How neighborhood environment modified the effects of power outages on multiple health outcomes in New York state?

•Health impact of power outage was shaped by numerous community predictors jointly.•Predictive models for the health impact were developed using machine learning method.•Greater impact of power outage was identified in more urbanized communities.•Downstate counties and those in northwest New York we...

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Vydáno v:Hygiene and Environmental Health Advances (Online) Ročník 4; s. 100039
Hlavní autoři: Zhang, Wangjian, Deng, Xinlei, Romeiko, Xiaobo X., Zhang, Kai, Sheridan, Scott C., Brotzge, Jerald, Chang, Howard H., Stern, Eric K., Guo, Zhijian, Dong, Guanghui, Reliene, Ramune, Hao, Yuantao, Lin, Shao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Netherlands Elsevier B.V 01.12.2022
Elsevier
Témata:
Machine learning
Multiple health outcomes
Neighborhood environment
Power outage
Vulnerability
Neighborhood environment
Power outage
Vulnerability
Multiple health outcomes
Machine learning
PO
ISSN:2773-0492, 2773-0492
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Shrnutí:•Health impact of power outage was shaped by numerous community predictors jointly.•Predictive models for the health impact were developed using machine learning method.•Greater impact of power outage was identified in more urbanized communities.•Downstate counties and those in northwest New York were the most susceptable. Although power outage (PO) is one of the most important consequences of increasing weather extremes and the health impact of POs has been reported previously, studies on the neighborhood environment underlying the population vulnerability in such situations are limited. This study aimed to identify dominant neighborhood environmental predictors which modified the impact of POs on multiple health outcomes in New York State. We applied a two-stage approach. In the first stage, we used time series analysis to determine the impact of POs (versus non-PO periods) on multiple health outcomes in each power operating division in New York State, 2001-2013. In the second stage, we classified divisions as risk-elevated and non-elevated, then developed predictive models for the elevation status based on 36 neighborhood environmental factors using random forest and gradient boosted trees. Consistent across different outcomes, we found predictors representing greater urbanization, particularly, the proportion of residents having access to public transportation (importance ranging from 4.9–15.6%), population density (3.3–16.1%), per capita income (2.3–10.7%), and the density of public infrastructure (0.8–8.5%), were associated with a higher possibility of risk elevation following power outages. Additionally, the percent of minority (-6.3–27.9%) and those with limited English (2.2–8.1%), the percent of sandy soil (6.5–11.8%), and average soil temperature (3.0–15.7%) were also dominant predictors for multiple outcomes. Spatial hotspots of vulnerability generally were located surrounding New York City and in the northwest, the pattern of which was consistent with socioeconomic status. Population vulnerability during power outages was dominated by neighborhood environmental factors representing greater urbanization. [Display omitted]
Bibliografie:ObjectType-Article-1
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ISSN:2773-0492
2773-0492
DOI:10.1016/j.heha.2022.100039