Social Determinants Of Health Data Research Articles

Development and validation of mortality prediction models based on the social determinants of health

BackgroundThere is no standardised approach to screening adults for social risk factors. The goal of this study was to develop mortality risk prediction models based on the social determinants of...

Abstract PO1-10-03: Prevalence of and Factors Associated with Food Insecurity and Other Adverse Social Determinants of Health Among Breast Oncology Patients

Abstract Background: Social determinants of health (SDOH), the non-medical factors that influence health status, have a profound impact on breast cancer (BC) outcomes and contribute to survival disparities. Food insecurity (FI), defined as a lack of consistent access to adequate food resources, is a modifiable SDOH associated with treatment disruptions, medication nonadherence, and decreased quality of life. To identify at-risk patients and inform future interventions, we implemented SDOH screening of patients seen in breast oncology clinic at our urban academic medical center. Methods: Screening was conducted through an 8-item questionnaire on the electronic patient portal. The questions covered FI, housing and transportation challenges, and emergency department (ED) visits. We conducted a retrospective analysis of SDOH data collected among patients scheduled for a breast oncology appointment between 11/1/22 and 5/25/23. Any level of food hardship or concern constituted a positive FI screen. Descriptive statistics were used to determine the prevalence of FI and other SDOH, to assess differences in FI rates by age, race/ethnicity, and clinical stage, and to compare characteristics of food-insecure vs food-secure patients. We also conducted a multivariable logistic regression analysis to estimate the odds ratio (OR) and 95% confidence interval (CI) associated with sociodemographic/clinical factors and FI. Results: A total of 2,585 patients were seen in our breast oncology practice, of which 1,189 (46.0%) were screened for SDOH, including 41.2% non-Hispanic White (NHW), 9.6% non-Hispanic Black (NHB) and 21.0% Hispanic patients. Among patients who responded to each SDOH question, 15.5% (146/940) reported some level of FI; 8.7% (84/962) were unable to pay their rent or mortgage on time; for 5.8% (57/989), lack of transportation interfered with their treatment. The FI rate differed significantly by race and ethnicity: 4.5% of NHW, 15.2% of NHB, and 41.8% of Hispanic patients reported FI (p< 0.001). In multivariable analysis, NHB and Hispanic patients also had significantly higher odds of FI compared to NHW patients (OR=3.68, 95% CI=1.7-7.7, p< 0.001 for NHB and OR=15.0, 95% CI=8.8-27.1, p< 0.001 for Hispanic patients). Patients with metastatic disease had higher rates of FI compared to other breast oncology patients (23.6% vs 14.8%, p=0.048), and a metastatic BC diagnosis was associated with over twice the odds of FI (OR=2.18, 95% CI=1.1-4.2, p=0.02). Among food-insecure patients, 41.1% were unable to pay their rent or mortgage on time and 22.6% reported transportation difficulties interfering with treatment (vs 2.6% and 2.4%, respectively, for food-secure patients (p< 0.001)). Compared to food-secure patients, those who were food-insecure had significantly higher rates of self-reported ED visits in the past 12 months: 18.8% vs. 33.6% reported at least 1 ED visit, respectively, and 5.2% vs. 16.4% reported multiple ED visits, respectively (p< 0.001). Conclusions: In this analysis of SDOH data among patients seen in breast oncology clinic, FI was the most common social health risk reported, with nearly a quarter of patients with metastatic BC reporting FI. Racial/ethnic minorities were also at considerably higher risk for FI. ED utilization was significantly higher among food-insecure patients. Targeted interventions to reduce FI in at-risk populations are warranted to improve clinical and healthcare utilization outcomes. Sociodemographic and Clinical Characteristics and Other Reported Social Determinants of Health Among Food-Secure vs Food-Insecure Patients Citation Format: Claire Sathe, David DeStephano, Anita Gul, Melissa Beauchemin, Justine Kahn, Melissa Accordino, Katherine Crew, Ngozika Emezienna, Dawn Hershman. Prevalence of and Factors Associated with Food Insecurity and Other Adverse Social Determinants of Health Among Breast Oncology Patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-10-03.

Leveraging data to support health equity in an integrated delivery and finance system

AbstractIntroductionTo accelerate healthcare transformation and advance health equity, scientists in learning health systems (LHSs) require ready access to integrated, comprehensive data that includes information on social determinants of health (SDOH).MethodsWe describe how an integrated delivery and finance system leveraged its learning ecosystem to advance health equity through (a) a cross‐sector initiative to integrate healthcare and human services data for better meeting clients' holistic needs and (b) a system‐level initiative to collect and use patient‐reported SDOH data for connecting patients to needed resources.ResultsThrough these initiatives, we strengthened our health system's capacity to meet diverse patient needs, address health disparities, and improve health outcomes. By sharing and integrating healthcare and human services data, we identified 281 000 Shared Services Clients and enhanced care management for 100 adult Medicaid/Special Needs Plan members. Over a 1‐year period, we screened 9173 (37%) patients across UPMC's Women's Health Services Line and connected over 700 individuals to social services and supports.ConclusionsOpportunities exist for LHSs to improve, expand, and sustain their innovative data practices. As learnings continue to emerge, LHSs will be well positioned to accelerate healthcare transformation and advance health equity.

The Current State of Neonatal Neurodevelopmental Follow-up Programs in North America: A Children's Hospitals Neonatal Consortium Report.

This study aimed to determine neonatal neurodevelopmental follow-up (NDFU) practices across academic centers. This study was a cross-sectional survey that addressed center-specific neonatal NDFU practices within the Children's Hospitals Neonatal Consortium (CHNC). Survey response rate was 76%, and 97% of respondents had a formal NDFU program. Programs were commonly staffed by neonatologists (80%), physical therapists (77%), and nurse practitioners (74%). Median gestational age at birth identified for follow-up was ≤32 weeks (range 26-36). Median duration was 3 years (range 2-18). Ninety-seven percent of sites used Bayley Scales of Infant and Toddler Development, but instruments used varied across ages. Scores were recorded in discrete electronic data fields at 43% of sites. Social determinants of health data were collected by 63%. Care coordination and telehealth services were not universally available. NDFU clinics are almost universal within CHNC centers. Commonalities and variances in practice highlight opportunities for data sharing and development of best practices. · Neonatal NDFU clinics help transition high-risk infants home.. · Interdisciplinary neonatal intensive care unit follow-up brings together previously separated outpatient service lines.. · This study reviews the current state of neonatal NDFU in North America..

Trends in publication and levels of social determinants of health reporting in Journal of Clinical and Translational Science from 2017 to 2023.

Social determinants of health affect clinical and translational research processes and outcomes but remain underreported in empirical studies. This scoping review examined the rate and types of social determinants of health (SDoH) variables included in the JCTS translational research studies published between 2017 and 2023 and included 129 studies. Most papers (91.7%) reported at least one SDoH variable with age, race and ethnicity, and sex included most often. Future studies to inform the role of SDoH data in translational research and science are recommended, and a draft SDoH data checklist is provided.

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.

Addressing Health Disparities using Social Determinants of Health: A Descriptive Analytics Approach

Purpose: This paper explores Social Determinants of Health (SDOH) data and its implications using descriptive data analytics. The analysis aims to help healthcare organizations understand the trends and impact on the industry and develop strategies to resolve disparities and eventually reduce healthcare costs.&#x0D; Methodology: This paper uses descriptive data analytics to analyze Social Determinants of Health (SDOH) data and its implications. The study focuses on the Centers for Disease Control and Prevention's (CDC) five critical areas of SDOH: healthcare quality and access, education quality and access, economic stability, social and economic context, and built and neighborhood environment.&#x0D; Findings: Health equity remains a significant challenge in the world and the United States, with persistent inequalities and injustice among different populations. Failure to address health equity issues could lead to social problems, including violent crimes, alcohol, and substance abuse, and potentially a global pandemic, which could significantly impact healthcare systems and increase costs. According to a report published by Deloitte, inequities in the United States cost more than 300 billion dollars in 2022. If not addressed, they could account for more than a trillion dollars in annual spending by 2040. Social Determinants of Health (SDOH) are critical in providing health equity and reducing disparities.&#x0D; Unique Contribution to Theory, Practice, and Policy: Understanding the implications and trends of SDOH data is crucial in developing targeted strategies to achieve health equity for all. Healthcare organizations can use this analysis's findings to establish policies and practices that address social determinants of health and reduce disparities. This, in turn, will help improve overall health outcomes and reduce healthcare costs.

Racial and Ethnic Differences in Social Determinants of Health and Health-Related Social Needs Among Adults - Behavioral Risk Factor Surveillance System, United States, 2022.

Social determinants of health (SDOH) are a broad array of social and contextual conditions where persons are born, live, learn, work, play, worship, and age that influence their physical and mental wellbeing and quality of life. Using 2022 Behavioral Risk Factor Surveillance System data, this study assessed measures of adverse SDOH and health-related social needs (HRSN) among U.S. adult populations. Measures included life satisfaction, social and emotional support, social isolation or loneliness, employment stability, food stability/security, housing stability/security, utility stability/security, transportation access, mental well-being, and health care access. Prevalence ratios were adjusted for age, sex, education, marital status, income, and self-rated health. Social isolation or loneliness (31.9%) and lack of social and emotional support (24.8%) were the most commonly reported measures, both of which were more prevalent among non-Hispanic (NH) American Indian or Alaska Native, NH Black or African American, NH Native Hawaiian or other Pacific Islander, NH multiracial, and Hispanic or Latino adults than among NH White adults. The majority of prevalence estimates for other adverse SDOH and HRSN were also higher across all other racial and ethnic groups (except for NH Asian) compared with NH White adults. SDOH and HRSN data can be used to monitor needed social and health resources in the U.S. population and help evaluate population-scale interventions.

Digital Health Implementations Using Tokenomics - Building Stakeholder Support for New Technologies

Speakers discuss how data is stored, shared, and monetized connecting an entire ecosystem with benefits. In addition, EHRs, genomic DNA, wearables, pharmacy and social determinants of health data and accelerated access to de-identified, tokenized, real-time data is addressed in shaping a new health data economy.

Social Determinants of Health Are Associated with Failed Bowel Management for Children with Anorectal Malformations.

Children with anorectal malformations (ARMs) benefit from bowel management programs (BMPs) to manage constipation or fecal incontinence. We aimed to understand the role of social determinants of health (SDOH) in outcomes following BMPs in this population. A single-institution, institutional review board (IRB) approved, retrospective review was performed in children with ARM who underwent BMP from 2014 to 2021. Clinical, surgical, and SDOH data were collected. Children were stratified as clean or not clean per the Rome IV criteria at the completion of BMP. Descriptive statistics were computed. Categorical variables were analyzed via Fisher's exact tests and continuous variables with Mood's median tests. In total, 239 patients who underwent BMP were identified; their median age was 6.62 years (interquartile range [IQR]: 4.78-9.83). Of these, 81 (34%) were not clean after completing BMP. Children with prior history of antegrade enema procedures had a higher rate of failure. Children who held public insurance, lived within driving distance, had unmarried parents, lived with extended family, and lacked formal support systems had a significant association with BMP failure (p < 0.05 for all). Type of ARM, age at repair, type of repair, age at BMP, and type of BMP regimen were not significantly associated with failure. There is a significant correlation of failure of BMPs with several SDOH elements in patients with ARM. Attention to SDOH may help identify high-risk patients in whom additional care may lead improved outcomes following BMP.

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.

The Utility of the Social Vulnerability Index as a Proxy for Social Disparities Following Total Knee Arthroplasty

BackgroundIn 2021, alternative payment models accounted for 40% of traditional Medicare reimbursements. As such, we sought to examine health disparities through a standardized categorization of social disparity using the social vulnerability index (SVI). We examined (1) risk factors for SVI ≥ 0.50, (2) incidences of complications, and (3) risk factors for total complications between patients who have SVI < 0.50 and SVI ≥ 0.50 who had a total knee arthroplasty (TKA). MethodsPatients who underwent TKA between January 1, 2022 and December 31, 2022 were identified in the state of Maryland. A total of 4,952 patients who had complete social determinants of health data were included. Patients were divided into 2 cohorts according to SVI: < 0.50 (n = 2,431) and ≥ 0.50 (n = 2,521) based on the national mean SVI of 0.50. The SVI identifies communities that may need support caused by external stresses on human health based on 4 themed scores: socioeconomic status, household composition and disability, minority status and language, and housing and transportation. The SVI theme of household composition and disability encompassed patients aged 65 years and more, patients aged 17 years and less, civilians who have a disability, single-parent households, and English language deficiencies. The higher the SVI, the more social vulnerability or resources are needed to thrive in a geographic area. ResultsWhen controlling for risk factors and patient comorbidities, the theme of household composition and disability (odds ratio 2.0, 95% confidence interval 1.1 to 5.0, P = .03) was the only independent risk factor for total complications. Patients who had an SVI ≥0.50 were more likely to be women (65.8% versus 61.0%, P < .001), Black (34.4% versus 12.9%, P < .001), and have a median household income < $87,999 (21.3% versus 10.2%, P < .001) in comparison to the patients who had an SVI < 0.50, respectively. ConclusionsThe SVI theme of household composition and disability, encompassing patients aged 65 years and more, patients aged 17 years and less, civilians who have a disability, single-parent households, and English language deficiencies, were independent risk factors for total complications following TKA. Together, these findings offer opportunities for interventions with selected patients to address social disparities.

Social determinants of hypertensive disorders of pregnancy trends in Mexican municipalities

ObjectivesTo analyze the hypertensive disorders of pregnancy (HDP) trends in the 2000–2020 period and its association with social determinants of health (SDH) in Mexican municipalities. Study designThis was a retrospective longitudinal study analyzing data from routine information systems on 21,493,076 hospital discharges due to maternal causes and SDH data of 2,439–2,450 municipalities between 2000 and 2020. Main outcomes measuresWe used joinpoint regression models to analyze HDP trends and hybrid negative binomial regression models to examine the association between SDH and HDP rates at intra- and inter-municipality levels. ResultsHDP rate increased from 0.54 in 2000 to 2.42 cases per 100 live births in 2020 (annual average percent change of 7.7 %, 95 % CI 6.6–8.9). Municipalities with higher marginalization index had higher HDP rates (IRR 1.12, 95 % CI 1.01–1.24; 1.36, 95 % CI 1.25–1.47; 1.30, 95 % CI 1.23–1.37, and 1.06, 95 % CI 1.00–1.12, in 2000–2004, 2005–2009, 2010–2014, 2015–2020, respectively). Municipal increases in the percentage of population with high schooling were associated with decreases in HDP rates in 2000–2009. Meanwhile, the role of socioeconomic status at the state level was less consistent, yet the HDP rates were lower in most socially advantaged regions. ConclusionDuring 21 years, HDP rates increased overall in Mexican municipalities, suggesting an increased maternal morbidity burden due to HDP. The association of municipal marginalization and socioeconomic level with HDP trends indicates a need to ensure equitable interventions to improve maternal health in Mexican municipalities.

Socio-Demographic and Behavioral Predictors of Admission for Kidney Disease in an Urban Hospital in Northwest Indiana

Background: The CDC estimates more than 1 in 7 American adults have Chronic Kidney Disease (CKD) and nearly 800,000 live with End-Stage Renal Disease (ESRD); the majority of whom require dialysis. Current literature demands further exploration on how social determinants of health (SDOH) influence inpatient and emergency services utilization for CKD/ESRD-related exacerbations. This study examined socio-demographic and behavioral factors associated with hospital admission for CKD/ESRD. It is part of a three-phased Community-Based Participatory Research (CBPR) partnership between IUSM-NW and St. Mary Medical Center (SMMC) to examine and address relationships between SDOH, patient demographics, health behaviors, and health outcomes from January 2022-January 2025.&#x0D; Methods: This retrospective study analyzed a limited dataset generated by SMMC from EPIC™ with SDOH, demographic, health behavior, and health outcomes data obtained from adult inpatients between January 2021 and March 2023. CKD/ESRD was determined by admission diagnosis ICD-10 Code N18. Data analysis utilized SPSS 29.0 to produce frequencies, Chi-Square tests (p&lt;0.05), and a binary logistic regression (p&lt;0.05). This study was exempted by Indiana University Human Research Protection Program (IRB # 14040).&#x0D; Results: This study included 10,953 inpatient admissions involving predominantly white (78.4%) and older adult (68 ± 21) patients. CKD/ESRD represented 96 admissions. The bivariate analysis found a significant association between CKD/ESRD admissions and age (p&lt;0.001), race (p&lt;0.001), sex (p=0.008), insurance type (p=0.030), and smoking tobacco use (p=0.004). After adjusting for all factors, being Asian (OR=12.172, p&lt;0.001), Black (OR=3.702; p&lt;0.001), female (OR=0.518; p=0.003), publicly insured (OR=2.76; p=0.014), and former smoking (OR=1.606; p=0.045) were significantly associated with CKD/ESRD admission.&#x0D; Conclusions: This study revealed several demographics, SDOH, and health behaviors associated with CKD/ESRD admission in an urban hospital. Uncovering predictors of CKD/ESRD admission enables focused interventions to decrease admissions. The next CBPR phases will apply advanced statistical analyses to further explore these relationships and codevelop interventions.

Neurovascular Conditions and Associated Socio-Demographic and Behavioral Factors in an Urban Hospital in Northwest Indiana

Background: Brain health equity remains an underexplored research area despite high prevalence of neurovascular conditions and related health impacts. This study examined the associations between socio-demographic, behavioral factors, and hospital admissions for neurovascular-related morbidity in an urban underserved community. It is part of a multi-phased Community-Based Participatory Research (CBPR) partnership between Indiana University School of Medicine-Northwest and St. Mary Medical Center (SMMC) to examine the prevalence, distribution, and relationships between social determinants of health (SDOH), demographics, health behaviors, and health outcomes in Northwest Indiana.&#x0D; Methods: This retrospective study analyzed a limited dataset generated by SMMC from EPIC™ with SDOH, demographic, behavioral and health outcomes data for adult inpatient visits from January 2021 to March 2023. Neurovascular admission was determined by ICD-10 Codes I67-69. Data analysis was conducted in SPSS 29.0 using descriptive statistics (i.e., frequencies and central tendency), bivariate analysis (Chi-square; p&lt;0.05), and multivariate analysis (binary logistic regression; p&lt;0.05). This study received exemption from Indiana University Human Research Protection Program (IRB #14040).&#x0D; Results: There were 1,489 participants included in this study. The majority were white (77.7%), older adults (67 ± 21.5) and publicly insured (77.8%). The bivariate analysis demonstrated significant relationships between admission for neurovascular conditions and age group (p&lt;0.001), veteran status (p&lt;0.001), insurance type (p&lt;0.037), and physical activity (p&lt;0.001). After adjusting for these factors in multivariate analysis, age group (p&lt; 0.003) and physical inactivity (p&lt;0.008) were significantly associated with admission for neurovascular conditions.&#x0D; Conclusion: Understanding how SDOH and behavioral factors influence neurovascular admissions and inequities in urban settings will enhance collaborative efforts to develop, implement, and evaluate evidence-based interventions. The subsequent CBPR phases will utilize these findings to explore how socioeconomic status affects these patients’ ability to seek emergent and/or surgical care. This will enable implementation of strategies that better account for SDOH in patient care.

Examination of Social Determinants of Health Characteristics Influencing Maternal Postpartum Symptom Experiences.

Postpartum women experience multiple, co-occurring postpartum symptoms. It is unknown if social determinants of health (SDOH) influence postpartum symptom typologies. This secondary analysis used the Community and Child Health Network study data. Participants included for analysis varied depending on the availability of the SDOH data (N = 851 to 1784). Bivariate and multiple regression analyses were conducted to examine the association between SDOH and previously identified postpartum symptom typologies. Area under the receiver operating characteristics curve (AUROC) was calculated to examine if adding SDOH variables contributes to predicting postpartum symptom typologies. The adjusted odds (aOR) of being in high symptom severity or occurrence typologies were greater for participants who had less than high school education (aOR = 2.29), experienced healthcare discrimination (aOR = 2.21), used governmental aid (aOR = 2.11), or were food insecure (aOR = 2.04). AUROC improved after adding SDOH. Considering experiences of different social-economic hardships influence postpartum symptom typologies, future practice and research should address SDOH to improve postpartum symptom experiences.

RocHealthData.org: Development and usage of a publicly available, geographic source of social determinants of health data.

Access to local, population specific, and timely data is vital in understanding factors that impact population health. The impact of place (neighborhood, census tract, and city) is particularly important in understanding the Social Determinants of Health. The University of Rochester Medical Center's Clinical and Translational Science Institute created the web-based tool RocHealthData.org to provide access to thousands of geographically displayed publicly available health-related datasets. The site has also hosted a variety of locally curated datasets (eg., COVID-19 vaccination rates and community-derived health indicators), helping set community priorities and impacting outcomes. Usage statistics (available through Google Analytics) show returning visitors with a lower bounce rate (leaving a site after a single page access) and spent longer at the site than new visitors. Of the currently registered 1033 users, 51.7% were from within our host university, 20.1% were from another educational institution, and 28.2% identified as community members. Our assessments indicate that these data are useful and valued across a variety of domains. Continuing site improvement depends on new sources of locally relevant data, as well as increased usage of data beyond our local region.

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration.

The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

Polysocial Risk Scores: Implications for Cardiovascular Disease Risk Assessment and Management.

To review current evidence, discuss key knowledge gaps and identify opportunities for development, validation and application of polysocial risk scores (pSRS) for cardiovascular disease (CVD) risk prediction and population cardiovascular health management. Limited existing evidence suggests that pSRS are promising tools to capture cumulative social determinants of health (SDOH) burden and improve CVD risk prediction beyond traditional risk factors. However, available tools lack generalizability, are cross-sectional in nature or do not assess social risk holistically across SDOH domains. Available SDOH and clinical risk factor data in large population-based databases are under-utilized for pSRS development. Recent advances in machine learning and artificial intelligence present unprecedented opportunities for SDOH integration and assessment in real-world data, with implications for pSRS development and validation for both clinical and healthcare utilization outcomes. pSRS presents unique opportunities to potentially improve traditional "clinical" models of CVD risk prediction. Future efforts should focus on fully utilizing available SDOH data in large epidemiological databases, testing pSRS efficacy in diverse population subgroups, and integrating pSRS into real-world clinical decision support systems to inform clinical care and advance cardiovascular health equity.

Abstract B074: A biobank of fecal immunochemical tests (FIT) to study gut microbiome-mediated cancer disparities

Abstract Introduction: Colorectal cancer (CRC) poses a disproportionate burden among certain Hispanic/Latino subgroups, such as Puerto Rican and Cuban individuals, and Black individuals. Causes of CRC disparities are poorly understood but are likely driven by social determinants of health (SDOH), which may directly and indirectly influence the gut microbiome (GM). In turn, the GM is increasingly implicated in colorectal carcinogenesis. Therefore, it is plausible that the GM may be targeted to reduce excess CRC burden among disproportionately affected populations. However, prior studies of the GM and CRC had limited inclusion of underserved populations and limited collection of SDOH data. Objective: To clearly define the role of the GM in CRC and other cancer disparities, we conducted a pilot study to establish a well-annotated cohort that includes longitudinally collected fecal specimens among diverse populations. Methods: We leveraged fecal immunochemical tests (FIT), a cost-effective method for CRC screening in patient populations with limited access to colonoscopy, in which the GM can be well characterized. Participants completed annual FIT testing following standard protocols as implemented in a Federally Qualified Health Center. FIT samples were processed and banked at Moffitt Cancer Center in -80ºC freezers. All participants completed stool and risk factor questionnaires capturing SDOH, medical history, diet, and lifestyle exposures. To further validate the FIT microbiome, we extracted DNA from 8 sets of triplicate FIT aliquots (200ul each) using the automated MagCore®️ DNA Kit and then performed shotgun sequencing to characterize the fecal microbiome. To test for quality of the data and to compare microbiome metrics (e.g., alpha diversity and microbial abundances) in replicate samples, we calculated intraclass correlation coefficients (ICCs) using linear mixed effects models. Results: In just one clinic, to date, more than N=79 individuals who are primarily Spanish speakers and persons from other racial/ethnic minority subgroups have been enrolled. The racial and ethnic distribution included 39% Mexican, 11% Black, 6% Puerto Rican, 9% Central or South American, and 5% Cuban individuals. Approximately, 59% of the consented population preferentially spoke Spanish and 14% tested positive on the FIT. In the pilot FIT testing samples, we found that the integrity of the DNA was high, with only a few (N=3 of 24) samples with insufficient DNA yield for shotgun metagenomic sequencing. ICCs were generally above ≥ 95% for alpha diversity metrics and abundance of species/genes indicating excellent handling and processing procedures for FIT microbiome extraction and sequencing. Conclusion: Our findings support the concept that FIT-based community screening programs offer exceptional opportunity for GM research in diverse, underrepresented populations. Our future studies will integrate the SDOH data and expand this cohort with the goal of contributing novel insights regarding associations of the GM with early pathogenesis of CRC across diverse populations.  Citation Format: Doratha Byrd, Stephanie Hogue, Xuefeng Wang, Sugriva Forsyth, Martha Fuentes, Kathleen Egan, Tiffany Carson, Youngchul Kim, Jessica Islam, Cathy Meade, Clement Gwede. A biobank of fecal immunochemical tests (FIT) to study gut microbiome-mediated cancer disparities [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B074.