Women’s healthcare decision-making capacity and HIV testing in sub-Saharan Africa: a multi-country analysis of demographic and health surveys

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Study Justification:
– The study aims to examine the association between women’s healthcare decision-making capacity and uptake of HIV testing in sub-Saharan Africa.
– This is important because global efforts to stop HIV and ensure access to treatment require women empowerment, as they play a major role in mother-to-child transmission.
– Understanding the factors that influence HIV testing uptake among women can help inform strategies to improve testing rates and reduce transmission.
Study Highlights:
– The study used data from the current Demographic and Health Surveys (DHS) conducted in 28 countries in sub-Saharan Africa between 2010 and 2018.
– The prevalence of HIV testing uptake in these countries was found to be 64.4%, with the lowest rate in Congo DR (20.2%) and the highest in Rwanda (97.4%).
– Women who made healthcare decisions alone or with their partners were more likely to test for HIV compared to those whose decisions were made by others.
– These findings were consistent even after controlling for other socio-demographic factors.
Study Recommendations:
– Sub-Saharan African countries should incorporate strategies to improve women’s healthcare decision-making capacity in order to improve HIV testing rates.
– These strategies can include education and counseling to empower women to make informed decisions about their healthcare.
– By addressing women’s decision-making capacity, countries can increase the likelihood that women will choose to test for their HIV status.
Key Role Players:
– Policy makers and government officials responsible for healthcare and HIV prevention programs.
– Healthcare providers and counselors who can deliver education and counseling services.
– Community leaders and organizations that can support and promote women’s empowerment and HIV testing.
Cost Items for Planning Recommendations:
– Education and training programs for healthcare providers and counselors.
– Development and dissemination of educational materials and resources.
– Implementation of counseling services for women.
– Awareness campaigns and community outreach programs.
– Monitoring and evaluation of the impact of the strategies implemented.
Please note that the cost items provided are general suggestions and may vary depending on the specific context and resources available in each country.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is rated 8 because it provides a clear description of the study methodology, including the data source, sample size, and statistical analysis. The results are presented with specific numerical values and confidence intervals. However, to improve the evidence, the abstract could include more details about the study limitations and potential biases. Additionally, it would be helpful to provide information on the generalizability of the findings and any implications for future research or interventions.

Background: Global commitment to stop Human Immunodeficiency Virus (HIV) and ensure access to HIV treatment calls for women empowerment, as these efforts play major roles in mother-to-child transmission. We examined the association between women’s healthcare decision-making capacity and uptake of HIV testing in sub-Saharan Africa. Methods: We used data from the current Demographic and Health Surveys (DHS) of 28 countries in sub-Saharan Africa, conducted between January 1, 2010 and December 31, 2018. At the descriptive level, we calculated the prevalence of HIV testing in each of the countries. This was followed by the distribution of HIV testing across the socio-demographic characteristics of women. Finally, we used binary logistic regression assess the likelihood of HIV testing uptake by women’s health care decision-making capacity and socio-demographic characteristics. The results were presented as Crude Odds Ratios (COR) and Adjusted Odds Ratios (AOR) with their corresponding 95% confidence intervals signifying precision. Statistical significance was set at p-value < 0.05. Results: We found that prevalence of HIV testing uptake in the 28 sub-Saharan African countries was 64.4%, with Congo DR having the least (20.2%) and the highest occurred in Rwanda (97.4%). Women who took healthcare decisions alone [COR = 3.183, CI = 2.880–3.519] or with their partners [COR = 2.577, CI = 2.335–2.844] were more likely to test for HIV, compared to those whose healthcare decisions were taken by others, and this persisted after controlling for significant covariates: [AOR = 1.507, CI = 1.321–1.720] and [AOR = 1.518, CI = 1.334–1.728] respectively. Conclusion: Sub-Saharan African countries intending to improve HIV testing need to incorporate women’s healthcare decision-making capacity strategies. These strategies can include education and counselling. This is essential because our study indicates that the capacity of women to make healthcare decisions has an association with decision to test for their HIV status.

We used pooled data from the current Demographic and Health Surveys (DHS) conducted from January 1, 2010 and December 31, 2018 in 28 countries in SSA (see Fig. 1). 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) and men between 15 and 64 years. DHS surveys followed the same standard procedures – sampling, questionnaire development, and data collection. However, data cleaning, coding, and analysis were done in this study for cross-country comparison. The survey employed a stratified two stage sampling technique. The initial stage involved the selection of points or clusters (enumeration areas [EAs]), followed by a systematic sampling of households listed in each cluster or EA. For this study, the women’s file of the DHS data was used. All the participants were women in their reproductive age (15–49), who were usual members of the selected households and/or visitors who slept in the household on the night before the survey. In this study, only women in unions who had complete information on all the variables of interest were included (N = 195,307). We relied on the “Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE) statement in writing the manuscript. Prevalence of HIV testing among women in SSA The outcome variable was HIV testing uptake. It was derived from the question “have you ever tested for HIV?” and the responses were coded as “1=Yes and 0=No”. Thirteen explanatory variables were considered in our study, including the key explanatory variable (women’s decision-making on healthcare). Women’s decision-making on healthcare was derived from the question “Who usually makes decisions about healthcare for yourself: you, your (husband/partner), you and your (husband/partner) jointly, or someone else?” The responses were categorised as respondent alone, respondent and husband/partner, husband/partner alone, someone else, and other. These were recoded into respondent/woman alone = 1, respondent and husband/partner = 2, husband/partner alone = 3 and other = 4 (family members and friends). Besides women’s decision-making on healthcare, 12 additional variables were included in the study. These are survey country, age, educational level, marital status, religion, wealth status, place of residence, parity, occupation, and exposure to mass media (radio, television, and newspaper). Apart from survey country which was predetermined based on the geographical scope of the study, the selection of the rest of the variables was based on their association with HIV testing uptake in previous studies [6–8, 20–25]. Marriage was recoded into ‘married (1)’ and ‘cohabiting (2)’. Occupation was captured as ‘not working (0)’, ‘managerial (1)’, ‘clerical (2)’, ‘sales (3)’, ‘agricultural (4)’, ‘household/domestic (5)’, ‘services (6)’, and ‘manual (7)’. We recoded parity (birth order) as ‘zero birth’(0), ‘one birth (1)’, ‘two births (2)’, ‘three births (3)’, and four or more births (4)’. Lastly, religion was recoded as ‘Traditional religion (1)’, ‘Christianity (2)’, ‘Islam (3)’, ‘No religion (4)’, and ‘Other religion (5)’ (e.g. Hinduism, Buddhism, Atheism, Juddaism, Taoism, Confucianism, Sikhism). The data was analysed with STATA version 14.2 for Mac OS. The analysis was done in three steps. The first step was the computation of the prevalence of HIV testing uptake in SSA (see Fig. ​Fig.1).1). The second step was a cross-tabulation by which we calculated the prevalence and proportions of HIV testing across the socio-demographic characteristics (see Table 1). Then, we conducted a bivariate logistic regression (Model I) and multivariable regression (Model II) analyses to assess the predictors of HIV testing among women in SSA (see Table 2). All frequency distributions were weighted and the survey command (svy) in STATA was used to adjust for the complex sampling structure of the data in the regression analyses. There was multicollinearity between knowing a place for HIV testing and HIV testing uptake. Due to this, it was taken out of the analysis. After it was taken out, there was no evidence of multicollinearity among the remaining variables (Mean VIF = 1.35, Maximum VIF = 1.70, Minimum VIF = 1.05). All results of the logistic regression analyses were presented as Crude Odds Ratios (CORs) and Adjusted Odds Ratios (AORs) at 95% confidence intervals (CIs). Socio-demographic characteristics and prevalence of HIV testing among women in SSA *P values are from chi-square test *Other religion (e.g. Hinduism, Buddhism, Atheism, Juddaism, Taoism, Confucianism, Sikhism) Logistic regression analysis on women’s healthcare decision-making capacity and HIV testing in SSA COR Crude Odds Ratio, AOR Adjusted Odds Ratio, CI Confidence Interval in square brackets, Ref Reference; *p < 0.05, **p < 0.01, ***p < 0.001 *Other religion (Hinduism, Buddhism, Atheism, Juddaism, Taoism, Confucianism, Sikhism)

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

1. Strengthen Women’s Healthcare Decision-Making Capacity: Implement strategies to empower women and enhance their decision-making capacity regarding healthcare, including maternal health. This can be achieved through education, counseling, and awareness programs.

2. Promote HIV Testing: Develop targeted interventions to increase the uptake of HIV testing among women in sub-Saharan Africa. This can include community-based campaigns, mobile testing units, and integration of HIV testing services into existing maternal health programs.

3. Improve Access to Healthcare Services: Enhance the availability and accessibility of healthcare services, particularly in rural and underserved areas. This can be achieved by expanding healthcare infrastructure, training healthcare providers, and ensuring the availability of essential maternal health supplies and medications.

4. Address Socio-Demographic Factors: Consider socio-demographic factors such as age, educational level, marital status, religion, wealth status, and place of residence when designing maternal health interventions. Tailoring programs to the specific needs and circumstances of different populations can improve their effectiveness.

5. Strengthen Health Systems: Invest in strengthening health systems to ensure efficient and effective delivery of maternal health services. This includes improving data collection and analysis, strengthening referral systems, and promoting collaboration between different healthcare providers and stakeholders.

6. Enhance Community Engagement: Engage communities in the planning, implementation, and monitoring of maternal health programs. This can be done through community mobilization, involvement of community leaders, and the establishment of community-based support networks.

7. Foster Partnerships and Collaboration: Foster partnerships and collaboration between governments, non-governmental organizations, healthcare providers, and other stakeholders to leverage resources, share best practices, and coordinate efforts to improve access to maternal health.

It is important to note that these recommendations are based on the provided information and may need to be further tailored and contextualized to specific settings and populations.
AI Innovations Description
Based on the information provided, the recommendation to improve access to maternal health and HIV testing in sub-Saharan Africa is to incorporate strategies that focus on women’s healthcare decision-making capacity. This can include education and counseling programs aimed at empowering women to make informed decisions about their healthcare, including HIV testing. By increasing women’s decision-making power and involvement in healthcare decisions, it is likely to lead to an increase in the uptake of HIV testing and overall improvement in maternal health outcomes.
AI Innovations Methodology
Based on the information provided, here are some potential recommendations to improve access to maternal health:

1. Strengthen Women’s Healthcare Decision-Making Capacity: Promote women’s empowerment and involvement in healthcare decision-making processes. This can be achieved through education and awareness programs that emphasize the importance of women’s autonomy in making healthcare decisions.

2. Enhance Education and Counseling: Provide comprehensive education and counseling services to women regarding maternal health, including the benefits of HIV testing and prevention. This can help address any misconceptions or fears surrounding HIV testing and encourage more women to seek testing.

3. Improve Availability and Accessibility of HIV Testing Services: Increase the availability and accessibility of HIV testing services, particularly in remote or underserved areas. This can be done by establishing mobile clinics, expanding the network of healthcare facilities, and integrating HIV testing services into existing maternal health programs.

4. Address Socio-Demographic Factors: Take into account socio-demographic factors that may influence access to maternal health, such as age, educational level, marital status, wealth status, and place of residence. Tailor interventions and strategies to specific populations to ensure equitable access to maternal health services.

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

1. Define the Outcome Measure: Determine a specific outcome measure that reflects improved access to maternal health, such as an increase in the proportion of women receiving timely prenatal care or an increase in the uptake of HIV testing among pregnant women.

2. Collect Baseline Data: Gather baseline data on the current status of access to maternal health services, including the proportion of women receiving prenatal care and the uptake of HIV testing. This data can be obtained from existing surveys, health records, or population-based studies.

3. Implement Interventions: Implement the recommended interventions, such as strengthening women’s healthcare decision-making capacity, enhancing education and counseling services, and improving the availability and accessibility of HIV testing services. Ensure that these interventions are implemented consistently and monitored closely.

4. Collect Post-Intervention Data: After a sufficient period of time, collect post-intervention data on the same outcome measure as in step 1. This data will reflect the impact of the interventions on improving access to maternal health.

5. Analyze and Compare Data: Analyze the baseline and post-intervention data to determine the impact of the interventions. Compare the outcome measure before and after the interventions to assess any changes or improvements in access to maternal health.

6. Evaluate and Adjust: Evaluate the results of the analysis and assess the effectiveness of the interventions. If necessary, make adjustments to the interventions or implement additional strategies to further improve access to maternal health.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of the recommended innovations on improving access to maternal health and make informed decisions on implementing these interventions.

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