Does Accounting for Community‐Level SDOH Matter for Readmission Policy? Insights from New York City

ObjectiveWe measured physicians’ (1) perceived importance of having access to social determinants of health (SDOH) data received from external providers in the electronic health record (EHR); (2) internal SDOH documentation practices in the EHR, including whether physicians document SDOH in a structured format that may facilitate use; and (3) use of EHR SDOH data to identify community resources and make referrals on behalf of patients.ApproachWe conducted a secondary analysis of two national physician surveys.ParticipantsRespondents from the American Board of Family Medicine Recertification Survey (ABFM, n = 4040), a survey of family physicians fielded 2021–2022, and the National Physician Health IT Survey (NPHIT, n = 3006), a survey of outpatient physicians across specialty areas fielded in 2022.Key ResultsUnder half of physicians felt that SDOH data were very important (ABFM: 44.8%, NPHIT: 30.8%). Although most physicians documented SDOH in the EHR (ABFM: 72%, NPHIT: 63.3%), fewer used structured documentation methods (ABFM: 56.3%, NPHIT: 33.2%). In both surveys, physicians who participated in value-based care initiatives, those for whom > 10% of their patient population was considered vulnerable, and those who felt that their clinic had the resources to address patients’ social needs had higher likelihood of documenting SDOH. Sixty-two percent of family physicians reported using SDOH data for identifying resources and making referrals.ConclusionsIn 2022, most physicians documented SDOH data in their EHR, yet fewer used structured methods, limiting data exchange opportunities to address patients’ social needs. Under half of physicians considered access to external SDOH data to be “very” important, suggesting greater reliance on data collected internally and missed opportunities to identify patients who need support. Variation in perceived importance of SDOH data access and SDOH documentation by physician characteristics indicate opportunities to support adoption of structured documentation tools facilitating SDOH data capture and exchange to improve patient-centered care.

Cervical cancer disparities exist in the United States with the highest incidence in Hispanic women and the highest mortality in Black women. Effective control of cervical cancer in the population requires targeted interventions tailored to community composition in terms of race, ethnicity, and social determinants of health (SDOH). Using cancer registry and SDOH data, geospatial hot spot analyses were carried out to identify statistically significant neighborhood clusters with high numbers of cervical cancer cases within the catchment area of an NCI-Designated Cancer Center. The locations, racial and ethnic composition, and SDOH resources of these hot spots were used by the center's community outreach and engagement office to deploy mobile screening units (MSU) for intervention in communities with women facing heightened risk for cervical cancer. Neighborhood hot spots with high numbers of cervical cancer cases in south Florida largely overlap with locations of poverty. Cervical cancer hot spots are associated with a high percentage of Hispanic cases and low SDOH status, including low income, housing tenure, and education attainment. A geospatially referenced cancer surveillance platform integrating cancer registry, SDOH, and cervical screening data can effectively identify targets for cervical cancer intervention in neighborhoods experiencing disparities. Guided with a data-driven surveillance system, MSUs proactively bringing prevention education and cervical screening to communities with more unscreened, at-risk women are an effective means for addressing disparities associated with cervical cancer control.

BackgroundSocial determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.MethodsWe searched PubMed using keywords “SDOH” and “EHR”, underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.ResultsWe identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.DiscussionDespite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

The effects of social determinants of health on patients with epilepsy.

BackgroundGrowing evidence documents the impacts that social determinants of health (SDoH) can have on medical and surgical outcomes, but many physicians report lacking the time to screen and address patients’ SDoH. This gap creates an opportunity for medical students to apply their knowledge of SDoH in a clinical setting, actively participate as members of the medical team, and address a critical aspect of patient care.MethodsIn a pilot project, medical students rotating on the urogynecology surgical service were tasked with calling patients the day prior to their surgical encounter and asking them SDoH screening questions. To evaluate the completion of SDoH screening by medical students, a data analysis team determined if medical students added or updated patients’ SDoH data in the medical record within 72 hours preoperatively or by 24 hours postoperatively.ResultsBetween May 21 and October 31, 2024, 18 medical students screened 44 patients for social risk factors. Excluding tobacco use and transportation needs screening, medical students conducted at least 90% of the SDoH screenings for these patients.ConclusionThis project demonstrates that medical students are a resource that can be used to increase the rates of patient SDoH screening, particularly for factors that are not routinely screened. In addition, this project demonstrates how SDoH screening can be efficiently implemented on a busy surgical service without interrupting patient flow.

Introduction: In the UK, the number of comorbidities seen in children has increased along with the worsening obesity rate. These comorbidities worsen into adulthood. Genome-wide association studies have highlighted single nucleotide polymorphisms associated with the weight status of adults and offspring individually. To date, in the UK, parental genetic, lifestyle, and social determinants of health have not been investigated alongside one another as influencers of offspring weight status. A comprehensive obesity prevention scheme would commence prior to conception and involve parental intervention including all known risk factors. This current study aims to identify the proportion of overweight that can be explained by known parental risk factors, including genetic, lifestyle, and social determinants of health with offspring weight status in the UK. Methods: A cross-sectional study was carried out on 123 parents. Parental and offspring anthropometric data and parental lifestyle and social determinants of health data were self-reported. Parental genetic data were collected by use of GeneFiX saliva collection vials and genotype were assessed for brain-derived neurotrophic factor (BDNF) gene rs6265, melanocortin 4 receptor (MC4R) gene rs17782313, transmembrane protein 18 (TMEM18) gene rs2867125, and serine/threonine-protein kinase (TNN13K) gene rs1514175. Associations were assessed between parental data and the weight status of offspring. Results: Maternal body mass index modestly predicted child weight status (p < 0.015; R² = 0.15). More mothers of overweight children carried the MC4R rs17782313 risk allele (77.8%; p = 0.007) compared to mothers of normal-weight children. Additionally, fathers who were not Caucasian and parents who slept for <7 h/night had a larger percentage of overweight children when compared to their counterparts (p = 0.039; p = 0.014, respectively). Conclusion: Associations exist between the weight status of offspring based solely on parental genetic, lifestyle, and social determinants of health data. Further research is required to appropriately address future interventions based on genetic and lifestyle risk groups on a pre-parent cohort.

Social determinants of health (SDH), including age, sex, ethnicity, socioeconomic status and rurality, influence health outcomes. Clinical trials investigating antifibrotic agents for people with idiopathic pulmonary fibrosis (IPF) have been conducted in predominantly White and male populations; it is unclear whether other SDH have been considered. This study aimed to investigate active consideration and reporting of SDH in clinical trials of antifibrotic agents for people with IPF. Three registries (ClinicalTrials.gov, ANZCTR and International Standard Randomised Controlled Trial Number (ISRCTN)) plus CENTRAL (Cochrane Central Register of Controlled Trials) were searched for clinical trials investigating antifibrotic agents for people with IPF or various progressive fibrotic ILD variants registered from 1 January 2000 until 3 September 2023. Data were extracted regarding trial phase/status, recruitment strategies and eligibility criteria. If trial results were available, SDH data from demographics and subgroup analyses were extracted. Of 313 records identified, 70 trials were included. The majority of trials were phase II or III (77%), 56% were completed and 61% had reported results that included eight terminated trials. All 70 trials specified age and sex, but not other SDH, within their eligibility criteria. Of 43 trials reporting results, all reported age and sex and 40 (95%) reported ethnicity. 10 387 participants were described (74% male, 77% White, 16% Asian and <1% Black). Descriptors for ethnicity varied considerably. Five trials (12%) included only White participants and three (7%) included only Asian participants. No other SDH were reported. SDH beyond age, sex and ethnicity were neither considered nor reported in antifibrotic IPF trials. Trial populations were predominantly male and White. There is a need to actively consider SDH to ensure diverse and representative clinical trial populations.

Background: While polygenic risk scores (PRS) and social determinants of health (SDOH) are established predictors of coronary artery disease (CAD), interactions between PRS and individual SDOH remain largely underexplored. Research Question: Do income, education, material deprivation, and social isolation modify the effect of PRS on incident CAD? Methods: We studied 409,877 participants aged 40-69 years from the UK Biobank free from CAD at baseline and with available genotyping and SDOH data. A CAD PRS consisting of 1,296,172 variants (GPSmult, PGS PGP000466) was calculated. Four SDOH were examined: income (defined as self-reported household income before tax), education (highest educational qualification), area-level material deprivation (quantified through the Townsend Deprivation Index), and social isolation (defined as living alone). Cox proportional hazards models were used to examine the associations between each SDOH and incident CAD. Interactions between PRS and each SDOH were assessed to understand whether social factors modify the effect of PRS on CAD risk. Results: Among 409,877 participants (mean age 56.0 ± 8.1 years, 53.5% female), 12,244 incident CAD events occurred over a median follow-up time of 13.7 years. Each standard deviation (SD) increase in PRS was associated with a 1.88-fold increase in incident CAD risk (95% CI: 1.84, 1.91). Lower income (Hazard Ratio (HR) 1.48, 95% CI: 1.33, 1.64), lower educational attainment (HR 1.34, 95% CI: 1.27, 1.41), and higher TDI (HR 1.18, 95% CI: 1.11, 1.25) were all independently associated with greater incident CAD risk. Living alone was not significantly associated with CAD risk (HR 1.04, 95% CI: 0.99, 1.09). The absolute CAD risk difference between the high and low income groups was 2.62% in the highest PRS decile, compared to only 0.77% in the lowest decile. Similarly, the absolute risk difference between college graduates and those without a high school diploma decreased from 1.40% in the highest PRS decile to 0.68% in the lowest. ( Figure ). Multiplicative interactions between PRS and both income ( P interaction = 4.9×10 -5 ) and education ( P interaction = 6.1×10 -6 ) were statistically significant. Conclusion: These findings suggest a complex interaction between genetic risk and SDOH in relation to CAD risk, particularly for income and education. Integrating these insights into predictive models could enable more accurate risk stratification and inform targeted interventions for those at greatest risk.

Purpose The screening process for social determinants of health (SDoH) includes questions regarding life circumstances and barriers to accessing health care. For patients, these questions may be intrusive, biased, and potentially risky. This article describes human-centered design methods to engage birthing parents and health care team members around SDoH screening and referral in maternity care. Methods Three phases of qualitative research with birthing parents, health care teams, and hospital administrators were conducted in the United States. Shadowing, interviews, focus groups, and participatory workshops addressed the explicit and tacit concerns of the stakeholders regarding SDoH during maternity care. Results Birthing parents wanted to be informed of the purpose of the clinic collecting SDoH information and how this information is used. Health care teams want to feel they are providing reliable and quality resources to their patients. They would like greater transparency that administrators are acting on SDoH data and the information is reaching people that can assist patients. Conclusion As clinics implement patient-centered strategies for addressing SDoH in maternity care, it is important to include patients’ perspectives. This human-centered design approach advances understanding of knowledge and emotional needs around SDoH and offers insights to meaningful engagement around sensitive health data.

e13559 Background: Social determinants of health (SDOH) are both drivers of health disparities and barriers to clinical research participation, particularly among historically underserved or marginalized populations. Using real-world data (RWD), diversity plans in cancer clinical trials frequently aim to reduce disparities and improve representation. Our aim was to describe the representativeness of patients with advanced non-small cell lung cancer (aNSCLC) with respect to race, ethnicity and person-level SDOH in a curated electronic health record (EHR) database. Methods: This was a retrospective cohort study of individuals diagnosed with aNSCLC between 2016–2022 in a US-based oncology EHR database (ConcertAI). Information on person-level SDOH was derived from linkage of curated EHR data to administrative claims and consumer financial data. These data were also compared to a sample of patients diagnosed with first primary NSCLC from the Surveillance, Epidemiology, and End Results (SEER) Program and American Community Survey (ACS), where possible. The Kaplan-Meier product limit estimator was used to compare time from advanced diagnosis to treatment initiation. Results: From an overall sample of 17,772 patients with aNSCLC, a total of 10,390 patients had 1 or more SDOH characteristics of interest documented. Compared with patients without linked SDOH data, a higher proportion of patients with linked person-level SDOH data were non-Hispanic (NH) White patients (79% vs 71%) and lower proportions were NH Black (10% vs 13%), NH Asian/Pacific Islander (2% vs 4%) and Hispanic patients (2% vs 4%). Patients with linked SDOH were also more likely to have aNSCLC progressed from an earlier stage and received biomarker testing for targetable alterations. Overall, 54% of patients were married, 38% completed a bachelor’s degree or higher, 14% worked in a blue-collar occupation, and 61% had an individual income of ≤$35,000. All 4 of these SDOH characteristics differed significantly by race/ethnicity. Differences in time-to-treatment initiation were statistically significant by marital status (median of 50 [married] vs 55 [not married] days; P=0.02) and individual income (median of 50 [&gt;$60,000] vs 55 [≤$30,000] days; P=0.04). Patients with aNSCLC in these data differed from patients in SEER and ACS estimates with respect to greater representation of patients who are NH White, have lower levels of education, and lower individual income. Conclusions: This analysis demonstrated the feasibility of measuring person-level SDOH using EHR from patients with aNSCLC treated at oncology clinics across the US. RWD with linked SDOH characteristics should be used cautiously to address the representativeness gap in evidence from oncology trials given their potential inherent selection biases.

Linking neighborhood- and patient-level data provides valuable information about the influence of upstream social determinants of health (SDOH). However, sharing of these data across health systems presents challenges. We set out to develop a pipeline to acquire, deidentify, and share neighborhood-level SDOH data across multiple health systems. We created a pipeline centered around Decentralized Geomarker Assessment for Multi-Site Studies (DeGAUSS) that utilizes containerization to geocode patient addresses and obtain neighborhood-level SDOH variables. We compared DeGAUSS to a third-party vendor geocoding tool available at Duke Health using a cohort of adult patients referred for abdominal transplant from January 1, 2016, to December 31, 2022. We calculated Cohen's Kappa and percent disagreement at census block group and tract levels, and by Area Deprivation Index, urbanicity, and year. The pipeline successfully generated SDOH data for 97.8% of addresses. There was high concordance between DeGAUSS and the vendor tool at the census block group (0.93) and tract levels (0.95). At the block group level, disagreement proportion differed by year and urbanicity, with larger disagreement in the rural category than in micropolitan and metropolitan categories (13%, 7%, 6.2%, respectively). We describe a novel pipeline that can facilitate the secure acquisition and sharing of neighborhood-level SDOH without sharing PHI. The pipeline can be scaled to include additional social, climate, and environmental variables, and can be extended to an unlimited number of health systems.

PurposeWe aimed to identify whether social determinants of health (SDoH) are associated with the development of sepsis and assess the differences between individuals living within systematically disadvantaged neighbourhoods compared with those living outside these neighbourhoods.MethodsWe conducted a single-centre case-control study including 300 randomly selected adult patients (100 patients with sepsis and 200 patients without sepsis) admitted to the emergency department of a large academic tertiary care hospital in Hamilton, ON, Canada. We collected data on demographics and a limited set of SDoH variables, including neighbourhood household income, smoking history, social support, and history of alcohol disorder. We analyzed study data using multivariate logistic regression models.ResultsThe study included 100 patients with sepsis with a median [interquartile range (IQR)] age of 75 [58–84] yr and 200 patients without sepsis with a median [IQR] age of 72 [60–83] yr. Factors significantly associated with sepsis included arrival by ambulance, absence of a family physician, higher Hamilton Early Warning Score, and a recorded history of dyslipidemia. Important SDoH variables, such as individual or household income and race, were not available in the medical chart. In patients with SDoH available in their medical records, no SDoH was significantly associated with sepsis. Nevertheless, compared with their proportion of the Hamilton population, the rate of sepsis cases and sepsis deaths was approximately two times higher among patients living in systematically disadvantaged neighbourhoods.ConclusionsThis study revealed the lack of available SDoH data in electronic health records. Despite no association between the SDoH variables available and sepsis, we found a higher rate of sepsis cases and sepsis deaths among individuals living in systematically disadvantaged neighbourhoods. Including SDoH in electronic health records is crucial to study their effect on the risk of sepsis and to provide equitable care.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12630-024-02790-6.

Social determinants of health (SDOH) are the conditions in which people are born, grow, work, and live. Although evidence suggests that SDOH influence a range of health outcomes, health systems lack the infrastructure to access and act upon this information. The purpose of this manuscript is to explain the methodology that a health system used to: 1) identify and integrate publicly available SDOH data into the health systems’ Data Warehouse, 2) integrate a HIPAA compliant geocoding software (via DeGAUSS), and 3) visualize data to inform SDOH projects (via Tableau). First, authors engaged key stakeholders across the health system to convey the implications of SDOH data for our patient population and identify variables of interest. As a result, fourteen publicly available data sets, accounting for &gt;30,800 variables representing national, state, county, and census tract information over 2016–2019, were cleaned and integrated into our Data Warehouse. To pilot the data visualization, we created county and census tract level maps for our service areas and plotted common SDOH metrics (e.g., income, education, insurance status, etc.). This practical, methodological integration of SDOH data at a large health system demonstrated feasibility. Ultimately, we will repeat this process system wide to further understand the risk burden in our patient population and improve our prediction models – allowing us to become better partners with our community.

BackgroundPostpartum contraception plays a vital role in extending interpregnancy intervals and reducing adverse maternal and neonatal outcomes. However, patients at elevated risk due to geographic and social barriers may experience disruptions in postpartum care. We examine associations between residential distance to the delivery hospital and social determinants of health (SDoH) with contraception uptake at delivery discharge.MethodsWe conducted a retrospective cohort study of live births born at 22.0 gestational age and greater at a single urban academic center from March 2020 to March 2022. Demographic, obstetric, and SDoH data were extracted from electronic medical records. Contraceptive method recorded for all births as part of the delivery discharge note was manually reviewed classified into long-acting reversible contraception (LARC) or non-LARC methods. Univariate analyses were used to compare characteristics across groups living within 10.0 miles and those living 10.1 miles or further from the hospital. Logistic regression was used to identify predictors of LARC uptake.ResultsA total of 3,644 births were included; 54.6% of patients were Black, 30.3% Hispanic, and 73.7% utilized public insurance. A total of 74.2% of patients resided within 10.0 miles of the delivery hospital. Patients living farther away were more likely to face transportation barriers, lower educational attainment, and higher SDoH personal risk scores. Overall, 18.6% living closer and 22.3% living further from the delivery hospital received LARC at discharge [X2 (2) = 7.45, p < .05]. After adjustment for race, ethnicity, insurance, maternal age, partnership status, and a combined risk factor that assigned a positive binary indicator if the neonate was delivered via cesarean section, had a NICU stay, and/or, the maternal length of hospital stay was greater than 3 days, odds for LARC uptake at delivery discharge was elevated for those living further from the hospital as compared to those living closer to the delivery hospital (OR = 1.20, 95% CI = 1.01–1.43). Those with high personal SDoH risk also had significantly higher odds of LARC uptake at discharge (OR = 1.24, 95% CI 1.02–1.50) as compared to those without SDoH personal risk noted.ConclusionSeveral factors remained associated with LARC uptake at delivery discharge including residential distance, SDoH, and insurance. Our findings support the importance of inpatient contraceptive counseling and provision, particularly for socially vulnerable populations.

Amputation Rates and Associated Social Determinants of Health in the Most Populous US Counties