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.