A logistic regression model for Ghana National Health Insurance claims

Authors

  • Samuel Antwi PhD Student, School of Finance and Economics Jiangsu University, P.R. China Lecturer, Koforidua Polytechnic, Ghana
  • Xicang Zhao Professor, School of Finance and Economics Jiangsu University, P.R. China

DOI:

https://doi.org/10.18533/ijbsr.v2i7.126

Keywords:

National Health Insurance, Claims, Logistic Regression, Odds Ratio, Ghana.

Abstract

In August 2003, the Ghanaian Government made history by implementing the first National Health Insurance System (NHIS) in Sub-Saharan Africa. Within three years, over half of the country’s population had voluntarily enrolled into the National Health Insurance Scheme. This study had three objectives: 1) To estimate the risk factors that influences the Ghana national health insurance claims. 2) To estimate the magnitude of each of the risk factors in relation to the Ghana national health insurance claims. In this work, data was collected from the policyholders of the Ghana National Health Insurance Scheme with the help of the National Health Insurance database and the patients’ attendance register of the Koforidua Regional Hospital, from 1st January to 31st December 2011. Quantitative analysis was done using the generalized linear regression (GLR) models. The results indicate that risk factors such as sex, age, marital status, distance and length of stay at the hospital were important predictors of health insurance claims. However, it was found that the risk factors; health status, billed charges and income level are not good predictors of national health insurance claim. The outcome of the study shows that sex, age, marital status, distance and length of stay at the hospital are statistically significant in the determination of the Ghana National health insurance premiums since they considerably influence claims. We recommended, among other things that, the National Health Insurance Authority should facilitate the institutionalization of the collection of appropriate data on a continuous basis to help in the determination of future premiums.

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