Burden of Disease

The highest burden of mortality among children under age five (under-five mortality or U5M) in Bangladesh in 2000 was due to neonatal disorders and lower respiratory infections, representing 62 percent of all deaths. Throughout the study period these two diseases remained as the top contributors, followed by diarrheal diseases. More than half of the reduction in overall U5M was due to reductions in deaths due to neonatal disorders and lower respiratory infections.

Under-five mortality in Bangladesh over time, death rates per 100,000 children under 5

Data Source: IHME GBD 2017


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). Bangladesh’s rating is 2 out of 5 stars, indicating that its mortality estimates – especially its cause-of-death estimates – contain large degrees of uncertainty.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 Bangladesh’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.1  

The decomposition analysis found an additional 10 percent reduction in child mortality (14 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).1  


Decomposition analysis

Data Source: Analysis from GBD Risk Factors Collaborators, GBD 2017, IHME

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 733,294 lives saved between 2000 and 2015 to specific health interventions, with the largest contributors being neonatal and antenatal interventions (including case management of neonatal sepsis/pneumonia, clean postnatal practices and labor and delivery management); Integrated Management of Childhood Illness (IMCI) interventions (including oral antibiotics for pneumonia and ORS); and vaccines (particularly Haemophilus influenzae type b [Hib] and measles).2

Lives Saved tool results for Bangladesh, 2000 – 2016

Data Source: Lives Saved Tool - Johns Hopkins Bloomberg School of Public Health


Across both modeling methods, neonatal and antenatal interventions and vaccines are estimated to have had a significant impact on reducing under-five deaths in Bangladesh. 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.

  1. 1
    Gakidou, Emmanuela, et al. "Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016." The Lancet 390.10100 (2017): 1345-1422. Institute for Health Metrics and Evaluation (IHME). Seattle, WA: IHME; 2018.
  2. 2
    Johns Hopkins Bloomberg School of Public Health. Lives Saved Tool. Baltimore, MD; 2018.

Detailed findings