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Background Realisation of universal health coverage is not possible without health financing systems that ensure financial risk protection. To ensure this, some African countries have instituted health insurance schemes as venues for ensuring universal access to health care for their populace. In this paper, we examined variations in health insurance coverage in Ghana, Kenya, Nigeria, and Tanzania. Methods We used data from demographic and health surveys of Ghana (2014), Kenya (2014), Nigeria (2013), and Tanzania (2015). Women aged 15–49 and men aged 15–59 years were included in the study. Our study population comprised 9,378 women and 4,371 men from Ghana, 14,656 women and 12,712 men from Kenya, 38,598 women and 17,185 men from Nigeria, and 10,123 women and 2,514 men from Tanzania. Bivariate and multivariate techniques were used to analyse the data. Results Coverage was highest in Ghana (Females = 62.4%, Males = 49.1%) and lowest in Nigeria (Females = 1.1%, Males = 3.1%). Age, level of education, residence, wealth status, and occupation were the socio-economic factors influencing variations in health insurance coverage. Conclusions There are variations in health insurance coverage in Ghana, Kenya, Nigeria, and Tanzania, with Ghana recording the highest coverage. Kenya, Tanzania, and Nigeria may not be able to achieve universal health coverage and meet the sustainable development goals on health by the year 2030 if the current fragmented public health insurance systems persist in those countries. Therefore, the various schemes of these countries should be harmonised to help maximise the size of their risk pools and increase the confidence of potential subscribers in the systems, which may encourage them to enrol.
We used data from demographic and health surveys (DHS) of Ghana (2014), Kenya (2014), Nigeria (2013), and Tanzania (2015) for this paper. DHS are nationwide surveys designed and conducted every five years in developing countries across the globe. The surveys mainly focus on maternal and child health and are designed to provide adequate data for monitoring the demographics and health conditions in developing countries. The data are specifically collected on maternal and child health outcomes, non-communicable diseases, fertility, physical activity, alcohol consumption, sexually transmitted infections, health insurance, and tobacco use. The surveys from which we drew data for this study were carried out by the Ghana Statistical Service (GSS), the Kenyan National Bureau of Statistics (KNBS), the National Population Commission of the Federal Republic of Nigeria, and the National Bureau of Statistics, Dar es Salaam in Ghana, Kenya, Nigeria, and Tanzania, respectively. All the surveys were conducted with technical support from ICF International through the MEASURE DHS programme. The demographic and health surveys were conducted among women of reproductive age (15–49 years) and productive men (15–59). Ethical approval for DHS is usually acquired from the ethics regulatory bodies of the various countries for the studies to be conducted. In the 2014 Ghana DHS, 9396 women aged 15–49 and 4388 men aged 15–59 from 12,831 households were interviewed throughout Ghana. In Kenya, 31,079 women and 12,818 men from 40,300 households were interviewed, while 39,948 women and 17,359 men from 38,522 households were interviewed in Nigeria. In Tanzania, 13,266 women and 3,512 men were interviewed. For the purpose of this study, the samples used were 9,378 women and 4,371 men for Ghana, and 14,656 women and 12,712 men for Kenya. For Nigeria, 38,598 women and 17,185 men were included, while 10,123 women and 2,514 men were used for the Tanzanian analysis. The men and women used in our analysis are those who provided responses to the question asked in relation to the outcome variable: ‘covered by health insurance’. Permission to use the data set was given by the MEASURE DHS following the assessment of a concept note. The data are available to the public at: Ghana: https://dhsprogram.com/data/dataset/Ghana_Standard-DHS_2014.cfm?flag=0; Kenya: https://dhsprogram.com/data/dataset/Kenya_Standard-DHS_2014.cfm?flag=1; Nigeria: https://dhsprogram.com/data/dataset/Nigeria_Standard-DHS_2013.cfm?flag=1; and Tanzania: https://dhsprogram.com/data/dataset/Tanzania_Standard-DHS_2015.cfm?flag=1 The outcome variable employed in this paper was ‘covered by health insurance’. It was coded as 1 = “Yes” and 0 = “No”. Age, level of education, residence, wealth status, and occupation were the explanatory variables. Our choice of the five explanatory variables was influenced by variables included in the DHS datasets and previous studies that found these variables to be important socio-economic variables influencing health care service utilisation [38–42]. Age for females was categorised into 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years (women of reproductive age). The age of males was categorised as 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, and 55–59 years (sexually active and productive men). Data were not available for males aged 50–54 or 55–59 years in Tanzania and Nigeria, respectively, nor were they available for males aged 55–59 years in Kenya. In our analysis, we separated the males from females because the DHS files were separated by sex, and, in the literature, ownership of insurance varies by sex. Educational level was separated into four categories: no education, primary level, secondary level, and higher education. Residence was categorised as rural and urban, while wealth status was grouped into poorest, poorer, middle, richer, and richest. Occupation was also placed into eight groups: not working, professional, clerical, sales, agriculture, services, skilled, and unskilled. There were no data on sales for Kenya or Tanzania. Descriptive and inferential statistics were used to analyse the data. The descriptive statistics comprised frequencies and percentages presented in the form of tables and line graphs, while the inferential statistics adopted were bivariate and multivariate analysis. The bivariate analysis was performed using chi-square, and the multivariate analysis was performed using binary logistic regression. The logistic regression model was used to investigate the relationship between the explanatory variables and the outcome variable. The acceptable level of significance for the inferential statistics was p<0.05. To make the findings representative, both the descriptive and inferential analyses were weighted using the probability weighted variable (v005). STATA version 13 (by StataCorp located at College Station, USA) was used to run all the analyses. All analysis was done using the women files and male files separately since they were both captured in different files.
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