Modeling the Number of Acute Hepatitis Sufferers in DKI Jakarta using Negative Binomial Regression
Hepatitis is an inflammation of the liver due to viral infections. All viral hepatitis can cause acute hepatitis. Hepatitis is an infectious disease that is a major health problem in the community because of its relatively easy transmission. DKI Jakarta is the province in Indonesia with the highest...
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| Format: | Article |
| Language: | English |
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cita konsultindo
2023-03-01
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| Series: | Asian Journal of Management, Entrepreneurship and Social Science |
| Subjects: | |
| Online Access: | http://www.ajmesc.com/index.php/ajmesc/article/view/324 |
| _version_ | 1797867447391354880 |
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| author | Wildan Alrasyid Dian Lestari Fevi Novkaniza Arman Haqqi Sindy Devila |
| author_facet | Wildan Alrasyid Dian Lestari Fevi Novkaniza Arman Haqqi Sindy Devila |
| author_sort | Wildan Alrasyid |
| collection | DOAJ |
| description |
Hepatitis is an inflammation of the liver due to viral infections. All viral hepatitis can cause acute hepatitis. Hepatitis is an infectious disease that is a major health problem in the community because of its relatively easy transmission. DKI Jakarta is the province in Indonesia with the highest cases of acute hepatitis. Therefore, efforts need to be made to reduce the number of acute hepatitis sufferers, especially in DKI Jakarta. Several factors are thought to be closely related to the high number of acute hepatitis cases. The purpose of this study is to find factors that can significantly explain the case of hepatitis disease in DKI Jakarta so that measures can be taken to prevent the emergence of acute hepatitis cases in the community. The data in this study was obtained from the DKI Jakarta health office in 2021. The appropriate modeling for the number of people with acute hepatitis is a poisson regression model because the number of people with acute hepatitis is a count of data. In overcoming cases of overdispersion in poisson regression models, a more suitable Negative Binomial regression model is used as an alternative. In this study, the estimation of model parameters was carried out using the Maximum Likelihood Estimation (MLE) method. The results of the analysis found 3 variables that significantly explain the number of acute hepatitis sufferers in DKI Jakarta, namely the number of places of management that meet health standards, the number of health workers, and the number of HIV sufferers.
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| first_indexed | 2024-04-09T23:40:20Z |
| format | Article |
| id | doaj.art-457336efd6074142a7c6767dac8881f1 |
| institution | Directory Open Access Journal |
| issn | 2808-7399 |
| language | English |
| last_indexed | 2024-04-09T23:40:20Z |
| publishDate | 2023-03-01 |
| publisher | cita konsultindo |
| record_format | Article |
| series | Asian Journal of Management, Entrepreneurship and Social Science |
| spelling | doaj.art-457336efd6074142a7c6767dac8881f12023-03-19T01:06:29Zengcita konsultindoAsian Journal of Management, Entrepreneurship and Social Science2808-73992023-03-0130210.98765/ajmesc.v3i02.324Modeling the Number of Acute Hepatitis Sufferers in DKI Jakarta using Negative Binomial RegressionWildan Alrasyid0Dian Lestari1Fevi Novkaniza2 Arman Haqqi3Sindy Devila4 Universitas Indonesia Universitas IndonesiaUniversitas IndonesiaUniversitas IndonesiaUniversitas Indonesia Hepatitis is an inflammation of the liver due to viral infections. All viral hepatitis can cause acute hepatitis. Hepatitis is an infectious disease that is a major health problem in the community because of its relatively easy transmission. DKI Jakarta is the province in Indonesia with the highest cases of acute hepatitis. Therefore, efforts need to be made to reduce the number of acute hepatitis sufferers, especially in DKI Jakarta. Several factors are thought to be closely related to the high number of acute hepatitis cases. The purpose of this study is to find factors that can significantly explain the case of hepatitis disease in DKI Jakarta so that measures can be taken to prevent the emergence of acute hepatitis cases in the community. The data in this study was obtained from the DKI Jakarta health office in 2021. The appropriate modeling for the number of people with acute hepatitis is a poisson regression model because the number of people with acute hepatitis is a count of data. In overcoming cases of overdispersion in poisson regression models, a more suitable Negative Binomial regression model is used as an alternative. In this study, the estimation of model parameters was carried out using the Maximum Likelihood Estimation (MLE) method. The results of the analysis found 3 variables that significantly explain the number of acute hepatitis sufferers in DKI Jakarta, namely the number of places of management that meet health standards, the number of health workers, and the number of HIV sufferers. http://www.ajmesc.com/index.php/ajmesc/article/view/324Link functionNegative Binomial regressionOverdispersionPoisson regressionGeneralized Linear Model |
| spellingShingle | Wildan Alrasyid Dian Lestari Fevi Novkaniza Arman Haqqi Sindy Devila Modeling the Number of Acute Hepatitis Sufferers in DKI Jakarta using Negative Binomial Regression Asian Journal of Management, Entrepreneurship and Social Science Link function Negative Binomial regression Overdispersion Poisson regression Generalized Linear Model |
| title | Modeling the Number of Acute Hepatitis Sufferers in DKI Jakarta using Negative Binomial Regression |
| title_full | Modeling the Number of Acute Hepatitis Sufferers in DKI Jakarta using Negative Binomial Regression |
| title_fullStr | Modeling the Number of Acute Hepatitis Sufferers in DKI Jakarta using Negative Binomial Regression |
| title_full_unstemmed | Modeling the Number of Acute Hepatitis Sufferers in DKI Jakarta using Negative Binomial Regression |
| title_short | Modeling the Number of Acute Hepatitis Sufferers in DKI Jakarta using Negative Binomial Regression |
| title_sort | modeling the number of acute hepatitis sufferers in dki jakarta using negative binomial regression |
| topic | Link function Negative Binomial regression Overdispersion Poisson regression Generalized Linear Model |
| url | http://www.ajmesc.com/index.php/ajmesc/article/view/324 |
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