No excess mortality detected in rural Bangladesh in 2020 from repeated surveys of a population of 81,000
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Abstract
Background
Excess mortality has demonstrated under-counting of COVID-19 deaths in many countries but cannot be measured in low-income countries where civil registration is incomplete.
Methods
Enumerators conducted an in-person census of all 16,054 households in a sample of 135 villages within a 350 km 2 region of Bangladesh followed by a census conducted again in May and November 2020 over the phone. The date and cause of any changes in household composition, as well as changes in income and food availability, were recorded. For analysis, we stratify the mortality data by month, age, gender, and household education. Mortality rates were modeled by Bayesian multilevel regression and the strata aggregated to the population by poststratification.
Results
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 phone, slightly below the 289 deaths reported for the same population over the same period in 2019. After adjustment for survey non-response and poststratification, 2020 mortality changed by -8% (95% CI, -21% to 7%) relative to an annualized mortality of 6.1 per thousand in 2019. However, salaried breadwinners reported a 40% decline in income and businesses a 60% decline in profits in May 2020.
Discussion
All-cause mortality in the surveyed portion of rural Bangladesh was if anything lower in 2020 compared to 2019. Our findings suggest various restrictions imposed by the government limited the scale of the pandemic, although they need to be accompanied by expanded welfare programs.
Key questions
What is already known?
Civil registry data from dozens of countries, where available, indicate gaps between official death counts attributed to COVID-19 and, usually, a larger increase in total mortality in 2020 compared to previous years. This approach is not available to gauge the impact of COVID-19 in countries such as Bangladesh where the civil registry system is slow and coverage incomplete. One year after the first COVID-19 case was reported in Bangladesh in 2020, the number of deaths attributed to COVID-19 was equivalent to 1% of annual mortality in previous years. Whether this low figure compared to many other countries is an accurate reflection of the situation or is distorted by massive under-counting has been much debated, albeit on the basis of little direct evidence. The lack of accurate mortality data has made it only more difficult for policy makers to balance the public health benefit of lockdowns and similar measures relative to the well-documented economic costs and hardship imposed by such measures on poor households in particular. A PubMed search conducted on May 4, 2021 under (Bangladesh[Title/Abstract]) AND (excess mortality[Title/Abstract]) limited to 2020-21 did not yield a single relevant study.
What are the new findings?
By conducting of repeated census of a large rural population over the course of 2020, once in person and twice over the phone, we document if anything a slight decline in mortality across a rural area of Bangladesh compared to 2019. We also place an upper limit on the level of under-reporting at the national level that is consistent with our observations. At the same time, interviewed households reported a large and sustained drop in income as well as reduced access to food.
What do the new findings imply?
The impact of the pandemic on mortality was thankfully limited in rural study area of Bangladesh in 2020. This suggests that officially recorded COVID-19 deaths may have been contributed largely by the urban population, about a third of the country overall. At the same time, the economic and nutritional impact of restrictions on trade and movement was substantial and probably underestimated in the rural population. As cases surge again, as they did in March–April 2021, policy makers may want to consider limiting strict restrictions to urban areas while expanding a financial support throughout the country.
Article activity feed
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SciScore for 10.1101/2021.05.07.21256865: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Another limitation of the study is that a quarter of the households surveyed in January 2020 could not be reached over the phone by November 2020. We cannot exclude that households with a member who recently died is less likely to pick up the phone or less willing to participate in the survey. Our approach to calculate aggregate mortality using demographic group-level mortality corrects for it, to the extent that mortality estimated …
SciScore for 10.1101/2021.05.07.21256865: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Another limitation of the study is that a quarter of the households surveyed in January 2020 could not be reached over the phone by November 2020. We cannot exclude that households with a member who recently died is less likely to pick up the phone or less willing to participate in the survey. Our approach to calculate aggregate mortality using demographic group-level mortality corrects for it, to the extent that mortality estimated for a particular age, sex and education group is not biased by attrition. The repeated census approach may also not have entirely eliminated a tendency not to report a recent death, especially if it was associated with COVID-19 symptoms because of widely reported stigma, especially at the beginning of the pandemic (21). We have separate evidence from our survey that COVID-19-like symptoms were under-reported by affected households on the basis of responses from neighboring households integrated at the village and para level. Various hypotheses have been proposed to explain the apparently lower impact of COVID-19 in some low-income countries (22). The effect of a relatively young population cannot be a factor in our study given that we are comparing the same population over two years. Spending more time outside or in well-ventilated houses has been invoked as an explanation, but another possibility is that previous infections in regions like rural Bangladesh could have dampened the symptoms of COVID-19 (23). Mobility data and our economic impact da...
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