Epidemiology of discoid lupus erythematosus among adults in the United States: a cross‐sectional analysis
Notice bibliographique
Résumé
Discoid lupus erythematosus (DLE) is a chronic cutaneous lupus erythematosus (CLE) characterized by well-demarcated, erythematous plaques, most commonly in the head and neck region.1 Prior estimates of DLE prevalence, ranging from 3.1 to 7.0 per 100,000, have been constrained by limited cohorts in urban areas with restricted patient demographic data.2, 3 We aim to ameliorate this gap via the National Institute of Health's All of Us research program, which prioritizes the inclusion of groups historically underrepresented in biomedical research. We specifically provide epidemiologic characteristics of DLE within the All of Us database, stratified by sociodemographic groups. We performed a cross-sectional analysis on the All of Us dataset on all patients with available electronic health record data. Using SNOMED code 200938002, we identified 922 DLE cases. Prevalence was estimated by calculating the proportion of DLE cases among all living patients with electronic health record (EHR) data. Univariable and multivariable logistic regression analyses were performed to determine the odds ratios (OR) of HS diagnosis within each patient category, with multivariable regression analysis controlled for demographic variables. 95% confidence intervals were calculated using the Wald method. Electronic health record data was retrieved for 287,011 patients, of which 922 had DLE. Table 1 reveals a greater than 5:1 ratio of female (0.46%, 95% CI 0.43–0.50) to male patients (0.09%, 95% CI 0.08–0.11). The prevalence of DLE was lowest among the oldest demographic, individuals aged 75 and above, with a prevalence of 0.22% (95% CI 0.18–0.27). Hispanic patients also had a higher prevalence of DLE at 0.37% (95% CI 0.32–0.42) compared to non-Hispanic patients. Among racial groups, African American patients exhibited the highest DLE prevalence (0.52%, 95% CI 0.46–0.58). Patients in the age range of 40–59 (aOR 1.75, 95% CI 1.45–2.11), individuals of Asian descent (aOR 1.65, 95% CI 1.11–2.37), and patients with an annual income between $10,000 and $25,000 (aOR 1.32, 95% CI 1.05–1.66) also exhibited heightened adjusted DLE odds in comparison to counterparts referenced in Table 2. Regarding education, those who attended college without earning a degree showed notably higher adjusted odds for DLE (aOR 1.75, 95% CI 1.35–2.28) compared to those with no high school degree (Table 2). Our study found a prevalence higher than previous estimates of 3.1 to 7.0 per 100,000 which were based on more restricted urban cohorts. This increase may reflect the broader diversity within the All of Us database. Furthermore, the higher prevalence of DLE among female patients is consistent with previous research, affirming the validity of our findings.2 However, current research is limited in explaining these phenomena within the DLE subclass. We, therefore, lean on the previously studied positive influences of cumulative smoke exposure and the X-linked genes, TLR7 and VGLL3, on the general CLE class as rationales behind the higher DLE affliction in older and female patients, respectively.4 Our results also suggest an association of DLE with reduced income levels. The impact of socioeconomic status has been researched concerning systemic lupus erythematosus and thus may extend to the DLE subclass.5 These findings underscore the presence of significant demographic disparities in DLE prevalence, emphasizing the importance of targeted screening and interventions for populations at elevated risk. Limitations include restricting the dataset to patients with EHRs and the potential for misclassification bias when using diagnostic codes to identify DLE cases. Furthermore, although diverse, this database is not a random sample of the U.S. population which may affect the generalizability of these findings. We attempt to limit such errors by combining a large, diverse sample size with controlling for confounding factors in statistical analysis but strongly recommend additional epidemiologic studies to further characterize DLE. The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition, the All of Us Research Program would not be possible without the partnership of its participants.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,005 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».