Estimation of health effects of prenatal methylmercury exposure using structural equation models

BACKGROUND: Observational studies in epidemiology always involve concerns regarding validity, especially measurement error, confounding, missing data, and other problems that may affect the study outcomes. Widely used standard statistical techniques, such as multiple regression analysis, may to some extent adjust for these shortcomings. However, structural equations may incorporate most of these considerations, thereby providing overall adjusted estimations of associations. This approach was used in a large epidemiological data set from a prospective study of developmental methyl-mercury toxicity. RESULTS: Structural equation models were developed for assessment of the association between biomarkers of prenatal mercury exposure and neuropsychological test scores in 7 year old children. Eleven neurobehavioral outcomes were grouped into motor function and verbally mediated function. Adjustment for local dependence and item bias was necessary for a satisfactory fit of the model, but had little impact on the estimated mercury effects. The mercury effect on the two latent neurobehavioral functions was similar to the strongest effects seen for individual test scores of motor function and verbal skills. Adjustment for contaminant exposure to poly chlorinated biphenyls (PCBs) changed the estimates only marginally, but the mercury effect could be reduced to non-significance by assuming a large measurement error for the PCB biomarker. CONCLUSIONS: The structural equation analysis allows correction for measurement error in exposure variables, incorporation of multiple outcomes and incomplete cases. This approach therefore deserves to be applied more frequently in the analysis of complex epidemiological data sets

The exposure metric: does including time since exposure in the calculation of working lifetime exposure provide a better understanding of disease risk than the cumulative exposure?

BACKGROUND: When exposure measurements are available for occupational epidemiology studies, the cumulative exposure (the sum of the products of duration and exposure intensity at all jobs) is generally selected as the summary metric for chronic diseases. For silica exposures, a metric that weights each exposure by the number of years since it occurred has been suggested as more biologically relevant. Comparative reports of analyses using both metrics have not been found in the literature, however. METHODS: We calculated both metrics for silica exposure, and evaluated exposure-response relations for lung cancer and silicosis in two separate case-control studies. RESULTS: Generally the results were consistent, due to the high correlation between the two metrics and the fact that the rate of time away from work during the employment years was low. CONCLUSION: The significant relation between exposure and silicosis using the weighted metric provides additional point estimates of risk, adding to the understanding of exposure-response

Comparison of sensitivity and timing of early signal detection of four frequently used signal detection methods: An empirical study based on the US FDA adverse event reporting system database

Background: There are limited published comparative data regarding the sensitivity and timing of early signal detection with commonly used signal detection methods (SDMs), including the reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC) and gamma Poisson shrinker (GPS). Objective: To examine the sensitivity and timing of early signal detection across four SDMs using the Adverse Events Reporting System (AERS) database of the US Food and Drug Administration. Methods: The four SDMs were applied to retrospectively detect ten confirmed drug-event combinations (DECs). The sensitivity to detect adverse events was defined as the percentage of DECs detected by the respective SDMs as positive signals. The timing of early signal detection was measured by comparing the index date of withdrawal (IDW), defined as the date on which the drug was removed from the market, with the index date of detection (IDD), defined as a date on which the signal was significantly detected by the SDM. Results: The estimated sensitivity was 100% for ROR, 90% for PRR and IC and 70% for GPS. The sensitivity increased with increasing numbers of reports per DEC. Compared with the IDW, the signals were detected on average 10 quarters earlier by ROR, 9 quarters earlier by PRR, 9.9 quarters earlier by the IC and 4.7 quarters earlier by GPS. Conclusions: The sensitivity and timing of early signal detection varies across the four SDMs. Numerically, the ROR showed better performance in sensitivity and early signal detection based on ten selected DECs. Given the limited number and range of DECs selected in this study and the unavailability of specificity assessment, further large-scale prospective studies are warranted in order to provide better guidance on the selection of SDMs. © 2008 Adis Data Information BV. All rights reserved.link_to_subscribed_fulltex