Quantitative modeling results

Burden of Disease

The largest contributors to under-five mortality in Senegal between 2000 and 2017 are shown below.

Caveats

Cause-of-death modeled estimates rely on available data sources, with limitations in data quality and completeness. The Institute for Health Metrics and Evaluation (IHME) provides a data quality assessment rating of the quality and completeness of cause-of-death estimates ranging from 0 (worst) to 5 stars (best). Peru’s rating is 3 out of 5 stars, meaning that the proportion of its data that is well-certified is between 35 and 65 percent; this indicates moderate levels of uncertainty in Peru’s mortality and cause-of-death estimates.¹

Quantitative Modeling Approach

The research team collaborated with IHME to look at quantitative modeling results using a decomposition method, and also collaborated with the Johns Hopkins Bloomberg School of Public Health to model results using the Lives Saved Tool (LiST). These analyses complement the primary research by looking at what the models suggest about the likely contribution of specific interventions in reducing child mortality. A summary of the results is presented in the following sections.

Modeling Results

The decomposition method estimates the percentage decline in U5M attributed to changes in risk factors and intervention coverage, based on efficacy assumptions derived from published literature.

From this method, the most significant contributor to the decline in Peru’s U5M rate is health interventions, which includes preventive measures like vaccines and curative treatments. Another significant contributor is program-related risk factors, which includes child growth failure, low birth weight, suboptimal breastfeeding, and vitamin A & zinc deficiency.

The decomposition analysis found an additional 14 percent reduction in child mortality (26 percent of the total reduction during this period) was attributed to risk factors corresponding to other communicable diseases, other non-communicable diseases, and other injuries. This reflects the portion of reduction in each of these causes of death (CoD) that is not accounted for by health systems interventions (bottom row).

We checked these results against LiST estimates of the number of lives saved attributed to each intervention based on estimates of disease incidence and deaths from the United Nations Inter-Agency Group for Child Mortality Estimation (IGME), as well as assumptions about effectiveness of interventions from published literature.

This model attributes a total of 60,600 lives saved between 2000 and 2016 to specific health interventions, with the largest contributors being neonatal and antenatal interventions (particularly labor and delivery management and case management of neonatal sepsis and pneumonia), vaccines and supplements (especially Hib vaccine and pneumococcal conjugate vaccine), and Integrated Management of Childhood Illness (IMCI) treatments (especially oral antibiotics for pneumonia).²

Summary

Across both modeling methods, neonatal and antenatal interventions and vaccines are estimated to have had a significant impact on reducing under-five deaths in Peru. The results of the primary research findings on implementation outcomes for each intervention are presented in the next section. A description of the quantitative modeling methods can be found in the Methodology section.