Pemodelan Generalized Poisson Regression (GPR) dan Negative Binomial Regression (NBR) untuk Mengatasi Overdispersi pada Jumlah Kematian Bayi di Kabupaten Probolinggo
Submission Date: 2022-07-27 20:58:42
Accepted Date: 2023-05-01 00:00:00
Abstract
Data Dinas Kesehatan Kabupaten Probolinggo tahun 2020 menyatakan Angka Kematian Bayi (AKB) di Kabupaten Probolinggo sebesar 8,11. Angka tersebut tergolong tinggi jika dibandingkan dengan AKB Provinsi Jawa Timur sebesar 6,3. Sehingga perlu dilakukan analisis untuk mengetahui faktor-faktor yang mempengaruhi jumlah kematian bayi di Kabupaten Probolinggo tahun 2020. Regresi poisson merupakan analisis yang sesuai dalam pemodelan kasus data diskrit. Namun regresi poisson mensyaratkan kondisi equidispersi yang sulit dipenuhi. Pada umumnya sering ditemui kondisi overdispersi. Metode yang dapat digunakan untuk mengatasi kondisi overdispersi diantaranya adalah metode Generalized Poisson Regression (GPR) dan Negative Binomial Regression (NBR). Objek penelitian terdiri dari variabel respon yaitu jumlah kematian bayi serta variabel prediktor yang diduga mempengaruhi jumlah kematian bayi diantaranya yaitu faktor kesehatan ibu hamil dan bayinya, faktor kebersihan dan gizi, serta faktor peningkatan imunitas bayi yang terdiri dari 9 variabel serta 2 variabel tambahan yaitu jumlah tenaga kesehatan dan jumlah fasilitas kesehatan. Hasil analisis diperoleh bahwa terjadi kasus overdispersi sehingga analisis GPR dan NBR perlu dilakukan. Berdasarkan analisis GPR dan NBR, model yang layak untuk digunakan adalah model dengan kombinasi variabel prediktor jumlah bayi lahir rendah (X7), jumlah ibu hamil mendapat imunisasi Td2+ (X8), dan jumlah tenaga kesehatan (X10). Keseluruhan variabel berpengaruh signifikan terhadap model. Diperoleh hasil bahwa metode yang paling baik digunakan untuk memodelkan jumlah kematian bayi untuk mengatasi overdispersi adalah metode GPR karena memiliki kriteria kebaikan model AIC, AICc, BIC, dan BICc yang lebih kecil dibandingkan dengan metode NBR.
Keywords
Generalized Poisson Regression; Kematian Bayi; Negative Binomial Regression; Overdispersi
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