Review for Retrospective Exposure Assessment Methods Used in Epidemiologic Cancer Risk Studies of Semiconductor Workers: Limitations and Recommendations.
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SUBMITTER: Park D
PROVIDER: S-EPMC6129997 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Peak exposures are of concern because they can potentially overwhelm normal defense mechanisms and induce adverse health effects. Metrics of peak exposure have been used in epidemiologic and exposure studies, but consensus is lacking on its definition. The relevant characteristics of peak exposure are dependent upon exposure patterns, biokinetics of exposure, and disease mechanisms. The objective of this review was to summarize the use of peak metrics in epidemiologic and exposure studies. A comprehensive search of Medline, Embase, Web of Science, and NIOSHTIC-2 databases was conducted using keywords related to peak exposures. The retrieved references were reviewed and selected for indexing if they included a peak metric and met additional criteria. Information on health outcomes and peak exposure metrics was extracted from each reference. A total of 1,215 epidemiologic or exposure references were identified, of which 182 were indexed and summarized. For the 72 epidemiologic studies, the health outcomes most frequently evaluated were: chronic respiratory effects, cancer and acute respiratory symptoms. Exposures were frequently assessed using task-based and full-shift time-integrated methods, qualitative methods, and real-time instruments. Peak exposure summary metrics included the presence or absence of a peak event, highest exposure intensity and frequency greater than a target. Peak metrics in the 110 exposure studies most frequently included highest exposure intensity, average short-duration intensity, and graphical presentation of the real-time data (plots). This review provides a framework for considering biologically relevant peak exposure metrics for epidemiologic and exposure studies to help inform risk assessment and exposure mitigation.
| S-EPMC7820770 | biostudies-literature
Project description:Epidemiologic studies use various biosample collection methods to study associations between human oral microbiota and health outcomes. However, the agreement between the different methods is unclear. We compared a commercially available OMNIgene ORAL kit to three alternative collection methods: Saccomanno's fixative, Scope mouthwash, and nonethanol mouthwash. Oral samples were collected from 40 individuals over 4 visits. Two samples were collected from each subject per visit: one with OMNIgene and one with an alternative method. DNA was extracted using the DSP DNA Virus Pathogen kit, and the V4 region of the 16S rRNA gene was PCR amplified and sequenced using MiSeq. Oral collection methods were compared based on alpha and beta diversity metrics and phylum- and genus-level relative abundances. All alpha diversity metrics were significantly lower for Saccomanno's fixative than for OMNIgene (P < 0.001), whereas the two mouthwashes were more similar to OMNIgene. Principal-coordinate analysis (PCoA) using the Bray-Curtis and weighted UniFrac beta diversity matrices showed large differences in the microbial compositions of samples collected with Saccomanno's compared to those with OMNIgene and the mouthwashes. Clustering by collection method was not observed in unweighted UniFrac PCoA plots, suggesting differences in relative abundances but not specific taxa detected by the collection methods. Relative abundances of most taxa were significantly different between OMNIgene and the other methods at each taxonomic level, with Saccomanno's showing the least agreement with OMNIgene. There were clear differences in oral microbial communities between the four oral collection methods, particularly for Saccomanno's fixative.IMPORTANCE We compared four different oral collection methods for studying the human oral microbiome: an OMNIgene ORAL kit, Scope mouthwash, nonethanol mouthwash, and Saccomanno's fixative. Our study shows that the type of the collection method can have a large impact on the results of an oral microbiome analysis. We recommend that one consistent oral collection method should be used for all oral microbiome comparisons. While Scope and nonethanol mouthwashes are less expensive and provide results similar to those with OMNIgene, Saccomanno's fixative may be unfavorable due to the microbial differences detected in this study. Our results will help guide the design of future oral microbiome studies.
| S-EPMC7343307 | biostudies-literature
Project description:Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or "hybrid" models for pollutant exposure estimates and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared with use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g., EC, CO, and NO(x)). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves existing data on human time-activity patterns and exposure to local source (e.g., traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space and investigating the impact of improved exposure estimates in chronic health studies.
| S-EPMC4088339 | biostudies-literature
Project description:Early life exposures influence numerous social determinants of health, as distal causes or confounders of later health outcomes. Although a growing literature is documenting how early life socioeconomic position affects later life health, few epidemiologic studies have tested measures for operationalizing early life neighborhood context, or examined their effects on later life health. In the Life-course Influences on Fetal Environments (LIFE) Study, a retrospective cohort study among Black women in Southfield, Michigan (71% response rate), we tested the validity and reliability of retrospectively-reported survey-based subjective measures of early life neighborhood context(N=693). We compared 3 subjective childhood neighborhood measures (disorder, informal social control, victimization), with 3 objective childhood neighborhood measures derived from 4 decades of historical census tract data 1970-2000, linked through geocoded residential histories (tract % poverty, tract % black, tract deprivation score derived from principal components analysis), as well as with 2 subjective neighborhood measures in adulthood. Our results documented that internal consistency reliability was high for the subjective childhood neighborhood scales (Cronbach's ? =0.89, 0.93). Comparison of subjective with objective childhood neighborhood measures found moderate associations in hypothesized directions. Associations with objective variables were strongest for neighborhood disorder (rhos=.40), as opposed to with social control or victimization. Associations between subjective neighborhood context in childhood versus adulthood were moderate and stronger for residentially-stable populations. We lastly formally tested for, but found little evidence of, recall bias of the retrospective subjective reports of childhood context. These results provide evidence that retrospective reports of subjective neighborhood context may be a cost-effective, valid, and reliable method to operationalize early life context for health studies.
| S-EPMC4405544 | biostudies-literature
Project description:OBJECTIVES:We previously developed a historical reconstruction model to estimate exposure to airborne polycyclic aromatic hydrocarbons (PAHs) from traffic back to 1960 for use in case-control studies of breast cancer risk. Here we report the results of four exercises to validate and calibrate the model. METHODS:Model predictions of benzo[a]pyrene (BaP) concentration in soil and carpet dust were tested against measurements collected at subjects' homes at interview. In addition, predictions of air intake of BaP were compared with blood PAH-DNA adducts. These same soil, carpet, and blood measurements were used for model optimization. In a separate test of the meteorological dispersion part of the model, predictions of hourly concentrations of carbon monoxide from traffic were compared with data collected at a U.S. Environmental Protection Agency monitoring station. RESULTS:The data for soil, PAH-DNA adducts, and carbon monoxide concentrations were all consistent with model predictions. The carpet dust data were inconsistent, suggesting possible spatial confounding with PAH-containing contamination tracked in from outdoors or unmodeled cooking sources. BaP was found proportional to other PAHs in our soil and dust data, making it reasonable to use BaP historical data as a surrogate for other PAHs. Road intersections contributed 40-80% of both total emissions and average exposures, suggesting that the repertoire of simple markers of exposure, such as traffic counts and/or distance to nearest road, needs to be expanded to include distance to nearest intersection.
| S-EPMC1513337 | biostudies-literature
Project description:Despite methodological advances in the field of economic evaluations of interventions, economic evaluations of obesity prevention programmes in early childhood are seldom conducted. The aim of the present study was to explore existing methods and applications of economic evaluations, examining their limitations and making recommendations for future cost-effectiveness assessments. A systematic literature search was conducted using PubMed, Cochrane Library, the British National Health Service Economic Evaluation Databases and EconLit. Eligible studies included trial-based or simulation-based cost-effectiveness analyses of obesity prevention programmes targeting preschool children and/or their parents. The quality of included studies was assessed. Of the six studies included, five were intervention studies and one was based on a simulation approach conducted on secondary data. We identified three main conceptual and methodological limitations of their economic evaluations: Insufficient conceptual approach considering the complexity of childhood obesity, inadequate measurement of effects of interventions, and lack of valid instruments to measure child-related quality of life and costs. Despite the need for economic evaluations of obesity prevention programmes in early childhood, only a few studies of varying quality have been conducted. Moreover, due to methodological and conceptual weaknesses, they offer only limited information for policy makers and intervention providers. We elaborate reasons for the limitations of these studies and offer guidance for designing better economic evaluations of early obesity prevention.
| S-EPMC5036744 | biostudies-literature
Project description:ObjectiveTo investigate whether workplace exposures to recognized lymphohematopoietic carcinogens were possibly related to cancers in six semiconductor-manufacturing workers.MethodsA job-exposure matrix was developed for chemical and physical process agents and anticipated by-products. Potential cumulative occupational exposures of the six cases were reconstructed. The role of workplace exposures in cancer was evaluated through quantitative risk assessment and by comparison with epidemiological literature.ResultsTwo workers were potentially exposed to agents capable of causing their diagnosed cancers. Reconstructed exposures were similar to levels in outdoor environments and lower than exposures associated with increased risks in epidemiological studies. Cancer risks were estimated to be less than 1 in 10,000 persons.ConclusionsThe development of cancer among the six workers was unlikely to be explained by occupational exposures to recognized lymphohematopoietic carcinogens.
| S-EPMC4448668 | biostudies-literature
Project description:BackgroundEnvironmental epidemiology, when focused on the life course of exposure to a specific pollutant, requires historical exposure estimates that are difficult to obtain for the full time period due to gaps in the historical record, especially in earlier years. We show that these gaps can be filled by applying multiple imputation methods to a formal risk equation that incorporates lifetime exposure. We also address challenges that arise, including choice of imputation method, potential bias in regression coefficients, and uncertainty in age-at-exposure sensitivities.MethodsDuring time periods when parameters needed in the risk equation are missing for an individual, the parameters are filled by an imputation model using group level information or interpolation. A random component is added to match the variance found in the estimates for study subjects not needing imputation. The process is repeated to obtain multiple data sets, whose regressions against health data can be combined statistically to develop confidence limits using Rubin's rules to account for the uncertainty introduced by the imputations. To test for possible recall bias between cases and controls, which can occur when historical residence location is obtained by interview, and which can lead to misclassification of imputed exposure by disease status, we introduce an "incompleteness index," equal to the percentage of dose imputed (PDI) for a subject. "Effective doses" can be computed using different functional dependencies of relative risk on age of exposure, allowing intercomparison of different risk models. To illustrate our approach, we quantify lifetime exposure (dose) from traffic air pollution in an established case-control study on Long Island, New York, where considerable in-migration occurred over a period of many decades.ResultsThe major result is the described approach to imputation. The illustrative example revealed potential recall bias, suggesting that regressions against health data should be done as a function of PDI to check for consistency of results. The 1% of study subjects who lived for long durations near heavily trafficked intersections, had very high cumulative exposures. Thus, imputation methods must be designed to reproduce non-standard distributions.ConclusionsOur approach meets a number of methodological challenges to extending historical exposure reconstruction over a lifetime and shows promise for environmental epidemiology. Application to assessment of breast cancer risks will be reported in a subsequent manuscript.
| S-EPMC3751034 | biostudies-literature
Project description:BackgroundMediation analysis methodology underwent many advancements throughout the years, with the most recent and important advancement being the development of causal mediation analysis based on the counterfactual framework. However, a previous review showed that for experimental studies the uptake of causal mediation analysis remains low. The aim of this paper is to review the methodological characteristics of mediation analyses performed in observational epidemiologic studies published between 2015 and 2019 and to provide recommendations for the application of mediation analysis in future studies.MethodsWe searched the MEDLINE and EMBASE databases for observational epidemiologic studies published between 2015 and 2019 in which mediation analysis was applied as one of the primary analysis methods. Information was extracted on the characteristics of the mediation model and the applied mediation analysis method.ResultsWe included 174 studies, most of which applied traditional mediation analysis methods (n = 123, 70.7%). Causal mediation analysis was not often used to analyze more complicated mediation models, such as multiple mediator models. Most studies adjusted their analyses for measured confounders, but did not perform sensitivity analyses for unmeasured confounders and did not assess the presence of an exposure-mediator interaction.ConclusionsTo ensure a causal interpretation of the effect estimates in the mediation model, we recommend that researchers use causal mediation analysis and assess the plausibility of the causal assumptions. The uptake of causal mediation analysis can be enhanced through tutorial papers that demonstrate the application of causal mediation analysis, and through the development of software packages that facilitate the causal mediation analysis of relatively complicated mediation models.
| S-EPMC8543973 | biostudies-literature
Project description:PurposeAir pollution epidemiology traditionally focuses on the relationship between individual air pollutants and health outcomes (e.g., mortality). To account for potential copollutant confounding, individual pollutant associations are often estimated by adjusting or controlling for other pollutants in the mixture. Recently, the need to characterize the relationship between health outcomes and the larger multipollutant mixture has been emphasized in an attempt to better protect public health and inform more sustainable air quality management decisions.MethodsNew and innovative statistical methods to examine multipollutant exposures were identified through a broad literature search, with a specific focus on those statistical approaches currently used in epidemiologic studies of short-term exposures to criteria air pollutants (i.e., particulate matter, carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone).ResultsFive broad classes of statistical approaches were identified for examining associations between short-term multipollutant exposures and health outcomes, specifically additive main effects, effect measure modification, unsupervised dimension reduction, supervised dimension reduction, and nonparametric methods. These approaches are characterized including advantages and limitations in different epidemiologic scenarios.DiscussionBy highlighting the characteristics of various studies in which multipollutant statistical methods have been used, this review provides epidemiologists and biostatisticians with a resource to aid in the selection of the most optimal statistical method to use when examining multipollutant exposures.
| S-EPMC5313327 | biostudies-literature