Mixed effects analysis of factors associated with barriers to accessing healthcare among women in sub-Saharan Africa: Insights from demographic and health surveys

PLoS ONE, Volume 15, No. 11 November, Year 2020

Interpretation

Background Access to healthcare is one of the key global concerns as treasured in the Sustainable Development Goals. This study, therefore, sought to assess the individual and contextual factors associated with barriers to accessing healthcare among women in sub-Saharan Africa (SSA). Materials and methods Data for this study were obtained from the latest Demographic and Health Surveys (DHS) conducted between January 2010 and December 2018 across 24 countries in SSA. The sample comprised 307,611 women aged 15-49. Data were analysed with STATA version 14.2 using both descriptive and multilevel logistic regression modelling. Statistical significance was set at p<0.05. Results It was found that 61.5% of women in SSA face barriers in accessing healthcare. The predominant barriers were getting money needed for treatment (50.1%) and distance to health facility (37.3%). Women aged 35-39 (AOR = 0.945, CI: 0.911-0.980), married women (AOR = 0.694, CI: 0.658-0.732), richest women (AOR = 0.457, CI:0.443-0.472), and those who read newspaper or magazine at least once a week (AOR = 0.893, CI:0.811-0.983) had lower odds of facing barriers in accessing healthcare. However, those with no formal education (AOR = 1.803, CI:1.718-1.891), those in manual occupations (AOR = 1.551, CI: 1.424- 1.689), those with parity 4 or more (AOR = 1.211, CI: 1.169-1.255), those who were not covered by health insurance (AOR = 1.284, CI: 1.248-1.322), and those in rural areas (AOR = 1.235, CI:1.209-1.26) had higher odds of facing barriers to healthcare access. Conclusion Both individual and contextual factors are associated with barriers to healthcare accessibility in SSA. Particularly, age, marital status, employment, parity, health insurance coverage, exposure to mass media, wealth status and place of residence are associated with barriers to healthcare accessibility. These factors ought to be considered at the various countries in SSA to strengthen existing strategies and develop new interventions to help mitigate the barriers. Some of the SSA African countries can adopt successful programs in other parts of SSA to suit their context such as the National Health Insurance Scheme (NHIS) and the Community-based Health Planning and Services concepts in Ghana. Data for this study were obtained from current Demographic and Health Surveys (DHS) conducted between January 1, 2010 and December 31, 2018 in 24 SSA countries (see Table 1). The choice of the 24 countries was influenced by the availability of the variables of interest in their datasets. DHS is a nationwide survey undertaken across low- and middle-income countries every five-year period[14]. The survey is representative of each of these countries and targets core maternal and child health indicators such as healthcare accessibility, unintended pregnancy, contraceptive use, skilled birth attendance, immunisation among under-fives, intimate partner violence, access to healthcare, and issues regarding men’s health such as tobacco and contraceptive use. In selecting the sample for each survey, multi-stage sampling approach was employed. The first step of this sampling approach involved the selection of clusters (i.e., enumeration areas [EAs]), followed by systematic household sampling within the selected EAs. In this study, the sample size consisted of women aged 15–49 who had complete information on all the variables of interest (N = 307,611). The Strengthening Reporting of Observational studies in Epidemiology (STROBE) guideline was used in the preparation of this manuscript [15]. The dataset is freely available for download at https://dhsprogram.com/data/available-datasets.cfm The outcome variable in this study was barriers to healthcare accessibility. It was derived from four questions on barriers to healthcare access that each woman responded to. These focused on difficulty in obtaining money (money), distance to health facility (distance), getting permission for treatment (permission), and not wanting to go alone (companionship). If a woman faced at least one or more of the problems (money, distance, companionship, and permission), she was considered to have barriers to healthcare access and coded as “1”. However, if she did not report difficulty in getting money, distance, companionship, and permission-related barriers, she was considered not to have barriers to healthcare access and coded as “0” [16–18]. Both individual and contextual level factors were considered in this study. These variables were chosen based on their statistically significant association with barriers to healthcare access in previous studies [16–18]. The individual level factors included age (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49), marital status (never married, married, cohabiting, widowed, divorced), educational level (no education, primary, secondary, higher), employment (not working, managerial, clerical, sales, house/domestic, agricultural, services, manual), parity (0,1–3, 4 or more), health insurance subscription (yes, no), and exposure to mass media, specifically, radio, newspaper, and television (not at all, less than once a week, at least once a week, almost every day). The contextual variables were sex of household head (male, female), household wealth status (poorest, poorer, middle, richer, richest), and type of residence (urban, rural) (see Table 2). The data were analysed with STATA version 14.2 for MacOS. Three basic steps were followed to analyse the data. The first step was the use of descriptive statistics to describe the sample and also cross-tabulation of all the independent variables against barriers to healthcare access. The second step was a bivariate analysis to select potential variables for the regression analysis. Variables that were statistically significant at the bivariate analysis at p<0.05 were moved to the regression stage, which involved a two-level multilevel binary logistic regression analyses to assess the individual and contextual factors associated with barriers to healthcare access. Clusters were considered as random effect to account for the unexplained variability at the community level [19]. Four models were fitted (see Table 3). Firstly, model I was an empty model and had no predictors (random intercept). Afterwards, the model II contained only the individual-level variables, model III contained only contextual level variables, while model IV contained both individual level and contextual level variables. For all models, adjusted odds ratios (AOR) and their associated 95% confidence intervals (CIs) were presented. These models were fitted by a STATA command “melogit” for the identification of predictors with the outcome variable. For model comparison, the log-likelihood ratio (LLR) and Akaike information criteria (AIC) test were used. Using the variance inflation factor (VIF), the multicollinearity test showed that there was no evidence of collinearity among the independent variables (Mean VIF = 1.51, Maximum VIF = 2.09 and Minimum VIF = 1.09). Sample weight (v005/1,000,000) was applied in all the analysis to correct for over- and under-sampling while the SVY command was used to account for the complex survey design and generalizability of the findings. Exponentiated coefficients; 95% confidence intervals in brackets. * p < 0.05 ** p < 0.01 *** p < 0.001. SE = Standard Error; ICC = Intra-Class Correlation; LR Test = Likelihood ratio Test; AIC = Akaike’s Information Criterion; BIC = Schwarz’s Bayesian Information Criteria. Model I is the null model, a baseline model without any determinant variable. Model II = individual level variables. Model III = Contextual level variables. Model IV is the final model adjusted for individual and Contextual level variables. Ethical clearance was obtained from the Ethics Committee of ORC Macro Inc. as well as Ethics Boards of partner organisations of the various countries, such as the Ministries of Health. The DHS follows the standards for ensuring the protection of respondents’ privacy. Inner City Fund International ensures that the survey complies with the U.S. Department of Health and Human Services regulations for the respect of human subjects. The survey also reports that both verbal and written informed consent were obtained from the respondents. However, this was a secondary analysis of data and, therefore, no further approval was required for this study. Further information about the DHS data usage and ethical standards are available at http://goo.gl/ny8T6X.

AI Digest

Study Justification:

– Access to healthcare is a global concern and a key focus of the Sustainable Development Goals.
– This study aimed to assess the factors associated with barriers to accessing healthcare among women in sub-Saharan Africa (SSA).

Highlights:

– 61.5% of women in SSA face barriers in accessing healthcare.
– The main barriers identified were difficulty in obtaining money for treatment (50.1%) and distance to health facilities (37.3%).
– Factors associated with lower odds of facing barriers included age (35-39), marital status (married), wealth status (richest), and regular reading of newspapers or magazines.
– Factors associated with higher odds of facing barriers included no formal education, manual occupations, higher parity, lack of health insurance coverage, and living in rural areas.
– Both individual and contextual factors contribute to barriers in accessing healthcare in SSA.

Recommendations:

– Consider age, marital status, employment, parity, health insurance coverage, exposure to mass media, wealth status, and place of residence when developing strategies to address barriers to healthcare access.
– Strengthen existing strategies and develop new interventions to mitigate barriers to healthcare access.
– Countries in SSA can learn from successful programs in other parts of SSA, such as the National Health Insurance Scheme (NHIS) and the Community-based Health Planning and Services concepts in Ghana.

Key Role Players:

– Ministries of Health in SSA countries
– International organizations focused on healthcare in SSA
– Non-governmental organizations (NGOs) working in healthcare in SSA
– Community leaders and organizations
– Healthcare providers and professionals

Cost Items for Planning Recommendations:

– Funding for healthcare infrastructure development and maintenance
– Training and capacity-building for healthcare providers
– Health education and awareness campaigns
– Implementation of health insurance schemes
– Research and data collection on healthcare access and barriers
– Monitoring and evaluation of interventions
– Collaboration and coordination between stakeholders

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study used a large sample size and conducted multilevel logistic regression modeling to analyze the data. The statistical significance was set at p<0.05. However, the abstract could be improved by providing more details about the methodology, such as the specific variables used and the inclusion criteria for the sample. Additionally, it would be helpful to include information about the limitations of the study and any potential biases. To improve the evidence, the authors could consider conducting a systematic review or meta-analysis to synthesize the findings from multiple studies. This would provide a more comprehensive and robust assessment of the factors associated with barriers to accessing healthcare among women in sub-Saharan Africa.

Abstract

Data for this study were obtained from current Demographic and Health Surveys (DHS) conducted between January 1, 2010 and December 31, 2018 in 24 SSA countries (see Table 1). The choice of the 24 countries was influenced by the availability of the variables of interest in their datasets. DHS is a nationwide survey undertaken across low- and middle-income countries every five-year period[14]. The survey is representative of each of these countries and targets core maternal and child health indicators such as healthcare accessibility, unintended pregnancy, contraceptive use, skilled birth attendance, immunisation among under-fives, intimate partner violence, access to healthcare, and issues regarding men’s health such as tobacco and contraceptive use. In selecting the sample for each survey, multi-stage sampling approach was employed. The first step of this sampling approach involved the selection of clusters (i.e., enumeration areas [EAs]), followed by systematic household sampling within the selected EAs. In this study, the sample size consisted of women aged 15–49 who had complete information on all the variables of interest (N = 307,611). The Strengthening Reporting of Observational studies in Epidemiology (STROBE) guideline was used in the preparation of this manuscript [15]. The dataset is freely available for download at https://dhsprogram.com/data/available-datasets.cfm The outcome variable in this study was barriers to healthcare accessibility. It was derived from four questions on barriers to healthcare access that each woman responded to. These focused on difficulty in obtaining money (money), distance to health facility (distance), getting permission for treatment (permission), and not wanting to go alone (companionship). If a woman faced at least one or more of the problems (money, distance, companionship, and permission), she was considered to have barriers to healthcare access and coded as “1”. However, if she did not report difficulty in getting money, distance, companionship, and permission-related barriers, she was considered not to have barriers to healthcare access and coded as “0” [16–18]. Both individual and contextual level factors were considered in this study. These variables were chosen based on their statistically significant association with barriers to healthcare access in previous studies [16–18]. The individual level factors included age (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49), marital status (never married, married, cohabiting, widowed, divorced), educational level (no education, primary, secondary, higher), employment (not working, managerial, clerical, sales, house/domestic, agricultural, services, manual), parity (0,1–3, 4 or more), health insurance subscription (yes, no), and exposure to mass media, specifically, radio, newspaper, and television (not at all, less than once a week, at least once a week, almost every day). The contextual variables were sex of household head (male, female), household wealth status (poorest, poorer, middle, richer, richest), and type of residence (urban, rural) (see Table 2). The data were analysed with STATA version 14.2 for MacOS. Three basic steps were followed to analyse the data. The first step was the use of descriptive statistics to describe the sample and also cross-tabulation of all the independent variables against barriers to healthcare access. The second step was a bivariate analysis to select potential variables for the regression analysis. Variables that were statistically significant at the bivariate analysis at p<0.05 were moved to the regression stage, which involved a two-level multilevel binary logistic regression analyses to assess the individual and contextual factors associated with barriers to healthcare access. Clusters were considered as random effect to account for the unexplained variability at the community level [19]. Four models were fitted (see Table 3). Firstly, model I was an empty model and had no predictors (random intercept). Afterwards, the model II contained only the individual-level variables, model III contained only contextual level variables, while model IV contained both individual level and contextual level variables. For all models, adjusted odds ratios (AOR) and their associated 95% confidence intervals (CIs) were presented. These models were fitted by a STATA command “melogit” for the identification of predictors with the outcome variable. For model comparison, the log-likelihood ratio (LLR) and Akaike information criteria (AIC) test were used. Using the variance inflation factor (VIF), the multicollinearity test showed that there was no evidence of collinearity among the independent variables (Mean VIF = 1.51, Maximum VIF = 2.09 and Minimum VIF = 1.09). Sample weight (v005/1,000,000) was applied in all the analysis to correct for over- and under-sampling while the SVY command was used to account for the complex survey design and generalizability of the findings. Exponentiated coefficients; 95% confidence intervals in brackets. * p < 0.05 ** p < 0.01 *** p < 0.001. SE = Standard Error; ICC = Intra-Class Correlation; LR Test = Likelihood ratio Test; AIC = Akaike’s Information Criterion; BIC = Schwarz’s Bayesian Information Criteria. Model I is the null model, a baseline model without any determinant variable. Model II = individual level variables. Model III = Contextual level variables. Model IV is the final model adjusted for individual and Contextual level variables. Ethical clearance was obtained from the Ethics Committee of ORC Macro Inc. as well as Ethics Boards of partner organisations of the various countries, such as the Ministries of Health. The DHS follows the standards for ensuring the protection of respondents’ privacy. Inner City Fund International ensures that the survey complies with the U.S. Department of Health and Human Services regulations for the respect of human subjects. The survey also reports that both verbal and written informed consent were obtained from the respondents. However, this was a secondary analysis of data and, therefore, no further approval was required for this study. Further information about the DHS data usage and ethical standards are available at http://goo.gl/ny8T6X.

Materials

N/A

Innovations

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide information and resources related to maternal health, such as prenatal care, nutrition, and postnatal care. These apps can be easily accessible on smartphones, making it convenient for women in sub-Saharan Africa to access important health information.

2. Telemedicine: Establish telemedicine services that allow pregnant women in remote areas to consult with healthcare professionals through video calls or phone calls. This can help overcome the barrier of distance to health facilities and provide timely medical advice and support.

3. Community Health Workers: Train and deploy community health workers who can provide basic maternal health services, education, and support in rural areas. These workers can conduct prenatal visits, provide health education, and refer women to appropriate healthcare facilities when necessary.

4. Financial Support Programs: Implement financial support programs that help women overcome the barrier of obtaining money for treatment. This could include microfinance initiatives, health insurance schemes, or conditional cash transfer programs specifically targeted at maternal health.

5. Infrastructure Development: Improve the infrastructure and transportation networks in rural areas to reduce the distance barrier to health facilities. This could involve building more health clinics, improving road networks, and providing transportation services for pregnant women to access healthcare services.

6. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of maternal health and the available healthcare services. These campaigns can be conducted through various channels, including radio, television, and community outreach programs.

7. Partnerships and Collaboration: Foster partnerships and collaboration between governments, non-governmental organizations, and private sector entities to pool resources and expertise in addressing barriers to maternal health access. This can lead to more comprehensive and sustainable solutions.

It is important to note that the specific implementation of these innovations should be tailored to the local context and needs of each country in sub-Saharan Africa.

AI Innovations Description

The recommendation based on the study is to consider both individual and contextual factors when developing interventions to improve access to maternal health in sub-Saharan Africa (SSA). The study found that factors such as age, marital status, employment, parity, health insurance coverage, exposure to mass media, wealth status, and place of residence are associated with barriers to healthcare accessibility.

To improve access to maternal health, countries in SSA can consider implementing successful programs from other parts of SSA, such as the National Health Insurance Scheme (NHIS) and the Community-based Health Planning and Services concepts in Ghana. These programs have shown positive results in addressing barriers to healthcare access. Additionally, efforts should be made to strengthen existing strategies and develop new interventions that target the identified individual and contextual factors. This can include initiatives to improve education, increase employment opportunities, expand health insurance coverage, and enhance transportation infrastructure in rural areas.

AI Innovations Methodology

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

1. Strengthening Health Insurance Coverage: Implementing or expanding health insurance schemes can help reduce financial barriers to accessing healthcare. This can include subsidizing premiums for low-income individuals and ensuring comprehensive coverage for maternal health services.

2. Improving Transportation Infrastructure: Addressing the issue of distance to health facilities can be achieved by improving transportation infrastructure, such as building roads and bridges, providing public transportation options, and establishing mobile health clinics to reach remote areas.

3. Enhancing Health Education and Awareness: Promoting health education and awareness programs can help address barriers related to knowledge and information. This can include educating women about the importance of maternal health, family planning, and available healthcare services.

4. Empowering Women: Empowering women through education and economic opportunities can contribute to reducing barriers to healthcare access. This can be achieved by providing vocational training, promoting gender equality, and supporting women’s participation in decision-making processes.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could involve the following steps:

1. Define the indicators: Identify specific indicators that measure access to maternal health, such as the percentage of women receiving antenatal care, skilled birth attendance, or postnatal care.

2. Collect baseline data: Gather data on the current status of access to maternal health services in the target population. This can be obtained from existing surveys, health records, or through primary data collection methods.

3. Develop a simulation model: Create a simulation model that incorporates the recommended interventions and their potential impact on the identified indicators. This model should consider factors such as population size, geographical distribution, and existing healthcare infrastructure.

4. Input intervention parameters: Define the parameters for each recommended intervention, such as the percentage of the population covered by health insurance, the number of new transportation routes, or the reach of health education programs.

5. Run simulations: Use the simulation model to run multiple scenarios with different combinations of intervention parameters. This will allow for the estimation of the potential impact of each intervention on the selected indicators.

6. Analyze results: Analyze the simulation results to determine the effectiveness of each intervention in improving access to maternal health. Compare the outcomes of different scenarios to identify the most impactful interventions or combinations of interventions.

7. Refine and iterate: Based on the analysis, refine the simulation model and intervention parameters as needed. Repeat the simulation process to further explore and optimize the potential impact of the recommendations.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of different interventions on improving access to maternal health and make informed decisions on which strategies to prioritize for implementation.

Author

Dima Health

Statistics:

Citations: 12

Identifiers:

DOI: 10.1371/journal.pone.0241409

Research Areas:

Community Interventions, Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Substance Abuse, Violence and Injury

Study Countries:

Ghana

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female