MODELING COUNT DATA WITH OVER-DISPERSION USING GENERALIZED POISSON REGRESSION: A CASE STUDY OF LOW BIRTH WEIGHT IN INDONESIA

Abstract

Poisson regression is commonly used in modeling count data in various research fields. An essential assumption must be met when using Poisson regression, which is that the count data of the response has the mean and variance must be equal, namely equi-dispersion. This assumption is often unmet because many data for the response that the variance is greater than the mean, called over-dispersion. If the Poisson regression model contains the over-dispersion, then will be produced an invalid model can under-estimate standard errors and misleading inference for regression parameters. Therefore, an approach is needed to overcome the over-dispersion problem in Poisson regression. The generalized Poisson regression can handle the over-dispersion in Poisson regression. This study aims to obtain the generalized Poisson regression model and the factors affecting the low birth weight in Indonesia in 2021. The result shows that the factors affecting the low birth weight in Indonesia based on the generalized Poisson regression model were: poverty rate, percentage of households with access to appropriate sanitation, percentage of pregnant women at risk of chronic energy deficiency receiving additional food, percentage of pregnant women who received blood-boosting tablets, and percentage of antenatal care.