PEMODELAN JUMLAH KEMATIAN BAYI AKIBAT TETANUS NEONATORUM DENGAN METODE GEOGRAPHICALLY WEIGHTED ZERO-INFLATED POISSON REGRESSION

A. Maulini, Nurfitri Imro'ah, Siti Aprizkiyandari
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Abstract

Tetanus Neonatorum (TN) is an infection in infants caused by the Clostridium tetani bacteria. In 2020, the Case Fatality Rate (CFR) due to TN in Indonesia increased to 50% compared to 2019, which was 11.76%. So it is necessary to study the number of infant deaths due to TN. This study discusses the modeling and factors that influence TN disease in Indonesia using the Geographically-Weighted Zero-Inflated Poisson Regression (GWZIPR) method. The GWZIPR model is divided into two based on the state: the ln model for the Poisson state and the logit model for the zero states. The data in this study are the number of infant deaths due to TN, the percentage of pregnant women carrying out Td2+ immunization, the percentage of pregnant women delivering at health facilities, and the percentage of puskesmas carrying out P4K in 34 provinces in Indonesia in 2020. The results of this study are that there is an excess zero of 58.82% and spatial heterogeneity occurs so that each region has a different model based on significant variables. The factors that influence the number of infant deaths due to TN are divided into four groups based on significant variables in the ln and logit models.
以地理加权零膨胀泊松回归为基础的新生儿破伤风发病率预测模型
新生儿破伤风(TN)是一种由破伤风梭菌引起的婴儿感染。与2019年的11.76%相比,2020年印度尼西亚由TN引起的病死率(CFR)上升至50%。因此,有必要研究因TN导致的婴儿死亡人数。本研究使用地理加权零膨胀泊松回归(GWZIPR)方法讨论了印度尼西亚TN疾病的建模和影响因素。GWZIPR模型根据状态分为两个部分:泊松状态的ln模型和零状态的logit模型。本研究的数据是:2020年印度尼西亚34个省因破伤风引起的婴儿死亡人数、开展破伤风2+免疫接种的孕妇百分比、在卫生机构分娩的孕妇百分比以及开展破伤风2+免疫接种的产妇百分比。本研究的结果是存在58.82%的过零,并且存在空间异质性,使得每个区域基于显著变量有不同的模型。根据ln和logit模型中的重要变量,将影响TN导致的婴儿死亡人数的因素分为四组。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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