Understanding variations in health insurance coverage in Ghana, Kenya, Nigeria, and Tanzania: Evidence from demographic and health surveys

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Study Justification:
– The study aims to understand variations in health insurance coverage in Ghana, Kenya, Nigeria, and Tanzania.
– It is important to examine these variations to ensure universal health coverage and meet the sustainable development goals on health by 2030.
– The study provides evidence on the socio-economic factors influencing health insurance coverage.
Highlights:
– Health insurance 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 found to be the socio-economic factors influencing variations in health insurance coverage.
Recommendations:
– The various health insurance schemes in Kenya, Tanzania, and Nigeria should be harmonized to maximize the size of their risk pools and increase confidence in the systems.
– This harmonization may encourage more people to enroll in health insurance, helping these countries achieve universal health coverage.
Key Role Players:
– Government health ministries and departments
– Health insurance regulatory bodies
– Health insurance providers
– Non-governmental organizations (NGOs) working in healthcare
– Community leaders and influencers
Cost Items for Planning Recommendations:
– Research and analysis costs
– Policy development and implementation costs
– Public awareness and education campaigns
– Administrative and operational costs for harmonizing health insurance schemes
– Monitoring and evaluation costs to assess the impact of the recommendations

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on data from demographic and health surveys conducted in Ghana, Kenya, Nigeria, and Tanzania. The surveys are nationwide and designed to provide adequate data for monitoring demographics and health conditions in developing countries. The study population is large, with a total of 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 analyze the data, and the results show significant variations in health insurance coverage among the countries. The conclusion suggests actionable steps to improve coverage, such as harmonizing the various health insurance schemes in Kenya, Tanzania, and Nigeria to maximize risk pools and increase confidence in the systems.

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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Based on the provided information, here are some potential innovations that could improve access to maternal health:

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women and new mothers with access to important health information, reminders for prenatal and postnatal care appointments, and educational resources.

2. Telemedicine: Implement telemedicine services to connect pregnant women in remote or underserved areas with healthcare professionals for virtual consultations, prenatal check-ups, and postnatal care.

3. Community Health Workers: Train and deploy community health workers to provide maternal health education, prenatal and postnatal care, and referrals for pregnant women in rural or marginalized communities.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access quality maternal healthcare services, including antenatal care, delivery, and postnatal care.

5. Maternity Waiting Homes: Establish maternity waiting homes near healthcare facilities to accommodate pregnant women who live far away, ensuring they have a safe place to stay before giving birth and reducing the risk of complications during transportation.

6. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services, such as partnering with private healthcare providers to offer subsidized or free services for pregnant women.

7. Transportation Solutions: Develop transportation initiatives, such as ambulance services or community transportation networks, to ensure pregnant women can easily access healthcare facilities for prenatal care, delivery, and emergency obstetric care.

8. Maternal Health Insurance: Implement or expand health insurance schemes specifically tailored to cover maternal health services, reducing financial barriers and increasing access to quality care for pregnant women.

9. Maternal Health Education Programs: Develop comprehensive maternal health education programs that target women, families, and communities, providing information on prenatal care, nutrition, breastfeeding, and postnatal care.

10. Quality Improvement Initiatives: Implement quality improvement initiatives in healthcare facilities, focusing on maternal health services, to ensure that pregnant women receive safe, effective, and respectful care during pregnancy, childbirth, and postpartum.

These innovations have the potential to address the variations in health insurance coverage and improve access to maternal health services in Ghana, Kenya, Nigeria, and Tanzania.
AI Innovations Description
Based on the information provided, the recommendation to improve access to maternal health based on the study’s findings is to harmonize the various health insurance schemes in Ghana, Kenya, Nigeria, and Tanzania. This harmonization would help maximize the size of the risk pools and increase the confidence of potential subscribers in the systems, which may encourage them to enroll. By creating a unified and streamlined health insurance system, these countries can work towards achieving universal health coverage and meeting the sustainable development goals on health by the year 2030.
AI Innovations Methodology
Based on the provided information, it seems that the focus of the study is on understanding variations in health insurance coverage in Ghana, Kenya, Nigeria, and Tanzania. The study aims to analyze the data from demographic and health surveys (DHS) conducted in these countries to identify factors influencing health insurance coverage and make recommendations for improving access to maternal health.

To simulate the impact of recommendations on improving access to maternal health, a possible methodology could include the following steps:

1. Data Collection: Gather data on various factors related to maternal health access, such as health insurance coverage, demographic information, education level, residence, wealth status, and occupation. This data can be obtained from the demographic and health surveys conducted in the respective countries.

2. Data Analysis: Use bivariate and multivariate techniques to analyze the collected data. This analysis can help identify the socio-economic factors that influence variations in health insurance coverage and access to maternal health services.

3. Identify Innovations: Based on the analysis of the data, identify potential recommendations or innovations that can improve access to maternal health. These innovations could include policy changes, interventions, or strategies to address the identified barriers and improve health insurance coverage and utilization of maternal health services.

4. Simulate Impact: Develop a simulation model to estimate the potential impact of the identified innovations on improving access to maternal health. This model can take into account various factors such as population size, coverage rates, and potential changes in health insurance enrollment and utilization.

5. Sensitivity Analysis: Conduct sensitivity analysis to assess the robustness of the simulation model and explore different scenarios. This analysis can help understand the potential range of outcomes and identify key factors that may influence the effectiveness of the recommended innovations.

6. Policy Recommendations: Based on the simulation results and sensitivity analysis, provide policy recommendations for implementing the identified innovations to improve access to maternal health. These recommendations can be tailored to the specific context of each country and take into account the socio-economic factors influencing health insurance coverage.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific objectives of the study and the available data. The steps outlined above provide a general framework for conducting such an analysis.

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