Crhd Deliverable1b Final 05 30 14

Analyzing the Impact of the Mental Health Services Act on Reducing Mental Health Disparities (Deliverable 1b): Analysis of the Mental Health System Response to Reducing Disparities Using the 2010 County-Submitted Cultural Competency Plans (CCP) Principal Investigator Sergio Aguilar-Gaxiola, M.D., Ph.D. Professor of Clinical Internal Medicine Director, Center for Reducing Health Disparities Co-Principal Investigator Estella M. Geraghty, M.D., M.S., M.P.H., GISP Associate Professor of Clinical Internal Medicine Funded by the Mental Health Services Oversight and Accountability Commission (MHSOAC) May 28, 2014 Table of Contents Introduction ........................................................................................................................ 1 Historical Initiation and Background ............................................................................................1 The CCP Framework ........................................................................................................................... 2 Revisions to the CCP Framework and Process ................................................................................... 3 Implementation and Integration of the CCP ...................................................................................... 5 Review of Related Literature ........................................................................................... 7 Culturally Competent Services and Mental Health Disparities .....................................................7 Workforce Availability .................................................................................................................7 Analytical Aims and Research Questions ......................................................................................9 Methods ...................................................................................................................................... 10 Data Requested by CCP Requirements ............................................................................................. 10 Data Sources and Analysis .......................................................................................................... 11 Data Quality and Completeness......................................................................................................... 12 Data Types .......................................................................................................................................... 12 Challenges With Data Extraction ...................................................................................................... 13 CSS Demographic Data Definitions and Sources ............................................................................ 14 Key Findings and Recommendations .......................................................................................... 15 Key Findings to Research Question 1: How does the county’s reported population compare to the 2010 U.S. Census population? To the California Department of Finance population? ..................... 15 Key Findings to Research Question 2: What groups did each county target for reducing disparities? How well do these findings align with disparities in mental health service access over time, as identified in deliverable 1a of this report? .......................................................................................... 17 Alignment of CSS targets with trends in access disparities identified in Deliverable 1a ........................................................................................................................................................ 19 Key Findings to Research Question 3: How does the county’s workforce compare to the general population? To the Medi-Cal population? To the CSS population? ................................................... 24 Conclusion, Practical Implications, Limitations, and Future Research .................. 28 Table 1. Population Data By County/Race/Ethnicity Using CCPs ............................ 31 Table 1. Population Data By County/Race/Ethnicity Using CCPs (Continued) ...... 32 Table 2. Special Population Data By Age and Gender Using CCPs ......................... 33 Table 2. Special Population Data By Age and Gender Using CCPs (Continued) ... 34 Table 3. Medi-Cal Population Data By Race/Ethnicity Using CCP (Continued) ...... 36 Table 4. Medi-Cal Population Data By Age and Gender Using CCP ........................ 37 Table 4. Medi-Cal Population Data By Age and Gender Using CCP (Continued) .. 38 Table 5. CSS Population Data By Race/Ethnicity Using CCP ................................... 39 i Table 5. CSS Population Data By Race/Ethnicity Using CCP (Continued) ............. 40 Table 6. CSS Population Data By Age and Gender Using CCP ................................ 41 Table 6. CSS Population Data By Age and Gender Using CCP (Continued) .......... 42 Table 7a. Alameda County Profile: Cultural Competency Plan ................................ 43 Table 7a. Alameda County Profile: Cultural Competency Plan (Continued) .......... 44 Table 7b. Alpine County Profile: Cultural Competency Plan .................................... 45 Table 7b. Alpine County Profile: Cultural Competency Plan (Continued) .............. 46 Table 7c. Amador County Profile: Cultural Competency Plan .................................. 47 Table 7c. Amador County Profile: Cultural Competency Plan (Continued) ............ 48 Table 7d. City of Berkeley Profile: Cultural Competency Plan ................................. 49 Table 7d. City of Berkeley Profile: Cultural Competency Plan (Continued)............ 50 Table 7e.Butte County Profile: Cultural Competency Plan ....................................... 51 Table 7e.Butte County Profile: Cultural Competency Plan (Continued) ................. 52 Table 7f. Calaveras County Profile: Cultural Competency Plan ............................... 53 Table 7f. Calaveras County Profile: Cultural Competency Plan (Continued) ......... 54 Table 7g. Colusa County Profile: Cultural Competency Plan ................................... 55 Table 7g. Colusa County Profile: Cultural Competency Plan (Continued) ............. 56 Table 7h. Contra Costa County Profile: Cultural Competency Plan ........................ 57 Table 7h. Contra Costa County Profile: Cultural Competency Plan (Continued) .. 58 Table 7i. Del Norte County Profile: Cultural Competency Plan ................................ 59 Table 7i. Del Norte County Profile: Cultural Competency Plan (Continued) .......... 60 Table 7j. El Dorado Hills County Profile: Cultural Competency Plan ...................... 61 Table 7j. El Dorado Hills County Profile: Cultural Competency Plan (Continued) . 62 Table 7k. Fresno County Profile: Cultural Competency Plan ................................... 63 Table 7k. Fresno County Profile: Cultural Competency Plan (Continued) .............. 64 Table 7l. Glenn County Profile: Cultural Competency Plan ...................................... 65 Table 7l. Glenn County Profile: Cultural Competency Plan (Continued) ................ 66 Table 7m. Humboldt County Profile: Cultural Competency Plan ............................. 67 ii Table 7m. Humboldt County Profile: Cultural Competency Plan (Continued) ....... 68 Table 7n. Imperial County Profile: Cultural Competency Plan ................................. 69 Table 7n. Imperial County Profile: Cultural Competency Plan (Continued)............ 70 Table 7o. Inyo County Profile: Cultural Competency Plan ........................................ 71 Table 7o. Inyo County Profile: Cultural Competency Plan (Continued) .................. 72 Table 7p. Kern County Profile: Cultural Competency Plan ....................................... 73 Table 7p. Kern County Profile: Cultural Competency Plan (Continued) ................. 74 Table 7q. Kings County Profile: Cultural Competency Plan ..................................... 75 Table 7q. Kings County Profile: Cultural Competency Plan (Continued) ............... 76 Table 7r. Lake County Profile: Cultural Competency Plan ........................................ 77 Table 7r. Lake County Profile: Cultural Competency Plan (Continued) .................. 78 Table 7s. Lassen County Profile: Cultural Competency Plan ................................... 79 Table 7s. Lassen County Profile: Cultural Competency Plan (Continued) ............. 80 Table 7t. Los Angeles County Profile: Cultural Competency Plan ........................... 81 Table 7t. Los Angeles County Profile: Cultural Competency Plan (Continued) ..... 82 Table 7u. Madera County Profile: Cultural Competency Plan................................... 83 Table 7u. Madera County Profile: Cultural Competency Plan (Continued) ............. 84 Table 7v. Marin County Profile: Cultural Competency Plan ...................................... 85 Table 7v. Marin County Profile: Cultural Competency Plan (Continued) ................ 86 Table 7w. Mariposa County Profile: Cultural Competency Plan ............................... 87 Table 7w. Mariposa County Profile: Cultural Competency Plan (Continued) ......... 88 Table 7x. Mendocino County Profile: Cultural Competency Plan ............................ 89 Table 7x. Mendocino County Profile: Cultural Competency Plan (Continued) ...... 90 Table 7y. Merced County Profile: Cultural Competency Plan ................................... 91 Table 7y. Merced County Profile: Cultural Competency Plan (Continued) ............. 92 Table 7z. Modoc County Profile: Cultural Competency Plan .................................... 93 Table 7z. Modoc County Profile: Cultural Competency Plan (Continued) .............. 94 Table 7aa. Mono County Profile: Cultural Competency Plan .................................... 95 iii Table 7aa. Mono County Profile: Cultural Competency Plan (Continued) .............. 96 Table 7ab. Monterey County Profile: Cultural Competency Plan ............................. 97 Table 7ab. Monterey County Profile: Cultural Competency Plan (Continued) ....... 98 Table 7ac. Napa County Profile: Cultural Competency Plan ..................................... 99 Table 7ac. Napa County Profile: Cultural Competency Plan (Continued) ............. 100 Table 7ad. Nevada County Profile: Cultural Competency Plan .............................. 101 Table 7ad. Nevada County Profile: Cultural Competency Plan (Continued) ........ 102 Table 7ae. Orange County Profile: Cultural Competency Plan ............................... 103 Table 7ae. Orange County Profile: Cultural Competency Plan (Continued) ......... 104 Table 7af. Placer County Profile: Cultural Competency Plan ................................. 105 Table 7af. Placer County Profile: Cultural Competency Plan (Continued) ............ 106 Table 7ag. Plumas County Profile: Cultural Competency Plan .............................. 107 Table 7ag. Plumas County Profile: Cultural Competency Plan (Continued) ........ 108 Table 7ah. Riverside County Profile: Cultural Competency Plan ........................... 109 Table 7ah. Riverside County Profile: Cultural Competency Plan (Continued) ..... 110 Table 7ai. Sacramento County Profile: Cultural Competency Plan ....................... 111 Table 7ai. Sacramento County Profile: Cultural Competency Plan (Continued) .. 112 Table 7aj. San Benito County Profile: Cultural Competency Plan ......................... 113 Table 7aj. San Benito County Profile: Cultural Competency Plan (Continued).... 114 Table 7ak. San Bernardino County Profile: Cultural Competency Plan ................ 115 Table 7ak. San Bernardino County Profile: Cultural Competency Plan (Continued) ......................................................................................................................................... 116 Table 7al. San Diego County Profile: Cultural Competency Plan ........................... 117 Table 7al. San Diego County Profile: Cultural Competency Plan (Continued) ..... 118 Table 7am. San Francisco County Profile: Cultural Competency Plan ................. 119 Table 7am. San Francisco County Profile: Cultural Competency Plan (Continued) ......................................................................................................................................... 120 Table 7an. San Joaquin County Profile: Cultural Competency Plan ..................... 121 Table 7an. San Joaquin County Profile: Cultural Competency Plan (Continued) 122 iv Table 7ao. San Luis Obispo County Profile: Cultural Competency Plan .............. 123 Table 7ao. San Luis Obispo County Profile: Cultural Competency Plan (Continued)..................................................................................................................... 124 Note: Sections with blanks indicates that data were not available. Table 7ap. San Mateo County Profile: Cultural Competency Plan .................................................... 124 Table 7ap. San Mateo County Profile: Cultural Competency Plan ......................... 125 Table 7ap. San Mateo County Profile: Cultural Competency Plan (Continued) ... 126 Table 7aq. Santa Barbara County Profile: Cultural Competency Plan .................. 127 Table 7aq. Santa Barbara County Profile: Cultural Competency Plan (Continued) ......................................................................................................................................... 128 Table 7ar. Santa Clara County Profile: Cultural Competency Plan ........................ 129 Table 7ar. Santa Clara County Profile: Cultural Competency Plan (Continued) .. 130 Table 7as. Santa Cruz County Profile: Cultural Competency Plan ........................ 131 Table 7as. Santa Cruz County Profile: Cultural Competency Plan (Continued) .. 132 Table 7at. Shasta County Profile: Cultural Competency Plan ................................ 133 Table 7at. Shasta County Profile: Cultural Competency Plan (Continued) ........... 134 Table 7au. Sierra County Profile: Cultural Competency Plan ................................. 135 Table 7au. Sierra County Profile: Cultural Competency Plan (Continued) ........... 136 Table 7v. Siskiyou County Profile: Cultural Competency Plan .............................. 137 Table 7v. Siskiyou County Profile: Cultural Competency Plan (Continued) ......... 138 Table 7aw. Solano County Profile: Cultural Competency Plan .............................. 139 Table 7aw. Solano County Profile: Cultural Competency Plan (Continued) ......... 140 Table 7ax. Sonoma County Profile: Cultural Competency Plan ............................. 141 Table 7ax. Sonoma County Profile: Cultural Competency Plan (Continued) ....... 142 Table 7ay. Stanislaus County Profile: Cultural Competency Plan ......................... 143 Table 7ay. Stanislaus County Profile: Cultural Competency Plan (Continued) ... 144 Table 7az. Sutter-Yuba County Profile: Cultural Competency Plan ....................... 145 Table 7az. Sutter-Yuba County Profile: Cultural Competency Plan (Continued) . 146 Table 7ba. Tehama County Profile: Cultural Competency Plan ............................. 147 v Table 7ba. Tehama County Profile: Cultural Competency Plan (Continued) ........ 148 Table 7bb. Tri-City County Profile: Cultural Competency Plan .............................. 149 Table 7bb. Tri-City County Profile: Cultural Competency Plan (Continued) ........ 150 Table 7bc. Trinity County Profile: Cultural Competency Plan ................................ 151 Table 7bc. Trinity County Profile: Cultural Competency Plan (Continued) .......... 152 Table 7bd. Tulare County Profile: Cultural Competency Plan ................................ 153 Table 7bd. Tulare County Profile: Cultural Competency Plan (Continued) .......... 154 Table 7be. Tuolumne County Profile: Cultural Competency Plan .......................... 155 Table 7be. Tuolumne County Profile: Cultural Competency Plan (Continued) .... 156 Table 7bf. Ventura County Profile: Cultural Competency Plan .............................. 157 Table 7bf. Ventura County Profile: Cultural Competency Plan (Continued) ......... 158 Table 7bg. Yolo County Profile: Cultural Competency Plan ................................... 159 Table 7bg. Yolo County Profile: Cultural Competency Plan (Continued) ............. 160 References ..................................................................................................................... 161 vi Cultural Competence Plan (CCP) Key Abbreviations ACS American Community Survey CAEQRO California External Quality Review Organization CCP Cultural Competence Plan CCPR Cultural Competence Plan Requirement CHIS California Health Information Survey CLAS Culturally and Linguistically Appropriate Services CMHDA California Mental Health Directors Association CMHPC California Mental Health Planning Council CMM Centers for Medicare and Medicaid CSI Client Services Information CSS Community Services and Support DBH Department of Behavioral Health DHCS Department of Health Care Services DHHS Department of Health and Human Services DMH Department of Mental Health DMHS District Mental Health Services DOF Department of Finance EQRO External Quality Review Organization FPL Federal Poverty Level FTE Full-time Equivalents HCFA Health Care Financing Administration LHJ Local Health Jurisdiction MHPs Mental Health Plans MHSA Mental Health Services Act MHSOAC Mental Health Services Oversight Accountability Commission OMS Office of Multicultural Services PEI Prevention and Early Intervention R&E Research and Evaluation Division SCMH MC Sonoma County Mental Health MHSA Coordinator SED Serious Emotional Disturbed SMI Serious Mental Illness WET Workforce Education and Training iv Introduction Cultural competence, according to Campinha-Bacote (2002), is a process in which the provider continuously strives to be culturally responsive and work effectively within the cultural context of a community from a diverse cultural and ethnic background. Reflecting on the work that mental health leaders do in local, regional, and state departments calls for an examination of the cultural capabilities that are essential to respond to mental health service inequalities and health disparities experienced by unserved, underserved, and inappropriately served communities. Anticipating and recognizing the need to reduce service inequalities and health disparities, the California Department of Mental Health introduced the cultural competence plans (CCPs) in the late 1990s, requiring all counties across the state to complete a CCP. This requirement remains a priority in the efforts to adequately assess and respond to potential disparities in mental health services throughout the state. Disparities in access to culturally relevant mental health services, and inconsistencies in culturally competent services offered, were thought to exist in many regions of California, and the CCPs were intended to enable introspective examination of potential deficiencies in services and solutions for the future. Therefore, data from the CCPs were used to examine California’s mental health system’s response to reducing disparities. This section of the report highlights CCPs in California, from their historical initiation and background, to the most updated data available, to recommendations for future CCP requirements and evaluation, with the goal of guiding and informing future steps toward equitable culturally competent mental health services throughout the state. Historical Initiation and Background Prior to the passage of the 2004 Mental Health Services Act (MHSA, Proposition 63), California’s community mental health systems had a long and well-documented history of attempts to reduce the disparities in access and quality of care experienced by diverse populations.1 In the mid-1990s, at the national and state levels, mental health systems were undergoing a major policy shift toward implementation of managed care programs. In order to consolidate resources and to create a more cost-effective Medi-Cal delivery system, California initiated a statewide movement away from “fee for service” providers through consolidation of Medi-Cal specialty mental health services. Consequently, mental health services were separated from health services, and a cohesive mental health system emerged. The consolidation of funding prevented “fee for service” providers from receiving reimbursement for Medi-Cal patients outside of the new managed care system. In essence, the new managed care system rendered county mental health systems the sole providers of community mental health services to California’s Medi-Cal beneficiary populations. In order to gain approval for this policy shift, the California Department of Mental Health (DMH) needed to apply for a waiver from the federal Department of Health and Human Services (DHHS), Health Care Financing Administration (HCFA), now known as the Centers for Medicare 1 The content of the historical initiation and background section is derived from personal communication with key informants previously and currently involved in the development and administration of the CCPs. 1 and Medicaid Services (CMS). In collaboration with the California Mental Health Directors Association (CMHDA), DMH submitted to HCFA a request for a waiver to grant California permission to implement the Consolidation of Medi-Cal Specialty Mental Health Services. HCFA approved the wavier. It was subsequently known as Phase II Consolidation of Medi-Cal Specialty Mental Health Services (Phase I referred to changes in state hospital funding). As part of the approved Phase II waiver, the county mental health systems became known as the new “mental health plans” (MHPs). In effect, the federal waiver granted county mental health plans the full responsibility for the mental health care of California’s Medi-Cal populations. The federal waiver required DMH to develop implementation plans for the rollout of Phase II. However, the DMH’s implementation plan for Phase II failed to include any requirements for the reporting of strategies to reduce disparities. In a state as highly diverse as California, this omission generated a strong response from various diverse communities, resulting in creation of an addendum to the Phase II consolidation plans. This addendum was known as the CCP requirements. The CCP Framework DMH issued the first ever-statewide CCP requirements in October 1997, as an addendum to the implementation plans of Phase II Consolidation of Medi-Cal Specialty Mental Health Services. The CCP requirements were added to state statute under Title 9, Rehabilitative and Developmental Services, Division 1, DMH, Chapter 11, Medi-Cal Specialty Mental Health Services, Article 4, Section 1810.410, culture and linguistic requirements. The regulation states “County mental health programs shall develop and implement cultural competence plans and submit these plans to DMH for review and approval.” This statute satisfied the requirements in the federal waiver. The CCP requirements were developed in partnership with community stakeholders through the newly formed Cultural Competence Task Force. This task force included representation from the California Mental Health Directors Association. The mental health directors were to be held responsible for writing and implementing their county mental health plans. The DMH required all mental health plans to include a CCP and submit the plan to DMH for review. Recognizing the need to oversee the CCP submissions and reviews, the DMH in 1998 established the Office of Multicultural Services (OMS). For the first time the DMH established standards and plan requirements for reducing disparities and moving toward achieving cultural and linguistic competence in service access and utilization. The Cultural Competence Task Force developed the standards as a means to synthesize the best research available in the cultural and linguistic competency literature. The overall goals of the CCP requirements were to: 1) Establish standards and requirements to create consistency in the reporting of data on cultural competency. This drive toward consistency was designed to enable the California DMH to monitor improvements in the creation of more culturally and linguistically competent county mental health systems over time. 2 2) Improve access and the quality of care in mental health services for underserved racially and ethnically diverse Medi-Cal beneficiaries. Recognizing the need to assess the mental health system and county-level response to reducing mental health disparities, DMH stipulated that each county must submit a CCP, allowing them nine months to gather and analyze their data and submit their final CCPs. The CCPs originally required each county to present population utilization profiles across four areas, including access rates stratified by race and ethnicity, age, gender, and language. In analyzing the data, counties were able to assess organizational and service provider capabilities, identify county-specific disparities, and develop a county-specific plan. The plan was to include targeted goals and measurable objectives to reduce specifically identified disparities within their counties. DMH offered technical assistance and training to help counties find population data sources (e.g., the California Department of Finance and the U.S. Census Bureau) and county Medi-Cal utilization data, and to use Client Services Information (CSI) data. Because the CCP requirements were part of Phase II of the Medi-Cal Consolidation of Specialty Mental Health Services, they were included as part of the required on-site Medi-Cal review protocols. The DMH Office of Multicultural Services trained the state review teams on CCP requirements to help ensure compliance with the CCP requirements as part of the Medi-Cal review protocol. The CCP, which constitutes a framework for identifying and improving cultural and linguistic competency, was well supported by national reports and initiatives. For example, in 2001, a federal report from the Office of the Surgeon General, U.S. Department of Health and Human Services (DHHS), titled Mental Health: Culture, Race, and Ethnicity, A Supplement to Mental Health declared that “racial and ethnic minorities bear a greater burden from unmet mental health needs and thus suffer a greater loss to their overall health and productivity.” Also in 2001, the U.S. DHHS Office of Minority Health issued the National Standards for Culturally and Linguistically Appropriate Services in Health Care, subsequently known as the CLAS standards. California’s CCP requirements were used as the template for development of these national standards. In subsequent CCP revisions, the federal CLAS standards were vetted and incorporated into the revised CCPs, thus tying the federal CLAS standards to the state plans. In July 2003, U.S. DHHS issued a report by the President’s New Freedom Commission on Mental Health titled Achieving the Promise: Transforming Mental Health Care in America. This report called for “A new vision for the future—action for mental health in the new millennium.” The report asserted that states should “tailor treatment to age, gender, race, and culture.” These reports placed a national spotlight upon national mental health systems, impelling them to take decisive action to reduce disparities that diverse, underserved populations experience. Revisions to the CCP Framework and Process In nearly two decades since DMH issued the first CCP requirements in 1997, three updates and revisions have occurred. The CCPs were updated in 2002, 2003, and most recently in 2010. Counties also were required to submit annual CCP updates. In the early submissions, DMH and community stakeholders reviewed, evaluated, and scored all CCPs. The review of the CCPs included a scoring protocol with criteria set to evaluate each component of the CCPs, which assigned a final score to each plan. After each CCP was scored, DMH maintained and stored all scoring sheets; whether or not these documents remain available is unclear. Use of the scoring 3 protocol to evaluate each of the CCPs demonstrated minimal investment and willingness by counties to set specific strategies to reduce disparities. In the review of earlier CCPs, prior to 2010, counties identified disparities in access to care for Latinos, Asian-Pacific Islanders, and Native Americans. Earlier CCP submissions did not identify disparities affecting African Americans because their data showed high utilization and overrepresentation in inpatient treatment settings. The California Mental Health Planning Council (CMHPC) can corroborate these findings. Between 2001 and 2005, the CMHPC, in meetings with its quality review committee, reviewed and reported county-specific data that reported utilization rates by various demographic variables. Despite the opportunity to measure county-level response to reducing mental health disparities, counties expressed hesitation to complete CCPs. Key informants familiar with the development and administration of the CCPs recall counties’ hesitation (personal communications, December 15, 2013). They reported that in the meetings held between County Mental Health Directors Association leadership and the state Department of Mental Health, counties expressed a concern that the CCPs were in fact an unfunded mandate. As a result, counties were reluctant to set realistic benchmarks for reducing disparities for fear of lack of reimbursement. Their position was that the CCP protocols required them to serve “new” populations without new dollars. Additionally, they were resistant to set realistic goals for reducing disparities for fear of consequences if they did not reach stated goals. Historically, the DMH position was that these were not new communities, but rather Medi-Cal beneficiaries whom counties were already responsible to serve under their Medi-Cal consolidation plans. Too often, the CCPs were seen as an added reporting responsibility to already overburdened and underfunded MHPs. The history of the CCP submissions to DMH continued to show poor results in the quality of data collection and analysis, thus resulting in inadequate and poorly conceived implementation goals and measurable strategies. The state DMH also failed to hold counties accountable. Poor reporting of data, inadequate plan submissions, and Medi-Cal site visits that indicated that sites were out of compliance were accepted with little to no consequences. Under pressure from the California Mental Health Directors Association, DMH issued no sanctions, and therefore no significant, meaningful changes were made. In California, all 58 counties as well as two mental health city sites are required to submit CCPs. Over a period encompassing multiple CCP submissions, prior to 2010, most counties scored poorly and did not seriously engage in the intended planning, implementation, and evaluation process. However, in the 2003 CCP submissions a few counties—notably San Diego, San Mateo, and Sacramento—began to stand out for their submission of comprehensive plans. Reviews of their CCPs revealed a supportive internal leadership, strong competent ethnic services managers, and investment in resources to work with their data, thus resulting in a willingness to set realistic and measureable objectives in order to make progress in reducing mental health disparities. In 2004, the CCPs began to head in a new direction with the passage of the Mental Health Services Act (MHSA, Proposition 63), which presented yet another major shift in California mental health delivery systems policy. However, unlike the mid-1990s Phase II Consolidation of Medi-Cal Specialty Mental Health Services move to a managed care delivery system, the MHSA brought with it a significant new infusion of financial resources to a historically underfunded 4 mental health system. The MHSA was responding to strong community advocacy for change in the California mental health system to reduce mental health disparities. The MHSA used much of the work done in the President’s New Freedom Commission on Mental Health 2003 report Achieving the Promise: Transforming Mental Health Care in America to guide its development. MHSA created a historic opportunity to totally redesign the delivery of mental health services in California. Once again, the DMH was charged with the responsibility to develop the criteria for implementation of each of the new components of the MHSA law, and related CCP requirements. Implementation and Integration of the CCP The DMH Office of Multicultural Services (OMS), which has been responsible for development and implementation of the CCPs since their introduction, did not want to perpetuate the previous missed opportunities to include cultural competence criteria and disparities data collection in the development of new MHSA programs and services. The rollout of the MHSA presented a new and unique opportunity to respond to the disparities that the previously submitted CCPs discerned. The OMS identified as a primary policy objective imprinting the appropriate cultural competency criteria, data collection, and organizational and service providers’ standards and strategies in all of the various levels of program development as they were being designed, thus avoiding the need for an addendum. The key to culturally competent services is embedding the criteria and requirements in program design and implementation at every level. Given this new opportunity, the OMS, and its advisory committee, sought to ensure that the CCP requirements, including the collection of data by race, ethnicity, gender, age, and language, would be ingrained into each of the new MHSA programs. The goal was to have accurate and timely data so that disparities could be identified and targeted with the hope of reducing the disparities. To achieve the aim of collecting data to identify and target disparities, and ultimately to respond to the service needs of California’s diverse communities, OMS included specific criteria in the five MHSA targeted components: (1) Community Services and Supports, (2) Prevention and Early Intervention, (3) Innovation, (4) Capital Facilities and Technological Needs, and (5) Workforce Education and Training. However, the OMS decided against extending these criteria to the Three-Year Program and Expenditure Plan because they were not intended to serve as a CCP nor would they report what counties were specifically doing to reduce disparities. In their MHSA Three-Year Program and Expenditure Plans, counties were required to submit a listing of all programs for which MHSA funding was requested, and to identify the proposed expenditures for each MHSA-funded program and targeted age group. During the initiation of MHSA, the California Mental Health Directors Association requested that the 2010 CCP requirements become more integrated and comprehensive as a planning document so that the CCPs could reflect the intent of the five MHSA components. The new resources allocated under the MHSA encouraged implementation of programs to expand services to multicultural communities, and the California Mental Health Directors Association wanted to make sure that the CCPs gave consideration to those activities. 5 In 2010, the submission of CCPs enabled counties to present their progress toward reducing mental health disparities with the help of MHSA resources. Between 2005 and 2010 counties were given a waiver from submitting annual CCPs, as they developed and submitted their MHSA plans to the state. Prior to the current report, the 2010 submissions of the CCPs had not been evaluated, limiting assessment of the mental health system response to mental health disparities. The current analysis in conjunction with other reports and analyses, begins to assess the potential influence that MHSA resources have wielded in reducing disparities in access to mental health treatment services, and the quality of mental health outcomes within the public mental health system. 6 Review of Related Literature Culturally Competent Services and Mental Health Disparities Despite the impediments that California has encountered in identifying, assessing, and reducing mental health disparities, continuation of the state's resolve to disseminate and implement culturally and linguistically competent care is of critical importance. Research indicates that disadvantaged groups respond well to culturally and linguistically competent services. For example, Snowden and colleagues (2006, 2011) highlight four areas that they found critically important in order for mental health ethnic service coordinators to be effective in improving access to services for diverse groups. The four areas are: (1) hiring bilingual staff, (2) outreach activities to promote awareness of mental health services, (3) collaboration with community- based organizations located in the areas where potential consumers reside, and (4) flexibility in hours and settings for conducting services. Matching providers with consumers of the same race and ethnicity also has proven to be beneficial for consumers (Cabral & Smith, 2011). That is, consumers tend to present with a strong preference for a provider of the same race and ethnicity and to be more responsive to them than to providers of a different race and ethnicity. Such affinity can lead to consumer retention in treatment. The general notion behind achieving beneficial cultural competency outcomes is to directly meet the needs of diverse populations by increasing culturally relevant organizational, structural, and clinical interventions (Betancourt, Green, Carrillo, & Ananeh-Firempong, 2003). Betancourt and colleagues elaborate on these three interventions. First, organizational interventions include establishment of a diverse workforce and leadership reflective of the communities served. Second, structural interventions refer to agency policies and practices that ensure access to quality care, such as interpreter services, bilingual staff, and written materials appropriate languages. Finally, studies have recommended tailoring clinical interventions to the unique needs of individual consumers and their families. To deliver culturally and linguistically competent services, providers must be equipped with the necessary training and tools. Workforce Availability The integration of a culturally and linguistically competent workforce can translate into a mental health system of care that is language- and culture-proficient while producing healthier outcomes for consumers and families of diverse cultures (Parks & Kreuter, 2007; Rogler, Malgady, Costantino, & Blumenthal, 1987). Model mental health programs have been known to offer bilingual and bicultural providers who are continually trained to treat diverse individuals, such as those with limited English proficiency (Aguilar-Gaxiola et al., 2012; Rogler et al., 1987). Despite the need for a diverse workforce, California is experiencing a significant shortage of diverse mental health workers. For example, the 2006–2010 American Community Survey estimates that only 30.7% of social workers in California are Hispanic or Latino, 41.7% are White, 13.6% are African American, 0.4% are American Indian or Alaska Native, 10.6% are Asian American, and 0.2% are Native Hawaiian or other Pacific Islander. Among psychologists in California, 8.6% are Hispanic or Latino, 80.6% are White, 3.0% are African American, 0.4% are American Indian or Alaska Native, 5.5% are Asian American, and 1% are Native Hawaiian 7 or other Pacific Islander (California Employment Development Department, 2010). The disproportional dispersal of providers throughout the state is an additional complication. Thus, the workforce also suffers from geographical disparities. A recent report by the California Healthcare Foundation (2013) highlights the availability of licensed mental health professionals throughout the state, per 100,000 populations, as depicted in Exhibit 1. While some regions, including the greater Bay Area, are staffed above the state average with mental health professionals, other regions (e.g., the San Joaquin Valley and the Inland Empire) are quite understaffed. Exhibit 1. Licensed Mental Health Professionals by Region in California Region Psychiatrists Psychologists Licensed Clinical Marriage and Social Workers Family Therapists Central Coast 20 45 46 117 Greater Bay Area 32 71 69 123 Inland Empire 9 16 27 40 Los Angeles 20 45 52 81 Northern and Sierra 10 25 46 91 Orange County 16 41 43 83 Sacramento Area 19 36 57 76 San Diego Area 22 53 53 72 San Joaquin Valley 2 17 25 34 State Average 19 43 48 81 *Per 100,000 Population Increasing the diversity and geographical availability of the mental health workforce is vital to achieve equity. McGuire and Miranda (2008) argue that a diverse mental health workforce throughout the state can serve as an asset to strengthen culturally and linguistically competent care to match those of consumers. With an emphasis on increasing and diversifying the workforce to represent the community being served, expanding culturally and linguistically competent trainings and engaging staff in community-defined practices and service delivery are essential for the provision of treatment. In light of these challenges, a more comprehensive review of MHSA programs is essential. In the current report, we present results of an analysis of the mental health system response to mental health disparities in California. We analyzed data from the 2010 CCPs for 52 California counties. In conjunction with statistics from other evaluation components carried out by our research team, these results contribute to a more comprehensive understanding of county data relevant to mental health service provision, and the cultural competence levels within the mental health systems across California counties. 8 Analysis of the Current Mental Health System and County-Level Response to Reduce Disparities In this section, we focus on our analysis of the county level response to reducing mental health disparities, as highlighted in the CCPs. Specifically, we focus on: (1) the objectives of the evaluation, including the research questions; (2) methods and data quality; and (3) key findings and recommendations. Analytical Aims and Research Questions Through the current analysis, we aimed to provide the MHSOAC, mental health services providers, consumers, and their family members, as well as advocates and all other relevant stakeholders, with an assessment of the impact that MHSA has had on mental health disparities among underserved and inappropriately served groups throughout the state. Specifically, we aimed to better understand how California counties assessed local mental health disparities, and how they responded to the need to reduce disparities in access to care. Guided by our findings, we conclude the current report with recommendations for the continued reduction of disparities that may help the state confront obstacles in the implementation of the MHSA. Based on initial discussions with MHSOAC, we developed three research questions to guide our evaluation. Research question 1: How does the county’s reported population compare to the 2010 U.S. Census population? To the California Department of Finance population? To address research question 1, we explored how counties reported their demographics, as well as the data source used to gather their information. We compared reported population numbers and percentages presented in the 2010 CCPs (general population, Medi-Cal population) to the U.S. Census Bureau’s 2010 Decennial Census and to the California Department of Finance (DOF) data. Variables of focus included: race, ethnicity, age, gender, and language. The U.S. Decennial Census and DOF data were compared to the county-reported demographics within CCPs to determine how well they measured up in terms of counts and percentages documented for each variable and within each demographic stratum (e.g., children, transitional-age youth, adults, older adults). Research question 2: What groups did each county target for reducing disparities? How well do these findings align with the trends in access disparities identified in deliverable 1a of this report? To answer this research question, we explored the populations targeted for mental health service disparity reduction by California counties. We assessed disparity targets for each county by: (1) documenting the number and type of targets, and (2) determining whether targets were realistic, when compared to the county’s demographic data, and to potential disparities highlighted in their respective CCPs. We assessed two types of county targets: Community Services and Supports (CSS) targets, and Workforce Education and Training (WET) targets. Research question 3: How does the county’s workforce compare to the general population? To the Medi-Cal population? To the MHSA/CSS population? 9 To attend to this final research question, we explored each county’s reported workforce data, where available, and compared the composition of these data to the composition of data for the general population, the Medi-Cal population, and the MHSA/CSS population with regard to race and ethnicity, and specific professional mental health categories (e.g., licensed, unlicensed, direct service providers, indirect service providers). The race and ethnicity workforce data yielded a preliminary assessment of the concordance between workforce data (i.e., the composition of health-care providers) and consumer data (i.e., the composition of clients likely to access services). Overall, our rationale for investigating these three research questions was to assess how well each county assessed local demographic and mental health service needs data, and identified and responded to potential mental health disparities. In the following sections, we highlight our methods, results, and recommendations based on our review of the CCPs made available by the California Department of Health Care Services. The counties without data, overall or for specific subsections of the report, either did not submit a CCP, or the data for specific subsections were not available. Data are presented in aggregate for all counties (Tables 1–6), allowing for county-by-county comparisons, and in individual tables for each of the counties assessed (Tables 7a-7bg). We focus on the data requested by CCP guidelines, data extraction methods, data quality, results, and recommendations tied to the CCPs. Methods Data Requested by CCP Requirements Specific CCP Requirements were developed by State Officials. The following section presents some of the specific instructions given to counties as they prepared their CCPs. For entry of general population data, CCP Requirements were as follows: “Provide a description of the county’s general population by race, ethnicity, age, gender, and other relevant small-county cultural populations. The summary may be [presented as] a narrative or as a display of data (other social or cultural groups may be addressed as data is available and collected locally). If appropriate, the county may use MHSA Annual Update Plan data here to respond to this requirement.” In the 2010 CCP Requirements, counties also were instructed to: “Summarize the following two categories by race, ethnicity, language, age, gender, and other relevant small-county cultural populations: The county’s Medi- Cal population (county may utilize data provided by DMH); the county’s client utilization data.” To capture MHSA/CSS population and Charles Holzer data, counties were instructed to: “Summarize the 200% of poverty (minus Medi-Cal population) and client utilization data by race, ethnicity, language, age, gender, and other relevant small-county cultural populations.” 10 Counties also were alerted that information for this section of the CCP was available at a web link they could access. In order to assess recruitment, hiring, and retention of a multicultural workforce whose members are part of, or experienced with, the identified unserved and underserved populations, the county was asked to include the following in the CCPR Modification (2010): “A. Extract and attach a copy of the MHSA workforce assessment submitted to DMH for the Workforce Education and Training (WET) component. B. Compare the WET Plan assessment data with the general population, Medi- Cal population, and 200% of poverty data.” To assess the 2010–2011 CCPs, we reviewed the data of 57 counties, the type and the quality of the data, the county-level sources from which the data were drawn, and the results highlighted. Specifically, we obtained electronic copies of the 2010 CCPs for each of the California Counties reviewed. We printed out hard copies of each CCP to facilitate review and data extraction. Data for each CCP were entered into a Microsoft (MS) Excel spreadsheet and database. Each row within the MS Excel spreadsheet represented one CCP, from one county or county collaborative (e.g., Sutter-Yuba). Columns included data for each variable of interest for the general, Medi-Cal, and CSS populations, as well as workforce data. Creation of additional columns in our database enabled insertion of US Census and DOF population data, as well as notes that highlighted data quality details. Data Sources and Analysis The following section highlights the data sources and analysis of data from the CCPs. We analyzed data for 57 counties that submitted a 2010 CCP. We were not able to access the Sierra County CCP because it was not submitted to DMH by the 2010 deadline. The Berkeley and Pasadena City CCPs also were not available to the Department of Health Care Services). CCPs reviewed ranged in length from 50 to 400 pages, and many CCPs included addendums and appendices that further extended their length. We reviewed Criterion 2, 3, and 6 of the CCPs. Below, we first deliver an assessment of data types, sources, impediments, quality, and consistency across counties. Next, we furnish a snapshot of the data that were presented in and extracted from CCPs. Tables that present the data from all counties side-by-side complement the text that describes these data. Tables 1–6 allow comparison of completeness of data across CCPs in all counties, and facilitate comparison of reported demographics across counties. Finally, we highlight county-specific data, presenting a two-page tabular summary for each CCP. Tables 7a–7bg constitute a snapshot of CCP data from each county, highlighting the general population, Medi-Cal population, MHSA/CSS population, US Census and DOF data, workforce data, and CSS and WET targets. We also present a breakdown of workforce data, comparing workforce full-time equivalents (FTEs), and percentages of FTEs to the proportion of the general, Medi-Cal, CSS, and DOF populations in each of the different race and ethnicity categories. Inclusion of all of these statistics is intended to present a picture of the data made 11 available for each county through their individual CCPs, and the gaps that exist in data collection, data extraction, and data reporting. Review of county CCP data also begins to highlight where potential disparities in mental health service provision may be present, and what mental health service priorities and targets were selected for current and future activities. To assess data quality we determined: the completeness of the data (e.g., number of items included, missing data); data types (e.g., counts, percentages, both, mixed data types); and data sources (e.g., American Community Survey [ACS], U.S. Census Bureau, DOF, California DMH). Data Quality and Completeness Data quality varied considerably across CCPs. Numerous counties compiled relatively comprehensive data. Such counties submitted data that were formatted and labeled clearly, embedded in appropriate sections of the CCPs, and well-structured with complementary tables and text entries. Data from counties with good reporting approaches relied upon reputable data sources, including the 2010 U.S. Decennial Census, the 2006–2008 American Community Survey (a U.S. Census Bureau Survey with a representative sample of U.S. citizens), and Holzer data sources. Further, many counties submitted data for mental health program targets that were focused and realistic, delineating specific subpopulations that needed additional mental health resources. Finally, counties that succeeded in presenting CCP data in an adequate manner included data of sound quality, presented in tabular formats, and included both counts and percentages for all variables. We emphasize that the data in this report is based on CCPs made available by the California Department of Health Care Services for analysis. The county and two cities without data either did not submit a CCP, or data were not available. The data that many other counties submitted in their respective CCPs, however, contained gaps (i.e., missing data), which rendered data more difficult to decipher, and complicated and compromised data extraction and analysis. At times, data within these CCPs were difficult to locate because they were not clearly embedded in the sections highlighted in CCP guidelines, or the data were buried in appendices or addendums to the CCP, which were challenging to navigate. Data were also unstructured in several CCPs, without tables, and in a format that was difficult to follow in text. Counties with gaps in CCP data also tended to leave out information on data sources or relied upon older data sources, such as the 2000 U.S. Census—which was significantly dated by 2010—and data other than Holzer data, complicating attempts to obtain strong indicators for local populations in need of mental health services. Data from counties with less structured CCPs also were presented, at times, using less favorable approaches, such as pie charts, rather than data tables, without corresponding numbers or proportions to facilitate extraction of counts and percentages for specific variables. Data Types Within CCPs, data were usually provided in one of four types (see Exhibit 2). These data types included: counts, percentages, both counts and percentages, or a mix of data types across different datasets and variables. In ideal situations, CCPs presented both counts and 12 percentages for all variables. However, when only counts were supplied, percentages could be calculated (and vice-versa) during data extraction and data entry processes. Exhibit 2 highlights the breakdown of data types that were detected during the data extraction and data entry processes. Exhibit 2. Data Types Within the CCP Characteristics General Population Data Type (n = 57) CSS Medi-Cal Only Count 3 16 3 Only Percent 15 6 2 Always Both 23 19 23 Mixed 15 9 14 Data Sources Data sources varied considerably by county across California. For general population data, sources included the U.S. Census Bureau, the California DOF, the American Community Survey (ACS), DMH, and the California Health Information Survey (CHIS). These data sources also varied by year (e.g., U.S. Census) and time range of focus (e.g., ACS 2006–2008, ACS 2006–2010). Exhibit 3 highlights examples of the variety of data sources reported for general and Medi-Cal demographic data. Challenges With Data Extraction Numerous hurdles made data extraction, data entry, data analysis, and interpretation of results tied to CCPs difficult. The reports were often exceedingly long, ranging from 50 to 400 pages, and numerous addendums and appendices often added to this length. Despite requests to format the reports in a similar template, data were often presented in differing formats across counties, such that counts, percentages, and rates often relied on different metrics, leading to inconsistencies in assessments of different variables. For example, some counties divided age- based data into three categories: children (0–17 years), adults (18–64 years), and older adults (65 years and over). Other counties divided age-based data into four categories: children (0–14 years, transitional-age youth (15–24 years), adults (25–64 years), and older adults (65 years and over). Such differences in variable definitions impeded comparison of strata-specific age categories across counties, and across different data sources. Data sources often varied across CCPs, by year and by the local, state, or federal agency from which the data were acquired. Thus, “apples to apples” comparison of general population data and Medi-Cal data on a county-by-county basis was difficult. Finally, the completeness of the data included in CCPs from across California counties and LHJs varied considerably. Some CCPs, for example, included comprehensive data for the general population but included less comprehensive Medi-Cal data. Other CCPs included strong data for race, ethnicity, and age 13 variables, but lacked comprehensive data for languages spoken. Finally, some CCPs included strong general and Medi-Cal population data but had limited targeting and workforce data. Exhibit 3. Data Sources That Counties Reported Using to Respond to CCP Questions General Population Medi-Cal Demographics US Census Bureau: 2000, 2008, 2009, 2010 California State MEDS File: 2010 US Census Bureau: ACS 2006-2008, 2005-2009 DHHD Mental Health Database (InSyst) US Census Bureau: American FactFinder QuickFacts Department of Mental Health: Medi-Cal eligible 2008 2009 California Department of Finance Medi-Cal approved claims data Department of Mental Health: 2007 APS Healthcare (EQRO): 2008 CHIS: 2007 California External Quality Review Organization MHSA Community Services CAEQRO: 2007, 2008, 2009 Chart from ___ County’s MHSA DCHS Website: Medi-Cal data 2011 Southern California Association of Governments 2009 DBH, R&E 2009 APS Healthcare Medi-Cal Approved Claims: 2009 http://www.dmh.ca.gov/Statistics_and_Data_Analysis/ docs/Population by County/ Combined sources CA County of Mental Health, ITWS File: 2008 Not identified No sources reported Note: Please see the list of key abbreviations at the beginning of this report. CSS Demographic Data Definitions and Sources CSS data are intended to highlight populations that are most in need of mental health services. CSS data, often referred to as Holzer data within CCPs, represent people living at 200% of the federal poverty level (FPL), who are presumed to have some of the highest mental health service needs. CSS data can be useful in highlighting potential disparities within counties, when compared to the general population data presented in CCPs. Data sources for CSS were very diverse across CCPs in California. Exhibit 4 highlights examples of CSS definitions and data sources from various CCPs. 14 Exhibit 4. Data Sources for Communities in Need from Various CCPs CSS Pop Data Holzer Data Additional Data Data in Spreadsheet “…population at or below Yes: 2004 Fully served, underserved, 200% FPL, unknown 200% FPL…” total served, county pop source County poverty population: 2004/2005 Utilization rates by gender, County poverty population 1999, 2000 ethnicity, age, language 2000 Census from Holzer Prevalence SMI 2007 SED/SMI prevalence County poverty data, 2009 census 200% of Poverty (minus Yes, 2000 Holzer Medical penetration rates Prevalence data county Medi-Cal prevalence under <200% FY 07/08 poverty population, poverty 2004 unknown “Residents in need” No, not close to Holzer Fully served, underserved “CSS pop assessment” data or inappropriately served county provided data; source? US Census 2000 No SCMH MC Consumers 1999 100% poverty, US Census No data provided Unknown No data None Note: Please see the list of key abbreviations at the beginning of this report. Key Findings and Recommendations The purpose of our evaluation was to analyze the mental health system response to disparities based on assessment of CCP data. In this section, we present the key findings connected to our three research questions. Key Findings to Research Question 1: How does the county’s reported population compare to the 2010 U.S. Census population? To the California Department of Finance population? Overall, counties present population data that are comparable to the 2010 Decennial Census and DOF population data. While data sources varied considerably across counties, and data were presented in a variety of ways across CCPs, the overall county-specific counts and proportions for specific variables and demographic groups were similar when comparing general population data and census and DOF data. Tables 1–6 display side-by-side comparisons of data by demographic categories. Tables 7a–7bg present county-specific CCP data, which permits comparison of county-level demographic data across different local-level data sources. The blue ribbon at the end of each table segment summarizes the county-specific reported population in comparison to the U.S. Census and DOF populations. For the year 2010, the Decennial Census and DOF data were identical. DOF estimates are based upon calculations derived from U.S. census data. During intercensal years (2011–2019), 15 DOF estimates will vary from decennial census data as they will take into consideration population growth and factors such as immigration and emigration across California counties. From a research perspective, our team recommends use of DOF data for county-level population estimates. State agencies are generally required to use DOF data for state and county-level population estimates as DOF estimates and projections are derived from US Census data, but take advantage of California demographic trends in calculating local population estimates during intercensal years. According to the DOF website on population estimates and projections, “Three demographic variables describe the reasons for population change. Change occurs because of fertility (births), mortality (deaths), or migration (the movement of people). Immigration, sometimes called international immigration, describes movements between countries while internal or domestic migration describes movements within the same country or state.” DOF estimates take these demographic variables and population shifts into consideration, arriving at more reliable population estimates for California counties. In fact, these “…data are used in determining the annual appropriations limit for all California jurisdictions, to distribute State subventions to cities and counties, to comply with various State codes, and for research and planning purposes by federal, state and local agencies, the academic community and the private sector.” (Source: http://www.dof.ca.gov/research/demographic/reports/view.php; accessed 4/28/14) Recommendations in response to research question 1 The key to more accurate and current population data from counties lies in using a consistent data collection and analysis plan and procedure for counties to track and monitor their CCP data. Based on our findings, we recommend the following actions be discussed, prioritized, and implemented by the following state agencies, including the Health and Human Services Agency, the California Department of Health care Services (DHCS), the Department of Public Health (DPH) including the Office of Health Equity, the Office of Statewide Health Planning and Development (OSHPD), and the Mental Health Services Act Oversight and Accountability Commission (MHSOAC); and county departments of mental health to effectively reduce disparities in unserved, underserved, and inappropriately served communities and improve mental health outcomes in MHSA-related and non-related programs: 1.1 Establish a consistent data compilation plan that counties can adopt and implement in a way that allows them to obtain and retrieve data with ease, and monitor progress over time. The plan should include templates for tables, figures, and text that counties can use to consistently insert their most recent data within a prescribed page limit. A consistent data collection procedure: (1) increases data accuracy, (2) enables adequate modifications to CCPs, and (3) focuses on persons from underrepresented groups (e.g., by race, age, sexual orientation and gender identity, and geographic regions) in receiving culturally responsive services. 1.2 Encourage counties to focus on reliable and consistent data sources. As noted above, we recommend use of demographic and population data from the California DOF. Consistent use of DOF population data will increase: (1) the reliability and validity of data and evaluation strategies relevant to populations being served, and (2) the effectiveness of 16 county program design and implementation that bridges gaps in service to underrepresented groups in high priority areas, accounting for each county’s unique blend of cultural demographics. 1.3 Formulate a routine procedure for counties to collect and monitor local county data, using websites and document downloads that are consistent across counties throughout California. This will facilitate consistent collection, management, and analysis of county- level data, for multiple years, allowing for high-quality comparisons to previous years and other counties. 1.4 Conduct on-site reviews of county-specific data collection, management, and analysis that can guide programs supported by MHSA funds. Ensure that measures are quantifiable, precise, and consistent over time. This action step can be accomplished by using a reliable data collection tool with specific measures. Key Findings to Research Question 2: What groups did each county target for reducing disparities? How well do these findings align with disparities in mental health service access over time, as identified in deliverable 1a of this report? CSS County Targets to Reduce Disparities Tables 7a–7bg present CSS and workforce education and training (WET) targets for each county (please see page 2 of each county-specific data table for targeting results). In this section, we focus on CSS targets (see below for WET targets). Overall, counties often targeted a large number of population groups and subpopulations. Of the 51 counties that presented mental health service targets, 22 reported less than five targets, 19 reported between six and nine, and 9 reported more than 10 targets (see Exhibit 5). Also, 18 counties reported targets that focused on a large portion of the general population (e.g., “children,” “adults,” “Latinos”), rather than pinpointing specific high-risk groups. This brings into question whether targeting was effective in numerous counties, since ‘targeted’ resources would be required to respond to mental health service needs in a broad portion of a county population, if not the entire county population. Frequently reported CSS targets were: 1. Race and ethnic groups, including:  Latinos  Asians and Pacific Islanders  Native Americans  African Americans 2. Age groups, including:  Children (0–5, 0–17)  Transitional-age Youth (18–25 years) 17 3. Risk groups, including:  Children with serious emotional disturbances  Individuals exposed to trauma  LGBTQ people vulnerable to discrimination Exhibit 5. Number of CSS Targets Reported by Counties Counties with 1-5 CSS Targets Counties with 6-9 CSS Targets Counties with 10 or More CSS Targets County CSS WET County CSS WET County CSS WET N N N N N N 1. El Dorado 5 4 1. Sutter-Yuba 9 8 1. San Diego 12 18 2. Stanislausa 5 5 2. Fresnoa 9 9 2. Orangec 13 NN 3. Kernb 5 ND 3. Alameda 9 7 3. Placerb 13 ND 4. Sacramento 4 6 4. San Joaquin 9 6 4. San Benito 13 2 5. Contra Costa 4 5 5. San Mateo 9 3 5. Montereyc 12 NN 6. Amadorc 4 NN 6. Shasta 9 3 6. Santa Clara 11 10 7. Del Norteb 4 ND 7. Siskiyouc 8 NN 7. San Francisco 11 3 8. Imperial 4 1 8. Los Angeles 7 4 8. San Luis 10 8 Obispo 9. Marinb 4 ND 9. Sonomac 7 NN 9. Yolo 10 4 10. Santa Barbara 4 2 10. Nevada 6 9 10. Napac 10 NN 11. Colusab 4 ND 11. Riversidea 6 6 12. Plumasb 4 ND 12. Modoc 6 3 13. Humboldt 3 4 13. Tuolumne 6 3 14. Butte 3 4 14. Madera 6 2 15. Lessenc 3 NN 15. Inyo 6 1 16. Mendocinoc 3 NN 16. Alpineb 6 ND 17. Tehamac 3 NN 17. Calaverasb 6 ND 18. Tularec 3 NN 18. Lakec 6 NN 19. Solanob 2 ND 19. Santa Cruzc 6 NN 20. Monoc 1 NN 21 San Bernardino 1 5 22. Trinitya 1 1 Note. a Counties with matching CSS and WET Targets, n = 4. b Counties with no differentiation (ND) between CSS and WET targets and other programs, n = 8. c Counties showing CSS targets but WET targets not noted (NN), n = 13. Note. Counties not in table is because data were not available A goal of the MHSA is to decrease disparities in access to mental health services across California counties. Analysis of CCP data is intended to yield insight into potential deficiencies in mental health agencies that are responsible for performing culturally competent services for unserved, underserved, and inappropriately served groups. Accordingly, the targeted groups mentioned above are consistent with the MHSA’s CSS components developed to improve access and quality of care, and to increase beneficial outcomes for these underrepresented 18 populations. Still, some of the targeted populations that highlighted specific demographic groups remained quite broad which could limit opportunities for focused interventions with the highest- risk populations. Counties that appeared to have sound methods for targeting priority groups were able to report three to five targets as shown in Exhibit 5 (e.g., Colusa, Contra Costa, El Dorado, Kern, Mendocino, San Bernardino, Solano). Our analysis suggests that these counties, after examining their general population data and populations in need of services, only focused on groups with specific characteristics that placed individuals within high priority groups that are not based on demographic data alone. That is, these targets consisted of specific high-risk groups (e.g., “trauma-exposed individuals,” “children and youth in stressed families,” “adults with serious mental illness who are imminently at risk of institutionalization or homelessness”). These counties also appeared to be more effective in matching their CSS target populations to programmatic developments (see discussion of WET targets below). One potential explanation for this finding is that effective, culturally competent programs are those that have responded to feedback from high-risk target groups. Community stakeholder feedback, when recognized and used to shape county-level plans to align funding resources with specific community needs, is essential to reduce disparities. Alignment of CSS targets with trends in access disparities identified in Deliverable 1a In reviewing the alignment of CSS targets with disparities highlighted in the statewide Client Services Information (CSI) data from Deliverable 1a, there was a general trend toward increasing access to mental health services in California among new and all clients following the implementation of the MHSA until 2008. This may reflect improvements in the outreach to clients and system wide changes to improve access to all clients following the MHSA. However, many population subgroups saw declining access for 2009 and 2010 with an upturn in 2011. We also noted that the trends in the CSI data varied by subgroup, region and county. The greatest disparities in access to mental health services were seen in older adults (age 60 and beyond). In the statewide data for both new clients and all clients, we noted increasing numbers and rates of access to mental health services among children, youth, and families (CYF) and transitional age youth (TAY) during the initial years following the implementation of MHSA. These trends indicate that counties were, in fact, showing signs of success in reaching higher numbers and proportions of children and youth, groups that many counties highlighted as “in need” in their CSS targets (Exhibits 5a, 5b). 19 Exhibit 5a. California mental health access trends by age group for new clients obtaining services Graph (a) shows the number of new clients by age group and year of service while graph (b) shows the number of new clients as a proportion of the state’s population for age subgroups. According to graph (a), the number of children, ages 0 to 15, that comprise the Children, Youth and Families (CYF) group, experienced a slow but steady increase in numbers accessing mental health services over the study period with the exception of a small drop in 2009. Transitional age youth (TAY), defined as the population age 16 to 25, also experienced increased numbers accessing services from the inception of the MHSA to 2008, but then there was a slow but continuing decrease in numbers through 2011. The adult population, ages 26- 59, comprise the dominant users (by overall numbers of new clients) of mental health services statewide and experienced increased access through 2008, but also saw a decline during 2009 and 2010. Then, a sharp increase in the numbers accessing services was seen in 2011. Older adults, those ages 60 plus, have the fewest numbers of new clients accessing mental health services in California and no discernable trend over the study period is seen. When reviewing the proportions accessing services by age group in graph (b), the CYF group showed increasing access over the study period with a steep rise from 2009 to 2011. In fact, for 2011, the CYF group had the highest proportional levels of access among all age groups to the mental health system. The TAY group had increased access from 2005 to 2008, but then declined through 2011. According to graph (b), although adults and older adults had an increase in access to mental health services from 2005 to 2008, both groups experienced a relative disparity in access to service compared to CYF and TAY groups. 20 Exhibit 5b. California mental health access trends by age group for all clients obtaining services. Graph (a) shows the number of all clients by age group and year of service while graph (b) shows the number of all clients as a proportion of the state’s population for age subgroups. Data were incomplete for 2012. According to graph (a), the number of children, ages 0 to 15, that comprise the CYF age group, experienced a slow but steady increase in numbers accessing mental health services over the study period with the exception of a very small drop in 2011. TAY also experienced increased numbers accessing services from the inception of the MHSA to 2008, but then there was a slow but continuing decrease in numbers through 2010. The adult population comprises the dominant users (by overall numbers of clients) of mental health services statewide and experienced increased access through 2008, but also saw a decline during 2009 and 2010. Older adults, those ages 60 plus, have the fewest numbers of clients accessing mental health services in California and no apparent trend over the study period is seen. When reviewing the proportions accessing services by age group in graph (b), the CYF group showed increasing access over the study period. For 2011, the CYF group had the highest proportional levels of access, among all age groups, to the mental health system. The TAY group had increased access from 2005 to 2009, but then declined through 2011. In graph (b), as in the data for new clients, although adults and older adults had an increase in access to mental health services from 2005 to 2008, both groups experienced a relative disparity in access to service compared to CYF and TAY groups. To portray the level of variability in CSS targeting on a county-by-county level, we present a selection of counties to highlight alignment across several targeting approaches in several geographic regions of California, allowing for comparison of CSI disparities to CSS targets within these counties. We highlight two counties that included less than five CSS targets in their CCP, two counties that included six to nine targets, and two counties that included greater than ten targets. For each county noted below, we did not include race and ethnicity due to data quality issues. Therefore, our analyses focus on gender and age targets combined with access trends from CSI data. Featured Counties with Less than Five CSS Targets Contra Costa County focused on four high-risk CSS populations with a specific emphasis on age groups within each target population. Overall, our CSI findings indicate that only the 21 transition-age youth (TAY) group made moderate increases in access to mental health services. However, between 2010 and 2012, access to care for this age group declined. The general trend for other age groups and both genders indicated a mild increase in access between 2005 and 2011. As noted in the analysis of Contra Costa’s CCP, focusing on specific age groups of a given population can be beneficial in strengthening the connection between that target group (e.g., children, youth, or older adults) and their increase in access to care. El Dorado County aimed to address the disparities of five CSS targets. All five groups were had age as the common variable. This county’s CSI data showed a positive trend in access to care for adults, and both males and females from 2005 to 2008. However a decrease in access was evident after 2008. While older adults had year-to-year variations in access, the overall trend was low access with no net increase or decrease over the study period. The greatest levels of mental health care was seen for children, youth, and family (CYF) and TAY groups with strong increases in access from 2005 to 2007. In examining the CSS targets for El Dorado County, we note that the first three target populations were very focused on specific subpopulations that would appear to be at high risk (e.g., youth at high risk, court-involved youth, adults with serious mental illness). The fourth and fifth targets are more general in nature (e.g., TAY adults and older adults). Overall, the TAY group appears to be the group with the highest disparities based on the CSI data. It is worth noting that there is convincing evidence that focusing on specific groups can lead to stronger outcomes. Featured Counties with Six to Nine CSS Targets Fresno County focused on nine CSS targets. In general, these nine groups in Fresno County experienced increased access to mental health services in the first year (2005-2006) after implementation of the MHSA (e.g., CYF, males, females, adults and older adults). However, these groups experienced a significant decrease in access for the balance of the study period. Moreover, CSS targets for Fresno County are relatively broadly focused on groups of all ages, which can be challenging to address in the short term. Veterans and members of the LGBTQ community are also listed as CSS targets which are even more focused. The decrease in access provides convincing evidence that disparities based on CSI data will persist for high specific CSS targets when they are no longer a priority. That is, the county focuses on too many targets at once. Santa Cruz County focused on six CSS targets. The common thread of these groups was high risk of a mental disorder. According to our CSI data, mental health access increased for all sex and age groups between 2005 and 2006 and for most groups increased again in 2007 (except TAY and older adults groups). From 2007 to 2010, decreases in access were seen for TAY, adult, male and female groups. All groups recovered in 2011 to levels above baseline, except for the TAY group, which although access increased in 2011, it did not return to baseline (2005) levels. The older adult group had the lowest overall access, but had the most consistent improvement in access over the study period. In reviewing the CSS targets for Santa Cruz, we note that the target populations listed are somewhat focused (e.g., trauma-exposed and at-risk), others are quite broad. While few of the highest-ranking CSS targets line up directly with disparities noted in the CSI disparities list, the underserved CSS targets, in general, align with the highest disparity (e.g., older adults) in access to mental health services. 22 Featured Counties with Ten or More CSS Targets Orange County concentrated on 13 CSS targets. Based on our CSI data, the groups that experienced increased access to mental health care between 2005 and 2010 including were females, males, adults, CYF, and TAY). All of these groups, however, had decreasing access after 2010. The TAY group had, by far, the highest levels of access overall while older adults had the lowest levels of access with no net change in access over the study period. While they are numerous, some of the CSS targets for Orange County are quite specific, and appear to be focused on subpopulations that are at high-risk, and that rank high on the list of groups with disparities in access to mental health services in the CSI data. Again, in general, focusing on too many CSS targets can be more challenging when addressing the disparities of the high-risk and high-priority groups. San Diego County focused on 18 CSS targets and the largest of all the counties in this report. According to our CSI data, the overall trend in access to mental health services for this county’s targets was positive between 2005 and 2011 with the exception of a mild dip of the trend between 2008 and 2010. The highest levels of access were seen for the children, youth, and families and the lowest levels occurred in the older adult group. Many of the 18 CSS targets for San Diego County are quite specific and primarily focused on age and gender. Veterans, LGBTQ, and immigrants are the other groups that are a bit broad. However, a number of the targets appear to be focused on subpopulations that align with subgroups experiencing the highest level of disparities in access to mental health services as noted in the CSI data (e.g., adults and older adults). Consistent with Orange County, the greater the number of targets, the more challenging it becomes to address disparities. However, it is important to note that San Diego County identified 12 WET targets that appear to be aligned with the CSS targets. Recommendations in response to research question 2: It is recommended that counties be provided with specific and streamlined guidelines to assess local mental health population needs and to select specific and focused CSS targets. Better targeting will lead to further improvement in the provision of mental health services, decreasing disparities in access to services among those who are disproportionately in need. The following recommendations are intended to help strengthen targeting on the county level: 2.1 Develop a targeting template for CCP requirements that assists counties in identifying three to five CSS targets that are specific (e.g., age group, racial and ethnic group, language, gender, high-risk community), relevant, accurate, and precise for each county and city. Provide counties with examples of effective targeting as “best practice examples” in an effort to improve focus on the high-risk populations that are in greatest need of services, and as substantiated by county level data. Data gathered and entered into this template can also be used as part of the counties' annual update, strengthening outreach and recruitment activities, while fostering service provision to key target populations. 2.2 Offer technical assistance to help each county explore gaps in gathering, managing, and analyzing CSS targeting data. Counties receiving MHSA funding to serve disadvantaged groups and overcome disparities should be required to produce specific targets that demonstrate a focused introspective assessment of needs within target groups that are 23 most disproportionately served, with a goal of increasing services to these populations. Technical assistance can help counties achieve this goal. 2.3 Ensure that counties specifically target five or less target populations as an approach to adequately identify not only disparities in services, but also strategies to address and eventually reduce disparities. Specifically, when it comes to workforce education and training, it is important that culturally congruent staff are hired to work with the counties’ CSS populations. Moreover, it is very likely that the 20 counties in Exhibit 5 that highlighted five or fewer CSS targets were more successful in matching their WET targets with their CSS target needs because they were more focused on priority populations. Counties that focus on groups with specific characteristics such as, high-risk groups (e.g., “trauma-exposed individuals,” “children and youth in stressed families,” “adults with serious mental illness who are imminently at risk of institutionalization or homelessness”), appeared to be more effective in matching their CSS target populations to programmatic developments. Funders of existing mental health services and/or outreach and education (including DHCS, DPH, OSHPD, and county departments of mental health) should coordinate and partner to engage on the development of specific plans to implement the above recommendations. Key Findings to Research Question 3: How does the county’s workforce compare to the general population? To the Medi-Cal population? To the CSS population? Tables 7a–7bg also present WET targets for each county (please see page 2 of each county- specific data table for targeting results). Overall, counties often targeted a large number of population groups and subpopulations for WET targets. Most counties that presented mental health service targets included more than five targets, and many included more than 10 targets. Counties were asked to differentiate between CSS and WET targets to ensure a more accurate assessment and to discern gaps. Completeness of data for these two target categories varied across counties. A total of eight counties did not differentiate between these two types of targets. Among the 25 counties that identified workforce targets, the targets frequently focused on the need to improve the diversity of mental health service staff. Many CCPs highlighted the need for hiring and training bilingual and bicultural mental health staff. References to hiring and working with Latino staff, Asian and Pacific Islander staff, and Spanish-speaking staff members appeared often. Several counties (e.g., Alameda, El Dorado, Stanislaus, Sutter-Yuba) also included WET targets that indicated a need for hiring and training mental health professionals with “lived experience,” meaning professionals who have experienced the effects of mental health conditions in their own lives, or in the lives of those around them. Frequently reported WET targets were: 1. Latino staff 2. Bilingual Spanish-speaking staff 3. Additional bilingual and bicultural staff 24 These findings point to the importance of language proficiency, cultural competency, and diversity in a workforce. These findings are also in accordance with the literature on cultural competence (Cabral & Smith, 2011) in that consumers will be more responsive to providers whom the consumer perceives to be knowledgeable about his or her cultural background and lived experiences. Completeness of workforce data was limited in several counties. When workforce data were included in CCPs, on numerous occasions data for several variables were missing. Among counties that did include workforce data, many workforce profiles appeared to reflect the larger general population of the county, and the population in need of services (as represented by CSI data). Connections between workforce data, the general population, and target populations deemed to have disparate needs typically were easier to see in well-organized CCPs. Exhibit 6 illustrates the number of workforce needs assessments included in CCP reports. Fifteen completed needs assessments were included in the narrative of county-level CCP reports and 22 were attached as tables at the end of county-level CCP reports. A total of 11 workforce needs assessments were not provided and 10 were incomplete or were not reviewed by our evaluation team. Although the findings in Exhibit 6 are limited by the incompleteness of the data, these findings provide insight into the opportunity for workforce self-assessment within future CCP reports and their applied utility within counties. The Department of Health Care Services (DHCS) could assist by working closely with counties to ensure that the data on workforce needs are relevant, and that the evidence generated could show a strong connection between having a diverse workforce and reducing disparities. An effective practice is for DHCS to reinforce a compliance mechanism that would consist of site visits to accurately assess each county’s workforce composition in relationship to the county’s population demographics. Exhibit 6. Workforce Needs Assessment Availability Needs Included Included Assessment in Body in Tables Sample Comments Provided N of Report of Report CCP does not contain formal Workforce Needs Assessment but does present workforce data by Yes 37 15 22 ethnicity, language and comparison to MC, 200% FPL, and general population. CCP contains table with data comparing workforce population to total population, MC population, 200% FPL population by ethnicity. No 11 0 0 CCP does not address prompts/requirements. CCP contains percentages of workforce by ethnicity but doesn’t provide a very good side-by-side comparison of this data to other populations required. Incomplete 10 0 0 No comments to report. Total 58 15 22 Note: Counties were required to provide a workforce needs assessment. 25 Focusing on counties that included workforce data in their CCPs, we found that, by and large, the composition of the mental health workforce was comparable to the general population data presented by counties in their CCPs. Workforce data focused on the breakdown of county mental health workforce by licensure status, agency status (e.g., internal, external), and race. Thus, to compare workforce data to the general, Medi-Cal, and CSS populations, we are limited to a focus on race. The proportions of mental health staff from each racial group in California counties, as garnered from the CCP workforce data, tended to be similar to the racial composition of the general population (see the second page of county-specific tables in the appendices) in most counties. When comparing county workforce data to the county-level Medi-Cal and CSS population data, however, more differences were noted. The racial composition of the mental health workforce in many counties demonstrated lower percentages of Latino staff than was needed according to the breakdown of Medical and CSS data, which portrayed higher percentages of clients within the Latino community. In some counties, there was also a disproportionately low percentage of mental health staff that were from the Asian/Pacific Islander or Native American community than was needed according to Medi-Cal and CSS data. The WET targets that were most often listed by counties reflected a need and desire to address these disparities. Many counties listed the training and hiring of Latino and Asian Pacific Islander staff as a highly ranked WET target, in order to better meet the needs of the local mental health community. That is, a number of counties also ranked the need for bilingual and bicultural staff to work with Latino and Asian clients, as well as clients from a number of other cultures and language groups. Recommendations in response to research question 3: Recognizing the need of culturally and linguistically competent staff that demonstrates a commitment to improve the delivery of services to unserved, underserved, and inappropriately served communities is critically important in rectifying disparities. To achieve that goal, the CCPs must reflect up-to-date workforce needs. We recommend the following actions to be addressed primarily by OSHPD in close collaboration with the county departments of mental health and other state agencies such as DHCS, DPH, and MHSOAC: 3.1 Develop a streamlined, easy to complete, workforce targeting template to examine and monitor staff diversity and the individual needs of each county. Provide all counties with these templates, and provide best practice workforce targeting examples to improve staff diversity, bilingual capabilities, and cultural competence. Monitoring data gathered from this workforce targeting template can inform county-level annual updates in an effort to strengthen their workforce training and recruitment programs. 3.2 Offer technical assistance to help each county explore gaps in gathering, managing, and analyzing workforce data. Existing templates that highlight the workforce composition on the county level can be streamlined and improved for clarity. Counties receiving MHSA funding to serve disadvantaged groups and overcome local disparities should be required to produce outcomes that demonstrate increases in workforce cultural competence, and evidence that disparities are decreasing. Technical assistance can help counties achieve this goal. 26 3.3 Expand the opportunities for hiring and inclusion of providers with lived experience for appropriate services, such as peer support groups. This recommendation’s core rests on the idea that lived experiences will strengthen relationships between providers and consumers within targeted communities. It may be worthwhile to share best practices from counties that specifically targeted people with lived experiences (e.g. Alameda, El Dorado, Stanislaus, and Sutter-Yuba). 3.3a Increase opportunities: (a) for career pathways for paraprofessionals, those with lived experiences, promotoras and community navigators to enhance the diversify the mental health workforce; (b) for interpreter training curricula for mental health professionals; and (c) for staff cultural competence training programs that will provide the most current cross-cultural knowledge and skills. 3.3b Emphasize language proficiency, cultural competency, and representative diversity to resolve disparities is a strategic means of increasing integration of consumers and families into the workforce. In analyses for the current report, most counties included workforce targets that focused on cultural and linguistic competence. Use of an updated and expanded version of the California Brief Multicultural Scale may be worth considering. Counties currently use this scale to assess and train county mental health staff in cultural competence, but stakeholders have expressed concerns that it should be updated to meet current day needs. 3.4 Strengthen counties’ data collection methods to ensure adequate collection of data from all MHSA programs. One way to achieve this is to involve target groups in the stakeholder community process to identify target population priorities that mirror county needs. Doing so is in line with the recommendations from 2013 MHSA audit by a California independent auditor. 3.4a Ensure that all counties and MHSA-funded cities put emphasis on WET targets that are specific (e.g., age group, racial and ethnic group, language, high-risk community), relevant, accurate, and precise for each county and city. This will help shape the next steps in improving evaluation approaches and mental health outcomes, thereby contributing to reductions in disparities. 3.4b Improve the quality of care for the targeted groups by ensuring that mental health staff members are able to communicate with consumers in a way that acknowledges the consumer’s needs and perspectives tied to mental health. In conjunction with this, counties should ascertain whether staff members implement mental health programs with fidelity to ensure best practice in service delivery. This speaks to our findings about counties being focused and directly connecting their WET target populations with their CSS populations. 27 Conclusion, Practical Implications, Limitations, and Future Research As the MHSA reaches its 10th anniversary, mental health leaders have recognized that the act has played and continues to fulfill an important role in transforming the mental health system by ensuring that counties are conducting culturally relevant mental health services and reducing disparities. The CCPs were designed to assess gaps in mental health service access and provision, and workforce diversity. They were also designed to assist counties in developing solutions to conquer current and future disparities in the mental health workforce and the delivery of services. A central goal of the CCPs, combined with the commitment of the counties, was to rectify the critical workforce shortages in the mental health industry by identifying gaps and solutions in the delivery of services. The desired solutions would translate into a transformation of the California mental health system that adequately responds to and addresses mental health disparities across the state. Overall, the findings from our analysis suggest that counties, especially those with strong data collection and monitoring capabilities, and those that focus on three to five specific target populations are in a position to: (1) meet the predominant requirement to increase language proficiency and cultural competency, and (2) provide representative diversity in the workforce in order to begin to transform mental health systems practices to reduce disparities. Practical Implications This report elucidates new points of reference that reveal the commitment of mental health service providers during the past several years to address disparities in access to mental health services and diversify their workforce. Evidence shows that MHSOAC, along with numerous counties, demonstrated a commitment to reduce disparities in access to mental health services. Our findings have several practical implications for MHSOAC as well as for counties. First, there are implications for DHCS to continue analyzing counties’ response to reducing mental health disparities using data from the CCPs. The use of the Department of Finance (DOF) data for county-level population estimates was found to be the most effective method in examining counties’ demographic variables and population changes. Additional requirements include, for instance, strengthening the communication with counties about completing and using CCP data to assess improvements in mental health access, disparities, and delivery and utilization of culturally and linguistically competent services. Moreover, our report suggests that counties that set clear objectives and realistic targets with fewer than five targets tend to be more effective in matching mental health services with target populations. Achieving this consists of three steps. First, streamlining the CCP requirements and submission processes reducing the number of forms, and keeping documentation manageable, will ensure a higher CCP completion rate and adequate time to review and assess how counties are responding to disparities. Next, adequate technical assistance for counties that require support and guidance in setting realistic targets, data identification, data management, data analysis, and data reporting. Finally, increase efficiency and promptness of reporting by creating an online CCP submission system. That not only would increase submission consistency, but also would expedite data entry, data management and data analysis. Such improvements are of critical importance to the utility and 28 scoring of the CCP to analyze and assess how counties are responding to reducing mental health disparities on an ongoing basis. Second, DHCS and MHSOAC may also use our findings to invest in continued evaluation activities to monitor future CCPs. Continued CCP evaluation can help monitor the adequate collection and assessment of CCP data. More importantly, monitor the availability of adequate resources to achieve MHSA’s goal to transform California’s mental health system into one that is adequately responding to and addressing mental health disparities of underrepresented communities as highlighted in the CCPs. That is, DHCS in close coordination with the MHSOAC, should continue analyzing all CCP data in order to monitor progress and help counties identify common trends in response to disparities. Third, our findings have implications for assessing the effectiveness of county-level workforce education, training and hiring practices related to language and cultural proficiency and workforce diversity to address mental health disparities. For instance, because several counties reported professionals with “lived experiences” as a priority, evaluations could focus on counties training and hiring of professionals who have experienced the effects of mental health conditions to reduce mental health disparities. Traditionally, consumers tend to be more responsive to providers whom they perceive to be knowledgeable about his or her cultural background and lived experiences. This is considered an important indicator of effectiveness in counties appropriately matching mental health services with target populations. Therefore, counties should continue offering incentives in the form of tuition stipends for graduate students in professional mental health programs, and loan repayment programs for individuals with graduate degrees—particularly for those working in rural areas or regions experiencing workforce shortages. Additional incentives should support the licensure of ethnically and racially diverse mental health providers (e.g., social workers and marriage and family therapists) to increase the number of licensed providers throughout the state. The key is to develop and sustain a culturally and linguistically competent mental health workforce consistent with the culture, language, and other important characteristics (e.g., gender identity and sexual orientation) of the targeted population. One strategy to diversify and sustain the mental health workforce and address the bilingual and bicultural shortages is for counties to explore the establishment of career pathways for immigrants who come from other countries with strong qualifications as mental health providers. Finally, strengthen the critical role that the Office of Statewide Health Planning & Development (OSHPD) and MHSOAC can serve in helping counties improve their recruitment and retention of bilingual and bicultural staff by facilitating provision of technical assistance and guidance, and sharing effective practices from other counties that have demonstrated success. Working with the CDPH’s Office of Health Equity (OHE) and to combine resources, could be a strategy to strengthen the recruitment and retention of bilingual and bicultural staff. 29 Limitations and Suggestions for Future Research This report was designed to analyze how the mental health system and counties respond to reducing mental health disparities. One limitation of this report is that CCPs were not submitted or where not available for one California county and two cities. Data in other counties, for which we were able to review CCPs, were not always complete. The missing data make it difficult to render absolute conclusions from findings with relationship to increasing workforce diversity and reducing disparities. Again, more resources and technical support are needed to support counties in compiling and presenting county-specific data in future CCPs. Nevertheless, we believe that our findings are noteworthy and provide good challenges for future research. For future research or analysis, MHSA researchers, at the local and state level, should continue to examine and determine how data from the CCP can be used and improved to assess counties’ continued commitment to respond to reducing mental health disparities. It became clear from our analysis that: (1) Using the DOF as a data source is the most effective approach for counties to obtain more consistent and concrete population estimates; (2) Counties with fewer program targets focused on specific subpopulations in particular need of mental health services seem to operate with more realistic benchmarks and yield better outcomes; and (3) improving the collection, management, and analysis of data tied to CCPs will allow for a more thorough and comprehensive understanding of the county-level response to mental health disparities across California. This is especially true for counties that serve and are targeting unserved, underserved, and inappropriately served populations in order to reduce mental health disparities. A statewide commitment to stronger and improved CCPs, and implementation of changes based on findings from this report, can continue to lead to a better understanding of and the reduction in mental healthcare disparities across California. Another future research recommendation is to put in place a mechanism that monitors the ongoing progress with documented corrective feedback for each county so that positive and negative trends in relationship to cultural competence are accurately measured and acted upon. Finally, future research and evaluation findings from the CCPs should be aligned with the Affordable Care Act (ACA) by: (1) exploring the integration of mental health services and primary care, and building workforce diversity in underrepresented communities; (2) ensuring that evaluation and data collection focuses on reducing mental health disparities for the most vulnerable communities; and (3) re-directing resources to target underrepresented groups and families, especially those traditionally low-income individuals and families, including immigrants, who have limited English proficiency and who do not have access to adequate resources. 30 Table 1. Population Data By County/Race/Ethnicity Using CCPs County General Pop. African Am. AA% API API% Latino Latino% Native Am. NA% White White% Other Other% Alameda 1,457,169 189,432 13.0% 349,721 24.0% 306,005 21.0% 539,153 37.0% 72,858 5.0% Alpine 1,145 218 19.0% 847 74.0% 80 7.0% Amador 4.0% 1.0% 9.0% 2.0% 82.0% 3.0% City of Berkeley Butte 204,752 2,457 1.2% 7,166 3.5% 29,689 14.5% 3,481 1.7% 156,021 76.2% 5,938 2.9% Calaveras 46,731 608 1.3% 794 1.7% 4,860 10.4% 794 1.7% 38,693 82.8% 1,308 2.8% Colusa 21,419 Contra Costa 1,042,478 96,803 9.3% 144,076 13.8% 252,553 24.2% 4,478 0.4% 506,949 48.6% 37,619 3.6% Del Norte 28,610 1,001 3.5% 1,001 3.5% 5,093 17.8% 2,232 7.8% 21,086 73.7% 1,287 4.5% El Dorado 156,299 813 0.5% 3,537 2.3% 14,566 9.3% 1,566 1.0% 140,209 89.7% 6,806 4.4% Fresno 930,450 81,880 8.8% 87,462 9.4% 468,016 50.3% 5,583 0.6% 304,257 32.7% 18,609 2.0% Glenn 28,111 166 0.6% 857 3.0% 9,741 34.7% 495 1.8% 16,411 58.4% 441 1.6% Humboldt 134,785 1,031 0.8% 2,321 1.7% 10,366 7.8% 9,146 6.9% 104,659 78.8% 5,271 4.0% Imperial 166,874 2,169 1.3% 834 0.5% 131,664 78.9% 501 0.3% 30,204 18.1% 1,502 0.9% Inyo 17,449 30 0.2% 261 1.5% 2,986 17.1% 1,751 10.0% 12,072 69.2% 349 2.0% Kern 786,000 47,160 6.0% 361,560 46.0% 495,180 63.0% Kings 141,225 11,722 8.3% 4,378 3.1% 64,964 46.0% 1,836 1.3% 56,066 39.7% 2,260 1.6% Lake 64,386 1,689 2.6% 677 1.1% 9,000 14.0% 2,335 3.6% 49,132 76.3% 0.0% Lassen 34,895 2,826 8.1% 523 1.5% 6,107 17.5% 1,221 3.5% 23,275 66.7% 1,221 3.5% Los Angeles 10,416,096 944,152 9.1% 1,391,495 13.4% 4,917,644 47.2% 27,612 0.3% 3,135,193 30.1% Madera 150,865 5,582 3.7% 3,017 2.0% 81,015 53.7% 4,073 2.7% 57,329 38.0% 6,336 4.2% Marin 248,794 7,713 3.1% 14,312 5.8% 35,016 14.1% 1,455 0.6% 218,870 88.0% 6,444 2.6% Mariposa 18,251 206 1.1% 170 0.9% 1,866 10.2% 602 3.3% 16,169 88.6% Mendocino 90,816 545 0.6% 2,180 2.4% 14,985 16.5% 4,023 4.4% 73,379 80.8% 11,534 12.7% Merced 273,935 6,920 2.5% 16,299 6.0% 153,698 56.1% 1,232 45.0% 91,799 33.5% 3,987 1.5% Modoc 9,197 75 0.8% 68 0.7% 1,201 13.1% 359 3.9% 7,286 79.2% 208 2.3% Mono 14,833 69 0.5% 185 1.2% 4,348 29.3% 303 2.0% 9,682 65.3% 246 1.7% Monterey 430,418 12,913 3.0% 30,129 7.0% 241,034 56.0% 137,734 32.0% 8,608 2.0% Napa 136,484 2,440 1.8% 8,986 6.6% 44,010 32.2% 544 0.4% 76,967 56.4% 3,537 2.6% Nevada 97,027 508 0.5% 1,253 1.3% 7,310 7.5% 767 0.8% 85,286 87.9% 1,903 2.0% Orange 3,048,000 45,000 1.5% 493,000 16.2% 705,000 23.1% 19,000 0.6% 1,495,000 49.0% 291,000 9.5% Placer 341,945 5,813 1.7% 18,807 5.5% 40,008 11.7% 3,078 0.9% 268,085 78.4% 8,891 2.6% Plumas 20,760 132 0.6% 130 0.6% 1,186 5.7% 489 2.4% 18,370 88.5% 453 2.2% Riverside 2,119,618 10,598 0.5% 97,502 4.6% 866,924 40.9% 10,598 0.5% 977,144 46.1% 36,034 1.7% Sacramento 1,400,949 135,892 9.7% 201,737 14.4% 287,195 20.5% 8,406 0.6% 715,885 51.1% 53,236 3.8% San Benito 54,667 493 0.9% 1,601 2.9% 28,984 53.0% 295 0.5% 22,508 41.2% 786 1.4% San Bernardino 2,017,673 181,591 9.0% 121,060 6.0% 968,483 48.0% 20,177 1.0% 686,009 34.0% 40,353 2.0% San Diego 2,974,859 145,227 4.9% 310,575 10.4% 901,369 30.3% 15,928 0.5% 1,528,568 51.4% 73,192 2.5% 31 Table 1. Population Data By County/Race/Ethnicity Using CCPs (Continued) County General Pop. African Am. AA% API API% Latino Latino% Native Am. NA% White White% Other Other% San Francisco 815,358 55,444 6.8% 259,284 31.8% 114,965 14.1% 4,892 0.6% 473,723 58.1% 22,830 2.8% San Joaquin 674,860 53,989 8.0% 97,855 14.5% 253,747 37.6% 9,448 1.4% 489,274 72.5% 24,295 3.6% San Luis Obispo 262,238 4,952 1.9% 8,385 3.2% 49,172 18.8% 2,435 0.9% 224,177 85.5% 22,289 8.5% San Mateo 736,667 26,520 3.6% 203,320 27.6% 188,587 25.6% 1,473 0.2% 313,820 42.6% 17,680 2.4% Santa Barbara 407,057 9,769 2.4% 19,132 4.7% 160,788 39.5% 6,920 1.7% 10,176 2.5% Santa Clara 1,748,976 43,999 2.5% 538,646 30.8% 449,133 25.7% 4,751 0.3% 674,765 38.6% 37,682 2.2% Santa Cruz 256,218 3,331 1.3% 7,430 2.9% 75,072 29.3% 3,075 1.2% 161,161 62.9% 6,149 2.4% Shasta 181,099 1,911 1.0% 4,773 2.4% 14,727 15.0% 3,648 1.9% 149,871 76.5% 6,169 3.2% Sierra Siskiyou 44,404 616 1.4% 738 1.7% 4,303 9.7% 1,183 2.7% 38,658 87.1% 3,209 7.2% Solano 407,515 57,622 14.1% 59,750 14.7% 92,094 22.6% 380 0.1% 176,317 43.3% 21,352 5.2% Sonoma 464,326 8,358 1.8% 19,966 4.3% 109,581 23.6% 7,429 1.6% 314,349 67.7% 13,465 2.9% Stanislaus 511,263 13,942 2.7% 26,667 5.2% 199,543 39.0% 3,843 0.8% 256,569 50.2% 10,699 2.1% Sutter-Yuba 164,138 4,279 2.6% 17,161 10.5% 41,229 25.1% 2,609 1.6% 94,501 57.6% 4,359 2.7% Tehama 61,138 611 1.0% 856 1.4% 12,961 21.2% 1,467 2.4% 56,613 92.6% 1,590 2.6% Tri-City Trinity 13,043 69 0.5% 171 1.3% 705 5.4% 204 1.6% 12,391 95.0% Tulare 368,021 5,852 1.6% 12,439 3.4% 186,844 50.8% 12,034 3.3% 213,747 58.1% 130,243 35.4% Tuolumne 56,910 1,138 2.0% 570 1.0% 5,691 10.0% 1,138 2.0% 47,235 83.0% 1,138 2.0% Ventura 798,364 17,212 2.2% 53,247 6.7% 296,745 37.2% 9,112 1.1% 417,425 52.3% Yolo 195,844 5,023 2.6% 23,917 12.2% 54,766 28.0% 1,378 0.7% 105,430 53.8% 5,330 2.7% Note: The data is based on CCPs made available by the California Department of Health Care Services for analysis. Sections with blanks indicate that data were not available. 32 Table 2. Special Population Data By Age and Gender Using CCPs County Child Child% TAY TAY% Adult Adult% Older Adult Old Adult% Male Male% Female Female% Alameda 276,862 19.0% 204,004 14.0% 816,015 56.0% 160,289 11.0% 714,013 49.0% 743,156 51.0% Alpine 207 18.1% 803 70.1% 134 11.7% 601 52.5% 544 47.5% Amador 14.0% 11.0% 46.0% 18.0% 54.0% 46.0% City of Berkeley Butte 46,250 22.5% 20,440 10.0% 119,281 58.3% 18,782 9.2% 93,732 45.8% 111,020 54.2% Calaveras 10,748 23.0% 27,057 57.9% 8,926 19.1% Colusa Contra Costa 263,156 25.2% 656,732 63.0% 122,589 11.8% 507,955 48.7% 534,522 51.3% Del Norte 6,151 21.5% 18,597 65.0% 3,862 13.5% 15,907 55.6% 12,703 44.4% El Dorado 44,688 28.6% 6,763 4.3% 85,652 54.8% 25,946 16.6% 77,993 49.9% 78,306 50.1% Fresno 277,088 29.8% 109,049 11.7% 453,967 48.8% 90,347 9.7% 464,853 50.0% 465,597 50.0% Glenn 7,652 27.2% 14,250 50.7% 6,209 22.1% 14,227 50.6% 13,884 49.4% Humboldt 22,431 16.6% 21,898 16.2% 64,247 47.7% 26,209 19.4% 66,901 49.6% 67,884 50.4% Imperial 56,570 33.9% 13,183 7.9% 79,599 47.7% 17,522 10.5% 86,674 51.9% 80,200 48.1% Inyo 3,648 20.9% 8,466 48.5% 5,335 30.6% 8,562 49.1% 8,887 50.9% Kern 322,260 41.0% 393,000 50.0% 70,740 9.0% 408,720 52.0% 377,280 48.0% Kings 39,543 28.0% 90,808 64.3% 10,733 7.6% 80,498 57.0% 60,727 43.0% Lake 14,850 23.1% 5,794 9.0% 36,587 56.8% 11,949 18.6% 31,694 49.2% 32,692 50.8% Lassen 6,456 18.5% 25,299 72.5% 3,141 9.0% Los Angeles 2,367,592 22.7% 1,560,167 15.0% 4,915,321 47.2% 1,573,016 15.1% 5,161,564 49.6% 5,254,532 50.4% Madera 44,354 29.4% 90,821 60.2% 15,690 10.4% 72,566 48.1% 78,299 51.9% Marin Mariposa 3,741 20.5% 9,121 50.0% 4,921 27.0% 9,081 49.8% 8,711 47.7% Mendocino 25,701 28.3% 16,256 17.9% 45,136 49.7% 45,680 50.3% Merced Modoc 1,825 19.8% 4,324 47.0% 3,048 33.1% 4,637 50.4% 4,560 49.6% Mono 3,471 23.4% 9,641 65.0% 1,721 11.6% Monterey 120,517 28.0% 262,555 61.0% 47,346 11.0% 219,513 51.0% 210,905 49.0% Napa Nevada 17,550 18.1% 47,840 49.3% 31,637 32.6% 48,172 49.6% 48,855 50.4% Orange 791,000 26.0% 292,000 9.6% 1,653,000 54.2% 309,000 10.1% 1,513,000 49.6% 1,535,000 50.4% Placer 74,202 21.7% 215,425 63.0% 52,318 15.3% 137,804 40.3% 173,366 50.7% Plumas Riverside 604,091 28.5% 1,214,541 57.3% 298,866 14.1% Sacramento 361,445 25.8% 815,352 58.2% 224,152 16.0% 689,267 49.2% 711,682 50.8% San Benito 15,838 29.0% 28,672 52.4% 10,157 18.6% 27,775 50.8% 26,892 49.2% San Bernardino 686,009 34.0% 1,331,664 66.0% 1,008,837 50.0% 1,008,837 50.0% San Diego 749,170 25.2% 1,894,869 63.7% 330,820 11.1% 1,494,127 50.2% 1,480,732 49.8% 33 Table 2. Special Population Data By Age and Gender Using CCPs (Continued) County Child Child% TAY TAY% Adult Adult% Older Adult Old Adult% Male Male% Female Female% San Francisco 146,764 18.0% 57,075 7.0% 513,676 63.0% 122,304 15.0% 415,833 51.0% 399,525 49.0% San Joaquin 259,821 38.5% 346,878 51.4% 68,161 10.1% 338,780 50.2% 336,080 49.8% San Luis Obispo 49,498 18.9% 212,740 81.1% 37,388 14.3% 135,551 51.7% 126,687 48.3% San Mateo 180,483 24.5% 416,954 56.6% 139,230 18.9% Santa Barbara 96,065 23.6% 258,074 63.4% 52,917 13.0% 205,564 50.5% 201,493 49.5% Santa Clara 419,608 24.0% 159,009 9.1% 983,694 56.2% 186,665 10.7% 895,003 51.2% 853,973 48.8% Santa Cruz 27,159 10.6% 128,621 50.2% 127,597 49.8% Shasta 33,969 18.8% 25,008 13.7% 81,796 45.2% 40,326 22.3% 88,539 48.9% 92,560 51.1% Sierra Siskiyou 2,330 5.2% 35,068 79.0% 8,348 18.8% 21,955 49.4% 22,449 50.6% Solano 102,650 25.2% 256,181 62.9% 45,684 11.2% 204,573 50.2% 202,942 49.8% Sonoma 28,324 6.1% 360,317 77.6% 60,362 13.0% 231,613 49.9% 237,993 51.3% Stanislaus 145,874 28.5% 313,163 61.3% 52,226 10.2% 253,014 49.5% 258,249 50.5% Sutter-Yuba 44,865 27.3% 101,401 61.8% 17,872 10.9% 81,813 49.8% 82,325 50.2% Tehama 19,381 31.7% 32,464 53.1% 9,293 15.2% 30,324 49.6% 30,814 50.4% Tri-City Trinity Tulare 124,391 33.8% 39,010 10.6% 168,554 45.8% 36,066 9.8% Tuolumne Ventura Yolo 48,798 24.9% 111,660 57.0% 35,386 18.1% 96,057 49.0% 99,787 51.0% Note: The data is based on CCPs made available by the California Department of Health Care Services for analysis. Sections with blanks indicate that data were not available. 34 Table 3. Medi-Cal Population Data By Race/Ethnicity Using CCP County Total Pop African Am. AA% API API% Latino Latino% Native Am. NA% White White% Other Other% Alameda 258,231 76,295 29.5% 59,690 23.1% 65,538 25.4% 943 0.4% 33,833 13.1% 21,932 8.5% Alpine 153 1 0.5% 1 0.5% 132 86.0% 20 13.0% Amador 1.0% 1.0% 11.0% 3.0% 81.0% 3.0% City of Berkeley Butte 48,778 1,951 4.0% 3,902 8.0% 3,902 8.0 1,463 3.0% 35,609 73.0% 1,951 4.0% Calaveras 524 3 0.6% 7 1.3% 37 7.1% 11 2.1% 441 84.2% 25 4.8% Colusa 4,880 53 1.1% 81 1.7% 3,410 69.9% 83 1.7% 1,169 24.0% 86 1.8% Contra Costa 125,645 27,190 21.6% 12,665 10.1% 48,172 38.3% 415 0.3% 27,064 21.5% 10,140 8.1% Del Norte 8,439 45 0.5% 733 8.7% 1,036 12.3% 825 9.8% 5,480 64.9% 323 3.8% El Dorado Fresno 300,405 23,151 7.7% 32,456 10.8% 193,806 64.5% 1,894 0.6% 42,381 14.1% 11,719 3.9% Glenn Humboldt 27,355 540 2.0% 1,000 3.7% 2,559 9.4% 2,844 10.4% 19,285 70.5% 1,127 4.1% Imperial 52,517 877 1.7% 283 0.5% 44,642 85.0% 478 0.9% 4,601 8.8% 1,636 3.1% Inyo 3,416 30 0.9% 21 0.6% 913 26.7% 687 20.1% 1,671 48.9% 94 2.8% Kern Kings 34,068 2,385 7.0% 1,022 3.0% 23,166 68.0% 102 0.3% 6,814 20.0% 920 2.7% Lake 1,236 53 4.3% 10 0.8% 79 6.4% 47 3.8% 998 80.7% 49 4.0% Lassen 4,967 122 2.5% 40 0.8% 598 12.0% 302 6.1% 3,642 73.3% 263 5.3% Los Angeles 2,030,535 233,394 11.5% 226,385 11.1% 1,242,950 61.2% 2,260 0.1% 246,041 12.1% 79,505 3.9% Madera 38,963 1,184 3.0% 536 1.4% 27,569 70.8% 302 0.8% 8,219 21.1% 1,153 3.0% Marin 21,978 1,903 8.7% 1,403 6.4% 9,598 43.7% 73 0.3% 8,381 38.1% 620 2.8% Mariposa Mendocino 22,688 231 1.0% 334 1.5% 7,000 30.9% 1,828 8.1% 12,611 55.6% 687 3.0% Merced 2,803 274 9.8% 337 12.0% 883 31.5% 17 0.6% 1,112 39.7% 180 6.4% Modoc 2,125 19 0.9% 19 0.9% 405 19.1% 144 6.8% 1,432 67.4% 106 5.0% Mono 1,300 773 59.5% 83 6.4% 392 30.2% Monterey 80,613 63,454 78.7% 9,588 11.9% Napa 14,423 299 2.1% 662 4.6% 7,816 54.2% 61 0.4% 5,095 35.3% 490 3.4% Nevada 1,302 11 0.8% 7 0.5% 137 10.5% 36 2.8% 1,093 83.9% 18 1.4% Orange 349,000 12,000 3.4% 52,000 14.9% 181,000 51.9% 2,000 0.6% 63,000 18.1% 39,000 11.2% Placer 27,420 701 2.6% 1,338 4.9% 5,712 20.8% 314 1.1% 17,185 62.7% 1,990 7.3% Plumas 2,921 76 2.6% 15 0.5% 237 8.1% 110 3.8% 2,363 80.9% 120 4.1% Riverside 336,844 30,653 9.1% 11,453 3.4% 198,738 59.0% 1,011 0.3% 75,453 22.4% 19,200 5.7% Sacramento 314,765 59,491 18.9% 51,936 16.5% 79,636 25.3% 2,518 0.8% 87,505 27.8% 33,365 10.6% San Benito 8,648 68 0.8% 114 1.3% 6,728 77.8% 14 0.2% 1,429 16.5% 295 3.4% San Bernardino 398,175 54,240 13.6% 15,459 3.9% 224,110 56.3% 1,431 0.4% 85,014 21.4% 17,921 4.5% San Diego 378,319 37,350 9.9% 37,183 9.8% 181,027 47.9% 1,556 0.4% 85,958 22.7% 35,248 9.3% 35 Table 3. Medi-Cal Population Data By Race/Ethnicity Using CCP (Continued) County Total Pop African Am. AA% API API% Latino Latino% Native Am. NA% White White% Other Other% San Francisco San Joaquin 159,367 19,823 12.4% 25,181 15.8% 72,863 45.7% 594 0.4% 34,413 21.6% 6,493 4.1% San Luis Obispo 33,089 602 1.8% 691 2.1% 13,287 40.2% 183 0.6% 16,834 50.9% 1,494 4.5% San Mateo 64,011 4,246 6.6% 11,784 18.4% 32,347 50.5% 118 0.2% 10,032 15.7% 31 0.0% Santa Barbara 74,073 1,888 2.5% 1,686 2.3% 37,085 50.1% 285 0.4% 30,390 41.0% 2,742 3.7% Santa Clara 245,333 9,696 4.0% 65,851 26.8% 124,781 50.9% 872 0.4% 31,976 13.0% 12,160 5.0% Santa Cruz 5,949 173 2.9% 83 1.4% 1,731 29.1% 61 1.0% 3,750 63.0% 151 2.6% Shasta 41,306 841 44.0% 1,613 33.8% 2,951 20.0% 1,549 42.5% 32,749 21.9% 1,603 26.0% Sierra Siskiyou 10,709 249 2.3% 210 2.0% 1,025 9.6% 687 6.4% 7,802 72.9% 737 6.9% Solano 62,794 16,617 26.5% 7,365 11.7% 20,012 31.9% 353 0.6% 14,495 23.1% 3,952 6.3% Sonoma 5,134 260 5.1% 126 2.5% 466 9.1% 95 1.9% 3,858 75.2% 141 2.8% Stanislaus 123,574 4,898 4.0% 6,793 5.5% 63,542 51.4% 398 0.3% 41,016 33.2% 6,927 5.6% Sutter-Yuba 42,815 1,362 3.2% 5,602 13.1% 14,464 33.8% 549 1.3% 19,366 45.2% 1,474 3.4% Tehama 14,916 131 0.9% 211 1.4% 3,732 25.0% 254 1.7% 10,127 67.9% 461 3.1% Tri-City Trinity 2,846 14 0.5% 19 0.7% 100 3.5% 145 5.1% 2,569 90.3% Tulare 151,320 2,969 2.0% 5,045 3.3% 108,628 71.8% 754 0.5% 28,073 18.6% 5,853 3.9% Tuolumne 1,525 5 0.3% 14 1.0% 54 3.5% 17 1.1% 1,351 88.6% 84 5.5% Ventura Yolo 31,271 1,443 4.6% 2,221 7.1% 14,882 47.6% 277 0.9% 9,381 30.0% 3,067 9.8% Note: The data is based on CCPs made available by the California Department of Health Care Services for analysis. Sections with blanks indicate that data were not available. 36 Table 4. Medi-Cal Population Data By Age and Gender Using CCP County Child Child% TAY TAY% Adult Adult% Older Adult Older Adult% Male Male% Female Female% Alameda 111,351 43% 23,782 9% 71,797 28% 51,301 20% 111,943 43% 146,288 57% Alpine 85 56% 57 37% 11 7% 63 41% 90 59% Amador 40% 43% 16% 42% 58% City of Berkeley Butte Calaveras 21 4% 133 25% 336 64% 34 6% 218 42% 306 58% Colusa 2,477 51% 1,766 36% 638 13% 2,210 45% 2,670 55% Contra Costa 55,560 44% 49,969 40% 20,116 16% 52,884 42% 72,761 58% Del Norte 3,310 39% 4,062 48% 1,069 13% 3,890 46% 4,549 54% El Dorado Fresno 155,112 52% 120,400 40% 29,894 10% 136,312 45% 169,093 56% Glenn Humboldt 9,733 36% 3,958 14% 10,463 38% 3,201 12% 12,387 45% 14,968 55% Imperial 23,202 44% 4,942 9% 15,612 30% 8,761 17% 22,967 44% 29,550 56% Inyo 1,067 31% 1,206 35% 1,143 33% 1,497 44% 1,919 56% Kern Kings 18,056 53% 12,946 38% 3,407 10% 14,990 44% 19,078 56% Lake 400 32% 738 60% 98 8% 570 46% 666 54% Lassen 1,950 39% 910 18% 1,841 37% 266 5% Los Angeles 1,013,346 50% 318,828 16% 375,689 19% 320,859 16% 911,809 45% 1,118,945 55% Madera 20,417 52% 3,840 10% 11,516 30% 3,190 8% 17,370 45% 21,593 55% Marin Mariposa Mendocino 9,773 43% 10,068 44% 2,848 13% 10,226 45% 12,462 55% Merced Modoc 607 29% 813 38% 705 33% 925 44% 1,200 56% Mono Monterey 40,964 51% 31,202 39% 8,448 10% 35,030 43% 45,583 57% Napa 6,595 46% 5,714 40% 2,114 15% 6,287 44% 8,136 56% Nevada 482 37.0% 644 49.5% 176 13.5% 618 52.5% 684 47.5% Orange 188,000 54% 25,000 7% 91,000 26% 47,000 13% 155,000 44% 194,000 56% Placer 11,582 42% 11,120 41% 4,538 17% 11,541 42% 15,699 57% Plumas 1,319 45% 1,286 44% 316 11% Riverside 180,885 54% 112,169 33% 43,116 13% 144,405 43% 192,439 57% Sacramento 143,848 46% 131,572 42% 39,346 13% 138,182 44% 176,583 56% San Benito 3,148 36% 3,020 35% 2,480 29% 3,570 41% 5,078 59% San Bernardino 188,718 47% 60,272 15% 104,075 26% 45,109 11% 173,302 44% 224,873 56% San Diego 178,766 47% 134,125 35% 65,430 17% 160,666 42% 217,654 58% 37 Table 4. Medi-Cal Population Data By Age and Gender Using CCP (Continued) County Child Child% TAY TAY% Adult Adult% Older Adult Older Adult% Male Male% Female Female% San Francisco San Joaquin 79,172 50% 8,832 6% 56,938 36% 14,425 9% 70,081 44% 89,286 56% San Luis Obispo 14,846 45% 14,074 43% 4,171 13% 14,362 43% 18,728 57% San Mateo 29,470 46% 21,615 34% 12,926 20% 26,360 41% 37,651 59% Santa Barbara 36,873 29,169 39% 8,032 11% 32,152 43% 41,921 57% Santa Clara 100,329 41% 91,851 37% 53,155 22% 105,249 43% 140,084 57% Santa Cruz 1,301 21.9% 1,136 19.1% 3,041 51.1% 471 7.9% 3,295 55.3% 2,654 44.7% Shasta 16,623 39.9% 19,334 19.5% 5,347 13.3% 18,427 20.8% 22,876 24.7% Sierra Siskiyou 4,265 40% 4,868 45% 1,577 15% 4,880 46% 5,829 54% Solano 28,765 46% 27,238 43% 6,791 11% 26,567 42% 36,227 58% Sonoma 1,813 35% 2,882 56% 439 9% 2,766 54% 2,349 46% Stanislaus 60,448 49% 51,529 42% 11,597 9% 54,243 44% 69,331 56% Sutter-Yuba 19,961 47% 17,568 41% 5,287 12% 19,150 45% 23,665 55% Tehama 6,838 46% 1,322 9% 6,544 44% 1,534 10% 6,418 43% 8,498 57% Tri-City Trinity 1,115 39% 1,403 49% 327 11% 1,316 46% 1,530 54% Tulare 77,958 52% 59,739 39% 13,624 9% 68,033 45% 83,288 55% Tuolumne 482 31.6% 221 14.5% 758 49.7% 64 4.2% 866 56.8% 659 43.2% Ventura Yolo 14,384 46% 12,414 40% 4,473 14% 13,676 44% 17,595 56% Note: The data is based on CCPs made available by the California Department of Health Care Services for analysis. Sections with blanks indicate that data were not available. 38 Table 5. CSS Population Data By Race/Ethnicity Using CCP County Total Pop African Am. AA% API API% Latino Latino% Native Am. NA% White White% Other Other% Alameda 447,723 101,894 22.8% 94,526 21.1% 115,645 25.8% 2,405 0.5% 110,791 24.8% 22,462 5.0% Alpine Amador City of Berkeley Butte 7,496 266 3.5% 407 5.4% 503 6.7% 314 4.2% 5,863 78.3% 143 1.9% Calaveras Colusa Contra Costa 184,550 34,488 18.7% 18,044 9.8% 59,429 32.2% 813 0.4% 63,111 34.2% 8,665 4.7% Del Norte El Dorado Fresno Glenn Humboldt 40,999 479 1.2% 919 2.2% 2,822 6.9% 2,519 6.1% 30,908 75.4% 3,352 8.2% Imperial 89,637 1,878 2.1% 2,107 2.4% 67,215 75.0% 2,129 2.4% 14,923 16.7% 1,385 1.6% Inyo Kern Kings Lake 4.4% 1.1% 10.5% 3.4% 80.1% 0.5% Lassen 9,059 89 1.0% 95 1.1% 1,077 11.9% 515 5.7% 6,983 77.1% 300 3.3% Los Angeles 3,734,626 364,446 9.8% 370,349 9.9% 2,426,069 65.0% 9,180 0.3% 564,582 15.1% Madera 11,103 635 5.7% 196 1.8% 5,314 47.9% 138 1.2% 4,197 37.8% 151 1.4% Marin Mariposa Mendocino 33,731 228 0.7% 561 1.7% 7,924 23.5% 2,014 6.0% 21,773 64.6% 1,232 3.7% Merced Modoc 3,760 14 0.4% 17 0.5% 652 17.3% 194 5.2% 2,784 74.0% 99 2.6% Mono 4,141 19 0.5% 32 0.8% 1,309 31.6% 173 4.2% 2,506 60.5% 102 2.5% Monterey 179,000 4,000 2.2% 155,000 86.6% 17,000 9.5% 3,000 1.7% Napa 29,555 355 1.2% 826 2.8% 11,616 39.3% 234 0.8% 15,931 53.9% 591 2.0% Nevada 1,345 10 0.7% 3 0.2% 28 2.1% 12 0.9% 1,279 95.1% 13 1.0% Orange 779,195 10,682 1.4% 112,790 14.5% 449,943 57.7% 2,852 0.4% 181,598 23.3% 21,330 2.7% Placer 28,665 411 1.4% 828 2.9% 3,006 10.5% 193 0.7% 23,554 82.2% 673 2.4% Plumas Riverside 142,511 8,799 6.2% 6,416 4.5% 62,259 43.7% 582 0.4% 61,744 43.3% 2,711 1.9% Sacramento 424,356 54,598 12.9% 68,459 16.1% 94,926 22.4% 4,485 1.1% 179,030 42.2% 22,858 5.4% San Benito San Bernardino 697,417 68,956 9.9% 37,647 5.4% 355,682 51.0% 4,607 0.7% 209,729 30.1% 20,796 3.0% San Diego 575,086 33,229 5.8% 48,438 8.4% 203,030 35.3% 3,457 0.6% 194,837 33.9% 92,095 16.0% 39 Table 5. CSS Population Data By Race/Ethnicity Using CCP (Continued) County Total Pop African Am. AA% API API% Latino Latino% Native Am. NA% White White% Other Other% San Francisco 183,622 20,214 11.0% 67,349 36.7% 36,714 20.0% 674 0.4% 54,084 29.5% 4,587 2.5% San Joaquin San Luis Obispo 90,766 1,189 1.3% 2,389 2.6% 29,379 32.4% 1,016 1.1% 54,662 60.2% 2,131 2.4% San Mateo 108,335 4,918 4.5% 18,428 17.0% 49,832 46.0% 616 0.6% 29,643 27.4% 4,898 4.5% Santa Barbara 129,140 2,281 1.8% 4,758 3.7% 73,140 56.6% 657 0.5% 45,652 35.4% 2,652 2.1% Santa Clara 309,672 8,239 2.7% 83,213 26.9% 144,342 46.6% 1,161 0.4% 65,560 21.2% 7,158 2.3% Santa Cruz 255,602 2,556 1.0% 8,691 3.4% 68,501 26.8% 167,419 65.5% 8,435 3.3% Shasta Sierra Siskiyou 7,904 145 1.8% 311 3.9% 933 11.8% 503 6.4% 6,012 76.1% Solano 395,426 55,959 14.2% 59,812 15.1% 84,121 21.3% 1,661 0.4% 176,872 44.7% 17,001 4.3% Sonoma 100,116 2,191 2.2% 3,587 3.6% 33,381 33.3% 1,811 1.8% 68,637 68.6% 18,016 18.0% Stanislaus 166,071 4,722 2.8% 8,768 5.3% 74,844 45.1% 1,155 0.7% 69,916 42.1% 6,666 4.0% Sutter-Yuba 31,647 809 2.6% 3,182 10.1% 9,536 30.1% 745 2.4% 15,308 48.4% 2,067 6.5% Tehama 22,150 129 0.6% 202 0.9% 5,192 23.4% 592 2.7% 15,345 69.3% 690 3.1% Tri-City Trinity Tulare Tuolumne Ventura 167,792 3,706 2.2% 11,580 6.9% 99,111 59.1% 1,887 1.1% 48,207 28.7% 3,301 2.0% Yolo Note: The data is based on CCPs made available by the California Department of Health Care Services for analysis. Sections with blanks indicate that data were not available. 40 Table 6. CSS Population Data By Age and Gender Using CCP County Child Child% TAY TAY% Adult Adult% Older Adult Older Adult% Male Male% Female Female% Alameda 102,559 22.9% 60,982 13.6% 238,151 53.2% 46,031 10.3% Alpine Amador City of Berkeley Butte 2,379 31.7% 1,076 14.4% 3,674 49.0% 367 4.9% 3,482 46.5% 4,014 53.5% Calaveras Colusa Contra Costa 63,188 34.2% 89,808 48.7% 31,554 17.1% Del Norte El Dorado Fresno Glenn Humboldt 16,069 39.2% 6,126 14.9% 17,665 43.1% 1,139 2.8% Imperial 28,244 31.5% 6,236 7.0% 45,948 51.3% 9,209 10.3% Inyo Kern Kings Lake 23.0% 25.0% Lassen 3,146 34.7% 5,913 65.3% 4,358 48.1% 4,701 51.9% Los Angeles 1,138,654 30.5% 585,904 15.7% 1,540,601 41.3% 469,376 12.6% 1,769,196 47.4% 1,965,430 52.6% Madera 2,806 25.3% 2,460 22.2% 4,176 37.6% 868 7.8% 4,004 36.1% 6,483 58.4% Marin Mariposa Mendocino 10,823 32.1% 3,869 11.5% 14,348 42.5% 3,377 10.0% Merced Modoc 1,201 31.9% 127 3.4% 1,844 49.0% 588 15.6% Mono 1,202 29.0% 2,939 71.0% Monterey 73,000 40.8% 93,000 52.0% 12,000 6.7% 96,000 53.6% 81,000 45.3% Napa 6,057 20.5% 3,948 13.4% 13,623 46.1% 5,927 20.1% Nevada 356 26.5% 67 4.9% 867 64.5% 55 4.1% 623 46.3% 722 53.7% Orange 243,228 31.2% 154,997 19.9% 303,837 39.0% 77,133 9.9% Placer 6,584 23.0% 3,525 12.3% 15,206 53.0% 3,350 11.7% Plumas Riverside 44,815 31.4% 77,359 54.3% 15,015 10.5% Sacramento 158,788 37.4% 55,282 13.0% 161,396 38.0% 48,890 11.5% 196,372 46.3% 227,984 53.7% San Benito San Bernardino 202,909 29.1% 111,849 16.0% 313,046 44.9% 69,613 10.0% San Diego 130,559 22.7% 347,595 60.4% 96,932 16.9% 41 Table 6. CSS Population Data By Age and Gender Using CCP (Continued) County Child Child% TAY TAY% Adult Adult% Older Adult Older Adult% Male Male% Female Female% San Francisco 32,241 17.6% 20,507 11.2% 94,147 51.3% 36,727 20.0% 87,317 47.6% 96,304 52.4% San Joaquin San Luis Obispo 17,111 18.9% 21,117 23.3% 43,409 47.8% 9,129 10.1% San Mateo 31,892 29.4% 76,443 70.6% Santa Barbara 36,682 28.4% 31,689 24.5% 44,546 34.5% 16,223 12.6% Santa Clara 92,738 29.9% 216,935 70.1% 150,153 48.5% 159,520 51.5% Santa Cruz 76,425 29.9% 25,622 10.0% 127,545 49.9% 128,057 50.1% Shasta Sierra Siskiyou 1,493 18.9% 931 11.8% 3,630 45.9% 1,850 23.4% Solano 113,146 28.6% 242,100 61.2% 40,180 10.2% 193,497 48.9% 201,929 51.1% Sonoma 28,262 28.2% 53,378 53.3% 18,476 18.5% Stanislaus 58,121 35.0% 28,008 16.9% 60,060 36.2% 19,882 12.0% Sutter-Yuba 11,315 35.8% 4,348 13.7% 13,914 44.0% 2,070 6.5% Tehama 7,354 33.2% 1,048 4.7% 10,600 47.9% 3,148 14.2% Tri-City Trinity Tulare Tuolumne Ventura 26,697 15.9% 20,461 12.2% 102,212 60.9% 18,422 11.0% Yolo Note: The data is based on CCPs made available by the California Department of Health Care Services for analysis. Sections with blanks indicate that data were not available. 42 Table 7a. Alameda County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 1,457,169 258,231 447,723 1,510,271 1,510,271 African American 189,432 76,295 101,894 190,451 190,451 African Am. % 13.0% 29.5% 22.8% 12.6% 12.6% API 349,721 59,690 94,526 407,362 407,362 API % 24.0% 23.1% 21.1% 27.0% 27.0% Latino 306,005 65,538 115,645 339,889 339,889 Latino% 21.0% 25.4% 25.8% 22.5% 22.5% Native Am. 943 2,405 9,799 9,799 Native Am. % 0.4% 0.5% 0.6% 0.6% White 539,153 33,833 110,791 649,122 649,122 White % 37.0% 13.1% 24.7% 43.0% 43.0% Other 72,858 21,932 22,462 162,540 162,540 Other% 5.0% 8.5% 5.0% 10.8% 10.8% Language English 154,578 English% 59.9% Spanish 42,696 Spanish% 16.5% Other 22,736 Other% 8.8% Age/Gender Total Population 1,457,169 258,231 447,723 1,510,271 1,510,271 Children 276,862 111,351 102,559 302,123 302,123 Children% 19.0% 43.1% 22.9% 20.0% 20.0% TAY 204,004 23,782 60,982 211,092 211,092 TAY% 14.0% 9.2% 13.6% 14.0% 14.0% Adult 816,015 71,797 238,151 750,456 750,456 Adult% 56.0% 27.8% 53.2% 49.7% 49.7% Older Adult 160,289 51,301 46,031 246,600 246,600 Older Adult% 11.0% 19.9% 10.3% 16.3% 16.3% Males 714,013 111,943 740,573 740,573 Male% 49.0% 43.3% 49.0% 49.0% Females 743,156 146,288 769,698 769,698 Female% 51.0% 56.7% 51.0% 51.0% In the socio-demographic data for Alameda County, we observe relatively complete data and good consistency across sources. 43 Table 7a. Alameda County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 539,153 33,833 110,791 649,122 WF Total (Latino) 306,005 65,538 115,645 339,889 WF Total (African Am.) 189,432 76,295 101,894 190,451 WF Total (API) 349,721 59,690 94,526 407,362 WF Total (Native Am.) 943 2,405 9,799 WF Total (Other) 72,858 21,932 22,462 162,540 WF Total (All) 1,457,169 258,231 447,723 1,510,271 WF % White 37.0% 13.1% 24.7% 43.0% WF % Latino 21.0% 25.4% 25.8% 22.5% WF % African Am. 13.0% 29.5% 22.8% 12.6% WF % API 24.0% 23.1% 21.1% 27.0% WF % Native Am. 0.4% 0.5% 0.6% WF % Other 5.0% 8.5% 5.0% 10.8% Workforce data for Alameda County were not found. CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Target Target Target Target Target Target Target Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Population 7 Population 8 Population 9 Children and African American API Latino Youth TAY Older adults LGBTQ Consumers Family members WET WET WET WET WET WET WET Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Target Population 6 Target Population 7 Bilingual Bilingual API Bilingual other Native Spanish staff languages staff languages staff Latino staff API staff American staff Lived experience staff Overall, Alameda targets appear to focus on racial/ethnic disparities and underrepresented groups. It may be challenging to meet the needs of all 16 targets. Note: Sections with blanks indicates that data were not available. 44 Table 7b. Alpine County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 1,145 153 1,175 1,175 African American 1 0 0 African Am. % 0.5% 0.0% 0.0% API 7 7 API % 0.6% 0.6% Latino 1 84 84 Latino% 0.5% 7.1% 7.1% Native Am. 218 132 240 240 Native Am. % 19.0% 86.0% 20.4% 20.4% White 847 20 881 881 White % 74.0% 13.0% 75.0% 75.0% Other 80 19 19 Other% 7.0% 1.6% 1.6% Language English English% Spanish Spanish% Other 80 Other% 7.0% Age/Gender Total Population 1,145 153 1,175 1,175 Children 207 85 234 234 Children% 18.1% 55.6% 19.9% 19.9% TAY 101 101 TAY% 8.6% 8.6% Adult 803 57 564 564 Adult% 70.1% 37.3% 48.0% 48.0% Older Adult 134 11 276 276 Older Adult% 11.7% 7.2% 23.5% 23.5% Males 601 63 606 606 Male% 52.5% 41.2% 51.6% 51.6% Females 544 90 569 569 Female% 47.5% 58.8% 48.4% 48.4% There are a number of gaps in sociodemographic data for Alpine county. Data for small counties have traditionally been more challenging to obtain leading up to and including 2010. 45 Table 7b. Alpine County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 847 20 881 WF Total (Latino) 1 84 WF Total (African Am.) 1 0 WF Total (API) WF Total (Native Am.) 218 132 240 WF Total (Other) 80 19 WF Total (All) 1,145 153 1,175 WF % White 74.0% 13.0% 75.0% WF % Latino 0.5% 7.1% WF % African Am. 0.5% 0.0% WF % API WF % Native Am. 19.0% 86.0% 20.4% WF % Other 7.0% 1.6% Workforce data for Alpine County were not found. CSS CSS CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Target Population 6 Trauma exposed individuals Individuals experiencing Children and youth in stressed Children and youth at risk for Children and youth at risk Underserved cultural onset of serious psychiatric families school failure for experiencing juvenile populations illness justice involvement CSS targets for Alpine County are relatively well focused on high-risk populations, especially children. Did not differentiate Medi-Cal, CSS, WET, and PEI. Note: Sections with blanks indicates that data were not available. 46 Table 7c. Amador County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 38,091 38,091 African American 962 962 African Am. % 4.0% 1.0% 2.5% 2.5% API 496 496 API % 1.0% 1.0% 1.3% 1.3% Latino 4,756 4,756 Latino% 9.0% 11.0% 12.5% 12.5% Native Am. 678 678 Native Am. % 2.0% 3.0% 1.8% 1.8% White 33,149 33,149 White % 82.0% 81.0% 87.0% 87.0% Other 1,450 1,450 Other% 3.0% 3.0% 3.8% 3.8% Language API API% 0.5% English English% 91.9% Spanish Spanish% 5.8% Other Other% 1.7% Age/Gender Total Population 38,091 38,091 Children 5,350 5,350 Children% 14.0% 40.0% 14.0% 14.0% TAY 3,882 3,882 TAY% 11.0% 10.2% 10.2% Adult 17,603 17,603 Adult% 46.0% 43.0% 46.2% 46.2% Older Adult 11,256 11,256 Older Adult% 18.0% 16.0% 29.6% 29.6% Males 20,749 20,749 Male% 54.0% 42.0% 54.5% 54.5% Females 17,342 17,342 Female% 46.0% 58.0% 45.5% 45.5% There are a number of gaps in sociodemographic data for Amador county. Data for small counties have traditionally been more challenging to obtain leading up to and including 2010. 47 Table 7c. Amador County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data Unlicensed WF 7.7 Licensed Direct WF 7.8 Other Direct WF 0.0 Direct Total FTE 15.5 Indirect Total FTE 10.5 WF Total (White) 20.6 33,149 WF Total (Latino) 2.9 4,756 WF Total (African Am.) 0.5 962 WF Total (API) 0.0 496 WF Total (Native Am.) 2.0 678 WF Total (Other) 0.0 1,450 WF Total (All) 26.0 38,091 WF % White 79.2% 82.0% 81.0% 87.0% WF % Latino 11.2% 9.0% 11.0% 12.5% WF % African Am. 1.9% 4.0% 1.0% 2.5% WF % API 0.0% 1.0% 1.0% 1.3% WF % Native Am. 7.7% 2.0% 3.0% 1.8% WF % Other 0.0% 3.0% 3.0% 3.8% Workforce data for Amador County were found. The composition of the mental health workforce appears to reflect the general, Medi-Cal, and DOF populations relatively well. CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Latinos Native Americans Youths Seniors Amador CSS targets appear to be relatively well focused. Specific sub-targets within the target populations highlighted could be beneficial. No WET target populations were noted. Note: Sections with blanks indicates that data were not available. 48 Table 7d. City of Berkeley Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population Incomplete 112,580 112,580 African American 11,241 11,241 African Am. % 10% 10% API 21,876 21,876 API % 19.4% 19.4% Latino 12,209 12,209 Latino% 10.8% 10.8% Native Am. 479 479 Native Am. % 0.4% 0.4% White 66,996 66,996 White % 59.5% 59.5% Other 4,994 4,994 Other% 4.4% 4.4% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 112,580 112,580 Children Children% TAY TAY% Adult Adult% Older Adult Older Adult% Males 55,031 55,031 Male% 48.8% 48.8% Females 57,549 57,549 Female% 51.1% 51.1% 49 Table 7d. City of Berkeley Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 66,996 WF Total (Latino) 12,209 WF Total (African Am.) 11,241 WF Total (API) 21,876 WF Total (Native Am.) 479 WF Total (Other) 4,994 WF Total (All) 112,580 WF % White 59.5% WF % Latino 10.8% WF % African Am. 10% WF % API 19.4% WF % Native Am. 0.4% WF % Other 4.4% Workforce data and population targets were not noted for City of Berkeley. Note: The missing data for the items were not reviewed. Table is incomplete. 50 Table 7e.Butte County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 204,752 48,778 7,496 220,000 220,000 African American 2,457 1,951 266 3,415 3,415 African Am. % 1.2% 4.0% 3.5% 1.6% 1.6% API 7,166 3,902 407 9,509 9,509 API % 3.5% 8.0% 5.4% 2.2% 2.2% Latino 29,689 3,902 503 31,116 31,116 Latino% 14.5% 8.0% 6.7% 14.1% 14.1% Native Am. 3,481 1,463 314 4,395 4,395 Native Am. % 1.7% 3.0% 4.2% 2.0% 2.0% White 156,021 35,609 5,863 180,096 180,096 White % 76.2% 73.0% 78.3% 81.8% 81.8% Other 5,938 1,951 143 12,141 12,141 Other% 2.9% 4.0% 1.9% 5.5% 5.5% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 204,752 7,496 220,000 220,000 Children 46,250 2,379 37,155 37,155 Children% 22.5% 31.7% 16.9% 16.9% TAY 20,440 1,076 40,313 40,313 TAY% 10.0% 14.4% 18.3% 18.3% Adult 119,281 3,674 93,716 93,716 Adult% 58.3% 49.0% 42.6% 42.6% Older Adult 18,782 367 33,817 33,817 Older Adult% 9.2% 4.9% 15.4% 15.4% Males 93,732 3,482 108,931 108,931 Male% 45.8% 46.5% 49.5% 49.5% Females 111,020 4,014 111,069 111,069 Female% 54.2% 53.5% 50.5% 50.5% 51 Table 7e.Butte County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 275.0 Licensed Direct WF 162.0 Other Direct WF 32.0 Direct Total FTE 469.0 Indirect Total FTE 173.0 WF Total (White) 515.0 156,021 35,609 5,863 180,096 WF Total (Latino) 44.0 29,689 3,902 503 31,116 WF Total (African Am.) 22.0 2,457 1,951 266 3,415 WF Total (API) 27.0 7,166 3,902 407 9,509 WF Total (Native Am.) 8.0 3,481 1,463 314 4,395 WF Total (Other) 26.0 5,938 1,951 143 12,141 WF Total (All) 642.0 204,752 48,778 7,496 220,000 WF % White 80.2% 76.2% 73.0% 78.3% 81.80% WF % Latino 6.9% 14.5% 8.0% 6.7% 14.10% WF % African Am. 3.4% 1.2% 4.0% 3.5% 1.60% WF % API 4.2% 3.5% 8.0% 5.4% 2.20% WF % Native Am. 1.2% 1.7% 3.0% 4.2% 2.00% WF % Other 4.0% 2.9% 4.0% 1.9% 5.50% Workforce data for Butte County were found. The composition of the mental health workforce appears to reflect the general, Medi-Cal, and DOF populations relatively well. CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Homeless and risk of homeless adults Homeless and risk of homeless TAY and foster children Older adults CSS targets for Butte County are relatively well focused on high-risk populations. WET WET WET WET Target Population 1 Target Population 2 Target Population 3 Target Population 4 Latino Staff API Staff Spanish speaking staff Hmong speaking staff WET targets for Butte County focus on the racial/ethnic composition and language skills among staff. Note: Sections with blanks indicates that data were not available. 52 Table 7f. Calaveras County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 46,731 524 1,150 45,578 45,578 African American 608 3 11 383 383 African Am. % 1.3% 0.6% 1.0% 0.8% 0.8% API 794 7 14 650 650 API % 1.7% 1.3% 1.2% 1.4% 1.4% Latino 4,860 37 130 4,703 4,703 Latino% 10.4% 7.1% 11.3% 10.3% 10.3% Native Am. 794 11 28 689 689 Native Am. % 1.7% 2.1% 2.4% 1.5% 1.5% White 38,693 441 1,033 40,522 40,522 White % 82.8% 84.2% 89.8% 88.9% 88.9% Other 1,308 25 31 1,534 1534 Other% 2.8% 4.8% 2.7% 3.4% 3.4% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 46,731 524 1,150 45,578 45,578 Children 10,748 21 334 7,665 7,665 Children% 23.0% 4.0% 29.0% 16.8% 16.8% TAY 133 94 4,479 4479 TAY% 25.4% 8.2% 9.8% 9.8% Adult 27,057 336 557 19,644 19,644 Adult% 57.9% 64.1% 48.4% 43.1% 43.1% Older Adult 8,926 34 261 13,790 13,790 Older Adult% 19.1% 6.5% 22.7% 30.3% 30.3% Males 218 432 22,822 22,822 Male% 41.6% 37.6% 50.1% 50.1% Females 306 718 22,756 22,756 Female% 58.4% 62.4% 49.9% 49.9% In the sociodemographic data for Calaveras County, we observe relatively complete data for race/ethnicity and age. 53 Table 7f. Calaveras County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data Unlicensed WF 10.5 Licensed Direct WF Other Direct WF 3.3 Direct Total FTE 0.0 Indirect Total FTE 12.1 WF Total (White) 29.6 38,693 441 1,033 40,522 WF Total (Latino) 2.0 4,860 37 130 4,703 WF Total (African Am.) 0.0 608 3 11 383 WF Total (API) 1.0 794 7 14 650 WF Total (Native Am.) 0.0 794 11 28 689 WF Total (Other) 0.0 1,308 25 31 1,534 WF Total (All) 32.6 46,731 524 1,150 45,578 WF % White 90.8% 82.8% 84.2% 89.8% 88.9% WF % Latino 6.1% 10.4% 7.1% 11.3% 10.3% WF % African Am. 0.0% 1.3% 0.6% 1.0% 0.8% WF % API 3.1% 1.7% 1.3% 1.2% 1.4% WF % Native Am. 0.0% 1.7% 2.1% 2.4% 1.5% WF % Other 0.0% 2.8% 4.8% 2.7% 3.4% Workforce data are a bit challenging to follow. Overall, the Calaveras mental health workforce appears to reflect the general, Medi-Cal, and CSS populations relatively well. There appears to be need for a slightly higher proportion of Latino mental health workforce staff. Target Target Target Target Target Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Native Americans Latinos Children TAY Adults Older adults Calaveras target populations are relatively broad and were not differentiated by CSS, or WET Note: Sections with blanks indicates that data were not available. 54 Table 7g. Colusa County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 21,419 4,880 21,419 21,419 African American 53 195 195 African Am. % 1.1% 0.9% 0.9% API 81 349 349 API % 1.7% 1.6% 1.6% Latino 3,410 11,804 11,804 Latino% 69.9% 55.1% 55.1% Native Am. 83 419 419 Native Am. % 1.7% 2.0% 2.0% White 1,169 13,854 13,854 White % 24.0% 64.7% 64.7% Other 86 5,838 5,838 Other% 1.8% 27.3% 27.3% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 4,880 21,419 21,419 Children 2,477 5,663 5,663 Children% 50.8% 26.4% 26.4% TAY 2,958 2,958 TAY% 13.8% 13.8% Adult 1,766 9,226 9,226 Adult% 36.2% 43.1% 43.1% Older Adult 638 3,572 3,572 Older Adult% 13.1% 16.7% 16.7% Males 2,210 11,012 11,012 Male% 45.3% 51.4% 51.4% Females 2,670 10,407 10,407 Female% 54.7% 48.6% 48.6% There are a number of gaps in sociodemographic data for Colusa county. Data for small counties have traditionally been more challenging to obtain leading up to and including 2010. 55 Table 7g. Colusa County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 1,169 13,854 WF Total (Latino) 3,410 11,804 WF Total (African Am.) 53 195 WF Total (API) 81 349 WF Total (Native Am.) 83 419 WF Total (Other) 86 5,838 WF Total (All) 21,419 48,80 21,419 WF % White 24.0% 64.7% WF % Latino 69.9% 55.1% WF % African Am. 1.1% 0.9% WF % API 1.7% 1.6% WF % Native Am. 1.7% 2.0% WF % Other 1.8% 27.3% Workforce data for Colusa County were not found. Target Target Target Target Population 1 Population 2 Population 3 Population 4 Children 0-5 Children 6-18 TAY 19-21 Adults 22-99 Colusa County appears to have broad target populations for different age groups. Colusa did not differentiate between CSS or WET targets. Note: Sections with blanks indicates that data were not available. 56 Table 7h. Contra Costa County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 1,042,478 125,645 184,550 1,049,025 1,049,025 African American 96,803 27,190 34,488 97,161 97,161 African Am. % 9.3% 21.6% 18.7% 9.3% 9.3% API 144,076 12,665 18,044 156,314 156,314 API % 13.8% 10.1% 9.8% 14.9% 14.9% Latino 252,553 48,172 59,429 255,560 255,560 Latino% 24.2% 38.3% 32.2% 24.4% 24.4% Native Am. 4,478 415 813 6,122 6,122 Native Am. % 0.4% 0.3% 0.4% 0.6% 0.6% White 506,949 27,064 63,111 614,512 614,512 White % 48.6% 21.5% 34.2% 58.6% 58.6% Other 37,619 10,140 8,665 112,691 112,691 Other% 3.6% 8.1% 4.7% 10.7% 10.7% Language API API% English 771,434 75,387 English% 74.0% 60.0% Spanish 271,044 37,693.5 Spanish% 26.0% 30.0% Other Other% Age/Gender Total Population 1,042,478 125,645 184,550 1,049,025 1,049,025 Children 263,156 55,560 63,188 229,115 229,115 Children% 25.2% 44.2% 34.2% 21.8% 21.8% TAY 131,257 131,257 TAY% 12.5% 12.5% Adult 656,732 49,969 89,808 498,857 498,857 Adult% 63.0% 39.8% 48.7% 47.6% 47.6% Older Adult 122,589 20,116 31,554 189,796 189,796 Older Adult% 11.8% 16.0% 17.1% 18.1% 18.1% Males 507,955 52,884 511,526 511,526 Male% 48.7% 42.1% 48.8% 48.8% Females 534,522 72,761 537,499 537,499 Female% 51.3% 57.9% 51.2% 51.2% Sociodemographic data for Contra Costa County are relatively complete with consistent measures for different population strata across different data sources. 57 Table 7h. Contra Costa County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 576.7 Licensed Direct WF 524.4 Other Direct WF 53.8 Direct Total FTE 1,154.9 Indirect Total FTE 368.6 WF Total (White) 701.0 506,949 27,064 63,111 614,512 WF Total (Latino) 169.9 252,553 48,172 59,429 255,560 WF Total (African Am.) 289.5 96,803 27,190 34,488 97,161 WF Total (API) 113.1 144,076 12,665 18,044 156,314 WF Total (Native Am.) 5.4 4,478 415 813 6,122 WF Total (Other) 244.7 37,619 10,140 8,665 112,691 WF Total (All) 1,523.5 1,042,478 125,645 184,550 1,049,025 WF % White 46.0% 48.6% 21.5% 34.2% 58.6% WF % Latino 11.2% 24.2% 38.3% 32.2% 24.4% WF % African Am. 19.0% 9.3% 21.6% 18.7% 9.3% WF % API 7.4% 13.8% 10.1% 9.8% 14.9% WF % Native Am. 0.4% 0.4% 0.3% 0.4% 0.6% WF % Other 16.1% 3.6% 8.1% 4.7% 10.7% Workforce data for Contra Costa County appear to reflect the general population for most racial groups in the county. The Medi-Cal, CSS, and DOF data indicate there may be a disparity in the proportion of Latino mental health workforce members, but that the proportion of Contra Costa staff that are African American is strong. CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Latinos, all age groups API, all ages Homeless or risk of homelessness, all ages Individuals at or belo w 200% FPL, all ages CSS targets for Contra County are relatively well focused on high-risk populations. A focus on specific age groups within each target population may be useful. WET WET WET WET WET Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Latino staff API staff Native American staff Spanish speaking staff Asian language speaking staff WET targets for Contra County focus on the racial/ethnic composition and language skills among staff. Note: Sections with blanks indicates that data were not available. 58 Table 7i. Del Norte County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 28,610 8,439 28,610 28,610 African American 1,001 45 993 993 African Am. % 3.5% 0.5% 3.5% 3.5% API 1,001 733 997 997 API % 3.5% 8.7% 3.5% 3.5% Latino 5,093 1,036 5,093 5,093 Latino% 17.8% 12.3% 17.8% 17.8% Native Am. 2,232 825 2,244 2244 Native Am. % 7.8% 9.8% 7.8% 7.8% White 21,086 5,480 21,098 21,098 White % 73.7% 64.9% 73.7% 73.7% Other 1,287 323 1,980 1,980 Other% 4.5% 3.8% 6.9% 6.9% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 8,439 28,610 28,610 Children 6,151 3,310 5,361 5361 Children% 21.5% 39.2% 18.7% 18.7% TAY 3,693 3693 TAY% 12.9% 12.9% Adult 18,597 4,062 13,941 13,941 Adult% 65.0% 48.1% 48.7% 48.7% Older Adult 3,862 1,069 5,615 5,615 Older Adult% 13.5% 12.7% 19.6% 19.6% Males 15,907 3,890 15,907 15,907 Male% 55.6% 46.1% 55.6% 55.6% Females 12,703 4,549 12,703 12,703 Female% 44.4% 53.9% 44.4% 44.4% Sociodemographic data for Del Norte County are relatively complete and consistent across different data sources. 59 Table 7i. Del Norte County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 21,086 5,480 21,098 WF Total (Latino) 5,093 1,036 5,093 WF Total (African Am.) 1,001 45 993 WF Total (API) 1,001 733 997 WF Total (Native Am.) 2,232 825 2,244 WF Total (Other) 1,287 323 1,980 WF Total (All) 28,610 8,439 28,610 WF % White 73.7% 64.9% 73.7% WF % Latino 17.8% 12.3% 17.8% WF % African Am. 3.5% 0.5% 3.5% WF % API 3.5% 8.7% 3.5% WF % Native Am. 7.8% 9.8% 7.8% WF % Other 4.5% 3.8% 6.9% No workforce data were found in the CCP for Del Norte County. CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Latinos, all age groups Asian, all age groups Homeless, all age groups Individuals at or below 200% FPL, all age groups CSS targets for Del Norte County focus on four distinct populations determined by race/ethnicity, housing status, or poverty. Targets focus on all age groups. Note: Sections with blanks indicates that data were not available. 60 Table 7j. El Dorado Hills County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 156,299 16,572 18,683 181,058 181,058 African American 813 149 230 1,409 1,409 African Am. % 0.5% 0.9% 1.2% 0.8% 0.8% API 3,537 409 350 6,591 6,591 API % 2.3% 2.5% 1.9% 3.6% 3.6% Latino 14,566 3,519 4,339 21,875 21,875 Latino% 9.3% 21.2% 23.2% 12.1% 12.1% Native Am. 1,566 209 136 2,070 2,070 Native Am. % 1.0% 1.3% 0.7% 1.1% 1.1% White 140,209 11,710 13,307 156,793 156,793 White % 89.7% 70.7% 71.2% 86.6% 86.6% Other 6,806 580 317 7,278 7.278 Other% 4.4% 3.5% 1.7% 4.0% 4.0% Language API 1,784 API% 1.1% English 132,474 English% 84.8% Spanish 9,470 Spanish% 6.1% Other 356 Other% 0.2% Age/Gender Total Population 156,299 16,572 18,683 181,058 181,058 Children 44,688 2,988 298 35,866 35,866 Children% 28.6% 18.0% 1.6% 19.8% 19.8% TAY 6763 4,016 1,778 20,523 20,523 TAY% 4.3% 24.2% 9.5% 11.3% 11.3% Adult 85,652 7,144 13,524 85,175 85,175 Adult% 54.8% 43.1% 72.4% 47.0% 47.0% Older Adult 25,946 2,425 3,082 39,494 39,494 Older Adult% 16.6% 14.6% 16.5% 21.8% 21.8% Males 77,993 7,176 9,123 90,571 90,571 Male% 49.9% 43.3% 48.8% 50.0% 50.0% Females 78,306 9,397 9,560 90,487 90,487 Female% 50.1% 56.7% 51.2% 50.0% 50.0% Sociodemographic data for El Dorado Hills County are complete, with the exception of language, and appear to follow similar proportional patterns across data sources. 61 Table 7j. El Dorado Hills County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 50.0 Licensed Direct WF 51.0 Other Direct WF 1.0 Direct Total FTE 0.0 Indirect Total FTE 32.0 WF Total (White) 141.0 140,209 11,710 13,307 156,793 WF Total (Latino) 25.0 14,566 3,519 4,339 21,875 WF Total (African Am.) 1.0 813 149 230 1,409 WF Total (API) 2.0 3,537 409 350 6,591 WF Total (Native Am.) 4.0 1,566 209 136 2,070 WF Total (Other) 1.0 6,806 580 317 7,278 WF Total (All) 174.0 156,299 16,572 18,683 181,058 WF % White 81.0% 89.7% 70.7% 71.2% 86.6% WF % Latino 14.4% 9.3% 21.2% 23.2% 12.1% WF % African Am. 0.6% 0.5% 0.9% 1.2% 0.8% WF % API 1.1% 2.3% 2.5% 1.9% 3.6% WF % Native Am. 2.3% 1.0% 1.3% 0.7% 1.1% WF % Other 0.6% 4.4% 3.5% 1.7% 4.0% Workforce data for El Dorado Hills County appear to reflect the general population of the county. Medi-Cal and CSS data indicate that there may be need for a slight increase in the proportion of Latinos on the mental health workforce. CSS CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Youth at risk of out of home Court-involved youth and Adults with SMI who are imminently at risk TAY adults Older adults placement their families of institutionalization or homelessness The first three target populations for El Dorado Hills County are very focused. The fourth and fifth targets are more general in nature. WET WET WET WET Target Population 1 Target Population 2 Target Population 3 Target Population 4 Bilingual, bicultural Spanish- Psychiatrists Persons with lived experience (particularly Generally-opportunities to "grow our own" in order to speaking clinicians consumers but inclusive of family members) at all increase the mental health professional pool levels of the system-including bilingual and committed to working within the county bicultural Spanish-speaking consumers and family members WET target populations for El Dorado Hills are focused on specific subgroups and people with lived experience. Note: Sections with blanks indicates that data were not available. 62 Table 7k. Fresno County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 930,450 300,405 930,450 930,450 African American 81,880 23,151 49,523 49,523 African Am. % 8.8% 7.7% 5.3% 5.3% API 87,462 32,456 90,762 90,762 API % 9.4% 10.8% 9.8% 9.8% Latino 468,016 193,806 468,070 468,070 Latino% 50.3% 64.5% 50.3% 50.3% Native Am. 5,583 1,894 15,649 15,649 Native Am. % 0.6% 0.6% 1.7% 1.7% White 304,257 42,381 515,145 515,145 White % 32.7% 14.1% 55.4% 55.4% Other 18,609 11,719 217,085 217,085 Other% 2.0% 3.9% 23.3% 23.3% Language API 47,572 API% 5.9% English 471,036 English% 58.1% Spanish 262,787 Spanish% 32.4% Other 29,852 Other% 3.7% Age/Gender Total Population 300,405 930,450 930,450 Children 277,088 155,112 245,088 245,088 Children% 29.8% 51.6% 26.3% 26.3% TAY 109,049 155,348 155,348 TAY% 11.7% 16.7% 16.7% Adult 453,967 120,400 396,646 396,646 Adult% 48.8% 40.1% 42.6% 42.6% Older Adult 90,347 29,894 133,368 133,368 Older Adult% 9.7% 10.0% 14.3% 14.3% Males 464,853 136,312 464,811 464,811 Male% 50.0% 45.4% 50.0% 50.0% Females 465,597 169,093 465,639 465,639 Female% 50.0% 56.3% 50.0% 50.0% Sociodemographic data for Fresno County are relatively complete, with the exception of CSS data. 63 Table 7k. Fresno County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal CSS Population DOF Population Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE 0.0 Indirect Total FTE 0.0 WF Total (White) 218.9 304,257 42,381 515,145 WF Total (Latino) 468,016 193,806 468,070 WF Total (African Am.) 81,880 23,151 49,523 WF Total (API) 87,462 32,456 90,762 WF Total (Native Am.) 5,583 1,894 15,649 WF Total (Other) 18,609 11,719 217,085 WF Total (All) 579.3 930,450 300,405 930,450 WF % White 32.7% 14.1% 55.4% WF % Latino 50.3% 64.5% 50.3% WF % African Am. 8.8% 7.7% 5.3% WF % API 9.4% 10.8% 9.8% WF % Native Am. 0.6% 0.6% 1.7% WF % Other 2.0% 3.9% 23.3% Workforce data found in the CCP for Fresno County are limited. CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Target Target Target Target Target Target Target Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Population 7 Population 8 Population 9 Latino, all ages African American, API, all ages Native American, Whites Females Males Veterans LGBTQ all ages all ages WET WET WET WET WET WET WET WET WET Target Target Target Target Target Target Target Target Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Population 7 Population 8 Population 9 Latino, all ages African American, API, all ages Native American, Whites Females Males Veterans LGBTQ all ages all ages CSS and WET targets for Fresno County are identical. With the exception of “Veterans” and “LGBTQ” targets, Fresno CSS and WET targets are quite broad, covering entire ethnic groups and ages. Note: Sections with blanks indicates that data were not available. 64 Table 7l. Glenn County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 28,111 28,122 28,122 African American 166 231 231 African Am. % 0.6% 0.8% 0.8% API 857 746 746 API % 3.0% 2.7% 2.7% Latino 9,741 10,539 10,539 Latino% 34.7% 37.5% 37.5% Native Am. 495 619 619 Native Am. % 1.8% 2.2% 2.2% White 16,411 19,990 19,990 White % 58.4% 71.1% 71.1% Other 441 5,522 5,522 Other% 1.6% 19.6% 19.6% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 28,122 28,122 Children 7,652 6,989 6,989 Children% 27.2% 24.9% 24.9% TAY 3,834 3,834 TAY% 13.6% 13.6% Adult 14,250 12,128 12,128 Adult% 50.7% 43.1% 43.1% Older Adult 6209 5,171 5,171 Older Adult% 22.1% 18.4% 18.4% Males 14,227 14,191 14,191 Male% 50.6% 50.5% 50.5% Females 13,884 13,931 13,931 Female% 49.4% 49.5% 49.5% 65 Table 7l. Glenn County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 16,411 19,990 WF Total (Latino) 9,741 10,539 WF Total (African Am.) 166 231 WF Total (API) 857 746 WF Total (Native Am.) 495 619 WF Total (Other) 441 5,522 WF Total (All) 28,111 28,122 WF % White 58.4% 71.1% WF % Latino 34.7% 37.5% WF % African Am. 0.6% 0.8% WF % API 3.0% 2.7% WF % Native Am. 1.8% 2.2% WF % Other 1.6% 19.6% Workforce data for Glenn County were not noted. Note: Sections with blanks indicates that data were not available. 66 Table 7m. Humboldt County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 134,785 27,355 40,999 134,623 134,623 African American 1,031 540 479 1,505 1,505 African Am. % 0.8% 2.0% 1.2% 1.1% 1.1% API 2,321 1,000 919 3,296 3,296 API % 1.7% 3.7% 2.2% 2.4% 2.4% Latino 10,366 2,559 2,822 13,211 13,211 Latino% 7.8% 9.4% 6.9% 9.8% 9.8% Native Am. 9,146 2,844 2,519 7,726 7,726 Native Am. % 6.9% 10.4% 6.1% 5.7% 5.7% White 104,659 19,285 30,908 109,920 109,920 White % 78.8% 70.5% 75.4% 81.7% 81.7% Other 5,271 1,127 3,352 5,003 5,003 Other% 4.0% 4.1% 8.2% 3.7% 3.7% Language API 1,276 API% 1.0% English 24,266 English% 88.7% Spanish 5,442 1,577 Spanish% 5.0% 5.8% Other 2567 1,140 Other% 2.0% 4.2% Age/Gender Total Population 134,785 27,355 16,069 134,623 134,623 Children 22,431 9,733 39.2% 23,832 23,832 Children% 16.6% 35.6% 6,126 17.7% 17.7% TAY 21,898 3,958 14.9% 22,166 22,166 TAY% 16.2% 14.5% 17,665 16.5% 16.5% Adult 64,247 10,463 43.1% 61,866 61,866 Adult% 47.7% 38.2% 1,139 46.0% 46.0% Older Adult 26,209 3,201 2.8% 26,759 26,759 Older Adult% 19.4% 11.7% 19.9% 19.9% Males 66,901 12,387 67,595 67,595 Male% 49.6% 45.3% 50.2% 50.2% Females 67,884 14,968 67,028 67,028 Female% 50.4% 54.7% 49.8% 49.8% Sociodemographic data are complete for Humboldt County and have similar proportional patterns across data sources. 67 Table 7m. Humboldt County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 117.5 Licensed Direct WF 82.5 Other Direct WF 33.0 Direct Total FTE 233.0 Indirect Total FTE 119.5 WF Total (White) 304.5 104,659 19,285 30,908 109,920 WF Total (Latino) 17.0 10,366 2,559 2,822 13,211 WF Total (African Am.) 13.0 1,031 540 479 1,505 WF Total (API) 10.0 2,321 1,000 919 3,296 WF Total (Native Am.) 3.0 9,146 2,844 2,519 7,726 WF Total (Other) 5.0 5,271 1,127 3,352 5,003 WF Total (All) 352.5 134,785 27,355 40,999 134,623 WF % White 86.4% 78.8% 70.5% 75.4% 81.7% WF % Latino 4.8% 7.8% 9.4% 6.9% 9.8% WF % African Am. 3.7% 0.8% 2.0% 1.2% 1.1% WF % API 2.8% 1.7% 3.7% 2.2% 2.4% WF % Native Am. 0.9% 6.9% 10.4% 6.1% 5.7% WF % Other 1.4% 4.0% 4.1% 8.2% 3.7% Workforce data for Humboldt County are complete and appear to be proportionate with general, Medi-Cal, CSS, and DOF population data, with one exception. It appears there is a need for greater representation of workforce staff that is Native American. CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Native American TAY Latino CSS targets for Humboldt County are quite focused. WET WET WET WET Target Population 1 Target Population 2 Target Population 3 Target Population 4 Native American Latino Spanish as primary language Peer client and peer family member staff In Humboldt County, WET target populations appear focused and are similar CSS target populations. Note: Sections with blanks indicates that data were not available. 68 Table 7n. Imperial County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 166,874 52,517 89,637 174,528 174,528 African American 2,169 877 1,878 5,773 5,773 African Am. % 1.3% 1.7% 2.1% 3.3% 3.3% API 834 283 2,107 3,008 3,008 API % 0.5% 0.5% 2.4% 1.7% 1.7% Latino 131,664 44,642 67,215 140,271 140,271 Latino% 78.9% 85.0% 75.0% 80.4% 80.4% Native Am. 501 478 2,129 3,059 3,059 Native Am. % 0.3% 0.9% 2.4% 1.8% 1.8% White 30,204 4,601 14,923 102,553 102,553 White % 18.1% 8.8% 16.6% 58.8% 58.8% Other 1,502 1,636 1,385 52,413 52,413 Other% 0.9% 3.1% 1.5% 30.0% 30.0% Language API API% English 48,727 20,887 English% 29.2% 39.8% Spanish 114,309 29,498 Spanish% 68.5% 56.2% Other 3,838 2,132 Other% 2.3% 4.1% Age/Gender Total Population 166,874 52,517 174,528 174,528 Children 56,570 23,202 28,244 44,878 44,878 Children% 33.9% 44.2% 31.5% 25.7% 25.7% TAY 13,183 4,942 6,236 27,649 27,649 TAY% 7.9% 9.4% 7.0% 15.8% 15.8% Adult 79,599 15,612 45,948 76,482 76,482 Adult% 47.7% 29.7% 51.3% 43.8% 43.8% Older Adult 17,522 8,761 9,209 25,519 25,519 Older Adult% 10.5% 16.7% 10.3% 14.6% 14.6% Males 86,674 22,967 89,646 89,646 Male% 51.9% 43.7% 51.4% 51.4% Females 80,200 29,550 84,882 84,882 Female% 48.1% 56.3% 48.6% 48.6% Sociodemographic data for Imperial County are relatively complete and appear to follow similar proportional patterns across subgroups within different data sources. There are a smaller proportion of white individuals in the general and Medi-Cal population data when compared to US Census data, which may be attributed to differences in definitions for race/ethnicity. 69 Table 7n. Imperial County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 85.0 Licensed Direct WF 28.5 Other Direct WF 11.0 Direct Total FTE 124.5 Indirect Total FTE 127.0 WF Total (White) 206.0 30,204 4,601 14,923 102,553 WF Total (Latino) 211.0 131,664 44,642 67,215 140,271 WF Total (African Am.) 3.0 2,169 877 1,878 5,773 WF Total (API) 3.0 834 283 2,107 3,008 WF Total (Native Am.) 2.0 501 478 2,129 3,059 WF Total (Other) 1.5 1,502 1,636 1,385 52,413 WF Total (All) 251.5 166,874 52,517 89,637 174,528 WF % White 81.9% 18.1% 8.8% 16.6% 58.8% WF % Latino 83.9% 78.9% 85.0% 75.0% 80.4% WF % African Am. 1.2% 1.3% 1.7% 2.1% 3.3% WF % API 1.2% 0.5% 0.5% 2.4% 1.7% WF % Native Am. 0.8% 0.3% 0.9% 2.4% 1.8% WF % Other 0.6% 0.9% 3.1% 1.5% 30.0% Workforce data for Imperial County appear complete. The workforce appears to be reflective of the genera, Medi-Cal, CSS, and DOF populations, with a high proportion of Latino mental health staff. CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Children and youth TAY Adult Older adults CSS target populations are general, including the entire population for Imperial County. WET Target Population 1 Latinos The only WET target population focuses on the most populous ethnic group, Latinos, in Imperial County. Note: Sections with blanks indicates that data were not available. 70 Table 7o. Inyo County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 17,449 3,416 18,546 18,546 African American 30 30 109 109 African Am. % 0.2% 0.9% 0.6% 0.6% API 261 21 259 259 API % 1.5% 0.6% 1.4% 1.4% Latino 2,986 913 3,597 3,597 Latino% 17.1% 26.7% 19.4% 19.4% Native Am. 1,751 687 2,121 2,121 Native Am. % 10.0% 20.1% 11.4% 11.4% White 12,072 1671 13,741 13,741 White % 69.2% 48.9% 74.1% 74.1% Other 349 94 1,676 1,676 Other% 2.0% 2.8% 9.0% 9.0% Language API API% English 2,602 English% 76.2% Spanish 669 Spanish% 19.6% Other 145 Other% 4.2% Age/Gender Total Population 17,449 3,416 18,546 18,546 Children 3648 1067 3,420 3420 Children% 20.9% 31.2% 18.4% 18.4% TAY 1,918 1918 TAY% 10.3% 10.3% Adult 8,466 1,206 8,334 8,334 Adult% 48.5% 35.3% 44.9% 44.9% Older Adult 5,335 1,143 4,874 4,874 Older Adult% 30.6% 33.5% 26.3% 26.3% Males 8,562 1,497 9,354 9,354 Male% 49.1% 43.8% 50.4% 50.4% Females 8,887 1,919 9,192 9,192 Female% 50.9% 56.2% 49.6% 49.6% Within Inyo County, the sociodemographic data for the Medi-Cal population is complete. There are a few gaps in the sociodemographic data from other sources. 71 Table 7o. Inyo County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 13.0 Licensed Direct WF 4.0 Other Direct WF 1.0 Direct Total FTE 18.0 Indirect Total FTE 9.3 WF Total (White) 21.2 12,072 1671 13,741 WF Total (Latino) 2.0 2,986 913 3,597 WF Total (African Am.) 0.0 30 30 109 WF Total (API) 1.0 261 21 259 WF Total (Native Am.) 3.0 1,751 687 2,121 WF Total (Other) 0.0 349 94 1,676 WF Total (All) 27.2 17,449 3,416 18,546 WF % White 77.9% 69.2% 48.9% 74.1% WF % Latino 7.4% 17.1% 26.7% 19.4% WF % African Am. 0.0% 0.2% 0.9% 0.6% WF % API 3.7% 1.5% 0.6% 1.4% WF % Native Am. 11.0% 10.0% 20.1% 11.4% WF % Other 0.0% 2.0% 2.8% 9.0% Workforce data for Inyo County appear to be complete. It appears there is a disparity in Latino mental health staff when workforce data are compared to the general, Medi-Cal, and DOF populations. CSS CSS CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Target Population 6 Latino Native Americans LGBTQ TAY Adults Older adults The first three target CSS populations for Inyo County seem to reflect and address some disparities in workforce FTEs and populations in need. WET Target Population 1 Spanish speaking staff The only WET target population focuses on a need that appears supported in the sociodemographic data. Note: Sections with blanks indicates that data were not available. 72 Table 7p. Kern County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 786,000 839,631 839,631 839,631 African American 47,160 48,921 48,921 48,921 African Am. % 6.0% 5.8% 5.8% 5.8% API 36,098 36,098 36,098 API % 4.3% 4.3% 4.3% Latino 361,560 413,033 413,033 413,033 Latino% 46.0% 49.2% 49.2% 49.2% Native Am. 12,676 12,676 12,676 Native Am. % 1.5% 1.5% 1.5% White 495,180 499,766 499,766 499,766 White % 63.0% 59.5% 59.5% 59.5% Other 204,314 204,314 204,314 Other% 24.3% 24.3% 24.3% Language API API% English 471,600 English% 60.0% Spanish 282,960 Spanish% 36.0% Other Other% Age/Gender Total Population 786,000 839,631 839,631 Children 322260 224,588 224,588 Children% 41.0% 26.7% 26.7% TAY 136,409 136,409 TAY% 16.2% 16.2% Adult 393,000 369,145 369,145 Adult% 50.0% 44.0% 44.0% Older Adult 70,740 109,489 109,489 Older Adult% 9.0% 13.0% 13.0% Males 408,720 433,108 433,108 Male% 52.0% 51.6% 51.6% Females 377,280 406,523 406,523 Female% 48.0% 48.4% 48.4% Sociodemographic data for Kern County have some gaps but contain most of the needed general population variables. 73 Table 7p. Kern County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 495,180 499,766 499,766 WF Total (Latino) 361,560 413,033 413,033 WF Total (African Am.) 47,160 48,921 48,921 WF Total (API) 36,098 36,098 WF Total (Native Am.) 12,676 12,676 WF Total (Other) 204,314 204,314 WF Total (All) 786,000 839,631 839,631 WF % White 63.0% 59.5% 59.5% WF % Latino 46.0% 49.2% 49.2% WF % African Am. 6.0% 5.8% 5.8% WF % API 4.3% 4.3% WF % Native Am. 1.5% 1.5% WF % Other 24.3% 24.3% Workforce data were not noted for Kern County in the CCP. CSS CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 People with severe and TAY 17-25 Older adults 55+ Individuals in recovery Individuals with co-occurring persistently mentally illness disorders Kern County provides a relatively unique and well-focused mix of target populations. There is no differentiation between target populations for CSS, Medi-Cal, MHSA, or other populations. Note: Sections with blanks indicates that data were not available. 74 Table 7q. Kings County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 14,1225 34,068 152,982 152,982 African American 1,1722 2,385 11,014 11,014 African Am. % 8.3% 7.0% 7.2% 7.2% API 4,378 1,022 5,891 5,891 API % 3.1% 3.0% 3.9% 3.9% Latino 64,964 23,166 77,866 77,866 Latino% 46.0% 68.0% 50.9% 50.9% Native Am. 1,836 102 2,562 2,562 Native Am. % 1.3% 0.3% 1.7% 1.7% White 56,066 6,814 83,027 83,027 White % 39.7% 20.0% 54.3% 54.3% Other 2,260 920 42,996 42,996 Other% 1.6% 2.7% 28.1% 28.1% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 141,225 152,982 152,982 Children 39,543 37,998 37,998 Children% 28.0% 24.8% 24.8% TAY 24,961 24,961 TAY% 16.3% 16.3% Adult 90,808 72,390 72,390 Adult% 64.3% 47.3% 47.3% Older Adult 10,733 17,633 17,633 Older Adult% 7.6% 11.5% 11.5% Males 80,498 86,344 86,344 Male% 57.0% 56.4% 56.4% Females 60,727 66,638 66,638 Female% 43.0% 43.6% 43.6% Sociodemographic data for Kings County has several gaps. 75 Table 7q. Kings County Profile: Cultural Competency Plan (Continued) Workforce Data (FTEs) Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 56,066 6,814 83,027 WF Total (Latino) 64,964 23,166 77,866 WF Total (African Am.) 1,1722 2,385 11,014 WF Total (API) 4,378 1,022 5,891 WF Total (Native Am.) 1,836 102 2,562 WF Total (Other) 2,260 920 42,996 WF Total (All) 14,1225 34,068 152,982 WF % White 39.7% 20.0% 54.3% WF % Latino 46.0% 68.0% 50.9% WF % African Am. 8.3% 7.0% 7.2% WF % API 3.1% 3.0% 3.9% WF % Native Am. 1.3% 0.3% 1.7% WF % Other 1.6% 2.7% 28.1% Workforce data were not noted for Kings County. Note: Sections with blanks indicates that data were not available. 76 Table 7r. Lake County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 64,386 1,236 64,665 64,665 African American 1,689 53 1,232 1,232 African Am. % 2.6% 4.3% 4.4% 1.9% 1.9% API 677 10 832 832 API % 1.1% 0.8% 1.1% 1.3% 1.3% Latino 9,000 79 11,088 11,088 Latino% 14.0% 6.4% 10.5% 17.1% 17.1% Native Am. 2,335 47 2,049 2,049 Native Am. % 3.6% 3.8% 3.4% 3.2% 3.2% White 49,132 998 52,033 52,033 White % 76.3% 80.7% 80.1% 80.5% 80.5% Other 49 5,455 5,455 Other% 0.0% 4.0% 0.5% 8.4% 8.4% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 64,386 1,236 64,665 64,665 Children 14,850 400 11,955 11,955 Children% 23.1% 32.4% 23.0% 18.5% 18.5% TAY 5,794 7,378 7,378 TAY% 9.0% 11.4% 11.4% Adult 36,587 738 28,735 28,735 Adult% 56.8% 59.7% 44.4% 44.4% Older Adult 11,949 98 16,597 16,597 Older Adult% 18.6% 7.9% 25.0% 25.7% 25.7% Males 31,694 570 32,469 32,469 Male% 49.2% 46.1% 50.2% 50.2% Females 32,692 666 32,196 32,196 Female% 50.8% 53.9% 49.8% 49.8% Sociodemographic data for Lake County are relatively complete for all data sources. No data on languages spoken were found in the CCP. 77 Table 7r. Lake County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 49,132 998 52,033 WF Total (Latino) 9,000 79 11,088 WF Total (African Am.) 1,689 53 1,232 WF Total (API) 677 10 832 WF Total (Native Am.) 2,335 47 2,049 WF Total (Other) 49 5,455 WF Total (All) 64,386 1,236 64,665 WF % White 76.3% 80.7% 80.1% 80.5% WF % Latino 14.0% 6.4% 10.5% 17.1% WF % African Am. 2.6% 4.3% 4.4% 1.9% WF % API 1.1% 0.8% 1.1% 1.3% WF % Native Am. 3.6% 3.8% 3.4% 3.2% WF % Other 0.0% 4.0% 0.5% 8.4% No workforce data were found for Lake County. CSS CSS CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Target Population 6 Individuals experiencing onset Underserved cultural Children and Youth at risk Trauma exposed Children and youth in Children and youth at of serious psychiatric illness populations of school failure individuals stressed families risk of or experiencing juvenile justice involvement CSS target populations for Lake County are quite specific, with a focus on individuals with severe mental illness and experience with trauma. No WET target populations were noted. Note: Sections with blanks indicates that data were not available. 78 Table 7s. Lassen County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 34,895 4,967 9,059 34,895 34,895 African American 2,826 122 89 2,834 2,834 African Am. % 8.1% 2.5% 1.0% 8.1% 8.1% API 523 40 95 521 521 API % 1.5% 0.8% 1.0% 1.5% 1.5% Latino 6,107 598 1,077 6,117 6,117 Latino% 17.5% 12.0% 11.9% 17.5% 17.5% Native Am. 1,221 302 515 1234 1,234 Native Am. % 3.5% 6.1% 5.7% 3.5% 3.5% White 23,275 3,642 6,983 25,532 25,532 White % 66.7% 73.3% 77.1% 73.2% 73.2% Other 1,221 263 300 3,562 3,562 Other% 3.5% 5.3% 3.3% 10.2% 10.2% Language API API% English 4,344 English% 87.5% Spanish 307 Spanish% 6.2% Other 5,583 313 Other% 16.0% 6.3% Age/Gender Total Population 34,895 4,967 9,059 34,895 34,895 Children 6,456 1,950 3,146 5,483 5,483 Children% 18.5% 39.3% 34.7% 15.7% 15.7% TAY 910 5,181 5,181 TAY% 18.3% 14.8% 14.8% Adult 25,299 1841 5,913 18,925 18,925 Adult% 72.5% 37.1% 65.3% 54.2% 54.2% Older Adult 3,141 266 5,306 5,306 Older Adult% 9.0% 5.4% 15.2% 15.2% Males 4,358 22,416 22,416 Male% 48.1% 64.2% 64.2% Females 4,701 12,479 12,479 Female% 51.9% 35.8% 35.8% For Lassen County, sociodemographic data are relatively complete and consistent across different data sources. 79 Table 7s. Lassen County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 12.0 Licensed Direct WF 8.0 Other Direct WF 2.5 Direct Total FTE 22.5 Indirect Total FTE 21.0 WF Total (White) 39.5 23,275 3,642 6,983 25,532 WF Total (Latino) 3.0 6,107 598 1,077 6,117 WF Total (African Am.) 0.0 2,826 122 89 2,834 WF Total (API) 0.0 523 40 95 521 WF Total (Native Am.) 1.0 1,221 302 515 1234 WF Total (Other) 0.0 1,221 263 300 3,562 WF Total (All) 43.5 34,895 4,967 9,059 34,895 WF % White 90.8% 66.7% 73.3% 77.1% 73.2% WF % Latino 6.9% 17.5% 12.0% 11.9% 17.5% WF % African Am. 0.0% 8.1% 2.5% 1.0% 8.1% WF % API 0.0% 1.5% 0.8% 1.0% 1.5% WF % Native Am. 2.3% 3.5% 6.1% 5.7% 3.5% WF % Other 0.0% 3.5% 5.3% 3.3% 10.2% Workforce data appear complete for Lassen County. The Lassen mental health workforce is predominantly white, while the general, Medi-Cal, CSS, and DOF data reflect staffing needs for slightly higher proportions of Latino, Native American, and African American populations. CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Children and youth at risk of or experiencing juvenile justice Geographically isolated communities (Herlong and Outreach to the older adult populations involvement Doyel, Big Valley, Westwood) CSS Target populations for Lassen County are quite focused on at-risk populations and geographies. No targets were noted for WET populations. Note: Sections with blanks indicates that data were not available. 80 Table 7t. Los Angeles County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 10,416,096 2,030,535 3,734,626 9,818,605 9,818,605 African American 944,152 233,394 364,446 856,874 856,874 African Am. % 9.1% 11.5% 9.8% 8.7% 8.7% API 1,391,495 226,385 370,349 1,372,959 1,372,959 API % 13.4% 11.1% 9.9% 14.0% 14.0% Latino 4,917,644 1,242,950 2,426,069 4,687,889 4,687,889 Latino% 47.2% 61.2% 65.0% 47.7% 47.7% Native Am. 27,612 2,260 9,180 72,828 72828 Native Am. % 0.3% 0.1% 0.2% 0.7% 0.7% White 3,135,193 246,041 564,582 4,936,599 4,936,599 White % 30.1% 12.1% 15.1% 50.3% 50.3% Other 79,505 2,140,632 2,140,632 Other% 3.9% 21.8% 21.8% Language API API% English 834,416 English% 46.2% Spanish 777,748 Spanish% 43.0% Other 225,850 Other% 12.5% Age/Gender Total Population 10,416,096 2,030,535 3,734,626 9,818,605 9,818,605 Children 2,367,592 1,013,346 1,138,654 2,103,652 2,103,652 Children% 22.7% 49.9% 30.5% 21.4% 21.4% TAY 1,560,167 318,828 585,904 1,514,741 1,514,741 TAY% 15.0% 15.7% 15.7% 15.4% 15.4% Adult 4,915,321 375,689 1,540,601 4,682,277 4,682,277 Adult% 47.2% 18.5% 41.3% 47.7% 47.7% Older Adult 1,573,016 320,859 469,376 1,517,935 1,517,935 Older Adult% 15.1% 15.8% 12.6% 15.5% 15.5% Males 5,161,564 911,809 1,769,196 4,839,654 4,839,654 Male% 49.6% 44.9% 47.4% 49.3% 49.3% Females 5,254,532 1,118,945 1,965,430 4,978,951 4,978,951 Female% 50.4% 55.1% 52.6% 50.7% 50.7% With the exception of language, the Los Angeles County sociodemographic data appear complete, and are consistent across sources. 81 Table 7t. Los Angeles County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 3,438.5 Licensed Direct WF 4,271.4 Other Direct WF 324.4 Direct Total FTE 8,034.4 Indirect Total FTE 4839.1 WF Total (White) 4,150.4 3,135,193 246,041 564,582 4,936,599 WF Total (Latino) 4,579.1 4,917,644 1,242,950 2,426,069 4,687,889 WF Total (African Am.) 1,943.9 944,152 233,394 364,446 856,874 WF Total (API) 10,131.4 1,391,495 226,385 370,349 1,372,959 WF Total (Native Am.) 6,694.2 27,612 2,260 9,180 72,828 WF Total (Other) 11,212.8 79,505 2,140,632 WF Total (All) 12,873.5 10,416,096 2,030,535 3,734,626 9,818,605 WF % White 32.2% 30.1% 12.1% 15.1% 50.3% WF % Latino 35.6% 47.2% 61.2% 65.0% 47.7% WF % African Am. 15.1% 9.1% 11.5% 9.8% 8.7% WF % API 78.7% 13.4% 11.1% 9.9% 14.0% WF % Native Am. 52.0% 0.3% 0.1% 0.2% 0.7% WF % Other 87.1% 3.9% 21.8% Workforce data are complete for Los Angeles County. The percent of the workforce population data by race and ethnicity are a bit challenging to follow. It appears, however, that the Los Angeles mental health workforce is relatively diverse. CSS CSS CSS CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Target Population 6 Target Population 7 API Latinos Children 0-15 TAY 16-25 Older adults +60 Threshold Language Communities Women (Arabic, Armenian, Cantonese, Farsi, Korean, Mandarin, Other Chinese, Russian, Spanish, Tagalog, and Vietnamese) Los Angeles County CSS targets are rather broad, including a large number of individuals and populations. WET WET WET WET Target Population 1 Target Population 2 Target Population 3 Target Population 4 API Latinos Older adults +60 Threshold Language Communities (Arabic, Armenian, Cantonese, Farsi, Korean, Mandarin, Other Chinese, Russian, Spanish, Tagalog, and Vietnamese) Los Angeles County WET target populations are relatively broad, including a large number of individuals and populations. Note: Sections with blanks indicates that data were not available. 82 Table 7u. Madera County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 150,865 38,963 11,103 150,865 150,865 African American 5,582 1,184 635 5,629 5,629 African Am. % 3.7% 3.0% 5.7% 3.7% 3.7% API 3,017 536 196 2,964 2,964 API % 2.0% 1.4% 1.8% 2.0% 2.0% Latino 81,015 27,569 5,314 80,992 80,992 Latino% 53.7% 70.8% 47.9% 53.7% 53.7% Native Am. 4,073 302 138 4,136 4,136 Native Am. % 2.7% 0.8% 1.2% 2.7% 2.7% White 57,329 8,219 4,197 94,456 94,456 White % 38.0% 21.1% 37.8% 62.6% 62.6% Other 6,336 1,153 151 37,380 37,380 Other% 4.2% 3.0% 1.4% 24.8% 24.8% Language API API% English 88,558 18,631 English% 58.7% 47.8% Spanish 49 Spanish% Other 62,307 1,366 Other% 41.3% 3.5% Age/Gender Total Population 38,963 11,103 150,865 150,865 Children 44,354 20,417 2,806 37,916 37,916 Children% 29.4% 52.4% 25.3% 25.1% 25.1% TAY 3,840 2,460 22,910 22,910 TAY% 9.9% 22.2% 15.2% 15.2% Adult 90,821 11,516 4,176 65,411 65,411 Adult% 60.2% 29.6% 37.6% 43.4% 43.4% Older Adult 15,690 3,190 868 24,628 24,628 Older Adult% 10.4% 8.2% 7.8% 16.3% 16.3% Males 72,566 17,370 4,004 72,682 72,682 Male% 48.1% 44.6% 36.1% 48.2% 48.2% Females 78,299 21,593 6,483 78,183 78,183 Female% 51.9% 55.4% 58.4% 51.8% 51.8% Sociodemographic data for Madera County appear solid overall, with limited data on languages spoken. 83 Table 7u. Madera County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 31.0 Licensed Direct WF 31.2 Other Direct WF 1.0 Direct Total FTE 63.2 Indirect Total FTE 52.0 WF Total (White) 52.0 57,329 8,219 4,197 94,456 WF Total (Latino) 48.0 81,015 27,569 5,314 80,992 WF Total (African Am.) 6.0 5,582 1,184 635 5,629 WF Total (API) 3.2 3,017 536 196 2,964 WF Total (Native Am.) 0.0 4,073 302 138 4,136 WF Total (Other) 6.0 6,336 1,153 151 37,380 WF Total (All) 115.2 150,865 38,963 11,103 150,865 WF % White 45.1% 38.0% 21.1% 37.8% 62.6% WF % Latino 41.7% 53.7% 70.8% 47.9% 53.7% WF % African Am. 5.2% 3.7% 3.0% 5.7% 3.7% WF % API 2.8% 2.0% 1.4% 1.8% 2.0% WF % Native Am. 0.0% 2.7% 0.8% 1.2% 2.7% WF % Other 5.2% 4.2% 3.0% 1.4% 24.8% Madera’s workforce data appear to reflect the composition of the general population. The Medi-Cal population, however, indicate that over 70% of Medi-Cal recipients are Latino in Madera County. Thus, it appears there is a need for greater representation of Latinos on the Madera mental health workforce. CSS CSS CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Target Population 6 Latinos Children 0-5 years TAY (18-25 years) Older Adults 65+ Males Adults CSS target populations for Madera County focus on a large part of the general population. WET WET Target Population 1 Target Population 2 Latinos Spanish speakers WET targets appear to reflect needs in the general population, with a larger Latino population. Note: Sections with blanks indicates that data were not available. 84 Table 7v. Marin County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 248,794 21,978 252,409 252,409 African American 7,713 1,903 6,987 6,987 African Am. % 3.1% 8.7% 2.8% 2.8% API 14,312 1,403 14,270 14,270 API % 5.8% 6.4% 5.7% 5.7% Latino 35,016 9,598 39,069 39,069 Latino% 14.1% 43.7% 15.5% 15.5% Native Am. 1,455 73 1,523 1,523 Native Am. % 0.6% 0.3% 0.6% 0.6% White 218,870 8,381 201,963 201,963 White % 88.0% 38.1% 80.0% 80.0% Other 6,444 620 16,973 16,973 Other% 2.6% 2.8% 6.7% 6.7% Language API API% English English% 89.8% Spanish Spanish% 7.7% Other Other% Age/Gender Total Population 252,409 252,409 Children 46,505 46,505 Children% 18.4% 18.4% TAY 22,595 22,595 TAY% 9.0% 9.0% Adult 121,855 121,855 Adult% 48.3% 48.3% Older Adult 61,454 61,454 Older Adult% 24.3% 24.3% Males 124,072 124,072 Male% 49.2% 49.2% Females 128,337 128,337 Female% 50.8% 50.8% Sociodemographic data are limited for Marin County. 85 Table 7v. Marin County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 180.0 Licensed Direct WF 310.0 Other Direct WF 36.0 Direct Total FTE 526.0 Indirect Total FTE 193.3 WF Total (White) 452.0 218,870 8,381 201,963 WF Total (Latino) 90.3 35,016 9,598 39,069 WF Total (African Am.) 43.5 7,713 1,903 6,987 WF Total (API) 47.5 14,312 1,403 14,270 WF Total (Native Am.) 0.0 1,455 73 1,523 WF Total (Other) 86.0 6,444 620 16,973 WF Total (All) 719.3 248,794 21,978 252,409 WF % White 62.8% 88.0% 38.1% 80.0% WF % Latino 12.5% 14.1% 43.7% 15.5% WF % African Am. 6.0% 3.1% 8.7% 2.8% WF % API 6.6% 5.8% 6.4% 5.7% WF % Native Am. 0.0% 0.6% 0.3% 0.6% WF % Other 12.0% 2.6% 2.8% 6.7% Workforce data for Marin County appear to reflect the general population composition. The Medi-Cal population data, however, indicate there is a higher proportion of Medi- Cal recipients who are Latino. Thus, it appears there is a need for greater representation of Latino mental health staff in Marin. CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Latino API African Americans Residents of West Marin CSS targets focus on the most populous minority populations in Marin County, and a geographic area in need. There was no differentiation between CSS, MHSA, Medi-Cal, and WET target populations. Note: Sections with blanks indicates that data were not available. 86 Table 7w. Mariposa County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 18,251 18,251 18,251 African American 206 138 138 African Am. % 1.1% 0.8% 0.8% API 170 230 230 API % 0.9% 1.3% 1.3% Latino 1,866 1,676 1,676 Latino% 10.2% 9.2% 9.2% Native Am. 602 527 527 Native Am. % 3.3% 2.9% 2.9% White 16,169 16,103 16,103 White % 88.6% 88.2% 88.2% Other 508 508 Other% 2.8% 2.8% Language API API% English 15,445 English% 84.6% Spanish 559 Spanish% 3.1% Other Other% Age/Gender Total Population 18,251 18,251 Children 3,741 2,802 2,802 Children% 20.5% 15.4% 15.4% TAY 1,794 1794 TAY% 9.8% 9.8% Adult 9,121 8,244 8,244 Adult% 50.0% 45.2% 45.2% Older Adult 4,921 5,411 5,411 Older Adult% 27.0% 29.6% 29.6% Males 9,081 9,269 9,269 Male% 49.8% 50.8% 50.8% Females 8,711 8,982 8,982 Female% 47.7% 49.2% 49.2% There are many gaps in the sociodemographic data for Mariposa County. 87 Table 7w. Mariposa County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 16,169 16,103 WF Total (Latino) 1,866 1,676 WF Total (African Am.) 206 138 WF Total (API) 170 230 WF Total (Native Am.) 602 527 WF Total (Other) 508 WF Total (All) 18,251 18,251 WF % White 88.6% 88.2% WF % Latino 10.2% 9.2% WF % African Am. 1.1% 0.8% WF % API 0.9% 1.3% WF % Native Am. 3.3% 2.9% WF % Other 2.8% No workforce data were noted in the Mariposa County CCP. Note: Sections with blanks indicates that data were not available. 88 Table 7x. Mendocino County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 90,816 22,688 33,731 87,841 87,841 African American 545 231 228 622 622 African Am. % 0.6% 1.0% 0.7% 0.7% 0.7% API 2,180 334 561 1,569 1,569 API % 2.4% 1.5% 1.7% 1.8% 1.8% Latino 14,985 7,000 7,924 19,505 19,505 Latino% 16.5% 30.9% 23.5% 22.2% 22.2% Native Am. 4,023 1,828 2,014 4,277 4,277 Native Am. % 4.4% 8.1% 6.0% 4.9% 4.9% White 73,379 12,611 21,773 67,218 67,218 White % 80.8% 55.6% 64.5% 76.5% 76.5% Other 11,534 687 1,232 10,185 10,185 Other% 12.7% 3.0% 3.7% 11.6% 11.6% Language API API% English 15,761 English% 69.5% Spanish 4,910 Spanish% 21.6% Other 988 Other% 4.4% Age/Gender Total Population 90,816 22,688 33,731 87,841 87,841 Children 25,701 9,773 10,823 17,186 17,186 Children% 28.3% 43.1% 32.1% 19.6% 19.6% TAY 3,869 10,414 10,414 TAY% 11.5% 11.9% 11.9% Adult 10,068 14,348 39,614 39,614 Adult% 44.4% 42.5% 45.1% 45.1% Older Adult 16,256 2,848 3,377 20,627 20,627 Older Adult% 17.9% 12.6% 10.0% 23.5% 23.5% Males 45,136 10,226 43,983 43,983 Male% 49.7% 45.1% 50.1% 50.1% Females 45,680 12,462 43,858 43,858 Female% 50.3% 54.9% 49.9% 49.9% Sociodemographic data are relatively complete, with the exception of language variables, and possess similar proportions across different data sources. 89 Table 7x. Mendocino County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 142.8 Licensed Direct WF 125.4 Other Direct WF 0.0 Direct Total FTE 268.2 Indirect Total FTE 117.9 WF Total (White) 313.6 73,379 12,611 21,773 67,218 WF Total (Latino) 28.0 14,985 7,000 7,924 19,505 WF Total (African Am.) 8.5 545 231 228 622 WF Total (API) 5.0 2,180 334 561 1,569 WF Total (Native Am.) 20.0 4,023 1,828 2,014 4,277 WF Total (Other) 6.4 11,534 687 1,232 10,185 WF Total (All) 381.5 90,816 22,688 33,731 87,841 WF % White 82.2% 80.8% 55.6% 64.5% 76.5% WF % Latino 7.3% 16.5% 30.9% 23.5% 22.2% WF % African Am. 2.2% 0.6% 1.0% 0.7% 0.7% WF % API 1.3% 2.4% 1.5% 1.7% 1.8% WF % Native Am. 5.2% 4.4% 8.1% 6.0% 4.9% WF % Other 1.7% 12.7% 3.0% 3.7% 11.6% Workforce data for Mendocino appear complete. In comparing workforce data to the general, Medi-Cal, CSS, and DOF populations, it appears there is a need for a greater proportion of mental health workforce representation from the Latino population. CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Ethnic groups Children TAY CSS targets are broad for Mendocino county. There are no WET targets. Note: Sections with blanks indicates that data were not available. 90 Table 7y. Merced County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 273,935 2,803 255,793 255,793 African American 6,920 274 9,926 9,926 African Am. % 2.5% 9.8% 3.9% 3.9% API 16,299 337 19,419 19,419 API % 6.0% 12.0% 7.6% 7.6% Latino 153,698 883 140,485 140,485 Latino% 56.1% 31.5% 54.9% 54.9% Native Am. 1,232 17 3,473 3,473 Native Am. % 45.0% 0.6% 1.4% 1.4% White 91,799 1,112 148,381 148,381 White % 33.5% 39.7% 58.0% 58.0% Other 3,987 180 62,665 62,665 Other% 1.5% 6.4% 24.5% 24.5% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 255,793 255,793 Children 70,994 70,994 Children% 27.8% 27.8% TAY 43,849 43,849 TAY% 17.1% 17.1% Adult 106,708 106,708 Adult% 41.7% 41.7% Older Adult 34,242 34,242 Older Adult% 13.4% 13.4% Males 128,737 128,737 Male% 50.3% 50.3% Females 127,056 127,056 Female% 49.7% 49.7% There are a number of gaps in the sociodemographic data for Merced County. 91 Table 7y. Merced County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 91,799 1,112 148,381 WF Total (Latino) 153,698 883 140,485 WF Total (African Am.) 6,920 274 9,926 WF Total (API) 16,299 337 19,419 WF Total (Native Am.) 1,232 17 3,473 WF Total (Other) 3,987 180 62,665 WF Total (All) 273,935 2,803 255,793 WF % White 33.5% 39.7% 58.0% WF % Latino 56.1% 31.5% 54.9% WF % African Am. 2.5% 9.8% 3.9% WF % API 6.0% 12.0% 7.6% WF % Native Am. 45.0% 0.6% 1.4% WF % Other 1.5% 6.4% 24.5% No workforce data were noted for Merced County. Note: Sections with blanks indicates that data were not available. 92 Table 7z. Modoc County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 9,197 2,125 3,760 9,686 9,686 African American 75 19 14 82 82 African Am. % 0.8% 0.9% 0.4% 0.8% 0.8% API 68 19 17 99 99 API % 0.7% 0.9% 0.5% 1.0% 1.0% Latino 1,201 405 652 1,342 1,342 Latino% 13.1% 19.1% 17.3% 13.9% 13.9% Native Am. 359 144 194 370 370 Native Am. % 3.9% 6.8% 5.2% 3.8% 3.8% White 7,286 1,432 2,784 8,084 8,084 White % 79.2% 67.4% 74.0% 83.5% 83.5% Other 208 106 99 680 680 Other% 2.3% 5.0% 2.6% 7.0% 7.0% Language API API% English 1,748 English% 82.3% Spanish 267 Spanish% 12.6% Other 110 Other% 5.2% Age/Gender Total Population 9,197 2,125 3,760 9,686 9,686 Children 1,825 607 1,201 1,862 1,862 Children% 19.8% 28.6% 31.9% 19.2% 19.2% TAY 127 956 956 TAY% 3.4% 9.9% 9.9% Adult 4,324 813 1,844 4,136 4,136 Adult% 47.0% 38.3% 49.0% 42.7% 42.7% Older Adult 3,048 705 588 2,732 2,732 Older Adult% 33.1% 33.2% 15.6% 28.2% 28.2% Males 4,637 925 4,878 4,878 Male% 50.4% 43.5% 50.4% 50.4% Females 4,560 1,200 4,808 4,808 Female% 49.6% 56.5% 49.6% 49.6% Sociodemographic data are complete, with the exception of language data, for Modoc County. 93 Table 7z. Modoc County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 6.8 Licensed Direct WF 8.0 Other Direct WF 1.0 Direct Total FTE 15.8 Indirect Total FTE 10.0 WF Total (White) 22.8 7,286 1,432 2,784 8,084 WF Total (Latino) 2.0 1,201 405 652 1,342 WF Total (African Am.) 0.0 75 19 14 82 WF Total (API) 0.0 68 19 17 99 WF Total (Native Am.) 1.0 359 144 194 370 WF Total (Other) 0.0 208 106 99 680 WF Total (All) 25.8 9,197 2,125 3,760 9,686 WF % White 88.3% 79.2% 67.4% 74.0% 83.5% WF % Latino 7.8% 13.1% 19.1% 17.3% 13.9% WF % African Am. 0.0% 0.8% 0.9% 0.4% 0.8% WF % API 0.0% 0.7% 0.9% 0.5% 1.0% WF % Native Am. 3.9% 3.9% 6.8% 5.2% 3.8% WF % Other 0.0% 2.3% 5.0% 2.6% 7.0% Workforce data are complete for Modoc County. Comparison of workforce data to data from the general, Medi-Cal, CSS, and DOF populations highlight a need for a greater representation of Latino mental health staff. CSS CSS CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Target Population 6 Native American, children Native American, TAY Latino, children Latino, TAY Adults with SMI Older adults CSS target populations for Modoc County appear to be relatively well focused. WET WET WET Target Population 1 Target Population 2 Target Population 3 Spanish language staff Native American staff Latino staff WET target populations focus on Latino and Native American staff. Targets were not easy to find in CCP report for Modoc County. Note: Sections with blanks indicates that data were not available. 94 Table 7aa. Mono County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 14,833 1,300 4,141 14,202 14,202 African American 69 19 47 47 African Am. % 0.5% 0.5% 0.3% 0.3% API 185 32 203 203 API % 1.2% 0.8% 1.4% 1.4% Latino 4,348 773 1,309 3,762 3,762 Latino% 29.3% 59.5% 31.6% 26.5% 26.5% Native Am. 303 83 173 302 302 Native Am. % 2.0% 6.4% 4.2% 2.1% 2.1% White 9,682 392 2,506 11,697 11,697 White % 65.3% 30.2% 60.5% 82.4% 82.4% Other 246 102 1,539 1,539 Other% 1.7% 2.5% 10.8% 10.8% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 4,141 14,202 14,202 Children 3,471 1,202 2,636 2,636 Children% 23.4% 29.0% 18.6% 18.6% TAY 2,037 2,037 TAY% 14.3% 14.3% Adult 9,641 2,939 7,339 7,339 Adult% 65.0% 71.0% 51.7% 51.7% Older Adult 1,721 2,190 2,190 Older Adult% 11.6% 15.4% 15.4% Males 7,548 7,548 Male% 53.1% 53.1% Females 6,654 6,654 Female% 46.9% 46.9% General and CSS data for Mono County are complete, with the exception of language data. Medi-Cal data are limited. 95 Table 7aa. Mono County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 9,682 392 2,506 11,697 WF Total (Latino) 4,348 773 1,309 3,762 WF Total (African Am.) 69 19 47 WF Total (API) 185 32 203 WF Total (Native Am.) 303 83 173 302 WF Total (Other) 246 102 1,539 WF Total (All) 14,833 1,300 4,141 14,202 WF % White 65.3% 30.2% 60.5% 82.4% WF % Latino 29.3% 59.5% 31.6% 26.5% WF % African Am. 0.5% 0.5% 0.3% WF % API 1.2% 0.8% 1.4% WF % Native Am. 2.0% 6.4% 4.2% 2.1% WF % Other 1.7% 2.5% 10.8% No workforce data were noted for Mono County. CSS Target Population Disenfranchised Whites Whites were noted as the only CSS target population. Other population targets, such as Latino’s, were considered but there is a belief in Mono County that the Holzer data do not apply to the county’s reality. No WET targets were noted. Note: Sections with blanks indicates that data were not available. 96 Table 7ab. Monterey County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 430,418 80,613 179,000 415,057 415,057 African American 12,913 12,785 12,785 African Am. % 3.0% 3.1% 3.1% API 30,129 4,000 27,329 27,329 API % 7.0% 2.2% 6.6% 6.6% Latino 241,034 63,454 155,000 230,003 230,003 Latino% 56.0% 78.7% 86.6% 55.4% 55.4% Native Am. 5,464 5,464 Native Am. % 1.3% 1.3% White 137,734 9,588 17,000 230,717 230,717 White % 32.0% 11.9% 9.5% 55.6% 55.6% Other 8,608 3,000 117,405 117,405 Other% 2.0% 1.7% 28.3% 28.3% Language API 30,129 API% 7.0% English 167,433 English% 38.9% Spanish 86,944 Spanish% 20.2% Other 19,799 Other% 4.6% Age/Gender Total Population 430,418 80,613 179,000 415,057 415,057 Children 120,517 40,964 73,000 98,235 98,235 Children% 28.0% 50.8% 40.8% 23.7% 23.7% TAY 65,507 65,507 TAY% 15.8% 15.8% Adult 262,555 31,202 93,000 187,233 187,233 Adult% 61.0% 38.7% 52.0% 45.1% 45.1% Older Adult 47,346 8,448 12,000 64,082 64,082 Older Adult% 11.0% 10.5% 6.7% 15.4% 15.4% Males 219,513 35,030 96,000 213,431 213,431 Male% 51.0% 43.5% 53.6% 51.4% 51.4% Females 210,905 45,583 81,000 201,626 201,626 Female% 49.0% 56.5% 45.3% 48.6% 48.6% Demographic data for Monterey County include most major variables, with the exception of language. Medi-Cal variables are limited. 97 Table 7ab. Monterey County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 136.5 137,734 9,588 17,000 230,717 WF Total (Latino) 115.3 241,034 63,454 155,000 230,003 WF Total (African Am.) 5.0 12,913 12,785 WF Total (API) 15.3 30,129 4,000 27,329 WF Total (Native Am.) 3.0 5,464 WF Total (Other) 8.0 8,608 3,000 117,405 WF Total (All) 283.1 430,418 80,613 179,000 415,057 WF % White 48.2% 32.0% 11.9% 9.5% 55.6% WF % Latino 40.7% 56.0% 78.7% 86.6% 55.4% WF % African Am. 1.8% 3.0% 3.1% WF % API 5.4% 7.0% 2.2% 6.6% WF % Native Am. 1.1% 1.3% WF % Other 2.8% 2.0% 1.7% 28.3% Overall workforce data are available by race/ethnicity, and appear to reflect the overall composition of the Monterey County general population. Review of Medi-Cal and CSS population data indicate there is a need for greater Latino mental health workforce representation. CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Target Target Target Target Target Target Target Target Target Target Target Population Population Population Population Population Population Population Population 8 Populatio Population Population Population 1 2 3 4 5 6 7 n 9 10 11 12 Medi-Cal Latinos Homeless TAY APIs Older Adults Trauma Individuals Children Children Children and Under- Population exposed experiencing and youth and youth youth at risk served and individuals onset of in stressed at risk for of unserved serious families school experiencing cultural psychiatric failure juvenile populations illness justice involvement CSS targets are numerous and broad for Monterey County. It may be challenging to address all noted targets. WET targets were not noted. Note: Sections with blanks indicates that data were not available. 98 Table 7ac. Napa County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 136,484 14,423 29,555 136,484 136,484 African American 2,440 299 355 2,668 2,668 African Am. % 1.8% 2.1% 1.2% 2.0% 2.0% API 8,986 662 826 9,595 9,595 API % 6.6% 4.6% 2.8% 7.0% 7.0% Latino 44,010 7,816 11,616 44,010 44,010 Latino% 32.2% 54.2% 39.3% 32.2% 32.2% Native Am. 544 61 234 1,058 1,058 Native Am. % 0.4% 0.4% 0.8% 0.8% 0.8% White 76,967 5,095 15,931 97,525 97,525 White % 56.4% 35.3% 53.9% 71.5% 71.5% Other 3,537 490 591 20,058 20,058 Other% 2.6% 3.4% 2.0% 14.7% 14.7% Language API 5,109 API% 4.1% English 84,219 7,061 English% 68.0% 49.0% Spanish 30,990 6,545 Spanish% 25.0% 45.4% Other 481 706 Other% 0.4% 4.9% Age/Gender Total Population 14,423 29,555 136,484 136,484 Children 6,595 6,057 27,638 27,638 Children% 45.7% 20.5% 20.2% 20.2% TAY 3,948 17,647 17,647 TAY% 13.4% 12.9% 12.9% Adult 5,714 13623 62,185 62,185 Adult% 39.6% 46.1% 45.6% 45.6% Older Adult 2,114 5,927 29,014 29,014 Older Adult% 14.7% 20.1% 21.3% 21.3% Males 6,287 68,159 68,159 Male% 43.6% 49.9% 49.9% Females 8,136 68,325 68,325 Female% 56.4% 50.1% 50.1% Demographic data for Napa County are complete, with the exception of age and gender-specific data for the general population. 99 Table 7ac. Napa County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 13.1 Licensed Direct WF 12.0 Other Direct WF Direct Total FTE 25.1 Indirect Total FTE 12.0 WF Total (White) 48.6 76,967 5,095 15,931 97,525 WF Total (Latino) 11.5 44,010 7,816 11,616 44,010 WF Total (African Am.) 1.7 2,440 299 355 2,668 WF Total (API) 2.8 8,986 662 826 9,595 WF Total (Native Am.) 0.0 544 61 234 1,058 WF Total (Other) 1.4 3,537 490 591 20,058 WF Total (All) 66.0 136,484 14,423 29,555 136,484 WF % White 73.6% 56.4% 35.3% 53.9% 71.5% WF % Latino 17.4% 32.2% 54.2% 39.3% 32.2% WF % African Am. 2.6% 1.8% 2.1% 1.2% 2.0% WF % API 4.2% 6.6% 4.6% 2.8% 7.0% WF % Native Am. 0.0% 0.4% 0.4% 0.8% 0.8% WF % Other 2.1% 2.6% 3.4% 2.0% 14.7% Workforce data appear complete for Napa County. A smaller proportion of the workforce is Latino compared to the proportion of Latinos in the general, Medi-Cal, CSS, and DOF populations. CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Target Target Target Target Target Target Target Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Population 7 Population 9 Population 10 Children Older adults TAY Latinos Consumers LGBTQ Trauma exposed Native Asian and Pacific children Americans Islander CSS targets are numerous and relatively broad. Note: Sections with blanks indicates that data were not available. 10 0 Table 7ad. Nevada County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 97,027 1,302 1,345 98,764 98,764 African American 508 11 10 389 389 African Am. % 0.5% 0.8% 0.7% 0.4% 0.4% API 1,253 7 3 1,297 1,297 API % 1.3% 0.5% 0.2% 1.3% 1.3% Latino 7,310 137 28 8,439 8,439 Latino% 7.5% 10.5% 2.1% 8.5% 8.5% Native Am. 767 36 12 1,044 1,044 Native Am. % 0.8% 2.8% 0.9% 1.1% 1.1% White 85,286 1,093 1,279 90,233 90,233 White % 87.9% 83.9% 95.1% 91.4% 91.4% Other 1,903 18 13 2,678 2,678 Other% 2.0% 1.4% 1.0% Language API API% English 8,188 1,270 English% 87.3% 97.6% Spanish 672 16 Spanish% 7.2% 1.2% Other 523 16 Other% 5.6% 1.2% Age/Gender Total Population 97,027 1,302 1,345 98,764 98,764 Children 17,550 482 356 16,430 16,430 Children% 18.1% 37.0% 26.5% 16.6% 16.6% TAY 67 10,197 10,197 TAY% 4.9% 10.3% 10.3% Adult 47,840 644 867 44,137 44,137 Adult% 49.3% 49.5% 64.5% 44.7% 44.7% Older Adult 31,637 176 55 28,000 28,000 Older Adult% 32.6% 13.5% 4.1% 28.4% 28.4% Males 48,172 618 623 48,835 48,835 Male% 49.6% 52.5% 46.3% 49.4% 49.4% Females 48,855 684 722 49,929 49,929 Female% 50.4% 47.5% 53.7% 50.6% 50.6% Demographic data for Nevada County are complete, with the exception of language data for the CSS and Census/DOF populations. 10 1 Table 7ad. Nevada County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 29.2 Licensed Direct WF 44.8 Other Direct WF 3.3 Direct Total FTE 77.3 Indirect Total FTE 33.7 WF Total (White) 93.5 85,286 1,093 1,279 90,233 WF Total (Latino) 2.0 7,310 137 28 8,439 WF Total (African Am.) 3.0 508 11 10 389 WF Total (API) 2.0 1,253 7 3 1,297 WF Total (Native Am.) 1.0 767 36 12 1,044 WF Total (Other) 1.0 1,903 18 13 2,678 WF Total (All) 102.5 97,027 1,302 1,345 98,764 WF % White 91.2% 87.9% 83.9% 95.1% 91.40% WF % Latino 2.0% 7.5% 10.5% 2.1% 8.50% WF % African Am. 2.8% 0.5% 0.8% 0.7% 0.40% WF % API 2.0% 1.3% 0.5% 0.2% 1.30% WF % Native Am. 1.0% 0.8% 2.8% 0.9% 1.10% WF % Other 1.0% 2.0% 1.4% 1.0% Workforce data for Nevada County are complete and appear to reflect the composition of the general population. Comparison of workforce data to Medi-Cal population data indicates there is a need for greater representation of Latino mental health staff. CSS Target CSS Target CSS Target CSS Target CSS Target CSS Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Children TAY Adults Older adults Latinos Males WET Target WET Target WET Target WET Target WET Target WET Target WET Target WET Target WET Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Population 7 Population 8 Population 9 Latino White African Asian American Indian Bilingual Children and TAY Older adults American Spanish- youth speaking Target populations are relatively broad. Target populations did not appear to be differentiated across CSS and WET categories. Note: Sections with blanks indicates that data were not available. 10 2 Table 7ae. Orange County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 3,048,000 349,000 779,195 3,010,232 3,010,232 African American 45,000 12,000 10,682 50,744 50,744 African Am. % 1.5% 3.4% 1.4% 1.7% 1.7% API 493,000 52,000 112,790 547,158 547,158 API % 16.2% 14.9% 14.5% 18.2% 18.2% Latino 705,000 181,000 449,943 1,012,973 1,012,973 Latino% 23.1% 51.9% 57.7% 33.7% 33.7% Native Am. 19,000 2,000 2,852 18,132 18,132 Native Am. % 0.6% 0.6% 0.4% 0.6% 0.6% White 1,495,000 63,000 181,598 1,830,758 1,830,758 White % 49.0% 18.1% 23.3% 60.8% 60.8% Other 291,000 39,000 21,330 435,641 435,641 Other% 9.5% 11.2% 2.7% 14.5% 14.5% Language API 74,000 API% 2.4% English 1,721,000 88,000 English% 56.5% 25.2% Spanish 247,000 68,000 Spanish% 8.1% 19.5% Other 116,000 10,000 Other% 3.8% 2.9% Age/Gender Total Population 3,048,000 349,000 779,195 3,010,232 3,010,232 Children 791,000 188,000 243,228 645,430 645,430 Children% 26.0% 53.9% 31.2% 21.4% 21.4% TAY 292,000 25,000 154,997 439,926 439,926 TAY% 9.6% 7.2% 19.9% 14.6% 14.6% Adult 1,653,000 91,000 303,837 1,428,472 1,428,472 Adult% 54.2% 26.1% 39.0% 47.5% 47.5% Older Adult 3,090,00 47,000 77,133 496,404 496,404 Older Adult% 10.1% 13.5% 9.9% 16.5% 16.5% Males 1,513,000 155,000 1,488,780 1,488,780 Male% 49.6% 44.4% 49.5% 49.5% Females 1,535,000 194,000 1,521,452 1,521,452 Female% 50.4% 55.6% 50.5% 50.5% Demographic data for Orange County appear to be relatively detailed, and portray similar proportions across different subgroups. 10 3 Table 7ae. Orange County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 1,089.9 Licensed Direct WF 432.1 Other Direct WF 30.5 Direct Total FTE 1,552.5 Indirect Total FTE 406.0 WF Total (White) 884.9 1,495,000 63,000 181,598 1,830,758 WF Total (Latino) 549.0 705,000 181,000 449,943 1,012,973 WF Total (African Am.) 59.5 45,000 12,000 10,682 50,744 WF Total (API) 263.7 493,000 52,000 112,790 547,158 WF Total (Native Am.) 6.5 19,000 2,000 2,852 18,132 WF Total (Other) 194.9 291,000 39,000 21,330 435,641 WF Total (All) 1,958.5 3,048,000 349,000 779,195 3,010,232 WF % White 45.2% 49.0% 18.1% 23.3% 60.8% WF % Latino 28.0% 23.1% 51.9% 57.7% 33.7% WF % African Am. 3.0% 1.5% 3.4% 1.4% 1.7% WF % API 13.5% 16.2% 14.9% 14.5% 18.2% WF % Native Am. 0.3% 0.6% 0.6% 0.4% 0.6% WF % Other 10.0% 9.5% 11.2% 2.7% 14.5% Workforce data for Orange County are complete and appear to reflect the composition of the general population. Comparison of workforce data to Medi-Cal and CSS population data indicate there is a need for greater representation of Latino mental health staff. CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Target Target Target Target Target Target Target Target Target Target Target Target Pop. 1 Pop. 2 Pop. 3 Pop. 4 Pop. 5 Pop. 6 Pop. 7 Pop. 8 Pop. 9 Pop. 10 Pop. 11 Pop. 12 Pop. 13 Latino Vietnamese Deaf and LGBTQ Limited Homeless Frail, Trauma ex- Children and Children Children Children Individuals hard of English individuals isolated posed TAY involved and TAY and TAY and TAY in experiencing hearing proficiency and older individuals, or at risk of at-risk of aging out stressed onset of families adults including becoming school of foster families psychiatric veterans involved in failure care illness juvenile system justice system CSS targets are numerous and several are quite specific. Note: Sections with blanks indicates that data were not available. 10 4 Table 7af. Placer County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 341,945 27,420 28,665 348,432 348,432 African American 5,813 701 411 4,751 4,751 African Am. % 1.7% 2.6% 1.4% 1.4% 1.4% API 18,807 1,338 828 21,213 21,213 API % 5.5% 4.9% 2.9% 6.1% 6.1% Latino 40,008 5,712 3,006 44,710 44,710 Latino% 11.7% 20.8% 10.5% 12.8% 12.8% Native Am. 3,078 314 193 3,011 3,011 Native Am. % 0.9% 1.1% 0.7% 0.9% 0.9% White 268,085 17,185 23,554 290,977 290,977 White % 78.4% 62.7% 82.2% 83.5% 83.5% Other 8,891 1,990 673 13,375 13,375 Other% 2.6% 7.3% 2.3% 3.8% 3.8% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 341,945 27,420 28,665 348,432 348,432 Children 74,202 11,582 6,584 74,653 74,653 Children% 21.7% 42.2% 23.0% 21.4% 21.4% TAY 3,525 40,848 40,848 TAY% 12.3% 11.7% 11.7% Adult 215,425 11,120 15,206 158223 158,223 Adult% 63.0% 40.6% 53.0% 45.4% 45.4% Older Adult 52,318 4,538 3,350 74,708 74,708 Older Adult% 15.3% 16.5% 11.7% 21.4% 21.4% Males 137,804 11,541 170,151 170,151 Male% 40.3% 42.1% 48.8% 48.8% Females 173,366 15,699 178,281 178,281 Female% 50.7% 57.3% 51.2% 51.2% With the exception of language variables, demographic data for Placer County are complete and relatively consistent across data sources. 10 5 Table 7af. Placer County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 268,085 17,185 23,554 290,977 WF Total (Latino) 40,008 5,712 3,006 44,710 WF Total (African Am.) 5,813 701 411 4,751 WF Total (API) 18,807 1,338 828 21,213 WF Total (Native Am.) 3,078 314 193 3,011 WF Total (Other) 8,891 1,990 673 13,375 WF Total (All) 341,945 27,420 28,665 348,432 WF % White 78.4% 62.7% 82.2% 83.5% WF % Latino 11.7% 20.8% 10.5% 12.8% WF % African Am. 1.7% 2.6% 1.4% 1.4% WF % API 5.5% 4.9% 2.9% 6.1% WF % Native Am. 0.9% 1.1% 0.7% 0.9% WF % Other 2.6% 7.3% 2.3% 3.8% No workforce data were noted for Placer County. CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Target Target Target Target Target Target Target Target Target Target Target Target Pop. 1 Pop. 2 Pop. 3 Pop. 4 Pop. 5 Pop. 6 Pop. 7 Pop. 8 Pop. 9 Pop. 10 Pop. 11 Pop. 12 Pop. 13 Native Latino TAY Older Mothers of Children and Recruitment Bilingual Stigma and Better under- LGBTQ Co- Multiple families, families, adults children youth at-risk /retention of and bias in work- standing of role occurring disabilities children, children, 0-5 with for school bilingual and bicultural force and benefit of youth youth depression failure, bicultural services regarding consumers, incarceration staff to Tahoe mental families, and and health issues youth in work- Lincoln force CSS target populations for Placer County are broad and numerous. Target populations were not differentiated across CSS and WET categories. Note: Sections with blanks indicates that data were not available. 10 6 Table 7ag. Plumas County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 20,760 2,921 20,007 20,007 African American 132 76 192 192 African Am. % 0.6% 2.6% 1.0% 1.0% API 130 15 152 152 API % 0.6% 0.5% 0.8% 0.8% Latino 1,186 237 1,605 1,605 Latino% 5.7% 8.1% 8.0% 8.0% Native Am. 489 110 539 539 Native Am. % 2.4% 3.8% 2.7% 2.7% White 18,370 2,363 17,797 17,797 White % 88.5% 80.9% 89.0% 89.0% Other 453 120 603 603 Other% 2.2% 4.1% 3.0% 3.0% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 2,921 20,007 20,007 Children 1,319 3,116 3,116 Children% 45.2% 15.6% 15.6% TAY 2,139 2,139 TAY% 10.7% 10.7% Adult 1,286 8,668 8,668 Adult% 44.0% 43.3% 43.3% Older Adult 316 6084 6,084 Older Adult% 10.8% 30.4% 30.4% Males 10,003 10,003 Male% 50.0% 50.0% Females 10,004 10,004 Female% 50.0% 50.0% Demographic data are relatively limited for Plumas County. 10 7 Table 7ag. Plumas County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE 34.3 WF Total (White) 27.3 18,370 2,363 17,797 WF Total (Latino) 4.0 1,186 237 1,605 WF Total (African Am.) 0.0 132 76 192 WF Total (API) 0.0 130 15 152 WF Total (Native Am.) 1.0 489 110 539 WF Total (Other) 2.0 453 120 603 WF Total (All) 34.3 20,760 2,921 20,007 WF % White 79.6% 88.5% 80.9% 89.0% WF % Latino 11.7% 5.7% 8.1% 8.0% WF % African Am. 0.0% 0.6% 2.6% 1.0% WF % API 0.0% 0.6% 0.5% 0.8% WF % Native Am. 2.9% 2.4% 3.8% 2.7% WF % Other 5.8% 2.2% 4.1% 3.0% Workforce data appear to reflect the composition of the general and Medi-Cal populations. Target Target Target Target Population 1 Population 2 Population 3 Population 4 Latino Native American Children and youth at risk Children and youth with juvenile justice involvement For Plumas County, target populations are not differentiated by CSS and WET categories. Target populations highlighted focus on two racial/ethnic groups and young populations. Note: Sections with blanks indicates that data were not available. 10 8 Table 7ah. Riverside County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 2,119,618 336,844 142,511 2,189,641 2,189,641 African American 10,598 30,653 8,799 140,543 140,543 African Am. % 0.5% 9.1% 6.2% 6.4% 6.4% API 97,502 11,453 6,416 137,342 137,342 API % 4.6% 3.4% 4.5% 6.3% 6.3% Latino 866,924 198,738 62,259 995,257 995,257 Latino% 40.9% 59.0% 43.7% 45.5% 45.5% Native Am. 10,598 1,011 582 23,710 23,710 Native Am. % 0.5% 0.3% 0.4% 1.1% 1.1% White 977,144 75,453 61,744 1,335,147 1,335,147 White % 46.1% 22.4% 43.3% 61.0% 61.0% Other 36,034 19,200 2,711 448,235 448,235 Other% 1.7% 5.7% 1.9% 20.5% 20.5% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 2,119,618 336,844 142,511 2,189,641 2,189,641 Children 604,091 180,885 44,815 544592 544,592 Children% 28.5% 53.7% 31.4% 24.9% 24.9% TAY 333,173 333,173 TAY% 15.2% 15.2% Adult 1,214,541 112,169 77,359 954,316 954,316 Adult% 57.3% 33.3% 54.3% 43.6% 43.6% Older Adult 298,866 43,116 15,015 357,560 357,560 Older Adult% 14.1% 12.8% 10.5% 16.3% 16.3% Males 144,405 1,089,576 1,089,576 Male% 42.9% 49.8% 49.8% Females 192,439 1,100,065 1,100,065 Female% 57.1% 50.2% 50.2% Demographic data for Riverside County are complete for most variables, and appear consistent across different data sources. Gaps are evident for language and gender data. 10 9 Table 7ah. Riverside County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 571.1 Licensed Direct WF 576.0 Other Direct WF 355.5 Direct Total FTE 1,502.6 Indirect Total FTE 607.9 WF Total (White) 692.5 977,144 75,453 61,744 1,335,147 WF Total (Latino) 393.1 866,924 198,738 62,259 995,257 WF Total (African Am.) 260.8 10,598 30,653 8,799 140,543 WF Total (API) 89.5 97,502 11,453 6,416 137,342 WF Total (Native Am.) 11.0 10,598 1,011 582 23,710 WF Total (Other) 663.7 36,034 19,200 2,711 448,235 WF Total (All) 2,110.5 2,119,618 336,844 142,511 2,189,641 WF % White 32.8% 46.1% 22.4% 43.3% 61.0% WF % Latino 18.6% 40.9% 59.0% 43.7% 45.5% WF % African Am. 12.4% 0.5% 9.1% 6.2% 6.4% WF % API 4.2% 4.6% 3.4% 4.5% 6.3% WF % Native Am. 0.5% 0.5% 0.3% 0.4% 1.1% WF % Other 31.4% 1.7% 5.7% 1.9% 20.5% Workforce data are complete and appear to highlight a relatively diverse mental health workforce. Comparison of workforce data to general, Medi-Cal, CSS, and DOF data indicate there is a need for a higher representation of Latino mental health staff but that representation of African American and API staff is good. Target Target Target Target Target Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Youth Older adults Latino Asian and Pacific Native Americans Deaf community Islanders Target populations are relatively broad. Target populations did not appear to be differentiated across CSS and WET categories. Note: Sections with blanks indicates that data were not available. 11 0 Table 7ai. Sacramento County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 1,400,949 314,765 424,356 1,418,788 1,418,788 African American 135,892 59,491 54,598 147,058 147,058 African Am. % 9.7% 18.9% 12.9% 10.4% 10.4% API 201,737 51,936 68,459 217,069 217,069 API % 14.4% 16.5% 16.1% 15.3% 15.3% Latino 287,195 79,636 94,926 306,196 306,196 Latino% 20.5% 25.3% 22.4% 21.6% 21.6% Native Am. 8,406 2,518 4,485 14,308 14,308 Native Am. % 0.6% 0.8% 1.1% 1.0% 1.0% White 715,885 87,505 179,030 815,151 815,151 White % 51.1% 27.8% 42.2% 57.5% 57.5% Other 53,236 33,365 22,858 131,691 131,691 Other% 3.8% 10.6% 5.4% 9.3% 9.3% Language API 41,430 API% 3.2% English 1,294,700 English% 70.4% Spanish 71,209 Spanish% 5.5% Other 271,887 Other% 21.0% Age/Gender Total Population 1,400,949 314,765 424,356 1,418,788 1,418,788 Children 361,445 143,848 158,788 320,083 320,083 Children% 25.8% 45.7% 37.4% 22.6% 22.6% TAY 55,282 208,508 208,508 TAY% 13.0% 14.7% 14.7% Adult 815,352 131,572 161,396 661,341 661,341 Adult% 58.2% 41.8% 38.0% 46.6% 46.6% Older Adult 224,152 39,346 48,890 228,856 228,856 Older Adult% 16.0% 12.5% 11.5% 16.1% 16.1% Males 689,267 138,182 196,372 694,793 694,793 Male% 49.2% 43.9% 46.3% 49.0% 49.0% Females 711,682 176,583 227,984 723,995 723,995 Female% 50.8% 56.1% 53.7% 51.0% 51.0% Demographic data are detailed for Sacramento County, and appear relatively consistent across data sources. Language data were only presented for the general population. 11 1 Table 7ai. Sacramento County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 936.7 Licensed Direct WF 484.3 Other Direct WF 201.9 Direct Total FTE 1,622.9 Indirect Total FTE 905.2 WF Total (White) 1,267.4 715,885 87,505 179,030 815,151 WF Total (Latino) 278.6 287,195 79,636 94,926 306,196 WF Total (African Am.) 422.8 135,892 59,491 54,598 147,058 WF Total (API) 329.8 201,737 51,936 68,459 217,069 WF Total (Native Am.) 23.7 8,406 2,518 4,485 14,308 WF Total (Other) 205.8 53,236 33,365 22,858 131,691 WF Total (All) 2,528.1 1,400,949 314,765 424,356 1,418,788 WF % White 50.1% 51.1% 27.8% 42.2% 57.5% WF % Latino 11.0% 20.5% 25.3% 22.4% 21.6% WF % African Am. 16.7% 9.7% 18.9% 12.9% 10.4% WF % API 13.0% 14.4% 16.5% 16.1% 15.3% WF % Native Am. 0.9% 0.6% 0.8% 1.1% 1.0% WF % Other 8.1% 3.8% 10.6% 5.4% 9.3% Workforce data are complete for Sacramento County and appear to reflect the general population composition for the county. Comparison of workforce data to the Medi-Cal, CSS, and DOF population data indicate there is a need for greater Latino representation but that there is relatively good representation of African American and API workforce. CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 API Latinos Native American Multi-racial CSS target populations for Sacramento County focus on racial and ethnic groups. WET WET WET WET WET WET Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Target Population 6 Licensed direct service LCSW, MFTs Psychiatrists Language diversity Career pathways for Career pathways that lead staff consumers and family bilingual staff to higher direct members service careers, and supervisory positions WET targets appear to be well thought out, and reflective of mental health needs in the county. Note: Sections with blanks indicates that data were not available. 11 2 Table 7aj. San Benito County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 54,667 8,648 55,269 55,269 African American 493 68 483 483 African Am. % 0.9% 0.8% 0.9% 0.9% API 1,601 114 1,537 1,537 API % 2.9% 1.3% 2.8% 2.8% Latino 28,984 6,728 31,186 31,186 Latino% 53.0% 77.8% 56.4% 56.4% Native Am. 295 14 895 895 Native Am. % 0.5% 0.2% 1.6% 1.6% White 22,508 1,429 35,181 35,181 White % 41.2% 16.5% 63.7% 63.7% Other 786 295 14,471 14,471 Other% 1.4% 3.4% 26.2% 26.2% Language API API% English 4,706 English% 54.4% Spanish 3,623 Spanish% 41.9% Other 319 Other% 3.7% Age/Gender Total Population 8,648 55,269 55,269 Children 15,838 3,148 14,064 14,064 Children% 29.0% 36.4% 25.4% 25.4% TAY 7,776 7,776 TAY% 14.1% 14.1% Adult 28,672 3,020 25,496 25,496 Adult% 52.4% 34.9% 46.1% 46.1% Older Adult 10,157 2,480 7,933 7,933 Older Adult% 18.6% 28.7% 14.4% 14.4% Males 27,775 3,570 27,629 27,629 Male% 50.8% 41.3% 50.0% 50.0% Females 26,892 5,078 27,640 27,640 Female% 49.2% 58.7% 50.0% 50.0% There are a few gaps in demographic data for San Benito County for the CSS and language variables. 11 3 Table 7aj. San Benito County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 8.5 Licensed Direct WF 13.5 Other Direct WF 0.5 Direct Total FTE 22.5 Indirect Total FTE 12.3 WF Total (White) 14.8 22,508 1,429 35,181 WF Total (Latino) 14.0 28,984 6,728 31,186 WF Total (African Am.) 0.0 493 68 483 WF Total (API) 4.0 1,601 114 1,537 WF Total (Native Am.) 0.0 295 14 895 WF Total (Other) 2.0 786 295 14,471 WF Total (All) 34.8 54,667 8,648 55,269 WF % White 42.4% 41.2% 16.5% 63.7% WF % Latino 40.3% 53.0% 77.8% 56.4% WF % African Am. 0.0% 0.9% 0.8% 0.9% WF % API 11.5% 2.9% 1.3% 2.8% WF % Native Am. 0.0% 0.5% 0.2% 1.6% WF % Other 5.8% 1.4% 3.4% 26.2% Workforce data are complete for San Benito County and appear to reflect a diverse staff that is representative of the general population for the county. When workforce data are compared to the Medi-Cal and CSS population data, it appears there may be a need for greater Latino mental health workforce representation. CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Target Target Target Target Target Target Target Target Target Target Target Target Population Population Population Population Population Population Population Population Population Population Population Population Population 1 2 3 4 5 6 7 8 9 10 11 12 13 Children 0-7 Children in "High risk" Youth in Homeless Latino youth LGBTQ Uninsured Undocumen Geographically Homeless Farm Rural and of all ethnic foster care children and criminal or children dropouts youth and ted adults isolated adults adults workers non-English groups, youth juvenile Underinsure speaking primarily justice d adults individuals Latino system Targets are numerous for San Benito County. WET WET Target Population 1 Target Population 2 Bilingual, bicultural Spanish-speaking staff Staff competent in gay/lesbian, co-occurring disorders, substance abuse recovery, consumer culture WET target populations focus on competencies with cultural, linguistic, sexual orientation, and co-morbidity needs. Note: Sections with blanks indicates that data were not available. 11 4 Table 7ak. San Bernardino County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 2,017,673 398,175 697,417 2,035,210 2,035,210 African American 181,591 54,240 68,956 181,862 181,862 African Am. % 9.0% 13.6% 9.9% 8.9% 8.9% API 121,060 15,459 37,647 135,473 135,473 API % 6.0% 3.9% 5.4% 6.7% 6.7% Latino 968,483 224,110 355,682 1,001,145 1,001,145 Latino% 48.0% 56.3% 51.0% 49.2% 49.2% Native Am. 20,177 1,431 4,607 22,689 22,689 Native Am. % 1.0% 0.4% 0.7% 1.1% 1.1% White 686,009 85,014 209,729 1,153,161 1,153,161 White % 34.0% 21.4% 30.1% 56.7% 56.7% Other 40,353 17,921 20,796 439,661 439,661 Other% 2.0% 4.5% 3.0% 21.6% 21.6% Language API API% English 266,777.25 English% 67.0% Spanish 107,507 Spanish% 27.0% Other 21,501.45 Other% 5.4% Age/Gender Total Population 2,017,673 398,175 697,417 2,035,210 2,035,210 Children 686,009 188,718 202,909 520,976 520,976 Children% 34.0% 47.4% 29.1% 25.6% 25.6% TAY 60,272 111,849 333,679 333,679 TAY% 15.1% 16.0% 16.4% 16.4% Adult 1331,664 104,075 313,046 912,784 912,784 Adult% 66.0% 26.1% 44.9% 44.8% 44.8% Older Adult 45,109 69,613 267,771 267,771 Older Adult% 11.3% 10.0% 13.2% 13.2% Males 1,008,837 173,302 1,011,507 1,011,507 Male% 50.0% 43.5% 49.7% 49.7% Females 1,008,837 224,873 1,023,703 1,023,703 Female% 50.0% 56.5% 50.3% 50.3% Demographic data appear relatively complete for San Bernardino County. Some differences between subgroup distributions are noted across different data sources. 11 5 Table 7ak. San Bernardino County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 378.5 Licensed Direct WF 492.4 Other Direct WF 155.0 Direct Total FTE 1,025.9 Indirect Total FTE 760.6 WF Total (White) 834.5 686,009 85,014 209,729 1,153,161 WF Total (Latino) 375.1 968,483 224,110 355,682 1,001,145 WF Total (African Am.) 339.1 181,591 54,240 68,956 181,862 WF Total (API) 142.1 121,060 15,459 37,647 135,473 WF Total (Native Am.) 19.2 20,177 1,431 4,607 22,689 WF Total (Other) 76.5 40,353 17,921 20,796 439,661 WF Total (All) 1,786.5 2,017,673 398,175 697,417 2,035,210 WF % White 46.7% 34.0% 21.4% 30.1% 56.7% WF % Latino 21.0% 48.0% 56.3% 51.0% 49.2% WF % African Am. 19.0% 9.0% 13.6% 9.9% 8.9% WF % API 8.0% 6.0% 3.9% 5.4% 6.7% WF % Native Am. 1.1% 1.0% 0.4% 0.7% 1.1% WF % Other 4.3% 2.0% 4.5% 3.0% 21.6% Workforce data reflect a diverse staff composition that is relatively reflective of the general, Medi-Cal, CSS, and DOF populations, with potential disparities in Latino staffing. CSS Target Population 1 African American One CSS target population was noted for San Bernardino County, with a focus on the African American community. WET WET WET WET WET Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Latinos African Americans Native Americans Spanish speakers Consumer and family members from diverse ethnic and linguistic backgrounds WET target populations appear to be focused and on par with noted disparities. Note: Sections with blanks indicates that data were not available. 11 6 Table 7al. San Diego County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 2,974,859 378,319 575,086 3,095,313 3,095,313 African American 145,227 37,350 33,229 158,213 158,213 African Am. % 4.9% 9.9% 5.8% 5.1% 5.1% API 310,575 37,183 48,438 351,428 351,428 API % 10.4% 9.8% 8.4% 11.4% 11.4% Latino 901,369 181,027 203,030 991,348 991,348 Latino% 30.3% 47.9% 35.3% 32.0% 32.0% Native Am. 15,928 1,556 3,457 26,340 26,340 Native Am. % 0.5% 0.4% 0.6% 0.9% 0.9% White 1,528,568 85,958 194,837 1,981,442 1,981,442 White % 51.4% 22.7% 33.9% 64.0% 64.0% Other 73,192 35,248 92,095 419,465 419,465 Other% 2.5% 9.3% 16.0% 13.6% 13.6% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 2,974,859 378,319 575,086 3,095,313 3,095,313 Children 749,170 178,766 638,216 638,216 Children% 25.2% 47.3% 20.6% 20.6% TAY 130,559 506,014 506,014 TAY% 22.7% 16.3% 16.3% Adult 1,894,869 134,125 347,595 1,450,347 1,450,347 Adult% 63.7% 35.5% 60.4% 46.9% 46.9% Older Adult 330,820 65,430 96,932 500,736 500,736 Older Adult% 11.1% 17.3% 16.9% 16.2% 16.2% Males 1,494,127 160,666 1,553,679 1,553,679 Male% 50.2% 42.5% 50.2% 50.2% Females 148,0732 217,654 1,541,634 1,541,634 Female% 49.8% 57.5% 49.8% 49.8% With the exception of language variables, San Diego County demographic data appear complete and relatively consistent across different data sources. 11 7 Table 7al. San Diego County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 363.9 Licensed Direct WF 656.9 Other Direct WF 79.5 Direct Total FTE 1,100.3 Indirect Total FTE 574.1 WF Total (White) 932.7 1,528,568 85,958 194,837 1,981,442 WF Total (Latino) 341.6 901,369 181,027 203,030 991,348 WF Total (African Am.) 165.4 145,227 37,350 33,229 158,213 WF Total (API) 171.9 310,575 37,183 48,438 351,428 WF Total (Native Am.) 5.6 15,928 1,556 3,457 26,340 WF Total (Other) 57.3 73,192 35,248 92,095 419,465 WF Total (All) 1,674.4 2,974,859 378,319 575,086 3,095,313 WF % White 55.7% 51.4% 22.7% 33.9% 64.0% WF % Latino 20.4% 30.3% 47.9% 35.3% 32.0% WF % African Am. 9.9% 4.9% 9.9% 5.8% 5.1% WF % API 10.3% 10.4% 9.8% 8.4% 11.4% WF % Native Am. 0.3% 0.5% 0.4% 0.6% 0.9% WF % Other 3.4% 2.5% 9.3% 16.0% 13.6% Workforce data are complete for San Diego County. Disparities in Latino staffing needs are apparent. CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Target Target Target Target Target Target Target Target Target Target Target Target Target Target Target Target Target Pop. 1 Pop. 2 Pop. 3 Pop. 4 Pop. 5 Pop. 6 Pop. 7 Pop. 8 Pop. 9 Pop. 10 Pop. 11 Pop. 12 Pop. 13 Pop. 14 Pop. 15 Pop. 16 Pop. 17 Pop. 18 Latino Latino African API API Native Native Whites Children Children TAY Adults Older Females Males Veterans LGBTQ Recent adults, children American adults, children American American 6-12 12-17 18-24 25-59 adults immigrants, older adults older adults children 60+ victims of adults adults violence CSS targets for San Diego County appear to include virtually all populations. WET WET WET WET WET WET WET WET WET WET WET WET Target Target Target Target Target Target Target Target Target Target Target Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Population 7 Population 8 Population 9 Population 10 Population 11 Population 12 Latino adults, Latino African African API adults, API children Children 6-12 Children 12-17 TAY 18-24 Adults 25-59 Older adults 60+ Recent older adults children American American older adults Immigrants, adults children victims of violence WET target populations are also numerous and inclusive of many, if not all, populations in San Diego County. Note: Sections with blanks indicates that data were not available. 11 8 Table 7am. San Francisco County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 815,358 183,622 805,235 805,235 African American 55,444 20,214 48,870 48,870 African Am. % 6.8% 11.0% 6.1% 6.1% API 259,284 67,349 271,274 271,274 API % 31.8% 36.7% 33.7% 33.7% Latino 114,965 36,714 121,774 121,774 Latino% 14.1% 20.0% 15.1% 15.1% Native Am. 4,892 674 4,024 4,024 Native Am. % 0.6% 0.4% 0.5% 0.5% White 473,723 54,084 390,387 390,387 White % 58.1% 29.5% 48.5% 48.5% Other 22,830 4,587 53,021 53,021 Other% 2.8% 2.5% 6.6% 6.6% Language API 211,993 API% 26.0% English 440,293 English% 54.0% Spanish 97,843 Spanish% 12.0% Other 57,075 Other% 7.0% Age/Gender Total Population 815,358 183,622 805,235 805,235 Children 146,764 32,241 95,772 95,772 Children% 18.0% 17.6% 11.9% 11.9% TAY 57,075 20,507 106,715 106,715 TAY% 7.0% 11.2% 13.3% 13.3% Adult 513,676 94,147 448,018 448,018 Adult% 63.0% 51.3% 55.6% 55.6% Older Adult 122,304 36,727 154,730 154,730 Older Adult% 15.0% 20.0% 19.2% 19.2% Males 415,833 87,317 408,462 408,462 Male% 51.0% 47.6% 50.7% 50.7% Females 399,525 96,304 396,773 396,773 Female% 49.0% 52.4% 49.3% 49.3% Medi-Cal population data were not noted among San Francisco demographic data. All other data sources and variables appear complete and are relatively consistent. 11 9 Table 7am. San Francisco County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 473,723 54,084 390,387 WF Total (Latino) 114,965 36,714 121,774 WF Total (African Am.) 55,444 20,214 48,870 WF Total (API) 259,284 67,349 271,274 WF Total (Native Am.) 4,892 674 4,024 WF Total (Other) 22,830 4,587 53,021 WF Total (All) 815,358 183,622 805,235 WF % White 58.1% 29.5% 48.5% WF % Latino 14.1% 20.0% 15.1% WF % African Am. 259,284 67,349 271,274 WF % API 31.8% 36.7% 33.7% WF % Native Am. 0.6% 0.4% 0.5% WF % Other 2.8% 2.5% 6.6% Workforce data were not noted for San Francisco County. CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Target Target Target Target Target Population Target Target Target Target Target Population 1 Population 2 Population 3 Population 4 Population 5 6 Population Population 8 Population 9 Population 10 Population 11 7 Homeless Native LGBTQ Youth in foster Adult People with co- Non- People with People Insured Veterans Americans care and offenders occurring English HIV/AIDS without health without juvenile with mental disorders speakers insurance mental health probation illness coverage CSS target populations for San Francisco County are numerous but appear focused. It may be challenging to address all noted target groups. WET WET WET Target Population 1 Target Population 2 Target Population 3 African Americans, underrepresented among licensed African Americans, underrepresented Latinos/as, underrepresented staff While workforce data were not noted, San Francisco County WET target populations appear to be relatively focused on potential disparities in representation of mental health staff by race and ethnicity. Note: Sections with blanks indicates that data were not available. 12 0 Table 7an. San Joaquin County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 674,860 159,367 685,306 685,306 African American 53,989 19,823 51,744 51,744 African Am. % 8.0% 12.4% 7.6% 7.6% API 97,855 25,181 102,230 102,230 API % 14.5% 15.8% 14.9% 14.9% Latino 253,747 72,863 266,341 266,341 Latino% 37.6% 45.7% 38.9% 38.9% Native Am. 9,448 594 7,196 7,196 Native Am. % 1.4% 0.4% 1.1% 1.1% White 489,274 34,413 349,287 349,287 White % 72.5% 21.6% 51.0% 51.0% Other 24,295 6,493 131,054 131,054 Other% 3.6% 4.1% 19.1% 19.1% Language API API% English 100,055 English% 62.8% Spanish 41,725 Spanish% 26.2% Other 7,987 Other% 5.0% Age/Gender Total Population 674,860 159,367 685,306 685,306 Children 259,821 79,172 176,865 176,865 Children% 38.5% 49.7% 25.8% 25.8% TAY 8,832 104,426 104,426 TAY% 5.5% 15.2% 15.2% Adult 346,878 56,938 301,786 301,786 Adult% 51.4% 35.7% 44.0% 44.0% Older Adult 68,161 14,425 102,229 102,229 Older Adult% 10.1% 9.1% 14.9% 14.9% Males 338,780 70,081 341,230 341,230 Male% 50.2% 44.0% 49.8% 49.8% Females 336,080 89,286 344,076 344,076 Female% 49.8% 56.0% 50.2% 50.2% While demographic data are relatively complete, some differences in proportions appear within subgroups across data sources. CSS data were not noted among San Joaquin County demographic data. 12 1 Table 7an. San Joaquin County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 222.1 Licensed Direct WF 204.5 Other Direct WF 30.8 Direct Total FTE 457.4 Indirect Total FTE 388.2 WF Total (White) 311.4 489,274 34,413 349,287 WF Total (Latino) 194.6 253,747 72,863 266,341 WF Total (African Am.) 138.7 53,989 19,823 51,744 WF Total (API) 135.8 97,855 25,181 102,230 WF Total (Native Am.) 10.6 9,448 594 7,196 WF Total (Other) 54.6 24,295 6,493 131,054 WF Total (All) 845.6 674,860 159,367 685,306 WF % White 36.8% 72.5% 21.6% 51.0% WF % Latino 23.0% 37.6% 45.7% 38.9% WF % African Am. 16.4% 8.0% 12.4% 7.6% WF % API 16.1% 14.5% 15.8% 14.9% WF % Native Am. 1.3% 1.4% 0.4% 1.1% WF % Other 6.5% 3.6% 4.1% 19.1% Workforce data appear complete for San Joaquin County and to reflect a relatively diverse staff composition. Comparison of workforce data to the general, Medi-Cal, and DOF population data indicate there is a need for greater representation of Latino mental health staff. CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Target Target Target Target Target Target Target Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Population 7 Population 8 Population 9 Muslim and Latino African Native LGBTQ Laotian Hmong Cambodian Vietnamese Middle Eastern American American CSS target populations are numerous and are primarily focused on specific racial and ethnic groups. WET WET WET Target Population 1 Target Population 2 Target Population 3 Behavioral health services Community-based organization and mental health Consumers and family workforce provider workforce members WET target populations are more focused but do not appear to be directly connected to CSS targets or potential disparities in demographic or workforce data. Note: Sections with blanks indicates that data were not available. 12 2 Table 7ao. San Luis Obispo County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 262,238 33,089 90,766 269,637 269,637 African American 4,952 602 1,189 5,550 5,550 African Am. % 1.9% 1.8% 1.3% 2.1% 2.1% API 8,385 691 2,389 8,896 8,896 API % 3.2% 2.1% 2.6% 3.3% 3.3% Latino 49,172 13,287 29,379 55,973 55,973 Latino% 18.8% 40.2% 32.4% 20.8% 20.8% Native Am. 2,435 183 1,016 2,536 2,536 Native Am. % 0.9% 0.6% 1.1% 0.9% 0.9% White 224,177 16,834 54,662 222,756 222,756 White % 85.5% 50.9% 60.2% 82.6% 82.6% Other 22,289 1,494 2,131 19,786 19,786 Other% 8.5% 4.5% 2.3% 7.3% 7.3% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 262,238 33,089 90,766 269,637 269,637 Children 49,498 14,846 17,111 44,440 44,440 Children% 18.9% 44.9% 18.9% 16.5% 16.5% TAY 21,117 49,630 49,630 TAY% 23.3% 18.4% 18.4% Adult 212,740 14,074 43,409 117,474 117,474 Adult% 81.1% 42.5% 47.8% 43.6% 43.6% Older Adult 37,388 4,171 9,129 58,093 58,093 Older Adult% 14.3% 12.6% 10.1% 21.5% 21.5% Males 135,551 14,362 137,999 137,999 Male% 51.7% 43.4% 51.2% 51.2% Females 126,687 18,728 131,638 131,638 Female% 48.3% 56.6% 48.8% 48.8% With the exception of language variables, the demographic data for San Luis Obispo County are complete and appear consistent across different data sources. 12 3 Table 7ao. San Luis Obispo County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 162.7 Licensed Direct WF 142.2 Other Direct WF 10.5 Direct Total FTE 315.4 Indirect Total FTE 138.1 WF Total (White) 375.6 224,177 16,834 54,662 222,756 WF Total (Latino) 57.2 49,172 13,287 29,379 55,973 WF Total (African Am.) 9.5 4,952 602 1,189 5,550 WF Total (API) 4.8 8,385 691 2,389 8,896 WF Total (Native Am.) 1.0 2,435 183 1,016 2,536 WF Total (Other) 5.5 22,289 1,494 2,131 19,786 WF Total (All) 453.5 262,238 33,089 90,766 269,637 WF % White 82.8% 85.5% 50.9% 60.2% 82.6% WF % Latino 12.6% 18.8% 40.2% 32.4% 20.8% WF % African Am. 2.1% 1.9% 1.8% 1.3% 2.1% WF % API 1.1% 3.2% 2.1% 2.6% 3.3% WF % Native Am. 0.2% 0.9% 0.6% 1.1% 0.9% WF % Other 1.2% 8.5% 4.5% 2.3% 7.3% Workforce data are complete for San Luis Obispo. There may be disparities in Latino workforce FTEs when compared to the composition of the general, Medi-Cal, CSS, and DOF populations. CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Pop. 1 Target Pop. 2 Target Pop. 3 Target Pop. 4 Target Pop. 5 Target Pop. 6 Target Pop. 7 Target Pop. 8 Target Pop. 9 Target Pop. 10 "High Foster youth Risk of out of Juvenile justice Co-occurring TAY, recently Adults at risk for Homebound Homeless or at Older adults, utilizers", all with multiple home placement, system, substance abuse diagnosed involuntary older adults risk of becoming presenting with ages placements, children and children and issues, youth, with mental institutionalizati home-less, mental illness at children and youth youth adults, older illness on adults and older primary care TAY adults adults provider's office CSS targets are numerous but appear to be quite focused on high-risk populations. WET WET WET WET WET WET WET WET Target Pop. 1 Target Pop. 2 Target Pop. 3 Target Pop. 4 Target Pop. 5 Target Pop. 6 Target Pop. 7 Target Pop. 8 Behavioral health Community based Bilingual and Clinicians Undergraduate and Mental Health consumers Criminal justice Consumers, family clinicians and organizations culturally diverse specializing in Graduate students seeking education and/or personnel who intervene members, reentry and support staff serving mental clinicians co-occurring seeking a career in a career in the behavioral with the mental health current students health clients disorders behavioral health health field population interested in working in mental health field WET target populations for San Luis Obispo are numerous but appear to be well thought out and connected to local staffing needs. Note: Sections with blanks indicates that data were not available. 12 4 Table 7ap. San Mateo County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 736,667 64,011 108,335 718,451 718,451 African American 26,520 4,246 4,918 20,436 20,436 African Am. % 3.6% 6.6% 4.5% 2.8% 2.8% API 203,320 11,784 18,428 188,435 188,435 API % 27.6% 18.4% 17.0% 26.2% 26.2% Latino 188,587 32,347 49,832 182,502 182,502 Latino% 25.6% 50.5% 46.0% 25.4% 25.4% Native Am. 1,473 118 616 3,306 3,306 Native Am. % 0.2% 0.2% 0.6% 0.5% 0.5% White 313,820 10,032 29,643 383,535 383,535 White % 42.6% 15.7% 27.4% 53.4% 53.4% Other 17,680 31 4,898 84,529 84,529 Other% 2.4% 0.0% 4.5% 11.8% 11.8% Language API API% English 29,579 English% 46.2% Spanish 26,607 Spanish% 41.6% Other Other% Age/Gender Total Population 736,667 64,011 108,335 718,451 718,451 Children 180,483 29,470 31,892 142,143 142,143 Children% 24.5% 46.0% 29.4% 19.8% 19.8% TAY 82,056 82,056 TAY% 11.4% 11.4% Adult 416,954 21,615 76,443 356,668 356,668 Adult% 56.6% 33.8% 70.6% 49.6% 49.6% Older Adult 139,230 12,926 137,584 137,584 Older Adult% 18.9% 20.2% 19.2% 19.2% Males 26,360 353,168 353,168 Male% 41.2% 49.2% 49.2% Females 37,651 365,283 365,283 Female% 58.8% 50.8% 50.8% Overall, demographic data for San Mateo County are relatively complete. Language and gender variables are missing for two data sources. 12 5 Table 7ap. San Mateo County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 277.0 Licensed Direct WF 17.0 Other Direct WF 10.0 Direct Total FTE 304.0 Indirect Total FTE 429.0 WF Total (White) 321.0 313,820 10,032 29,643 383,535 WF Total (Latino) 146.0 188,587 32,347 49,832 182,502 WF Total (African Am.) 73.0 26,520 4,246 4,918 20,436 WF Total (API) 104.0 203,320 11,784 18,428 188,435 WF Total (Native Am.) 1.0 1,473 118 616 3,306 WF Total (Other) 87.0 17,680 31 4,898 84,529 WF Total (All) 733.0 736,667 64,011 108,335 718,451 WF % White 43.8% 42.6% 15.7% 27.4% 53.4% WF % Latino 19.9% 25.6% 50.5% 46.0% 25.4% WF % African Am. 10.0% 3.6% 6.6% 4.5% 2.8% WF % API 14.2% 27.6% 18.4% 17.0% 26.2% WF % Native Am. 0.1% 0.2% 0.2% 0.6% 0.5% WF % Other 11.9% 2.4% 0.0% 4.5% 11.8% Workforce data for San Mateo County are complete and reflect a relatively diverse mental health workforce. Comparison of workforce data to general, Medi-Cal, CSS and DOF population data indicate a need for increased representation of Latino mental health staff. CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Target Target Target Target Target Target Target Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Population 7 Population 8 Population 9 API, children African Amer., Latino, Latinos involved Pacific Islanders African Americans Asian, older Pacific Islander, Latino, older and TAY children and children and in criminal involved in criminal involved in criminal adults older adults adults TAY TAY justice system justice system justice system CSS target populations for San Mateo County are numerous and are focused primarily on younger, older, and criminally involved people from distinct racial/ethnic groups. WET WET WET Target Population 1 Target Population 2 Target Population 3 Latino Asian Spanish speaking staff WET targets for San Mateo are focused on cultural and language needs in the Latino and Asian population. Note: Sections with blanks indicates that data were not available. 12 6 Table 7aq. Santa Barbara County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 407,057 74,073 129,140 423,895 423,895 African American 9,769 1,888 2,281 8,513 8,513 African Am. % 2.4% 2.5% 1.8% 2.0% 2.0% API 19,132 1,686 4,758 21,471 21,471 API % 4.7% 2.3% 3.7% 5.1% 5.1% Latino 160,788 37,085 73,140 181,687 181,687 Latino% 39.5% 50.1% 56.6% 42.9% 42.9% Native Am. 6,920 285 657 5,485 5,485 Native Am. % 1.7% 0.4% 0.5% 1.3% 1.3% White 30,390 45,652 295,124 295,124 White % 41.0% 35.4% 69.6% 69.6% Other 10,176 2,742 2,652 73,860 73,860 Other% 2.5% 3.7% 2.1% 17.4% 17.4% Language API API% English 3,218 English% 84.8% Spanish 258 Spanish% 6.8% Other 130,989 14 Other% 32.8% 0.4% Age/Gender Total Population 407,057 74,073 129,140 423,895 423,895 Children 96,065 36,873 36,682 85,850 85,850 Children% 23.6% 49.8% 28.4% 20.3% 20.3% TAY 31,689 81,980 81,980 TAY% 24.5% 19.3% 19.3% Adult 258,074 29,169 44,546 181,070 181,070 Adult% 63.4% 39.4% 34.5% 42.7% 42.7% Older Adult 52,917 8,032 16,223 74,995 74,995 Older Adult% 13.0% 10.8% 12.6% 17.7% 17.7% Males 205,564 32,152 212,786 212,786 Male% 50.5% 43.4% 50.2% 50.2% Females 201,493 41,921 211,109 211,109 Female% 49.5% 56.6% 49.8% 49.8% Overall, demographic data for Santa Barbara are relatively complete. There are a few gaps in racial and ethnic, language, and gender variables. 12 7 Table 7aq. Santa Barbara County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 30,390 45,652 295,124 WF Total (Latino) 160,788 37,085 73,140 181,687 WF Total (African Am.) 9,769 1,888 2,281 8,513 WF Total (API) 19,132 1,686 4,758 21,471 WF Total (Native Am.) 6,920 285 657 5,485 WF Total (Other) 41.0% 35.4% 69.6% WF Total (All) 407,057 74,073 129,140 423,895 WF % White 10,176 2,742 2,652 73,860 WF % Latino 39.5% 50.1% 56.6% 42.9% WF % African Am. 2.4% 2.5% 1.8% 2.0% WF % API 4.7% 2.3% 3.7% 5.1% WF % Native Am. 1.7% 0.4% 0.5% 1.3% WF % Other 2.5% 3.7% 2.1% 17.4% No workforce data were noted for Santa Barbara County CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Female Latino children Homeless population Children with serious emotional Latino population disturbance CSS targets for Santa Barbara are relatively well focused on 3 or 4 distinct subpopulations. WET WET Target Population 1 Target Population 2 Latino population Whites While focused on two populations, the WET targets for Santa Barbara County are relatively broad and it is not clear whether they are tied to workforce data. Note: Sections with blanks indicates that data were not available. 12 8 Table 7ar. Santa Clara County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 1,748,976 245,333 309,672 1,781,642 1,781,642 African American 43,999 9,696 8,239 46,428 46,428 African Am. % 2.5% 4.0% 2.7% 2.6% 2.6% API 538,646 65,851 83,213 577,584 577,584 API % 30.8% 26.8% 26.9% 32.4% 32.4% Latino 449,133 124,781 144,342 479,210 479,210 Latino% 25.7% 50.9% 46.6% 26.9% 26.9% Native Am. 4,751 872 1,161 12,960 12,960 Native Am. % 0.3% 0.4% 0.4% 0.7% 0.7% White 674,765 31,976 65,560 836,616 836,616 White % 38.6% 13.0% 21.2% 47.0% 47.0% Other 37,682 12,160 7,158 220,806 220,806 Other% 2.2% 5.0% 2.3% 12.4% 12.4% Language API 265,844 API% 15.2% English 954,941 85,255 English% 54.6% 40.5% Spanish 307,820 79,625 Spanish% 17.6% 37.9% Other 8,745 8401 Other% 0.5% 4.0% Age/Gender Total Population 1,748,976 245,333 309,672 1,781,642 1,781,642 Children 419,608 100,329 92,738 382,908 382,908 Children% 24.0% 40.9% 29.9% 21.5% 21.5% TAY 159,009 230,646 230,646 TAY% 9.1% 12.9% 12.9% Adult 983,694 91,851 216,935 888,011 888,011 Adult% 56.2% 37.4% 70.1% 49.8% 49.8% Older Adult 186,665 53,155 280,077 280,077 Older Adult% 10.7% 21.7% 15.7% 15.7% Males 895,003 105,249 150,153 893,851 893,851 Male% 51.2% 42.9% 48.5% 50.2% 50.2% Females 853,973 140,084 159,520 887,791 887,791 Female% 48.8% 57.1% 51.5% 49.8% 49.8% Demographic data for Santa Clara County are complete and appear consistent across different data sources. 12 9 Table 7ar. Santa Clara County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 778.0 Licensed Direct WF 640.0 Other Direct WF 65.0 Direct Total FTE 1,483.0 Indirect Total FTE 489.0 WF Total (White) 749.0 674,765 31,976 65,560 836,616 WF Total (Latino) 451.0 449,133 124,781 144,342 479,210 WF Total (African Am.) 221.0 43,999 9,696 8,239 46,428 WF Total (API) 453.0 538,646 65,851 83,213 577,584 WF Total (Native Am.) 32.0 4,751 872 1,161 12,960 WF Total (Other) 66.0 37,682 12,160 7,158 220,806 WF Total (All) 1,972.0 1,748,976 245,333 309,672 1,781,642 WF % White 38.0% 38.6% 13.0% 21.2% 47.0% WF % Latino 22.9% 25.7% 50.9% 46.6% 26.9% WF % African Am. 11.2% 2.5% 4.0% 2.7% 2.6% WF % API 23.0% 30.8% 26.8% 26.9% 32.4% WF % Native Am. 1.6% 0.3% 0.4% 0.4% 0.7% WF % Other 3.3% 2.2% 5.0% 2.3% 12.4% Workforce data are complete for Santa Clara and depict a diverse mental health workforce. CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Pop. 1 Target Pop. 2 Target Pop. 3 Target Pop. 4 Target Pop. 5 Target Pop. 6 Target Pop. 7 Target Pop. 8 Target Pop. 9 Target Pop. 10 Target Pop. 11 0-5 high risk Foster care Juvenile 0-15 SMI/SED 16-25 aging TAY with first Adults in jail, Adults un- 60+ high risk Survivors of Homeless, or youth justice youth out of youth break homeless, and served and and isolated torture at-risk of systems psychosis dually under-served SMI homeless & diagnosed SMI SMI unemployment and substance abuse CSS targets populations for Santa Clara County are numerous and appear focused on age-specific and high-risk groups. WET WET WET WET WET WET WET WET WET WET Target Pop. 1 Target Pop. 2 Target Pop. 3 Target Pop. 4 Target Pop. 5 Target Pop. 6 Target Pop. 7 Target Pop. 8 Target Pop. 9 Target Pop. 10 Psychiatrists TAY Non-English Hearing Consumers Consumer and Direct care Non-white persons in Current direct service Consumers, for children monolingual impaired and family family from ethnic providers managerial, licensed, providers and staff family, cultural, and older who are not in cultural and and advanced degree need additional cultural linguistic adults workforce linguistic groups positions competency training groups Santa Clara presents a number of WET target populations, some of which are quite specific, and some that are relatively broad. Note: Sections with blanks indicates that data were not available. 13 0 Table 7as. Santa Cruz County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 256,218 5,949 255,602 262,382 262,382 African American 3,331 173 2,556 2,766 2,766 African Am. % 1.3% 2.9% 1.0% 1.1% 1.1% API 7,430 83 8,691 11,461 11,461 API % 2.9% 1.4% 3.4% 4.4% 4.4% Latino 75,072 1,731 68,501 84,092 84,092 Latino% 29.3% 29.1% 26.8% 32.0% 32.0% Native Am. 3,075 61 2,253 2,253 Native Am. % 1.2% 1.0% 0.9% 0.9% White 161,161 3,750 167,419 190,208 190,208 White % 62.9% 63.0% 65.5% 72.5% 72.5% Other 6,149 151 8,435 43,376 43,376 Other% 2.4% 2.6% 3.3% 16.5% 16.5% Language API API% English 184,989 228,034 English% 72.2% 89.0% Spanish 56,881 23,059 Spanish% 22.2% 9.0% Other 14,348 5,125 Other% 5.6% 2.0% Age/Gender Total Population 5,949 262,382 262,382 Children 1,301 76,425 48,726 48,726 Children% 21.9% 29.9% 18.6% 18.6% TAY 1,136 46,762 46,762 TAY% 19.1% 17.8% 17.8% Adult 3,041 121,582 121,582 Adult% 51.1% 46.3% 46.3% Older Adult 27,159 471 25,622 45,312 45,312 Older Adult% 10.6% 7.9% 10.0% 17.3% 17.3% Males 128,621 3,295 127,545 130,913 130,913 Male% 50.2% 55.3% 49.9% 49.9% 49.9% Females 127,597 2,654 128,057 131,469 131,469 Female% 49.8% 44.7% 50.1% 50.1% 50.1% Demographic data for Santa Cruz County are complete and appear consistent across different data sources. 13 1 Table 7as. Santa Cruz County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 266.1 Licensed Direct WF 146.1 Other Direct WF 38.0 Direct Total FTE 450.2 Indirect Total FTE 209.3 WF Total (White) 407.8 161,161 3,750 167,419 190,208 WF Total (Latino) 191.1 75,072 1,731 68,501 84,092 WF Total (African Am.) 16.9 3,331 173 2,556 2,766 WF Total (API) 17.0 7,430 83 8,691 11,461 WF Total (Native Am.) 4.3 3,075 61 2,253 WF Total (Other) 20.7 6,149 151 8,435 43,376 WF Total (All) 657.8 256,218 262,382 WF % White 62.0% 62.9% 63.0% 65.5% 72.50% WF % Latino 29.1% 29.3% 29.1% 26.8% 32.00% WF % African Am. 2.6% 1.3% 2.9% 1.0% 1.10% WF % API 2.6% 2.9% 1.4% 3.4% 4.40% WF % Native Am. 0.6% 1.2% 1.0% 0.90% WF % Other 3.1% 2.4% 2.6% 3.3% 16.50% Workforce data for Santa Cruz County are complete and appear to reflect the composition of the general population. Target Target Target Target Target Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Underserved cultural Individuals experiencing Children and youth in Trauma-exposed Children and youth at risk Children and youth at risk populations onset of serious stressed families individuals of school failure of experiencing juvenile psychiatric illness justice involvement A number of CSS targets are presented for Santa Cruz, some of which are quite broad. WET targets were not noted. Note: Sections with blanks indicates that data were not available. 13 2 Table 7at. Shasta County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 181,099 41,306 177,223 177,223 African American 1,911 841 1,548 1,548 African Am. % 1.0% 2.0% 0.9% 0.9% API 4,773 1,613 4,662 4,662 API % 2.4% 3.9% 2.6% 2.6% Latino 14,727 2,951 14,878 14,878 Latino% 15.0% 7.1% 8.4% 8.4% Native Am. 3,648 1,549 4,950 4,950 Native Am. % 1.9% 3.8% 2.8% 2.8% White 149,871 32,749 153,726 153,726 White % 76.5% 79.3% 86.7% 86.7% Other 6,169 1,603 4,501 4,501 Other% 3.2% 3.9% 2.5% 2.5% Language API 3,010 API% 1.8% English 151,467 English% 88.9% Spanish 9,766 Spanish% 5.7% Other 1,768 Other% 1.0% Age/Gender Total Population 181,099 41,304 177,223 177,223 Children 33,969 16,624 34,610 34,610 Children% 18.8% 40.3% 19.5% 19.5% TAY 25,008 23,245 23,245 TAY% 13.7% 13.1% 13.1% Adult 81,796 19,334 77,262 77,262 Adult% 45.2% 46.8% 43.6% 43.6% Older Adult 40,326 5,347 42,106 42,106 Older Adult% 22.3% 12.9% 23.8% 23.8% Males 88,539 18,428 87,130 87,130 Male% 48.9% 44.6% 49.2% 49.2% Females 92,560 22,876 90,093 90,093 Female% 51.1% 55.4% 50.8% 50.8% Demographic data for Shasta County are complete and appear consistent across different data sources. The percent for Latinos on the CCP is a bit higher and for White a bit lower when compared to the DOF. 13 3 Table 7at. Shasta County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 59.4 Licensed Direct WF 72.0 Other Direct WF 13.0 Direct Total FTE 144.4 Indirect Total FTE 95.5 WF Total (White) 187.7 149,871 32,749 153,726 WF Total (Latino) 7.4 14,727 2,951 14,878 WF Total (African Am.) 1.0 1,911 841 1,548 WF Total (API) 3.0 4,773 1,613 4,662 WF Total (Native Am.) 5.0 3,648 1,549 4,950 WF Total (Other) 7.0 6,169 1,603 4,501 WF Total (All) 211.1 181,099 41,306 177,223 WF % White 89.0% 76.5% 79.3% 86.70% WF % Latino 3.5% 15.0% 7.1% 8.40% WF % African Am. 0.5% 1.0% 2.0% 0.90% WF % API 1.4% 2.4% 3.9% 2.60% WF % Native Am. 2.4% 1.9% 3.8% 2.80% WF % Other 3.3% 3.2% 3.9% 2.50% Workforce data appear complete for Shasta County. Comparison of workforce data to general, Medi-Cal, CSS and DOF population data indicate a need for increased representation of Latino mental health staff. CSS Target CSS Target CSS Target CSS Target CSS Target CSS Target CSS Target CSS Target CSS Target Population 1 Population 2 Population 3 Population 4 Population 5 Population 6 Population 7 Population 8 Population 9 Children and TAY Adults Older adults Children with Children Individuals with Females head of Unserved and youth serious involved in the serious mental household with underserved emotional juvenile justice illness dependent populations disturbance and system children hospitalization A number of CSS targets are presented for Shasta County, some of which are quite broad. WET targets were not noted. WET WET WET WET WET WET Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Target Population 6 Psychiatrist Registered nurse Licensed clinician Clinical program Community health Social workers coordinator workers WET Targets mainly focused on staff with experience with specific unserved and underserved populations. Note: Sections with blanks indicates that data were not available. 13 4 Table 7au. Sierra County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 3,240 3,240 African American 6 6 African Am. % 0.2% 0.2% API 14 14 API % 0.4% 0.4% Latino 269 269 Latino% 8.3% 8.3% Native Am. 44 44 Native Am. % 1.4% 1.4% White 3,022 3,022 White % 93.3% 93.3% Other 75 75 Other% 2.3% 2.3% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 3,240 3,240 Children 474 474 Children% 14.6% 14.6% TAY 274 274 TAY% 8.5% 8.5% Adult 1470 1,470 Adult% 45.4% 45.4% Older Adult 1,022 1,022 Older Adult% 31.5% 31.5% Males 1,646 1,646 Male% 50.8% 50.8% Females 1,594 1,594 Female% 49.2% 49.2% Demographic data are incomplete for Sierra County. 13 5 Table 7au. Sierra County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 3,022 WF Total (Latino) 269 WF Total (African Am.) 6 WF Total (API) 14 WF Total (Native Am.) 44 WF Total (Other) 75 WF Total (All) 3,240 WF % White 93.30% WF % Latino 8.30% WF % African Am. 0.20% WF % API 0.40% WF % Native Am. 1.40% WF % Other 2.30% Workforce data and target populations were not noted for Sierra County. Note: The missing data for the items were not reviewed. Table is incomplete. 13 6 Table 7v. Siskiyou County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 44,404 10,709 7,904 44,900 44,900 African American 616 249 145 571 571 African Am. % 1.4% 2.3% 1.8% 1.3% 1.3% API 738 210 311 620 620 API % 1.7% 2.0% 3.9% 1.4% 1.4% Latino 4,303 1025 933 4,615 4,615 Latino% 9.7% 9.6% 11.8% 10.3% 10.3% Native Am. 1,183 687 503 1,814 1,814 Native Am. % 2.7% 6.4% 6.4% 4.0% 4.0% White 38,658 7,802 6,012 38,030 38,030 White % 87.1% 72.9% 76.1% 84.7% 84.7% Other 3,209 737 1,491 1,491 Other% 7.2% 6.9% 3.3% 3.3% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 44,404 10,709 7,904 44,900 44,900 Children 2,330 4,265 1,493 8,138 8,138 Children% 5.2% 39.8% 18.9% 18.1% 18.1% TAY 931 4,895 4,895 TAY% 11.8% 10.9% 10.9% Adult 35,068 4,868 3,630 19,263 19,263 Adult% 79.0% 45.5% 45.9% 42.9% 42.9% Older Adult 8,348 1,577 1,850 12,604 12,604 Older Adult% 18.8% 14.7% 23.4% 28.1% 28.1% Males 21,955 4,880 22,395 22,395 Male% 49.4% 45.6% 49.9% 49.9% Females 22,449 5,829 22,505 22,505 Female% 50.6% 54.4% 50.1% 50.1% For Siskiyou County, demographic data are relatively complete with the exception of language variables. Differences in the proportions of racial/ethnic and age subgroups are notable across data sources. 13 7 Table 7v. Siskiyou County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 62.0 Licensed Direct WF 36.0 Other Direct WF 0.0 Direct Total FTE 98.0 Indirect Total FTE 72.0 WF Total (White) 147.0 38,658 7,802 6,012 38,030 WF Total (Latino) 2.0 4,303 1025 933 4,615 WF Total (African Am.) 2.0 616 249 145 571 WF Total (API) 2.0 738 210 311 620 WF Total (Native Am.) 9.0 1,183 687 503 1,814 WF Total (Other) 8.0 3,209 737 1,491 WF Total (All) 170.0 44,404 10,709 7,904 44,900 WF % White 86.5% 87.1% 72.9% 76.1% 84.7% WF % Latino 1.2% 9.7% 9.6% 11.8% 10.3% WF % African Am. 1.2% 1.4% 2.3% 1.8% 1.3% WF % API 1.2% 1.7% 2.0% 3.9% 1.4% WF % Native Am. 5.3% 2.7% 6.4% 6.4% 4.0% WF % Other 4.7% 7.2% 6.9% 3.3% Workforce data appear complete for Siskiyou County. Comparison of workforce data to general, Medi-Cal, CSS and DOF population data indicate a need for increased representation of Latino mental health staff. CSS CSS CSS CSS CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Target Population 6 Target Population 7 Target Population 8 Older adults TAY LGBTQ Native American Latino Asian African American Agricultural industry workers A number of CSS targets are presented for Siskiyou, some of which are quite broad. WET targets were not noted. Note: Sections with blanks indicates that data were not available. 13 8 Table 7aw. Solano County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 407,515 62,794 395,426 413,344 413,344 African American 57,622 16,617 55,959 60,750 60,750 African Am. % 14.1% 26.5% 14.2% 14.7% 14.7% API 59,750 7,365 59,812 64,037 64,037 API % 14.7% 11.7% 15.1% 15.5% 15.5% Latino 92,094 20,012 84,121 99,356 99,356 Latino% 22.6% 31.9% 21.3% 24.0% 24.0% Native Am. 380 353 1,661 3,212 3,212 Native Am. % 0.1% 0.6% 0.4% 0.8% 0.8% White 176,317 14,495 176,872 210,751 210,751 White % 43.3% 23.1% 44.7% 51.0% 51.0% Other 21,352 3,952 17,001 43,236 43,236 Other% 5.2% 6.3% 4.3% 10.5% 10.5% Language API 39,751 API% 10.5% English 267,559 43,424 English% 70.4% 69.2% Spanish 61,905 13,927 Spanish% 16.3% 22.2% Other 11,062 4,051 Other% 2.9% 6.5% Age/Gender Total Population 407,515 62,794 395,426 413,344 413,344 Children 102,650 28,765 113,146 88,842 88,842 Children% 25.2% 45.8% 28.6% 21.5% 21.5% TAY 58,992 58,992 TAY% 14.3% 14.3% Adult 256,181 27,238 242,100 195,445 195,445 Adult% 62.9% 43.4% 61.2% 47.3% 47.3% Older Adult 45,684 6,791 40,180 70,065 70,065 Older Adult% 11.2% 10.8% 10.2% 17.0% 17.0% Males 204,573 26,567 193,497 206,195 206,195 Male% 50.2% 42.3% 48.9% 49.9% 49.9% Females 202,942 36,227 201,929 207,149 207,149 Female% 49.8% 57.7% 51.1% 50.1% 50.1% Demographic data for Solano County are detailed and complete, and there appear to be consistencies across data sources. 13 9 Table 7aw. Solano County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE 0.0 Indirect Total FTE 465.8 WF Total (White) 233.6 176,317 14,495 176,872 210,751 WF Total (Latino) 49.1 92,094 20,012 84,121 99,356 WF Total (African Am.) 70.9 57,622 16,617 55,959 60,750 WF Total (API) 38.6 59,750 7,365 59,812 64,037 WF Total (Native Am.) 1.0 380 353 1,661 3,212 WF Total (Other) 72.6 21,352 3,952 17,001 43,236 WF Total (All) 465.8 407,515 62,794 395,426 413,344 WF % White 50.2% 43.3% 23.1% 44.7% 51.0% WF % Latino 10.5% 22.6% 31.9% 21.3% 24.0% WF % African Am. 15.2% 14.1% 26.5% 14.2% 14.7% WF % API 8.3% 14.7% 11.7% 15.1% 15.5% WF % Native Am. 0.2% 0.1% 0.6% 0.4% 0.8% WF % Other 15.6% 5.2% 6.3% 4.3% 10.5% Workforce data are complete for Solano County and appear to be reflective of the general population in the county. Comparison of workforce data to general, Medi-Cal, CSS and DOF population data indicate a need for increased representation of Latino and API mental health staff. CSS CSS Target Population 1 Target Population 2 Latinos Spanish speakers CSS target populations are quite focused, and appear appropriate for Solano County. WET target populations are identical to CSS target populations for Solano. Note: Sections with blanks indicates that data were not available. 14 0 Table 7ax. Sonoma County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 464,326 5,134 100,116 483,878 483,878 African American 8,358 260 2,191 7,610 7,610 African Am. % 1.8% 5.1% 2.2% 1.6% 1.6% API 19,966 126 3,587 19,899 19,899 API % 4.3% 2.5% 3.6% 4.1% 4.1% Latino 109,581 466 33,381 120,430 120,430 Latino% 23.6% 9.1% 33.3% 24.9% 24.9% Native Am. 7,429 95 1,811 6,489 6,489 Native Am. % 1.6% 1.9% 1.8% 1.3% 1.3% White 314,349 3,858 68,637 371,412 371,412 White % 67.7% 75.2% 68.6% 76.8% 76.8% Other 13,465 141 18,016 56,966 56,966 Other% 2.9% 2.8% 18.0% 11.8% 11.8% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 464,326 5,134 100,116 483,878 483,878 Children 28,324 1,813 28,262 93,395 93,395 Children% 6.1% 35.3% 28.2% 19.3% 19.3% TAY 65,507 65,507 TAY% 13.5% 13.5% Adult 360,317 2,882 53,378 225,423 225,423 Adult% 77.6% 56.1% 53.3% 46.6% 46.6% Older Adult 60,362 439 18,476 99,553 99,553 Older Adult% 13.0% 8.6% 18.5% 20.6% 20.6% Males 231,613 2,766 237,902 237,902 Male% 49.9% 53.9% 49.2% 49.2% Females 237,993 2,349 245,976 245,976 Female% 51.3% 45.8% 50.8% 50.8% For Sonoma County, demographic data are relatively complete, with the exception of language variables. 14 1 Table 7ax. Sonoma County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE 0.0 Indirect Total FTE 392.0 WF Total (White) 329.0 314,349 3,858 68,637 371,412 WF Total (Latino) 44.0 109,581 466 33,381 120,430 WF Total (African Am.) 19.0 8,358 260 2,191 7,610 WF Total (API) 10.0 19,966 126 3,587 19,899 WF Total (Native Am.) 5.0 7,429 95 1,811 6,489 WF Total (Other) 9.0 13,465 141 18,016 56,966 WF Total (All) 416.0 464,326 5,134 100,116 483,878 WF % White 79.1% 67.7% 75.2% 68.6% 76.8% WF % Latino 10.6% 23.6% 9.1% 33.3% 24.9% WF % African Am. 4.6% 1.8% 5.1% 2.2% 1.6% WF % API 2.4% 4.3% 2.5% 3.6% 4.1% WF % Native Am. 1.2% 1.6% 1.9% 1.8% 1.3% WF % Other 2.2% 2.9% 2.8% 18.0% 11.8% Sonoma County workforce data are limited for a few categories. The percentage of FTEs among Latino staff appears low when compared to the general, CSS, and DOF population compositions. CSS CSS CSS CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Target Population 6 Target Population 7 Trauma exposed Children and youth Children at risk for Children at risk for Underserved cultural Individuals Latino population individuals in stressed families school failure juvenile justice populations experiencing onset of involvement serious psychiatric illness Several CSS targets for Sonoma County are focused on age and risk-specific groups. No WET target populations were noted. Note: Sections with blanks indicates that data were not available. 14 2 Table 7ay. Stanislaus County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 511,263 123,574 166,071 514,453 514,453 African American 13,942 4,898 4,722 14,721 14,721 African Am. % 2.7% 4.0% 2.8% 2.9% 2.9% API 26,667 6,793 8,768 29,491 29,491 API % 5.2% 5.5% 5.3% 5.7% 5.7% Latino 199,543 63,542 74,844 215,658 215,658 Latino% 39.0% 51.4% 45.1% 41.9% 41.9% Native Am. 3,843 398 1,155 5,902 5,902 Native Am. % 0.8% 0.3% 0.7% 1.1% 1.1% White 256,569 41,016 69,916 337,342 337,342 White % 50.2% 33.2% 42.1% 65.6% 65.6% Other 10,699 6,927 6,666 99,210 99,210 Other% 2.1% 5.6% 4.0% 19.3% 19.3% Language API API% English 77,037 English% 62.3% Spanish 35,844 Spanish% 29.0% Other 10,693 Other% 8.7% Age/Gender Total Population 511,263 123,574 166,071 514,453 514,453 Children 145,874 60,448 58,121 129,617 129,617 Children% 28.5% 48.9% 35.0% 25.2% 25.2% TAY 28,008 78,817 78,817 TAY% 16.9% 15.3% 15.3% Adult 313,163 51,529 60,060 227,583 227,583 Adult% 61.3% 41.7% 36.2% 44.2% 44.2% Older Adult 52,226 11,597 19,882 78,436 78,436 Older Adult% 10.2% 9.4% 12.0% 15.2% 15.2% Males 253,014 54,243 254,489 254,489 Male% 49.5% 43.9% 49.5% 49.5% Females 258,249 69,331 259,964 259,964 Female% 50.5% 56.1% 50.5% 50.5% Demographic data for Solano County are relatively complete, with the exception of language variables. Different proportions across data sources may be attributed to differences in definitions for some subgroups. 14 3 Table 7ay. Stanislaus County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 220.0 Licensed Direct WF 307.9 Other Direct WF 2.4 Direct Total FTE 530.3 Indirect Total FTE 210.2 WF Total (White) 439.5 314,349 3,858 68,637 371,412 WF Total (Latino) 168.9 109,581 466 33,381 120,430 WF Total (African Am.) 43.1 13,942 4,898 4,722 14,721 WF Total (API) 68.4 26,667 6,793 8,768 29,491 WF Total (Native Am.) 8.6 7,429 95 1,811 6,489 WF Total (Other) 11.9 13,465 141 18,016 56,966 WF Total (All) 740.5 511,263 123,574 166,071 514,453 WF % White 59.4% 67.7% 75.2% 68.6% 76.8% WF % Latino 22.8% 23.6% 9.1% 33.3% 24.9% WF % African Am. 5.8% 2.7% 4.0% 2.8% 2.9% WF % API 9.2% 5.2% 5.5% 5.3% 5.7% WF % Native Am. 1.2% 1.6% 1.9% 1.8% 1.3% WF % Other 1.6% 2.9% 2.8% 18.0% 11.8% Workforce data appear complete and reflective of the general population for Stanislaus County. CSS CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Latino Native American API Older adults Individuals living in outlying areas CSS target populations for Stanislaus County are relatively broad. WET WET WET WET WET Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Bilingual, bicultural staff in all African American direct service Bilingual, bicultural licensed Bilingual clinicians Individuals with lived experience, both consumers and classifications, especially staff staff trained for children family members, especially Spanish speaking and Spanish speaking Assyrian WET targets largely focus on bilingual staffing needs, and experience with specific racial/ethnic and mental health issues. Note: Sections with blanks indicates that data were not available. 14 4 Table 7az. Sutter-Yuba County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 164,138 42,815 31,647 94,737 94,737 African American 4,279 1,362 809 1,919 1,919 African Am. % 2.6% 3.2% 2.6% 2.0% 2.0% API 17,161 5,602 3,182 13,944 13,944 API % 10.5% 13.1% 10.1% 14.7% 14.7% Latino 41,229 14,464 9,536 27,251 27,251 Latino% 25.1% 33.8% 30.1% 28.8% 28.8% Native Am. 2,609 549 745 1,365 1,365 Native Am. % 1.6% 1.3% 2.4% 1.4% 1.4% White 94,501 19,366 15,308 57,749 57,749 White % 57.6% 45.2% 48.4% 61.0% 61.0% Other 4,359 1,474 2,067 14,463 14,463 Other% 2.7% 3.4% 6.5% 15.3% 15.3% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 164,138 42,815 31,647 94,737 94,737 Children 44,865 19,961 11,315 23,060 23,060 Children% 27.3% 46.6% 35.8% 24.3% 24.3% TAY 4,348 13,576 13,576 TAY% 13.7% 14.3% 14.3% Adult 101,401 17,568 13,914 41,418 41,418 Adult% 61.8% 41.0% 44.0% 43.7% 43.7% Older Adult 17,872 5,287 2,070 16,683 16,683 Older Adult% 10.9% 12.3% 6.5% 17.6% 17.6% Males 81,813 19,150 47,001 47,001 Male% 49.8% 44.7% 49.6% 49.6% Females 82,325 23,665 47,736 47,736 Female% 50.2% 55.3% 50.4% 50.4% With the exception of language variables, demographic data for Sutter-Yuba Counties are detailed, complete, and consistent across data sources. 14 5 Table 7az. Sutter-Yuba County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 58.0 Licensed Direct WF 75.0 Other Direct WF 12.0 Direct Total FTE 145.0 Indirect Total FTE 54.0 WF Total (White) 155.0 94,501 19,366 15,308 57,749 WF Total (Latino) 18.0 41,229 14,464 9,536 27,251 WF Total (African Am.) 5.0 4,279 1,362 809 1,919 WF Total (API) 7.0 17,161 5,602 3,182 13,944 WF Total (Native Am.) 1.0 2,609 549 745 1,365 WF Total (Other) 13.0 4,359 1,474 2,067 14,463 WF Total (All) 199.0 164,138 42,815 31,647 94,737 WF % White 77.9% 57.6% 45.2% 48.4% 61.0% WF % Latino 9.0% 25.1% 33.8% 30.1% 28.8% WF % African Am. 2.5% 2.6% 3.2% 2.6% 2.0% WF % API 3.5% 10.5% 13.1% 10.1% 14.7% WF % Native Am. 0.5% 1.6% 1.3% 2.4% 1.4% WF % Other 6.5% 2.7% 3.4% 6.5% 15.3% Workforce data appear complete for Sutter-Yuba Counties. There appears to be low proportions of Latino and API FTEs compared to the proportions noted in the general, Medi- Cal, CSS, and DOF demographic data. CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Population Target Population Target Population Target Population Target Population Target Population Target Population Target Population Target Population 1 2 3 4 5 6 7 8 9 Latino American Indian Hmong Asian Indian Spanish, preferred 0-5 year olds 16-25 year olds Homebound LGBTQ language seniors CSS target populations are numerous for Sutter-Yuba Counties. Some targets are broad while others are very specific, with subgroups delineated by age, language, and sexual orientation. WET WET WET WET WET WET WET WET Target Population Target Population Target Population Target Population Target Population Target Population Target Population Target Population 1 2 3 4 5 6 7 8 Bilingual Spanish Bilingual Spanish Licensed staff, all Administrative Consumers and Consumers, family Increase opportunity for Registered interns staff interpreters ethnicities and staff, all ethnicities family members, all members and individuals with lived experience languages and languages ethnicities and community to pursue license and unlicensed languages stakeholders positions in mental health Some WET targets appear to be well focused on local cultural and bilingual needs. Some targets are broad. Note: Sections with blanks indicates that data were not available. 14 6 Table 7ba. Tehama County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 61,138 14,916 22,150 63,463 63,463 African American 611 131 129 406 406 African Am. % 1.0% 0.9% 0.6% 0.6% 0.6% API 856 211 202 732 732 API % 1.4% 1.4% 0.9% 1.2% 1.2% Latino 12,961 3,732 5,192 13,906 13,906 Latino% 21.2% 25.0% 23.4% 21.9% 21.9% Native Am. 1,467 254 592 1,644 1,644 Native Am. % 2.4% 1.7% 2.7% 2.6% 2.6% White 56,613 10,127 15,345 51,721 51,721 White % 92.6% 67.9% 69.3% 81.5% 81.5% Other 1,590 461 690 6,258 6,258 Other% 2.6% 3.1% 3.1% 9.9% 9.9% Language API API% English 11,544 English% 77.4% Spanish 2,625 Spanish% 17.6% Other 8,803 726 Other% 14.4% 4.9% Age/Gender Total Population 61,138 14,916 22,150 63,463 63,463 Children 19,381 6,838 7,354 14,165 14,165 Children% 31.7% 45.8% 33.2% 22.3% 22.3% TAY 1,322 1,048 8,029 8,029 TAY% 8.9% 4.7% 12.7% 12.7% Adult 32,464 6,544 10,600 27,137 27,137 Adult% 53.1% 43.9% 47.9% 42.8% 42.8% Older Adult 9,293 1,534 3,148 14,132 14,132 Older Adult% 15.2% 10.3% 14.2% 22.3% 22.3% Males 30,324 6,418 31,610 31,610 Male% 49.6% 43.0% 49.8% 49.8% Females 30,814 8,498 31,853 31,853 Female% 50.4% 57.0% 50.2% 50.2% Tehama County demographic data appear detailed and complete, with the exception of language variables, and depict similar distributions across data sources. 14 7 Table 7ba. Tehama County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 27.0 Licensed Direct WF 19.0 Other Direct WF 11.0 Direct Total FTE 57.0 Indirect Total FTE 23.0 WF Total (White) 65.0 56,613 10,127 15,345 51,721 WF Total (Latino) 9.0 12,961 3,732 5,192 13,906 WF Total (African Am.) 2.0 611 131 129 406 WF Total (API) 1.0 856 211 202 732 WF Total (Native Am.) 2.0 1,467 254 592 1,644 WF Total (Other) 1.0 1,590 461 690 6,258 WF Total (All) 80.0 61,138 14,916 22,150 63,463 WF % White 81.3% 92.6% 67.9% 69.3% 81.5% WF % Latino 11.3% 21.2% 25.0% 23.4% 21.9% WF % African Am. 2.5% 1.0% 0.9% 0.6% 0.6% WF % API 1.3% 1.4% 1.4% 0.9% 1.2% WF % Native Am. 2.5% 2.4% 1.7% 2.7% 2.6% WF % Other 1.3% 2.6% 3.1% 3.1% 9.9% Workforce data appear complete for Tehama County. The proportion of FTEs among Latino staff appears lower than the proportion of Latinos in the general, Medi-Cal, CSS, and DOF populations. CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Latino Native American LGBTQ CSS target populations for Tehama appear to be well focused on local needs. WET target populations were not noted. Note: Sections with blanks indicates that data were not available. 14 8 Table 7bb. Tri-City County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 366,248 366,248 African American 12,389 12,389 African Am. % 3.4% 3.4% API 24,939 24,939 API % 6.8% 6.8% Latino 119,315 119,315 Latino% 32.6% 32.6% Native Am. 3,002 3,002 Native Am. % 0.8% 0.8% White 255,776 255,776 White % 69.8% 69.8% Other 50,498 50,498 Other% 13.8% 13.8% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 366,248 366,248 Children Children% TAY TAY% Adult Adult% Older Adult Older Adult% Males 178,994 178,994 Male% 48.9% 48.9% Females 185,254 185,254 Female% 50.6% 50.6% 14 9 Table 7bb. Tri-City County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF Licensed Direct WF Other Direct WF Direct Total FTE Indirect Total FTE WF Total (White) 255,776 WF Total (Latino) 119,315 WF Total (African Am.) 12,389 WF Total (API) 24,939 WF Total (Native Am.) 3,002 WF Total (Other) 50,498 WF Total (All) 366,248 WF % White 69.80% WF % Latino 32.60% WF % African Am. 3.40% WF % API 6.80% WF % Native Am. 0.80% WF % Other 13.80% Workforce data and population targets were not noted for Tri-City County. Note: The missing data for the items were not reviewed. Table is incomplete. 15 0 Table 7bc. Trinity County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 13,043 2,846 13,786 13,786 African American 69 14 59 59 African Am. % 0.5% 0.5% 0.4% 0.4% API 171 19 110 110 API % 1.3% 0.7% 0.8% 0.8% Latino 705 100 959 959 Latino% 5.4% 3.5% 7.0% 7.0% Native Am. 204 145 655 655 Native Am. % 1.6% 5.1% 4.8% 4.8% White 12,391 2,569 12,033 12,033 White % 95.0% 90.3% 87.3% 87.3% Other 217 217 Other% 1.6% 1.6% Language API API% English 2,731 English% 96.0% Spanish 37 Spanish% 1.3% Other 78 Other% 2.7% Age/Gender Total Population 2,846 13,786 13,786 Children 1,115 2,172 2,172 Children% 39.2% 15.8% 15.8% TAY 1,296 1,296 TAY% 9.4% 9.4% Adult 1,403 6,253 6,253 Adult% 49.3% 45.4% 45.4% Older Adult 327 4,065 4,065 Older Adult% 11.5% 29.5% 29.5% Males 1,316 7,113 7,113 Male% 46.2% 51.6% 51.6% Females 1,530 6,673 6,673 Female% 53.8% 48.4% 48.4% Demographic data for Trinity County exhibit several gaps in general and CSS populations. 15 1 Table 7bc. Trinity County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 15.3 Licensed Direct WF 8.0 Other Direct WF 0.0 Direct Total FTE 23.3 Indirect Total FTE 12.0 WF Total (White) 31.3 12,391 2,569 12,033 WF Total (Latino) 1.0 705 100 959 WF Total (African Am.) 0.0 69 14 59 WF Total (API) 0.0 171 19 110 WF Total (Native Am.) 3.0 204 145 655 WF Total (Other) 0.0 217 WF Total (All) 35.3 13,043 2,846 13,786 WF % White 88.7% 95.0% 90.3% 87.3% WF % Latino 2.8% 5.4% 3.5% 7.0% WF % African Am. 0.0% 0.5% 0.5% 0.4% WF % API 0.0% 1.3% 0.7% 0.8% WF % Native Am. 8.5% 1.6% 5.1% 4.8% WF % Other 0.0% 1.6% Workforce data appear complete for Trinity County and the composition of the workforce appears to reflect the racial/ethnic composition of the general, Medi-Cal, and DOF populations. CSS Target Population 1 Rural poor, all ages One CSS target population is noted. WET Target Population 1 Consumers and family members The WET target population noted for Trinity County focuses on individuals with lived experience. Note: Sections with blanks indicates that data were not available. 15 2 Table 7bd. Tulare County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 368,021 151,320 442,179 442,179 African American 5,852 2,969 7,196 7,196 African Am. % 1.6% 2.0% 1.6% 1.6% API 12,439 5,045 15,685 15,685 API % 3.4% 3.3% 3.5% 3.5% Latino 186,844 108,628 268,065 268,065 Latino% 50.8% 71.8% 60.6% 60.6% Native Am. 12,034 754 6,993 6,993 Native Am. % 3.3% 0.5% 1.6% 1.6% White 213,747 28,073 265,618 265,618 White % 58.1% 18.6% 60.1% 60.1% Other 130,243 5,853 128,263 128,263 Other% 35.4% 3.9% 29.0% 29.0% Language API API% English 207,196 English% 56.3% Spanish 143,160 Spanish% 38.9% Other 4,048 Other% 1.1% Age/Gender Total Population 368,021 151,320 442,179 442,179 Children 124,391 77,958 127,780 127,780 Children% 33.8% 51.5% 28.9% 28.9% TAY 39,010 70,081 70,081 TAY% 10.6% 15.8% 15.8% Adult 168,554 59,739 184,141 184,141 Adult% 45.8% 39.5% 41.6% 41.6% Older Adult 36,066 13,624 60,177 60,177 Older Adult% 9.8% 9.0% 13.6% 13.6% Males 68,033 221,442 221,442 Male% 45.0% 50.1% 50.1% Females 83,288 220,737 220,737 Female% 55.0% 49.9% 49.9% Demographic data for Tulare County have gaps in CSS data and in gender and language variables elsewhere. 15 3 Table 7bd. Tulare County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 202.0 Licensed Direct WF 238.9 Other Direct WF 55.0 Direct Total FTE 495.9 Indirect Total FTE 228.0 WF Total (White) 276.9 213,747 28,073 265,618 WF Total (Latino) 371.1 186,844 108,628 268,065 WF Total (African Am.) 17.0 5,852 2,969 7,196 WF Total (API) 32.9 12,439 5,045 15,685 WF Total (Native Am.) 1.0 12,034 754 6,993 WF Total (Other) 24.0 130,243 5,853 128,263 WF Total (All) 723.9 368,021 151,320 442,179 WF % White 38.3% 58.1% 18.6% 60.1% WF % Latino 51.3% 50.8% 71.8% 60.6% WF % African Am. 2.3% 1.6% 2.0% 1.6% WF % API 4.5% 3.4% 3.3% 3.5% WF % Native Am. 0.1% 3.3% 0.5% 1.6% WF % Other 3.3% 35.4% 3.9% 29.0% Workforce data appear complete for Tulare County and reflect relatively strong coverage for Latino and White mental health staffing. CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Children with SMI and/or SED Unserved and underserved in rural communities, all TAY with SMI and/or SED ages Target populations 1 and 3 for Tulare County are focused on young, high-risk populations, while target 2 is broad. WET targets were not noted. Note: Sections with blanks indicates that data were not available. 15 4 Table 7be. Tuolumne County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 56,910 1,525 55,365 55,365 African American 1,138 5 1,143 1,143 African Am. % 2.0% 0.3% 2.1% 2.1% API 570 14 648 648 API % 1.0% 1.0% 1.2% 1.2% Latino 5,691 54 5,918 5,918 Latino% 10.0% 3.5% 10.7% 10.7% Native Am. 1,138 17 1,039 1,039 Native Am. % 2.0% 1.1% 1.9% 1.9% White 47,235 1,351 48,274 48,274 White % 83.0% 88.6% 87.2% 87.2% Other 1,138 84 2,238 2,238 Other% 2.0% 5.5% 4.0% 4.0% Language API API% English English% Spanish Spanish% Other Other% Age/Gender Total Population 1,525 55,365 55,365 Children 482 8,365 8,365 Children% 31.6% 4.4% 15.1% 15.1% TAY 221 6,146 6,146 TAY% 14.5% 11.1% 11.1% Adult 758 24,978 24,978 Adult% 49.7% 10.1% 45.1% 45.1% Older Adult 64 15,876 15,876 Older Adult% 4.2% 2.6% 28.7% 28.7% Males 866 29,245 29,245 Male% 56.8% 52.8% 52.8% Females 659 26,120 26,120 Female% 43.2% 47.2% 47.2% Several gaps in demographic data were noted for Tuolumne County. 15 5 Table 7be. Tuolumne County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 19.0 Licensed Direct WF 22.5 Other Direct WF 5.0 Direct Total FTE 46.5 Indirect Total FTE 23.0 WF Total (White) 65.5 47,235 1,351 48,274 WF Total (Latino) 4.0 5,691 54 5,918 WF Total (African Am.) 1,138 5 1,143 WF Total (API) 570 14 648 WF Total (Native Am.) 1,138 17 1,039 WF Total (Other) 1,138 84 2,238 WF Total (All) 69.5 55,365 WF % White 94.2% 83.0% 88.6% 87.20% WF % Latino 5.8% 10.0% 3.5% 10.70% WF % African Am. 2.0% 0.3% 2.10% WF % API 1.0% 1.0% 1.20% WF % Native Am. 2.0% 1.1% 1.90% WF % Other 2.0% 5.5% 4.00% Workforce data appear complete for Tuolumne County. Since Latino population continues to increase, workforce data to general (i.e., Medi-Cal and DOF population data), indicate a need for increased representation of Latino mental health staff. CSS CSS CSS CSS CSS CSS Target Population 1 Target Population 2 Target Population 3 Target Population 4 Target Population 5 Target Population 6 Native Americans Latinos Children and families All age groups – All age groups – All age groups – co- homelessness and at-risk incarceration and at-risk of occurring disorders and of homelessness incarceration dual diagnosis CSS target populations for Tuolumne County are focused on very specific subpopulations, while others are relatively broad. WET WET WET Target Population 1 Target Population 2 Target Population 3 Native American Latino Spanish speaking staff WET target populations appear to be well focused on specific cultural and linguistic subpopulations, Note: Sections with blanks indicates that data were not available. 15 6 Table 7bf. Ventura County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 798,364 167,792 823,318 823,318 African American 17,212 3,706 15,163 15,163 African Am. % 2.2% 2.2% 1.8% 1.8% API 53,247 11,580 57,089 57,089 API % 6.7% 6.9% 6.9% 6.9% Latino 296,745 99,111 331,567 331,567 Latino% 37.2% 59.1% 40.3% 40.3% Native Am. 9,112 1,887 8,068 8,068 Native Am. % 1.1% 1.1% 1.0% 1.0% White 417,425 48,207 565,804 565,804 White % 52.3% 28.7% 68.7% 68.7% Other 3,301 140,253 140,253 Other% 2.0% 17.0% 17.0% Language API API% English 502,969 English% 63.0% Spanish Spanish% Other 295,395 Other% 37.0% Age/Gender Total Population 167,792 823,318 823,318 Children 26,697 185,487 185,487 Children% 15.9% 22.5% 22.5% TAY 20,461 118,834 118,834 TAY% 12.2% 14.4% 14.4% Adult 102,212 380,376 380,376 Adult% 60.9% 46.2% 46.2% Older Adult 18,422 138,621 138,621 Older Adult% 11.0% 16.8% 16.8% Males 408,969 408,969 Male% 49.7% 49.7% Females 414,349 414,349 Female% 50.3% 50.3% A number of gaps were evident in Ventura County demographic data. 15 7 Table 7bf. Ventura County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 361.8 Licensed Direct WF 230.3 Other Direct WF 59.2 Direct Total FTE 651.3 Indirect Total FTE 233.8 WF Total (White) 506.8 417,425 48,207 565,804 WF Total (Latino) 264.2 296,745 99,111 331,567 WF Total (African Am.) 49.6 17,212 3,706 15,163 WF Total (API) 50.1 53,247 11,580 57,089 WF Total (Native Am.) 4.5 9,112 1,887 8,068 WF Total (Other) 10.1 3,301 140,253 WF Total (All) 885.1 798,364 167,792 823,318 WF % White 57.3% 52.3% 28.7% 68.7% WF % Latino 29.8% 37.2% 59.1% 40.3% WF % African Am. 5.6% 2.2% 2.2% 1.8% WF % API 5.7% 6.7% 6.9% 6.9% WF % Native Am. 0.5% 1.1% 1.1% 1.0% WF % Other 1.1% 2.0% 17.0% Workforce data appear complete. Comparison of workforce data to general, CSS and DOF population data indicate a need for increased representation of Latino mental health staff. CSS and WET target populations were not noted. Note: Sections with blanks indicates that data were either not available. 15 8 Table 7bg. Yolo County Profile: Cultural Competency Plan Variable General Population (CCP) Medi-Cal Population CSS Population US Census DOF Total Population 195,844 31,271 200,849 200,849 African American 5,023 1,443 5,208 5,208 African Am. % 2.6% 4.6% 2.6% 2.6% API 23,917 2,221 26,962 26,962 API % 12.2% 7.1% 13.4% 13.4% Latino 54,766 14,882 60,953 60,953 Latino% 28.0% 47.6% 30.3% 30.3% Native Am. 1,378 277 2,214 2,214 Native Am. % 0.7% 0.9% 1.1% 1.1% White 105,430 9,381 126,883 126,883 White % 53.8% 30.0% 63.2% 63.2% Other 5,330 3,067 27,882 27,882 Other% 2.7% 9.8% 13.9% 13.9% Language API API% English 17,727 English% 55.6% Spanish 9,630 Spanish% 30.2% Other 2,713 Other% 8.5% Age/Gender Total Population 195,844 31,271 200,849 200,849 Children 48,798 14,384 40,192 40,192 Children% 24.9% 46.0% 20.0% 20.0% TAY 47,062 47,062 TAY% 23.4% 23.4% Adult 111,660 12,414 84,858 84,858 Adult% 57.0% 39.7% 42.2% 42.2% Older Adult 35,386 4,473 28,737 28,737 Older Adult% 18.1% 14.3% 14.3% 14.3% Males 96,057 13,676 97,935 97,935 Male% 49.0% 43.7% 48.8% 48.8% Females 99,787 17,595 102,914 102,914 Female% 51.0% 56.3% 51.2% 51.2% Several gaps in demographic data were noted for Yolo County. 15 9 Table 7bg. Yolo County Profile: Cultural Competency Plan (Continued) Variable Full-Time Equivalent/% General Population (CCP) Medi-Cal Population CSS Population DOF Population Workforce Data (FTEs) Unlicensed WF 239.7 Licensed Direct WF 241.0 Other Direct WF 195.6 Direct Total FTE 676.3 Indirect Total FTE 284.4 WF Total (White) 635.1 105,430 9,381 126,883 WF Total (Latino) 106.7 54,766 14,882 60,953 WF Total (African Am.) 88.1 5,023 1,443 5,208 WF Total (API) 73.9 23,917 2,221 26,962 WF Total (Native Am.) 12.2 1,378 277 2,214 WF Total (Other) 44.8 5,330 3,067 27,882 WF Total (All) 960.7 195,844 31,271 200,849 WF % White 66.1% 53.8% 30.0% 63.2% WF % Latino 11.1% 28.0% 47.6% 30.3% WF % African Am. 9.2% 2.6% 4.6% 2.6% WF % API 7.7% 12.2% 7.1% 13.4% WF % Native Am. 1.3% 0.7% 0.9% 1.1% WF % Other 4.7% 2.7% 9.8% 13.9% Workforce data appear complete for Yolo County. Staff appears to be diverse. Comparison of workforce data to general, Medi-Cal and DOF population data indicate a need for increased representation of Latino mental health staff. CSS CSS CSS CSS CSS CSS CSS CSS CSS CSS Target Pop. 1 Target Pop. 2 Target Pop. 3 Target Pop. 4 Target Pop. 5 Target Pop. 6 Target Pop. 7 Target Pop. 8 Target Pop. 9 Target Pop. 10 Children 0-17 Latino, adult API White, Non Homeless TAY LGBTQ Older adults with Rural SMI individuals and children Latino emancipating Spanish, Russian, or populations with co-occurring from foster care south-east substance abuse or juvenile hall languages disorders CSS target populations for Yolo County are numerous. While some of the targets are focused on very specific subpopulations, others are relatively broad. WET WET WET WET Target Population 1 Target Population 3 Target Population 3 Target Population 4 Additional bilingual and bicultural staff Staff trained for LGBTQ community Consumers and Family Member staff Non-English speakers: Spanish, Russian, Ukrainian, Deaf/Hearing Impaired WET target populations appear to be well focused on specific cultural and linguistic subpopulations, as well as staff with lived experience. Note: Sections with blanks indicates that data were not available. 16 0 References Aguilar-Gaxiola, S., Loera, G., Méndez, L., Sala, M., Latino Mental Health Concilio, and Nakamoto, J. (2012). Community-defined solutions for Latino mental health care disparities: California Reducing Disparities Project, Latino Strategic Planning Workgroup population report. Sacramento, CA: UC Davis. Betancourt, J. R., Green, A. R., Carrillo, E., Ananeh-Firempong, O. (2003). 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