Reduction in child mortality in Ethiopia: Analysis of data from demographic and health surveys

Journal of Global Health, Volume 6, No. 2, Year 2016

Interpretation

Background To examine changes in under-5 mortality, coverage of child survival interventions and nutritional status of children in Ethiopia between 2000 and 2011. Using the Lives Saved Tool, the impact of changes in coverage of child survival interventions on under-5 lives saved was estimated. Methods Estimates of child mortality were generated using three Ethiopia Demographic and Health Surveys undertaken between 2000 and 2011. Coverage indicators for high impact child health interventions were calculated and the Lives Saved Tool (LiST) was used to estimate child lives saved in 2011. Results The mortality rate in children younger than 5 years decreased rapidly from 218 child deaths per 1000 live births (95% confidence interval 183 to 252) in the period 1987-1991 to 88 child deaths per 1000 live births in the period 2007-2011 (78 to 98). The prevalence of moderate or severe stunting in children aged 6-35 months also declined significantly. Improvements in the coverage of interventions relevant to child survival in rural areas of Ethiopia between 2000 and 2011 were found for tetanus toxoid, DPT3 and measles vaccination, oral rehydration solution (ORS) and care-seeking for suspected pneumonia. The LiST analysis estimates that there were 60 700 child deaths averted in 2011, primarily attributable to decreases in wasting rates (18%), stunting rates (13%) and water, sanitation and hygiene (WASH) interventions (13%). Conclusions Improvements in the nutritional status of children and increases in coverage of high impact interventions most notably WASH and ORS have contributed to the decline in under-5 mortality in Ethiopia. These proximal determinants however do not fully explain the mortality reduction which is plausibly also due to the synergistic effect of major child health and nutrition policies and delivery strategies. We used full birth and death history data collected from women aged 15 to 49 years in nationally representative surveys: namely the 2000 Demographic and Health Survey (DHS) the first DHS to be undertaken in Ethiopia, 2005 DHS and the 2011 DHS to calculate under–5 mortality. The surveys covered 14 072, 13 721, and 16 702 households respectively. To assess trends in coverage of child survival interventions and nutritional status we used the same three Ethiopian DHS surveys. The surveys provide detailed information about the health and nutritional status of women and children and coverage of health care services. The analysis included all survey data sets available with full data, including sampling weights, to allow for re–analysis (see Table S1 in the Online Supplementary Document(Online Supplementary Document) for further details on the surveys). To assess coverage of malaria interventions two separate Malaria Indicator Surveys (MIS) were used since these surveys sample specifically from malaria endemic areas. Malaria is seasonal in most parts of Ethiopia, with variable transmission and prevalence patterns affected by the large diversity in altitude, rainfall, and population movement. The MIS from 2007 [6] and 2011 [7] focus on malarious areas defined as <2000m in altitude mapped by global positioning system (GPS); hence these provide a more appropriate estimate of coverage of malaria interventions than the DHS surveys [7]. All of the surveys provided cross–sectional data on intervention coverage in their respective years; however for the MIS, primary data are not available and only point estimates are presented. Definitions and data sources for all indicators can be found in Table S2 in Online Supplementary Document(Online Supplementary Document). We used a direct method for estimating under–5 mortality based on the synthetic cohort approach [8,9]. Under this concept, age–specific mortality probabilities for narrow age ranges and defined periods are calculated using death events and exposures. These probabilities are combined to compute the probability that a child has not died before reaching age 5 years [9]. Under–five mortality rates were computed for successive five year periods preceding the 2011 DHS. For the purposes of this analysis, mortality rates were calculated for 5–year periods starting from 1987–1991 up until 2007–2011 (the 5–year period immediately prior to the 2011 DHS). Survival probabilities were calculated over age ranges; 0, 1–2, 3–5, 6–11, 12–23, 24–35, 36–47, 48–59 months as recommended by DHS (Section B in Online Supplementary Document(Online Supplementary Document)) [9]. The standard errors for the computed mortality estimates were obtained using the Jackknife variance estimation, a repeated sampling method [8]. A series of mortality estimates were obtained by deleting and replacing each primary sampling unit; this produced a sample of under–5 estimates, from which the variance was computed in turn. We also estimated the average annual change (AAC) in mortality using mortality estimates for the periods 1987–1991 and 2007–2011 (Section B in the Online Supplementary Document(Online Supplementary Document)). We analyzed primary data from three Ethiopia DHS surveys to assess coverage trends for 10 indicators which represent high impact maternal and child health interventions; three additional malaria intervention indicators are presented as point estimates. We re–calculated all coverage indicators using standard indicator definitions [10] for tracking progress toward MDG 4. The sampling design of these DHS surveys, such as clustering at enumeration areas and sampling weights (due to non–proportional sampling), were taken into account. Except for the malaria indicators, coverage estimates for rural areas are presented to reflect the focus of the HEP on universal access. We considered malaria indicators for endemic areas only. The 95% confidence intervals were used to assess whether the changes were significantly different across the three time periods. We computed anthropometric indicators for stunting (height–for–age) and underweight (weight–for–age) in children younger than three years of age from information on age, height and weight in the surveys applying the WHO child growth standards [11]. Moderate or severe (below minus two standard deviations (SD) from the median) and severe (below minus three standard deviations (SD) from the median) were calculated for both nutritional measures. Infant feeding indicators such as exclusive breastfeeding and micronutrient intake (vitamin A supplementation) were calculated by age of the child. We used Stata (version 13) (Stata Corporation, College Station, Texas, USA) for all mortality and coverage analyses. We used the Lives Saved Tool (LiST) to estimate the number of deaths averted in 2011 due to changes in coverage since 2000. We compared the changes in mortality produced in LiST with single year estimates from IGME [12] as well as the five–year estimates produced in this analysis using DHS data. LiST uses country–specific or region–specific baseline information on mortality rates and causes of death as well as background variables (fertility, exposure to Plasmodium falciparum, stunting rates) and current coverage of more than 60 interventions and their associated effectiveness values [13–16] relative to specific causes of death and risk factors to estimate the deaths averted, overall and by specific interventions. The modeling methods have been widely published including discussion of the limitations [16–18]. We used 2000 as the baseline year and projected forward to 2011 using all available national data on changes in intervention coverage and nutritional status (Section C and Table S5 in the Online Supplementary Document(Online Supplementary Document)). Specific input values used in this LiST application are available in Table S6 in Online Supplementary Data(Online Supplementary Document). The analysis was done with the program Spectrum/Lives Saved Tool, version 5.04 (Johns Hopkins University, Baltimore Maryland, USA).

AI Digest

Study Justification:

The study aimed to examine the changes in under-5 mortality, coverage of child survival interventions, and nutritional status of children in Ethiopia between 2000 and 2011. The goal was to understand the factors contributing to the reduction in child mortality in order to inform future policies and interventions.

Highlights:

– The mortality rate in children under 5 years decreased rapidly from 218 child deaths per 1000 live births in the period 1987-1991 to 88 child deaths per 1000 live births in the period 2007-2011.
– The prevalence of moderate or severe stunting in children aged 6-35 months also declined significantly.
– Improvements in the coverage of interventions relevant to child survival were found for tetanus toxoid, DPT3 and measles vaccination, oral rehydration solution (ORS), and care-seeking for suspected pneumonia.
– The analysis estimated that there were 60,700 child deaths averted in 2011, primarily due to decreases in wasting rates, stunting rates, and water, sanitation, and hygiene (WASH) interventions.

Recommendations:

– Continue to prioritize and invest in interventions that have shown significant impact, such as WASH interventions, ORS, and vaccination programs.
– Strengthen efforts to address wasting and stunting in children through targeted interventions and nutrition programs.
– Improve access to and utilization of healthcare services, particularly in rural areas, to ensure timely and appropriate care for children.
– Enhance coordination and collaboration between different sectors, including health, nutrition, and water and sanitation, to maximize the impact of interventions.

Key Role Players:

– Ministry of Health: Responsible for policy development, planning, and implementation of child health and nutrition programs.
– Ministry of Water, Irrigation, and Energy: Involved in implementing WASH interventions and ensuring access to clean water and sanitation facilities.
– Ministry of Education: Plays a role in promoting health and nutrition education in schools and communities.
– Non-governmental organizations (NGOs): Provide support and implementation of interventions at the community level.
– Development partners and donors: Provide financial and technical support for child health and nutrition programs.

Cost Items for Planning Recommendations:

– Funding for vaccination programs, including procurement of vaccines and cold chain equipment.
– Investment in WASH infrastructure, such as clean water sources, sanitation facilities, and hygiene promotion.
– Resources for nutrition programs, including provision of therapeutic foods and supplements.
– Health system strengthening, including training of healthcare workers and improving access to healthcare services.
– Monitoring and evaluation activities to track the progress and impact of interventions.
Please note that the cost items provided are general categories and not actual cost estimates. The actual cost will depend on the specific context and implementation strategies.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on data from nationally representative surveys and uses a robust methodology. However, to improve the evidence, the abstract could provide more specific details about the surveys and the sampling design, as well as the specific indicators used to assess coverage of child survival interventions and nutritional status. Additionally, it would be helpful to include information on any potential limitations or biases in the data or analysis.

Abstract

We used full birth and death history data collected from women aged 15 to 49 years in nationally representative surveys: namely the 2000 Demographic and Health Survey (DHS) the first DHS to be undertaken in Ethiopia, 2005 DHS and the 2011 DHS to calculate under–5 mortality. The surveys covered 14 072, 13 721, and 16 702 households respectively. To assess trends in coverage of child survival interventions and nutritional status we used the same three Ethiopian DHS surveys. The surveys provide detailed information about the health and nutritional status of women and children and coverage of health care services. The analysis included all survey data sets available with full data, including sampling weights, to allow for re–analysis (see Table S1 in the Online Supplementary Document(Online Supplementary Document) for further details on the surveys). To assess coverage of malaria interventions two separate Malaria Indicator Surveys (MIS) were used since these surveys sample specifically from malaria endemic areas. Malaria is seasonal in most parts of Ethiopia, with variable transmission and prevalence patterns affected by the large diversity in altitude, rainfall, and population movement. The MIS from 2007 [6] and 2011 [7] focus on malarious areas defined as <2000m in altitude mapped by global positioning system (GPS); hence these provide a more appropriate estimate of coverage of malaria interventions than the DHS surveys [7]. All of the surveys provided cross–sectional data on intervention coverage in their respective years; however for the MIS, primary data are not available and only point estimates are presented. Definitions and data sources for all indicators can be found in Table S2 in Online Supplementary Document(Online Supplementary Document). We used a direct method for estimating under–5 mortality based on the synthetic cohort approach [8,9]. Under this concept, age–specific mortality probabilities for narrow age ranges and defined periods are calculated using death events and exposures. These probabilities are combined to compute the probability that a child has not died before reaching age 5 years [9]. Under–five mortality rates were computed for successive five year periods preceding the 2011 DHS. For the purposes of this analysis, mortality rates were calculated for 5–year periods starting from 1987–1991 up until 2007–2011 (the 5–year period immediately prior to the 2011 DHS). Survival probabilities were calculated over age ranges; 0, 1–2, 3–5, 6–11, 12–23, 24–35, 36–47, 48–59 months as recommended by DHS (Section B in Online Supplementary Document(Online Supplementary Document)) [9]. The standard errors for the computed mortality estimates were obtained using the Jackknife variance estimation, a repeated sampling method [8]. A series of mortality estimates were obtained by deleting and replacing each primary sampling unit; this produced a sample of under–5 estimates, from which the variance was computed in turn. We also estimated the average annual change (AAC) in mortality using mortality estimates for the periods 1987–1991 and 2007–2011 (Section B in the Online Supplementary Document(Online Supplementary Document)). We analyzed primary data from three Ethiopia DHS surveys to assess coverage trends for 10 indicators which represent high impact maternal and child health interventions; three additional malaria intervention indicators are presented as point estimates. We re–calculated all coverage indicators using standard indicator definitions [10] for tracking progress toward MDG 4. The sampling design of these DHS surveys, such as clustering at enumeration areas and sampling weights (due to non–proportional sampling), were taken into account. Except for the malaria indicators, coverage estimates for rural areas are presented to reflect the focus of the HEP on universal access. We considered malaria indicators for endemic areas only. The 95% confidence intervals were used to assess whether the changes were significantly different across the three time periods. We computed anthropometric indicators for stunting (height–for–age) and underweight (weight–for–age) in children younger than three years of age from information on age, height and weight in the surveys applying the WHO child growth standards [11]. Moderate or severe (below minus two standard deviations (SD) from the median) and severe (below minus three standard deviations (SD) from the median) were calculated for both nutritional measures. Infant feeding indicators such as exclusive breastfeeding and micronutrient intake (vitamin A supplementation) were calculated by age of the child. We used Stata (version 13) (Stata Corporation, College Station, Texas, USA) for all mortality and coverage analyses. We used the Lives Saved Tool (LiST) to estimate the number of deaths averted in 2011 due to changes in coverage since 2000. We compared the changes in mortality produced in LiST with single year estimates from IGME [12] as well as the five–year estimates produced in this analysis using DHS data. LiST uses country–specific or region–specific baseline information on mortality rates and causes of death as well as background variables (fertility, exposure to Plasmodium falciparum, stunting rates) and current coverage of more than 60 interventions and their associated effectiveness values [13–16] relative to specific causes of death and risk factors to estimate the deaths averted, overall and by specific interventions. The modeling methods have been widely published including discussion of the limitations [16–18]. We used 2000 as the baseline year and projected forward to 2011 using all available national data on changes in intervention coverage and nutritional status (Section C and Table S5 in the Online Supplementary Document(Online Supplementary Document)). Specific input values used in this LiST application are available in Table S6 in Online Supplementary Data(Online Supplementary Document). The analysis was done with the program Spectrum/Lives Saved Tool, version 5.04 (Johns Hopkins University, Baltimore Maryland, USA).

Materials

https://efashare.b-cdn.net/materials/58c5569a9d815b878a9e33cf1aae7ee7a4ae323c672e0db9a0dcfa06d727f094/jogh-06-020401-s001.pdf

Innovations

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

1. Mobile health (mHealth) interventions: Utilizing mobile phones and other digital technologies to provide maternal health information, reminders for prenatal care appointments, and access to telemedicine consultations.

2. Community health worker programs: Training and deploying community health workers to provide education, counseling, and basic maternal health services in rural and underserved areas.

3. Telemedicine: Expanding access to maternal health services through telemedicine consultations, allowing pregnant women to receive care remotely and reducing the need for travel to healthcare facilities.

4. Maternal health vouchers: Implementing voucher programs that provide pregnant women with financial assistance to access essential maternal health services, such as antenatal care, skilled birth attendance, and postnatal care.

5. Maternal waiting homes: Establishing safe and comfortable facilities near healthcare facilities where pregnant women from remote areas can stay before and after giving birth, ensuring timely access to skilled care.

6. Task-shifting: Training and empowering non-physician healthcare providers, such as nurses and midwives, to perform certain maternal health services, thereby increasing the availability of skilled care.

7. Integration of maternal health services: Integrating maternal health services with other healthcare services, such as family planning and HIV/AIDS prevention and treatment, to provide comprehensive care to women.

8. Quality improvement initiatives: Implementing quality improvement programs to enhance the quality of maternal health services, including training healthcare providers, improving infrastructure, and ensuring the availability of essential supplies and equipment.

9. Public-private partnerships: Collaborating with private sector organizations to improve access to maternal health services, leveraging their resources, expertise, and networks.

10. Health financing reforms: Implementing health financing reforms, such as health insurance schemes or social health protection programs, to ensure financial risk protection and increase access to maternal health services for all women.

These innovations have the potential to address barriers to accessing maternal health services, improve the quality of care, and ultimately reduce maternal and child mortality rates.

AI Innovations Description

Based on the provided information, the recommendation to improve access to maternal health in Ethiopia is to focus on the following strategies:

1. Strengthening high-impact interventions: Continue to improve coverage of interventions such as tetanus toxoid, DPT3 and measles vaccination, oral rehydration solution (ORS), and care-seeking for suspected pneumonia. These interventions have shown significant improvements in coverage in rural areas of Ethiopia between 2000 and 2011.

2. Enhancing water, sanitation, and hygiene (WASH) interventions: Increase efforts to improve access to clean water, sanitation facilities, and hygiene practices. The analysis suggests that WASH interventions have contributed to the decline in under-5 mortality in Ethiopia. This includes initiatives to provide safe drinking water, proper sanitation facilities, and hygiene education.

3. Addressing malnutrition: Continue efforts to improve the nutritional status of children by reducing wasting rates and stunting rates. This can be achieved through interventions such as promoting exclusive breastfeeding, micronutrient supplementation, and implementing nutrition-sensitive programs.

4. Strengthening health policies and delivery strategies: Recognize the synergistic effect of major child health and nutrition policies and delivery strategies in reducing child mortality. This includes ensuring effective implementation of policies and strategies that prioritize maternal and child health, and improving the overall health system to provide accessible and quality care.

By focusing on these recommendations, Ethiopia can further improve access to maternal health and contribute to reducing child mortality rates.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals can improve access to maternal health services. This includes building and upgrading hospitals, clinics, and birthing centers, as well as ensuring an adequate number of skilled healthcare providers.

2. Increasing community awareness and education: Conducting awareness campaigns and educational programs can help improve knowledge about maternal health and the importance of seeking timely care. This can be done through community health workers, local media, and educational institutions.

3. Enhancing transportation and logistics: Improving transportation systems and logistics can help overcome geographical barriers and ensure timely access to maternal health services. This can involve providing ambulances or transportation vouchers for pregnant women in remote areas.

4. Expanding telemedicine and mobile health solutions: Utilizing technology, such as telemedicine and mobile health applications, can help overcome distance barriers and provide remote access to maternal health services. This includes virtual consultations, remote monitoring, and access to health information.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define indicators: Identify key indicators that measure access to maternal health, such as the number of antenatal care visits, skilled birth attendance, postnatal care coverage, and maternal mortality rates.

2. Collect baseline data: Gather data on the current status of these indicators, including coverage rates and mortality rates, using surveys, health records, and other relevant sources.

3. Define intervention scenarios: Develop different scenarios that represent the implementation of the recommended innovations. For example, scenario 1 could represent the strengthening of healthcare infrastructure, scenario 2 could represent the combination of infrastructure improvements and community awareness programs, and so on.

4. Simulate the impact: Use modeling tools, such as the Lives Saved Tool (LiST) mentioned in the provided information, to estimate the impact of each scenario on the defined indicators. This involves inputting the baseline data, intervention parameters, and relevant demographic and health information into the modeling tool.

5. Analyze results: Compare the simulated outcomes of each scenario to the baseline data to assess the potential impact of the recommendations on improving access to maternal health. This can include estimating the number of lives saved, changes in coverage rates, and reductions in maternal mortality.

6. Refine and iterate: Based on the results, refine the intervention scenarios and repeat the simulation process to further optimize the recommendations and assess their potential impact.

It is important to note that the methodology may vary depending on the specific context and available data. Collaboration with experts in maternal health, epidemiology, and health systems research can help ensure the accuracy and validity of the simulation methodology.

Author

Dima Health

Statistics:

Citations: 12

Identifiers:

DOI: 10.7189/jogh.06.020401

ISSN: 20472978

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female