Social determinants of antenatal care service use in ethiopia: Changes over a 15-year span

Frontiers in Public Health, Volume 7, No. JUN, Year 2019

Ukuhunyushwa

Background: Improving maternal health in Ethiopia is a major public health challenge. International studies indicate that it is possible to improve maternal health outcomes through action on the Social Determinants of Health (SDH). This study aimed to explore the SDH that influence the antenatal care (ANC) utilization in Ethiopia over time. Methods: The study used data from the nation-wide surveys conducted by the Ethiopian Central Statistical Agency (CSA) and ORC Macro International, USA in 2005, 2011, and 2016. A negative binomial with random effects at cluster level was used to model the number of ANC visits whereas a multilevel binary logistic regression modeled binary responses relating to whether a woman had at least 4 ANC visits or not. The model estimates were obtained with the statistical software Stata SE 15 using the restricted maximum likelihood method. Results: Although the median number of ANC visits significantly increased between 2005 and 2016, the majority of the women do not obtain the four ANC visits during pregnancy as recommended. The odds of having at least four ANC visits were significantly lower among women: below 20 years, those living in the rural areas, having higher birth order, or Muslim. In contrast, higher educational attainment, higher socio-economic status, exposure to mass media, and self-reporting decision empowerment were significantly associated with having at least four ANC visits. Conclusion: The use of ANC visits is driven mostly by the social determinants of health rather than individual health risk. The importance of the various SDHs needs to be recognized by Ministry of Health policy and program managers as a key driving force behind the country’s challenges with reaching targets in the health agenda related to maternal health, particularly related to the recommended number of ANC visits. The study used data from the three latest EDHSs, conducted by the Ethiopian Central Statistical Agency (CSA) and ORC Macro International, USA, between April 2005—August 2005, December 2010—June 2011, and January 2016—June 2016. The full details of the methods and procedures used in the data collection of each EDHS, are published elsewhere (2, 14, 15). In the current study. We included total of 22, 799 weighted data from: 7306 women collected from 570 Enumeration Areas (EA) (clusters) in 2005; 7908 women from 548 clusters in 2011; and data from 7585 women from 575 clusters in 2016. The eligibility criteria were: being in the reproductive ages 15–49 years, reporting at least one births in the last 5 years preceding the actual survey, and participating in one of the three surveys from any region in the country. The number of interviewed females were 14,070 in the 2005 EDHS, 16,515 in the 2011 EDHS, and 18,500 in the 2016, making a total of 49,085 respondents (2, 14, 15). However, among all female respondents, 22, 799 (46.5%) met the eligibility criteria, and those with complete data on one or more of the variables of interest. Data on these eligible women were pooled from the survey datasets allowing the analysis to span the period 2001 to 2016. The analyses in the current study were based on two ANC-related outcomes: (1) The total number of ANC visits each participant had in the index pregnancy; (2) A binary outcome based on whether a woman had had four or more visits during the course of the pregnancy or not, according to at that time recommended four visits in the WHO guidelines for FANC (4), as recommended by the Ethiopian Ministry of Health during this period (13). Important individual and community level social determinants (SD) were considered in the analyses. Individual level SD included: marital status, religion, education level, employment status for both the participant and her partner, empowerment (relating to household decision making and whether the women were involved or not: on her own health care; large household purchases; and visits to family or relatives), household wealth index (low, middle, high), mass media (radio and TV) exposure (no exposure, exposed to either a radio or TV and exposed to both), sex of the household head, maternal age at last birth, birth order. The following community level SD were considered: place of residence, urban or rural, and if the region were classified as agrarian, pastoral, or urban. The data available contained a significant number of zero counts due to the high number of women not attending ANC at all (71.5% in 2005, and 57.1% in 2011). We addressed these distributional challenges by fitting a negative binomial random effects (NBRE) model to our count data. It is important to note two key study assumptions that should be borne in mind when interpreting our findings: First, given the cross-sectional nature of DHS data, some of the information used in the analysis related to the time of the surveys rather than the time of birth and pregnancy. Secondly, we used 2005 as the reference survey year and estimated the incidence rate ratios (IRR), for 2011 and 2016. Estimates of IRR, which represents the change in the number of ANC visits in 2011 compared to 2016, relative to the number of ANC visits in 2005, were obtained from the NBRE model. Due to data clustering at the survey level, binary data relating to whether a woman had at least four ANC visits in pregnancy or not, were modeled using a binary logistic multilevel regression model after adjustments for several confounders. We identified the main confounding variables from the literature as: age while giving last birth, order of the last birth, place of residence, and husband’s education. Multiple multilevel logistic regression model was used to control the effects of potential confounders and from the model, adjusted odds ratios (AOR) with 95% confidence intervals were obtained. In addition, we computed an estimate of intra-cluster correlation coefficient (ICC), which described the amount of variability in the response variable attributable to differences between the clusters. We then used the McKelvey & Zavoina Pseudo R2 to assess the fit of the model (20, 21). Both bivariate (data not given) “see Tables S1a,b.” and adjusted models were fitted to count and binary response data. Individual and cluster level SD that were significantly (P ≤ 0.05) associated with having ANC visits were included in the multiple Poisson and logistic regression models while controlling for the effect of other variables contained in the model. The model parameter estimates were obtained in the statistical software StataSE 15 using the restricted maximum likelihood method (REML). The level of significance was set at α = 0.05. The study was conducted by confirming to national and international ethical guidelines for biomedical research involving human subjects (22) including the Helsinki declaration. This study was reviewed and approved from the Regional Committee for Medical and Health Research Ethics (REK) and Norwegian Center for Research data (NSD) at the University of Oslo. Our team also requested permission to have access to the data from the CSA and ICF international by registering online on the website www.dhsprogram.com1 and submitting the study protocol (see Additional File 2). We also highlighted the objectives of the study as part of the online registration process. The ORC Macro Inc removed all information that could be used to identify the respondents; hence, confidentiality of the data was maintained.

I-AI Digest

Study Justification:

– Improving maternal health in Ethiopia is a major public health challenge.
– International studies indicate that action on the Social Determinants of Health (SDH) can improve maternal health outcomes.
– This study aimed to explore the SDH that influence antenatal care (ANC) utilization in Ethiopia over time.

Study Highlights:

– Data from nationwide surveys conducted in 2005, 2011, and 2016 were used.
– The median number of ANC visits significantly increased between 2005 and 2016, but the majority of women did not obtain the recommended four ANC visits during pregnancy.
– Factors associated with lower odds of having at least four ANC visits included being below 20 years, living in rural areas, having higher birth order, or being Muslim.
– Factors associated with higher odds of having at least four ANC visits included higher educational attainment, higher socio-economic status, exposure to mass media, and self-reporting decision empowerment.

Study Recommendations:

– The Ministry of Health policy and program managers should recognize the importance of social determinants of health in driving ANC utilization.
– Efforts should be made to address the challenges in reaching targets related to maternal health, particularly the recommended number of ANC visits.

Key Role Players:

– Ministry of Health policy and program managers
– Healthcare providers
– Community health workers
– Non-governmental organizations (NGOs)
– Women’s advocacy groups

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers and community health workers
– Development and implementation of awareness campaigns targeting women and communities
– Improvement of healthcare infrastructure and facilities
– Provision of resources and supplies for ANC services
– Monitoring and evaluation of ANC utilization programs

Amandla Obufakazi

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 data from nationwide surveys conducted by reputable organizations, which adds credibility to the findings. The study also used appropriate statistical methods to analyze the data. However, the abstract could be improved by providing more specific details about the sample size, data collection methods, and statistical analyses. Additionally, it would be helpful to include information about any limitations or potential biases in the study. Overall, the evidence is fairly strong, but providing more detailed information and addressing potential limitations would further enhance the strength of the evidence.

Abstract

The study used data from the three latest EDHSs, conducted by the Ethiopian Central Statistical Agency (CSA) and ORC Macro International, USA, between April 2005—August 2005, December 2010—June 2011, and January 2016—June 2016. The full details of the methods and procedures used in the data collection of each EDHS, are published elsewhere (2, 14, 15). In the current study. We included total of 22, 799 weighted data from: 7306 women collected from 570 Enumeration Areas (EA) (clusters) in 2005; 7908 women from 548 clusters in 2011; and data from 7585 women from 575 clusters in 2016. The eligibility criteria were: being in the reproductive ages 15–49 years, reporting at least one births in the last 5 years preceding the actual survey, and participating in one of the three surveys from any region in the country. The number of interviewed females were 14,070 in the 2005 EDHS, 16,515 in the 2011 EDHS, and 18,500 in the 2016, making a total of 49,085 respondents (2, 14, 15). However, among all female respondents, 22, 799 (46.5%) met the eligibility criteria, and those with complete data on one or more of the variables of interest. Data on these eligible women were pooled from the survey datasets allowing the analysis to span the period 2001 to 2016. The analyses in the current study were based on two ANC-related outcomes: (1) The total number of ANC visits each participant had in the index pregnancy; (2) A binary outcome based on whether a woman had had four or more visits during the course of the pregnancy or not, according to at that time recommended four visits in the WHO guidelines for FANC (4), as recommended by the Ethiopian Ministry of Health during this period (13). Important individual and community level social determinants (SD) were considered in the analyses. Individual level SD included: marital status, religion, education level, employment status for both the participant and her partner, empowerment (relating to household decision making and whether the women were involved or not: on her own health care; large household purchases; and visits to family or relatives), household wealth index (low, middle, high), mass media (radio and TV) exposure (no exposure, exposed to either a radio or TV and exposed to both), sex of the household head, maternal age at last birth, birth order. The following community level SD were considered: place of residence, urban or rural, and if the region were classified as agrarian, pastoral, or urban. The data available contained a significant number of zero counts due to the high number of women not attending ANC at all (71.5% in 2005, and 57.1% in 2011). We addressed these distributional challenges by fitting a negative binomial random effects (NBRE) model to our count data. It is important to note two key study assumptions that should be borne in mind when interpreting our findings: First, given the cross-sectional nature of DHS data, some of the information used in the analysis related to the time of the surveys rather than the time of birth and pregnancy. Secondly, we used 2005 as the reference survey year and estimated the incidence rate ratios (IRR), for 2011 and 2016. Estimates of IRR, which represents the change in the number of ANC visits in 2011 compared to 2016, relative to the number of ANC visits in 2005, were obtained from the NBRE model. Due to data clustering at the survey level, binary data relating to whether a woman had at least four ANC visits in pregnancy or not, were modeled using a binary logistic multilevel regression model after adjustments for several confounders. We identified the main confounding variables from the literature as: age while giving last birth, order of the last birth, place of residence, and husband’s education. Multiple multilevel logistic regression model was used to control the effects of potential confounders and from the model, adjusted odds ratios (AOR) with 95% confidence intervals were obtained. In addition, we computed an estimate of intra-cluster correlation coefficient (ICC), which described the amount of variability in the response variable attributable to differences between the clusters. We then used the McKelvey & Zavoina Pseudo R2 to assess the fit of the model (20, 21). Both bivariate (data not given) “see Tables S1a,b.” and adjusted models were fitted to count and binary response data. Individual and cluster level SD that were significantly (P ≤ 0.05) associated with having ANC visits were included in the multiple Poisson and logistic regression models while controlling for the effect of other variables contained in the model. The model parameter estimates were obtained in the statistical software StataSE 15 using the restricted maximum likelihood method (REML). The level of significance was set at α = 0.05. The study was conducted by confirming to national and international ethical guidelines for biomedical research involving human subjects (22) including the Helsinki declaration. This study was reviewed and approved from the Regional Committee for Medical and Health Research Ethics (REK) and Norwegian Center for Research data (NSD) at the University of Oslo. Our team also requested permission to have access to the data from the CSA and ICF international by registering online on the website www.dhsprogram.com1 and submitting the study protocol (see Additional File 2). We also highlighted the objectives of the study as part of the online registration process. The ORC Macro Inc removed all information that could be used to identify the respondents; hence, confidentiality of the data was maintained.

Izinto zokwakha

Emisha

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

1. Mobile health (mHealth) interventions: Develop mobile applications or text messaging services to provide pregnant women with information and reminders about antenatal care visits, nutrition, and other important aspects of maternal health.

2. Community health worker programs: Train and deploy community health workers to provide education, support, and referrals for pregnant women in rural areas who may have limited access to healthcare facilities.

3. Telemedicine: Implement telemedicine services to enable pregnant women in remote areas to consult with healthcare professionals and receive prenatal care remotely.

4. Transportation solutions: Develop transportation initiatives, such as mobile clinics or transportation vouchers, to help pregnant women in rural areas overcome geographical barriers and reach healthcare facilities for antenatal care.

5. Financial incentives: Introduce financial incentives, such as conditional cash transfers or vouchers, to encourage pregnant women to attend antenatal care visits and ensure they receive the recommended number of visits.

6. Public-private partnerships: Foster collaborations between the government, private sector, and non-profit organizations to improve infrastructure, resources, and access to maternal health services in underserved areas.

7. Maternal health awareness campaigns: Launch targeted campaigns to raise awareness about the importance of antenatal care and address cultural and social barriers that may prevent women from seeking care.

8. Integration of services: Integrate maternal health services with other existing healthcare programs, such as immunization or family planning services, to provide comprehensive care and improve access for pregnant women.

9. Quality improvement initiatives: Implement quality improvement programs to enhance the quality of antenatal care services, ensuring that pregnant women receive evidence-based, respectful, and culturally sensitive care.

10. Empowerment and education programs: Develop programs that empower women with knowledge and skills to make informed decisions about their maternal health, including family planning, birth preparedness, and newborn care.

These innovations can help address the social determinants of health and improve access to maternal health services in Ethiopia.

AI Innovations Description

Based on the description provided, the recommendation to improve access to maternal health is to address the social determinants of health (SDH) that influence antenatal care (ANC) utilization in Ethiopia. The study found that factors such as age, place of residence, education level, socioeconomic status, exposure to mass media, and decision empowerment were significantly associated with having at least four ANC visits.

To develop this recommendation into an innovation, the following steps can be taken:

1. Develop targeted interventions: Based on the identified social determinants, design interventions that specifically address the barriers faced by different groups of women. For example, interventions could focus on improving education and awareness among younger women, providing transportation services for women in rural areas, or implementing community-based education programs to increase knowledge about the importance of ANC.

2. Strengthen health systems: Enhance the capacity of healthcare facilities to provide quality ANC services. This can include training healthcare providers on best practices, ensuring the availability of necessary equipment and supplies, and improving the overall infrastructure of healthcare facilities.

3. Utilize technology: Explore the use of technology to improve access to maternal health services. This could involve implementing telemedicine programs to provide remote ANC consultations, developing mobile applications to provide information and reminders about ANC visits, or using SMS messaging to send appointment reminders to pregnant women.

4. Collaborate with community organizations: Partner with local community organizations to reach women who may face additional barriers to accessing ANC. These organizations can help raise awareness, provide support, and facilitate access to ANC services for marginalized or hard-to-reach populations.

5. Monitor and evaluate: Establish a monitoring and evaluation system to track the impact of the innovation on access to maternal health. Regularly assess the effectiveness of the interventions and make necessary adjustments based on the findings.

By implementing these recommendations and continuously improving upon them, it is possible to develop innovative solutions that address the social determinants of health and improve access to maternal health services in Ethiopia.

AI Innovations Methodology

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

1. Strengthening Health Education: Implement comprehensive health education programs that target women and their families, focusing on the importance of antenatal care (ANC) visits, the benefits of early and regular check-ups, and the potential risks of not seeking care.

2. Community-Based Interventions: Establish community-based programs that provide information, support, and resources to pregnant women, such as mobile clinics, community health workers, and outreach programs. These interventions can help overcome barriers to accessing ANC, especially in rural areas.

3. Addressing Social Determinants of Health: Develop strategies to address the social determinants of health that influence ANC utilization, such as poverty, education, and cultural beliefs. This may involve providing financial incentives, scholarships, or vocational training to empower women and improve their socio-economic status.

4. Improving Health Infrastructure: Invest in improving the availability and quality of health facilities, especially in rural and underserved areas. This includes ensuring the availability of skilled healthcare providers, essential medical supplies, and equipment necessary for ANC services.

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

1. Data Collection: Gather data on the current utilization of ANC services, including the number of visits, demographic information, and social determinants of health. This can be done through surveys, interviews, or analysis of existing data sources.

2. Define Metrics: Identify key metrics to measure the impact of the recommendations, such as the increase in the number of ANC visits, the reduction in maternal mortality rates, or improvements in maternal health outcomes.

3. Develop a Simulation Model: Create a simulation model that incorporates the collected data and simulates the potential impact of the recommendations. This model should consider factors such as population demographics, healthcare infrastructure, and the effectiveness of the proposed interventions.

4. Run Simulations: Run multiple simulations using different scenarios, varying the implementation strategies, resource allocation, and target populations. This will help assess the potential impact of each recommendation and identify the most effective approaches.

5. Analyze Results: Analyze the simulation results to determine the potential impact of the recommendations on improving access to maternal health. Compare the outcomes of different scenarios to identify the most promising strategies.

6. Refine and Implement: Based on the simulation results, refine the recommendations and develop an implementation plan. Consider factors such as feasibility, cost-effectiveness, and scalability. Collaborate with stakeholders, policymakers, and healthcare providers to ensure successful implementation.

7. Monitor and Evaluate: Continuously monitor and evaluate the implemented interventions to assess their effectiveness and make necessary adjustments. Collect data on the actual impact of the recommendations and compare it with the simulated results to validate the model’s accuracy.

By following this methodology, policymakers and program managers can make informed decisions and allocate resources effectively to improve access to maternal health services.

Umbhali

Dima Health

Statistics:

Citations: 36

Identifiers:

DOI: 10.3389/fpubh.2019.00161

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female

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