The application of polygenic scores has transformed our ability to investigate whether and how genetic and environmental factors jointly contribute to the variation of complex traits. Modelling the complex interplay between genes and environment, however, raises serious methodological challenges. Here we illustrate the largely unrecognised impact of gene-environment dependencies on the identification of the effects of genes and their variation across environments. We show that controlling for heritable covariates in regression models that include polygenic scores as independent variables introduces endogenous selection bias when one or more of these covariates depends on unmeasured factors that also affect the outcome. This results in the problem of conditioning on a collider, which in turn leads to spurious associations and effect sizes. Using graphical and simulation methods we demonstrate that the degree of bias depends on the strength of the gene-covariate correlation and of hidden heterogeneity linking covariates with outcomes, regardless of whether the main analytic focus is mediation, confounding, or gene × covariate (commonly gene × environment) interactions. We offer potential solutions, highlighting the importance of causal inference. We also urge further caution when fitting and interpreting models with polygenic scores and non-exogenous environments or phenotypes and demonstrate how spurious associations are likely to arise, advancing our understanding of such results.
Background: COVID-19 poses one of the most profound public health crises for a hundred years. As of mid-May 2020, across the world, almost 300,000 deaths and over 4 million confirmed cases were registered. Reaching over 30,000 deaths by early May, the UK had the highest number of recorded deaths in Europe, second in the world only to the USA. Hospitalization and death from COVID-19 have been linked to demographic and socioeconomic variation. Since this varies strongly by location, there is an urgent need to analyse the mismatch between health care demand and supply at the local level. As lockdown measures ease, reinfection may vary by area, necessitating a real-time tool for local and regional authorities to anticipate demand. Methods: Combining census estimates and hospital capacity data from ONS and NHS at the Administrative Region, Ceremonial County (CC), Clinical Commissioning Group (CCG) and Lower Layer Super Output Area (LSOA) level from England and Wales, we calculate the number of individuals at risk of COVID-19 hospitalization. Combining multiple sources, we produce geospatial risk maps on an online dashboard that dynamically illustrate how the precrisis health system capacity matches local variations in hospitalization risk related to age, social deprivation, population density and ethnicity, also adjusting for the overall infection rate and hospital capacity. Results: By providing fine-grained estimates of expected hospitalization, we identify areas that face higher disproportionate health care burdens due to COVID-19, with respect to pre-crisis levels of hospital bed capacity. Including additional risks beyond age-composition of the area such as social deprivation, race/ethnic composition and population density offers a further nuanced identification of areas with disproportionate health care demands. Conclusions: Areas face disproportionate risks for COVID-19 hospitalization pressures due to their socioeconomic differences and the demographic composition of their populations. Our flexible online dashboard allows policymakers and health officials to monitor and evaluate potential health care demand at a granular level as the infection rate and hospital capacity changes throughout the course of this pandemic. This agile knowledge is invaluable to tackle the enormous logistical challenges to re-allocate resources and target susceptible areas for aggressive testing and tracing to mitigate transmission.
COVID-19 poses one of the most profound public health crises for a hundred years. As of late March 2020, over 25,000 deaths and above a half million confirmed cases were registered across more than 175 countries or regions. The virus will infect a sizeable proportion of the worlds population, leading to unprecedented pressures on national health care systems. Although national estimates of hospital bed capacity are available, these obscure important differences at local and regional levels. COVID-19 appears especially dangerous for the oldest age groups and those with serious comorbidities. It is crucial to understand how health system capacity matches local variations in population structure. Using England and Wales, we illustrate how the interaction of local demography, a high burden of COVID-19 hospitalization at older ages, and regional variation in hospital resources may culminate in "hospital deserts" with too few resources to cope with a wave of critical cases. We demonstrate how local capacity could rapidly become overwhelmed. By providing fine-grained local estimates of expected hospitalization, we identify areas that have higher risks of health care burden. An online companion to this paper can be used by policymakers to identify and monitor high-risk areas, and predict the expected healthcare demand in real-time as the actual epidemic spreads. This agile knowledge will be invaluable to tackle the enormous logistical challenge COVID-19 will pose to health care systems.
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