# Improving Age- and Cause-Specific Under-Five Mortality Rates (ACSU5MR) by Systematically Accounting Measurement Errors to Inform Child Survival Decision Making in Low Income Countries

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $533,027

## Abstract

Project Summary
An estimated 5.0 million children died before age 5 years globally in 2020. To improve child survival, the US
government and international community invest in the development, evaluation and implementation of age-
targeted, disease-specific life-saving childhood interventions, such as a malaria vaccine or azithromycin to
address leading causes of under-five mortality including malaria, diarrhea, pneumonia and meningitis. Routine
and timely estimates of age-and cause-specific under-five mortality (ACSU5M) are critical for understanding
heterogeneity in causes of deaths within the under-five window and evaluating child survival policy and
program effectiveness. ACSU5M estimates mandate precision well beyond what’s required to effectively target
policies and programs in adults yet empirical data are scarce. Demographic and epidemiological evidence
amounts to the conclusion that child cause of death is not uniform in the 1-59-month period. National empirical
data at levels of specificity below 1-59 months are often not available in low resource settings with limited civil
registration systems. Such data and estimates bear considerable scientific value to inform the development
and impact evaluation of age-specific childhood interventions and their scale-up. Previous research has
suffered from four main drawbacks: (i) using custom-collected data to understand age dynamics in a single
cause; (ii) estimating ACSU5M only in broad age groups; (iii) ignoring uncertainty that arises from the empirical
measurements of ACSU5M, such as prevalence measurement errors from routine household surveys; and (iv)
failing to address cost effectiveness in data collection strategies. We leverage a team with extensive
experience in both cause-specific and under-five mortality measurement and estimation to propose a series of
Aims targeted at these drawbacks by specifically assessing and accounting for measurement errors to improve
ACSU5M estimation in low-income countries. Our proposal evaluates data collection strategies through
validation studies, focus group discussions and cluster randomized trials, and develops state-of-the-art
statistical methodology to improve both the inputs into and the methodology behind ACSU5M estimation. Our
statistical work builds on our ongoing NICHD R21HD095451 to develop a flexible Bayesian model which
incorporates multiple sources of uncertainty using partial registration data. Partnerships with Country wide
Mortality Surveillance for Action in Mozambique (COMSA-Mozambique) and the Matlab, Bangladesh Health
and Demographic Surveillance System (HDSS) provide both infrastructure to evaluate and innovate on data
collection strategies, high quality data for methodology development, and target end users for dissemination. If
successful, the proposed study will further improve understanding of measurement errors in ACSU5M
originated from major data collection strategies and significantly advance ACSU5M estimation to systematical...

## Key facts

- **NIH application ID:** 10840394
- **Project number:** 5R01HD107015-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Li Liu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $533,027
- **Award type:** 5
- **Project period:** 2023-05-11 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10840394

## Citation

> US National Institutes of Health, RePORTER application 10840394, Improving Age- and Cause-Specific Under-Five Mortality Rates (ACSU5MR) by Systematically Accounting Measurement Errors to Inform Child Survival Decision Making in Low Income Countries (5R01HD107015-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10840394. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*