Generalized Linear Models in Determining Factors Affecting the Number of Community Visits to Health Service with Bayesian Inference Approach

Selfinia Selfinia; Dodi Devianto; Ferra Yanuar

doi:10.31764/jtam.v7i3.15186

Generalized Linear Models in Determining Factors Affecting the Number of Community Visits to Health Service with Bayesian Inference Approach

Selfinia Selfinia, Dodi Devianto, Ferra Yanuar

Abstract

Public health plays an important role in achieving the Sustainable Development Goals (SDGs) set by the United Nations. The SDGs are a series of global targets and commitments aimed at addressing various challenges facing the world today, such as poverty, hunger, gender inequality, climate change, and others. Public health, as one of the important aspects of the SDGs, is closely linked to several sustainable development goals. Efforts made to achieve the SDGs in the health sector are to improve health services. The objective of this study was to identify factors that influence the number of community visits to health services. The data used is a small sample size as one hundred community respondents in Padang City, West Sumatra Province. In this study, the number of respondents' visits to health service was the measured variable, while the predictor variables consisted of five variables, namely the status of the implementation of clean and healthy living behavior, health history, distance to health services, type of insurance owned, and consumption patterns. The generalized linear models is used to identify predictor variables that have significance using the Bayesian inference approach. It was found that there are two predictor variables that are significant in influencing the number of community visits to health services, namely the consumption patterns of respondents and the health history of respondents. These two variables have a very dominant effect on the number of visits to health service facilities in Padang City. This result indicates the community has to pay attention to their consumption patterns and living behavior to prevent periodic disease outbreaks and take care of their health history factors.

Keywords

Generalized Linear Models; Health Service; Bayesian Inference.

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DOI: https://doi.org/10.31764/jtam.v7i3.15186