Adaptive Gaussian Markov Random Fields for Child Mortality Estimation

The under-5 mortality rate (U5MR), a critical health indicator, is typically estimated from household surveys in lower and middle income countries. Spatio-temporal disaggregation of household survey data can lead to highly variable estimates of U5MR, necessitating the usage of smoothing models which borrow information across space and time. The assumptions of common smoothing models may be unrealistic when certain time periods or regions are expected to have shocks in mortality relative to their neighbors, which can lead to oversmoothing of U5MR estimates. In this paper, we develop a spatial and temporal smoothing approach based on Gaussian Markov random field models which incorporate knowledge of these expected shocks in mortality. We demonstrate the potential for these models to improve upon alternatives not incorporating knowledge of expected shocks in a simulation study. We apply these models to estimate U5MR in Rwanda at the national level from 1985-2019, a time period which includes the Rwandan civil war and genocide.Comment: 59 page

Statistical Methods for Human Rights and Child Mortality Estimation

Thesis (Ph.D.)--University of Washington, 2022This dissertation addresses statistical methodology commonly used in human rights research and child mortality estimation. We first consider two related problems, record linkage and multiple-systems estimation, typically used to estimate the number of civilian casualties in the wake of a conflict when probability surveys are not available, and then consider the problem of estimating child mortality over time in a country that has experienced conflict. In Chapter 2, we propose a novel Bayesian approach for record linkage in the general setting where there may be any number of files, with arbitrary patterns of duplication across files. In Chapter 3, we present a re-framing of multiple-systems estimation which places identifying assumptions front and center in the multiple-systems estimation workflow, and examine how common models fit into this framing. In Chapter 4, we develop spatial and temporal smoothing models which incorporate knowledge of expected shocks in child mortality, such as the timing of a conflict, leading to estimates of child mortality which are not oversmoothed. Finally, we conclude with discussion of future work in Chapter 5

Estimating Global and Country-Specific Excess Mortality During the COVID-19 Pandemic

Estimating the true mortality burden of COVID-19 for every country in the world is a difficult, but crucial, public health endeavor. Attributing deaths, direct or indirect, to COVID-19 is problematic. A more attainable target is the "excess deaths", the number of deaths in a particular period, relative to that expected during "normal times", and we estimate this for all countries on a monthly time scale for 2020 and 2021. The excess mortality requires two numbers, the total deaths and the expected deaths, but the former is unavailable for many countries, and so modeling is required for these countries. The expected deaths are based on historic data and we develop a model for producing expected estimates for all countries and we allow for uncertainty in the modeled expected numbers when calculating the excess. We describe the methods that were developed to produce the World Health Organization (WHO) excess death estimates. To achieve both interpretability and transparency we developed a relatively simple overdispersed Poisson count framework, within which the various data types can be modeled. We use data from countries with national monthly data to build a predictive log-linear regression model with time-varying coefficients for countries without data. For a number of countries, subnational data only are available, and we construct a multinomial model for such data, based on the assumption that the fractions of deaths in sub-regions remain approximately constant over time. Based on our modeling, the point estimate for global excess mortality, over 2020-2021, is 14.9 million, with a 95% credible interval of (13.3, 16.6) million. This leads to a point estimate of the ratio of excess deaths to reported COVID-19 deaths of 2.75, which is a huge discrepancy

The WHO estimates of excess mortality associated with the COVID-19 pandemic

The WHO estimates of excess mortality associated with the COVID-19 pandemic for years 2020 and 2021 by country and month for each of the 194 WHO members states