Assessor burden, inter-rater agreement and user experience of the RoB-SPEO tool for assessing risk of bias in studies estimating prevalence of exposure to occupational risk factors: An analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury

Protocol for designing INVITES-IN, a tool for assessing the internal validity of in vitro studies

This protocol describes the design and development of a tool for evaluation of the internal validity of in vitro studies, which is needed to include the data as evidence in systematic reviews and chemical risk assessments. The tool will be designed specifically to be applied to cell culture studies, including, but not restricted to, studies meeting the new approach methodology (NAM) definition. The tool is called INVITES-IN (IN VITro Experimental Studies INternal validity). In this protocol, three of the four studies that will be performed to create the release version of INVITES-IN are described. In the first study, evaluation of existing assessment tools will be combined with focus group discussions to identify how characteristics of the design or conduct of an in vitro study can affect its internal validity. Bias domains and items considered to be of relevance for in vitro studies will be identified. In the second study, group agreement on internal validity domains and items of importance for in vitro studies will be identified via a modified Delphi methodology. In the third study, the draft version of the tool will be created, based on the data on relevance and importance of bias domains and items collected in Studies 1 and 2. A separate protocol will be prepared for the fourth study, which includes the user testing and validation of the tool, and collection of users' experience.

Inter-Rater Agreement in Assessing Risk of Bias in Melanoma Prediction Studies Using the Prediction Model Risk of Bias Assessment Tool (PROBAST): Results from a Controlled Experiment on the Effect of Specific Rater Training

Assessing the risk of bias (ROB) of studies is an important part of the conduct of systematic reviews and meta-analyses in clinical medicine. Among the many existing ROB tools, the Prediction Model Risk of Bias Assessment Tool (PROBAST) is a rather new instrument specifically designed to assess the ROB of prediction studies. In our study we analyzed the inter-rater reliability (IRR) of PROBAST and the effect of specialized training on the IRR. Six raters independently assessed the risk of bias (ROB) of all melanoma risk prediction studies published until 2021 (n = 42) using the PROBAST instrument. The raters evaluated the ROB of the first 20 studies without any guidance other than the published PROBAST literature. The remaining 22 studies were assessed after receiving customized training and guidance. Gwet's AC₁ was used as the primary measure to quantify the pairwise and multi-rater IRR. Depending on the PROBAST domain, results before training showed a slight to moderate IRR (multi-rater AC₁ ranging from 0.071 to 0.535). After training, the multi-rater AC₁ ranged from 0.294 to 0.780 with a significant improvement for the overall ROB rating and two of the four domains. The largest net gain was achieved in the overall ROB rating (difference in multi-rater AC₁: 0.405, 95%-CI 0.149-0.630). In conclusion, without targeted guidance, the IRR of PROBAST is low, questioning its use as an appropriate ROB instrument for prediction studies. Intensive training and guidance manuals with context-specific decision rules are needed to correctly apply and interpret the PROBAST instrument and to ensure consistency of ROB ratings.

Estimating the population exposed to a risk factor over a time window: A microsimulation modelling approach from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury

OBJECTIVES

Burden of disease estimation commonly requires estimates of the population exposed to a risk factor over a time window (yeart to yeart+n). We present a microsimulation modelling approach for producing such estimates and apply it to calculate the population exposed to long working hours for one country (Italy).

METHODS

We developed a three-model approach: Model 1, a multilevel model, estimates exposure to the risk factor at the first year of the time window (yeart). Model 2, a regression model, estimates transition probabilities between exposure categories during the time window (yeart to yeart+n). Model 3, a microsimulation model, estimates the exposed population over the time window, using the Monte Carlo method. The microsimulation is carried out in three steps: (a) a representative synthetic population is initiated in the first year of the time window using prevalence estimates from Model 1, (b) the exposed population is simulated over the time window using the transition probabilities from Model 2; and (c) the population is censored for deaths during the time window.

RESULTS

We estimated the population exposed to long working hours (i.e. 41-48, 49-54 and ≥55 hours/week) over a 10-year time window (2002-11) in Italy. We populated all three models with official data from Labour Force Surveys, United Nations population estimates and World Health Organization life tables. Estimates were produced of populations exposed over the time window, disaggregated by sex and 5-year age group.

CONCLUSIONS

Our modelling approach for estimating the population exposed to a risk factor over a time window is simple, versatile, and flexible. It however requires longitudinal exposure data and Model 3 (the microsimulation model) is stochastic. The approach can improve accuracy and transparency in exposure and burden of disease estimations. To improve the approach, a logical next step is changing Model 3 to a deterministic microsimulation method, such as modelling of microflows.

Towards a framework for systematic reviews of the prevalence of exposure to environmental and occupational risk factors

Exposure prevalence studies (as here defined) record the prevalence of exposure to environmental and occupational risk factors to human health. Applying systematic review methods to the synthesis of these studies would improve the rigour and transparency of normative products produced based on this evidence (e.g., exposure prevalence estimates). However, a dedicated framework, including standard methods and tools, for systematically reviewing exposure prevalence studies has yet to be created. We describe the need for this framework and progress made towards it through a series of such systematic reviews that the World Health Organization and the International Labour Organization conducted for their WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury (WHO/ILO Joint Estimates).We explain that existing systematic review frameworks for environmental and occupational health cannot be directly applied for the generation of exposure prevalence estimates because they seek to synthesise different types of evidence (e.g., intervention or exposure effects on health) for different purposes (e.g., identify intervention effectiveness or exposure toxicity or carcinogenicity). Concepts unique to exposure prevalence studies (e.g., "expected heterogeneity": the real, non-spurious variability in exposure prevalence due to exposure changes over space and/or time) also require new assessment methods. A framework for systematic reviews of prevalence of environmental and occupational exposures requires adaptation of existing methods (e.g., a standard protocol) and development of new tools or approaches (e.g., for assessing risk of bias and certainty of a body of evidence, including exploration of expected heterogeneity).As part of the series of systematic reviews for the WHO/ILO Joint Estimates, the World Health Organization collaborating with partners has created a preliminary framework for systematic reviews of prevalence studies of exposures to occupational risk factors. This included development of protocol templates, data extraction templates, a risk of bias assessment tool, and an approach for assessing certainty of evidence in these studies. Further attention and efforts are warranted from scientific and policy communities, especially exposure scientists and policy makers, to establish a standard framework for comprehensive and transparent systematic reviews of studies estimating prevalence of exposure to environmental and occupational risk factors, to improve estimates, risk assessments and guidelines.

Assessing the quality of evidence in studies estimating prevalence of exposure to occupational risk factors: The QoE-SPEO approach applied in the systematic reviews from the WHO/ILO Joint Estimates of the Work-related burden of disease and Injury

BACKGROUND

The World Health Organization (WHO) and the International Labour Organization (ILO) have produced the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury (WHO/ILO Joint Estimates). For these, systematic reviews of studies estimating the prevalence of exposure to selected occupational risk factors have been conducted to provide input data for estimations of the number of exposed workers. A critical part of systematic review methodology is to assess the quality of evidence across studies. In this article, we present the approach applied in these WHO/ILO systematic reviews for performing such assessments on studies of prevalence of exposure. It is called the Quality of Evidence in Studies estimating Prevalence of Exposure to Occupational risk factors (QoE-SPEO) approach. We describe QoE-SPEO's development to date, demonstrate its feasibility reporting results from pilot testing and case studies, note its strengths and limitations, and suggest how QoE-SPEO should be tested and developed further.

METHODS

Following a comprehensive literature review, and using expert opinion, selected existing quality of evidence assessment approaches used in environmental and occupational health were reviewed and analysed for their relevance to prevalence studies. Relevant steps and components from the existing approaches were adopted or adapted for QoE-SPEO. New steps and components were developed. We elicited feedback from other systematic review methodologists and exposure scientists and reached consensus on the QoE-SPEO approach. Ten individual experts pilot-tested QoE-SPEO. To assess inter-rater agreement, we counted ratings of expected (actual and non-spurious) heterogeneity and quality of evidence and calculated a raw measure of agreement (Pi) between individual raters and rater teams for the downgrade domains. Pi ranged between 0.00 (no two pilot testers selected the same rating) and 1.00 (all pilot testers selected the same rating). Case studies were conducted of experiences of QoE-SPEO's use in two WHO/ILO systematic reviews.

RESULTS

We found no existing quality of evidence assessment approach for occupational exposure prevalence studies. We identified three relevant, existing approaches for environmental and occupational health studies of the effect of exposures. Assessments using QoE-SPEO comprise three steps: (1) judge the level of expected heterogeneity (defined as non-spurious variability that can be expected in exposure prevalence, within or between individual persons, because exposure may change over space and/or time), (2) assess downgrade domains, and (3) reach a final rating on the quality of evidence. Assessments are conducted using the same five downgrade domains as the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach: (a) risk of bias, (b) indirectness, (c) inconsistency, (d) imprecision, and (e) publication bias. For downgrade domains (c) and (d), the assessment varies depending on the level of expected heterogeneity. There are no upgrade domains. The QoE-SPEO's ratings are "very low", "low", "moderate", and "high". To arrive at a final decision on the overall quality of evidence, the assessor starts at "high" quality of evidence and for each domain downgrades by one or two levels for serious concerns or very serious concerns, respectively. In pilot tests, there was reasonable agreement in ratings for expected heterogeneity; 70% of raters selected the same rating. Inter-rater agreement ranged considerably between downgrade domains, both for individual rater pairs (range Pi: 0.36-1.00) and rater teams (0.20-1.00). Sparse data prevented rigorous assessment of inter-rater agreement in quality of evidence ratings.

CONCLUSIONS

We present QoE-SPEO as an approach for assessing quality of evidence in prevalence studies of exposure to occupational risk factors. It has been developed to its current version (as presented here), has undergone pilot testing, and was applied in the systematic reviews for the WHO/ILO Joint Estimates. While the approach requires further testing and development, it makes steps towards filling an identified gap, and progress made so far can be used to inform future work in this area.