Why do women deliver at home? Multilevel modeling of Ethiopian national demographic and health survey data

PLoS ONE, Volume 10, No. 4, Year 2015

Interpretation

Background: Despite of the existing intensive efforts to improve maternal health in Ethiopia, the proportion of birth delivered at home remains high and is still the top priority among the national health threats. Objective: The study aimed to examine effects of individual women and community-level factors of women’s decision on place of delivery in Ethiopia. Methods: Data were obtained from the nationally representative 2011 Ethiopian Demographic and Health Survey (EDHS) which used a two-stage cluster sampling design with rural-urban and regions as strata. The EDHS collected data from a big sample size but our study focused on a sample of 7,908 women whose most recent birth was within five years preceding 2011 and 576 communities in which the women were living in. The data were analyzed using a two-level mixed-effects logistic regression to determine fixed-effects of individual-and community-level factors and random-intercept of between-cluster characteristics. Results: In the current study, 6980 out of 7908 deliveries (88.3%) took place at home. Lower educational levels (OR=2.74, 95%CI:1.84,4.70; p<0.0001), making no or only a limited number of ANC visits (OR=3.72,95%CI:2.85, 4.83; p<0.0001), non-exposure to media (OR=1.51, 95% CI 1.13, 2.01; p=0.004), higher parity (OR=2.68, 95%CI:1.96,3.68; p< 0.0001), and perceived distance problem to reach health facilities (OR=1.29, 95%CI:1.03,1.62; p=0.022) were positively associated with home delivery. About 75% of the total variance in the odds of giving birth at home was accounted for the between-community differences of characteristics (ICC=0.75, p<0.0001). With regard to community-level characteristics, rural communities (OR=4.67, 95%CI:3.06,7.11; p<0.0001), pastoralist communities (OR=4.53, 95% CI:2.81,7.28; p<0.0001), communities with higher poverty levels (OR=1.49 95% CI:1.08,2.22; p=0.048), with lower levels of ANC utilization (OR=2.01, 95%CI:1.42,2.85; p<0.0001) and problem of distance to a health facility (OR=1.29, 95%CI:1.03,1.62; p=0.004) had a positive influence on women to give birth at home. Conclusions: Not only individual characteristics of women, but also community-level factors determine women's decision to deliver at home. We used data from the EDHS 2011, particularly data on individual women. Ethiopia Demography and Health Survey used a two-stage cluster sampling design with rural-urban and regions as strata. In EDHS 2011, a sample of 624 clusters was drawn by the Ethiopian Central Statistical Agency from its master sampling frame of census 2007. Cluster (community) was defined as a randomly selected area, which contained 150–200 households. In total, 17,817 households and 16,515 reproductive women age 15–49 were sampled using random selection from these clusters (Fig 1). With respect to structure of the data, women are nested within household and household are nested within clusters. The survey was conducted from December 27, 2010 to June 3, 2010 in all the nine regions and two administrative councils of Ethiopia. We included individual data of 7,908 women (weighted) whose last birth was alive and delivered within five years preceding 2011 and community characteristics of 576 clusters (weighted). For mothers with more than one births, we used the most recent birth for the study (Fig 1). The DHS captures a wide scope of data, generally concerning the health of women, men and children. However, for the current study, we used specific data, related with maternal health. The primary entry criterion to this study was women having a live birth within five years preceding the DHS. For the analysis, we included individual variables of socioeconomic and demographic characteristics, obstetric, fertility, perception of women to access health facilities, access to health facility, mass media, and others. The outcome of interest for the study was place of delivery and was grouped into two categories: home and facility based delivery. Home delivery was defined as any birth that had taken place in the women’s or others’ home; while deliveries that occurred in governmental health post, health center, hospital and private clinic and hospital and NG health facilities were grouped as facility-based delivery. In Ethiopia if a birth takes place at home, it is unlikely skilled health professionals assist it. In this context, no home delivery in the EDHS survey was assisted by a nurse, doctor or midwife. The study also focused on community characteristics. We took place of residence as urban versus rural without changing the original coding in the DHS dataset. However, the regions in Ethiopia are divided into eleven for administrative purpose; but, the delineation of the regions may not necessarily be related to the health status of their population. For this study, we have classified the regions into three contextual—agrarian, pastoralist and city dwellers—based on the characteristics of their population in relation to maternal health, particularly place of delivery. Based on their living ways, Ministry of Health of Ethiopia has clearly identified which regions are agrarian, pastoralist or city dwellers so as to make a contextual intervention for each region [19]. Except place of residence and geographic regions, the EDHS did not capture variables that can describe the characteristics of the communities. Yet, we created more community characteristics by aggregating the individual mothers’ characteristics within their clusters. The aggregates for clusters were computed using mean of the proportions of women in each category of a given variable. We categorized the aggregate of a cluster into groups based on the National Median Values. We used median since all distributions of the aggregates were not normally distributed. For the community ANC utilization, for example, we computed the proportion of ANC utilization in each cluster. Finally, we categorized these aggregate values into lower and higher ANC utilization based on the National median of ANC utilization. Ultimately, we used these individual and community level factors to answer why Ethiopian women still deliver at home. The data were analyzed using STATA 11 (Stata Corporation, College Station, TX, USA). The different characteristics of women and communities were described using descriptive statistics. The proportions and frequencies were estimated after applying sample weights to the data to adjust for disproportionate sampling and non-responses. Since DHS data are hierarchical, i.e. mothers are nested within households, and households are nested within clusters, use of flat models could underestimate standard errors of the effect sizes, which consequently can affect decision on null hypothesis. In such data, mothers within same cluster may be more similar to each other than mothers in the rest of the country. This violates the assumption of flat models—independence of observations and equal variance across the clusters. Thus, we used two-level mixed-effects logistic regression model to test the effect sizes of individual and community factors on women’s decision to place of delivery and estimate the between-cluster variability of odds of home delivery. We ran four models: Empty model, model containing only individual factors, model containing community—level factors and model combining both individual and community-level factors. We fit the data into the model: Where The distribution of u 0j is normal with mean 0 and variance σ2 u0. The Intra-Class Correlation (ICC) was calculated using between-cluster variance and within cluster variance [ICC = σu 2 /(σu 2 + π 2 /3)]. In log distribution, the residual variance of women within a cluster is zero but variance is considered constant at π2/3. This helped to show the level of between-cluster correlation within a model and to compare the successive models by looking at the decline of the ICC. The Proportional Change in Variance (PCV) was also computed for each model with respect to the empty model to show how much of variability on the odds of home delivery be explained by the successive models. The PCV was calculated as PCV = (V e—V mi)/V e where Ve is variance in women’s decision in the empty model and Vmi is variance in successive models. We used Variance Inflator Factor (VIF) to scrutiny high multicollinearity among the explanatory variables. The fixed effect sizes of individual and community-level factors on place of delivery were expressed using the Odds Ratio (OR) and the population effect sizes were estimated using 95% Confidence Interval (CI). We accessed the data from MEASURE DHS database at http://dhsprogram.com/data/available-datasets.cfm. We retrieved data of women only. As the data were obtained from records, we could not consent women for accessing their records. However, the records were anonymized and de-identified prior to analysis. MEASUSRE DHS governs the DHS data of all countries and researchers can use the data obeying the data sharing policy. The organization accessed us the data after reviewing our proposal. We accepted the terms and conditions attached to data sharing policy; i.e, we need to keep the data confidential and we would not use the data for purposes other than the current study.

AI Digest

Study Justification:

– Despite efforts to improve maternal health in Ethiopia, the proportion of home deliveries remains high and is a top priority among national health threats.
– This study aims to examine the individual and community-level factors that influence women’s decision to deliver at home.

Study Highlights:

– Data was obtained from the 2011 Ethiopian Demographic and Health Survey (EDHS), which used a two-stage cluster sampling design.
– The study focused on a sample of 7,908 women whose most recent birth was within five years preceding 2011 and 576 communities.
– Lower educational levels, limited ANC visits, non-exposure to media, higher parity, and perceived distance problems were positively associated with home delivery.
– Community-level factors such as rural or pastoralist communities, higher poverty levels, lower ANC utilization, and distance to health facilities also influenced home delivery.
– About 75% of the variance in home delivery odds was accounted for by between-community differences.

Recommendations:

– Improve access to education for women to reduce the likelihood of home delivery.
– Increase ANC utilization and promote media exposure to encourage facility-based delivery.
– Address the specific needs of rural and pastoralist communities, including poverty reduction and improving access to health facilities.

Key Role Players:

– Ministry of Health of Ethiopia
– Ethiopian Central Statistical Agency
– Health professionals (nurses, doctors, midwives)
– Community leaders and organizations
– NGOs and international organizations working in maternal health

Cost Items for Planning Recommendations:

– Education programs for women
– ANC services and outreach programs
– Media campaigns and communication strategies
– Infrastructure development for health facilities in rural and pastoralist areas
– Poverty reduction initiatives
– Training and capacity building for health professionals
– Monitoring and evaluation systems for tracking progress in reducing home deliveries

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study used a nationally representative dataset and employed a two-level mixed-effects logistic regression model to analyze the data. The findings indicate that lower educational levels, limited ANC visits, non-exposure to media, higher parity, and perceived distance problems are positively associated with home delivery. Additionally, rural and pastoralist communities, higher poverty levels, lower ANC utilization, and distance to health facilities also influence women’s decision to deliver at home. The study provides valuable insights into the factors contributing to home delivery in Ethiopia. However, to improve the evidence, the abstract could include more details on the methodology, such as the specific variables used and the statistical significance of the findings. Additionally, it would be helpful to include information on any limitations of the study and suggestions for future research.

Abstract

We used data from the EDHS 2011, particularly data on individual women. Ethiopia Demography and Health Survey used a two-stage cluster sampling design with rural-urban and regions as strata. In EDHS 2011, a sample of 624 clusters was drawn by the Ethiopian Central Statistical Agency from its master sampling frame of census 2007. Cluster (community) was defined as a randomly selected area, which contained 150–200 households. In total, 17,817 households and 16,515 reproductive women age 15–49 were sampled using random selection from these clusters (Fig 1). With respect to structure of the data, women are nested within household and household are nested within clusters. The survey was conducted from December 27, 2010 to June 3, 2010 in all the nine regions and two administrative councils of Ethiopia. We included individual data of 7,908 women (weighted) whose last birth was alive and delivered within five years preceding 2011 and community characteristics of 576 clusters (weighted). For mothers with more than one births, we used the most recent birth for the study (Fig 1). The DHS captures a wide scope of data, generally concerning the health of women, men and children. However, for the current study, we used specific data, related with maternal health. The primary entry criterion to this study was women having a live birth within five years preceding the DHS. For the analysis, we included individual variables of socioeconomic and demographic characteristics, obstetric, fertility, perception of women to access health facilities, access to health facility, mass media, and others. The outcome of interest for the study was place of delivery and was grouped into two categories: home and facility based delivery. Home delivery was defined as any birth that had taken place in the women’s or others’ home; while deliveries that occurred in governmental health post, health center, hospital and private clinic and hospital and NG health facilities were grouped as facility-based delivery. In Ethiopia if a birth takes place at home, it is unlikely skilled health professionals assist it. In this context, no home delivery in the EDHS survey was assisted by a nurse, doctor or midwife. The study also focused on community characteristics. We took place of residence as urban versus rural without changing the original coding in the DHS dataset. However, the regions in Ethiopia are divided into eleven for administrative purpose; but, the delineation of the regions may not necessarily be related to the health status of their population. For this study, we have classified the regions into three contextual—agrarian, pastoralist and city dwellers—based on the characteristics of their population in relation to maternal health, particularly place of delivery. Based on their living ways, Ministry of Health of Ethiopia has clearly identified which regions are agrarian, pastoralist or city dwellers so as to make a contextual intervention for each region [19]. Except place of residence and geographic regions, the EDHS did not capture variables that can describe the characteristics of the communities. Yet, we created more community characteristics by aggregating the individual mothers’ characteristics within their clusters. The aggregates for clusters were computed using mean of the proportions of women in each category of a given variable. We categorized the aggregate of a cluster into groups based on the National Median Values. We used median since all distributions of the aggregates were not normally distributed. For the community ANC utilization, for example, we computed the proportion of ANC utilization in each cluster. Finally, we categorized these aggregate values into lower and higher ANC utilization based on the National median of ANC utilization. Ultimately, we used these individual and community level factors to answer why Ethiopian women still deliver at home. The data were analyzed using STATA 11 (Stata Corporation, College Station, TX, USA). The different characteristics of women and communities were described using descriptive statistics. The proportions and frequencies were estimated after applying sample weights to the data to adjust for disproportionate sampling and non-responses. Since DHS data are hierarchical, i.e. mothers are nested within households, and households are nested within clusters, use of flat models could underestimate standard errors of the effect sizes, which consequently can affect decision on null hypothesis. In such data, mothers within same cluster may be more similar to each other than mothers in the rest of the country. This violates the assumption of flat models—independence of observations and equal variance across the clusters. Thus, we used two-level mixed-effects logistic regression model to test the effect sizes of individual and community factors on women’s decision to place of delivery and estimate the between-cluster variability of odds of home delivery. We ran four models: Empty model, model containing only individual factors, model containing community—level factors and model combining both individual and community-level factors. We fit the data into the model: Where The distribution of u 0j is normal with mean 0 and variance σ2 u0. The Intra-Class Correlation (ICC) was calculated using between-cluster variance and within cluster variance [ICC = σu 2 /(σu 2 + π 2 /3)]. In log distribution, the residual variance of women within a cluster is zero but variance is considered constant at π2/3. This helped to show the level of between-cluster correlation within a model and to compare the successive models by looking at the decline of the ICC. The Proportional Change in Variance (PCV) was also computed for each model with respect to the empty model to show how much of variability on the odds of home delivery be explained by the successive models. The PCV was calculated as PCV = (V e—V mi)/V e where Ve is variance in women’s decision in the empty model and Vmi is variance in successive models. We used Variance Inflator Factor (VIF) to scrutiny high multicollinearity among the explanatory variables. The fixed effect sizes of individual and community-level factors on place of delivery were expressed using the Odds Ratio (OR) and the population effect sizes were estimated using 95% Confidence Interval (CI). We accessed the data from MEASURE DHS database at http://dhsprogram.com/data/available-datasets.cfm. We retrieved data of women only. As the data were obtained from records, we could not consent women for accessing their records. However, the records were anonymized and de-identified prior to analysis. MEASUSRE DHS governs the DHS data of all countries and researchers can use the data obeying the data sharing policy. The organization accessed us the data after reviewing our proposal. We accepted the terms and conditions attached to data sharing policy; i.e, we need to keep the data confidential and we would not use the data for purposes other than the current study.

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) Solutions: Develop and implement mobile applications or text messaging services to provide pregnant women with important information about prenatal care, safe delivery practices, and postnatal care. These tools can also be used to remind women about upcoming appointments and provide support during pregnancy and childbirth.

2. Community Health Workers: Train and deploy community health workers to provide education, support, and basic healthcare services to pregnant women in rural and remote areas. These workers can help identify high-risk pregnancies, provide antenatal care, and refer women to appropriate healthcare facilities for delivery.

3. Telemedicine: Establish telemedicine networks to connect healthcare providers in urban areas with pregnant women in rural and remote areas. This would allow women to receive virtual consultations, advice, and support from healthcare professionals without having to travel long distances.

4. Maternal Waiting Homes: Build and operate maternal waiting homes near healthcare facilities in rural areas. These homes would provide a safe and comfortable place for pregnant women to stay during the final weeks of pregnancy, ensuring that they are close to a healthcare facility when it’s time to deliver.

5. Transportation Solutions: Improve transportation infrastructure and services in rural areas to make it easier for pregnant women to access healthcare facilities. This could include providing affordable transportation options, improving road conditions, and implementing emergency transportation systems for women in labor.

6. Financial Incentives: Introduce financial incentives, such as cash transfers or vouchers, to encourage pregnant women to seek antenatal care and deliver at healthcare facilities. This could help offset the costs associated with transportation, healthcare services, and other expenses related to childbirth.

7. Quality Improvement Initiatives: Implement quality improvement initiatives in healthcare facilities to ensure that pregnant women receive safe and respectful care. This could involve training healthcare providers, improving infrastructure and equipment, and implementing protocols and guidelines for maternal health services.

8. Public Awareness Campaigns: Launch public awareness campaigns to educate communities about the importance of antenatal care and facility-based delivery. These campaigns could address cultural beliefs and misconceptions surrounding childbirth and promote the benefits of skilled attendance during delivery.

9. Partnerships and Collaboration: Foster partnerships and collaboration between government agencies, non-governmental organizations, healthcare providers, and community leaders to collectively address the barriers to accessing maternal health services. This could involve sharing resources, coordinating efforts, and leveraging expertise to improve maternal health outcomes.

10. Research and Data Collection: Conduct further research and data collection to better understand the factors influencing women’s decisions to deliver at home and identify effective strategies for improving access to maternal health services. This could involve conducting qualitative studies, analyzing existing data, and monitoring and evaluating the impact of interventions.

AI Innovations Description

Based on the study titled “Why do women deliver at home? Multilevel modeling of Ethiopian national demographic and health survey data,” the following recommendation can be developed into an innovation to improve access to maternal health:

1. Strengthening education and awareness: Since lower educational levels were found to be positively associated with home delivery, it is important to focus on improving education and awareness among women. This can be done through community-based education programs, providing information on the benefits of facility-based delivery and the risks associated with home delivery.

2. Increasing antenatal care (ANC) utilization: The study found that making no or only a limited number of ANC visits was positively associated with home delivery. To address this, efforts should be made to increase ANC utilization by providing accessible and quality ANC services, including regular check-ups, health education, and counseling on the importance of facility-based delivery.

3. Addressing distance and transportation challenges: Perceived distance problems to reach health facilities were found to be positively associated with home delivery. To overcome this barrier, innovative solutions such as mobile health clinics or transportation services can be implemented to ensure that women have easy access to health facilities during pregnancy and childbirth.

4. Targeting specific communities: The study found that rural and pastoralist communities had a higher likelihood of home delivery. It is important to develop targeted interventions for these communities, taking into account their specific needs and challenges. This can include establishing or upgrading health facilities in rural areas and providing culturally sensitive maternal health services.

5. Empowering women: The study highlighted the influence of individual and community-level factors on women’s decision to deliver at home. Empowering women through education, economic opportunities, and involvement in decision-making processes can help shift the cultural norms and beliefs that contribute to home delivery.

Overall, the recommendation is to develop an innovative approach that combines education, improved access to ANC services, addressing distance and transportation challenges, targeting specific communities, and empowering women to improve access to maternal health and reduce home deliveries in Ethiopia.

AI Innovations Methodology

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

1. Increase educational opportunities for women: Lower educational levels were found to be positively associated with home delivery. By increasing access to education, especially for women, it can empower them to make informed decisions about their health and seek appropriate maternal healthcare.

2. Improve antenatal care (ANC) utilization: Making no or only a limited number of ANC visits was positively associated with home delivery. Implementing strategies to increase ANC utilization, such as community outreach programs, mobile clinics, and education campaigns, can help ensure that pregnant women receive the necessary care and information during pregnancy.

3. Enhance media exposure: Non-exposure to media was found to be positively associated with home delivery. Increasing media exposure through targeted campaigns and community engagement can help disseminate important information about the benefits of facility-based delivery and the risks associated with home delivery.

4. Address perceived distance problems: Perceived distance problems to reach health facilities were positively associated with home delivery. Improving transportation infrastructure, establishing more health facilities in remote areas, and providing transportation subsidies or incentives can help address the perceived distance problem and encourage women to seek facility-based delivery.

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

1. Collect baseline data: Gather data on the current rates of home delivery, educational levels, ANC utilization, media exposure, and perceived distance problems in the target population.

2. Define indicators: Identify specific indicators to measure the impact of the recommendations, such as the percentage change in home delivery rates, increase in ANC utilization, increase in media exposure, and reduction in perceived distance problems.

3. Implement interventions: Implement the recommended interventions, such as educational programs, ANC outreach initiatives, media campaigns, and infrastructure improvements.

4. Monitor and evaluate: Continuously monitor and evaluate the implementation of the interventions, collecting data on the indicators identified in step 2.

5. Analyze data: Use statistical analysis software, such as STATA, to analyze the collected data. Apply appropriate statistical methods, such as logistic regression, to determine the impact of the interventions on improving access to maternal health.

6. Simulate impact: Use the analyzed data to simulate the impact of the recommendations on improving access to maternal health. This can be done by comparing the baseline data with the post-intervention data and calculating the percentage change in the indicators.

7. Interpret results: Interpret the simulation results to understand the effectiveness of the recommendations in improving access to maternal health. Identify any limitations or challenges encountered during the simulation process.

8. Refine and adjust interventions: Based on the simulation results, refine and adjust the interventions as necessary to further improve access to maternal health.

9. Continuously monitor and evaluate: Maintain ongoing monitoring and evaluation to track the long-term impact of the interventions and make any necessary adjustments to ensure sustained improvements in access to maternal health.

Authors & Co-Authors

Statistics:

Citations: 48

Authors: 3

Identifiers:

DOI: 10.1371/journal.pone.0124718

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Male & Female