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Principal Investigator: JONATHAN C WAKEFIELD
Organization: UNIVERSITY OF WASHINGTON
Fiscal Year: 2024
Award: $308,048
Funding agency: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Project Summary: The goal of the proposal is to develop a Bayesian statistical framework for mortality estimation
from disparate data sources. Using this framework we will produce a suite of principled methods to be used in
those situations in which vital registration data are lacking. We will emphasize efficient implementations that
can be used by researchers in low- and middle-income countries (LMICs), who may have limited computing
resources. In Aim 1, we will develop guidelines on a general statistical framework for mortality estimation. Aim 2
will focus on subnational child mortality with particular emphasis on the under-5 mortality rate (U5MR), which is
a key indicator of the health of a population, and the neonatal mortality rate (NMR). Excess mortality estimation
during the Covid-19 pandemic, by month, at the country level, will be the subject of Aim 3. We will disseminate
results widely and provide software and training in the developed methods.
We will produce yearly estimates of U5MR and NMR at the geographical level at which health decisions are
made. To achieve this goal, household survey, VR and census data must be combined in a coherent way. Census
data on child mortality typically provide summary birth history (SBH) data, which consist of mother's age along
with the number of children born and the number who died, but without the times at which those events occurred.
We will develop a framework for combining the different data sources, which will entail dealing with the design
issues in the household survey, accounting for unknown birth and death times in the SBH data, and estimating the
completeness of the VR data (births and deaths). We will also incorporate demographic information via a form
of Bayesian benchmarking. Effective and appropriate use of the models will require rigorous model assessment,
careful interpretation of results and meaningful and informative graphical summaries.
We will develop robust models to evaluate the excess mortality, i.e., the difference between the deaths ob-
served in the pandemic and those expected if the pandemic had not occurred. We will model the expected deaths,
and incorporate the uncertainty in this endeavor in the excess mortality calculation. Completeness of mortality
counts, that is, under-reporting and delays in reporting, will also be considered. For countries who do not report
deaths in the pandemic, we must predict the mortality count using available country-level covariate data, and we
will adopt flexible yet interpretable regression forms, and acknowledge uncertainty in the covariate data.
We will produce user-friendly software for the methods, along with vignettes and training materials, including
short courses. The endpoint is to have software that can be used by researchers in LMICs. All aims will be
informed by the collaborative team's close links with the United Nations Inter-agency Group for Child Mortality
Estimation (for the subnational child mortality aim) and the World Health Organization Division of Data, Analytics
and Delivery for Impact (for the excess mortality aim). Together we will develop methods to highlight disparities
and inform interventions.
Terms: <0-11 years old><Accounting><Address><Adopted><Age><Area><Bayesian Modeling><Bayesian adaptive designs><Bayesian adaptive models><Bayesian belief network><Bayesian belief updating model><Bayesian framework><Bayesian hierarchical model><Bayesian network model><Bayesian nonparametric models><Bayesian spatial data model><Bayesian spatial image models><Bayesian spatial models><Bayesian statistical models><Bayesian tracking algorithms><Benchmarking><Best Practice Analysis><Birth><Birth History><COVID crisis><COVID epidemic><COVID pandemic><COVID-19 crisis><COVID-19 epidemic><COVID-19 era><COVID-19 global health crisis><COVID-19 global pandemic><COVID-19 health crisis><COVID-19 pandemic><COVID-19 period><COVID-19 public health crisis><COVID-19 years><Caring><Censuses><Cessation of life><Child><Child Mortality><Child Youth><Childhood><Children (0-21)><Collaborations><Computer software><Country><Data><Data Collection><Data Reporting><Data Sources><Death><Death Rate><Decision Making><Dedications><Development><Disease><Disorder><Disparate><Disparities><Disparity><Elements><Event><Excess Mortality><Exercise><Geography><Goals><Guidelines><Health><Household><Individual><Intervention><Intervention Strategies><Investigators><LMIC><Link><Manuscripts><Measures><Methodology><Methods><Modeling><Mothers><Neonatal Mortality><Paper><Parturition><Peer Review><Population><Procedures><Process><Production><Public Health><Publishing><Reporting><Reproducibility><Reproducibility of Findings><Reproducibility of Results><Research Personnel><Researchers><SARS-CoV-2 epidemic><SARS-CoV-2 global health crisis><SARS-CoV-2 global pandemic><SARS-CoV-2 pandemic><SARS-coronavirus-2 epidemic><SARS-coronavirus-2 pandemic><Severe Acute Respiratory Syndrome CoV 2 epidemic><Severe Acute Respiratory Syndrome CoV 2 pandemic><Severe acute respiratory syndrome coronavirus 2 epidemic><Severe acute respiratory syndrome coronavirus 2 pandemic><Software><Software Validation><Software Verification><Statistical Methods><Stratification><Survey Instrument><Surveys><Sustainable Development><System><Time><Training><Translations><Twitter><Uncertainty><United Nations><Update><Walking><Work><World Health Organization><ages><benchmark><complex modeling><computational resources><computer based prediction><computing resources><coronavirus disease 2019 crisis><coronavirus disease 2019 epidemic><coronavirus disease 2019 global health crisis><coronavirus disease 2019 global pandemic><coronavirus disease 2019 health crisis><coronavirus disease 2019 pandemic><coronavirus disease 2019 public health crisis><coronavirus disease crisis><coronavirus disease epidemic><coronavirus disease pandemic><coronavirus disease-19 global pandemic><coronavirus disease-19 pandemic><data modeling><data representation><data representations><data streams><death among neonates><death among newborns><death in neonates><death in newborn><design><designing><developmental><discrete data><doubt><emergent pandemic><emerging pandemic><flexibility><flexible><global health><interest><interventional strategy><kids><low and middle-income countries><model of data><model the data><modeling of the data><mortality><mortality among neonates><mortality among newborns><mortality in neonates><mortality in newborns><mortality rate><mortality ratio><neonatal death><neonatal demise><new approaches><new pandemic><newborn death><newborn mortality><novel approaches><novel pandemic><novel strategies><novel strategy><open source><pandemic><pandemic disease><pediatric><predictive modeling><public health intervention><response><severe acute respiratory syndrome coronavirus 2 global health crisis><severe acute respiratory syndrome coronavirus 2 global pandemic><sex><statistic methods><success><temporal measurement><temporal resolution><theories><time measurement><translation><user friendly computer software><user friendly software><web site><website><youngster>