Child Health and Mortality Prevention Surveillance (CHAMPS)

Understanding the Problem

Despite reductions, childhood mortality remains high in low- and middle-income countries (LMICs) in sub-Saharan Africa and South Asia.^1,2 Policy makers and other stakeholders need high-quality data on the cause of death for stillbirths and children under five, so they can target interventions toward the leading causes of death in their regions. The Child Health and Mortality Prevention Surveillance (CHAMPS) Network provides robust, standardized, longitudinal mortality data on stillbirths and deaths in children under age 5 in Sub-Saharan Africa and South Asia from many sources.³ These data help global child health policymakers as they work to prevent childhood deaths and illness.

CHAMPS is committed to making its data accessible to the scientific, clinical, and public health communities through their website (https://champshealth.org/data/). However, a lack of tools makes getting started with CHAMPS data more daunting for new researchers. The different types of data often require complex filtering and joining to get to a desired result. Without tools, pulling data together to build reports and statistical summaries is time-consuming and potentially error prone, given the complexity of the data. The goal of this data science rally was to develop programmatic tools that researchers need to build reports, compute statistics, and complete custom data analyses from CHAMPS data.

These programmatic tools are owned and made available by the CHAMPS Network at https://champshealth.org.

Objectives

1. Provide tools that help researchers generate reports from the data reliably and efficiently.
2. Provide tools that make it easier for new researchers to get acquainted with the data.

Results

Researchers can register and request access to the CHAMPS dataset at https://champs.emory.edu/redcap/surveys/?s=PCEERX993Y. The primary output of this rally is an R package with a companion website containing seven articles that guide the user through topics including how to access the data, getting acquainted with the data, creating standard reports, and doing custom analyses. (https://eghi-champs.github.io/champs-L2-statistics/index.html)

Four levels of data access are available to researchers. The CHAMPS R package supports level 2 deidentified data. This dataset has been transformed to remove or replace identifying information such as case identifiers, dates, and summary or narrative fields.

For each case, a range of data is included such as demographics, lab results, histopathology, abstracted clinical records, and verbal autopsy findings. A verbal autopsy is a structured interview recommended by the World Health Organization to help determine the cause of death.⁴ The laboratory and histopathology data are collected postmortem through a procedure called minimally invasive tissue sampling (MITS).⁵ In addition to microbiological and histological methods, molecular methods called TaqMan Array Cards (TACs) are used to evaluate MITS samples. TACs test postmortem MITS samples, such as blood, cerebrospinal fluid, lung tissue, respiratory tract swabs, and rectal swabs using real-time polymerase chain reaction (PCR) for multiple pathogens relevant to the regions where CHAMPS data is collected.⁶ Finally, a Determination of Cause of Death (DeCoDe) panel composed of experts from a local CHAMPS site analyzes the information to determine the underlying cause (event that precipitated the fatal sequence of events) and other antecedent, immediate, and maternal causes of death.⁷ CHAMPS sites have local community-based mortality notification systems that allow these data to be collected.

In addition to resources that introduce the dataset and guide users to understand the data structure, other resources in the package help analysts learn important nuances in the data. The package provides utilities to read and transform the data into convenient formats, functions to compute several statistics of interest, and some utilities for presenting these statistics in various formats such as plots and HTML tables. It also provides detailed explanations of how to compute statistics of interest and complete custom data analyses.

Actionable Outcomes

CHAMPS partners with governments and ministries of health to integrate insights from CHAMPS data into their evidence-based decision-making processes. By providing accurate and complete data on the likely causes of deaths of children under five, CHAMPS helps countries, public health programs, and local and global child health policymakers in their efforts to prevent childhood deaths and illness.

A major objective for CHAMPS is that the data and its corresponding insights have as broad a reach as possible. The multiple types of data collected in CHAMPS and the often complex way in which it is combined to provide insights can be daunting for new researchers. This data science rally has produced tools and resources that are being disseminated to help make CHAMPS data more widely accessible.

References

1. Dicker D, Nguyen G, Abate D, et al. The Lancet. 2018;392(10159):1684-1735. doi:10.1016/S0140-6736(18)31891-9
2. Wang H, Abbas KM, Abbasifard M, et al. The Lancet. 2020;396(10258):1160-1203. doi:10.1016/S0140-6736(20)30977-6
3. Salzberg NT, Sivalogan K, Bassat Q, et al. Clin Infect Dis. 2019;69(Suppl 4):S262-S273. doi:10.1093/cid/ciz599
4. Verbal autopsy standards: ascertaining and attributing causes of death tool. Accessed November 14, 2023. https://www.who.int/standards/classifications/other-classifications/verbal-autopsy-standards-ascertaining-and-attributing-causes-of-death-tool
5. Rakislova N, Fernandes F, Lovane L, et al. Clinical Infectious Diseases. 2019;69(Supplement_4):S302-S310. doi:10.1093/cid/ciz565
6. Diaz MH, Waller JL, Theodore MJ, et al. Clinical Infectious Diseases. 2019;69(Supplement_4):S311-S321. doi:10.1093/cid/ciz571
7. Blau DM, Caneer JP, Philipsborn RP, et al. Clinical Infectious Diseases. 2019;69(Supplement_4):S333-S341. doi:10.1093/cid/ciz572