PubMed · 10871964
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L R Wulsin. 2000-06-26. Does depression kill?. https://doi.org/10.1001/archinte.160.12.1731
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We present a comprehensive analysis of the sensitivity of mercury (Hg) human exposure to environmental variables using a multimedia model of the fate and transport of Hg in the environment. The results of the analysis show that the Hg dose is most sensitive to the lake pH, the burial rate of Hg adsorbed to sediments, and the chemical speciation of Hg emissions to the atmosphere. The lake pH has a strong non-linear effect on the methylation rate and bioaccumulation of Hg in fish. The burial of sediments is a major pathway for removing Hg from the lake cycling. The speciation of Hg emissions is important because Hg(II) is deposited much more rapidly than Hg(0). These results highlight the importance of key variables that should be investigated through well-designed field programs, so that we can minimize the overall uncertainties associated with the modeling of mercury fate and transport.
The generalizability of intervention research is dependent of the degree of success in including patients who represent the disease category for which the intervention might be considered. To be relevant for medical practice, estimates of intervention effects need to be quantitatively generalizable. This requires specification of the characteristics of the intervention (e.g. dosage and timing) and of the disease outcome, and of socalled effect-modifying factors: disease severity, co-interventions and the characteristics of the patients which influence the magnitude of the intervention effect. Literature remains less clear about the necessity to specify these effect-modifying factors. If effect modification is seriously considered, the investigator must decide either to design the trial in such a way that the subgroup-specific effects may be estimated, or to restrict the study to one homogeneous stratum of patients. Despite many theoretical reasons and some empirical examples of substantial effect modification by characteristics of patients, systematic comparison of randomised (selected populations) and non-randomised studies (less selection) fail to show many differences. This suggests that differences between subgroups are not very frequent and that trial results, provided they are adequately specified, generally do have a high degree of generalizability.
Knowledge concerning aetiologic hypotheses can be obtained through systematic reviews of observational studies. In observational studies, heterogeneity between studies is expected, because of differences in study design and analysis. Data from observational studies have to be made comparable before pooling of results from component studies is possible. In this, definition of exposure and disease and adjustment for confounding have to be taken into account. Three methods to deal with heterogeneity are presented: ignoring (fixed effects model), modelling (random effects model) and exploring (meta regression). Any combination of data in meta-analysis of observational studies has to be based on statistical, methodological and clinical considerations. The same considerations are applied for the choice which method is used.