Lessons from a scoping review: Clinical presentations of central centrifugal cicatricial alopecia

BACKGROUND

Central centrifugal cicatricial alopecia (CCCA) nomenclature describes a typical clinical presentation of cicatricial hair loss that begins on the vertex scalp with progressive, symmetric, and centrifugal evolution. However, atypical presentations have been noted clinically by the authors and reported in the literature.

OBJECTIVE

We sought to characterize the distribution of hair loss in published cases of adult patients with CCCA.

METHODS

A 3-step search process was used to evaluate research articles in Cumulative Index to Nursing & Allied Health, EMBASE, Google Scholar, MEDLINE, Scopus, and Web of Science databases. Studies with scalp photography or description of hair loss distribution were included. Three researchers evaluated eligible studies for clinical subtypes. Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review was used to report results.

RESULTS

Ninety-nine studies consisting of 281 cases of CCCA were included. Hair loss distributions included variants of the classic presentation along with distinct subtypes such as patchy, occipital, parietal, frontal, temporal, and trichorrhexis.

LIMITATIONS

Studies had significant homogeneity, as the classic distribution of CCCA was commonly reported. Additionally, clinically diagnosed cases may have concurrent diagnoses, and numerous studies did not report trichoscopy findings.

CONCLUSION

CCCA terminology may not always be reflective of clinical presentation. Understanding atypical presentations is essential to inform appropriate and targeted treatment.

Minority hair tax: pricing bias in haircare products

Background

Black haircare is an estimated $2.51-billion-dollar industry. Black women spend 9 times more on ethnic hair products than non-Black consumers. The haircare industry has adapted to these market trends by developing products catering to the needs of "natural hair," referring to curly to tightly coiled hair texture that has not been chemically straightened with a relaxer. Anecdotally, natural haircare products are relatively expensive.

Objective

We aimed to investigate texture-based price differences for haircare products targeting coily/curly compared to straight hair types.

Methods

Data were collected in August 2022 from 6 brands available on www.amazon.com. After stratifying the data by manufacturer, hair texture, and average price/oz, we used 2 sample t-test with equal variances to examine cost differences.

Results

Overall, there was a significant difference in average price/oz between all coily/curly and straight hair products, with coily/curly hair products being more expensive. When stratified by manufacturer, one leading US manufacturer sold coily/curly hair products at a higher price ($0.66/oz ±$0.05) compared with straight hair products ($0.46/oz ±$0.04), t14 = 2.8967, P < 0.0134.

Limitations

A small sample size of only shampoos and conditioners were analyzed, which may not represent the number of haircare products that consumers use.

Conclusion

Pricing policies should ensure that all individuals have access to effective, affordable haircare products. Dermatologists should also be cognizant of pricing differences to direct patients with natural hair to fairly priced products.

Unconscious Bias

Unconscious biases (also known as implicit biases) are involuntary stereotypes or attitudes held about certain groups of people that may influence our behaviors, understandings, and actions, often with unintended detrimental consequences. Implicit bias appears in multiple facets of medical education, training, and promotion with negative effects on diversity and equity efforts. Notable health disparities exist among minority groups in the United States, which may partly be attributable to unconscious biases. Although there is little evidence supporting the effectiveness of current bias/diversity training programming, standardization and blinding may be helpful, evidence-based methods to reduce implicit bias.

Automating Hair Loss Labels for Universally Scoring Alopecia From Images: Rethinking Alopecia Scores

Importance

Clinical estimation of hair density has an important role in assessing and tracking the severity and progression of alopecia, yet to the authors' knowledge, no automation currently exists for this process. While some algorithms have been developed to assess alopecia presence on a binary level, their scope has been limited by focusing on a re-creation of the Severity of Alopecia Tool (SALT) score for alopecia areata (AA). Yet hair density loss is common to all alopecia forms, and an evaluation of that loss is used in established scoring systems for androgenetic alopecia (AGA), central centrifugal cicatricial alopecia (CCCA), and many more.

Objective

To develop and validate a new model, HairComb, to automatically compute the percentage hair loss from images regardless of alopecia subtype.

Design, Setting, and Participants

In this research study to create a new algorithmic quantification system for all hair loss, computational imaging analysis and algorithm design using retrospective image data collection were performed. This was a multicenter study, where images were collected at the Children's Hospital of Philadelphia, University of Pennsylvania (Penn), and via a Penn Dermatology web interface. Images were collected from 2015 to 2021, and they were analyzed from 2019 to 2021.

Main Outcomes and Measures

Scoring systems correlation analysis was measured by linear and logarithmic regressions. Algorithm performance was evaluated using image segmentation accuracy, density probability regression error, and average percentage hair loss error for labeled images, and Pearson correlation for manual scores.

Results

There were 404 participants aged 2 years and older that were used for designing and validating HairComb. Scoring systems correlation analysis was performed for 250 participants (70.4% female; mean age, 35.3 years): 75 AGA, 66 AA, 50 CCCA, 27 other alopecia diagnoses (frontal fibrosing alopecia, lichen planopilaris, telogen effluvium, etc), and 32 unaffected scalps without alopecia. Scoring systems showed strong correlations with underlying percentage hair loss, with coefficient of determination R2 values of 0.793 and 0.804 with respect to log of percentage hair loss. Using HairComb, 92% accuracy, 5% regression error, 7% hair loss difference, and predicted scores with errors comparable to annotators were achieved.

Conclusions and Relevance

In this research study,it is shown that an algorithm quantitating percentage hair loss may be applied to all forms of alopecia. A generalizable automated assessment of hair loss would provide a way to standardize measurements of hair loss across a range of conditions.

Photographing Alopecia: How Many Pixels Are Needed for Clinical Evaluation?

Determining the minimum image resolution needed for clinical assessment is crucial for computational efficiency, image standardization, and storage needs alleviation. In this paper, we explore the image resolution requirements for the assessment of alopecia by analyzing how clinicians detect the presence of characteristics needed to quantify the disorder in the clinic. By setting the image resolution as a function of width of the patient's head, we mimicked experiments conducted in the computer vision field to understand human perception in the context of scene recognition and object detection and asked 6 clinicians to identify the regions of interest on a set of retrospectively collected de-identified images at different resolutions. The experts were able to detect the presence of alopecia at very low resolutions, while significantly higher resolution was required to identify the presence of vellus-like hair. Furthermore, the accuracy with which alopecia was detected as a function of resolution followed the same trend as the one obtained when we classified normal versus abnormal hair density using a standard neural network architecture, hinting that the resolution needed by an expert human observer may also provide an upper bound for future image processing algorithms.

Skin-lightening practices among shoppers in select markets in Kumasi, Ghana: A cross-sectional survey

Background

Despite warnings about its harmful adverse effects and the implementation of regulatory bans, anecdotal reports suggest high use of skin-lightening agents in Kumasi, Ghana.

Objective

To estimate the prevalence and motivation for skin-lightening agent use and nonuse among shoppers in Kumasi, Ghana.

Methods

This is a cross-sectional survey of a convenience sample of shoppers in 3 of Kumasi's largest markets.

Results

Of the 350 shoppers who were approached, 334 consented, for a response rate of 95%. Data were analyzed on 331 completed surveys. A total of 40.4% of individuals reported current or past use of skin-lightening products, and 26.6% of men reported history of product use compared with 56.5% of women. Female sex was significantly associated with skin-lightening product use (odds ratio 3.59; 95% confidence interval 2.26-5.70). Users of skin-lightening agents were more likely to agree with statements that associated lighter skin with beauty, self-esteem, attractiveness, and wealth.

Limitations

Using a nonrandomized convenience sampling method on a single city limits the generalizability of the results. Response, recall, and social desirability biases may have skewed the results, especially given the stigma associated with skin-lightening agent use.

Conclusion

The practice of skin lightening among all participants is high and is associated with perceptions of increased beauty, attractiveness, self-esteem, and wealth.

Expediting patient appointments with dermatology rapid access clinics

BACKGROUND

Lengthy wait times for dermatology appointments in the U.S. limit care access. The University of Pennsylvania's Department of Dermatology has established an urgent care clinic (UCC) and an intermediate care clinic (ICC) to expedite appointments for higher acuity patients.

OBJECTIVE

To describe our rapid access clinics' operations, referral patterns, and distributions of diagnoses.

METHODS

We performed a retrospective review of dermatology consult order and appointment data for UCC, ICC, and routine care to determine the number of orders, consult appointments, and follow-up appointments; appointment wait times; and frequencies of diagnoses in referring provider and consult appointments. Press Ganey patient satisfaction ratings were also analyzed.

RESULTS

The median (interquartile range) wait times for UCC, ICC, and routine care, appointments were 3 (1-8) days, 36 (15-64) days, and 45 (12-97) days, respectively (P&lt;0.001). The proportion of referrals originating from subspecialists varied among UCC (47.6%), ICC (20.2%) and routine care (15.8%), (P&lt;0.001). Distributions of diagnoses differed among UCC, ICC, and routine care. Ratings for most satisfaction metrics were similar across clinic settings.

CONCLUSIONS

Dermatology rapid access clinics within an academic medical center can reduce wait times for higher acuity patients while maintaining patient satisfaction.