Analysis on time delay of tuberculosis among adolescents and young adults in Eastern China

Abstract

Background: Tuberculosis (TB) is recognized as a significant global public health concern. Still, there remains a dearth of comprehensive evaluation regarding the specific indicators and their influencing factors of delay for adolescents and young adults.

Methods: All notified pulmonary TB (PTB) patients in Jiaxing City were collected between 2005 and 2022 from China's TB Information Management System. Logistic regression models were conducted to ascertain the factors that influenced patient and health system delays for PTB cases, respectively. Furthermore, the impact of the COVID-19 pandemic on local delays has been explored.

Results: From January 1, 2005 to December 31, 2022, a total of 5,282 PTB cases were notified in Jiaxing City, including 1,678 adolescents and 3,604 young adults. For patient delay, female (AOR: 1.18, 95%CI: 1.05-1.32), PTB complicated with extra-pulmonary TB (AOR: 1.70, 95% CI: 1.28-2.26), passive case finding (AOR: 1.46, 95% CI: 1.07-1.98) and retreatment (AOR: 1.52, 95% CI: 1.11-2.09) showed a higher risk of delay. For health system delay, minorities (AOR: 0.69, 95% CI: 0.53-0.90) and non-students (AOR: 0.83, 95% CI: 0.71-0.98) experienced a lower delay. Referral (AOR: 1.46, 95% CI: 1.29-1.65) had a higher health system delay compared with clinical consultation. Furthermore, county hospitals (AOR: 1.47, 95% CI: 1.32-1.65) and etiological positive results (AOR: 1.46, 95% CI: 1.30-1.63) were associated with comparatively high odds of patient delay. Contrarily, county hospitals (AOR: 0.88, 95% CI: 0.78-1.00) and etiological positive results (AOR: 0.67, 95% CI: 0.59-0.74) experienced a lower health system delay. Besides, the median of patient delay, health system delay, and total delay during the COVID-19 pandemic were significantly lower than that before.

Conclusion: In general, there has been a noteworthy decline in the notification rate of PTB among adolescents and young adults in Jiaxing City while the declining trend was not obvious in patient delay, health system delay, and total delay, respectively. It also found factors such as gender, case-finding method, and the hospital level might influence the times of seeking health care and diagnosis in health agencies. These findings will provide valuable insights for refining preventive and treatment strategies for TB among adolescents and young adults.

Keywords: delay; health system delay; influencing factors; logistic regression; patient delay; tuberculosis.