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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| Published in: | Hygiene and Environmental Health Advances (Online) Vol. 4; p. 100039 |
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| Main Authors: | , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Netherlands
Elsevier B.V
01.12.2022
Elsevier |
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| ISSN: | 2773-0492, 2773-0492 |
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| Abstract | •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.
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| AbstractList | Background: 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. Methods: 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. Results: 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. Conclusion: Population vulnerability during power outages was dominated by neighborhood environmental factors representing greater urbanization. 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.BackgroundAlthough 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.MethodsWe 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.ResultsConsistent 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.ConclusionPopulation vulnerability during power outages was dominated by neighborhood environmental factors representing greater urbanization. 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. •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] |
| ArticleNumber | 100039 |
| Author | Deng, Xinlei Chang, Howard H. Reliene, Ramune Brotzge, Jerald Zhang, Kai Sheridan, Scott C. Stern, Eric K. Zhang, Wangjian Lin, Shao Romeiko, Xiaobo X. Hao, Yuantao Dong, Guanghui Guo, Zhijian |
| Author_xml | – sequence: 1 givenname: Wangjian surname: Zhang fullname: Zhang, Wangjian organization: Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China – sequence: 2 givenname: Xinlei orcidid: 0000-0001-8129-6007 surname: Deng fullname: Deng, Xinlei organization: Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA – sequence: 3 givenname: Xiaobo X. surname: Romeiko fullname: Romeiko, Xiaobo X. organization: Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA – sequence: 4 givenname: Kai surname: Zhang fullname: Zhang, Kai organization: Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA – sequence: 5 givenname: Scott C. surname: Sheridan fullname: Sheridan, Scott C. organization: Department of Geography, Kent State University, Kent, OH, USA – sequence: 6 givenname: Jerald orcidid: 0000-0002-0120-0325 surname: Brotzge fullname: Brotzge, Jerald organization: New York State Mesonet, College of Arts and Sciences, State University of New York, Rensselaer, NY, USA – sequence: 7 givenname: Howard H. surname: Chang fullname: Chang, Howard H. organization: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA – sequence: 8 givenname: Eric K. surname: Stern fullname: Stern, Eric K. organization: College of Emergency Preparedness, Homeland Security and Cybersecurity, State University of New York, Albany, NY, USA – sequence: 9 givenname: Zhijian surname: Guo fullname: Guo, Zhijian organization: Department of Mathematics and Statistics, State University of New York, Albany, NY, USA – sequence: 10 givenname: Guanghui surname: Dong fullname: Dong, Guanghui organization: Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, China – sequence: 11 givenname: Ramune surname: Reliene fullname: Reliene, Ramune organization: Cancer Research Center, State University of New York, Rensselaer, NY, USA – sequence: 12 givenname: Yuantao surname: Hao fullname: Hao, Yuantao email: haoyt@mail.sysu.edu.cn organization: Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China – sequence: 13 givenname: Shao orcidid: 0000-0002-5535-7504 surname: Lin fullname: Lin, Shao email: slin@albany.edu organization: Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA |
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| Keywords | Neighborhood environment Power outage Vulnerability Multiple health outcomes Machine learning PO |
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| Snippet | •Health impact of power outage was shaped by numerous community predictors jointly.•Predictive models for the health impact were developed using machine... 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,... Background: Although power outage (PO) is one of the most important consequences of increasing weather extremes and the health impact of POs has been reported... |
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| SubjectTerms | Machine learning Multiple health outcomes Neighborhood environment Power outage Vulnerability |
| Title | How neighborhood environment modified the effects of power outages on multiple health outcomes in New York state? |
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