Recurrent Pulmonary Tuberculosis in China, 2005 to 2021.


Journal

JAMA network open
ISSN: 2574-3805
Titre abrégé: JAMA Netw Open
Pays: United States
ID NLM: 101729235

Informations de publication

Date de publication:
01 Aug 2024
Historique:
medline: 12 8 2024
pubmed: 12 8 2024
entrez: 12 8 2024
Statut: epublish

Résumé

Despite posing a significant challenge to global tuberculosis (TB) elimination efforts, recurrent TB remains understudied due to the challenges of long-term observation. To investigate the burden of recurrent TB using data from patients with pulmonary TB (PTB) in China. This retrospective cohort study included all bacteriologically confirmed or clinically diagnosed PTB cases reported to the Tuberculosis Information Management System with completed or successful treatment outcomes from January 1, 2005, to December 31, 2021. Data were analyzed from July 15, 2022, to October 28, 2023. Newly diagnosed PTB was classified into primary, hematogenous disseminated, or secondary PTB. The primary outcome was the annual recurrence rate, stratified by disease classification, over the 17-year observation period. The recurrence rate for year n was calculated by dividing the number of patients with recurrent TB in year n by observed person-years in year n. The secondary outcome was the annual proportion of recurrent TB among reported cases and associated risk factors. Of 13 833 249 patients with TB reported to the Tuberculosis Information Management System, 10 482 271 with PTB met the inclusion criteria. Of these, 68.9% were male, 22.3% were 65 years or older, 89.6% were of Han ethnicity, and 68.4% were agricultural workers. A total of 413 936 patients experienced a recurrent TB episode after successful treatment, resulting in an overall recurrence rate of 0.47 (95% CI, 0.47-0.48) per 100 person-years. The recurrence rate for patients with primary PTB was 0.24 (95% CI, 0.22-0.26) per 100 person-years; for hematogenous disseminated PTB, 0.37 (95% CI, 0.36-0.38) per 100 person-years; and for secondary PTB, 0.48 (95% CI, 0.47-0.48) per 100 person-years. The cumulative proportion of recurrences within the first 2 years accounted for 48.9% of all recurrent cases. The proportion of recurrent cases among notified incident cases increased 1.9-fold from 4.7% in 2015 to 8.8% in 2021. Among other factors, ages 45 to 64 years (adjusted hazard ratio, 1.77 [95% CI, 1.65-1.89]) and having completed treatment (adjusted hazard ratio, 1.16 [95% CI, 1.14-1.18]) were identified as associated with recurrence. In this retrospective cohort study, the PTB recurrence rate was substantially higher than the incidence, and the proportion of recurrent cases increased. Almost half of the recurrence occurred within the first 2 years, suggesting that routine posttreatment follow-up may represent an important strategy for accelerating TB elimination.

Identifiants

pubmed: 39133484
pii: 2822208
doi: 10.1001/jamanetworkopen.2024.27266
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2427266

Auteurs

Tao Li (T)

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing, China.

Bo Zhang (B)

School of Environmental Science and Engineering, Hainan University, Haikou, China.
Center for Drug Abuse Control and Prevention, National Institute of Health Data Science, Peking University, Beijing, China.
Department of Global Health, School of Public Health, Peking University, Beijing, China.

Xin Du (X)

National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.

Shaojun Pei (S)

Department of Global Health, School of Public Health, Peking University, Beijing, China.

Zhongwei Jia (Z)

Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing, China.
Center for Drug Abuse Control and Prevention, National Institute of Health Data Science, Peking University, Beijing, China.
Department of Global Health, School of Public Health, Peking University, Beijing, China.

Yanlin Zhao (Y)

National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.

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