Changing Epidemiology of TB in Shandong, China Driven by Demographic Changes

Qianying Lin; Sourya Shrestha; Shi Zhao; Alice P Y Chiu; Yao Liu; Chunbao Yu; Ningning Tao; Yifan Li; Yang Shao; Daihai He; Huaichen Li

doi:10.3389/fmed.2022.810382

Changing Epidemiology of TB in Shandong, China Driven by Demographic Changes

Front Med (Lausanne). 2022 Mar 9:9:810382. doi: 10.3389/fmed.2022.810382. eCollection 2022.

Authors

Qianying Lin^{1

2}, Sourya Shrestha³, Shi Zhao^{1

4}, Alice P Y Chiu¹, Yao Liu⁵, Chunbao Yu⁶, Ningning Tao⁵, Yifan Li⁵, Yang Shao⁵, Daihai He¹, Huaichen Li⁷

Affiliations

¹ Department of Applied Mathematics, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
² Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, United States.
³ Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
⁴ JC School of Public Health and Primary Care, Chinese University of Hong Kong, Johns Hopkins University, Shatin, Hong Kong SAR, China.
⁵ Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China.
⁶ Shandong Chest Hospital, Jinan, China.
⁷ Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.

Abstract

Tuberculosis (TB) incidence has been in steady decline in China over the last few decades. However, ongoing demographic transition, fueled by aging, and massive internal migration could have important implications for TB control in the future. We collated data on TB notification, demography, and drug resistance between 2004 and 2017 across seven cities in Shandong, the second most populous province in China. Using these data, and age-period-cohort models, we (i) quantified heterogeneities in TB incidence across cities, by age, sex, resident status, and occupation and (ii) projected future trends in TB incidence, including drug-resistant TB (DR-TB). Between 2006 and 2017, we observed (i) substantial variability in the rates of annual change in TB incidence across cities, from -4.84 to 1.52%; (ii) heterogeneities in the increments in the proportion of patients over 60 among reported TB cases differs from 2 to 13%, and from 0 to 17% for women; (iii) huge differences across cities in the annual growths in TB notification rates among migrant population between 2007 and 2017, from 2.81 cases per 100K migrants per year in Jinan to 22.11 cases per 100K migrants per year in Liaocheng, with drastically increasing burden of TB cases from farmers; and (iv) moderate and stable increase in the notification rates of DR-TB in the province. All of these trends were projected to continue over the next decade, increasing heterogeneities in TB incidence across cities and between populations. To sustain declines in TB incidence and to prevent an increase in Multiple DR-TB (MDR-TB) in the future in China, future TB control strategies may (i) need to be tailored to local demography, (ii) prioritize key populations, such as elderly and internal migrants, and (iii) enhance DR-TB surveillance.

Keywords: Tuberculosis; age-period-cohort model; aging; heterogeneity; urbanization.