Mapping Mental Health Disparities: The COVID-19 Mental Health Impact on Latino and African-American Communities in Los Angeles County

Mapping Mental Health Disparities: The COVID-19 Mental Health Impact on Latino and African-American Communities in Los Angeles County | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mapping Mental Health Disparities: The COVID-19 Mental Health Impact on Latino and African-American Communities in Los Angeles County Jesus Jasso Verduzco This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7190511/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper seeks to investigate mental health disparities in minority communities in Los Angeles County. We sought to answer the following questions: (1) Have minority communities faced a disproportionate impact of the Coronavirus Disease (COVID-19) infection in Los Angeles County? And if so, (2) Will these minority communities face a mental health crisis? Throughout the COVID-19 pandemic, the Los Angeles Department of Public Health (LADPH) collected data on COVID-19 cases and death rates in minority communities. This paper utilized LADPH data to conduct three statistical tests, including the following: Geographically Weighted Regression (GWR), Getis-Ord Gi*, and Moran’s I. In addition, a collection of scholarly work on the effects of respiratory virus pandemics on mental health suggested that respiratory-related pandemics exacerbate Post-Traumatic Stress Disorder (PTSD) and depression. Our findings revealed that trends in COVID-19 cases can be densely concentrated in a geographical area, which we identify as a COVID-19 cluster. Through statistical analysis and a literature review, we determined that a COVID-19 cluster was in the area between I-10 and I-105. A close analysis of the COVID-19 cluster revealed that the top five cities within the cluster were predominantly comprised of Latinos or African Americans. Our research questions were answered, and we concluded that the Latino communities within the City of Bell Gardens, City of Cudahy, City of Commerce, Boyle Heights Community, City of Maywood, and City of Lynwood are at greatest risk of facing a mental health crisis post-COVID-19 pandemic. To conclude, the paper presents four recommendations for mental health providers serving minority populations in the Los Angeles County region. COVID-19 Case Rate COVID-19 cluster Latino Mental Health culturally sensitive Figures Figure 1 Figure 2 INTRODUCTION The COVID-19 pandemic had a severe impact on healthcare systems nationwide. With much of the focus on protecting physical health, newly emerging mental health issues were consequently understudied. For this reason, this paper seeks to examine the impact of the COVID-19 pandemic on mental health needs of minority communities in Los Angeles County. This paper sought to answer two questions: (1) Have minority communities faced a disproportionate impact of the Coronavirus Disease (COVID-19) infection in Los Angeles County? And if so, (2) Will these minority communities face a mental health crisis? Our in-depth analysis of COVID-19 data from the Los Angeles Department of Public Health revealed the need to address mental health disparities in communities of color in Los Angeles County. Through a literature review and our quantitative analysis, clinicians can gain a deeper understanding of the mental health landscape in Los Angeles County. A socio-spatial analysis conducted with ArcGIS identified the specific geographical zones in Los Angeles County that will likely experience an increased risk of developing mental health disorders. The COVID-19 cluster identified in this study aligns with the statement that minority communities in Los Angeles County faced a disproportionate burden of disease during the pandemic. In California, healthcare providers are accountable for serving a diverse clientele that is largely comprised of a growing Latino population. In Los Angeles County alone, approximately 49% (or 4,725,059 individuals) identify as Latino. The term "Latino" used in this paper refers to any individual with ancestry from Latin America. In Los Angeles County, an estimated 65% of the total population, or 3,575,000 individuals, are foreign-born. The federal legislation created during the pandemic, such as the Coronavirus Aid, Relief, and Economic Security Act (CARES Act), excluded immigrants and mixed-status families from receiving any economic relief during the COVID-19 pandemic. Many mixed-status families, spouses of immigrants, and their children who have full citizenship were excluded by the CARES Act. Civil rights organizations have filed lawsuits against the federal government over provisions contained in the CARES Act. The federal economic stimulus policy during the COVID-19 pandemic blatantly discriminated against mixed-status families and married couples, including spouses of immigrants who filed their income taxes with an ITIN (Individual Tax Identification Number). In the recent decade, lawsuits have been brought against California’s Department of Health Care Services, claiming that the state has not done enough to remove unnecessary obstacles to healthcare access for low-income patients and has been reluctant to adequately implement mechanisms to monitor and oversee the program. (CBS News, 2017, Local News). A combination of long-term disinvestment from Medi-Cal and exclusion from economic relief through the CARES Act has contributed to the risk factors within minority communities for developing a mental health disorder during the COVID-19 pandemic. LITERATURE REVIEW Respiratory infections have been the cause of multiple pandemics in the past, such as the 2002 severe acute respiratory syndrome (SARS) pandemic in China and the 2015 Middle Eastern Respiratory Syndrome (MERS) pandemic in South Korea. A review of scholarly work on previous pandemics shows that social factors experienced in a pandemic era can lead to an increase in post-traumatic stress disorder (PTSD), depression, and anxiety disorders. PTSD can be defined as intense feelings of stress, or feeling afraid after experiencing a traumatic event (CDC, 2021, Types of Mental Disorders). Depression is when an individual experiences constant feelings of being in a bad mood, to the point that their mental, as well as psychological, well-being, is impacted nearly every day and for much of the day (CDC, 2021, Types of Mental Disorder). According to Lam et. al. (2009), from the Department of Psychiatry at The Chinese University of Hong Kong, a psychiatric evaluation of 181 SARS virus survivors in Hong Kong, China, found that PTSD and depression were prevalent among interview participants. Study participants were issued a psychiatric evaluation to determine if SARS survivors had developed a mental disorder after recovery. The evaluation was issued between three to four years after the participant had been diagnosed with the SARS virus. Among the 181 study participants, only six had a history of mental disorder. However, at the time of follow-up, 77 of the 181 (or 42.5%) of participants experienced at least one mental disorder (Lam et. al., 2009, P. 2043). More specifically, among the participants who were diagnosed with a disorder, the most common mental disorder found was PTSD (42 of 77) or 54.5%, and depression (30 of 77) or 39.0% (Lam et. al., 2009, P. 2043). Moreover, social factors such as stigmatization, stress, grief, and fear have been found to contribute to the development of a mental disorder. According to Abdelhafiz and Alorabi (2020) from the National Cancer Institute at Cairo University, fear and anxiety arise during a pandemic as a result of the unknown cause of the disease and possible fatal outcome. A respiratory virus can be life-threatening, and the stigmatization of the infected flourishes with dramatic stories in the media and on the internet (Abdelhafiz and Alorabi, 2020, p. 429). Studies have also shown that the SARS pandemic led to adverse mental health conditions among virus survivors in Beijing, China. A study by Hong et al. (2009), from the University of Rochester Medical Center in New York, measured the incidence of PTSD among 68 SARS virus survivors. Clinical assessments were conducted periodically with subjects after they had been discharged from the hospital following a diagnosis of SARS. The assessments were conducted by a trained psychiatrist using the Chinese Classification of Mental Disorders (CCMD-III) and Diagnostic and Statistical Manual of Mental Disorders. The clinical assessments revealed that among the 68 subjects who were hospitalized with SARS, 30 (44.1%) of survivors developed PTSD after being discharged. (Hong et. al., 2009, P. 546-554) In 2015, the Middle Eastern Respiratory Syndrome (MERS) outbreak in South Korea presented evidence that links mental disorders to respiratory virus pandemics. A study by Park et. al. (2020), from the Seoul National University Hospital in Seoul, revealed that PTSD and depression were commonly found among pandemic survivors in South Korea. A nationwide study was conducted one year after the initial MERS outbreak in South Korea. Trained clinicians interviewed 63 participants using the Impact of Event Scale and the Patient Health Questionnaire-9 frameworks to determine mental health outcomes. The questionnaire results showed that out of the 63 participants, 42.9% reportedly had significant PTSD symptoms, and 27% reportedly had depression. (Park et. al., 2020, P. 605) In the United States, a household pulse survey conducted by the Centers for Disease Control and Prevention (CDC) during the COVID-19 pandemic revealed that anxiety and depression disorders had increased across the country. In 2019, the CDC found that 10.8% of adults ages 18 and older had anxiety or depressive disorders. After the COVID-19 outbreak in late 2019, the CDC found that mental disorders had increased to 26.4% for adults ages 18 and older (CDC, 2020, Household Pulse Survey). The CDC stated that COVID-19 survivors experience social factors such as stigmatization, isolation, depression, anxiety, or public embarrassment. Additionally, studies on the SARS and MERS pandemics align with the results from the CDC household pulse survey. The literature discussed in this section ties mental disorders, such as PTSD and depression, to the experience of minority groups most impacted by the COVID-19 pandemic. In the sections below, this paper identifies the minority communities in Los Angeles County that have been disproportionately affected by the COVID-19 pandemic. METHODS AND DATA This study employed an instrumentalist perspective in its analysis methods . The goal of the analysis is to provide grassroots mental health providers with information that guides their mental health services in minority communities post-pandemic. In other words, the instrumentalist perspective asserts that organizations must utilize the appropriate instruments at their disposal, depending on specific conditions or situations. Selecting and combining programs to change is a key factor for community-based mental health providers post-pandemic. During the COVID-19 pandemic, the Los Angeles County Department of Mental Health (LADMH) published Strategic Plan 2030. 1 The plan issued by the County outlines the structure, goals, and requirements for mental health partners post-pandemic. This study provides critical insight into how mental health providers in minority communities can effectively distribute mental health services to clients most impacted by COVID-19. The analysis portion of this study consisted of two main approaches: a geospatial analysis that identified the spread of COVID-19 in Los Angeles County, and a statistical analysis that correlated race with a higher COVID-19 case rate. The geospatial analysis, used to identify the spread of COVID-19 in Los Angeles County, enabled this study to locate emerging mental health hotspots. For the COVID-19 analysis, this project used ArcGIS Pro to conduct three statistical analyses. The Moran's I and Getis-Ord Gi* statistical tests were used to identify the pattern of the spread of COVID-19 cases. The outcomes of these tests show the autocorrelation between COVID-19 and space. Furthermore, the analysis included the socio-demographics of the population most affected by COVID-19 infections. A Geographically Weighted Regression analysis was conducted to determine the correlation between the African-American and Latino populations and COVID-19. This methodology is widely used among statisticians to understand the spatial characteristics and their relationships with socio-economic variables. The data used to build the ArcGIS geospatial analysis has been collected from three different sources. Demographic information for Los Angeles County, including race and household income, was collected from the U.S. Census Bureau. The data used for statistical analysis of COVID-19 were collected from the Los Angeles County Department of Public Health. The COVID-19 data includes the final death count, death rate, total case count, and case rate from March 2020 to March 2021 2 . The COVID-19 data is not organized by census tract, rather by Community Standard District (CSD). The Community Standard District is the unit of analysis used by the Los Angeles Planning District to organize entire cities or unincorporated land for special topics, such as a pandemic. The case rate was calculated by dividing the final count of COVID-19 cases within a given district by the total population of the corresponding CSD. Furthermore, from aggregating case rates in the COVID-19 data, the average infection rate was 8.7%. This study considers a “high COVID-19 case rate,” defined as a CSD with one to two standard deviations above the sample mean, rather than the average. This was elaborated upon in the analysis section of this project. DEFINITIONS Throughout this paper, the term “mental health hotspot” refers to COVID-19 clusters with one or two standard deviations above the sample mean. The reason is that virus survivors living in high COVID-19 impact zones are at greater risk of developing mental health illnesses, such as PTSD or depression. Another requirement for a zone to be considered a “hotspot” is the prevalence of Latino or African-American residents living within the CSD. The Latino or African American population must comprise 40% or greater of the total population in the CSD to be considered in the findings. The study does not use the term “hotspot” alone, but rather in conjunction with other words (i.e., mental health hotspot). All in all, the COVID-19 burden areas correspond with the mental health hotspots identified by the ArcGIS spatial analysis. Below, Table 1 provides an elaboration on the terms and definitions used throughout this paper. (see Table 1) To narrow the scope of this project, PTSD and depression are the major mental health disorders that will arguably be most common within the COVID-19 cluster. Finally, the “COVID-19 cluster” is also defined. The data and analysis in this study include the number of COVID-19 confirmed cases. The Geographically Weighted Regression conducted in this study has identified that the spread of the virus happens in clusters and is correlated to space. Therefore, a “COVID-19 cluster” refers to a concentration of high virus infections in a specific geographical area. This analysis color-coded the geographical areas within Los Angeles County that were most severely impacted by the COVID-19 virus, identifying them as COVID-19 clusters. The large red spot in the center of the LAC map means that residents in that area experienced more COVID-19 cases at least 1 standard deviation above the sample mean. This large red spot has been identified as the most significant COVID-19 cluster in the county, due to its disproportionate impact on minority communities and its high concentration of infections. Table 1 Overview of definitions Suggested term and alternatives Definition Mental health hotspot (a) An area within a Latino or African-American community within a high COVID-19 zone. Based on the literature, residents are at greater risk of experiencing adverse mental health effects because of the pandemic. COVID-19 cluster (a) Any areas colored in dark red/red on the maps. This indicates that there is at least 1 to 2 standard deviations above the sample mean. Identified by the Getis-Ord Gi* and Moran’s I statistical tests as a concentration of high COVID-19 cases. Mental health/mental health illness (a) Mental health includes a person’s emotional, psychological, and social well-being. Based on the literature, Post-Traumatic Stress Disorder (PTSD) and Depression are common mental health illnesses that increase during a pandemic. STATISTICAL ANALYSIS During the peak of the pandemic, this study sought to pinpoint the exact communities that were disproportionately impacted by the virus, and at-risk of developing a mental disorder. The first step was to visually represent the geographical spread of COVID-19 infections. The Getis-Ord Gi* test was used to identify areas with high or low COVID-19 case rates within the county. To be considered a statistically significant COVID-19 cluster, a feature will showcase a high value (highlighted in Red on Map 1) and be surrounded by other features with high values as well (ESRI, 2021, Spatial Statistics). The Getis-Ord Gi* test also shows features that will have a high value (red). Map 2 and Map 3, below, show the statistically significant areas (red) where the spread of the virus was clustered between Interstate 105 and Interstate 10 (see Map 2 and Map 3). The map below shows the statistically significant areas (Red) that are clustered with high COVID-19 case rates (see Map 1). The Red and Blue areas on the map are described by the legend below (see Table 2). The high and low clusters are determined based on the distance that the area’s COVID-19 case rate is from the sample mean of distribution. Red indicates that the area is 1 or more standard deviations above the sample mean. In other words, the Red areas have a high number of COVID-19 cases and have a statistically significantly high COVID-19 infection rate. Table 2: Color Legends for Map 1 Confidence Red Gray Blue % 99% 95% 90% - 90% 95% 99% p-value 0.01 0.05 0.10 - 0.10 0.05 0.01 St. Deviation >2.58 1.96~2.58 1.65-1.96 -1.65-1.65 -1.65~-1.96 -1.96~-2.58 <-2.58 Secondly, a Geographically Weighted Regression (GWR) test was employed to examine the relationship between race and COVID-19 cases. The GWR statistical tool is a linear regression test within the ArcGIS program (ESRI, 2021, GeoAnalytics). The COVID-19 case rate was used as the dependent variable, and race was used as the explanatory variable. After inputting the variables, the GWR determined that there was a positive relationship between race and the COVID-19 case rate, with a model fit (R²) of 0.14 to 0.2. More specifically, the greater the fit (R2) that race variables have with the COVID-19 case rates, the higher the percentage that race explains of the high COVID-19 case rates. The output features of the GWR show that the Latino communities and African American communities within the COVID-19 cluster are correlated to having experienced a greater burden of disease in Los Angeles County (see Figure 1 and Table 3). Table 3: Explanatory Power from GWR Model Geographically Weighted Regression (GWR) Explanatory Power Latino Only African-American and Latino AICs 7130.7792 7130.7910 R2 0.179 0.199 Thirdly, a Global Moran’s I test was conducted to determine if space and the spread of infection had any correlation. The Global Moran's I test produces an index value within a range of −1.0 to +1.0. The index value options are (> 0) clustered, (= 0) randomly distributed, and ( 0) clustered (see Figure 2). The results indicate that a spatial auto-correlation exists between COVID-19 and space. In other words, the spread of infection can be densely concentrated in a single geographical space. Therefore, the residents located in a statistically significant COVID-19 cluster are particularly vulnerable to experiencing adverse mental health conditions. The Moran’s I test shows that COVID-19 can disproportionately impact a specific community or city. MENTAL HEALTH DISPARITY In the analysis above, this paper has determined that the Latino and African American communities living between Interstate I-105 and Interstate I-10 are at greatest risk of experiencing a post-COVID-19 pandemic mental health disorder. According to the National Alliance of Mental Health (NAMI), only 34% of Latino adults with mental disorders receive treatment each year compared to the U.S. average of 45%. A Latino might describe what they are feeling with a phrase like “Me duele el corazón,” which means “my heart hurts.” However, it is an expression of emotional distress and not of physical pain. (NAMI, 2021, Hispanic/Latino Tab) The misdiagnosis of mental or emotional disorders in the Latino community is a problem that can be mitigated. In the section below, this paper includes recommendations for policymakers and mental health organizations on how they can better serve minority populations in Los Angeles County. The GWR, Getis-Ord Gi*, and Moran’s I statistical tests work together to show that high COVID-19 case rates are found in dense clusters throughout Los Angeles County. The location of the COVID-19 cluster is critical information for mental health providers, as it indicates the potential locations of new clients. Policymakers should utilize this information to consider innovative and culturally sensitive ways to serve the high-impacted minority communities of Los Angeles County. Policymakers at federal, state, and local levels can address risk by providing comprehensive support, such as economic stimulus and access to quality healthcare, to mitigate the disproportionate burden of disease for minority populations. Various studies on the burden of disease state that economic hardship is a risk factor for developing mental disorders (Cordes and Castro, 2020, P. 100355). Below, this study has identified income data that shows specific cities that can benefit from COVID-19 economic relief. (See Table 4). Similar urban characteristics, such as ethnic enclaves, are commonly found within the Top 5 cities/communities located within the COVID-19 cluster. An ethnic enclave can be defined as an urban area where a particular racial group is densely concentrated and is socially and economically distinct from the majority of the city (Lim, 2017, p. 138). The cities and communities in the mental health hotspot are comprised of Latinos (See Table 5). In 2019, during the peak of the COVID-19 pandemic, the Latino population made up 48.6% of the total population in Los Angeles County. (US Census, 2019, Demographics Table) Furthermore, this study found that more than 90% of residents in the City of Bell Gardens, Cudahy, Maywood, and Lynwood were Latino. This study found that the Latino ethnic enclaves comprised the majority of residents living in the COVID-19 cluster. (see Map 4) Table 4: Demographic Characteristics Cities/Communities within a COVID-19 Cluster Income Per Capita Unemployment Rate Below the Poverty Level* SNAP/ Food Stamps** No Health Insurance Coverage*** City of Compton $17,707 8.72% 16.98% 19.25% 16.03% City of Commerce $18,508 8.23% 12.76% 12.85% 17.68 City of Cudahy $14,545 8.66% 23.71% 20.85% 25.60% City of Maywood $15,845 7.58% 20.58% 16.67% 21.26% City of Vernon $36,450 5.1% 5.25% 9.65% 8% C.A $39,393 5.1% 11.8% 8.4% 10.9% U.S. $35,672 4.5% 12.03% 10.7% 12.9% (Source: ACS, US Census, 2019) Note: *Percentage of families and people whose income in the past month is below the Poverty Level. ** Percentage of households with Food Stamp/SNAP benefits in the past 12 months. ***No health insurance coverage, including the civilian non-institutionalized population 19 to 64 years in the labor force. Table 5: Evidence of Ethnic Enclaves Within the COVID-19 Cluster (Source: Data collected from US Census Bureau and Data USA, 2021, https://datausa.io/profile/geo) Community Standard District COVID-19 Case Rate Total Population* Latino Population** SPA Location City of Bell Gardens 17.60% 43,071 40,800 7 City of Cudahy 17.93% 24,347 23,000 7 City of Commerce 17.20% 13,069 9,090 7 Boyle Heights Community 18.70% 86,884 74,800 7 City of Maywood 17.10% 28,049 27,00 7 City of Lynwood 16.90% 72,047 71,000 6 RECOMMENDATIONS SECTION Below, this study presents recommendations aimed at supporting a county-wide approach to reducing mental health disparities in the identified mental health hotspots: Recommendation 1. A COVID-19 cluster indicates a geographical area with a high COVID-19 case rate. These areas should be a priority for local mental health service providers. The majority of high COVID-19 clusters are located between the I-10 and I-110 freeways/highways/interstates, which is also known as Service Planning Area (SPA) 7. It is recommended that mental health providers develop an outreach plan to target services and increase patient intake capacity, thereby better serving low-income clients living within COVID-19 clusters. Recommendation 2. Providing culturally sensitive treatment is a critical recommendation that will help reduce the mental health disparities in Los Angeles County. More studies are needed to fully understand the impact of culturally competent mental health services. However, building from philosophy of Instituto Familiar de la Raza ( El Instituto ) in San Francisco, California, mental health providers can begin to incorporate a long-standing culturally sensitive framework into their clinical services. For example, one of the core philosophies of El Instituto is the concept of “ La Cultura Cura ,” or the “Culture Can Heal.” By configuring treatment programs to embrace a shared Latino identity, such as indigenous and Meso-American roots, clinicians can be more effective in treating Latino clients. El Instituto practices this concept by incorporating culturally relevant coping skills into treatments such as knitting, pottery, painting, and other traditional arts native to Mexico and Central America. It is also crucial to hire translators or clinicians with Spanish-speaking capabilities to effectively incorporate the Spanish language into treatment. In addition, El Instituto, required therapists to provide flexible meeting locations for Latino patients. On occasion, therapists were asked to meet clients in public spaces closer to their work or home during the pandemic. Thirdly, it is recommended that therapists in Los Angeles County create a resource list of suitable public spaces near their clients' workplaces or homes to increase attendance at sessions. Recommendation 3. Collaboration with local CBOs that have a strong rapport with the Latino population in Los Angeles is recommended so that clinicians can recruit Spanish translators. CBOs such as the Coalition of Humane Immigrant Rights (CHIRLA), Mexican American Opportunity Foundation (MAOF), and Esperanza Community Housing have a long-standing rapport with the Latino population in Los Angeles County. They could be a vital starting point for recruiting translators and clinician support. Recommendation 4. The Integrated Behavioral Information Systems (IBHIS) is an information system established by the LACDMH to enhance the administrative role of mental health providers. It is recommended that local providers utilize the IBHIS system to share information with community-based organizations (CBOs). The IBHIS can be a system that connects grassroots and community-based organizations to Latino-serving organizations that may have stronger ties to communities of interest. An example of information that can be shared is medication information, recent changes in mental health assessments, laboratory and psychological test results, and clinician notes from prior visits, when appropriate. CONCLUSION This study has identified a COVID-19 cluster in the region of Los Angeles County, located south of Interstate 10 and north of Interstate 105. Communities located in the COVID-19 cluster are at greater risk of experiencing a mental health impact from the COVID-19 pandemic, as evidenced by the literature. Based on previous research on mental health and respiratory pandemics, victims of the pandemic can develop adverse mental health conditions. Studies show multiple social factors lead to an increase in PTSD and/or depression for COVID-19 victims post-pandemic. Through the GWR, Getis-Ord Gi* and Moran’s I statistical analysis, our results show that most COVID-19 cases are densely located in low-income neighborhoods that lack healthcare coverage and are mostly comprised of Latinos. The recommendations provided in this research paper aim to help reduce mental health disparities within the COVID-19 cluster in Los Angeles County. Recommendations emphasize the importance of strong collaboration, allocating resources to local grassroots organizations, and providing support for establishing culturally sensitive services. In conclusion, mental health organizations serving minority communities should consider the literature presented in this paper. The data in this paper suggests a correlation between race and higher infection rates during the COVID-19 pandemic. Through our analysis, we have identified that the mental health hotspots in low-income communities are mostly comprised of Latino residents. Case in point, mental health providers will be wise to increase outreach and service to Latino clients residing in SPA 7. Creating a more robust outpatient treatment program, increasing culturally competent services, and collaborating with other organizations using the IBHIS system will address new mental health needs. Declarations AKNOWLEDGEMENTS: No acknowledgments. AUTHOR CONTRIBUTIONS : K.C. contributed to the geospatial analysis presented in the Statistical Analysis section. K.C. contributed to Map 1, Table 2, Map 2, Map 3, Figure 1, Table 3, and Figure 2, all of which are located within the Statistical Analysis section. FUNDING DECLARATION: This research received no specific grant from any funding agency, public or private institution, commercial, or not-for-profit sectors. AVAILABILITY OF DATA: The data used to determine the COVID-19 cluster was drawn from the Los Angeles County Department of Public Health, COVID-19 Surveillance Dashboard. (2020) http://dashboard.publichealth.lacounty.gov/covid19_surveillance_dashboard/ CODE AVAILABILITY: Not applicable. CONFLICT OF INTEREST: The author(s) declare that there is not conflict of interest regarding the publication of this article. ETHICAL APPROVAL: The author(s) declare that they have no know competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Abdelhafiz, Ahmed Samir; Alorabi, Mohamed. “Social Stigma: The Hidden Threat of COVID-19.” Frontiers in Public Health. August 28, 2020. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484807/ Center for Disease Control and Prevention (CDC). Household Pulse Survey. 2020. https://www.cdc.gov/nchs/covid19/pulse/mental-health.htm Center for Disease Control and Prevention (CDC). Types of Mental Disorder. May, 2021. https://www.cdc.gov/mentalhealth/learn/index.htm Cordes, J., & Castro, M. C. (2020). “Spatial analysis of COVID-19 clusters and contextual factors in New York City.” Spat Spatiotemporal Epidemiol. doi: 10.1016/j.sste.2020.100355 ESRI. (n.d.). Geographically Weighted Regression (GWR) (GeoAnalytics). Retrieved May 6, 2021.\https://pro.arcgis.com/en/pro-app/latest/tool-reference/big-data- analytics/geographically-weighted-regression.htm ESRI. (n.d.). Hot Spot Analysis (Getis-Ord Gi*) (Spatial Statistics). Retrieved May 6, 2021.https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/hot-spot- analysis.htm ESRI. (n.d.). Spatial Autocorrelation (Global Moran's I) (Spatial Statistics). Retrieved May 6, 2021. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial- statistics/spatial-autocorrelation.htm Hong, Xia; Currier, Glenn W.; Zhao, Xiaohui; Jiang, Yina; Zhou, Wei and Wei, Jing. “Posttraumatic stress disorder in convalescent severe acute respiratory syndrome patients: a 4-year follow-up study.” General Hospital Psychiatry. 27 August 2009.https://pubmed.ncbi.nlm.nih.gov/19892213/ Kolinovsky, Sarah . “Some 1.2 million Americans won't get stimulus checks because they're married to immigrants.” ABC News. May 21, 2020. https://abcnews.go.com/Politics/12-million-americans-stimulus-checks-married- immigrants/story?id=70493620 Lam MH, Wing YK, Yu MW, Leung CM, Ma RC, Kong AP, So WY, Fong SY, Lam SP .“Mental Morbidities and Chronic Fatigue in Severe Acute Respiratory Syndrome Survivors: Long-Term Follow-Up.” Arch Intern Med. 2009 Dec 14;169(22):2142-7.doi:10.1001/archinternmed.2009.384. PMID: 20008700. Lim, Sungwoo; Yi, Stella; De La Cruz, Nneka; and Trinh-Shevrin, Chau. (2017) “Defining ethnic enclaves and their associations with self-reported health outcomes among Asian American adults in New York City.”NCBI. PMCID: PMC4919243 “Lawsuit Claims Medi-Cal Discriminates Against Low-Income Californians”. CBS News. April 16, 2024. July 13, 2017.https://www.cbsnews.com/sanfrancisco/news/lawsuit-medi-cal-discriminates-poor- californians/. National Alliance on Mental Health. Hispanic/Latino. Retrieved 2021.https://www.nami.org/Your-Journey/Identity-and-Cultural-Dimensions/Hispanic- Latino Park, H., Park, W., Lee, S., Kim, J., Lee, J., Lee, H., & Shin, H. (2020). “Posttraumatic stress disorder and depression of survivors 12 months after the outbreak of Middle East respiratory syndrome in South Korea.” BMC Public Health, 20(605). https://doi.org/10.1186/s12889-020-08726-1 US Census Bureau. Demographics Table for Los Angeles County . 2019. https://www.census.gov/data.html Footnotes LA County Department of Mental Health, 2030 Strategic Plan , .dmh.lacountygov Los Angeles County Department of Public Health, COVID-19 Surveillance Dashboard. (2020) http://dashboard.publichealth.lacounty.gov/covid19_surveillance_dashboard/ Maps Maps 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files map1.png Color Map of High COVID-19 Case Rate Areas map2.png Color Map of High Covid-19 Case Rate Areas map3.png Detailed View of COVID-19 Cluster map4.png Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Verduzco","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACxoYDQJKNgYEfLkC0FskGYrVAAFCLwQFitTA3njF8XFF22N74RvLjDz8YbGQ3HCDosDPGhmfOHWY2u5FmJtnDkGZMhJZjaZKNbYfZzG7ksDEzMBxOJEZL+k+gFh7jGTnMnxkY/hOj5fAxRqAWCQOJHAZpBoYDRGk5LNlwLt1A4swzoF8Mko1nEtJiOONg48eGMmt7/nZQiFXYyfYR1oKiwoCAchCQ528gQtUoGAWjYBSMbAAAVx9GZAYftRIAAAAASUVORK5CYII=","orcid":"","institution":"University of California, Irvine","correspondingAuthor":true,"prefix":"","firstName":"Jesus","middleName":"Jasso","lastName":"Verduzco","suffix":""}],"badges":[],"createdAt":"2025-07-22 21:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7190511/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7190511/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87903125,"identity":"1f8f8bec-1b6e-4fb8-bb9d-b15b0f8eea43","added_by":"auto","created_at":"2025-07-30 08:29:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":442295,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGWR Model Type for COVID-19 Case Rate and Race\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7190511/v1/61d908eaf122efcc0fcd387f.png"},{"id":87903122,"identity":"f547e453-b8b1-4f59-a143-3e635aef4f29","added_by":"auto","created_at":"2025-07-30 08:29:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153112,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGlobal Moran’s I Test Output\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7190511/v1/18e8fe243eaba004d3900e63.png"},{"id":88248574,"identity":"cf1c4952-3733-46a6-a1c0-821e307b3c37","added_by":"auto","created_at":"2025-08-04 13:02:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1368940,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7190511/v1/ef548ae8-e9c8-40e8-b064-c0c0f84806a3.pdf"},{"id":87903123,"identity":"28e25a6d-8496-448c-8abb-9e8c10d63fd2","added_by":"auto","created_at":"2025-07-30 08:29:00","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":346589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eColor Map of High COVID-19 Case Rate Areas\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"map1.png","url":"https://assets-eu.researchsquare.com/files/rs-7190511/v1/391c6e9f7004b85d76caf153.png"},{"id":87903622,"identity":"c5a71532-dfba-4ab3-b902-ec3854325dd0","added_by":"auto","created_at":"2025-07-30 08:37:00","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":367439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eColor Map of High Covid-19 Case Rate Areas\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"map2.png","url":"https://assets-eu.researchsquare.com/files/rs-7190511/v1/a0ecbb44412dd4637ed41b23.png"},{"id":87903130,"identity":"d40b5939-1505-4e0c-83fd-5996ec2386e2","added_by":"auto","created_at":"2025-07-30 08:29:00","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":561871,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDetailed View of COVID-19 Cluster\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"map3.png","url":"https://assets-eu.researchsquare.com/files/rs-7190511/v1/69df40b7de255ace6e4815ca.png"},{"id":87903134,"identity":"f2ad0e8a-1d39-41ed-98b5-42141207406b","added_by":"auto","created_at":"2025-07-30 08:29:01","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":565455,"visible":true,"origin":"","legend":"","description":"","filename":"map4.png","url":"https://assets-eu.researchsquare.com/files/rs-7190511/v1/aefefcbb35c714d9ff3aafdd.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping Mental Health Disparities: The COVID-19 Mental Health Impact on Latino and African-American Communities in Los Angeles County","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe COVID-19 pandemic had a severe impact on healthcare systems nationwide. With much of the focus on protecting physical health, newly emerging mental health issues were consequently understudied. For this reason, this paper seeks to examine the impact of the COVID-19 pandemic on mental health needs of minority communities in Los Angeles County. This paper sought to answer two questions: (1) Have minority communities faced a disproportionate impact of the Coronavirus Disease (COVID-19) infection in Los Angeles County? And if so, (2) Will these minority communities face a mental health crisis? Our in-depth analysis of COVID-19 data from the Los Angeles Department of Public Health revealed the need to address mental health disparities in communities of color in Los Angeles County. Through a literature review and our quantitative analysis, clinicians can gain a deeper understanding of the mental health landscape in Los Angeles County. A socio-spatial analysis conducted with ArcGIS identified the specific geographical zones in Los Angeles County that will likely experience an increased risk of developing mental health disorders. The COVID-19 cluster identified in this study aligns with the statement that minority communities in Los Angeles County faced a disproportionate burden of disease during the pandemic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn California, healthcare providers are accountable for serving a diverse clientele that is largely comprised of a growing Latino population. In Los Angeles County alone, approximately 49% (or 4,725,059 individuals) identify as Latino. The term \u0026quot;Latino\u0026quot; used in this paper refers to any individual with ancestry from Latin America. In Los Angeles County, an estimated 65% of the total population, or 3,575,000 individuals, are foreign-born. The federal legislation created during the pandemic, such as the Coronavirus Aid, Relief, and Economic Security Act (CARES Act), excluded immigrants and mixed-status families from receiving any economic relief during the COVID-19 pandemic. Many mixed-status families, spouses of immigrants, and their children who have full citizenship were excluded by the CARES Act. Civil rights organizations have filed lawsuits against the federal government over provisions contained in the CARES Act. The federal economic stimulus policy during the COVID-19 pandemic blatantly discriminated against mixed-status families and married couples, including spouses of immigrants who filed their income taxes with an ITIN (Individual Tax Identification Number). In the recent decade, lawsuits have been brought against California\u0026rsquo;s Department of Health Care Services, claiming that the state has not done enough to remove unnecessary obstacles to healthcare access for low-income patients and has been reluctant to adequately implement mechanisms to monitor and oversee the program. (CBS News, 2017, Local News). A combination of long-term disinvestment from Medi-Cal and exclusion from economic relief through the CARES Act has contributed to the risk factors within minority communities for developing a mental health disorder during the COVID-19 pandemic.\u0026nbsp;\u003c/p\u003e"},{"header":"LITERATURE REVIEW","content":"\u003cp\u003eRespiratory infections have been the cause of multiple pandemics in the past, such as the 2002 severe acute respiratory syndrome (SARS) pandemic in China and the 2015 Middle Eastern Respiratory Syndrome (MERS) pandemic in South Korea. A review of scholarly work on previous pandemics shows that social factors experienced in a pandemic era can lead to an increase in post-traumatic stress disorder (PTSD), depression, and anxiety disorders. PTSD can be defined as intense feelings of stress, or feeling afraid after experiencing a traumatic event (CDC, 2021, Types of Mental Disorders). Depression is when an individual experiences constant feelings of being in a bad mood, to the point that their mental, as well as psychological, well-being, is impacted nearly every day and for much of the day (CDC, 2021, Types of Mental Disorder).\u003c/p\u003e\n\u003cp\u003eAccording to Lam et. al. (2009), from the Department of Psychiatry at The Chinese University of Hong Kong, a psychiatric evaluation of 181 SARS virus survivors in Hong Kong, China, found that PTSD and depression were prevalent among interview participants. Study participants were issued a psychiatric evaluation to determine if SARS survivors had developed a mental disorder after recovery. The evaluation was issued between three to four years after the participant had been diagnosed with the SARS virus. Among the 181 study participants, only six had a history of mental disorder. However, at the time of follow-up, 77 of the 181 (or 42.5%) of participants experienced at least one mental disorder (Lam et. al., 2009, P. 2043). More specifically, among the participants who were diagnosed with a disorder, the most common mental disorder found was PTSD (42 of 77) or 54.5%, and depression (30 of 77) or 39.0% (Lam et. al., 2009, P. 2043). Moreover, social factors such as stigmatization, stress, grief, and fear have been found to contribute to the development of a mental disorder. According to Abdelhafiz and Alorabi (2020) from the National Cancer Institute at Cairo University, fear and anxiety arise during a pandemic as a result of the unknown cause of the disease and possible fatal outcome. A respiratory virus can be life-threatening, and the stigmatization of the infected flourishes with dramatic stories in the media and on the internet (Abdelhafiz and Alorabi, 2020, p. 429).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudies have also shown that the SARS pandemic led to adverse mental health conditions among virus survivors in Beijing, China. A study by Hong et al. (2009), from the University of Rochester Medical Center in New York, measured the incidence of PTSD among 68 SARS virus survivors. Clinical assessments were conducted periodically with subjects after they had been discharged from the hospital following a diagnosis of SARS. The assessments were conducted by a trained psychiatrist using the Chinese Classification of Mental Disorders (CCMD-III) and Diagnostic and Statistical Manual of Mental Disorders. The clinical assessments revealed that among the 68 subjects who were hospitalized with SARS, 30 (44.1%) of survivors developed PTSD after being discharged. (Hong et. al., 2009, P. 546-554)\u003c/p\u003e\n\u003cp\u003eIn 2015, the Middle Eastern Respiratory Syndrome (MERS) outbreak in South Korea presented evidence that links mental disorders to respiratory virus pandemics. A study by Park et. al. (2020), from the Seoul National University Hospital in Seoul, revealed that PTSD and depression were commonly found among pandemic survivors in South Korea. A nationwide study was conducted one year after the initial MERS outbreak in South Korea. Trained clinicians interviewed 63 participants using the Impact of Event Scale and the Patient Health Questionnaire-9 frameworks to determine mental health outcomes. The questionnaire results showed that out of the 63 participants, 42.9% reportedly had significant PTSD symptoms, and 27% reportedly had depression. (Park et. al., 2020, P. 605)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the United States, a household pulse survey conducted by the Centers for Disease Control and Prevention (CDC) during the COVID-19 pandemic revealed that anxiety and depression disorders had increased across the country. In 2019, the CDC found that 10.8% of adults ages 18 and older had anxiety or depressive disorders. After the COVID-19 outbreak in late 2019, the CDC found that mental disorders had increased to 26.4% for adults ages 18 and older (CDC, 2020, Household Pulse Survey). The CDC stated that COVID-19 survivors experience social factors such as stigmatization, isolation, depression, anxiety, or public embarrassment. Additionally, studies on the SARS and MERS pandemics align with the results from the CDC household pulse survey. The literature discussed in this section ties mental disorders, such as PTSD and depression, to the experience of minority groups most impacted by the COVID-19 pandemic. In the sections below, this paper identifies the minority communities in Los Angeles County that have been disproportionately affected by the COVID-19 pandemic. \u0026nbsp;\u003c/p\u003e"},{"header":"METHODS AND DATA","content":"\u003cp\u003eThis study employed an instrumentalist perspective in its analysis methods\u003cem\u003e.\u0026nbsp;\u003c/em\u003eThe goal of the analysis is to provide grassroots mental health providers with information that guides their mental health services in minority communities post-pandemic. In other words, the \u003cem\u003einstrumentalist perspective\u0026nbsp;\u003c/em\u003easserts that organizations must utilize the appropriate instruments at their disposal, depending on specific conditions or situations. Selecting and combining programs to change is a key factor for community-based mental health providers post-pandemic. During the COVID-19 pandemic, the Los Angeles County Department of Mental Health (LADMH) published Strategic Plan 2030.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e The plan issued by the County outlines the structure, goals, and requirements for mental health partners post-pandemic. This study provides critical insight into how mental health providers in minority communities can effectively distribute mental health services to clients most impacted by COVID-19.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis portion of this study consisted of two main approaches: a geospatial analysis that identified the spread of COVID-19 in Los Angeles County, and a statistical analysis that correlated race with a higher COVID-19 case rate. The geospatial analysis, used to identify the spread of COVID-19 in Los Angeles County, enabled this study to locate emerging mental health hotspots. For the COVID-19 analysis, this project used ArcGIS Pro to conduct three statistical analyses. The Moran's I and Getis-Ord Gi* statistical tests were used to identify the pattern of the spread of COVID-19 cases. The outcomes of these tests show the autocorrelation between COVID-19 and space. Furthermore, the analysis included the socio-demographics of the population most affected by COVID-19 infections. A Geographically Weighted Regression analysis was conducted to determine the correlation between the African-American and Latino populations and COVID-19. This methodology is widely used among statisticians to understand the spatial characteristics and their relationships with socio-economic variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data used to build the ArcGIS geospatial analysis has been collected from three different sources. Demographic information for Los Angeles County, including race and household income, was collected from the U.S. Census Bureau. The data used for statistical analysis of COVID-19 were collected from the Los Angeles County Department of Public Health. The COVID-19 data includes the final death count, death rate, total case count, and case rate from March 2020 to March 2021\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e. The COVID-19 data is not organized by census tract, rather by Community Standard District (CSD). The Community Standard District is the unit of analysis used by the Los Angeles Planning District to organize entire cities or unincorporated land for special topics, such as a pandemic. \u0026nbsp;The case rate was calculated by dividing the final count of COVID-19 cases within a given district by the total population of the corresponding CSD. Furthermore, from aggregating case rates in the COVID-19 data, the average infection rate was 8.7%. This study considers a “high COVID-19 case rate,” defined as a CSD with one to two standard deviations above the sample mean, rather than the average. This was elaborated upon in the analysis section of this project. \u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEFINITIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThroughout this paper, the term “mental health hotspot” refers to COVID-19 clusters with one or two standard deviations above the sample mean. The reason is that virus survivors living in high COVID-19 impact zones are at greater risk of developing mental health illnesses, such as PTSD or depression. Another requirement for a zone to be considered a “hotspot” is the prevalence of Latino or African-American residents living within the CSD. The Latino or African American population must comprise 40% or greater of the total population in the CSD to be considered in the findings. The study does not use the term “hotspot” alone, but rather in conjunction with other words (i.e., mental health hotspot). All in all, the COVID-19 burden areas correspond with the mental health hotspots identified by the ArcGIS spatial analysis. Below, Table 1 provides an elaboration on the terms and definitions used throughout this paper. (see Table 1)\u003c/p\u003e\n\u003cp\u003eTo narrow the scope of this project, PTSD and depression are the major mental health disorders that will arguably be most common within the COVID-19 cluster. Finally, the “COVID-19 cluster” is also defined. The data and analysis in this study include the number of COVID-19 confirmed cases. The Geographically Weighted Regression conducted in this study has identified that the spread of the virus happens in clusters and is correlated to space. Therefore, a “COVID-19 cluster” refers to a concentration of high virus infections in a specific geographical area. This analysis color-coded the geographical areas within Los Angeles County that were most severely impacted by the COVID-19 virus, identifying them as COVID-19 clusters. The large red spot in the center of the LAC map means that residents in that area experienced more COVID-19 cases at least 1 standard deviation above the sample mean. This large red spot has been identified as the most significant COVID-19 cluster in the county, due to its disproportionate impact on minority communities and its high concentration of infections. \u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1 Overview of definitions\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSuggested term and alternatives\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 317px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eMental health hotspot\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 317px;\"\u003e\n \u003cp\u003e(a) An area within a Latino or African-American community within a high COVID-19 zone. Based on the literature, residents are at greater risk of experiencing adverse mental health effects because of the pandemic.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eCOVID-19 cluster\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 317px;\"\u003e\n \u003cp\u003e(a) Any areas colored in dark red/red on the maps. This indicates that there is at least 1 to 2 standard deviations above the sample mean. Identified by the Getis-Ord Gi* and Moran’s I statistical tests as a concentration of high COVID-19 cases.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eMental health/mental health illness\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 317px;\"\u003e\n \u003cp\u003e(a) Mental health includes a person’s emotional, psychological, and social well-being. Based on the literature, Post-Traumatic Stress Disorder (PTSD) and Depression are common mental health illnesses that increase during a pandemic.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTATISTICAL ANALYSIS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the peak of the pandemic, this study sought to pinpoint the exact communities that were disproportionately impacted by the virus, and at-risk of developing a mental disorder. The first step was to visually represent the geographical spread of COVID-19 infections. The Getis-Ord Gi* test was used to identify areas with high or low COVID-19 case rates within the county. To be considered a statistically significant COVID-19 cluster, a feature will showcase a high value (highlighted in Red on Map 1) and be surrounded by other features with high values as well (ESRI, 2021, Spatial Statistics). The Getis-Ord Gi* test also shows features that will have a high value (red). Map 2 and Map 3, below, show the statistically significant areas (red) where the spread of the virus was clustered between Interstate 105 and Interstate 10 (see Map 2 and Map 3). The map below shows the statistically significant areas (Red) that are clustered with high COVID-19 case rates (see Map 1). The Red and Blue areas on the map are described by the legend below (see Table 2). The high and low clusters are determined based on the distance that the area’s COVID-19 case rate is from the sample mean of distribution. Red indicates that the area is 1 or more standard deviations above the sample mean. In other words, the Red areas have a high number of COVID-19 cases and have a statistically significantly high COVID-19 infection rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2: Color Legends for Map 1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse; border: none; width: 632px;\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd style=\"width: 81.1pt; border-width: 3pt 1pt 1pt 3pt; border-color: black; border-style: solid; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 16px; line-height: 107%; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003eConfidence\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd colspan=\"3\" style=\"width: 155.95pt; border-top: 3pt solid black; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 16px; line-height: 107%; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003eRed\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: 3pt solid black; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 16px; line-height: 107%; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003eGray\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd colspan=\"3\" style=\"width: 177.8pt; border-top: 3pt solid black; border-left: none; border-bottom: 1pt solid black; border-right: 3pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 16px; line-height: 107%; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003eBlue\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 81.1pt; border-top: none; border-left: 3pt solid black; border-bottom: 1pt solid black; border-right: 1pt solid black; background: rgb(192, 0, 0); padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 40.55pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; background: rgb(192, 0, 0); padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63.05pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; background: rgb(197, 89, 17); padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 52.35pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; background: rgb(247, 203, 172); padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; background: rgb(165, 165, 165); padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; background: rgb(217, 226, 243); padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; background: rgb(142, 170, 219); padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 3pt solid black; background: rgb(31, 56, 100); padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 81.1pt; border-top: none; border-left: 3pt solid black; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 16px; line-height: 107%; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e%\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 40.55pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e99%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63.05pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e95%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 52.35pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e90%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e90%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e95%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 3pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e99%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 81.1pt; border-top: none; border-left: 3pt solid black; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 16px; line-height: 107%; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003ep-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 40.55pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e0.01\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63.05pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e0.05\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 52.35pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e0.10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e0.10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e0.05\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 1pt solid black; border-right: 3pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e0.01\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 81.1pt; border-top: none; border-left: 3pt solid black; border-bottom: 3pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 16px; line-height: 107%; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003eSt. Deviation\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 40.55pt; border-top: none; border-left: none; border-bottom: 3pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e\u0026gt;2.58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63.05pt; border-top: none; border-left: none; border-bottom: 3pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e1.96~2.58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 52.35pt; border-top: none; border-left: none; border-bottom: 3pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e1.65-1.96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 3pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e-1.65-1.65\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 3pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e-1.65~-1.96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 3pt solid black; border-right: 1pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e-1.96~-2.58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 59.25pt; border-top: none; border-left: none; border-bottom: 3pt solid black; border-right: 3pt solid black; padding: 0cm 5.4pt; height: 18.3pt; vertical-align: top;\"\u003e\n \u003cp style=\"margin: 0cm 0cm 8pt; font-size: 11pt; font-family: Calibri, sans-serif; text-align: justify;\"\u003e\u003cspan style=\"font-size: 16px; font-family: \u0026quot;Times New Roman\u0026quot;, serif;\"\u003e\u0026lt;-2.58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eSecondly, a Geographically Weighted Regression (GWR) test was employed to examine the relationship between race and COVID-19 cases. The GWR statistical tool is a linear regression test within the ArcGIS program (ESRI, 2021, GeoAnalytics). \u0026nbsp;The COVID-19 case rate was used as the dependent variable, and race was used as the explanatory variable. After inputting the variables, the GWR determined that there was a positive relationship between race and the COVID-19 case rate, with a model fit (R²) of 0.14 to 0.2. More specifically, the greater the fit (R2) that race variables have with the COVID-19 case rates, the higher the percentage that race explains of the high COVID-19 case rates. The output features of the GWR show that the Latino communities and African American communities within the COVID-19 cluster are correlated to having experienced a greater burden of disease in Los Angeles County (see Figure 1 and Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 3: Explanatory Power from GWR Model\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"616\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd colspan=\"2\" valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographically Weighted Regression (GWR)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExplanatory\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eLatino Only\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003eAfrican-American and Latino\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAICs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e7130.7792\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003e7130.7910\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eThirdly, a Global Moran’s I test was conducted to determine if space and the spread of infection had any correlation. The Global Moran's I test produces an index value within a range of −1.0 to +1.0. The index value options are (\u0026gt; 0) clustered, (= 0) randomly distributed, and (\u0026lt; 0) dispersed (ESRI, 2021, Spatial Statistics). The Moran’s I test resulted in an index value of 0.3, which is (\u0026gt; 0) clustered (see Figure 2). The results indicate that a spatial auto-correlation exists between COVID-19 and space. In other words, the spread of infection can be densely concentrated in a single geographical space. Therefore, the residents located in a statistically significant COVID-19 cluster are particularly vulnerable to experiencing adverse mental health conditions. The Moran’s I test shows that COVID-19 can disproportionately impact a specific community or city.\u0026nbsp;\u003c/p\u003e\n\n\n\n\n\n\n\n\n\n\n\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e"},{"header":"MENTAL HEALTH DISPARITY","content":"\u003cp\u003eIn the analysis above, this paper has determined that the Latino and African American communities living between Interstate I-105 and Interstate I-10 are at greatest risk of experiencing a post-COVID-19 pandemic mental health disorder. According to the National Alliance of Mental Health (NAMI), only 34% of Latino adults with mental disorders receive treatment each year compared to the U.S. average of 45%. A Latino might describe what they are feeling with a phrase like “Me duele el corazón,” which means “my heart hurts.” However, it is an expression of emotional distress and not of physical pain. (NAMI, 2021, Hispanic/Latino Tab) The misdiagnosis of mental or emotional disorders in the Latino community is a problem that can be mitigated. In the section below, this paper includes recommendations for policymakers and mental health organizations on how they can better serve minority populations in Los Angeles County.\u003c/p\u003e\u003cp\u003eThe GWR, Getis-Ord Gi*, and Moran’s I statistical tests work together to show that high COVID-19 case rates are found in dense clusters throughout Los Angeles County. The location of the COVID-19 cluster is critical information for mental health providers, as it indicates the potential locations of new clients. Policymakers should utilize this information to consider innovative and culturally sensitive ways to serve the high-impacted minority communities of Los Angeles County. Policymakers at federal, state, and local levels can address risk by providing comprehensive support, such as economic stimulus and access to quality healthcare, to mitigate the disproportionate burden of disease for minority populations. Various studies on the burden of disease state that economic hardship is a risk factor for developing mental disorders (Cordes and Castro, 2020, P. 100355). Below, this study has identified income data that shows specific cities that can benefit from COVID-19 economic relief. (See Table 4).\u003c/p\u003e\u003cp\u003eSimilar urban characteristics, such as ethnic enclaves, are commonly found within the Top 5 cities/communities located within the COVID-19 cluster. An ethnic enclave can be defined as an urban area where a particular racial group is densely concentrated and is socially and economically distinct from the majority of the city (Lim, 2017, p. 138). The cities and communities in the mental health hotspot are comprised of Latinos (See Table 5). In 2019, during the peak of the COVID-19 pandemic, the Latino population made up 48.6% of the total population in Los Angeles County. (US Census, 2019, Demographics Table) Furthermore, this study found that more than 90% of residents in the City of Bell Gardens, Cudahy, Maywood, and Lynwood were Latino. This study found that the Latino ethnic enclaves comprised the majority of residents living in the COVID-19 cluster. (see Map 4)\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 4: Demographic Characteristics Cities/Communities within a COVID-19 Cluster\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"660\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCapita\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnemployment\u0026nbsp;Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBelow the Poverty Level*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNAP/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFood Stamps**\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Health Insurance Coverage***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCity of Compton\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e$17,707\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e8.72%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e16.98%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e19.25%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e16.03%\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCity of Commerce\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e$18,508\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e8.23%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e12.76%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e12.85%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e17.68\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCity of Cudahy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e$14,545\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e8.66%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e23.71%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e20.85%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e25.60%\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCity of Maywood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e$15,845\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e7.58%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e20.58%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16.67%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e21.26%\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCity of Vernon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e$36,450\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e5.25%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9.65%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e$39,393\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e11.8%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.4%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10.9%\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eU.S.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e$35,672\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e12.03%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e12.9%\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003e\u003cstrong\u003e(Source: ACS, US Census, 2019)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eNote: *Percentage of families and people whose income in the past month is below the Poverty Level. \u0026nbsp;** Percentage of households with Food Stamp/SNAP benefits in the past 12 months. ***No health insurance coverage, including the civilian non-institutionalized population 19 to 64 years in the labor force.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 5: Evidence of Ethnic Enclaves Within the COVID-19 Cluster\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Source: Data collected from US Census Bureau and Data USA, 2021, https://datausa.io/profile/geo)\u003c/strong\u003e\u003c/p\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Standard District\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 Case Rate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Population*\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatino Population**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSPA\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003eCity of Bell Gardens\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e17.60%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e43,071\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e40,800\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003eCity of Cudahy\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e17.93%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e24,347\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e23,000\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003eCity of Commerce\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e17.20%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e13,069\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e9,090\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003eBoyle Heights Community\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e18.70%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e86,884\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e74,800\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003eCity of Maywood\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e17.10%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e28,049\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e27,00\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003eCity of Lynwood\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e16.90%\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e72,047\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e71,000\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e"},{"header":"RECOMMENDATIONS SECTION","content":"\u003cp\u003eBelow, this study presents recommendations aimed at supporting a county-wide approach to reducing mental health disparities in the identified mental health hotspots:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRecommendation 1.\u003c/em\u003eA COVID-19 cluster indicates a geographical area with a high COVID-19 case rate. These areas should be a priority for local mental health service providers. The majority of high COVID-19 clusters are located between the I-10 and I-110 freeways/highways/interstates, which is also known as Service Planning Area (SPA) 7. It is recommended that mental health providers develop an outreach plan to target services and increase patient intake capacity, thereby better serving low-income clients living within COVID-19 clusters. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRecommendation 2.\u003c/em\u003e Providing culturally sensitive treatment is a critical recommendation that will help reduce the mental health disparities in Los Angeles County. More studies are needed to fully understand the impact of culturally competent mental health services. However, building from philosophy of Instituto Familiar de la Raza (\u003cem\u003eEl Instituto\u003c/em\u003e) in San Francisco, California, mental health providers can begin to incorporate a long-standing culturally sensitive framework into their clinical services. For example, one of the core philosophies of \u003cem\u003eEl Instituto\u003c/em\u003e is the concept of \u0026ldquo;\u003cem\u003eLa Cultura Cura\u003c/em\u003e,\u0026rdquo; or the \u0026ldquo;Culture Can Heal.\u0026rdquo; By configuring treatment programs to embrace a shared Latino identity, such as indigenous and Meso-American roots, clinicians can be more effective in treating Latino clients. \u003cem\u003eEl Instituto\u003c/em\u003e practices this concept by incorporating culturally relevant coping skills into treatments such as knitting, pottery, painting, and other traditional arts native to Mexico and Central America. \u003c/p\u003e\n\u003cp\u003eIt is also crucial to hire translators or clinicians with Spanish-speaking capabilities to effectively incorporate the Spanish language into treatment. In addition, \u003cem\u003eEl Instituto, \u003c/em\u003erequired therapists to provide flexible meeting locations for Latino patients. On occasion, therapists were asked to meet clients in public spaces closer to their work or home during the pandemic. Thirdly, it is recommended that therapists in Los Angeles County create a resource list of suitable public spaces near their clients\u0026apos; workplaces or homes to increase attendance at sessions. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRecommendation 3.\u003c/em\u003e Collaboration with local CBOs that have a strong rapport with the Latino population in Los Angeles is recommended so that clinicians can recruit Spanish translators. CBOs such as the Coalition of Humane Immigrant Rights (CHIRLA), Mexican American Opportunity Foundation (MAOF), and Esperanza Community Housing have a long-standing rapport with the Latino population in Los Angeles County. They could be a vital starting point for recruiting translators and clinician support. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRecommendation 4.\u003c/em\u003e The Integrated Behavioral Information Systems (IBHIS) is an information system established by the LACDMH to enhance the administrative role of mental health providers. It is recommended that local providers utilize the IBHIS system to share information with community-based organizations (CBOs). The IBHIS can be a system that connects grassroots and community-based organizations to Latino-serving organizations that may have stronger ties to communities of interest. An example of information that can be shared is medication information, recent changes in mental health assessments, laboratory and psychological test results, and clinician notes from prior visits, when appropriate. \u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study has identified a COVID-19 cluster in the region of Los Angeles County, located south of Interstate 10 and north of Interstate 105. Communities located in the COVID-19 cluster are at greater risk of experiencing a mental health impact from the COVID-19 pandemic, as evidenced by the literature. Based on previous research on mental health and respiratory pandemics, victims of the pandemic can develop adverse mental health conditions. Studies show multiple social factors lead to an increase in PTSD and/or depression for COVID-19 victims post-pandemic. Through the GWR, Getis-Ord Gi* and Moran\u0026rsquo;s I statistical analysis, our results show that most COVID-19 cases are densely located in low-income neighborhoods that lack healthcare coverage and are mostly comprised of Latinos. The recommendations provided in this research paper aim to help reduce mental health disparities within the COVID-19 cluster in Los Angeles County. Recommendations emphasize the importance of strong collaboration, allocating resources to local grassroots organizations, and providing support for establishing culturally sensitive services.\u003c/p\u003e\u003cp\u003eIn conclusion, mental health organizations serving minority communities should consider the literature presented in this paper. The data in this paper suggests a correlation between race and higher infection rates during the COVID-19 pandemic. Through our analysis, we have identified that the mental health hotspots in low-income communities are mostly comprised of Latino residents. Case in point, mental health providers will be wise to increase outreach and service to Latino clients residing in SPA 7. Creating a more robust outpatient treatment program, increasing culturally competent services, and collaborating with other organizations using the IBHIS system will address new mental health needs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAKNOWLEDGEMENTS:\u0026nbsp;\u003c/strong\u003eNo acknowledgments. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e: K.C. contributed to the geospatial analysis presented in the Statistical Analysis section. K.C. contributed to Map 1, Table 2, Map 2, Map 3, Figure 1, Table 3, and Figure 2, all of which are located within the Statistical Analysis section.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING DECLARATION:\u0026nbsp;\u003c/strong\u003eThis research received no specific grant from any funding agency, public or private institution, commercial, or not-for-profit sectors. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILABILITY OF DATA:\u0026nbsp;\u003c/strong\u003eThe data used to determine the COVID-19 cluster was drawn from the Los Angeles County Department of Public Health, COVID-19 Surveillance Dashboard. (2020) http://dashboard.publichealth.lacounty.gov/covid19_surveillance_dashboard/\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCODE AVAILABILITY:\u0026nbsp;\u003c/strong\u003eNot applicable. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST:\u0026nbsp;\u003c/strong\u003eThe author(s) declare that there is not conflict of interest regarding the publication of this article. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICAL APPROVAL:\u0026nbsp;\u003c/strong\u003eThe author(s) declare that they have no know competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cem\u003eAbdelhafiz, Ahmed Samir; Alorabi, Mohamed.\u003c/em\u003e \u0026ldquo;Social Stigma: The Hidden Threat of COVID-19.\u0026rdquo; Frontiers in Public Health. August 28, 2020. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484807/\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCenter for Disease Control and Prevention (CDC). \u003cem\u003eHousehold Pulse Survey.\u003c/em\u003e 2020. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;https://www.cdc.gov/nchs/covid19/pulse/mental-health.htm\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eCenter for Disease Control and Prevention (CDC). \u003cem\u003eTypes of Mental Disorder.\u0026nbsp;\u003c/em\u003eMay, 2021. https://www.cdc.gov/mentalhealth/learn/index.htm\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eCordes, J., \u0026amp; Castro, M. C. (2020).\u003c/em\u003e\u0026ldquo;Spatial analysis of COVID-19 clusters and contextual factors in New York City.\u0026rdquo; Spat \u0026nbsp; \u0026nbsp; Spatiotemporal Epidemiol. doi: 10.1016/j.sste.2020.100355\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eESRI. (n.d.). \u003cem\u003eGeographically Weighted Regression (GWR) (GeoAnalytics).\u003c/em\u003e Retrieved May 6, 2021.\\https://pro.arcgis.com/en/pro-app/latest/tool-reference/big-data- \u0026nbsp; \u0026nbsp; \u0026nbsp;analytics/geographically-weighted-regression.htm\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eESRI. (n.d.). \u003cem\u003eHot Spot Analysis (Getis-Ord Gi*) (Spatial Statistics).\u0026nbsp;\u003c/em\u003eRetrieved May 6, 2021.https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/hot-spot- \u0026nbsp; \u0026nbsp; analysis.htm\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eESRI. (n.d.). \u003cem\u003eSpatial Autocorrelation (Global Moran\u0026apos;s I) (Spatial Statistics).\u003c/em\u003eRetrieved May 6, 2021. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial- \u0026nbsp; \u0026nbsp; \u0026nbsp; statistics/spatial-autocorrelation.htm\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eHong, Xia; Currier, Glenn W.; Zhao, Xiaohui; Jiang, Yina; Zhou, Wei and Wei, Jing. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u0026ldquo;Posttraumatic stress disorder in convalescent severe acute respiratory syndrome patients: a 4-year follow-up study.\u0026rdquo;\u003cem\u003e\u0026nbsp;\u003c/em\u003eGeneral Hospital Psychiatry. 27 August 2009.https://pubmed.ncbi.nlm.nih.gov/19892213/\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eKolinovsky, Sarah\u003c/em\u003e. \u0026ldquo;Some 1.2 million Americans won\u0026apos;t get stimulus checks because they\u0026apos;re married to immigrants.\u0026rdquo; \u0026nbsp;ABC News. May 21, 2020. https://abcnews.go.com/Politics/12-million-americans-stimulus-checks-married- immigrants/story?id=70493620\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eLam MH, Wing YK, Yu MW, Leung CM, Ma RC, Kong AP, So WY, Fong SY, Lam SP\u003c/em\u003e.\u0026ldquo;Mental Morbidities and Chronic Fatigue in Severe Acute Respiratory Syndrome \u0026nbsp;Survivors: Long-Term Follow-Up.\u0026rdquo; Arch Intern Med. 2009 Dec 14;169(22):2142-7.doi:10.1001/archinternmed.2009.384. PMID: 20008700.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eLim, Sungwoo; Yi, Stella; De La Cruz, Nneka; and Trinh-Shevrin, Chau. (2017)\u003c/em\u003e\u0026ldquo;Defining ethnic enclaves and their associations with self-reported health outcomes \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;among Asian American adults in New York City.\u0026rdquo;NCBI. PMCID: PMC4919243\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u0026ldquo;Lawsuit Claims Medi-Cal Discriminates Against Low-Income Californians\u0026rdquo;.\u003cem\u003eCBS News.\u003c/em\u003e April 16, 2024. July 13, 2017.https://www.cbsnews.com/sanfrancisco/news/lawsuit-medi-cal-discriminates-poor- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;californians/.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNational Alliance on Mental Health. Hispanic/Latino. Retrieved 2021.https://www.nami.org/Your-Journey/Identity-and-Cultural-Dimensions/Hispanic- \u0026nbsp;Latino\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003ePark, H., Park, W., Lee, S., Kim, J., Lee, J., Lee, H., \u0026amp; Shin, H.\u0026nbsp;\u003c/em\u003e\u003cem\u003e(2020).\u003c/em\u003e \u0026ldquo;Posttraumatic stress disorder and depression of survivors 12 months after the outbreak of Middle East respiratory syndrome in South Korea.\u0026rdquo; BMC Public Health, 20(605). https://doi.org/10.1186/s12889-020-08726-1\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eUS Census Bureau. \u003cem\u003eDemographics Table for Los Angeles County\u003c/em\u003e. 2019. \u0026nbsp; \u0026nbsp; https://www.census.gov/data.html\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e LA County Department of Mental Health, \u003cem\u003e2030 Strategic Plan\u003c/em\u003e, .dmh.lacountygov\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Los Angeles County Department of Public Health, \u003cem\u003eCOVID-19 Surveillance Dashboard. (2020)\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dashboard.publichealth.lacounty.gov/covid19_surveillance_dashboard/\u003c/span\u003e\u003cspan address=\"http://dashboard.publichealth.lacounty.gov/covid19_surveillance_dashboard/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Maps","content":"\u003cp\u003eMaps 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19 Case Rate, COVID-19 cluster, Latino Mental Health, culturally sensitive","lastPublishedDoi":"10.21203/rs.3.rs-7190511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7190511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper seeks to investigate mental health disparities in minority communities in Los Angeles County. We sought to answer the following questions: (1) Have minority communities faced a disproportionate impact of the Coronavirus Disease (COVID-19) infection in Los Angeles County? And if so, (2) Will these minority communities face a mental health crisis? Throughout the COVID-19 pandemic, the Los Angeles Department of Public Health (LADPH) collected data on COVID-19 cases and death rates in minority communities. This paper utilized LADPH data to conduct three statistical tests, including the following: Geographically Weighted Regression (GWR), Getis-Ord Gi*, and Moran\u0026rsquo;s I. In addition, a collection of scholarly work on the effects of respiratory virus pandemics on mental health suggested that respiratory-related pandemics exacerbate Post-Traumatic Stress Disorder (PTSD) and depression. Our findings revealed that trends in COVID-19 cases can be densely concentrated in a geographical area, which we identify as a COVID-19 cluster. Through statistical analysis and a literature review, we determined that a COVID-19 cluster was in the area between I-10 and I-105. A close analysis of the COVID-19 cluster revealed that the top five cities within the cluster were predominantly comprised of Latinos or African Americans. Our research questions were answered, and we concluded that the Latino communities within the City of Bell Gardens, City of Cudahy, City of Commerce, Boyle Heights Community, City of Maywood, and City of Lynwood are at greatest risk of facing a mental health crisis post-COVID-19 pandemic. To conclude, the paper presents four recommendations for mental health providers serving minority populations in the Los Angeles County region.\u003c/p\u003e","manuscriptTitle":"Mapping Mental Health Disparities: The COVID-19 Mental Health Impact on Latino and African-American Communities in Los Angeles County","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 08:28:56","doi":"10.21203/rs.3.rs-7190511/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e968f4a8-b822-49f5-b875-61fdf7272e70","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T12:54:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 08:28:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7190511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7190511","identity":"rs-7190511","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}