How the choice of ethnic indicator influences ethnicity-based inequities in maternal health care in four Latin American countries: who is indigenous?

Abstract

Abstract Background The current focus on monitoring health inequalities and the complexity around ethnicity requires careful consideration of how ethnic disparities are measured and presented. This paper aims to determine how inequalities in maternal healthcare by ethnicity change according to different criteria used to classify indigenous populations. Methods Nationally representative demographic surveys from Bolivia, Guatemala, Mexico, and Peru (2008–2016) were used to explore coverage gaps across maternal health care by ethnicity using different criteria. Women were classified as indigenous through self-identification (SI), spoken indigenous language (SIL), or indigenous household (IH). We compared the gaps through measuring coverage ratios (CR) with adjusted Poisson regression models. Results Proportions of indigenous women changed significantly according to the identification criterion (Bolivia:SI-63.1%/SIL-37.7%; Guatemala:SI-49.7%/SIL-28.2%; Peru:SI-34%/SIL-6.3% & Mexico:SI-29.7%/SIL-6.9%). Indigenous in all countries, regardless of their identification, had less coverage. Gaps in care between indigenous and non-indigenous populations changed, for all indicators and countries, depending on the criterion used (e.g., Bolivia CR for contraceptive-use SI = 0.70, SIL = 0.89; Guatemala CR for skilled-birth-attendant SI = 0.77, SIL = 0.59). The heterogeneity persists when the reference groups are modified and compare just to non-indigenous (e.g., Bolivia CR for contraceptive-use under SI = 0.64, SIL = 0.70; Guatemala CR for Skilled-birth-attendant under SI = 0.77, SIL = 0.57). Conclusions The indigenous identification criteria could have an impact on the measurement of inequalities in the coverage of maternal health care. Given the complexity and diversity observed, it is not possible to provide a definitive direction on the best way to define indigenous populations to measure inequalities. In practice, the categorization will depend on the information available. Our results call for greater care in the analysis of ethnicity-based inequalities. A greater understanding on how the indigenous are classified when assessing inequalities by ethnicity can help stakeholders to deliver interventions responsive to the needs of these groups.