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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.

Reducing COVID-19 burden for populations will require equitable and effective risk-based allocations of scarce preventive resources, including vaccinations1. To aid in this effort, we developed a general population risk calculator for COVID-19 mortality based on various sociodemographic factors and pre-existing conditions for the US population, combining information from the UK-based OpenSAFELY study with mortality rates by age and ethnicity across US states. We tailored the tool to produce absolute risk estimates in future time frames by incorporating information on pandemic dynamics at the community level. We applied the model to data on risk factor distribution from a variety of sources to project risk for the general adult population across 477 US cities and for the Medicare population aged 65 years and older across 3,113 US counties, respectively. Validation analyses using 54,444 deaths from 7 June to 1 October 2020 show that the model is well calibrated for the US population. Projections show that the model can identify relatively small fractions of the population (for example 4.3%) that might experience a disproportionately large number of deaths (for example 48.7%), but there is wide variation in risk across communities. We provide a web-based risk calculator and interactive maps for viewing community-level risks.

Reducing COVID-19 illness and mortality for populations in the future will require equitable and effective risk-based allocations of scarce preventive resources, including early available vaccines. To aid in this effort, we develop a risk calculator for COVID-19 mortality based on various socio-demographic factors and pre-existing conditions for the US adult population by combining information from the UK-based OpenSAFELY study, with mortality rates by age and ethnicity available across US states. We tailor the tool to produce absolute risks for individuals in future time frames by incorporating information on pandemic dynamics at the community level as available from forecasting models. We apply this risk calculation model to available data on prevalence and co-occurrences of the risk-factors from a variety of data sources to project risk for the general adult population across 477 US cities (defined as Census Places) and for the 65 years and older Medicare population across 3,113 US counties, respectively. Validation analyses based on these projected risks and data on tens of thousands of recent deaths show that the model is well calibrated for the US population. Projections show that the model can identify relatively small fractions of the population (e.g. 4.3%) which will lead to a disproportionately large number of deaths (e.g. 49.8%), and thus will be useful for effectively targeting individuals for early vaccinations, but there will be wide variation in risk distribution across US communities. We provide a web-based tool for individualized risk calculations and interactive maps for viewing the city-, county- and state-level risk projections.