Objectives. We explored the relationship between social isolation and mortality in a nationally representative US sample and compared the predictive power of social isolation with that of traditional clinical risk factors.Methods. We used data on 16 849 adults from the Third National Health and Nutrition Examination Survey and the National Death Index. Predictor variables were 4 social isolation factors and a composite index. Comparison predictors included smoking, obesity, elevated blood pressure, and high cholesterol. Unadjusted Kaplan–Meier tables and Cox proportional hazards regression models controlling for sociodemographic characteristics were used to predict mortality.Results. Socially isolated men and women had worse unadjusted survival curves than less socially isolated individuals. Cox models revealed that social isolation predicted mortality for both genders, as did smoking and high blood pressure. Among men, individual social predictors included being unmarried, participating infrequently in religious activities, and lacking club or organization affiliations; among women, significant predictors were being unmarried, infrequent social contact, and participating infrequently in religious activities.Conclusions. The strength of social isolation as a predictor of mortality is similar to that of well-documented clinical risk factors. Our results suggest the importance of assessing patients’ level of social isolation.

As evidence of the associations between social factors and health outcomes continues to mount, capturing and acting on social determinants of health (SDOH) in clinical settings has never been more relevant. Many professional medical organizations have endorsed screening for SDOH, and the U.S. Office of the National Coordinator for Health Information Technology has recommended increased capacity of health information technology to integrate and support use of SDOH data in clinical settings. As these efforts begin their translation to practice, a new subfield of health informatics is emerging, focused on the application of information technologies to capture and apply social data in conjunction with health data to advance individual and population health. Developing this dedicated subfield of informatics—which we term social informatics—is important to drive research that informs how to approach the unique data, interoperability, execution, and ethical challenges involved in integrating social and medical care.

Recent studies have explored clinician impacts of health care–based interventions that respond to patients’ social and economic needs. These studies were limited by available clinician data. We used the Commonwealth International Health Policy Survey of 890 primary care physicians to examine associations between clinic capacity to respond to patients’ social needs and physician satisfaction, stress, and perceived medical care quality. Results suggest that perceived capacity to address social needs is strongly associated with both clinician satisfaction and perceived medical care quality. Our findings add to a growing literature on the potential return on investment of clinical interventions to address social needs.

BACKGROUND AND OBJECTIVES: Social risk factors are linked to children’s health, but little is known about how frequently these factors are documented using the International Classification of Diseases (ICD) or whether documentation is associated with health care use outcomes. Using a large administrative database of pediatric hospitalizations, we examined the prevalence of ICD social risk code documentation and hypothesized that social code documentation would be associated with longer length of stay (LOS) and readmission. METHODS: We analyzed hospitalizations of children ages ≤18 using the 2012 Nationwide Readmissions Database. The following ICD social codes were used as predictors: family member with alcohol and/or drug problem, history of abuse, parental separation, foster care, educational circumstance, housing instability, other economic strain, and legal circumstance. Outcomes included long LOS (top quintile) and readmission within 30 days after discharge. Covariates included individual, hospital, and season variables. RESULTS: Of 926 073 index hospitalizations, 7432 (0.8%) had International Classification of Diseases, Ninth Revision, social codes. Social code documentation was significantly associated with long LOS. Adjusting for covariates, family alcohol and/or drug problem (odds ratio [OR] 1.65; 95% confidence interval [CI] 1.16–2.35), foster care (OR 2.37, 95% CI 1.53–3.65), other economic strain (OR 2.12, 95% CI 1.38–3.26), and legal circumstances (OR 1.66; 95% CI 1.02–2.71) remained significant predictors of long LOS. Social code documentation was not associated with readmission after adjusting for covariates. CONCLUSIONS: Social ICD codes are associated with prolonged LOS and readmission in pediatric hospitalizations, but they are infrequently documented. Future work exploring these associations could help to determine if addressing social risk factors in inpatient settings might improve child health outcomes.

Background: Prior work has found relationships between childhood social adversity and biomarkers of stress, but knowledge gaps remain. To help address these gaps, we explored associations between social adversity and biomarkers of inflammation (interleukin-1β [IL-1β], IL-6, IL-8, tumor necrosis factor-alpha [TNF-α], and salivary cytokine hierarchical “clusters” based on the three interleukins), neuroendocrine function (cortisol, cortisone, dehydroepiandrosterone, testosterone, and progesterone), neuromodulation (N-arachidonoylethanolamine, stearoylethanolamine, oleoylethanolamide, and palmitoylethanolamide), and epigenetic aging (Pediatric-Buccal-Epigenetic clock). Methods: We collected biomarker samples of children ages 0–17 recruited from an acute care pediatrics clinic and examined their associations with caregiver-endorsed education, income, social risk factors, and cumulative adversity. We calculated regression-adjusted means for each biomarker and compared associations with social factors using Wald tests. We used logistic regression to predict being in the highest cytokine cluster based on social predictors. Results: Our final sample included 537 children but varied based on each biomarker. Cumulative social adversity was significantly associated with having higher levels of all inflammatory markers and with cortisol, displaying a U-shaped distribution. There were no significant relationships between cumulative social adversity and cortisone, neuromodulation biomarkers or epigenetic aging. Conclusion: Our findings support prior work suggesting that social stress exposures contribute to increased inflammation in children. Impact: Our study is one of the largest studies examining associations between childhood social adversity and biomarkers of inflammation, neuroendocrine function, neuromodulation, and epigenetic aging. It is one of the largest studies to link childhood social adversity to biomarkers of inflammation, and the first of which we are aware to link cumulative social adversity to cytokine clusters. It is also one of the largest studies to examine associations between steroids and epigenetic aging among children, and one of the only studies of which we are aware to examine associations between social adversity and endocannabinoids among children. Clinical Trial Registration: NCT02746393