JMIR Publications

ABSTRACT

Background:

The control of pulmonary tuberculosis (PTB) burden was critical to achieving the vision of the End-TB goal.

Objective:

The study analyzed the temporal trends in PTB incidence associated with age, period and birth cohort from 2006 to 2020 in Yunnan Province of China, then projected the PTB burden to 2030, and explored the drivers of incidence.

Methods:

The aggregated PTB incidence rate and number between 2005 and 2020 was obtained from the National Notifiable Disease Reporting System. We used the age-period-cohort (APC) model to evaluate the age, period and cohort effects in PTB incidence. We evaluated the net drift, local drift, longitudinal age curve, period and cohort effects. We applied the Bayesian age-period-cohort model to project future PTB incidence from 2021 to 2030. We applied the decomposition algorithm to attribute the trending changes in PTB incidence to population aging, population growth, and age-specific changes from 2006 to 2030.

Results:

From 2006 to 2020, the PTB incidence in Yunnan was relatively stable, although the absolute number of PTB incidence showed an increase. The net drift (overall annual percentage changes) was -1.56% (95% confidence interval, CI: -2.41% to -0.70%), with sex difference -1.94% (95% CI: -2.79% to -1.07%) for males and -1.07% (95%CI: -1.94% to -0.21%) for females, respectively. The M-shaped bimodal local drift (age-specific annual percentage changes) and longitudinal age curve were observed. Overall local drift was below zero for most age groups except ages 15 to 19 years (2.37%, 95% CI -0.28% to 5.09%) and ages 50 to 54 years (0.41%, 95% CI -1.78% to 2.64%). The highest risk of PTB incidence was in the ages of 65–69 years, with another peak around the ages of 20–24 years. The downward trends presented for both period and cohort effects, but the trends for cohort effect were uneven, with the higher risk of the birth cohort 1961–1970 and 2001–2010 than adjacent cohorts. The Bayesian age-period-cohort model projected PTB incidence number would continue to increase from 2021 to 2030. There would be 61,306 PTB incidence in 2030, which increased 2.28 times to 2006. The age-specific changes, especially the cohort effects were the leading cause and the driver of the growing disease burden.

Conclusions:

Although there were serial progresses for PTB control, the disease burden was likely to increase in the future. To bridge the gap of TB-free vision, the study suggested public health practices should be put in place instantly, including large-scale active case finding, priority prevention policy to the high-risk elderly and young population, and the reduction of possible grandparent-grandchildren transmission pattern.