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

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.

References

Beaney T , Clarke JM , Jain V , et al. Excess mortality: the gold standard in measuring the impact of COVID-19 worldwide? J R Soc Med. 2020;113(9):329-334. doi:10.1177/0141076820956802 PubMed Google Scholar Crossref

Karlinsky A , Kobak D . Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset. Elife. 2021;10:e69336. doi:10.7554/eLife.69336 PubMed Google Scholar

Setel PW , Macfarlane SB , Szreter S , et al; Monitoring of Vital Events. A scandal of invisibility: making everyone count by counting everyone. Lancet. 2007;370(9598):1569-1577. doi:10.1016/S0140-6736(07)61307-5 PubMed Google Scholar Crossref

Mikkelsen L , Phillips DE , AbouZahr C , et al. A global assessment of civil registration and vital statistics systems: monitoring data quality and progress. Lancet. 2015;386(10001):1395-1406. doi:10.1016/S0140-6736(15)60171-4 PubMed Google Scholar Crossref

Chatterjee P . Is India missing COVID-19 deaths? Lancet. 2020;396(10252):657. doi:10.1016/S0140-6736(20)31857-2 PubMed Google Scholar Crossref

Checchi F , Roberts L . Documenting mortality in crises: what keeps us from doing better. PLoS Med. 2008;5(7):e146. doi:10.1371/journal.pmed.0050146 PubMed Google Scholar

Egger D , Miguel E , Warren SS , et al. Falling living standards during the COVID-19 crisis: quantitative evidence from nine developing countries. Sci Adv. 2021;7(6):eabe0997. doi:10.1126/sciadv.abe0997 PubMed Google Scholar

World Health Organization. WHO COVID-19 dashboard. Accessed October 15, 2021. https://covid19.who.int

Bangladesh Bureau of Statistics. Report on sample vital registration system (SVRS) 2019. Accessed August 20, 2021. http://www.bbs.gov.bd/site/page/b588b454-0f88-4679-bf20-90e06dc1d10b/-

10.

Uddin M , Ashrafi SAA , Azad AK , et al. Improving coverage of civil registration and vital statistics, Bangladesh. Bull World Health Organ. 2019;97(9):637-641. doi:10.2471/BLT.18.219162 PubMed Google Scholar Crossref

11.

Elyazar IR , Surendra H , Ekawati L , et al. Excess mortality during the first ten months of COVID-19 epidemic at Jakarta, Indonesia. medRxiv. Published online December 14, 2020. doi:10.1101/2020.12.14.20248159 Google Scholar

12.

Koum Besson ES , Norris A , Bin Ghouth AS , et al. Excess mortality during the COVID-19 pandemic: a geospatial and statistical analysis in Aden governorate, Yemen. BMJ Glob Health. 2021;6(3):e004564. doi:10.1136/bmjgh-2020-004564 PubMed Google Scholar

13.

Biswas RK , Afiaz A , Huq S . Underreporting COVID-19: the curious case of the Indian subcontinent. Epidemiol Infect. 2020;148:e207. doi:10.1017/S0950268820002095 PubMed Google Scholar

14.

Mukherjee S . Why does the pandemic seem to be hitting some countries harder than others? The New Yorker. March 1, 2021. Accessed October 15, 2021. https://www.newyorker.com/magazine/2021/03/01/why-does-the-pandemic-seem-to-be-hitting-some-countries-harder-than-others

15.

London School of Hygiene & Tropical Medicine. The use of epidemiological tools in conflict-affected populations: open-access educational resources for policy-makers. 2009. Accessed October 15, 2021. http://conflict.lshtm.ac.uk/page_02.htm

16.

Bangladesh Bureau of Statistics. Household income and expenditure survey 2016-2017. September 19, 2018. Accessed October 15, 2021. https://catalog.ihsn.org/index.php/catalog/7399

17.

The American Association for Public Opinion Research. Standard definitions: final dispositions of case codes and outcome rates for surveys, 9th ed. Revised 2016. Accessed October 15, 2021. https://www.aapor.org/AAPOR_Main/media/publications/Standard-Definitions20169theditionfinal.pdf

18.

Google, LLC. Google COVID-19 community mobility reports. Accessed March 10, 2021. https://www.google.com/covid19/mobility/

19.

Gelman A , Little TC . Poststratification into many categories using hierarchical logistic regression. Surv Methodol. 1997;23:127–35. Accessed October 15, 2021. http://www.stat.columbia.edu/~gelman/research/published/poststrat3.pdf Google Scholar

20.

Park DK , Gelman A , Bafumi J . Bayesian multilevel estimation with poststratification: state-level estimates from national polls. Polit Anal. 2004;12:375–85. doi:10.1093/pan/mph024 Google Scholar Crossref

21.

Stan Development Team. Stan user’s guide. Accessed October 15, 2021. https://mc-stan.org

22.

Becker S , Weng S . Seasonal patterns of deaths in Matlab, Bangladesh. Int J Epidemiol. 1998;27(5):814-823. doi:10.1093/ije/27.5.814 PubMed Google Scholar Crossref

23.

Financial Express Report. Bangladesh’s inflation recorded at 5.56pc in 2020-21 fiscal. July 20, 2021. Accessed October 15, 2021. https://thefinancialexpress.com.bd/economy/bangladeshs-inflation-recorded-at-556pc-in-2020-21-fiscal-1626747662

24.

Bangladesh Bureau of Statistics. Report on sample vital registration system (SVRS) 2020. Accessed August 20, 2021. http://www.bbs.gov.bd/site/page/b588b454-0f88-4679-bf20-90e06dc1d10b/-

25.

De Weerdt J , Van Damme W . Health, wealth and the double paradox of COVID-19 mortality in low-income countries. March 2, 2021. Accessed October 15, 2021. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3793427

26.

Cepon-Robins TJ , Gildner TE . Old friends meet a new foe: a potential role for immune-priming parasites in mitigating COVID-19 morbidity and mortality. Evol Med Public Health. 2020;2020(1):234-248. doi:10.1093/emph/eoaa037 PubMed Google Scholar Crossref

27.

Ge Y , Zhang W , Liu H , et al Effects of worldwide interventions and vaccination on COVID-19 between waves and countries. medRxiv. April 6, 2021. Accessed October 15, 2021. https://www.medrxiv.org/content/10.1101/2021.03.31.21254702v1 Google Scholar

28.

Hamadani JD , Hasan MI , Baldi AJ , et al. Immediate impact of stay-at-home orders to control COVID-19 transmission on socioeconomic conditions, food insecurity, mental health, and intimate partner violence in Bangladeshi women and their families: an interrupted time series. Lancet Glob Health. 2020;8(11):e1380-e1389. doi:10.1016/S2214-109X(20)30366-1 PubMed Google Scholar Crossref

29.

Gerard F , Imbert C , Orkin K . Social protection response to the COVID-19 crisis: options for developing countries. Oxford Rev Econ Policy. 2020;36(1)(suppl):S281-S296. doi:10.1093/oxrep/graa026 Google Scholar

30.

Mahmud A , Islam MR . Social stigma as a barrier to COVID-19 responses to community well-being in Bangladesh. Int J Com WB. 2020;4:315-321. doi:10.1007/s42413-020-00071-w Google Scholar Crossref

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

Measuring the Toll of the COVID-19 Pandemic in Rural Bangladesh

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