Evidence map›Paper›PMID 41080610›Full record

ArticleLancet regional health. Americas2025

Regional and national estimates of children affected by all-cause and COVID-19-associated orphanhood and caregiver death in Brazil, by age and family circumstance: a modeling study.

Nicholas Steyn, H Juliette T Unwin, Jamie Ponmattam, Andrés Villaveces, Luiza Martins, Lorraine Sherr, Alexandra Blenkinsop, Elizaveta Semenova, Alice Stuart-Brown, André Victor Ribeiro Amaral and 9 more

Abstract read
In one paragraph

Article in Lancet regional health. Americas, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

19 authors.

Nicholas SteynDepartment of Statistics, University of Oxford, Oxford, United Kingdom.
H Juliette T UnwinSchool of Mathematics, University of Bristol, Bristol, United Kingdom.
Jamie PonmattamDepartment of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, United States of America.
Andrés VillavecesDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
Luiza MartinsFederal University of Minas Gerais, Minas Gerais, Brazil.
Lorraine SherrInstitute of Global Health, University College London, London, United Kingdom.
Alexandra BlenkinsopDepartment of Mathematics, Imperial College London, London, United Kingdom.
Elizaveta SemenovaSchool of Public Health, Imperial College London, London, United Kingdom.
Alice Stuart-BrownSchool of Public Health, Imperial College London, London, United Kingdom.
André Victor Ribeiro AmaralSchool of Mathematical Sciences, University of Southampton, Southampton, United Kingdom.
Oliver RatmannDepartment of Mathematics, Imperial College London, London, United Kingdom.
Ricardo Parolin SchnekenbergInstitute of Neurology, University College London, London, United Kingdom.
Lucie CluverDepartment of Social Policy and Intervention, University of Oxford, Oxford, United Kingdom.
Susan HillisDepartment of Mathematics, Imperial College London, London, United Kingdom.
Laura RawlingsWorld Bank Group, Washington, DC, United States of America.
Lorena BarberiaDepartment of Political Science, University of São Paulo, São Paulo, Brazil.
Andrea Santos SouzaMinistério Público do Estado de São Paulo, São Paulo, Brazil.
Marcia C CastroDepartment of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, United States of America.
Seth FlaxmanDepartment of Computer Science, University of Oxford, Oxford, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Orphanhood and caregiver death can have severe consequences for children. Timely and accurate data can guide policy, particularly during health crises like COVID-19. The aim of our study is to present national and subnational analysis of both all-cause and COVID-19-associated orphanhood and caregiver death in Brazil and compare our model outputs with bespoke administrative datasets. Methods: We use publicly available national datasets to estimate the number of Brazilian children experiencing parent and caregiver loss due to all causes and COVID-19 in 2020-2021. Findings: An estimated 1,300,000 (95% uncertainty interval, 1,190,000, 1,430,000) children in Brazil experienced loss of one or multiple parents and/or co-residing caregivers. 673,000 (652,000, 690,000) were estimated to have lost one or both parents, of which 149,000 (144,000, 154,000) were COVID-19 associated; 635,000 (534,000, 758,000) children were estimated to have lost a co-residing grandparent or other kin, of which 135,000 (85,900, 199,000) were COVID-19 associated. Orphanhood varied substantially across states, with the rate of all cause parental orphanhood highest in Roraima at 17.5 (15.6, 20.6) per 1000 children and the lowest in Santa Catarina at 9.5 (8.7, 10.4) per 1000 children. COVID-19-associated orphanhood was also unevenly distributed, with Mato Grosso experiencing the greatest rate, at 4.4 (3.9, 5.3) per 1000 children, while Pará experienced the lowest rate of 1.4 (1.2, 1.8) per 1000 children. Comparisons with limited data from Brazil's civil registry offices and (manually reviewed death certificates in Campinas found a similar demographic distribution of orphanhood. However, our estimates suggested that administrative sources undercount orphanhood. Interpretation: Our findings highlight the extent of orphanhood in Brazil and large inequalities between states. Comparisons between administrative data and model estimates show similar temporal patterns and proportions of maternal and paternal orphanhood but different magnitudes. This suggests that strengthening vital registration systems can put children at the center of public health responses globally. Funding: This study was funded by "Building Global Public Health Capacity to Link Real-Time Modelling Data on COVID-19-associated Orphanhood and Caregiver Deaths to Inform Prevention, Preparedness and Protection from COVID-19 consequences" (2023 CDC/WHO grant) and the Moderna Charitable Foundation.

Indexed as

BrazilCaregiver deathCOVID-19Excess mortalityOrphanhood

Identifiers

PMID41080610
PMCPMC12513060

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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.