Epidemiology, Behavioral Determinants, Symptom Burden, and Severity of Traction Alopecia Among Ghanaian Women: A Cross‐Sectional Study
Gloria Apegwine Ayugane, Jeduthun Osei‐Sekyereh, Christabel Afi Tonugble, Akua Yiadomah Asare, Ellwin Kwabena‐Adade, Princess Y. Benson, Angela Kyei, Ulrick Sidney KanmounyeABSTRACT
Background and Aims
Traction alopecia (TA) is preventable, yet population‐level data on cumulative exposures in African cohorts remain scarce. This study characterized the epidemiology of TA among Ghanaian women and evaluated a multi‐behavioral risk‐stratification early‐screening framework.
Methods
We surveyed Ghanaian women in May 2026 using a structured questionnaire. Hairline status was classified using the Marginal Traction Alopecia Score (M‐TAS). Associations between exposures and TA were evaluated using bivariable analyses and multivariable logistic regression. Model discrimination was assessed using the area under the receiver‐operating characteristic curve (AUROC), and participants were stratified into risk tiers using predicted probabilities.
Results
The mean age of the 197 respondents was 23.1 ± 4.3 years, and 40.6% (80/197) had hairline recession consistent with TA. In the regression model, each additional year of age was associated with an 8.0% increase in the odds of recession (aOR, 1.08; 95% CI, 1.00–1.16; p = 0.04). Keeping a single hairstyle for over 6 weeks showed a non‐significant increase in estimated odds (aOR, 1.81; 95% CI, 0.59–5.60; p = 0.30) while > 2 high‐frequency thermal styling sessions/month (aOR, 5.65; 95% CI, 1.35–23.62; p = 0.02) were associated with increased odds compared to unexposed individuals (AUROC, 0.68; 95% CI, 0.60–0.76). The proportion of participants with hairline recession consistent with TA increased from 27.7% in the lowest‐risk group to 58.5% in the highest‐risk group ( p < 0.001).
Conclusions
TA risk scales non‐linearly, driven heavily by prolonged installations and frequent heat exposure. Integrating these distinct exposures into a unified, model‐predicted risk stratification framework could provide a well‐calibrated backend architecture for consumer‐facing digital health tools in Africa.