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Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services.

ABSTRACT: Introduction:A growing variety of diverse data sources is emerging to better inform health care delivery and health outcomes. We sought to evaluate the capacity for clinical, socioeconomic, and public health data sources to predict the need for various social service referrals among patients at a safety-net hospital. Materials and Methods:We integrated patient clinical data and community-level data representing patients' social determinants of health (SDH) obtained from multiple sources to build random forest decision models to predict the need for any, mental health, dietitian, social work, or other SDH service referrals. To assess the impact of SDH on improving performance, we built separate decision models using clinical and SDH determinants and clinical data only. Results:Decision models predicting the need for any, mental health, and dietitian referrals yielded sensitivity, specificity, and accuracy measures ranging between 60% and 75%. Specificity and accuracy scores for social work and other SDH services ranged between 67% and 77%, while sensitivity scores were between 50% and 63%. Area under the receiver operating characteristic curve values for the decision models ranged between 70% and 78%. Models for predicting the need for any services reported positive predictive values between 65% and 73%. Positive predictive values for predicting individual outcomes were below 40%. Discussion:The need for various social service referrals can be predicted with considerable accuracy using a wide range of readily available clinical and community data that measure socioeconomic and public health conditions. While the use of SDH did not result in significant performance improvements, our approach represents a novel and important application of risk predictive modeling.

SUBMITTER: Kasthurirathne SN

PROVIDER: S-EPMC7647142 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services.

Kasthurirathne Suranga N SN Vest Joshua R JR Menachemi Nir N Halverson Paul K PK Grannis Shaun J SJ

Journal of the American Medical Informatics Association : JAMIA 20180101 1

<h4>Introduction</h4>A growing variety of diverse data sources is emerging to better inform health care delivery and health outcomes. We sought to evaluate the capacity for clinical, socioeconomic, and public health data sources to predict the need for various social service referrals among patients at a safety-net hospital.<h4>Materials and methods</h4>We integrated patient clinical data and community-level data representing patients' social determinants of health (SDH) obtained from multiple s ...[more]

PMID: 29177457

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Project description:ObjectivesEarly hospital readmissions or deaths are key healthcare quality measures in pay-for-performance programs. Predictive models could identify patients at higher risk of readmission or death and target interventions. However, existing models usually do not incorporate social determinants of health (SDH) information, although this information is of great importance to address health disparities related to social risk factors. The objective of this study is to examine the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission.MethodsWe extracted electronic health record data for 19,941 hospital admissions between January 2015 and November 2017 at an academic medical center in New York City. We applied the Simplified HOSPITAL score model to predict potentially avoidable 30-day readmission or death and examined if incorporating individual- and community-level SDH could improve the prediction using cross-validation. We calculated the C-statistic for discrimination, Brier score for accuracy, and Hosmer-Lemeshow test for calibration for each model using logistic regression. Analysis was conducted for all patients and three subgroups that may be disproportionately affected by social risk factors, namely Medicaid patients, patients who are 65 or older, and obese patients.ResultsThe Simplified HOSPITAL score model achieved similar performance in our sample compared to previous studies. Adding SDH did not improve the prediction among all patients. However, adding individual- and community-level SDH at the US census tract level significantly improved the prediction for all three subgroups. Specifically, C-statistics improved from 0.70 to 0.73 for Medicaid patients, from 0.66 to 0.68 for patients 65 or older, and from 0.70 to 0.73 for obese patients.ConclusionsPatients from certain subgroups may be more likely to be affected by social risk factors. Incorporating SDH into predictive models may be helpful to identify these patients and reduce health disparities associated with vulnerable social conditions.

Project description:Preterm birth (PTB; <37 weeks gestation), is a leading cause of infant mortality and morbidity. Among those born preterm, risk increases as gestational age at birth decreases. Psychosocial factors such as depression symptoms and social determinants of health (SDH) may increase risk for PTB. Research is needed to understand these risk factors and identify effective interventions. This retrospective cohort study recruited English- and Spanish-speaking women presenting symptoms of preterm labor or admitted for PTB from an urban county hospital in the San Francisco Bay Area (n = 47). We used an iterative analytic approach by which qualitative data informed an exploratory quantitative analysis. Key exposures were presence of self-reported depression symptoms during pregnancy, SDH along eight domains, and receipt of behavioral health services. The outcome was gestational age at birth. T-tests, Wilcoxon rank sum tests, and linear regression models were used to test associations between the exposures and gestational age. Most participants were Black (25.5%) or Latina (59.6%). After adjusting for covariates, participants with depression symptoms had an average gestational age 3.1 weeks shorter (95% CI: -5.02, -1.20) than women reporting no symptoms. After adjusting for covariates, high number of adverse social determinants (≥ 4) suggested an association with shorter gestational age (p = 0.07, 1.65 weeks, 95% CI: -3.44, 0.14). Receipt of behavioral health services was associated with a significantly later gestational age; the median difference was 5.5 weeks longer for depression symptoms, 3.5 weeks longer for high social determinants, and 6 weeks longer for depression symptoms and high social determinants. Among a cohort of high-risk pregnant women, both depression symptoms during pregnancy and co-occurring with exposure to high adverse SDH are associated with shorter gestational age at birth, after controlling for psychosocial factors. Receipt of behavioral health services may be an effective intervention to address disparities in PTB.

Project description:BackgroundRisk models in the private insurance setting may systematically underpredict in the socially disadvantaged. In this study, we sought to determine whether US minority Medicare beneficiaries had disproportionately low costs compared with their clinical outcomes and whether adding social determinants of health (SDOH) into risk prediction models improves prediction accuracy.Methods and resultsRetrospective observational cohort study of 2016 to 2017 Medicare Current Beneficiary Survey data (n=3614) linked to Medicare fee-for-service claims. Logistic and linear regressions were used to determine the relationship between race/ethnicity and annual costs of care, all-cause hospitalization, cardiovascular hospitalization, and death. We calculated the observed-to-expected (O:E) ratios for all outcomes under 4 risk models: (1) age+sex, (2) model 1+clinical comorbidity adjustment, (3) model 2+SDOH, and (4) SDOH alone. Our sample was 44% male and 11% black or Hispanic. Among minorities, adverse clinical outcomes were inversely related to cost. After multivariable adjustment, blacks/Hispanics had higher rates of cardiovascular hospitalization (incidence rate ratio, 1.78; P=0.012) but similar annual costs ($-336, P=0.77) compared with whites. Among whites, models 1 to 4 all showed similar O:E ratios, suggesting high accuracy in risk prediction using current models. Among minorities, adjustment for age, sex, and comorbidities underpredicted all-cause hospitalization by 20% (O:E, 1.20) and cardiovascular hospitalization by 70% (O:E, 1.70) and overpredicted death by 21% (O:E, 0.79); adding SDOH brought O:E near 1 for all outcomes. Among both groups, the SDOH risk model alone performed with equal or superior accuracy to the model based on clinical comorbidities.ConclusionsA paradoxical relationship was observed between clinical outcomes and costs among racial and ethnic minorities. Because of systematic differences in access to care, cost may not be an appropriate surrogate for predicting clinical risk among vulnerable populations. Adjustment for SDOH improves the accuracy of risk models among racial and ethnic minorities and could guide use of prevention strategies.

Dataset Information

Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services.

Publications

Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services.

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