Relationship between Neighborhood-Level Social Risk Factor Measures and Presenting Glaucoma Severity Utilizing Multilevel Modeling.

Purpose: The neighborhood and built environment social determinant of health domain has several social risk factors (SRFs) that are modifiable through policy efforts. We investigated the impact of neighborhood-level SRFs on presenting glaucoma severity at a tertiary eye care center. Design: A cross-sectional study from August 2012 to May 2022 in the University of Michigan electronic health record (EHR). Participants: Patients with a diagnosis of any open-angle glaucoma with ≥1 eye care visit at the University of Michigan Kellogg Eye Center and ≥1 reliable visual field (VF). Methods: Participants who met inclusion criteria were identified by International Classification of Diseases ninth and tenth revision codes (365.x/H40.x). Data extracted from the EHR included patient demographics, address, presenting mean deviation (MD), and VF reliability. Addresses were mapped to SRF measures at the census tract, block group, and county levels. Multilevel linear regression models were used to estimate the fixed effects of each SRF on MD, after adjusting for patient-level demographic factors and a random effect for neighborhood. Interactions between each SRF measure with patient-level race and Medicaid status were tested for an additive effect on MD. Main Outcome Measures: The main outcome measure was the effect of SRF on presenting MD. Results: In total, 4428 patients were included in the analysis who were, on average, 70.3 years old (standard deviation = 11.9), 52.6% self-identified as female, 75.8% self-identified as White race, and 8.9% had Medicaid. The median value of presenting MD was -4.94 decibels (dB) (interquartile range = -11.45 to -2.07 dB). Neighborhood differences accounted for 4.4% of the variability in presenting MD. Neighborhood-level measures, including worse area deprivation (estimate, ß = -0.31 per 1-unit increase; P < 0.001), increased segregation (ß = -0.92 per 0.1-unit increase in Theil's H index; P < 0.001), and increased neighborhood Medicaid (ß = -0.68; P < 0.001) were associated with worse presenting MD. Significant interaction effects with race and Medicaid status were found in several neighborhood-level SRF measures. Conclusions: Although patients' neighborhood SRF measures accounted for a minority of the variability in presenting MD, most neighborhood-level SRFs are modifiable and were associated with clinically meaningful differences in presenting MD. Policies that aim to reduce neighborhood inequities by addressing allocation of resources could have lasting impacts on vision outcomes. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Effect of multi-level social risk factors on developmental trajectories of sexual risk behaviors among Bahamian middle-to-late adolescents.

Background: Few studies have examined how multi-level social factors interact and affect developmental patterns of sexual risk among middle-to-late adolescents who are at risk of experiencing sexual risk behaviors. We examined developmental trajectories of sexual risk behaviors of boys and girls in middle-to-late adolescence and the effects of exposure to three social risk factors (poor parental monitoring, peer risk, and neighborhood risk). Methods: We followed 2,332 Bahamian adolescents every six months from Grades 10-12. We used group-based trajectory modeling to identify distinct trajectories of sexual risk behaviors for boys and girls. Results: We identified three trajectories each for boys and girls. Peer risk and neighborhood risk predicted a high sexual-risk trajectory for boys, and peer risk (alone or combined with other risk factors) had the greatest impact on the membership of moderate-to-high-risk trajectory for girls. Parental monitoring had a relatively small effect on adolescents' sexual risk behavior. Conclusion: Our results underscore the importance of early identification of adolescents with sexual risk behavior and development of targeted prevention interventions to improve adolescent health outcomes.

From Policy to Practice: Assessing the State Innovation Models Initiative's Early Success in Incorporating Social Determinants of Health in ASCVD Hospitalizations in the United States.

The study examines effects of the CMS State Innovation Models(SIM) on capturing social risk factors in adults hospitalized with Atherosclerotic Cardiovascular Disease (ASCVD). Using a difference-in-differences(DID) approach with propensity score weights, the study compared documentation of secondary diagnosis of SDOH/social factors using ICD-9 V codes ("SDOH codes") in adults hospitalized with ASCVD as a primary diagnosis (N= 1,485,354). Data were gathered from January 1, 2010, to September 30, 2015, covering the period before and after the SIM implementation in October 2013. From January 2010 to September 2015, SDOH codes were infrequently utilized among adults with ASCVD(0.55%, 95% CI: 0.43%-0.67%). SDOH codes with ASCVD increased from pre- to post-period in SIM states(0.56% to 0.93%) and comparison states (0.46% to 0.56%). SIM implementation was associated with greater improvement in SDOH codes utilization (adjusted OR 1.30, 95%CI: 1.18-1.43) during ASCVD hospitalizations. The odds of SDOH codes utilization were 86% higher in ED admissions(AOR 1.86, 95%CI: 1.76-1.97) than in routine admissions with ASCVD. Findings were similar when limiting population to older adults(>=65 years) enrolled in Medicare(AOR 1.50, 95%CI 1.31-1.71), whereas not significant for Medicaid beneficiaries. The study points to challenges for healthcare providers in documenting SDOH in adults with ASCVD.

Understanding social risk factors in patients presenting to the emergency Department for Acute Heart Failure: A pilot study.

BACKGROUND: The standard of care for congestive heart failure (CHF) aims to slow disease progression and maximize patient function, however there is an increase in emergency department (ED) revisits and readmissions. Social risk factors play a role in the disease management and prognosis of CHF. There is a gap in the identification of low-risk CHF patient who would be safely discharged using an initial social risk factor stratification. OBJECTIVES: To generate a social risk profile for patients presenting to the ED with acute CHF exacerbation and identify variables that may increase the risk of 7-day and overall mortality, 30-day ED revisit, and readmission. METHODS: We conducted a pilot prospective survey-based study among adult patients presenting to the ED with acute CHF exacerbation. The combination of a self-report questionnaire and retrospective chart review was used to generate a CHF risk profile. RESULTS: A total of 62 patients were recruited in the pilot study with a mean age of 69.5 years. The preliminary data indicated that prior to this ED visit, 21% of patients were not aware of a previous CHF diagnosis; 64.5% of patients rated their sleep quality as poor or very poor; 72.6% reported orthopnea; and 43.5% reported recent weight gain. 37.1% of patients did not adhere to dietary recommendations and some patients did not adhere to their medication regime 100%. CONCLUSION: This study suggests establishing a social risk profile for patients presenting to the ED with CHF can help formulate a CHF-specific care plan and optimize multidisciplinary management to reduce ED revisits and readmissions.

Using social risks to predict unplanned hospital readmission and emergency care among hospitalized Veterans.

OBJECTIVES: (1) To estimate the association of social risk factors with unplanned readmission and emergency care after a hospital stay. (2) To create a social risk scoring index. DATA SOURCES AND SETTING: We analyzed administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse. Settings were VA medical centers that participated in a national social work staffing program. STUDY DESIGN: We grouped socially relevant diagnoses, screenings, assessments, and procedure codes into nine social risk domains. We used logistic regression to examine the extent to which domains predicted unplanned hospital readmission and emergency department (ED) use in 30 days after hospital discharge. Covariates were age, sex, and medical readmission risk score. We used model estimates to create a percentile score signaling Veterans' health-related social risk. DATA EXTRACTION: We included 156,690 Veterans' admissions to a VA hospital with discharged to home from 1 October, 2016 to 30 September, 2022. PRINCIPAL FINDINGS: The 30-day rate of unplanned readmission was 0.074 and of ED use was 0.240. After adjustment, the social risks with greatest probability of readmission were food insecurity (adjusted probability = 0.091 [95% confidence interval: 0.082, 0.101]), legal need (0.090 [0.079, 0.102]), and neighborhood deprivation (0.081 [0.081, 0.108]); versus no social risk (0.052). The greatest adjusted probabilities of ED use were among those who had experienced food insecurity (adjusted probability 0.28 [0.26, 0.30]), legal problems (0.28 [0.26, 0.30]), and violence (0.27 [0.25, 0.29]), versus no social risk (0.21). Veterans with social risk scores in the 95th percentile had greater rates of unplanned care than those with 95th percentile Care Assessment Needs score, a clinical prediction tool used in the VA. CONCLUSIONS: Veterans with social risks may need specialized interventions and targeted resources after a hospital stay. We propose a scoring method to rate social risk for use in clinical practice and future research.

Families' Perspectives on Social Services Navigation After Pediatric Urgent Care.

BACKGROUND: Interest is growing in clinic-based programs that screen for and intervene on patients' social risk factors, including housing, food, and transportation. Though several studies suggest these programs can positively impact health, few examine the mechanisms underlying these effects. This study explores pathways through which identifying and intervening on social risks can impact families' health. METHODS: This qualitative study was embedded in a randomized clinical trial that examined the health impacts of participation in a social services navigation program. We conducted semi-structured interviews with 27 English or Spanish-speaking caregivers of pediatric patients who had participated in the navigation program. Interviews were analyzed using thematic analysis. RESULTS: Caregivers described 3 pathways through which the navigation program affected overall child and/or caregiver health: 1) increasing families' knowledge of and access to social services; 2) helping families connect with health care services; and 3) providing emotional support that reduced caregiver isolation and anxiety. Participants suggested that navigation programs can influence health even when they do not directly impact resource access. DISCUSSION: Social care programs may impact health through multiple potential pathways. Program impacts seem to be mediated by the extent to which programs increase knowledge of and access to social and health care services and support positive relationships between families and program personnel.

Adjusting Clinical Plans Based on Social Context.

BACKGROUND: Social risk data collection is expanding in community health centers (CHCs). We explored clinicians' practices of adjusting medical care based on their awareness of patients' social risk factors-that is, changes they make to care plans to mitigate the potential impacts of social risk factors on their patients' care and health outcomes-in a set of Texas CHCs. METHODS: Convergent mixed methods. Surveys/interviews explored clinician perspectives on adjusting medical care based on patient social risk factors. Survey data were analyzed with descriptive statistics; interviews were analyzed using thematic analysis and inductive coding. RESULTS: Across 4 CHCs, we conducted 15 clinician interviews and collected 97 surveys. Interviews and surveys overall indicated support for adjustment activities. Two main themes emerged: 1) clinicians reported making frequent adjustments to patient care plans based on their awareness of patients' social contexts, while simultaneously expressing concerns about adjustment; and 2) awareness of patients' social risk factors, and clinician time, training, and experience all influenced clinician adjustments. CONCLUSIONS: Clinicians at participating CHCs described routinely adjusting patient care plans based on their patients' social contexts. These adjustments were being made without specific guidelines or training. Standardization of adjustments may facilitate the contextualization of patient care through shared decision making to improve outcomes.

Assessing New York City's COVID-19 Vaccine Rollout Strategy: A Case for Risk-Informed Distribution.

This study reviews the impact of eligibility policies in the early rollout of the COVID-19 vaccine on coverage and probable outcomes, with a focus on New York City. We conducted a retrospective ecological study assessing age 65+, area-level income, vaccination coverage, and COVID-19 mortality rates, using linked Census Bureau data and New York City Health administrative data aggregated at the level of modified zip code tabulation areas (MODZCTA). The population for this study was all individuals in 177 MODZCTA in New York City. Population data were obtained from Census Bureau and New York City Health administrative data. The total mortality rate was examined through an ordinary least squares (OLS) regression model, using area-level wealth, the proportion of the population aged 65 and above, and the vaccination rate among this age group as predictors. Low-income areas with high proportions of older people demonstrated lower coverage rates (mean vaccination rate 52.8%; maximum coverage 67.9%) than wealthier areas (mean vaccination rate 74.6%; maximum coverage 99% in the wealthiest quintile) in the first 3 months of vaccine rollout and higher mortality over the year. Despite vaccine shortages, many younger people accessed vaccines ahead of schedule, particularly in high-income areas (mean coverage rate 60% among those 45-64 years in the wealthiest quintile). A vaccine program that prioritized those at greatest risk of COVID-19-associated morbidity and mortality would have prevented more deaths than the strategy that was implemented. When rolling out a new vaccine, policymakers must account for local contexts and conditions of high-risk population groups. If New York had focused limited vaccine supply on low-income areas with high proportions of residents 65 or older, overall mortality might have been lower.

The relationship between community social risk factors and regional hospital-reported cash, negotiated, and chargemaster prices for 14 common services.

BACKGROUND: Social risk factors are key drivers of the geographic variation in spending in the United States but little is known how community-level social risk factors are associated with hospital prices. Our objective was to describe the relationship between regional hospital-reported prices and social risk factors by price type (chargemaster, cash, commercial, Medicare, and Medicaid). METHODS: This cross-sectional analysis used newly available hospital-reported prices from acute general hospitals in 2022. The prices were for 14 common services. Prices were winsorized at 98%, wage index-adjusted, standardized by service, and aggregated to hospital service areas (HSAs). For social risk, we used 23 measures across 5 domains of social risk (socioeconomic position; race, ethnicity, and culture; gender; social relationships; and residential and community context). Spearman's correlation was used to estimate associations between median prices and social risk by price type. RESULTS: Prices were reported from 2,386 acute general hospitals in 45% (1,502 of 3,436) HSAs. Correlations between regional prices and other social risk factors varied by price type (range: -0.19 to 0.31). Chargemaster and cash prices were significantly correlated with the most community characteristics (10 of 23, 43%) followed by commercial prices (8, 35%). Medicare and Medicaid prices were only significantly correlated with 1 measure (all p < 0.01). All price types were significantly correlated with the percentage of uninsured (all p < 0.01). Chargemaster, cash, and commercial prices were positively correlated with percentage of Hispanic residents, residents with limited English proficiency, and non-citizens (all p < 0.05). CONCLUSIONS: While regional correlations between prices and social risk factors were weak across all prices, chargemaster, cash, and commercial prices were more like closely aligned with community-level social risk factors than the two public payers (Medicare and Medicaid). Chargemaster, cash, and commercial hospital prices appeared to be higher in socially disadvantaged communities. Further research is needed to clarify the relationship between prices and community social risk factors.

Trajectories and correlates of mental health among urban, school-age children during the COVID-19 pandemic: a longitudinal study.

BACKGROUND: The COVID-19 pandemic posed numerous obstacles to psychosocial wellbeing for children. We conducted a longitudinal study to evaluate child mental health and social risks during the pandemic. METHODS: Participants were 172 caregivers of children aged 6-11 years old who attended well child visits within 6 months before pandemic onset at an urban safety net hospital in the US. Prepandemic data was extracted from the electronic medical record, and surveys were administered at three time points between August 2020 and July 2021. We measured mental health symptoms with the Pediatric Symptom Checklist-17, social risks (e.g., food and housing insecurity) with the THRIVE questionnaire, and school modality (in-person, hybrid, remote). RESULTS: Compared to pre-pandemic, children had significantly higher PSC-17 total scores (overall mental health symptoms) and THRIVE total scores (total burden of social risks) at all three mid-pandemic waves. Using longitudinal mixed models accounting for time, social risks, and school modality, both social risks (B = 0.37, SE = 0.14, p < 0.01) and school modality were significantly associated with PSC-17 scores (B = - 1.95, SE = 0.63, p < 0.01). Children attending in-person school had fewer mental health symptoms than those attending remote or hybrid school. CONCLUSION: Mental health symptoms and social risks remained significantly higher fifteen months after the onset of the COVID-19 pandemic compared to prepandemic. In-person attendance at school appeared protective against persistently elevated mental health symptoms.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions.

Background: Social 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. Methods: We 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. Results: We 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. Discussion: Despite 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.

Optimizing Glycemic Outcomes for Minoritized and Medically Underserved Adults Living with Type 1 Diabetes.

Individuals living with type 1 diabetes (T1D) from medically underserved communities have poorer health outcomes. Efforts to improve outcomes include a focus on team-based care, activation of behavior change, and enhancing self-management skills and practices. Advanced diabetes technologies are part of the standard of care for adults with T1D. However, health care providers often carry implicit biases and may be uncomfortable with recommending technologies to patients who have traditionally been excluded from efficacy trials or have limited real-world exposure to devices. We review the literature on this topic and provide an approach to address these issues in clinical practice.

The social determinants of mental health and disorder: evidence, prevention and recommendations.

People exposed to more unfavourable social circumstances are more vulnerable to poor mental health over their life course, in ways that are often determined by structural factors which generate and perpetuate intergenerational cycles of disadvantage and poor health. Addressing these challenges is an imperative matter of social justice. In this paper we provide a roadmap to address the social determinants that cause mental ill health. Relying as far as possible on high-quality evidence, we first map out the literature that supports a causal link between social determinants and later mental health outcomes. Given the breadth of this topic, we focus on the most pervasive social determinants across the life course, and those that are common across major mental disorders. We draw primarily on the available evidence from the Global North, acknowledging that other global contexts will face both similar and unique sets of social determinants that will require equitable attention. Much of our evidence focuses on mental health in groups who are marginalized, and thus often exposed to a multitude of intersecting social risk factors. These groups include refugees, asylum seekers and displaced persons, as well as ethnoracial minoritized groups; lesbian, gay, bisexual, transgender and queer (LGBTQ+) groups; and those living in poverty. We then introduce a preventive framework for conceptualizing the link between social determinants and mental health and disorder, which can guide much needed primary prevention strategies capable of reducing inequalities and improving population mental health. Following this, we provide a review of the evidence concerning candidate preventive strategies to intervene on social determinants of mental health. These interventions fall broadly within the scope of universal, selected and indicated primary prevention strategies, but we also briefly review important secondary and tertiary strategies to promote recovery in those with existing mental disorders. Finally, we provide seven key recommendations, framed around social justice, which constitute a roadmap for action in research, policy and public health. Adoption of these recommendations would provide an opportunity to advance efforts to intervene on modifiable social determinants that affect population mental health.

Relationship between Unstable Housing, Food Insecurity, and Vision Status in the MI-SIGHT Community Eye Disease Screening Program.

PURPOSE: Assess rate of visual impairment (VI) from uncorrected refractive error (URE) and associations with demographic and socioeconomic factors among low-income patients presenting to the Michigan Screening and Intervention for Glaucoma and Eye Health through Telemedicine (MI-SIGHT) program. DESIGN: Cross-sectional study. PARTICIPANTS: Adults ≥ 18 years without acute ocular symptoms. METHODS: MI-SIGHT program participants received a telemedicine-based eye disease screening and ordered glasses through an online optical shop. Participants were categorized based on refractive error (RE) status: VI from URE (presenting visual acuity [PVA] ≤ 20/50, best corrected visual acuity [BCVA] ≥ 20/40), URE without VI (PVA ≥ 20/40, had ≥ 2 lines of improvement to BCVA), and no or adequately corrected RE (PVA ≥ 20/40, < 2 line improvement to BCVA). Patient demographics, self-reported visual function, and satisfaction with glasses obtained through the program were compared between groups using analysis of variance, Kruskal-Wallis, chi-square, and Fisher exact testing. MAIN OUTCOME MEASURES: PVA, BCVA, and presence of VI (defined as PVA ≤ 20/50). RESULTS: Of 1171 participants enrolled in the MI-SIGHT program during the first year, the average age was 55.1 years (standard deviation = 14.5), 37.7% were male, 54.1% identified as Black, and 1166 (99.6%) had both PVA and BCVA measured. VI was observed in 120 (10.3%); 96 had VI from URE (8.2%), 168 (14.4%) had URE without VI, and 878 (75.3%) had no or adequately corrected RE. A smaller percentage of participants with VI from URE reported having a college degree and a larger percentage reported income < $10 000 compared to participants with no or adequately corrected RE (3.2% versus 14.2%, P = 0.02; 45.5% versus 21.6%, respectively, P < 0.0001. Visual function was lowest among participants with VI from URE, followed by those with URE without VI, and then those with no or adequately corrected RE (VFQ9 composite score 67.3 ± 19.6 versus 77.0 ± 14.4 versus 82.2 ± 13.3, respectively; P < 0.0001). 71.2% (n = 830) ordered glasses for an average cost of $36.80 ± $32.60; 97.7% were satisfied with their glasses. CONCLUSIONS: URE was the main cause of VI at 2 clinics serving low-income communities and was associated with reduced vision-related quality of life. An online optical shop with lower prices made eyeglasses accessible to low-income patients. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Social Determinants of Health: An Overview for the Primary Care Provider.

Social determinants of health (SDoH) are reflected in how people live (access to health care, economic stability, built environment, food security, climate), learn (the educational environment), work (occupational environment), and play/socialize (social context and digital domain). All of these day-to-day conditions play a vital role in a patient's overall health, and a primary care provider should be prepared to understand their role to screen, assess, and address SDoH in clinical practice.

Social Risk Factors That Increase Cardiovascular and Breast Cancer Risk.

PURPOSE OF REVIEW: Cardiovascular disease (CVD) and breast cancer (BC) are significant causes of mortality globally, imposing a substantial health burden. This review article aims to examine the shared risk factors and social determinants that contribute to the high prevalence of both diseases, with a focus on social risk factors. RECENT FINDINGS: The common risk factors for CVD and BC, such as hypertension, diabetes, obesity, aging, and physical inactivity, are discussed, emphasizing their modifiability. Adhering to ideal cardiovascular health behaviors has shown a trend toward lower BC incidence. Increased risk of CVD-related mortality is significantly impacted by age and race in BC patients, especially those over 45 years old. Additionally, racial disparities in both diseases highlight the need for targeted interventions. Social determinants of health, including socioeconomic status, education, employment, and neighborhood context, significantly impact outcomes for both CVD and BC. Addressing social factors is vital in reducing the burden of both CVD and BC and improving overall health equity.

Developing a brief assessment of social risks for the Veterans Health Administration Survey of Healthcare Experiences of Patients.

OBJECTIVE: To determine whether a 6- or 12-month look-back period affected rates of reported social risks in a social risk survey for use in the Veterans Health Administration and to assess associations of social risks with overall health and mental health. STUDY DESIGN: Cross-sectional survey of respondents randomized to 6- or 12-month look-back period. DATA SOURCES AND STUDY SETTING: Online survey with a convenience sample of Veterans in June and July 2021. DATA COLLECTION/EXTRACTION METHODS: Veteran volunteers were recruited by email to complete a survey assessing social risks, including financial strain, adult caregiving, childcare, food insecurity, housing, transportation, internet access, loneliness/isolation, stress, discrimination, and legal issues. Outcomes included self-reported overall health and mental health. Chi-squared tests compared the prevalence of reported social risks between 6- and 12-month look-back periods. Spearman correlations assessed associations among social risks. Bivariate and multivariable logistic regression models estimated associations between social risks and fair/poor overall and mental health. PRINCIPAL FINDINGS: Of 3418 Veterans contacted, 1063 (31.10%) responded (87.11% male; 85.61% non-Hispanic White; median age = 70, interquartile range [IQR] = 61-74). Prevalence of most reported social risks did not significantly differ by look-back period. Most social risks were weakly intercorrelated (median |r| = 0.24, IQR = 0.16-0.31). Except for legal issues, all social risks were associated with higher odds of fair/poor overall health and mental health in bivariate models. In models containing all significant social risks from bivariate models, adult caregiving and stress remained significant predictors of overall health; food insecurity, housing, loneliness/isolation, and stress remained significant for mental health. CONCLUSIONS: Six- and 12-month look-back periods yielded similar rates of reported social risks. Although most individual social risks are associated with fair/poor overall and mental health, when examined together, only adult caregiving, stress, loneliness/isolation, food, and housing remain significant.

Evaluating the comparability of patient-level social risk data extracted from electronic health records: A systematic scoping review.

Objective: To evaluate how and from where social risk data are extracted from EHRs for research purposes, and how observed differences may impact study generalizability. Methods: Systematic scoping review of peer-reviewed literature that used patient-level EHR data to assess 1 ± 6 social risk domains: housing, transportation, food, utilities, safety, social support/isolation. Results: 111/9022 identified articles met inclusion criteria. By domain, social support/isolation was most often included (N = 68/111), predominantly defined by marital/partner status (N = 48/68) and extracted from structured sociodemographic data (N = 45/48). Housing risk was defined primarily by homelessness (N = 39/49). Structured housing data was extracted most from billing codes and screening tools (N = 15/30, 13/30, respectively). Across domains, data were predominantly sourced from structured fields (N = 89/111) versus unstructured free text (N = 32/111). Conclusion: We identified wide variability in how social domains are defined and extracted from EHRs for research. More consistency, particularly in how domains are operationalized, would enable greater insights across studies.

Implementation of Social Isolation Screening and an Integrated Community Resource Referral Platform.

BACKGROUND: Screening and referral programs for social isolation and loneliness in older patients increased during the COVID-19 pandemic in primary care settings to mitigate associated adverse health outcomes. This study explores community health centers' experiences implementing a social isolation and loneliness screening program involving a community resource referral platform integrated into the electronic health record to support referrals. METHODS: A formative mixed methods evaluation in 4 community health centers. Semistructured interviews, observation of implementation meetings, facilitated group discussions, surveys, and utilization data extracted from the electronic health record and community resource referral platform were collected and analyzed concurrently. RESULTS: Screening for social isolation and loneliness can heighten health center staff knowledge and prioritization of socially isolated older patients. Participants indicate using an integrated community resource referral platform may only be useful in certain circumstances, particularly for those located outside urban areas. The experiences of these health centers indicate that when implementing interventions to mitigate patients' social isolation and loneliness, it is necessary to consider other resource directories, needed adjustments to referral and documentation workflows, and potential impacts on patients and care teams. CONCLUSION: Screening older patients for social isolation could increase care team awareness of social risk; assistance related referral options should be considered carefully.

Association of Insurance Type with Inpatient Surgical 30-day Readmissions, Emergency Department Visits/Observation Stays, and Costs.

OBJECTIVE: To assess the association of Private, Medicare, and Medicaid/Uninsured insurance type with 30-day Emergency Department visits/Observation Stays (EDOS), readmissions, and costs in a safety-net hospital (SNH) serving diverse socioeconomic status patients. SUMMARY BACKGROUND DATA: Medicare's Hospital Readmission Reduction Program (HRRP) disproportionately penalizes SNHs. METHODS: This retrospective cohort study used inpatient National Surgical Quality Improvement Program (2013-2019) data merged with cost data. Frailty, expanded Operative Stress Score, case status, and insurance type were used to predict odds of EDOS and readmissions, as well as index hospitalization costs. RESULTS: The cohort had 1,477 Private; 1,164 Medicare; and 3,488 Medicaid/Uninsured cases with a patient mean age 52.1 years [SD=14.7] and 46.8% of the cases were performed on male patients. Medicaid/Uninsured (aOR=2.69, CI=2.38-3.05, P<.001) and Medicare (aOR=1.32, CI=1.11-1.56, P=.001) had increased odds of urgent/emergent surgeries and complications versus Private patients. Despite having similar frailty distributions, Medicaid/Uninsured compared to Private patients had higher odds of EDOS (aOR=1.71, CI=1.39-2.11, P<.001), and readmissions (aOR=1.35, CI=1.11-1.65, P=.004), after adjusting for frailty, OSS, and case status, while Medicare patients had similar odds of EDOS and readmissions versus Private. Hospitalization variable cost %change was increased for Medicare (12.5%) and Medicaid/Uninsured (5.9%), but Medicaid/Uninsured was similar to Private after adjusting for urgent/emergent cases. CONCLUSIONS: Increased rates and odds of urgent/emergent cases in Medicaid/Uninsured patients drive increased odds of complications and index hospitalization costs versus Private. SNHs care for higher cost populations while receiving lower reimbursements and are further penalized by the unintended consequences of HRRP. Increasing access to care, especially for Medicaid/Uninsured patients, could reduce urgent/emergent surgeries resulting in fewer complications, EDOS/readmissions, and costs.