Assessing a measure for Quality of Life in patients with severe Alopecia Areata: a multicentric Italian study

Abstract

Objective: The prevalence of anxiety and depression in patients diagnosed with Alopecia Areata (AA) is very high and this significant burden of psychological symptoms threatens the Health-Related Quality of Life (HRQoL) of affected patients. Indeed, AA often does not produce significant physical symptoms, but it nonetheless disrupts many areas of mental health. Clinical assessment of disease severity may not reliably predict patient's HRQoL, nor may it predict the patient's perception of illness. For this reason, considerable effort has been made to apply and develop measures that consider patient's perception and assess the HRQoL of individuals affected by AA. The aim of this multicentric study was to provide the Italian version of the Skindex-16AA and to evaluate its psychometric properties in a clinical sample of consecutive patients with moderate-to-severe AA.

Methods: This is a longitudinal, multicenter, observational study. Patients returned for follow-up visits at 4-, 12-, and 24-weeks. The analyses of the current work aimed to confirm the factorial structure of the Skindex-16AA. In the case of non-fit, an alternative structure for the model was proposed, using an Exploratory Graph Analysis and the Bayesian approach.

Results: The sample was composed of 106 patients with AA. Alopecia Universalis was the most frequently diagnosed type of alopecia at all time points. The analyses on the Skindex-16AA revealed that a two-factor structure with eight items fit the data best (Bayesian Posterior Predictive Checking using 95% Confidence Interval for the Difference Between the Observed and the Replicated Chi-Square values = -6.246/56.395, Posterior Predictive P-value = 0.06), and reported satisfactory psychometric properties (i.e., internal consistency and convergent validity).

Conclusion: The Skindex-8AA demonstrated optimal psychometric properties (i.e., convergent and construct validity, and test-retest reliability) measured in a sample of patients with AA, that may suggest that it is an appropriate tool to measure the HRQoL in AA patients. However, further studies are needed in order to confirm and tested other psychometric features of this tool.

Keywords: Alopecia Areata (AA); Bayesian confirmatory factor analysis; Quality of Life; patients reported outcomes; psychodermatology.

Figure 1

Item stability analysis for the Exploratory Graph Analysis. Different colors indicate the different communities found with the Exploratory Graph Analysis. Nodes (i.e., items) with a value < 0.70 are not considered stable.