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Lag Spatial Modeling for Finding the Factors Affecting Infants Mortality Rate in East Java

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Background: Auto regressive spatial model or spatial lag is a model that combines a simple regression model with spatial lag dependent variable using cross section data. East Java Province was one of the five largest contributors to IMR in Indonesia. In 2014, the IMR in East Java was 9 per 1,000 live births. Thus, this research aimed to find the factors affecting IMR using the spatial lag. Method: This research was using a non-reactive or non-obstructive study method, by analyzing secondary data from the health profile and Susenas (National Social Economic Survey) of East Java Province in 2014. The dependent variable was the IMR and the independent variable were the coverage of pregnancy visit K1 (X2), coverage of pregnancy visit K4 (X3), delivery by health personnel (X4), neonatal visit 1 (X5), and complete neonatal visit (X6), the ratio of midwives (X7) and the ratio of Posyandu (X8). Results: The result of the study showed: (1) Variables that affect the IMR were the coverage of the first antenatal visit or K1 (p = 0.003), the coverage of the fourth ante natal visit or K4 (p = 0.01), and the ratio of Posyandu (p = 0.03). (2) IMR model that being obtained was = 1.68 * coverage of first ante natal visit K1 − 1.20 * coverage of fourth ante natal visit or K4 − 0.12 * Posyandu ratio + 0.29 WY. Conclusion: Variables of coverage of the first ante natal visit or K1, coverage of the fourth ante natal visit or K4 and the ratio of Posyandu supported the IMR model by 67%.
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Keywords: IMR; Indonesia; Infant Mortality Rate; Spatial Lag Model

Document Type: Research Article

Affiliations: Department of Biostatistics and Population Study, Faculty of Public Health, Airlangga University, Jl. Mulyosari, Kampus C Universitas Airlangga, Surabaya, 60115, Indonesia

Publication date: April 1, 2017

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