How equitable is health spending on curative services and institutional delivery in Malawi? Evidence from a quasi-longitudinal benefit incidence analysis

International Journal for Equity in Health, Volume 21, No. 1, Year 2022

Interpretation

Background: Malawi is one of a handful of countries that had resisted the implementation of user fees, showing a commitment to providing free healthcare to its population even before the concept of Universal Health Coverage (UHC) acquired global popularity. Several evaluations have investigated the effects of key policies, such as the essential health package or performance-based financing, in sustaining and expanding access to quality health services in the country. Understanding the distributional impact of health spending over time due to these policies has received limited attention. Our study fills this knowledge gap by assessing the distributional incidence of public and overall health spending between 2004 and 2016. Methods: We relied on 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. We used data from household surveys and National Health Accounts. We used a concentration index (CI) to determine the health benefits accrued by each socioeconomic group. Results: Socioeconomic inequality in both public and overall health spending substantially decreased over time, with higher inequality observed in overall spending, non-public health facilities, curative health services, and at higher levels of care. Between 2004 and 2016, the inequality in public spending on curative services decreased from a CI of 0.037 (SE 0.013) to a CI of 0.004 (SE 0.011). Whiles, it decreased from a CI of 0.084 (SE 0.014) to a CI of 0.068 (SE 0.015) for overall spending in the same period. For institutional delivery, inequality in public and overall spending decreased between 2004 and 2016 from a CI of 0.032 (SE 0.028) to a CI of -0.057 (SE 0.014) and from a CI of 0.036 (SE 0.022) to a CI of 0.028 (SE 0.018), respectively. Conclusions: Through its free healthcare policy, Malawi has reduced socioeconomic inequality in health spending over time, but some challenges still need to be addressed to achieve a truly egalitarian health system. Our findings indicate a need to increase public funding for the health sector to ensure access to care and financial protection. We applied a Benefit Incidence Analysis (BIA) to assess the distributional incidence of both public and overall health spending on curative services and institutional delivery at three time points. Public health spending refers to public subsidies allocated to health facilities for the provisions of care. Overall spending refers to all sources of health spending allocated to health facilities in terms of public spending, external support and out-of-pocket expenditure (OOPE). BIA measures whether the financial benefits of health services reach individuals across socioeconomic groups equally at a specific point in time [34, 35]. BIA has traditionally been employed to assess the distributional incidence of public spending. Given the growing global emphasis on fostering UHC by combining multiple financing mechanisms, McIntyre and Ataguba recently argues in favor of expanding the scope of the BIA methodological approach to assess the distribution incidence of overall health system spending. Our work builds on their theoretical postulations and their specific methodological guidance [34]. Due to the nature of BIA methodology and the available data, it was impossible to conduct strictly-speaking longitudinal analysis. Therefore, we describe our study as quasi-longitudinal. The computation of BIA relies on two datasets: data on health service utilization stratified by socio-economic status and data on the unit costs of different types of health services. In other words, BIA expresses in monetary terms the distribution of health benefits. As such, BIA aims to capture the extent to which investments in the health sector reach equally all strata of the population. To perform this analysis, we used data from available nationally representative repeated cross-sectional household surveys and National Health Accounts (NHA) for health service utilization and health spending, respectively. Before deciding on the time points of our analysis, we depicted and attempted to match, to the extent possible, for three time points: (1) available health policies and interventions (Fig. (Fig.1)1) that were implemented in Malawi to foster progress towards universal coverage of curative and maternal services; (2) the household survey data on utilization of curative services and institutional delivery; and (3) available data on health spending on curative services and institutional delivery available. We repeated the BIA at the selected three time points to explore changes in the distributional incidence of health spending in relation to the different UHC reforms implemented in the country. Table S1 in the Additional file 1 illustrates which variables were used from each household survey, for each year, and briefly describes the sampling strategy of each survey. The repositories of the used datasets are provided under availability of data and materials. We derived our data on health care utilization from the Integrated Household Living Condition surveys (IHLCS) for curative services and the Demographic and Health Surveys (DHS) for institutional delivery. These nationally representative household surveys, normally conducted every five years, contain data on the utilization of curative services and institutional deliveries differentiated by provider typology and a measure of socioeconomic status (SES), allowing us to categorize individuals by weighted SES quintiles. Table Table11 indicates the health variables we extracted from each household survey. Variables and data sources 2004 2010 2015 2004 2010 2015 As a ranking variable to build socioeconomic strata, we used per capita consumption expenditure based on the total household food and non-food expenditure for IHLCS data sets and the household-wealth-index factor scores generated through the principal components analysis based on household material asset ownership for DHS data sets. We estimated the annual visits to curative services and institutional deliveries in the study year for individuals across different socioeconomic groups. We used a binary variable for curative services indicating whether the respondent had used curative services in the previous 14 days and a binary variable for institutional delivery indicating whether a woman had delivered in a facility in the prior twelve months. Counts of curatives services were annualized to obtain yearly counts by multiplying the visits recorded for the 14-day recall period by 26. We categorized curative services and institutional delivery by different health facilities types depending on data availability in each survey and NHA. Due to seasonal variation of disease incidences and use of health services, the literature indicates that the annualized utilization of health services may be underestimated or overestimated based on the period of data collection [36]. To account for these seasonal variations, we conducted a sensitivity analysis by adjusting the utilization of curative services and institutional delivery from the household surveys by the monthly seasonal variations in the use of these services. We built a monthly seasonality index using data from the 2014-2018 Health Management Information System (HMIS). We estimated averages of monthly health care utilization reported in the HMIS between 2014 and 2018 and used these averages to calculate the monthly seasonality indices. We then accounted for seasonal variations in the utilization of curative services and institutional delivery using corresponding seasonality indices depending on the months for which health service utilization was reported in the household surveys. We derived data on health spending from the National Health Accounts (see Fig. Fig.11 and table table1).1). We estimated the unit cost using recurrent public spending, donor spending and household OOPE from the National Health Accounts. We applied the constant unit subsidy assumption for the public and donor spending to estimate the unit subsidy at different types of health facilities. We determined the unity subsidy of each type of health service at each type of health facility by dividing the total health spending for one type of service by the total utilization of that service at this health facility. For OOPE, we relied on the constant unit cost assumption for each quintile based on the percentage of OOPE incurred by each quintile at different types of health facilities. We adjusted the OOPE across quintiles based on the works by Nakovics et al. [27] for curative services and by Chinkhumba et al. [37] for institutional delivery. The OOPE adjustment was based on the fact that the individuals belonging to different SES quintiles generally display different OOPE at different types of facilities. Hence, using a constant unit OOPE at each type of facility would overestimate the OOPE incurred by the bottom SES quintiles. The studies by Nakovics et al. [27] and Chinkhumba et al. [37] indicated that the least poor incurred approximately twice as much OOPE for curative services and one third more OOPE for institutional delivery than the poorest segment of the population. We estimated the unit cost for each quintile and each type of health service at each health facility by dividing the total OOPE incurred by that quintile for that service at the same health facility by the total utilization accrued to that quintile for that service at that health facility. We computed traditional BIA by measuring only the distributional incidence of public spending and comprehensive BIA by looking at the distributional incidence of overall health spending, including public and donor subsidy allocation to facilities as well as OOPE incurred by individuals. Our choice was motivated by a wish to provide a comprehensive health system assessment, in line with the policy intention of the reforms implemented to promote and sustain increased service coverage in the country. Based on the data availability (table (table1),1), we decomposed our analysis by different health facility typologies for both curative services (public facilities vs faith-based facilities vs private facilities) and institutional delivery (public health centers vs public hospitals vs faith-based health centers vs faith-based hospitals vs private facilities) and each year. To determine the total financial health benefits at each type of health facility, we multiplied the unit subsidy or unit cost by the total utilization of health services at each 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 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 benefit is concentrated disproportionately 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 an absence of wealth-related inequality [35]. We adjusted the concentration indices by the sampling weights of the IHLCS and DHS household surveys to scale up sample-specific estimates to reflect the national population. The standardized concentration index (Ch) is estimated as follows [35]: Where hi is the health variable (e.g. health care 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 [38] to calculate 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. We performed a dominance test at a significance level of 5%. The dominance test is used to statistically verify if a determined pro-poor or pro-least-poor distribution holds across the entire distribution of the socioeconomic variables [34, 35], especially when it is not clear if a distribution is pro-poor or pro-least-poor [39].

AI Digest

Study Justification:

This study aims to assess the distributional impact of health spending on curative services and institutional delivery in Malawi between 2004 and 2016. The study fills a knowledge gap by examining the socioeconomic inequality in health spending over time, particularly in relation to the implementation of key health policies and interventions. The findings of this study can provide valuable insights into the equity of health spending and inform policy decisions to achieve a more egalitarian health system.

Highlights:

– Malawi has shown a commitment to providing free healthcare to its population even before the concept of Universal Health Coverage (UHC) gained global popularity.
– The study uses a Benefit Incidence Analysis (BIA) to measure the socioeconomic inequality in public and overall health spending on curative services and institutional delivery.
– Socioeconomic inequality in health spending has decreased over time, but challenges still remain to achieve a truly equitable health system.
– The study highlights the need to increase public funding for the health sector to ensure access to care and financial protection.

Recommendations:

– Increase public funding for the health sector to ensure equitable access to quality health services.
– Strengthen policies and interventions aimed at reducing socioeconomic inequality in health spending, particularly in non-public health facilities, curative health services, and higher levels of care.
– Continuously monitor and evaluate the distributional impact of health spending to inform evidence-based policy decisions.

Key Role Players:

– Ministry of Health: Responsible for overall health sector planning and policy implementation.
– National Health Accounts Unit: Provides data on health spending for analysis.
– Health Facility Managers: Play a crucial role in the allocation and utilization of health resources.
– Researchers and Academics: Conduct studies and provide evidence to inform policy decisions.

Cost Items for Planning Recommendations:

– Increased public funding for the health sector: Includes budget allocation for health facilities, human resources, medical supplies, infrastructure development, and health information systems.
– Strengthening policies and interventions: Requires funding for program implementation, monitoring, and evaluation.
– Research and data collection: Funding for surveys, data analysis, and dissemination of findings.
– Capacity building: Investment in training and development of healthcare professionals to ensure equitable service delivery.
Please note that the cost items provided are general categories and not actual cost estimates. The specific cost implications would depend on the context and scale of the recommended interventions.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study uses a Benefit Incidence Analysis (BIA) to assess the distributional incidence of health spending in Malawi. It relies on data from household surveys and National Health Accounts, which provides a solid foundation for the analysis. The study also considers socioeconomic inequality in both public and overall health spending on curative services and institutional delivery, and finds that inequality has decreased over time. The findings suggest a need to increase public funding for the health sector. To improve the evidence, the study could provide more details on the sampling strategy of the household surveys and the data sources used for health spending. Additionally, it would be helpful to include information on the statistical methods used to calculate the concentration indices and the dominance test.

Abstract

We applied a Benefit Incidence Analysis (BIA) to assess the distributional incidence of both public and overall health spending on curative services and institutional delivery at three time points. Public health spending refers to public subsidies allocated to health facilities for the provisions of care. Overall spending refers to all sources of health spending allocated to health facilities in terms of public spending, external support and out-of-pocket expenditure (OOPE). BIA measures whether the financial benefits of health services reach individuals across socioeconomic groups equally at a specific point in time [34, 35]. BIA has traditionally been employed to assess the distributional incidence of public spending. Given the growing global emphasis on fostering UHC by combining multiple financing mechanisms, McIntyre and Ataguba recently argues in favor of expanding the scope of the BIA methodological approach to assess the distribution incidence of overall health system spending. Our work builds on their theoretical postulations and their specific methodological guidance [34]. Due to the nature of BIA methodology and the available data, it was impossible to conduct strictly-speaking longitudinal analysis. Therefore, we describe our study as quasi-longitudinal. The computation of BIA relies on two datasets: data on health service utilization stratified by socio-economic status and data on the unit costs of different types of health services. In other words, BIA expresses in monetary terms the distribution of health benefits. As such, BIA aims to capture the extent to which investments in the health sector reach equally all strata of the population. To perform this analysis, we used data from available nationally representative repeated cross-sectional household surveys and National Health Accounts (NHA) for health service utilization and health spending, respectively. Before deciding on the time points of our analysis, we depicted and attempted to match, to the extent possible, for three time points: (1) available health policies and interventions (Fig. (Fig.1)1) that were implemented in Malawi to foster progress towards universal coverage of curative and maternal services; (2) the household survey data on utilization of curative services and institutional delivery; and (3) available data on health spending on curative services and institutional delivery available. We repeated the BIA at the selected three time points to explore changes in the distributional incidence of health spending in relation to the different UHC reforms implemented in the country. Table S1 in the Additional file 1 illustrates which variables were used from each household survey, for each year, and briefly describes the sampling strategy of each survey. The repositories of the used datasets are provided under availability of data and materials. We derived our data on health care utilization from the Integrated Household Living Condition surveys (IHLCS) for curative services and the Demographic and Health Surveys (DHS) for institutional delivery. These nationally representative household surveys, normally conducted every five years, contain data on the utilization of curative services and institutional deliveries differentiated by provider typology and a measure of socioeconomic status (SES), allowing us to categorize individuals by weighted SES quintiles. Table Table11 indicates the health variables we extracted from each household survey. Variables and data sources 2004 2010 2015 2004 2010 2015 As a ranking variable to build socioeconomic strata, we used per capita consumption expenditure based on the total household food and non-food expenditure for IHLCS data sets and the household-wealth-index factor scores generated through the principal components analysis based on household material asset ownership for DHS data sets. We estimated the annual visits to curative services and institutional deliveries in the study year for individuals across different socioeconomic groups. We used a binary variable for curative services indicating whether the respondent had used curative services in the previous 14 days and a binary variable for institutional delivery indicating whether a woman had delivered in a facility in the prior twelve months. Counts of curatives services were annualized to obtain yearly counts by multiplying the visits recorded for the 14-day recall period by 26. We categorized curative services and institutional delivery by different health facilities types depending on data availability in each survey and NHA. Due to seasonal variation of disease incidences and use of health services, the literature indicates that the annualized utilization of health services may be underestimated or overestimated based on the period of data collection [36]. To account for these seasonal variations, we conducted a sensitivity analysis by adjusting the utilization of curative services and institutional delivery from the household surveys by the monthly seasonal variations in the use of these services. We built a monthly seasonality index using data from the 2014-2018 Health Management Information System (HMIS). We estimated averages of monthly health care utilization reported in the HMIS between 2014 and 2018 and used these averages to calculate the monthly seasonality indices. We then accounted for seasonal variations in the utilization of curative services and institutional delivery using corresponding seasonality indices depending on the months for which health service utilization was reported in the household surveys. We derived data on health spending from the National Health Accounts (see Fig. Fig.11 and table table1).1). We estimated the unit cost using recurrent public spending, donor spending and household OOPE from the National Health Accounts. We applied the constant unit subsidy assumption for the public and donor spending to estimate the unit subsidy at different types of health facilities. We determined the unity subsidy of each type of health service at each type of health facility by dividing the total health spending for one type of service by the total utilization of that service at this health facility. For OOPE, we relied on the constant unit cost assumption for each quintile based on the percentage of OOPE incurred by each quintile at different types of health facilities. We adjusted the OOPE across quintiles based on the works by Nakovics et al. [27] for curative services and by Chinkhumba et al. [37] for institutional delivery. The OOPE adjustment was based on the fact that the individuals belonging to different SES quintiles generally display different OOPE at different types of facilities. Hence, using a constant unit OOPE at each type of facility would overestimate the OOPE incurred by the bottom SES quintiles. The studies by Nakovics et al. [27] and Chinkhumba et al. [37] indicated that the least poor incurred approximately twice as much OOPE for curative services and one third more OOPE for institutional delivery than the poorest segment of the population. We estimated the unit cost for each quintile and each type of health service at each health facility by dividing the total OOPE incurred by that quintile for that service at the same health facility by the total utilization accrued to that quintile for that service at that health facility. We computed traditional BIA by measuring only the distributional incidence of public spending and comprehensive BIA by looking at the distributional incidence of overall health spending, including public and donor subsidy allocation to facilities as well as OOPE incurred by individuals. Our choice was motivated by a wish to provide a comprehensive health system assessment, in line with the policy intention of the reforms implemented to promote and sustain increased service coverage in the country. Based on the data availability (table (table1),1), we decomposed our analysis by different health facility typologies for both curative services (public facilities vs faith-based facilities vs private facilities) and institutional delivery (public health centers vs public hospitals vs faith-based health centers vs faith-based hospitals vs private facilities) and each year. To determine the total financial health benefits at each type of health facility, we multiplied the unit subsidy or unit cost by the total utilization of health services at each 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 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 benefit is concentrated disproportionately 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 an absence of wealth-related inequality [35]. We adjusted the concentration indices by the sampling weights of the IHLCS and DHS household surveys to scale up sample-specific estimates to reflect the national population. The standardized concentration index (Ch) is estimated as follows [35]: Where hi is the health variable (e.g. health care 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 [38] to calculate 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. We performed a dominance test at a significance level of 5%. The dominance test is used to statistically verify if a determined pro-poor or pro-least-poor distribution holds across the entire distribution of the socioeconomic variables [34, 35], especially when it is not clear if a distribution is pro-poor or pro-least-poor [39].

Materials

https://efashare.b-cdn.net/materials/7c645b3780296d36dbeabd515ad67d60dae4627121f17195e03bce4bc5aaba5b/12939_2022_1624_MOESM1_ESM.docx

Innovations

Based on the information provided, it seems that the study is focused on analyzing the distributional impact of health spending on curative services and institutional delivery in Malawi. The study uses a Benefit Incidence Analysis (BIA) to measure the socioeconomic inequality of public and overall health spending across different health facility typologies. The analysis is conducted at three time points to explore changes in the distributional incidence of health spending in relation to Universal Health Coverage (UHC) reforms implemented in the country.

Some potential innovations that could improve access to maternal health based on the findings of this study could include:

1. Targeted investment in public health facilities: The study highlights higher inequality in overall spending and non-public health facilities. To improve access to maternal health, there could be a focus on increasing public funding for health facilities, particularly those serving disadvantaged populations.

2. Strengthening performance-based financing: The study mentions performance-based financing as one of the key policies evaluated in Malawi. This approach could be further enhanced and expanded to incentivize health facilities to improve the quality and accessibility of maternal health services.

3. Addressing financial barriers: The study indicates a need to increase public funding for the health sector to ensure access to care and financial protection. Innovations such as health insurance schemes or cash transfer programs could be explored to reduce out-of-pocket expenditure for maternal health services and improve financial access for vulnerable populations.

4. Enhancing data collection and monitoring: The study relies on household surveys and National Health Accounts for data on health service utilization and health spending. Investing in robust data collection systems and monitoring mechanisms could help identify gaps in access to maternal health services and inform evidence-based decision-making.

5. Strengthening collaboration and coordination: The study highlights the need for a comprehensive health system assessment. Innovations that promote collaboration and coordination among different stakeholders, including government agencies, healthcare providers, and community organizations, could help improve the efficiency and effectiveness of maternal health services.

It is important to note that these recommendations are based on the information provided and should be further explored and tailored to the specific context and needs of Malawi’s maternal health system.

AI Innovations Description

Based on the description provided, the recommendation to improve access to maternal health in Malawi is to increase public funding for the health sector. While Malawi has made progress in reducing socioeconomic inequality in health spending through its free healthcare policy, there are still challenges that need to be addressed to achieve a truly egalitarian health system.

The study found that socioeconomic inequality in both public and overall health spending has decreased over time, but higher inequality is observed in overall spending, non-public health facilities, curative health services, and at higher levels of care. To ensure access to care and financial protection, it is important to increase public funding for the health sector.

By increasing public funding, the government can allocate more resources to maternal health services, including improving infrastructure, training healthcare professionals, and providing necessary medical supplies and equipment. This will help to expand access to quality maternal health services, particularly in underserved areas.

Additionally, it is crucial to continue monitoring the distributional impact of health spending over time to identify any remaining gaps and address them effectively. This can be done through regular assessments using methods like Benefit Incidence Analysis (BIA) to measure the socioeconomic inequality of health spending.

Overall, increasing public funding for the health sector and ensuring equitable distribution of resources will contribute to improving access to maternal health in Malawi.

AI Innovations Methodology

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

1. Increase public funding for the health sector: To ensure access to care and financial protection, it is important to allocate more resources to the health sector. This can help improve the availability and quality of maternal health services.

2. Strengthen performance-based financing: Performance-based financing can be used as a mechanism to incentivize health facilities and providers to deliver quality maternal health services. By linking financial rewards to the achievement of specific targets and outcomes, this approach can help improve access and quality of care.

3. Expand the essential health package: The essential health package should be expanded to include a comprehensive range of maternal health services. This can ensure that all women have access to essential interventions, such as antenatal care, skilled birth attendance, and postnatal care.

4. Improve infrastructure and equipment: Investing in the improvement of health facilities, including the availability of essential equipment and supplies, can help ensure that women have access to safe and effective maternal health services.

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

1. Define indicators: Identify key indicators that reflect access to maternal health services, such as the percentage of women receiving antenatal care, the percentage of births attended by skilled health personnel, and the percentage of women receiving postnatal care.

2. Collect baseline data: Gather data on the current status of these indicators in the target population. This can be done through surveys, health facility records, and other relevant sources.

3. Develop a simulation model: Create a simulation model that incorporates the various recommendations and their potential impact on the selected indicators. This model should consider factors such as population size, resource allocation, and the effectiveness of interventions.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. This can involve adjusting variables such as funding levels, performance-based financing mechanisms, and the scope of the essential health package.

5. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health services. This can include assessing changes in the selected indicators and identifying any potential trade-offs or unintended consequences.

6. Refine and iterate: Based on the analysis of the simulation results, refine the recommendations and the simulation model as needed. Iterate the process to further optimize the interventions and their potential impact.

By following this methodology, policymakers and stakeholders can gain insights into the potential effects of different recommendations on improving access to maternal health services. This can inform decision-making and help prioritize interventions that are likely to have the greatest impact.

Author

Dima Health

Identifiers:

DOI: 10.1186/s12939-022-01624-5

Research Areas:

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

Study Countries:

Malawi

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative