Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Oct 4:383:e075081.
doi: 10.1136/bmj-2023-075081.

Mortality risks associated with floods in 761 communities worldwide: time series study

Zhengyu Yang  1 , Wenzhong Huang  1 , Joanne E McKenzie  1 , Rongbin Xu  1 , Pei Yu  1 , Tingting Ye  1 , Bo Wen  1 , Antonio Gasparrini  2   3   4 , Ben Armstrong  2 , Shilu Tong  5   6   7 , Eric Lavigne  8   9 , Joana Madureira  10   11   12 , Jan Kyselý  13   14 , Yuming Guo  1 , Shanshan Li  15 ; MCC Collaborative Research Network
Collaborators, Affiliations

Mortality risks associated with floods in 761 communities worldwide: time series study

Zhengyu Yang et al. BMJ. .

Abstract

Objective: To evaluate lag-response associations and effect modifications of exposure to floods with risks of all cause, cardiovascular, and respiratory mortality on a global scale.

Design: Time series study.

Setting: 761 communities in 35 countries or territories with at least one flood event during the study period.

Participants: Multi-Country Multi-City Collaborative Research Network database, Australian Cause of Death Unit Record File, New Zealand Integrated Data Infrastructure, and the International Network for the Demographic Evaluation of Populations and their Health Network database.

Main outcome measures: The main outcome was daily counts of deaths. An estimation for the lag-response association between flood and daily mortality risk was modelled, and the relative risks over the lag period were cumulated to calculate overall effects. Attributable fractions of mortality due to floods were further calculated. A quasi-Poisson model with a distributed lag non-linear function was used to examine how daily death risk was associated with flooded days in each community, and then the community specific associations were pooled using random effects multivariate meta-analyses. Flooded days were defined as days from the start date to the end date of flood events.

Results: A total of 47.6 million all cause deaths, 11.1 million cardiovascular deaths, and 4.9 million respiratory deaths were analysed. Over the 761 communities, mortality risks increased and persisted for up to 60 days (50 days for cardiovascular mortality) after a flooded day. The cumulative relative risks for all cause, cardiovascular, and respiratory mortality were 1.021 (95% confidence interval 1.006 to 1.036), 1.026 (1.005 to 1.047), and 1.049 (1.008 to 1.092), respectively. The associations varied across countries or territories and regions. The flood-mortality associations appeared to be modified by climate type and were stronger in low income countries and in populations with a low human development index or high proportion of older people. In communities impacted by flood, up to 0.10% of all cause deaths, 0.18% of cardiovascular deaths, and 0.41% of respiratory deaths were attributed to floods.

Conclusions: This study found that the risks of all cause, cardiovascular, and respiratory mortality increased for up to 60 days after exposure to flood and the associations could vary by local climate type, socioeconomic status, and older age.

PubMed Disclaimer

Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: support from the Australian Research Council, Australian National Health and Medical Research Council, and European Union’s Horizon 2020 Project Exhaustion; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Figures

Fig 1
Fig 1
Annual flood days in 761 communities from 35 countries or territories during study period
Fig 2
Fig 2
Relative risks of mortality associated with exposure to floods during lag 0-60 days in 761 communities from 35 countries or territories
Fig 3
Fig 3
Cumulative relative risks of all cause, cardiovascular, and respiratory mortality associated with exposure to floods during lag 0-60 days of all locations, and by country or territory and region. CI=confidence interval; NA=not available
See this image and copyright information in PMC

Comment in

References

    1. Hu P, Zhang Q, Shi P, Chen B, Fang J. Flood-induced mortality across the globe: Spatiotemporal pattern and influencing factors. Sci Total Environ 2018;643:171-82. 10.1016/j.scitotenv.2018.06.197. - DOI - PubMed
    1. Rentschler J, Salhab M, Jafino BA. Flood exposure and poverty in 188 countries. Nat Commun 2022;13:3527. 10.1038/s41467-022-30727-4. - DOI - PMC - PubMed
    1. Tellman B, Sullivan JA, Kuhn C, et al. Satellite imaging reveals increased proportion of population exposed to floods. Nature 2021;596:80-6. 10.1038/s41586-021-03695-w. - DOI - PubMed
    1. Yan M, Wilson A, Peel JL, et al. Community-wide Mortality Rates in Beijing, China, During the July 2012 Flood Compared with Unexposed Periods. Epidemiology 2020;31:319-26. 10.1097/EDE.0000000000001182. - DOI - PMC - PubMed
    1. Rocha LA, Fromknecht CQ, Redman SD, Brady JE, Hodge SE, Noe RS. Medicolegal Death Scene Investigations After Natural Disaster- and Weather-Related Events: A Review of the Literature. Acad Forensic Pathol 2017;7:221-39. 10.23907/2017.023. - DOI - PMC - PubMed