Negative Bias in Exposure-Response Trends in Occupational Studies: Modeling the Healthy Worker Survivor Effect

Abstract

Many occupational studies analyze trends between cumulative exposure and mortality. The authors show that such trends are, in general, negatively confounded by employment status. Mortality rates for workers who leave work ("inactive" workers) are higher than for active workers because some workers leave because they are ill. The percentage of inactive relative to active person-time is higher in low categories of cumulative exposure, causing employment status to act as a negative confounder of exposure-response trends (the opposite occurs for time-since-hire). We illustrate these phenomena using 10 "negative" mortality studies, in which adjustment for employment status removes false trends. However, adjustment for employment status will lead to biased estimates when it acts as an intermediate variable between cumulative exposure and death, as occurs directly when exposure causes a disabling disease that, in turn, causes death or indirectly when exposure causes workers to leave work. The authors illustrate this problem using simulated follow-up data for leaving, disease incidence, and mortality. In the null case in which cumulative exposure affects neither disease incidence (or mortality) nor leaving rates, employment status indeed acts as a negative confounder of exposure-response trends, and traditional adjustment eliminates this confounding. However, when cumulative exposure affects disease incidence or rates of leaving, adjustment for employment status will not be adequate. Employment status falls under the general rubric of variables that are simultaneously confounders and intermediate variables.

Brain tumors and occupational exposures in a cohort of female textile workers in Shanghai, China

“…We did not have a priori biological reasons for suspecting protective effects of these exposures for brain tumors. We hypothesize that these observations may be the result of a healthy worker survivor effect, as women in the 20-year exposure categories lagged by 20 years had longer total employment in the industry than women of similar ages in the lower-duration exposure categories, possibly indicating that they were in better overall health (41).…”

A critical review of the relationship between occupational exposure to diesel emissions and lung cancer risk

“…Several strategies have been developed in order to deal with this kind of bias (Arrighi & Hertz-Picciotto 1994;Arrighi & Hertz-Picciotto 1995;Steenland et al 1996;Li & Sung 1999;Baillargeon 2001;Richardson et al 2004;Applebaum et al 2007Applebaum et al , 2011Chevrier et al 2012;Joffe 2012;Naimi et al 2013;Picciotto et al 2013;Buckley et al 2015), but as recently formulated: the HWSE is a still-evolving concept (Picciotto & Hertz-Picciotto 2015). Some of the strategies referenced above do not take into account the primary aim of occupational epidemiology, which is to investigate the relationship between exposure and the first diagnosis (incidence) of a disease.…”

Attenuation of exposure-response curves in occupational cohort studies at high exposure levels

“…The bias related to the "healthy initial hire effect" can be avoided by the use of comparisons within the cohort, and such avoidance is generally feasible when exposureresponse relationships are being explored. The healthy worker survivor effect is more difficult to deal with and may introduce bias in exposure-response relationships because short-term workers are often less healthy than long-term workers (18,19) and thus may leave the workplace for reasons either causally or incidentally related to the disease under study. Since short-term workers have higher disease rates than long-term workers, and since, by definition, their exposure is lower, this situation creates a problem of confounding, which could yield negative or flat exposure-response trends when duration or cumulative exposure (product of duration and intensity) is the exposure metric used.…”