Evidence map›Paper›PMID 34429282›Full record

ArticleBMJ open2021

How many infants may have died in low-income and middle-income countries in 2020 due to the economic contraction accompanying the COVID-19 pandemic? Mortality projections based on forecasted declines in economic growth.

Gil Shapira, Damien de Walque, Jed Friedman

Abstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  12. Infant Mortality Trends and Determinants in Kazakhstan.Children (Basel, Switzerland) · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Gil ShapiraDevelopment Research Group, World Bank, Washington, Columbia, USA gshapira@worldbank.org.ORCID 0000-0002-1138-7269
Damien de WalqueDevelopment Research Group, World Bank, Washington, Columbia, USA.ORCID 0000-0003-1592-7602
Jed FriedmanDevelopment Research Group, World Bank, Washington, Columbia, USA.ORCID 0000-0002-6763-5890

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWhile COVID-19 has a relatively small direct impact on infant mortality, the pandemic is expected to indirectly increase mortality of this vulnerable group in low-income and middle-income countries through its effects on the economy and health system performance. Previous studies projected indirect mortality by modelling how hypothesised disruptions in health services will affect health outcomes. We provide alternative projections, relying on modelling the relationship between aggregate income shocks and mortality.

designWe construct a sample of 5.2 million births by pooling retrospective birth histories reported by women in Demographic and Health Surveys conducted in 83 low-income and middle-income countries between 1985 and 2018. We employ regression models with country-specific fixed-effects and flexible time trends to estimate the impact of gross domestic product per capita on infant mortality rate. We then use growth projections by the International Monetary Fund to predict the effect of the economic downturn in 2020 on infant mortality.

resultsWe estimate 267 208 (95% CI 112 000 to 422 415) excess infant deaths in 128 countries, corresponding to a 6.8% (95% CI 2.8% to 10.7%) increase in the total number of infant deaths expected in 2020.

conclusionsThe findings underscore the vulnerability of infants to the negative income shocks such as those imposed by the COVID-19 pandemic. While efforts towards prevention and treatment of COVID-19 remain paramount, the global community should also strengthen social safety nets and assure continuity of essential health services.

Indexed as

COVID-19PandemicsDeveloping CountriesEconomic DevelopmentFemaleHumansInfantRetrospective StudiesSARS-CoV-2COVID-19health economicshealth policy

Identifiers

PMID34429282
PMCPMC8413467

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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.