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Assessment of Excess Mortality and Household Income in Rural Bangladesh During the COVID-19 Pandemic in 2020

Educational Objective
To identify the key insights or developments described in this article
1 Credit CME
Key Points

Question  Is the low COVID-19–related mortality reported in Bangladesh for 2020 associated with massive undercounting?

Findings  This repeated survey study including households from a sample of 135 villages in rural Bangladesh found that all-cause mortality in the surveyed are was lower in 2020 compared with 2019, but measures to control the COVID-19 pandemic were associated with a reduction in rural income and food availability.

Meaning  These findings suggest that government restrictions designed to curb the spread of COVID-19 may have been effective in 2020 but needed to be accompanied by expanded welfare support.

Abstract

Importance  A slow or incomplete civil registry makes it impossible to determine excess mortality due to COVID-19 and difficult to inform policy.

Objective  To quantify the association of the COVID-19 pandemic with excess mortality and household income in rural Bangladesh in 2020.

Design, Setting, and Participants  This repeated survey study is based on an in-person census followed by 2 rounds of telephone calls. Data were collected from a sample of 135 villages within a densely populated 350-km2 rural area of Bangladesh. Household data were obtained first in person and subsequently over the telephone. For the analysis, mortality data were stratified by month, age, sex, and household education. Mortality rates were modeled by bayesian multilevel regression, and the strata were aggregated to the population by poststratification. Data analysis was performed from February to April 2021.

Exposures  Date and cause of any changes in household composition, as well as changes in income and food availability.

Main Outcomes and Measures  Mortality rates were compared for 2019 and 2020, both without adjustment and after adjustment for nonresponse and differences in demographic variables between surveys. Income and food availability reported for January, May, and November 2020 were also compared.

Results  Enumerators collected data from an initial 16 054 households in January 2020; 14 551 households (91%) responded when contacted again by telephone in May 2020, and 11 933 households (74%)responded when reached again over the telephone in November 2020, for a total of 58 806 individuals (29 726 female participants [50.5%]; mean [SD] age, 26.4 [19.8] years). A total of 276 deaths were reported between February and the end of October 2020 for the subset of the population that could be contacted twice over the telephone, slightly below the 289 deaths reported for the same population over the same period in 2019. After adjustment for survey nonresponse and poststratification, 2020 mortality changed by −8% (95% CI, −21% to 7%) compared with an annualized mortality of 6.1 deaths per 1000 individuals in 2019. However, in May 2020, salaried primary income earners reported a 40% decrease in monthly income (from 17 485 to 10 835 Bangladeshi Taka), and self-employed earners reported a 60% decrease in monthly income (23 083 to 8521 Bangladeshi Taka), with only a small recovery observed by November 2020.

Conclusions and Relevance  In this study of households in rural Bangladesh, all-cause mortality was lower in 2020 compared with 2019. Restrictions imposed by the government may have limited the scale of the COVID-19 pandemic in rural areas, although economic data suggest that these restrictions need to be accompanied by expanded welfare programs.

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Article Information

Accepted for Publication: September 1, 2021.

Published: November 15, 2021. doi:10.1001/jamanetworkopen.2021.32777

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Barnwal P et al. JAMA Network Open.

Corresponding Author: Alexander van Geen, PhD, Lamont-Doherty Earth Observatory, Columbia University, 61 Route 9W, Palisades, NY 10964 (avangeen@ldeo.columbia.edu).

Author Contributions: Drs Barnwal and van Geen had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Barnwal and Yao contributed equally to the study.

Concept and design: Barnwal, Yao, Wang, Haque, van Geen.

Acquisition, analysis, or interpretation of data: Barnwal, Yao, Wang, Juy, Raihan, van Geen.

Drafting of the manuscript: Barnwal, Yao, Wang, van Geen.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Yao, Wang, Raihan.

Obtained funding: Barnwal, van Geen.

Administrative, technical, or material support: Barnwal, Juy, Raihan, Haque.

Supervision: Barnwal, Juy, Raihan, Haque, van Geen.

Conflict of Interest Disclosures: None reported.

Funding/Support: The arsenic mitigation trial that set the stage for this study was supported by National Science Foundation SBE awards 1851928 (to Dr Barnwal) and 1853289 (to Dr van Geen).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We thank the large teams of enumerators involved in the field and telephone surveys. We particularly thank the residents of our study villages whose patience in a stressful situation was tested by a number of long telephone calls. Joel Cohen, PhD (Rockefeller University and Columbia University), Mushfiq Mobarak, PhD (Yale University), Stephen Luby, MD (Stanford University), and Jeffrey Shaman, PhD (Columbia University), provided helpful advice over the course of the study; they were not compensated for their contributions.

Additional Information: Data and code are available at https://github.com/yao-yl/mortalityPaper. All confidential information was removed from the posted survey data.

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