Bayesian spatial analysis of factors influencing neonatal mortality and its geographic variation in Ethiopia

PLoS ONE, Volume 17, No. 7 July, Year 2022

Interpretation

Background Ethiopia is a Sub-Saharan country with very high neonatal mortality rates, varying across its regions. The rate of neonatal mortality reduction in Ethiopia is slow, and Ethiopia may not meet the third United Nations sustainable development target by 2030. This study aimed to investigate the spatial variations and contributing factors for neonatal mortality rates in Ethiopia. Methods We analysed data from the 2016 Ethiopian Demographic and Health Survey (EDHS), which used a two-stage cluster sampling technique with a census enumeration area as primary and households as secondary sampling units. A Bayesian spatial logistic regression model using the Stochastic Partial Differential Equation (SPDE) method was fitted accounting for socio-economic, health service-related and geographic factors. Results Higher neonatal mortality rates were observed in eastern, northeastern and southeastern Ethiopia, and the Somali region had higher risks of neonatal mortality. Neonates from frequently drought-affected areas had a higher mortality risk than less drought-affected areas. Application of traditional substances on the cord increased the risk of neonatal mortality (Adjusted Odds Ratio (AOR) = 2.07, 95% Credible Interval (CrI): 1.12 to 4.30) and getting health facility delivery services had a lower odds of neonatal mortality (AOR = 0.60, 95% CrI: 0.37, 0.98). Conclusions Residing in drought-affected areas, applying traditional substances on the umbilical cord and not delivering at health facilities were associated with a higher risk of neonatal mortality. Policy-makers and resource administrators at different administrative levels could leverage the findings to prioritise and target areas identified with higher neonatal mortality rates. Ethiopia is the second-most populous country in Africa, with a population of more than 112 million and a growth rate of 2.6% in 2019 [36]. The majority (80%) of the people reside in rural areas, with agriculture being the primary income source [37]. The study is based on secondary data analysis of the 2016 Ethiopian Demographic and Health Survey (EDHS) [22]. The EDHS is a cross-sectional survey with a nationally representative sample. The datasets contain socio-economic, neonatal, maternal, geospatial and health service use related variables. The demographic, Global Positioning System (GPS) coordinates and geospatial data were combined using the cluster (enumeration area) code. Permission was granted to access the dataset through the Demographic and Health Surveys Program [38]. The detailed descriptions of the DHS design and sampling procedures can be found elsewhere [39]. The target population of the study were newborns from birth to the 28th day from birth in Ethiopia. The samples were selected using a two-stage cluster sampling technique using a census enumeration area (EA) as the primary sampling unit and households as the secondary sampling unit. An EA or cluster is a geographic area covering, on average, 181 households. In the 2016 EDHS survey, a total of 645 clusters were sampled. For the survey to be cost-effective and produce representative data at a national and sub-national level, the DHS applies an oversampling in regions with a small population and under-sampling in regions with a larger population. Therefore, DHS applies sampling weight to restore the representativeness of the samples and correct the deliberate under-sampling and over-sampling [40]. In the computation of means, totals, and percentages, we applied sample weighting based on the DHS recommendations [41]. The most recent births of mothers were included in the study to identify the factors associated with neonatal mortality because most of the health services use related variables such as antenatal care, place of delivery, and postnatal care were recorded for only the most recent live births. The sampled total number of live births in the past five years of 2016 EDHS was 10,571, and health service use related data were collected from 7,180 most recent live births, of which 7,071 samples were collected from permanently residing respondents. One hundred nine survey participants were visitors and not included in the analysis. After data cleaning, the final analysis included 6,868 samples (see Fig 2). We adopted the Mosley and Chen conceptual framework for child survival [35] to organise variables (see Fig 1). The primary outcome of interest was neonatal mortality, defined as death within 28 days of birth. The explanatory variables at the individual level include multiple births, birth order, child sex, umbilical cord care practice, antenatal care use, place of delivery, duration of pregnancy, counselling about neonatal danger signs and living situation of the mother. Factors such as residing in urban or rural areas, the proportion of postnatal care use per cluster and episodes of drought were considered at the community level (see S1 Table for definitions of variables).

AI Digest

Study Justification:

– Neonatal mortality rates in Ethiopia are high and vary across regions.
– The rate of neonatal mortality reduction in Ethiopia is slow, and the country may not meet the United Nations sustainable development target by 2030.
– Understanding the spatial variations and contributing factors for neonatal mortality is crucial for targeted interventions and resource allocation.

Study Highlights:

– Higher neonatal mortality rates were observed in eastern, northeastern, and southeastern Ethiopia.
– The Somali region had higher risks of neonatal mortality.
– Neonates from frequently drought-affected areas had a higher mortality risk.
– Application of traditional substances on the cord increased the risk of neonatal mortality.
– Getting health facility delivery services had a lower odds of neonatal mortality.

Study Recommendations for Lay Reader and Policy Maker:

– Residing in drought-affected areas, applying traditional substances on the umbilical cord, and not delivering at health facilities were associated with a higher risk of neonatal mortality.
– Policy-makers and resource administrators should prioritize and target areas identified with higher neonatal mortality rates.

Key Role Players Needed to Address Recommendations:

– Policy-makers at different administrative levels
– Resource administrators
– Health officials
– Community leaders
– Healthcare providers

Cost Items to Include in Planning the Recommendations:

– Increased access to health facilities in areas with high neonatal mortality rates
– Training and capacity building for healthcare providers
– Awareness campaigns on safe umbilical cord care practices
– Investments in drought mitigation strategies and resources in affected areas
– Monitoring and evaluation systems to track progress and outcomes

Strength of Evidence

N/A

Abstract

Ethiopia is the second-most populous country in Africa, with a population of more than 112 million and a growth rate of 2.6% in 2019 [36]. The majority (80%) of the people reside in rural areas, with agriculture being the primary income source [37]. The study is based on secondary data analysis of the 2016 Ethiopian Demographic and Health Survey (EDHS) [22]. The EDHS is a cross-sectional survey with a nationally representative sample. The datasets contain socio-economic, neonatal, maternal, geospatial and health service use related variables. The demographic, Global Positioning System (GPS) coordinates and geospatial data were combined using the cluster (enumeration area) code. Permission was granted to access the dataset through the Demographic and Health Surveys Program [38]. The detailed descriptions of the DHS design and sampling procedures can be found elsewhere [39]. The target population of the study were newborns from birth to the 28th day from birth in Ethiopia. The samples were selected using a two-stage cluster sampling technique using a census enumeration area (EA) as the primary sampling unit and households as the secondary sampling unit. An EA or cluster is a geographic area covering, on average, 181 households. In the 2016 EDHS survey, a total of 645 clusters were sampled. For the survey to be cost-effective and produce representative data at a national and sub-national level, the DHS applies an oversampling in regions with a small population and under-sampling in regions with a larger population. Therefore, DHS applies sampling weight to restore the representativeness of the samples and correct the deliberate under-sampling and over-sampling [40]. In the computation of means, totals, and percentages, we applied sample weighting based on the DHS recommendations [41]. The most recent births of mothers were included in the study to identify the factors associated with neonatal mortality because most of the health services use related variables such as antenatal care, place of delivery, and postnatal care were recorded for only the most recent live births. The sampled total number of live births in the past five years of 2016 EDHS was 10,571, and health service use related data were collected from 7,180 most recent live births, of which 7,071 samples were collected from permanently residing respondents. One hundred nine survey participants were visitors and not included in the analysis. After data cleaning, the final analysis included 6,868 samples (see Fig 2). We adopted the Mosley and Chen conceptual framework for child survival [35] to organise variables (see Fig 1). The primary outcome of interest was neonatal mortality, defined as death within 28 days of birth. The explanatory variables at the individual level include multiple births, birth order, child sex, umbilical cord care practice, antenatal care use, place of delivery, duration of pregnancy, counselling about neonatal danger signs and living situation of the mother. Factors such as residing in urban or rural areas, the proportion of postnatal care use per cluster and episodes of drought were considered at the community level (see S1 Table for definitions of variables).

Materials

Innovations

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women and new mothers with access to important health information, reminders for prenatal and postnatal care appointments, and guidance on proper neonatal care practices.

2. Telemedicine Services: Establish telemedicine services that allow pregnant women in remote areas to consult with healthcare professionals and receive prenatal care advice and guidance through video or phone calls.

3. Community Health Worker Training: Implement comprehensive training programs for community health workers to improve their knowledge and skills in providing maternal and neonatal healthcare services. This can help bridge the gap between healthcare facilities and remote communities.

4. Maternal Health Vouchers: Introduce a voucher system that provides pregnant women with financial assistance to access quality maternal healthcare services, including antenatal care, delivery, and postnatal care.

5. Maternal Waiting Homes: Establish maternal waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to travel long distances to reach a healthcare facility. These homes can provide a safe and comfortable place for women to stay before and after delivery.

6. Transportation Support: Develop transportation support systems, such as ambulances or community transportation networks, to ensure that pregnant women can easily access healthcare facilities for prenatal care, delivery, and emergency obstetric care.

7. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of prenatal and postnatal care, safe delivery practices, and proper neonatal care. These campaigns can be conducted through various channels, including radio, television, and community outreach programs.

8. Strengthening Health Infrastructure: Invest in improving and expanding healthcare infrastructure, particularly in rural areas, by constructing and equipping health centers and maternity wards. This will ensure that there are enough facilities to accommodate pregnant women and provide quality maternal healthcare services.

9. Maternal Health Insurance: Introduce or expand maternal health insurance schemes to provide financial protection for pregnant women and ensure that they can afford necessary healthcare services throughout their pregnancy and postpartum period.

10. Research and Data Analysis: Conduct further research and data analysis, like the Bayesian spatial analysis mentioned in the provided information, to identify specific factors influencing maternal and neonatal health outcomes in different regions of Ethiopia. This will help policymakers and healthcare providers target interventions and resources more effectively.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health based on the study “Bayesian spatial analysis of factors influencing neonatal mortality and its geographic variation in Ethiopia” includes:

1. Strengthening health facility delivery services: The study found that getting health facility delivery services had a lower odds of neonatal mortality. Therefore, it is recommended to focus on improving access to and quality of health facility delivery services. This can be achieved by increasing the number of health facilities, ensuring availability of skilled birth attendants, and providing necessary resources and equipment for safe deliveries.

2. Promoting awareness and education on umbilical cord care: The study found that applying traditional substances on the cord increased the risk of neonatal mortality. To address this, it is important to promote awareness and education on proper umbilical cord care practices. This can be done through community health education programs, antenatal care visits, and engaging traditional birth attendants to promote safe practices.

3. Targeting drought-affected areas: The study found that residing in drought-affected areas was associated with a higher risk of neonatal mortality. To address this, it is recommended to target and prioritize resources and interventions in these areas. This can include providing additional support for healthcare services, nutrition programs, and emergency response systems to mitigate the impact of drought on maternal and neonatal health.

4. Implementing spatial analysis for targeted interventions: The study utilized Bayesian spatial analysis to identify geographic variations and contributing factors for neonatal mortality rates. This approach can be further developed into an innovation by integrating it into the existing health information systems. By incorporating spatial analysis techniques, policymakers and resource administrators can identify areas with higher neonatal mortality rates and allocate resources and interventions accordingly.

Overall, the recommendations focus on improving access to health facility delivery services, promoting safe umbilical cord care practices, targeting drought-affected areas, and utilizing spatial analysis for targeted interventions. Implementing these recommendations can help improve access to maternal health and reduce neonatal mortality rates in Ethiopia.

AI Innovations Methodology

The study described above focuses on investigating the spatial variations and contributing factors for neonatal mortality rates in Ethiopia. The methodology used in this study includes the following steps:

1. Data Collection: The study utilizes data from the 2016 Ethiopian Demographic and Health Survey (EDHS), which is a nationally representative survey. The survey collects data on various socio-economic, neonatal, maternal, geospatial, and health service-related variables.

2. Sampling Technique: The EDHS survey uses a two-stage cluster sampling technique. The primary sampling unit is the census enumeration area (EA), and households are the secondary sampling unit. A total of 645 clusters were sampled in the 2016 survey.

3. Data Analysis: A Bayesian spatial logistic regression model using the Stochastic Partial Differential Equation (SPDE) method is fitted to the data. This model accounts for socio-economic, health service-related, and geographic factors. The model allows for the identification of factors associated with neonatal mortality rates and their spatial variations.

4. Outcome of Interest: The primary outcome of interest in this study is neonatal mortality, defined as death within 28 days of birth. The study aims to identify factors at both the individual and community levels that contribute to neonatal mortality.

5. Statistical Analysis: The study applies statistical techniques, such as adjusted odds ratios (AOR) and credible intervals (CrI), to assess the associations between various factors and neonatal mortality rates. The analysis includes factors such as the application of traditional substances on the umbilical cord, health facility delivery services, residing in drought-affected areas, and geographic variations.

6. Policy Implications: The study concludes by highlighting the factors associated with higher neonatal mortality rates and suggests that policymakers and resource administrators can use these findings to prioritize and target areas with higher neonatal mortality rates. This can help in developing interventions and allocating resources effectively to improve access to maternal health.

In summary, the methodology used in this study combines data analysis techniques, spatial modeling, and statistical analysis to investigate the factors influencing neonatal mortality rates in Ethiopia. The findings can inform policy decisions and interventions aimed at improving access to maternal health in the country.

Authors & Co-Authors

Statistics:

Citations: 2

Authors: 3

Identifiers:

DOI: 10.1371/journal.pone.0270879

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:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative