The global burden of occupational disease

Purpose of Review: Burden of occupational disease estimation contributes to understanding of both magnitude and relative importance of different occupational hazards and provides essential information for targeting risk reduction. This review summarises recent key findings and discusses their impact on occupational regulation and practice. Recent Findings: New methods have been developed to estimate burden of occupational disease that take account of the latency of many chronic diseases and allow for exposure trends and workforce turnover. Results from these studies have shown in several countries and globally that, in spite of improvements in workplace technology, practices and exposures over the last decades, occupational hazards remain an important cause of ill health and mortality worldwide. Summary: Major data gaps have been identified particularly regarding exposure information. Reliable data on employment and disease are also lacking especially in developing countries. Burden of occupational disease estimates form an important part of decision-making processes

Estimating the burden of occupational cancer: assessing bias and uncertainty.

BACKGROUND AND OBJECTIVES: We aimed to estimate credibility intervals for the British occupational cancer burden to account for bias uncertainty, using a method adapted from Greenland's Monte Carlo sensitivity analysis. METHODS: The attributable fraction (AF) methodology used for our cancer burden estimates requires risk estimates and population proportions exposed for each agent/cancer pair. Sources of bias operating on AF estimator components include non-portability of risk estimates, inadequate models, inaccurate data including unknown cancer latency and employment turnover and compromises in using the available estimators. Each source of bias operates on a component of the AF estimator. Independent prior distributions were estimated for each bias, or graphical sensitivity analysis was used to identify plausible distribution ranges for the component variables, with AF recalculated following Monte Carlo repeated sampling from these distributions. The methods are illustrated using the example of lung cancer due to occupational exposure to respirable crystalline silica in men. RESULTS: Results are presented graphically for a hierarchy of biases contributing to an overall credibility interval for lung cancer and respirable crystalline silica exposure. An overall credibility interval of 2.0% to 16.2% was estimated for an AF of 3.9% in men. Choice of relative risk and employment turnover were shown to contribute most to overall estimate uncertainty. Bias from using an incorrect estimator makes a much lower contribution. CONCLUSIONS: The method illustrates the use of credibility intervals to indicate relative contributions of important sources of uncertainty and identifies important data gaps; results depend greatly on the priors chosen

Patients with ALS show highly correlated progression rates in left and right limb muscles.

ObjectiveAmyotrophic lateral sclerosis (ALS) progresses at different rates between patients, making clinical trial design difficult and dependent on large cohorts of patients. Currently, there are few data showing whether the left and right limbs progress at the same or different rates. This study addresses rates of decline in specific muscle groups of patients with ALS and assesses whether there is a relationship between left and right muscles in the same patient, regardless of overall progression.MethodsA large cohort of patients was used to assess decline in muscle strength in right and left limbs over time using 2 different methods: The Tufts Quantitative Neuromuscular Exam and Accurate Test of Limb Isometric Strength protocol. Then advanced linear regression statistical methods were applied to assess progression rates in each limb.ResultsThis report shows that linearized progression models can predict general slopes of decline with good accuracy. Critically, the data demonstrate that while overall decline is variable, there is a high degree of correlation between left and right muscle decline in ALS. This implies that irrespective of which muscle starts declining soonest or latest, their rates of decline following onset are more consistent.ConclusionsFirst, this study demonstrates a high degree of power when using unilateral treatment approaches to detect a slowing in disease progression in smaller groups of patients, thus allowing for paired statistical tests. These findings will be useful in transplantation trials that use muscle decline to track disease progression in ALS. Second, these findings discuss methods, such as tactical selection of muscle groups, which can improve the power efficiency of all ALS clinical trials

Robust Bayesian sensitivity analysis for case-control studies with uncertain exposure misclassification probabilities

Exposure misclassification in case–control studies leads to bias in odds ratio estimates. There has been considerable interest recently to account for misclassification in estimation so as to adjust for bias as well as more accurately quantify uncertainty. These methods require users to elicit suitable values or prior distributions for the misclassification probabilities. In the event where exposure misclassification is highly uncertain, these methods are of limited use, because the resulting posterior uncertainty intervals tend to be too wide to be informative. Posterior inference also becomes very dependent on the subjectively elicited prior distribution. In this paper, we propose an alternative “robust Bayesian” approach, where instead of eliciting prior distributions for the misclassification probabilities, a feasible region is given. The extrema of posterior inference within the region are sought using an inequality constrained optimization algorithm. This method enables sensitivity analyses to be conducted in a useful way as we do not need to restrict all of our unknown parameters to fixed values, but can instead consider ranges of values at a time.published_or_final_versio