021 - Regional and Socio-economic inequality in facility delivery in Bangladesh

Background:
Bangladesh Maternal Mortality Survey 2016 estimated that maternal mortality ratio is 196 per 100,000 live births; this rate has remained almost unchanged in Bangladesh since 2010.With a view to reducing existing gaps and inequalities in the delivery of maternal health services, the Bangladesh government is adopting and implementing policies such as National Strategy for Maternal Health (BNSMH) 2017-2030. Thus, the overall percentage of institutional delivery has increased, but there is still variation among the districts. This study aims to identify districts with low institutional delivery and to find out socio-economic inequalities' contribution to the regional inequality of institutional deliveries.
Methods:
In this study information of 1189, 7950, and 9183 15-49-year-old women, who gave birth in the previous two years, have been used from multiple cluster surveys of 2006, 2012-13, and 2019.
To describe regional inequality on health facility delivery within unobserved sub-population (districts), growth mixture model was used. The concentration indexing measure was used to explore the socioeconomic inequality, and a Blinder-Oaxaca decomposition analysis was performed to decompose the gap among classes which was found by using growth mixture modeling approach.

Results:
The mean percentage of health facility delivery overall in Bangladesh was increasing (16 percent in 2006, 30 percent in 2012-13, and 52 percent in 2019). Several growth mixture models were fitted and AIC values showed random intercept and slope model with 3 classes (high, moderate, and low) was best. The average intercepts and slopes for each latent class indicate the trajectories vary from them, and the coefficient value was statistically highly significant among the three classes. The high, moderate, and low classes have consisted of 46, 5, and 13 districts, respectively. Facility delivery was 60 percent, 31 percent, and 16.3 for high, moderate, and low-class districts, respectively. The concentration index confirms wealth inequality in the facility delivery for all three classes. Blinder-Oaxaca decomposition indicated a significant gap in the facility delivery among the classes of the districts. The differences were 0.249, 0.415, and 0.166 for high vs moderate, high vs low, and moderate vs low, respectively. These differences were decomposed into endowments, coefficients, and interaction, and found the endowments, as well as the coefficient effect, was statistically significant. The extension part of the Blinder-Oaxaca decomposition analysis showed that education, area of residence, and utilization of antenatal care were the most significant contributors reducing the gap in the high vs low and moderate vs low latent classes, respectively (p-value <0.001). In terms of high vs moderate latent class this study found wealth status education, division, and antenatal care were the significant contributor to changing the high vs moderate gap (p-value <0.001).

Conclusions:
The result of latent trajectory analysis illustrated three different sub-groups (high, middle, and low) of districts. Districts with a lower rate of and lower increase in institutional delivery have significant wealth inequality in service consumption. Therefore, the government needs to adopt a strategy that focuses on the lowest wealth quantile of lower districts. Moreover, the decomposition results also demonstrate that the gap in educational attainment and place of residence also contribute to the significant difference in institutional delivery among the classes of districts. For a ubiquitous high institutional delivery as well as safe and improved maternal health, the government of Bangladesh should come up with a poor people-focused health strategy for the districts of low class, as well as a strategy to educate all people and close the urban-rural gap.

Inequality

Facility Delivery

Growth mixture model

Blinder-Oaxaca decomposition

Spatial variation

Bangladesh 

Expert

Knowledge

International Conference on Health Policy Statistics 2023