A Mixture of Urinary Phthalate Metabolite Concentrations During Pregnancy and Offspring Social Responsiveness Scale Scores.

BACKGROUND: Phthalates are a group of chemicals with ubiquitous exposure worldwide. Exposures to phthalates during pregnancy may play a role in autism spectrum disorder (ASD) etiology by disrupting hormone levels or directly impacting fetal neurodevelopment. However, there is little research quantifying the aggregate effect of phthalates on child ASD-related behaviors. METHODS: We used data from two prospective pregnancy and birth cohorts-the Health Outcomes and Measures of the Environment (HOME) and the Early Autism Risk Longitudinal Investigation (EARLI). HOME is a general population cohort while participants in EARLI were at higher familial risk for ASD. Using quantile g-computation and linear regression models, we assessed the joint and individual associations of a mixture of six phthalate metabolites during pregnancy with child ASD-related traits measured by Social Responsiveness Scale (SRS) scores at ages 3-8 years. RESULTS: Our analyses included 271 participants from HOME and 166 participants from EARLI. There were imprecise associations between the phthalate mixture and SRS total raw scores in HOME (difference in SRS scores per decile increase in every phthalate = 1.3; 95% confidence interval [CI] = -0.2, 2.8) and EARLI (difference in SRS scores per decile increase in every phthalate = -0.9; 95% CI = -3.5, 1.7). CONCLUSIONS: The cohort-specific effect sizes of the pthalates-SRS associations were small and CIs were imprecise. These results suggest that if there are associations between phthalate metabolites during pregnancy and child SRS scores, they may differ across populations with different familial liabilities. Further studies with larger sample sizes are warranted.

The Environmental Influences on Child Health Outcomes (ECHO)-Wide Cohort.

The Environmental Influences on Child Health Outcomes (ECHO)-Wide Cohort Study (EWC), a collaborative research design comprising 69 cohorts in 31 consortia, was funded by the National Institutes of Health (NIH) in 2016 to improve children's health in the United States. The EWC harmonizes extant data and collects new data using a standardized protocol, the ECHO-Wide Cohort Data Collection Protocol (EWCP). EWCP visits occur at least once per life stage, but the frequency and timing of the visits vary across cohorts. As of March 4, 2022, the EWC cohorts contributed data from 60,553 children and consented 29,622 children for new EWCP data and biospecimen collection. The median (interquartile range) age of EWCP-enrolled children was 7.5 years (3.7-11.1). Surveys, interviews, standardized examinations, laboratory analyses, and medical record abstraction are used to obtain information in 5 main outcome areas: pre-, peri-, and postnatal outcomes; neurodevelopment; obesity; airways; and positive health. Exposures include factors at the level of place (e.g., air pollution, neighborhood socioeconomic status), family (e.g., parental mental health), and individuals (e.g., diet, genomics).

Prenatal Exposure to Per- and Polyfluoroalkyl Substances and Childhood Autism-related Outcomes.

BACKGROUND: Epidemiologic evidence linking prenatal exposure to per- and polyfluoroalkyl substances (PFAS) with altered neurodevelopment is inconclusive, and few large studies have focused on autism-related outcomes. We investigated whether blood concentrations of PFAS in pregnancy are associated with child autism-related outcomes. METHODS: We included 10 cohorts from the National Institutes of Health (NIH)-funded Environmental influences on Child Health Outcomes (ECHO) program (n = 1,429). We measured 14 PFAS analytes in maternal blood collected during pregnancy; eight analytes met detection criteria for analysis. We assessed quantitative autism-related traits in children via parent report on the Social Responsiveness Scale (SRS). In multivariable linear models, we examined relationships of each PFAS (natural log-transformed) with SRS scores. We further modeled PFAS as a complex mixture using Bayesian methods and examined modification of these relationships by child sex. RESULTS: Most PFAS in maternal blood were not associated with child SRS T-scores. Perfluorononanoic acid (PFNA) showed the strongest and most consistent association: each 1-unit increase in ln-transformed PFNA was associated with greater autism-related traits (adjusted ß [95% confidence interval (CI)] = 1.5 [-0.1, 3.0]). The summed mixture, which included six PFAS detected in >70% of participants, was not associated with SRS T-scores (adjusted ß [95% highest posterior density interval] = 0.7 [-1.4, 3.0]). We did not observe consistent evidence of sex differences. CONCLUSIONS: Prenatal blood concentrations of PFNA may be associated with modest increases in child autism-related traits. Future work should continue to examine the relationship between exposures to both legacy and emerging PFAS and additional dimensional, quantitative measures of childhood autism-related outcomes.

Greater Gestational Vitamin D Status is Associated with Reduced Childhood Behavioral Problems in the Environmental Influences on Child Health Outcomes Program.

BACKGROUND: Vitamin D deficiency is common in pregnancy. Vitamin D plays an important role in the developing brain, and deficiency may impair childhood behavioral development. OBJECTIVES: This study examined the relationship between gestational 25(OH)D concentrations and childhood behavior in the Environmental influences on Child Health Outcomes (ECHO) Program. METHODS: Mother-child dyads from ECHO cohorts with data available on prenatal (first trimester through delivery) or cord blood 25(OH)D and childhood behavioral outcomes were included. Behavior was assessed using the Strengths and Difficulties Questionnaire or the Child Behavior Checklist, and data were harmonized using a crosswalk conversion. Linear mixed-effects models examined associations of 25(OH)D with total, internalizing, and externalizing problem scores while adjusting for important confounders, including age, sex, and socioeconomic and lifestyle factors. The effect modification by maternal race was also assessed. RESULTS: Early (1.5-5 y) and middle childhood (6-13 y) outcomes were examined in 1688 and 1480 dyads, respectively. Approximately 45% were vitamin D deficient [25(OH)D < 20 ng/mL], with Black women overrepresented in this group. In fully adjusted models, 25(OH)D concentrations in prenatal or cord blood were negatively associated with externalizing behavior T-scores in middle childhood [-0.73 (95% CI: -1.36, -0.10) per 10 ng/mL increase in gestational 25(OH)D]. We found no evidence of effect modification by race. In a sensitivity analysis restricted to those with 25(OH)D assessed in prenatal maternal samples, 25(OH)D was negatively associated with externalizing and total behavioral problems in early childhood. CONCLUSIONS: This study confirmed a high prevalence of vitamin D deficiency in pregnancy, particularly among Black women, and revealed evidence of an association between lower gestational 25(OH)D and childhood behavioral problems. Associations were more apparent in analyses restricted to prenatal rather than cord blood samples. Interventions to correct vitamin D deficiency during pregnancy should be explored as a strategy to improve childhood behavioral outcomes.

Associations of prenatal exposure to a mixture of persistent organic pollutants with social traits and cognitive and adaptive function in early childhood: Findings from the EARLI study.

BACKGROUND: Literature suggests that maternal exposure to persistent organic pollutants (POPs) may influence child neurodevelopment. Evidence linking prenatal POPs and autism spectrum disorder has been inconclusive and few studies have examined the mixture effect of the POPs on autism-related traits. OBJECTIVE: To evaluate the associations between prenatal exposure to a mixture of POPs and autism-related traits in children from the Early Autism Risk Longitudinal Investigation study. METHODS: Maternal serum concentrations of 17 POPs (11 polychlorinated biphenyls [PCBs], 4 polybrominated diphenyls [PBDEs], and 2 persistent pesticides) in 154 samples collected during pregnancy were included in this analysis. We examined the independent associations of the natural log-transformed POPs with social, cognitive, and behavioral traits at 36 months of age, including Social Responsiveness Scale (SRS), Mullen Scales of Early Learning-Early Learning Composite (MSEL-ELC), and Vineland Adaptive Behavior Scales (VABS) scores, using linear regression models. We applied Bayesian kernel machine regression and quantile g-computation to examine the joint effect and interactions of the POPs. RESULTS: Higher ln-PBDE47 was associated with greater deficits in social reciprocity (higher SRS score) (ß = 6.39, 95% CI: 1.12, 11.65) whereas higher ln-p,p'-DDE was associated with lower social deficits (ß = -8.34, 95% CI: -15.32, -1.37). Positive associations were observed between PCB180 and PCB187 and cognitive (MSEL-ELC) scores (ß = 5.68, 95% CI: 0.18, 11.17; ß = 4.65, 95% CI: 0.14, 9.17, respectively). Adaptive functioning (VABS) scores were positively associated with PCB170, PCB180, PCB187, PCB196/203, and p,p'-DDE. In the mixture analyses, we did not observe an overall mixture effect of POPs on the quantitative traits. Potential interactions between PBDE99 and other PBDEs were identified in association with MSEL-ELC scores. CONCLUSIONS: We observed independent effects of PCB180, PCB187, PBDE47, and p,p' DDE with ASD-related quantitative traits and potential interactions between PBDEs. Our findings highlight the importance of assessing the effect of POPs as a mixture.

Invited Commentary: Is Bias Towards the Null From Nondifferential Misclassification Wishful Thinking?

In their article, Yland et al. (Am J Epidemiol. 2022;191(8):1485-1495) bring renewed attention to the oft-cited heuristic "nondifferential misclassification biases results towards the null." They make a compelling case, as others have before, that this heuristic is misguided. Herein, I briefly discuss the history of this heuristic, discuss why I believe it became and stayed popular, and note the very limited circumstances for which it may be appropriate.

Combining Effect Estimates Across Cohorts and Sufficient Adjustment Sets for Collaborative Research: A Simulation Study.

BACKGROUND: Collaborative research often combines findings across multiple, independent studies via meta-analysis. Ideally, all study estimates that contribute to the meta-analysis will be equally unbiased. Many meta-analyses require all studies to measure the same covariates. We explored whether differing minimally sufficient sets of confounders identified by a directed acyclic graph (DAG) ensures comparability of individual study estimates. Our analysis applied four statistical estimators to multiple minimally sufficient adjustment sets identified in a single DAG. METHODS: We compared estimates obtained via linear, log-binomial, and logistic regression and inverse probability weighting, and data were simulated based on a previously published DAG. RESULTS: Our results show that linear, log-binomial, and inverse probability weighting estimators generally provide the same estimate of effect for different estimands that are equally sufficient to adjust confounding bias, with modest differences in random error. In contrast, logistic regression often performed poorly, with notable differences in effect estimates obtained from unique minimally sufficient adjustment sets, and larger standard errors than other estimators. CONCLUSIONS: Our findings do not support the reliance of collaborative research on logistic regression results for meta-analyses. Use of DAGs to identify potentially differing minimally sufficient adjustment sets can allow meta-analyses without requiring the exact same covariates.

Motor Vehicle Crashes Involving a Bicycle Before and After Introduction of a Bike Share Program in Philadelphia, Pennsylvania, 2010-2018.

Objectives. To quantify the impact of a citywide bicycle share program on rates of motor vehicle collisions involving a bicycle.Methods. We conducted an interrupted time series analysis, using crash records from the Pennsylvania Department of Transportation for Philadelphia County from 2010 through 2018. We also calculated summary statistics to illustrate annual and monthly trends in rates of motor vehicle crashes involving a bicycle.Results. The baseline rate of bike events was 106% greater (95% confidence interval [CI] = 1.25, 3.38) at the time bicycle share was implemented compared with January 2010. Before bicycle share implementation, the rate of bicycle events decreased 1% (95% CI = 0.95, 1.03) annually. After the bicycle share program started, the rate of bicycle events decreased 13% (95% CI = 0.82, 0.94) annually.Conclusions. In the long term, programs that increase the number of bicycles on the road, such as bike share, may reduce rates of motor vehicle crashes involving a bicycle.

Prenatal Exposure to Endocrine-disrupting Chemicals in Relation to Autism Spectrum Disorder and Intellectual Disability.

BACKGROUND: Exposure to endocrine disruptors is unavoidable. Many such compounds are suspected to impact neurologic development of children, but most studies conducted have considered effects of individual chemicals in isolation. Because exposures co-occur, it is important to consider their health impacts in a single regression framework. METHODS: We applied Bayesian statistical tools (including shared mean and mixture priors for 25 unique chemicals) to study independent associations of endocrine disruptor biomarkers with autism spectrum disorder (ASD) (n = 491) and intellectual disability (n = 155), compared with 373 general population controls, in the Early Markers for Autism study. We measured biomarkers in maternal serum collected and stored from midpregnancy and considered them individually or as a class (i.e., summed polychlorinated biphenyls). We adjusted all models for original matching factors (child sex and month and year of birth), maternal age, maternal race/ethnicity, parity, and maternal education at the time samples were collected. We estimated the change in the odds of ASD or intellectual disability per 1 SD increase in the z-score of measured biomarker concentration for each chemical. RESULTS: Odds of ASD and intellectual disability did not change with increasing concentration for any specific endocrine disruptor. The effect estimates for each chemical were centered on or near an odds ratio of 1.00 in both models where we applied a shared mean or a mixture prior. CONCLUSION: Our mixtures analyses do not suggest an independent relationship with ASD or intellectual disability with any of the 25 chemicals examined together in this mixtures analysis.

Lung Cancer Risk Associated with Regulated and Unregulated Chrysotile Asbestos Fibers.

BACKGROUND: Regulation of asbestos fibers in the workplace is partly determined by which fibers can be visually counted. However, a majority of fibers are too short and thin to count this way and are, consequently, not subject to regulation. METHODS: We estimate lung cancer risk associated with asbestos fibers of varying length and width. We apply an order-constrained prior both to leverage external information from toxicological studies of asbestos health effects. This prior assumes that risk from asbestos fibers increases with increasing length and decreases with increasing width. RESULTS: When we apply a shared mean for the effect of all asbestos fiber exposure groups, the rate ratios for each fiber group per unit exposure appear mostly equal. Rate ratio estimates for fibers of diameter <0.25 µm and length <1.5 and 1.5-5.0 µm are the most precise. When applying an order-constrained prior, we find that estimates of lung cancer rate ratio per unit of exposure to unregulated fibers 20-40 and >40 µm in the thinnest fiber group are similar in magnitude to estimates of risk associated with long fibers in the regulated fraction of airborne asbestos fibers. Rate ratio estimates for longer fibers are larger than those for shorter fibers, but thicker and thinner fibers do not differ as the toxicologically derived prior had expected. CONCLUSION: Credible intervals for fiber size-specific risk estimates overlap; thus, we cannot conclude that there are substantial differences in effect by fiber size. Nonetheless, our results suggest that some unregulated asbestos fibers may be associated with increased incidence of lung cancer.

Bayesian Analysis of Silica Exposure and Lung Cancer Using Human and Animal Studies.

BACKGROUND: Bayesian methods can be used to incorporate external information into epidemiologic exposure-response analyses of silica and lung cancer. METHODS: We used data from a pooled mortality analysis of silica and lung cancer (n = 65,980), using untransformed and log-transformed cumulative exposure. Animal data came from chronic silica inhalation studies using rats. We conducted Bayesian analyses with informative priors based on the animal data and different cross-species extrapolation factors. We also conducted analyses with exposure measurement error corrections in the absence of a gold standard, assuming Berkson-type error that increased with increasing exposure. RESULTS: The pooled animal data exposure-response coefficient was markedly higher (log exposure) or lower (untransformed exposure) than the coefficient for the pooled human data. With 10-fold uncertainty, the animal prior had little effect on results for pooled analyses and only modest effects in some individual studies. One-fold uncertainty produced markedly different results for both pooled and individual studies. Measurement error correction had little effect in pooled analyses using log exposure. Using untransformed exposure, measurement error correction caused a 5% decrease in the exposure-response coefficient for the pooled analysis and marked changes in some individual studies. CONCLUSION: The animal prior had more impact for smaller human studies and for one-fold versus three- or 10-fold uncertainty. Adjustment for Berkson error using Bayesian methods had little effect on the exposure-response coefficient when exposure was log transformed or when the sample size was large. See video abstract at, http://links.lww.com/EDE/B160.

A Time Series Analysis of Associations between Daily Temperature and Crime Events in Philadelphia, Pennsylvania.

Urban crime may be an important but overlooked public health impact of rising ambient temperatures. We conducted a time series analysis of associations between temperature and crimes in Philadelphia, PA, for years 2006-2015. We obtained daily crime data from the Philadelphia Police Department, and hourly temperature and dew point data from the National Centers for Environmental Information. We calculated the mean daily heat index and daily deviations from each year's seasonal mean heat index value. We used generalized additive models with a quasi-Poisson distribution, adjusted for day of the week, public holiday, and long-term trends and seasonality, to estimate relative rates (RR) and 95% confidence intervals. We found that the strongest associations were with violent crime and disorderly conduct. For example, relative to the median of the distribution of mean daily heat index values, the rate of violent crimes was 9% (95% CI 6-12%) higher when the mean daily heat index was at the 99th percentile of the distribution. There was a positive, linear relationship between deviations of the daily mean heat index from the seasonal mean and rates of violent crime and disorderly conduct, especially in cold months. Overall, these analyses suggest that disorderly conduct and violent crimes are highest when temperatures are comfortable, especially during cold months. This work provides important information regarding the temporal patterns of crime activity.

Hierarchical regression for analyses of multiple outcomes.

In cohort mortality studies, there often is interest in associations between an exposure of primary interest and mortality due to a range of different causes. A standard approach to such analyses involves fitting a separate regression model for each type of outcome. However, the statistical precision of some estimated associations may be poor because of sparse data. In this paper, we describe a hierarchical regression model for estimation of parameters describing outcome-specific relative rate functions and associated credible intervals. The proposed model uses background stratification to provide flexible control for the outcome-specific associations of potential confounders, and it employs a hierarchical "shrinkage" approach to stabilize estimates of an exposure's associations with mortality due to different causes of death. The approach is illustrated in analyses of cancer mortality in 2 cohorts: a cohort of dioxin-exposed US chemical workers and a cohort of radiation-exposed Japanese atomic bomb survivors. Compared with standard regression estimates of associations, hierarchical regression yielded estimates with improved precision that tended to have less extreme values. The hierarchical regression approach also allowed the fitting of models with effect-measure modification. The proposed hierarchical approach can yield estimates of association that are more precise than conventional estimates when one wishes to estimate associations with multiple outcomes.

The researcher and the consultant: from testing to probability statements.

In the first instalment of this series, Stang and Poole provided an overview of Fisher significance testing (ST), Neyman-Pearson null hypothesis testing (NHT), and their unfortunate and unintended offspring, null hypothesis significance testing. In addition to elucidating the distinction between the first two and the evolution of the third, the authors alluded to alternative models of statistical inference; namely, Bayesian statistics. Bayesian inference has experienced a revival in recent decades, with many researchers advocating for its use as both a complement and an alternative to NHT and ST. This article will continue in the direction of the first instalment, providing practicing researchers with an introduction to Bayesian inference. Our work will draw on the examples and discussion of the previous dialogue.

Examining the association of lung cancer and highly correlated fibre size-specific asbestos exposures with a hierarchical Bayesian model.

BACKGROUND: Asbestos is a known carcinogen. However, little is known about the differential effects of size-specific asbestos fibres. Previous research has examined the relationship with lung cancer of each fibre group in the absence of others. Attempts to model all fibre groups within a single regression model have failed due to high correlations across fibre size groups. METHODS: We compare results from frequentist models for individual fibre size groups, and a hierarchical Bayesian model that included all fibre groups to estimate the relationship of size-specific asbestos fibre groups to lung cancer mortality. The hierarchical model assumes partial exchangeability of the effects of size-specific asbestos fibre groups to lung cancer, and is capable of handling the strong correlation of the exposure data. RESULTS: When fibre groups are modelled independently with a frequentist model, there appears to be an increase in the dose-response with increasing fibre size. However, when subject to a hierarchical structure, this trend vanishes, and the effects of distinct fibre groups appear largely similar. CONCLUSIONS: This is the first occasion where distinct asbestos fibre groups have been assessed in a single regression model; however, even the use of a hierarchical modelling structure does not appear to overcome all the statistical fluctuations arising from the high correlations across fibre groups. We believe these results should be compared with other occupational cohorts with similar fibre group information. Finally, results for the smallest fibre group may be suggestive of a carcinogenic potential for nanofibres.

Modelling complex mixtures in epidemiologic analysis: additive versus relative measures for differential effectiveness.

OBJECTIVES: Mixed exposures are often combined into single exposure measures using weighting factors. This occurs for many complex mixtures in environmental and occupational epidemiology including multiple congeners, air pollutants and unique forms of ionising radiation, among others. METHODS: The weights used for combining exposures are most often determined from experimental animal and cellular research. However, evidence from observational research is necessary to support their use in risk analyses, since results from experimental research do not directly translate to observational epidemiology. RESULTS: Using simulated data, we show that ratio-based relative weights cannot be reliably estimated from observational research. As a solution to this problem, we propose an approach for estimating differences in effectiveness of distinct exposures based on their excess effectiveness compared with a reference exposure. CONCLUSIONS: This alternative is easy to calculate and provides reliable estimates of differences in effectiveness of distinct exposures. This is important to regulatory bodies using relative measures for policy decisions, as well as practicing epidemiologists conducting risk analyses.

Prenatal Metal Exposures and Child Social Responsiveness Scale Scores in 2 Prospective Studies.

Background: Prenatal exposure to metals is hypothesized to be associated with child autism. We aim to investigate the joint and individual effects of prenatal exposure to urine metals including lead (Pb), mercury (Hg), manganese (Mn), and selenium (Se) on child Social Responsiveness Scale (SRS) scores. Methods: We used data from 2 cohorts enriched for likelihood of autism spectrum disorder (ASD): Early Autism Risk Longitudinal Investigation (EARLI) and the Markers of Autism Risk in Babies-Learning Early Signs (MARBLES) studies. Metal concentrations were measured in urine collected during pregnancy. We used Bayesian Kernel Machine Regression and linear regression models to investigate both joint and independent associations of metals with SRS Z-scores in each cohort. We adjusted for maternal age at delivery, interpregnancy interval, maternal education, child race/ethnicity, child sex, and/or study site. Results: The final analytic sample consisted of 251 mother-child pairs. When Pb, Hg, Se, and Mn were at their 75th percentiles, there was a 0.03 increase (95% credible interval [CI]: -0.11, 0.17) in EARLI and 0.07 decrease (95% CI: -0.29, 0.15) in MARBLES in childhood SRS Z-scores, compared to when all 4 metals were at their 50th percentiles. In both cohorts, increasing concentrations of Pb were associated with increasing values of SRS Z-scores, fixing the other metals to their 50th percentiles. However, all the 95% credible intervals contained the null. Conclusions: There were no clear monotonic associations between the overall prenatal metal mixture in pregnancy and childhood SRS Z-scores at 36 months. There were also no clear associations between individual metals within this mixture and childhood SRS Z-scores at 36 months. The overall effects of the metal mixture and the individual effects of each metal within this mixture on offspring SRS Z-scores might be heterogeneous across child sex and cohort. Further studies with larger sample sizes are warranted.