Understanding intersections of social determinants of maternal healthcare utilization in Uttar Pradesh, India

• Published in: PloS one Vol. 13; no. 10; p. e0204810
• Main Authors: Dey, Arnab, Hay, Katherine, Afroz, Bilal, Chandurkar, Dharmendra, Singh, Kultar, Dehingia, Nabamallika, Raj, Anita, Silverman, Jay G.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.10.2018; Public Library of Science (PLoS)
• Subjects: Adult; Age; Algorithms; Analysis; Biology and Life Sciences; Births; Censuses; Children; Classification; Colleges & universities; Coverage; Cross-Sectional Studies; Determinants; Developing countries; Female; Gender equity; Global health; Health care; Health care facilities; Health Care Surveys; Health centres; Health disparities; Health services; Health Services Accessibility; Health services utilization; Health surveys; Households; Humans; India; Infant; Infant, Newborn; Intersectionality; Intersections; LDCs; Learning algorithms; Literacy; Machine Learning; Marginalized groups; Marriage; Maternal & child health; Maternal Age; Maternal Health Services; Maternal mortality; Medical care utilization; Medical research; Medicine; Medicine and Health Sciences; Methods; Patient Acceptance of Health Care - psychology; People and Places; Population; Prediction models; Pregnancy; Prenatal Care - psychology; Public health; Regression analysis; Religion; Rural Health; Social determinants of health; Social Sciences; Socioeconomic Factors; Studies; Utilization; Womens health; Young Adult; India; La Jolla California; Uttar Pradesh India; California; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0204810

To explore intersections of social determinants of maternal healthcare utilization using the Classification and Regression Trees (CART) algorithm which is a machine-learning method used to construct prediction models. Institutional review board approval for this study was granted from Public Health Service-Ethical Review Board (PHS-ERB) and from the Health Ministry Screening Committee (HMSC) facilitated by Indian Council for Medical Research (ICMR). IRB review and approval for the current analyses was obtained from University of California, San Diego. Cross-sectional data were collected from women with children aged 0-11 months (n = 5,565) from rural households in 25 districts of Uttar Pradesh, India. Participants were surveyed on maternal healthcare utilization including registration of pregnancy (model-1), receipt of antenatal care (ANC) during pregnancy (model-2), and delivery at health facilities (model -3). Social determinants of health including wealth, social group, literacy, religion, and early age at marriage were captured during the survey. The Classification and Regression Tree (CART) algorithm was used to explore intersections of social determinants of healthcare utilization. CART analyses highlight the intersections, particularly of wealth and literacy, in maternal healthcare utilization in Uttar Pradesh. Model-1 documents that women who are poorer, illiterate and Muslim are less likely to have their pregnancies registered (71.4% vs. 86.0% in the overall sample). Model-2 documents that poorer, illiterate women had the lowest ANC coverage (37.7% vs 45% in the overall sample). Model-3, developed for deliveries at health facilities, highlighted that illiterate and poor women have the lowest representation among facility deliveries (59.6% vs. 69% in the overall sample). This paper explores the interactions between determinants of maternal healthcare utilization indicators. The findings in this paper highlights that the interaction of wealth and literacy can play a very strong role in accentuating or diminishing healthcare utilization among women. The study also reveals that religion and women's age at marriage also interact with wealth and literacy to create substantial disparities in utilization. The study provides insights into the effect of intersections of determinants, and highlights the importance of using a more nuanced understanding of the impact of co-occurring forms of marginalization to effectively tackle inequities in healthcare utilization.