780. Incidence and Prevalence of Nontuberculous Mycobacterial Lung Disease in US Medicare, 2008–2015
Notice bibliographique
Résumé
Abstract Background Previous research has reported nontuberculous mycobacterial lung disease (NTMLD) prevalence of 47 per 100,000 among Medicare beneficiaries ≥65 years in 2007, with an average increase of 8.2% annually between 1997 and 2007. In this study, we have evaluated NTMLD incidence and prevalence in Medicare between 2008 and 2015. Methods Patients diagnosed for NTMLD with an ICD9 031.0 were identified from the Medicare database (N≈30 million yearly), not including the Part C portion. Individuals who incurred at least 2 medical claims ≥30 days apart between 2007 and 2015 were considered as a positive NTMLD case, yielding 58,294 patients. All individuals fulfilling the case definition each calendar year were considered as prevalent cases. Incident cases included those meeting case criteria and who did not have a Medicare claim for NTMLD in the prior year. Poisson regression was used to estimate yearly confidence intervals. ARIMA models were used to forecast incidence and prevalence over 2016–2025. Results Patients with NTMLD in the Medicare database had a mean age of 74 (standard deviation: ±10) years. Sixty-nine percent were women and 89% white. Yearly NTMLD incidence increased from 20.7 (95% CI: 20.2–21.3) in 2008 to 28.1 (27.5–28.7) in 2013 per 100,000 Medicare beneficiaries and leveled to 27.6 (26.9–28.2) in 2014 and 25.9 (25.3–26.5) in 2015 per 100,000. Yearly NTMLD prevalence increased throughout the observation period from 41.6 (40.9–42.3) in 2008 to 63.1 (62.2–64.0) in 2015 per 100,000 Medicare beneficiaries. Incidence was 28.1 vs. 14.7 per 100,000 in 2015 in Medicare beneficiaries ≥65 years vs. those <65 years, respectively. Prevalence was 70.2 vs. 27.9 per 100,000 in 2015 in Medicare beneficiaries ≥65 years vs. those <65 years, respectively. In 2015, incidence and prevalence were higher in women than men (33.9 vs. 16.0/100,000 and 86.2 vs. 34.6/100,000, respectively) and among individuals of Asian origin compared with White (41.1 vs. 27.6/100,000 and 89.4 vs. 68.7/100,000, respectively). The 10-year incidence and prevalence forecasts were presented in figures. Conclusion In US Medicare beneficiaries, NTMLD incidence increased from 2008 through 2013 and leveled off in more recent years, while NTMLD prevalence continued to rise through 2015. Disclosures K. L. Winthrop, Insmed Incorporated: Scientific Advisor, Consulting fee and Research grant. T. Marras, Insmed Incorporated: Investigator, Consulting fee and Research grant, Horizon Pharmaceuticals: Consultant, Consulting fee, Red Hill: Consultant, Consulting fee, AstraZeneca: CME, Speaker honorarium. G. Eagle, Insmed Incorporated: Employee, Salary. R. Zhang, Insmed Incorporated: Consultant, Consulting fee. P. Wang, Insmed Incorporated: Employee, Salary. E. Chou, Insmed Incorporated: Employee, Salary. Q. Zhang, Insmed Incorporated: Employee, Salary.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».