title: Inequalities in socioeconomic status and race and the odds of undergoing a mammogram in Brazil.
abstract: Access 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.
  
  
  
  This 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.
  
  
  
  Having 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.
  
  
  
  This 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.
authors.affiliation: Department 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.|Health Information and Networks Research Group, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil. enirtes@fiocruz.br.
authors.forename: Enirtes Caetano Prates
authors.initials: EC
authors.lastname: Melo
authors.affiliation: Health Information and Networks Research Group, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil.
authors.forename: Evangelina Xavier Gouveia
authors.initials: EX
authors.lastname: de Oliveira
authors.affiliation: Department 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.
authors.forename: Dóra
authors.initials: D
authors.lastname: Chor
authors.affiliation: Health Information and Networks Research Group, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil.|Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil.
authors.forename: Marilia Sá
authors.initials: MS
authors.lastname: Carvalho
authors.affiliation: Health Information and Networks Research Group, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil.|Institute for Studies in Collective Health, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
authors.forename: Rejane Sobrino
authors.initials: RS
authors.lastname: Pinheiro
mesh_terms: D000328:Adult; D000368:Aged; D001938:Brazil; D005260:Female; D006297:Health Services Accessibility / Q000706:statistics & numerical data*; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data*; D054625:Healthcare Disparities / Q000706:statistics & numerical data*; D006801:Humans; D007182:Income / Q000706:statistics & numerical data; D007348:Insurance, Health / Q000706:statistics & numerical data*; D016015:Logistic Models; D008327:Mammography / Q000706:statistics & numerical data*; D008875:Middle Aged; D015999:Multivariate Analysis; D044469:Racial Groups; D012923:Social Class; D012959:Socioeconomic Factors
keywords: Access to health services; Brazil; Health equity; Health inequalities; Information systems; Mammogram; Race