Screening for social needs during routine medical visits is increasingly common. To date, there are limited data on which social needs are most predictive of health outcomes. The aim of this study is to build a predictive model from integrated social needs screening and health data to identify individual or clusters of social needs that are predictive of chronic illnesses. Using the electronic medical record data from a Federally Qualified Health Center collected from January 2016 to December 2020, demographic, diagnosis, and social needs screening data were used to look at adjusted and unadjusted associations of individual unmet social needs with chronic illnesses (n = 2497). The least absolute shrinkage and selection operator (LASSO) model was used to identify which social need(s) were associated with overall burden of chronic illness, and individual diagnoses of hypertension, obesity, diabetes, and psychiatric illness. The LASSO model identified age, race, language, gender, insurance, transportation, and food insecurity as significant predictors of any chronic illness. Using these variables in a multivariable model, transportation (adjusted odds ratio [aOR] 1.66) was the only social need that remained significantly associated with chronic illness diagnosis. Transportation need was also significantly associated with diabetes (aOR 1.44) and psychiatric illness (aOR 1.98). Food insecurity was associated with obesity (aOR 10.21). Using LASSO models to identify significant social needs, transportation was identified as a predictor in 3 of the 5 models. Further research is warranted to evaluate if addressing patients' transportation needs has the potential to mitigate chronic disease sequelae for vulnerable adults to advance health equity.
Keywords: LASSO; community health centers; social determinants of health.