Exposure-disease Associations Research Articles

Generalized Linear Models with Covariate Measurement Error and Zero-Inflated Surrogates.

Epidemiological studies often encounter a challenge due to exposure measurement error when estimating an exposure-disease association. A surrogate variable may be available for the true unobserved exposure variable. However, zero-inflated data are encountered frequently in the surrogate variables. For example, many nutrient or physical activity measures may have a zero value (or a low detectable value) among a group of individuals. In this paper, we investigate regression analysis when the observed surrogates may have zero values among some individuals of the whole study cohort. A naive regression calibration without taking into account a probability mass of the surrogate variable at 0 (or a low detectable value) will be biased. We developed a regression calibration estimator which typically can have smaller biases than the naive regression calibration estimator. We propose an expected estimating equation estimator which is consistent under the zero-inflated surrogate regression model. Extensive simulations show that the proposed estimator performs well in terms of bias correction. These methods are applied to a physical activity intervention study.

A flexible matching strategy for matched nested case-control studies

PurposeIndividual matching in case-control studies improves statistical efficiency over random selection of controls but can lead to selection bias if cases are excluded due to the lack of appropriate controls or residual confounding with less strict matching criteria. We introduce flex matching, an algorithm using multiple rounds of control selection with successively relaxed matching criteria to select controls for cases. MethodsWe simulated exposure-disease relationships in multiple cohort data sets with a range of confounding scenarios and conducted 16,800,000 nested case-control studies, comparing random selection of controls, strict matching, and flex matching. We computed average bias and statistical efficiency in estimates of exposure-disease relationships under each matching strategy. ResultsOn average, flex matching produced the least biased estimates of exposure-disease associations with the smallest standard errors. Strict matching algorithms that excluded cases for whom matched controls could not be identified produced biased estimates with larger standard errors. Estimates from studies with random assignment of controls were relatively unbiased, but the standard errors were larger than from studies using flex matching. ConclusionsFlex matching should be considered for case-control designs, especially for biomarker studies where matching on technical artifacts is necessary and maximizing efficiency is a priority.

Environmental and occupational exposures in interstitial lung disease.

We highlight recent advances in the understanding of how environmental and occupational exposures increase the risk of developing interstitial lung disease (ILD), and how to evaluate a patient for potential exposures. A review of emerging literature suggests that environmental and occupational exposures can be directly causal, as in the case of the pneumoconioses and smoking-related ILDs, or one of many contributors to disease, as in the case of idiopathic pulmonary fibrosis (IPF). Regardless of the level of association, exposures are clearly prevalent across all ILD subtypes studied. Inhalational exposures are increasingly recognized as an important component in the development of ILDs, and novel exposure-disease associations continue to be discovered. These exposures represent potential opportunities for further understanding the pathobiology of disease and for the prevention of these often progressive and debilitating disorders. Prospective, comprehensive data collection regarding occupational and environmental exposures are needed in ILD patients to fully elucidate specific antigens and their relationships to disease incidence and outcomes. Systematically collected exposure information will also inform potential interventions to remediate exposures and thus mitigate the course of frequently progressive and fatal diseases.

Bespoke Instruments: A new tool for addressing unmeasured confounders.

Suppose that an investigator is interested in quantifying an exposure-disease causal association in a setting where the exposure, disease, and some potential confounders of the association of interest have been measured. However, there remains concern about residual confounding of the association of interest by unmeasured confounders. We propose an approach to account for residual bias due to unmeasured confounders. The proposed approach uses a measured confounder to derive a "bespoke" instrumental variable that is tailored to the study population and is used to control for bias due to residual confounding. The approach may provide a useful tool for assessing and accounting for bias due to residual confounding. We provide a formal description of the conditions for identification of causal effects, illustrate the method using simulations, and provide an empirical example concerning mortality among Japanese atomic bomb survivors.

Network biology approach to human tissue-specific chemical exposome

Human exposure to environmental chemicals is a major contributor to the global disease burden. To characterize the external exposome it is important to assess its chemical components and to study their impact on human health. Biomonitoring studies measure the body burden of environmental chemicals detected in biospecimens from a wide range of the population. The detection of these chemicals in biospecimens (and, hence, human tissues) is considered an important biomarker of human exposure. However, there is no readily available resource that compiles such exposure data for human tissues from published literature, and no studies that explore the patterns in the associations between tissue-specific exposures and human diseases. We present Human Tissue-specific Exposome Atlas (TExAs), a compilation of 380 environmental chemicals detected across 27 human tissues. TExAs is accessible via a user friendly webserver: https://cb.imsc.res.in/texas. We compare the chemicals in TExAs with 55 global chemical regulations, guidelines, and inventories, which represent several categories of the external exposome of humans. Further to understand the potential implications on human health of chemicals detected across human tissues, we employ a network biology approach and explore possible chemical exposure-disease associations. Ensuing analyses reveal the possibilities of disease comorbidities and demonstrate the application of network biology in unraveling complex disease associations due to chemical exposure.

894Molecular epidemiology: building causal evidence within highly dimensioned studies

Abstract Focus of Presentation This presentation provides a practical perspective on molecular epidemiologic analysis within a single high dimensional study to consider a causal question. Findings The work steps include special issues. Those include composite exposures and understanding of collinearity across predictors. Laboratory artefact needs to be minimised. Non-causal confounding is also important. This can occur when a factor is a determinant of outcome and the underlying association between exposure and the factor is non-causal. That is, the association arises due to chance, confounding or other bias rather than reflecting that exposure and the factor are causally related. The influence of laboratory processing features must be accounted for. This can be achieved by pre-processing measures to remove artefact or controlling for process factors. The work plan should allow the evaluation of alternative non-causal explanations for observed exposure-disease associations and strategies to obtain the highest level of causal inference possible within the study. Conclusions/Implications A systematic approach is required to work through a question a question set and obtain insights on not only the exposure-disease association but the multifactorial causal structure of the underlying data where possible. The appropriate inclusion of molecular findings will enhance the quest to better understand multifactorial disease causation in modern observational epidemiological studies. Key messages A systematic set of work steps is outlined to assess whether detected exposure-disease associations have a low and high likelihood of being attributed to non-causal and causal explanations respectively.

Comparison of prevalence and exposure-disease associations using self-report and hospitalization data among enrollees of the world trade center health registry

BackgroundAlthough many studies have investigated agreement between survey and hospitalization data for disease prevalence, it is unknown whether exposure-chronic disease associations vary based on data collection method. We investigated agreement between self-report and administrative data for the following: 1) disease prevalence, and 2) the accuracy of self-reported hospitalization in the last 12 months, and 3) the association of seven chronic diseases (rheumatoid arthritis, hypertension, heart attack, stroke, asthma, diabetes, hyperlipidemia) with four measures of 9/11 exposure.MethodsEnrollees of the World Trade Center Health Registry who resided in New York State were included (N = 18,206). Hospitalization data for chronic diseases were obtained from the New York State Planning and Research Cooperative System (SPARCS). Prevalence for each disease and concordance measures (kappa, sensitivity, specificity, positive agreement, and negative agreement) were calculated. In addition, the associations of the seven chronic diseases with the four measures of exposure were evaluated using logistic regression.ResultsSelf-report disease prevalence ranged from moderately high (40.5% for hyperlipidemia) to low (3.8% for heart attack). Self-report prevalence was at least twice that obtained from administrative data for all seven chronic diseases. Kappa ranged from 0.35 (stroke) to 0.04 (rheumatoid arthritis). Self-reported hospitalizations within the last 12 months showed little overlap with actual hospitalization data. Agreement for exposure-disease associations was good over the twenty-eight exposure-disease pairs studied.ConclusionsAgreement was good for exposure-disease associations, modest for disease prevalence, and poor for self-reported hospitalizations. Neither self-report nor administrative data can be treated as the “gold standard.” Which source to use depends on the availability and context of data, and the disease under study.

An approximate quasi-likelihood approach for error-prone failure time outcomes and exposures.

Measurement error arises commonly in clinical research settings that rely on data from electronic health records or large observational cohorts. In particular, self-reported outcomes are typical in cohort studies for chronic diseases such as diabetes in order to avoid the burden of expensive diagnostic tests. Dietary intake, which is also commonly collected by self-report and subject to measurement error, is a major factor linked to diabetes and other chronic diseases. These errors can bias exposure-disease associations that ultimately can mislead clinical decision-making. We have extended an existing semiparametric likelihood-based method for handling error-prone, discrete failure time outcomes to also address covariate error. We conduct an extensive numerical study to compare the proposed method to the naive approach that ignores measurement error in terms of bias and efficiency in the estimation of the regression parameter of interest. In all settings considered, the proposed method showed minimal bias and maintained coverage probability, thus outperforming the naive analysis which showed extreme bias and low coverage. This method is applied to data from the Women's Health Initiative to assess the association between energy and protein intake and the risk of incident diabetes mellitus. Our results show that correcting for errors in both the self-reported outcome and dietary exposures leads to considerably different hazard ratio estimates than those from analyses that ignore measurement error, which demonstrates the importance of correcting for both outcome and covariate error.

Reflection on modern methods: building causal evidence within high-dimensional molecular epidemiological studies of moderate size.

This commentary provides a practical perspective on epidemiological analysis within a single high-dimensional study of moderate size to consider a causal question. In this setting, non-causal confounding is important. This occurs when a factor is a determinant of outcome and the underlying association between exposure and the factor is non-causal. That is, the association arises due to chance, confounding or other bias rather than reflecting that exposure and the factor are causally related. In particular, the influence of technical processing factors must be accounted for by pre-processing measures to remove artefact or to control for these factors such as batch run. Work steps include the evaluation of alternative non-causal explanations for observed exposure-disease associations and strategies to obtain the highest level of causal inference possible within the study. A systematic approach is required to work through a question set and obtain insights on not only the exposure-disease association but also the multifactorial causal structure of the underlying data where possible. The appropriate inclusion of molecular findings will enhance the quest to better understand multifactorial disease causation in modern observational epidemiological studies.

A Spatiotemporal Prediction Model for Black Carbon in the Denver Metropolitan Area, 2009-2020.

Studies on health effects of air pollution from local sources require exposure assessments that capture spatial and temporal trends. To facilitate intraurban studies in Denver, Colorado, we developed a spatiotemporal prediction model for black carbon (BC). To inform our model, we collected more than 700 weekly BC samples using personal air samplers from 2018 to 2020. The model incorporated spatial and spatiotemporal predictors and smoothed time trends to generate point-level weekly predictions of BC concentrations for the years 2009-2020. Our results indicate that our model reliably predicted weekly BC concentrations across the region during the year in which we collected data. We achieved a 10-fold cross-validation R2 of 0.83 and a root-mean-square error of 0.15 μg/m3 for weekly BC concentrations predicted at our sampling locations. Predicted concentrations displayed expected temporal trends, with the highest concentrations predicted during winter months. Thus, our prediction model improves on typical land use regression models that generally only capture spatial gradients. However, our model is limited by a lack of long-term BC monitoring data for full validation of historical predictions. BC predictions from the weekly spatiotemporal model will be used in traffic-related air pollution exposure-disease associations more precisely than previous models for the region have allowed.

Addressing inequality in health in sub-Saharan Africa through robust measurement of socio-economic status

Socioeconomic status (SES) is an important determinant of the likelihood that individuals and populations are exposed to environmental and other risk factors for health. Both single and composite measures from census and administrative databases of local authorities have been used to define the socio-economic characteristics of an area in developed countries. The situation in Nigeria, like many other developing countries, is that demographic data at district level is scarce, while the methodologies for defining socio-economic groups are less developed. For a study of air pollution and respiratory health of school children in Warri, Nigeria, a purpose-designed measure of deprivation had to be devised, using data that could be gathered in the field. Data on socio-economic and material deprivation indicators were gathered through a surveillance exercise and interacting with residents of the study area on multiple visits. Low SES in the selected neighbourhood and schools were characterised by the metrics of sixteen indicators such as lack of electricity, no clean water, use of pit latrines, open drainage, untarred roads, living in a multi-tenant houses with metal roofing sheets, cooking with firewood or kerosene, high traffic density and densely populated areas. Other metrics such as access to metered electricity, living in a flat or bungalow with aluminium roofing sheet and water-closet, in a sparsely populated, low traffic density, planned neighbourhood with tarred road and closed drainage; availability of pipe-borne water and use of gas for cooking characterised affluent neighbourhood. Understanding the strengths and limitations of different indicators and the context is vital not only for designing the study, but also to interpret its results. Controlling for SES in the study design such as this, and/or statistical analysis is important in defining the extent of exposure disease association and, more importantly, to determine the size of health benefit achievable through feasible exposure reductions.

Integrated approaches for the assessment of health impacts of environmental chemicals: our experience in the GraMo cohort study

Abstract The assessment of the health implications of human exposure to low doses of multiple environmental chemical pollutants represents an important challenge for environmental epidemiology. Current studies must go beyond simple exposure-disease associations and need further characterization of a complete exposure disease continuum, including exposure routes, adverse outcome pathways, potential subclinical effects, interactions between chemicals, and intra-individual susceptibility. This needs an effective synergism with basic sciences, which can enrich epidemiologic findings with very detailed information at a molecular level, but also with clinicians, who would help to provide accurate diagnoses and interpretation in a real-world scenario. On the other hand, environmental exposures can affect the general population at different levels, including economic and social dimensions, which have been frequently overlooked. These approaches would require specific methodologies that are relatively novel in the field of environmental epidemiology, such as cost-benefit analyses and qualitative techniques. Public health campaigns can clearly benefit from these mixed approaches.

Improving Exposure Assessment Methodologies for Epidemiological Studies on Pesticides: Study Protocol.

BackgroundExposure to certain pesticides has been associated with several chronic diseases. However, to determine the role of pesticides in the causation of such diseases, an assessment of historical exposures is required. Exposure measurement data are rarely available; therefore, assessment of historical exposures is frequently based on surrogate self-reported information, which has inherent limitations. Understanding the performance of the applied surrogate measures in the exposure assessment of pesticides is therefore important to allow proper evaluation of the risks.ObjectiveThe Improving Exposure Assessment Methodologies for Epidemiological Studies on Pesticides (IMPRESS) project aims to assess the reliability and external validity of the surrogate measures used to assign exposure within individuals or groups of individuals, which are frequently based on self-reported data on exposure determinants. IMPRESS will also evaluate the size of recall bias on the misclassification of exposure to pesticides; this in turn will affect epidemiological estimates of the effect of pesticides on human health.MethodsThe IMPRESS project will recruit existing cohort participants from previous and ongoing research studies primarily of epidemiological origin from Malaysia, Uganda, and the United Kingdom. Consenting participants of each cohort will be reinterviewed using an amended version of the original questionnaire addressing pesticide use characteristics administered to that cohort. The format and relevant questions will be retained but some extraneous questions from the original (eg, relating to health) will be excluded for ethical and practical reasons. The reliability of pesticide exposure recall over different time periods (<2 years, 6-12 years, and >15 years) will then be evaluated. Where the original cohort study is still ongoing, participants will also be asked if they wish to take part in a new exposure biomonitoring survey, which involves them providing urine samples for pesticide metabolite analysis and completing questionnaire information regarding their work activities at the time of sampling. The participant’s level of exposure to pesticides will be determined by analyzing the collected urine samples for selected pesticide metabolites. The biomonitoring measurement results will be used to assess the performance of algorithm-based exposure assessment methods used in epidemiological studies to estimate individual exposures during application and re-entry work.ResultsThe project was funded in September 2017. Enrollment and sample collection was completed for Malaysia in 2019 and is on-going for Uganda and the United Kingdom. Sample and data analysis will proceed in 2020 and the first results are expected to be submitted for publication in 2021.ConclusionsThe study will evaluate the consistency of questionnaire data and accuracy of current algorithms in assessing pesticide exposures. It will indicate where amendments can be made to better capture exposure data for future epidemiology studies and thus improve the reliability of exposure-disease associations.International Registered Report Identifier (IRRID)PRR1-10.2196/16448

Shift work and migraine: A systematic review.

ObjectivesMigraine is a chronic neurological disorder characterized by recurrent attacks of headache, mainly affecting the working age population with a great socioeconomic impact. The etiology of migraine is still uncertain, and various individual and/or environmental risk factors have been suggested as triggers of the attacks, including irregularities in the sleep‐wake rhythm. In this perspective, it is possible that shift and night work, affecting circadian rhythms, may play a key function in the disease pathogenesis. Therefore, aim of this review was to provide an overview on the possible association between shift works and migraine development or clinical outcomes.MethodsA systematic review of literature studies available in Pubmed, Scopus, and ISI Web of Science databases, addressing the possible shift work‐migraine relationship was performed.ResultsConflicting data emerged from the revised studies. Some results supported a positive association between migraine prevalence and shift works, according to peculiar job tasks, seniority in shift works, specific work schedules, and number of night shifts performed in a month. However, other investigations failed to confirm such findings.ConclusionsThe limited number of available studies, their cross‐sectional nature, the different criteria employed for migraine diagnosis, and the various shift work schedules analyzed, together with exposure to other confounding factors on workplace do not allow to extrapolate definite conclusions on shift work‐migraine relationship. From an occupational health perspective, further studies appear necessary to better understand such exposure‐disease association and possibly define risk assessment and management strategies to protect the health of susceptible and/or migraine affected workers.

Applying two general population job exposure matrices to predict incident carpal tunnel syndrome: A cross-national approach to improve estimation of workplace physical exposures.

Objectives A job exposure matrix (JEM) is a tool to estimate workers' exposure to occupational physical risk factors. We evaluated the performance of two general population JEM (CONSTANCES and O*NET) to detect known exposure-disease relationships in an American prospective cohort study. We compared exposure estimates from three data sources and explored whether combining exposures from these two JEM, or combining exposure from each JEM with individual-level measures, improved prediction of carpal tunnel syndrome (CTS). Methods Using Cox proportional hazard models, we evaluated relationships between physical work exposure and incident CTS of 2393 workers using JEM-assigned and individual-level measure exposure information. We compared exposure estimates using Spearman's rank correlation and Cohen's kappa. We compared combined exposure models to single source exposure models by using binomial logistic regression and examined differences based on model fit and performance. Results The O*NET JEM [hazard ratio (HR) range 1.3-2.01] demonstrated generally similar exposure-disease associations as individual-level measures (HR range 1.00-1.42); we found fewer associations with the CONSTANCES JEM (HR range 1.08-2.05). Comparisons between the three sources showed stronger correlations and agreement at the job versus worker level. Combined models improved goodness-of-fit and had lower Akaike information criterion (AIC) values compared to single-source models. Conclusions JEM can be applied cross nationally and there is potential to combine complementary exposure methods to improve estimation of workplace physical exposures in the prediction of CTS. More investigations are needed to explore exposure-disease associations in other samples and combinations of exposure data from different methods.

Visualization and Interpretation of Multivariate Associations with Disease Risk Markers and Disease Risk-The Triplot.

Metabolomics has emerged as a promising technique to understand relationships between environmental factors and health status. Through comprehensive profiling of small molecules in biological samples, metabolomics generates high-dimensional data objectively, reflecting exposures, endogenous responses, and health effects, thereby providing further insights into exposure-disease associations. However, the multivariate nature of metabolomics data contributes to high complexity in analysis and interpretation. Efficient visualization techniques of multivariate data that allow direct interpretation of combined exposures, metabolome, and disease risk, are currently lacking. We have therefore developed the ‘triplot’ tool, a novel algorithm that simultaneously integrates and displays metabolites through latent variable modeling (e.g., principal component analysis, partial least squares regression, or factor analysis), their correlations with exposures, and their associations with disease risk estimates or intermediate risk factors. This paper illustrates the framework of the ‘triplot’ using two synthetic datasets that explore associations between dietary intake, plasma metabolome, and incident type 2 diabetes or BMI, an intermediate risk factor for lifestyle-related diseases. Our results demonstrate advantages of triplot over conventional visualization methods in facilitating interpretation in multivariate risk modeling with high-dimensional data. Algorithms, synthetic data, and tutorials are open source and available in the R package ‘triplot’.

Adjusting for differential misclassification in matched case-control studies utilizing health administrative data.

In epidemiological studies of secondary data sources, lack of accurate disease classifications often requires investigators to rely on diagnostic codes generated by physicians or hospital systems to identify case and control groups, resulting in a less-than-perfect assessment of the disease under investigation. Moreover, because of differences in coding practices by physicians, it is hard to determine the factors that affect the chance of an incorrectly assigned disease status. What results is a dilemma where assumptions of non-differential misclassification are questionable but, at the same time, necessary to proceed with statistical analyses. This paper develops an approach to adjust exposure-disease association estimates for disease misclassification, without the need of simplifying non-differentiality assumptions, or prior information about a complicated classification mechanism. We propose to leverage rich temporal information on disease-specific healthcare utilization to estimate each participant's probability of being a true case and to use these estimates as weights in a Bayesian analysis of matched case-control data. The approach is applied to data from a recent observational study into the early symptoms of multiple sclerosis (MS), where MS cases were identified from Canadian health administrative databases and matched to population controls that are assumed to be correctly classified. A comparison of our results with those from non-differentially adjusted analyses reveals conflicting inferences and highlights that ill-suited assumptions of non-differential misclassification can exacerbate biases in association estimates.

Matched case-control data with a misclassified exposure: what can be done with instrumental variables?

Matched case-control studies are used for finding the association between a disease and an exposure after controlling the effect of important confounding variables. It is a known fact that the disease-exposure association parameter estimators are biased when the exposure is misclassified, and a matched case-control study is of no exception. Any bias correction method relies on validation data that contain the true exposure and the misclassified exposure value, and in turn the validation data help to estimate the misclassification probabilities. The question is what we can do when there are no validation data and no prior knowledge on the misclassification probabilities, but some instrumental variables are observed. To answer this unexplored and unanswered question, we propose two methods of reducing the exposure misclassification bias in the analysis of a matched case-control data when instrumental variables are measured for each subject of the study. The significance of these approaches is that the proposed methods are designed to work without any validation data that often are not available when the true exposure is impossible or too costly to measure. A simulation study explores different types of instrumental variable scenarios and investigates when the proposed methods work, and how much bias can be reduced. For the purpose of illustration, we apply the methods to a nested case-control data sampled from the 1989 US birth registry.

Proceedings of the fourth international molecular pathological epidemiology (MPE) meeting.

An important premise of epidemiology is that individuals with the same disease share similar underlying etiologies and clinical outcomes. In the past few decades, our knowledge of disease pathogenesis has improved, and disease classification systems have evolved to the point where no complex disease processes are considered homogenous. As a result, pathology and epidemiology have been integrated into the single, unified field of molecular pathological epidemiology (MPE). Advancing integrative molecular and population-level health sciences and addressing the unique research challenges specific to the field of MPE necessitates assembling experts in diverse fields, including epidemiology, pathology, biostatistics, computational biology, bioinformatics, genomics, immunology, and nutritional and environmental sciences. Integrating these seemingly divergent fields can lead to a greater understanding of pathogenic processes. The International MPE Meeting Series fosters discussion that addresses the specific research questions and challenges in this emerging field. The purpose of the meeting series is to: discuss novel methods to integrate pathology and epidemiology; discuss studies that provide pathogenic insights into population impact; and educate next-generation scientists. Herein, we share the proceedings of the Fourth International MPE Meeting, held in Boston, MA, USA, on 30 May-1 June, 2018. Major themes of this meeting included 'integrated genetic and molecular pathologic epidemiology', 'immunology-MPE', and 'novel disease phenotyping'. The key priority areas for future research identified by meeting attendees included integration of tumor immunology and cancer disparities into epidemiologic studies, further collaboration between computational and population-level scientists to gain new insight on exposure-disease associations, and future pooling projects of studies with comparable data.

When exposure is subject to nondifferential misclassification, are validation data helpful in testing for an exposure–disease association?

AbstractConsider assessing the evidence for an exposure variable and a disease variable being associated, when the true exposure variable is more costly to obtain than an error‐prone but nondifferential surrogate exposure variable. From a study design perspective, there are choices regarding the best use of limited resources. Should one acquire the true exposure status for fewer subjects or the surrogate exposure status for more subjects? The issue of validation is also central, i.e., should we simultaneously measure the true and surrogate exposure variables on a subset of study subjects? Using large‐sample theory, we provide a framework for quantifying the power of testing for an exposure–disease association as a function of study cost. This enables us to present comparisons of different study designs under different suppositions about both the relative cost and the performance (sensitivity and specificity) of the surrogate variable. We present simulations to show the applicability of our theoretical framework, and we provide a case‐study comparing results from an actual study to what could have been seen had true exposure status been ascertained for a different proportion of study subjects. We also describe an extension of our ideas to a more complex situation involving covariates. The Canadian Journal of Statistics 47: 222–237; 2019 © 2019 Statistical Society of Canada