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The lifetime risk and impact of vitiligo across sociodemographic groups: a UK population-based cohort study
Abstract Background Vitiligo is an autoimmune skin disorder characterized by depigmented patches of skin, which can have significant psychological impacts. Objectives To estimate the lifetime incidence of vitiligo, overall, by ethnicity and across other sociodemographic subgroups, and to investigate the impacts of vitiligo on mental health, work and healthcare utilization. Methods Incident cases of vitiligo were identified in the Optimum Patient Care Database of primary care records in the UK between 1 January 2004 and 31 December 2020. The lifetime incidence of vitiligo was estimated at age 80 years using modified time-to-event models with age as the timescale, overall and stratified by ethnicity, sex and deprivation. Depression, anxiety, sleep disturbance, healthcare utilization and work-related outcomes were assessed in the 2 years after vitiligo diagnosis and compared with matched controls without vitiligo. The study protocol for this retrospective observational study was registered with ClinicalTrials.gov (NCT06097494). Results In total, 9460 adults and children were newly diagnosed with vitiligo during the study period. The overall cumulative lifetime incidence was 0.92% at 80 years of age [95% confidence interval (CI) 0.90–0.94]. Cumulative incidence was similar in female (0.94%, 95% CI 0.92–0.97) and male patients (0.89%, 95% CI 0.86–0.92). There were substantial differences in lifetime incidence across ethnic groups, listed by Office for National Statistics criteria [Asian 3.58% (95% CI 3.38–3.78); Black 2.18% (95% CI 1.85–2.50); Mixed/multiple 2.03% (95% CI 1.58–2.47); Other 1.05% (95% CI 0.94–1.17); and White 0.73% (95% CI 0.71–0.76)]. Compared with matched controls, people with vitiligo had an increased risk of depression [adjusted odds ratio (aOR) 1.08, 95% CI 1.01–1.15]; anxiety (aOR 1.19, 95% CI 1.09–1.30); depression or anxiety (aOR 1.10, 95% CI 1.03–1.17); and sleep disturbance [adjusted hazard ratio (aHR) 1.15, 95% CI 1.02–1.31]. People with vitiligo also had a greater number of primary care encounters (adjusted incidence rate ratio 1.29, 95% CI 1.26–1.32) and a greater risk of time off work (aHR 1.15, 95% CI 1.06–1.24). There was little evidence of disparities in vitiligo-related impacts across ethnic subgroups. Conclusions Clinicians should be aware of the markedly increased incidence of vitiligo in people belonging to Asian, Black, Mixed/multiple and Other groups. The negative impact of vitiligo on mental health, work and healthcare utilization highlights the importance of monitoring people with vitiligo to identify those who need additional support.
Explaining Health Risk Behaviors in the U.S. with Social Deprivation at Local and Regional Levels
Health risk behaviors are precursors to many chronic health outcomes, and hence, they pose a challenge to public health. Social deprivation undoubtedly creates circumstances that limit access to healthy habits. Moreover, broad regional effects (weather patterns, political ideology, social norms), and local characteristics (cultural notions and barriers, urban places) also influence lifestyle choices and must be accounted for to truly understand the impact of social deprivation on risky behaviors. This research fills the knowledge gap in epidemiological modeling of health risk behaviors by leveraging machine learning to find associations between social deprivation and health risk behaviors, when adjusted by regional and local effects. Four health risk behaviors, namely, binge drinking, smoking, lack of sleep, and lack of physical activity from the CDC PLACES project are considered in a single framework to understand and compare the interplay between local/regional characteristics and seven measures of social deprivation. Our results indicate that local and/or regional factors rise to the top for three out of four risk behaviors (binge drinking, smoking and lack of sleep) out-competing social deprivation measures. Un-entangling the geographical effects reveals that poverty, educational attainment and non-employment are the three deprivation measures most significantly associated with all four health risk factors. The research thus indicates that public health policies to promote healthy lifestyle behaviors must seek to remedy social deprivation, but using socially and culturally sensitive interventions.