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

The mockery that confounds better treatment of confounding in epidemiology: The change in estimate fallacy

Confounding is one of the most infamous bugbears of epidemiology, used by some to dismiss the field's utility outright. The subject has received considerable attention from epidemiologists and the field boasts a remarkable arsenal for addressing the issue. However, it appears that there are still misconceptions about how to identify variables that cause confounding (a lack of exchangeability) in epidemiologic practice. In this commentary, I examine whether analysis of the properties of change-in-estimate method for identification of confounding, exemplified by two highly cited papers, has been appropriately cited in published reports and whether it was utilized to improve epidemiologic practice. I conclude that the myth that a change-in-estimate criterion of 10 % is legitimate for identifying confounding persists in epidemiological practice, despite having been discredited by several independent research groups decades ago. Speculations on possible solutions to this problem are offered, but my work's main contribution is identification of a problem of how methodological advances in epidemiology may be misapplied. There currently do not exist any universal criteria for identification of confounding! "Citation without representation" or biased presentation of conclusions of methodological research may be pervasive.

A New Criterion for Determining a Cutoff Value Based on the Biases of Incidence Proportions in the Presence of Non-differential Outcome Misclassifications

When conducting database studies, researchers sometimes use an algorithm known as "case definition," "outcome definition," or "computable phenotype" to identify the outcome of interest. Generally, algorithms are created by combining multiple variables and codes, and we need to select the most appropriate one to apply to the database study. Validation studies compare algorithms with the gold standard and calculate indicators such as sensitivity and specificity to assess their validities. As the indicators are calculated for each algorithm, selecting an algorithm is equivalent to choosing a pair of sensitivity and specificity. Therefore, receiver operating characteristic curves can be utilized, and two intuitive criteria are commonly used. However, neither was conceived to reduce the biases of effect measures (e.g., risk difference and risk ratio), which are important in database studies. In this study, we evaluated two existing criteria from perspectives of the biases and found that one of them, called the Youden index always minimizes the bias of the risk difference regardless of the true incidence proportions under nondifferential outcome misclassifications. However, both criteria may lead to inaccurate estimates of absolute risks, and such property is undesirable in decision-making. Therefore, we propose a new criterion based on minimizing the sum of the squared biases of absolute risks to estimate them more accurately. Subsequently, we apply all criteria to the data from the actual validation study on postsurgical infections and present the results of a sensitivity analysis to examine the robustness of the assumption our proposed criterion requires.

Exposure variables in veterinary epidemiology: are they telling us what we think they are?

This manuscript summarizes a presentation delivered by the first author at the 2024 symposium for the Calvin Schwabe Award for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine, which was awarded to Dr. Jan Sargeant. Epidemiologic research plays a crucial role in understanding the complex relationships between exposures and health outcomes. However, the accuracy of the conclusions drawn from these investigations relies upon the meticulous selection and measurement of exposure variables. Appropriate exposure variable selection is crucial for understanding disease etiologies, but it is often the case that we are not able to directly measure the exposure variable of interest and use proxy measures to assess exposures instead. Inappropriate use of proxy measures can lead to erroneous conclusions being made about the true exposure of interest. These errors may lead to biased estimates of associations between exposures and outcomes. The consequences of such biases extend beyond research concerns as health decisions can be made based on flawed evidence. Recognizing and mitigating these biases are essential for producing reliable evidence that informs health policies and interventions, ultimately contributing to improved population health outcomes. To address these challenges, researchers must adopt rigorous methodologies for exposure variable selection and validation studies to minimize measurement errors.

The potential impact fraction of population weight reduction scenarios on non-communicable diseases in Belgium: application of the g-computation approach

BACKGROUND

Overweight is a major risk factor for non-communicable diseases (NCDs) in Europe, affecting almost 60% of all adults. Tackling obesity is therefore a key long-term health challenge and is vital to reduce premature mortality from NCDs. Methodological challenges remain however, to provide actionable evidence on the potential health benefits of population weight reduction interventions. This study aims to use a g-computation approach to assess the impact of hypothetical weight reduction scenarios on NCDs in Belgium in a multi-exposure context.

METHODS

Belgian health interview survey data (2008/2013/2018, n = 27 536) were linked to environmental data at the residential address. A g-computation approach was used to evaluate the potential impact fraction (PIF) of population weight reduction scenarios on four NCDs: diabetes, hypertension, cardiovascular disease (CVD), and musculoskeletal (MSK) disease. Four scenarios were considered: 1) a distribution shift where, for each individual with overweight, a counterfactual weight was drawn from the distribution of individuals with a "normal" BMI 2) a one-unit reduction of the BMI of individuals with overweight, 3) a modification of the BMI of individuals with overweight based on a weight loss of 10%, 4) a reduction of the waist circumference (WC) to half of the height among all people with a WC:height ratio greater than 0.5. Regression models were adjusted for socio-demographic, lifestyle, and environmental factors.

RESULTS

The first scenario resulted in preventing a proportion of cases ranging from 32.3% for diabetes to 6% for MSK diseases. The second scenario prevented a proportion of cases ranging from 4.5% for diabetes to 0.8% for MSK diseases. The third scenario prevented a proportion of cases, ranging from 13.6% for diabetes to 2.4% for MSK diseases and the fourth scenario prevented a proportion of cases ranging from 36.4% for diabetes to 7.1% for MSK diseases.

CONCLUSION

Implementing weight reduction scenarios among individuals with excess weight could lead to a substantial and statistically significant decrease in the prevalence of diabetes, hypertension, cardiovascular disease (CVD), and musculoskeletal (MSK) diseases in Belgium. The g-computation approach to assess PIF of interventions represents a straightforward approach for drawing causal inferences from observational data while providing useful information for policy makers.

Leveraging External Validation Data: The Challenges of Transporting Measurement Error Parameters

Approaches to address measurement error frequently rely on validation data to estimate measurement error parameters (e.g., sensitivity and specificity). Acquisition of validation data can be costly, thus secondary use of existing data for validation is attractive. To use these external validation data, however, we may need to address systematic differences between these data and the main study sample. Here, we derive estimators of the risk and the risk difference that leverage external validation data to account for outcome misclassification. If misclassification is differential with respect to covariates that themselves are differentially distributed in the validation and study samples, the misclassification parameters are not immediately transportable. We introduce two ways to account for such covariates: (1) standardize by these covariates or (2) iteratively model the outcome. If conditioning on a covariate for transporting the misclassification parameters induces bias of the causal effect (e.g., M-bias), the former but not the latter approach is biased. We provide proof of identification, describe estimation using parametric models, and assess performance in simulations. We also illustrate implementation to estimate the risk of preterm birth and the effect of maternal HIV infection on preterm birth. Measurement error should not be ignored and it can be addressed using external validation data via transportability methods.

Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey

BACKGROUND

In many countries, the prevalence of non-communicable diseases risk factors is commonly assessed through self-reported information from health interview surveys. It has been shown, however, that self-reported instead of objective data lead to an underestimation of the prevalence of obesity, hypertension and hypercholesterolemia. This study aimed to assess the agreement between self-reported and measured height, weight, hypertension and hypercholesterolemia and to identify an adequate approach for valid measurement error correction.

METHODS

Nine thousand four hundred thirty-nine participants of the 2018 Belgian health interview survey (BHIS) older than 18 years, of which 1184 participated in the 2018 Belgian health examination survey (BELHES), were included in the analysis. Regression calibration was compared with multiple imputation by chained equations based on parametric and non-parametric techniques.

RESULTS

This study confirmed the underestimation of risk factor prevalence based on self-reported data. With both regression calibration and multiple imputation, adjusted estimation of these variables in the BHIS allowed to generate national prevalence estimates that were closer to their BELHES clinical counterparts. For overweight, obesity and hypertension, all methods provided smaller standard errors than those obtained with clinical data. However, for hypercholesterolemia, for which the regression model's accuracy was poor, multiple imputation was the only approach which provided smaller standard errors than those based on clinical data.

CONCLUSIONS

The random-forest multiple imputation proves to be the method of choice to correct the bias related to self-reported data in the BHIS. This method is particularly useful to enable improved secondary analysis of self-reported data by using information included in the BELHES. Whenever feasible, combined information from HIS and objective measurements should be used in risk factor monitoring.

Nondifferential Treatment Misclassification Biases Toward the Null? Not a Safe Bet for Active Comparator Studies

Active comparator studies are increasingly common, particularly in pharmacoepidemiology. In such studies, the parameter of interest is a contrast (difference or ratio) in the outcome risks between the treatment of interest and the selected active comparator. While it may appear treatment is dichotomous, treatment is actually polytomous as there are at least 3 levels: no treatment, the treatment of interest, and the active comparator. Because misclassification may occur between any of these groups, independent nondifferential treatment misclassification may not be toward the null (as expected with a dichotomous treatment). In this work, we describe bias from independent nondifferential treatment misclassification in active comparator studies with a focus on misclassification that occurs between each active treatment and no treatment. We derive equations for bias in the estimated outcome risks, risk difference, and risk ratio, and we provide bias correction equations that produce unbiased estimates, in expectation. Using data obtained from US insurance claims data, we present a hypothetical comparative safety study of antibiotic treatment to illustrate factors that influence bias and provide an example probabilistic bias analysis using our derived bias correction equations.

Drug exposure misclassification in pharmacoepidemiology: Sources and relative impact

BACKGROUND

Drug exposure assessment based on dispensing data can be misclassified when patients do not adhere to their therapy or when information about over-the-counter drugs is not captured in the study database. Previous research has considered hypothetical sensitivity and specificity values, whereas this study aims to assess the impact of literature-based real values of exposure misclassification.

METHODS

A synthetic cohort study was constructed based on the proportion of exposure theoretically captured in a database (range 0.5-1.0) and the level of adherence (0.5-1.0). Three scenarios were explored: nondifferential misclassification, differential misclassification (misclassifications dependent on an unmeasured risk factor doubling the outcome risk), and nondifferential misclassification in a comparative effectiveness study (RR_A and RR_B both 2.0 compared to nonuse, RR_A-B 1.0).

RESULTS

For the scenarios with nondifferential misclassification, 25% nonadherence or 25% uncaptured exposure changed the RR from 2.0 to 1.75, and 1.95, respectively. Applying different proportions of nonadherence or uncaptured use (20% vs. 40%) for subgroups with and without the risk factor, an RR of 0.95 was observed in the absence of a true effect (i.e., true RR = 1). In the comparative effectiveness study, no effect on RR was seen for different proportions of uncaptured exposure; however, different levels of nonadherence for the drugs (20% vs. 40%) led to an underestimation of RR_A-B (0.89).

DISCUSSION

All scenarios led to biased estimates, but the magnitude of the bias differed across scenarios. When testing the robustness of findings of pharmacoepidemiologic studies, we recommend using realistic values of nonadherence and uncaptured exposure based on real-world data.

Bias Analysis Gone Bad

Quantitative bias analysis comprises the tools used to estimate the direction, magnitude, and uncertainty from systematic errors affecting epidemiologic research. Despite the availability of methods and tools, and guidance for good practices, few reports of epidemiologic research incorporate quantitative estimates of bias impacts. The lack of familiarity with bias analysis allows for the possibility of misuse, which is likely most often unintentional but could occasionally include intentional efforts to mislead. We identified 3 examples of suboptimal bias analysis, one for each common bias. For each, we describe the original research and its bias analysis, compare the bias analysis with good practices, and describe how the bias analysis and research findings might have been improved. We assert no motive to the suboptimal bias analysis by the original authors. Common shortcomings in the examples were lack of a clear bias model, computed example, and computing code; poor selection of the values assigned to the bias model's parameters; and little effort to understand the range of uncertainty associated with the bias. Until bias analysis becomes more common, community expectations for the presentation, explanation, and interpretation of bias analyses will remain unstable. Attention to good practices should improve quality, avoid errors, and discourage manipulation.

Calibrating validation samples when accounting for measurement error in intervention studies

Many lifestyle intervention trials depend on collecting self-reported outcomes, such as dietary intake, to assess the intervention's effectiveness. Self-reported outcomes are subject to measurement error, which impacts treatment effect estimation. External validation studies measure both self-reported outcomes and accompanying biomarkers, and can be used to account for measurement error. However, in order to account for measurement error using an external validation sample, an assumption must be made that the inferences are transportable from the validation sample to the intervention trial of interest. This assumption does not always hold. In this paper, we propose an approach that adjusts the validation sample to better resemble the trial sample, and we also formally investigate when bias due to poor transportability may arise. Lastly, we examine the performance of the methods using simulation, and illustrate them using PREMIER, a lifestyle intervention trial measuring self-reported sodium intake as an outcome, and OPEN, a validation study measuring both self-reported diet and urinary biomarkers.

Propensity score trimming mitigates bias due to covariate measurement error in inverse probability of treatment weighted analyses: A plasmode simulation

BACKGROUND

Inverse probability of treatment weighting (IPTW) may be biased by influential observations, which can occur from misclassification of strong exposure predictors.

METHODS

We evaluated bias and precision of IPTW estimators in the presence of a misclassified confounder and assessed the effect of propensity score (PS) trimming. We generated 1000 plasmode cohorts of size N = 10 000, sampled with replacement from 6063 NHANES respondents (1999-2014) age 40 to 79 with labs and no statin use. We simulated statin exposure as a function of demographics and CVD risk factors; and outcomes as a function of 10-year CVD risk score and statin exposure (rate ratio [RR] = 0.5). For 5% of the people in selected populations (eg, all patients, exposed, those with outcomes), we randomly misclassified a confounder that strongly predicted exposure. We fit PS models and estimated RRs using IPTW and 1:1 PS matching, with and without asymmetric trimming.

RESULTS

IPTW bias was substantial when misclassification was differential by outcome (RR range: 0.38-0.63) and otherwise minimal (RR range: 0.51-0.53). However, trimming reduced bias for IPTW, nearly eliminating it at 5% trimming (RR range: 0.49-0.52). In one scenario, when the confounder was misclassified for 5% of those with outcomes (0.3% of cohort), untrimmed IPTW was more biased and less precise (RR = 0.37 [SE(logRR) = 0.21]) than matching (RR = 0.50 [SE(logRR) = 0.13]). After 1% trimming, IPTW estimates were unbiased and more precise (RR = 0.49 [SE(logRR) = 0.12]) than matching (RR = 0.51 [SE(logRR) = 0.14]).

CONCLUSIONS

Differential misclassification of a strong predictor of exposure resulted in biased and imprecise IPTW estimates. Asymmetric trimming reduced bias, with more precise estimates than matching.

An underappreciated misclassification mechanism: implications of nondifferential dependent misclassification of covariate and exposure

Misclassification is a pervasive problem in assessing relations between exposures and outcomes. While some attention has been paid to the impact of dependence in measurement error between exposures and outcomes, there is little awareness of the potential impact of dependent error between exposures and covariates, despite the fact that this latter dependency may occur much more frequently, for example, when both are assessed by questionnaire. We explored the impact of nondifferential dependent exposure-confounder misclassification bias by simulating a dichotomous exposure (E), disease (D) and covariate (C) with varying degrees of non-differential dependent misclassification between C and E. We demonstrate that under plausible scenarios, an adjusted association can be a poorer estimate of the true association than the crude. Correlated errors in the measurement of covariate and exposure distort the covariate-exposure, covariate-outcome and exposure-outcome associations creating observed associations that can be greater than, less than, or in the opposite direction of the true associations. Under these circumstances adjusted associations may not be bounded by the crude association and true effect, as would be expected with nondifferential independent confounder misclassification. The degree and direction of distortion depends on the amount of dependent error, prevalence of covariate and exposure, and magnitude of true effect.

Approaches to addressing missing values, measurement error, and confounding in epidemiologic studies

OBJECTIVES

Epidemiologic studies often suffer from incomplete data, measurement error (or misclassification), and confounding. Each of these can cause bias and imprecision in estimates of exposure-outcome relations. We describe and compare statistical approaches that aim to control all three sources of bias simultaneously.

STUDY DESIGN AND SETTING

We illustrate four statistical approaches that address all three sources of bias, namely, multiple imputation for missing data and measurement error, multiple imputation combined with regression calibration, full information maximum likelihood within a structural equation modeling framework, and a Bayesian model. In a simulation study, we assess the performance of the four approaches compared with more commonly used approaches that do not account for measurement error, missing values, or confounding.

RESULTS

The results demonstrate that the four approaches consistently outperform the alternative approaches on all performance metrics (bias, mean squared error, and confidence interval coverage). Even in simulated data of 100 subjects, these approaches perform well.

CONCLUSION

There can be a large benefit of addressing measurement error, missing values, and confounding to improve the estimation of exposure-outcome relations, even when the available sample size is relatively small.

A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications

Joint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a new maximum likelihood based estimator for marginal causal effects that simultaneously adjusts for confounding and several forms of joint misclassification of the exposure and outcome variables. The proposed method relies on validation data for the construction of weights that account for both sources of bias. The weighting estimator, which is an extension of the outcome misclassification weighting estimator proposed by Gravel and Platt (Weighted estimation for confounded binary outcomes subject to misclassification. Stat Med 2018; 37: 425–436), is applied to reinfarction data. Simulation studies were carried out to study its finite sample properties and compare it with methods that do not account for confounding or misclassification. The new estimator showed favourable large sample properties in the simulations. Further research is needed to study the sensitivity of the proposed method and that of alternatives to violations of their assumptions. The implementation of the estimator is facilitated by a new R function (ipwm) in an existing R package (mecor).

A critical review of talc and ovarian cancer

The association between perineal talc use and ovarian cancer has been evaluated in several epidemiology studies. Some case-control studies reported weak positive associations, while other case-control and three large prospective cohort investigations found this association to be null. A weight-of-evidence evaluation was conducted of the epidemiology, toxicity, exposure, transport, in vitro, and mechanistic evidence to determine whether, collectively, these data support a causal association. Our review of the literature indicated that, while both case-control and cohort studies may be impacted by bias, the possibility of recall and other biases from the low participation rates and retrospective self-reporting of talc exposure cannot be ruled out for any of the case-control studies. The hypothesis that talc exposure induces ovarian cancer is only supported if one discounts the null results of the cohort studies and the fact that significant bias and/or confounding are likely reasons for the associations reported in some case-control investigations. In addition, one would need to ignore the evidence from animal experiments that show no marked association with cancer, in vitro and genotoxicity studies that did not indicate a carcinogenic mechanism of action for talc, and mechanistic and transport investigations that did not support the retrograde transport of talc to the ovaries. An alternative hypothesis that talc does not produce ovarian cancer, and that bias and confounding contribute the reported positive associations in case-control studies, is better supported by the evidence across all scientific disciplines. It is concluded that the evidence does not support a causal association between perineal talc use and ovarian cancer.

Reflection on modern methods: five myths about measurement error in epidemiological research

Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study's inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.

Source apportionment of primary and secondary PM2.5: Associations with pediatric respiratory disease emergency department visits in the U.S. State of Georgia

We developed a hybrid chemical transport model and receptor model (CTM-RM) to conduct source apportionment of both primary and secondary PM_2.5 (particulate matter ≤2.5 μm in diameter) at 36 km resolution throughout the U.S. State of Georgia for the years 2005 and 2007. This novel source apportionment model enabled us to estimate and compare associations of short-term changes in 12 PM_2.5 source concentrations (agriculture, biogenic, coal, dust, fuel oil, metals, natural gas, non-road mobile diesel, non-road mobile gasoline, on-road mobile diesel, on-road mobile gasoline, and all other sources) with emergency department (ED) visits for pediatric respiratory diseases. ED visits for asthma (N = 49,651), pneumonia (N = 25,558), and acute upper respiratory infections (acute URI, N = 235,343) among patients aged ≤18 years were obtained from patient claims records. Using a case-crossover study, we estimated odds ratios per interquartile range (IQR) increase for 3-day moving average PM_2.5 source concentrations using conditional logistic regression, matching on day-of-week, month, and year, and adjusting for average temperature, humidity, and holidays. We fit both single-source and multi-source models. We observed positive associations between several PM_2.5 sources and ED visits for asthma, pneumonia, and acute URI. For example, for asthma, per IQR increase in the source contribution in the single-source model, odds ratios were 1.022 (95% CI: 1.013, 1.031) for dust; 1.050 (95% CI: 1.036, 1.063) for metals, and 1.091 (95% CI: 1.064, 1.119) for natural gas. These sources comprised 5.7%, 2.2%, and 6.3% of total PM_2.5 mass, respectively. PM_2.5 from metals and natural gas were positively associated with all three respiratory outcomes. In addition, non-road mobile diesel was positively associated with pneumonia and acute URI.

Development of a Job-Exposure Matrix for Assessment of Occupational Exposure to High-Frequency Electromagnetic Fields (3 kHz-300 GHz)

OBJECTIVES

The aim of this work was to build a job-exposure matrix (JEM) using an international coding system and covering the non-thermal intermediate frequency (IF) (3-100 kHz, named IFELF), thermal IF (100 kHz-10 MHz, named IFRF), and radiofrequency (RF) (>10 MHz) bands.

METHODS

Detailed occupational data were collected in a large population-based case-control study, INTEROCC, with occupations coded into the International Standard Classification of Occupations system 1988 (ISCO88). The subjects' occupational source-based ancillary information was combined with an existing source-exposure matrix and the reference levels of the International Commission on Non-Ionizing Radiation Protection (ICNIRP) for occupational exposure to calculate estimates of level (L) of exposure to electric (E) and magnetic (H) fields by ISCO88 code and frequency band as ICNIRP ratios (IFELF) or squared ratios (IFRF and RF). Estimates of exposure probability (P) were obtained by dividing the number of exposed subjects by the total number of subjects available per job title.

RESULTS

With 36 011 job histories collected, 468 ISCO88 (four-digit) codes were included in the JEM, of which 62.4% are exposed to RF, IFRF, and/or IFELF. As a reference, P values for RF E-fields ranged from 0.3 to 65.0% with a median of 5.1%. L values for RF E-fields (ICNIRP squared ratio) ranged from 6.94 × 10-11 to 33.97 with a median of 0.61.

CONCLUSIONS

The methodology used allowed the development of a JEM for high-frequency electromagnetic fields containing exposure estimates for the largest number of occupations to date. Although the validity of this JEM is limited by the small number of available observations for some codes, this JEM may be useful for epidemiological studies and occupational health management programs assessing high-frequency electromagnetic field exposure in occupational settings.

The Prospective Association Between the Five Factor Personality Model With Health Behaviors and Health Behavior Clusters

To examine the prospective association of personality with individual behavior, multibehavior and clustered health behavior profiles. A prospective study design was employed. Two hundred young adults provided baseline data and 126 (mean age: 21.6 yrs) provide complete data for a 5-month follow-up assessment (63% response rate). Personality and health behaviors (and covariates) were assessed via validated questionnaires. A multibehavior index variable was created ranging from 0-5; two separate health behavior cluster indices were created, including high (4-5 behaviors) vs. low (2 or fewer) behavior adoption and an energy balance cluster (MVPA and diet). When examining MVPA as a continuous variable, the personality trait conscientiousness was prospectively associated with MVPA and a healthy diet. Extraversion was prospectively associated with high (vs. low) behavioral clustering (OR = 1.18; 95% CI: 1.00-1.40) and conscientiousness was prospectively associated with energy balance clustering (OR = 1.09; 95% CI: 1.01-1.17). Extraversion, conscientiousness, openness to experience, and agreeableness were associated with select health-related behaviors. Further, extraversion and conscientiousness were associated with health behavior clustering.

Prevalence of and risk factors for fatty liver in the general population of Northern Italy: the Bagnacavallo Study

Background

The estimation of the burden of disease attributable to fatty liver requires studies performed in the general population.

Methods

The Bagnacavallo Study was performed between October 2005 and March 2009. All the citizens of Bagnacavallo (Ravenna, Italy) aged 30 to 60 years as of January 2005 were eligible. Altered liver enzymes were defined as alanine transaminase > 40 U/l and/or aspartate transaminase > 37 U/l.

Results

Four thousand and thirty-three (58%) out of 6920 eligible citizens agreed to participate and 3933 (98%) had complete data. 393 (10%) of the latter had altered liver enzymes and 3540 had not. After exclusion of subjects with HBV or HCV infection, liver ultrasonography was available for 93% of subjects with altered liber enzymes and 52% of those with normal liver enzymes. The prevalence of fatty liver, non-alcoholic fatty liver disease (NAFLD) and alcoholic fatty liver disease (AFLD) was 0.74 (95%CI 0.70 to 0.79) vs. 0.35 (0.33 to 0.37), 0.46 (0.41 to 0.51) vs. 0.22 (0.21 to 0.24) and 0.28 (0.24 to 0.33) vs. 0.13 (0.11 to 0.14) in citizens with than in those without altered liver enzymes. Ethanol intake was not associated and all the components of the metabolic syndrome (MS) were associated with fatty liver. All potential risk factors were associated with a lower odds of normal liver vs. NAFLD while they were unable to discriminate AFLD from NAFLD.

Conclusions

Fatty liver as a whole was highly prevalent in Bagnacavallo in 2005/9 and was more common among citizens with altered liver enzymes.

Electronic supplementary material

The online version of this article (10.1186/s12876-018-0906-8) contains supplementary material, which is available to authorized users.

Epidemiologic analyses with error-prone exposures: review of current practice and recommendations

PURPOSE

Variables in observational studies are commonly subject to measurement error, but the impact of such errors is frequently ignored. As part of the STRengthening Analytical Thinking for Observational Studies Initiative, a task group on measurement error and misclassification seeks to describe the current practice for acknowledging and addressing measurement error.

METHODS

Task group on measurement error and misclassification conducted a literature survey of four types of research studies that are typically impacted by exposure measurement error: (1) dietary intake cohort studies, (2) dietary intake population surveys, (3) physical activity cohort studies, and (4) air pollution cohort studies.

RESULTS

The survey revealed that while researchers were generally aware that measurement error affected their studies, very few adjusted their analysis for the error. Most articles provided incomplete discussion of the potential effects of measurement error on their results. Regression calibration was the most widely used method of adjustment.

CONCLUSIONS

Methods to correct for measurement error are available but require additional data regarding the error structure. There is a great need to incorporate such data collection within study designs and improve the analytical approach. Increased efforts by investigators, editors, and reviewers are needed to improve presentation of research when data are subject to error.

Measurement error is often neglected in medical literature: a systematic review

OBJECTIVES

In medical research, covariates (e.g., exposure and confounder variables) are often measured with error. While it is well accepted that this introduces bias and imprecision in exposure-outcome relations, it is unclear to what extent such issues are currently considered in research practice. The objective was to study common practices regarding covariate measurement error via a systematic review of general medicine and epidemiology literature.

STUDY DESIGN AND SETTING

Original research published in 2016 in 12 high impact journals was full-text searched for phrases relating to measurement error. Reporting of measurement error and methods to investigate or correct for it were quantified and characterized.

RESULTS

Two hundred and forty-seven (44%) of the 565 original research publications reported on the presence of measurement error. 83% of these 247 did so with respect to the exposure and/or confounder variables. Only 18 publications (7% of 247) used methods to investigate or correct for measurement error.

CONCLUSIONS

Consequently, it is difficult for readers to judge the robustness of presented results to the existence of measurement error in the majority of publications in high impact journals. Our systematic review highlights the need for increased awareness about the possible impact of covariate measurement error. Additionally, guidance on the use of measurement error correction methods is necessary.

Randomized clinical trials and observational studies in the assessment of drug safety

Randomized clinical trials are considered as the preferred design to assess the potential causal relationships between drugs or other medical interventions and intended effects. For this reason, randomized clinical trials are generally the basis of development programs in the life cycle of drugs and the cornerstone of evidence-based medicine. Instead, randomized clinical trials are not the design of choice for the detection and assessment of rare, delayed and/or unexpected effects related to drug safety. Moreover, the highly homogeneous populations resulting from restrictive eligibility criteria make randomized clinical trials inappropriate to describe comprehensively the safety profile of drugs. In that context, observational studies have a key added value when evaluating the benefit-risk balance of the drugs. However, observational studies are more prone to bias than randomized clinical trials and they have to be designed, conducted and reported judiciously. In this article, we discuss the strengths and limitations of randomized clinical trials and of observational studies, more particularly regarding their contribution to the knowledge of medicines' safety profile. In addition, we present general recommendations for the sensible use of observational data.

Radiofrequency Electromagnetic Radiation and Memory Performance: Sources of Uncertainty in Epidemiological Cohort Studies

Uncertainty in experimental studies of exposure to radiation from mobile phones has in the past only been framed within the context of statistical variability. It is now becoming more apparent to researchers that epistemic or reducible uncertainties can also affect the total error in results. These uncertainties are derived from a wide range of sources including human error, such as data transcription, model structure, measurement and linguistic errors in communication. The issue of epistemic uncertainty is reviewed and interpreted in the context of the MoRPhEUS, ExPOSURE and HERMES cohort studies which investigate the effect of radiofrequency electromagnetic radiation from mobile phones on memory performance. Research into this field has found inconsistent results due to limitations from a range of epistemic sources. Potential analytic approaches are suggested based on quantification of epistemic error using Monte Carlo simulation. It is recommended that future studies investigating the relationship between radiofrequency electromagnetic radiation and memory performance pay more attention to treatment of epistemic uncertainties as well as further research into improving exposure assessment. Use of directed acyclic graphs is also encouraged to display the assumed covariate relationship.

Random measurement error: Why worry? An example of cardiovascular risk factors

With the increased use of data not originally recorded for research, such as routine care data (or ‘big data’), measurement error is bound to become an increasingly relevant problem in medical research. A common view among medical researchers on the influence of random measurement error (i.e. classical measurement error) is that its presence leads to some degree of systematic underestimation of studied exposure-outcome relations (i.e. attenuation of the effect estimate). For the common situation where the analysis involves at least one exposure and one confounder, we demonstrate that the direction of effect of random measurement error on the estimated exposure-outcome relations can be difficult to anticipate. Using three example studies on cardiovascular risk factors, we illustrate that random measurement error in the exposure and/or confounder can lead to underestimation as well as overestimation of exposure-outcome relations. We therefore advise medical researchers to refrain from making claims about the direction of effect of measurement error in their manuscripts, unless the appropriate inferential tools are used to study or alleviate the impact of measurement error from the analysis.

Development of a source-exposure matrix for occupational exposure assessment of electromagnetic fields in the INTEROCC study

To estimate occupational exposures to electromagnetic fields (EMF) for the INTEROCC study, a database of source-based measurements extracted from published and unpublished literature resources had been previously constructed. The aim of the current work was to summarize these measurements into a source-exposure matrix (SEM), accounting for their quality and relevance. A novel methodology for combining available measurements was developed, based on order statistics and log-normal distribution characteristics. Arithmetic and geometric means, and estimates of variability and maximum exposure were calculated by EMF source, frequency band and dosimetry type. The mean estimates were weighted by our confidence in the pooled measurements. The SEM contains confidence-weighted mean and maximum estimates for 312 EMF exposure sources (from 0 Hz to 300 GHz). Operator position geometric mean electric field levels for radiofrequency (RF) sources ranged between 0.8 V/m (plasma etcher) and 320 V/m (RF sealer), while magnetic fields ranged from 0.02 A/m (speed radar) to 0.6 A/m (microwave heating). For extremely low frequency sources, electric fields ranged between 0.2 V/m (electric forklift) and 11,700 V/m (high-voltage transmission line-hotsticks), whereas magnetic fields ranged between 0.14 μT (visual display terminals) and 17 μT (tungsten inert gas welding). The methodology developed allowed the construction of the first EMF-SEM and may be used to summarize similar exposure data for other physical or chemical agents.

Self-Report Dietary Assessment Tools Used in Canadian Research: A Scoping Review

Choosing the most appropriate dietary assessment tool for a study can be a challenge. Through a scoping review, we characterized self-report tools used to assess diet in Canada to identify patterns in tool use and to inform strategies to strengthen nutrition research. The research databases Medline, PubMed, PsycINFO, and CINAHL were used to identify Canadian studies published from 2009 to 2014 that included a self-report assessment of dietary intake. The search elicited 2358 records that were screened to identify those that reported on self-report dietary intake among nonclinical, non-Aboriginal adult populations. A pool of 189 articles (reflecting 92 studies) was examined in-depth to assess the dietary assessment tools used. Food-frequency questionnaires (FFQs) and screeners were used in 64% of studies, whereas food records and 24-h recalls were used in 18% and 14% of studies, respectively. Three studies (3%) used a single question to assess diet, and for 3 studies the tool used was not clear. A variety of distinct FFQs and screeners, including those developed and/or adapted for use in Canada and those developed elsewhere, were used. Some tools were reported to have been evaluated previously in terms of validity or reliability, but details of psychometric testing were often lacking. Energy and fat were the most commonly studied, reported by 42% and 39% of studies, respectively. For ∼20% of studies, dietary data were used to assess dietary quality or patterns, whereas close to half assessed ≤5 dietary components. A variety of dietary assessment tools are used in Canadian research. Strategies to improve the application of current evidence on best practices in dietary assessment have the potential to support a stronger and more cohesive literature on diet and health. Such strategies could benefit from national and global collaboration.

Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence

BACKGROUND

Spatial modelling of STH and schistosomiasis epidemiology is now commonplace. Spatial epidemiological studies help inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration; however, limited attention has been given to propagated uncertainties, their interpretation, and consequences for the mapped values. Using currently published literature on the spatial epidemiology of helminth infections we identified: (1) the main uncertainty sources, their definition and quantification and (2) how uncertainty is informative for STH programme managers and scientists working in this domain.

METHODOLOGY/PRINCIPAL FINDINGS

We performed a systematic literature search using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol. We searched Web of Knowledge and PubMed using a combination of uncertainty, geographic and disease terms. A total of 73 papers fulfilled the inclusion criteria for the systematic review. Only 9% of the studies did not address any element of uncertainty, while 91% of studies quantified uncertainty in the predicted morbidity indicators and 23% of studies mapped it. In addition, 57% of the studies quantified uncertainty in the regression coefficients but only 7% incorporated it in the regression response variable (morbidity indicator). Fifty percent of the studies discussed uncertainty in the covariates but did not quantify it. Uncertainty was mostly defined as precision, and quantified using credible intervals by means of Bayesian approaches.

CONCLUSION/SIGNIFICANCE

None of the studies considered adequately all sources of uncertainties. We highlighted the need for uncertainty in the morbidity indicator and predictor variable to be incorporated into the modelling framework. Study design and spatial support require further attention and uncertainty associated with Earth observation data should be quantified. Finally, more attention should be given to mapping and interpreting uncertainty, since they are relevant to inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration.

Potential sources of bias in the use of Escherichia coli to measure waterborne diarrhoea risk in low-income settings

OBJECTIVES

Escherichia coli is the standard water quality indicator for diarrhoea risk. Yet, the association between E. coli and diarrhoea is inconsistent across studies without a systematic assessment of methodological differences behind this variation. Most studies measure water quality cross-sectionally with diarrhoea, risking exposure misclassification and reverse causation. Studies use different recall windows for self-reported diarrhoea; longer periods increase potential outcome misclassification through misrecall. Control of confounding is inconsistent across studies. Additionally, diarrhoea measured in unblinded intervention trials can present courtesy bias. We utilised measurements from a randomised trial of water interventions in Bangladesh to assess how these factors affect the E. coli-diarrhoea association.

METHODS

We compared cross-sectional versus prospective measurements of water quality and diarrhoea, 2-versus 7-day symptom recall periods, estimates with and without controlling for confounding and using measurements from control versus intervention arms of the trial.

RESULTS

In the control arm, 2-day diarrhoea prevalence, measured prospectively 1 month after water quality, significantly increased with log₁₀ E. coli (PR = 1.50, 1.02-2.20). This association weakened when we used 7-day recall (PR = 1.18, 0.88-1.57), cross-sectional measurements of E. coli and diarrhoea (PR = 1.11, 0.79-1.56) or did not control for confounding (PR = 1.20, 0.88-1.62). Including data from intervention arms led to less interpretable associations, potentially due to courtesy bias, effect modification and/or reverse causation.

CONCLUSIONS

By systematically addressing potential sources of bias, our analysis demonstrates a clear relationship between E. coli in drinking water and diarrhoea, suggesting that the continued use of E. coli as an indicator of waterborne diarrhoea risk is justified.

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.

Sex differences in in-hospital mortality following a first acute myocardial infarction: symptomatology, delayed presentation, and hospital setting

BACKGROUND

Women generally wait longer than men prior to seeking treatment for acute myocardial infarction (AMI). They are more likely to present with atypical symptoms, and are less likely to be admitted to coronary or intensive care units (CCU or ICU) compared to similarly-aged males. Women are more likely to die during hospital admission. Sex differences in the associations of delayed arrival, admitting ward, and mortality have not been thoroughly investigated.

METHODS

Focusing on presenting symptoms and time of presentation since symptom onset, we evaluated sex differences in in-hospital mortality following a first AMI in 4859 men and women presenting to three emergency departments (ED) from December 2008 to February 2014. Sex-specific risk of mortality associated with admission to either CCU/ICU or medical wards was calculated after adjusting for age, socioeconomic status, triage-assigned urgency of presentation, blood pressure, heart rate, presenting symptoms, timing of presentation since symptom onset, and treatment in the ED. Sex-specific age-adjusted attributable risks were calculated.

RESULTS

Compared to males, females waited longer before seeking treatment, presented more often with atypical symptoms, and were less likely to be admitted to CCU or ICU. Age-adjusted mortality in CCU/ICU or medical wards was higher among females (3.1 and 4.9 % respectively in CCU/ICU and medical wards in females compared to 2.6 and 3.2 % in males). However, after adjusting for variation in presenting symptoms, delayed arrival and other risk factors, risk of death was similar between males and females if they were admitted to CCU or ICU. This was in contrast to those admitted to medical wards. Females admitted to medical wards were 89 % more likely to die than their male counterparts. Arriving in the ED within 60 min of onset of symptoms was not associated with in-hospital mortality. Among males, 2.2 % of in-hospital mortality was attributed to being admitted to medical wards rather than CCU or ICU, while for females this age-adjusted attributable risk was 4.1 %.

CONCLUSIONS

Our study stresses the need to reappraise decision making in patient selection for admission to specialised care units, whilst raising awareness of possible sex-related bias in management of patients diagnosed with an AMI.

Identification of confounder in epidemiologic data contaminated by measurement error in covariates

Background

Common methods for confounder identification such as directed acyclic graphs (DAGs), hypothesis testing, or a 10 % change-in-estimate (CIE) criterion for estimated associations may not be applicable due to (a) insufficient knowledge to draw a DAG and (b) when adjustment for a true confounder produces less than 10 % change in observed estimate (e.g. in presence of measurement error).

Methods

We compare previously proposed simulation-based approach for confounder identification that can be tailored to each specific study and contrast it with commonly applied methods (significance criteria with cutoff levels of p-values of 0.05 or 0.20, and CIE criterion with a cutoff of 10 %), as well as newly proposed two-stage procedure aimed at reduction of false positives (specifically, risk factors that are not confounders). The new procedure first evaluates potential for confounding by examination of correlation of covariates and applies simulated CIE criteria only if there is evidence of correlation, while rejecting a covariate as confounder otherwise. These approaches are compared in simulations studies with binary, continuous, and survival outcomes. We illustrate the application of our proposed confounder identification strategy in examining the association of exposure to mercury in relation to depression in the presence of suspected confounding by fish intake using the National Health and Nutrition Examination Survey (NHANES) 2009–2010 data.

Results

Our simulations showed that the simulation-determined cutoff was very sensitive to measurement error in exposure and potential confounder. The analysis of NHANES data demonstrated that if the noise-to-signal ratio (error variance in confounder/variance of confounder) is at or below 0.5, roughly 80 % of the simulated analyses adjusting for fish consumption would correctly result in a null association of mercury and depression, and only an extremely poorly measured confounder is not useful to adjust for in this setting.

Conclusions

No a prior criterion developed for a specific application is guaranteed to be suitable for confounder identification in general. The customization of model-building strategies and study designs through simulations that consider the likely imperfections in the data, as well as finite-sample behavior, would constitute an important improvement on some of the currently prevailing practices in confounder identification and evaluation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-016-0159-6) contains supplementary material, which is available to authorized users.

Caffeine: Friend or Foe?

The debate on the safety of and regulatory approaches for caffeine continues among various stakeholders and regulatory authorities. This decision-making process comes with significant challenges, particularly when considering the complexities of the available scientific data, making the formulation of clear science-based regulatory guidance more difficult. To allow for discussions of a number of key issues, the North American Branch of the International Life Sciences Institute (ILSI) convened a panel of subject matter experts for a caffeine-focused session entitled "Caffeine: Friend or Foe?," which was held during the 2015 ILSI Annual Meeting. The panelists' expertise covered topics ranging from the natural occurrence of caffeine in plants and interindividual metabolism of caffeine in humans to specific behavioral, reproductive, and cardiovascular effects related to caffeine consumption. Each presentation highlighted the potential risks, benefits, and challenges that inform whether caffeine exposure warrants concern. This paper aims to summarize the key topics discussed during the session.

Uses of NHANES Biomarker Data for Chemical Risk Assessment: Trends, Challenges, and Opportunities

BACKGROUND

Each year, the U.S. NHANES measures hundreds of chemical biomarkers in samples from thousands of study participants. These biomarker measurements are used to establish population reference ranges, track exposure trends, identify population subsets with elevated exposures, and prioritize research needs. There is now interest in further utilizing the NHANES data to inform chemical risk assessments.

OBJECTIVES

This article highlights a) the extent to which U.S. NHANES chemical biomarker data have been evaluated, b) groups of chemicals that have been studied, c) data analysis approaches and challenges, and d) opportunities for using these data to inform risk assessments.

METHODS

A literature search (1999-2013) was performed to identify publications in which U.S. NHANES data were reported. Manual curation identified only the subset of publications that clearly utilized chemical biomarker data. This subset was evaluated for chemical groupings, data analysis approaches, and overall trends.

RESULTS

A small percentage of the sampled NHANES-related publications reported on chemical biomarkers (8% yearly average). Of 11 chemical groups, metals/metalloids were most frequently evaluated (49%), followed by pesticides (9%) and environmental phenols (7%). Studies of multiple chemical groups were also common (8%). Publications linking chemical biomarkers to health metrics have increased dramatically in recent years. New studies are addressing challenges related to NHANES data interpretation in health risk contexts.

CONCLUSIONS

This article demonstrates growing use of NHANES chemical biomarker data in studies that can impact risk assessments. Best practices for analysis and interpretation must be defined and adopted to allow the full potential of NHANES to be realized.

Evaluating Evidence for Association of Human Bladder Cancer with Drinking-Water Chlorination Disinfection By-Products

Exposure to chlorination disinfection by-products (CxDBPs) is prevalent in populations using chlorination-based methods to disinfect public water supplies. Multifaceted research has been directed for decades to identify, characterize, and understand the toxicology of these compounds, control and minimize their formation, and conduct epidemiologic studies related to exposure. Urinary bladder cancer has been the health risk most consistently associated with CxDBPs in epidemiologic studies. An international workshop was held to (1) discuss the qualitative strengths and limitations that inform the association between bladder cancer and CxDBPs in the context of possible causation, (2) identify knowledge gaps for this topic in relation to chlorine/chloramine-based disinfection practice(s) in the United States, and (3) assess the evidence for informing risk management. Epidemiological evidence linking exposures to CxDBPs in drinking water to human bladder cancer risk provides insight into causality. However, because of imprecise, inaccurate, or incomplete estimation of CxDBPs levels in epidemiologic studies, translation from hazard identification directly to risk management and regulatory policy for CxDBPs can be challenging. Quantitative risk estimates derived from toxicological risk assessment for CxDBPs currently cannot be reconciled with those from epidemiologic studies, notwithstanding the complexities involved, making regulatory interpretation difficult. Evidence presented here has both strengths and limitations that require additional studies to resolve and improve the understanding of exposure response relationships. Replication of epidemiologic findings in independent populations with further elaboration of exposure assessment is needed to strengthen the knowledge base needed to better inform effective regulatory approaches.

Potential sensitivity of bias analysis results to incorrect assumptions of nondifferential or differential binary exposure misclassification

BACKGROUND

Results of bias analyses for exposure misclassification are dependent on assumptions made during analysis. We describe how adjustment for misclassification is affected by incorrect assumptions about whether sensitivity and specificity are the same (nondifferential) or different (differential) for cases and noncases.

METHODS

We adjusted for exposure misclassification using probabilistic bias analysis, under correct and incorrect assumptions about whether exposure misclassification was differential or not. First, we used simulated data sets in which nondifferential and differential misclassification were introduced. Then, we used data on obesity and diabetes from the National Health and Nutrition Examination Survey (NHANES) in which both self-reported (misclassified) and measured (true) obesity were available, using literature estimates of sensitivity and specificity to adjust for bias. The ratio of odds ratio (ROR; observed odds ratio divided by true odds ratio) was used to quantify magnitude of bias, with ROR = 1 signifying no bias.

RESULTS

In the simulated data sets, under incorrect assumptions (eg, assuming nondifferential misclassification when it was truly differential), results were biased, with RORs ranging from 0.18 to 2.46. In NHANES, results adjusted based on incorrect assumptions also produced biased results, with RORs ranging from 1.26 to 1.55; results were more biased when making these adjustments than when using the misclassified exposure values (ROR = 0.91).

CONCLUSIONS

Making an incorrect assumption about nondifferential or differential exposure misclassification in bias analyses can lead to more biased results than if no adjustment is performed. In our analyses, incorporating uncertainty using probabilistic bias analysis was not sufficient to overcome this problem.

Throwing out the baby with the bathwater?: Comparing 2 approaches to implausible values of change in body size

BACKGROUND

In childhood obesity research, the appearance of height loss, or "shrinkage," indicates measurement error. It is unclear whether a common response--excluding "shrinkers" from analysis--reduces bias.

METHODS

Using data from the National Longitudinal Study of Adolescent Health, we sampled 816 female adolescents (≥17 years) who had attained adult height by 1996 and for whom adult height was consistently measured in 2001 and 2008 ("gold-standard" height). We estimated adolescent obesity prevalence and the association of maternal education with adolescent obesity under 3 conditions: excluding shrinkers (for whom gold-standard height was less than recorded height in 1996), retaining shrinkers, and retaining shrinkers but substituting their gold-standard height.

RESULTS

When we estimated obesity prevalence, excluding shrinkers decreased precision without improving validity. When we regressed obesity on maternal education, excluding shrinkers produced less valid and less precise estimates.

CONCLUSION

In some circumstances, ignoring shrinkage is a better strategy than excluding shrinkers.

The two-dimensional Monte Carlo: a new methodologic paradigm for dose reconstruction for epidemiological studies

Retrospective dose estimation, particularly dose reconstruction that supports epidemiological investigations of health risk, relies on various strategies that include models of physical processes and exposure conditions with detail ranging from simple to complex. Quantification of dose uncertainty is an essential component of assessments for health risk studies since, as is well understood, it is impossible to retrospectively determine the true dose for each person. To address uncertainty in dose estimation, numerical simulation tools have become commonplace and there is now an increased understanding about the needs and what is required for models used to estimate cohort doses (in the absence of direct measurement) to evaluate dose response. It now appears that for dose-response algorithms to derive the best, unbiased estimate of health risk, we need to understand the type, magnitude and interrelationships of the uncertainties of model assumptions, parameters and input data used in the associated dose estimation models. Heretofore, uncertainty analysis of dose estimates did not always properly distinguish between categories of errors, e.g., uncertainty that is specific to each subject (i.e., unshared error), and uncertainty of doses from a lack of understanding and knowledge about parameter values that are shared to varying degrees by numbers of subsets of the cohort. While mathematical propagation of errors by Monte Carlo simulation methods has been used for years to estimate the uncertainty of an individual subject's dose, it was almost always conducted without consideration of dependencies between subjects. In retrospect, these types of simple analyses are not suitable for studies with complex dose models, particularly when important input data are missing or otherwise not available. The dose estimation strategy presented here is a simulation method that corrects the previous deficiencies of analytical or simple Monte Carlo error propagation methods and is termed, due to its capability to maintain separation between shared and unshared errors, the two-dimensional Monte Carlo (2DMC) procedure. Simply put, the 2DMC method simulates alternative, possibly true, sets (or vectors) of doses for an entire cohort rather than a single set that emerges when each individual's dose is estimated independently from other subjects. Moreover, estimated doses within each simulated vector maintain proper inter-relationships such that the estimated doses for members of a cohort subgroup that share common lifestyle attributes and sources of uncertainty are properly correlated. The 2DMC procedure simulates inter-individual variability of possibly true doses within each dose vector and captures the influence of uncertainty in the values of dosimetric parameters across multiple realizations of possibly true vectors of cohort doses. The primary characteristic of the 2DMC approach, as well as its strength, are defined by the proper separation between uncertainties shared by members of the entire cohort or members of defined cohort subsets, and uncertainties that are individual-specific and therefore unshared.

A proposal for assessing study quality: Biomonitoring, Environmental Epidemiology, and Short-lived Chemicals (BEES-C) instrument

The quality of exposure assessment is a major determinant of the overall quality of any environmental epidemiology study. The use of biomonitoring as a tool for assessing exposure to ubiquitous chemicals with short physiologic half-lives began relatively recently. These chemicals present several challenges, including their presence in analytical laboratories and sampling equipment, difficulty in establishing temporal order in cross-sectional studies, short- and long-term variability in exposures and biomarker concentrations, and a paucity of information on the number of measurements required for proper exposure classification. To date, the scientific community has not developed a set of systematic guidelines for designing, implementing and interpreting studies of short-lived chemicals that use biomonitoring as the exposure metric or for evaluating the quality of this type of research for WOE assessments or for peer review of grants or publications. We describe key issues that affect epidemiology studies using biomonitoring data on short-lived chemicals and propose a systematic instrument--the Biomonitoring, Environmental Epidemiology, and Short-lived Chemicals (BEES-C) instrument--for evaluating the quality of research proposals and studies that incorporate biomonitoring data on short-lived chemicals. Quality criteria for three areas considered fundamental to the evaluation of epidemiology studies that include biological measurements of short-lived chemicals are described: 1) biomarker selection and measurement, 2) study design and execution, and 3) general epidemiological study design considerations. We recognize that the development of an evaluative tool such as BEES-C is neither simple nor non-controversial. We hope and anticipate that the instrument will initiate further discussion/debate on this topic.

Evaluating uncertainty to strengthen epidemiologic data for use in human health risk assessments

BACKGROUND

There is a recognized need to improve the application of epidemiologic data in human health risk assessment especially for understanding and characterizing risks from environmental and occupational exposures. Although there is uncertainty associated with the results of most epidemiologic studies, techniques exist to characterize uncertainty that can be applied to improve weight-of-evidence evaluations and risk characterization efforts.

METHODS

This report derives from a Health and Environmental Sciences Institute (HESI) workshop held in Research Triangle Park, North Carolina, to discuss the utility of using epidemiologic data in risk assessments, including the use of advanced analytic methods to address sources of uncertainty. Epidemiologists, toxicologists, and risk assessors from academia, government, and industry convened to discuss uncertainty, exposure assessment, and application of analytic methods to address these challenges.

SYNTHESIS

Several recommendations emerged to help improve the utility of epidemiologic data in risk assessment. For example, improved characterization of uncertainty is needed to allow risk assessors to quantitatively assess potential sources of bias. Data are needed to facilitate this quantitative analysis, and interdisciplinary approaches will help ensure that sufficient information is collected for a thorough uncertainty evaluation. Advanced analytic methods and tools such as directed acyclic graphs (DAGs) and Bayesian statistical techniques can provide important insights and support interpretation of epidemiologic data.

CONCLUSIONS

The discussions and recommendations from this workshop demonstrate that there are practical steps that the scientific community can adopt to strengthen epidemiologic data for decision making.

Using bayesian models to assess the effects of under-reporting of cannabis use on the association with birth defects, national birth defects prevention study, 1997-2005

BACKGROUND

Studies on associations between periconceptional cannabis exposure and birth defects have mainly relied on self-reported exposure. Therefore, the results may be biased due to under-reporting of the exposure. The aim of this study was to quantify the potential effects of this form of exposure misclassification.

METHODS

Using multivariable logistic regression, we re-analysed associations between periconceptional cannabis use and 20 specific birth defects using data from the National Birth Defects Prevention Study from 1997-2005 for 13 859 case infants and 6556 control infants. For seven birth defects, we implemented four Bayesian models based on various assumptions concerning the sensitivity of self-reported cannabis use to estimate odds ratios (ORs), adjusted for confounding and under-reporting of the exposure. We used information on sensitivity of self-reported cannabis use from the literature for prior assumptions.

RESULTS

The results unadjusted for under-reporting of the exposure showed an association between cannabis use and anencephaly (posterior OR 1.9 [95% credible interval (CRI) 1.1, 3.2]) which persisted after adjustment for potential exposure misclassification. Initially, no statistically significant associations were observed between cannabis use and the other birth defect categories studied. Although adjustment for under-reporting did not notably change these effect estimates, cannabis use was associated with esophageal atresia (posterior OR 1.7 [95% CRI 1.0, 2.9]), diaphragmatic hernia (posterior OR 1.8 [95% CRI 1.1, 3.0]), and gastroschisis (posterior OR 1.7 [95% CRI 1.2, 2.3]) after correction for exposure misclassification.

CONCLUSIONS

Under-reporting of the exposure may have obscured some cannabis-birth defect associations in previous studies. However, the resulting bias is likely to be limited.

Atrazine and pregnancy outcomes: a systematic review of epidemiologic evidence

Atrazine (ATR) is a commonly used agricultural herbicide that has been the subject of epidemiologic studies assessing its relation to reproductive health problems. This review evaluates both the consistency and the quality of epidemiologic evidence testing the hypothesis that ATR exposure, at usually encountered levels, is a risk factor for birth defects, small for gestational age birth weight, prematurity, miscarriages, and problems of fetal growth and development. We followed the current methodological guidelines for systematic reviews by using two independent researchers to identify, retrieve, and evaluate the relevant epidemiologic literature on the relation of ATR to various adverse outcomes of birth and pregnancy. Each eligible paper was summarized with respect to its methods and results with particular attention to study design and exposure assessment, which have been cited as the main areas of weakness in ATR research. As a quantitative meta-analysis was not feasible, the study results were categorized qualitatively as positive, null, or mixed. The literature on ATR and pregnancy-related health outcomes is growing rapidly, but the quality of the data is poor with most papers using aggregate rather than individual-level information. Without good quality data, the results are difficult to assess; however, it is worth noting that none of the outcome categories demonstrated consistent positive associations across studies. Considering the poor quality of the data and the lack of robust findings across studies, conclusions about a causal link between ATR and adverse pregnancy outcomes are not warranted.

Bisphenol A and indicators of obesity, glucose metabolism/type 2 diabetes and cardiovascular disease: a systematic review of epidemiologic research

INTRODUCTION

Bisphenol A (BPA), a high-volume chemical with weak estrogenic properties, has been linked to obesity, cardiovascular diseases (CVD) and diabetes mellitus (DM). This review evaluates both the consistency and the quality of epidemiological evidence from studies testing the hypothesis that BPA exposure is a risk factor for these health outcomes.

METHODS

We followed the current methodological guidelines for systematic reviews by using two independent researchers to identify, review and summarize the relevant epidemiological literature on the relation of BPA to obesity, CVD, DM, or related biomarkers. Each paper was summarized with respect to its methods and results with particular attention to study design and exposure assessment, which have been cited as the main areas of weakness in BPA epidemiologic research. As quantitative meta-analysis was not feasible, the study results were categorized qualitatively as positive, inverse, null, or mixed.

RESULTS

Nearly all studies on BPA and obesity-, DM- or CVD-related health outcomes used a cross-sectional design and relied on a single measure of BPA exposure, which may result in serious exposure misclassification. For all outcomes, results across studies were inconsistent. Although several studies used the same data and the same or similar statistical methods, when the methods varied slightly, even studies that used the same data produced different results.

CONCLUSION

Epidemiological study design issues severely limit our understanding of health effects associated with BPA exposure. Considering the methodological limitations of the existing body of epidemiology literature, assertions about a causal link between BPA and obesity, DM, or CVD are unsubstantiated.