Imani Kapungu, Emmanuel Evarest, Nyimvua Shaban, Andongwisye J. Mwakisisile
{"title":"Modelling and Forecasting Claim Payments of Tanzania National Health Insurance Fund","authors":"Imani Kapungu, Emmanuel Evarest, Nyimvua Shaban, Andongwisye J. Mwakisisile","doi":"10.4314/tjs.v49i4.12","DOIUrl":null,"url":null,"abstract":"The expenses of medical services are increasing across the globe. As a result, pressure is placed on government and insurance companies’ budgets. The amounts of money collected are not enough to cover the claim payments. Therefore, it threatens the sustainability of the health insurance companies due to the mismatch of income and expenditures. This study aimed to model and forecast claim payments for the national health insurance fund (NHIF) in Tanzania. The claim payment data for the period of 2001–2021 from NHIF were used in building the ARIMA model. It was proven that ARIMA (0, 2, 2) is the most accurate model for forecasting the claim payments from 2022 to 2031. Furthermore, numerical results show that the claim payments for NHIF will grow by 68% by 2031.","PeriodicalId":22207,"journal":{"name":"Tanzania Journal of Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tanzania Journal of Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4314/tjs.v49i4.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The expenses of medical services are increasing across the globe. As a result, pressure is placed on government and insurance companies’ budgets. The amounts of money collected are not enough to cover the claim payments. Therefore, it threatens the sustainability of the health insurance companies due to the mismatch of income and expenditures. This study aimed to model and forecast claim payments for the national health insurance fund (NHIF) in Tanzania. The claim payment data for the period of 2001–2021 from NHIF were used in building the ARIMA model. It was proven that ARIMA (0, 2, 2) is the most accurate model for forecasting the claim payments from 2022 to 2031. Furthermore, numerical results show that the claim payments for NHIF will grow by 68% by 2031.