0348 The Healthy Worker Survivor Effect Dissected: Addressing Component Parts. (23rd June 2014)

• Record Type: Journal Article
• Authors: Chevrier, Jonathan; Brown, Dan; Picciotto, Sally; Costello, Sadie; Neophytou, Andreas; Eisen, Ellen
• Is Part Of: Occupational and environmental medicine. Volume 71(2014)Supplement 1
• Page Start: A120
• Page End: A120
• Publication Date: 2014-06-23
• Subjects: Medicine, Industrial -- Periodicals; Environmental health -- Periodicals; 616.980305
• DOI: 10.1136/oemed-2014-102362.379
• Languages: English
• ISSNs: 1351-0711

Abstract

Abstract : Objectives: The healthy worker survivor effect (HWSE) is a well-recognised bias usually described as a form of selection bias or confounding. A more precise epidemiologic explanation, however, has been elusive. We distinguish several components of the HWSE and suggest methods for bias correction in occupational cohort studies. Method: Although generally referred to a single effect, we demonstrate using simulation studies that there are in fact four distinct aspects of the HWSE. Two aspects, (1) time-varying confounding by variables on the causal pathway and (2) heterogeneity in susceptibility, are functions of the underlying process of the exposure and disease under study. The other two, (3) left truncation and (4) right truncation, are functions of how the data are collected, ie the study design. We quantify the bias induced by each aspect of HWSE on dose-response parameter estimates and apply methods designed to reduce the bias. Results: We find that causal techniques, eg, g-estimation and IPTW, can correct for time-varying confounding. Heterogeneous susceptibility in combination with either left or right truncationcan be corrected using inverse probability of censoring weights. The health related variables needed to make either of these methods succeed in reducing the bias are often unmeasured. Conclusions: HWSE occurs due to the presence of any of four factors that may function separately or in concert to produce a downward bias if not accounted for. We provide guidance for methodologic approaches to reduce the bias.