Long-term impact of national tuberculosis program interventions on incidence and disparities by age and geography
Background Evaluations of the national tuberculosis control programs (NTPs) frequently lacked insights into the uneven long-term impacts on distinct age groups and geographic regions. Methods We analyzed tuberculosis cases notified from 1990 to 2019 in Changhua, Taiwan. A Bayesian hierarchical chang...
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| Published in: | BMC infectious diseases Vol. 25; no. 1; pp. 1087 - 10 |
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| Main Authors: | , , , , , , , , , , |
| Format: | Journal Article |
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BioMed Central
01.09.2025
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| ISSN: | 1471-2334, 1471-2334 |
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| Abstract | Background
Evaluations of the national tuberculosis control programs (NTPs) frequently lacked insights into the uneven long-term impacts on distinct age groups and geographic regions.
Methods
We analyzed tuberculosis cases notified from 1990 to 2019 in Changhua, Taiwan. A Bayesian hierarchical change-point model was developed to estimate the NTP’s effects on different age groups and townships and project the long-term tuberculosis incidence trend to 2035.
Results
A total of 23,149 tuberculosis cases were identified. Tuberculosis incidence peaked at 106.6 per 100,000 in 2002, then declined steadily. Annual reductions estimated after the establishment of the laboratory network, directly observed therapy implementation, and post-2015 projections were 3.3%, 4.5%, and 5.7%, respectively. Tuberculosis incidence fell by 44.8% from its peak to 2016, with our model projecting a further reduction of 69.0% by 2035. The predicted overall incidence (per 100,000) is 15.7 in 2035, with age-specific rates and corresponding projected reductions expected to be 1.6 (68.1%), 3.6 (75.1%), 12.3 (76.6%), and 113.1 (65.4%) for ages 0 to 29, 30 to 49, 50 to 69, and over 70 years old, respectively. Older adults constituted over two-thirds of cases, showing intractable reduction. Geographical disparities persisted, with higher incidences noted in remote rural townships.
Conclusions
The NTP has significantly decreased tuberculosis incidence in Changhua; however, additional efforts will likely be required to reach the END TB targets by 2035. Future programs must expand preventive treatments for latent tuberculosis infection and consistently address barriers within the TB care cascade, with a specific focus on the elderly and residents of underserved areas. |
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| AbstractList | We analyzed tuberculosis cases notified from 1990 to 2019 in Changhua, Taiwan. A Bayesian hierarchical change-point model was developed to estimate the NTP's effects on different age groups and townships and project the long-term tuberculosis incidence trend to 2035. A total of 23,149 tuberculosis cases were identified. Tuberculosis incidence peaked at 106.6 per 100,000 in 2002, then declined steadily. Annual reductions estimated after the establishment of the laboratory network, directly observed therapy implementation, and post-2015 projections were 3.3%, 4.5%, and 5.7%, respectively. Tuberculosis incidence fell by 44.8% from its peak to 2016, with our model projecting a further reduction of 69.0% by 2035. The predicted overall incidence (per 100,000) is 15.7 in 2035, with age-specific rates and corresponding projected reductions expected to be 1.6 (68.1%), 3.6 (75.1%), 12.3 (76.6%), and 113.1 (65.4%) for ages 0 to 29, 30 to 49, 50 to 69, and over 70 years old, respectively. Older adults constituted over two-thirds of cases, showing intractable reduction. Geographical disparities persisted, with higher incidences noted in remote rural townships. The NTP has significantly decreased tuberculosis incidence in Changhua; however, additional efforts will likely be required to reach the END TB targets by 2035. Future programs must expand preventive treatments for latent tuberculosis infection and consistently address barriers within the TB care cascade, with a specific focus on the elderly and residents of underserved areas. Section BackgroundEvaluations of the national tuberculosis control programs (NTPs) frequently lacked insights into the uneven long-term impacts on distinct age groups and geographic regions.AbstractSection MethodsWe analyzed tuberculosis cases notified from 1990 to 2019 in Changhua, Taiwan. A Bayesian hierarchical change-point model was developed to estimate the NTP’s effects on different age groups and townships and project the long-term tuberculosis incidence trend to 2035.AbstractSection ResultsA total of 23,149 tuberculosis cases were identified. Tuberculosis incidence peaked at 106.6 per 100,000 in 2002, then declined steadily. Annual reductions estimated after the establishment of the laboratory network, directly observed therapy implementation, and post-2015 projections were 3.3%, 4.5%, and 5.7%, respectively. Tuberculosis incidence fell by 44.8% from its peak to 2016, with our model projecting a further reduction of 69.0% by 2035. The predicted overall incidence (per 100,000) is 15.7 in 2035, with age-specific rates and corresponding projected reductions expected to be 1.6 (68.1%), 3.6 (75.1%), 12.3 (76.6%), and 113.1 (65.4%) for ages 0 to 29, 30 to 49, 50 to 69, and over 70 years old, respectively. Older adults constituted over two-thirds of cases, showing intractable reduction. Geographical disparities persisted, with higher incidences noted in remote rural townships.AbstractSection ConclusionsThe NTP has significantly decreased tuberculosis incidence in Changhua; however, additional efforts will likely be required to reach the END TB targets by 2035. Future programs must expand preventive treatments for latent tuberculosis infection and consistently address barriers within the TB care cascade, with a specific focus on the elderly and residents of underserved areas. Background Evaluations of the national tuberculosis control programs (NTPs) frequently lacked insights into the uneven long-term impacts on distinct age groups and geographic regions. Methods We analyzed tuberculosis cases notified from 1990 to 2019 in Changhua, Taiwan. A Bayesian hierarchical change-point model was developed to estimate the NTP’s effects on different age groups and townships and project the long-term tuberculosis incidence trend to 2035. Results A total of 23,149 tuberculosis cases were identified. Tuberculosis incidence peaked at 106.6 per 100,000 in 2002, then declined steadily. Annual reductions estimated after the establishment of the laboratory network, directly observed therapy implementation, and post-2015 projections were 3.3%, 4.5%, and 5.7%, respectively. Tuberculosis incidence fell by 44.8% from its peak to 2016, with our model projecting a further reduction of 69.0% by 2035. The predicted overall incidence (per 100,000) is 15.7 in 2035, with age-specific rates and corresponding projected reductions expected to be 1.6 (68.1%), 3.6 (75.1%), 12.3 (76.6%), and 113.1 (65.4%) for ages 0 to 29, 30 to 49, 50 to 69, and over 70 years old, respectively. Older adults constituted over two-thirds of cases, showing intractable reduction. Geographical disparities persisted, with higher incidences noted in remote rural townships. Conclusions The NTP has significantly decreased tuberculosis incidence in Changhua; however, additional efforts will likely be required to reach the END TB targets by 2035. Future programs must expand preventive treatments for latent tuberculosis infection and consistently address barriers within the TB care cascade, with a specific focus on the elderly and residents of underserved areas. Background Evaluations of the national tuberculosis control programs (NTPs) frequently lacked insights into the uneven long-term impacts on distinct age groups and geographic regions. Methods We analyzed tuberculosis cases notified from 1990 to 2019 in Changhua, Taiwan. A Bayesian hierarchical change-point model was developed to estimate the NTP's effects on different age groups and townships and project the long-term tuberculosis incidence trend to 2035. Results A total of 23,149 tuberculosis cases were identified. Tuberculosis incidence peaked at 106.6 per 100,000 in 2002, then declined steadily. Annual reductions estimated after the establishment of the laboratory network, directly observed therapy implementation, and post-2015 projections were 3.3%, 4.5%, and 5.7%, respectively. Tuberculosis incidence fell by 44.8% from its peak to 2016, with our model projecting a further reduction of 69.0% by 2035. The predicted overall incidence (per 100,000) is 15.7 in 2035, with age-specific rates and corresponding projected reductions expected to be 1.6 (68.1%), 3.6 (75.1%), 12.3 (76.6%), and 113.1 (65.4%) for ages 0 to 29, 30 to 49, 50 to 69, and over 70 years old, respectively. Older adults constituted over two-thirds of cases, showing intractable reduction. Geographical disparities persisted, with higher incidences noted in remote rural townships. Conclusions The NTP has significantly decreased tuberculosis incidence in Changhua; however, additional efforts will likely be required to reach the END TB targets by 2035. Future programs must expand preventive treatments for latent tuberculosis infection and consistently address barriers within the TB care cascade, with a specific focus on the elderly and residents of underserved areas. Keywords: Tuberculosis elimination, END TB, National tuberculosis program, Age, Geographical regions, Disparity, Tuberculosis laboratory network, Directly Observed Therapy (DOT) Abstract Background Evaluations of the national tuberculosis control programs (NTPs) frequently lacked insights into the uneven long-term impacts on distinct age groups and geographic regions. Methods We analyzed tuberculosis cases notified from 1990 to 2019 in Changhua, Taiwan. A Bayesian hierarchical change-point model was developed to estimate the NTP’s effects on different age groups and townships and project the long-term tuberculosis incidence trend to 2035. Results A total of 23,149 tuberculosis cases were identified. Tuberculosis incidence peaked at 106.6 per 100,000 in 2002, then declined steadily. Annual reductions estimated after the establishment of the laboratory network, directly observed therapy implementation, and post-2015 projections were 3.3%, 4.5%, and 5.7%, respectively. Tuberculosis incidence fell by 44.8% from its peak to 2016, with our model projecting a further reduction of 69.0% by 2035. The predicted overall incidence (per 100,000) is 15.7 in 2035, with age-specific rates and corresponding projected reductions expected to be 1.6 (68.1%), 3.6 (75.1%), 12.3 (76.6%), and 113.1 (65.4%) for ages 0 to 29, 30 to 49, 50 to 69, and over 70 years old, respectively. Older adults constituted over two-thirds of cases, showing intractable reduction. Geographical disparities persisted, with higher incidences noted in remote rural townships. Conclusions The NTP has significantly decreased tuberculosis incidence in Changhua; however, additional efforts will likely be required to reach the END TB targets by 2035. Future programs must expand preventive treatments for latent tuberculosis infection and consistently address barriers within the TB care cascade, with a specific focus on the elderly and residents of underserved areas. |
| ArticleNumber | 1087 |
| Audience | Academic |
| Author | Lin, Ting-Yu Chen, Hung-Pin Luh, Dih-Ling Chien, Yuan-Shan Lin, Shin-Yi Hsu, Chen-Yang Chen, Hsiu-Hsi Lai, Chao-Chih Wang, Hsiao Chi Hsieh, Yu-Chu Yeh, Yen-Po |
| Author_xml | – sequence: 1 givenname: Yuan-Shan surname: Chien fullname: Chien, Yuan-Shan organization: Department of Public Health, Chung Shan Medical University, Changhua Public Health Bureau – sequence: 2 givenname: Ting-Yu surname: Lin fullname: Lin, Ting-Yu organization: Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University – sequence: 3 givenname: Chao-Chih surname: Lai fullname: Lai, Chao-Chih organization: Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Emergency Department of Taipei City Hospital – sequence: 4 givenname: Chen-Yang surname: Hsu fullname: Hsu, Chen-Yang organization: Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University – sequence: 5 givenname: Yu-Chu surname: Hsieh fullname: Hsieh, Yu-Chu organization: Changhua Public Health Bureau – sequence: 6 givenname: Shin-Yi surname: Lin fullname: Lin, Shin-Yi organization: Changhua Public Health Bureau – sequence: 7 givenname: Hsiao Chi surname: Wang fullname: Wang, Hsiao Chi organization: Changhua Public Health Bureau – sequence: 8 givenname: Hung-Pin surname: Chen fullname: Chen, Hung-Pin organization: Department of Public Health, Chung Shan Medical University, Ershuei Township Health Center – sequence: 9 givenname: Hsiu-Hsi surname: Chen fullname: Chen, Hsiu-Hsi organization: Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University – sequence: 10 givenname: Dih-Ling surname: Luh fullname: Luh, Dih-Ling email: luhdihling@gmail.com organization: Department of Public Health, Chung Shan Medical University, Department of Family and Community Medicine, Chung Shan Medical University Hospital – sequence: 11 givenname: Yen-Po surname: Yeh fullname: Yeh, Yen-Po email: yeh.leego@gmail.com organization: Changhua Public Health Bureau, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University |
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| Cites_doi | 10.2471/blt.08.054510 10.1016/S0140-6736(15)60570-0 10.1093/cid/civ365 10.1086/322777 10.1016/S2214-109X(23)00312-1 10.1136/bmjopen-2019-029948 10.2471/BLT.11.101436 10.1164/rccm.201702-0377OC 10.1093/cid/ciac121 10.1017/S0950268815002265 10.1177/00333549131286S314 10.1016/S2213-2600(18)30057-2 10.3201/eid2905.220796 10.1016/S0140-6736(15)00323-2 10.1098/rsif.2007.1138 10.1111/resp.13303 10.15585/mmwr.mm7012a4 10.1016/j.lanwpc.2023.100770 10.1183/13993003.00266-2021 10.1183/16000617.0107-2018 10.1016/S1473-3099(24)00007-0 10.1001/jama.280.19.1679 10.1186/1471-2458-11-915 10.1183/13993003.00499-2023 10.1164/rccm.201907-1289OC |
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Evaluations of the national tuberculosis control programs (NTPs) frequently lacked insights into the uneven long-term impacts on distinct age groups... We analyzed tuberculosis cases notified from 1990 to 2019 in Changhua, Taiwan. A Bayesian hierarchical change-point model was developed to estimate the NTP's... Background Evaluations of the national tuberculosis control programs (NTPs) frequently lacked insights into the uneven long-term impacts on distinct age groups... Section BackgroundEvaluations of the national tuberculosis control programs (NTPs) frequently lacked insights into the uneven long-term impacts on distinct age... Abstract Background Evaluations of the national tuberculosis control programs (NTPs) frequently lacked insights into the uneven long-term impacts on distinct... |
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| SubjectTerms | Age Age groups Antitubercular agents Bayesian analysis BCG BCG vaccines Control programs Disease control Disparity Dosage and administration Drug therapy END TB Epidemics Geographical regions Geography Infectious Diseases Internal Medicine Intervention Medical Microbiology Medicine Medicine & Public Health Medicine, Preventive Methods National tuberculosis program Older people Parasitology Patient outcomes Patients Preventive health services Trends Tropical Medicine Tuberculosis Tuberculosis elimination |
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| Title | Long-term impact of national tuberculosis program interventions on incidence and disparities by age and geography |
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