Quantitative modeling results

Burden of disease

Over half of the burden of under-five mortality in Nepal in 2000 is represented by deaths due to neonatal disorders and lower respiratory infections, according to burden of disease estimates from the Institute for Health Metrics and Evaluation (IHME).¹ Additionally, over half of the overall reduction in deaths between 2000 and 2015 were due to reductions in these two causes.¹ A timeline of key policies and new interventions that likely contributed to the reductions in under-five mortality is shown in the figure below, along with the estimated reductions by cause of death.

Caveats

Cause of death modeled estimates rely on available data sources, with limitations in data quality and completeness. IHME provides a data quality assessment rating of the quality and completeness of cause of death estimates rating from a score of 0 (worst) to 5 (best). Nepal’s rating is 1, indicating that mortality estimates – and cause of death estimates in particular - have large degrees of uncertainty.

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 Nepal’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 18 percent reduction in under-five mortality (27 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 Lives Saved Tool (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.²

This model attributes a total of 156,703 lives saved between 2000 and 2016 to specific health interventions, with the largest contributors being neonatal and antenatal interventions, vaccines and supplements, and Integrated Management of Childhood Illness (IMCI) treatments.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.