Maskne prevalence and associated factors in Irish healthcare workers during the COVID‐19 pandemic
Bibliographic record
Abstract
In early 2020, mask usage was mandated for healthcare workers (HCWs) to limit the transmission of COVID-19.1, 2 Since then, dermatoses related to personal protective equipment (PPE) have become well-recognized and widely reported, predominantly related to pressure-related damage and irritant contact dermatitis (ICD).3 A previous Irish study showed that 82% of staff developed ICD, with 26% reporting PPE-related facial dermatoses.4 We sought to evaluate the prevalence and contributory factors in ‘maskne’ development amongst Irish HCWs during the COVID-19 pandemic. In April-May 2021, 700 self-administered questionnaires were distributed to staff in three university hospitals in Cork, Ireland. The questionnaire enquired about history of acne, PPE exposure, maskne development, contributing/alleviating factors and whether advice/treatment was sought. In total, 337 completed the questionnaire (48% response rate). Most (84.6%, n = 285) were female. Forty-nine per cent were aged between 20 and 30 years, 27.6% between 31 and 40 years and 23.3% were over 40 years. Nursing staff comprised 64.1% of participants, doctors 22%, healthcare assistants (HCAs) 4.7% and allied health professionals 3.9%. Most respondents (72.5%) worked on general wards, 10% on COVID-19 wards, 4.5% in intensive care units and 3.2% in emergency departments. A quarter (26%, n = 87) had a previous history of acne, and a quarter (25.5%, n = 84) had a family history of acne (first degree relative). The majority were White (82.7%; n = 278) followed by South Asian (7.7%; n = 26), East Asian (3.3%; n = 11) and Black (2.2%; 7). As per the Fitzpatrick scale, most (52.7%) participants reported type I (19.2%, n = 64) or type II (33.5%, n = 112) skin. More darkly pigmented skin types were reported in 47.8% [type III 22.5% (n = 75); type IV 13.8% (n = 46); type V 9.9% (n = 33) and 1.2% (n = 4) type VI]. Over half (53.4%, n = 180) of respondents reported developing maskne since the onset of the COVID-19 pandemic. The majority were (85.5%, n = 154) self-reported papulopustular eruptions, 46% (n = 83) comedonal breakouts and 22.5% (n = 44) nodulocystic lesions. Only 12.8% of HCW with maskne sought medical advice (Fig. 1). Factors associated with increased rates of maskne included female gender (OR 4.26; 95% CI 2.11–8.20; P < 0.001), younger age [64.1% of 20–30 year olds compared with 48.8% and 46.7% of the 31–40 and >40 year categories respectively (P = 0.037)], history of acne (OR 2.16 95% CI 1.28–3.64; P = 0.004), family history of acne (OR 1.7 95% CI 1.02–2.89; P = 0.04), working in a ‘hot and sweaty’ environment (OR 1.89; 95% CI 1.14–3.15; P = 0.014), use of emollients under the masks (OR 1.89; CI 1.21–2.95; P = 0.005) and use of face shields and goggles (OR 1.59; 95% CI 0.97–2.60; P = 0.031; Table 1). There was no correlation with duration of mask use (P = 0.097), number of shifts worked per week (P = 0.52), job description (P = 0.793), use of hormonal contraception amongst female staff members (P = 0.474), Fitzpatrick skin type (P = 0.844) or ethnicity (P = 0.22). Over half of our HCWs developed maskne since the onset of the COVID-19. Our findings suggest that female HCW, younger HCW and those with a personal or family history of acne are at increased risk. The reduction in duration of usage may not prevent maskne, but application of emollients under masks should not be recommended.5 Limitations of our study include self-reporting of maskne. Our study was vulnerable to responder bias. However, with 55% of the 48% respondents reporting maskne, even if all non-respondents did not develop maskne, the overall prevalence would still have been at least 26%. We did not account for confounding variables such as the use of other comedogenic products, such as make-up. This study highlights the pervasiveness of maskne in the COVID-19 era. Most HCW with maskne do not seek medical attention, so the impact of this occupational dermatosis may be under-estimated. None declared by LK, COC, GOB, COB, JG and JB. The data are available on request.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".