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Identifying the healthy places to live in Australia with a new environmental quality health index

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Title: Identifying the healthy places to live in Australia with a new environmental quality health index
Authors: Shuang Zhou, Zhihu Xu, Wenzhong Huang, Yao Wu, Rongbin Xu, Zhengyu Yang, Pei Yu, Wenhua Yu, Tingting Ye, Bo Wen, Shanshan Li, Yuming Guo
Source: Environment International, Vol 195, Iss , Pp 109268- (2025)
Publisher Information: Elsevier, 2025.
Publication Year: 2025
Collection: LCC:Environmental sciences
Subject Terms: New environmental quality health index, Environmental exposure, Socio-economic factors, Health risk, Environmental sciences, GE1-350
More Details: Background: Existing environmental quality indices often fail to account for the varying health impacts of different exposures and exclude socio-economic status indicators (SES). Objectives: To develop and validate a comprehensive Environmental Quality Health Index (EQHI) that integrates multiple environmental exposures and SES to assess mortality risks across Australia. Methods: We combined all-cause, cardiovascular, and respiratory mortality data (2016–2019) from 2,180 Statistical Areas Level 2 with annual mean values of 12 environmental exposures, including PM2.5, ozone, temperature, humidity, normalized difference vegetation index, night light, road and building density, and socioeconomic status. Exposure-mortality relationships were estimated using a spatial age-period-cohort model, and EQHIs (scored 0–100, with higher values indicating better conditions) were constructed. Validation was performed using K-fold cross-validation and spatial regression models. Results: Validation showed strong model performance (R-squared = 83.53 %, 75.55 %, and 52.44 % for EQHI-all cause, EQHI-CVD, and EQHI-Resp). Each interquartile increase in EQHI-all cause reduced all-cause mortality risk by 10 %, with similar reductions for cardiovascular and respiratory mortality. Geographically, EQHIs were higher in south, east, and southeast coastal regions. From 2016 to 2019, SA2s with the highest EQHI (>75) decreased from 27.1 % to 21.1 %. The population weighted EQHI was highest in Hobart and lowest in Darwin. Conclusions: We established, to our knowledge, the first tool to quantify and communicate environmental health risks using three types of mortality data and 12 environmental factors. This EQHI provides a robust framework to assess environmental health risks and guide targeted interventions. Our methodology can be adapted globally to standardize risk evaluation.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 0160-4120
Relation: http://www.sciencedirect.com/science/article/pii/S0160412025000194; https://doaj.org/toc/0160-4120
DOI: 10.1016/j.envint.2025.109268
Access URL: https://doaj.org/article/ae49b7e2f447480884b291763b6a3ba4
Accession Number: edsdoj.49b7e2f447480884b291763b6a3ba4
Database: Directory of Open Access Journals
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  Data: Environment International, Vol 195, Iss , Pp 109268- (2025)
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  Data: Background: Existing environmental quality indices often fail to account for the varying health impacts of different exposures and exclude socio-economic status indicators (SES). Objectives: To develop and validate a comprehensive Environmental Quality Health Index (EQHI) that integrates multiple environmental exposures and SES to assess mortality risks across Australia. Methods: We combined all-cause, cardiovascular, and respiratory mortality data (2016–2019) from 2,180 Statistical Areas Level 2 with annual mean values of 12 environmental exposures, including PM2.5, ozone, temperature, humidity, normalized difference vegetation index, night light, road and building density, and socioeconomic status. Exposure-mortality relationships were estimated using a spatial age-period-cohort model, and EQHIs (scored 0–100, with higher values indicating better conditions) were constructed. Validation was performed using K-fold cross-validation and spatial regression models. Results: Validation showed strong model performance (R-squared = 83.53 %, 75.55 %, and 52.44 % for EQHI-all cause, EQHI-CVD, and EQHI-Resp). Each interquartile increase in EQHI-all cause reduced all-cause mortality risk by 10 %, with similar reductions for cardiovascular and respiratory mortality. Geographically, EQHIs were higher in south, east, and southeast coastal regions. From 2016 to 2019, SA2s with the highest EQHI (>75) decreased from 27.1 % to 21.1 %. The population weighted EQHI was highest in Hobart and lowest in Darwin. Conclusions: We established, to our knowledge, the first tool to quantify and communicate environmental health risks using three types of mortality data and 12 environmental factors. This EQHI provides a robust framework to assess environmental health risks and guide targeted interventions. Our methodology can be adapted globally to standardize risk evaluation.
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