Abstract
BACKGROUND: Trends in the incidence of bacteriologically confirmed pulmonary tuberculosis (PTB), a primary source of transmission, are important for targeted prevention and control. Hunan Province is a high-PTB-burden region in China. However, conventional trend analyses cannot separate the independent effects of age, period, and birth cohort, and this question has not been specifically examined in Hunan Province. METHODS: Using PTB surveillance data from Hunan Province (2009-2023), this study combined joinpoint regression and an age-period-cohort (APC) model to assess long-term trends in the reported incidence of bacteriologically confirmed PTB and quantify the independent effects of age, period, and birth cohort. RESULTS: Over the study period, the age-standardized reported incidence of bacteriologically confirmed PTB declined (average annual percent change [AAPC], -2.20%). This decrease was more marked among males (AAPC, -2.54%), whereas the decline among females was not statistically significant. The decline plateaued after 2017, with an inflection point in 2020. APC analysis indicated a bimodal age pattern, with peaks in the 20-24 and 80-84-year age groups. The increase in incidence with age was greatest in the oldest age group. The period effects were statistically significant. The post-2017 plateau may be associated with changes in diagnostic practices and/or reporting, and the 2020 inflection point coincided with the COVID-19 pandemic. Cohort effects showed that risk peaked in the 1949-1953 birth cohort and then declined steadily; however, among females, cohorts born after 1994 showed early indications of a possible increasing risk. CONCLUSION: APC analysis of bacteriologically confirmed PTB in Hunan Province showed a shifting epidemiology. These findings suggest that tuberculosis control efforts should prioritize the increasing burden in older adults and closely monitor younger female cohorts for possible increases in risk. These results may help refine interventions for high-risk groups and optimize surveillance strategies.