Prevalence and factors associated with health insurance coverage in urban sub-Saharan Africa: Multilevel analyses of demographic and health survey data

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Study Justification:
– The study examines the prevalence and factors associated with health insurance coverage in urban sub-Saharan Africa (SSA).
– It addresses the vision of achieving Universal Health Coverage (UHC) by 2030.
– The study provides valuable insights into the current state of health insurance coverage in SSA and its implications for achieving UHC.
Highlights:
– The overall prevalence of health insurance coverage in urban SSA is low, with rates of 10.6% among females and 14% among males.
– Education level is a significant factor associated with health insurance coverage, with higher education levels having higher odds of being covered.
– Mass media, such as reading newspapers or magazines, listening to the radio, and watching television, is also associated with higher odds of health insurance coverage.
Recommendations:
– Increase public education on the benefits of health insurance coverage, targeting less educated urban dwellers, males in the lowest wealth quintile, and young adults (15-29 years).
– Utilize mass media platforms to disseminate information about health insurance and its importance.
– Develop targeted interventions to improve health insurance coverage among specific demographic groups.
Key Role Players:
– Ministry of Health: Responsible for policy development and implementation.
– Health Insurance Providers: Involved in expanding coverage and improving accessibility.
– Media Organizations: Collaborate in disseminating information and raising awareness.
– Community Leaders and Organizations: Engage in community-level education and outreach.
Cost Items for Planning Recommendations:
– Public Education Campaign: Budget for designing and implementing mass media campaigns, including advertisements, radio spots, and TV commercials.
– Training and Capacity Building: Allocate funds for training healthcare workers and community leaders to effectively communicate the benefits of health insurance.
– Research and Evaluation: Set aside resources for monitoring and evaluating the impact of interventions and adjusting strategies accordingly.
– Collaboration and Coordination: Allocate funds for meetings, workshops, and coordination efforts among key stakeholders to ensure effective implementation of recommendations.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study used a large sample size and employed multivariable analyses to examine the prevalence and factors associated with health insurance coverage in urban sub-Saharan Africa. The study also provided specific odds ratios and confidence intervals to support their findings. However, the abstract lacks information on the representativeness of the sample and the generalizability of the results to the broader population. To improve the strength of the evidence, the authors could include details on the sampling methodology, such as the sampling frame and the sampling weights used. Additionally, providing information on the response rate and any potential biases in the data collection process would enhance the credibility of the findings.

Introduction With the vision of achieving Universal Health Coverage (UHC) by the year 2030, many sub-Saharan African (SSA) countries have implemented health insurance schemes that seek to improve access to healthcare for their populace. In this study, we examined the prevalence and factors associated with health insurance coverage in urban sub-Saharan Africa (SSA). Materials and methods We used the most recent Demographic and Health Survey (DHS) data from 23 countries in SSA. We included 120,037 women and 54,254 men residing in urban centres in our analyses which were carried out using both bivariable and multivariable analyses. Results We found that the overall prevalence of health insurance coverage was 10.6% among females and 14% among males. The probability of being covered by health insurance increased by level of education. Men and women with higher education, for instance, had 7.61 times (95%CI = 6.50–8.90) and 7.44 times (95%CI = 6.77–8.17) higher odds of being covered by health insurance than those with no formal education. Males and females who read newspaper or magazine (Males: AOR = 1.47, 95%CI = 1.37–1.57; Females: AOR = 2.19, 95%CI = 1.31–3.66) listened to radio (Males: AOR = 1.29, 95%CI = 1.18–1.41; Females: AOR = 1.42, 95%CI = 1.35–1.51), and who watched television (Males: AOR = 1.80, 95%CI = 1.64–1.97; Females: AOR = 1.86, 95%CI = 1.75–1.99) at least once a week had higher odds of being covered by health insurance. Conclusion The coverage of health insurance in SSA is generally low among urban dwellers. This has negative implications for the achievement of universal health coverage by the year 2030. We recommend increased public education on the benefits of being covered by health insurance using the mass media which we found to be an important factor associated with health insurance coverage. The focus of such mass media education could target the less educated urban dwellers, males in the lowest wealth quintile, and young adults (15–29 years).

The study used data from the Demographic and Health Surveys (DHS) were collected in 23 countries across SSAs. The DHS conducts nationally representative surveys in over 85 low- and middle-income countries between 2010 and 2019 around the world using a recurrent cross-sectional research design. The surveys concentrate on maternal and child health, physical activity, sexually transmitted infections, fertility, health insurance, tobacco use, and alcohol consumption. They mainly provide data to monitor the demographic and health profiles of the respective countries [20]. Our study, however, focused on those aged 15–64 as coverage of health insurance has implications for maternal and overall adult health. The surveys’ data collection technique includes using a standard questionnaire that is equivalent across nations to collect information from women aged 15–49 and men aged 15–59, as well as information on their children. The questionnaire is frequently translated into the major local languages of the participating countries. The DHS claims that the translated questionnaires, along with the English-language version, are pretested in English and the local dialect to guarantee their validity. After that, the pre-test field workers engaged in a lively discussion of the questions, making suggestions to improve all versions. Following field practice, a debriefing session with the pre-test field personnel is held, and the questionnaires are modified depending on the lessons learned. Details on the sampling methodology, procedures, and implementation can be found elsewhere [21]. The sampling procedure employed in the surveys involves a two-stage stratified sampling procedure, where countries are grouped into urban and rural areas. The first stage involves the selection of clusters usually called enumeration areas (EAs) and the second stage consists of the selection of a household for the survey. The study by Aliaga and Ruilin [21] provides details of the sampling process. For this study, only women and men residing in urban centres were included in our analyses. A total of 54,254 men and 120,037 women who had complete information on all the variables of interest were included in the study (Table 1). We relied on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement in writing the manuscript [22]. The dataset is freely available for download at https://dhsprogram.com/data/available-datasets.cfm (accessed on 17th February 2021) ‘-’ indicate no values. The outcome variable of this study was health insurance coverage. This was derived from the question “are you covered with any health insurance?”. Response is coded as 0 = “No” and 1 = “Yes”. The explanatory variables were age, wealth status, level of education, marital status, frequency of reading newspaper or magazine, frequency of listening to the radio, and frequency of watching television. Age was recoded as 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64. Wealth status was categorized as poorest, poorer, middle, richer, and richest. Education was classified into four categories: no education, primary education, secondary education, and higher education. The frequency of reading newspaper or magazine, frequency of listening to radio, and frequency of watching television were respectively captured as not at all, less than once a week, at least once a week, and almost every day. Our study variables and codings were based on previous literature [12, 14, 15] and their availability in the DHS dataset of selected SSA countries. We employed both descriptive and inferential analytical approaches. First, we computed the proportion of males and females who were covered by health insurance (see Table 1). Following the hierarchical nature of the data set, a multilevel logistic regression model was employed. This comprises fixed effects and random effects [23]. The fixed effects of the model were gauged with binary logistic regression which resulted in odds ratios (ORs) and adjusted odds ratios (AORs) (see Tables ​Tables22 & 3). The random effects on the other hand were assessed with Intra-Cluster Correlation (ICC) [24] (see Tables ​Tables22 & 3). Regarding the model building process, Model 1 is an empty model that looked at the ICC. Model 2 looks at the individual variables. It looks at the effects of the individual variables on the empty model. Model 3 looks at the effects of the Household variables on the empty model. Model 4 is the complete model that combined both the individual and the household variables. The complete model looks at the relationship of the explanatory variables (individual and household) on the outcome variables. *p<0.05 **p<0.01 *** p<0.001. *p<0.05 **p<0.01 *** p<0.001. The sample weight (v005/1,000,000) was applied in all the analyses to control for over and under-sampling. All the analyses were carried out using STATA version 14.2. We assess the fitness of the models with the Likelihood Ratio (LR) test. The presence of multicollinearity between the independent variables was checked before fitting the models. The variance inflation factor (VIF) test revealed the absence of high multicollinearity between the variables (Mean VIF = 2.67 for males and, mean VIF = 2.27 for females).

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 information and resources related to maternal health, including prenatal care, nutrition, and postnatal care. These apps can also offer reminders for appointments and medication, as well as connect women with healthcare providers through telemedicine.

2. Community Health Workers: Train and deploy community health workers who can provide education, support, and basic healthcare services to pregnant women and new mothers in urban areas. These workers can help bridge the gap between healthcare facilities and the community, ensuring that women have access to necessary care.

3. Telemedicine Services: Implement telemedicine services that allow pregnant women to consult with healthcare providers remotely. This can be particularly beneficial for women who face barriers to accessing healthcare facilities, such as transportation issues or long distances.

4. Health Insurance Education Campaigns: Develop targeted public education campaigns to raise awareness about the benefits of health insurance coverage for maternal health. These campaigns can use mass media platforms, such as television, radio, and newspapers, to reach a wide audience and emphasize the importance of being covered by health insurance.

5. Financial Incentives: Explore the possibility of providing financial incentives, such as subsidies or cash transfers, to encourage pregnant women to enroll in health insurance programs. This can help reduce financial barriers and increase the affordability of maternal healthcare services.

6. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers to expand service delivery, implementing public-private insurance schemes, or establishing referral systems between public and private facilities.

7. Maternal Health Information Hotlines: Set up toll-free hotlines staffed by trained healthcare professionals who can provide information, guidance, and support to pregnant women and new mothers. These hotlines can be accessed via phone calls or text messages, ensuring that women have access to reliable and timely information.

8. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with subsidized or free access to essential maternal healthcare services, such as antenatal care visits, delivery services, and postnatal care. These vouchers can be distributed through healthcare facilities or community outreach programs.

9. Maternal Health Clinics in Urban Areas: Establish dedicated maternal health clinics in urban areas to provide comprehensive and specialized care for pregnant women. These clinics can offer a range of services, including prenatal care, childbirth support, and postnatal care, in a convenient and accessible location.

10. Maternal Health Outreach Programs: Develop targeted outreach programs that specifically focus on reaching vulnerable populations, such as young adults, low-income individuals, and those with low levels of education. These programs can provide tailored information, support, and resources to address the unique needs and challenges faced by these groups.

It is important to note that the effectiveness and feasibility of these innovations may vary depending on the specific context and resources available in each urban sub-Saharan African country. Further research and evaluation would be needed to determine the most appropriate and impactful strategies for improving access to maternal health in these settings.
AI Innovations Description
Based on the study’s findings, the following recommendation can be developed into an innovation to improve access to maternal health:

Increase public education on the benefits of health insurance coverage using mass media: The study found that individuals who read newspapers or magazines, listened to the radio, and watched television at least once a week had higher odds of being covered by health insurance. Therefore, an innovation could involve developing targeted public education campaigns that utilize various mass media platforms to raise awareness about the importance of health insurance for maternal health. These campaigns can provide information on the benefits of health insurance coverage, how to enroll in health insurance schemes, and the specific maternal health services that are covered. The focus of these campaigns should be on reaching less educated urban dwellers, males in the lowest wealth quintile, and young adults (15-29 years), as these groups were found to have lower health insurance coverage rates. By increasing public education through mass media, more individuals may be encouraged to enroll in health insurance schemes, leading to improved access to maternal health services.
AI Innovations Methodology
Based on the provided description, the study focused on examining the prevalence and factors associated with health insurance coverage in urban sub-Saharan Africa (SSA). The study used data from the most recent Demographic and Health Survey (DHS) conducted in 23 countries in SSA. The analysis included 120,037 women and 54,254 men residing in urban centers.

The study found that the overall prevalence of health insurance coverage was 10.6% among females and 14% among males. The probability of being covered by health insurance increased with the level of education. Individuals with higher education had higher odds of being covered by health insurance compared to those with no formal education. Additionally, individuals who read newspapers or magazines, listened to the radio, and watched television at least once a week had higher odds of being covered by health insurance.

To simulate the impact of recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Identify the recommendations: Based on the study findings and other relevant research, identify specific recommendations that can improve access to maternal health. For example, one recommendation could be to increase public education on the benefits of health insurance coverage using mass media.

2. Define the simulation parameters: Determine the variables and parameters that will be used to simulate the impact of the recommendations. This could include variables such as the target population, the coverage of health insurance, and the potential increase in coverage due to the recommendations.

3. Develop a simulation model: Create a simulation model that incorporates the identified variables and parameters. This model should simulate the impact of the recommendations on improving access to maternal health. It could use statistical techniques such as regression analysis or mathematical modeling to estimate the potential impact.

4. Validate the simulation model: Validate the simulation model by comparing its outputs with real-world data or other validated models. This step ensures that the model accurately represents the potential impact of the recommendations.

5. Run the simulation: Input the relevant data and parameters into the simulation model and run the simulation. This will generate results that estimate the potential impact of the recommendations on improving access to maternal health.

6. Analyze the results: Analyze the simulation results to understand the potential impact of the recommendations. This could include quantifying the increase in health insurance coverage, estimating the number of individuals who would benefit from improved access to maternal health, and assessing the cost-effectiveness of the recommendations.

7. Communicate the findings: Present the simulation findings in a clear and concise manner, highlighting the potential benefits of the recommendations for improving access to maternal health. This information can be used to inform policy decisions and guide interventions aimed at improving maternal health outcomes.

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

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