Introduction:Urban landscape patterns impact population health;however,traditional indices are limited by single-dimensional focus,multicollinearity,and weak health relevance.Developing a holistic Landscape Pattern He...Introduction:Urban landscape patterns impact population health;however,traditional indices are limited by single-dimensional focus,multicollinearity,and weak health relevance.Developing a holistic Landscape Pattern Health Index(LPHI)is critical for planning healthy cities.Methods:Using data from Ningbo(China),this study integrated 2001–2023 land use data(reclassified into 7 types)and 2009–2016 street-level stroke mortality data.A two-stage Generalized Weighted Quantile Sum(GWQS)regression addressed the temporal data discrepancy,first deriving weights from 2009–2016 health data,then calculating the LPHI for the full 2001–2023 period.Quasi-Poisson regression was used to validate the association between the LPHI and stroke mortality.Results:An interquartile-range increase in the Protective Composite Index reduced stroke mortality by 20%(warm seasons)and 22%(cold seasons),while the Hazard Composite Index increased risk by 29%(warm)and 20%(cold).The LPHI demonstrated significant associations with stroke mortality,with the Protective Composite Index reducing risk and the Hazard Composite Index increasing it across both seasons.Conclusion:The study suggests that the LPHI can serve as a bridge between landscape ecology and public health,with the potential to identify high-risk areas and seasonal priorities.This approach could guide targeted interventions through landscape optimization,supporting evidence-based healthy urban planning.展开更多
基金Supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project(2024ZD0531603).
文摘Introduction:Urban landscape patterns impact population health;however,traditional indices are limited by single-dimensional focus,multicollinearity,and weak health relevance.Developing a holistic Landscape Pattern Health Index(LPHI)is critical for planning healthy cities.Methods:Using data from Ningbo(China),this study integrated 2001–2023 land use data(reclassified into 7 types)and 2009–2016 street-level stroke mortality data.A two-stage Generalized Weighted Quantile Sum(GWQS)regression addressed the temporal data discrepancy,first deriving weights from 2009–2016 health data,then calculating the LPHI for the full 2001–2023 period.Quasi-Poisson regression was used to validate the association between the LPHI and stroke mortality.Results:An interquartile-range increase in the Protective Composite Index reduced stroke mortality by 20%(warm seasons)and 22%(cold seasons),while the Hazard Composite Index increased risk by 29%(warm)and 20%(cold).The LPHI demonstrated significant associations with stroke mortality,with the Protective Composite Index reducing risk and the Hazard Composite Index increasing it across both seasons.Conclusion:The study suggests that the LPHI can serve as a bridge between landscape ecology and public health,with the potential to identify high-risk areas and seasonal priorities.This approach could guide targeted interventions through landscape optimization,supporting evidence-based healthy urban planning.
