Identification of confounders in the assessment of the relationship between lead exposure and child development

To explore the best approach to identify and adjust for confounders in epidemiologic practice.

In the Port Pirie cohort study, the selection of covariates was based on both a priori and an empirical consideration. In an assessment of the relationship between exposure to environmental lead and child development, change-in-estimate (CE) and significance testing (ST) criteria were compared in identifying potential confounders. The Pearson correlation coefficients were used to evaluate the potential for collinearity between pairs of major quantitative covariates. In multivariate analyses, the effects of confounding factors were assessed with multiple linear regression models.

The nature and number of covariates selected varied with different confounder selection criteria and different cutoffs. Four covariates (i.e., quality of home environment, socioeconomic status (SES), maternal intelligence, and parental smoking behaviour) met the conventional CE criterion (> or =10%), whereas 14 variables met the ST criterion (p < or = 0.25). However, the magnitude of the relationship between blood lead concentration and children's IQ differed slightly after adjustment for confounding, using either the CE (partial regression coefficient: -4.4; 95% confidence interval (CI): -0.5 to -8.3) or ST criterion (-4.3; 95% CI: -0.2 to -8.4).

Identification and selection of confounding factors need to be viewed cautiously in epidemiologic studies. Either the CE (e.g., > or = 10%) or ST (e.g., p < or = 0.25) criterion may be implemented in identification of a potential confounder if a study sample is sufficiently large, and both the methods are subject to arbitrariness of selecting a cut-off point. In this study, the CE criterion (i.e., > or = 10%) appears to be more stringent than the ST method (i.e., p < or = 0.25) in the identification of confounders. However, the ST rule cannot be used to determine the trueness of confounding because it cannot reflect the causal relationship between the confounder and outcome. This study shows the complexities one can expect to encounter in the identification of and adjustment for confounders.