Epidemiology confounding

According to EPA (IRIS 1999), the available human epidemiological studies lack quantitative exposure data for lead and for possible confounding exposures (e.g., arsenic, smoking). Cancer excesses in the lung and stomach of lead-exposed workers that are reported are relatively small, dose-response relationships are not demonstrated neither is there consistency in the site of cancers reported. EPA (IRIS 1999) concluded that the human data are inadequate to refute or demonstrate the potential carcinogenicity of lead exposure. 

Epidemiological and Human Dosimetry Studies. There are studies on the adverse effects of acrylonitrile in humans. These studies link acrylonitrile exposure and lung cancer. It has also been suggested that acrylonitrile may have the potential to cause prostate cancer. Many of the studies have major limitations including insufficient quantification of exposure, short follow-up, small study population, and inadequate evaluation of confounding associations. Additional studies would be useful in clarifying the cancer risk and estimating the exposure levels that lead to these effects. 

Again, in epidemiological studies or clinical trials there is nearly always a degree of uncertainty due to bias, chance and confounders. In these studies uncertainty is measured in terms of / -values, odd ratios, and relative risks, and so on. 

Epidemiology evidence is rated as sufficient to establish a causal link to some form of cancer or as limited (a role for bias or confounding cannot be eliminated) or as inadequate (insufficient in quality or quantity for evaluation). Some of the substances for which lARC panels have concluded there is sufficient evidence of a causal role in human cancer are listed in Table 5.1. 

Limited epidemiological information exists for carcinogenicity in humans following inhalation exposure to kerosene (vapor) (Chan et al. 1979) and other fuel oils such as diesel fuel (vapor) (Partanen et al. 1991). These studies either test kerosene exposure by use of kerosene stoves, and so are limited for the same reasons as the respiratory studies described above, or measure fuel oil exposures according to occupation. In the latter case, confounding from exposure to other chemicals, such as gasoline, exists. Both studies are limited since the duration and level of fuel oil exposure were not identified. Other available data are also reported to be inadequate to assess the carcinogenic potential of fuel oils (lARC 1989 Lam and Du 1988). 

Bias Occurs when there is a tendency to produce results that differ in a systematic manner from the true values. A study with small systematic bias is said to have high accuracy. Bias may lead to over- or underestimation of the strength of an association. The sources of bias in epidemiology are many and over 30 specific types of bias have been identified. The main biases are selection bias, information bias, and bias due to confounding. 

Tobacco smoke is considered to be one of the more severe confounders in epidemiological smdies, due, e.g., to its ability to affect enzyme activities and to cause various health effects (KEMI 2003). 

There is no conclusive evidence from epidemiological studies that mercury increases cancer risk in humans. In the few studies in which increases have been reported, concomitant exposure to other known carcinogens has confounded the results. The lARC has determined that there is inadequate evidence in humans for the carcinogenicity of mercury and mercury compounds. In animals there is inadequate evidence for carcinogenicity of metallic mercury and limited evidence for the carcinogenicity of mercuric chloride. 

Some epidemiological studies have suggested increased risks for lymphatic and hematopoietic neoplasms. However, the risks are generally small, statistically unstable, and often based on subgroup analyses. The possibility that the observations are the results of confounding by other occupational exposures cannot be ruled out. ... 

When epidemiological data are available, the issues to be dealt with include selection of the appropriate study and control populations, evaluation of exposure levels and tissue doses, determination of the reliability of cancer ascertainment, allowance for the latent period and age distribution of cancers, control of biases and confounding factors, fitting of models to the data to characterize the dose-incidence relationship, and derivation of risk estimates with their associated ranges of uncertainty. 

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