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...