Evidence map›Paper›PMID 31726368›Full record

ArticleEnvironment international2020

How community vulnerability factors jointly affect multiple health outcomes after catastrophic storms.

Wangjian Zhang, Patrick L Kinney, David Q Rich, Scott C Sheridan, Xiaobo Xue Romeiko, Guanghui Dong, Eric K Stern, Zhicheng Du, Jianpeng Xiao, Wayne R Lawrence and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environment international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
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  3. Article
  4. Article
  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 6 institutions in 2 countries.

Wangjian ZhangDepartment of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China; Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA.
Patrick L KinneyDepartment of Environmental Health, School of Public Health, Boston University, MA, USA.
David Q RichDepartment of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA.
Scott C SheridanDepartment of Geography, Kent State University, Kent, OH, USA.
Xiaobo Xue RomeikoDepartment of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA.
Guanghui DongDepartment of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Eric K SternCollege of Emergency Preparedness, Homeland Security, and Cyber-Security, University at Albany, State University of New York, Albany, NY, USA.
Zhicheng DuDepartment of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Jianpeng XiaoDepartment of Occupational Health and Occupational Medicine, School of Public Health, Southern Medical University, Guangzhou, China.
Wayne R LawrenceDepartment of Epidemiology and Biostatistics, University at Albany, State University of New York, Rensselaer, NY, USA.
Ziqiang LinDepartment of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA; Department of Mathematics, University at Albany, Albany, NY, USA.
Yuantao HaoDepartment of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China. Electronic address: haoyt@mail.sysu.edu.cn.
Shao LinDepartment of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA. Electronic address: slin@albany.edu.
University at Albany, State University of New York · USSun Yat-sen University · CNBoston University · USKent State University · USSouthern Medical University · CNUniversity of Rochester Medical Center · US

Funding

The Endowment for Community-based Health Disparities Research and TrainingS21MD010692 · NIMHD · STATE UNIVERSITY OF NEW YORK AT ALBANY · PI RODRIGUEZ, HAVIDAN, SCHELL, LAWRENCE M · 2016 to 2020
$10.0M
Weather extremes, natural disasters, and health outcomes among vulnerable older adults: New improvements on exposure assessment, disparity identification, and risk communication strategiesR01AG070949 · NIA · STATE UNIVERSITY OF NEW YORK AT ALBANY · PI LIN, SHAO · 2022 to 2025
$1.6M
Assess and Model the Health Effects, Population, and Infrastructural Vulnerabilities of Power OutageR15ES028000 · NIEHS · STATE UNIVERSITY OF NEW YORK AT ALBANY · PI LIN, SHAO · 2018 to 2018
$444k
NIA NIH HHS R01 AG070949NIEHS NIH HHS R15 ES028000NIMHD NIH HHS S21 MD010692OPHPR CDC HHS U01 TP000566
6 · The paper itself

Abstract

backgroundWhile previous studies uncovered individual vulnerabilities to health risks during catastrophic storms, few evaluated the population vulnerability which is more important for identifying areas in greatest need of intervention.

objectivesWe assessed the association between community factors and multiple health outcomes, and developed a community vulnerability index.

methodsWe retained emergency department visits for several health conditions from the 2005-2014 New York Statewide Planning and Research Cooperative System. We developed distributed lag nonlinear models at each spatial cluster across eight counties in downstate New York to evaluate the health risk associated with Superstorm Sandy (10/28/2012-11/9/2012) compared to the same period in other years, then defined census tracts in clusters with an elevated risk as "risk-elevated communities", and all others as "unelevated". We used machine-learning techniques to regress the risk elevation status against community factors to determine the contribution of each factor on population vulnerability, and developed a community vulnerability index (CVI).

resultsOverall, community factors had positive contributions to increased community vulnerabilities to Sandy-related substance abuse (91.35%), injuries (70.51%), cardiovascular diseases (8.01%), and mental disorders (2.71%) but reversely contributed to respiratory diseases (-34.73%). The contribution of low per capita income (max: 22.08%), the percentage of residents living in group quarters (max: 31.39%), the percentage of areas prone to flooding (max: 38.45%), and the percentage of green coverage (max: 29.73%) tended to be larger than other factors. The CVI based on these factors achieved an accuracy of 0.73-0.90 across outcomes.

conclusionsOur findings suggested that substance abuse was the most sensitive disease susceptible to less optimal community indicators, whereas respiratory diseases were higher in communities with better social environment. The percentage of residents in group quarters and areas prone to flooding were among dominant predictors for community vulnerabilities. The CVI based on these factors has an appropriate predictive performance.

Indexed as

Outcome Assessment, Health CareCyclonic StormsFloodsNew YorkRisk FactorsCatastrophic stormsCommunity vulnerability indexMultiple health outcomesROC

Identifiers

PMID31726368
PMCPMC13008362
OpenAlexW2986933146

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.