The cardiometabolic consequences of workplace sexual harassment
Bibliographic record
Abstract
This editorial refers to ‘Exposure to workplace sexual harassment and risk of cardiometabolic disease: a prospective cohort study of 88 904 Swedish men and women’, by P. KC et al., https://doi.org/10.1093/eurjpc/zwae178. For many years, we have known nine risk factors that account for over 90% of the population attributable risk of cardiovascular (CV) disease.1 Many programmes have been developed to identify and treat eight of these risk factors: hypertension, diabetes, abnormal lipids, abdominal obesity, nutrition, physical activity, alcohol consumption, and tobacco use, with varying levels of success.2,3 However, the ninth risk factor, psychosocial factors—including depression, locus of control, and stress—is rarely considered. Despite advancements in understanding the underlying pathophysiological mechanisms of stress,4 programmes for its assessment and intervention lag far behind those of other CV risk factors. Workplace stress, in particular, has been associated with both cardiovascular disease5–7 and mental health7 issues, yet is rarely considered. The study by KC et al.8 highlights a particular element of workplace stress: sexual harassment, identifying it as a potentially important consideration for CV risk. The authors examined the associations of workplace sexual harassment—defined in this study as undesirable advances or offensive references to what is generally associated with sexual relations—with incident cardiovascular disease (CVD) and type-2 diabetes in nearly 89 000 Swedish workers followed for 11 years. Using responses to the Swedish Work Environment Survey (SWES) from 1995 to 2015, linked with National Patient Register and Causes of Death Register, the authors found that 1.9% of men and 7.5% of women reported sexual harassment in the workplace. After adjusting for sociodemographic factors (e.g. sex, birth country, family situation, education, and income) and work-related factors (e.g. job demands, job control, job support, and physical strain at work), the researchers found that workplace sexual harassment was associated with an increased risk of incident CVD [hazard ration (HR) 1.25, 95% confidence interval (CI) 1.03–1.51], and type 2 diabetes [HR 1.45, 95% CI 1.21–1.73]. The study also highlighted that the frequency of exposure to harassment and the type of harasser played a crucial role in determining the risk. Sexual harassment by a supervisor or fellow worker was associated with a higher risk of CVD (HR 1.57, 95% CI 1.15–2.15) and type-2 diabetes (HR 1.85, 95% CI 1.39–2.46) compared to harassment by others. Frequent exposure to harassment further suggested a potential increased risk, with HRs of 1.31 (95% CI 0.95–1.81) for CVD and 1.72 (95% CI 1.30–2.28) for type-2 diabetes. While the prevalence of workplace sexual harassment was higher among women, the authors did not find statistically significant interactions between workplace sexual harassment and biological sex. This study highlights the significant health impacts of workplace sexual harassment. The strengths of the study include the long-term follow-up and exploration of potential dose-relationships by examining associations with the type of harasser and frequency of harassment. The importance of establishing the long-term impact of workplace sexual harassment cannot be understated. In Europe, ∼9% of women report experiencing sexual harassment at work and a similar proportion of National Health Service workers in the UK reported workplace sexual harassment.9,10 Though these figures likely underestimate the real scope of the issue given the sensitive nature of sexual harassment, along with fear of retaliation, stigma, and perceived lack of support systems in the workplace. There are two key issues that remain unclear: (i) whether the associations reported by the authors are direct or mediated through secondary mechanisms, and (ii) if they are direct, what can be done to address this? It is plausible that workplace sexual harassment increases the likelihood of deleterious behaviours such as smoking, alcohol consumption, unhealthy diet choices, and physical inactivity. Future research should explore whether there is an independent association between workplace sexual harassment and incident CVD and type-2 diabetes, apart from these behavioural/lifestyle risk factors. In the meantime, CV risk factor screening programmes should include assessment of workplace stress and explicitly inquire about workplace sexual harassment. This, of course, implies that the employers need to be prepared to address these issues if identified. There are limitations to this study. The authors did not account for traditional metabolic risk factors, including systolic blood pressure, dyslipidaemia, elevated blood pressure, and abdominal obesity, which may have confounded the associations. Irrespective of the precise mechanisms, this study, along with the broader literature on the detrimental effects of workplace sexual harassment on health and well-being, should serve as a clarion call for employers across all sectors to better understand and take decisive actions against misconduct in the workplace. By implementing preventative measures, explicitly addressing and providing comprehensive support systems, employers can significantly reduce—or even prevent—sexual harassment in the workplace. These actions will not only promote a more equitable environment but may also contribute to the health of workers, at least in part due to cardiovascular risk factor reduction. J.B. received funding for event adjudication from Bayer AG outside of the current work. No new data were generated or analysed in support of this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".