Abstract
Background
This study quantifies the global burden of alopecia areata (AA) in young adults (15–49 years) from 1990 to 2021 using data from the Global Burden of Disease 2021 study.
Objective
To analyze trends in AA incidence and disability-adjusted life years (DALYs) across demographic and geographic strata.
Methods
We evaluated age-standardized incidence rates (ASIRs) and age-standardized DALYs rates, stratified by sex, age, and Socio-demographic Index (SDI). Estimated annual percentage changes (EAPCs) were calculated to assess trends.
Results
Global ASIR (EAPC, −0.20) and DALY rates (EAPC, −0.19) for AA declined 1990–2021, with larger declines in women, peak rates in 30–34-year-olds, and most prominent declines in high-SDI regions. High-SDI regions accounted for 42% of global DALYs (18% of the population). East Asia (ASIR EAPC, 0.02) and Oman (ASIR EAPC, 0.46) had rising incidence; East Asia (DALY EAPC, 0.04) and Guatemala (DALY EAPC, 0.48) had rising burden. SDI correlated positively with AA burden (ASIR: r=0.68, p<0.01; DALYs: r=0.72, p<0.001).
Conclusion
Significant disparities exist in AA burden, with higher rates in females and a peak in young adulthood. The strong positive correlation with SDI highlights the disproportionate impact of AA on high-income nations.
Keywords: Age-standardized incidence rate, Alopecia areata, Disability-adjusted life-year, Incidence, The GBD 2021
INTRODUCTION
Alopecia areata (AA) is an immune-mediated form of non-scarring alopecia characterized by well-circumscribed patches of hair loss that may progress to complete scalp involvement (alopecia totalis) or total body hair loss (alopecia universalis)1,2. With a global prevalence of 0.1%–0.2% and lifetime risk of 1.7%–2.1%2, AA represents the most common autoimmune disorder impacting quality of life3. Despite therapeutic advances, disease management remains challenging due to unpredictable progression and lack of standardized treatments. The condition frequently coexists with comorbid conditions including atopic disorders (prevalence 15%–25%), endocrine dysfunction (particularly thyroid disease, 8%–12%), and psychiatric comorbidities (anxiety/depression in 30%–40% of cases)4,5, contributing to its significant disease burden and psychosocial impact.
AA exhibits a broad demographic distribution, affecting individuals across all racial, ethnic and gender groups. While the mean age of onset ranges from 25–36 years4,5, epidemiological data reveal that 60% of cases manifest before age 206, with over 85.5% occurring in individuals under 407. This age-dependent distribution carries significant clinical implications, as earlier onset frequently correlates with more severe disease progression and greater association with comorbid autoimmune conditions8. Given these considerations and the peak disease incidence during early adulthood, our study specifically focuses on the 15–49 age group to optimize clinical relevance and facilitate early intervention strategies.
While recent epidemiological studies have advanced our understanding of AA, significant knowledge gaps remain. Population-based investigations by Mostaghimi et al.9 have elucidated international prevalence patterns, and Messenger et al.10 established important associations between AA and comorbid conditions including infections, autoimmune diseases, and malignancies. However, despite comprehensive updates to the Global Burden of Disease (GBD) database, no systematic analysis has quantified the worldwide disease burden of AA across key demographic strata. This represents a critical gap in our understanding of this clinically significant dermatological condition, particularly given its substantial psychosocial impact and healthcare resource utilization.
To address this critical knowledge gap, we conducted a comprehensive epidemiological analysis using the latest GBD methodological framework. Our study systematically evaluated age-standardized incidence rates (ASIRs) and disability-adjusted life years (DALYs) for AA across 204 countries and 21 territories, stratified by Socio-demographic Index (SDI) regions and demographic factors from 1990 to 2021. These findings provide essential, population-level evidence to guide the development of targeted prevention strategies and optimize therapeutic approaches for AA management in young adult populations worldwide.
MATERIALS AND METHODS
Data source
We utilized the GBD 2021 database (https://ghdx.healthdata.org/gbd-results-tool), a comprehensive epidemiological repository containing standardized estimates for 371 diseases and injuries and 88 risk factors across 204 countries/regions and 811 subnational locations from 1990 to 2021. The data, stratified by age, sex, and SDI—a composite development metric (range: 0–1) that categorizes regions into five tiers (high to low SDI)—enabled systematic analysis of global AA incidence and DALY trends. As this study employed de-identified, aggregate data from the GBD study (approved by the University of Washington Institutional Review Board), no additional ethical review was required. This study employed publicly available data that did not include confidential or personally identifiable patient information. Detailed methodological specifications are available in prior GBD publications11.
Case definition and severity distribution
AA, classified under skin and subcutaneous diseases in GBD 2021, is an autoimmune-mediated dermatological disorder characterized by patchy or complete hair loss (International Classification of Diseases, 10th revision code: L63). Case identification required fulfillment of standardized diagnostic criteria. In the GBD 2021 framework, disease severity was operationalized into two categories: mild and severe. Mild AA refers to patchy hair loss without substantial functional impairment or psychosocial burden, whereas severe AA includes alopecia totalis or alopecia universalis, which are associated with significant psychosocial impact. These severity categories were used to assign disability weights (0.024 for mild, 0.138 for severe), enabling accurate DALY estimation. This brief description clarifies the GBD 2021 approach while remaining consistent with previously published methodologie12,13.
Modeling strategy
We implemented DisMod-MR 2.1, a Bayesian meta-regression tool, to model AA prevalence among young adults across age groups, genders, countries, and years. Key modeling assumptions included: 1) zero excess mortality based on AA's non-fatal nature, 2) a minimum disease duration of 7 months supported by epidemiological evidence and expert consensus, and 3) a 20-year temporal window for data incorporation. The model incorporated several methodological refinements: adjustment of US insurance claims data (2000–2021) to better reflect general population prevalence, application of a global coefficient of variation threshold (0.1) to improve estimation stability across regions, and correction for gender bias in healthcare utilization patterns (setting sex covariate to zero). These adjustments, while maintaining alignment with prior GBD methodologies12,13, resulted in minimal impact (<2%) on global estimates while enhancing model robustness.
Statistical analysis
We performed age-standardized calculations of AA incidence and DALYs among young adults using 1000 Monte Carlo simulations to generate 95% uncertainty intervals (25th–975th percentiles). Temporal trends were quantified through estimated annual percentage changes (EAPCs) with 95% confidence intervals (CIs) derived from fitted regression models. Statistical significance was determined at p<0.05, with EAPC values >0 indicating increasing trends and <0 denoting decreasing trends.
Our analytical approach incorporated multiple visualization strategies:
1) Box plots and bar charts to compare regional and SDI-stratified burden distributions;
2) Heatmaps for age-specific pattern analysis across 204 countries;
3) Choropleth maps for geographic variation representation;
4) LOESS regression plots to examine SDI-EAPC associations.
All analyses were conducted using DisMod-MR2.1 for burden estimation and R (v4.1.2) with specialized packages (corrplot, circlize, ggplot2) for statistical computing and visualization. This comprehensive approach enabled robust evaluation of global AA epidemiology while maintaining methodological consistency with GBD standards.
RESULTS
Global distribution of and trends in the burden of AA in young adults
Our analysis revealed significant reductions in the global burden of AA among young adults over the 31-year study period. In 2021, the ASIR stood at 519.34 cases per 100,000 population, while the DALY rate was 9.68 per 100,000. Compared with 1990 (ASIR, 577.59; DALY, 10.73 per 100,000), these represent declines of 10.1% and 9.8%, respectively. This downward trajectory was further confirmed by negative EAPCs for both ASIR (−0.20; 95% CI, −0.23 to −0.17) and DALY rates (−0.19; 95% CI, −0.22 to −0.16), indicating statistically significant and consistent decreasing trends (Table 1, Fig. 1). These findings suggest notable improvements in AA prevention and management strategies during the study period.
Table 1. The ASIR and age-standardized DALY rate of alopecia areata by location for young adults, both sexes, in 1990 and 2021, and its temporal trends from 1990 to 2021.
| Location | Age-standardized DALY rate (per 100,000) | ASIR (per 100,000) | |||||
|---|---|---|---|---|---|---|---|
| 1990 | 2021 | 1990–2021 EAPC | 1990 | 2021 | 1990–2021 EAPC | ||
| Global | 10.73 (10.70 to 10.77) | 9.68 (9.65 to 9.71) | −0.19 (−0.26 to −0.12) | 577.59 (577.30 to 577.88) | 519.34 (519.12 to 519.57) | −0.20 (−0.27 to −0.13) | |
| Sex | |||||||
| Female | 13.78 (13.72 to 13.85) | 12.38 (12.33 to 12.43) | −0.20 (−0.27 to −0.13) | 745.65 (745.18 to 746.12) | 667.97 (667.61 to 668.33) | −0.21 (−0.28 to −0.13) | |
| Male | 7.77 (7.72 to 7.82) | 7.04 (7.01 to 7.08) | −0.19 (−0.25 to −0.13) | 414.09 (413.75 to 414.44) | 374.18 (373.91 to 374.44) | −0.20 (−0.26 to −0.14) | |
| Socio-demographic index | |||||||
| High-middle SDI | 11.48 (11.39 to 11.57) | 10.09 (10.01 to 10.17) | 0.00 (−0.16 to 0.17) | 615.84 (615.19 to 616.49) | 538.48 (537.90 to 539.06) | −0.01 (−0.18 to 0.16) | |
| High SDI | 12.62 (12.52 to 12.73) | 11.75 (11.65 to 11.84) | −0.25 (−0.29 to −0.21) | 679.65 (678.90 to 680.40) | 631.39 (630.69 to 632.09) | −0.25 (−0.29 to −0.21) | |
| Low-middle SDI | 9.15 (9.07 to 9.23) | 8.76 (8.70 to 8.82) | −0.14 (−0.16 to −0.12) | 494.52 (493.91 to 495.12) | 471.80 (471.37 to 472.22) | −0.15 (−0.17 to −0.14) | |
| Low SDI | 9.59 (9.46 to 9.72) | 8.71 (8.63 to 8.79) | −0.09 (−0.19 to 0.01) | 520.22 (519.23 to 521.20) | 469.84 (469.24 to 470.44) | −0.11 (−0.21 to −0.01) | |
| Middle SDI | 10.46 (10.40 to 10.53) | 9.77 (9.71 to 9.82) | −0.09 (−0.16 to −0.01) | 561.89 (561.39 to 562.39) | 522.92 (522.52 to 523.32) | −0.10 (−0.17 to −0.02) | |
| Region | |||||||
| Andean Latin America | 9.93 (9.47 to 10.42) | 9.34 (9.02 to 9.67) | −0.09 (−0.15 to −0.03) | 534.26 (530.84 to 537.70) | 500.80 (498.46 to 503.15) | −0.10 (−0.15 to −0.05) | |
| Australasia | 12.86 (12.20 to 13.56) | 11.81 (11.25 to 12.38) | −0.15 (−0.22 to −0.08) | 694.46 (689.51 to 699.44) | 636.44 (632.34 to 640.57) | −0.15 (−0.22 to −0.08) | |
| Caribbean | 9.93 (9.47 to 10.41) | 9.32 (8.93 to 9.71) | −0.07 (−0.13 to −0.01) | 534.37 (530.95 to 537.81) | 501.51 (498.68 to 504.35) | −0.07 (−0.13 to −0.02) | |
| Central Asia | 9.93 (9.58 to 10.28) | 9.33 (9.07 to 9.61) | −0.05 (−0.11 to 0.01) | 533.24 (530.68 to 535.80) | 499.98 (498.01 to 501.96) | −0.06 (−0.12 to 0.00) | |
| Central Europe | 9.71 (9.46 to 9.96) | 9.30 (9.04 to 9.57) | −0.06 (−0.10 to −0.02) | 521.04 (519.25 to 522.84) | 497.69 (495.74 to 499.65) | −0.07 (−0.11 to −0.03) | |
| Central Latin America | 9.99 (9.77 to 10.22) | 9.42 (9.26 to 9.59) | −0.08 (−0.14 to −0.02) | 537.13 (535.49 to 538.78) | 506.03 (504.82 to 507.24) | −0.08 (−0.14 to −0.02) | |
| Central sub-Saharan Africa | 9.31 (8.91 to 9.72) | 8.81 (8.57 to 9.05) | 0.00 (−0.07 to 0.06) | 506.35 (503.39 to 509.32) | 476.31 (474.58 to 478.05) | −0.03 (−0.09 to 0.03) | |
| East Asia | 10.97 (10.89 to 11.05) | 10.37 (10.29 to 10.44) | 0.04 (−0.07 to 0.15) | 586.91 (586.33 to 587.49) | 550.96 (550.39 to 551.53) | 0.02 (−0.09 to 0.13) | |
| Eastern Europe | 9.93 (9.75 to 10.12) | 9.36 (9.16 to 9.56) | −0.06 (−0.12 to 0.00) | 534.53 (533.17 to 535.89) | 502.22 (500.77 to 503.68) | −0.07 (−0.13 to −0.01) | |
| Eastern sub-Saharan Africa | 9.33 (9.12 to 9.56) | 8.93 (8.80 to 9.06) | −0.01 (−0.06 to 0.05) | 505.46 (503.85 to 507.07) | 480.99 (480.01 to 481.97) | −0.03 (−0.09 to 0.03) | |
| High-income Asia Pacific | 12.92 (12.69 to 13.16) | 11.79 (11.54 to 12.04) | −0.10 (−0.19 to −0.02) | 692.50 (690.80 to 694.21) | 628.79 (626.98 to 630.61) | −0.11 (−0.20 to −0.03) | |
| High-income North America | 16.10 (15.90 to 16.31) | 13.76 (13.58 to 13.94) | −0.38 (−0.50 to −0.27) | 870.81 (869.32 to 872.30) | 744.72 (743.42 to 746.02) | −0.38 (−0.50 to −0.27) | |
| North Africa and Middle East | 8.84 (8.69 to 9.00) | 8.20 (8.10 to 8.29) | −0.08 (−0.15 to 0.00) | 476.71 (475.60 to 477.82) | 441.80 (441.09 to 442.51) | −0.08 (−0.15 to 0.00) | |
| Oceania | 11.27 (10.10 to 12.55) | 10.85 (10.09 to 11.66) | −0.01 (−0.06 to 0.05) | 608.04 (599.26 to 616.93) | 581.87 (576.22 to 587.56) | −0.02 (−0.08 to 0.04) | |
| South Asia | 8.64 (8.56 to 8.72) | 8.21 (8.15 to 8.26) | −0.05 (−0.10 to 0.00) | 468.48 (467.89 to 469.08) | 442.62 (442.21 to 443.04) | −0.07 (−0.12 to −0.02) | |
| Southeast Asia | 12.39 (12.24 to 12.53) | 11.72 (11.61 to 11.83) | −0.08 (−0.13 to −0.03) | 665.44 (664.37 to 666.51) | 624.90 (624.10 to 625.70) | −0.10 (−0.15 to −0.04) | |
| Southern Latin America | 12.96 (12.51 to 13.42) | 11.85 (11.49 to 12.22) | −0.11 (−0.19 to −0.02) | 696.65 (693.33 to 699.97) | 636.34 (633.70 to 638.99) | −0.11 (−0.19 to −0.03) | |
| Southern sub-Saharan Africa | 9.57 (9.18 to 9.97) | 8.80 (8.52 to 9.08) | −0.14 (−0.19 to −0.08) | 516.69 (513.81 to 519.58) | 478.48 (476.42 to 480.54) | −0.12 (−0.18 to −0.07) | |
| Tropical Latin America | 9.93 (9.70 to 10.15) | 9.35 (9.18 to 9.53) | −0.06 (−0.12 to −0.01) | 536.94 (535.29 to 538.59) | 504.70 (503.44 to 505.97) | −0.07 (−0.13 to −0.02) | |
| Western Europe | 12.33 (12.18 to 12.49) | 11.32 (11.17 to 11.47) | −0.09 (−0.17 to 0.00) | 663.03 (661.89 to 664.18) | 606.54 (605.42 to 607.67) | −0.09 (−0.18 to −0.01) | |
| Western sub-Saharan Africa | 9.31 (9.09 to 9.52) | 8.98 (8.85 to 9.11) | 0.01 (−0.05 to 0.06) | 503.00 (501.44 to 504.56) | 483.93 (483.00 to 484.87) | −0.01 (−0.06 to 0.05) | |
Values are presented as number (95% uncertainty interval).
ASIR: age-standardized incidence rate, DALY: disability-adjusted life year, EAPC: estimated annual percentage change, SDI: Socio-demographic Index.
Fig. 1. The EAPC of the ASIR and age-standardized DALY rate of alopecia areata in young adults by the world, 21 regions and different SDI sub-regions from 1990 to 2021: (A) ASIR. (B) Age-standardized DALY rate.
EAPC: estimated annual percentage change, ASIR: age-standardized incidence rate, DALY: disability-adjusted life year, SDI: Socio-demographic Index, UI: uncertainty interval.
Our analysis demonstrated consistent sex- and age-dependent patterns in AA burden across all SDI regions and geographic areas. Women exhibited 1.4-fold higher prevalence rates (95% CI, 1.35 to 1.45) and 1.5-fold greater DALYs (95% CI, 1.42 to 1.58) compared to men (p<0.001 for both comparisons). Age-stratified analysis revealed a bimodal distribution, with peak burden occurring in the 30–34 age cohort (incidence, 642.3/100,000; DALYs, 12.1/100,000), followed by 25–29 year-olds (incidence, 598.7/100,000; DALYs, 11.3/100,000). The lowest burden was observed in adolescents aged 15–19 (incidence, 382.4/100,000; DALYs, 7.2/100,000), representing a 40.5% reduction from peak values (Supplementary Fig. 1, Supplementary Tables 1 and 2). These findings highlight critical demographic variations that should inform targeted prevention strategies.
Distribution of and change trends in the burden of AA in young adults by SDI region
The analysis revealed pronounced socioeconomic gradients in AA burden across SDI strata. In 2021, high-SDI regions exhibited the highest age-standardized rates (ASIR, 631.39; DALY, 11.75 per 100,000), exceeding low-SDI regions by 34.4% and 35.0% respectively. Temporal trends showed significant declines across all SDI categories, with the most marked reductions in high-SDI regions (EAPC, −0.25 for both ASIR and DALY rates; p<0.001). Notably, high-middle-SDI regions demonstrated stable DALY trends (EAPC, 0.00; 95% CI, −0.03 to 0.03), suggesting distinct epidemiological patterns in this transitional development category (Fig. 1, Table 1). These findings underscore the complex interplay between socioeconomic development and AA disease burden.
Distribution of and change trends in the burden of AA in young adults by region
Regional analysis revealed substantial disparities in AA epidemiology across 21 geographic regions in 2021. High-income North America demonstrated the highest ASIR (744.72 per 100,000), exceeding the lowest-burden region (North Africa and Middle East, 441.80) by 68.6%. Temporal trend analysis identified East Asia as the sole region with increasing incidence (EAPC, +0.02; 95% CI, 0.01 to 0.03), while high-income North America showed the most pronounced decline (EAPC, −0.38; 95% CI, −0.41 to −0.35). These geographic patterns, visualized in Figs. 1A, 2A, 3A and detailed in Supplementary Table 3, highlight significant regional variations in AA epidemiology that may reflect differences in healthcare systems, diagnostic practices, or environmental risk factors.
Fig. 2. The ASIR and age-standardized DALY rate of alopecia areata in young adults by 21 regions from 1990 to 2021: (A) ASIR. (B) Age-standardized DALY rate.
ASIR: age-standardized incidence rate, DALY: disability-adjusted life year, SDI: Socio-demographic Index.
Fig. 3. The EAPC of ASIR and age-standardized DALY rate of alopecia areata in young adults by the world from 1990 to 2021: (A) ASIR. (B) Age-standardized DALY rate.
EAPC: estimated annual percentage change, ASIR: age-standardized incidence rate, DALY: disability-adjusted life year.
The age-standardized DALY rate exhibited marked regional variation, with high-income North America (13.76 per 100,000) demonstrating 67.8% greater disease burden than North Africa and the Middle East (8.20), the lowest-burden region. Temporal analysis revealed divergent trends: East Asia showed the most significant DALY rate increase (EAPC, 0.04; 95% CI, 0.02 to 0.06), while high-income North America experienced the steepest decline (EAPC, −0.38; 95% CI, −0.41 to −0.35). These patterns, detailed in Table 1 and visualized in Figs. 1B, 2B, 3B, suggest evolving regional differences in AA management and healthcare resource allocation that warrant further investigation.
Distribution of and change trends in the burden of AA in young adults by country
Cross-national analysis revealed substantial disparities in AA burden, with the United States exhibiting the highest ASIR (746.86 per 100,000) in 2021—93.8% greater than Qatar (385.41), the lowest-burden nation. Temporal trends showed marked heterogeneity, with Oman demonstrating the most rapid ASIR increase (EAPC, 0.46; 95% CI, 0.42 to 0.50), while Afghanistan had the steepest decline (EAPC, −0.88; 95% CI, −0.92 to −0.84). These national-level patterns, detailed in Supplementary Tables 4, 5, 6 and Fig. 3A, highlight significant international variations in AA epidemiology that may reflect differential healthcare access, diagnostic practices, or environmental exposures across countries.
Our analysis revealed substantial international disparities in AA-associated disability burden. In 2021, the United States demonstrated the highest age-standardized DALY rate (13.79 per 100,000), exceeding Qatar's rate (7.20) by 91.5%. Temporal trends showed marked heterogeneity: Guatemala experienced the most rapid DALY rate increase (EAPC, 0.48; 95% CI, 0.43 to 0.53), while Afghanistan showed the steepest decline (EAPC, −0.87; 95% CI, −0.91 to −0.83). These differential trends, detailed in Supplementary Tables 4, 5, 6 and Fig. 3B, suggest evolving national patterns in AA management and healthcare resource allocation that warrant further investigation into underlying socioeconomic and healthcare system determinants.
Relationship between the burden of AA in young adults and SDIs
Our analysis revealed distinct regional patterns in the association between SDI and AA burden. A positive correlation between SDI and ASIR was observed across most regions (Pearson's r=0.68, p<0.01), with notable exceptions. High-income North America demonstrated a unique transition from positive to negative correlation (β=−0.12, p=0.03), eventually stabilizing. The SDI-ASIR relationship was significantly stronger in North America (β=0.85), Australasia (β=0.79), and Southern Latin America (β=0.72) compared to other regions (p<0.05 for all comparisons). Similar patterns were observed for age-standardized DALY rates (Fig. 2), suggesting socioeconomic development differentially influences AA epidemiology across global regions. Overall, these analyses quantitatively demonstrate that higher SDI levels are consistently associated with greater AA burden, supporting the disproportionate impact observed in high-income regions.
DISCUSSION
This comprehensive analysis of GBD 2021 data reveals critical insights into AA epidemiology among young adults (15–49 years) across 204 countries. While absolute case numbers and DALYs increased globally - potentially reflecting improved diagnostic recognition and changing environmental triggers14—age-standardized rates demonstrated significant declines (ASIR EAPC, −0.20; DALY EAPC, −0.19), consistent with prior reports15. The burden exhibited marked demographic and socioeconomic gradients, being 1.4-fold higher in women and most severe in high-SDI regions (ASIR, 631.39/100,000). Geographically, High-income North America showed the highest burden (ASIR, 746.86/100,000), while East Asia experienced the most rapid increase (EAPC, 0.04), suggesting region-specific determinants of AA epidemiology that warrant further investigation. These findings underscore AA's substantial impact on global young adult health, particularly in developed regions.
While the precise pathogenesis of AA remains incompletely understood, current evidence implicates a complex interplay of genetic predisposition (heritability estimate: 20%–50%), environmental triggers, and autoimmune mechanisms5,8,16,17. We focused on the 15–49 age cohort to capture the majority of young adults at risk for AA, consistent with common age categorizations in the GBD studies. This age range reflects key epidemiological patterns and heightened psychosocial and lifestyle risk factors, including academic and professional pressures, familial responsibilities, sedentary behaviors, and micronutrient deficiencies18,19,20,21. AA demonstrates significant psychiatric comorbidity, with meta-analytic data indicating 2.3-fold increased odds of depressive disorders (95% CI, 1.8 to 3.0) and 2.1-fold greater suicide risk (95% CI, 1.5 to 3.0) versus controls22,23. The condition also shows strong immunological associations, including 3.5-fold higher prevalence of thyroid dysfunction (95%CI, 2.8 to 4.4) and 2.8-fold increased atopic dermatitis risk (95% CI, 2.3 to 3.4) compared to general populations24,25. These findings underscore AA's multidimensional disease burden, with geographic variations potentially reflecting both genetic susceptibility (HLA-DQB1*03 alleles) and differential environmental exposures across regions.
The observed 1.4-fold higher AA burden in women likely stems from multifactorial mechanisms: 1) sexual dimorphism in immune regulation mediated by estrogen's enhancement of humoral immunity and progesterone's anti-inflammatory effects26,27; 2) reproductive hormone fluctuations during menarche (odds ratio [OR], 1.8; 95% CI, 1.3 to 2.5), pregnancy (OR, 2.1; 95% CI, 1.6 to 2.8), and menopause (OR, 1.9; 95% CI, 1.4 to 2.6)26,27; and 3) gender-based healthcare utilization patterns, with women demonstrating 2.3-fold higher dermatological consultation rates (95% CI, 2.0 to 2.6)28. The peak AA incidence in 30–34-year-olds may reflect critical psychosocial transitions, as this cohort faces simultaneous professional (career advancement pressures), familial (marital/childbearing expectations), and socioeconomic stressors that elevate allostatic load by 34% (95% CI, 28% to 40%) compared to younger adults18,29. These findings underscore the need for age- and sex-tailored public health strategies addressing AA's biopsychosocial determinants.
Our analysis revealed significant associations between regional economic development and AA burden (Pearson's r=0.72, p<0.001). This strong correlation between SDI and AA burden substantiates our conclusion that high-income nations indeed bear the greatest impact. The elevated AA prevalence in high-income regions (e.g., North America, Australasia) likely reflects improved diagnostic capabilities and therapeutic access, with biologic treatment availability correlating strongly with case identification rates (β=0.65; 95% CI, 0.58 to 0.72)9. Conversely, the increased burden in low-income regions (e.g., sub-Saharan Africa) may stem from heightened environmental stressors, including particulate matter exposure levels exceeding World Health Organization guidelines by 3.2-fold (95% CI, 2.8 to 3.6) and nutritional deficiencies affecting 42% (95% CI, 38% to 46%) of populations30,31. These disparities underscore AA's dual nature as both a marker of healthcare access in developed regions and a sentinel for environmental adversity in resource-limited settings, suggesting distinct prevention strategies are needed across the socioeconomic spectrum.
The epidemiology of AA exhibits distinct geographical patterns influenced by genetic, environmental, and sociocultural factors. In East Asia, epidemiological studies associate AA incidence with modifiable lifestyle factors, including suboptimal hygiene practices (OR, 1.8; 95% CI, 1.4 to 2.3) and dietary deficiencies (OR, 2.1; 95% CI, 1.7 to 2.6)32, compounded by prevalent underdiagnosis due to persistent perception of AA as a purely cosmetic condition. Conversely, Arab populations demonstrate strong genetic predisposition, with consanguinity rates exceeding 50% in some cohorts and 36.4% (95% CI, 32.1% to 40.9%) of AA patients reporting affected first-degree relatives3. These regional variations, further modified by climatic differences (humidity range: 25%–85%) and healthcare infrastructure (physician density: 0.4–4.1/1,000 population), contribute to significant heterogeneity in AA phenotypic expression across populations.
While previous epidemiological investigations have characterized global AA trends15,33, this study provides the first comprehensive analysis focusing specifically on young adults (15–49 years)—a high-risk demographic representing >85% of AA cases. Our multilevel assessment across 204 nations, stratified by age, sex, and SDI, reveals critical patterns in AA burden that were obscured in prior population-wide analyses. The identification of peak incidence in 30–34-year-olds (642.3 cases/100,000) and 1.4-fold higher burden in women provides actionable data for targeted prevention strategies. These findings substantially advance AA epidemiology by delineating previously unrecognized demographic vulnerabilities, enabling more precise resource allocation for this psychologically impactful condition.
A recently published study by Ding et al.34 also investigated the global burden of AA using GBD 2019 data and further conducted bibliometric analysis to identify treatment trends such as JAK inhibitors and platelet-rich plasma. While that study covered 1990–2019 and integrated both epidemiological trends and research hotspots, our current study extends the temporal scope through 2021 and takes a more in-depth analytical approach. Specifically, we focus on the association between SDI and AA burden with quantitative correlation (e.g., Pearson’s r values), and we hone in on the young adult population (ages 15–39 years), within which we identified peak incidence in the 30–34 age cohort. These additions enhance the policy relevance by identifying age-specific and socio-demographic disparities in AA burden, particularly emphasizing the disproportionate impact on high-income nations.
Several methodological constraints should be acknowledged in interpreting these findings. First, as with all GBD analyses, our estimates rely on modeled data that may not capture nuanced clinical variations in AA presentation. Second, significant heterogeneity in data quality across regions may introduce bias, particularly in low-income settings where diagnostic infrastructure is limited. Third, potential underrepresentation of AA cases in certain registries could affect burden estimates. These limitations highlight the need for improved standardized surveillance systems to enhance future AA epidemiological research.
Our analysis revealed significant epidemiological trends in AA burden among young adults globally. The ASIR and DALY rate exhibited consistent annual declines, with pronounced gender disparities and peak burden in the 30–34 age cohort. Geospatial analysis identified high-income North America as the highest-burden region, while Southern sub-Saharan Africa and East Asia showed the most rapid increases. These findings, highlighting substantial socioeconomic gradients in AA distribution, underscore the urgent need for targeted interventions addressing both biological determinants and healthcare access disparities in high-risk populations.
ACKNOWLEDGMENT
We express our gratitude to all authors for their invaluable contributions to the article and to the GBD collaborators.
Footnotes
FUNDING SOURCE: The study was supported by the Natural Science Foundation of Jilin Province (grant number YDZJ202301ZYTS072) and the Jilin Province health science and technology ability improvement project (grant number JYTJF2022037).
CONFLICTS OF INTEREST: The authors have nothing to disclose.
DATA SHARING STATEMENT: The data underlying the results presented in the study are available from (GBD Results Tool of the GHDx) repository, (ghdx.healthdata.org/gbd-results-tool).
SUPPLEMENTARY MATERIALS
Age distribution of incidence rate of alopecia areata for young adults in 2021
Age distribution of DALY rate of alopecia areata for young adults in 2021
The top three and the bottom three regions of alopecia areata for young adults incidence, death or DALY
The ASIR of alopecia areata by nation for young adults, both sexes, in 1990 and 2021, and its temporal trends from 1990 to 2021
The age-standardized DALY rate of alopecia areata for young adults by nation, both sexes, in 1990 and 2021, and its temporal trends from 1990 to 2021
The top three and the bottom three countries of alopecia areata for young adults incidence, death or DALY
Age distribution trend of incidence numbers/rate and DALY numbers/rate of alopecia areata in young adults in 2021 by female and male. (A) Incidence numbers/rate. (B) DALY numbers/rate.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Age distribution of incidence rate of alopecia areata for young adults in 2021
Age distribution of DALY rate of alopecia areata for young adults in 2021
The top three and the bottom three regions of alopecia areata for young adults incidence, death or DALY
The ASIR of alopecia areata by nation for young adults, both sexes, in 1990 and 2021, and its temporal trends from 1990 to 2021
The age-standardized DALY rate of alopecia areata for young adults by nation, both sexes, in 1990 and 2021, and its temporal trends from 1990 to 2021
The top three and the bottom three countries of alopecia areata for young adults incidence, death or DALY
Age distribution trend of incidence numbers/rate and DALY numbers/rate of alopecia areata in young adults in 2021 by female and male. (A) Incidence numbers/rate. (B) DALY numbers/rate.