Exposure to an obesogenic diet during adolescence leads to abnormal maturation of neural and behavioral substrates underpinning fear and anxiety.

BACKGROUND: Post-traumatic stress disorder (PTSD) and obesity are highly prevalent in adolescents. Emerging findings from our laboratory and others are consistent with the novel hypothesis that obese individuals may be predisposed to developing PTSD. Given that aberrant fear responses are pivotal in the pathogenesis of PTSD, the objective of this study was to determine the impact of an obesogenic Western-like high-fat diet (WD) on neural substrates associated with fear. METHODS: Adolescent Lewis rats (n = 72) were fed with either the experimental WD (41.4% kcal from fat) or the control diet. The fear-potentiated startle paradigm was used to determine sustained and phasic fear responses. Diffusion tensor imaging metrics and T2 relaxation times were used to determine the structural integrity of the fear circuitry including the medial prefrontal cortex (mPFC) and the basolateral complex of the amygdala (BLA). RESULTS: The rats that consumed the WD exhibited attenuated fear learning and fear extinction. These behavioral impairments were associated with oversaturation of the fear circuitry and astrogliosis. The BLA T2 relaxation times were significantly decreased in the WD rats relative to the controls. We found elevated fractional anisotropy in the mPFC of the rats that consumed the WD. We show that consumption of a WD may lead to long-lasting damage to components of the fear circuitry. CONCLUSIONS: Our findings demonstrate that consumption of an obesogenic diet during adolescence has a profound impact in the maturation of the fear neurocircuitry. The implications of this research are significant as they identify potential biomarkers of risk for psychopathology in the growing obese population.

Quantitative bias analysis in an asthma study of rescue-recovery workers and volunteers from the 9/11 World Trade Center attacks.

PURPOSE: When learning bias analysis, epidemiologists are taught to quantitatively adjust for multiple biases by correcting study results in the reverse order of the error sequence. To understand the error sequence for a particular study, one must carefully examine the health study's epidemiologic data-generating process. In this article, we describe the unique data-generating process of a man-made disaster epidemiologic study. METHODS: We described the data-generating process and conducted a bias analysis for a study associating September 11, 2001 dust cloud exposure and self-reported newly physician-diagnosed asthma among rescue-recovery workers and volunteers. We adjusted an odds ratio (OR) estimate for the combined effect of missing data, outcome misclassification, and nonparticipation. RESULTS: Under our assumptions about systematic error, the ORs adjusted for all three biases ranged from 1.33 to 3.84. Most of the adjusted estimates were greater than the observed OR of 1.77 and were outside the 95% confidence limits (1.55, 2.01). CONCLUSIONS: Man-made disasters present some situations that are not observed in other areas of epidemiology. Future epidemiologic studies of disasters could benefit from a proactive approach that focuses on the technical aspect of data collection and gathers information on bias parameters to provide more meaningful interpretations of results.

The role of counterfactual theory in causal reasoning.

In this commentary I review the fundamentals of counterfactual theory and its role in causal reasoning in epidemiology. I consider if counterfactual theory dictates that causal questions must be framed in terms of well-defined interventions. I conclude that it does not. I hypothesize that the interventionist approach to causal inference in epidemiology stems from elevating the randomized trial design to the gold standard for thinking about causal inference. I suggest that instead the gold standard we should use for thinking about causal inference in epidemiology is the thought experiment that, for example, compares an actual disease frequency under one exposure level with a counterfactual disease frequency under a different exposure level (as discussed in Greenland and Robins (1986) and Maldonado and Greenland (2002)). I also remind us that no method should be termed "causal" unless it addresses the effect of other biases in addition to the problem of confounding.

Quantifying and Adjusting for Disease Misclassification Due to Loss to Follow-Up in Historical Cohort Mortality Studies.

The purpose of this analysis was to quantify and adjust for disease misclassification from loss to follow-up in a historical cohort mortality study of workers where exposure was categorized as a multi-level variable. Disease classification parameters were defined using 2008 mortality data for the New Zealand population and the proportions of known deaths observed for the cohort. The probability distributions for each classification parameter were constructed to account for potential differences in mortality due to exposure status, gender, and ethnicity. Probabilistic uncertainty analysis (bias analysis), which uses Monte Carlo techniques, was then used to sample each parameter distribution 50,000 times, calculating adjusted odds ratios (ORDM-LTF) that compared the mortality of workers with the highest cumulative exposure to those that were considered never-exposed. The geometric mean ORDM-LTF ranged between 1.65 (certainty interval (CI): 0.50-3.88) and 3.33 (CI: 1.21-10.48), and the geometric mean of the disease-misclassification error factor (εDM-LTF), which is the ratio of the observed odds ratio to the adjusted odds ratio, had a range of 0.91 (CI: 0.29-2.52) to 1.85 (CI: 0.78-6.07). Only when workers in the highest exposure category were more likely than those never-exposed to be misclassified as non-cases did the ORDM-LTF frequency distributions shift further away from the null. The application of uncertainty analysis to historical cohort mortality studies with multi-level exposures can provide valuable insight into the magnitude and direction of study error resulting from losses to follow-up.

Good practices for quantitative bias analysis.

Quantitative bias analysis serves several objectives in epidemiological research. First, it provides a quantitative estimate of the direction, magnitude and uncertainty arising from systematic errors. Second, the acts of identifying sources of systematic error, writing down models to quantify them, assigning values to the bias parameters and interpreting the results combat the human tendency towards overconfidence in research results, syntheses and critiques and the inferences that rest upon them. Finally, by suggesting aspects that dominate uncertainty in a particular research result or topic area, bias analysis can guide efficient allocation of sparse research resources. The fundamental methods of bias analyses have been known for decades, and there have been calls for more widespread use for nearly as long. There was a time when some believed that bias analyses were rarely undertaken because the methods were not widely known and because automated computing tools were not readily available to implement the methods. These shortcomings have been largely resolved. We must, therefore, contemplate other barriers to implementation. One possibility is that practitioners avoid the analyses because they lack confidence in the practice of bias analysis. The purpose of this paper is therefore to describe what we view as good practices for applying quantitative bias analysis to epidemiological data, directed towards those familiar with the methods. We focus on answering questions often posed to those of us who advocate incorporation of bias analysis methods into teaching and research. These include the following. When is bias analysis practical and productive? How does one select the biases that ought to be addressed? How does one select a method to model biases? How does one assign values to the parameters of a bias model? How does one present and interpret a bias analysis?. We hope that our guide to good practices for conducting and presenting bias analyses will encourage more widespread use of bias analysis to estimate the potential magnitude and direction of biases, as well as the uncertainty in estimates potentially influenced by the biases.

Adjusting for outcome misclassification: the importance of accounting for case-control sampling and other forms of outcome-related selection.

PURPOSE: Special care must be taken when adjusting for outcome misclassification in case-control data. Basic adjustment formulas using either sensitivity and specificity or predictive values (as with external validation data) do not account for the fact that controls are sampled from a much larger pool of potential controls. A parallel problem arises in surveys and cohort studies in which participation or loss is outcome related. METHODS: We review this problem and provide simple methods to adjust for outcome misclassification in case-control studies, and illustrate the methods in a case-control birth certificate study of cleft lip/palate and maternal cigarette smoking during pregnancy. RESULTS: Adjustment formulas for outcome misclassification that ignore case-control sampling can yield severely biased results. In the data we examined, the magnitude of error caused by not accounting for sampling is small when population sensitivity and specificity are high, but increases as (1) population sensitivity decreases, (2) population specificity decreases, and (3) the magnitude of the differentiality increases. Failing to account for case-control sampling can result in an odds ratio adjusted for outcome misclassification that is either too high or too low. CONCLUSIONS: One needs to account for outcome-related selection (such as case-control sampling) when adjusting for outcome misclassification using external information.

Toward a clearer understanding of causal concepts in epidemiology.

PURPOSE: In this manuscript, I share insights into causal concepts that emerged from creating and refining a simple example originally designed for teaching causal epidemiologic concepts. METHODS: The insights that emerged are primarily related to the difference between how a causal effect occurs in an individual and what our methods assume about how a causal effect occurs when we estimate its effect in a population. In an individual, the causal effect of exposure on disease occurrence results from the interaction of several causal factors in that individual, not from a single factor in isolation. The result of this interaction within an individual determines an individual's causal type (e.g., doomed, exposure causative, exposure preventive, immune) with respect to a particular exposure contrast and target (etiologic) time period. In a population, the causal effect of exposure on disease frequency depends on the distribution of causal types of individuals in that population, not necessarily on the population distribution of covariates. Yet in epidemiology, when we attempt to estimate the effect of a potential cause of interest, we (through the methods we use) usually do not account for this within individual causal interaction. RESULTS: This failure to account for within-individual causal interactions has interesting implications for causal inference, as I illustrate here: (1) an effect estimate can be simultaneously confounded and unconfounded, (2) there can be confounding even if no variables satisfy the traditional criteria for being considered a confounder, (3) there can be no confounding even if there are variables that do satisfy the traditional confounder criteria, (4) the magnitude of confounding caused by a variable need not depend on the strength of the exposure-variable association, (5) a directed acyclic graph does not always correctly identify the presence of confounding, (6) the common-cause confounder criterion is imperfect, and (7) a time-varying confounder does not necessarily lead to time-varying confounding. CONCLUSIONS: Our example illustrates that confounding is a "team sport": single variables do not confound by themselves; confounding depends on how variables interact in individuals, not just on how variables are distributed within and across populations. Because confounding depends on how variables interact in individuals, methods that ignore causal interactions in individuals are not guaranteed to be confounding identification methods.

The effect of uncertainty in exposure estimation on the exposure-response relation between 1,3-butadiene and leukemia.

In a follow-up study of mortality among North American synthetic rubber industry workers, cumulative exposure to 1,3-butadiene was positively associated with leukemia. Problems with historical exposure estimation, however, may have distorted the association. To evaluate the impact of potential inaccuracies in exposure estimation, we conducted uncertainty analyses of the relation between cumulative exposure to butadiene and leukemia. We created the 1,000 sets of butadiene estimates using job-exposure matrices consisting of exposure values that corresponded to randomly selected percentiles of the approximate probability distribution of plant-, work area/job group-, and year specific butadiene ppm. We then analyzed the relation between cumulative exposure to butadiene and leukemia for each of the 1,000 sets of butadiene estimates. In the uncertainty analysis, the point estimate of the RR for the first non zero exposure category (>0-<37.5 ppm-years) was most likely to be about 1.5. The rate ratio for the second exposure category (37.5-<184.7 ppm-years) was most likely to range from 1.5 to 1.8. The RR for category 3 of exposure (184.7-<425.0 ppm-years) was most likely between 2.1 and 3.0. The RR for the highest exposure category (425.0+ ppm-years) was likely to be between 2.9 and 3.7. This range off RR point estimates can best be interpreted as a probability distribution that describes our uncertainty in RR point estimates due to uncertainty in exposure estimation. After considering the complete probability distributions of butadiene exposure estimates, the exposure-response association of butadiene and leukemia was maintained. This exercise was a unique example of how uncertainty analyses can be used to investigate and support an observed measure of effect when occupational exposure estimates are employed in the absence of direct exposure measurements.

Update: Greenland and Robins (1986). Identifiability, exchangeability and epidemiological confounding.

We are pleased to publish an update to "Identifiabiliity, exchangeability and epidemiological confounding" (IEEC) by Sander Greenland and James Robins, originally published in 1986 in the International Journal of Epidemiology. This is the first in a series of updates to classic epidemiologic-methods papers that EP&I has commissioned.

Specifying exposure classification parameters for sensitivity analysis: family breast cancer history.

One of the challenges to implementing sensitivity analysis for exposure misclassification is the process of specifying the classification proportions (eg, sensitivity and specificity). The specification of these assignments is guided by three sources of information: estimates from validation studies, expert judgment, and numerical constraints given the data. The purpose of this teaching paper is to describe the process of using validation data and expert judgment to adjust a breast cancer odds ratio for misclassification of family breast cancer history. The parameterization of various point estimates and prior distributions for sensitivity and specificity were guided by external validation data and expert judgment. We used both nonprobabilistic and probabilistic sensitivity analyses to investigate the dependence of the odds ratio estimate on the classification error. With our assumptions, a wider range of odds ratios adjusted for family breast cancer history misclassification resulted than portrayed in the conventional frequentist confidence interval.

How far from non-differential does exposure or disease misclassification have to be to bias measures of association away from the null?

A well-known heuristic in epidemiology is that non-differential exposure or disease misclassification biases the expected values of an estimator toward the null value. This heuristic works correctly only when additional conditions are met, such as independence of classification errors. We present examples to show that, even when the additional conditions are met, if the misclassification is only approximately non-differential, then bias is not guaranteed to be toward the null. In light of such examples, we advise that evaluation of misclassification should not be based on the assumption of exact non-differentiality unless the latter can be deduced logically from the facts of the situation.

Uncertainty analysis: an example of its application to estimating a survey proportion.

Uncertainty analysis is a method, established in engineering and policy analysis but relatively new to epidemiology, for the quantitative assessment of biases in the results of epidemiological studies. Each uncertainty analysis is situation specific, but usually involves four main steps: (1) specify the target parameter of interest and an equation for its estimator; (2) specify the equation for random and bias effects on the estimator; (3) specify prior probability distributions for the bias parameters; and (4) use Monte-Carlo or analytic techniques to propagate the uncertainty about the bias parameters through the equation, to obtain an approximate posterior probability distribution for the parameter of interest. A basic example is presented illustrating uncertainty analyses for four proportions estimated from a survey of the epidemiological literature.

Exposure-measurement error is frequently ignored when interpreting epidemiologic study results.

INTRODUCTION: One important source of error in study results is error in measuring exposures. When interpreting study results, one should consider the impact that exposure-measurement error (EME) might have had on study results. METHODS: To assess how often this consideration is made and the form it takes, journal articles were randomly sampled from original articles appearing in the American Journal of Epidemiology and Epidemiology in 2001, and the International Journal of Epidemiology between December 2000 and October 2001. RESULTS: Twenty-two (39%) of the 57 articles surveyed mentioned nothing about EME. Of the 35 articles that mentioned something about EME, 16 articles described qualitatively the effect EME could have had on study results. Only one study quantified the impact of EME on study results; the investigators used a sensitivity analysis. Few authors discussed the measurement error in their study in any detail. CONCLUSIONS: Overall, the potential impact of EME on error in epidemiologic study results appears to be ignored frequently in practice.

Can landscape ecology untangle the complexity of antibiotic resistance?

Bacterial resistance to antibiotics continues to pose a serious threat to human and animal health. Given the considerable spatial and temporal heterogeneity in the distribution of resistance and the factors that affect its evolution, dissemination and persistence, we argue that antibiotic resistance must be viewed as an ecological problem. A fundamental difficulty in assessing the causal relationship between antibiotic use and resistance is the confounding influence of geography: the co-localization of resistant bacterial species with antibiotic use does not necessarily imply causation and could represent the presence of environmental conditions and factors that have independently contributed to the occurrence of resistance. Here, we show how landscape ecology, which links the biotic and abiotic factors of an ecosystem, might help to untangle the complexity of antibiotic resistance and improve the interpretation of ecological studies.

An updated study of mortality among North American synthetic rubber industry workers.

This study evaluated mortality rates from leukemia and other diseases during the time period 1944 through 1998 among 17,924 men employed in the synthetic rubber industry. In this group, there were 6237 deaths, which is 14% fewer than the 7242 deaths expected based on general population rates. Numbers of observed versus expected deaths (shown hereafter as observed/expected) were 1608/1741 for all cancers combined, including 71/61 for leukemia, 53/53 for non-Hodgkin lymphoma (NHL*), and 26/27 for multiple myeloma. The higher than expected number of deaths from leukemia (16% increase) was concentrated in workers paid hourly who had started work 20 to 29 years earlier, had worked 10 or more years in the industry, and had worked in subgroups employed in polymerization, coagulation, maintenance labor, and laboratory operations. The overall higher leukemia mortality rate, as well as the higher rate in the subgroup of hourly workers who had 20 or more years since hire and 10 or more years worked, was not limited to a particular form of leukemia. Cumulative exposure to 1,3-butadiene (BD) was associated positively with all leukemias, with chronic myelogenous leukemia and, to a lesser extent, with chronic lymphocytic leukemia (CLL). Exposure to styrene or to dimethyldithiocarbamate (DMDTC) also was associated positively with leukemia. Exposures to these two agents were correlated with exposure to BD; data were limited on the independent effects of each of the three chemicals on leukemia. After controlling for the effects of BD, we found no consistent exposure-response relation between either styrene or DMDTC and all leukemias, chronic myelogenous leukemia, or CLL. However, a positive association between any exposure to DMDTC and leukemia persisted. The data from this study indicate that employment in the synthetic rubber industry is related causally to leukemia. Uncertainty remains about the specific agent or agents responsible for the association. The carcinogenic mechanisms through which BD, styrene, or DMDTC could cause leukemia in humans have not been established, and epidemiologic support for a leukemogenic role is limited for these agents. Styrene and DMDTC were associated positively with NHL. External support for this relation has not been reported from other epidemiologic studies. The study did not find any clear relation between exposure to BD, styrene, or DMDTC and multiple myeloma. Some subgroups of subjects had more than the expected number deaths from colorectal cancer, prostate cancer, and other diseases. These increases did not appear to be related to occupational exposure in the industry.