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Burstyn I, Galarneau JM, Cherry N. Does recall bias explain the association of mood disorders with workplace harassment? GLOBAL EPIDEMIOLOGY 2024; 7:100144. [PMID: 38711843 PMCID: PMC11070321 DOI: 10.1016/j.gloepi.2024.100144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/08/2024] Open
Abstract
Purpose To determine the contribution of recall bias to the observed excess in mental ill-health in those reporting harassment at work. Methods A prospective cohort of 1885 workers in welding and electrical trades was contacted every six months for up to 5 years, asking whether they were currently anxious or depressed and whether this was made worse by work. Only at the end of the study did we ask about any workplace harassment they had experienced at work. We elicited sensitivity and specificity of self-reported bullying from published reliability studies and formulated priors that reflect the possibility of over-reporting of workplace harassment (exposure) by those whose anxiety or depression was reported to be made worse by work (cases). We applied the resulting misclassification models to probabilistic bias analysis (PBA) of relative risks. Results We observe that PBA implies that it is unlikely that biased misclassification due to the study subjects' states of mind could have caused the entire observed association. Indeed, the results demonstrated that doubling of risk of anxiety or depression following workplace harassment is plausible, with the unadjusted relative risk attenuated with understated uncertainty. Conclusions It seems unlikely that risk of anxiety or depression following workplace harassment can be explained by the form of recall bias that we proposed.
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Affiliation(s)
- Igor Burstyn
- Department of Environmental and Occupational Health, Drexel University, Philadelphia, PA, USA
| | - Jean-Michel Galarneau
- Division of Preventive Medicine, University of Alberta, Edmonton, Alberta, Canada
- Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada
| | - Nicola Cherry
- Division of Preventive Medicine, University of Alberta, Edmonton, Alberta, Canada
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Burstyn I. Does adjustment for non-differential misclassification of dichotomous exposure induce positive bias if there is no true association? GLOBAL EPIDEMIOLOGY 2024; 7:100132. [PMID: 38152554 PMCID: PMC10749869 DOI: 10.1016/j.gloepi.2023.100132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/29/2023] Open
Abstract
This article is a response to an off-the-record discussion that I had at an international meeting of epidemiologists more than decade ago. It centered on a concern, perhaps widely spread, that adjustment for exposure misclassification can induce a false positive result. I trace the possible history of this supposition and test it in a simulated case-control study under the assumption of non-differential misclassification of binary exposure, in which a Bayesian adjustment is applied. Probabilistic bias analysis is also briefly considered. The main conclusion is that adjustment for the presumed non-differential exposure misclassification of dichotomous does not "induce" positive associations, especially if the focus of the interpretation of the result is taken away from the point estimate. The misconception about positive bias induced by adjustment for exposure misclassification, if more clearly explained during the training of epidemiologists, may promote appropriate (and wider) use of the adjustment techniques. The simple message that can be derived from this paper is: "Exposure misclassification as a tractable problem that deserves much more attention than just a typical qualitative throw-away discussion".
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Burstyn I, Jones RM. The chronicles of statistical methods employed in occupational hygiene. Ann Work Expo Health 2023; 67:920-925. [PMID: 37494458 DOI: 10.1093/annweh/wxad042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/28/2023] Open
Affiliation(s)
- Igor Burstyn
- Department of Environmental and Occupational Health, Dornsife School of Public Health, Drexel University, Nesbitt Hall Room 614, 3215 Market Street, Philadelphia, PA 19104, United States
| | - Rachael M Jones
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, 650 Charles E. Young Dr S., Los Angeles, CA 90095, United States
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Scott LJ, Toumpakari Z, Nobles J, Sillero-Rejon C, Jago R, Cummins S, Blake S, Horwood J, Vocht FD. Assessing exposure to outdoor advertisement for products high in fat, salt and sugar (HFSS); is self-reported exposure a useful exposure metric? BMC Public Health 2023; 23:668. [PMID: 37041569 PMCID: PMC10088263 DOI: 10.1186/s12889-023-15567-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/30/2023] [Indexed: 04/13/2023] Open
Abstract
BACKGROUND Exposure to advertising of unhealthy commodities such as fast-food and gambling is recognised as a risk factor for developing non-communicable diseases. Assessment of the impact of such advertisement and the evaluation of the impact of any policies to restrict such advertisements on public health are reliant on the quality of the exposure assessment. A straightforward method for assessing exposure is to ask people whether they noticed any such advertisements in their neighbourhoods. However, the validity of this method is unclear. We assessed the associations between measured exposure to outdoor advertising, self-reported exposure, and self-reported consumption. METHODS We collected exposure information in January-March 2022 using two methods: (i) through a resident survey investigating advertising and consumption of unhealthy products, distributed across Bristol and neighbouring South Gloucestershire, and (ii) through in-person auditing. Self-reported exposure was obtained from the resident survey (N = 2,560) and measured exposure from photos obtained for all Council owned advertisement sites (N = 973 bus stops). Both data sources were geographically linked at lower-super-output-area level. Reporting ratios (RRs), 95% confidence intervals (CIs), and Cohen's kappas, are presented. RESULTS 24% of advertisements displayed food and/or drink advertising. Bristol respondents in neighbourhoods displaying food/drink adverts were more likely to also report seeing these adverts compared to those in neighbourhoods without food/drink adverts (59% vs. 51%, RR = 1.15, 95%CI 1.01-1.31). There was no such association in South Gloucestershire (26% vs. 32%, RR = 0.82, 95%CI 0.58-1.14). Respondents in both Bristol and South Gloucestershire who recalled seeing advertising for unhealthy food and drink products were more likely to consume them (e.g. for fast-food: 22% vs. 11%, RR = 2.01, 95%CI 1.68-2.42). There was no such association between measured food and drink adverts in respondents' local areas and self-reported consumption of HFSS product (90.1% vs. 90.7%, RR = 0.99, 95%CI 0.96-1.03). CONCLUSIONS Self-reported outdoor advertisement exposure is correlated with measured exposure, making this a useful methodology for population studies. It has the added advantage that it correlates with consumption. However, given that measurement error can be significant and self-reported exposure is known to be susceptible to various biases, inferences from studies using this exposure metric should be made with caution.
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Affiliation(s)
- Lauren J Scott
- National Institute for Health Research Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
- Population health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Zoi Toumpakari
- Centre for Exercise, Nutrition & Health Sciences, School for Policy Studies, University of Bristol, Bristol, UK
| | - James Nobles
- Obesity Institute, School of Health, Leeds Beckett University, Leeds, UK
| | - Carlos Sillero-Rejon
- National Institute for Health Research Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
- Population health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Russell Jago
- National Institute for Health Research Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
- Population health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Centre for Exercise, Nutrition & Health Sciences, School for Policy Studies, University of Bristol, Bristol, UK
| | - Steven Cummins
- Faculty of Public Health and Policy, London School of Hygiene and tropical medicine, London, UK
| | - Sarah Blake
- National Institute for Health Research Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Jeremy Horwood
- National Institute for Health Research Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
- Population health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Frank de Vocht
- National Institute for Health Research Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.
- Population health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- NIHR ARC West, 9th Floor, Whitefriars Lewins Mead Bristol, Bristol, BS1 2NT, UK.
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Wan W, Ge CB, Friesen MC, Locke SJ, Russ DE, Burstyn I, Baker CJO, Adisesh A, Lan Q, Rothman N, Huss A, van Tongeren M, Vermeulen R, Peters S. Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison. Ann Work Expo Health 2023:7025461. [PMID: 36734402 DOI: 10.1093/annweh/wxad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 01/09/2023] [Indexed: 02/04/2023] Open
Abstract
OBJECTIVES Automatic job coding tools were developed to reduce the laborious task of manually assigning job codes based on free-text job descriptions in census and survey data sources, including large occupational health studies. The objective of this study is to provide a case study of comparative performance of job coding and JEM (Job-Exposure Matrix)-assigned exposures agreement using existing coding tools. METHODS We compared three automatic job coding tools [AUTONOC, CASCOT (Computer-Assisted Structured Coding Tool), and LabourR], which were selected based on availability, coding of English free-text into coding systems closely related to the 1988 version of the International Standard Classification of Occupations (ISCO-88), and capability to perform batch coding. We used manually coded job histories from the AsiaLymph case-control study that were translated into English prior to auto-coding to assess their performance. We applied two general population JEMs to assess agreement at exposure level. Percent agreement and PABAK (Prevalence-Adjusted Bias-Adjusted Kappa) were used to compare the agreement of results from manual coders and automatic coding tools. RESULTS The coding per cent agreement among the three tools ranged from 17.7 to 26.0% for exact matches at the most detailed 4-digit ISCO-88 level. The agreement was better at a more general level of job coding (e.g. 43.8-58.1% in 1-digit ISCO-88), and in exposure assignments (median values of PABAK coefficient ranging 0.69-0.78 across 12 JEM-assigned exposures). Based on our testing data, CASCOT was found to outperform others in terms of better agreement in both job coding (26% 4-digit agreement) and exposure assignment (median kappa 0.61). CONCLUSIONS In this study, we observed that agreement on job coding was generally low for the three tools but noted a higher degree of agreement in assigned exposures. The results indicate the need for study-specific evaluations prior to their automatic use in general population studies, as well as improvements in the evaluated automatic coding tools.
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Affiliation(s)
- Wenxin Wan
- Department Population Health Sciences, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Calvin B Ge
- Department Population Health Sciences, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Melissa C Friesen
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Sarah J Locke
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Daniel E Russ
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Igor Burstyn
- Department of Environmental and Occupational Health, Drexel University, Dornsife School of Public Health, Philadelphia, PA, USA
| | - Christopher J O Baker
- Department of Computer Science, Faculty of Science, Applied Science and Engineering, University of New Brunswick, Saint John, NB, Canada
| | - Anil Adisesh
- Division of Occupational Medicine, Department of Medicine, University of Toronto, Toronto, ON, Canada.,Department of Medicine, Dalhousie Medicine New Brunswick, Saint John, NB, Canada
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Rockville, MD, USA
| | - Nathaniel Rothman
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Anke Huss
- Department Population Health Sciences, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Martie van Tongeren
- Centre for Occupational and Environmental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Roel Vermeulen
- Department Population Health Sciences, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Susan Peters
- Department Population Health Sciences, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
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Rémen T, Richardson L, Siemiatycki J, Lavoué J. Impact of Variability in Job Coding on Reliability in Exposure Estimates Obtained via a Job-Exposure Matrix. Ann Work Expo Health 2021; 66:551-562. [PMID: 34931220 DOI: 10.1093/annweh/wxab106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 09/27/2021] [Accepted: 11/01/2021] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVES The use of a job-exposure matrix (JEM) to assess exposure to potential health hazards in occupational epidemiological studies requires coding each participant's job history to a standard occupation and/or industry classification system recognized by the JEM. The objectives of this study were to assess the impact of inter-coder variability in job coding on reliability in exposure estimates derived from linking the job codes to the Canadian job-exposure matrix (CANJEM) and to identify influent parameters. METHOD Two trained coders independently coded 1000 jobs sampled from a population-based case-control study to the ISCO-1968 occupation classification at the five-digit resolution level, of which 859 could be linked to CANJEM using both assigned codes. Each of the two sets of codes was separately linked to CANJEM and thereby generated, for each of the 258 occupational agents available in CANJEM, two exposure estimates: exposure status (yes/no) and intensity of exposure (low, medium, and high) for exposed jobs only. Then, inter-rater reliability (IRR) was computed (i) after stratifying agents in 4 classes depending, for each, on the proportion of occupation codes in CANJEM defined as 'exposed' and (ii) for two additional scenarios restricted to jobs coded differently: the first one using experts' codes, the other one using codes randomly selected. IRR was computed using Cohen's kappa, PABAK and Gwet's AC1 index for exposure status, and weighted kappa and Gwet's AC2 for exposure intensity. RESULTS Across all agents and based on all jobs, median (Q1, Q3; Nagents) values were 0.68 (0.59, 0.75; 220) for kappa, 0.99 (0.95, 1.00; 258) for PABAK, and 0.99 (0.97, 1.00; 258) for AC1. For the additional scenarios, median kappa was 0.28 (0.00, 0.45; 209) and -0.01 (-0.02, 00; 233) restricted to jobs coded differently using experts' and random codes, respectively. A similar decreasing pattern was observed for PABAK and AC1 albeit with higher absolute values. Median kappa remained stable across exposure prevalence classes but was more variable for low prevalent agents. PABAK and AC1 decreased with increasing prevalence. Considering exposure intensity and all exposed jobs, median values were 0.79 (0.68, 0.91; 96) for weighted kappa, and 0.95 (0.89, 0.99; 102) for AC2. For the additional scenarios, median kappa was, respectively, 0.28 (-0.04, 0.42) and -0.05 (-0.18, 0.09) restricted to jobs coded differently using experts' and random codes, with a similar though attenuated pattern for AC2. CONCLUSION Despite reassuring overall reliability results, our study clearly demonstrated the loss of information associated with jobs coded differently. Especially, in cases of low exposure prevalence, efforts should be made to reliably code potentially exposed jobs.
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Affiliation(s)
- Thomas Rémen
- Health Innovation and Evaluation Hub Department, University of Montreal Hospital Research Center (CRCHUM), Pavillon S, 850 Rue Saint-Denis, Montréal QC, Canada
| | - Lesley Richardson
- Health Innovation and Evaluation Hub Department, University of Montreal Hospital Research Center (CRCHUM), Pavillon S, 850 Rue Saint-Denis, Montréal QC, Canada
| | - Jack Siemiatycki
- Health Innovation and Evaluation Hub Department, University of Montreal Hospital Research Center (CRCHUM), Pavillon S, 850 Rue Saint-Denis, Montréal QC, Canada
| | - Jérôme Lavoué
- Health Innovation and Evaluation Hub Department, University of Montreal Hospital Research Center (CRCHUM), Pavillon S, 850 Rue Saint-Denis, Montréal QC, Canada
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Burstyn I. Occupational epidemiologist's quest to tame measurement error in exposure. GLOBAL EPIDEMIOLOGY 2020. [DOI: 10.1016/j.gloepi.2020.100038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Orak NH, Small MJ, Druzdzel MJ. Bayesian network-based framework for exposure-response study design and interpretation. Environ Health 2019; 18:23. [PMID: 30902096 PMCID: PMC6431017 DOI: 10.1186/s12940-019-0461-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Accepted: 03/04/2019] [Indexed: 05/08/2023]
Abstract
Conventional environmental-health risk-assessment methods are often limited in their ability to account for uncertainty in contaminant exposure, chemical toxicity and resulting human health risk. Exposure levels and toxicity are both subject to significant measurement errors, and many predicted risks are well below those distinguishable from background incident rates in target populations. To address these issues methods are needed to characterize uncertainties in observations and inferences, including the ability to interpret the influence of improved measurements and larger datasets. Here we develop a Bayesian network (BN) model to quantify the joint effects of measurement errors and different sample sizes on an illustrative exposure-response system. Categorical variables are included in the network to describe measurement accuracies, actual and measured exposures, actual and measured response, and the true strength of the exposure-response relationship. Network scenarios are developed by fixing combinations of the exposure-response strength of relationship (none, medium or strong) and the accuracy of exposure and response measurements (low, high, perfect). Multiple cases are simulated for each scenario, corresponding to a synthetic exposure response study sampled from the known scenario population. A learn-from-cases algorithm is then used to assimilate the synthetic observations into an uninformed prior network, yielding updated probabilities for the strength of relationship. Ten replicate studies are simulated for each scenario and sample size, and results are presented for individual trials and their mean prediction. The model as parameterized yields little-to-no convergence when low accuracy measurements are used, though progressively faster convergence when employing high accuracy or perfect measurements. The inferences from the model are particularly efficient when the true strength of relationship is none or strong with smaller sample sizes. The tool developed in this study can help in the screening and design of exposure-response studies to better anticipate where such outcomes can occur under different levels of measurement error. It may also serve to inform methods of analysis for other network models that consider multiple streams of evidence from multiple studies of cumulative exposure and effects.
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Affiliation(s)
- Nur H Orak
- Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Environmental Engineering, Duzce University, Duzce, Turkey.
| | - Mitchell J Small
- Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Marek J Druzdzel
- School of Computing and Information Sciences, University of Pittsburgh, Pittsburgh, PA, USA
- Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland
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