Prioritizing child health interventions in Ethiopia: Modeling impact on child mortality, life expectancy and inequality in age at death

PLoS ONE, Volume 7, No. 8, Year 2012

Interpretation

Background: The fourth Millennium Development Goal calls for a two-thirds reduction in under-5 mortality between 1990 and 2015. Under-5 mortality rate is declining, but many countries are still far from achieving the goal. Effective child health interventions that could reduce child mortality exist, but national decision-makers lack contextual information for priority setting in their respective resource-constrained settings. We estimate the potential health impact of increasing coverage of 14 selected health interventions on child mortality in Ethiopia (2011-2015). We also explore the impact on life expectancy and inequality in the age of death (Ginihealth). Methods and Findings: We used the Lives Saved Tool to estimate potential impact of scaling-up 14 health interventions in Ethiopia (2011-2015). Interventions are scaled-up to 1) government target levels, 2) 90% coverage and 3) 90% coverage of the five interventions with the highest impact. Under-5 mortality rate, neonatal mortality rate and deaths averted are primary outcome measures. We used modified life tables to estimate impact on life expectancy at birth and inequality in the age of death (Ginihealth). Under-5 mortality rate declines from 101.0 in 2011 to 68.8, 42.1 and 56.7 per 1000 live births under these three scenarios. Prioritizing child health would also increase life expectancy at birth from expected 60.5 years in 2015 to 62.5, 64.2 and 63.4 years and reduce inequality in age of death (Ginihealth) substantially from 0.24 to 0.21, 0.18 and 0.19. Conclusions: The Millennium Development Goal for child health is reachable in Ethiopia. Prioritizing child health would also increase total life expectancy at birth and reduce inequality in age of death substantially (Ginihealth). © 2012 Onarheim et al. In support of evidence-based policy making and priority-setting, we have recently seen a new focus on modeling tools analyzing possible impacts of increased access to health interventions. Rudan and colleagues argue that an optimal priority setting tool “should be able to draw on the best local evidence and guide policy makers and governments to identify, prioritize, and implement evidence-based health interventions for scale-up and delivery” [27]. PopMod [20], Marginal Budgeting for Bottlenecks (MBB) [23] and the Lives Saved Tool (LiST) [24] are current examples of modeling tools which assess the impacts of scaling-up health care interventions using evidence-based data. PopMod and MBB also include costing opportunities. The modeling tools assume that the interventions will be fully implemented. However, actual effects of scale-up depend on factors like human resources and equipment available, health seeking behavior and other context depended factors. We chose to use LiST, as the most user-friendly and the most recently updated tool available at time of analysis, as we believe these concerns are relevant for policy makers. LiST and MBB is also a part of the new OneHealth tool, which harmonize current modeling tools on costing and impact assessment in the health sector [28]. This paper addresses impacts on health of scaling-up interventions, but does not address costs. We used the Lives Saved Tool (LiST) version 4.43. LiST models changes in maternal and child mortality by scaling-up coverage of health care interventions. The tool operates within the Spectrum model, where maternal and child health data are integrated with demographic (DemProj) and HIV/AIDS (AIM) projections [29]. Evidence-based effectiveness data are from the Child Health Epidemiology Research Group (CHERG), which is an expert group conducting technical reviews on maternal, neonatal and child morbidity and mortality and effectiveness of interventions [30]. We selected 14 essential child health interventions relevant for Ethiopian decision makers through the following process: first, we identified recent recommendations on which interventions to give priority to in resource-constrained settings from the literature [12], [13], [17], [18]. Second, we included interventions that are explicitly considered in Ethiopia, but are not yet implemented, such as the pneumococcal vaccine [31]. The interventions were chosen to reflect the diversity of intervention options and delivery platforms, i.e. according to a) level of care, b) preventive or curative concerns and c) early and late mortality. All interventions target conditions with high burden of disease in Ethiopia [4]. Through meetings with key persons in the Ethiopian Ministry of Health in November 2010 we got access to drafts of the Health Sector and Development Program IV (HSDP IV) and the final draft in August 2011. Access to HSDP IV planning provided information on which interventions were conceived as relevant to the Ethiopian Ministry of Health and also current coverage estimates. This study does not look at the impact of all interventions included in HSDP IV, but the 14 selected child health interventions only. The complete list and description of the 14 interventions are provided in the Appendix S1. We modified the newest available LiST projection for Ethiopia (June 2011) by use of contextualized data and evidence. We adjusted life expectancy (UN Population Data) and life table (WHO 2008) in the demographic projection and updated epidemiological and current coverage data in the LiST model (2011) [1], [7], [10]. We used effectiveness data from the Child Health Epidemiology Research Group (CHERG), as we did not have contextualized effectiveness data [30]. We modeled three different scenarios where interventions were scaled-up in one package, as seen in Figure 1. We scaled-up by linear interpolation from current coverage in 2011 to target levels in 2015: The scenarios include packages of interventions. In the analysis, preventive interventions are scaled-up before treatment interventions scaled-up. The different interventions included will therefore influence each other, and the estimated number of deaths averted by interventions. For example; when pneumococcal vaccine is not included in the package (Scenario 3), LiST estimates that there will be more cases of pneumonia, and therefore also more lives to be averted by case-management of pneumonia (compared to Scenario 2). Under-5 mortality rate (U5MR), neonatal mortality rate (NMR) and deaths averted are primary outcome measures. Based on the LiST analysis, we estimated two secondary outcome measures: life expectancy at birth and inequality in age at death (Ginihealth). Life expectancy is a long-term outcome and an indicator of overall average population health. Ginihealth adds information on distribution of age at death around the average. The LiST analysis provided life tables with data on mortality for the different age groups in a given population, here presented as estimated number of deaths per 100 000. The life tables included impacts on mortality rates among the population below 5 years of age after interventions. We assume that the life tables from LiST represent the average Ethiopian population in 2011 and 2015. In a spreadsheet we included Ethiopian data on fertility and population data from PopMod (2005) to also estimate impact on maternal mortality for scale-up of such interventions, as this is not included in current life tables from LiST [32]. We calculated life expectancy at birth based on the life tables from LiST by use of standard methods [33]. The calculations are simplified, as the life tables are non-dynamic. The life tables were then used to estimate inequality in age at death (Ginihealth) by a method first suggested by LeGrand [34], later developed by others [35], [36], [37]. Ginihealth is here used as a measure of overall health inequality in a population [34], [36], [38]. In the literature, the Gini index is commonly used to analyze income distribution within populations [39]. Wagstaff and others have applied the Gini index to analyze distribution of health within populations. Ginihealth hereby describes the degree of overall health inequality, defined as inequality in age at death in a given population [35]. We calculated Ginihealth by use of equation 1 [37]: Where μ is the average health in this population, h i is age of death, f i is the sample proportion in the ith group, and Ri is the rank of this group (rank 1 is the rank of the best-off group with highest age of death). The parameter v is a parameter reflecting aversion to inequality that we have set to v = 2 in this study (the value used in the standard Gini) [35]. The Gini coefficient equals to zero for perfect equality and one for the most unequal distribution. Ginihealth normally varies between 0.10 and 0.50 [36]. Socioeconomic inequality in health is an important contributor to overall inequality in health, and can be measured within a population ranked by wealth by the analogous Concentration Index [35]. This is however not done in this paper, where we study pure health inequality, by differences in age at death, not by differences in wealth.

AI Digest

Study Justification:

The study aims to provide evidence-based information to support policy-making and priority-setting in the area of child health interventions in Ethiopia. The Millennium Development Goal of reducing under-5 mortality by two-thirds between 1990 and 2015 is still far from being achieved in many countries, including Ethiopia. This study seeks to estimate the potential impact of increasing coverage of 14 selected health interventions on child mortality, life expectancy, and inequality in age at death in Ethiopia.

Highlights:

– The study uses the Lives Saved Tool (LiST) to estimate the potential impact of scaling up health interventions in Ethiopia.
– Three different scenarios are modeled: scaling up to government target levels, 90% coverage, and 90% coverage of the five interventions with the highest impact.
– Under-5 mortality rate, neonatal mortality rate, and deaths averted are the primary outcome measures.
– Prioritizing child health interventions could lead to a significant reduction in under-5 mortality rate, increase in life expectancy at birth, and reduction in inequality in age at death (Ginihealth).

Recommendations:

– The Millennium Development Goal for child health is achievable in Ethiopia by prioritizing child health interventions.
– Increasing coverage of these interventions would not only reduce child mortality but also improve overall population health and reduce health inequality.
– Policy-makers should consider scaling up the 14 selected child health interventions to achieve these positive outcomes.

Key Role Players:

– Ethiopian Ministry of Health: Responsible for implementing and coordinating the scaling up of child health interventions.
– Health care providers: Involved in delivering the health interventions at various levels of care.
– Community health workers: Play a crucial role in reaching remote and underserved populations with the interventions.
– Non-governmental organizations (NGOs): Provide support and resources for implementing and monitoring the interventions.
– Donors and international organizations: Contribute funding and technical assistance to support the scaling up of interventions.

Cost Items for Planning Recommendations:

– Human resources: Budget for training and hiring additional health care providers and community health workers.
– Equipment and supplies: Allocate funds for procuring necessary medical equipment, vaccines, and other supplies.
– Infrastructure: Consider the need for upgrading health facilities and improving access to health services.
– Monitoring and evaluation: Set aside resources for monitoring the implementation and impact of the interventions.
– Health promotion and community engagement: Allocate funds for community outreach activities and health education campaigns.
– Research and data collection: Include a budget for conducting further research and collecting data to inform decision-making.
Please note that the cost items provided are general categories and not actual cost estimates. The specific costs will depend on the context and scale of implementation.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on the use of the Lives Saved Tool (LiST) and effectiveness data from the Child Health Epidemiology Research Group (CHERG). The study provides estimates of the potential health impact of increasing coverage of 14 selected health interventions on child mortality in Ethiopia. The outcomes measured include under-5 mortality rate, neonatal mortality rate, deaths averted, life expectancy at birth, and inequality in age at death. The study also provides a clear methodology and justification for the selection of interventions. To improve the evidence, the study could include a discussion of the limitations and potential biases of the modeling tool used, as well as a discussion of the generalizability of the findings to other resource-constrained settings.

Abstract

In support of evidence-based policy making and priority-setting, we have recently seen a new focus on modeling tools analyzing possible impacts of increased access to health interventions. Rudan and colleagues argue that an optimal priority setting tool “should be able to draw on the best local evidence and guide policy makers and governments to identify, prioritize, and implement evidence-based health interventions for scale-up and delivery” [27]. PopMod [20], Marginal Budgeting for Bottlenecks (MBB) [23] and the Lives Saved Tool (LiST) [24] are current examples of modeling tools which assess the impacts of scaling-up health care interventions using evidence-based data. PopMod and MBB also include costing opportunities. The modeling tools assume that the interventions will be fully implemented. However, actual effects of scale-up depend on factors like human resources and equipment available, health seeking behavior and other context depended factors. We chose to use LiST, as the most user-friendly and the most recently updated tool available at time of analysis, as we believe these concerns are relevant for policy makers. LiST and MBB is also a part of the new OneHealth tool, which harmonize current modeling tools on costing and impact assessment in the health sector [28]. This paper addresses impacts on health of scaling-up interventions, but does not address costs. We used the Lives Saved Tool (LiST) version 4.43. LiST models changes in maternal and child mortality by scaling-up coverage of health care interventions. The tool operates within the Spectrum model, where maternal and child health data are integrated with demographic (DemProj) and HIV/AIDS (AIM) projections [29]. Evidence-based effectiveness data are from the Child Health Epidemiology Research Group (CHERG), which is an expert group conducting technical reviews on maternal, neonatal and child morbidity and mortality and effectiveness of interventions [30]. We selected 14 essential child health interventions relevant for Ethiopian decision makers through the following process: first, we identified recent recommendations on which interventions to give priority to in resource-constrained settings from the literature [12], [13], [17], [18]. Second, we included interventions that are explicitly considered in Ethiopia, but are not yet implemented, such as the pneumococcal vaccine [31]. The interventions were chosen to reflect the diversity of intervention options and delivery platforms, i.e. according to a) level of care, b) preventive or curative concerns and c) early and late mortality. All interventions target conditions with high burden of disease in Ethiopia [4]. Through meetings with key persons in the Ethiopian Ministry of Health in November 2010 we got access to drafts of the Health Sector and Development Program IV (HSDP IV) and the final draft in August 2011. Access to HSDP IV planning provided information on which interventions were conceived as relevant to the Ethiopian Ministry of Health and also current coverage estimates. This study does not look at the impact of all interventions included in HSDP IV, but the 14 selected child health interventions only. The complete list and description of the 14 interventions are provided in the Appendix S1. We modified the newest available LiST projection for Ethiopia (June 2011) by use of contextualized data and evidence. We adjusted life expectancy (UN Population Data) and life table (WHO 2008) in the demographic projection and updated epidemiological and current coverage data in the LiST model (2011) [1], [7], [10]. We used effectiveness data from the Child Health Epidemiology Research Group (CHERG), as we did not have contextualized effectiveness data [30]. We modeled three different scenarios where interventions were scaled-up in one package, as seen in Figure 1. We scaled-up by linear interpolation from current coverage in 2011 to target levels in 2015: The scenarios include packages of interventions. In the analysis, preventive interventions are scaled-up before treatment interventions scaled-up. The different interventions included will therefore influence each other, and the estimated number of deaths averted by interventions. For example; when pneumococcal vaccine is not included in the package (Scenario 3), LiST estimates that there will be more cases of pneumonia, and therefore also more lives to be averted by case-management of pneumonia (compared to Scenario 2). Under-5 mortality rate (U5MR), neonatal mortality rate (NMR) and deaths averted are primary outcome measures. Based on the LiST analysis, we estimated two secondary outcome measures: life expectancy at birth and inequality in age at death (Ginihealth). Life expectancy is a long-term outcome and an indicator of overall average population health. Ginihealth adds information on distribution of age at death around the average. The LiST analysis provided life tables with data on mortality for the different age groups in a given population, here presented as estimated number of deaths per 100 000. The life tables included impacts on mortality rates among the population below 5 years of age after interventions. We assume that the life tables from LiST represent the average Ethiopian population in 2011 and 2015. In a spreadsheet we included Ethiopian data on fertility and population data from PopMod (2005) to also estimate impact on maternal mortality for scale-up of such interventions, as this is not included in current life tables from LiST [32]. We calculated life expectancy at birth based on the life tables from LiST by use of standard methods [33]. The calculations are simplified, as the life tables are non-dynamic. The life tables were then used to estimate inequality in age at death (Ginihealth) by a method first suggested by LeGrand [34], later developed by others [35], [36], [37]. Ginihealth is here used as a measure of overall health inequality in a population [34], [36], [38]. In the literature, the Gini index is commonly used to analyze income distribution within populations [39]. Wagstaff and others have applied the Gini index to analyze distribution of health within populations. Ginihealth hereby describes the degree of overall health inequality, defined as inequality in age at death in a given population [35]. We calculated Ginihealth by use of equation 1 [37]: Where μ is the average health in this population, h i is age of death, f i is the sample proportion in the ith group, and Ri is the rank of this group (rank 1 is the rank of the best-off group with highest age of death). The parameter v is a parameter reflecting aversion to inequality that we have set to v = 2 in this study (the value used in the standard Gini) [35]. The Gini coefficient equals to zero for perfect equality and one for the most unequal distribution. Ginihealth normally varies between 0.10 and 0.50 [36]. Socioeconomic inequality in health is an important contributor to overall inequality in health, and can be measured within a population ranked by wealth by the analogous Concentration Index [35]. This is however not done in this paper, where we study pure health inequality, by differences in age at death, not by differences in wealth.

Materials

https://efashare.b-cdn.net/materials/633332699f99d369b004c074b8f07dadaae032a79859df5e84f2fbb9e0292575/pone.0041521.s001.doc

Innovations

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women with information and reminders about prenatal care, nutrition, and vaccinations. These apps can also connect women with healthcare providers for virtual consultations and appointment scheduling.

2. Telemedicine: Establish telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls. This can help overcome geographical barriers and provide access to prenatal care and advice.

3. Community Health Workers: Train and deploy community health workers who can provide basic prenatal care, education, and support to pregnant women in their communities. These workers can also identify high-risk pregnancies and refer women to appropriate healthcare facilities.

4. Maternal Health Vouchers: Implement a voucher system that provides pregnant women with financial assistance to access maternal healthcare services. These vouchers can cover the cost of prenatal check-ups, delivery, and postnatal care, ensuring that women can afford the necessary care.

5. Maternity Waiting Homes: Establish maternity waiting homes near healthcare facilities, where pregnant women from remote areas can stay during the final weeks of pregnancy. This ensures that they are close to medical assistance when labor begins, reducing the risk of complications during childbirth.

6. Transportation Support: Provide transportation support for pregnant women to reach healthcare facilities for prenatal check-ups, delivery, and postnatal care. This can include subsidized transportation services or the use of ambulances in emergency situations.

7. Health Education Programs: Develop and implement comprehensive health education programs that focus on maternal health. These programs can educate women and their families about the importance of prenatal care, nutrition, hygiene, and birth preparedness.

8. Maternal Health Clinics: Establish specialized maternal health clinics that provide comprehensive prenatal, delivery, and postnatal care. These clinics can be equipped with skilled healthcare professionals and necessary medical equipment to ensure quality care for pregnant women.

9. Public-Private Partnerships: Foster collaborations between the government, private healthcare providers, and non-profit organizations to improve access to maternal health services. This can involve sharing resources, expertise, and funding to expand healthcare facilities and services.

10. Data-driven Decision Making: Utilize data and modeling tools, such as the Lives Saved Tool (LiST), to analyze the impact of scaling up health interventions on maternal and child mortality. This can help policymakers prioritize and allocate resources effectively to improve maternal health outcomes.

It’s important to note that the specific innovations implemented may vary depending on the local context, resources, and healthcare system of each country or region.

AI Innovations Description

The recommendation to improve access to maternal health based on the provided description is to prioritize child health interventions in Ethiopia. This recommendation is supported by evidence-based modeling tools such as the Lives Saved Tool (LiST), which assesses the potential impact of scaling up health care interventions using data from the Child Health Epidemiology Research Group (CHERG). By increasing coverage of 14 selected health interventions, including preventive and curative measures, Ethiopia can reduce under-5 mortality rate, neonatal mortality rate, and overall child mortality. This prioritization of child health interventions would also increase life expectancy at birth and reduce inequality in the age of death (Ginihealth). It is important for decision-makers to have contextual information and evidence-based data to guide policy-making and resource allocation in order to achieve the Millennium Development Goal for child health in Ethiopia.

AI Innovations Methodology

The methodology described in the provided text is focused on using the Lives Saved Tool (LiST) to estimate the potential impact of scaling up 14 selected health interventions on child mortality in Ethiopia from 2011 to 2015. The goal is to provide evidence-based information to support policy-making and priority-setting in resource-constrained settings.

The LiST tool operates within the Spectrum model, which integrates maternal and child health data with demographic and HIV/AIDS projections. It uses evidence-based effectiveness data from the Child Health Epidemiology Research Group (CHERG) to model changes in maternal and child mortality by scaling up coverage of health care interventions.

To simulate the impact of scaling up interventions, the tool assumes that the interventions will be fully implemented. However, the actual effects of scale-up depend on factors such as the availability of human resources and equipment, health-seeking behavior, and other context-dependent factors.

The methodology involves selecting 14 essential child health interventions based on recent recommendations from the literature and input from the Ethiopian Ministry of Health. These interventions reflect a diversity of options and delivery platforms, targeting conditions with a high burden of disease in Ethiopia.

Three different scenarios are modeled, where interventions are scaled up in packages. The analysis includes primary outcome measures such as under-5 mortality rate, neonatal mortality rate, and deaths averted. Secondary outcome measures include life expectancy at birth and inequality in age at death (Ginihealth).

Life tables from LiST are used to estimate life expectancy at birth and inequality in age at death. Ginihealth is calculated using the Gini coefficient, which measures overall health inequality in a population based on differences in age at death.

It’s important to note that this methodology focuses on the health impact of scaling up interventions and does not address costs. The LiST tool is chosen for its user-friendliness and up-to-date data availability, and it is part of the OneHealth tool that harmonizes modeling tools for costing and impact assessment in the health sector.

Overall, this methodology provides a systematic approach to simulate the potential impact of scaling up health interventions on child mortality, life expectancy, and inequality in age at death in Ethiopia. It aims to inform decision-makers and guide priority-setting in improving access to maternal health.

Authors & Co-Authors

Statistics:

Citations: 8

Authors: 6

Identifiers:

DOI: 10.1371/journal.pone.0041521

Research Areas:

Community Interventions, Disparities, Health System and Policy, Infectious Diseases, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Technology and Innovations

Study Countries:

Ethiopia

Study Design:

Cross Sectional Study, randomised-control-trial