Social Determinants Of Health Measures Research Articles

Abstract 15643: Social Determinants of Health, Epigenetic Aging, and Subclinical Cardiovascular Disease: The Coronary Artery Risk Development in Young Adults (CARDIA) Study

Introduction: Associations of social determinants of health (SDOH) with subclinical cardiovascular disease (CVD), such as left ventricular mass index (LVMI) and coronary artery calcification (CAC), are poorly described. Mechanisms of such associations may involve epigenetic changes like DNA methylation. We sought to examine associations between selected SDOH measures and subclinical CVD, and potential mediation by GrimAge acceleration (GrimAA), a measure of epigenetic aging. Methods: We included 2932 Black and White adults from the CARDIA year 20 (Y20) exam (mean age 45y, 48% Black, 57% female). Associations between SDOH measures at Y20 (or cumulative up to Y20) and Y25 ln(CAC score +1) or LVMI (in g/m 2.7 ) were examined using linear regression. Statistical mediation analysis was used to estimate the proportion of associations attributable to GrimAA among 880 participants in whom it was measured. Results: All selected SDOH measures at Y20 - residential racial segregation (G* Score); census tract poverty; food insecurity; difficulty paying for basics, medications, or healthcare; and maximum years of participant or parental education - were associated with LVMI at Y25 (Table). For example, each 1% higher census tract poverty was associated with an average 0.95 g/m 2.7 higher LVMI. Several SDOH measures were also associated with CAC score at Y25. Similar associations were observed between cumulative SDOH exposures from Y0-Y20 and Y25 subclinical CVD. GrimAA mediated >30% of the statistical association between participant education and LVMI or CAC (Table), and >20% of the associations of residential segregation and census tract poverty with LVMI. Conclusions: SDOH metrics were significantly and longitudinally associated with subclinical CVD in midlife. DNA methylation, as represented by GrimAge acceleration, appears to statistically mediate these associations, suggesting possible molecular mechanisms by which SDOH may accelerate CVD risk.

Abstract 12492: Relation of Medical and Social Determinants of Health to Use of Guideline-Directed Medical Therapy for Heart Failure With Reduced Ejection Fraction in the All of Us Research Program

Background: HFrEF is associated with significant morbidity and mortality. The standard of care is guideline-directed medical therapy (GDMT) consisting of evidence-based pharmacological interventions which have demonstrated improved outcomes in HFrEF. However, paltry adherence to GDMT remains an ongoing challenge. We examined in US adults the relationship between social determinants of health (SDOH), demographics, and risk factors on GDMT adherence for HFrEF. Methods: The NIH All of Us Research Program started in May 2018 with the goal of enrolling over 1 million US residents. Over 315,000 participants have shared electronic health records, physical measurements, surveys, and lab results. Demographics, risk factors, and SDOH measures were captured for HFrEF in US adults 18 years or older. Patients with HFrEF who were on quadruple therapy (ACE-inhibitor/angiotensin receptor/combination angiotensin receptor-neprilysin inhibitor, beta-blocker, SGLT2 inhibitor, mineralocorticoid antagonist) were compared to those on fewer. Multiple logistic regression analyses were conducted to examine the association between risk factors/SDOH measures and medical therapy optimization. Results: In the All of Us data, 6049 patients were identified with HFrEF. Of those patients, 5838 (97%) patients were on less than 4 GDMT, while 210 (3%) patients were on quadruple therapy. Multiple logistics regression showed participants with ASCVD, and diabetes had higher odds (95% CI) of being on quadruple therapy, 2.19 [1.28, 3.99] and 9.07 [4.88, 18.84], respectively, while hypertension had lower odds (0.64 [0.43, 0.95]). Race, income, education, and health insurance types did not predict medical therapy optimization. Conclusion: Various cardiovascular conditions were associated with higher odds of being on quadruple therapy. However, sociodemographic factors did not show a correlation of predicting the odds of achieving maximal medical therapy.

Abstract 17880: The Association of Social Determinants of Health at Birth With Cardiovascular Health in Young Adulthood: The Future of Families and Child Wellbeing Study

Background: Social determinants of health (SDoH) are known to influence an individual’s risk for CVD. Childhood is a vulnerable period during which the influences of SDoH may have a stronger impact on future health outcomes. Little is known about early life SDoH and associations with cardiovascular health (CVH) in early adulthood. Methods: In 1998-2000, the Future of Families and Child Wellbeing Study (FFCWS) enrolled 4898 mother-baby dyads from large cities across the US. Births to unmarried mothers were oversampled. When the offspring were 22 yrs, they completed an in-person exam for CVH metrics. SDoH at birth included maternal and paternal education, mother living in a public housing project, having income within the past year from public assistance/welfare/food stamps. Life’s Essential 8 (LE8) Score (without sleep) was assessed at age 22. Linear regression models examined the association of individual SDoH measures with LE8 score adjusting for age, gender and race/ethnicity. Results: This study included 1211 participants, mean age 22 yrs (55% female; 51% Black, 25% Hispanic, 20% White, 4% Other). Adverse SDoH were common with 10% living in a public housing project at birth and 36.7% of mothers having received public assistance within the past year. Adjusted CVH scores were 3.0 (95% CI 1.2-4.8) pts lower for individuals whose mother reported having received public assistance in the past year. Compared to mothers with a college or graduate degree, those with less than high school, high school, and some college had 8.0 (5.0-10.9), 7.1 (4.2-10.0) and 3.2 (0.3-6.1) lower scores CVH scores. Findings were similar for paternal education. No significant associations were observed with living in a public housing project. Conclusion: SDoH at the time of birth have long lasting influence on the CVH of offspring. Interventions to promote CVH within children and families experiencing adverse SDoH are critical to promote health equity and break the inter-generational cycle of CVD.

Individual-Level Social Determinants of Health (SDOH) Measures and Treatment Complications in Acute Leukemia

Background Population studies demonstrate widening survival disparities in AML survival. In our previous analysis, census tract-based measures of socioeconomic status accounted for most of the AML death disparity for non-Hispanic Black (NHB) and Hispanic patients (pts) suggesting that patients' social and physical environments contribute to poor AML endpoints (Abraham et al. Blood 2022). However, these data are retrospective and only provide neighborhood-level information. We prospectively collected individual level data on SDOH and linked this to treatment delivery and complications. Methods Patients were prospectively enrolled from two institutions: a comprehensive cancer center and a minority-serving cancer center for a planned 2 year follow-up. A mixed-methods approach utilized validated questionnaires and semi-structured interviews administered to patients 30-90 days from diagnosis and data was collected on adherence, falls, infections, ER visits, hospitalizations, and ICU admission. Quantitative measures included patient neighborhood and environmental living conditions (AHC HRSN), health literacy (CHLT-6), financial toxicity (COST-FACIT), medication affordability/adherence (ARMS), and mental health (PHQ-4). Quantitative data was supplemented with qualitative data from semi-structured interviews from a subset of patients. We used linear regression to estimate statistical differences in means between groups. Results We report results from 52 patients, predominantly with AML and high-risk MDS followed for up to 6 months. Baseline characteristics are summarized in Table 1. Median age was 62 years. 38% were non-Hispanic White (NHW), 25% NHB, and 23% Hispanic and lived in socioeconomically diverse census tracts. Half the cohort had a high school education or less. 40% had an annual income level < $25,000. 40% pts had a Charlson comorbidity index (CCI) ≥ 5. On the AHC HRSN tool, housing and food insecurity were reported in 10% and 15%, respectively, 40% reported difficulty paying for basic needs and 22% loneliness/isolation. In terms of care delivery, pts aged ≥ 65 years were more likely to miss clinic visits (p=0.026) and chemotherapy treatments (p=0.005). Non-white (p=0.13) pts and those on public insurance (p=0.04) were also more likely to miss clinic visits. Education level, income, and marital status were not associated with missed visits/treatments. Assessing treatment complications 60% of ER visits occurred with annual household incomes under $50,000 (p=0.048). Education and insurance were also linked to treatment morbidity, with 75% of ICU admissions having a high school education or less (p=0.09) and 83% having Medicare or Medicaid (p=0.087). Rates of mechanical ventilation mirrored these findings. Education was associated with lower rates of discharge to SNF or SAR, 72% of college or higher-educated pts were discharged home (p=0.035). Fall rates and infectious complications did not differ based on SDOH. Interestingly age and CCI did not distinguish between any of these treatment complications. Validated patient reported measures of social vulnerability were next examined as predictors of treatment adherence and complications (Table 2). The mean COSTFACIT was 9.8 lower (95% CI -17.6 to -1.93) in those with ER visits than those without ER visits (p=0.061). The mean CHLT-6 score was 0.96 lower (95% CI -1.8 to -0.09) in pts admitted to the ICU (p = 0.03). The PHQ-4 score did not differ based on any complication. Discussion This study represents the first attempt to prospectively link patient reported SDOH to treatment delivery and early complications in leukemia and provides valuable insights into targetable barriers to overcome disparities. Age was the most significant risk factor for missed clinic visits and chemotherapy doses. Older pts may benefit from navigators, caregiver engagement, tailored education given increased reliance on complex outpatient regimens. Treatment complications were strongly associated with SDOH, including education and household income. These findings suggest that pre-treatment ‘fitness’ assessments and greater home-based visits during treatment may mitigate ER visits and life-threatening complications. Importantly, quantitative SDOH measures such as COST-FACIT and CHLT-6 can prospectively identify patients at risk of early complications and become an important tool to triage these resources.

Are Commonly Used Geographically Based Social Determinant of Health Indices in Orthopaedic Surgery Research Correlated With Each Other and With PROMIS Global-10 Physical and Mental Health Scores?

Are Commonly Used Geographically Based Social Determinant of Health Indices in Orthopaedic Surgery Research Correlated With Each Other and With PROMIS Global-10 Physical and Mental Health Scores?

Cumulative social disadvantage and health-related quality of life: national health interview survey 2013–2017

BackgroundEvidence for the association between social determinants of health (SDoH) and health-related quality of life (HRQoL) is largely based on single SDoH measures, with limited evaluation of cumulative social disadvantage. We examined the association between cumulative social disadvantage and the Health and Activity Limitation Index (HALex).MethodsUsing adult data from the National Health Interview Survey (2013–2017), we created a cumulative disadvantage index by aggregating 47 deprivations across 6 SDoH domains. Respondents were ranked using cumulative SDoH index quartiles (SDoH-Q1 to Q4), with higher quartile groups being more disadvantaged. We used two-part models for continuous HALex scores and logistic regression for poor HALex (< 20th percentile score) to examine HALex differences associated with cumulative disadvantage. Lower HALex scores implied poorer HRQoL performance.ResultsThe study sample included 156,182 respondents, representing 232.8 million adults in the United States (mean age 46 years; 51.7% women). The mean HALex score was 0.85 and 17.7% had poor HALex. Higher SDoH quartile groups had poorer HALex performance (lower scores and increased prevalence of poor HALex). A unit increase in SDoH index was associated with − 0.010 (95% CI [-0.011, -0.010]) difference in HALex score and 20% higher odds of poor HALex (odds ratio, OR = 1.20; 95% CI [1.19, 1.21]). Relative to SDoH-Q1, SDoH-Q4 was associated with HALex score difference of -0.086 (95% CI [-0.089, -0.083]) and OR = 5.32 (95% CI [4.97, 5.70]) for poor HALex. Despite a higher burden of cumulative social disadvantage, Hispanics had a weaker SDoH-HALex association than their non-Hispanic White counterparts.ConclusionsCumulative social disadvantage was associated with poorer HALex performance in an incremental fashion. Innovations to incorporate SDoH-screening tools into clinical decision systems must continue in order to accurately identify socially vulnerable groups in need of both clinical risk mitigation and social support. To maximize health returns, policies can be tailored through community partnerships to address systemic barriers that exist within distinct sociodemographic groups, as well as demographic differences in health perception and healthcare experience.

Social Determinants of Health Measures &amp; Successful Community Discharge Across Rehabilitation Service Areas

Abstract Date Presented 04/21/2023 Social determinants of health across rehabilitation service areas are related to successful community discharge. Using the Agency for Healthcare Research and Quality Beta Dataset, 23 measures were identified, representing economic, social, physical, educational, and health care contexts. Primary Author and Speaker: Timothy Reistetter Contributing Authors: Alex Bokov, Susanne Schmidt, Mei-Ling A. Lin, Julianna Dean, Chih-Ying Li, Allen Haas, John Prochaska

The Association between Social Determinants of Health and Depressive Disorders: A 2017 Behavioral Risk Factor Surveillance System (BRFSS) Analysis

Background: Major Depressive Disorder is a leading cause of disability worldwide and one of the most common disorders in the United States. Contributors to an individual’s risk for experiencing depressive disorders include individual and social factors. Although the social determinants of health (SDOH) are conditions that contribute to healthy functioning, health outcomes, and quality of life, it is unclear to what extent adverse SDOH experiences are associated with self-reporting depressive disorder (DD). Methods: Data from the 2017 Behavioral Risk Factor Surveillance System (BRFSS), a population-based telephone survey of noninstitutionalized U.S. adults, was employed in this study. Lifetime diagnosis of DD was self-reported among survey participants who also completed the Social Determinants of Health optional module within 17 participating BRFSS states and entities. Rao-Scott chi-square analysis was used to determine the association of individual and SDOH measures with DD. Results: The prevalence of DD among participating states was 19.85%. Among respondents, significant proportional differences were observed for each SDOH indicator regarding DD status. However, significant differences in health insurance coverage were not observed. Conclusion: Survey respondents with a lifetime diagnosis of DD experienced adverse SDOH conditions in greater proportion than individuals not reporting DD. Individual and community-based approaches to address the contextual influences of depressive disorders should be aggressively implemented.

Impact of social determinants of health (SDOH) measures on prescribing advanced androgen blockade (AAB) in patients with metastatic hormone sensitive prostate cancer (mHSPC).

e17086 Background: Use of AAB (abiraterone acetate, apalutamide or enzalutamide) has been shown to improve survival in mHSPC, but many patients are not prescribed this treatment. There is substantial interest in the broader role of SDOH in cancer. We explored how SDOH influences prescribing patterns of AAB in patients with mHSPC. Methods: Patients diagnosed with mHSPC between 1/1/2017 and 12/31/2021 were identified using the iKnowMed electronic health record database from The US Oncology Network of community oncology practices. Records were searched for prescriptions for AAB (based on intent to treat). Individual level measures were age, diagnosis year, ECOG, race and type of health insurance. Area level measures were Area Deprivation Index (ADI) at national and state level, and rural status. ADI is a validated metric based on demographic variables from census block groups; high ADI scores for state ( &gt; 8) and national ( &gt; 80) level are markers of low socioeconomic status. Logistic regression models were run on each SDOH variable and adjusted for confounding variables, including a joint distribution model of African American (AA) and ADI (significance at p &lt; 0.05). Results: There were 3,855 patients identified with mHSPC: 40% had a prescription for AAB. Summary measures overall were mean age: 71 years, AA: 10%, Medicaid: 5%, ECOG 0-1 and 2+: 60% and 14% respectively; and diagnosis year 2017-21: 15%, 20%, 21%, 22%, and 21% respectively. Area level scores: National ADI high: 8%, State ADI high: 18%, rural: 5%. Interaction variable: AA + State ADI (high): 2%. Statistical differences between those with/without AAB (not shown) were diagnosis year, ECOG and age. Multivariate logistic models were adjusted for these 3 variables with the single SDOH measure as the primary independent variable and the binary variable AAB as the dependent variable (Table). Conclusions: There was no significant association between AAB prescriptions and AA race, rural status, ADI (state or national), or Medicaid status. These data indicate that SDOH measures do not appear to influence prescribing of these treatments. We believe this study is among the first to examine these particular SDOH measures and their relationship to the prescribing of oncology treatments. Further research should be conducted into the impact of SDOH measures on the fulfillment and compliance of these drugs. [Table: see text]

Social determinants of health and utilization of neoadjuvant chemotherapy in patients with triple negative breast cancer (TNBC) in the community oncology setting.

610 Background: TNBC has a higher incidence in African American women compared to European American, often presents at a younger age or more advanced stage, and is associated with worse outcomes. The use of multi-agent neoadjuvant chemotherapy (NC) improves event-free survival and overall survival in patients with early-stage TNBC and is considered standard of care. Race and other social determinants of health (SDOH) have been shown to affect outcomes in patients with TNBC. Differences in use of NC associated with SDOH may be a contributing factor to inferior outcomes. The aim of this study is to determine if SDOH measures affect utilization of NC in patients with early-stage TNBC. Methods: This retrospective observational cross-sectional study examined patient profiles, treatment patterns and SDOH indicators among patients newly diagnosed with early-stage breast cancer in The US Oncology Network. Using iKnowMed EHR data, patients diagnosed with TNBC between 03/31/2017 and 09/30/2021 with stages II-IIIB disease or tumor size ≥2cm (T2 or higher) were included. The initial TNBC diagnosis date was used as the index date for each patient and records were assessed from 6 months pre- to 6 months post-diagnosis for NC initiation, baseline characteristics, and social determinants, including Area Deprivation Index (ADI), a validated measure of socioeconomic status. Results: 3321 patients with TNBC were identified, with a mean age of 58 years; 59% were White, 15% Black, 3% Asian, and 23% other/unknown; and 2139 (64%) received NC. Patients who received NC relative to those who did not were younger (mean 55.9 vs 61.8; p=0.001), had higher BMIs (70% with BMI&gt;25 vs ≤25, p=0.045), and had commercial or Medicaid insurance (67.5% with commercial insurance or Medicaid vs. 52.5% with Medicare; p=0.001). Regional differences were observed, with proportionally fewer patients receiving neoadjuvant chemotherapy in the south US census regions and more in the west. There was no difference in NC use in Black vs White patients (66% vs 64%). No significant differences in receipt of NC were found for race, ethnicity, or ADI. Conclusions: In the community oncology setting, we did not observe inequities in the use of NC based on social determinants analyzed in this study, such as race, ethnicity, and ADI. Poorer outcomes observed in Black patients with TNBC may not be a result of underutilization of NC.

Geo-spatial risk factor analysis for drug overdose death in South Florida from 2014 to 2019, and the independent contribution of social determinants of health

PurposeThe physical environment and social determinants of health have been shown to influence health behaviors including drug use and fatal drug overdose. The current research examines the effects of the built environment, social determinants of health measures and aggregated risk from the built environment at neighborhood-level on drug overdose death locations in Miami-Dade County, Florida. MethodsRisk Terrain Modeling (RTM) was used to assess the place features risk factors that significantly increase the risk of drug overdose death spatially in Miami-Dade County ZIP Code Tabulation Areas, Florida from 2014 to 2019. An aggregated neighborhood risk of fatal drug overdose measure was developed by averaging the risk per grid cell from the RTM within census block groups each year. Six logistic and zero-inflated regression models were built to examine the effects of three indices of incident-specific social determinants of health (IS-SDH) measures and aggregated risk measures separately, and simultaneously on drug overdose death locations each year. ResultsSeven place features including parks, bus stops, restaurants and grocery stores were significantly related to the occurrence of fatal drug overdoses. When examined separately, one or more indices of the IS-SDH were significant covariates of drug overdose locations in some years. When examined simultaneously, the three indices of the IS-SDH and aggregated risk of fatal drug overdose measure could be all significant in certain years. ConclusionsThe patterns of high-risk areas and place features identified from the RTM related to drug overdose deaths may be used to inform the placement of treatment and prevention resources. A multi-factor approach that combines an aggregated neighborhood risk measure reflecting the risk from the built environment and the incident-specific social determinants of health measures can be used to identify the drug overdose death locations in certain years.

Social determinants of health among family caregiver centered outcomes in lung cancer: a systematic review.

Lung cancer is the leading cause of cancer death globally. Both lung cancer patients and family caregivers (FCGs) have unmet quality of life (QOL) needs. An understudied topic in lung cancer research is the role of social determinants of health (SDOH) on QOL outcomes for this population. The purpose of this review was to explore the state of research on SDOH FCGs centered outcomes in lung cancer. The databases PubMed/MEDLINE, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and American Psychological Association (APA) PsycInfo were searched for peer-reviewed manuscripts evaluating defined SDOH domains on FCGs published within the last ten years. The information extracted using Covidence included patients, FCGs and study characteristics. Level of evidence and quality of articles were assessed using the Johns Hopkins Nursing Evidence-Based Practice Rating Scale. Of the 344 full-text articles assessed, 19 were included in this review. The social and community context domain focused on caregiving stressors and interventions to reduce its effects. The health care access and quality domain showed barriers and underuse of psychosocial resources. The economic stability domain indicated marked economic burdens for FCGs. Four interconnected themes emerged among articles on the influence of SDOH on FCG-centered outcomes in lung cancer: (I) psychological well-being, (II) overall quality of life, (III) relationship quality, and (IV) economic hardship. Notably, most participants in the studies were White females. The tools used to measure SDOH factors included primarily demographic variables. Current studies provide evidence on the role of SDOH factors on lung cancer FCGs' QOL. Expanded utilization of validated SDOH measures in future studies would provide greater consistency in data, that could in turn inform interventions to improve QOL. Further research focusing on the domains of education quality and access and neighborhood and built environment should be carried out to bridge gaps in knowledge.

Social Determinants of Health and Adverse Health Outcomes in an Urban Community Hospital in Northwest Indiana

Background: Social determinants of health (SDOH) are underlying contributing factors ofhealth inequities. Facility-based SDOH screenings inform targeted care, community resource referrals, and actions. St. Mary Medical Center (SMMC), an urban hospital in Northwest Indiana, was first acute care hospital in the state to pilot comprehensive SDOH screenings and referrals using the Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences(PRAPARE), a nationally recognized and validated tool. This study is part of a three-phased Community Based Participatory Research (CBPR) partnership between SMMC and IUSM-NWto examine and address SDOH from January 2021-January 2025. This study’s research questions are: What is the distribution of adverse health outcomes by demographics and by SDOH in SMMC’s inpatients?&#x0D; Methods: This descriptive study analyzed a limited dataset generated from EPIC™ by SMMC. It included demographics, behavioral, health outcomes, and SDOH measures for adult inpatient visits from January 2021 to June 2022. Data analysis was conducted in SPSS 28.0 usingdescriptive statistics and tests of association (p&lt;0.05) including One-way ANOVA, Kruskal Wallis H, Chi-square, and Fisher’s Exact. This study was exempted by Indiana UniversityHuman Research Protection Program (IRB #14040).&#x0D; Results: The sample comprised 4370 patients, majority non-Hispanic (90.4%), White (75.7%), older adults (median age 65, IQR = 24), and publicly insured (76.3%). 30-day readmissions accounted for 9.1%. The top three discharge diagnoses were diseases of the circulatory system (20%), digestive system (12.4%), and injury (7.5%). Discharge diagnoses differed significantly (p&lt;.001) by family income, insurance type, stress presence, race, ethnicity, and sex. 30-day readmission was significantly associated with housing risk (p=.036) and insurance type (p&lt;.001).&#x0D; Conclusions: This study underscores the role of SDOH in influencing health outcomes and the importance of continuous SDOH screenings. This study will inform subsequent advanced analysis, the program evaluation, and the translation-to-action phase of the CBPR project.

Area-level social determinants of health and individual-level social risks: Assessing predictive ability and biases in social risk screening.

Area-level social determinants of health (SDoH) and individual-level social risks are different, yet area-level measures are frequently used as proxies for individual-level social risks. This study assessed whether demographic factors were associated with patients being screened for individual-level social risks, the percentage who screened positive for social risks, and the association between SDoH and patient-reported social risks in a nationwide network of community-based health centers. Electronic health record data from 1,330,201 patients with health center visits in 2021 were analyzed using multilevel logistic regression. Associations between patient characteristics, screening receipt, and screening positive for social risks (e.g., food insecurity, housing instability, transportation insecurity) were assessed. The predictive ability of three commonly used SDoH measures (Area Deprivation Index, Social Deprivation Index, Material Community Deprivation Index) in identifying individual-level social risks was also evaluated. Of 244,155 (18%) patients screened for social risks, 61,414 (25.2%) screened positive. Sex, race/ethnicity, language preference, and payer were associated with both social risk screening and positivity. Significant health system-level variation in both screening and positivity was observed, with an intraclass correlation coefficient of 0.55 for social risk screening and 0.38 for positivity. The three area-level SDoH measures had low accuracy, sensitivity, and area under the curve when used to predict individual social needs. Area-level SDoH measures may provide valuable information about the communities where patients live. However, policymakers, healthcare administrators, and researchers should exercise caution when using area-level adverse SDoH measures to identify individual-level social risks.

Association of Social Determinants of Health Measures and Successful Community Discharge Across Rehabilitation Service Areas

<h3>Research Objectives</h3> Evaluate: 1) variation in successful community discharge across Rehabilitation Service Areas (RSAs) nationally, and 2) the relationship between social determinants of health (SDH) variables/indices and successful community discharge across RSAs. <h3>Design</h3> Retrospective cross-sectional analysis of Centers for Medicare & Medicaid Services (CMS) and AHRQ SDH dataset. We performed maximum likelihood factor analysis with varimax rotation and principled mean analysis to identify how many factors to extract from the AHRQ-SDH dataset and calculated a rehabilitation-specific SDH measure (SDH-R) based on these factors and variables. Next, we calculated the Social Deprivation Index (SDI) and the SDH-R for a random sample of 800 RSAs and examined the association between SDI and SDH-R and successful community discharge. <h3>Setting</h3> We examined discharges following post-acute care from long-term care hospitals, home health agencies, and skilled nursing and inpatient rehabilitation facilities. <h3>Participants</h3> The population included 2013–2015 Medicare records from fee-for-service beneficiaries >65 years. The cohort included 2,287,210 records across all diagnostic groups. <h3>Interventions</h3> N/A. <h3>Main Outcome Measures</h3> Successful community discharge across the 1711 RSAs nationally using CMS quality measure specifications. <h3>Results</h3> The national average for successful community discharge across RSAs was 55.1%. The pattern of successful community discharge indicates the majority of RSAs(1239, 72.4%) perform at the national average for successful community discharge with 253 RSAs (14.8%) significantly below and 219(12.8%) significantly above the national average. Factor analysis with AHRQ-SDH variables identified five factors with eigenvalues >1.0. The resultant model explained 23.1% of the variation in successful community discharge. The SDH-R model performed significantly better than the SDI (RMSE:SDH-R=0.88 vs SDI=0.99). <h3>Conclusions</h3> Successful community discharge varied across RSAs nationally. We identified SDH variables related to successful community discharge following rehabilitation. More research is needed to understand how SDH impacts community discharge across high- and low-performing RSAs and explore disparities and equity issues related to successful community discharge following post-acute care rehabilitation. <h3>Author(s) Disclosures</h3> This work was supported by the Agency for Healthcare Research and Quality (AHRQ) R01-HS024711 (TR,JP,AH,YK,KE), T32-HS026133 (YK,JD). All authors declare no conflicts.

Methods for development and application of data standards in an ontology-driven information model for measuring, managing, and computing social determinants of health for individuals, households, and communities evaluated through an example of asthma

ObjectiveTo describe methods to approach application of data standards to integrate social determinants of health (SDoH) into EHRs through evaluation of a case of clinical decision support for pediatric asthma. Materials and MethodsWe identified a list of environmental factors important for managing pediatric asthma. We identified and integrated data from local outdoor air quality monitors with elements available from the clinic’s EHR and self-reported indoor air quality questionnaire data. We assessed existing SDoH frameworks, assessment tools, and terminologies to identify representative data standards for these environmental SDoH measures. ResultsWe found many-to-many relationships between the multiple framework domains, the environmental exposure measures collected, and existing standards. The majority of concepts did not accurately align with environmental exposure measurements. We propose an ontology-driven information framework methodology to apply standards for SDoH measurements to support measuring, managing, and computing SDoH data. DiscussionTo support methods of integrating SDoH data in the EHR via an ontology-driven information framework, a common SDoH ecosystem should be developed descriptively and prescriptively integrating framework domains, assessment tools, and standard ontologies to support future data sharing, aggregation, and interoperability. A hierarchical object-oriented information model should be adopted to manage SDoH to extend beyond patient-centered orientation of EHRs to orient to households and communities. ConclusionSDoH data pose unique challenges and opportunities in collecting, measuring, and managing health information. Future work is needed to define data standards for implementing SDoH in a hierarchical, object-oriented information model representing multiple units of orientation including individuals, households, and communities.

Abstract 13552: Associations Between Food Security and Social Determinants of Health With Dietary Intakes- A Pilot Study

Introduction: Food insecurity remains prevalent in the United States and has disproportionately affected those living in poverty or experiencing financial hardships. Social determinants of health (SODH) influence food security; yet, little is known on which aspects of the built environment influence the most food insecurity and dietary intake, especially among minority groups living in urban food deserts. We conducted a pilot study to examine the association between individuals’ dietary intake, food insecurity and other SODH measures among individuals residing in an urban food desert. Methods: We recruited 45 adults, residing in the targeted study area. Following consent, participants completed surveys to measure sociodemographic and health status, dietary intake, social determinants of health (food insecurity, perceived neighborhood safety, healthy food environment, perceived discrimination). All demographic and survey data were examined graphically and summarized descriptively. Analysis of variance was used to examine associations between dietary data and SODH measures. Results: Overall, ~78% of the participants were African American, female (80%), 53 years old on average. More than 50% lived in a household with a yearly income below $12,000. Education level and neighborhood safety were the major SODH influencing dietary intake. Majority of participants were food insecure and 54% reported not feeling safe in their neighborhood. The effect of food insecurity was significant on the average intake of processed meat (F = 4.72, p = .036), fast food (F= 4.384, p= .042) and whole grains (F= 4.573, p= .038). The effect of education level on average intake of processed meat was significant, F = 3.59, p=.02. Lastly, neighborhood safety significantly influenced the average intake of processed meat (F= 3.593, p= 0.022). Conclusions: Findings from this pilot study confirm the importance of examining neighborhood contextual factors such as SODH in relation to food insecurity and diet. A better understanding of the relationships between dietary intake and SODH have important implications for clinical practice, behavioral interventions and policies aiming to decrease diet-related health disparities.

Abstract 13643: Examining the Association Between Social Determinants of Health and Health-Related Quality of Life Among Children With Cardiac Conditions

Introduction: Patient-reported outcomes like health-related quality of life (HRQOL) measure the health and well-being of patients, providing insights not communicated by traditional outcomes. The degree to which social determinants of health (SDOH) influence HRQOL is under-studied. Methods: A cross-sectional analysis of subjects aged 8-18 years referred for cardiac catheterization from 1/1/18-12/31/21 was performed, evaluating the association between HRQOL (measured through child and caregiver-proxy reports of cardiac-specific HRQOL (Pediatric Cardiac Quality of Life Inventory (PCQLI)) and general HRQOL (Pediatric Quality of Life Inventory (PedsQL))) and a panel of potential SDOH. Potential SDOH measures were calculated at subject’s neighborhood level by linking their addresses to block group/census tract data. SDOH measures included Childhood Opportunity Index, which measures neighborhood resources supporting child development, and Social Deprivation Index, which measures neighborhoods’ levels of disadvantage. Linear regression was used to evaluate the association between SDOH and HRQOL (PCQLI and PedsQL total scores), adjusting for presence of cardiac symptoms (New York Heart Association Class). Results: In total, 111 subjects (age 12.8±2.9 years, of which 89% identified as White, 5% Black, and 8% Hispanic, and 27% Medicaid-insured) were studied. Primary cardiac diagnoses included single ventricle heart disease (19%), complex biventricular repairs (20%), and orthotopic heart transplantation (12%). The majority (69%) denied having symptoms. Out of a potential 100, subject-reported scores were 67±18 on the PCQLI and 74±19 on the PedsQL. Caregiver-proxy reported scores were 70±17 on the PCQLI and 71±19 on the PedsQL. Worse SDOH was consistently associated with lower HRQOL adjusted for presence of symptoms (Table 1). Conclusions: Worse SDOH was associated with worse HRQOL in pediatric cardiac patients independent of symptoms severity

Are Detailed, Patient-level Social Determinant of Health Factors Associated With Physical Function and Mental Health at Presentation Among New Patients With Orthopaedic Conditions?

Are Detailed, Patient-level Social Determinant of Health Factors Associated With Physical Function and Mental Health at Presentation Among New Patients With Orthopaedic Conditions?

Exploring the role of discretionary income as a social determinant of health.

The object of this study is to explore the impact of discretionary income on the overall well-being of Americans. The data source used for this study was 2017 Behavioral Risk Factor Surveillance System data comprising 12 states that used the Social Determinants of Health (SDOH) module. Multivariable logistic regression models were used to analyze the relationship between discretionary income and self-reported health status after adjusting for common SDOH measures, sociodemographic factors, chronic conditions, and perspectives and experiences of respondents regarding specific aspects of their health. Average marginal effects (AME) were reported. Not applicable. At all income levels, those with discretionary income at the end of the month were 6-7 percentage points more likely to report better than average health than those with none, controlling for other factors (AME: 0.07, 95% CI: 0.02-0.12). Our study suggests that discretionary income has a role to play in overall health and well-being that goes beyond that of disposable income and may be an important resource for diverse communities.