RESUMO
Although the policy in Rwanda aims at ensuring quality healthcare, a portion of the Rwandan population still does not have access to it due to the lack of health insurance. This study investigates the impact of health insurance on healthcare utilization in all 30 administrative districts of Rwanda, using secondary data from the 5th Integrated Household Living Conditions Survey (EICV 5) in Rwanda, with a total of 14,580 households. A logistic regression model was used to evaluate the effects of health insurance on healthcare utilization, and a decision tree model was adopted to categorize districts based on the use of health services. This study has made a novel contribution to the existing research by classifying districts based on similarities in the use of health care services, regarding households with or without health insurance. The results showed a significant age effect on the use of health care services for household heads with an age range of 56-65, a significant increase was observed with an adjusted odds ratio of AO = 1.308, (95% CI: 1.044-1.639). It was the same for the household heads whose age range is 66-75 with an adjusted odds ratio of A0 = 1.589 with (95% CI: 1.244-2.028) and those aged 76 and older with an adjusted odds ratio of AO = 1.524, with (95% CI: 1.170-1.985). Households with health insurance interacted with districts (A0 = 2.76) increased health service use threefold compared to households without health insurance, female-headed households increased health service use (AO = 1.423, 95% CI:1.293-1.566) 1.4-fold compared to male-headed households, while households in the third quintile (AO = 1.198, 95% CI: 1.035-1.385) used health services 1.2 times compared to those in the first quintile; households in the fourth quintile (AO = 1.307, 95% CI: 1.134-1.506) and in the fifth quintile (AO = 1.307, 95% CI: 1.136 1.504) used health services 1.3 times compared to those in the first quintile. Similarly, for the households located in the main district group 4 variable had an odds ratio of 1.386 with (95% CI: 1.242-1.547), indicating that the households located in the main district group 4 use the health care services 1.4 times higher compared to those located in Ruhango district. Households in Rwanda who lack health insurance do not utilize health services to their full capacity, which has a negative influence on the wellbeing of the country's population. The researchers recommend that future policies target households in rural areas with an elderly head of household and those without health insurance that have a low usage of health care services in Rwanda. They also recommend that health insurance fees are reduced in order to increase health coverage rate as recommended by the World Health Organization.
RESUMO
INTRODUCTION: in Rwanda, the estimated out-of-pocket health expenditure has been increased from 24.46% in 2000 to 26% in 2015. Despite the existence of guideline in estimation of out-of-pocket health expenditures provided by WHO (2018), the estimation of out-of-pocket health expenditure still have difficulties in many countries including Rwanda. METHODS: the purpose of this paper was to figure out the best model which predicts the out-of-pocket health expenditures in Rwanda during the process of considering various techniques of machine learning by using the Rwanda Integrated Living Conditions Surveys (EICV5) of 14580 households (2018). RESULTS: our findings presented the model which predict the out-of-pocket health expenditures with higher accuracy and was found as treenet model. Furthermore, machine learning techniques were used to judge which predictor variable was important in our prediction process and comparison of the performance of the algorithms through train accuracy and test accuracy metric measures. Finally, the findings show that the tests of accuracy of the models were 50.16% for multivariate adaptive regression splines (MARS) model, 74% decision tree model, 87% for treenet model, 83% for random forest model, gradient boosting 81%, predictor total consumption played a significant role in the model for all tested models. CONCLUSION: finally, we conclude that the total consumption of the household came out to be the most important variable which is consistently true to all the algorithms tested. The findings from our study have policy implications for policy makers in Rwanda and in the world generally. We recommend the government to significantly increase public spending on health. Domestic financial resources are key to moving closer to universal health coverage (UHC) and should be increased on a long-term basis. In addition, these results will be useful for the future to assess the out-of-pocket health expenditures dataset.
