Forecasting the National Health Insurance Fund Membership Enrolment in Tanzania Using the SARIMA Model

https://doi.org/10.51867/scimundi.4.2.4

Authors

Keywords:

ARIMA, Box- Jenkins, National Health Insurance, SARIMA, Seasonal

Abstract

This paper aimed at forecasting membership enrolment in the National Health Insurance Fund (NHIF) in Tanzania using quarterly time series data. This study used 88 time series data to fit the seasonal Autoregressive Integrated Moving Average model (SARIMA). ARIMA (3,1,1) (0,1,0)[4] model was built and used for forecasting. The results show that there will be an increasing membership enrolment overtime over the years and no signs of decreasing. Thus, the government, apart from continuing  subsidizing the cost of accessing health insurance services,  should also improve the National Health Insurance (NHI) coverage to accommodate the increased enrolment and discourage dropouts. In turn, this will help to achieve the Universal Health Coverage (UHC) ultimate aim of ensuring equitable access to essential and manageable healthcare services, regardless of individuals’ financial situations, their location, and personality.

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Afriyie, D. O., Masiye, F., Tediosi, F., & Fink, G. (2023). Confidence in the health system and health insurance enrollment among the informal sector population in Lusaka, Zambia. Social Science & Medicine, 321, 115750. https://doi.org/10.1016/j.socscimed.2023.115750 DOI: https://doi.org/10.1016/j.socscimed.2023.115750

Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705 DOI: https://doi.org/10.1109/TAC.1974.1100705

Alesane, A., & Anang, B. T. (2018). Uptake of health insurance by the rural poor in Ghana: determinants and implications for policy. Pan African Medical Journal, 31(1), 1-10. https://doi.org/10.11604/pamj.2018.31.124.16265 DOI: https://doi.org/10.11604/pamj.2018.31.124.16265

Boateng, R. (2024). Micro Level Analysis on Health Insurance Enrolment among Selected Women in Ghana: Barriers and Predictors. Journal of Health Statistics Reports. SRC/JHSR-118, 3(1), 2-5.

Box, G., & Jenkins, G. (1970). Time series analysis: Forecasting and control. San Francisco: Holden-Day

Chatfield, C., & Prothero, D. L. (1973). Box‐Jenkins seasonal forecasting: Problems in a case study. Journal of the Royal Statistical Society: Series A (General), 136(3), 295-315. https://doi.org/10.2307/2344994 DOI: https://doi.org/10.2307/2344994

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427-431. https://doi.org/10.2307/2286348, https://doi.org/10.1080/01621459.1979.10482531 DOI: https://doi.org/10.1080/01621459.1979.10482531

Ghimire, P., Sapkota, V. P., & Poudyal, A. K. (2019). Factors Associated with Enrolment of Households in Nepal's National Health Insurance Program. International Journal of Health Policy and Management, 8(11), 636-645. https://doi.org/10.15171/ijhpm.2019.54 DOI: https://doi.org/10.15171/ijhpm.2019.54

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting Principles and Practice. Melbourne: OTexts.

Jalalpour, M., Gel, Y., & Levin, S. (2015). Forecasting demand for health services: Development of a publicly available toolbox. Operations research for health care, 5, 1-9. https://doi.org/10.1016/j.orhc.2015.03.001 DOI: https://doi.org/10.1016/j.orhc.2015.03.001

Kathrin, D., Günther, I., & Harttgen, K. (2021). Using machine learning to predict health insurance enrolment and take-up of health services.

Kornelio, S., Balan, R., & Deogratias, E. (2024). Forecasting students' enrolment in Tanzania government primary schools from 2021 to 2035 using ARIMA model. International Journal of Curriculum & Instruction, 16(1), 162–174.

Kotoh, A. M., Aryeetey, G. C., & Van der Geest, S. (2018). Factors that influence enrolment and retention in National Health Insurance Scheme. International Journal of Health Policy and Management, 7(5), 443

https://doi.org/10.15171/ijhpm.2017.117 DOI: https://doi.org/10.15171/ijhpm.2017.117

Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The annals of mathematical statistics, 22(1), 79-86. https://doi.org/10.1214/aoms/1177729694 DOI: https://doi.org/10.1214/aoms/1177729694

Kusi, A., Enemark, U., Hansen, K. S., & Asante, F. A. (2015). Refusal to enroll in Ghana's National Health Insurance Scheme: is affordability the problem? International journal for equity in health, 14, 1-14.

https://doi.org/10.1186/s12939-014-0130-2 DOI: https://doi.org/10.1186/s12939-014-0130-2

Lwaho, J., & Ilembo, B. (2023). Unfolding the potential of the ARIMA model in forecasting maize production in Tanzania. Business Analyst Journal, 44(2), 128-139. https://doi.org/10.1108/BAJ-07-2023-0055 DOI: https://doi.org/10.1108/BAJ-07-2023-0055

Marinova, G., & Todorova, M. (2023, November). Regression Analysis for Predicting Health Insurance. In 2023 4th International Conference on Communications, Information, Electronic and Energy Systems (CIEES) (pp. 1-4). IEEE. https://doi.org/10.1109/CIEES58940.2023.10378755 DOI: https://doi.org/10.1109/CIEES58940.2023.10378755

Morgan, A. K., Adei, D., Agyemang-Duah, W., & Mensah, A. A. (2022). An integrative review on individual determinants of enrolment in National Health Insurance Scheme among older adults in Ghana. BMC Primary Care, 23(1), 190. https://doi.org/10.1186/s12875-022-01797-6 DOI: https://doi.org/10.1186/s12875-022-01797-6

Naylor, T. H., Seaks, T. G., & Wichern, D. W. (1972). Box-Jenkins methods: An alternative to econometric models. International Statistical Review/Revue Internationale de Statistique, 123-137.

https://doi.org/10.2307/1402755 DOI: https://doi.org/10.2307/1402755

Ng'ang'a, E. W. (2021). Determinants of Health Insurance Uptake Among Low-Income Populations in Kibera-Nairobi, Kenya (Doctoral dissertation, University of Nairobi).

Nsiah-Boateng, E., & Aikins, M. (2018). Trends and characteristics of enrolment in the National Health Insurance Scheme in Ghana: a quantitative analysis of longitudinal data. Global health research and policy, 3, 1-10. https://doi.org/10.1186/s41256-018-0087-6 DOI: https://doi.org/10.1186/s41256-018-0087-6

Nyman, J. A. (2001). The theory of the demand for health insurance (No. 311). Discussion Paper.

Osei Afriyie, D., Krasniq, B., Hooley, B., Tediosi, F., & Fink, G. (2022). Equity in health insurance schemes enrollment in low and middle-income countries: A systematic review and meta-analysis. International Journal for Equity in Health, 21(1), 21. https://doi.org/10.1186/s12939-021-01608-x DOI: https://doi.org/10.1186/s12939-021-01608-x

Putri, N. K., Laksono, A. D., & Rohmah, N. (2023). Predictors of national health insurance membership among the poor with different education levels in Indonesia. BMC Public Health, 23(1), 373.

https://doi.org/10.1186/s12889-023-15292-9 DOI: https://doi.org/10.1186/s12889-023-15292-9

Ramasubramanian, V. (2007). Time series analysis. New Delhi: I.S.R.I

Sharma, R. (2023). Inequality and disparities in health insurance enrolment in India. Journal of Medicine Surgery and Public Health, 1, 100009. https://doi.org/10.1016/j.glmedi.2023.100009 DOI: https://doi.org/10.1016/j.glmedi.2023.100009

Soyiri, I. N., & Reidpath, D. D. (2012). Evolving forecasting classifications and applications in health forecasting. International journal of general medicine, 381-389. https://doi.org/10.2147/IJGM.S31079 DOI: https://doi.org/10.2147/IJGM.S31079

Soyiri, I. N., & Reidpath, D. D. (2013). An overview of health forecasting. Environmental health and preventive medicine, 18, 1-9. https://doi.org/10.1007/s12199-012-0294-6 DOI: https://doi.org/10.1007/s12199-012-0294-6

Wiah, E. N., Buabeng, A., & Agyarko, K. (2022). Statistical Model for the Forecast of Electricity Power Generation in Ghana. Open Journal of Statistics, 12(3), 373-384. https://doi.org/10.4236/ojs.2022.123024 DOI: https://doi.org/10.4236/ojs.2022.123024

Yego, N. K. K., Nkurunziza, J., & Kasozi, J. (2023). Predicting health insurance uptake in Kenya using Random Forest: An analysis of socio-economic and demographic factors. Plos one, 18(11), e0294166.

https://doi.org/10.1371/journal.pone.0294166 DOI: https://doi.org/10.1371/journal.pone.0294166

Yego, N. K., Kasozi, J., & Nkurunziza, J. (2021). A Comparative Analysis of Machine Learning Models for the Prediction of Insurance Uptake in Kenya. Data, 6(11), 116. https://doi.org/10.3390/data6110116 DOI: https://doi.org/10.3390/data6110116

Published

2024-07-20

How to Cite

Tembo, A., Ilembo, B., & Lwaho, J. (2024). Forecasting the National Health Insurance Fund Membership Enrolment in Tanzania Using the SARIMA Model. SCIENCE MUNDI, 4(2), 29–39. https://doi.org/10.51867/scimundi.4.2.4

Issue

Vol. 4 No. 2 (2024): Jul-Dec 2024

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Articles
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Copyright (c) 2024 Alfred Tembo, Bahati Ilembo, Joseph Lwaho

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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