Mixtures of urinary concentrations of phenols and phthalate biomarkers in relation to the ovarian reserve among women attending a fertility clinic.

Although prior studies have found associations of the ovarian reserve with urinary concentrations of some individual phenols and phthalate metabolites, little is known about the potential associations of these chemicals as a mixture with the ovarian reserve. We investigated whether mixtures of four urinary phenols (bisphenol A, butylparaben, methylparaben, propylparaben) and eight metabolites of five phthalate diesters including di(2-ethylhexyl) phthalate were associated with markers of the ovarian reserve among 271 women attending a fertility center who enrolled in the Environment and Reproductive Health study (2004-2017). The analysis was restricted to one outcome per study participant using the earliest outcome after the last exposure assessment. Ovarian reserve markers included lower antral follicle count (AFC) defined as AFC < 7, circulating serum levels of day 3 follicle stimulating hormone (FSH) assessed by immunoassays, and diminished ovarian reserve (DOR) defined as either AFC < 7, FSH > 10 UI/L or primary infertility diagnosis of DOR. We applied Bayesian Kernel Machine Regression (BKMR) and quantile g-computation to estimate the joint associations and assess the interactions between chemical exposure biomarkers on the markers of the ovarian reserve while adjusting for confounders. Among all 271 women, 738 urine samples were collected. In quantile g-computation models, a quartile increase in the exposure biomarkers mixture was not significantly associated with lower AFC (OR = 1.10, 95 % CI = 0.52, 2.30), day 3 FSH levels (Beta = 0.30, 95 % CI = -0.32, 0.93) or DOR (OR = 1.02, 95 % CI = 0.52, 2.05). Similarly, BKMR did not show any evidence of associations between the mixture and any of the studied outcomes, or interactions between chemicals. Despite the lack of associations, these results need to be explored among women in other study cohorts.

Associations of Maternal Urinary Concentrations of Phenols, Individually and as a Mixture, with Serum Biomarkers of Thyroid Function and Autoimmunity: Results from the EARTH Study.

The associations between urinary phenol concentrations and markers of thyroid function and autoimmunity among potentially susceptible subgroups, such as subfertile women, have been understudied, especially when considering chemical mixtures. We evaluated cross-sectional associations of urinary phenol concentrations, individually and as a mixture, with serum markers of thyroid function and autoimmunity. We included 339 women attending a fertility center who provided one spot urine and one blood sample at enrollment (2009-2015). We quantified four phenols in urine using isotope dilution high-performance liquid chromatography-tandem mass spectrometry, and biomarkers of thyroid function (thyroid-stimulating hormone (TSH), free and total thyroxine (fT4, TT4), and triiodothyronine (fT3, TT3)), and autoimmunity (thyroid peroxidase (TPO) and thyroglobulin (Tg) antibodies (Ab)) in serum using electrochemoluminescence assays. We fit linear and additive models to investigate the association between urinary phenols-both individually and as a mixture-and serum thyroid function and autoimmunity, adjusted for confounders. As a sensitivity analysis, we also applied Bayesian Kernel Machine Regression (BKMR) to investigate non-linear and non-additive interactions. Urinary bisphenol A was associated with thyroid function, in particular, fT3 (mean difference for a 1 log unit increase in concentration: -0.088; 95% CI [-0.151, -0.025]) and TT3 (-0.066; 95% CI [-0.112, -0.020]). Urinary methylparaben and triclosan were also associated with several thyroid hormones. The overall mixture was negatively associated with serum fT3 concentrations (mean difference comparing all four mixture components at their 75th vs. 25th percentiles: -0.19, 95% CI [-0.35, -0.03]). We found no evidence of non-linearity or interactions. These results add to the current literature on phenol exposures and thyroid function in women, suggesting that some phenols may alter the thyroid system.

Incorporating biological knowledge in analyses of environmental mixtures and health.

A key goal of environmental health research is to assess the risk posed by mixtures of pollutants. As epidemiologic studies of mixtures can be expensive to conduct, it behooves researchers to incorporate prior knowledge about mixtures into their analyses. This work extends the Bayesian multiple index model (BMIM), which assumes the exposure-response function is a nonparametric function of a set of linear combinations of pollutants formed with a set of exposure-specific weights. The framework is attractive because it combines the flexibility of response-surface methods with the interpretability of linear index models. We propose three strategies to incorporate prior toxicological knowledge into construction of indices in a BMIM: (a) imposing directional homogeneity constraints on the weights, (b) structuring index weights by exposure transformations, and (c) placing informative priors on the index weights. We propose a novel prior specification that combines spike-and-slab variable selection with an informative Dirichlet distribution based on relative potency factors often derived from previous toxicological studies. In simulations we show that the proposed priors improve inferences when prior information is correct and can protect against misspecification suffered by naïve toxicological models when prior information is incorrect. Moreover, different strategies may be mixed-and-matched for different indices to suit available information (or lack thereof). We demonstrate the proposed methods on an analysis of data from the National Health and Nutrition Examination Survey and incorporate prior information on relative chemical potencies obtained from toxic equivalency factors available in the literature.

Metals and per- and polyfluoroalkyl substances mixtures and birth outcomes in the New Hampshire Birth Cohort Study: Beyond single-class mixture approaches.

We aimed to investigate the joint, class-specific, and individual impacts of (i) PFAS, (ii) toxic metals and metalloids (referred to collectively as "metals"), and (iii) essential elements on birth outcomes in a prospective pregnancy cohort using both established and recent mixture modeling approaches. Participants included 537 mother-child pairs from the New Hampshire Birth Cohort Study. Concentrations of 6 metals and 5 PFAS were measured in maternal toenail clippings and plasma, respectively. Birth weight, birth length, and head circumference at birth were abstracted from medical records. Joint, index-wise, and individual associations of the metals and PFAS concentrations with birth outcomes were evaluated using Bayesian Kernel Machine Regression (BKMR) and Bayesian Multiple Index Models (BMIM). After controlling for potential confounders, the metals-PFAS mixture was associated with a larger head circumference at birth, which was driven by manganese. When using BKMR, the difference in the head circumference z-score when changing manganese from its 25th to 75th percentiles while holding all other mixture components at their medians was 0.22 standard deviations (95% posterior credible interval [CI]: -0.02, 0.46). When using BMIM, the posterior mean of index weight estimates assigned to manganese for head circumference z-score was 0.72 (95% CI: 0, 0.99). Prenatal exposure to the metals-PFAS mixture was not associated with birth weight or birth length by either BKMR or BMIM. Using both traditional and new mixture modeling approaches, prenatal exposure to manganese was associated with a larger head circumference at birth after accounting for exposure to PFAS and multiple toxic and essential metals.

Bayesian multiple index models for environmental mixtures.

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.

Associations of sleep-related behaviors and the sleep environment at infant age one month with sleep patterns in infants five months later.

OBJECTIVE: To characterize family and environmental correlates of sleep patterns that may contribute to differences in infant sleep. METHODS: We studied 313 infants in the Rise & SHINE (Sleep Health in Infancy & Early Childhood study) cohort. Our main exposures were the parent-reported sleep environment, feeding method and sleep parenting strategies at infant age one month. The main outcomes were nighttime sleep duration, longest nighttime sleep and number of awakenings measured by actigraphy at age six months. We used multivariable linear regression models to examine associations, and secondarily also explored the role of sleep-related environmental exposures in mediating previously observed associations of racial/ethnicity and parental education with infant sleep characteristics. RESULTS: In adjusted models, a non-dark sleep environment (versus an always dark sleep location) and taking the baby to parent's bed when awake at night (versus no co-sleeping) were associated with 28 (95% CI, -45, -11) and 18 (95% CI, -33, -4) minutes less sleep at night, respectively. Bottle feeding at bedtime was associated with 62 (95% CI, 21, 103) minutes additional longest nighttime sleep period. Exploratory mediation analyses suggested a modest mediating role of a non-dark sleep environment on racial/ethnic and educational differences in sleep duration. CONCLUSIONS: Infant sleep duration was positively associated with a dark sleep environment and a focal feed at bedtime while taking the baby to the parent's bed was associated with reduced infant sleep. Modifying the sleep environment and practices may improve infant sleep and reduce sleep health disparities.

On the Nature of Informative Presence Bias in Analyses of Electronic Health Records.

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.

Fitting marginal models in small samples: A simulation study of marginalized multilevel models and generalized estimating equations.

In correlated data settings, analysts typically choose between fitting conditional and marginal models, whose parameters come with distinct interpretations, and as such the choice between the two should be made on scientific grounds. For settings where interest lies in marginal-or population-averaged-parameters, the question of how best to estimate those parameters is a statistical one, and analysts have at their disposal two distinct modeling frameworks: generalized estimating equations (GEE) and marginalized multilevel models (MMMs). The two have been contrasted theoretically and in large sample settings, but asymptotic theory provides no guarantees in the small sample settings that are commonplace. In a comprehensive series of simulation studies, we shed light on the relative performance of GEE and MMMs in small-sample settings to help guide analysis decisions in practice. We find that both GEE and MMMs exhibit similar small-sample bias when the correct correlation structure is adopted (ie, when the random effects distribution is correctly specified or moderately misspecified)-but MMMs can be sensitive to misspecification of the correlation structure. When there are a small number of clusters, MMMs only slightly underestimate standard errors (SEs) for within-cluster associations but can severely underestimate SEs for between-cluster associations. By contrast, while GEE severely underestimates SEs, the Mancl and DeRouen correction provides approximately valid inference.

Impact of pneumococcal conjugate vaccine on pneumonia hospitalization and mortality in children and elderly in Ecuador: Time series analyses.

BACKGROUND: Pneumococcal conjugate vaccines (PCV) reduce the burden of invasive pneumococcal disease and pneumonia hospitalizations. However, there is limited evidence of the effect of PCVs on pneumonia mortality in children. It is anticipated that indirect effects resulting from PCV use among children might further reduce the remaining burden of adult pneumococcal disease caused by pneumococcal serotypes contained in PCV. Whether this will result in reduced pneumonia mortality in children and adults is still not known. METHODS: We investigated the impact of PCV on pneumonia hospitalization and mortality in in Ecuador, where PCV was introduced in 2010, considering national data from secondary data sources from 2005 to 2015. Time series analysis using regression models were used to evaluate the decline in the number of all-cause pneumonia hospitalizations and deaths in the period post-PCV introduction. The target populations were children under 5 years and adults aged 50 years and over. Outcomes of interest were hospitalizations and mortality in which the main cause of hospital admission and death, respectively, were coded as ICD10 codes J12-18 (pneumonia). Three different models were fitted. RESULTS: We demonstrate a sizeable impact of PCV in pneumonia hospitalization in children < 1 year (27% reduction, 95%CI 12-42%), and < 5 years of age (33% reduction, 95%CI 11-43%). The estimated impact of PCV in pneumonia mortality was a reduction of 14% in < 1 year (95%CI 0-33%), 10% in < 5 years (95%CI 0-25%), and 22% (95%CI 7-34%) in adults aged 50-64 years. Little evidence of a change was detected in elderly ≥ 65 years. CONCLUSION: This study is the first to report on the impact of PCV in pneumonia morbidity and mortality in children and older adults, being relevant to policy makers and global donors. Findings were consistent when using different models. Additional studies on the indirect effect of PCV in older adults are needed.

Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing.

Despite the widespread implementation of public health measures, coronavirus disease 2019 (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, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 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, occupational 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 behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic.

Methodological Issues in Population-Based Studies of Multigenerational Associations.

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 association. Finally, we identify areas requiring further methodological development.

Population-scale Longitudinal Mapping of COVID-19 Symptoms, Behavior, and Testing Identifies Contributors to Continued Disease Spread in the United States.

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.

Outcome-dependent sampling in cluster-correlated data settings with application to hospital profiling.

Hospital readmission is a key marker of quality of healthcare and an important policy measure, used by the Centers for Medicare and Medicaid Services to determine, in part, reimbursement rates. Currently, analyses of readmissions are based on a logistic-normal generalized linear mixed model that permits estimation of hospital-specific measures while adjusting for case mix differences. Recent moves to identify and address healthcare disparities call for expanding case mix adjustment to include measures of socio-economic status while minimizing additional burden to hospitals associated with collecting data on such measures. Towards resolving this dilemma, we propose that detailed socio-economic data be collected on a subsample of patients via an outcome-dependent sampling scheme, specifically the cluster-stratified case-control design. Estimation and inference, for both the fixed and the random-effects components, are performed via pseudo-maximum-likelihood wherein inverse probability weights are incorporated in the usual integrated likelihood to account for the design. In comprehensive simulations, cluster-stratified case-control sampling proves to be an efficient design whenever interest lies in fixed or random effects of a generalized linear mixed model and covariates are unobserved or expensive to collect. The methods are motivated by and illustrated with an analysis of N = 889661 Medicare beneficiaries hospitalized between 2011 and 2013 with congestive heart failure at one of K = 3116 hospitals. Results highlight that the framework proposed provides a means of mitigating disparities in terms of which hospitals are indicated as being poor performers, relative to a naive analysis that fails to adjust for missing case mix variables.

Informatively empty clusters with application to multigenerational studies.

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.

Characteristics of Paid Malpractice Claims Among Resident Physicians From 2001 to 2015 in the United States.

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.

Corrections for measurement error due to delayed onset of illness for case-crossover designs.

Epidemiologic studies of the short-term effects of ambient particulate matter (PM) on the risk of acute cardiovascular or cerebrovascular events often use data from administrative databases in which only the date of hospitalization is known. A common study design for analyzing such data is the case-crossover design, in which exposure at a time when a patient experiences an event is compared to exposure at times when the patient did not experience an event within a case-control paradigm. However, the time of true event onset may precede hospitalization by hours or days, which can yield attenuated effect estimates. In this article, we consider a marginal likelihood estimator, a regression calibration estimator, and a conditional score estimator, as well as parametric bootstrap versions of each, to correct for this bias. All considered approaches require validation data on the distribution of the delay times. We compare the performance of the approaches in realistic scenarios via simulation, and apply the methods to analyze data from a Boston-area study of the association between ambient air pollution and acute stroke onset. Based on both simulation and the case study, we conclude that a two-stage regression calibration estimator with a parametric bootstrap bias correction is an effective method for correcting bias in health effect estimates arising from delayed onset in a case-crossover study.