Evidence map›Paper›PMID 35564404›Full record

ArticleInternational journal of environmental research and public health2022

Long-Term Health Impacts of Wildfire Exposure: A Retrospective Study Exploring Hospitalization Dynamics Following the 2016 Wave of Fires in Israel.

Odeya Cohen, Stav Shapira, Eyal Furman

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Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Odeya CohenDepartment of Nursing, Recanati School for Community Health Professions, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O. Box 653, Beer Sheva 8410501, Israel.ORCID 0000-0002-2427-6381
Stav ShapiraSchool of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O. Box 653, Beer Sheva 8410501, Israel.ORCID 0000-0001-6258-4935
Eyal FurmanMaccabi Healthcare Services, Haifa 3508510, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClimate-related events, including wildfires, which adversely affect human health, are gaining the growing attention of public-health officials and researchers. Israel has experienced several disastrous fires, including the wave of fires in November 2016 that led to the evacuation of 75,000 people. The fires lasted six days (22-27 November) with no loss of life or significant immediate health impacts. The objective of this study is to explore the long-term hospitalization dynamics in a population exposed to this large-scale fire, including the effects of underlying morbidity and socio-economic status (SES).

methodsThis is a retrospective crossover study, conducted in 2020, analyzing the electronic medical records of residents from areas exposed to a wildfire in northern Israel. The study spans from one year before exposure to two years after it (22 November 2015-27 November 2018). The hospitalization days during the study period were analyzed using the Poisson regression model. The rate of hospitalization days along with 95% confidence intervals (CIs) were plotted.

resultsThe study included 106,595 participants. The median age was 37 (IQR = 17-56), with a mean socio-economic ranking of 6.47 out of 10 (SD = 2.01). Analysis revealed that people with underlying morbidity were at greater risk of experiencing long-term effects following fires, which was manifested in higher hospitalization rates that remained elevated for two years post-exposure. This was also evident among individuals of low socio-economic status without these background illnesses.

conclusionsHealthcare services should prepare for increased hospitalization rates during the two years following wildfires for populations with underlying morbidity and those of low socio-economic status. Implementing preventive-medicine approaches may increase the resiliency of communities in the face of extreme climate-related events and prevent future health burdens. Additional research should focus on the specific mechanisms underpinning the long-term effects of wildfire exposure.

Indexed as

FiresWildfiresAdultCross-Over StudiesHospitalizationHumansIsraelRetrospective Studiesclimate changehealthcare utilizationlong-term health impactnatural disasterssocio-economic factorswildfires

Identifiers

PMID35564404
PMCPMC9099700

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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.