Universal health coverage and the poor: to what extent are health financing policies making a difference? Evidence from a benefit incidence analysis in Zambia

BMC Public Health, Volume 22, No. 1, Year 2022

Interpretação

Background: Zambia has invested in several healthcare financing reforms aimed at achieving universal access to health services. Several evaluations have investigated the effects of these reforms on the utilization of health services. However, only one study has assessed the distributional incidence of health spending across different socioeconomic groups, but without differentiating between public and overall health spending and between curative and maternal health services. Our study aims to fill this gap by undertaking a quasi-longitudinal benefit incidence analysis of public and overall health spending between 2006 and 2014. Methods: We conducted a Benefit Incidence Analysis (BIA) to measure the socioeconomic inequality of public and overall health spending on curative services and institutional delivery across different health facility typologies at three time points. We combined data from household surveys and National Health Accounts. Results: Results showed that public (concentration index of − 0.003; SE 0.027 in 2006 and − 0.207; SE 0.011 in 2014) and overall (0.050; SE 0.033 in 2006 and − 0.169; SE 0.011 in 2014) health spending on curative services tended to benefit the poorer segments of the population while public (0.241; SE 0.018 in 2007 and 0.120; SE 0.007 in 2014) and overall health spending (0.051; SE 0.022 in 2007 and 0.116; SE 0.007 in 2014) on institutional delivery tended to benefit the least-poor. Higher inequalities were observed at higher care levels for both curative and institutional delivery services. Conclusion: Our findings suggest that the implementation of UHC policies in Zambia led to a reduction in socioeconomic inequality in health spending, particularly at health centres and for curative care. Further action is needed to address existing barriers for the poor to benefit from health spending on curative services and at higher levels of care. We applied BIA to assess the distributional incidence of both public and overall health spending on curative services and institutional delivery at three time points. BIA measures the share of benefits accruing to different socioeconomic groups from using health services at a specific point in time, thereby determining whether financial health benefits are reaching the poor segments of the population ([18, 33]. BIA relies on two sets of data: health service utilization stratified by socioeconomic status and recurrent health spending on different types of health services. In other words, BIA expresses in monetary terms the distribution of health benefits. We performed a quasi-longitudinal analysis using data from available nationally representative repeated cross-sectional household surveys and national health accounts (NHA) for the health service utilization and health spending, respectively. Before deciding on the time points of our analysis, we mapped all the health policies and interventions (Fig. (Fig.1)1) that were implemented in Zambia with the aim of achieving universal coverage of curative and maternal health services. Based on the available data, we then chose the time points that could allow us to assess the changes of socioeconomic inequality in financial health benefits over time in line with the implemented UHC-reforms. We derived data on healthcare utilization from the 2006 and 2010 Living Condition and Monitoring surveys (LCMS) and the 2014 Zambia Household Health Expenditure and Utilization Survey (ZHHEUS) for the curative services and the 2007 Demographic and Health Surveys (DHS) and the 2014 ZHHEUS for institutional delivery. As summarized in Table 1, these household surveys are nationally representative and contain data on the utilization of curative services and institutional deliveries differentiated by provider typology and socioeconomic status (SES). The latter allowed us to group individuals into weighted SES quintiles, from the poorest to the least poor. Table 2 indicates the health variables we extracted from each household survey. Given data availability, we relied on different data to compute household SES, the basis for our classification of individuals into groups. For analyses relying on LCMS and ZHHEUS, we used the per capita consumption expenditure based on the total household food and non-food expenditure. For analyses relying on DHS, we used the household-wealth-index factor scores generated through the principal component analysis based on the household material asset ownership from the DHS. Summary information on population survey data employed in the study Stratified two-stage sampling technique: In the first stage, the primary units or enumeration areas (EAs) were drawn to probability proportional to the number of households counted in the EA (for a total of approximately 1000 EAs). In the second stage, households were drawn in equal probability in each of the enumeration areas (for a total of approximately 20,000 households). Stratified two-stage sampling technique: In the first stage, 320 EAs were selected with probability proportional to the SEA size. An EA is a convenient geographical area with an average size of 130 households or 600 people. In the second stage, households were drawn with equal probability in each EA (for a total of approximately 8000 households). Variables and data sources LCMS (2006; 2010) ZHH EUS (2014) 2006 2010 2014 DHS (2007) ZHHEUS (2014) To estimate the annual visits for curative healthcare services and institutional deliveries, we adopt the methodological guidance provided by McIntyre and Ataguba [18] For curative services, we used a binary variable indicating whether the individuals used curative services in the previous 14 days and for the institutional delivery, we used a binary variable indicating whether the women delivered in the study year. Curative care visits were annualized to obtain visits per year by multiplying the visits in a recall period of 14 days by 26. We categorized curative services and institutional delivery by different providers and types of health facilities depending on data availability in each survey and NHA. We derived data on health spending from the NHA. We estimated the unit cost of curative health services and institutional deliveries using recurrent public spending, donor spending and household OOPE from the NHA. We applied a constant unit subsidy assumption to estimate the unity subsidy for public and donor spending at different providers/types of health facilities. For the OOPE, we relied on a constant unit cost for each quintile based on the percentage of OOPE incurred by each quintile at different providers/types of health facilities. The OOPE adjustment was made because individuals belonging to different SES quintiles have different abilities to pay for OOPE at different providers/types of facilities. Hence using a constant unit OOPE at each provider/type of facility would overestimate the OOPE incurred by the bottom SES quintiles. We used the data on household health expenditure from the ZHHEUS survey to quantify the distribution of OOPE on health across socioeconomic quintiles. To determine the unit subsidy or the unit cost at each provider/type of health facility, we divided the total health spending by the total utilization of health services at each health facility. We computed the traditional BIA by measuring the distributional incidence of public spending and comprehensive BIA by looking at the distributional incidence of overall health spending, including public and donor subsidies allocated to different health facilities and OOPE incurred by individuals. We repeated the same analysis at three time points for the curative services and at two time points for institutional delivery to capture changes in the distribution of health spending over time. Based on data availability (Table (Table2),2), we stratified our analysis by health facility typologies (public health centres, public hospitals and mission health facilities) for each year. Given the limited number of private health facilities in Zambia, they were excluded from the analysis. To determine the total financial health benefits at each provider/type of health facility, we multiplied the unit subsidy or unit cost by the total utilization of health services at each provider/type of health facility. We used concentration indices to measure the degree of inequality in the distribution of public and overall health spending on curative services and institutional delivery across different socioeconomic groups. The concentration index (CI) quantifies the degree of wealth-related inequality and ranges from − 1.0 to + 1.0. The CI takes a negative (positive) value when the financial health benefits is concentrated among the poor (least-poor). If the CI is close to zero, a lower degree of inequality is present; and if it is zero, there is no wealth-related inequality [33]. The standardized concentration index (Ch) is estimated as follows [33]: Where hi is the health variable (e.g. healthcare utilization) for individual ί, μ is the mean of health variable, Ri is individual i’s fraction socioeconomic rank, and Cov (hi, Ri) is the covariance. We used convenient regression ([34] to allow the calculation of the standard errors of the concentration index. The formula is: Where 2σ2R is the variance of the fractional rank variable. β is the estimator of the concentration index.

AI Digest

Study Justification:

– The study aims to assess the distributional incidence of health spending in Zambia, specifically focusing on the effects of healthcare financing reforms on the utilization of health services.
– Previous evaluations have examined the impact of these reforms on health service utilization, but there is a lack of research on the distributional incidence of health spending across socioeconomic groups.
– By conducting a benefit incidence analysis (BIA), this study fills the gap in knowledge and provides valuable insights into the extent to which health financing policies are making a difference in achieving universal health coverage (UHC) for the poor.

Study Highlights:

– The study used data from household surveys and National Health Accounts to analyze the socioeconomic inequality of public and overall health spending on curative services and institutional delivery.
– Results showed that health spending on curative services tended to benefit the poorer segments of the population, while spending on institutional delivery tended to benefit the least-poor.
– Higher inequalities were observed at higher levels of care for both curative and institutional delivery services.
– The findings suggest that the implementation of UHC policies in Zambia has led to a reduction in socioeconomic inequality in health spending, particularly at health centers and for curative care.
– However, further action is needed to address existing barriers for the poor to benefit from health spending on curative services and at higher levels of care.

Recommendations:

– The study recommends addressing barriers that prevent the poor from benefiting from health spending on curative services and at higher levels of care.
– Policy interventions should focus on improving access to curative services for the poor and ensuring that health spending on institutional delivery is more equitable.
– Efforts should be made to reduce inequalities in health spending across different socioeconomic groups, especially at higher levels of care.
– Continued monitoring and evaluation of health financing policies and their impact on the distribution of health spending is crucial to inform future interventions and ensure progress towards UHC.

Key Role Players:

– Ministry of Health: Responsible for implementing and monitoring health financing policies and interventions.
– National Health Insurance Authority: Involved in the design and implementation of health insurance schemes to improve access to healthcare services.
– Health Facilities: Play a critical role in delivering health services and ensuring equitable distribution of health spending.
– Civil Society Organizations: Advocate for the rights of the poor and marginalized populations, and can contribute to shaping health financing policies and interventions.

Cost Items for Planning Recommendations:

– Health Facility Infrastructure: Investments may be required to improve the capacity and quality of health facilities, especially at higher levels of care.
– Human Resources for Health: Additional funding may be needed to recruit and train healthcare professionals to ensure adequate staffing levels and quality of care.
– Health Information Systems: Investments in data collection and management systems are necessary to monitor the distribution of health spending and evaluate the impact of interventions.
– Health Promotion and Education: Resources should be allocated to raise awareness about available health services and promote health-seeking behaviors among the poor.
– Monitoring and Evaluation: Funding is required to conduct regular monitoring and evaluation activities to assess the effectiveness of health financing policies and interventions.
Please note that the cost items provided are general categories and the actual cost estimates would depend on the specific context and implementation strategies.

Força da prova

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are areas for improvement. The study conducted a quasi-longitudinal benefit incidence analysis using data from household surveys and National Health Accounts to assess the distributional incidence of public and overall health spending on curative services and institutional delivery in Zambia. The study found that health spending on curative services tended to benefit the poorer segments of the population, while spending on institutional delivery tended to benefit the least-poor. However, the abstract does not provide details on the sample size, methodology, or statistical analysis used, which limits the ability to fully evaluate the strength of the evidence. To improve the evidence, the abstract should include more information on the study design, sample size, and statistical methods used. Additionally, providing more context on the limitations of the study and potential sources of bias would enhance the overall strength of the evidence.

Resumo

We applied BIA to assess the distributional incidence of both public and overall health spending on curative services and institutional delivery at three time points. BIA measures the share of benefits accruing to different socioeconomic groups from using health services at a specific point in time, thereby determining whether financial health benefits are reaching the poor segments of the population ([18, 33]. BIA relies on two sets of data: health service utilization stratified by socioeconomic status and recurrent health spending on different types of health services. In other words, BIA expresses in monetary terms the distribution of health benefits. We performed a quasi-longitudinal analysis using data from available nationally representative repeated cross-sectional household surveys and national health accounts (NHA) for the health service utilization and health spending, respectively. Before deciding on the time points of our analysis, we mapped all the health policies and interventions (Fig. (Fig.1)1) that were implemented in Zambia with the aim of achieving universal coverage of curative and maternal health services. Based on the available data, we then chose the time points that could allow us to assess the changes of socioeconomic inequality in financial health benefits over time in line with the implemented UHC-reforms. We derived data on healthcare utilization from the 2006 and 2010 Living Condition and Monitoring surveys (LCMS) and the 2014 Zambia Household Health Expenditure and Utilization Survey (ZHHEUS) for the curative services and the 2007 Demographic and Health Surveys (DHS) and the 2014 ZHHEUS for institutional delivery. As summarized in Table 1, these household surveys are nationally representative and contain data on the utilization of curative services and institutional deliveries differentiated by provider typology and socioeconomic status (SES). The latter allowed us to group individuals into weighted SES quintiles, from the poorest to the least poor. Table 2 indicates the health variables we extracted from each household survey. Given data availability, we relied on different data to compute household SES, the basis for our classification of individuals into groups. For analyses relying on LCMS and ZHHEUS, we used the per capita consumption expenditure based on the total household food and non-food expenditure. For analyses relying on DHS, we used the household-wealth-index factor scores generated through the principal component analysis based on the household material asset ownership from the DHS. Summary information on population survey data employed in the study Stratified two-stage sampling technique: In the first stage, the primary units or enumeration areas (EAs) were drawn to probability proportional to the number of households counted in the EA (for a total of approximately 1000 EAs). In the second stage, households were drawn in equal probability in each of the enumeration areas (for a total of approximately 20,000 households). Stratified two-stage sampling technique: In the first stage, 320 EAs were selected with probability proportional to the SEA size. An EA is a convenient geographical area with an average size of 130 households or 600 people. In the second stage, households were drawn with equal probability in each EA (for a total of approximately 8000 households). Variables and data sources LCMS (2006; 2010) ZHH EUS (2014) 2006 2010 2014 DHS (2007) ZHHEUS (2014) To estimate the annual visits for curative healthcare services and institutional deliveries, we adopt the methodological guidance provided by McIntyre and Ataguba [18] For curative services, we used a binary variable indicating whether the individuals used curative services in the previous 14 days and for the institutional delivery, we used a binary variable indicating whether the women delivered in the study year. Curative care visits were annualized to obtain visits per year by multiplying the visits in a recall period of 14 days by 26. We categorized curative services and institutional delivery by different providers and types of health facilities depending on data availability in each survey and NHA. We derived data on health spending from the NHA. We estimated the unit cost of curative health services and institutional deliveries using recurrent public spending, donor spending and household OOPE from the NHA. We applied a constant unit subsidy assumption to estimate the unity subsidy for public and donor spending at different providers/types of health facilities. For the OOPE, we relied on a constant unit cost for each quintile based on the percentage of OOPE incurred by each quintile at different providers/types of health facilities. The OOPE adjustment was made because individuals belonging to different SES quintiles have different abilities to pay for OOPE at different providers/types of facilities. Hence using a constant unit OOPE at each provider/type of facility would overestimate the OOPE incurred by the bottom SES quintiles. We used the data on household health expenditure from the ZHHEUS survey to quantify the distribution of OOPE on health across socioeconomic quintiles. To determine the unit subsidy or the unit cost at each provider/type of health facility, we divided the total health spending by the total utilization of health services at each health facility. We computed the traditional BIA by measuring the distributional incidence of public spending and comprehensive BIA by looking at the distributional incidence of overall health spending, including public and donor subsidies allocated to different health facilities and OOPE incurred by individuals. We repeated the same analysis at three time points for the curative services and at two time points for institutional delivery to capture changes in the distribution of health spending over time. Based on data availability (Table (Table2),2), we stratified our analysis by health facility typologies (public health centres, public hospitals and mission health facilities) for each year. Given the limited number of private health facilities in Zambia, they were excluded from the analysis. To determine the total financial health benefits at each provider/type of health facility, we multiplied the unit subsidy or unit cost by the total utilization of health services at each provider/type of health facility. We used concentration indices to measure the degree of inequality in the distribution of public and overall health spending on curative services and institutional delivery across different socioeconomic groups. The concentration index (CI) quantifies the degree of wealth-related inequality and ranges from − 1.0 to + 1.0. The CI takes a negative (positive) value when the financial health benefits is concentrated among the poor (least-poor). If the CI is close to zero, a lower degree of inequality is present; and if it is zero, there is no wealth-related inequality [33]. The standardized concentration index (Ch) is estimated as follows [33]: Where hi is the health variable (e.g. healthcare utilization) for individual ί, μ is the mean of health variable, Ri is individual i’s fraction socioeconomic rank, and Cov (hi, Ri) is the covariance. We used convenient regression ([34] to allow the calculation of the standard errors of the concentration index. The formula is: Where 2σ2R is the variance of the fractional rank variable. β is the estimator of the concentration index.

Materiais

N/A

Inovações

Based on the provided description, it seems that the study focused on assessing the distributional incidence of health spending on curative services and institutional delivery in Zambia. The study used a Benefit Incidence Analysis (BIA) to measure the socioeconomic inequality of public and overall health spending across different health facility typologies at three time points. The results showed that health spending on curative services tended to benefit the poorer segments of the population, while health spending on institutional delivery tended to benefit the least-poor. Higher inequalities were observed at higher levels of care for both curative and institutional delivery services.

In terms of innovations to improve access to maternal health, based on the information provided, it is not explicitly mentioned. However, some potential recommendations could include:

1. Targeted financial support: Implement targeted financial support programs to ensure that the poorest segments of the population have access to maternal health services. This could involve subsidizing the cost of maternal health services or providing cash transfers to pregnant women or families in need.

2. Strengthening primary healthcare: Invest in strengthening primary healthcare services, particularly in rural areas, to ensure that pregnant women have access to quality maternal health services closer to their communities. This could involve improving infrastructure, training healthcare workers, and providing essential equipment and supplies.

3. Community-based interventions: Implement community-based interventions to raise awareness about the importance of maternal health and promote early and regular antenatal care visits. This could involve training community health workers to provide education, counseling, and support to pregnant women and their families.

4. Mobile health (mHealth) solutions: Utilize mobile health technologies to improve access to maternal health information and services. This could include mobile apps or text message reminders for antenatal care appointments, access to teleconsultations with healthcare providers, and educational resources for pregnant women.

5. Public-private partnerships: Foster collaborations between the public and private sectors to expand access to maternal health services. This could involve partnering with private healthcare providers to increase the availability of quality maternal health services, especially in underserved areas.

It is important to note that these recommendations are based on general knowledge and may not specifically align with the findings or recommendations of the study mentioned in the description.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health based on the provided information is to implement Universal Health Coverage (UHC) policies that prioritize the needs of the poor and address existing barriers for them to benefit from health spending on curative services and at higher levels of care. This recommendation is supported by the findings of the Benefit Incidence Analysis (BIA) conducted in Zambia, which showed that public and overall health spending on curative services tended to benefit the poorer segments of the population, while spending on institutional delivery tended to benefit the least-poor.

To implement this recommendation, the following steps can be taken:

1. Design and implement UHC policies: Develop and implement policies that aim to achieve universal access to maternal health services. These policies should prioritize the needs of the poor and ensure that financial health benefits are reaching them.

2. Improve healthcare financing: Ensure that healthcare financing mechanisms are in place to support the implementation of UHC policies. This may include increasing public spending on maternal health services and exploring innovative financing options such as health insurance schemes or public-private partnerships.

3. Address barriers to access: Identify and address existing barriers that prevent the poor from accessing maternal health services. This may include improving transportation infrastructure, increasing the availability of healthcare facilities in rural areas, and providing financial assistance or subsidies for maternal health services.

4. Strengthen healthcare delivery: Improve the quality and availability of maternal health services by investing in healthcare infrastructure, training healthcare workers, and ensuring the availability of essential medical supplies and equipment.

5. Monitor and evaluate progress: Regularly monitor and evaluate the impact of the implemented UHC policies on access to maternal health services. This will help identify any gaps or areas for improvement and inform future policy decisions.

By implementing these recommendations, it is expected that access to maternal health services will be improved, particularly for the poor. This will contribute to reducing maternal mortality rates and improving the overall health outcomes of mothers and their children.

Autor

Dima Health

Statistics:

Citations: 1

Identifiers:

DOI: 10.1186/s12889-022-13923-1

Research Areas:

Community Interventions, Disparities, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants

Study Countries:

Zambia

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

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