Evidence map›Paper›PMID 39070074›Full record

ArticleLancet regional health. Americas2024

Ethno-racial inequalities on adverse birth and neonatal outcomes: a nationwide, retrospective cohort study of 21 million Brazilian newborns.

Poliana Rebouças, Enny S Paixão, Dandara Ramos, Julia Pescarini, Elzo Pereira Pinto-Junior, Ila R Falcão, Maria Yury Ichihara, Samila Sena, Rafael Veiga, Rita Ribeiro and 3 more

Abstract read
In one paragraph

Article in Lancet regional health. Americas, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Maternal Mortality Among Black Women in Brazil: A Retrospective Cohort Study.International journal of environmental research and public health · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Predicting Low Birth Weight in Big Cities in the United States Using a Machine Learning Approach.International journal of environmental research and public health · 2025
    Article
  13. Article
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

13 authors.

Poliana RebouçasCenter for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
Enny S PaixãoCenter for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
Dandara RamosCenter for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
Julia PescariniCenter for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
Elzo Pereira Pinto-JuniorCenter for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
Ila R FalcãoCenter for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
Maria Yury IchiharaCenter for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
Samila SenaCenter for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
Rafael VeigaCenter for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
Rita RibeiroCenter for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
Laura C RodriguesFaculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Maurício L BarretoCenter for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
Emanuelle F GoesCenter for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ethno-racial inequalities are critical determinants of health outcomes. We quantified ethnic-racial inequalities on adverse birth outcomes and early neonatal mortality in Brazil. Methods: We conducted a cohort study in Brazil using administrative linked data between 2012 and 2019. Estimated the attributable fractions for the entire population (PAF) and specific groups (AF), as the proportion of each adverse outcome that would have been avoided if all women had the same baseline conditions as White women, both unadjusted and adjusted for socioeconomics and maternal risk factors. AF was also calculated by comparing women from each maternal race/skin colour group in different groups of mothers' schooling, with White women with 8 or more years of education as the reference group and by year. Findings: 21,261,936 newborns were studied. If all women experienced the same rate as White women, 1.7% of preterm births, 7.2% of low birth weight (LBW), 10.8% of small for gestational age (SGA) and 11.8% of early neonatal deaths would have been prevented. Percentages preventable were higher among Indigenous (22.2% of preterm births, 17.9% of LBW, 20.5% of SGA and 19.6% of early neonatal deaths) and Black women (6% of preterm births, 21.4% of LBW, 22.8% of SGA births and 20.1% of early neonatal deaths). AF was higher in groups with fewer years of education among Indigenous, Black and Interpretation: A considerable portion of adverse birth outcomes and neonatal deaths could be avoided if ethnic-racial inequalities were non-existent in Brazil. Acting on the causes of these inequalities must be central in maternal and child health policies. Funding: Bill & Melinda Gates Foundation and Wellcome Trust.

Indexed as

Health inequalitiesLow birth weightNewbornPrematuritySmall for gestational age

Identifiers

PMID39070074
PMCPMC11269955

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.