Asthma caused by occupational exposures is common – A systematic analysis of estimates of the population-attributable fraction

<p>Abstract</p> <p>Background</p> <p>The aim of this paper is to highlight emerging data on occupational attributable risk in asthma. Despite well documented outbreaks of disease and the recognition of numerous specific causal agents, occupational exposures previously had been relegated a fairly minor role relative to other causes of adult onset asthma. In recent years there has been a growing recognition of the potential importance of asthma induced by work-related exposures</p> <p>Methods</p> <p>We searched Pub Med from June 1999 through December 2007. We identified six longitudinal general population-based studies; three case-control studies and eight cross-sectional analyses from seven general population-based samples. For an integrated analysis we added ten estimates prior to 1999 included in a previous review.</p> <p>Results</p> <p>The longitudinal studies indicate that 16.3% of all adult-onset asthma is caused by occupational exposures. In an overall synthesis of all included studies the overall median PAR value was 17.6%.</p> <p>Conclusion</p> <p>Clinicians should consider the occupational history when evaluating patients in working age who have asthma. At a societal level, these findings underscore the need for further preventive action to reduce the occupational exposures to asthma-causing agents.</p

Approches d’analyse causale en épidémiologie

Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, "causal models", though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the formulation of causal hypotheses, which will be a basis for all methodological choices. Beyond this step, statistical analysis tools recently developed offer new possibilities to delineate complex relationships, in particular in life course epidemiology

Indoor air and respiratory health: volatile organic compounds and cleaning products.

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