Relative performance of different exposure modeling approaches for sulfur dioxide concentrations in the air in rural western Canada

Proximity to wood industries and respiratory symptoms in children: a sensitivity analysis

Increased prevalence of respiratory and irritation symptoms was found in children who live near a large wood industrial park. Proximity to the wood industries was used as indicator of exposure. This study describes a sensitivity analysis for the results of the survey. All the children (3-14 years) living in the area were surveyed through a parental questionnaire (n=3854) and their addresses were geocoded. The distances from each child's home and school to the closest industry were combined, weighted and used as an indicator of exposure. A sensitivity analysis was performed to check 1) the robustness of the results to the choice of weights used for defining the exposure indicator, 2) the effect of outliers on risk estimates and 3) the sensitivity on the functional form used for modeling the dose-response function. The choice of the weights did not influence the association between proximity to the industries and respiratory symptoms. Excluding the subjects who lived far away from the industries showed that in a radius of 5km from the industries the study did not had enough power to estimate a gradient in the dose-response function. Besides, results were sensitive to the choice of the functional form used for modeling the minimum distance. The sensitivity analyses confirmed the overall increasing trend of respiratory symptoms with proximity to the industries and pointed out that all the assumptions made for defining a proxy of exposure need to be carefully checked.

Optimizing cost-efficiency in mean exposure assessment--cost functions reconsidered

Background

Reliable exposure data is a vital concern in medical epidemiology and intervention studies. The present study addresses the needs of the medical researcher to spend monetary resources devoted to exposure assessment with an optimal cost-efficiency, i.e. obtain the best possible statistical performance at a specified budget. A few previous studies have suggested mathematical optimization procedures based on very simple cost models; this study extends the methodology to cover even non-linear cost scenarios.

Methods

Statistical performance, i.e. efficiency, was assessed in terms of the precision of an exposure mean value, as determined in a hierarchical, nested measurement model with three stages. Total costs were assessed using a corresponding three-stage cost model, allowing costs at each stage to vary non-linearly with the number of measurements according to a power function. Using these models, procedures for identifying the optimally cost-efficient allocation of measurements under a constrained budget were developed, and applied on 225 scenarios combining different sizes of unit costs, cost function exponents, and exposure variance components.

Results

Explicit mathematical rules for identifying optimal allocation could be developed when cost functions were linear, while non-linear cost functions implied that parts of or the entire optimization procedure had to be carried out using numerical methods.

For many of the 225 scenarios, the optimal strategy consisted in measuring on only one occasion from each of as many subjects as allowed by the budget. Significant deviations from this principle occurred if costs for recruiting subjects were large compared to costs for setting up measurement occasions, and, at the same time, the between-subjects to within-subject variance ratio was small. In these cases, non-linearities had a profound influence on the optimal allocation and on the eventual size of the exposure data set.

Conclusions

The analysis procedures developed in the present study can be used for informed design of exposure assessment strategies, provided that data are available on exposure variability and the costs of collecting and processing data. The present shortage of empirical evidence on costs and appropriate cost functions however impedes general conclusions on optimal exposure measurement strategies in different epidemiologic scenarios.