Modeling Hospitalization Decision and Utilization for the Elderly in China

Getting medical services has become more difficult and expensive in China, which led to a problem of illness not being treated and a large number of zeros in the statistics of being hospitalized for the elderly. Traditional classic models such as the Poisson model and the negative binomial model cannot fit this kind of data well. One aim of this study was to use zero-inflated and hurdle models to better solve the problem of excess zeros. Another aim was to discover the factors affecting the decision-making behavior of the elderly being hospitalized and hospitalization service utilization. Therefore, the XGBoost model was firstly introduced to rank the importance of influencing factors in this paper. It was found that the zero-inflated negative binomial model performed best. The results showed that the elderly who had enjoyed NRCM or ERBMI/URBMI were more likely to have a higher number of hospitalizations. This indicated that the high cost of hospitalization had prevented the willingness of the elderly being hospitalized, but the basic medical insurance had increased the times of their repeated hospital readmissions. Policy efforts should be made to improve the level of basic medical insurance.

PENANGANAN OVERDISPERSI PADA REGRESI POISSON

Penyakit demam berdarah dengue (DBD) telah menjadi penyakit endemik di kota-kota besar di Indonesia. Angka kejadian DBD merupakan data diskrit (count) yang mengikuti distribusi Poisson. Dalam penelitian ini dilakukan pemodelan pengaruh iklim terhadap angka kejadian DBD menggunakan metode regresi Poisson. Masalah overdispersi sering ditemukan dalam regresi poisson, dimana nilai ekspektasi tidak sama dengan nilai variannya seperti yang disyaratkan dalam regresi poisson. Selanjutnya untuk menangani masalah overdispersi yang terjadi pada regresi poisson dilakukan pendekatan dengan menggunakan beberapa model regresi lain yaitu model Regresi Negative Binomial, Zero-Truncated Poisson dan Zero-Truncated Negative Binomial. Model terpilih dengan kriteria terbaik adalah Negative Binomial. Pada Negative Binomial hanya peubah rataan kelembapan per tahun yang memiliki pengaruh yang nyata terhadap jumlah penderita penyakit DBD di Kota Bogor pada tahun 2013-2019 pada taraf nyata 5%. Peubah rataan suhu rata-rata dan peubah jumlah curah hujan memiliki pengaruh yang nyata terhadap jumlah penderita penyakit DBD di Kota Bogor pada taraf nyata 15%.