Modeling Transitions in Prenatal Care and Institutional Delivery Using Markov Chains

Abstract

Continuity of maternal care is a critical determinant of perinatal outcomes. In Honduras, despite advances in service coverage, gaps persist in the retention of women along the continuum of care. To model the transitions between prenatal care and institutional delivery states using Markov chains, to simulate scenarios of improvement in coverage and retention, and to estimate their impact on continuity of care indicators. Data from Honduras' ENDESA/MICS 2019 were used to parameterize a six-state Markov model representing the maternal care continuum. Stratified transition matrices were estimated by area of residence and wealth quintile, and four intervention scenarios were simulated using Monte Carlo simulation with 10,000 iterations. The probability of achieving institutional delivery was 95.0% (95%CI: 66.2-96.0%) at the national level, with significant disparities between urban (97.1%) and rural (91.7%) areas, and between the richest (97.1%) and poorest (89.5%) quintiles. The main bottleneck identified was the transition from partial prenatal care to institutional delivery in rural populations. The simulations indicated that interventions focused on improving access to institutional childbirth (+15%) would generate the greatest incremental impact (0.7%). The Markov model allows us to identify critical points of loss in the maternal care continuum. Interventions should be prioritized in rural and lower-income populations, focusing on the effective linkage between prenatal care and institutional delivery.