Benchmark dose profiles for bivariate exposures

While benchmark dose (BMD) methodology is well-established for settings with a single exposure, these methods cannot easily handle multidimensional exposures with nonlinear effects. We propose a framework for BMD analysis to characterize the joint effect of a two-dimensional exposure on a continuous outcome using a generalized additive model while adjusting for potential confounders via propensity scores. This leads to a dose-response surface which can be summarized in two dimensions by a contour plot in which combinations of exposures leading to the same expected effect are identified. In our motivating study of prenatal alcohol exposure, cognitive deficits in children are found to be associated with both the frequency of drinking as well as the amount of alcohol consumed on each drinking day during pregnancy. The general methodological framework is useful for a broad range of settings, including combinations of environmental stressors, such as chemical mixtures, and in explorations of the impact of dose rate rather than simply cumulative exposure on adverse outcomes.

A dose-response analysis of the effects of prenatal alcohol exposure on cognitive development

BACKGROUND

Most studies of the effects of prenatal alcohol exposure (PAE) on cognitive function have assumed that the dose-response curve is linear. However, data from a few animal and human studies suggest that there may be an inflection point in the dose-response curve above which PAE effects are markedly stronger and that there may be differences associated with pattern of exposure, assessed in terms of alcohol dose per drinking occasion and drinking frequency.

METHODS

We performed second-order confirmatory factor analysis on data obtained at school age, adolescence, and early adulthood from 2227 participants in six US longitudinal cohorts to derive a composite measure of cognitive function. Regression models were constructed to examine effects of PAE on cognitive function, adjusted for propensity scores. Analyses based on a single predictor (absolute alcohol (AA)/day) were compared with analyses based on two predictors (dose/occasion and drinking frequency), using (1) linear models and (2) nonparametric general additive models (GAM) that allow for both linear and nonlinear effects.

RESULTS

The single-predictor GAM model showed virtually no nonlinearity in the effect of AA/day on cognitive function. However, the two-predictor GAM model revealed differential effects of maternal drinking pattern. Among offspring of infrequent drinkers, PAE effects on cognitive function were markedly stronger in those whose mothers drank more than ~3 drinks/occasion, and the effect of dose/occasion was strongest among the very frequent drinkers. Frequency of drinking did not appear to alter the PAE effect on cognitive function among participants born to mothers who limited their drinking to ~1 drink/occasion or less.

CONCLUSIONS

These findings suggest that linear models based on total AA/day are appropriate for assessing whether PAE affects a given cognitive outcome. However, examination of alcohol dose/occasion and drinking frequency is needed to fully characterize the impact of different levels of alcohol intake on cognitive impairment.

Meta-analysis on studies with heterogeneous and partially observed covariates

Individual participant data meta-analysis is a commonly used alternative to the traditional aggregate data meta-analysis. It is popular because it avoids relying on published results and enables direct adjustment for relevant covariates. However, a practical challenge is that the studies being combined often vary in terms of the potential confounders that were measured. Furthermore, it will inevitably be the case that some individuals have missing values for some of those covariates. In this paper, we demonstrate how these challenges can be resolved using a propensity score approach, combined with multiple imputation, as a strategy to adjust for covariates in the context of individual participant data meta-analysis. To illustrate, we analyze data from the Bill and Melinda Gates Foundation-funded Healthy Birth, Growth, and Development Knowledge Integration project to investigate the relationship between physical growth rate in the first year of life and cognition measured later during childhood. We found that the overall effect of average growth velocity on cognitive outcome is slightly, but significantly, positive with an estimated effect size of 0.36 (95% CI 0.18, 0.55).

Bayesian outcome selection modeling

In psychiatric and social epidemiology studies, it is common to measure multiple different outcomes using a comprehensive battery of tests thought to be related to an underlying construct of interest. In the research that motivates our work, researchers wanted to assess the impact of in utero alcohol exposure on child cognition and neuropsychological development, which are evaluated using a range of different psychometric tests. Statistical analysis of the resulting multiple outcomes data can be challenging, because the outcomes measured on the same individual are not independent. Moreover, it is unclear, a priori, which outcomes are impacted by the exposure under study. While researchers will typically have some hypotheses about which outcomes are important, a framework is needed to help identify outcomes that are sensitive to the exposure and to quantify the associated treatment or exposure effects of interest. We propose such a framework using a modification of stochastic search variable selection, a popular Bayesian variable selection model and use it to quantify an overall effect of the exposure on the affected outcomes. The performance of the method is investigated empirically and an illustration is given through application using data from our motivating study.

A hierarchical meta-analysis for settings involving multiple outcomes across multiple cohorts

Evidence from animal models and epidemiological studies has linked prenatal alcohol exposure (PAE) to a broad range of long-term cognitive and behavioural deficits. However, there is a paucity of evidence regarding the nature and levels of PAE associated with increased risk of clinically significant cognitive deficits. To derive robust and efficient estimates of the effects of PAE on cognitive function, we have developed a hierarchical meta-analysis approach to synthesize information regarding the effects of PAE on cognition, integrating data on multiple outcomes from six U.S. Iongitudinal cohort studies. A key assumption of standard methods of meta-analysis, effect sizes are independent, is violated when multiple intercorrelated outcomes are synthesized across studies. Our approach involves estimating the dose-response coefficients for each outcome and then pooling these correlated dose-response coefficients to obtain an estimated "global" effect of exposure on cognition. In the first stage, we use individual participant data to derive estimates of the effects of PAE by fitting regression models that adjust for potential confounding variables using propensity scores. The correlation matrix characterizing the dependence between the outcome-specific dose-response coefficients estimated within each cohort is then run, while accommodating incomplete information on some outcome. We also compare inferences based on the proposed approach to inferences based on a full multivariate analysis.

Propensity score analysis for a semi-continuous exposure variable: a study of gestational alcohol exposure and childhood cognition

Propensity score methodology has become increasingly popular in recent years as a tool for estimating causal effects in observational studies. Much of the related research has been directed at settings with binary or discrete exposure variables with more recent work involving continuous exposure variables. In environmental epidemiology, a substantial proportion of individuals is often completely unexposed while others may experience heavy exposure leading to an exposure distribution with a point mass at zero and a heavy right tail. We suggest a new approach to handle this type of exposure data by constructing a propensity score based on a two-part model and show how this model can be used to more reliably adjust for covariates of a semi-continuous exposure variable. We also consider the case when a misspecified propensity score is used in a regression adjustment and derive an explicit form of the bias. We show that the potential bias gets smaller as the estimated propensity score gets closer to the true expectation of the exposure variable given a set of observed covariates. While this result pertains to a more general setting, we use it to evaluate the potential bias in settings in which the true exposure has a semi-continuous structure. We also evaluate and compare the performance of our proposed method through simulation studies relative to a simpler linear regression-based propensity score for a continuous exposure variable as well as through direct covariate adjustment. Overall, we find that using a propensity score constructed via a two-part model significantly improves the regression estimate when the exposure variable is semi-continuous in nature. Specifically when the proportion of non-exposed subjects is high and the effects of covariates on exposure and outcome are strong, the proposed two-part propensity score method outperforms the more standard competing methods. We illustrate our method using data from the Detroit Longitudinal Cohort Study in which the exposure variable reflects gestational alcohol exposure featuring zero values and a long tail.

Effects of prenatal alcohol exposure on cognitive and behavioral development: Findings from a hierarchical meta-analysis of data from six prospective longitudinal U.S. cohorts

BACKGROUND

Cognitive and behavioral sequelae of prenatal alcohol exposure (PAE) continue to be prevalent in the United States and worldwide. Because these sequelae are also common in other neurodevelopmental disorders, researchers have attempted to identify a distinct neurobehavioral profile to facilitate the differential diagnosis of fetal alcohol spectrum disorders (FASD). We used an innovative, individual participant meta-analytic technique to combine data from six large U.S. longitudinal cohorts to provide a more comprehensive and reliable characterization of the neurobehavioral deficits seen in FASD than can be obtained from smaller samples.

METHODS

Meta-analyses were performed on data from 2236 participants to examine effects of PAE (measured as oz absolute alcohol/day (AA/day)) on IQ, four domains of cognition function (learning and memory, executive function, reading achievement, and math achievement), sustained attention, and behavior problems, after adjusting for potential confounders using propensity scores.

RESULTS

The effect sizes for IQ and the four domains of cognitive function were strikingly similar to one another and did not differ at school age, adolescence, or young adulthood. Effect sizes were smaller in the more middle-class Seattle cohort and larger in the three cohorts that obtained more detailed and comprehensive assessments of AA/day. PAE effect sizes were somewhat weaker for parent- and teacher-reported behavior problems and not significant for sustained attention. In a meta-analysis of five aspects of executive function, the strongest effect was on set-shifting.

CONCLUSIONS

The similarity in the effect sizes for the four domains of cognitive function suggests that PAE affects an underlying component or components of cognition involving learning and memory and executive function that are reflected in IQ and academic achievement scores. The weaker effects in the more middle-class cohort may reflect a more cognitively stimulating environment, a different maternal drinking pattern (lower alcohol dose/occasion), and/or better maternal prenatal nutrition. These findings identify two domains of cognition-learning/memory and set-shifting-that are particularly affected by PAE, and one, sustained attention, which is apparently spared.

A Longitudinal Analysis of the Executive Functions in High-Level Soccer Players

INTRODUCTION

Assessments of executive functions (EFs) with varying levels of perceptual information or action fidelity are common talent-diagnostic tools in soccer, yet their validity still has to be established. Therefore, a longitudinal development of EFs in high-level players to understand their relationship with increased exposure to training is required.

METHODS

A total of 304 high-performing male youth soccer players (10-21 years old) in Germany were assessed across three seasons on various sport-specific and non-sport-specific cognitive functioning assessments.

RESULTS

The posterior means (90% highest posterior density) of random slopes indicated that both abilities predominantly developed between 10 and 15 years of age. A plateau was apparent for domain-specific abilities during adolescence, whereas domain-generic abilities improved into young adulthood.

CONCLUSION

The developmental trajectories of soccer players' EFs follow the general populations' despite long-term exposure to soccer-specific training and game play. This brings into question the relationship between high-level experience and EFs and renders including EFs in talent identification questionable.

Analysis of grouped data using conjugate generalized linear mixed models

This article concerns a class of generalized linear mixed models for two-level grouped data, where the random effects are uniquely indexed by groups and are independent. We derive necessary and sufficient conditions for the marginal likelihood to be expressed in explicit form. These models are unified under the conjugate generalized linear mixed models framework, where conjugate refers to the fact that the marginal likelihood can be expressed in closed form, rather than implying inference via the Bayesian paradigm. The proposed framework allows simultaneous conjugacy for Gaussian, Poisson and gamma responses, and thus can accommodate both unit- and group-level covariates. Only group-level covariates can be incorporated for the binomial distribution. In a simulation of Poisson data, our framework outperformed its competitors in terms of computational time, and was competitive in terms of robustness against misspecification of the random effects distributions.

A novel case-control subsampling approach for rapid model exploration of large clustered binary data

In many settings, an analysis goal is the identification of a factor, or set of factors associated with an event or outcome. Often, these associations are then used for inference and prediction. Unfortunately, in the big data era, the model building and exploration phases of analysis can be time-consuming, especially if constrained by computing power (ie, a typical corporate workstation). To speed up this model development, we propose a novel subsampling scheme to enable rapid model exploration of clustered binary data using flexible yet complex model set-ups (GLMMs with additive smoothing splines). By reframing the binary response prospective cohort study into a case-control-type design, and using our knowledge of sampling fractions, we show one can approximate the model estimates as would be calculated from a full cohort analysis. This idea is extended to derive cluster-specific sampling fractions and thereby incorporate cluster variation into an analysis. Importantly, we demonstrate that previously computationally prohibitive analyses can be conducted in a timely manner on a typical workstation. The approach is applied to analysing risk factors associated with adverse reactions relating to blood donation.

Localisation and substrate specificities of transglycanases in charophyte algae relate to development and morphology

Cell wall-modifying enzymes have been previously investigated in charophyte green algae (CGA) in cultures of uniform age, giving limited insight into their roles. Therefore, we investigated the in situ localisation and specificity of enzymes acting on hemicelluloses in CGA genera of different morphologies and developmental stages. In vivo transglycosylation between xyloglucan and an endogenous donor in filamentous Klebsormidium and Zygnema was observed in longitudinal cell walls of young (1 month) but not old cells (1 year), suggesting that it has a role in cell growth. By contrast, in parenchymatous Chara, transglycanase action occurred in all cell planes. In Klebsormidium and Zygnema, the location of enzyme action mainly occurred in regions where xyloglucans and mannans, and to a lesser extent mixed-linkage β-glucan (MLG), were present, indicating predominantly xyloglucan:xyloglucan endotransglucosylase (XET) activity. Novel transglycosylation activities between xyloglucan and xylan, and xyloglucan and galactomannan were identified in vitro in both genera. Our results show that several cell wall-modifying enzymes are present in CGA, and that differences in morphology and cell age are related to enzyme localisation and specificity. This indicates an evolutionary significance of cell wall modifications, as similar changes are known in their immediate descendants, the land plants. This article has an associated First Person interview with the first author of the paper.

A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia

The field of spatio-temporal modelling has witnessed a recent surge as a result of developments in computational power and increased data collection. These developments allow analysts to model the evolution of health outcomes in both space and time simultaneously. This paper models the trends in ischaemic heart disease (IHD) in New South Wales, Australia over an eight-year period between 2006 and 2013. A number of spatio-temporal models are considered, and we propose a novel method for determining the goodness-of-fit for these models by outlining a spatio-temporal extension of the Moran's I statistic. We identify an overall decrease in the rates of IHD, but note that the extent of this health improvement varies across the state. In particular, we identified a number of remote areas in the north and west of the state where the risk stayed constant or even increased slightly.

A flexible parametric approach to examining spatial variation in relative survival

Most of the few published models used to obtain small-area estimates of relative survival are based on a generalized linear model with piecewise constant hazards under a Bayesian formulation. Limitations of these models include the need to artificially split the time scale, restricted ability to include continuous covariates, and limited predictive capacity. Here, an alternative Bayesian approach is proposed: a spatial flexible parametric relative survival model. This overcomes previous limitations by combining the benefits of flexible parametric models: the smooth, well-fitting baseline hazard functions and predictive ability, with the Bayesian benefits of robust and reliable small-area estimates. Both spatially structured and unstructured frailty components are included. Spatial smoothing is conducted using the intrinsic conditional autoregressive prior. The model was applied to breast, colorectal, and lung cancer data from the Queensland Cancer Registry across 478 geographical areas. Advantages of this approach include the ease of including more realistic complexity, the feasibility of using individual-level input data, and the capacity to conduct overall, cause-specific, and relative survival analysis within the same framework. Spatial flexible parametric survival models have great potential for exploring small-area survival inequalities, and we hope to stimulate further use of these models within wider contexts. Copyright © 2016 John Wiley & Sons, Ltd.

Cementum and Dentin in Hypophosphatasia

Hypophosphatasia (HPP) often leads to premature loss of deciduous teeth, due to disturbed cementum formation. We addressed the question to what extent cementum and dentin are similarly affected. To this end, we compared teeth from children with HPP with those from matched controls and analyzed them microscopically and chemically. It was observed that both acellular and cellular cementum formation was affected. For dentin, however, no differences in mineral content were recorded. To explain the dissimilar effects on cementum and dentin in HPP, we assessed pyrophosphate (an inhibitor of mineralization) and the expression/activity of enzymes related to pyrophosphate metabolism in both the periodontal ligament and the pulp of normal teeth. Expression of nucleotide pyrophosphatase phosphodiesterase 1 (NPP1) in pulp proved to be significantly lower than in the periodontal ligament. Also, the activity of NPP1 was less in pulp, as was the concentration of pyrophosphate. Our findings suggest that mineralization of dentin is less likely to be under the influence of the inhibitory action of pyrophosphate than mineralization of cementum.

Exposure Enriched Case-Control (EECC) Design for the Assessment of Gene-Environment Interaction

Genetic susceptibility and environmental exposure both play an important role in the aetiology of many diseases. Case-control studies are often the first choice to explore the joint influence of genetic and environmental factors on the risk of developing a rare disease. In practice, however, such studies may have limited power, especially when susceptibility genes are rare and exposure distributions are highly skewed. We propose a variant of the classical case-control study, the exposure enriched case-control (EECC) design, where not only cases, but also high (or low) exposed individuals are oversampled, depending on the skewness of the exposure distribution. Of course, a traditional logistic regression model is no longer valid and results in biased parameter estimation. We show that addition of a simple covariate to the regression model removes this bias and yields reliable estimates of main and interaction effects of interest. We also discuss optimal design, showing that judicious oversampling of high/low exposed individuals can boost study power considerably. We illustrate our results using data from a study involving arsenic exposure and detoxification genes in Bangladesh.

Spatial regression with covariate measurement error: A semiparametric approach

Spatial data have become increasingly common in epidemiology and public health research thanks to advances in GIS (Geographic Information Systems) technology. In health research, for example, it is common for epidemiologists to incorporate geographically indexed data into their studies. In practice, however, the spatially defined covariates are often measured with error. Naive estimators of regression coefficients are attenuated if measurement error is ignored. Moreover, the classical measurement error theory is inapplicable in the context of spatial modeling because of the presence of spatial correlation among the observations. We propose a semiparametric regression approach to obtain bias-corrected estimates of regression parameters and derive their large sample properties. We evaluate the performance of the proposed method through simulation studies and illustrate using data on Ischemic Heart Disease (IHD). Both simulation and practical application demonstrate that the proposed method can be effective in practice.

Analysis of repeated pregnancy outcomes

Women tend to repeat reproductive outcomes, with past history of an adverse outcome being associated with an approximate two-fold increase in subsequent risk. These observations support the need for statistical designs and analyses that address this clustering. Failure to do so may mask effects, result in inaccurate variance estimators, produce biased or inefficient estimates of exposure effects. We review and evaluate basic analytic approaches for analysing reproductive outcomes, including ignoring reproductive history, treating it as a covariate or avoiding the clustering problem by analysing only one pregnancy per woman, and contrast these to more modern approaches such as generalized estimating equations with robust standard errors and mixed models with various correlation structures. We illustrate the issues by analysing a sample from the Collaborative Perinatal Project dataset, demonstrating how the statistical model impacts summary statistics and inferences when assessing etiologic determinants of birth weight.

Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis

BACKGROUND

Preventing risk factor exposure is vital to reduce the high burden from lung cancer. The leading risk factor for developing lung cancer is tobacco smoking. In Australia, despite apparent success in reducing smoking prevalence, there is limited information on small area patterns and small area temporal trends. We sought to estimate spatio-temporal patterns for lung cancer risk factors using routinely collected population-based cancer data.

METHODS

The analysis used a Bayesian shared component spatio-temporal model, with male and female lung cancer included separately. The shared component reflected lung cancer risk factors, and was modelled over 477 statistical local areas (SLAs) and 15 years in Queensland, Australia. Analyses were also run adjusting for area-level socioeconomic disadvantage, Indigenous population composition, or remoteness.

RESULTS

Strong spatial patterns were observed in the underlying risk factor estimates for both males (median Relative Risk (RR) across SLAs compared to the Queensland average ranged from 0.48 to 2.00) and females (median RR range across SLAs 0.53-1.80), with high risks observed in many remote areas. Strong temporal trends were also observed. Males showed a decrease in the underlying risk across time, while females showed an increase followed by a decrease in the final 2 years. These patterns were largely consistent across each SLA. The high underlying risk estimates observed among disadvantaged, remote and indigenous areas decreased after adjustment, particularly among females.

CONCLUSION

The modelled underlying risks appeared to reflect previous smoking prevalence, with a lag period of around 30 years, consistent with the time taken to develop lung cancer. The consistent temporal trends in lung cancer risk factors across small areas support the hypothesis that past interventions have been equally effective across the state. However, this also means that spatial inequalities have remained unaddressed, highlighting the potential for future interventions, particularly among remote areas.

Phase II Study of Ifosfamide+Doxorubicin in Patients With Advanced Synovial Sarcomas (E1793): A Trial of the Eastern Cooperative Oncology Group

Purpose Because we had observed in the synovial sarcoma subgroup of a broad phase III advanced soft tissue sarcoma study a significantly greater objective regression rate from ifosfamide+doxorubicin (88%) than from doxorubicin alone (20%) (P = 0.02), the Eastern Cooperative Oncology Group (ECOG) decided to further assess this two drug combination in a subsequent Phase II study.Patients Between 1994 and 1999, twelve adult patients with advanced synovial sarcomas were enrolled to receive, as their initial chemotherapy, ifosfamide 7.5 gm/m(2) plus doxorubicin 60 mg/m(2), given intravenously over two consecutive days every 3 weeks.Methods Each day for 2 days doxorubicin 30 mg/m(2) was infused over 5 min through a running i.v., followed by ifosfamide 3750 mg/m(2) over 4 h. Continuous i.v. fluid was infused at 300 mL/h for 3 h on day 1, before chemotherapy was begun; then the infusion was continued at 100 mL/h for a total of 3 days. Mesna 750 mg/m(2) was given 15 min before ifosfamide and at 4 and 8 h after ifosfamide on days 1 and 2 of each treatment cycle. Filgrastim (G-CSF) 5 mug/kg was given subcutaneously each day for 14 days beginning on day 3 of each treatment cycle to limit the severity of neutropenia.Results Five of our 12 patients (42%) experienced partial regression of their advanced synovial sarcomas; however, this first stage result was borderline for proceeding to the second planned stage of accrual and our case accrual was quite poor. Thus, the study was closed after stage one accrual. Our patients received a median of four cycles of chemotherapy (range: 1 to 6). All patients experienced at least grade 3 neutropenia (grade 4 in nine of them), and one patient died of treatment-related sepsis following the initial cycle of chemotherapy. Median survival was 11 months.

Prediction and surveillance of influenza epidemics

OBJECTIVE

To describe the use of surveillance and forecasting models to predict and track epidemics (and, potentially, pandemics) of influenza.

METHODS

We collected 5 years of historical data (2005-2009) on emergency department presentations and hospital admissions for influenza-like illnesses (International Classification of Diseases [ICD-10-AM] coding) from the Emergency Department Information System (EDIS) database of 27 Queensland public hospitals. The historical data were used to generate prediction and surveillance models, which were assessed across the 2009 southern hemisphere influenza season (June-September) for their potential usefulness in informing response policy. Three models are described: (i) surveillance monitoring of influenza presentations using adaptive cumulative sum (CUSUM) plan analysis to signal unusual activity; (ii) generating forecasts of expected numbers of presentations for influenza, based on historical data; and (iii) using Google search data as outbreak notification among a population.

RESULTS

All hospitals, apart from one, had more than the expected number of presentations for influenza starting in late 2008 and continuing into 2009. (i) The CUSUM plan signalled an unusual outbreak in December 2008, which continued in early 2009 before the winter influenza season commenced. (ii) Predictions based on historical data alone underestimated the actual influenza presentations, with 2009 differing significantly from previous years, but represent a baseline for normal ED influenza presentations. (iii) The correlation coefficients between internet search data for Queensland and statewide ED influenza presentations indicated an increase in correlation since 2006 when weekly influenza search data became available.

CONCLUSION

This analysis highlights the value of health departments performing surveillance monitoring to forewarn of disease outbreaks. The best system among the three assessed was a combination of routine forecasting methods coupled with an adaptive CUSUM method.

Effects of Caffeine Consumption by Women and Men on the Outcome of In Vitro Fertilization

OBJECTIVE

The objective of this study was to estimate the association between caffeine consumption and in vitro fertilization (IVF) outcomes.

METHODS

A total of 2474 couples were prospectively enrolled prior to undergoing their first cycle of IVF, contributing a total of 4716 IVF cycles. Discrete survival analysis adjusting for observed confounders was applied to quantify the relation between caffeine consumption and livebirth. Secondary outcomes of interest were oocyte retrieval, peak estradiol level, implantation rate, and fertilization rate.

RESULTS

Overall, caffeine consumption by women was not significantly associated with livebirth (p_trend=0.74). Compared with women who do not drink caffeine, the likelihood of livebirth was not significantly different for women who drank low (>0-800 mg/week; odds ratio [OR]=1.00, 95% confidence interval [CI])=0.83-1.21), moderate (>800-1400 mg/week; OR=0.89, 95% CI=0.71-1.12), or high levels of caffeine (>1400 mg/week; OR=1.07, 95% CI=0.85-1.34). Greater caffeine intake by women was associated with a significantly lower peak estradiol level (p_trend=0.03), but was not associated with the number of oocytes retrieved (p_trend=0.75), fertilization rate (p_trend=0.10), or implantation rate (p_trend=0.23). There was no significant association between caffeine intake by men and livebirth (p_trend=0.27), fertilization (p_trend=0.72), or implantation (p_trend=0.24). The individual effects of consumption of coffee, tea, or soda by women or men were not related to livebirth.

CONCLUSION

Caffeine consumption by women or men was not associated with IVF outcomes.

Early-life or lifetime sun exposure, sun reaction, and the risk of squamous cell carcinoma in an Asian population

BACKGROUND

It has been widely accepted that sun exposure is a risk factor of squamous cell carcinoma (SCC) among fair-skinned populations. However, sun exposure and sun reaction have not been explored in Asians and no gender-specific data were available.

METHOD

In a case-control study, 176 incident skin cancer cases were recruited from National Cheng-Kung University Medical Center from 1996 to 1999. Controls included 216 age-, gender-, and residency-matched subjects from the southwestern Taiwan. A questionnaire was administered to collect information on life style and other risk factors. Logistic regression analysis was performed to evaluate the association between sun exposure or sun reaction and the risk of SCC by gender.

RESULTS

Early-age (age 15 to 24) and lifetime sun exposure were significantly associated with increased risk of SCC in a dose-response pattern [odds ratio (OR) = 1.49-3.08, trend p = 0.009 and 0.0007, respectively]. After stratified by gender, the third tertile of early-age sun exposure was significantly associated with the SCC risk among men (OR = 3.08). The second and third tertiles of lifetime sun exposure was significantly associated with SCC risk among women (OR = 3.78 and 4.53, respectively). Skin reaction after 2-h sun exposure during childhood and adolescence was not significantly associated with the risk of SCC.

CONCLUSIONS

Lifetime sun exposure was more related to SCC risk in women, while early-age sun exposure was more relevant to men's SCC risk. This may be attributable to different lifestyle between men and women.

Cumulative exposure to lead in relation to cognitive function in older women

BACKGROUND

Recent data indicate that chronic low-level exposure to lead is associated with accelerated declines in cognition in older age, but this has not been examined in women.

OBJECTIVE

We examined biomarkers of lead exposure in relation to performance on a battery of cognitive tests among older women.

METHODS

Patella and tibia bone lead--measures of cumulative exposure over many years--and blood lead, a measure of recent exposure, were assessed in 587 women 47-74 years of age. We assessed their cognitive function 5 years later using validated telephone interviews.

RESULTS

Mean +/- SD lead levels in tibia, patella, and blood were 10.5 +/- 9.7 microg/g bone, 12.6 +/- 11.6 microg/g bone, and 2.9 +/- 1.9 microg/dL, respectively, consistent with community-level exposures. In multivariable-adjusted analyses of all cognitive tests combined, levels of all three lead biomarkers were associated with worse cognitive performance. The association between bone lead and letter fluency score differed dramatically from the other bone lead-cognitive score associations, and exclusion of this particular score from the combined analyses strengthened the associations between bone lead and cognitive performance. Results were statistically significant only for tibia lead: one SD increase in tibia lead corresponded to a 0.051-unit lower standardized summary cognitive score (95% confidence interval: -0.099 to -0.003; p = 0.04), similar to the difference in cognitive scores we observed between women who were 3 years apart in age.

CONCLUSIONS

These findings suggest that cumulative exposure to lead, even at low levels experienced in community settings, may have adverse consequences for women's cognition in older age.

Effects of retinoic acid on Dominant hemimelia expression in mice

BACKGROUND

Dominant hemimelia (Dh) is an autosomal dominant mutation that arose spontaneously in mice. Dh animals are asplenic and they exhibit asymmetric hindlimb defects in association with reduced numbers of lumbar vertebrae. These defects suggest that Dh acts early in embryonic development to affect patterning of the anterior-posterior (A-P) and left-right axes. This study was undertaken to determine whether retinoic acid (RA), which is involved in A-P patterning and coordination of bilaterally synchronized somitogenesis, affects phenotypic expression of the Dh gene.

METHODS

Thirty-four pregnant females were given, by oral intubation, a single dose of 50 or 75 mg all-trans RA per kilogram body weight at GD 9, 10, or 11. The pregnant females were then euthanized at GD 18 and fetuses removed by cesarean section. A total of 326 fetuses were identified by phenotype and linked DNA and their skeletons were analyzed.

RESULTS

There was a differential effect of RA on the axial skeleton and hindlimb of Dh/+ mice as compared to their wild-type littermates. Dose- and stage-specific effects on sternebrae and vertebrae were observed.

CONCLUSIONS

The effects of RA dosing on numbers of sternebrae and vertebrae suggest that Dh embryos have a primary defect in retinoid-mediated A-P patterning. Dosing with RA may produce the observed effects on phenotypic expression of Dh/+ by indirectly or directly modifying an already existing altered Hox expression pattern. As the relationship between axial patterning and the asymmetric limb is unknown, Dh is an important model for studying this relationship.

Mouse allergen exposure, wheeze and atopy in the first seven years of life

BACKGROUND

Little is known about mouse allergen exposure in home environments and the development of wheezing, asthma and atopy in childhood.

OBJECTIVE

To examine the relation between mouse allergen exposure and wheezing, atopy, and asthma in the first 7 years of life.

METHODS

Prospective study of 498 children with parental history of allergy or asthma followed from birth to age 7 years, with longitudinal questionnaire ascertainment of reported mouse exposure and dust sample mouse urinary protein allergen levels measured at age 2-3 months.

RESULTS

Parental report of mouse exposure in the first year of life was associated with increased risk of transient wheeze and wheezing in early life. Current report of mouse exposure was also significantly associated with current wheeze throughout the first 7 years of life in the longitudinal analysis (P = 0.03 for overall relation of current mouse to current wheeze). However, early life mouse exposure did not predict asthma, eczema or allergic rhinitis at age 7 years. Exposure to detectable levels of mouse urinary protein in house dust samples collected at age 2-3 months was associated with a twofold increase in the odds of atopy (sensitization to >=1 allergen) at school age (95% confidence interval for odds ratio = 1.1-3.7; P = 0.03 in a multivariate analysis.

CONCLUSIONS

Among children with parental history of asthma or allergies, current mouse exposure is associated with increased risk of wheeze during the first 7 years of life. Early mouse exposure was associated with early wheeze and atopy later in life.

Polymorphisms inGSTT1andp53and urinary transitional cell carcinoma in south-western Taiwan: A preliminary study

Little is known about the relevance of genetic polymorphisms to arsenic-related bladder cancer. A preliminary case-control study was conducted to explore the association between genetic polymorphisms of GSTT1, p53 codon 72 and bladder cancer in southern Taiwan, a former high arsenic exposure area. Fifty-nine urinary transitional cell carcinoma (TCC) patients from a referral centre in south-western Taiwan and 81 community controls matched on residence were recruited from 1996 to 1999. A questionnaire was administered to obtain arsenic exposure and general health information. Genotypes of p53 codon 72 and GSTT1 were analysed by polymerase chain reaction-restriction fragment length polymerase. The combined variant genotypes (heterozygous or homozygous variant) of p53 codon 72 and GSTT1 null were observed in 29% of cases and in 44% of controls, respectively. In this preliminary study, bladder cancer risk was slightly elevated for subjects carrying the variant genotype of p53 codon 72 or in subjects carrying the GSTT1 null genotype. Variants in p53 codon 72 increased the risk of bladder cancer among smokers. However, the results were not statistically significant and larger confirmatory studies are needed to clarify the role of candidate gene polymorphisms and bladder cancer risk in arsenic exposed populations.

Diversity of the gut microbiota and eczema in early life

BACKGROUND

A modest number of prospective studies of the composition of the intestinal microbiota and eczema in early life have yielded conflicting results.

OBJECTIVE

To examine the relationship between the bacterial diversity of the gut and the development of eczema in early life by methods other than stool culture.

METHODS

Fecal samples were collected from 21 infants at 1 and 4 months of life. Nine infants were diagnosed with eczema by the age of 6 months (cases) and 12 infants were not (controls). After conducting denaturating gradient gel electrophoresis (DGGE) of stool samples, we compared the microbial diversity of cases and controls using the number of electrophoretic bands and the Shannon index of diversity (H') as indicators.

RESULTS

Control subjects had significantly greater fecal microbial diversity than children with eczema at ages 1 (mean H' for controls = 0.75 vs. 0.53 for cases, P = 0.01) and 4 months (mean H' for controls = 0.92 vs. 0.59 for cases, P = 0.02). The increase in diversity from 1 to 4 months of age was significant in controls (P = 0.04) but not in children who developed eczema by 6 months of age (P = 0.32).

CONCLUSION

Our findings suggest that reduced microbial diversity is associated with the development of eczema in early life.

Residual-based diagnostics for structural equation models

Classical diagnostics for structural equation models are based on aggregate forms of the data and are ill suited for checking distributional or linearity assumptions. We extend recently developed goodness-of-fit tests for correlated data based on subject-specific residuals to structural equation models with latent variables. The proposed tests lend themselves to graphical displays and are designed to detect misspecified distributional or linearity assumptions. To complement graphical displays, test statistics are defined; the null distributions of the test statistics are approximated using computationally efficient simulation techniques. The properties of the proposed tests are examined via simulation studies. We illustrate the methods using data from a study of in utero lead exposure.

Maternal depressive symptoms, parenting self-efficacy, and child growth

OBJECTIVES

We assessed whether maternal depressive symptoms and parenting self-efficacy were associated with child growth delay.

METHODS

We collected data from a random sample of 595 low-income mothers and their children aged 6 to 24 months in Teresina, Piauí, Brazil, including information on sociodemographic characteristics, mothers' depressive symptoms and parenting self-efficacy, and children's anthropometric characteristics. We used adjusted logistic regression models in our analyses.

RESULTS

Depressive symptoms among mothers were associated with 1.8 times higher odds (95% confidence interval [CI] = 1.1, 2.9) of short stature among children. Parenting self-efficacy was not associated with short stature, nor did it mediate or modify the relationship between depressive symptoms and short stature. Maternal depressive symptoms and self-efficacy were not related to child underweight.

CONCLUSIONS

Our results showed that among low-income Brazilian families maternal depressive symptoms, but not self-efficacy, were associated with short stature in children aged 6 to 24 months after adjustment for known predictors of growth.

Penalized Item Response Theory Models: Application to Epigenetic Alterations in Bladder Cancer

Increasingly used in health-related applications, latent variable models provide an appealing framework for handling high-dimensional exposure and response data. Item response theory (IRT) models, which have gained widespread popularity, were originally developed for use in the context of educational testing, where extremely large sample sizes permitted the estimation of a moderate-to-large number of parameters. In the context of public health applications, smaller sample sizes preclude large parameter spaces. Therefore, we propose a penalized likelihood approach to reduce mean square error and improve numerical stability. We present a continuous family of models, indexed by a tuning parameter, that range between the Rasch model and the IRT model. The tuning parameter is selected by cross validation or approximations such as Akaike Information Criterion. While our approach can be placed easily in a Bayesian context, we find that our frequentist approach is more computationally efficient. We demonstrate our methodology on a study of methylation silencing of gene expression in bladder tumors. We obtain similar results using both frequentist and Bayesian approaches, although the frequentist approach is less computationally demanding. In particular, we find high correlation of methylation silencing among 16 loci in bladder tumors, that methylation is associated with smoking and also with patient survival.

Dose-response relationship of prenatal mercury exposure and IQ: an integrative analysis of epidemiologic data

BACKGROUND

Prenatal exposure to mercury has been associated with adverse childhood neurologic outcomes in epidemiologic studies. Dose-response information for this relationship is useful for estimating benefits of reduced mercury exposure.

OBJECTIVES

We estimated a dose-response relationship between maternal mercury body burden and subsequent childhood decrements in intelligence quotient (IQ), using a Bayesian hierarchical model to integrate data from three epidemiologic studies.

METHODS

Inputs to the model consist of dose-response coefficients from studies conducted in the Faroe Islands, New Zealand, and the Seychelles Islands. IQ coefficients were available from previous work for the latter two studies, and a coefficient for the Faroe Islands study was estimated from three IQ subtests. Other tests of cognition/achievement were included in the hierarchical model to obtain more accurate estimates of study-to-study and end point-to-end point variability.

RESULTS

We find a central estimate of -0.18 IQ points (95% confidence interval, -0.378 to -0.009) for each parts per million increase of maternal hair mercury, similar to the estimates for both the Faroe Islands and Seychelles studies, and lower in magnitude than the estimate for the New Zealand study. Sensitivity analyses produce similar results, with the IQ coefficient central estimate ranging from -0.13 to -0.25.

CONCLUSIONS

IQ is a useful end point for estimating neurodevelopmental effects, but may not fully represent cognitive deficits associated with mercury exposure, and does not represent deficits related to attention and motor skills. Nevertheless, the integrated IQ coefficient provides a more robust description of the dose-response relationship for prenatal mercury exposure and cognitive functioning than results of any single study.

Predicting Hospital Mortality, Length of Stay, and Transfer to Long-Term Care for Injured Patients

BACKGROUND

Using hospital length of stay (LOS) to measure trauma care efficiency is complicated by short LOS resulting from early mortality or transfer to long-term care (LTC).

METHODS

Records from the 1999 to 2003 National Trauma Data Bank were used to create a multistate model divided into four time periods, each with constant rates of death, discharge home, and LTC transfer. Estimated hospital mortality and LOS for patient subgroups were calculated from this model, and time-varying covariate effects were estimated.

RESULTS

A total of 369,829 cases with adequate data were available. Early mortality was increased most by hypotension or coma, and also by anatomic injury severity or penetrating mechanism, but these effects diminished with time; age remained a strong predictor of mortality at any time but sex was insignificant. Rates of discharge home decreased with time, whereas rates of LTC transfer peaked at 6 to 11 days. Increased age strongly predicted transfer to LTC, whereas penetrating or burn mechanisms made it less likely. Predicted and observed outcomes were similar for multiple subgroups, and about 17% of individual variation in LOS was explained by the model.

CONCLUSIONS

Multistate models of patient status can accurately predict mortality and resource use after injury, and describe time-varying effects of other factors.

Maternal social and pyschological conditions and physical growth in low-income children in Piauí, Northeast Brazil

Prevalence of child undernutrition remains high in many developing countries. In settings with scarce resources, modifiable maternal social conditions may influence feeding and parenting practices, in turn affecting child growth. This study aims to quantify the association between maternal social support and depression to children's physical growth outcomes in Teresina, Piauí, northeast Brazil. Interviews were conducted with a random sample of 595 mothers of children 6-24 months old in four low-income sections of Teresina, Piauí. We collected data on sociodemographic factors, mothers' social support, mothers' depressive symptomatology, and child's weight and recumbent length. Weight-for-height z-scores (WHZ), height-for-age z-scores (HAZ) and weight-for-age z-scores (WAZ) were calculated using the National Center for Chronic Disease Prevention and Health Promotion Center SAS program based on the 2000 Centers for Disease Control reference growth curves. Multivariable linear regression was used to model the association between maternal social support and depression to child growth, adjusting for biological and socio-demographic variables. Interviewer and neighborhood variation was accounted for through the inclusion of random effects. In adjusted models, material support, measured by number of friends or family members available to mothers when needing food or milk, was related to 0.3 higher average WHZ and 0.2 higher average WAZ in their children. Maternal positive social interaction, which reflects engagement in leisure-time activities with others, was associated with 0.3 higher average WHZ. Mothers' affectionate support was related to 0.2 higher average children's WHZ and WAZ, whereas social support for resolving a conflict was associated with 0.2 lower average HAZ. Maternal depression was not associated with child growth. It is concluded that inadequate growth in children may be sensitive to maternal social support.

Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study

We present a case study illustrating the challenges of analyzing accelerometer data taken from a sample of children participating in an intervention study designed to increase physical activity. An accelerometer is a small device worn on the hip that records the minute-by-minute activity levels of the child throughout the day for each day it is worn. The resulting data are irregular functions characterized by many peaks representing short bursts of intense activity. We model these data using the wavelet-based functional mixed model. This approach incorporates multiple fixed effect and random effect functions of arbitrary form, the estimates of which are adaptively regularized using wavelet shrinkage. The method yields posterior samples for all functional quantities of the model, which can be used to perform various types of Bayesian inference and prediction. In our case study, a high proportion of the daily activity profiles are incomplete, i.e. have some portion of the profile missing, so cannot be directly modeled using the previously described method. We present a new method for stochastically imputing the missing data that allows us to incorporate these incomplete profiles in our analysis. Our approach borrows strength from both the observed measurements within the incomplete profiles and from other profiles, from the same child as well as other children with similar covariate levels, while appropriately propagating the uncertainty of the imputation throughout all subsequent inference. We apply this method to our case study, revealing some interesting insights into children's activity patterns. We point out some strengths and limitations of using this approach to analyze accelerometer data.

Mode of delivery and cord blood cytokines: a birth cohort study

Background

The mechanisms for the association between birth by cesarean section and atopy and asthma are largely unknown.

Objective

To examine whether cesarean section results in neonatal secretion of cytokines that are associated with increased risk of atopy and/or asthma in childhood. To examine whether the association between mode of delivery and neonatal immune responses is explained by exposure to the maternal gut flora (a marker of the vaginal flora).

Methods

CBMCs were isolated from 37 neonates at delivery, and secretion of IL-13, IFN-γ, and IL-10 (at baseline and after stimulation with antigens [dust mite and cat dander allergens, phytohemagglutinin, and lipopolysaccharide]) was quantified by ELISA. Total and specific microbes were quantified in maternal stool. The relation between mode of delivery and cord blood cytokines was examined by linear regression. The relation between maternal stool microbes and cord blood cytokines was examined by Spearman's correlation coefficients.

Results

Cesarean section was associated with increased levels of IL-13 and IFN-γ. In multivariate analyses, cesarean section was associated with an increment of 79.4 pg/ml in secretion of IL-13 by CBMCs after stimulation with dust mite allergen (P < 0.001). Among children born by vaginal delivery, gram-positive anaerobes and total anaerobes in maternal stool were positively correlated with levels of IL-10, and gram-negative aerobic bacteria in maternal stool were negatively correlated with levels of IL-13 and IFN-γ.

Conclusion

Cesarean section is associated with increased levels of IL-13 and IFN-γ, perhaps because of lack of labor and/or reduced exposure to specific microbes (e.g., gram-positive anaerobes) at birth.

Modeling particle exposure in U.S. trucking terminals

Multi-tiered sampling approaches are common in environmental and occupational exposure assessment, where exposures for a given individual are often modeled based on simultaneous measurements taken at multiple indoor and outdoor sites. The monitoring data from such studies is hierarchical by design, imposing a complex covariance structure that must be accounted for in order to obtain unbiased estimates of exposure. Statistical methods such as structural equation modeling (SEM) represent a useful alternative to simple linear regression in these cases, providing simultaneous and unbiased predictions of each level of exposure based on a set of covariates specific to the exposure setting. We test the SEM approach using data from a large exposure assessment of diesel and combustion particles in the U.S.trucking industry. The exposure assessment includes data from 36 different trucking terminals across the United States sampled between 2001 and 2005, measuring PM2.5 and its elemental carbon (EC), organic carbon (OC) components, by personal monitoring, and sampling at two indoor work locations and an outdoor "background" location. Using the SEM method, we predict the following: (1) personal exposures as a function of work-related exposure and smoking status; (2) work-related exposure as a function of terminal characteristics, indoor ventilation, job location, and background exposure conditions; and (3) background exposure conditions as a function of weather, nearby source pollution, and other regional differences across terminal sites. The primary advantage of SEMs in this setting is the ability to simultaneously predict exposures at each of the sampling locations, while accounting for the complex covariance structure among the measurements and descriptive variables. The statistically significant results and high R2 values observed from the trucking industry application supports the broader use of this approach in exposure assessment modeling.

Does living near a Superfund site contribute to higher polychlorinated biphenyl (PCB) exposure?

We assessed determinants of cord serum polychlorinated biphenyl (PCB) levels among 720 infants born between 1993 and 1998 to mothers living near a PCB-contaminated Superfund site in Massachusetts, measuring the sum of 51 PCB congeners (capital sigmaPCB) and ascertaining maternal address, diet, sociodemographics, and exposure risk factors. Addresses were geocoded to obtain distance to the Superfund site and neighborhood characteristics. We modeled log10(capital sigmaPCB) as a function of potential individual and neighborhood risk factors, mapping model residuals to assess spatial correlates of PCB exposure. Similar analyses were performed for light (mono-tetra) and heavy (penta-deca) PCBs to assess potential differences in exposure pathways as a function of relative volatility. PCB-118 (relatively prevalent in site sediments and cord serum) was assessed separately. The geometric mean of capital sigmaPCB levels was 0.40 (range, 0.068-18.14) ng/g serum. Maternal age and birthplace were the strongest predictors of capital sigmaPCB levels. Maternal consumption of organ meat and local dairy products was associated with higher and smoking and previous lactation with lower capital sigmaPCB levels. Infants born later in the study had lower capital sigmaPCB levels, likely due to temporal declines in exposure and site remediation in 1994-1995. No association was found between capital sigmaPCB levels and residential distance from the Superfund site. Similar results were found with light and heavy PCBs and PCB-118. Previously reported demographic (age) and other (lactation, smoking, diet) correlates of PCB exposure, as well as local factors (consumption of local dairy products and Superfund site dredging) but not residential proximity to the site, were important determinants of cord serum PCB levels in the study community.

Associations of cord blood fatty acids with lymphocyte proliferation, IL-13, and IFN-gamma

BACKGROUND

N-3 and n-6 polyunsaturated fatty acids (PUFAs) have been hypothesized to have opposing influences on neonatal immune responses that might influence the risk of allergy or asthma. However, both n-3 eicosapentaenoic acid (EPA) and n-6 arachidonic acid (AA) are required for normal fetal development.

OBJECTIVE

We evaluated whether cord blood fatty acid levels were related to neonatal immune responses and whether n-3 and n-6 PUFA responses differed.

METHODS

We examined the relation of cord blood plasma n-3 and n-6 PUFAs (n = 192) to antigen- and mitogen-stimulated cord blood lymphocyte proliferation (n = 191) and cytokine (IL-13 and IFN-gamma; n = 167) secretion in a US birth cohort.

RESULTS

Higher levels of n-6 linoleic acid were correlated with higher IL-13 levels in response to Bla g 2 (cockroach, P = .009) and Der f 1 (dust mite, P = .02). Higher n-3 EPA and n-6 AA levels were each correlated with reduced lymphocyte proliferation and IFN-gamma levels in response to Bla g 2 and Der f 1 stimulation. Controlling for potential confounders, EPA and AA had similar independent effects on reduced allergen-stimulated IFN-gamma levels. If neonates had either EPA or AA levels in the highest quartile, their Der f 1 IFN-gamma levels were 90% lower (P = .0001) than those with both EPA and AA levels in the lowest 3 quartiles. Reduced AA/EPA ratio was associated with reduced allergen-stimulated IFN-gamma level.

CONCLUSION

Increased levels of fetal n-3 EPA and n-6 AA might have similar effects on attenuation of cord blood lymphocyte proliferation and IFN-gamma secretion.

CLINICAL IMPLICATIONS

The implications of these findings for allergy or asthma development are not yet known.