Huang Wen-Zhong, He Wei-Yun, Knibbs Luke D, Jalaludin Bin, Guo Yu-Ming, Morawska Lidia, Heinrich Joachim, Chen Duo-Hong, Yu Yun-Jiang, Zeng Xiao-Wen, Yu Hong-Yao, Yang Bo-Yi, Hu Li-Wen, Liu Ru-Qing, Feng Wen-Ru, Dong Guang-Hui
Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne VIC, 3004, Australia.
Department of Environmental Health, Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China.
Environ Res. 2022 Mar;204(Pt D):112397. doi: 10.1016/j.envres.2021.112397. Epub 2021 Nov 17.
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.
In this study, we aimed to evaluate the utility of AQHIs constructed by different models and health outcomes, and determine a better strategy.
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.
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 NO and O to the final AQHI were prominent, while the contribution of SO and PM were relatively small.
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.
广泛使用的空气质量指数（AQI）因其不准确而受到批评，这促使了空气质量健康指数（AQHI）的发展，它是对AQI的一种改进。然而，目前对于AQHI最合适的构建策略尚无共识。
在本研究中，我们旨在评估由不同模型和健康结局构建的AQHI的效用，并确定更好的策略。
基于中国广州299家医院的每日时间序列门诊就诊和住院情况（2016年1月至2018年12月）以及死亡率（2017年1月至2019年12月），我们采用累积风险指数（CRI）法、贝叶斯多污染物加权（BMW）模型和标准方法为不同健康结局构建AQHI。通过两阶段验证分析评估不同策略构建的AQHI的有效性，并检查它们与特定病因发病率和死亡率的暴露 - 反应关系。
不同模型的验证表明，在空气质量指数中，采用BMW模型构建的AQHI（BMW - AQHI）在总体人群或亚人群中与健康结局的关联最强，其次是采用CRI法构建的AQHI（CRI - AQHI），然后是普通AQHI和AQI。不同健康结局的进一步验证表明，基于门诊就诊风险构建的AQHI在呈现死亡率和发病率方面通常表现出最高的效用，其次是基于住院风险构建的AQHI，然后是基于死亡率的AQHI和AQI。NO和O对最终AQHI的贡献显著，而SO和PM的贡献相对较小。
BMW模型在构建AQHI方面可能比CRI和标准方法更有效。基于BMW模型，用门诊数据构建的AQHI在呈现与空气污染物共同暴露相关的短期健康风险方面可能比基于死亡率的AQHI和现有AQI更有效。
