Can Social Policies Improve Health? A Systematic Review and Meta-Analysis of 38 Randomized Trials

Context: Insurers and health care providers are investing heavily in nonmedical social interventions in an effort to improve health and potentially reduce health care costs. Methods: We performed a systematic review and meta-analysis of all known randomized social experiments in the United States that included health outcomes. We reviewed 5,880 papers, reports, and data sources, ultimately in- cluding 61 publications from 38 randomized social experiments. After synthe- sizing the main findings narratively, we conducted risk of bias analyses, power analyses, and random-effects meta-analyses where possible. Finally, we used multivariate regressions to determine which study characteristics were associ- ated with statistically significant improvements in health outcomes. Findings: The risk of bias was low in 17 studies, moderate in 11, and high in 33. Of the 451 parameter estimates reported, 77% were underpowered to detect health outcomes. Among adequately powered parameters, 49% demonstrated a significant health improvement, 44% had no effect on health, and 7% were associated with significant worsening of health. In meta-analyses, early life and education interventions were associated with a reduction in smoking (odds ratio [OR] = 0.92, 95% confidence interval [CI] 0.86-0.99). Income maintenance and health insurance interventions were associated with significant improve- ments in self-rated health (OR = 1.20, 95% CI 1.06-1.36, and OR = 1.38, 95% CI 1.10-1.73, respectively), whereas some welfare-to-work interventions had a negative impact on self-rated health (OR = 0.77, 95% CI 0.66-0.90). Housing and neighborhood trials had no effect on the outcomes included in the meta-analyses. A positive effect of the trial on its primary socioeconomic outcome was associated with higher odds of reporting health improvements. We found evidence of publication bias for studies with null findings. Conclusions: Early life, income, and health insurance interventions have the potential to improve health. However, many of the included studies were underpowered to detect health effects and were at high or moderate risk of bias. Future social policy experiments should be better designed to measure the association between interventions and health outcomes