Socioeconomic inequality in barriers for accessing health care among married reproductive aged women in sub-Saharan African countries: a decomposition analysis

BMC Women's Health, Volume 22, No. 1, Year 2022

Interprétation

Background: Accessibility of health care is an essential for promoting healthy life, preventing diseases and deaths, and enhancing health equity for all. Barriers in accessing health care among reproductive-age women creates the first and the third delay for maternal mortality and leads to the occurrence of preventable complications related to pregnancy and childbirth. Studies revealed that barriers for accessing health care are concentrated among individuals with poor socioeconomic status which creates health inequality despite many international organizations top priority is enhancing universal health coverage. Therefore, this study aimed to assess the presence of socioeconomic inequality in barriers for accessing health care and its contributors in Sub-Saharan African countries. Methods: The most recent DHS data of 33 sub-Saharan African countries from 2010 to 2020 were used. A total sample of 278,501 married reproductive aged were included in the study. Erreygers normalized concentration index (ECI) and its concentration curve were used while assessing the socioeconomic-related inequality in barriers for accessing health care. A decomposition analysis was performed to identify factors contributing for the socioeconomic-related inequality. Results: The weighted Erreygers normalized Concentration Index (ECI) for barriers in accessing health care was − 0.289 with Standard error = 0.005 (P value < 0.0001); indicating that barriers in accessing health care was disproportionately concentrated among the poor. The decomposition analysis revealed that wealth index (42.58%), place of residency (36.42%), husband educational level (5.98%), women educational level (6.34%), and mass media exposure (3.07%) were the major contributors for the pro-poor socioeconomic inequalities in barriers for accessing health care. Conclusion: In this study, there is a pro-poor inequality in barriers for accessing health care. There is a need to intensify programs that improve wealth status, education level of the population, and mass media coverage to tackle the barriers for accessing health care among the poor. The most recent sub-Saharan African Countries Demographic and Health Surveys (DHS) data conducted from 2010 to 2020 was used for this study. This study analyzed a multi-country DHS dataset that is collected every 5-year across low-and middle-income countries because the program uses standardized tools and follows similar procedure. The DHS program employs two-stage stratified cluster sampling technique where clusters/enumeration areas (EAs) were randomly selected from the sampling frame (i.e. are usually developed from the available latest national census) in the first stage. In the second stage, systematic random sampling was employed to select households in each cluster or EA. Finally, interviews were conducted from the selected households with target populations that are women aged 15–49 and men aged 15–64. In this study, a total weighted sample of 278,501 married reproductive aged women who had given birth within the 5 years preceding the survey of each country were included. In addition, the reproductive aged women with missing value of the outcome variable were excluded from the study (Table (Table11). Overall sample size and sample per each country DHS and survey year Socioeconomic-related inequality in barriers for accessing health care was the outcome variable in this study. Barriers for accessing health care were composite variable from four questions related to challenge for health care access (obtaining money, distance to health facilities, permission to consult the doctor, and not wanting to go alone). If women reported at least one challenge of the health care access were considered as having barriers for accessing health care while if a woman didn’t report none of the above challenges were considered as no barriers for accessing health care [29]. The socioeconomic-related inequality of barriers for accessing health care was expressed as the covariance between barriers for accessing health care and the measurement for socioeconomic class which was wealth index in our case. Then, it was classified into either pro-poor, pro-rich, or no inequality. Women’s age, educational level, wealth index, sex of household head, mass media exposure, place of residence, husbands educational level, current working status, parity, ownership of the assets, women involvements on decision making [30] were incorporated as explanatory variables. The socioeconomic status was measured using the wealth index from DHS data sets. In the DHS data, the wealth index was constructed using principal component analysis for urban and rural separately and then categorized as poorest (quintile 1), poorer (quintile 2), middle (quintile 3), richer (quintile 4), richest (quintile 5) [13, 31–33]. Data were managed and analyzed using STATA 14 software according to the DHS guideline. Sampling weight was considered to adjust for the unequal probability of selection of the sample and the possible differences in response rates. The frequency and different summary measures were used. Pearson’s chi-squared test with its P values was reported to indicate the distribution of respondents’ background characteristics. A concentration index (CI) was computed to measure the socioeconomic-related inequality in barriers for accessing healthcare. For an unbound variable, the concentration index ranges between − 1 and 1, and for unbounded variables, it ranges from μ − 1 to 1 − μ [34]. Decomposition of the healthcare inequality depends on the assumption that the health variable is a linear function of the explanatory variables. Our health variable is a barrier for accessing health care is a binary variable which ranges from 0 to 1 and can’t be negative. Therefore, we used Erreygers normalized concentration index (ECI) which is a modified version of the concentration index was computed [35]. Mathematically, ECI can be defined as: where ECI is Erreygers concentration index, CI(y) is the generalized concentration index and μ is the mean of the health variable, barriers for accessing healthcare. Then, the ECI with the standard error (SE) was reported in this study. To graphically depict the socioeconomic related inequality in barriers for accessing health care, Concentration curves were used and the curves demonstrate the cumulative share of barriers for accessing health care on the y-axis against and the cumulative share of women ranked by the wealth index on the x-axis, arranged from the poorest to the richest. The ECI will be zero in the case when there is no socioeconomic-related inequality. This means if everyone, regardless of wealth status, has the same condition for accessing health care, the concentration curve lies at a 45-degree line (the line of perfect equality). When the curve lies above the line of equality (when the ECI takes a negative value) the health variable in this case barrier is concentrated among the poor (pro-poor). However, the ECI value can be positive, the curve will be below the line of equality indicating the health variable is concentrated among the rich (pro-rich) [13, 36]. Visual inspection of a concentration curve can give information regarding whether the concentration curve lies above or below the line of equality. To assess the statistical significance of the difference between the concentration curve and the line of perfect equality (45-degree or diagonal line), the ECI with its p-value was calculated. To identify the relative contribution of various factors to the socioeconomic-related inequality in barriers for accessing health care, decomposition of the ECI was performed [13, 34, 36]. For any linear additive regression model of health outcome (y) [13], The concentration index for y, CI, is given as: where “y” is the health outcome variable (in this case socioeconomic related inequality of barriers for accessing health care), Xk is a set of the socioeconomic determinants of the health outcome, α is the intercept, βk is the coefficient of Xk, µ is the mean of y, X¯k is the mean of Xk, Ck is the CI for Xk, gc∈ is the generalized CI for the error term (∈), βkX¯kμ is the elasticity of y with respect to X¯k [34, 37]. This study is a data from the DHS program, so it does not require ethical approval. However, online registration and request for measure DHS were conducted for accessing the data. The dataset was downloaded from DHS on-line archive (http://www.dhsprogram.com) after getting permission. All methods were carried out in accordance with the Declaration of Helsinki.

Résumé de l’IA

Study Justification:

This study aimed to assess the presence of socioeconomic inequality in barriers for accessing health care among married reproductive-aged women in sub-Saharan African countries. The study is important because accessibility to health care is crucial for promoting a healthy life, preventing diseases and deaths, and enhancing health equity. Previous studies have shown that barriers to accessing health care are concentrated among individuals with poor socioeconomic status, which creates health inequality despite efforts to enhance universal health coverage. By identifying the factors contributing to this inequality, the study provides valuable insights for policymakers and stakeholders to develop targeted interventions and programs.

Highlights:

– The study used the most recent Demographic and Health Surveys (DHS) data from 33 sub-Saharan African countries conducted between 2010 and 2020.
– A total sample of 278,501 married reproductive-aged women was included in the study.
– The study found a pro-poor socioeconomic inequality in barriers for accessing health care, indicating that barriers were disproportionately concentrated among the poor.
– Factors contributing to this inequality were identified, including wealth index, place of residency, husband’s educational level, women’s educational level, and mass media exposure.
– The study highlights the need to intensify programs that improve wealth status, education level of the population, and mass media coverage to tackle barriers for accessing health care among the poor.

Recommendations:

– Intensify programs aimed at improving wealth status, such as poverty alleviation initiatives and income generation projects.
– Enhance educational opportunities for both men and women, focusing on increasing literacy rates and promoting higher education.
– Increase mass media coverage and awareness campaigns to educate the population about the importance of accessing health care and available services.
– Strengthen health systems and infrastructure, particularly in rural areas, to improve access to health care facilities and services.
– Promote community-based interventions and outreach programs to reach marginalized populations and address their specific barriers to accessing health care.

Key Role Players:

– Government health ministries and departments
– International organizations and donor agencies
– Non-governmental organizations (NGOs) working in the health sector
– Community health workers and volunteers
– Health care providers and professionals
– Education ministries and departments
– Media organizations and journalists
– Researchers and academics in the field of public health

Cost Items for Planning Recommendations:

– Funding for poverty alleviation initiatives and income generation projects
– Investment in education infrastructure and resources
– Budget for mass media campaigns and advertisements
– Allocation for health system strengthening, including infrastructure development and equipment procurement
– Resources for training and capacity building of health care providers and professionals
– Funding for community-based interventions and outreach programs, including transportation and logistics
– Research grants and funding for further studies and evaluations

Force de la preuve

N/A

Abstrait

The most recent sub-Saharan African Countries Demographic and Health Surveys (DHS) data conducted from 2010 to 2020 was used for this study. This study analyzed a multi-country DHS dataset that is collected every 5-year across low-and middle-income countries because the program uses standardized tools and follows similar procedure. The DHS program employs two-stage stratified cluster sampling technique where clusters/enumeration areas (EAs) were randomly selected from the sampling frame (i.e. are usually developed from the available latest national census) in the first stage. In the second stage, systematic random sampling was employed to select households in each cluster or EA. Finally, interviews were conducted from the selected households with target populations that are women aged 15–49 and men aged 15–64. In this study, a total weighted sample of 278,501 married reproductive aged women who had given birth within the 5 years preceding the survey of each country were included. In addition, the reproductive aged women with missing value of the outcome variable were excluded from the study (Table (Table11). Overall sample size and sample per each country DHS and survey year Socioeconomic-related inequality in barriers for accessing health care was the outcome variable in this study. Barriers for accessing health care were composite variable from four questions related to challenge for health care access (obtaining money, distance to health facilities, permission to consult the doctor, and not wanting to go alone). If women reported at least one challenge of the health care access were considered as having barriers for accessing health care while if a woman didn’t report none of the above challenges were considered as no barriers for accessing health care [29]. The socioeconomic-related inequality of barriers for accessing health care was expressed as the covariance between barriers for accessing health care and the measurement for socioeconomic class which was wealth index in our case. Then, it was classified into either pro-poor, pro-rich, or no inequality. Women’s age, educational level, wealth index, sex of household head, mass media exposure, place of residence, husbands educational level, current working status, parity, ownership of the assets, women involvements on decision making [30] were incorporated as explanatory variables. The socioeconomic status was measured using the wealth index from DHS data sets. In the DHS data, the wealth index was constructed using principal component analysis for urban and rural separately and then categorized as poorest (quintile 1), poorer (quintile 2), middle (quintile 3), richer (quintile 4), richest (quintile 5) [13, 31–33]. Data were managed and analyzed using STATA 14 software according to the DHS guideline. Sampling weight was considered to adjust for the unequal probability of selection of the sample and the possible differences in response rates. The frequency and different summary measures were used. Pearson’s chi-squared test with its P values was reported to indicate the distribution of respondents’ background characteristics. A concentration index (CI) was computed to measure the socioeconomic-related inequality in barriers for accessing healthcare. For an unbound variable, the concentration index ranges between − 1 and 1, and for unbounded variables, it ranges from μ − 1 to 1 − μ [34]. Decomposition of the healthcare inequality depends on the assumption that the health variable is a linear function of the explanatory variables. Our health variable is a barrier for accessing health care is a binary variable which ranges from 0 to 1 and can’t be negative. Therefore, we used Erreygers normalized concentration index (ECI) which is a modified version of the concentration index was computed [35]. Mathematically, ECI can be defined as: where ECI is Erreygers concentration index, CI(y) is the generalized concentration index and μ is the mean of the health variable, barriers for accessing healthcare. Then, the ECI with the standard error (SE) was reported in this study. To graphically depict the socioeconomic related inequality in barriers for accessing health care, Concentration curves were used and the curves demonstrate the cumulative share of barriers for accessing health care on the y-axis against and the cumulative share of women ranked by the wealth index on the x-axis, arranged from the poorest to the richest. The ECI will be zero in the case when there is no socioeconomic-related inequality. This means if everyone, regardless of wealth status, has the same condition for accessing health care, the concentration curve lies at a 45-degree line (the line of perfect equality). When the curve lies above the line of equality (when the ECI takes a negative value) the health variable in this case barrier is concentrated among the poor (pro-poor). However, the ECI value can be positive, the curve will be below the line of equality indicating the health variable is concentrated among the rich (pro-rich) [13, 36]. Visual inspection of a concentration curve can give information regarding whether the concentration curve lies above or below the line of equality. To assess the statistical significance of the difference between the concentration curve and the line of perfect equality (45-degree or diagonal line), the ECI with its p-value was calculated. To identify the relative contribution of various factors to the socioeconomic-related inequality in barriers for accessing health care, decomposition of the ECI was performed [13, 34, 36]. For any linear additive regression model of health outcome (y) [13], The concentration index for y, CI, is given as: where “y” is the health outcome variable (in this case socioeconomic related inequality of barriers for accessing health care), Xk is a set of the socioeconomic determinants of the health outcome, α is the intercept, βk is the coefficient of Xk, µ is the mean of y, X¯k is the mean of Xk, Ck is the CI for Xk, gc∈ is the generalized CI for the error term (∈), βkX¯kμ is the elasticity of y with respect to X¯k [34, 37]. This study is a data from the DHS program, so it does not require ethical approval. However, online registration and request for measure DHS were conducted for accessing the data. The dataset was downloaded from DHS on-line archive (http://www.dhsprogram.com) after getting permission. All methods were carried out in accordance with the Declaration of Helsinki.

Matériaux

N/A

Innovations

Innovation 1: Economic empowerment programs
– Develop microfinance programs that provide financial support and resources to individuals and families in lower socioeconomic groups, allowing them to improve their wealth status.
– Implement vocational training programs that equip individuals with skills and knowledge to generate income and improve their economic situation.
– Promote income-generating activities, such as small-scale entrepreneurship or agricultural initiatives, to empower individuals and families economically.

Innovation 2: Education and awareness initiatives
– Enhance educational opportunities for women and girls, as well as their partners, to improve their knowledge and understanding of maternal health.
– Provide access to quality education, including reproductive health education, to ensure individuals are equipped with the necessary information to make informed decisions about their health.
– Conduct awareness campaigns on the importance of seeking timely and appropriate maternal health care, targeting both women and their communities.

Innovation 3: Strengthening healthcare infrastructure
– Invest in improving the availability and accessibility of healthcare facilities, particularly in rural and underserved areas.
– Build and equip health centers with necessary resources and equipment to provide quality maternal health services.
– Train healthcare providers, including midwives and nurses, to ensure they have the skills and knowledge to provide comprehensive maternal care.

Innovation 4: Mobile health interventions
– Utilize mobile technology to deliver maternal health information and services to remote and marginalized communities.
– Develop mobile health applications that provide educational resources, appointment reminders, and access to telemedicine consultations.
– Implement text messaging services to send reminders and important information to pregnant women, such as prenatal care schedules and nutrition advice.

Innovation 5: Community engagement
– Foster community participation and involvement in maternal health initiatives.
– Establish community-based health programs that provide support and education to pregnant women and their families.
– Engage community leaders and traditional birth attendants to promote awareness and encourage the utilization of maternal health services.

These innovations aim to address the socioeconomic inequalities in barriers for accessing health care and improve access to maternal health services for all women in sub-Saharan African countries.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health based on the study is to intensify programs that focus on improving wealth status, education level of the population, and mass media coverage. These factors were identified as major contributors to the pro-poor socioeconomic inequalities in barriers for accessing health care among married reproductive-aged women in sub-Saharan African countries.

To address these barriers and improve access to maternal health, the following recommendations can be considered:

1. Economic empowerment: Implement programs that aim to improve the wealth status of individuals and families, particularly those in lower socioeconomic groups. This can be done through initiatives such as microfinance programs, vocational training, and income-generating activities.

2. Education and awareness: Enhance educational opportunities for women and girls, as well as their partners, to improve their knowledge and understanding of maternal health. This can include providing access to quality education, promoting health literacy, and conducting awareness campaigns on the importance of seeking timely and appropriate maternal health care.

3. Strengthen healthcare infrastructure: Invest in improving the availability and accessibility of healthcare facilities, particularly in rural and underserved areas. This can involve building and equipping health centers, training healthcare providers, and ensuring the availability of essential maternal health services and supplies.

4. Mobile health interventions: Utilize mobile technology to deliver maternal health information and services to remote and marginalized communities. This can include mobile health applications, text messaging services, and telemedicine consultations, which can help overcome geographical barriers and provide timely support and guidance to pregnant women.

5. Community engagement: Foster community participation and involvement in maternal health initiatives. This can be achieved through community-based health programs, the establishment of support groups, and the engagement of community leaders and traditional birth attendants to promote awareness and encourage the utilization of maternal health services.

By implementing these recommendations, it is possible to address the socioeconomic inequalities in barriers for accessing health care and improve access to maternal health services for all women in sub-Saharan African countries.

AI Innovations Methodology

To simulate the impact of the main recommendations on improving access to maternal health, the following methodology can be used:

1. Selection of intervention areas: Identify specific sub-Saharan African countries or regions where the intervention will be implemented. Consider factors such as the prevalence of barriers to accessing maternal health, socioeconomic disparities, and availability of resources.

2. Baseline data collection: Collect baseline data on key indicators related to maternal health, including wealth status, education level, mass media coverage, and barriers to accessing health care. This can be done through surveys, interviews, or analysis of existing data sources.

3. Implementation of interventions: Implement the recommended interventions in the selected areas. This may involve partnering with local organizations, government agencies, and community leaders to ensure effective implementation.

4. Monitoring and evaluation: Continuously monitor and evaluate the impact of the interventions on improving access to maternal health. Collect data on indicators such as the proportion of women accessing maternal health services, changes in wealth status and education level, and improvements in mass media coverage.

5. Data analysis: Analyze the collected data to assess the impact of the interventions. Use statistical methods such as regression analysis, concentration index, and decomposition analysis to measure changes in socioeconomic inequalities and identify the contributions of different factors to the overall impact.

6. Comparison with control groups: If feasible, include control groups or comparison areas where the interventions are not implemented. This will allow for a comparison of the outcomes between intervention and control groups, helping to determine the specific impact of the interventions.

7. Iterative improvement: Based on the findings from the analysis, make adjustments and improvements to the interventions as needed. This may involve refining strategies, targeting specific population groups, or addressing any unforeseen challenges or barriers.

8. Reporting and dissemination: Prepare reports and disseminate the findings to relevant stakeholders, including policymakers, healthcare providers, and the community. Share the lessons learned and best practices to inform future interventions and policies.

By following this methodology, it will be possible to simulate the impact of the main recommendations on improving access to maternal health and provide evidence-based insights for decision-making and further interventions.

Auteurs et co-auteurs

Statistics:

Citations: 2

Authors: 9

Identifiers:

DOI: 10.1186/s12905-022-01716-y

Research Areas:

Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Mali

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female

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