COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022
In line with the Sustainable Development Goals (SDGs), the Infant Mortality Rate (AKB) is a very important health indicator, especially in neonatal and perinatal care. West Java Province consistently ranks third nationally in terms of infant mortality in 2020 and 2021. This study analyzes the factor...
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Universitas Pattimura
2025-01-01
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12102 |
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| author | Toha Saifudin Fatiha Nadia Salsabila Mubadi'ul Fitriani Azizatul Kholidiyah Nina Auliyah Fildzah Tri Januar Ariani Suliyanto Suliyanto |
| author_facet | Toha Saifudin Fatiha Nadia Salsabila Mubadi'ul Fitriani Azizatul Kholidiyah Nina Auliyah Fildzah Tri Januar Ariani Suliyanto Suliyanto |
| author_sort | Toha Saifudin |
| collection | DOAJ |
| description | In line with the Sustainable Development Goals (SDGs), the Infant Mortality Rate (AKB) is a very important health indicator, especially in neonatal and perinatal care. West Java Province consistently ranks third nationally in terms of infant mortality in 2020 and 2021. This study analyzes the factors influencing infant mortality in West Java in 2022 using secondary data from the 2022 West Java Provincial Health Profile. The response variable is the number of infant deaths, while the predictor variables include the percentage of K-4 coverage (X1), high-risk pregnancy (X2), family with PHBS (X3), exclusive breastfeeding (X4), and complete immunization coverage (X5). Given the count-based nature of the data, Poisson regression was used, which assumes equidispersion where the variance is equal to the mean. However, the analysis found overdispersion, where the variance significantly exceeds the mean, making Poisson regression unsuitable. To address this, Generalized Poisson Regression (GPR) was applied, as GPR introduces a dispersion parameter that accounts for overdispersion, thus better fitting the data. The initial Poisson regression results showed that X1, X2, X4, and X5 significantly influenced infant mortality, while the GPR model showed that only X2 and X3 were significant factors, with a dispersion parameter of -3.116. The GPR model shows that every additional one high-risk pregnancy increases the infant mortality rate by 1.00006, while an increase of one unit of clean and healthy living practices reduces the mortality rate by 2.66%. Model evaluation using AIC, BIC, and RMSE confirmed that the GPR model better described the relationship between predictor variables and infant mortality rates compared to Poisson regression. These findings emphasize the need to use GPR to model cases with overdispersion in count data, so as to provide more reliable information for policy and intervention strategies. |
| format | Article |
| id | doaj-art-258dba973a0c4a8a9d8f81f6a1160fe6 |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-258dba973a0c4a8a9d8f81f6a1160fe62025-08-20T03:37:34ZengUniversitas PattimuraBarekeng1978-72272615-30172025-01-01191355010.30598/barekengvol19iss1pp35-5012102COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022Toha Saifudin0Fatiha Nadia Salsabila1Mubadi'ul Fitriani2Azizatul Kholidiyah3Nina Auliyah4Fildzah Tri Januar Ariani5Suliyanto Suliyanto6Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, IndonesiaDepartment of Mathematics, Faculty of Science and Technology, Universitas Airlangga, IndonesiaDepartment of Mathematics, Faculty of Science and Technology, Universitas Airlangga, IndonesiaDepartment of Mathematics, Faculty of Science and Technology, Universitas Airlangga, IndonesiaDepartment of Mathematics, Faculty of Science and Technology, Universitas Airlangga, IndonesiaDepartment of Mathematics, Faculty of Science and Technology, Universitas Airlangga, IndonesiaDepartment of Mathematics, Faculty of Science and Technology, Universitas Airlangga, IndonesiaIn line with the Sustainable Development Goals (SDGs), the Infant Mortality Rate (AKB) is a very important health indicator, especially in neonatal and perinatal care. West Java Province consistently ranks third nationally in terms of infant mortality in 2020 and 2021. This study analyzes the factors influencing infant mortality in West Java in 2022 using secondary data from the 2022 West Java Provincial Health Profile. The response variable is the number of infant deaths, while the predictor variables include the percentage of K-4 coverage (X1), high-risk pregnancy (X2), family with PHBS (X3), exclusive breastfeeding (X4), and complete immunization coverage (X5). Given the count-based nature of the data, Poisson regression was used, which assumes equidispersion where the variance is equal to the mean. However, the analysis found overdispersion, where the variance significantly exceeds the mean, making Poisson regression unsuitable. To address this, Generalized Poisson Regression (GPR) was applied, as GPR introduces a dispersion parameter that accounts for overdispersion, thus better fitting the data. The initial Poisson regression results showed that X1, X2, X4, and X5 significantly influenced infant mortality, while the GPR model showed that only X2 and X3 were significant factors, with a dispersion parameter of -3.116. The GPR model shows that every additional one high-risk pregnancy increases the infant mortality rate by 1.00006, while an increase of one unit of clean and healthy living practices reduces the mortality rate by 2.66%. Model evaluation using AIC, BIC, and RMSE confirmed that the GPR model better described the relationship between predictor variables and infant mortality rates compared to Poisson regression. These findings emphasize the need to use GPR to model cases with overdispersion in count data, so as to provide more reliable information for policy and intervention strategies.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12102generalized poisson regressioninfant mortalityoverdispersionpoisson regression |
| spellingShingle | Toha Saifudin Fatiha Nadia Salsabila Mubadi'ul Fitriani Azizatul Kholidiyah Nina Auliyah Fildzah Tri Januar Ariani Suliyanto Suliyanto COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022 Barekeng generalized poisson regression infant mortality overdispersion poisson regression |
| title | COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022 |
| title_full | COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022 |
| title_fullStr | COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022 |
| title_full_unstemmed | COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022 |
| title_short | COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022 |
| title_sort | comparison of poisson regression and generalized poisson regression in modeling the number of infant mortality in west java 2022 |
| topic | generalized poisson regression infant mortality overdispersion poisson regression |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12102 |
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