Predictors of Availing Maternal Health Schemes: A community based study in Gujarat, India
Background: India continues to face challenges in improving key maternal health indicators with about 1/3rd of global maternal deaths happening in India. Utilization of health care services is an important issue in India with significant proportion of home deliveries and majority of mothers not receiving adequate antenatal care. Mortality among poor rural women is the highest with lowest utilization. To make maternal healthcare more equitable, numerous schemes such as Janani Suraksha Yojana, Chiranjeevi Yojana, Kasturba Poshan Sahay Yojana have been introduced. Studies suggest that utilization of such schemes by target population is low and there is a need to understand factors affecting maternal health care utilization in the context of these schemes. Current community based study was done in rural Gujarat to understand characteristics of women who utilize such schemes and predictors of utilization. Methodology: Data collection was done in two districts of Gujarat from June to August, 2013 as a pilot phase of MATIND project. Community based cross-sectional study included 827 households and socio-demographic details of 1454 women of 15-49 years age groups were collected. 265 mothers, who had delivered after 1st January, 2013 are included in the regression analyses. The data analysis carried out with R version 3.0.1 software. Results: The analysis indicates socioeconomic variables such as caste, maternal variables such as education and health system variables such as use of government facility are important predictors of maternal health scheme utilization. Results suggest that socioeconomic and health system factors are the best predictors for availing scheme. Conclusion: Health system variables along with individual level variables are important predictors for availing maternal health schemes. The study indicates the need to examine all levels of predictors for utilizing government health schemes to maximize the benefit for underserved populations such as poor rural mothers.