Association between diagnostic delay and prognosis of pulmonary tuberculosis in Shandong, China: a retrospective study

Assessing heterogeneity of patient and health system delay among TB in a population with internal migrants in China

Backgrounds

The diagnostic delay of tuberculosis (TB) contributes to further transmission and impedes the implementation of the End TB Strategy. Therefore, we aimed to describe the characteristics of patient delay, health system delay, and total delay among TB patients in Shanghai, identify areas at high risk for delay, and explore the potential factors of long delay at individual and spatial levels.

Method

The study included TB patients among migrants and residents in Shanghai between January 2010 and December 2018. Patient and health system delays exceeding 14 days and total delays exceeding 28 days were defined as long delays. Time trends of long delays were evaluated by Joinpoint regression. Multivariable logistic regression analysis was employed to analyze influencing factors of long delays. Spatial analysis of delays was conducted using ArcGIS, and the hierarchical Bayesian spatial model was utilized to explore associated spatial factors.

Results

Overall, 61,050 TB patients were notified during the study period. Median patient, health system, and total delays were 12 days (IQR: 3-26), 9 days (IQR: 4-18), and 27 days (IQR: 15-43), respectively. Migrants, females, older adults, symptomatic visits to TB-designated facilities, and pathogen-positive were associated with longer patient delays, while pathogen-negative, active case findings and symptomatic visits to non-TB-designated facilities were associated with long health system delays (LHD). Spatial analysis revealed Chongming Island was a hotspot for patient delay, while western areas of Shanghai, with a high proportion of internal migrants and industrial parks, were at high risk for LHD. The application of rapid molecular diagnostic methods was associated with reduced health system delays.

Conclusion

Despite a relatively shorter diagnostic delay of TB than in the other regions in China, there was vital social-demographic and spatial heterogeneity in the occurrence of long delays in Shanghai. While the active case finding and rapid molecular diagnosis reduced the delay, novel targeted interventions are still required to address the challenges of TB diagnosis among both migrants and residents in this urban setting.

Time-trend analysis of tuberculosis diagnosis in Shenzhen, China between 2011 and 2020

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.