Despite the widespread implementation of public health measures, COVID-19 continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behavior, and demographics. Here we report results from over 500,000 users in the United States from April 2, 2020 to May 12, 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19 positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation, show a variety of exposure, occupation, and demographic risk factors for COVID-19 beyond symptoms, reveal factors for which users have been SARS-CoV-2 PCR tested, and highlight the temporal dynamics of symptoms and self-isolation behavior. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure, and behavioral self-reported data to fight the COVID-19 pandemic.

An important goal of environmental health research is to assess the risk posed by mixtures of environmental exposures. Two popular classes of models for mixtures analyses are response‐surface methods and exposure‐index methods. Response‐surface methods estimate high‐dimensional surfaces and are thus highly flexible but difficult to interpret. In contrast, exposure‐index methods decompose coefficients from a linear model into an overall mixture effect and individual index weights; these models yield easily interpretable effect estimates and efficient inferences when model assumptions hold, but, like most parsimonious models, incur bias when these assumptions do not hold. In this paper, we propose a Bayesian multiple index model framework that combines the strengths of each, allowing for non‐linear and non‐additive relationships between exposure indices and a health outcome, while reducing the dimensionality of the exposure vector and estimating index weights with variable selection. This framework contains response‐surface and exposure‐index models as special cases, thereby unifying the two analysis strategies. This unification increases the range of models possible for analysing environmental mixtures and health, allowing one to select an appropriate analysis from a spectrum of models varying in flexibility and interpretability. In an analysis of the association between telomere length and 18 organic pollutants in the National Health and Nutrition Examination Survey (NHANES), the proposed approach fits the data as well as more complex response‐surface methods and yields more interpretable results.

Despite social distancing and shelter-in-place policies, COVID-19 continues to spread in the United States. A lack of timely information about factors influencing COVID-19 spread and testing has hampered agile responses to the pandemic. We developed How We Feel, an extensible web and mobile application that aggregates self-reported survey responses, to fill gaps in the collection of COVID-19-related data. How We Feel collects longitudinal and geographically localized information on users' health, behavior, and demographics. Here we report results from over 500,000 users in the United States from April 2, 2020 to May 12, 2020. We show that self- reported surveys can be used to build predictive models of COVID-19 test results, which may aid in identification of likely COVID-19 positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation, as well as for household and community exposure, occupation, and demographics being strong risk factors for COVID-19. We further reveal factors for which users have been SARS-CoV-2 PCR tested, as well as the temporal dynamics of self- reported symptoms and self-isolation behavior in positive and negative users. These results highlight the utility of collecting a diverse set of symptomatic, demographic, and behavioral self- reported data to fight the COVID-19 pandemic.

Electronic health records (EHRs) offer unprecedented opportunities to answer epidemiologic questions. However, unlike in ordinary cohort studies or randomized trials, EHR data are collected somewhat idiosyncratically. In particular, patients who have more contact with the medical system have more opportunities to receive diagnoses, which are then recorded in their EHRs. The goal of this article is to shed light on the nature and scope of this phenomenon, known as informative presence, which can bias estimates of associations. We show how this can be characterized as an instance of misclassification bias. As a consequence, we show that informative presence bias can occur in a broader range of settings than previously thought, and that simple adjustment for the number of visits as a confounder may not fully correct for bias. Additionally, where previous work has considered only underdiagnosis, investigators are often concerned about overdiagnosis; we show how this changes the settings in which bias manifests. We report on a comprehensive series of simulations to shed light on when to expect informative presence bias, how it can be mitigated in some cases, and cases in which new methods need to be developed.

Summary Exposures with multigenerational effects have profound implications for public health, affecting increasingly more people as the exposed population reproduces. Multigenerational studies, however, are susceptible to informative cluster size, occurring when the number of children to a mother (the cluster size) is related to their outcomes, given covariates. A natural question then arises: what if some women bear no children at all? The impact of these potentially informative empty clusters is currently unknown. This article first evaluates the performance of standard methods for informative cluster size when cluster size is permitted to be zero. We find that if the informative cluster size mechanism induces empty clusters, standard methods lead to biased estimates of target parameters. Joint models of outcome and size are capable of valid conditional inference as long as empty clusters are explicitly included in the analysis, but in practice empty clusters regularly go unacknowledged. In contrast, estimating equation approaches necessarily omit empty clusters and therefore yield biased estimates of marginal effects. To resolve this, we propose a joint marginalized approach that readily incorporates empty clusters and even in their absence permits more intuitive interpretations of population-averaged effects than do current methods. Competing methods are compared via simulation and in a study of the impact of in-utero exposure to diethylstilbestrol on the risk of attention-deficit/hyperactivity disorder (ADHD) among 106 198 children to 47 540 nurses from the Nurses Health Study.

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Abstract Laboratory-based animal research has revealed a number of exposures with multigenerational effects—ones that affect the children and grandchildren of those directly exposed. An important task for epidemiology is to investigate these relationships in human populations. Without the relative control achieved in laboratory settings, however, population-based studies of multigenerational associations have had to use a broader range of study designs. Current strategies to obtain multigenerational data include exploiting birth registries and existing cohort studies, ascertaining exposures within them and measuring outcomes across multiple generations. In this paper, we describe the methodological challenges inherent to multigenerational studies in human populations. After outlining standard taxonomy to facilitate discussion of study designs and target exposure associations, we highlight the methodological issues, focusing on the interplay between study design, analysis strategy, and the fact that outcomes may be related to family size. In a simulation study, we show that different multigenerational designs lead to estimates of different exposure associations with distinct scientific interpretations. Nevertheless, target associations can be recovered by incorporating (possibly) auxiliary information, and we provide insights into choosing an appropriate target associations. Finally, we identify areas requiring further methodological development.

Purpose Limited information exists about medical malpractice claims against physicians-in-training. Data on residents’ involvement in malpractice actions may inform perceptions about medicolegal liability and influence clinical decision-making at a formative stage. This study aimed to characterize rates and payment amounts of paid malpractice claims on behalf of resident physicians in the United States. Method Using data from the National Practitioner Data Bank, 1,248 paid malpractice claims against resident physicians (interns, residents, and fellows) from 2001 to 2015, representing 1,632,471 residents-years, were analyzed. Temporal trends in overall and specialty-specific paid claim rates, payment amounts, catastrophic (> $1 million) and small (< $100,000) payments, and other claim characteristics were assessed. Payment amounts were compared with attending physicians during the same time period. Results The overall paid malpractice claim rate was 0.76 per 1,000 resident-years from 2001 to 2015. Among 1,194 unique residents with paid claims, 95.7% had exactly 1 claim, while 4.3% had 2–4 claims during training. Specialty-specific paid claim rates ranged from 0.12 per 1,000 resident-years (pathology) to 2.96 (obstetrics and gynecology). Overall paid claim rates decreased by 52% from 2001–2005 to 2011–2015 (95% confidence interval [CI]: 0.45, 0.59). Median inflation-adjusted payment amount was $199,024 (2015 dollars), not significantly different from payments made on behalf of attending physicians during the same period. Proportions of catastrophic (11.2%) and small (33.1%) claims did not significantly change over the study period. Conclusions From 2001 to 2015, paid malpractice claim rates on behalf of resident physicians decreased by 52%, while median payment amounts were stable. Resident paid claim rates were lower than attending physicians, while payment amounts were similar.