Geographic variation and factors associated with under-five mortality in Ethiopia. A spatial and multilevel analysis of Ethiopian mini demographic and health survey 2019

PLoS ONE, Volume 17, No. 10 October, Year 2022

Interprétation

Background The distribution of under-five mortality (U5M) worldwide is uneven and the burden is higher in Sub-Saharan African countries, which account for more than 53% of the global under-five mortality. In Ethiopia, though U5M decreased substantially between 1990 and 2019, it remains excessively high and unevenly distributed. Therefore, this study aimed to assess geographic variation and factors associated with under-five mortality (U5M) in Ethiopia. Methods We sourced data from the most recent nationally representative 2019 Ethiopian Mini-Demographic and Health Survey for this study. A sample size of 5,695 total births was considered. Descriptive, analytical analysis and spatial analysis were conducted using STATA version 16. Both multilevel and spatial analyses were employed to ascertain the factors associated with U5M in Ethiopia. Results The U5M was 5.9% with a 95% CI 5.4% to 6.6%. Based on the multivariable multilevel logistic regression model results, the following characteristics were associated with under-five mortality: family size (AOR = 0.92, 95% CI: 0.84,0.99), number of under-five children in the family (AOR = 0.17, 95% CI: 0.14, 0.21), multiple birth (AOR = 14.4, 95% CI: 8.5, 24.3), children who were breastfed for less than 6 months (AOR = 5.04, 95% CI: 3.81, 6.67), people whose main roof is palm (AOR = 0.57, 95% CI: 0.34, 0.96), under-five children who are the sixth or more child to be born (AOR = 2.46, 95% CI: 1.49, 4.06), institutional delivery (AOR = 0.57, 95% CI: 0.41, 0.81), resident of Somali and Afar region (AOR = 3.46, 95% CI: 1.58, 7.55) and (AOR = 2.54, 95% CI: 1.10, 5.85), respectively. Spatial analysis revealed that hot spot areas of under-five mortality were located in the Dire Dawa and Somali regions. Conclusion Under-five mortality in Ethiopia is high and unacceptable when compared to the 2030 sustainable development target, which aims for 25 per 1000 live births. Breastfeeding for less than 6 months, twin births, institutional delivery and high-risk areas of under-five mortality (Somali and Dire Dawa) are modifiable risk factors. Therefore, maternal and community education on the advantages of breastfeeding and institutional delivery is highly recommended. Women who deliver twins should be given special attention. An effective strategy should be designed for intervention in under-five mortality hot spot areas such as Somali and Dire Dawa. Our source was the 2019 EMDHS, the second mini demographic health survey (DHS) conducted in Ethiopia (a land-locked country located in the Horn of Africa that lies between the 30N and 150N Latitude or 330E and 480E Longitude) [21]. Data collection was conducted from March 21, 2019, to June 28, 2019, the nine regions (Tigray, Afar, Amhara, Oromia, Somali, Benishangul Gumuz, Southern nation nationalities and People region (SNNPR), Harari, and Gambella) and two administrative cities (Addis Ababa and Dire Dawa). The study design was a population-based cross-sectional study. A frame of all census Enumeration areas (EAs) was used as a sampling frame for the 2019 EMDHS. 149,093 EAs were created which cover an average of 131 Households (HHS). A two-stage stratified cluster sampling technique was employed and each region was stratified into urban and rural areas, yielding 21 sampling strata were selected independently in each stratum. In the first stage, 305 clusters (93 urban and 212 rural) were selected with probability proportional to EAs size and with independent selection in each sampling stratum. In the second stage, a fixed number of 30 households per cluster was selected. Finally, women aged 15–49 in 9,150 (2,790 urban and 6,360 rural) households from 305 clusters were selected. The whole procedure of sampling is found in the full 2019 EMDHS report [21]. The outcome variable was under-five mortality status, which was categorized as (child alive: Yes = 0 and No = 1). The age was recorded in months. The community-level predictors, were place of residence, region, community place of delivery, community wealth, community media exposure, and community toilet facility. The individual-level predictors were further categorized as socio-demographic and economic factors like the educational level of the mother, sex of the household head, age of household head, number of household members, number of children under the age of five, marital status of the mother, source of water, time to get water, type of toilet facility, household electricity, types of cooking fuel, main floor material, main wall material, and main roof material and maternal and child factors (such as maternal age at first birth, sex of the child), utilization of contraception, order of birth, mode of delivery, duration of breastfeeding and multiple births (Fig 1). The data for spatial analysis was cleaned and merged using STATA version 16 and Microsoft Excel. ArcGIS version 10.8 and saTScan version 9.7 were used for the spatial analysis. Spatial autocorrelation (Global Moran’s I) analysis was conducted to examine whether under-five mortality was dispersed (Moran’s I value closer to -1), clustered (Moran’s I value closer to 1), or randomly distributed (Moran’s I value of 0) in Ethiopia [22]. The under-five mortality was known in enumerated areas, while in areas that were not selected, the under-five mortality rates were predicted. Spatial interpolation was applied using the geostatistical ordinary Kriging spatial interpolation technique to predict under-five mortality from existing sample data points to un-sampled areas [23]. The scan analysis was performed using SaTscan, based on the Bernoulli test for cases (child is not alive) and controls (child is alive). The upper limit used was the default maximum spatial cluster size of less than 50% of the population, allowing both small and large clusters to be detected, while clusters that contained more than the maximum limit with the circular shape of the window were avoided [24]. Most likely clusters were identified using p-values and likelihood ratio tests, which is the ratio of the likelihood of the alternative hypothesis (higher activity level inside the window) over the likelihood of the null hypothesis (same activity level inside and outside). We used STATA version 16 and R statistical software version 4.0.5 to analyze the data. A total of 31 variables were retained for the analysis. Residents who were not De jure were excluded which affected under-five mortality, as they could not respond to most of the socio-demographic and economic characteristics even though they could answer the maternal and child characteristics. This exclusion changed our sample size from 5,753 to 5,695. The outcome variable was re-coded to (child alive: Yes = 1 and No = 0). Four community variables were generated by taking the individual variable, calculating their proportion, and dichotomizing them based on their mean or median according to their distribution. In the end, we had 29 predictor variables, of which 6 were community-level predictor variables and 23 were individual-level predictor variables. Based on EMDHS, respondents in the same cluster showed similar outcomes or functions at the same level and the data has a hierarchical structure. This made binary logistic regression not the most appropriate as it violates the assumption of independence of the residuals. Instead, a model that considers clustering effect should be used [7, 10]. Multilevel logistic modeling separates the within-cluster effects from the between-cluster effects [25]. Therefore, to assess the predictors associated with U5M, a non-weighted multilevel logistic regression model was used. Bivariable multilevel logistic regression was used to screen each predictor variable for a p-value less than 0.2. Significant variables were included in multivariable multilevel logistic models. Twenty predictor variables (3 of which were community-level predictor variables) were included in the multivariable analysis. In the multivariable analysis, a p-value less than 0.05 was considered a factor associated with U5M. The first model fitted was the null model (intercept model), which contained the outcome variable only (under-five mortality status) with the cluster number. The intra-cluster correlation (ICC) was used to assess whether there was a random effect. An ICC of 0.130 which meant there was a minimum of 13% under-five mortality was explained by between-cluster differences. We found that 87% of under-five mortality was explained by within-cluster differences, which was not negligible. The second model was fitted using the outcome, the cluster and the individual-level predictor variables only. The probability of U5M was predicted as a function of individual-level predictors. For the third model the outcome variable, the cluster number, and the community-level predictor variables were accounted for. Then the final model was fitted by taking both the individual-level and the community-level predictor variables into account. The models were compared by using a log-likelihood statistic, where the best model was selected based on smallest deviance. Permission for data access was obtained from a major demographic and health survey through an online request from http://www.dhsprogram.com. The data used for this study were publicly available with no personal identifiers.

Résumé de l’IA

Study Justification:

– The study aimed to assess the geographic variation and factors associated with under-five mortality (U5M) in Ethiopia.
– Under-five mortality remains excessively high and unevenly distributed in Ethiopia, despite a substantial decrease between 1990 and 2019.
– Understanding the geographic variation and factors associated with U5M is crucial for developing targeted interventions and reducing child mortality in Ethiopia.

Highlights:

– The study used data from the 2019 Ethiopian Mini-Demographic and Health Survey, with a sample size of 5,695 total births.
– Multilevel logistic regression and spatial analysis were employed to identify factors associated with U5M and identify hot spot areas.
– The study found that family size, number of under-five children in the family, multiple births, duration of breastfeeding, type of roof material, birth order, institutional delivery, and residence in certain regions were associated with U5M.
– Hot spot areas of under-five mortality were identified in the Dire Dawa and Somali regions.

Recommendations for Lay Reader and Policy Maker:

– Under-five mortality in Ethiopia is high and unacceptable compared to the 2030 sustainable development target.
– Breastfeeding for less than 6 months, twin births, institutional delivery, and high-risk areas (Somali and Dire Dawa) are modifiable risk factors.
– Maternal and community education on the benefits of breastfeeding and institutional delivery is highly recommended.
– Special attention should be given to women delivering twins.
– Effective strategies should be designed for intervention in under-five mortality hot spot areas such as Somali and Dire Dawa.

Key Role Players:

– Ministry of Health: Responsible for implementing interventions and policies related to child health and reducing under-five mortality.
– Non-governmental organizations (NGOs): Involved in implementing community education programs and providing support to mothers and children.
– Health workers: Play a crucial role in delivering healthcare services, including promoting breastfeeding and safe delivery practices.
– Community leaders: Engage in community mobilization and advocacy efforts to raise awareness about under-five mortality and the importance of interventions.

Cost Items for Planning Recommendations:

– Development and dissemination of educational materials on breastfeeding and institutional delivery.
– Training programs for health workers on providing quality maternal and child healthcare services.
– Community outreach programs and campaigns to raise awareness and promote behavior change.
– Monitoring and evaluation systems to track the progress of interventions and identify areas for improvement.
– Infrastructure improvements in under-five mortality hot spot areas, such as healthcare facilities and access to clean water and sanitation.

Force de la preuve

N/A

Abstrait

Our source was the 2019 EMDHS, the second mini demographic health survey (DHS) conducted in Ethiopia (a land-locked country located in the Horn of Africa that lies between the 30N and 150N Latitude or 330E and 480E Longitude) [21]. Data collection was conducted from March 21, 2019, to June 28, 2019, the nine regions (Tigray, Afar, Amhara, Oromia, Somali, Benishangul Gumuz, Southern nation nationalities and People region (SNNPR), Harari, and Gambella) and two administrative cities (Addis Ababa and Dire Dawa). The study design was a population-based cross-sectional study. A frame of all census Enumeration areas (EAs) was used as a sampling frame for the 2019 EMDHS. 149,093 EAs were created which cover an average of 131 Households (HHS). A two-stage stratified cluster sampling technique was employed and each region was stratified into urban and rural areas, yielding 21 sampling strata were selected independently in each stratum. In the first stage, 305 clusters (93 urban and 212 rural) were selected with probability proportional to EAs size and with independent selection in each sampling stratum. In the second stage, a fixed number of 30 households per cluster was selected. Finally, women aged 15–49 in 9,150 (2,790 urban and 6,360 rural) households from 305 clusters were selected. The whole procedure of sampling is found in the full 2019 EMDHS report [21]. The outcome variable was under-five mortality status, which was categorized as (child alive: Yes = 0 and No = 1). The age was recorded in months. The community-level predictors, were place of residence, region, community place of delivery, community wealth, community media exposure, and community toilet facility. The individual-level predictors were further categorized as socio-demographic and economic factors like the educational level of the mother, sex of the household head, age of household head, number of household members, number of children under the age of five, marital status of the mother, source of water, time to get water, type of toilet facility, household electricity, types of cooking fuel, main floor material, main wall material, and main roof material and maternal and child factors (such as maternal age at first birth, sex of the child), utilization of contraception, order of birth, mode of delivery, duration of breastfeeding and multiple births (Fig 1). The data for spatial analysis was cleaned and merged using STATA version 16 and Microsoft Excel. ArcGIS version 10.8 and saTScan version 9.7 were used for the spatial analysis. Spatial autocorrelation (Global Moran’s I) analysis was conducted to examine whether under-five mortality was dispersed (Moran’s I value closer to -1), clustered (Moran’s I value closer to 1), or randomly distributed (Moran’s I value of 0) in Ethiopia [22]. The under-five mortality was known in enumerated areas, while in areas that were not selected, the under-five mortality rates were predicted. Spatial interpolation was applied using the geostatistical ordinary Kriging spatial interpolation technique to predict under-five mortality from existing sample data points to un-sampled areas [23]. The scan analysis was performed using SaTscan, based on the Bernoulli test for cases (child is not alive) and controls (child is alive). The upper limit used was the default maximum spatial cluster size of less than 50% of the population, allowing both small and large clusters to be detected, while clusters that contained more than the maximum limit with the circular shape of the window were avoided [24]. Most likely clusters were identified using p-values and likelihood ratio tests, which is the ratio of the likelihood of the alternative hypothesis (higher activity level inside the window) over the likelihood of the null hypothesis (same activity level inside and outside). We used STATA version 16 and R statistical software version 4.0.5 to analyze the data. A total of 31 variables were retained for the analysis. Residents who were not De jure were excluded which affected under-five mortality, as they could not respond to most of the socio-demographic and economic characteristics even though they could answer the maternal and child characteristics. This exclusion changed our sample size from 5,753 to 5,695. The outcome variable was re-coded to (child alive: Yes = 1 and No = 0). Four community variables were generated by taking the individual variable, calculating their proportion, and dichotomizing them based on their mean or median according to their distribution. In the end, we had 29 predictor variables, of which 6 were community-level predictor variables and 23 were individual-level predictor variables. Based on EMDHS, respondents in the same cluster showed similar outcomes or functions at the same level and the data has a hierarchical structure. This made binary logistic regression not the most appropriate as it violates the assumption of independence of the residuals. Instead, a model that considers clustering effect should be used [7, 10]. Multilevel logistic modeling separates the within-cluster effects from the between-cluster effects [25]. Therefore, to assess the predictors associated with U5M, a non-weighted multilevel logistic regression model was used. Bivariable multilevel logistic regression was used to screen each predictor variable for a p-value less than 0.2. Significant variables were included in multivariable multilevel logistic models. Twenty predictor variables (3 of which were community-level predictor variables) were included in the multivariable analysis. In the multivariable analysis, a p-value less than 0.05 was considered a factor associated with U5M. The first model fitted was the null model (intercept model), which contained the outcome variable only (under-five mortality status) with the cluster number. The intra-cluster correlation (ICC) was used to assess whether there was a random effect. An ICC of 0.130 which meant there was a minimum of 13% under-five mortality was explained by between-cluster differences. We found that 87% of under-five mortality was explained by within-cluster differences, which was not negligible. The second model was fitted using the outcome, the cluster and the individual-level predictor variables only. The probability of U5M was predicted as a function of individual-level predictors. For the third model the outcome variable, the cluster number, and the community-level predictor variables were accounted for. Then the final model was fitted by taking both the individual-level and the community-level predictor variables into account. The models were compared by using a log-likelihood statistic, where the best model was selected based on smallest deviance. Permission for data access was obtained from a major demographic and health survey through an online request from http://www.dhsprogram.com. The data used for this study were publicly available with no personal identifiers.

Matériaux

Innovations

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

1. Telemedicine: Implementing telemedicine services can provide remote access to healthcare professionals for pregnant women in remote or underserved areas. This allows for virtual consultations, monitoring, and guidance throughout the pregnancy and postpartum period.

2. Mobile clinics: Setting up mobile clinics that travel to rural or hard-to-reach areas can provide essential maternal health services, including prenatal care, vaccinations, and postnatal check-ups. This helps overcome geographical barriers and ensures that women in remote areas have access to necessary healthcare.

3. Community health workers: Training and deploying community health workers can help bridge the gap between healthcare facilities and communities. These workers can provide education, support, and basic healthcare services to pregnant women, ensuring they receive appropriate care and guidance throughout their pregnancy.

4. Health information systems: Implementing robust health information systems can improve data collection and analysis, allowing for better monitoring and evaluation of maternal health outcomes. This can help identify areas with high under-five mortality rates and target interventions accordingly.

5. Maternal health education programs: Developing and implementing comprehensive maternal health education programs can empower women with knowledge about pregnancy, childbirth, and postpartum care. These programs can be delivered through various channels, such as community workshops, mobile apps, or radio programs, to reach a wide audience.

6. Improved transportation infrastructure: Enhancing transportation infrastructure, particularly in rural areas, can facilitate access to healthcare facilities for pregnant women. This can include building roads, improving public transportation systems, or providing transportation vouchers for pregnant women to ensure they can reach healthcare facilities in a timely manner.

7. Strengthening referral systems: Establishing efficient referral systems between primary healthcare centers and higher-level facilities can ensure that pregnant women with complications receive timely and appropriate care. This includes clear protocols for transferring patients, communication channels between facilities, and training healthcare providers on emergency obstetric care.

These innovations can help address the geographic variation and factors associated with under-five mortality in Ethiopia, improving access to maternal health services and ultimately reducing under-five mortality rates.

AI Innovations Description

Based on the information provided, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Implement targeted maternal and community education programs: Develop and implement educational programs that specifically target pregnant women and their communities, focusing on the importance of breastfeeding for at least 6 months and the benefits of institutional delivery. These programs should provide accurate information, address misconceptions, and promote the advantages of these practices for maternal and child health.

2. Establish specialized support for mothers of twins: Recognize the increased risk associated with multiple births and provide specialized support and care for mothers of twins. This can include additional antenatal care visits, counseling on nutrition and breastfeeding for twins, and access to resources and support groups specifically tailored to their needs.

3. Design interventions for high-risk areas: Identify and target high-risk areas, such as the Somali and Dire Dawa regions, with specific interventions to address the underlying factors contributing to under-five mortality. These interventions can include improving access to healthcare facilities, training healthcare providers, and implementing community-based initiatives to increase awareness and utilization of maternal and child health services.

4. Utilize spatial analysis for targeted interventions: Use spatial analysis techniques, such as the geostatistical ordinary Kriging spatial interpolation technique, to identify areas with high under-five mortality rates. This information can guide the allocation of resources and the implementation of targeted interventions in these areas, ensuring that resources are directed where they are most needed.

By implementing these recommendations, it is possible to improve access to maternal health services, reduce under-five mortality rates, and work towards achieving the Sustainable Development Goal target of 25 per 1000 live births in Ethiopia.

AI Innovations Methodology

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

1. Increase awareness and education: Implement comprehensive maternal and community education programs to raise awareness about the importance of maternal health, including the benefits of breastfeeding and institutional delivery. This can be done through community health workers, antenatal care visits, and mass media campaigns.

2. Improve access to healthcare facilities: Strengthen the healthcare infrastructure by increasing the number of healthcare facilities, particularly in under-served areas. This includes establishing more health centers, maternity clinics, and hospitals equipped with skilled healthcare professionals and necessary medical equipment.

3. Enhance transportation services: Develop and improve transportation systems to ensure that pregnant women can easily access healthcare facilities, especially in remote and rural areas. This can involve providing ambulances, mobile clinics, or transportation vouchers to pregnant women in need.

4. Promote family planning services: Increase access to family planning services to empower women to make informed decisions about their reproductive health. This can help reduce the number of high-risk pregnancies and improve maternal and child health outcomes.

5. Strengthen community engagement: Engage local communities, including religious and traditional leaders, in promoting maternal health practices and addressing cultural barriers. This can be done through community dialogues, support groups, and community-led initiatives.

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 state of maternal health access, including indicators such as maternal mortality rates, institutional delivery rates, and breastfeeding practices. This can be done through surveys, interviews, and existing health records.

2. Define indicators: Identify specific indicators that will be used to measure the impact of the recommendations. For example, indicators could include the percentage increase in institutional delivery rates or the reduction in maternal mortality rates.

3. Establish a baseline: Determine the current values of the selected indicators to establish a baseline for comparison. This will serve as a reference point to measure the impact of the recommendations.

4. Simulate interventions: Use modeling techniques to simulate the potential impact of the recommendations on the selected indicators. This can involve creating different scenarios based on the proposed interventions and estimating the expected changes in the indicators.

5. Analyze results: Analyze the simulated results to assess the potential impact of the recommendations on improving access to maternal health. Compare the projected changes in the indicators with the baseline values to determine the effectiveness of the interventions.

6. Refine and adjust: Based on the analysis, refine the recommendations and interventions as needed. Consider factors such as feasibility, cost-effectiveness, and scalability to ensure practical implementation.

7. Monitor and evaluate: Implement the recommended interventions and continuously monitor and evaluate their impact on improving access to maternal health. This will help identify any challenges or areas for improvement and inform future decision-making.

It is important to note that the methodology for simulating the impact may vary depending on the specific context and available data. It is recommended to consult with experts in the field of maternal health and utilize appropriate statistical and modeling techniques for accurate analysis.

Auteur

Dima Health

Identifiers:

DOI: 10.1371/journal.pone.0275586

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

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

Study Approach:

Quantitative

Participants Gender:

Female

Partagez ceci :