Negative Binomial Regression for Low Birth Weight (LBW) Cases in Kolaka Regency

Baharuddin

doi:10.35580/variansiunm505

Baharuddin Universitas Halu Oleo

DOI: https://doi.org/10.35580/variansiunm505

Keywords: Kolaka Regency, low birth weight, negative binomial regression, overdispersion

Abstract

Low birth weight (LBW) is a major risk factor for neonatal mortality. Kolaka Regency experienced a 41.98% increase in LBW cases in 2024, necessitating an analysis of influencing factors. This study aims to model the average number of LBW cases using negative binomial regression. Data were obtained from the Kolaka District Health Office with 12 sub-districts as observation units. The dependent variable was the number of LBW cases, while independent variables included the percentage of K4 antenatal care coverage (X₁), number of pregnant women consuming 90 iron supplement tablets (X₂), and number of pregnant women with chronic energy deficiency (CED, X₃). Results showed overdispersion in the data, making negative binomial regression appropriate. Two variables significantly influenced LBW cases: the number of pregnant women consuming 90 iron supplement tablets had a negative effect (β₂ = -0.0025; p = 0.004), and the number of pregnant women with CED had a positive effect (β₃ = 0.0231; p < 0.001). Increasing consumption of 90 iron supplement tablets reduces the average number of LBW cases, while increasing the number of pregnant women with CED increases the average number of LBW cases in Kolaka Regency.

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