To prepare this report, the University of Global Health Equity (UGHE) and Gates Ventures undertook an extensive review of available information and published data on under-five mortality in Nepal and worldwide. This review focused on the policies, strategies, and evidence-based interventions that are available to potential exemplar countries, and the uptake and implementation of these interventions in Nepal.
Gates Ventures researchers conducted initial research through MEDLINE (PubMed) and Google Scholar using the search terms “child mortality” or “under-five mortality” and “Nepal.” Further searches included specific evidence-based interventions, causes of death, or contextual factors as search terms (e.g., “insecticide-treated nets,” “malaria,” or “community health workers”). The UGHE team then reviewed this preliminary desk research for accuracy and completeness and identified additional potential sources.
To deepen the research, UGHE collaborated with their Nepali consultant, Nepal Public Health Foundation (NPHF), to identify individuals to interview for further insights on the implementation strategies, policies, and contextual factors most relevant to the success in reducing under-five mortality in Nepal - and to glean lessons on approaches that could be implemented in other countries.
These informants included current and former MOH employees who were deeply involved in addressing specific disease or intervention areas. Other interviewees included policy makers, academics, donors, and implementing partners both from within and outside Nepal.
In selecting the interviewees, the teams focused on individuals active between 2000 and 2015 who could knowledgeably discuss one or more phases of Nepal’s implementation process of evidence-based interventions during that period.
The program co-lead, the research and project coordinator, and NPHF led telephone or in-person interviews with 21 key informants. The teams informed interviewees about the goals and structure of the project and obtained consent for their participation and for recording the conversations.
The recording was solely for the purpose of verifying the accuracy of interview notes. All recordings and interview notes were kept in password-protected computers and stored on a limited-access Google Drive. All recordings were destroyed once the interview-coding process was complete. No quotations or other materials that might be identified with a specific individual are included without that person’s explicit permission.
Even with the focus on the 2000–2015 period, the interviews generated useful insights about important events preceding and following those years. Interviewees also identified sources of data and information that could supplement those that had already been unearthed during the desk review.
The interviews and the entire research process took place with the awareness, approval, and support of the MOH and the NHRC, whose ethical review board gave its approval prior to the start of the data collection period. The project was conducted entirely retrospectively, using de-identified data and report materials.
The research team partnered with the Institute for Health Metrics and Evaluation (IHME) and the Johns Hopkins Bloomberg School of Public Health to conduct quantitative modeling using a decomposition method and the Lives Saved Tool. These analyses complemented the primary research by examining what the models suggested about the likely contribution of specific interventions in reducing child mortality.
Other collaborations included working with IHME to generate health-equity maps, and with the International Center for Equity in Health to explore changes in equity indices for mortality and evidence-based intervention coverage. In addition, the researchers consulted the findings of other organizations and initiatives, including WHO and Countdown 2015.
Both the desk review and the primary research phases of the project were informed by an implementation science framework designed specifically for this report. Use of this framework informed the design and implementation of the research to ensure that itwent beyond measuring coverage of evidence-based interventions and included an analysis of implementation strategies and outcomes - areas not covered in more traditional research processes.
For example, while it is often possible to identify evidence-based interventions and other policies that a country deploys to reduce under-five mortality, highly relevant information on how these interventions and policies were chosen, adapted, implemented, and sustained are often missing from available published or gray literature. Implementation science can provide that information.
In addition, implementation science offers important tools for how to think more holistically about how and why countries were able to reduce under-five mortality - and how to identify potential lessons for other countries. This is especially important in light of the fact that two countries implementing the same policies and interventions could experience divergent results. Implementation science helps researchers better understand the reasons behind such differences.
Toward these ends, the teams built on existing implementation science frameworks to develop a model for understanding the contribution of contextual factors and the different levels of actors involved: global, national, ministerial, subnational, facility, and community.
The UGHE team used a mixed-methods explanatory approach, applying the framework to understand the progress (or lack thereof) of evidence-based interventions in reducing each cause of death, and the coverage levels of chosen interventions. The framework also helped identify systemic factors that advanced or impeded progress at the local, national, and global levels.
Finally, it is worth pointing out that the health and survival of children under five depend upon countless social and structural factors, many of which fall outside the scope of this project. For example, the research did not include in-depth reviews of Nepal’s water and sanitation systems, antipoverty programs, or agricultural policies. It focused instead on health-related factors amenable to readily identifiable medical interventions.
Quantitative Modeling Analysis and Synthesis
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 quantitative analyses complement the primary research by looking at what the models suggest about the likely contribution of specific interventions in reducing child mortality.
The decomposition analysis conducted by IHME breaks down changes over time for a series of factors that directly influence child mortality levels using the Das Gupta method. The overall change in mortality between years is divided into contributions from:
- Interventions and risk factors: Interventions and risk factors influence mortality rates through changes in the proportion of the population exposed to each, and through changes in their corresponding relative risks of mortality. Increased coverage of specific interventions is known to reduce mortality rates, whereas increased exposure to certain risk factors increases mortality rates. The relative risk for each specific disease outcome is established through a literature review.
- Population changes: The total number of deaths in a given year is a product of both age-specific mortality rates and the population size in each age group, so changes in both population growth and population age structure are factored into the decomposition. One example of the effect of population changes is if mortality rates are cut in half while the population size doubles in each age group, total deaths remain the same.
The Lives Saved Tool (LiST), developed by the Johns Hopkins Bloomberg School of Public Health, calculates changes in cause-specific mortality based on intervention coverage change, intervention effectiveness for that cause, and the percentage of cause-specific mortality sensitive to that intervention. Coverage data come from large-scale household surveys – typically Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), as well as WHO/UNICEF and the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP).
Default effectiveness values come from systematic reviews, meta-analyses, Delphi estimations, and randomized control trials based upon the Child Health Epidemiology Reference Group guidelines. Baseline mortality is drawn from country-level estimates from DHS, WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division and the UN Inter-Agency Group for Child Mortality Estimation (IGME). Additionally, users who have more recent or alternative data sources can easily replace default data with their own.