Geometric classification of scalp hair for valid drug testing, 6 more reliable than 8 hair curl groups

K Mkentane, J C Van Wyk, N Sishi, F Gumedze, M Ngoepe, L M Davids and N P Khumalo

PLOS ONE, 2017, vol. 12, issue 6, 1-10

Abstract: Introduction: Curly hair is reported to contain higher lipid content than straight hair, which may influence incorporation of lipid soluble drugs. The use of race to describe hair curl variation (Asian, Caucasian and African) is unscientific yet common in medical literature (including reports of drug levels in hair). This study investigated the reliability of a geometric classification of hair (based on 3 measurements: the curve diameter, curl index and number of waves). Materials and methods: After ethical approval and informed consent, proximal virgin (6cm) hair sampled from the vertex of scalp in 48 healthy volunteers were evaluated. Three raters each scored hairs from 48 volunteers at two occasions each for the 8 and 6-group classifications. One rater applied the 6-group classification to 80 additional volunteers in order to further confirm the reliability of this system. The Kappa statistic was used to assess intra and inter rater agreement. Results: Each rater classified 480 hairs on each occasion. No rater classified any volunteer’s 10 hairs into the same group; the most frequently occurring group was used for analysis. The inter-rater agreement was poor for the 8-groups (k = 0.418) but improved for the 6-groups (k = 0.671). The intra-rater agreement also improved (k = 0.444 to 0.648 versus 0.599 to 0.836) for 6-groups; that for the one evaluator for all volunteers was good (k = 0.754). Conclusions: Although small, this is the first study to test the reliability of a geometric classification. The 6-group method is more reliable. However, a digital classification system is likely to reduce operator error. A reliable objective classification of human hair curl is long overdue, particularly with the increasing use of hair as a testing substrate for treatment compliance in Medicine.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0172834

DOI: 10.1371/journal.pone.0172834

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