Abstract
In Indonesia, especially Bandung, there are still many cases of maternal deaths during pregnancy, birth, and postpartum which can increase the Maternal Mortality Rate (MMR). Cases of maternal deaths are mostly due to exsanguination, infection, anemia, and the other causes that related to pregnancy. To reduce the MMR of Bandung, we can analyze to determine the factors that influence the MMR in order to maximize the maternal health care programs so as to prevent the possibility of death. The analysis is using Zero-Inflated Poisson (ZIP) regression because maternal mortality data is the result of counting that allows overdispersion due to excess zeros. Regression parameter estimation use expectation maximization algorithm followed by the Newton-Raphson iteration method. The analysis result showed that of the five suspected factors to affect the MMR -such as the first tetanus toxoid immunization (TT1), the provision of 90 Fe tablets (Fe3), postpartum care, pregnancy complications, and the first antenatal care (K1)- only TT1 and postpartum care are significantly affect the MMR.
Highlights
The Maternal Mortality Rate (MMR) is the number of maternal deaths due to pregnancy, childbirth, and within 42 days postpartum every 100,000 live births
The MMR is a measurement of the woman's death risk that related to pregnancy, as well as being an indicator of the quality of health care and health status of people in a region
Analysis of the factors that influence MMR is done through the data of number of maternal deaths in Bandung 2016 obtained from the official website of Bandung’s health service
Summary
The Maternal Mortality Rate (MMR) is the number of maternal deaths due to pregnancy, childbirth, and within 42 days postpartum every 100,000 live births. Analysis of the factors that influence MMR is done through the data of number of maternal deaths in Bandung 2016 obtained from the official website of Bandung’s health service. The data of number of maternal deaths in Bandung 2016 shown that the data are counting data that classified into discrete data The results of this analysis are suppossed to help the government programs in resolving the MMR problems that are concerned to increase. The data about death is a rare event data that contains many zero values (excess zeros) which has important meaning for the research. This can cause one of the assumption violations called overdispersion. Regression parameter estimation in this paper using the Expectation Maximization (EM) algorithm and followed by the NewtonRaphson iteration method
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