PERBANDINGAN MODEL REGRESI BINOMIAL NEGATIF BIVARIAT DENGAN MODEL GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL BIVARIAT REGRESSION (GWNBBR) PADA KASUS ANGKA KEMATIAN BAYI DAN KEMATIAN IBU DI JAWA TENGAH

Infant mortality (0-11 months) and maternal mortality (during pregnancy, childbirth, and postpartum) are significant indicators in determining the level of public health. Central Java Province which has 35 regencies/cities is included in the top five regions with the highest number of infant and maternal mortality in Indonesia. The data characteristics of the number of infants and maternal mortality are count data. Therefore, the Poisson Regression method can be used to analyze the factors that influence the number of infants and maternal mortality. In Poisson regression analysis, there must be a fulfilled assumption, called equidispersion. Frequently, the variance of count data is greater than the mean, which is known as the overdispersion. The research, binomial negative bivariate regression is used as a solutions to overcome the problem of overdispersion in poisson regression. This method produce a global model. In reality, the geographical, socio-cultural, and economic conditions of each region will be different. This illustrates the effect of spatial heterogeneity, so it needs to be developed into Geographically Weighted Negative Binomial Bivariate Regression (GWNBBR). The model of GWNBBR provides weighting based on the position or distance from one observation area to another. Significant variables for modeling infant mortality cases included the percentage of obstetric complications treated (X1), the percentage of infants who were exclusively breastfed (X3), and the percentage of poor people (X5). Significant variable for modeling maternal mortality cases is the percentage of poor people (X5). Based on the AIC value, GWNBBR model is better than binomial negatif bivariat regression model because it has a smaller AIC value.

Spatial Distribution of Infant Mortality in Ethiopia: Using Demographic Health Survey, the Ethiopia

Background Mortality is one of the demographic variables that affect population trends. Among mortality of children, Infant mortality contributed to more than 75% of all under-five deaths globally. It disproportionately affects those living in the different regions of countries and within the region. Exploring the spatial distribution and identifying associated factors is important to design effective intervention programs to reduce infant mortality. Thus, this study aimed to assess the spatial distribution and associated factors of infant mortality in Ethiopia using the 2016 Ethiopian Demographic and Health Survey (EDHS). Method The Data this study were used Ethiopian Demographic and Health Survey in 2016. A total of 11,023 live births from the EDHS data were included in the analysis. Spatial analysis was done to explore spatial distribution of infant mortality using ArcGIS version 10.4. Results This study revealed that the spatial distribution of infant mortality was non-random in the country with Moran’s index 0.1546 (P-value=0.0185). The Afar and Somali regions of Ethiopia were identified in this study on the hot spot of infant mortality. Conclusions The spatial distribution of infant mortality varies across the country. ANC usage, sex of a child, birth interval, birth size, birth type, birth order, wealth index, residence, region, and the spatial variable (Si) were significant predictors of infant mortality. Therefore, it needs interventions in the hot spot areas. Focusing on maternal health care services, rural residences, multiple births, infants having a smaller birth size, and male infants deserves special attention.

Co-establishing an infrastructure for routine data collection to address disparities in infant mortality: planning and implementation

Background Efforts to address infant mortality disparities in Ohio have historically been adversely affected by the lack of consistent data collection and infrastructure across the community-based organizations performing front-line work with expectant mothers, and there is no established template for implementing such systems in the context of diverse technological capacities and varying data collection magnitude among participating organizations. Methods Taking into account both the needs and limitations of participating community-based organizations, we created a data collection infrastructure that was refined by feedback from sponsors and the organizations to serve as both a solution to their existing needs and a template for future efforts in other settings. Results By standardizing the collected data elements across participating organizations, integration on a scale large enough to detect changes in a rare outcome such as infant mortality was made possible. Datasets generated through the use of the established infrastructure were robust enough to be matched with other records, such as Medicaid and birth records, to allow more extensive analysis. Conclusion While a consistent data collection infrastructure across multiple organizations does require buy-in at the organizational level, especially among participants with little to no existing data collection experience, an approach that relies on an understanding of existing barriers, iterative development, and feedback from sponsors and participants can lead to better coordination and sharing of information when addressing health concerns that individual organizations may struggle to quantify alone.