Evaluating the association of social needs assessment data with cardiometabolic health status in a federally qualified community health center patient population

Abstract

Background Health systems are increasingly using standardized social needs screening and response protocols including the Protocol for Responding to and Assessing Patients’ Risks, Assets, and Experiences (PRAPARE) to improve population health and equity; despite established relationships between the social determinants of health and health outcomes, little is known about the associations between standardized social needs assessment information and patients’ clinical condition. Methods In this cross-sectional study, we examined the relationship between social needs screening assessment data and measures of cardiometabolic clinical health from electronic health records data using two modelling approaches: a backward stepwise logistic regression and a least absolute selection and shrinkage operation (LASSO) logistic regression. Primary outcomes were dichotomized cardiometabolic measures related to obesity, hypertension, and atherosclerotic cardiovascular disease (ASCVD) 10-year risk. Nested models were built to evaluate the utility of social needs assessment data from PRAPARE for risk prediction, stratification, and population health management. Results Social needs related to lack of housing, unemployment, stress, access to medicine or health care, and inability to afford phone service were consistently associated with cardiometabolic risk across models. Model fit, as measured by the c-statistic, was poor for predicting obesity (logistic = 0.586; LASSO = 0.587), moderate for stage 1 hypertension (logistic = 0.703; LASSO = 0.688), and high for borderline ASCVD risk (logistic = 0.954; LASSO = 0.950). Conclusions Associations between social needs assessment data and clinical outcomes vary by cardiometabolic condition. Social needs assessment data may be useful for prospectively identifying patients at heightened cardiometabolic risk; however, there are limits to the utility of social needs data for improving predictive performance.

Screening for social determinants of health among populations at risk for MASLD: a scoping review

“…Among the 2,192 participants, 58.4% were uninsured, 34.6% obtained less than a high school education, 29% were unemployed and not seeking work, 19.4% were without housing and 16.2% reported food insecurity. Additional values can be found in the study ( 51 ). Palacio and colleagues reported the following among their study population: 39% were born outside the US, 21% had less than a high school education, 9% reported health illiteracy, 33% had financial strain, 22% described “quite a lot or very much stress,” a range from 9 to 56% responded yes to questions indicative of social isolation, 2% of participants’ income was at or below the federal poverty level and 31% reported a delay in medical care in the prior 12 months ( 55 ).…”

“…Among the studies that included specifics regarding survey administration, the most common approaches were collection via patient interview by the provider ( 51 , 54 ) or clinic staff; in Palacio et al, patients were sent messages via text or email asking them to access their patient portal to complete the SDoH survey. The two remaining studies described collection of SDoH in clinic but did not provide additional details.…”

“…Among the 10 studies reviewed, four of the studies described clear action plans for the SDoH data collected from patients. Drake et al referred patients to community resources or social services based on their identified needs ( 51 ). Similarly, Roth et al referred individuals to their community resource desk staffed by multilingual and multicultural resource specialists employed by community partners to assist with the navigation of community services including nutrition assistance, housing and employment support, and dental care.…”

“…Nineteen percent were unhoused, which was 16.6% among those with obesity, 21.4% with elevated blood pressure, and 21.6% in those with high ASCVD risk. Food insecurity overall was 16.2%, increased to 16.4, 20, and 20.4% among those with obesity, high blood pressure, and high ASCVD risk, respectively ( 51 ).…”

Impact of Social Determinants of Health in Spine Surgery

“…Future studies examining patient outcomes should not only include SDH elements but controls in statistical analysis. We propose the inclusion of a questionary or survey (i.e., Accountability Health Communities Screen Tool) along with propensity matching to more accurately incorporate reliable SDH qualities within spine surgery [ 99 ].…”