Improved morbidity-based air quality health index development using Bayesian multi-pollutant weighted model

Wen-Zhong Huang; Wei-Yun He; Luke D Knibbs; Bin Jalaludin; Yu-Ming Guo; Lidia Morawska; Joachim Heinrich; Duo-Hong Chen; Yun-Jiang Yu; Xiao-Wen Zeng; Hong-Yao Yu; Bo-Yi Yang; Li-Wen Hu; Ru-Qing Liu; Wen-Ru Feng; Guang-Hui Dong

doi:10.1016/j.envres.2021.112397

Improved morbidity-based air quality health index development using Bayesian multi-pollutant weighted model

Environ Res. 2022 Mar;204(Pt D):112397. doi: 10.1016/j.envres.2021.112397. Epub 2021 Nov 17.

Authors

Wen-Zhong Huang¹, Wei-Yun He², Luke D Knibbs³, Bin Jalaludin⁴, Yu-Ming Guo¹, Lidia Morawska⁵, Joachim Heinrich⁶, Duo-Hong Chen⁷, Yun-Jiang Yu⁸, Xiao-Wen Zeng⁹, Hong-Yao Yu⁹, Bo-Yi Yang⁹, Li-Wen Hu⁹, Ru-Qing Liu⁹, Wen-Ru Feng¹⁰, Guang-Hui Dong¹¹

Affiliations

¹ Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne VIC, 3004, Australia.
² Department of Environmental Health, Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China.
³ School of Public Health, The University of Queensland, Herston, Queensland, 4006, Australia.
⁴ Centre for Air Quality and Health Research and Evaluation, Glebe, NSW, 2037, Australia; Ingham Institute for Applied Medial Research, Liverpool, NSW, 2170, Australia; School of Public Health and Community Medicine, The University of New South Wales, Kensington, NSW, 2052, Australia.
⁵ International Laboratory for Air Quality and Health, Queensland University of Technology (QUT), GPO Box 2434, Brisbane, Queensland, 4001, Australia.
⁶ Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, 80336, Germany; Comprehensive Pneumology Center Munich, German Center for Lung Research, Munich, 80336, Germany.
⁷ Department of Air Quality Forecasting and Early Warning, Guangdong Environmental Monitoring Center, State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Protection Key Laboratory of Atmospheric Secondary Pollution, Guangzhou, 510308, China.
⁸ State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, 510655, China.
⁹ Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
¹⁰ Department of Environmental Health, Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China. Electronic address: wenrf2020@yeah.net.
¹¹ Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China. Electronic address: donggh5@mail.sysu.edu.cn.

PMID: 34798120
DOI: 10.1016/j.envres.2021.112397

Abstract

Background: The widely used Air Quality Index (AQI) has been criticized due to its inaccuracy, leading to the development of the air quality health index (AQHI), an improvement on the AQI. However, there is currently no consensus on the most appropriate construction strategy for the AQHI.

Objectives: In this study, we aimed to evaluate the utility of AQHIs constructed by different models and health outcomes, and determine a better strategy.

Methods: Based on the daily time-series outpatient visits and hospital admissions from 299 hospitals (January 2016-December 2018), and mortality (January 2017-December 2019) in Guangzhou, China, we utilized cumulative risk index (CRI) method, Bayesian multi-pollutant weighted (BMW) model and standard method to construct AQHIs for different health outcomes. The effectiveness of AQHIs constructed by different strategies was evaluated by a two-stage validation analysis and examined their exposure-response relationships with the cause-specific morbidity and mortality.

Results: Validation by different models showed that AQHI constructed with the BMW model (BMW-AQHI) had the strongest association with the health outcome either in the total population or subpopulation among air quality indexes, followed by AQHI constructed with the CRI method (CRI-AQHI), then common AQHI and AQI. Further validation by different health outcomes showed that AQHI constructed with the risk of outpatient visits generally exhibited the highest utility in presenting mortality and morbidity, followed by AQHI constructed with the risk of hospitalizations, then mortality-based AQHI and AQI. The contributions of NO₂ and O₃ to the final AQHI were prominent, while the contribution of SO₂ and PM_2.5 were relatively small.

Conclusions: The BMW model is likely to be more effective for AQHI construction than CRI and standard methods. Based on the BMW model, the AQHI constructed with the outpatient data may be more effective in presenting short-term health risks associated with the co-exposure to air pollutants than the mortality-based AQHI and existing AQIs.

Keywords: Air quality health index; Bayesian multi-pollutant weighted model; Health risk assessment; Short-term effects.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Bayes Theorem
China
Environmental Pollutants*
Humans
Morbidity
Particulate Matter / analysis

Substances

Air Pollutants
Environmental Pollutants
Particulate Matter