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Assessing risk of bias in human environmental epidemiology studies using three tools: different conclusions from different tools.

ABSTRACT: BACKGROUND:Systematic reviews are increasingly prevalent in environmental health due to their ability to synthesize evidence while reducing bias. Different systematic review methods have been developed by the US National Toxicology Program's Office of Health Assessment and Translation (OHAT), the US Environmental Protection Agency's (EPA) Integrated Risk Information System (IRIS), and by the US EPA under the Toxic Substances Control Act (TSCA), including the approach to assess risk of bias (ROB), one of the most vital steps which is used to evaluate internal validity of the studies. Our objective was to compare the performance of three tools (OHAT, IRIS, TSCA) in assessing ROB. METHODS:We selected a systematic review on polybrominated diphenyl ethers and intelligence quotient and/or attention deficit hyperactivity disorder because it had been endorsed by the National Academy of Sciences. Two reviewers followed verbatim instructions from the tools and independently applied each tool to assess ROB in 15 studies previously identified. We documented the time to apply each tool and the impact the ROB ratings for each tool had on the final rating of the quality of the overall body of evidence. RESULTS:The time to complete the ROB assessments varied widely (mean = 20, 32, and 40?min per study for the OHAT, IRIS, and TSCA tools, respectively). All studies were rated overall "low" or "uninformative" using IRIS, due to "deficient" or "critically deficient" ratings in one or two domains. Similarly, all studies were rated "unacceptable" using the TSCA tool because of one "unacceptable" rating in a metric related to statistical power. Approximately half of the studies had "low" or "probably low ROB" ratings across all domains with the OHAT and Navigation Guide tools. CONCLUSIONS:Tools that use overall ROB or study quality ratings, such as IRIS and TSCA, may reduce the available evidence to assess the harms of environmental exposures by erroneously excluding studies, which leads to inaccurate conclusions about the quality of the body of evidence. We recommend using ROB tools that circumvents these issues, such as OHAT and Navigation Guide. SYSTEMATIC REVIEW REGISTRATION:This review has not been registered as it is not a systematic review.

SUBMITTER: Eick SM

PROVIDER: S-EPMC7596989 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Assessing risk of bias in human environmental epidemiology studies using three tools: different conclusions from different tools.

Eick Stephanie M SM Goin Dana E DE Chartres Nicholas N Lam Juleen J Woodruff Tracey J TJ

Systematic reviews 20201029 1

<h4>Background</h4>Systematic reviews are increasingly prevalent in environmental health due to their ability to synthesize evidence while reducing bias. Different systematic review methods have been developed by the US National Toxicology Program's Office of Health Assessment and Translation (OHAT), the US Environmental Protection Agency's (EPA) Integrated Risk Information System (IRIS), and by the US EPA under the Toxic Substances Control Act (TSCA), including the approach to assess risk of bi ...[more]

PMID: 33121530

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Project description:PurposeThere is limited knowledge on the reliability of risk of bias (ROB) tools for assessing internal validity in systematic reviews of exposure and frequency studies. We aimed to identify and then compare the inter-rater reliability (IRR) of six commonly used tools for frequency (Loney scale, Gyorkos checklist, American Academy of Neurology [AAN] tool) and exposure (Newcastle-Ottawa scale, SIGN50 checklist, AAN tool) studies.MethodsSix raters independently assessed the ROB of 30 frequency and 30 exposure studies using the three respective ROB tools. Articles were rated as low, intermediate, or high ROB. We calculated an intraclass correlation coefficient (ICC) for each tool and category of ROB tool. We compared the IRR between ROB tools and tool type by inspection of overlapping ICC 95% CIs and by comparing their coefficients after transformation to Fisher's Z values. We assessed the criterion validity of the AAN ROB tools by calculating an ICC for each rater in comparison with the original ratings from the AAN.ResultsAll individual ROB tools had an IRR in the substantial range or higher (ICC point estimates between 0.61 and 0.80). The IRR was almost perfect (ICC point estimate > 0.80) for the AAN frequency tool and the SIGN50 checklist. All tools were comparable in IRR, except for the AAN frequency tool which had a significantly higher ICC than the Gyorkos checklist (p = 0.021) and trended towards a higher ICC when compared to the Loney scale (p = 0.085). When examined by category of ROB tool, scales, and checklists had a substantial IRR, whereas the AAN tools had an almost perfect IRR. For the criterion validity of the AAN ROB tools, the average agreement between our raters and the original AAN ratings was moderate.ConclusionAll tools had substantial IRRs except for the AAN frequency tool and the SIGN50 checklist, which both had an almost perfect IRR. The AAN ROB tools were the only category of ROB tools to demonstrate an almost perfect IRR. This category of ROB tools had fewer and simpler criteria. Overall, parsimonious tools with clear instructions, such as those from the AAN, may provide more reliable ROB assessments.

Project description:BackgroundDietary exposure assessments are a critical issue in evaluating human nutrition studies; however, nutrition-specific criteria are not consistently included in existing bias assessment tools.ObjectivesOur objective was to develop a set of risk of bias (RoB) tools that integrated nutrition-specific criteria into validated generic assessment tools to address RoB issues, including those specific to dietary exposure assessment.MethodsThe Nutrition QUality Evaluation Strengthening Tools (NUQUEST) development and validation process included 8 steps. The first steps identified 1) a development strategy; 2) generic assessment tools with demonstrated validity; and 3) nutrition-specific appraisal issues. This was followed by 4) generation of nutrition-specific items and 5) development of guidance to aid users of NUQUEST. The final steps used established ratings of selected studies and feedback from independent raters to 6) assess reliability and validity; 7) assess formatting and usability; and 8) finalize NUQUEST.ResultsNUQUEST is based on the Scottish Intercollegiate Guidelines Network checklists for randomized controlled trials, cohort studies, and case-control studies. Using a purposive sample of 45 studies representing the 3 study designs, interrater reliability was high (Cohen's κ: 0.73; 95% CI: 0.52, 0.93) across all tools and at least moderate for individual tools (range: 0.57-1.00). The use of a worksheet improved usability and consistency of overall interrater agreement across all study designs (40% without worksheet, 80%-100% with worksheet). When compared to published ratings, NUQUEST ratings for evaluated studies demonstrated high concurrent validity (93% perfect or near-perfect agreement). Where there was disagreement, the nutrition-specific component was a contributing factor in discerning exposure methodological issues.ConclusionsNUQUEST integrates nutrition-specific criteria with generic criteria from assessment tools with demonstrated reliability and validity. NUQUEST represents a consistent and transparent approach for evaluating RoB issues related to dietary exposure assessment commonly encountered in human nutrition studies.

Project description:BackgroundEvidence based medicine aims to integrate scientific evidence, clinical experience, and patient values and preferences. Individual health care professionals need to appraise the evidence from randomized trials and observational studies when guidelines are not yet available. To date, tools for assessment of bias and terminologies for bias are specific for each study design. Moreover, most tools appeal only to methodological knowledge to detect bias, not to subject matter knowledge, i.e. in-depth medical knowledge about a topic. We propose a unified framework that enables the coherent assessment of bias across designs.MethodsEpidemiologists traditionally distinguish between three types of bias in observational studies: confounding, information bias, and selection bias. These biases result from a common cause, systematic error in the measurement or common effect of the intervention and outcome respectively. We applied this conceptual framework to randomized trials and show how it can be used to identify bias. The three sources of bias were illustrated with graphs that visually represent researchers' assumptions about the relationships between the investigated variables (causal diagrams).ResultsCritical appraisal of evidence started with the definition of the research question in terms of the population of interest, the compared interventions and the main outcome. Next, we used causal diagrams to illustrate how each source of bias can lead to over- or underestimated treatment effects. Then, we discussed how randomization, blinded outcome measurement and intention-to-treat analysis minimize bias in trials. Finally, we identified study aspects that can only be appraised with subject matter knowledge, irrespective of study design.ConclusionsThe unified framework encompassed the three main sources of bias for the effect of an assigned intervention on an outcome. It facilitated the integration of methodological and subject matter knowledge in the assessment of bias. We hope that graphical diagrams will help clarify debate among professionals by reducing misunderstandings based on different terminology for bias.

Project description:Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this can undermine statistical inference. In other disciplines, it is common for researchers to complete 'risk-of-bias' assessments to expose and document the potential for biases to undermine conclusions. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar assessments are urgently needed in ecology.We introduce ROBITT, a structured tool for assessing the 'Risk-Of-Bias In studies of Temporal Trends in ecology'. ROBITT has a similar format to its counterparts in other disciplines: it comprises signalling questions designed to elicit information on the potential for bias in key study domains. In answering these, users will define study inferential goal(s) and relevant statistical target populations. This information is used to assess potential sampling biases across domains relevant to the research question (e.g. geography, taxonomy, environment), and how these vary through time. If assessments indicate biases, then users must clearly describe them and/or explain what mitigating action will be taken.Everything that users need to complete a ROBITT assessment is provided: the tool, a guidance document and a worked example. Following other disciplines, the tool and guidance document were developed through a consensus-forming process across experts working in relevant areas of ecology and evidence synthesis.We propose that researchers should be strongly encouraged to include a ROBITT assessment when publishing studies of biodiversity trends, especially when using aggregated data. This will help researchers to structure their thinking, clearly acknowledge potential sampling issues, highlight where expert consultation is required and provide an opportunity to describe data checks that might go unreported. ROBITT will also enable reviewers, editors and readers to establish how well research conclusions are supported given a dataset combined with some analytical approach. In turn, it should strengthen evidence-based policy and practice, reduce differing interpretations of data and provide a clearer picture of the uncertainties associated with our understanding of reality.

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Assessing risk of bias in human environmental epidemiology studies using three tools: different conclusions from different tools.

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Assessing risk of bias in human environmental epidemiology studies using three tools: different conclusions from different tools.

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