Women’s reproductive health decisionmaking: A multi-country analysis of demographic and health surveys in sub-Saharan Africa

PLoS ONE, Volume 14, No. 1, Year 2019

Interpretation

Introduction :Women’s ability to make decisions regarding their reproductive health has important implications for their health and well-being. We studied the socio-demographic factors affecting reproductive health decision-making among women in 27 sub-Sahara African countries. Materials and methods: The study made use of pooled data from current Demographic and Health Survey (DHS) conducted from January 1, 2010 and December 31, 2016 in 27 countries in sub-Sahara African. Binary and multivariate logistic regression models were used to investigate the associations of women’s socio-demographic factors with decision-making regarding sexual reproductive health. Results: The proportion of women who can ask their partners to use a condom during sexual intercourse ranged from lowest in Mali (16.6%) to highest in Namibia (93.4%). Furthermore, the proportion of women who can refuse sex ranged from 18.3% in Mali to 92.4% in Namibia. Overall, approximately every five out of ten women can ask their partners to use a condom, six out ten women could refuse their partners sex and seven out of ten women could make at least 1 decision. Women from rural areas (OR = 0.51, CI = 0.48-0.54), those with no education (OR = 0.11, CI = 0.10-0.12), Muslim women (OR = 0.29, CI = 0.27-0.31), women not working (OR = 0.53, CI = 0.51-0.56) and women whose partners had no education (OR = 0.17, CI = 0.16-0.19) were less likely to make a decision on their reproductive health. Conclusion: Residence, age, level of education, religion, occupation and partner’s education were found to be associated with women’s decision-making about sexual intercourse, condom use and reproductive health decision-making index. This study contributes to the discourse on reproductive health decision-making in Africa. Policies and intervention targeted at improving women’s autonomy and empowering them to take charge of their sexual and reproductive health issues should be focused on these factors. The study made use of pooled data from current Demographic and Health Survey (DHS) conducted from January 1, 2010 and December 31, 2016 in 27 countries in sub- Saharan Africa. DHS is a nationwide survey collected every five-year period across low and middle-income countries. DHS focuses on maternal and child health by interviewing women of reproductive age (15–49 years). DHS surveys follow the same standard procedures—sampling, questionnaires, data collection, cleaning, coding and analysis—which allows for cross–country comparison. The survey employs a stratified two-stage sampling technique. The first stage involved the selecting of points or clusters (enumeration areas [EAs]). The second stage is the systematic sampling of households listed in each cluster or EA. All women in their reproductive age (15–49) who were usual of selected households or visitors who slept in the households on the night before the survey were interviewed. The response rate varied from 86.2% to 100.0%. For the purpose of this, only women who had information on reproduction health decision-making were used (N = 210,536), thus, women who were either married or living with a partner. Women gave oral and written consent. Ethical approval for DHS is usually obtained from the ethics regulatory boards of the countries for which the studies are conducted and by ICF International’s institutional review board. Permission to use the data set was sort from MEASURE DHS. Data set is available to the public at https://dhsprogram.com/data/available-datasets.cfm (data was not collected or owned by the authors; potential users would be given access once a concept note is sent to MEASURE DHS) The three main outcome variables used were: (1) decision-making on sexual intercourse, (2) decision-making on condom use, and (3) reproductive health decision-making index. For the first variable, women were asked if they can refuse their partner sex. For the second variable (i.e. decision-making on condom use), women were asked if the can ask their partners to use condoms. The response category of these variables were: 1 = “yes”, 2 = “no” and 3 = “don’t know/ not sure”. This response was categorized as 0 = “no and don’t know” and 1 = “yes” (see Darteh et al. [10]). The third outcome variable, reproductive health decision-making index, is generated from the combination of the decision-making on sexual intercourse and the decision-making on condom use variables. This was categorized as 0 = no decision and 1 = at least 1 decision. The explanatory variables consist of: residence, age, wealth status, education, religion, occupation and partner’s education. Residence was categorized as urban and rural. Age was grouped in 5 –year interval: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49. Wealth status was derived from the ownership of a variety of household assets and categorized as poorest, poorer, middle, richer and richest. Level of education and partner’s education was captured as no education, primary, secondary and higher education. Religion was recorded as Christian, Muslims and Others. Religion was not available for Niger. Occupation was categorized as not working, working. All data sets from the 29 countries downloaded from MEASURE DHS were merged and appended as one data set before the analysis was done. Descriptive and inferential statistics were used. Descriptive figures are reported in percentages by countries. Binary and multivariate logistic regression models were used to investigate the relationship between the explanatory variables and the outcome variables. Two models were used to assess the predictors of women’s decision-making on sexual intercourse, decision-making on condom use, and reproductive health decision-making index. Model I looked at a bivariate analysis between each of the predictor variables and the outcome variable. Model II was fitted to investigate the association between the independent variables and the outcome variables (decision-making on sexual intercourse, decision making on condom use, and reproductive health decision-making index). All frequency distributions were weighted whilst the survey command in Stata was used to adjust for the complex sampling structure of the data in the regression analyses. All results of the logistic analyses were presented as odds ratios (ORs) with 95% confidence intervals (CIs).

AI Digest

Study Justification:

The study aimed to investigate the socio-demographic factors influencing women’s decision-making regarding their reproductive health in 27 sub-Saharan African countries. This research is important because women’s ability to make decisions about their reproductive health has significant implications for their overall health and well-being. By understanding the factors that influence decision-making, policymakers and stakeholders can develop targeted interventions to empower women and improve their reproductive health outcomes.

Highlights:

– The study analyzed data from the Demographic and Health Surveys (DHS) conducted between 2010 and 2016 in 27 sub-Saharan African countries.
– The proportion of women who could ask their partners to use a condom during sexual intercourse varied across countries, ranging from 16.6% in Mali to 93.4% in Namibia.
– Similarly, the proportion of women who could refuse sex ranged from 18.3% in Mali to 92.4% in Namibia.
– Overall, approximately half of the women could ask their partners to use a condom, six out of ten women could refuse sex, and seven out of ten women could make at least one reproductive health decision.
– Factors associated with lower decision-making autonomy included living in rural areas, having no education, being Muslim, not working, and having a partner with no education.

Recommendations:

Based on the study findings, the following recommendations can be made:

1. Develop policies and interventions that focus on improving women’s autonomy and decision-making power regarding their sexual and reproductive health.
2. Implement educational programs to increase awareness and knowledge about reproductive health rights and options.
3. Target interventions towards women living in rural areas, those with no education, and those belonging to religious or cultural groups with lower decision-making autonomy.
4. Promote gender equality and women’s empowerment through initiatives that address socio-economic disparities and provide opportunities for education and employment.

Key Role Players:

To address the recommendations, the involvement of the following key role players is crucial:

1. Government agencies responsible for health and women’s empowerment policies.
2. Non-governmental organizations (NGOs) working in the field of reproductive health and women’s rights.
3. Community leaders and religious institutions to promote cultural and social change.
4. Health professionals and educators to provide information and support to women.

Cost Items for Planning Recommendations:

While the actual cost may vary depending on the specific interventions and context, the following cost items should be considered in planning the recommendations:

1. Development and implementation of educational programs and awareness campaigns.
2. Training and capacity-building for healthcare providers and educators.
3. Research and data collection to monitor the impact of interventions.
4. Infrastructure and resources for healthcare facilities and services.
5. Collaboration and coordination efforts among stakeholders.
6. Evaluation and monitoring of the effectiveness of interventions.
Please note that the provided cost items are general and may require further analysis and budgeting based on the specific context and requirements of the interventions.

Strength of Evidence

N/A

Abstract

The study made use of pooled data from current Demographic and Health Survey (DHS) conducted from January 1, 2010 and December 31, 2016 in 27 countries in sub- Saharan Africa. DHS is a nationwide survey collected every five-year period across low and middle-income countries. DHS focuses on maternal and child health by interviewing women of reproductive age (15–49 years). DHS surveys follow the same standard procedures—sampling, questionnaires, data collection, cleaning, coding and analysis—which allows for cross–country comparison. The survey employs a stratified two-stage sampling technique. The first stage involved the selecting of points or clusters (enumeration areas [EAs]). The second stage is the systematic sampling of households listed in each cluster or EA. All women in their reproductive age (15–49) who were usual of selected households or visitors who slept in the households on the night before the survey were interviewed. The response rate varied from 86.2% to 100.0%. For the purpose of this, only women who had information on reproduction health decision-making were used (N = 210,536), thus, women who were either married or living with a partner. Women gave oral and written consent. Ethical approval for DHS is usually obtained from the ethics regulatory boards of the countries for which the studies are conducted and by ICF International’s institutional review board. Permission to use the data set was sort from MEASURE DHS. Data set is available to the public at https://dhsprogram.com/data/available-datasets.cfm (data was not collected or owned by the authors; potential users would be given access once a concept note is sent to MEASURE DHS) The three main outcome variables used were: (1) decision-making on sexual intercourse, (2) decision-making on condom use, and (3) reproductive health decision-making index. For the first variable, women were asked if they can refuse their partner sex. For the second variable (i.e. decision-making on condom use), women were asked if the can ask their partners to use condoms. The response category of these variables were: 1 = “yes”, 2 = “no” and 3 = “don’t know/ not sure”. This response was categorized as 0 = “no and don’t know” and 1 = “yes” (see Darteh et al. [10]). The third outcome variable, reproductive health decision-making index, is generated from the combination of the decision-making on sexual intercourse and the decision-making on condom use variables. This was categorized as 0 = no decision and 1 = at least 1 decision. The explanatory variables consist of: residence, age, wealth status, education, religion, occupation and partner’s education. Residence was categorized as urban and rural. Age was grouped in 5 –year interval: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49. Wealth status was derived from the ownership of a variety of household assets and categorized as poorest, poorer, middle, richer and richest. Level of education and partner’s education was captured as no education, primary, secondary and higher education. Religion was recorded as Christian, Muslims and Others. Religion was not available for Niger. Occupation was categorized as not working, working. All data sets from the 29 countries downloaded from MEASURE DHS were merged and appended as one data set before the analysis was done. Descriptive and inferential statistics were used. Descriptive figures are reported in percentages by countries. Binary and multivariate logistic regression models were used to investigate the relationship between the explanatory variables and the outcome variables. Two models were used to assess the predictors of women’s decision-making on sexual intercourse, decision-making on condom use, and reproductive health decision-making index. Model I looked at a bivariate analysis between each of the predictor variables and the outcome variable. Model II was fitted to investigate the association between the independent variables and the outcome variables (decision-making on sexual intercourse, decision making on condom use, and reproductive health decision-making index). All frequency distributions were weighted whilst the survey command in Stata was used to adjust for the complex sampling structure of the data in the regression analyses. All results of the logistic analyses were presented as odds ratios (ORs) with 95% confidence intervals (CIs).

Materials

N/A

Innovations

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide information and resources on maternal health, including decision-making regarding sexual reproductive health. These apps can be easily accessible to women in sub-Saharan Africa, providing them with accurate and reliable information.

2. Community Health Workers: Train and deploy community health workers who can educate women about their reproductive health rights and empower them to make informed decisions. These workers can provide counseling, support, and referrals to appropriate healthcare services.

3. Telemedicine: Implement telemedicine services that allow women in remote areas to consult with healthcare professionals via video calls or phone calls. This can help overcome geographical barriers and provide access to expert advice and guidance.

4. Health Education Programs: Develop comprehensive health education programs that focus on reproductive health decision-making. These programs can be integrated into schools, community centers, and healthcare facilities to reach a wide audience and promote awareness and empowerment.

5. Policy and Advocacy: Advocate for policies that prioritize women’s reproductive health rights and ensure access to quality maternal healthcare services. This can involve working with governments, NGOs, and international organizations to address barriers and improve healthcare infrastructure.

It’s important to note that these recommendations are based on the information provided and may need to be tailored to specific contexts and challenges in sub-Saharan Africa.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health would be to focus on empowering women and improving their autonomy in making decisions regarding their reproductive health. This can be achieved through the following strategies:

1. Education and awareness: Implement programs that provide comprehensive reproductive health education to women, including information on sexual intercourse, condom use, and reproductive health decision-making. This can help women make informed choices and assert their rights.

2. Community engagement: Engage community leaders, religious leaders, and influential individuals to promote gender equality and women’s empowerment. This can help challenge traditional norms and beliefs that limit women’s decision-making power.

3. Access to healthcare services: Improve access to quality maternal healthcare services, including family planning, prenatal care, and safe delivery options. This can be done by increasing the number of healthcare facilities, trained healthcare providers, and ensuring affordability and availability of services.

4. Legal and policy reforms: Advocate for the implementation and enforcement of laws and policies that protect women’s rights and promote gender equality. This can include laws against gender-based violence, discrimination, and ensuring access to reproductive healthcare services.

5. Supportive partnerships: Collaborate with international organizations, NGOs, and local stakeholders to provide financial and technical support for initiatives aimed at improving access to maternal health. This can include funding for research, capacity building, and program implementation.

By implementing these recommendations, it is possible to empower women, improve their decision-making abilities, and ultimately enhance access to maternal health services in sub-Saharan Africa.

AI Innovations Methodology

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

1. Education and awareness programs: Implement comprehensive education and awareness programs targeting women, their partners, and communities to increase knowledge about reproductive health, decision-making, and the importance of maternal health.

2. Empowerment initiatives: Develop initiatives that empower women to make informed decisions about their reproductive health, including access to family planning methods, safe childbirth practices, and postnatal care.

3. Strengthen healthcare infrastructure: Invest in improving healthcare infrastructure, particularly in rural areas, by increasing the number of skilled healthcare providers, establishing well-equipped maternal health facilities, and ensuring the availability of essential medicines and supplies.

4. Mobile health (mHealth) interventions: Utilize mobile technology to provide information, reminders, and support to pregnant women and new mothers, including appointment reminders, educational materials, and access to teleconsultations with healthcare providers.

5. Community-based interventions: Implement community-based interventions that involve local leaders, traditional birth attendants, and community health workers to promote maternal health practices, provide support, and facilitate access to healthcare services.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include 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 the selected indicators in the target population or region.

3. Define the intervention scenarios: Develop different scenarios based on the recommendations mentioned above, specifying the expected changes in the selected indicators for each scenario.

4. Simulate the impact: Use statistical modeling or simulation techniques to estimate the potential impact of each scenario on the selected indicators. This could involve analyzing the data using regression models, predictive modeling, or simulation software.

5. Evaluate the results: Compare the simulated outcomes of each scenario to the baseline data to assess the potential improvements in access to maternal health. This evaluation can help identify the most effective interventions and guide decision-making for implementation.

6. Refine and iterate: Based on the evaluation results, refine the recommendations and simulation methodology as needed, and iterate the process to further optimize the impact on improving access to maternal health.

It is important to note that the specific methodology for simulating the impact may vary depending on the available data, resources, and expertise. Collaboration with experts in public health, statistics, and data analysis would be beneficial to ensure the accuracy and validity of the simulation results.

Author

Dima Health

Statistics:

Citations: 45

Identifiers:

DOI: 10.1371/journal.pone.0209985

Research Areas:

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

Study Countries:

Mali, Multi-Countries, Namibia, Niger

Study Design:

Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female