Abstract

Several studies have provided strong evidence that long-term exposure to air pollution, even at low levels, increases risk of mortality. As regulatory actions are becoming prohibitively expensive, robust evidence to guide the development of targeted interventions to protect the most vulnerable is needed. In this article, we introduce a novel statistical method that (i) discovers subgroups whose effects substantially differ from the population mean, and (ii) uses randomization-based tests to assess discovered heterogeneous effects. Also, we develop a sensitivity analysis method to assess the robustness of the conclusions to unmeasured confounding bias. Via simulation studies and theoretical arguments, we demonstrate that hypothesis testing focusing on the discovered subgroups can substantially increase statistical power to detect heterogeneity of the exposure effects. We apply the proposed de novo method to the data of 1,612,414 Medicare beneficiaries in the New England region in the United States for the period 2000–2006. We find that seniors aged between 81 and 85 with low income and seniors aged 85 and above have statistically significant greater causal effects of long-term exposure to PM_2.5 on 5-year mortality rate compared to the population mean.

摘要

几项研究提供了强有力的证据表明，长期接触空气污染，即使是低水平的空气污染，也会增加死亡风险。随着监管行动变得极其昂贵，需要强有力的证据来指导制定有针对性的干预措施以保护最弱势群体。在本文中，我们介绍了一种新颖的统计方法，该方法 (i) 发现其效果与总体平均值存在显着差异的子组，以及 (ii) 使用基于随机化的测试来评估发现的异质效应。此外，我们开发了一种敏感性分析方法来评估结论对未测量的混杂偏差的稳健性。通过模拟研究和理论论证，我们证明，针对已发现子组的假设检验可以显着提高统计功效，以检测暴露效应的异质性。我们对 2000-2006 年期间美国新英格兰地区 1,612,414 名医疗保险受益人的数据应用了拟议的从头方法。我们发现 81 至 85 岁的低收入老年人和 85 岁及以上的老年人长期接触 PM2.5 的因果效应具有统计学显着性_2.5与人口平均值相比的 5 年死亡率。