Epidemiology and Health

Review Paper

Beyond cohort versus case-control: the primacy of exposure assessment over study design in occupational cancer epidemiology: Yangwoo Kim, Dong-Uk Park; Epidemiol Health. 2026;48:e2026026. Published online June 8, 2026; DOI: https://doi.org/10.4178/epih.e2026026

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In occupational cancer epidemiology, prospective cohort studies are conventionally ranked above case-control studies. However, exposure assessment quality—central to occupational risk estimation—receives less methodological attention than study design. This review argues that divergent cohort and case-control risk estimates reflect exposure assessment limitations rather than study design. Three findings support this. First, variance decomposition demonstrates that group-level exposure assignment attenuates effect estimates by a reliability ratio approaching zero as within-worker variability becomes dominant. Second, Berkson error in job-exposure matrices, combined with model non-linearity, biases estimates toward the null. Third, the healthy worker effect, immortal time bias, and left truncation compound misclassification and shift cohort estimates further toward the null. Two case studies illustrate this pattern. For night shift work and breast cancer, the case-control odds ratio (OR) of 1.34 contrasted with the cohort relative risk (RR) of 0.98; at 30 years’ cumulative exposure, these estimates were 1.88 and 1.13. For welding fumes and lung cancer, the pooled cohort RR of 1.29 contrasted with a case-control OR of 1.87; Sørensen’s cohort, using individual breathing-zone measurements, recovered a hazard rate ratio of 2.34 at the highest cumulative exposure. Thus, an occupational cancer study’s inferential value depends on its exposure assessment method rather than its design label. Group-level assignment attenuates effect estimates and may compromise etiological inference regardless of nominal design. Individual exposure reconstruction—through occupational interviews, industrial hygiene assessment, or personal monitoring—supports precise dose-response estimation. Quality assessment tools and meta-analyses should stratify evidence by exposure assessment method rather than design label.; Summary

Korean summary
본 연구에서는 직업성 암 역학 연구에서 코호트 연구, 환자-대조군 연구 등 역학 연구 설계보다 노출평가의 질이 효과 추정의 정확성을 좌우한다고 주장하고자 한다. 직업명이나 직무노출매트릭스와 같은 집단수준 노출 할당은 측정오차와 구조적 편향이 결합되어 연관성을 영가설 방향으로 약화시킬 수 있는 반면, 개인수준에서의 노출 측정과 그에 따른 노출 재구성은 용량-반응 관계를 더 잘 포착할 수 있다. 따라서 직업성 암 역학 연구에서 코호트와 환자-대조군 연구의 근거를 평가하거나 메타분석 연구를 진행할 때에는 역학 연구 설계를 다루는 것에만 머무르지 않고 노출평가 방법과 그 정밀도에 따라 층화하는 연구 방법론의 정립이 필요하다.

Key Message
For occupational carcinogens with long latency, group-level exposure assignment in prospective cohort studies systemati- cally attenuates risk estimates. Regulatory and quality-assessment frameworks should evaluate studies by the precision of their exposure information—distinguishing individual-level reconstruction from group-level assignment—rather than by their nominal design label. Null findings from cohorts relying on job-title classification warrant caution due to this systematic attenuation.

Methods

Equivalence model: A new graphical model for causal inference: Jalal Poorolajal; Epidemiol Health. 2020;42:e2020024. Published online April 9, 2020; DOI: https://doi.org/10.4178/epih.e2020024

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Abstract

Although several causal models relevant to epidemiology have been proposed, a key question that has remained unanswered is why some people at high-risk for a particular disease do not develop the disease while some people at low-risk do develop it. The equivalence model, proposed herein, addresses this dilemma. The equivalence model provides a graphical description of the overall effect of risk and protective factors at the individual level. Risk factors facilitate the occurrence of the outcome (the development of disease), whereas protective factors inhibit that occurrence. The equivalence model explains how the overall effect relates to the occurrence of the outcome. When a balance exists between risk and protective factors, neither can overcome the other; therefore, the outcome will not occur. Similarly, the outcome will not occur when the units of the risk factor(s) are less than or equal to the units of the protective factor(s). In contrast, the outcome will occur when the units of the risk factor(s) are greater than the units of the protective factor(s). This model can be used to describe, in simple terms, causal inferences in complex situations with multiple known and unknown risk and protective factors. It can also justify how people with a low level of exposure to one or more risk factor(s) may be affected by a certain disease while others with a higher level of exposure to the same risk factor(s) may remain unaffected.

Summary

Citations

Citations to this article as recorded by

Association between birth weight and risk of nonneurological childhood cancers: a systematic review and meta-analysis
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Updated dose–response meta-analysis of sexual activity and prostate cancer risk
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Note

Theory and practice of Case-Crossover Study Design.: Moran Ki; Korean J Epidemiol. 2008;30(1):1-11. Published online June 30, 2008; DOI: https://doi.org/10.4178/kje.2008.30.1.1

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Abstract

A case-crossover study design is a method to assess the effect of transient exposures on the risk of onset of acute events. Which was introduced by Maclure in 1991 for Myocardial Infarction Onset Study. The design has been used to diverse fields of epidemiology such as injury, drug adverse events, air pollution and so on. The most valuable advantage of this design is unnecessary of control selection. To estimate relative risk, the exposure frequency during a window just before outcome onset is compared with exposure frequencies during control times rather than in control persons. One or more control times are supplied by each of the cases themselves. Self-matching of cases eliminates the threat of control-selection bias and increases efficiency. To application of the case-crossover design, we need to make sure several criteria and the possibility of specific bias. This review is designed to help the reader apply a case-crossover study design to their research fields by understanding general ideas, prior conditions and limitations of the design.

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Citations

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Original Articles

Smoking and lung cancer: foundation of modern epidemiology.: Hae Kwan Cheong; Korean J Epidemiol. 2005;27(2):1-19.

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Abstract PDF: Abstract
Since its introduction to western world in 16th century, smoking has been one of the most popular parts of human life. Its health hazards, however, has rarely been evaluated before mid 20th century. After early suggestion of association with lip cancer and pipe smoking, which was falsely associated with the heat of the pipe smoking, association between rapidly increasing incidence of lung cancer and increasing popularity of smoking habit in the western world has been suggested in late 1940s. Initial case-control studies, in spite of its proneness to various biases, aroused the relevance of the relationship. It was supported by following well-designed case-control studies and new method, cohort studies in both coast of the Atlantic. Consistency of the results of epidemiologic studies and additional support from animal experiments made the causal relationship to be accepted from scientific community, and finally from public and governments. Establishment of criteria of causal relationship was also established in the process of investigation of the relationship between smoking and lung cancer. Smoking is most common cause attributable to lung cancers in most of the world. It is also responsible for the many cancers, including larynx, bladder, oral cavity, esophagus, pancreas, kidney, stomach, liver, and myeloid leukemia; and cardiovascular disorders, respiratory disorders, and other degenerative disorders. Passive (or environmental tobacco) smoking has also been found to be hazardous. Establishment of causal relationship between smoking and lung cancer has been a landmark in the development of epidemiologic methods and concepts, which played the key role in the evaluation of risk factors and preventive intervention on the chronic degenerative disorders.; Summary

Population-adjusted Mean Age at Incidence (PAMA) for Comparing Incidence Patterns with Age in Different Populations.: Yoon Ok Ahn, Moo Song Lee, Weechang Kang, Chung Min Lee, Youngjo Lee; Korean J Epidemiol. 1999;21(1):31-35.

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Standardized incidence rates have been widely used for comparing incidence patterns between populations, adjusting for differences in demographic structure. These rates can compare overall incidence levels, but to fully understand incidence patterns, an index which links incidence with age is also needed. The authors proposed a statistical method for estimating population-adjusted mean age of incidence (PAMA), based on Poisson distribution and Fieller's theorem. The index was applied with several modifications to data relating to the incidence of breast cancer among Caucasian women living in Los Angeles.; Summary

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