Social Determinants Of Health Measures Research Articles

QOL-45. SOCIAL DETERMINANTS OF HEALTH PREDICT NEUROPSYCHOLOGICAL OUTCOMES IN PEDIATRIC BRAIN TUMOR SURVIVORS

Abstract BACKGROUND Pediatric brain tumors and their treatment impact cognition including intellectual functioning (IQ), attention, working memory, and processing speed. Few studies have examined the role of social determinants of health (SDoH) on cognition in pediatric brain tumors. To date, studies have primarily focused on family-related SDoH factors such as insurance status or parental education as predictors of cognition. To our knowledge, no study has examined multiple indicators of SDoH to determine which family-based and community-based predict cognition. METHODS Participants included 151 pediatric brain tumor patients (M diagnosis age=8.82) seen for a clinical neuropsychological evaluation (M evaluation age=13.71). IQ, brief attention (WISC/DAS-II Digits Forward), working memory (WISC/DAS-II Digits Backwards), and processing speed (WISC Coding) were obtained from age-appropriate measures (WISC>6 years, DAS-II<6 years). Family-based SDoH indicators include insurance type (public, private) and maternal education (<high school degree, some college, ≥college degree). Community-based SDoH include the Area Deprivation Index (ADI) and Childhood Opportunity Index (COI). Multiple regressions examined variance in IQ, attention, working memory, and processing speed accounted for by the SDoH measures, after controlling for treatment exposures using the Neurological Predictor Scale. RESULTS Insurance accounted for 14.9% of the IQ variance [β=-.316, t(151)=-4.18; p<.001]; COI accounted for 18.3% of the IQ variance [β=.202, t(151)=2.70; p=.008]. Insurance accounted for 12% of the variance in attention [β=-.324, t(152)=-4.22; p<.001], and 17.5% of the variance in processing speed [F(2,134)=15.399, p<.001]. In the working memory model, insurance [β=-.179, t(148)=-2.19; p=.03] and ADI [β=-.171, t(148)=-2.11; p=.04], accounted for 5.3% and 7.5% of the variance, respectively. CONCLUSIONS Findings highlight the important contribution of both family and community factors on neuropsychological outcomes in pediatric brain tumors. Nuances of the data will be discussed as they relate to clinical care and research.

QOL-43. COMPARISON OF SOCIAL DETERMINANTS OF HEALTH MEASURES ON IQ AND ACADEMIC OUTCOMES IN PEDIATRIC BRAIN TUMORS

Abstract BACKGROUND Treatment for pediatric brain tumors impacts IQ and academic achievement; fewer studies have examined the role of social determinants of health (SDoH). To date, studies have primarily focused on family-based SDoH such as insurance type or parental education as predictors of cognition. Limited studies have examined community factors, though our work highlighted the role of education quality on IQ and academics in pediatric brain tumors. To our knowledge, no study has examined the predictive value of family and community-based SDoH. METHODS Participants included 151 pediatric brain tumor patients (M age at diagnosis=8.82) seen for a clinical neuropsychological evaluation (M age at evaluation=13.71). IQ was obtained from age-appropriate intelligence measures (WISC>6 years, DAS-II<6 years); untimed reading and math calculation were examined via WIAT/KTEA/WJ subtests. Family-based SDoH included insurance type (public, private) and maternal education (<high school degree, some college, >college degree). Community-based SDoH included the Area Deprivation Index (ADI) and Education, Health-Environment, and Social-Economic indices from the Childhood Opportunity Index (COI). Multiple regressions examined variance in IQ, reading, and math accounted for by each of the SDoH measures, after controlling for treatment exposures using the Neurological Predictor Scale (NPS). RESULTS Insurance [β=-.302, t(151)=-4.00; p<001] and Education [β=.235, t(151)=3.14; p=.002] accounted for 14.9% and 19.7% of the IQ variance, respectively. Education also accounted for 12.8% of the reading variance (β=.304, t(109)=3.40; p<.001). Both the Education [β=.570, t(107)=3.41; p<.001] and Social-Economic [β=-.381, t(107)=-2.28; p=.03] indices accounted for 7.7% and 11.3% of the math calculation variance, respectively. CONCLUSIONS Community, particularly those related to education and social & economic resources, and family factors (e.g., insurance status) were associated with IQ and academic performance. Findings indicate the importance of investigating both family and community factors that may contribute to outcomes in pediatric brain tumors.

Associations of mid-to-late-life inflammation with late-life mobility and the influences of chronic comorbidities, race, and social determinants of health: The Atherosclerosis Risk in Communities Study.

Relationships of midlife inflammation with late-life mobility and influences of chronic health conditions, race, and social determinants of health (SDoH) on these relationships are poorly understood. Among 4758 community-dwelling participants (41% men, 20% Black), high-sensitivity C-reactive protein (hsCRP) was measured over 20+ years: in midlife at study visit 2 (V2: 1990-1992, 47-68 years); at V4 (1996-1998, 53-74 years); and with concurrent late-life 4-m gait speed at V5 (2011-2013, 67-88 years, mean 75 years). SDoH measures included race, the national-rank area deprivation index, education, and income. We examined associations of late-life gait speed with midlife hsCRP (V2 continuous and clinically high ≥3 mg/L), with 20-year hsCRP history from midlife (V2-V5 average continuous hsCRP and clinically high ≥3 mg/L) and with inflammation accumulation (visits and years with high hsCRP). Regression models adjusted for demographic, cardiovascular, and SDoH measures; effect modification by the presence of other common chronic conditions (obesity, diabetes, hypertension) and race were examined, with and without accounting for SDoH. High midlife hsCRP was associated with slower late-life gait speed, even among those without chronic conditions in midlife: -4.6 cm/s (95% CI: -6.4, -2.8). Importantly, sustained high hsCRP was associated with a 20-year slowing of -10.0 cm/s (-14.9, -5.1) among those who never experienced obesity, diabetes, or hypertension over the 20-year period. Associations were similar between Black participants, -3.8 cm/s (-6.9, -0.7) and White participants -3.3 (-4.5, -2.2) per interquartile range of midlife hsCRP; effect modifications by chronic conditions and race were unsupported throughout. Results were robust to accounting for SDoH or otherwise; however, worse SDoH was associated with higher inflammation and slower gait speed in both Black and White participants. Inflammation in midlife may contribute to clinically meaningful late-life slowing of gait speed, even among otherwise healthy-appearing adults and regardless of race and socioeconomic disadvantage. Regular monitoring and interventions for inflammation may be warranted from midlife.

Social Determinants of Health and Other Predictors in Initiation of Treatment with CDK4/6 Inhibitors for HR+/HER2- Metastatic Breast Cancer.

In hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) metastatic breast cancer (MBC), cyclin-dependent kinase 4/6 inhibitors (CDK4/6is) have replaced endocrine therapy alone as the standard of care; however, several barriers to treatment initiation still exist. We assessed social determinants of health (SDOH) and other factors associated with the initiation of CDK4/6i for HR+/HER2- MBC in the Medicare population. Using a retrospective cohort design, patients aged ≥65 years and diagnosed during 2015-2017 were selected from the SEER-Medicare database. Time from MBC diagnosis to first CDK4/6i initiation was the study outcome. The effect of SDOH measures and other predictors on the outcome was assessed using the multivariable Fine and Gray hazard modeling. Of 752 eligible women, 352 (46.8%) initiated CDK4/6i after MBC diagnosis (median time to initiation: 27.9 months). In adjusted analysis, SDOH factors significantly associated with CDK4/6i initiation included high versus low median household income (HHI) (hazard ratio [HR] = 1.70; 95% CI = 1.03-2.81) and the percentage of population with high versus low Medicare-only coverage (HR = 1.54; 95% CI = 1.04-2.27). In summary, older Medicare patients with HR+/HER2- MBC residing in areas with high median HHI and a high proportion of Medicare-only coverage had higher rates of initiating CDK4/6i, suggesting inequitable access to these novel, effective treatments and a need for policy intervention.

Social Determinants of Health and Health-Related Quality of Life following Pediatric Septic Shock: Secondary Analysis of the Life After Pediatric Sepsis Evaluation Dataset, 2014-2017.

Social determinants of health (SDOH) are associated with disparities in disease severity and in-hospital outcomes among critically ill children. It is unknown whether SDOH are associated with later outcomes. We evaluated associations between SDOH measures and mortality, new functional morbidity, and health-related quality of life (HRQL) decline among children surviving septic shock. Secondary analysis of the Life After Pediatric Sepsis Evaluation (LAPSE) prospective cohort study was conducted between 2014 and 2017. Twelve academic U.S. PICUs were involved in the study. Children younger than 18 years with community-acquired septic shock were involved in the study. None. We assessed associations between race, ethnicity, income, education, marital status, insurance, language, and home U.S. postal code with day 28 mortality, new functional morbidity at discharge per day 28, and HRQL decline using logistic regression. Of 389 patients, 32% (n = 98) of families had household income less than $50,000 per year. Median Pediatric Risk of Mortality (PRISM) score was 11 (interquartile range 6, 17). We found that English language and Area Deprivation Index less than 50th percentile were associated with higher PRISM scores. Mortality was 6.7% (n = 26), new functional morbidity occurred in 21.8% (n = 78) of patients, and HRQL decline by greater than 10% occurred in 31.0% of patients (n = 63). We failed to identify any association between SDOH measures and mortality, new functional morbidity, or HRQL decline. We are unable to exclude the possibility that annual household income greater than or equal to $50,000 was associated with up to 81% lesser odds of mortality and, in survivors, more than three-fold greater odds of HRQL decline by greater than 10%. In this secondary analysis of the 2014-2017 LAPSE dataset, we failed to identify any association between SDOH measures and in-hospital or postdischarge outcomes following pediatric septic shock. This finding may be reflective of the high illness severity and single disease (sepsis) of the cohort, with contribution of clinical factors to functional and HRQL outcomes predominating over prehospital and posthospital SDOH factors.

Associations of social determinants of health with avoidance of information, treatment receipt, and physician mistrust for women with breast cancer.

1507 Background: Social determinants of health (SDoH) are key factors contributing to breast cancer disparities but are difficult to measure. We surveyed a diverse population of women with breast cancer to identify SDoH and demographic measures associated with avoiding information, treatment receipt, and physician mistrust. Methods: During 2018-2020, we interviewed 297 women (60% White, 25% Black, 15% Hispanic) with a history of breast cancer treated at three academic institutions. We used multivariable regression to assess the associations of SDoH, demographics, and related factors (social support, discrimination in daily life and within healthcare, education, financial strain, faith, age, birth country, race and ethnicity, marital status) with three outcomes: (1) avoiding information about cancer, (2) lack of initiation of recommended treatments, and (3) physician mistrust. For each model, we included factors statistically significant (p<0.05) in unadjusted analyses. Results: Overall, 79 participants (26.6%) reported avoiding information about cancer. Age <40, discrimination (treated with less courtesy or as if dishonest), and financial strain (worry about medical bills, uninsured status) were all significantly associated with avoiding information (p<0.05 for all). In multivariable analyses, discrimination in daily life (OR 3.37, 95% CI 1.40-8.10 for being treated as if dishonest) and age (OR 3.36 for age <40 compared to >60) were associated with avoiding information. Twenty-two participants (7.4%) reported not initiating at least one recommended treatment. Discrimination in daily life and in healthcare were both significantly associated with treatment receipt, as was financial strain (all p<0.01). In multivariable analyses, discrimination (OR 3.51, 95% CI 1.05-11.76 for being treated as if dishonest) and worrying about medical bills (OR 3.53, 95% CI 1.40-8.93) were associated with lack of treatment initiation. Twelve participants (4.0%) reported not trusting physician’s judgements about medical care. Discrimination in daily life and in health care and insurance status were significantly associated with physician mistrust (both p<0.05). Multivariable analysis found that discrimination in both daily life and in health care (OR 5.95, 95% CI 1.40-25.3 for being treated with less courtesy in daily life, and OR 12.79, 95% CI 2.49-65.6 for reporting being treated as dishonest in health care setting) were associated with mistrust. Race and ethnicity were not significantly associated with any of the outcome measures in unadjusted or adjusted analyses (all p>0.05). Conclusions: SDoH measures and not race were associated with lack of engagement and trust within the health care system, with experiences of discrimination and financial strain associated with avoiding information, not initiating treatments, and physician mistrust.

Census tract–level social determinants of health and variability in stage at diagnosis of bladder, breast, and non-small cell lung cancers.

11170 Background: Social determinants of health (SDOH) can identify underserved populations, inform the patient journey, and identify opportunities to improve outcomes. In cancer, SDOH may influence stage at diagnosis through exposure to risk factors for aggressive cancer and decreased access to care. However, privacy restrictions can limit research with SDOH in real world datasets, which often only know a patient’s broad location. Methods: Geocoding was undertaken on addresses of patients diagnosed between 1/1/17 and 9/30/22 with bladder (n=6078), breast (n=28,958), and non-small cell lung cancer (NSCLC, n=14,957) in the Syapse Learning Health Network of U.S. community health systems. Location was linked to five indicators of SDOH at the census tract level: the Social Vulnerability Index (SVI); percent (%) of tract spending >30% of income on housing; % of tract with broadband internet; county designation as a primary care shortage area; and rural urban commuting area (RUCA). Nested multivariable ordinal logistic regression models estimated independent associations between demographic, clinical, and SDOH factors with stage at initial cancer diagnosis. The statistical significance of including SDOH variables was assessed using a likelihood ratio test (LRT) comparing the models before and after including SDOH. Results: After successful geocoding and linkage to census tracts, there were marginal differences in the distribution of the five SDOH across stages at diagnosis within the three tumor types. In multivariable models adjusted for year of diagnosis, age, sex, race, ethnicity, smoking, and primary payor, among the tested SDOH measures, county designated as primary care shortage (proportional odds ratios (OR): 1.31, 95% confidence interval (95% CI): 1.05-1.64), and non-metropolitan area (0.69, 0.50-0.94) were statistically significantly associated with stage at bladder cancer diagnosis. SVI (1.52, 1.31-1.76) was also statistically significantly associated and directly proportional with stage at breast cancer diagnosis. Among patients with NSCLC, broadband internet access (1.01, 1.00-1.01), primary care shortage (1.15, 1.03-1.27) and non-metropolitan area (0.85, 0.74-0.99) were statistically associated with stage at diagnosis. In all three tumors, LRTs found that models with SDOH were statistically significantly better at predicting stage at diagnosis than those without (p <0.001). Conclusions: Using granular, census-tract geospatial resolution for geocoding of SDOH, we find that patients who live in greater vulnerability are diagnosed at later stages than those who live in areas of less vulnerability, suggesting that consideration of SDOH variables is important in assessing stage at diagnosis and identify patients with an unmet need for outreach and screening.

Measuring social determinants of health in the All of Us Research Program

To accelerate medical breakthroughs, the All of Us Research Program aims to collect data from over one million participants. This report outlines processes used to construct the All of Us Social Determinants of Health (SDOH) survey and presents the psychometric characteristics of SDOH survey measures in All of Us. A consensus process was used to select SDOH measures, prioritizing concepts validated in diverse populations and other national cohort surveys. Survey item non-response was calculated, and Cronbach’s alpha was used to analyze psychometric properties of scales. Multivariable logistic regression models were used to examine associations between demographic categories and item non-response. Twenty-nine percent (N = 117,783) of eligible All of Us participants submitted SDOH survey data for these analyses. Most scales had less than 5% incalculable scores due to item non-response. Patterns of item non-response were seen by racial identity, educational attainment, income level, survey language, and age. Internal consistency reliability was greater than 0.80 for almost all scales and most demographic groups. The SDOH survey demonstrated good to excellent reliability across several measures and within multiple populations underrepresented in biomedical research. Bias due to survey non-response and item non-response will be monitored and addressed as the survey is fielded more completely.

Social Determinants of Health, Risk and Resilience against Alzheimer's Disease and Related Dementias: The Healthy Brain Initiative.

Few studies have investigated associations between perceived social determinants of health (SDOH) and Alzheimer's disease and related dementia (ADRD) biomarkers or between SDOH and resilience against ADRD. To examine associations between perceived and objective SDOH and ADRD-related outcomes. We used cross-sectional data on≥50-year-olds without dementia in the Healthy Brain Initiative (n = 162). Questionnaires captured trust in neighbors and indices of perceived neighborhood greenspace access, time spent in neighborhood greenspaces, and interpersonal discrimination. Residential addresses were linked to 2021 Area Deprivation Index scores. The Vulnerability Index (VI) is based on 12 dementia risk factors (e.g., age, race/ethnicity, diabetes) and Resilience Index (RI) is based on 6 protective factors (e.g., diet, mindfulness, physical activity). Cognitive measured included number symbol coding task and Montreal Cognitive Assessment. Biomarkers included Aβ42/40 and pTau-217/npTau-217, hippocampal and white matter hyperintensity volume, lipoprotein A, and high-sensitivity c-reactive protein. Perceived greater access to greenspaces (estimate = 2.83, 95% CI = 1.40-4.26) and greater time in neighborhood greenspaces were associated with greater RI scores (estimate = 2.30, 95% CI = 1.24-3.35). Reporting greater discrimination (estimate = 0.10, 95% CI = 0.04-0.16) and living in higher deprivation neighborhoods were associated with greater VI scores (estimate = 0.017, 95% CI = 0.003-0.032). Greater discrimination was associated with greater white matter hyperintensity volume (estimate = 0.27, 95% CI = 0.04-0.51). Perceived greenspace access and time spent in greenspaces were associated with resilience against ADRD, and interpersonal discrimination was associated with vulnerability to ADRD. Future work needs to validate perceived SDOH measures, examine associations in racially/ethnic diverse populations, and investigate longitudinal associations between SDOH and ADRD-related biomarkers.

Combining zip code-based population data and pharmacy administrative claims data to create measures of social determinants of health.

Social determinants of health (SDoH) are key factors that impact health outcomes. However, there are many barriers to collecting SDoH data (eg, cost of data collection, technological barriers, and lack of standardized measures). Population data may provide an accessible alternative to collecting SDoH data for patients. To explain how population data can be leveraged to create SDoH measures, assess the association of population SDoH measures with diabetic medication adherence, and discuss how understanding a patient's SDoH can inform care plans and patient engagement. A nationally representative commercial sample of patients who were aged 18 years and older and met Pharmacy Quality Alliance inclusion criteria for diabetes mellitus were analyzed (N = 37,789). US Census and North American Industry Classification System data were combined with pharmacy administrative claims data to create SDoH measures. Derived measures represent 2 SDoH domains: (1) economic stability (housing density, housing relocation, jobs per resident, and average salary) and (2) health care access and quality (urban/rural classification, distance traveled to prescriber and pharmacy, use of a primary care provider [PCP], and residents per PCP). The association of population SDoH measures with diabetic medication adherence (proportion of days covered) was assessed via logistic regression, which included covariates (eg, sex, age, comorbidities, and prescription plan attributes). As housing density (houses per resident) increased, so did the likelihood of adherence (odds ratio = 1.54, 95% CI = 1.21-1.97, P = 0.001). Relative to patients who did not move, patients who moved once had 0.87 (95% CI = 0.81-0.93, P < 0.001) the odds of being adherent, and patients who moved 2 or more times had 0.82 (95% CI = 0.71-0.95, P = 0.008) the odds of being adherent. Compared with areas with fewer jobs per resident, patients living within a zip code with 0.16 to 0.26 jobs per resident were 1.12 (95% CI = 1.04-1.20, P = 0.002) times more likely to be adherent. Patients who lived in an urban cluster were 1.11 (95% CI = 1.01-1.22, P = 0.037) times more likely to be adherent than patients living in a rural area. Patients who travel at least 25 miles to their prescriber had 0.82 (95% CI = 0.77-0.86, P < 0.001) the odds of being adherent. Community pharmacy users had 0.65 (95% CI = 0.59-0.71, P < 0.001) the odds of being adherent compared with mail order pharmacy users. Patients who had a PCP were 1.26 (95% CI = 1.18-1.34, P < 0.001) times more likely to be adherent to their medication. Leveraging publicly available population data to create SDoH measures is an accessible option to overcome barriers to SDoH data collection. Derived measures can be used to increase equity in care received by identifying patients who could benefit from assistance with medication adherence.

Abstract 3451: Association of multi-level factors with cervical cancer screening in healthcare settings

Abstract Background- Few studies have examined the independent contribution of patient, clinician and system factors associated with completing cervical cancer screening. Methods- Using a large retrospective cohort created from electronic health record data, we examined the association of patient- and provider-level characteristics with cervical cancer screening among unknown- and average-risk, screen eligible women who entered the cohort at a primary care visit in 1 of 3 U.S. healthcare systems (Kaiser Permanente Washington, Parkland Health, Mass General Brigham) between 2010-2019. The outcome was time, in days, from cohort entry to screening. We fit piecewise exponential (PWE) survival models, nesting patients hierarchically within provider seen and the facility at the time of their analytic cohort entry visit. We excluded patients whose visit was with providers at their non-principal facility (principal defined as the facility that they saw most patients in 2010). The analysis was stratified by healthcare setting. Results- Median time from cohort entry until screening varied across settings (202-1063 days) and across providers and clinics (between cluster variance= 0.11-0.77, 0.06-1.18, respectively). Many patient-level characteristics (age, cohort entry year, race/ethnicity, number of comorbidities, smoking status, primary care visit within 12 months before cohort entry, and insurance), some of the provider-level characteristics (provider specialty and sex) and social determinants of health measures (Local Isolation Score Hispanic) were associated with time to screening. In the adjusted PWE models, for all three settings: a) Younger patients (21-49 years old) (vs older patients (50-65 years)); b) Hispanic and Non-Hispanic Asian/Pacific Islander (vs Non-Hispanic White); and c) patients seeing ob-gyn providers (vs family medicine providers) had shorter time to screening. Patients with a) higher (vs lower) comorbidity burden, and b) Medicare insurance (vs Commercial) were less likely to receive screened. Conclusion- By identifying characteristics at multiple levels that are associated with the outcome, our results help us identify targets for interventions to improve and increase timely cervical cancer screening in healthcare settings. Citation Format: Rutu Rathod, Ellen Hu, Sandi Pruitt, Jasmin A. Tiro, Jessica Chubak, Jennifer Haas, Steven J. Atlas, Gina Kruse, Amy E. Hughes. Association of multi-level factors with cervical cancer screening in healthcare settings [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3451.

Comparison of predicting cardiovascular disease hospitalization using individual, ZIP code-derived, and machine learning model-predicted educational attainment in New York City.

Area-level social determinants of health (SDOH) based on patients' ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. To our knowledge, whether machine learning (ML) could be used to derive individual SDOH measures, specifically individual educational attainment, is unknown. This is a retrospective study using data from the Mount Sinai BioMe Biobank. We included participants that completed a validated questionnaire on educational attainment and had home addresses in New York City. ZIP code-level education was derived from the American Community Survey matched for the participant's gender and race/ethnicity. We tested several algorithms to predict individual educational attainment from routinely collected clinical and demographic data. To evaluate how using different measures of educational attainment will impact model performance, we developed three distinct models for predicting cardiovascular (CVD) hospitalization. Educational attainment was imputed into models as either survey-derived, ZIP code-derived, or ML-predicted educational attainment. A total of 20,805 participants met inclusion criteria. Concordance between survey and ZIP code-derived education was 47%, while the concordance between survey and ML model-predicted education was 67%. A total of 13,715 patients from the cohort were included into our CVD hospitalization prediction models, of which 1,538 (11.2%) had a history of CVD hospitalization. The AUROC of the model predicting CVD hospitalization using survey-derived education was significantly higher than the model using ZIP code-level education (0.77 versus 0.72; p < 0.001) and the model using ML model-predicted education (0.77 versus 0.75; p < 0.001). The AUROC for the model using ML model-predicted education was also significantly higher than that using ZIP code-level education (p = 0.003). The concordance of survey and ZIP code-level educational attainment in NYC was low. As expected, the model utilizing survey-derived education achieved the highest performance. The model incorporating our ML model-predicted education outperformed the model relying on ZIP code-derived education. Implementing ML techniques can improve the accuracy of SDOH data and consequently increase the predictive performance of outcome models.

Understanding individual health-related social needs in the context of area-level social determinants of health: The case for granularity.

Screening for health-related social needs (HRSNs) within health systems is a widely accepted recommendation, however challenging to implement. Aggregate area-level metrics of social determinants of health (SDoH) are easily accessible and have been used as proxies in the interim. However, gaps remain in our understanding of the relationships between these measurement methodologies. This study assesses the relationships between three area-level SDoH measures, Area Deprivation Index (ADI), Social Deprivation Index (SDI) and Social Vulnerability Index (SVI), and individual HRSNs among patients within one large urban health system. Patients screened for HRSNs between 2018 and 2019 (N = 45,312) were included in the analysis. Multivariable logistic regression models assessed the association between area-level SDoH scores and individual HRSNs. Bivariate choropleth maps displayed the intersection of area-level SDoH and individual HRSNs, and the sensitivity, specificity, and positive and negative predictive values of the three area-level metrics were assessed in relation to individual HRSNs. The SDI and SVI were significantly associated with HRSNs in areas with high SDoH scores, with strong specificity and positive predictive values (∼83% and ∼78%) but poor sensitivity and negative predictive values (∼54% and 62%). The strength of these associations and predictive values was poor in areas with low SDoH scores. While limitations exist in utilizing area-level SDoH metrics as proxies for individual social risk, understanding where and how these data can be useful in combination is critical both for meeting the immediate needs of individuals and for strengthening the advocacy platform needed for resource allocation across communities.

Neighborhood-level social determinants of health measures independently predict receipt of living donor liver transplantation in the United States.

Disparities exist in the access to living donor liver transplantation (LDLT) in the United States. However, the association of neighborhood-level social determinants of health (SDoH) on the receipt of LDLT is not well-established. This was a retrospective cohort study of adult liver transplant recipients between January 1, 2005 and December 31, 2021 at centers performing LDLT using the United Network for Organ Sharing database, which was linked through patients' ZIP code to a set of 24 neighborhood-level SDoH measures from different data sources. Temporal trends and center differences in neighborhood Social Deprivation Index (SDI), a validated scale of socioeconomic deprivation ranging from 0 to 100 (0=least disadvantaged), were assessed by transplant type. Multivariable logistic regression evaluated the association of increasing SDI on receipt of LDLT [vs. deceased donor liver transplantation (DDLT)]. There were 51,721 DDLT and 4026 LDLT recipients at 59 LDLT-performing centers during the study period. Of the 24 neighborhood-level SDoH measures studied, the SDI was most different between the 2 transplant types, with LDLT recipients having lower SDI (ie, less socioeconomic disadvantage) than DDLT recipients (median SDI 37 vs. 47; p < 0.001). The median difference in SDI between the LDLT and DDLT groups significantly decreased from 13 in 2005 to 3 in 2021 ( p = 0.003). In the final model, the SDI quintile was independently associated with transplant type ( p < 0.001) with a threshold SDI of ~40, above which increasing SDI was significantly associated with reduced odds of LDLT (vs. reference SDI 1-20). As a neighborhood-level SDoH measure, SDI is useful for evaluating disparities in the context of LDLT. Center outreach efforts that aim to reduce disparities in LDLT could preferentially target US ZIP codes with SDI > 40.

School-Based Screening of Social Determinants of Health: A Scoping Review

School-based screening instruments have traditionally focused on assessing within-child factors, such as a student’s academic, social, emotional, behavioral, or physical development. This emphasis in school-based screening may be a missed opportunity to assess and ameliorate contextual factors (i.e., social determinants of health) influencing child development. In this scoping review, we aim to describe the current landscape of screening for social determinants of health (SDOH) in school settings. Following established practices for scoping reviews, we searched PsycInfo, ERIC, and CINAHL Plus in December 2022 for articles describing the development or use of an SDOH measure in a school setting. From each eligible article, two coders independently extracted (1) study characteristics, (2) measure characteristics, (3) available psychometric or usability information, and (4) reported outcomes of measure implementation. Descriptive and content analyses were used to examine data. We identified six articles describing the development or use of SDOH measures in elementary, high school, or university settings. These articles yielded six unique SDOH measures, intended for either adolescent or young adult self-report, caregiver proxy-report, or both. Measures included 6–25 SDOH items, with additional items assessing demographics, health behaviors, or mental health (e.g., depression). Reported outcomes included increased referrals to services and implementation of school-based supports to reduce social risk (e.g., school food pantry). We discuss next steps for research evaluating the feasibility and social consequences of school-based SDOH screening.

Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis.

Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA). We conducted a cluster analysis for a cohort of OHCA survivors to examine the association of clinical and social factors for mortality at 1 year. We used a retrospective observational OHCA cohort identified from Medicare claims data, including area-level social determinants of health (SDOH) features and hospital-level data sets. We applied k-means clustering algorithms to identify subphenotypes of beneficiaries who had survived an OHCA and examined associations of outcomes by subphenotype. We identified 27,028 unique beneficiaries who survived to discharge after OHCA. We derived 4 distinct subphenotypes. Subphenotype 1 included a distribution of more urban, female, and Black beneficiaries with the least robust area-level SDOH measures and the highest 1-year mortality (2375/4417, 53.8%). Subphenotype 2 was characterized by a greater distribution of male, White beneficiaries and had the strongest zip code-level SDOH measures, with 1-year mortality at 49.9% (4577/9165). Subphenotype 3 had the highest rates of cardiac catheterization at 34.7% (1342/3866) and the greatest distribution with a driving distance to the index OHCA hospital from their primary residence >16.1 km at 85.4% (8179/9580); more were also discharged to a skilled nursing facility after index hospitalization. Subphenotype 4 had moderate median household income at US $51,659.50 (IQR US $41,295 to $67,081) and moderate to high median unemployment at 5.5% (IQR 4.2%-7.1%), with the lowest 1-year mortality (1207/3866, 31.2%). Joint modeling of these features demonstrated an increased hazard of death for subphenotypes 1 to 3 but not for subphenotype 4 when compared to reference. We identified 4 distinct subphenotypes with differences in outcomes by clinical and area-level SDOH features for OHCA. Further work is needed to determine if individual or other SDOH domains are specifically tied to long-term survival after OHCA.

Recent Patterns of Care with BTK Inhibitors and Distribution of Social Determinants of Health Among Patients with CLL/SLL in the US Community Setting

BACKGROUND: The treatment landscape for patients with chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL/SLL) has shifted with the introduction of second-generation Bruton tyrosine kinase (BTK) inhibitors. Few real-world studies, however, have examined sequencing patterns among BTK inhibitors for CLL/SLL and the patient characteristics across different sequences, including social determinants of health (SDOH). The aim of this study is to examine the characteristics, treatment patterns, and SDOH among patients with CLL/SLL who received BTK inhibitors in a large network of community oncology practices. METHODS: This was a retrospective study of adult patients diagnosed with CLL/SLL who were prescribed a BTK inhibitor between 1/1/2020-4/30/2023 in The US Oncology Network. Study data were captured from structured fields in the iKnowMed electronic health record (EHR), with linkage to external sources to evaluate patients' Area Deprivation Index (ADI) scores and rural/urban status. ADI scores were determined by mapping patients' home addresses to a census block group (CBG), which was then linked to the ADI. ADI rankings are based on CBG and determined by the domains of income, education, employment, and housing quality, where high scores indicate increased disadvantage. Low socioeconomic status (SES) was defined as the top 20% of ADI scores on a national and state level (cut-off based on literature review). Rural/urban status was based on the RUCA codes published by the US Department of Agriculture. Insurance coverage was reported “as is” in the claims system with Medicaid an indicator of low SES. Results were assessed by treatment sequence, and SDOH measures results were reported as counts, frequency, and 95% confidence intervals (CI). Chi Square p-values were reported for selected SDOH measures and are shown in text. Data are presented for treatment subgroups with at least 30 eligible patients. RESULTS: In total, 2,082 patients were included in the analysis (median age 73 years; 61.9% male; 73.5% white; 35.0% Rai stage 0-I; median follow-up 14.1 months). The highest proportion of patients received acalabrutinib or ibrutinib without an observed subsequent BTK inhibitor (n=1,159 and n=628, respectively). Patient characteristics by the top 6 treatment sequences are provided in Table 1. Demographic and clinical characteristics across the treatment sequences were similar, including sex, race, and ethnicity distributions, along with Rai stage and ECOG performance status at baseline. Across the subgroups, 8.7% (n=182) were observed to receive a subsequent BTK inhibitor. Among patients who received a BTK inhibitor in 2020 (n=597), 62.3% (n=372) received ibrutinib and a single patient received zanubrutinib. Among the 206 patients who received a BTK inhibitor in 2023, 6.8% (n=14) and 28.6% (n=59) received ibrutinib and zanubrutinib, respectively. In total, 137 patients (6.6%; 95% CI 5.6-7.7) were classified as living in rural locations. Low SES status based on state and national indicators was observed among 361 (17.3%; 95% CI 15.7-19.0) and 125 (6.0%; 95% CI 5.0-7.1) patients, respectively, and was also similar across the subgroups. The highest proportion of patients had Medicare insurance coverage (42.1%; n=877; 95% CI 40-44.3), followed by managed Medicare (24.4%; n=508; 95% CI 22.6-26.3). A statistical difference was found between the treatment regimens for rural/urban ( P&amp;lt;0.01), but not for state ADI ( P=0.52), national ADI ( P=0.36), or Medicaid insurance coverage vs. all others ( P=0.18). The actual values for each regimen are in the Table 1. CONCLUSIONS: The results of this study provide insight into the shifting treatment landscape of BTK inhibitor use among patients with CLL/SLL. During the observation period, the use of ibrutinib decreased while acalabrutinib and zanubrutinib increased. When evaluating measures of SDOH, similarities across the treatment sequence subgroups suggest that patients' SES and Medicaid do not influence prescribing patterns, but rural/urban status show differences. Further real-world research and longitudinal follow-up are needed to examine the impact of SDOH on treatment choice, treatment switching, reasons for switching, and outcomes among users of BTK inhibitors. Additionally, a better understanding of the specific treatment regimen(s) and reasons driving rural/urban differences is needed.

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 &gt;30% of the statistical association between participant education and LVMI or CAC (Table), and &gt;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.