A variety of predisposing factors have been associated with serious illness and death from COVID-19. Understanding the distribution of risks associated with these factors by local communities can provide important opportunities for targeting interventions. We characterize the distribution of risk for COVID-19 mortality for populations at large across 442 US cities, by utilizing recently published estimates of risk associated with age, gender, ethnicity, social deprivation and 12 health conditions from a very large UK-based study, combined with the information available on prevalence and co-occurrence of these factors in the US through a variety of population-based public databases. We estimate that across all the cities, an underlying weighted risk-score can identify a total of approximately 12.65 million, 4.09 million and 1.34 million individuals who are at 2-, 5- and 10-fold higher risk, respectively, compared to the average risk for the US population. The percentage of population which exceed the respective risk thresholds varies across the cities in the range (1st-99th percentile), 3.6%-20.1%, 0.7%-8.0% and 0.1%-3.2%, respectively. The percentage of deaths within a city that are expected to occur above these risk-thresholds varies in the range of 20.1%-53.5%, 8.5%-38.2% and 2.9%-25.4%, respectively. Our analysis can provide guidance to national and local policy makers regarding resources needed to protect the most vulnerable populations in these communities, and how much utility such interventions may have in reducing the total population burden of death.
Often both aggregate data (AD) studies and individual participant data (IPD) studies are available for specific treatments. Combining these two sources of data could improve the overall meta‐analytic estimates of treatment effects. Moreover, often for some studies with AD, the associated IPD maybe available, albeit at some extra effort or cost to the analyst. We propose a method for combining treatment effects across trials when the response is from the exponential family of distribution and hence a generalized linear model structure can be used. We consider the case when treatment effects are fixed and common across studies. Using the proposed combination method, we study the relative efficiency of analyzing all IPD studies vs combining various percentages of AD and IPD studies. For many different models, design constraints under which the AD estimators are the IPD estimators, and hence fully efficient, are known. For such models, we advocate a selection procedure that chooses AD studies over IPD studies in a manner that force least departure from design constraints and hence ensures an efficient combined AD and IPD estimator.
Often both Aggregate Data (AD) studies and Individual Patient Data (IPD) studies are available for specific treatments. Combining these two sources of data could improve the overall meta-analytic estimates of treatment effects. Moreover, often for some studies with AD, the associated IPD maybe available, albeit at some extra effort or cost to the analyst. We propose a method for combining treatment effects across trials when the response is from the exponential family of distribution and hence a generalized linear model structure can be used. We consider the case when treatment effects are fixed and common across studies. Using the proposed combination method, we evaluate the wisdom of choosing AD when IPD is available by studying the relative efficiency of analyzing all IPD studies versus combining various percentages of AD and IPD studies. For many different models design constraints under which the AD estimators are the IPD estimators, and hence fully efficient, are known. For such models we advocate a selection procedure that chooses AD studies over IPD studies in a manner that force least departure from design constraints and hence ensures a fully efficient combined AD and IPD estimator.
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