Hairstyling Practices to Prevent Hair Damage and Alopecia in Women of African Descent.

Highly textured hair has been found to be more susceptible to breakage than other hair types due to an increased proportion of spirals and relatively fewer elastic fibers anchoring the hair follicles to the dermis. Women of African descent frequently employ hairstyles and hair treatments for ease of management and as a form of self-expression, but a number of these practices have been implicated as risk factors for alopecia. Herein, we provide an overview of hairstyles for patients with highly textured hair so that physicians may better identify high-risk hairstyles, provide individualized recommendations for safer alternatives, and manage and stop the progression of hair loss before it becomes permanent.

Brigham Eyebrow Tool for Alopecia: A Reliable Assessment of Eyebrow Alopecia Areata.

There are no tools to evaluate eyebrow involvement in patients with alopecia areata. We developed and assessed the reliability of the Brigham Eyebrow Tool for Alopecia (BETA) as a quantitative evaluation of eyebrow alopecia areata. BETA uses facial landmarks of eyebrow anatomy and is calculated using surface area and density. A total of 50 eyebrow images with varying levels of hair loss were distributed to six board-certified dermatologists at three academic medical centers with standardized instructions and examples. Interrater and intrarater reliability were calculated using intraclass correlation coefficients (ICCs). BETA demonstrated high interrater (ICC = 0.88, confidence interval = 0.83-0.92 right eyebrow scores and ICC = 0.90, confidence interval = 0.85-0.94 left eyebrow scores) and intrarater (ICC = 0.90, confidence interval = 0.85-0.93 right eyebrow scores and ICC = 0.91, confidence interval = 0.87-0.94 left eyebrow scores) reliability. When measured in the same patient with varying degrees of hair loss over time, BETA demonstrated sensitivity to change. BETA is a simple and reliable objective assessment of eyebrow alopecia areata. BETA is easy-to-use and quick to calculate, making it feasible for a variety of clinical and research settings. Although developed for alopecia areata, we hope that BETA will be investigated in other etiologies of eyebrow alopecia to serve as a universal tool for monitoring disease progression, improvement, and response to treatment.

Development and pilot-testing of the Alopecia Areata Assessment Tool (ALTO).

BACKGROUND: Alopecia areata (AA) is an autoimmune disease characterized by non-scarring hair loss. The lack of a definitive biomarker or formal diagnostic criteria for AA limits our ability to define the epidemiology of the disease. In this study, we developed and tested the Alopecia Areata Assessment Tool (ALTO) in an academic medical center to validate the ability of this questionnaire in identifying AA cases. METHODS: The ALTO is a novel, self-administered questionnaire consisting of 8 closed-ended questions derived by the Delphi method. This prospective pilot study was administered during a 1-year period in outpatient dermatology clinics. Eligible patients (18 years or older with chief concern of hair loss) were recruited consecutively. No patients declined to participate. The patient's hair loss diagnosis was determined by a board-certified dermatologist. Nine scoring algorithms were created and used to evaluate the accuracy of the ALTO in identifying AA. RESULTS: 239 patients (59 AA cases and 180 non-AA cases) completed the ALTO and were included for analysis. Algorithm 5 demonstrated the highest sensitivity (89.8%) while algorithm 3 demonstrated the highest specificity (97.8%). Select questions were also effective in clarifying disease phenotype. CONCLUSION: In this study. we have successfully demonstrated that ALTO is a simple tool capable of discriminating AA from other types of hair loss. The ALTO may be useful to identify individuals with AA within large populations.

Identifying work related injuries: comparison of methods for interrogating text fields.

BACKGROUND: Work-related injuries in Australia are estimated to cost around $57.5 billion annually, however there are currently insufficient surveillance data available to support an evidence-based public health response. Emergency departments (ED) in Australia are a potential source of information on work-related injuries though most ED's do not have an 'Activity Code' to identify work-related cases with information about the presenting problem recorded in a short free text field. This study compared methods for interrogating text fields for identifying work-related injuries presenting at emergency departments to inform approaches to surveillance of work-related injury. METHODS: Three approaches were used to interrogate an injury description text field to classify cases as work-related: keyword search, index search, and content analytic text mining. Sensitivity and specificity were examined by comparing cases flagged by each approach to cases coded with an Activity code during triage. Methods to improve the sensitivity and/or specificity of each approach were explored by adjusting the classification techniques within each broad approach. RESULTS: The basic keyword search detected 58% of cases (Specificity 0.99), an index search detected 62% of cases (Specificity 0.87), and the content analytic text mining (using adjusted probabilities) approach detected 77% of cases (Specificity 0.95). CONCLUSIONS: The findings of this study provide strong support for continued development of text searching methods to obtain information from routine emergency department data, to improve the capacity for comprehensive injury surveillance.