Association between PM2.5 and daily pharmacy visit tendency in China: A time series analysis using mobile phone cellular signaling data

Qi Zhou, Shen Qu, Jiongchao Ding, Miaomiao Liu*, Xianjin Huang, Jun Bi, John S. Ji, Patrick L. Kinney

*此作品的通讯作者

    科研成果: 期刊稿件文章同行评审

    3 引用 (Scopus)

    摘要

    While much is known about the effects of PM2.5 pollution on severe health outcomes, less is known about the heterogeneous impacts of air pollution on less severe health effects experienced by large numbers of people, such as those resulting in pharmacy visits. Based on anonymized daily pharmacy visits extracted from about 136,000 mobile phone station macrocells and 28,170 pharmacy locations, we used the generalized additive model and meta-analysis to investigate the effects of PM2.5 exposure on pharmacy visit tendencies (PVTs), as a proxy variable of health facility utilization, across regions, groups and social contexts. Overall, PVTs increased by 0.338% (95% CI: 0.335%–0.342%) for per 10 μg/m3 increases in PM2.5. The exposure-response curve was nonlinear, having a sharp slope below 60 μg/m3 and then flattening. Sensitivity analyses showed that results were robust to modeling choices and existence of noise in captured PVT data. At pharmacy level, higher percentage increases in PVTs were observed in near-residency pharmacies (0.474%), designated medical insurance pharmacies (0.360%), and pharmacies in urban areas (0.457%) than their counterparts (0.156%, 0.321%, 0.180%). This indicates that social contexts, including inconvenient access to the pharmacy and limited social medical insurance coverage, inhibited residents from utilizing health facilities for the health care needed. Interactions of social contexts led to strong heterogeneity among cities, between rural and urban regions as well as across communities, which call for differentiated health resources allocations to ensure health equity.

    源语言英语
    文章编号130688
    期刊Journal of Cleaner Production
    340
    DOI
    出版状态已出版 - 15 3月 2022

    指纹

    探究 'Association between PM2.5 and daily pharmacy visit tendency in China: A time series analysis using mobile phone cellular signaling data' 的科研主题。它们共同构成独一无二的指纹。

    引用此