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Towards An Understanding Of The Multilevel Factors Associated With Maternal Health Care Utilization In Uttar Pradesh, India

Sanjeev Sridharan, Arnab Dey, Aparna Seth, Dharmendra Chandurkar, Kultar Singh, Katherine Hay, Rachael Gibson
Published 2017 · Medicine
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ABSTRACT Background: This paper explores the multilevel factors associated with maternal health utilization in India’s most populous state, Uttar Pradesh. 3 key utilization practices: registration of pregnancy, receipt of antenatal care, and delivery at home are examined for district and individual level predictors. The data is based on 5666 household surveys conducted as part of a baseline evaluation of the Uttar Pradesh Technical Support Unit (UPTSU.) program. Objectives: This intervention aims to assist the Government of Uttar Pradesh in increasing the efficiency, effectiveness, and equity of service delivery across a continuum of reproductive, maternal, new-born, child, and adolescent health (RMNCH+A) outcomes. Methods: The paper employs multilevel models that control for individuals being nested within districts in order to understand the predictors of maternal health care utilization. Results: The study identifies several individual-level predictors of health care utilization, including: literacy of the woman, the husband’s schooling, age at marriage, and socio-economic factors. Key predictors of pregnancy registration include husband’s schooling (OR 1.49, 95% CI 1.26–1.76), having a bank account (OR 1.36, 95% CI 1.11–1.68), and owning a house (OR 2.28, 95% CI 1.85–2.80). Factors affecting antenatal care include the woman’s literacy (OR 1.49, 95% CI 1.28–1.73), the respondent having had a job in the last year (OR 1.39, 95% CI 1.10–1.77), and owning a house (OR 2.83, 95% CI 2.27–3.53). Home delivery tends to be associated with woman’s literacy (OR 0.62, 95% CI 0.54–0.72) and marriage age of 15 and younger (OR 1.48, 95% CI 1.26–1.73). Conclusions: Interventions having equity considerations need to disrupt existing patterns of the health gradient. Successful implementation of such interventions, necessitate understanding the mechanisms that can disrupt the unequal utilization patterns and target domains of disadvantage. Knowledge of key predictors of utilization can aid in the implementation of such complex interventions.
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