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. 2025 May 28;37(1):175.
doi: 10.1007/s40520-025-03070-z.

The impact of population aging on tuberculosis prevention and control in Shanghai: a prediction based on age-period-cohort models

Rong Du  1   2 , Lixin Rao  3   4 , Xiao Xiao  3   4 , Qi Zhao  5   6 , Xin Shen  7   8 , Biao Xu  1   2
Affiliations

The impact of population aging on tuberculosis prevention and control in Shanghai: a prediction based on age-period-cohort models

Rong Du et al. Aging Clin Exp Res. .

Abstract

Background: The number and proportion of people aged 65 and above in Shanghai are increasing, posing challenges to tuberculosis (TB) control. This study aims to assess the impact of this demographic change on the TB epidemic in Shanghai.

Methods: Data were obtained from the TB Information Management System, with case counts and notification rates calculated by gender, age, and year. TB notification rates and trends were analyzed under two demographic scenarios: a constant aging scenario and an increasing aging scenario. Grey models (GM (1,1)) and age-period-cohort (APC) models were employed to forecast changes in the elderly population as well as age-specific TB notification rates. The estimated annual percentage change (EAPC) quantified trends over time.

Results: From 2015 to 2023, a total of 29,694 TB cases were reported, with males accounting for 69.79%. In 2023, the notification rate was 19.55 per 100,000, with the highest rate observed among individuals aged 65 years and older, reaching 48.47 per 100,000. The proportion of older adults among TB patients increased annually. Predictions indicated a peak notification rate among those aged 70-79 over the next five years. Compared to the constant aging scenario, the increasing aging scenario was associated with a more moderate reduction in TB notification rates (34.49% vs. 42.81%) and a slower declining trend over the study period (EAPC = -3.50, 95% CI: -4.70 to -2.29 vs. EAPC = -4.45, 95% CI: -5.47 to -3.42).

Conclusion: Population aging poses challenges to TB control, highlighting the need for targeted strategies for older adults.

Keywords: Age-period-cohort modeling; Epidemiology; Population aging; Tuberculosis.

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Conflict of interest statement

Declarations. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Tuberculosis (TB) cases among Shanghai household-registered residents, 2015–2023. (A) the gender distribution of TB cases by year. (B) the age distribution of TB cases. (C) the age distribution of TB cases among female residents by year. (D) the age distribution of TB cases among male residents by year
Fig. 2
Fig. 2
Matrix of tuberculosis notification rates and cases by year and age among Shanghai household-registered residents, 2015–2028. (A-C) notification rate matrix for: (A) female, (B) male, (C) overall. (D-F) case count matrix for: (D) female, (E) male, (F) overall. (G-I) Estimated annual percentage change (EAPC) in crude notification rates by age group: (G) female, (H) male, (I) overall
Fig. 3
Fig. 3
Projections and reduction in tuberculosis notification rates under different aging scenarios. (A) projected TB notification rates from 2015 to 2028 under the constant aging scenario (“---“) and the increasing aging scenario (“—“) for the total, male, and female populations. (B) estimated annual percentage change (EAPC) of TB notification rates by gender under both aging scenarios. (C) projected reductions in TB notification rates by gender in 2020, 2025 and 2028
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