Determinants of enrolment and renewing of community-based health insurance in households with under-5 children in rural South-Western Uganda

  • International Journal of Health Policy and Management, Volume 8, No. 10, Year 2019

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Study Justification:
– The study aims to shed light on the determinants of households’ decisions to enroll and renew membership in community-based health insurance (CBHI) schemes in rural South-Western Uganda.
– This is important because the government of Uganda is currently considering integrating existing CBHI schemes into a larger national health insurance program.
– While enrollment in CBHI has been studied in other countries, there is a lack of research from a Ugandan perspective.
Study Highlights:
– The study collected data from 464 households in 14 villages served by a large CBHI scheme in South-Western Uganda.
– Logistic and zero-inflated negative binomial regressions were used to analyze the data and understand the determinants of enrollment and renewing membership in CBHI.
– The results showed that household socioeconomic status, husband’s employment in rural casual work, and knowledge of health insurance premiums were significant predictors of enrollment.
– Social capital and connectivity, as measured by the number of voluntary groups a household belonged to, were also positively associated with CBHI participation.
– Positive perceptions of insurance and access to information were also important factors for enrollment and renewing.
Study Recommendations:
– Mechanisms to promote inclusion should be devised to improve the participation of communities in CBHI.
– Trust in insurance and eventual coverage can be enhanced by improving community participation.
– Access to correct information and strengthening social network information pathways can increase the chances of renewing CBHI.
Key Role Players:
– Government officials responsible for health policy and the national health insurance program.
– Community leaders and organizations involved in CBHI schemes.
– Health insurance providers and administrators.
– Researchers and academics studying health insurance and healthcare access.
Cost Items for Planning Recommendations:
– Public awareness campaigns to promote CBHI and educate communities about its benefits.
– Training programs for community leaders and health insurance providers to improve their understanding of CBHI and their ability to communicate its importance.
– Infrastructure development to improve access to healthcare facilities in rural areas.
– Information and communication technology systems to facilitate the sharing of information and enrollment processes.
– Monitoring and evaluation systems to assess the effectiveness of CBHI schemes and identify areas for improvement.

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Background: The desire for universal health coverage in developing countries has brought attention to community-based health insurance (CBHI) schemes in developing countries. The government of Uganda is currently debating policy for the national health insurance programme, targeting the integration of existing CBHI schemes into a larger national risk pool. However, while enrolment has been largely studied in other countries, it remains a generally under-covered issue from a Ugandan perspective. Using a large CBHI scheme, this study, therefore, aims at shedding more light on the determinants of households’ decisions to enrol and renew membership in these schemes. Methods: We collected household data from 464 households in 14 villages served by a large CBHI scheme in south-western Uganda. We then estimated logistic and zero-inflated negative binomial (ZINB) regressions to understand the determinants of enrolment and renewing membership in CBHI, respectively. Results: Results revealed that household’s socioeconomic status, husband’s employment in rural casual work (odds ratio [OR]: 2.581, CI: 1.104-6.032) and knowledge of health insurance premiums (OR: 17.072, CI: 7.027-41.477) were significant predictors of enrolment. Social capital and connectivity, assessed by the number of voluntary groups a household belonged to, was also positively associated with CBHI participation (OR: 5.664, CI: 2.927-10.963). More positive perceptions on insurance (OR: 2.991, CI: 1.273-7.029), access to information were also associated with enrolment and renewing among others. Burial group size and number of burial groups in a village, were all significantly associated with increased the likelihood of renewing CBHI. Conclusion: While socioeconomic factors remain important predictors of participation in insurance, mechanisms to promote inclusion should be devised. Improving the participation of communities can enhance trust in insurance and eventual coverage. Moreover, for households already insured, access to correct information and strengthening their social network information pathways enhances their chances of renewing.

Data used in this study comes from a cross-sectional survey conducted between August and December 2015, in Kabale and Rukungiri districts in south-western Uganda. A multi-stage simple random sampling criterion was applied to select a population representative sample of 464 households in 14 villages. The first stage was the selection of villages from 3 sub-counties of Nyakishenyi and Nyarushanje in Rukungiri district and Kashambya sub-county in Kabale district, which have the highest coverage of Kisiizi CBHI scheme. The 3 sub-counties represented a population of 106 000 people in 23 500 households as of the 2014 national census.42 We invited leaders from 23 parishes in the 3 sub-counties for a first stage sampling workshop. Fifteen of the 23 parish leaders attended in person or were represented by a committee member. Eight parishes that did not have a representative were excluded. All parish leaders were requested to list all the villages in their area. In addition, they were requested to classify the villages into rich and poor, using access to road, school or health facility or market as a criterion. Altogether, 174 villages were listed, 104 as poor and 70 as rich villages. All the listed villages’ names were put in a raffle box according to their categorisation and a leader randomly selected 7 villages from each box in the presence of other leaders and the research team. Leaders who attended the village sampling workshop provided the contacts of lower level leaders in the selected villages for household listing. The second stage of sampling was household listing and selection of households for the survey. Fourteen lower level leaders were invited for a household listing workshop and requested to generate a list of households in their villages who had a child between 6 months and less than 59 months (5 years) . A total of 511 households were listed and 464 were interviewed. A data collection tool was developed by the first and fourth authors and was duly assessed by the respective ethical committees in Germany and Uganda. The tool included a household demographic module collecting data on household occupancy; a child and maternal health module recording data on healthcare seeking behaviour for mothers and children and a nutrition module recording household food availability and intake data. Data on durable assets holdings and other endowments in agriculture, water and sanitation, and housing was recorded as an indicator for household social and economic welfare. The health insurance and social connectivity modules collected data regarding household insurance status, group membership and participation, and knowledge of insurance such as premiums and benefits package. In line with,22 data on various perceptions on insurance were collected. Moreover, village level information is also collected and used to control for village heterogeneity. Data were collected using Open Data Kit, a computer-assisted personal interviewing platform. Open Data Kit and other platforms of similar fashion are becoming increasingly suggested for their overall cost-effectiveness and reducing of common survey errors.43 Data analysis was conducted in Stata version 14.44 We employ 2 models to understand the determinants of enrolment and renewing CBHI. Since the outcome for CBHI participation (1 if CBHI member and 0 otherwise), the suitable model is a binary logistic model to estimate the determinants of household’s CBHI status. The model is given as: Pr (Insure=1)i = β0+β1X1i+ β2X2i+β3X3i+ϵi Where the probability that a household i was enrolled depends on X1i – a vector of household socioeconomic and demographic variables, X2i – a vector of household enabling variables and X3i is a vector of village level variables and an error term ϵi. All household socioeconomic variables, household enabling variables and village level variables are shown in Table 1. We show odds ratios of the association between the covariates and the decision to enrol in CBHI. To ascertain that the model is well fit, we first re-centre some variables to overcome multi-collinearity.45 We then show the Variance Inflation Factor statistic. Abbreviations: CBHI, community-based health insurance; PCA, principal components analysis. The decision to renew membership in CBHI is modelled in the form of the length of time households are insured. The more the years a household was in CBHI implies the number of annual renewing decisions taken by the households. As seen in the Figure, majority households (56%) are not in CBHI. These are therefore coded as zeros regarding the decision the renew insurance. Number of Years in CBHI. Abbreviation: CBHI, community-based health insurance. Because the outcome is a non-negative count outcome – years of participation in CBHI, a suitable model would be of a Poisson distribution, such as Poisson, Tobit, or negative binomial model. However, as the Figure shows, we are worried about excess zeros (over-dispersion) since more than half the sample does not renew participation. To model the determinants of renewing CBHI, we, therefore, use a zero-inflated negative binomial (ZINB) model. The ZINB model facilitates the estimation of a non-negative count outcome with possible over-dispersion better than other models for count outcomes.46 The ZINB model performs the inflation equation and an outcome equation. The inflation equation is a logistic estimation of the probability that the outcome is observed as a zero. After accounting for the excess zero in the model estimates the probability of the outcome.47 In order to show that the ZINB is the appropriate model over negative binomial model and other models of count outcomes, we show the Vuong test, which shows a significantly positive test statistic if the data is suitable for zero-inflated models. The basic model is then given as follows. Years i = β0+β1X1i+ β2X2i+β3X3i+ϵi Similar to determinants of CBHI enrolment status, renewing (Yearsi) is a function of vectors for household socioeconomic and demographic variables, household enabling variables and village covariates. In the results, we report incident rate ratios (IRRs) for renewing CBHI.

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

1. Mobile Health (mHealth) Solutions: Develop mobile applications or SMS-based systems to provide pregnant women with information on prenatal care, nutrition, and postnatal care. These solutions can also be used to send reminders for appointments and medication.

2. Community Health Workers (CHWs): Train and deploy CHWs to provide maternal health education, counseling, and support in rural areas. CHWs can conduct home visits, facilitate group discussions, and refer women to healthcare facilities when necessary.

3. Telemedicine: Establish telemedicine services to enable pregnant women in remote areas to consult with healthcare professionals through video conferencing or phone calls. This can help address the shortage of healthcare providers in rural areas and improve access to specialized care.

4. Financial Incentives: Implement financial incentives, such as cash transfers or subsidies, to encourage pregnant women to enroll and renew their membership in community-based health insurance schemes. This can help reduce financial barriers to accessing maternal healthcare services.

5. Health Education Campaigns: Conduct targeted health education campaigns to raise awareness about the importance of maternal health and the benefits of enrolling in community-based health insurance schemes. These campaigns can be conducted through various channels, including radio, television, community meetings, and social media.

6. Strengthening Social Networks: Develop interventions to strengthen social networks and community support systems for pregnant women. This can include promoting the formation of women’s groups, support groups, and community-based savings groups that can provide emotional support, share knowledge, and pool resources for maternal health expenses.

7. Improving Information Access: Enhance access to accurate and reliable information on health insurance premiums, benefits, and enrollment procedures. This can be done through the development of user-friendly information materials, hotlines, and online platforms.

8. Addressing Socioeconomic Factors: Implement interventions to address socioeconomic factors that influence enrollment and renewal of community-based health insurance. This can include income-generating activities, vocational training, and microfinance initiatives to improve household economic status and affordability of health insurance.

9. Collaboration and Integration: Foster collaboration and coordination between community-based health insurance schemes, government health insurance programs, and other stakeholders involved in maternal health. This can help streamline processes, reduce duplication of efforts, and ensure comprehensive coverage for pregnant women.

10. Continuous Monitoring and Evaluation: Establish systems for continuous monitoring and evaluation of community-based health insurance schemes and maternal health programs. This can help identify gaps, measure impact, and inform evidence-based decision-making for further improvements.

It is important to note that the specific implementation of these innovations should be tailored to the local context and needs of the target population.
AI Innovations Description
The study titled “Determinants of enrolment and renewing of community-based health insurance in households with under-5 children in rural South-Western Uganda” provides insights into the factors that influence households’ decisions to enroll and renew membership in community-based health insurance (CBHI) schemes. The study collected data from 464 households in 14 villages served by a large CBHI scheme in south-western Uganda.

The study found several significant predictors of enrollment in CBHI. These include household socioeconomic status, husband’s employment in rural casual work, and knowledge of health insurance premiums. Social capital and connectivity, measured by the number of voluntary groups a household belonged to, were also positively associated with CBHI participation. Positive perceptions of insurance and access to information were also linked to enrollment.

For households already insured, access to correct information and strengthening social network information pathways were found to enhance the chances of renewing CBHI. Factors such as burial group size and the number of burial groups in a village were also associated with increased likelihood of renewing CBHI.

The study used a cross-sectional survey conducted between August and December 2015 in Kabale and Rukungiri districts in south-western Uganda. A multi-stage simple random sampling criterion was applied to select a representative sample of 464 households in 14 villages. Data collection included household demographic information, child and maternal health data, nutrition data, durable assets holdings, and information on health insurance and social connectivity.

To analyze the determinants of enrollment and renewing CBHI, the study employed logistic and zero-inflated negative binomial (ZINB) regressions, respectively. Logistic regression was used to estimate the determinants of household CBHI enrollment status, while ZINB regression was used to model the length of time households were insured.

Overall, the study highlights the importance of socioeconomic factors, access to information, and social networks in influencing enrollment and renewing of CBHI. The findings suggest that mechanisms to promote inclusion and improve community participation can enhance trust in insurance and increase coverage.
AI Innovations Methodology
Based on the provided description, the study aims to understand the determinants of households’ decisions to enroll and renew membership in community-based health insurance (CBHI) schemes in rural South-Western Uganda. The study collected household data from 464 households in 14 villages served by a large CBHI scheme. Logistic and zero-inflated negative binomial regressions were used to estimate the determinants of enrollment and renewing membership in CBHI, respectively.

To improve access to maternal health, here are some potential recommendations based on the study findings:

1. Promote socioeconomic inclusion: Since socioeconomic factors were found to be important predictors of participation in insurance, it is crucial to devise mechanisms that promote inclusion. This could involve targeted interventions to improve the socioeconomic status of households, such as providing income-generating opportunities and access to education.

2. Enhance knowledge of health insurance premiums: The study found that knowledge of health insurance premiums was a significant predictor of enrollment. Therefore, efforts should be made to improve awareness and understanding of the costs and benefits of health insurance among households. This could be done through community education programs and information campaigns.

3. Strengthen social capital and connectivity: The study revealed that social capital and connectivity, assessed by the number of voluntary groups a household belonged to, were positively associated with CBHI participation. Therefore, interventions should focus on strengthening social networks and community engagement to enhance trust in insurance and encourage participation.

4. Improve access to correct information: The study highlighted that access to correct information was associated with enrollment and renewing. It is important to ensure that households have access to accurate and up-to-date information about health insurance, including the services covered, the claims process, and the renewal procedures. This can be achieved through targeted communication strategies and the use of community health workers.

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

1. Define the indicators: Identify specific indicators that reflect improved access to maternal health, such as the percentage of pregnant women receiving prenatal care, the percentage of births attended by skilled health personnel, or the percentage of women accessing postnatal care within a specified timeframe.

2. Collect baseline data: Gather baseline data on the selected indicators before implementing the recommendations. This could involve conducting surveys, reviewing existing health records, or using other data collection methods.

3. Implement the recommendations: Introduce the recommended interventions, such as socioeconomic inclusion programs, health education campaigns, and community engagement initiatives. Ensure that these interventions are implemented consistently and monitored closely.

4. Collect post-intervention data: After a suitable period of time, collect post-intervention data on the selected indicators. This could involve repeating the surveys or reviewing updated health records.

5. Analyze the data: Compare the baseline and post-intervention data to assess the impact of the recommendations on improving access to maternal health. Use statistical methods, such as regression analysis or hypothesis testing, to determine the significance of any observed changes.

6. Interpret the results: Interpret the findings to understand the extent to which the recommendations have improved access to maternal health. Identify any challenges or limitations encountered during the implementation process.

7. Adjust and refine: Based on the results, make any necessary adjustments or refinements to the recommendations. This could involve scaling up successful interventions, addressing identified barriers, or exploring additional strategies to further enhance access to maternal health.

By following this methodology, it will be possible to simulate the impact of the recommendations on improving access to maternal health and make evidence-based decisions for future interventions.

Statistics:
Citations: 14
Authors: 5
Identifiers:
DOI: 10.15171/ijhpm.2019.49
Research Areas:
Community Interventions, Environmental, Food Security, Genetics and Genomics, Health System and Policy, Maternal and Child Health, Quality of Care, Social Determinants
Study Countries:
Uganda
Study Design:
Case-Control Study, Cross Sectional Study, Grounded Theory
Study Approach:
Quantitative
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