Epidemiological Data and Cancer Risk Assessment: Cessation Lag and Lingering Effect Concepts

By Bencko, Vladimír; Chen, Chao | Central European Journal of Public Health, September 2014 | Go to article overview

Epidemiological Data and Cancer Risk Assessment: Cessation Lag and Lingering Effect Concepts


Bencko, Vladimír, Chen, Chao, Central European Journal of Public Health


SUMMARY

Cessation lag and lingering effect are two important aspects of risk assessment, and have potential applications to dose-response analysis in risk assessment. In addition to providing insight into biological mode of action, the concept of cessation lag is useful for economic benefit analysis. Concept of effect lingering can be used to analyze epidemiological data by uncovering the hidden biological implications related to disease endpoints, thereby advancing current efforts to characterize and reduce risk assessment uncertainties.

Multicentre study design is proposed as a way to increase study size and to mitigate criticism of meta-analysis of independent studies. Individual studies from a multicentre study can be either pooled using original data, or combined by meta-analysis of summarized results. A multicentre study of large cohort or case-control study also offers an exciting opportunity to study the contribution of epigenetic events that may be associated with life-style and environmental risk factors for human health. Methods for optimizing exposure assessment and reducing exposure misclassification represent important but difficult components in epidemiological studies. Biomarkers present a potentially useful approach for improving exposure estimates.

Key words: epidemiological data, carcinogenicity, multicentre studies, lingering effect, cessation lag, risk assessment

INTRODUCTION

The field of epidemiology has reached a crucial point with challenges and opportunities. On one hand, it seems that most of the major occupational carcinogens have already been identified. Many chemicals classified as carcinogens by the International Agency for Research on Cancer (IARC) were first evaluated in the workplace. In the last decades, occupational exposure to known human carcinogens has diminished in many countries as awareness of their hazards has increased. On the other hand, we are still confronted with a long list of substances for which epidemiological data are lacking or inconclusive. The estimated number of chemicals in commerce ranges from tens of thousands to over 140,000; for most of them, relevant toxicological information is needed to set regulatory standards (1-3).

We are now at an important crossroad; advances in the interrelated disciplines on which health risk assessment depends hold promise for comprehensive understanding of the influence of environmental stressors on human health. The last decade has been marked with major developments in the field of cancer risk assessment. There have been remarkable advances in the broad area of cancer epidemiology. This includes research not only on human exposures to major cancer risk factors in environmental and occupational settings, but also on lifestyle and nutrition related risks. Traditional approaches and study design in cancer epidemiology have not been successful in identifying and evaluating these potential risk and/or protective factors.

Two main reasons for this failure are often due to insufficient study size and inadequate exposure assessment. In this paper, we discuss issues and approaches relevant to these two challenges, and the opportunity of using emerging genomics information in epidemiology studies.

Increasing Study Size

An important characteristic of research in the last decade is the increasing number of collaborative studies involving various countries, and as a consequence, sample size is greatly increased. By increasing the sample size, the power of a study to identify significant associations between exposures and a disease endpoint is enhanced. For example, considering that lifetime prevalence of occupation-related exposures in the population is low (typically below 5 or 10%), and the associated risk can be small as well (e.g. relative risk of 2). The study sample size for a community based study must be large in order to identify statistically significant associations. This is even more crucial if the exposure or disease outcome is misclassified in a study population. …

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