The effect of transient exposures on the risk of an acute illness with low hazard rate

There are many situations where intermittent short-term exposures of a certain kind are thought to temporarily enhance the risk of onset of an adverse health event (illness). When the hazard rate of the illness is small it is desirable to investigate this possible association using only data on cases occurring in a finite observation period. Here we extend a method for such an analysis by allowing the baseline hazard for the illness to depend on the increasing age over the observation period and using age at the times of exposure, a time dependent variable, as a covariate in the effect of the transient exposure. The method is illustrated with a study of the possible association of long-haul air travel and hospitalization for venous thromboembolism over an observation period of 19 years. It is demonstrated that allowing for aging over the observation period can avoid bias in the estimated effect size when the baseline hazard for the illness increases with age and exposures occur irregularly over time.     

Identification of Risks in the Life Cycle of Nanotechnology‐Based Products

In order to realize the projected market potential of nanotechnology, the environmental, health, and safety (EHS) uncertainties posed by a nano‐product (i.e., a nanotechnology‐enabled product) need to be characterized through the identification of risks and opportunities in early stages of product development. We present a methodology to identify risks from nano‐products using a scenario analysis approach that allows for expert elicitation on a set of preidentified use and disposal scenarios and what we have labeled “risk triggers” to obtain scores on their likelihood of occurrence and severity. Use and disposal scenarios describe product life‐cycle stages that could result in risk attributed to the nano‐product, whereas risk triggers are particular to nanoparticle properties. These are potential risks, as the risk assessment community is currently debating the specific risks attributed to nanotechnology. Through such a framework, our goal is to identify which products pose greater risks, where these risks occur in the product life cycle, and the impacts of these environmental risks on society. The comparison of risk triggers across nano‐products allows relative risk ranking on axes of exposure‐ and hazard‐related risk triggers. For the specific case of air fresheners, areas of acute risks resulted from bioavailability of nanoparticles in air release and water entrainment exposure scenarios; catalytic activity of nanoparticles in inhalation and air release exposure scenarios; the harmful effects due to the antibacterial property on useful bacteria particularly in susceptible populations; and, finally, risks from the lack of nanoparticle coating stability in air release scenarios.     

DNA methylation in neonates born to women receiving psychiatric care

Prenatal exposure both to maternal psychiatric illness and psychiatric medication has been linked with adverse child outcomes that affect physiological, emotional and psychiatric development. Studies suggest that epigenetic mechanisms, such as DNA methylation, may facilitate these effects. In this report, we explore the association between maternal psychiatric illness and treatment during pregnancy and neonatal DNA methylation patterns in a prospectively-characterized clinical cohort of 201 dyads. Associations between the percent of umbilical cord blood DNA methylated at 27,578 CpG sites and maternal psychiatric diagnosis, symptoms and antidepressant use were evaluated by fitting a separate linear mixed effects model for each CpG site. There were no significant changes in neonatal DNA methylation attributable to maternal psychiatric diagnosis or depressive symptoms during pregnancy. Exposure to an antidepressant medication was associated with differential methylation of CpG sites in TNFRSF21 and CHRNA2 (false discovery rate < 0.05), but the average difference in methylation for both CpG sites was less than 3% between each group. The results were not specific to type of antidepressant or duration of the exposure. This study suggests that there are no large effects of maternal psychiatric illness, depressive symptoms or prenatal exposure to antidepressants on neonatal DNA methylation. Delineation of the influence of maternal psychiatric illness and pharmacological exposures on the developing fetuses has critical implications for clinical care during pregnancy.     

DNA methylation in neonates born to women receiving psychiatric care

Prenatal exposure both to maternal psychiatric illness and psychiatric medication has been linked with adverse child outcomes that affect physiological, emotional and psychiatric development. Studies suggest that epigenetic mechanisms, such as DNA methylation, may facilitate these effects. In this report, we explore the association between maternal psychiatric illness and treatment during pregnancy and neonatal DNA methylation patterns in a prospectively-characterized clinical cohort of 201 dyads. Associations between the percent of umbilical cord blood DNA methylated at 27,578 CpG sites and maternal psychiatric diagnosis, symptoms and antidepressant use were evaluated by fitting a separate linear mixed effects model for each CpG site. There were no significant changes in neonatal DNA methylation attributable to maternal psychiatric diagnosis or depressive symptoms during pregnancy. Exposure to an antidepressant medication was associated with differential methylation of CpG sites in TNFRSF21 and CHRNA2 (false discovery rate < 0.05), but the average difference in methylation for both CpG sites was less than 3% between each group. The results were not specific to type of antidepressant or duration of the exposure. This study suggests that there are no large effects of maternal psychiatric illness, depressive symptoms or prenatal exposure to antidepressants on neonatal DNA methylation. Delineation of the influence of maternal psychiatric illness and pharmacological exposures on the developing fetuses has critical implications for clinical care during pregnancy.     

Do people with idiopathic environmental intolerance attributed to electromagnetic fields display physiological effects when exposed to electromagnetic fields? A systematic review of provocation studies

Idiopathic environmental intolerance attributed to electromagnetic fields (IEI‐EMF) is a controversial illness in which people report symptoms that they believe are triggered by exposure to EMF. Double‐blind experiments have found no association between the presence of EMF and self‐reported outcomes in people with IEI‐EMF. No systematic review has assessed whether EMF exposure triggers physiological or cognitive changes in this group. Using a systematic literature search, we identified 29 single or double‐blind experiments in which participants with IEI‐EMF were exposed to different EMF levels and in which objectively measured outcomes were assessed. Five studies identified significant effects of exposure such as reduced heart rate and blood pressure, altered pupillary light reflex, reduced visual attention and perception, improved spatial memory, movement away from an EMF source during sleep and altered EEG during sleep. In most cases, these were isolated results that other studies failed to replicate. For the sleep EEG findings, the results reflected similar changes in the IEI‐EMF participants and a non‐IEI‐EMF control group. At present, there is no reliable evidence to suggest that people with IEI‐EMF experience unusual physiological reactions as a result of exposure to EMF. This supports suggestions that EMF is not the main cause of their ill health. Bioelectromagnetics. Bioelectromagnetics 32:593–609, 2011. © 2011 Wiley Periodicals, Inc.     

Data-adaptive longitudinal model selection in causal inference with collaborative targeted minimum loss-based estimation

Causal inference methods have been developed for longitudinal observational study designs where confounding is thought to occur over time. In particular, one may estimate and contrast the population mean counterfactual outcome under specific exposure patterns. In such contexts, confounders of the longitudinal treatment-outcome association are generally identified using domain-specific knowledge. However, this may leave an analyst with a large set of potential confounders that may hinder estimation. Previous approaches to data-adaptive model selection for this type of causal parameter were limited to the single time-point setting. We develop a longitudinal extension of a collaborative targeted minimum loss-based estimation (C-TMLE) algorithm that can be applied to perform variable selection in the models for the probability of treatment with the goal of improving the estimation of the population mean counterfactual outcome under a fixed exposure pattern. We investigate the properties of this method through a simulation study, comparing it to G-Computation and inverse probability of treatment weighting. We then apply the method in a real-data example to evaluate the safety of trimester-specific exposure to inhaled corticosteroids during pregnancy in women with mild asthma. The data for this study were obtained from the linkage of electronic health databases in the province of Quebec, Canada. The C-TMLE covariate selection approach allowed for a reduction of the set of potential confounders, which included baseline and longitudinal variables.     

Demonstration of the facts and research of the causes in environmental epidemiology: methodological aspects

There are usually three major steps in the study of the possible impact of environmental factors on health: 1) to demonstrate that there is an association between exposure to the factor and the disease under study; 2) to demonstrate that this association is causal; 3) to evaluate the health benefit that could be obtained by removing the source of exposure. Statistical methods are commonly assumed to provide an objective way of achieving these three steps. This paper reviews some of the conditions that have to be met to allow proper interpretations and to avoid some of the controversies that are often found in health-environment studies. First, it should be remembered that the so-called P value which is used to qualify 'statistically significant' associations between risk factors and diseases does not give any indication of the probability that this association is actual, while far too often it is believed that it does. The probability that an association between an environmental factor and a disease is real could, however, be estimated by using Bayesian methods. These methods require that the a priori probabilities be stated, which is difficult to do in practice. Some directions to overcome this difficulty are presented. Second, the analysis of causality cannot be carried out on statistical grounds alone and the so-called 'causality criteria' are of limited practical interest. Definition of what is a cause, and upon which conditions a candidate factor of a disease can be considered as a cause, deserves much research effort, and careful consideration of the huge literature (mostly outside of the epidemiological field, for example in logic) which is devoted to this subject. Finally, the measurement of the role of a factor in a disease is very often assessed through the use of 'attributable fraction' or 'attributable mortality'. This should be done only when it is demonstrated that the considered factor is causal. Moreover, the interpretation of attributable fractions to a specific factor may be difficult in the (general) case where there are multiple causal factors implied in the development of the disease. Demographic measures of 'potential years (or days, etc.) of life lost' should in general be used, rather than 'numbers of deaths' to quantify the possible impact of environmental factors. Also, as the personal factors are generally extremely important in the determinism of the causes of death associated to environmental factors, and as they cannot be controlled through ecological studies, epidemiological designs where these cofactors can be evaluated individually on cases and controls should be preferred.     

p53 displacement from centrosomes and p53-mediated G1 arrest following transient inhibition of the mitotic spindle

Growing evidence indicates a central role for p53 in mediating cell cycle arrest in response to mitotic spindle defects so as to prevent rereplication in cells in which the mitotic division has failed. Here we report that a transient inhibition of spindle assembly induced by nocodazole, a tubulin-depolymerizing drug, triggers a stable activation of p53, which can transduce a cell cycle inhibitory signal even when the spindle-damaging agent is removed and the spindle is allowed to reassemble. Cells transiently exposed to nocodazole continue to express high levels of p53 and p21 in the cell cycle that follows the transient exposure to nocodazole and become arrested in G(1), regardless of whether they carry a diploid or polyploid genome after mitotic exit. We also show that p53 normally associates with centrosomes in mitotic cells, whereas nocodazole disrupts this association. Together these results suggest that the induction of spindle damage, albeit transient, interferes with the subcellular localization of p53 at specific mitotic locations, which in turn dictates cell cycle arrest in the offspring of such defective mitoses.     

A General Framework For Prioritizing Research To Reduce Injuries And Diseases in Mining

A strategy for prioritizing mining health and safety research by evaluating the potential for risk reduction through interventions is proposed. Mining has one of the highest incidence rates of injury and disease found in major industries. The main premise of this paper is that often the best opportunities to reduce these rates are not revealed by retrospective analysis of injury and illness data. Instead, a proactive approach is needed that accounts for risks to specific hazards that can be abated by engineering or behavioral interventions. The process proposed here begins with development of prospective interventions. The degree of reduction in risk to be expected from an intervention then is determined from statistics on the mining worker population, the expected degree of success of the intervention, and the expected change in the severity of injuries resulting from the intervention. Three disparate mining health and safety concerns are presented to demonstrate common problems in assessing risks of injury and illness and describe additional data needs. Information on events preceding injuries and illnesses and more detailed demographic data on the mining work force are needed to analyze injury and illness data more precisely. Detailed information on exposure to specific hazards is necessary to evaluate the potential for an intervention to reduce risk of injury or illness.     

Impact of measurement error in radon exposure on the estimated excess relative risk of lung cancer death in a simulated study based on the French Uranium Miners’ Cohort

Measurement error (ME) can lead to bias in the analysis of epidemiologic studies. Here a simulation study is described that is based on data from the French Uranium Miners’ Cohort and that was conducted to assess the effect of ME on the estimated excess relative risk (ERR) of lung cancer death associated with radon exposure. Starting from a scenario without any ME, data were generated containing successively Berkson or classical ME depending on time periods, to reflect changes in the measurement of exposure to radon (²²²Rn) and its decay products over time in this cohort. Results indicate that ME attenuated the level of association with radon exposure, with a negative bias percentage on the order of 60% on the ERR estimate. Sensitivity analyses showed the consequences of specific ME characteristics (type, size, structure, and distribution) on the ERR estimates. In the future, it appears important to correct for ME upon analyzing cohorts such as this one to decrease bias in estimates of the ERR of adverse events associated with exposure to ionizing radiation.     

Shiftwork and metabolic dysfunction

Many of the health problems that are more prevalent among shiftworkers are thought to be linked to their heightened susceptibility to metabolic syndrome, i.e., the association of even moderate degrees of visceral obesity, dyslipidemia, abnormal blood pressure, and serum glucose levels in the same individual. Although previous studies have identified associations between shiftwork and metabolic syndrome, there is relatively little evidence to date of how the risk of developing it varies as a function of exposure to shiftwork. The current study seeks to confirm earlier findings of an association between shiftwork exposure and metabolic dysfunction, and to examine the impact of exposure duration, while adjusting for a number of covariates in the analyses. The analyses were based on data from VISAT, a study involving the measurement of physiological, behavioral, and subjective outcomes from 1757 participants, 989 being current or former shiftworkers. The sample comprised employed and retired wage earners, male and female, who were 32, 42, 52, and 62 yrs old. The first analysis sought to confirm previous findings of an association between exposure to shiftwork and the risk of developing metabolic syndrome. It indicated that participants who were or who had previously been shiftworkers (i.e., working schedules that involved rotating shifts; not being able to go to bed before midnight; having to get up before 05:00 h; or being prevented from sleeping during the night) were more likely to exhibit symptoms of metabolic syndrome, after adjusting for age, sex, socioeconomic status, smoking, alcohol intake, perceived stress, and sleep difficulty (odds ratio [OR] 1.78; 95% confidence interval [CI] 1.03-3.08). The results suggest the association between shiftwork and metabolic syndrome cannot be fully accounted for by either higher levels of strain or increased sleep difficulty among shiftworkers, although it remains a possibility that either one or both of these factors may have played a contributing role. The second analysis addressed the issue of duration of exposure to shiftwork. Participants with >10 yrs' experience of working rotating shifts were more likely to exhibit symptoms of metabolic syndrome than participants without exposure to shiftwork, i.e., dayworkers, even after adjusting for age and sex (OR 1.96; 95% CI 1.03-3.75). Thus, the current study confirms the association between shiftwork exposure and metabolic syndrome. It also provides new information regarding the time course of the development of the illness as function of exposure duration, although this was only examined in relation to rotating shiftwork. It is concluded that those responsible for monitoring workers' health should pay particular attention to indices of metabolic dysfunction in workers who have been exposed to shiftwork for >10 yrs.     

Analysis and Portrayal of Uncertainty in a Food-Web Exposure Model

The results of quantitative risk assessments are key factors in a risk manager's decision of the necessity to implement actions to reduce risk. The extent of the uncertainty in the assessment will play a large part in the degree of confidence a risk manager has in the reported significance and probability of a given risk. The two main sources of uncertainty in such risk assessments are variability and incertitude. In this paper we use two methods, a second-order two-dimensional Monte Carlo analysis and probability bounds analysis, to investigate the impact of both types of uncertainty on the results of a food-web exposure model. We demonstrate how the full extent of uncertainty in a risk estimate can be fully portrayed in a way that is useful to risk managers. We show that probability bounds analysis is a useful tool for identifying the parameters that contribute the most to uncertainty in a risk estimate and how it can be used to complement established practices in risk assessment. We conclude by promoting the use of probability analysis in conjunction with Monte Carlo analyses as a method for checking how plausible Monte Carlo results are in the full context of uncertainty.     

Measuring Exposure in Hurricane Katrina: A Meta-Analysis and an Integrative Data Analysis

To date there is no consensus on the operationalization of exposure severity in the study of the impact of natural disasters. This is problematic because incomplete and inconsistent measurement of exposure limits the internal and external validity of disaster studies. The current paper examined the predictive validity of severity measures in two interrelated studies of Hurricane Katrina survivors. First, in a meta-analysis of eight studies that measured both exposure severity and posttraumatic stress, the effect size was estimated to be r = .266. The moderating effects of sample and study characteristics were examined and we found that minority status and number of stressors assessed were significant moderators. Second, in an integrative data analysis of five independent samples of Hurricane Katrina survivors, the impact of specific disaster-related stressors on mental health was compared. Threat to physical integrity of self and others were found to have the strongest association with posttraumatic stress (PTS) and general psychological distress (GPD). The lack of basic necessities, such as food, water, and medical care, and loss of pet were also found to be strongly associated with both PTS and GPD. The results from the two studies are integrated and their implication for disaster research and relief are discussed.     

Molecular Dynamics of the Association of L-Selectin and FERM Regulated by PIP2

Phosphatidylinositol 4,5-bisphosphate (PIP2) acts as a signaling lipid, mediating membrane trafficking and recruitment of proteins to membranes. A key example is the PIP2-dependent regulation of the adhesion of L-selectin to the cytoskeleton adaptors of the N-terminal subdomain of ezrin-radixin-moesin (FERM). The molecular details of the mediating behavior of multivalent anionic PIP2 lipids in this process, however, remain unclear. Here, we use coarse-grained molecular dynamics simulation to explore the mechanistic details of PIP2 in the transformation, translocation, and association of the FERM/L-selectin complex. We compare membranes of different compositions and find that anionic phospholipids are necessary for both FERM and the cytoplasmic domain of L-selectin to absorb on the membrane surface. The subsequent formation of the FERM/L-selectin complex is strongly favored by the presence of PIP2, which clusters around both proteins and triggers a conformational transition in the cytoplasmic domain of L-selectin. We are able to quantify the effect of PIP2 on the association free energy of the complex by means of a potential of mean force. We conclude that PIP2 behaves as an adhesive agent to enhance the stability of the FERM/L-selectin complex and identify key residues involved. The molecular information revealed in this study highlights the specific role of membrane lipids such as PIP2 in protein translocation and potential signaling.     

Marginal mark regression analysis of recurrent marked point process data

Summary .  Longitudinal studies typically collect information on the timing of key clinical events and on specific characteristics that describe those events. Random variables that measure qualitative or quantitative aspects associated with the occurrence of an event are known as marks. Recurrent marked point process data consist of possibly recurrent events, with the mark (and possibly exposure) measured if and only if an event occurs. Analysis choices depend on which aspect of the data is of primary scientific interest. First, factors that influence the occurrence or timing of the event may be characterized using recurrent event analysis methods. Second, if there is more than one event per subject, then the association between exposure and the mark may be quantified using repeated measures regression methods. We detail assumptions required of any time-dependent exposure process and the event time process to ensure that linear or generalized linear mixed models and generalized estimating equations provide valid estimates. We provide theoretical and empirical evidence that if these conditions are not satisfied, then an independence estimating equation should be used for consistent estimation of association. We conclude with the recommendation that analysts carefully explore both the exposure and event time processes prior to implementing a repeated measures analysis of recurrent marked point process data.     

Classification of worker exposures

The classification of jobs or workers by exposure is an important undertaking in any occupational epidemiological study. Hitherto, the exposure classification designs have been strongly motivated by a desire to generate a sufficient number of exposure classes for the determination of a potential exposure-response relationship. Thus, the partitioning of exposures has been more or less arbitrary. The misclassification problems created by the selection of an arbitrary number of exposure assignment classes have not been addressed. In any quantitative exposure classification scheme, specific job titles may be indistinguishable in existing employment records; therefore, between worker variability must be addressed when characterizing worker exposures. Also, industrial hygiene exposure measurements frequently used to characterize worker exposures are often treated as valid representations of exposures; but they are neither random nor systematic evaluations of worker exposures. As a result they do not represent sampling from the proper exposure stratification of workers. These observations suggest that the selection of exposure groups should be based on a more rigorous examination of the data and its limitations. Considering the probability of any given worker being placed into the proper class as the probability of finding the mean exposure for that worker within the class boundary, the general equations to quantify the misclassification rates for any classification design as well as the exposure class limits and their width for any acceptable misclassification rate are developed. If between worker variability could not be calculated from the available exposure measurements, then it might be estimated from the proper data compiled from the literature. By considering an acceptable level of exposure misclassification, it is possible to calculate the allowable number of exposure classes and the proper partitioning ratio for these classes. Thus, the trade-off between misclassification and number of exposure classes might be a satisfactory solution to this difficulty encountered in occupational epidemiology.     

Multi-locus association study of schizophrenia susceptibility genes with a posterior probability method

Schizophrenia is a serious neuropsychiatric illness affecting about 1% of the world’s population. Family, twin and adoption studies have demonstrated that 65%―85% of the susceptibility to schizophrenia can be attributed to genes. On the basis of genetic model-ing of epidemiological data and recent results of a number of whole-genome screens for susceptibility genes, schizophrenia has been considered a complex disorder. A number of genes with small to moderate effects are involved in combinat…     

Shiftwork and Metabolic Dysfunction

Many of the health problems that are more prevalent among shiftworkers are thought to be linked to their heightened susceptibility to metabolic syndrome, i.e., the association of even moderate degrees of visceral obesity, dyslipidemia, abnormal blood pressure, and serum glucose levels in the same individual. Although previous studies have identified associations between shiftwork and metabolic syndrome, there is relatively little evidence to date of how the risk of developing it varies as a function of exposure to shiftwork. The current study seeks to confirm earlier findings of an association between shiftwork exposure and metabolic dysfunction, and to examine the impact of exposure duration, while adjusting for a number of covariates in the analyses. The analyses were based on data from VISAT, a study involving the measurement of physiological, behavioral, and subjective outcomes from 1757 participants, 989 being current or former shiftworkers. The sample comprised employed and retired wage earners, male and female, who were 32, 42, 52, and 62 yrs old. The first analysis sought to confirm previous findings of an association between exposure to shiftwork and the risk of developing metabolic syndrome. It indicated that participants who were or who had previously been shiftworkers (i.e., working schedules that involved rotating shifts; not being able to go to bed before midnight; having to get up before 05:00?h; or being prevented from sleeping during the night) were more likely to exhibit symptoms of metabolic syndrome, after adjusting for age, sex, socioeconomic status, smoking, alcohol intake, perceived stress, and sleep difficulty (odds ratio [OR] 1.78; 95% confidence interval [CI] 1.03–3.08). The results suggest the association between shiftwork and metabolic syndrome cannot be fully accounted for by either higher levels of strain or increased sleep difficulty among shiftworkers, although it remains a possibility that either one or both of these factors may have played a contributing role. The second analysis addressed the issue of duration of exposure to shiftwork. Participants with >10 yrs' experience of working rotating shifts were more likely to exhibit symptoms of metabolic syndrome than participants without exposure to shiftwork, i.e., dayworkers, even after adjusting for age and sex (OR 1.96; 95% CI 1.03–3.75). Thus, the current study confirms the association between shiftwork exposure and metabolic syndrome. It also provides new information regarding the time course of the development of the illness as function of exposure duration, although this was only examined in relation to rotating shiftwork. It is concluded that those responsible for monitoring workers' health should pay particular attention to indices of metabolic dysfunction in workers who have been exposed to shiftwork for >10 yrs. (Author correspondence: p.t.tucker@swansea.ac.uk)     

Shift Work and Obesity among Canadian Women: A Cross-Sectional Study Using a Novel Exposure Assessment Tool

Background/Objectives

It has been suggested that the association between shift work and chronic disease is mediated by an increase in obesity. However, investigations of the relationship between shift work and obesity reveal mixed findings. Using a recently developed exposure assessment tool, this study examined the association between shift work and obesity among Canadian women from two studies: a cohort of university alumni, and a population-based study.

Methods

Self-administered questionnaire data were used from healthy, currently employed females in a population-based study, the Ontario Women’s Diet and Health case-control study (n = 1611 controls), and from a subset of a of university alumni from the Canadian Study of Diet, Lifestyle, and Health (n = 1097) cohort study. Overweight was defined as BMI≥25 to <30, and obesity as BMI≥30. Reported occupation was converted to occupational codes and linked to a probability of shift work value derived from Survey of Labour and Income Dynamics data. Regular evenings, nights, or rotating work comprised shift work. Polytomous logistic regression estimated the association between probability of shift work, categorized as near nil, low, medium, and high probability of shift work, on overweight and obesity, controlling for detected confounders.

Results

In the population-based sample, high probability of shift work was associated with obesity (reference = near nil probability of shift work, OR: 1.88, 95% CI: 1.01–3.51, p = 0.047). In the alumni cohort, no significant association was detected between shift work and overweight or obesity.

Conclusions

As these analyses found a positive association between high probability of shift work exposure and obesity in a population-based sample, but not in an alumni cohort, it is suggested that the relationship between shift work and obesity is complex, and may be particularly susceptible to occupational and education-related factors within a given population.