Individual-level Social Determinants Of Health Research Articles

The Role of Social Determinants of Health in Moral Injury: Implications and Future Directions.

Purpose of ReviewExposure to potentially morally injurious events (PMIEs) and the development of moral injury have yet to be conceptualized as they relate to social determinants of health (SDoH).Recent Findings.In this paper, the extant literature on moral injury and SDoH is reviewed. Specific individual-level SDoH, including gender, sex, sexual orientation, race, and ethnicity, are explored relative to PMIEs and moral injury. The relationship among environmental SDoH, including childhood environment, justice involvement, and homelessness, is described.SummaryAssessment and treatment implications are discussed, and future research directions highlighting the need for additional work addressing health inequities in moral injury are presented.

Impact of social determinants of health on improving the LACE index for 30-day unplanned readmission prediction.

ObjectiveEarly and accurate prediction of patients at risk of readmission is key to reducing costs and improving outcomes. LACE is a widely used score to predict 30-day readmissions. We examine whether adding social determinants of health (SDOH) to LACE can improve its predictive performance.MethodsThis is a retrospective study that included all inpatient encounters in the state of Maryland in 2019. We constructed predictive models by fitting Logistic Regression (LR) on LACE and different sets of SDOH predictors. We used the area under the curve (AUC) to evaluate discrimination and SHapley Additive exPlanations values to assess feature importance.ResultsOur study population included 316 558 patients of whom 35 431 (11.19%) patients were readmitted after 30 days. Readmitted patients had more challenges with individual-level SDOH and were more likely to reside in communities with poor SDOH conditions. Adding a combination of individual and community-level SDOH improved LACE performance from AUC = 0.698 (95% CI [0.695–0.7]; ref) to AUC = 0.708 (95% CI [0.705–0.71]; P < .001). The increase in AUC was highest in black patients (+1.6), patients aged 65 years or older (+1.4), and male patients (+1.4).DiscussionWe demonstrated the value of SDOH in improving the LACE index. Further, the additional predictive value of SDOH on readmission risk varies by subpopulations. Vulnerable populations like black patients and the elderly are likely to benefit more from the inclusion of SDOH in readmission prediction.ConclusionThese findings provide potential SDOH factors that health systems and policymakers can target to reduce overall readmissions.

Social Vulnerability Is Associated with Poorer Outcomes in Preschool Children With Recurrent Wheezing Despite Standardized and Supervised Medical Care

Social determinants of health are associated with disparate asthma outcomes in school-age children. Social determinants have not been studied in preschool children with recurrent wheezing. We hypothesized that preschool children with recurrent wheezing at highest risk of social vulnerability would have more frequent symptoms and exacerbations when followed over 1 year, despite receiving standardized and supervised asthma care. A multicenter population of adherent preschool children receiving standardized and supervised care for wheezing was stratified by a composite measure of social vulnerability based on individual-level variables. Primary outcomes included days with upper respiratory infections and days with asthma symptom flares. Other outcomes included symptom scores during upper respiratory infections and respiratory symptom flare days, exacerbation occurrence, quality of life during the exacerbation, and hospitalization. Preschool children at highest risk of social vulnerability did not have more frequent upper respiratory infections, respiratory symptoms, or exacerbations, but instead had more severe symptoms during upper respiratory infections and respiratory flare days, as well as more severe exacerbations with significantly poorer caregiver quality of life. Children at highest risk of social vulnerability also lived in poorer housing conditions with differing exposures and self-reported triggers. Individual-level social determinants of health reflecting social vulnerability are associated with poorer outcomes in preschool children with recurrent wheezing despite access to supervised and standardized care. Comprehensive assessment of social determinants of health is warranted in even the youngest children with wheezing, because mitigation of these social inequities is an essential first step toward improving outcomes in pediatric patients.

Association Between Neighborhood Disadvantage and Pediatric Readmissions.

Although individual-level social determinants of health (SDH) are known to influence 30-day readmission risk, contextual-level associations with readmission are poorly understood among children. This study explores associations between neighborhood disadvantage measured by Area Deprivation Index (ADI) and pediatric 30-day readmissions. This retrospective cohort study included discharges of patients aged < 20years from Maryland's 2013-2016 all-payer dataset. The ADI, which quantifies 17 indicators of neighborhood socioeconomic disadvantage within census block groups, is used as a proxy for contextual-level SDH. Readmissions were identified with the 30-day Pediatric All-Condition Readmissions measure. Associations between ADI and readmission were identified with generalized estimating equations adjusted for patient demographics and clinical severity (Chronic Condition Indicator [CCI], Pediatric Medical Complexity Algorithm [PMCA], Index Hospital All Patients Refined Diagnosis Related Groups [APR-DRG]), and hospital discharge volume. Discharges (n = 138,998) were mostly female (52.7%), publicly insured (55.1%), urban-dwelling (93.0%), with low clinical severity levels (0-1 CCIs [82.3%], minor APR-DRG severity [48.4%]). Overall readmission rate was 4.0%. Compared to the least disadvantaged ADI quartile, readmissions for the most disadvantaged quartile were significantly more likely (aOR 1.19, 95% CI 1.09-1.30). After adjustment, readmissions were associated with public insurance and indicators of medical complexity (higher number of CCIs, complex-chronic disease PMCA, and APR-DRG severity). In this all-payer, statewide sample, living in the most socioeconomically disadvantaged neighborhoods independently predicted pediatric readmission. While the relative magnitude of neighborhood disadvantage was modest compared to medical complexity, disadvantage is modifiable and thus represents an important consideration for prevention and risk stratification efforts.

Moving Beyond Simple Risk Prediction: Segmenting Patient Populations Using Consumer Data.

There has been growing interest among health systems in population health (1, 2). Population health aims to improve the overall health of a population across the full continuum of care by more targeted, effective and coordinated health services (3). Given the rising trend of aging population and chronic disease burden, managing population health becomes more important for health systems trying to control cost (4). In order to improve outcomes and efficiency, health systems need to customize care and interventions based on identified risks and costs (5). One of the systematic approaches in the literature for targeting interventions to subgroups of patients with different needs is population segmentation or risk-stratification (referred as patient segmentation in the remainder of this paper). Population segmentation that divides a population into groups with related service needs is an important foundation for effective and sustainable care delivery (6–8). Segmentation divides patients into distinct groups with specific needs, characteristics or behaviors and allows for health services to be organized around patients with similar needs (7). Patient segmentation models are becoming essential element of healthcare management due to the increase in the number of programs that incentivize value-based care (9). Although patient segmentation models can help design interventions targeting subgroups of patients, they are often based on International Classification of Diseases (ICD) codes found in electronic health records (EHRs) and/or insurance claims data and lack important social risk factors that are essential for designing interventions. World Health Organization defines social determinants of health (SDOH) as the conditions in which people are born, grow, work, live, and age (10). These factors include economic policies and systems, development agendas, social norms, social policies and political systems (10). There are numerous studies demonstrating social factors acting as powerful determinants on multiple health outcomes including coronary heart disease (11), breast cancer (12), childhood obesity (13) and end-stage renal failure (14). Literature suggests that high utilizers of healthcare resources among Medicaid and uninsured population often have multiple chronic conditions (15, 16) and programs targeting this population collectively argue that social risk factors including but not limited to language, health literacy, unemployment, substance abuse and housing are important drivers of healthcare utilization (17, 18). Most of the current patient segmentation models use administrative billing data because insurance claims data provides a nearly complete view of patients' interactions with health care delivery system; therefore, it is a reliable source to extract utilization outcomes (19). Majority of the EHRs on the other hand contain data from clinical encounters occurring between individuals and providers within a single health system and hence miss out of network events (19). On the positive side of EHRs is that they offer more extensive data including family history, lab results, vital signs and symptoms which could help improve the population segmentation model (20). One drawback of reliance on insurance claims data and EHRs is that they miss social and behavioral factors that complicate care (21). Although, there is a subset of ICD-10-CM codes, the Z codes, for documenting SDOH in EHRs, these codes are underutilized (22, 23). As such, SDOH Z codes may not reflect the actual burden of social needs experienced by patients. To address this gap, this paper presents the complementary benefit of consumer data when it is linked to EHRs or insurance claims data. The consumer marketing data include individual-level SDOH (including income, education, lifestyle variables, language spoken, household size, smoking status, life events, shopping activity) that are not available in the insurance claims data or majority of EHR data. The combined data provides 360-degree view of patients and can help predict the risk of repeat emergency room visits or hospital admissions (24). Inclusion of SDOH is essential to improve population health as medical interventions without addressing social determinants are not sustainable and effective. This unprecedented view into the lives of patients has significant potential to improve upon segmentation approaches relying exclusively on health plan or EHR data that lack measures or even decent proxies for fitness, diet and other SDOH which can profoundly alter the course of chronic diseases. A number of commercial companies provide marketing data that is well-utilized by organizations that subscribe to their services. Experian's ConsumerViewSM U.S. database is one of the world's largest consumer database on more than 300 million individuals and 126 million households (25). ConsumerViewSM U.S. database is compiled from hundreds of resources. For example, property and mortgage data are compiled from public records and county deeds while lifestyle and interest data are compiled from consumers who have completed self-reported surveys (25). Marketing companies match and mange patient identity across the healthcare ecosystems enabling the linkage of datasets across channels and silos (26, 27). According to Acxiom, two-thirds of hospitals actively use or want third-party consumer and lifestyle data to improve patient care (24).

Social determinants of multimorbidity in Jamaica: application of latent class analysis in a cross-sectional study

BackgroundNon-communicable disease (NCD) multimorbidity is associated with impaired functioning, lower quality of life and higher mortality. Susceptibility to accumulation of multiple NCDs is rooted in social, economic and cultural contexts, with important differences in the burden, patterns, and determinants of multimorbidity across settings. Despite high prevalence of individual NCDs within the Caribbean region, exploration of the social epidemiology of multimorbidity remains sparse. This study aimed to examine the social determinants of NCD multimorbidity in Jamaica, to better inform prevention and intervention strategies.MethodsLatent class analysis (LCA) was used to examine social determinants of identified multimorbidity patterns in a sample of 2551 respondents aged 15–74 years, from the nationally representative Jamaica Health and Lifestyle Survey 2007/2008. Multimorbidity measurement was based on self-reported presence/absence of 11 chronic conditions. Selection of social determinants of health (SDH) was informed by the World Health Organization’s Commission on SDH framework. Multinomial logistic regression models were used to estimate the association between individual-level SDH and class membership.ResultsApproximately one-quarter of the sample (24.05%) were multimorbid. LCA revealed four distinct profiles: a Relatively Healthy class (52.70%), with a single or no morbidity; and three additional classes, characterized by varying degrees and patterns of multimorbidity, labelled Metabolic (30.88%), Vascular-Inflammatory (12.21%), and Respiratory (4.20%). Upon controlling for all SDH (Model 3), advancing age and recent healthcare visits remained significant predictors of all three multimorbidity patterns (p < 0.001). Private insurance coverage (relative risk ratio, RRR = 0.63; p < 0.01) and higher educational attainment (RRR = 0.73; p < 0.05) were associated with lower relative risk of belonging to the Metabolic class while being female was a significant independent predictor of Vascular-Inflammatory class membership (RRR = 2.54; p < 0.001). Material circumstances, namely housing conditions and features of the physical and neighbourhood environment, were not significant predictors of any multimorbidity class.ConclusionThis study provides a nuanced understanding of the social patterning of multimorbidity in Jamaica, identifying biological, health system, and structural determinants as key factors associated with specific multimorbidity profiles. Future research using longitudinal designs would aid understanding of disease trajectories and clarify the role of SDH in mitigating risk of accumulation of diseases.

Medication Adherence: Expanding the Conceptual Framework.

Interventions targeting traditional barriers to antihypertensive medication adherence have been developed and evaluated, with evidence of modest improvements in adherence. Translation of these interventions into population-level improvements in adherence and clinical outcomes among older adults remains suboptimal. From the Cohort Study of Medication Adherence among Older adults (CoSMO), we evaluated traditional barriers to antihypertensive medication adherence among older adults with established hypertension (N = 1,544; mean age = 76.2 years, 59.5% women, 27.9% Black, 24.1% and 38.9% low adherence by proportion of days covered (i.e., PDC <0.80) and the 4-item Krousel-Wood Medication Adherence Scale (i.e., K-Wood-MAS-4 ≥1), respectively), finding that they explained 6.4% and 14.8% of variance in pharmacy refill and self-reported adherence, respectively. Persistent low adherence rates, coupled with low explanatory power of traditional barriers, suggest that other factors warrant attention. Prior research has investigated explicit attitudes toward medications as a driver of adherence; the roles of implicit attitudes and time preferences (e.g., immediate vs. delayed gratification) as mechanisms underlying adherence behavior are emerging. Similarly, while associations of individual-level social determinants of health (SDOH) and medication adherence are well reported, there is growing evidence about structural SDOH and specific pathways of effect. Building on published conceptual models and recent evidence, we propose an expanded conceptual framework that incorporates implicit attitudes, time preferences, and structural SDOH, as emerging determinants that may explain additional variation in objectively and subjectively measured adherence. This model provides guidance for design, implementation, and assessment of interventions targeting sustained improvement in implementation medication adherence and clinical outcomes among older women and men with hypertension.

Abstract P882: A Multi-Level Exploration of the Social Determinants of Health in Florida: Initial Findings From the Transitions of Care Stroke Disparities Study

Introduction: Social determinants of health (SDH) are major contributors to stroke incidence and disparities, yet their relationship with severity and disability is relatively unexplored. The purpose of this study is to provide preliminary findings on SDH measures at the individual- and ZIP code-level across outcomes of stroke severity and disability. Methods: We used data from 344 stroke patients from the ongoing Transitions of Care Stroke Disparities Study (TCSDS) enrolled 2018-2020 from sites throughout Florida. TCSDS aims to identify disparities in hospital-to-home transition of stroke care. Individual-level SDH were collected by a trained interviewer at discharge; ZIP-level SDH data were obtained from a contracted data company. Outcomes included stroke severity at admission measured by the NIH Stroke Scale (NIHSS; Mild: 0-4; Moderate: 5-14; Severe: ≥15) and obtained from the AHA Get with the Guidelines-Stroke program; and disability at discharge measured by the modified Rankin Scale (mRS; 0-1; 2-5). Non-parametric statistical tests were used to compare individual- and ZIP-level SDH by NIHSS and mRS scores. Results: Most patients were older (median age 62 years, IQR: 19), male (58%), non-Hispanic White (39%) or Hispanic (35%) and suffered mild strokes (median NIHSS, IQR: 2, 4) with mild disability (median mRS: 1, 4). Those living with children (LWC) had more moderate strokes, while those living with a spouse/partner (LWP) had more severe strokes (p=0.004). Less than high school (HS)-, HS-, and college-educated patients had more moderate, mild, and severe strokes, respectively (p=0.02). ZIP-level unemployment rate was positively associated with NIHSS (p=0.031). Higher mRS was seen among Spanish and Haitian Creole speakers (p=0.005); HS- and less than HS-educated patients (p=0.005); and those with lower levels of social support (p=0.019). LWC had higher mRS scores, while the opposite was true for LWP (p=0.001). Conclusions: Despite the intersectional nature of SDH, these findings highlight possible mechanisms by which education, economic conditions and psychosocial factors may influence stroke severity and disability after stroke. More data are necessary to determine whether these SDH influence long term stroke outcomes post-discharge.

Social Determinants of Health Mediate COVID-19 Disparities in South Florida.

BackgroundThere are several reports of health disparities related to COVID-19. Understanding social determinants of health (SDoH) could help develop mitigation strategies to prevent further COVID-19 spread. Our aim is to evaluate self-reported and census-based SDoH as a mediator of health disparities in COVID-19.MethodsWe conducted a cross-sectional ecological study and included all COVID-19 cases report by the COVID-19 Florida dashboard as the dependent variable. The independent variables were census-based median household income, population and household size, and self-reported SDoH using a validated survey. We calculated the incidence rate ratio (IRR) of COVID-19 by zip code using Poisson regression and structured equation modelling to evaluate the mediation effect of income and SDoH on COVID-19 cases.ResultsWe included 97,594 COVID-19 positive cases across 79 Miami-Dade ZIP codes with a median age of 43 years; females represented 50.7% of the cases. The highest IRR (4.44) were for ZIP code 33125 (income $21,106, 6% Black, 93% Hispanic), while the lowest IRR (0.86) was for ZIP code 33146 (median household incomes $96,609, 3% Black and 53% Hispanic). In structured equation models, the indirect coefficient of income in the relationship between race/ethnicity and COVID-19 were only significant for Blacks and not Hispanics.ConclusionsThis ecological analysis using ZIP code and aggregate individual-level SDoH shows that in Miami-Dade county, COVID infection is associated with economic disadvantage in a particular geographical area and not with racial/ethnic distribution.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11606-020-06341-9.

Social determinants of health in electronic health records and their impact on analysis and risk prediction: A systematic review.

This integrative review identifies and analyzes the extant literature to examine the integration of social determinants of health (SDoH) domains into electronic health records (EHRs), their impact on risk prediction, and the specific outcomes and SDoH domains that have been tracked. In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a literature search in the PubMed, CINAHL, Cochrane, EMBASE, and PsycINFO databases for English language studies published until March 2020 that examined SDoH domains in the context of EHRs. Our search strategy identified 71 unique studies that are directly related to the research questions. 75% of the included studies were published since 2017, and 68% were U.S.-based. 79% of the reviewed articles integrated SDoH information from external data sources into EHRs, and the rest of them extracted SDoH information from unstructured clinical notes in the EHRs. We found that all but 1 study using external area-level SDoH data reported minimum contribution to performance improvement in the predictive models. In contrast, studies that incorporated individual-level SDoH data reported improved predictive performance of various outcomes such as service referrals, medication adherence, and risk of 30-day readmission. We also found little consensus on the SDoH measures used in the literature and current screening tools. The literature provides early and rapidly growing evidence that integrating individual-level SDoH into EHRs can assist in risk assessment and predicting healthcare utilization and health outcomes, which further motivates efforts to collect and standardize patient-level SDoH information.

Using electronic health records for population health sciences: a case study to evaluate the associations between changes in left ventricular ejection fraction and the built environment.

ObjectiveElectronic health record (EHR) data linked with address-based metrics using geographic information systems (GIS) are emerging data sources in population health studies. This study examined this approach through a case study on the associations between changes in ejection fraction (EF) and the built environment among heart failure (HF) patients.Materials and MethodsWe identified 1287 HF patients with at least 2 left ventricular EF measurements that are minimally 1 year apart. EHR data were obtained at an academic medical center in New York for patients who visited between 2012 and 2017. Longitudinal clinical information was linked with address-based built environment metrics related to transportation, air quality, land use, and accessibility by GIS. The primary outcome is the increase in the severity of EF categories. Statistical analyses were performed using mixed-effects models, including a subgroup analysis of patients who initially had normal EF measurements.ResultsPreviously reported effects from the built environment among HF patients were identified. Increased daily nitrogen dioxide concentration was associated with the outcome while controlling for known HF risk factors including sex, comorbidities, and medication usage. In the subgroup analysis, the outcome was significantly associated with decreased distance to subway stops and increased distance to parks.ConclusionsPopulation health studies using EHR data may drive efficient hypothesis generation and enable novel information technology-based interventions. The availability of more precise outcome measurements and home locations, and frequent collection of individual-level social determinants of health may further drive the use of EHR data in population health studies.

Abstract 360: Neighborhood-level Poverty and Incident Atrial Fibrillation: Analysis of Electronic Health Records From 2005-2018

Introduction: Atrial fibrillation (AF) is a leading cause of cardiovascular morbidity and mortality. Disparities in AF outcomes have been related to individual-level social determinants of health, including race/ethnicity and socioeconomic status. While neighborhood-level factors, such as poverty, have been related to prevalence of key risk factors (e.g. obesity, hypertension), the association between neighborhood poverty and incident AF has not been previously examined. Methods: Using the Northwestern Medicine Enterprise Data Warehouse, we identified adults free of cardiovascular disease, with at least 5 years of follow-up from January 1, 2005 - December 31, 2013. Residential addresses were geocoded and matched to census tract level poverty estimates from the American Community Survey. Neighborhood poverty was defined as the proportion of residents in the census tract living below the US-defined poverty threshold. We categorized neighborhood poverty levels into tertiles. Generalized linear mixed effects models were used to examine the association between tertiles of neighborhood poverty and incident AF, adjusting for demographic (age, sex, race/ethnicity, insurance type) and AF risk factors (hypertension, diabetes, obesity and smoking status). Results: The cohort comprised 29,069 adults with a mean (SD) age of 51.4 (11.3) years, which included 58% women and 10% non-Hispanic Blacks. Higher rates of obesity, diabetes, hypertension, and smoking were observed in higher poverty groups. Approximately, 3.4% of patients developed incident AF over a follow-up of 5 years. The adjusted odds of incident AF were higher for the medium poverty compared to the low poverty group (adjusted odds ratio, aOR 1.30 (95% CI 1.05-1.56). The aOR of incident AF was similarly higher in the high poverty compared to low poverty group though not statistically significant (Table). Conclusions: In a cohort of adults free of cardiovascular disease at baseline, we found that residence in a more deprived neighborhood was associated with higher rates of incident AF, even after adjustment for traditional risk factors. Understanding how neighborhood and individual-level clinical factors interact to increase the incidence of AF is critical to developing equitable prevention strategies in this increasingly common condition.

Using the barriers and facilitators to linkage to HIV care to inform hepatitis C virus (HCV) linkage to care strategies for people released from prison: Findings from a systematic review.

Elimination of hepatitis C virus (HCV) among short-term sentenced prison populations will require improved access to HCV care and specific strategies dedicated to linkage upon release. Prison-based HCV care has lagged behind HIV care, but much can be learned from HIV studies. We performed a systematic review to identify individual-, provider- and system-level barriers and facilitators to linkage to HCV and HIV care among released inmates. We searched MEDLINE, Scopus, the Cochrane Central Register of Controlled Trials and Embase, and performed a grey literature search for English articles published up to November 2018. Two searches were conducted, one each for HCV and HIV; 323 and 684 unique articles were identified of which two and 27 studies were included, respectively. Facilitators to linkage to HCV care included social support, having an existing primary care provider, and receipt of methadone, whereas barriers included a perceived lack of healthcare information and a lack ofspecialized linkage to care programs. The principal facilitators to linkage to HIV care included social support, treatment for substance use and mental illness, the provision of education, case management, discharge planning and transportation assistance. Important barriers were unstable housing, age <30years, HIV-related stigma, poor providers' attitudes and the lack of post-release reintegration assistance. While HCV care-specific studies are needed, much can be learned from linkage to HIV care studies. Ultimately, a multi-pronged approach, addressing several individual-level social determinants of health, and key provider- and system-level barriers may be an appropriate starting point for the development of HCV linkage to care strategies.

Abstract B26: The effect of neighborhood context on physical and mental health among ethnic minority breast cancer survivors

Abstract Background: Contextual determinants of health risk and outcomes, including physical and social environments, are increasing priority areas for health disparities research and practice. Individual-level social determinants of health among ethnic minority cancer survivors, such as education and income, have been well documented. However, little is known about the effect of neighborhood context on health among this vulnerable population. Learning more about the source and relative impact of health determinants is especially critical to public health and policy strategies to promote address health disparities and improve health equity. Purpose: This study examined the relationship between neighborhood context and patient reported outcomes. Specifically, we hypothesized that greater neighborhood stress would be significantly related to poorer physical and mental health status in post-treatment Latina (LBCS) and African-American breast cancer survivors (AABCS). Method: We used a mixed-methods recruitment approach to enroll 320 LBCS and AABCS from population-based sources. Neighborhood context was assessed using a self-rated neighborhood stress scale for the following aspects of neighborhood context: housing situation, neighborhood environment, transportation, availability of public services, crime and violence, and relation with police. Two domains of health were used: general health, measured by five-point Likert-type item along with the number of self-reported comorbidities; and mental health, assessed using the Center for Epidemiologic Studies Depression (CES-D) Scale, in conjunction with self-reported psychological difficulties. Results: Of the participants, 88 (27.5%) self-identified as African American and 232 (72.5%) self-identified as Latina. Participant age ranged from 26 to 89 years with a mean of 54.49 years (SD =11.49). LBCS were older, less educated, and more financially disadvantaged (p&amp;lt;0.5) compared to AABCS. In addition, LBCS were more likely to be married, to be homemakers, to be born outside of the US, to have spent fewer years residing in the U.S., and to receive chemotherapy. Generalized linear regression analyses show that among ethnic minority breast cancer survivors, higher self-reported neighborhood stress was significantly associated with worse self-rated health status (p &amp;lt;0.05), more frequent depressive symptoms (p &amp;lt;0.01), higher number of comorbidities (p &amp;lt;0.01), and increased likelihood of having psychological difficulties (p &amp;lt;0.001) after adjusting for important individual sociodemographic and medical covariates such as ethnicity, age, income, education, cancer treatment and cancer stage. Significantly, LBCS had higher likelihood of experiencing psychological difficulties than AABCS (OR=2.27, 95% CI: 1.03-5.00). In addition, self-rated health was shown to be significantly impacted by working status (p&amp;lt;0.05). Unexpectedly, some seemingly important sociodemographic factors were not shown to have an impact on self-reported health status: education level and annual household income were not significantly correlated with health. However, individuals with annual incomes at or above the national median reported significantly less depressive symptoms than those individuals who made less than $15,000 per year. Conclusion: Self-reported neighborhood contextual factors were shown to be an influential predictor of physical and mental health among both AABCS and LBCS. These findings underscore the importance of taking neighborhood context into account in investigating health outcomes. The impact of contextual determinants, including neighborhood resources, on cancer-related and overall physical and mental health requires further investigations. Future studies should incorporate population level data, e.g., census tract, to obtain a fuller picture of neighborhood status and resources to better inform interventions and/or policies to improve health outcomes among lower income and vulnerable populations. Citation Format: Mayra Serrano, Kimlin Ashing, Lisa Barcelo, Chenkai Wu. The effect of neighborhood context on physical and mental health among ethnic minority breast cancer survivors. [abstract]. In: Proceedings of the Seventh AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 9-12, 2014; San Antonio, TX. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2015;24(10 Suppl):Abstract nr B26.