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The World Health Organization has a mandate to compile and disseminate statistics on mortality, and we have been tracking the progression of the COVID-19 pandemic since the beginning of 20201. Reported statistics on COVID-19 mortality are problematic for many countries owing to variations in testing access, differential diagnostic capacity and inconsistent certification of COVID-19 as cause of death. Beyond what is directly attributable to it, the pandemic has caused extensive collateral damage that has led to losses of lives and livelihoods. Here we report a comprehensive and consistent measurement of the impact of the COVID-19 pandemic by estimating excess deaths, by month, for 2020 and 2021. We predict the pandemic period all-cause deaths in locations lacking complete reported data using an overdispersed Poisson count framework that applies Bayesian inference techniques to quantify uncertainty. We estimate 14.83 million excess deaths globally, 2.74 times more deaths than the 5.42 million reported as due to COVID-19 for the period. There are wide variations in the excess death estimates across the six World Health Organization regions. We describe the data and methods used to generate these estimates and highlight the need for better reporting where gaps persist. We discuss various summary measures, and the hazards of ranking countries’ epidemic responses.Msemburi et al. describe how the World Health Organization has estimated the excess mortality associated with the COVID-19 pandemic, by month and for 2020 and 2021, and analyse their estimates across the WHO member states, with 14.83 million global excess deaths estimated. [ABSTRACT FROM AUTHOR]

Predisposition to a disease is usually caused by cumulative effects of a multitude of exposures and lifestyle factors in combination with individual susceptibility. Failure to include all relevant variables may result in biased risk estimates and decreased power, whereas inclusion of all variables may lead to computational difficulties, especially when variables are correlated. We describe a Bayesian Mixture Model (BMM) incorporating a variable-selection prior and compared its performance with logistic multiple regression model (LM) in simulated case-control data with up to twenty exposures with varying prevalences and correlations. In addition, as a practical example we re analyzed data on male infertility and occupational exposures (Chaps-UK). BMM mean-squared errors (MSE) were smaller than of the LM, and were independent of the number of model parameters. BMM type I errors were minimal (1), whereas for the LM this increased with the number of parameters and correlation between exposures. The numbers of type II errors were comparable. Re analysis of Chaps-UK data demonstrated more convincingly than by using a LM that occupational exposure to glycol ethers and VOCs are likely risk factors for male infertility. This BMM proves an appealing alternative to standard logistic regression when dealing with the analysis of (correlated) exposures in case-control studies. [ABSTRACT FROM AUTHOR]

Objectives: Incidence of myelodysplastic syndromes (MDS) has been described in the United States since its inclusion in the Surveillance, Epidemiology, and End Results program in 2001, and the Seattle-Puget Sound region of Washington State has among the highest rates of the registries. In this investigation, we described small-scale incidence patterns of MDS within the Seattle-Puget Sound region from 2002 to 2006 and identified potential spatial clusters to inform planning of future studies of MDS etiology.Methods: We used a spatial disease mapping model to estimate smoothed relative risks for each census tract and to describe the spatial component of variability in the incidence rates. We also used two methods to describe the location of potential MDS clusters: the approach of Besag and Newell and the Kulldorff spatial scan statistic.Results: Our findings from all three approaches indicated the most likely areas of increased MDS incidence were located on Whidbey Island in Island County.Conclusion: Interpretation is limited because our data are based on the residential location of the MDS case only at the time of diagnosis. Nevertheless, inclusion of identified cluster regions in future population-based research and investigation of individual-level exposures could shed light on environmental risk factors for MDS. [ABSTRACT FROM AUTHOR]

We are conducting an epidemiological study on the association between disinfection by-product concentrations in drinking water and adverse birth outcomes in the UK, using trihalomethane (THM) concentrations over defined water zones as an exposure index. Here we construct statistical models using sparse routinely collected THMs measurements to obtain quarterly estimates of mean THM concentrations for each water zone. We modelled the THM measurements using a Bayesian hierarchical mixture model, taking into account heterogeneity in THM concentrations between water originating from different source types, quarterly variation in THM concentrations and uncertainty in the true value of undetected and rounded measurements. Quarterly estimates of mean THM concentrations plus estimates of the water source type (ground, lowland surface or upland surface) were obtained for each water zone. THM concentration estimates were typically highest from July to September (third quarter), and varied considerably between water sources. Our exposure estimates were categorized into‘low’,‘medium’and‘high’THM classes. Our modelled quarterly exposure estimates were compared to a simple alternative: annual means of the raw data for each water zone. In all, 15-25%of exposure estimates were classified differently. The modelled THM estimates led to slightly stronger and more precise estimates of association with risk of still birth and low birth weight than did the raw annual means. We conclude that our modelling approach enabled us to provide robust quarterly estimates of ecological exposure to THMs in a situation where the raw data were too sparse to base exposure assessment on empirical summaries alone.Journal of Exposure Analysis and Environmental Epidemiology (2005) 15, 138-146. doi:10.1038/sj.jea.7500380 Published online 19 May 2004 [ABSTRACT FROM AUTHOR]

Population approaches to modeling pharmacokinetic and or pharmacodynamic data attempt to separate the variability in observed data into within - and between -individual components. This is most naturally achieved via a multistage model. At the first stage of the model the data of a particular individual is modeled with each individual having his own set of parameters. At the second stage these individual parameters are assumed to have arisen from some unknown population distribution which we shall denote F. The importance of the choice of second stage distribution has led to a number of flexible approaches to the modeling of F. A nonparametric maximum likelihood estimate of F was suggested by Mallet whereas Davidian and Gallant proposed a semiparametric maximum likelihood approach where the maximum likelihood estimate is obtained over a smooth class of distributions. Previous Bayesian work has concentrated largely on F being assigned to a parametric family, typically the normal or Student's t. We describe a Bayesian nonparametric approach using the Dirichlet process. We use Markov chain Monte Carlo simulation to implement the procedure. We discuss each procedure and compare our approach with those of Mallet and Davidian and Gallant, using simulated data for a pharmacodynamic dose-response model. [ABSTRACT FROM AUTHOR]