Racial and ethnic disparities in mental health among breast cancer patients and survivors in the United States.

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Learn more: PMC Disclaimer | PMC Copyright Notice Health Serv Res . 2024 Oct 14;60(3):e14391. doi: 10.1111/1475-6773.14391 Racial and ethnic disparities in mental health among breast cancer patients and survivors in the United States Ahmad Reshad Osmani 1 , Asako Moriya 2, ✉ , Shelley White‐Means 3, 4 Author information Article notes Copyright and License information 1 Department of Economics and Finance, Louisiana State University (LSU) Shreveport, Shreveport, Louisiana, USA 2 Division of Research and Modeling, Center for Financing, Access and Cost Trends, Agency for Healthcare Research and Quality, Rockville, Maryland, USA 3 College of Graduate Health Sciences, Department of Interprofessional Education, The University of Tennessee Health Science Center, Memphis, Tennessee, USA 4 Consortium for Health Education, Economic Empowerment and Research (CHEER), The University of Tennessee Health Science Center, Memphis, Tennessee, USA * Correspondence , Asako Moriya, Division of Research and Modeling, Center for Financing, Access and Cost Trends, Agency for Healthcare Research and Quality, 5600 Fishers Lane, Rockville, MD 20857, USA. Email: asako.moriya@ahrq.hhs.gov ✉ Corresponding author. Issue date 2025 Jun. © 2024 Health Research and Educational Trust. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. PMC Copyright notice PMCID: PMC12120510  PMID: 39402858 Abstract Objective To decompose the mental health disparities between breast cancer patients and survivors (hereafter survivors) of racial and ethnic minority groups and non‐Hispanic White survivors into the contributions of individual‐, interpersonal‐, community‐, and societal‐level determinants. Data Sources and Study Setting We used data from the 2010–2020 Medical Expenditure Panel Survey Household Component (MEPS‐HC). Our primary outcome was whether the person had mental health conditions or not. Study Design We employed the Kitagawa–Oaxaca–Blinder (KOB) method to understand to what extent the differences in outcomes were explained by the differences in the determinants between non‐Hispanic Black or Hispanic breast cancer survivors and non‐Hispanic White survivors. We also bifurcated the Hispanic sample analysis by the US‐born status (and county of origin). Data Collection/Extraction Methods Confidential geographic identifiers are utilized to supplement the MEPS‐HC data with information on community characteristics and local healthcare resources. Principal Findings The prevalence of mental health conditions among non‐Hispanic Black and Hispanic breast cancer survivors was 26.1% (95% CI: 20.4, 31.7) and 28.3% (95% CI: 21.9, 34.6), respectively. These rates were higher than those for their non‐Hispanic White counterparts, 19.7% (95% CI: 17.4, 21.9). In our KOB model, the disparity between non‐Hispanic Black and White survivors was fully explained by differences in education, health, and family structure, with community‐ and societal‐level determinants playing no significant role. Conversely, our KOB model did not explain any of the overall differences between Hispanic and non‐Hispanic White survivors. However, for foreign‐born Hispanic survivors, the disparity was fully explained by a combination of individual‐ and societal‐level determinants. Conclusions Our findings, which identify specific individual‐, interpersonal‐, and societal‐ determinants that were associated with racial and ethnic differences in mental health, can be used by clinicians and policymakers to proactively address racial and ethnic disparities in health. Keywords: chronic disease, determinants of health/population health/socioeconomic cause of health, health equity, racial/ethnic differences in health and healthcare What is known on this topic Race and ethnicity play significant roles in determining health outcomes for breast cancer patients and survivors. Previous evidence on the prevalence of mental health conditions among non‐Hispanic Black and Hispanic breast cancer patients and survivors was limited and mixed. What this study adds Compared to non‐Hispanic White breast cancer patients and survivors, their non‐Hispanic Black and Hispanic counterparts experienced 6.4 and 8.6 percentage points, respectively, higher rates of mental health conditions. The disparity between non‐Hispanic Black and White breast cancer patients and survivors was explained by the differences in individual‐ and interpersonal‐level determinants, such as education, health status, and family structure. The disparity was explained by the differences in determinants for foreign‐born Hispanic patients and survivors but not for their US‐born counterparts, highlighting the heterogeneous nature of the Hispanic population. 1. INTRODUCTION As of 2022, more than 3.8 million women had been diagnosed with breast cancer in the US. 1 Existing literature has shown that more than a quarter of individuals treated for cancer have experienced common mental health issues, including depressive disorders and anxiety. 2 In addition to the direct impacts of cancer diagnosis, fear of cancer recurrence and its treatment outcomes, loss of income, and fear of isolation may exacerbate pre‐existing mental health conditions and increase the likelihood of chronic depressive disorders. 3 Mental health conditions are more prevalent among breast cancer survivors (42%) compared to survivors of other types of cancers and have been associated with poor cancer treatment outcomes, lower quality of life, risk of suicidal thoughts, lengthy hospitalizations, and mortality. 4 , 5 , 6 Breast cancer mortality differs substantially between non‐Hispanic Black (henceforth Black) and non‐Hispanic White (henceforth White) women. Even though Black women are frequently diagnosed at a relatively young age, they are 40% more likely to die of breast cancer compared to White women. 7 The persistent mortality gap between Black and White women could be linked to multiple economic, social, behavioral, health, and genetic factors. Besides a higher prevalence of chronic health conditions such as heart disease, diabetes, obesity, and mental health conditions, 8 , 9 health‐system and community risk factors can increase cancer‐related mortality among racial and ethnic minority groups. Despite the link between mental health and mortality among cancer patients, 6 there was limited evidence on the disparities in mental health among breast cancer patients and survivors (henceforth survivors) of racial and ethnic minority groups in the US. While most studies on Hispanic breast cancer survivors found their mental health to be worse than that of White survivors, 10 , 11 , 12 , 13 , 14 the variability in mental health between the US‐born and the foreign‐born Hispanic people documented in the literature 15 was not accounted for. The studies on Black survivors have shown mixed results, with their mental health being either worse than or similar to that of White survivors, and were often limited by older data or restricted sample populations. 10 , 11 , 16 Hispanic survivors are not a monolithic group and can vary significantly by US‐born status and place of origin. The US‐born Hispanic cancer survivors report better overall quality of life compared to foreign‐born Hispanic survivors due to better access to healthcare, more positive attitudes towards seeking mental healthcare, and fewer language barriers. 17 Foreign‐born Hispanic survivors may rely more on extended family and community support, which can provide emotional comfort but might not address all mental healthcare needs. 18 Also, cancer mortality rates differ depending on their countries of origin, partially reflecting their different propensities to return to their home country when they become severely ill (“salmon bias”). 19 Such selection can affect the mental health of survivors differently depending on their countries of origin. In this study, we investigated differences in the prevalence of mental health conditions among White, Black, and Hispanic women ever diagnosed with breast cancer. We employed the Kitagawa–Oaxaca–Blinder (KOB) decomposition method to identify potential sources of those differences at the individual, interpersonal, community, and societal levels, using nationally representative data from the 2010–2020 Medical Expenditure Panel Survey Household Component (MEPS‐HC). We also examined the disparities for the US‐born and the foreign‐born Hispanic survivors separately and conducted an exploratory analysis for Hispanic survivors of Mexican origin, the largest group of origin in our data. We included both patients and survivors in the study sample to understand the effects of experiencing a breast cancer diagnosis on women's mental health. To our knowledge, this is the first study to utilize the KOB method to understand the racial and ethnic disparities in mental health among breast cancer survivors and also to disaggregate Hispanic breast cancer survivors by place of birth and country of origin. 2. METHODS 2.1. Conceptual framework Our conceptual framework (see Figure 1 ) was adapted from the National Institute on Minority Health and Health Disparities (NIMHD) Research Framework, a multiple‐level, multiple‐domain model that captures the complex nature of minority health and health disparities. 20 Our selection of domains and determinants was guided by the literature on mental health, especially among breast cancer survivors and racial and ethnic minority groups. 10 , 21 FIGURE 1. Open in a new tab Conceptual framework of disparities in mental health conditions among breast cancer patients and survivors. This conceptual framework is adapted from the National Institute on Minority Health and Health Disparities Research Framework by the authors. At the individual level, we hypothesized that the determinants in the following three domains could influence survivors' mental health and whether they receive adequate care: biological and sociodemographic characteristics, access to care and insurance coverage, and health status. The previous studies identified age, education, comorbidities, and cancer characteristics as the predictors of mental health among breast cancer survivors. 10 , 21 At the interpersonal level, we assumed that family functioning could predict the mental health of survivors, given that living arrangements were a predictor observed in the literature. We also included the US‐born status and English fluency, the predictors of mental health among Hispanic people, 22 in the interpersonal domain, recognizing how cultural differences and language barriers may impact an individual's social interactions and ability to access support networks. 23 This was a departure from the NIMHD research framework that classified these as individual‐level determinants. The determinants at the community and societal levels can also directly impact the mental health of survivors. At the community level, we hypothesized that residential racial and ethnic composition, availability of health services, and community health could affect their mental health. A higher concentration of racial or ethnic minority groups in the community can have negative effects on the mental health of minority members due to discrimination and scarcer economic resources, 24 while it can provide protective effects of enclaves. 24 , 25 We included laws and policies, and social norms in the framework at the societal level. More generous healthcare laws and policies can improve access to care and alleviate financial stress among survivors. However, laws and policies, and social norms can also hinder the equitable distribution of health opportunities and outcomes across racial and ethnic groups. 26 The relationship between mental health and its determinants can evolve as cancer progresses or as patients transition to survivorship. 27 , 28 We acknowledge that our conceptual framework did not account for the temporal dynamics of this relationship, and discuss the importance of incorporating these dynamics in future research later. 2.2. Data We used 2010–2020 data from the MEPS‐HC, an annual survey of a nationally representative sample of the U.S. civilian noninstitutionalized population administered by the Agency for Healthcare Research and Quality (AHRQ). MEPS includes detailed information on demographic and socioeconomic characteristics and health conditions and oversamples Black and Hispanic people. We used geographic identifiers in the confidential MEPS‐HC file to merge information at the county and census tract levels. The detailed breast cancer information was obtained from the 2010–2012 public‐use files and the 2013–2020 confidential files. Our study sample comprised female breast cancer survivors aged 18 or older who reported their race and ethnicity as White, Black, or Hispanic. As all data were de‐identified, this study was deemed exempt from the institutional review board's review. Our primary outcome was an indicator variable that took a value of one if the person was deemed to have a probable depressive disorder or severe psychological distress and zero otherwise. We used the Patient Health Questionnaire (PHQ‐2) depression symptom checklist and the Kessler Screening Scale for Psychological Distress (K6) to identify those with a probable depressive disorder (PHQ‐2 score ≥2) or severe psychological distress (K6 score ≥13). These measures are widely used in the literature on racial and ethnic disparities 29 , 30 and have been validated among Spanish‐speaking populations in the literature. 31 , 32 , 33 We included age, educational attainment, employment status, and family income (as a percentage of the federal poverty level) as the biological and sociodemographic characteristics at the individual level. The measures of access to care and insurance coverage included whether the person had a usual source of care and type of coverage (any private, public only or uninsured). Health status measures included whether the cancer was in remission, the age at which the person was diagnosed with breast cancer, the presence of other comorbidities, self‐reported health status, and the degree to which pain limited work. At the interpersonal level, we included marital status, the presence of another adult in the family, and the presence of children as the measures of family function. We further included the US‐born versus foreign‐born status and English proficiency in the analysis of Hispanic survivors. At the community level, we included the percentage of non‐Hispanic Black and Hispanic residents at the census tract level as a measure of residential racial and ethnic composition and the percentage of adults who were obese at the county level as a measure of community health. We used the number of primary care physicians per 1000 people at the county level to measure the availability and quality of healthcare in the community. These variables were obtained from AHRQ's Social Determinants of Health (SDOH) database 34 (see the Appendix for details). Although we considered including alternative or multiple measures of community health and the availability of health services, these measures tended to correlate highly within each domain. We confirmed that our results were robust to the choice of the measures (results available upon request). We used residence in an MSA (urban) versus non‐MSA (rural) area and region of the country as measures of societal‐level determinants. We do not know specific laws and policies that directly affect the mental health of breast cancer survivors; however, the literature documented that the differences in Medicare reimbursements for non‐MSA versus MSAs lead to challenges in access to and quality of mental healthcare. 35 , 36 Healthcare policies are the responsibility of federal, state, and local governments, with recent efforts increasingly executed at the state and local levels. We captured this potential influence by including measures of regional residence but not state of residence. Including state‐fixed effects would yield unreliable estimates because the number of observations can be very low for Black and Hispanic Survivors in certain states, reflecting the variation in racial and ethnic composition across US states. For example, in 2015, Black and Hispanic populations accounted for less than 3% in 11 and four states, respectively, while exceeding 30% in four states (including the District of Columbia for the Black population). 37 2.3. Analytic approach The purpose of our empirical analysis was to present racial and ethnic disparities in mental health among breast cancer survivors and to understand how these disparities can be explained by the differences in the determinants laid out in the conceptual framework. Identifying the determinants correlated with disparities can be helpful to policymakers for future planning. We ran a linear probability model (LPM) of mental health separately for each racial and ethnic group and utilized the KOB 38 , 39 , 40 method to decompose the difference in mental health into (1) the difference explained by the differences in our determinant measures between the survivors of racial or ethnic minority groups and White survivors (an explained difference) and (2) an unexplained difference. The explained difference represents how the mean outcome would change for the survivors of minority groups if they had the same observable characteristics as White survivors on average, assuming that the correlation between these characteristics and mental health remains the same (see the Appendix for details). We applied survey weights to obtain nationally representative estimates. We corrected standard errors for the complex design of the MEPS to obtain mean characteristics, 41 and they were corrected for clustering at the state level in the LPM and decomposition analysis. We tested the robustness of the primary analysis in the following ways. First, the changes in disparities due to the changes in insurance status were already accounted for in the primary analysis. However, ACA Medicaid expansions may have affected the disparities through the channels beyond insurance expansions, such as increased access to care among new and existing Medicaid beneficiaries. We attempted to account for such Medicaid expansion effects by adding an indicator for ACA Medicaid expansions that were in effect and indicators for a set of the states that implemented the ACA Medicaid expansions in 2014, 2015–2016, and 2017–2020 to our primary model as a robustness check. We obtained data on Medicaid expansion status from the Kaiser Family Foundation, 42 and included early expansion states as the 2014 expansion states. Note that this analysis was exploratory because we could not control for state‐fixed effects. Second, we excluded the year 2020, the first year of the coronavirus disease 2019 (COVID‐19) pandemic, from the analysis as a robustness check since the pandemic potentially affected the mental health of breast cancer survivors differently by race and ethnicity. 3. RESULTS 3.1. Observed differences by race and ethnicity In the US, the rate of mental health conditions was 6.4 and 8.6 percentage points higher for Black and Hispanic breast cancer survivors (26.1% and 28.3%) compared to White survivors (19.7%) from 2010 to 2020 (Table 1 ). In contrast, among women never diagnosed with cancer, the rates were similar between White and Hispanic women (19.8% and 19.3%), and the difference between Black and White women was 1.7 percentage points (Appendix Table 1 ). These indicate that a pre‐existing difference in mental health may have existed between Black and White survivors but not between Hispanic and White survivors. TABLE 1. Mean characteristics of female adult breast cancer patients and survivors by race and ethnicity in the United States, 2010–2020. White survivors Black survivors Hispanic survivors Dependent variable Have mental health conditions 0.197 (0.011) 0.261 ** (0.029) 0.283 ** (0.032) Individual‐level determinants Age 67.8 (0.413) 62.7 *** (0.790) 59.9 *** (1.149) Education Less than HS diploma 0.072 (0.008) 0.163 *** (0.026) 0.327 *** (0.039) HS diploma 0.301 (0.014) 0.320 (0.029) 0.277 (0.035) Some college 0.266 (0.014) 0.289 (0.033) 0.246 (0.041) BA or more 0.361 (0.016) 0.227 *** (0.032) 0.149 *** (0.027) Employed 0.332 (0.015) 0.377 (0.032) 0.458 ** (0.037) Family income <125% FPL 0.120 (0.008) 0.218 *** (0.024) 0.238 *** (0.034) ≥125% FPL, <200% FPL 0.130 (0.009) 0.176 ** (0.020) 0.183 * (0.024) ≥200% FPL, <400%FPL 0.236 (0.011) 0.262 (0.024) 0.252 (0.029) ≥400% FPL 0.514 (0.016) 0.344 *** (0.035) 0.327 *** (0.042) Has a usual source of care 0.927 (0.008) 0.922 (0.016) 0.900 (0.021) Health insurance status Uninsured 0.024 (0.004) 0.030 (0.008) 0.084 *** (0.021) Public 0.669 (0.016) 0.624 (0.032) 0.572 * (0.040) Any private insurance 0.306 (0.016) 0.346 (0.033) 0.343 (0.042) Cancer in remission 0.929 (0.009) 0.903 (0.019) 0.915 (0.024) Age of breast cancer diagnosis 56.2 (0.470) 53.2 *** (0.868) 50.5 *** (1.187) Has other comorbidities 0.885 (0.011) 0.921 (0.019) 0.823 (0.043) Self‐reported physical health Excellent 0.151 (0.011) 0.071 *** (0.016) 0.125 (0.024) Very good 0.350 (0.014) 0.230 *** (0.029) 0.223 ** (0.043) Good 0.316 (0.013) 0.383 ** (0.026) 0.359 (0.028) Fair/poor 0.184 (0.010) 0.316 *** (0.028) 0.292 ** (0.032) Self‐reported pain level Not at all 0.385 (0.014) 0.378 (0.029) 0.391 (0.040) A little bit 0.312 (0.012) 0.245 ** (0.027) 0.234 ** (0.024) Moderately 0.162 (0.009) 0.145 (0.019) 0.140 (0.020) Quite a bit/extremely 0.142 (0.009) 0.232 *** (0.024) 0.235 ** (0.031) Interpersonal‐level determinants Married 0.576 (0.017) 0.352 *** (0.035) 0.455 ** (0.036) Have another adult in a family 0.678 (0.015) 0.575 *** (0.036) 0.713 (0.037) Have a child (age < 19) in a family 0.084 (0.008) 0.192 *** (0.024) 0.239 *** (0.044) Born in the United States 0.954 (0.007) 0.928 (0.021) 0.492 *** (0.043) Speaks English less than very well 0.008 (0.003) 0.017 (0.010) 0.331 *** (0.039) Community‐level determinants Census‐tract level % non‐Hispanic Black people 0.069 (0.004) 0.462 *** (0.029) 0.110 (0.013) % Hispanic people 0.092 (0.005) 0.142 *** (0.014) 0.423 *** (0.029) County level % adults who are obese 0.289 (0.002) 0.299 * (0.005) 0.262 *** (0.005) # primary care physicians/1000 people 0.761 (0.015) 0.798 (0.019) 0.770 (0.028) Societal‐level determinants Region Northeast 0.190 (0.016) 0.213 (0.036) 0.202 (0.033) Midwest 0.249 (0.016) 0.132 *** (0.025) 0.136 ** (0.044) South 0.366 (0.020) 0.561 *** (0.040) 0.306 (0.038) West 0.195 (0.014) 0.094 *** (0.022) 0.357 *** (0.045) MSA 0.817 (0.018) 0.940 *** (0.019) 0.967 *** (0.014) Year 2015.2 (0.109) 2015.1 (0.237) 2015.4 (0.261) N 2226 536 393 Open in a new tab Note : Standard errors in parentheses. Estimates were weighted and adjusted for the complex survey design of MEPS. Source : 2010–2020 Medical Expenditure Panel Survey and 2010–2020 AHRQ's SDOH databases. Abbreviations: AHRQ, Agency for Healthcare Research and Quality; BA, Bachelor of Arts; FPL, federal poverty level; HS, high school; MSA, metropolitan statistical area; SDOH, Social Determinants of Health. * Statistically different from White survivors at the 10% level. ** Statistically different from White survivors at the 5% level. *** Statistically different from White survivors at the 1% level. Black and Hispanic survivors were generally younger, less educated, had lower incomes, were diagnosed with breast cancer at a younger age, and reported worse health and pain compared to White survivors (Table 1 ). Hispanic survivors were also more likely to be uninsured. Black and Hispanic survivors were more often unmarried and had children under 19. Black survivors were less likely to have another adult in the family, while Hispanic survivors were often foreign‐born with limited English proficiency. At the community level, there was significant residential segregation, with Black and Hispanic survivors living in areas with higher proportions of residents of their race or ethnicity. Black survivors were more likely to live in the South, while Hispanic survivors were more likely to live in the West and counties with lower obesity rates. Both groups were more likely to live in MSAs than White survivors. 3.2. Linear probability models (LPMs) Older age, employment, and marriage were linked to better mental health for White survivors, while these associations were similar but not statistically significant for Black survivors (Columns 2 and 3 of Table 2 ). Higher education and moderate family income were linked to better mental health for Black survivors, and pain limitation was associated with worse mental health for both Black and White survivors. At the community level, higher obesity rates and more primary care physicians correlated with worse mental health for Black survivors, but the racial composition of their census tract did not. Living in the Midwest or South was associated with worse mental health for White survivors while living in the West was linked to worse mental health for both Black and White survivors. TABLE 2. Association between mental health conditions and individual‐level, interpersonal‐level, community‐level, and societal‐level determinants among female adult breast cancer patients and survivors by race and ethnicity in the United States, 2010–2020. Black versus White survivors comparison Hispanic versus White survivors comparison White survivors Black survivors White survivors Hispanic survivors Individual‐level determinants Age (reference = 18–54 year olds) 55–64 year olds −0.069 (0.042) −0.086 (0.062) −0.074 * (0.041) −0.035 (0.089) 65–74 year olds −0.160 ** (0.064) −0.124 (0.090) −0.165 *** (0.064) −0.103 (0.149) 75+ year olds −0.164 ** (0.067) −0.083 (0.110) −0.172 *** (0.067) −0.205 (0.133) Education (reference = less than HS diploma) HS diploma 0.034 (0.036) −0.021 (0.048) 0.032 (0.037) 0.047 (0.053) Some college −0.044 (0.033) −0.044 (0.067) −0.045 (0.034) 0.012 (0.053) BA or more 0.026 (0.034) −0.153 *** (0.059) 0.024 (0.035) 0.096 (0.106) Employed −0.050 *** (0.019) −0.055 (0.064) −0.051 *** (0.019) −0.074 (0.069) Family income (reference = <125%FPL) > = 125% FPL, < 200% FPL −0.007 (0.039) −0.144 ** (0.071) −0.007 (0.041) 0.032 (0.114) > = 200% FPL, < 400% FPL −0.018 (0.034) −0.117 * (0.061) −0.016 (0.034) −0.002 (0.083) > = 400% FPL −0.006 (0.032) −0.102 (0.073) −0.006 (0.032) −0.090 (0.093) Has a usual source of care −0.005 (0.031) −0.019 (0.086) −0.005 (0.031) 0.067 (0.113) Health insurance status (reference = uninsured) Public 0.013 (0.077) −0.158 (0.149) 0.020 (0.076) 0.071 (0.078) Any private insurance −0.077 (0.070) −0.163 (0.152) −0.071 (0.069) −0.035 (0.109) Cancer in remission 0.048 (0.040) −0.033 (0.080) 0.046 (0.040) −0.015 (0.064) Age of breast cancer diagnosis 0.000 (0.001) −0.002 (0.002) 0.000 (0.001) 0.002 (0.003) Has other comorbidities −0.017 (0.032) −0.078 (0.089) −0.020 (0.033) 0.132 ** (0.060) Self‐reported physical health (reference = excellent) Very good 0.023 (0.018) −0.113 (0.084) 0.018 (0.018) −0.005 (0.063) Good 0.078 *** (0.026) −0.045 (0.072) 0.072 *** (0.025) 0.044 (0.061) Fair/Poor 0.264 *** (0.041) 0.063 (0.110) 0.260 *** (0.041) 0.134 * (0.074) Pain limits normal work (reference = not at all) A little bit 0.049 ** (0.020) 0.101 * (0.057) 0.045 ** (0.020) 0.023 (0.062) Moderately 0.148 *** (0.027) 0.127 ** (0.059) 0.146 *** (0.027) 0.096 (0.101) Quite a bit/extremely 0.236 *** (0.040) 0.223 *** (0.049) 0.233 *** (0.040) 0.180 ** (0.089) Interpersonal‐level determinants Married −0.042 ** (0.021) −0.089 (0.056) −0.042 * (0.022) 0.015 (0.048) Have another adult in a family 0.029 (0.025) −0.050 (0.059) 0.025 (0.026) −0.058 (0.062) Have a child (age < 19 years) in a family 0.069 * (0.037) 0.048 (0.082) 0.062 * (0.037) −0.037 (0.055) Born in the United States −0.025 (0.046) 0.069 (0.068) Speaks English less than very well 0.094 (0.124) 0.095 (0.089) Community‐level determinants Census‐Tract level % non‐Hispanic Black people (reference = first or second quartile) Third quartile −0.025 (0.020) 0.079 (0.100) Fourth quartile −0.016 (0.029) 0.030 (0.081) % Hispanic people (reference = first or second quartile) Third quartile −0.021 (0.024) −0.113 (0.086) Fourth quartile −0.041 * (0.021) −0.087 (0.095) County level % adults who are obese (reference = first quartile) Second quartile −0.009 (0.026) 0.067 * (0.038) −0.013 (0.026) −0.040 (0.084) Third quartile −0.019 (0.021) −0.005 (0.056) −0.033 (0.023) −0.117 (0.074) Fourth quartile −0.035 (0.028) 0.194 *** (0.049) −0.043 (0.028) −0.324 *** (0.092) # primary care physicians/1000 people (reference = first quartile) Second quartile 0.004 (0.028) 0.005 (0.066) −0.002 (0.027) −0.155 ** (0.060) Third quartile −0.047 * (0.024) 0.067 (0.061) −0.042 * (0.024) −0.185 ** (0.078) Fourth quartile −0.022 (0.027) 0.163 ** (0.076) −0.031 (0.025) −0.176 (0.125) Societal‐level determinants Region (reference = Northeast) Midwest 0.051 *** (0.018) −0.051 * (0.030) 0.054 *** (0.018) 0.103 (0.093) South 0.054 ** (0.027) 0.067 * (0.035) 0.053 ** (0.023) −0.059 (0.097) West 0.077 *** (0.022) 0.229 *** (0.054) 0.092 *** (0.024) −0.187 ** (0.083) MSA −0.032 (0.031) −0.083 (0.079) −0.032 (0.031) 0.157 (0.097) Year indicators 2011 0.016 (0.032) −0.075 (0.068) 0.017 (0.032) −0.119 ** (0.055) 2012 0.040 (0.049) 0.017 (0.096) 0.042 (0.050) −0.112 (0.105) 2013 0.017 (0.047) −0.035 (0.102) 0.021 (0.048) −0.046 (0.117) 2014 −0.019 (0.043) −0.096 (0.088) −0.017 (0.044) −0.150 (0.099) 2015 −0.017 (0.034) −0.076 (0.111) −0.013 (0.035) −0.071 (0.081) 2016 0.009 (0.039) −0.125 (0.107) 0.014 (0.039) 0.080 (0.090) 2017 −0.010 (0.040) −0.088 (0.089) −0.006 (0.042) 0.063 (0.083) 2018 0.032 (0.044) −0.145 ** (0.070) 0.035 (0.044) 0.022 (0.076) 2019 0.012 (0.030) −0.206 * (0.112) 0.017 (0.032) −0.081 (0.098) 2020 0.017 (0.040) −0.201 ** (0.085) 0.024 (0.042) −0.125 (0.091) N 2226 536 2226 393 Open in a new tab Note : Standard errors in parentheses. Regressions are run separately for each racial and ethnic group. The first and second sets of columns show the regression results for Black versus White comparison analyses, and the third and fourth sets of columns show the regression results used for Hispanic versus White comparison analyses. We included indicator variables for the quartiles of county‐level and census‐tract‐level variables. Estimates were weighted, and standard errors were clustered at the state level. Source : 2010–2020 Medical Expenditure Panel Survey and 2010–2020 AHRQ's SDOH databases. Abbreviations: AHRQ, Agency for Healthcare Research and Quality; BA, Bachelor of Arts; FPL, federal poverty level; HS, high school; MSA, metropolitan statistical area; SDOH, Social Determinants of Health. * Statistically different from White survivors at the 10% level. ** Statistically different from White survivors at the 5% level. *** Statistically different from White survivors at the 1% level. The rest of Table 2 show LPM results for White and Hispanic survivors. The model for White survivors in the third set of columns includes more covariates than the first set, but the results remain similar. In contrast, for Hispanic survivors (see column 5, Table 2 ), only comorbidities and extreme pain were significantly correlated with mental health at the individual level, likely due to the small sample size and population heterogeneity. At the community and societal levels, higher obesity rates, more primary care physicians, and residing in the West were associated with better mental health for Hispanic survivors. 3.3. Decomposition analysis Consistent with Table 1 , there was a 6.4‐percentage‐point difference in mental health condition rates between Black and White survivors (Table 3 ). The explained difference in the second row suggests that if Black survivors had the same observable determinants as White survivors, their mental health condition rate would decrease by 11.1 percentage points (note that this explained difference is not statistically different from the actual difference, 6.4 percentage points). The remaining rows break down the explained difference by sets of determinants. Having the same age (at the time of the survey) distribution as White survivors would not significantly change the mental health of Black survivors. However, having the same educational attainment, health status (excluding cancer characteristics), and family structure as White survivors would decrease the rate by 1.9, 2.7, and 3.0 percentage points, respectively, accounting for 17.2%, 24.6%, and 27.3% of the explained difference. Other individual‐level determinants, including income, employment, healthcare access, and cancer characteristics, as well as community‐ and societal‐level determinants, did not significantly explain the differences. TABLE 3. Actual differences in the rate of mental health conditions and explained differences, overall and by sets of determinants, for Black and Hispanic survivors compared to White survivors (in percentage points). Black survivors versus White survivors SE Hispanic survivors versus White survivors SE Actual differences in rates of mental health conditions 6.41 ** 2.76 8.61 ** 4.08 Amount of difference attributable to differences in means 11.09 ** 4.46 0.18 6.93 Amount of difference attributable to differences in means of specific sets of variables Individual‐level determinants Age 0.93 1.48 3.57 2.98 Education 1.91 ** 0.76 −2.16 2.24 Income and employment 0.52 1.09 0.91 1.80 Healthcare access 0.08 0.31 −1.00 0.65 Cancer characteristics 0.77 0.56 −0.91 1.74 Other health status 2.73 ** 1.38 2.18 1.63 Interpersonal‐level determinants Family structure 3.03 ** 1.24 −0.96 0.98 US‐born status and English proficiency −0.11 2.03 Community‐level determinants 1.74 4.06 0.93 4.65 Societal‐level determinants −0.87 1.76 −3.24 2.48 Year 0.26 0.63 0.96 0.64 Open in a new tab Note : Estimates were weighted and standard errors were clustered at the state level. The healthcare access category includes whether the person had a usual source of care and insurance status. The cancer characteristics category includes whether the cancer was in remission or not and age when breast cancer was diagnosed. The other health status category includes whether the person had other comorbidities or not, self‐reported physical health status, and pain limitation. The family structure category includes marital status, whether there was another adult in the family or not, and whether there was a child in the family or not. The community‐ and societal‐level determinants include the variables listed in Table 1 . See Appendix Table A2 for the results for each variable. Abbreviations: AHRQ, Agency for Healthcare Research and Quality; SDOH, Social Determinants of Health; SE, standard error. ** Statistically different from zero at the 5% level. Source : 2010–2020 Medical Expenditure Panel Survey and 2010–2020 AHRQ's SDOH databases. Appendix Table A2 shows detailed decomposition analysis results, attributing the explained difference to each variable. While no single variable under family structure significantly explained the outcome difference, the signs indicate that if Black survivors were less likely to be single, to be the sole adult in a family, or to have children, they would be less likely to have mental health conditions. The difference in self‐reported pain accounted for 1.1 percentage points ( p < 0.10) of the mental health outcome. Community‐ and societal‐level determinants, including the percentage of Black residents in the census tract, did not explain the mental health difference between Black and White survivors. In contrast to the results for Black survivors, the 8.6 percentage‐point difference in mental health condition rates between Hispanic and White survivors was not explained by the differences in determinants. Table 3 shows no significant set of determinants explaining this gap. Appendix Table A2 indicates that the county‐level prevalence of obesity was positively correlated with the difference in mental health conditions. These decomposition findings remained robust when accounting for ACA Medicaid expansion effects (beyond insurance coverage changes) and excluding the COVID year, 2020 (see Appendix Table A3 ). 3.4. Decomposition analysis for Hispanic survivors by US‐born status The rate of mental health conditions was 25.0% for the US‐born and 31.4% for the foreign‐born Hispanic survivors (Appendix Table A4 ). Foreign‐born Hispanic survivors had lower educational attainment, lower family income, and higher uninsured rates compared to the US‐born Hispanic survivors, consistent with the literature. 43 Foreign‐born Hispanic survivors tended to live in neighborhoods with more Hispanic residents, in counties with lower obesity rates and more primary care physicians, and in the Northeast compared to the US‐born Hispanic survivors. For the US‐born Hispanic survivors, moderate family income, other comorbidities, and self‐reported fair/poor health were positively associated with having mental health conditions (Appendix Table A5 ). Conversely, more primary care physicians per 1000 people, higher obesity rates at the county level, and residency in the West were linked to better mental health. For foreign‐born Hispanic survivors, having cancer in remission showed a significant negative association with having mental health conditions, while living in an MSA was associated with worse mental health. The US‐born Hispanic survivors had a 5.3‐percentage‐point higher rate of mental health conditions compared to White survivors (Table 4 ), although the small sample size limited the statistical power to detect significance (power = 0.371; see Appendix for details). In contrast, foreign‐born Hispanic survivors had a significantly higher rate of 11.8 percentage points compared to White survivors. The estimated explained difference suggests that if foreign‐born Hispanic survivors had the same characteristics as White survivors, their mental health condition rate would decrease by 26.7 percentage points. Specifically, matching individual health status and societal‐level factors would reduce their mental health condition rate by 5.5 and 7.8 percentage points, respectively. Appendix Table A6 shows that differences in self‐reported health and MSA residence accounted for 5.1 and 4.7 percentage points, respectively. Additionally, the county‐level obesity rate partially explained the mental health difference for the US‐born Hispanic survivors, although overall determinants did not fully explain the outcome difference. Some results became less significant when accounting for ACA Medicaid expansion, likely due to small sample sizes affecting the precision of estimates. However, the robustness check results remained reasonable overall (Appendix Table A7 ). TABLE 4. Actual differences in the rate of mental health conditions and explained differences, overall and by sets of determinants, for the US‐born and the foreign‐born Hispanic survivors compared to the White survivors (in Percentage Points). US‐born Hispanic survivors versus White survivors SE Foreign‐born Hispanic survivors versus White survivors SE Actual differences in rate of mental health conditions 5.34 4.21 11.77 ** 4.89 Amount of difference attributable to differences in means −10.18 7.94 26.67 *** 9.09 Amount of difference attributable to differences in means of specific sets of variables: Individual‐level determinants Age 7.21 4.49 3.33 2.12 Education −2.43 1.94 0.80 2.81 Income and employment −2.40 2.17 5.23 * 2.84 Healthcare access −5.05 * 2.64 −1.58 * 0.85 Cancer status −1.82 2.24 −2.31 1.57 Other health status 3.06 2.25 5.45 ** 2.48 Interpersonal‐level determinants Family structure −1.68 2.25 −1.92 1.93 English proficiency 0.62 0.95 3.04 3.99 Community‐level determinants −4.70 8.66 6.80 7.31 Societal‐level determinants −4.73 3.96 7.84 *** 2.82 Year 1.73 1.70 −0.02 1.13 Open in a new tab Note : See Notes under Table 3 for the list of the variables included under each category. Estimates were weighted and standard errors were clustered at the state level. Abbreviations: AHRQ, Agency for Healthcare Research and Quality; SDOH, social determinants of health; SE, standard error. * Statistically different from zero at the 10% level. ** Statistically different from zero at the 5% level. *** Statistically different from zero at the 1% level. Source : 2010–2020 Medical Expenditure Panel Survey and 2010–2020 AHRQ's SDOH databases. 3.5. Exploratory analysis for Hispanic survivors of Mexican origin We conducted decomposition analysis for Hispanic survivors of Mexican origin (see Appendix Tables A4 and A5 for descriptive statistics and LPM results). We consider this analysis exploratory because we could not analyze survivors of other countries of origin due to small sample sizes. For survivors of Mexican origin, older age (at the time of the survey) was uniquely associated with worse mental health, and a higher percentage of Hispanic people in the community was linked to better mental health. The analysis suggests that while their mental health condition rate was similar to White survivors', the rate of mental health conditions would increase by 26.6 percentage points if they had the same characteristics as White survivors (Appendix Table A8 ). Specifically, if they lived in a community with fewer Hispanic people or in the same regions as White survivors, their mental health condition rate would increase by 17.3 and 11.8 percentage points, respectively (Appendix Table A9 ). 4. DISCUSSION Existing literature has consistently shown disparities in access to mental healthcare among Black and Hispanic populations. 44 While there is extensive and well‐established literature on the racial and ethnic disparities in health outcomes, 45 limited evidence exists on the prevalence of mental health conditions among breast cancer survivors of racial and ethnic minority groups in the US. In the present study, we filled the gap by showing that the rates were 6.4‐ and 8.6‐percentage‐point higher for Black and Hispanic survivors, respectively, compared to their White counterparts. Our decomposition estimates imply that the difference in mental health conditions between Black and White survivors was fully explained by the differences in determinants, such as educational attainment, health status, and family structure. Education and living arrangements are predictors of mental health identified in the literature. More education can potentially improve mental health behavior and knowledge and increase the resilience of survivors, while family support can alleviate their stress. Physical health can affect the mental health of cancer survivors, 46 yet we acknowledge that our self‐reported physical health measure may include cancer characteristics not included in our data, such as tumor grades and subtypes of cancer. Black women are disproportionally affected by invasive subtypes of breast cancer, including triple‐negative breast cancer. 47 Our findings also indicated that the higher rates of mental health conditions among Hispanic survivors, compared to White survivors, were not statistically attributable to differences in determinants. By exploring the heterogeneity of Hispanic breast cancer survivors by birthplace, we found that an 11.8‐percentage‐point difference in the prevalence of mental health conditions between foreign‐born Hispanic and White survivors was explained by the differences in health status and residence in an MSA. Residing in an MSA was associated with worse mental health for foreign‐born Hispanic survivors, and the estimates suggest that having a rate of residing in MSAs as low as that of White survivors can improve their mental health. The literature suggests that living in an urban area is associated with worse mental health in other countries but not necessarily in the US. 48 , 49 Our exploratory analysis of the survivors of Mexican origin revealed that if they lived in the same region and in a neighborhood with the same (i.e., lower) percentage of Hispanic people as White survivors do, their mental health would be worse. The latter community results may reflect the protective effects of immigrant communities as well as “salmon bias” documented for Hispanic people, especially those of Mexican origin in the literature, 50 since living in a community with a higher percentage of Hispanic people can indicate closer ties to their home country. The positive relationship between older age and mental health, which contrasts with the negative relationship observed for other subgroups of survivors, can potentially suggest that younger patients of Mexican origin with severe breast cancer tended to return to their home country and were not included in the sample. These findings for the survivors of Hispanic subgroups warrant investigation in future research. Our results can support stakeholders’ informed decision‐making in the short and long run. In the near term, policies that recognize the mental health needs of Black survivors with lower educational attainment, worse physical health, or in a single‐parent household, as well as foreign‐born Hispanic survivors with worse physical health, could be helpful. Educating survivors, families, healthcare providers, and communities about these risk factors and promoting culturally sensitive programs can also mitigate mental health disparities among survivors. In the longer term, policies aimed at improving education and self‐perceived health and supporting single‐parent households can positively impact on mental health inequities. In addition, understanding and addressing structural factors would decrease the difference in mental health outcomes between foreign‐born Hispanic and White survivors. The limitations of our study are as follows. First, our data did not include detailed information on cancer, such as tumor grades and cancer stages. Second, we had a relatively small sample size, which hindered us from controlling for more determinants at the community and societal levels, including state‐fixed effects. Consequently, our measures of societal‐level determinants were limited to residence in an MSA and census regions. We note that unexplained differences in our analysis could potentially include the differences due to omitted determinants, especially the differences in state laws and policies not captured by regional differences. Additionally, if changes occurred in the relationship between determinants and mental health over our 11‐year data period, our estimates could only capture the average of such trends. Finally, a cancer diagnosis is self‐reported in MEPS and may contain measurement errors. Despite these limitations of the MEPS data, there are no other nationally representative data that include detailed demographic, socioeconomic, and clinical characteristics and geographic information required to conduct analyses such as ours. Our study has several important implications for future research. First, we found suggestive evidence that mental health conditions affect Hispanic breast cancer populations differently based on their birthplace and country of origin. Therefore, considering the key characteristics of individuals and communities in the development and implementation of mental health interventions is critically important for addressing mental health disparities, as it ensures that interventions are culturally sensitive and responsive to the unique needs of ethnic minority groups. Second, our results showed the substantial role of determinants in explaining the difference in mental health conditions between Black and White breast cancer survivors. A substantial divide exists between breast cancer survivors with different levels of education, family structure, and health status, possibly exacerbating differences in mental health conditions. This may help explain why, in recent years, minorities in the lower distribution of socioeconomic indicators have witnessed a deterioration in health outcomes. 51 , 52 Future research should aim to understand the exact pathways through which the determinants identified in the current study contribute to mental health inequities among Black and Hispanic survivors, which might require utilizing a mixed methods strategy. Further research on how the differences in cancer characteristics across race and ethnicity, societal‐level determinants (including state and local laws and policies), and countries of origin for Hispanic people correlate with the disparities in mental health among breast cancer survivors would be valuable. Finally, taking a longitudinal approach to understanding how the relationship between determinants and mental health disparities evolves throughout cancer journeys while accounting for pre‐diagnosis mental health would yield further insights. Such research would require larger data with potentially less detailed information than MEPS or data with detailed clinical information and history, which may be available only for a convenience sample and can complement the current study. In conclusion, this study presented substantial disparities in mental health for Black and Hispanic breast cancer survivors and potential mitigating determinants that can be used for targeted interventions. It underscores the importance of considering the heterogeneity of Hispanic people and invites future research into this topic. Supporting information Data S1. Supplementary information. HESR-60-0-s001.docx (97.2KB, docx) ACKNOWLEDGMENTS There is no funding to report. We are grateful for the helpful comments from Terceira Berdahl, Joel Cohen, G. Edward Miller, and participants of the Agency for Healthcare Research and Quality, Center for Financing, Access and Cost Trends (AHRQ – CFACT) seminar. The views expressed in this article are those of the authors, and no official endorsement by the Department of Health and Human Services or the Agency for Healthcare Research and Quality is intended or should be inferred. Osmani AR, Moriya A, White‐Means S. Racial and ethnic disparities in mental health among breast cancer patients and survivors in the United States. Health Serv Res. 2025;60(3):e14391. doi: 10.1111/1475-6773.14391 REFERENCES 1. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17‐48. [ DOI ] [ PubMed ] [ Google Scholar ] 2. Nakash O, Levav I, Aguilar‐Gaxiola S, et al. Comorbidity of common mental disorders with cancer and their treatment gap: findings from the world mental health surveys. Psychooncology. 2014;23(1):40‐51. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Rodin G, Lo C, Mikulincer M, Donner A, Gagliese L, Zimmermann C. Pathways to distress: the multiple determinants of depression, hopelessness, and the desire for hastened death in metastatic cancer patients. Soc Sci Med. 2009;68(3):562‐569. [ DOI ] [ PubMed ] [ Google Scholar ] 4. Alcalá HE. Differential mental health impact of cancer across racial/ethnic groups: findings from a population‐based study in California. BMC Public Health. 2014;14(1):1‐9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Hewitt M, Rowland JH. Mental health service use among adult cancer survivors: analyses of the National Health Interview Survey. J Clin Oncol. 2002;20(23):4581‐4590. [ DOI ] [ PubMed ] [ Google Scholar ] 6. Satin JR, Linden W, Phillips MJ. Depression as a predictor of disease progression and mortality in cancer patients: a meta‐analysis. Cancer. 2009;115(22):5349‐5361. [ DOI ] [ PubMed ] [ Google Scholar ] 7. Politi MC, Yen RW, Elwyn G, et al. Women who are young, non‐white, and with lower socioeconomic status report higher financial toxicity up to 1 year after breast cancer surgery: a mixed‐effects regression analysis. Oncologist. 2021;26(1):e142‐e152. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Hsieh N, Ruther M. Despite increased insurance coverage, nonwhite sexual minorities still experience disparities in access to care. Health Aff. 2017;36(10):1786‐1794. [ DOI ] [ PubMed ] [ Google Scholar ] 9. McGuire TG, Miranda J. New evidence regarding racial and ethnic disparities in mental health: policy implications. Health Aff. 2008;27(2):393‐403. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Jackson I, Rowan P, Padhye N, Hwang L, Vernon S. Racial/ethnic differences in health‐related quality of life among female breast cancer survivors: cross‐sectional findings from the medical expenditure panel survey. Public Health. 2021;196:74‐81. [ DOI ] [ PubMed ] [ Google Scholar ] 11. Jackson I, Rowan P, Padhye N, Hwang L‐Y, Vernon SW. Trends in health‐related quality of life of female breast cancer survivors using the medical expenditure panel survey (MEPS), 2008–2016. Qual Life Res. 2021;30(12):3547‐3558. [ DOI ] [ PubMed ] [ Google Scholar ] 12. Lee S, Held ML. Variation in mental health service use among US Latinos by place of origin and service provider type. Psychiatr Serv. 2015;66(1):56‐64. [ DOI ] [ PubMed ] [ Google Scholar ] 13. Lee S, Laiewski L, Choi S. Racial‐ethnic variation in US mental health service use among Latino and Asian non‐US citizens. Psychiatr Serv. 2014;65(1):68‐74. [ DOI ] [ PubMed ] [ Google Scholar ] 14. Yanez B, Thompson EH, Stanton AL. Quality of life among Latina breast cancer patients: a systematic review of the literature. J Cancer Surviv. 2011;5:191‐207. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Alegría M, Álvarez K, DiMarzio K. Immigration and mental health. Curr Epidemiol Rep. 2017;4:145‐155. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Paskett ED, Alfano CM, Davidson MA, et al. Breast cancer survivors' health‐related quality of life: racial differences and comparisons with noncancer controls. Cancer. 2008;113(11):3222‐3230. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Lui F, Lewicka M, Bao GC, Moyer A, Boyce L, Leng J. A systematic review and meta‐analysis of psychosocial interventions for immigrant and limited english proficient cancer patients. Psychooncology. 2023;32(4):516‐557. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Stoddard SA, Garcia CM. Hopefulness among non‐US‐born Latino youth and young adults. J Child Adolesc Psychiatr Nurs. 2011;24(4):216‐222. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Fernandez J, García‐Pérez M, Orozco‐Aleman S. Unraveling the Hispanic health paradox. J Econ Perspect. 2023;37(1):145‐167. [ Google Scholar ] 20. NIMHD . NIMHD Minority Health and Health Disparities Research Framework. Updated 10/09/2023. Accessed 01/05/2024, 2023 https://nimhd.nih.gov/researchFramework 21. Carreira H, Williams R, Müller M, Harewood R, Stanway S, Bhaskaran K. Associations between breast cancer survivorship and adverse mental health outcomes: a systematic review. J Natl Cancer Inst. 2018;110(12):1311‐1327. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Alarcón RD, Parekh A, Wainberg ML, Duarte CS, Araya R, Oquendo MA. Hispanic immigrants in the USA: social and mental health perspectives. Lancet Psychiatry. 2016;3(9):860‐870. [ DOI ] [ PubMed ] [ Google Scholar ] 23. Soto Saavedra C, Lopez JL, Shaw SA, Gibbs BG. “It happened when I was connecting to the community…”: multiple pathways to migrant (non) belonging in a new destination setting. Int J Environ Res Public Health. 2023;20(3):2172. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Williams DR, Collins C. Racial residential segregation: a fundamental cause of racial disparities in health. Public Health Rep. 2001;116:404‐416. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Osypuk TL, Roux AVD, Hadley C, Kandula NR. Are immigrant enclaves healthy places to live? The multi‐ethnic study of atherosclerosis. Soc Sci Med. 2009;69(1):110‐120. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Shim RS. Dismantling structural racism in psychiatry: a path to mental health equity. Am J Psychiatry. 2021;178(7):592‐598. [ DOI ] [ PubMed ] [ Google Scholar ] 27. Fortin J, Leblanc M, Elgbeili G, Cordova MJ, Marin M‐F, Brunet A. The mental health impacts of receiving a breast cancer diagnosis: a meta‐analysis. Br J Cancer. 2021;125(11):1582‐1592. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Yang H, Brand JS, Fang F, et al. T ime‐dependent risk of depression, anxiety, and stress‐related disorders in patients with invasive and in situ breast cancer. Int J Cancer. 2017;140(4):841‐852. [ DOI ] [ PubMed ] [ Google Scholar ] 29. Cook BL, Zuvekas SH, Chen J, Progovac A, Lincoln AK. Assessing the individual, neighborhood, and policy predictors of disparities in mental health care. Med Care Res Rev. 2017;74(4):404‐430. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Kessler RC, Berglund P, Demler O, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS‐R). JAMA. 2003;289(23):3095‐3105. [ DOI ] [ PubMed ] [ Google Scholar ] 31. Reuland DS, Cherrington A, Watkins GS, Bradford DW, Blanco RA, Gaynes BN. Diagnostic accuracy of Spanish language depression‐screening instruments. Ann Fam Med. 2009;7(5):455‐462. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Stolk Y, Kaplan I, Szwarc J. Clinical use of the Kessler psychological distress scales with culturally diverse groups. Int J Methods Psychiatr Res. 2014;23(2):161‐183. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Valencia‐Garcia D, Simoni JM, Alegría M, Takeuchi DT. Social capital, acculturation, mental health, and perceived access to services among Mexican American women. J Consult Clin Psychol. 2012;80(2):177‐185. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 34. Agency for Healthcare Research and Quality . SDOH Data and Analytics. 2023. [ DOI ] [ PubMed ] 35. Reschovsky JD, Staiti AB. Access and quality: does rural America lag behind? Health Aff. 2005;24(4):1128‐1139. [ DOI ] [ PubMed ] [ Google Scholar ] 36. Altman DE, Morgan DH. The role of state and local government in health. Health Aff. 1983;2(4):7‐31. [ DOI ] [ PubMed ] [ Google Scholar ] 37. Kaiser Family Foundation . State Health Facts. https://www.kff.org/other/state‐indicator/distribution‐by‐raceethnicity/?currentTimeframe=6&sortModel=%7B%22colId%22:%22Black%22,%22sort%22:%22asc%22%7D 38. Blinder AS. Wage discrimination: reduced form and structural estimates. J Hum Resour. 1973;8:436‐455. [ Google Scholar ] 39. Kitagawa EM. Components of a difference between two rates. J Am Stat Assoc. 1955;50(272):1168‐1194. [ Google Scholar ] 40. Oaxaca R. Male‐female wage differentials in urban labor markets. Int Econ Rev. 1973;693–709:693. [ Google Scholar ] 41. AHRQ . Accounting for Clustering in the Analysis of MEPS Data: Frequently Asked Questions (FAQ). https://meps.ahrq.gov/survey_comp/hc_clustering_faq.pdf 42. Kaiser Family Foundation . Status of state Medicaid expansion decisions: interactive map. https://www.kff.org/medicaid/issue-brief/status-of-state-medicaid-expansion-decisions-interactive-map/ 43. Saenz R. Social and Economic Well‐Being and the Future for Latinos in the United States. 2010. https://www.prb.org/resources/social‐and‐economic‐well‐being‐and‐the‐future‐for‐latinos‐in‐the‐united‐states/ 44. Jimenez DE, Cook B, Bartels SJ, Alegría M. Disparities in mental health service use of racial and ethnic minority elderly adults. J Am Geriatr Soc. 2013;61(1):18‐25. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Baciu A, Negussie Y, Geller A, Weinstein JN. Communities in Action: Pathways to Health Equity. 2017. [ PubMed ] 46. Naughton MJ, Weaver KE. Physical and mental health among cancer survivors: considerations for long‐term care and quality of life. N C Med J. 2014;75(4):283‐286. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. McCarthy AM, Friebel‐Klingner T, Ehsan S, et al. Relationship of established risk factors with breast cancer subtypes. Cancer Med. 2021;10(18):6456‐6467. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 48. Peen J, Schoevers RA, Beekman AT, Dekker J. The current status of urban‐rural differences in psychiatric disorders. Acta Psychiatr Scand. 2010;121(2):84‐93. [ DOI ] [ PubMed ] [ Google Scholar ] 49. Breslau J, Marshall GN, Pincus HA, Brown RA. Are mental disorders more common in urban than rural areas of the United States? J Psychiatr Res. 2014;56:50‐55. [ DOI ] [ PubMed ] [ Google Scholar ] 50. Riosmena F, Wong R, Palloni A. Migration selection, protection, and acculturation in health: a binational perspective on older adults. Demography. 2013;50(3):1039‐1064. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Johnston DW, Lordan G. Discrimination makes me sick! An examination of the discrimination–health relationship. J Health Econ. 2012;31(1):99‐111. [ DOI ] [ PubMed ] [ Google Scholar ] 52. Woolf SH, Braveman P. Where health disparities begin: the role of social and economic determinants—and why current policies may make matters worse. Health Aff. 2011;30(10):1852‐1859. [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Data S1. Supplementary information. 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