Cintron Dakota W, Gottlieb Laura M, Hagan Erin, Tan May Lynn, Vlahov David, Glymour M Maria, Matthay Ellicott C
Center for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143, USA.
Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, 2nd Floor, Campus Box 0560, San Francisco, CA, 94143, USA.
SSM Popul Health. 2023 Feb 4;22:101352. doi: 10.1016/j.ssmph.2023.101352. eCollection 2023 Jun.
Substantial heterogeneity in effects of social policies on health across subgroups may be common, but has not been systematically characterized. Using a sample of 55 contemporary studies on health effects of social policies, we recorded how often heterogeneous treatment effects (HTEs) were assessed, for what subgroups (e.g., male, female), and the subgroup-specific effect estimates expressed as Standardized Mean Differences (SMDs). For each study, outcome, and dimension (e.g., gender), we fit a random-effects meta-analysis. We characterized the magnitude of heterogeneity in policy effects using the standard deviation of the subgroup-specific effect estimates (τ). Among the 44% of studies reporting subgroup-specific estimates, policy effects were generally small (<0.1 SMDs) with mixed impacts on health (67% beneficial) and disparities (50% implied narrowing of disparities). Across study-outcome-dimensions, 54% indicated any heterogeneity in effects, and 20% had τ > 0.1 SMDs. For 26% of study-outcome-dimensions, the magnitude of τ indicated that effects of opposite signs were plausible across subgroups. Heterogeneity was more common in policy effects not specified . Our findings suggest social policies commonly have heterogeneous effects on health of different populations; these HTEs may substantially impact disparities. Studies of social policies and health should routinely evaluate HTEs.
社会政策对不同亚组健康的影响存在显著异质性，这可能很常见，但尚未得到系统的描述。我们使用了55项关于社会政策对健康影响的当代研究样本，记录了异质性治疗效果（HTE）的评估频率、针对哪些亚组（如男性、女性）以及以标准化均值差异（SMD）表示的亚组特定效应估计值。对于每项研究、结果和维度（如性别），我们进行了随机效应荟萃分析。我们使用亚组特定效应估计值的标准差（τ）来描述政策效应异质性的程度。在报告亚组特定估计值的44%的研究中，政策效应通常较小（<0.1 SMD），对健康有混合影响（67%有益），对差异有影响（50%意味着差异缩小）。在所有研究-结果-维度中，54%表明效应存在任何异质性，20%的τ>0.1 SMD。对于26%的研究-结果-维度，τ的大小表明各亚组之间效应符号相反是合理的。政策效应未明确规定时，异质性更为常见。我们的研究结果表明，社会政策通常对不同人群的健康有不同的影响；这些HTE可能会对差异产生重大影响。社会政策与健康的研究应常规评估HTE。
