A longitudinal analysis of PM2.5 exposure and multimorbidity clusters and accumulation among adults aged 45-85 in China.

• Type of Material: Article
• Authors: Kai Hu, University of St Andrews, Fife, United Kingdom; Katherine Keenan, University of St Andrews, Fife, United Kingdom; Jo Mhairi Hale, University of St Andrews, Fife, United Kingdom; Yang Liu, Emory University; Hill Kulu, University of St Andrews, Fife, United Kingdom
• Language: English
• Date: 2022
• Publisher: PLOS
• Publication Version: Final Published Version
• Copyright Statement: © 2022 Hu et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pgph.0000520
• Title of Journal or Parent Work: PLOS Glob Public Health
• Volume: 2
• Issue: 6
• Start Page: e0000520
• End Page: e0000520
• Grant/Funding Information: This study is supported by China Scholarship Council (CSC No. 201703780011), People’s Republic of China, and Population and Health Research Group (PHRG), School of Geography and Sustainable Development, University of St Andrews, United Kingdom. PM2.5 data in this study is from the work of Yang Liu, supported by the National Institute of Environmental Health Sciences of the National Institutes of Health, USA (Grant No. 1R01ES032140). This study is also supported by the Centre for Population Change (CPC) (ES/R009139/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• Keywords: China; longitudinal analysis; adults
• Research Categories: Health Sciences, Medicine and Surgery

Abstract

While previous studies have emphasised the role of individual factors in understanding multimorbidity disparities, few have investigated contextual factors such as air pollution (AP). We first use cross-sectional latent class analysis (LCA) to assess the associations between PM2.5 exposure and multimorbidity disease clusters, and then estimate the associations between PM2.5 exposure and the development of multimorbidity longitudinally using growth curve modelling (GCM) among adults aged 45-85 in China. The results of LCA modelling suggest four latent classes representing three multimorbidity patterns (respiratory, musculoskeletal, cardio-metabolic) and one healthy pattern. The analysis shows that a 1 μg/m3 increase in cumulative exposure to PM2.5 is associated with a higher likelihood of belonging to respiratory, musculoskeletal or cardio-metabolic clusters: 2.4% (95% CI: 1.02, 1.03), 1.5% (95% CI: 1.01, 1.02) and 3.3% (95% CI: 1.03, 1.04), respectively. The GCM models show that there is a u-shaped association between PM2.5 exposure and multimorbidity, indicating that both lower and higher PM2.5 exposure is associated with increased multimorbidity levels. Higher multimorbidity in areas of low AP is explained by clustering of musculoskeletal diseases, whereas higher AP is associated with cardio-metabolic disease clusters. The study shows how multimorbidity clusters vary contextually and that PM2.5 exposure is more detrimental to health among older adults.