痴呆
Probit模型
杠杆(统计)
工具变量
环境卫生
计量经济学
选择偏差
随机效应模型
医学
有序概率单位
空气污染
疾病
人口学
经济
统计
数学
荟萃分析
内科学
病理
社会学
有机化学
化学
作者
Kelly Bishop,Jonathan D. Ketcham,Nicolai V. Kuminoff
标识
DOI:10.1093/restud/rdac078
摘要
Abstract We study whether long-term cumulative exposure to airborne small particulate matter (PM2.5) affects the probability that an individual receives a new diagnosis of Alzheimer's disease or related dementias. We track the health, residential location, and PM2.5 exposures of Americans aged sixty-five and above from 2001 through 2013. The expansion of Clean Air Act regulations led to quasi-random variation in individuals’ subsequent exposures to PM2.5. We leverage these regulations to construct instrumental variables for individual-level decadal PM2.5 that we use within flexible probit models that also account for any potential sample selection based on survival. We find that a 1 µg/m3 increase in decadal PM2.5 increases the probability of a new dementia diagnosis by an average of 2.15 percentage points (pp). All else equal, we find larger effects for women, older people, and people with more clinical risk factors for dementia. These effects persist below current regulatory thresholds.
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