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Abstract The current mode of use of Electronic Health Records (EHR) elicits text redundancy. Clinicians often populate new documents by duplicating existing notes, then updating accordingly. Data duplication can lead to propagation of errors, inconsistencies and misreporting of care. Therefore, measures to quantify information redundancy play an essential role in evaluating innovations that operate on clinical narratives. This work is a quantitative examination of information redundancy in EHR notes. We present and evaluate two methods to measure redundancy: an information-theoretic approach and a lexicosyntactic and semantic model. Our first measure trains large Transformer-based language models using clinical text from a large openly available US-based ICU dataset and a large multi-site UK based Hospital. By comparing the information-theoretic efficient encoding of clinical text against open-domain corpora, we find that clinical text is ∼ 1.5 × to ∼ 3 × less efficient than open-domain corpora at conveying information. Our second measure, evaluates automated summarisation metrics Rouge and BERTScore to evaluate successive note pairs demonstrating lexicosyntactic and semantic redundancy, with averages from ∼ 43 to ∼ 65%.

Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter). Peer reviewed

Abstract Introduction: In the past decade, the minimal school leaving age has been raised twice. Previous studies have found evidence for a link between this type of policy and myopia. We aim to use the 1972 raising of school leaving age to estimate the effect of the raising of school leaving age in 2013 and 2015. Methods: We use a segmented regression model to conduct an instrumental time series analyses of the effect of years of education on myopia using the 1972 raising of school leaving age. To recover the effect of a one-year change, we use the effect of the change on years of education and reflective error in an instrumental variables analysis. Results: We found evidence for a 0.60 (SE = 0.10) increase in years of education and, after adjusting for probability of having missing data and sex, a -0.14d (SE = 0.03) for refractive error. Instrumental variables analyse implies a -0.24 d/year (SE = 0.05) change in refractive error for each additional year in education. Conclusion: Our results triangulate the findings of pervious quasi-experimental methods on the effect of years of education on myopia and imply that each raising of school leaving age in the 2010s should be expected to a lead to -0.07 d/yr change in refractive error in the UK population.

Economists and social scientists have debated the relative importance of nature (one's genes) and nurture (one's environment) for decades, if not centuries. This debate can now be informed by the ready availability of genetic data in a growing number of social science datasets. This paper explores the potential uses of genetic data in economics, with a focus on estimating the interplay between nature (genes) and nurture (environment). We discuss how economists can benefit from incorporating genetic data into their analyses even when they do not have a direct interest in estimating genetic effects. We argue that gene-environment (G × E) studies can be instrumental for (i) testing economic theory, (ii) uncovering economic or behavioral mechanisms, and (iii) analyzing treatment effect heterogeneity, thereby improving the understanding of how (policy) interventions affect population subgroups. We introduce the reader to essential genetic terminology, develop a conceptual economic model to interpret gene-environment interplay, and provide practical guidance to empirical researchers.

Background: Households are an important location for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, especially during periods when travel and work was restricted to essential services. We aimed to assess the association of close-range contact patterns with SARS-CoV-2 transmission.Methods: We deployed proximity sensors for two weeks to measure face-to-face interactions between household members after SARS-CoV-2 was identified in the household, in South Africa, 2020–2021. We calculated the duration, frequency, and average duration of close-range proximity events with SARS-CoV-2 index cases. We assessed the association of contact parameters with SARS-CoV-2 transmission using mixed effects logistic regression accounting for index and household member characteristics.Results: We included 340 individuals (88 SARS-CoV-2 index cases and 252 household members). On multivariable analysis, factors associated with SARS-CoV-2 acquisition were index cases with minimum Ct value 35, and female contacts (aOR 2.5 95% CI 1.3–5.0). No contact parameters were associated with acquisition (aOR 1.0–1.1) for any of the duration, frequency, cumulative time in contact, or average duration parameters.Conclusions: We did not find an association between close-range proximity events and SARS-CoV-2 household transmission. Our findings may be due to study limitations, that droplet-mediated transmission during close-proximity contacts plays a smaller role than airborne transmission of SARS-CoV-2 in the household, or due to high contact rates in households. International audience

Abstract We analyse the dynamic evolution of disease outbreak risk after the main waves of the 1918-19 “Spanish flu” pandemic in the US and in major cities in the UK, and after the 1890-91 “Russian flu” pandemic in England and Wales. We compile municipal public health records and use national data to model the stochastic process of mortality rates after the main pandemic waves as a sequence of bounded Pareto distributions with an exponentially decaying tail parameter. In all cases, we find elevated mortality risk lasting nearly two decades. An application to COVID-19 under model uncertainty shows that in 90% of model-predicted time series, the annual probability of outbreaks exceeding 500 deaths per million is above 20% for a decade, remaining above 10% for two decades.