Evidence map›Paper›PMID 27628786›Full record

ArticleInternational journal for equity in health2016

Inequalities in socioeconomic status and race and the odds of undergoing a mammogram in Brazil.

Enirtes Caetano Prates Melo, Evangelina Xavier Gouveia de Oliveira, Dóra Chor, Marilia Sá Carvalho, Rejane Sobrino Pinheiro

Abstract read
In one paragraph

Article in International journal for equity in health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Tuberculosis Treatment Outcomes in Brazil: Different Predictors for Each Type of Unsuccessful Outcome.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023
    Observational
  6. Article
  7. Article
  8. Life Expectancy of White and Non-White Elite Heavyweight Boxers.Journal of racial and ethnic health disparities · 2020
    Article
  9. Article
  10. Article
  11. 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

5 authors.

Enirtes Caetano Prates MeloDepartment of Epidemiology, National School of Public Health, Oswaldo Cruz Foundation -DEMQS/ENSP, Rua Leopoldo Bulhões, 1480, room 806. Manguinhos, Rio de Janeiro, RJ, 21041-210, Brazil. enirtes@fiocruz.br.
Evangelina Xavier Gouveia de OliveiraHealth Information and Networks Research Group, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil.
Dóra ChorDepartment of Epidemiology, National School of Public Health, Oswaldo Cruz Foundation -DEMQS/ENSP, Rua Leopoldo Bulhões, 1480, room 806. Manguinhos, Rio de Janeiro, RJ, 21041-210, Brazil.
Marilia Sá CarvalhoHealth Information and Networks Research Group, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil.
Rejane Sobrino PinheiroHealth Information and Networks Research Group, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccess to mammograms, in common with other diagnostic procedures, is strongly conditioned by socioeconomic disparities. Which aspects of inequality affect the odds of undergoing a mammogram, and whether they are the same in different localities, are relevant issues related to the success of health policies.

methodsThis study analyzed data from the 2008 PNAD - Brazilian National Household Sample Survey (11.607 million women 40 years of age or older), on having had at least one mammogram over life for women 40 years of age or older in each of Brazil's nine Metropolitan Regions (MR), according to socioeconomic position. The effects of income, schooling, health insurance and race in the different regions were investigated using multivariate logistical regression for each region individually, and for all MRs combined. The age-adjusted odds of a woman having had a mammogram according to race and stratified by two income strata (and two schooling strata) were also analyzed.

resultsHaving a higher income increases four to seven times a woman's odds of having had at least one mammogram in all MRs except Curitiba. For schooling, the gradient, though less steep, is favorable to women with more years of study. Having health insurance increases two to three times the odds in all MRs. Multivariate analysis did not show differences due to race (except for the Fortaleza MR), but the stratified analysis by income and schooling shows effects of race in most MRs, with greater differences for women with higher socioeconomic status.

conclusionsThis study confirms that income and schooling, as well as having health insurance, are still important determinants of inequality in health service use in Brazil. Additionally, race also contributes to the odds of having had a mammogram. The point is not to isolate the effect of each factor, but to evaluate how their interrelations may exacerbate differences, generating patterns of cumulative adversity, a theme that is still little explored in Brazil. This is much more important when we consider that race has only recently started be included in analyses of health outcomes in Brazil.

Indexed as

AdultAgedBrazilFemaleHealthcare DisparitiesHealth Services AccessibilityHealth Services Needs and DemandHumansIncomeInsurance, HealthLogistic ModelsMammographyMiddle AgedMultivariate AnalysisRacial GroupsSocial ClassAccess to health servicesBrazilHealth equityHealth inequalitiesInformation systemsMammogramRace

Identifiers

PMID27628786
PMCPMC5024478

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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.