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Updated: Aug 7, 2025

Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates
Published on: September 5, 2017
Time-trend analysis of tuberculosis diagnosis in Shenzhen, China between 2011 and 2020
Chuang-Yue Hong1, Fu-Lin Wang2, You-Tong Zhang2
1Department of Tuberculosis Prevention and Control, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China.
Objective:
To describe the trend of tuberculosis (TB) diagnosis in the migrant city Shenzhen, China, and analyze the risk factors of diagnosis delays.
Methods:
Demographic and clinical information of TB patients from 2011 to 2020 in Shenzhen were extracted. A bundle of measures to enhance TB diagnosis had been implemented since late 2017. We calculated the proportions of patients who underwent a patient delay (>30 days from syndrome onset to first care-seeking) or a hospital delay (>4 days from first care-seeking to TB diagnosis). Multivariable logistic regression was used to analyze the risk factors of diagnosis delays.
Results:
During the study period, 43,846 patients with active pulmonary TB were diagnosed and registered in Shenzhen. On average, the bacteriological positivity rate of the patients was 54.9%, and this increased from 38.6% in 2017 to 74.2% in 2020. Overall, 30.3 and 31.1% of patients had a patient delay or a hospital delay, respectively. Molecular testing significantly increased bacteriological positivity and decreased the risk of hospital delay. People >35 years old, the unemployed, and residents had a higher risk of delays in both patient care-seeking and hospital diagnosis than younger people, workers, or migrants. Compared with passive case-finding, active case-finding significantly decreased the risk of patient delay by 5.47 (4.85-6.19) times.
Conclusion:
The bacteriological positivity rate of TB patients in Shenzhen increased significantly but the diagnosis delays were still serious, which may need more attention when active case-finding in risk populations and optimization of molecular testing.
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