Statistics from Altmetric.com
Confounding should always be addressed in studies concerned with causality. When present, it results in a biased estimate of the effect of exposure on disease. The bias can be negative—resulting in underestimation of the exposure effect—or positive, and can even reverse the apparent direction of effect. It is a concern no matter what the design of the study or what statistic is used to measure the effect of exposure.
The potential for confounding can be reduced by good study design, but in non-randomised studies this is unlikely to resolve the problem fully. Hence statistical adjustment methods, to reduce the bias caused by measured confounders, are also frequently considered. Such adjustment presupposes that one knows which factors are confounders. However, recent literature on methods for identifying confounders suggest that these are not always obvious. Indeed, in pursuit of guidelines, authors have had to reexamine the meanings of confounding and confounders with some ambiguity and conflict emerging. This literature is reviewed and a recent modification to the traditional definition of a confounder, which emphasises causal rather than statistical relationships, is described and illustrated. Some well known problems in occupational epidemiology, arising from health related selection, are considered in the light of recent ideas.
Control of confounding through study design is not addressed, nor is the article concerned with details of statistical methods for adjustment. An overview of design and analysis in relation to confounding by age may be useful additional reading.1 It is assumed that the reader has at least a basic knowledge of epidemiological methods. Unless otherwise stated, definitions and comments apply to all causal study designs including case–control studies.
Consider a study of the relationship between exposure to silica dust and lung cancer where the rate of lung cancer in exposed workers is twice that in unexposed subjects, giving …
Review history and Supplementary material
Confounding and confounders
Neyman J. Sur les applications de la thar des probabilities aux experiences Agaricales: essay des principles. Transl. D Dabrowska, T Speed in Statistical Sciences 1990;5:463-472.
Lewis D. Causation. J Philos 1973; 70:556-67.
Rubin DB. Estimating causal effects of treatments in randomized and non-randomized studies. J Educ Psych 1974;66:688-701.
Rothman KJ. Causes. Am J Epidemiology 1976;104:587-92.
Sobel MS. Causal inference in the social sciences. J Am Statist Assoc 2000; 95:647-651.
On adjusting for pregnancy history:
Nurminen T. On adjusting for the outcome of previous pregnancies in epidemiologic reproductive studies. Epidemiology 1994;6:84-86.
Weinberg C. Should we adjust for pregnancy history when the exposure effect is transient? (Letter) Epidemiology 1995;6:335-336.
Nurminen T Should we adjust for pregnancy history when the exposure effect is transient? (Reply) Epidemiology 1995;6:336-337.
On selection effects in occupational cohorts:
Fox AJ, Collier PF. Low mortality rates in industrial cohort studies due to selection for work and survival in the industry. Br J Prev Soc Med 1976;30:225-230.
McMicheal AJ. Standardised mortality ratios and the �healthy worker effect�: scratching beneath the surface. J Occup Med 1976;18:165-168.
Wen CP, Tsai SP, Gibson RL. Anatomy of the healthy worker effect: a critical review. J Occup Med 1983;25:283-289.
Sterling TD, Weinkam JJ. Extent, persistence and constancy of the healthy worker or healthy person effect by all and selected causes of death. J Occup Med 1986;28:348-353.
Monson RR. Observations on the healthy worker effect. J Occup Med 1986;28:425-433.
On collapsibility definition of a confounder:
Whittemore AS. Collapsing multidimensional tables. J R Stat Soc B 1978;40:328-340.
Boivin JF, Wacholder S. Conditions for confounding of the risk ratio and of the odds ratio. Am J Epidemiology 1985;121:152-158.
Grayson DA.. Confounding confounding. Am J Epidemiology 1987;126:546-63.
Greenland S, Morgenstern H. Poole C, Robins JM. Re: �confounding confounding�. (Letter). Am J Epidemiology 1989;129:1086-9.
Grayson DA.. Re: �confounding confounding�. (Reply). Am J Epidemiology 1989;129:1089-1091.
On control of the healthy (shift)worker hire effect in case-control studies:
McNamee R, Binks K, Jones S, Slovak A, Cherry NM. Shiftwork and mortality from ischaemic heart disease. Occupational and Environmental Medicine 1996;53:367-373.
On control of the healthy worker survivor effect and similar problems:
Hertz-Picciotto, Michael Arrighi H, Suh-Woan H. Does arsenic exposure increase the risk of circulatory disease? Am J Epidemiology 2000;151:174-181.
Steenland K, Deddens J, Salvan A, Staynew L. Negative bias in exposure-response trends in occupational studies: modelling the healthy worker survivor effect. Am J Epidemiology 1996;143:202-210.
Robins JM, Blevins D, Ritter G, Wulfson M. G-estimation of the effect of prophylaxis therapy for pneumocystis carinii pneumonia on the survival of AIDS patients. Epidemiology 1992;3:319-336.
Robins J. A new approach to causal inference in mortality studies with a sustained exposure period: application to control of the healthy worker effect. Math Modelling 1986;7:1393-1512.
Sterne J, Tilling K. G-estimation of causal effects, allowing for time-varying confounding. The Stata Journal 2002;2:164-182
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.