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Further analysis of tuberculosis in eight high-burden countries based on the Global Burden of Disease Study 2021 data
Open accessAbstract
BACKGROUNDS: Most significant findings from the Global Tuberculosis (TB) Report 2023 indicate that India, Indonesia, China, the Philippines, Pakistan, Nigeria, Bangladesh, and the Democratic Republic of the Congo (DRC) collectively contribute to approximately two-thirds of global TB cases. This study aims to provide crucial data-driven insights and references to improve TB control measures through a comprehensive analysis of these eight high-burden countries. METHODS: The eight high-burden TB countries analyzed in this study include India, Indonesia, China, the Philippines, Pakistan, Nigeria, Bangladesh, and the DRC. Age-standardized incidence rates (ASIR) of TB were derived from the Global Burden of Diseases Study 2021 data. Temporal trends were analyzed using Joinpoint regression. An age-period-cohort model was applied to examine the risk ratios (RR) of TB across diverse age groups, periods, and birth cohorts. A Bayesian age-period-cohort framework was employed to predict the ASIR of TB by 2030. RESULTS: The study found that the Philippines (average annual percentage change = 3.1%, P < 0.001) exhibited an upward trend from 1990 to 2021. In India, the Philippines, Pakistan, and Bangladesh, the RR of TB incidence exceeded 1 after individuals reached 25 years old. Notably, the RR has shown a consistent upward trend since 2001, peaking during the period of 2017-2021 with an estimated RR of 1.5 (P < 0.001) in the Philippines. Similarly, the highest RR was observed during the period of 2017-2021 reaching 1.1 (P < 0.001) in the DRC. In the Philippines, the markedly increasing RR values for TB have been observed among individuals born after 1997-2001. Projections suggest that the ASIR of TB is expected to follow a continued upward trajectory, with an estimated rate of 392.9 per 100,000 by 2030 in the Philippines; India and Indonesia are projected to achieve less than 20.0% of the target set by the World Health Organization (WHO). CONCLUSIONS: Among the eight high-burden countries, the Philippines, India and Indonesia are diverging from the goals set by the WHO, and the risk of TB in the Philippines and the DRC shows a trend toward affecting younger populations, which suggests that the management strategies for TB patients need to be further strengthened.
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“The incidence of head and neck TB is higher in developing countries, where TB continues to be a significant public health concern [3].”
From Head and Neck Tuberculosis: A Rare Diagnosis and the Role of Surgical Biopsy and Histopathological Evaluation in Extrapulmonary Disease (2025) · cites this paper as [3]
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Cites
Ferrari, A. J., Santomauro, D. F., Aali, A., Abate, Y. H., Abbafati, C., Abbastabar, H., Abd ElHafeez, S., Abdelmasseh, M., Abd-Elsalam, S., Abdollahi, A., et al. (2024) · The Lancet · doi:10.1016/s0140-6736(24)00757-8 · Cited by 5,112
Self-citationThe Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling
Houben, R. M. G. J., & Dodd, P. J. (2016) · PLOS Medicine · doi:10.1371/journal.pmed.1002152 · Cited by 2,054
WHO's Global Tuberculosis Report 2022
Bagcchi, S. (2022) · The Lancet Microbe · doi:10.1016/s2666-5247(22)00359-7 · Cited by 1,174
Riebler, A., & Held, L. (2017) · Biometrical Journal · doi:10.1002/bimj.201500263 · Cited by 661
The Intrinsic Estimator for Age‐Period‐Cohort Analysis: What It Is and How to Use It
Yang, Y., Schulhofer‐Wohl, S., Fu, W. J., & Land, K. C. (2008) · American Journal of Sociology · doi:10.1086/587154 · Cited by 506
Air pollution and development in Africa: impacts on health, the economy, and human capital
Fisher, S., Bellinger, D. C., Cropper, M., Kumar, P., Binagwaho, A., Koudenoukpo, J. B., Park, Y., Taghian, G., & Landrigan, P. J. (2021) · The Lancet Planetary Health · doi:10.1016/s2542-5196(21)00201-1 · Cited by 300
Haakenstad, A., Yearwood, J. A., Fullman, N., Bintz, C., Bienhoff, K., Weaver, M. R., Nandakumar, V., LeGrand, K. E., Knight, M., Abbafati, C., et al. (2022) · The Lancet Global Health · doi:10.1016/s2214-109x(22)00429-6 · Cited by 258
Africa and the Nexus of poverty, malnutrition and diseases
Adeyeye, S. A. O., Ashaolu, T. J., Bolaji, O. T., Abegunde, T. A., & Omoyajowo, A. O. (2021) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2021.1952160 · Cited by 191
Trimmed constrained mixed effects models: formulations and algorithms
Zheng, P., Barber, R., Sorensen, R. J. D., Murray, C. J. L., & Aravkin, A. Y. (2021) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2020.1868303 · Cited by 174
Tuberculosis origin: The Neolithic scenario
Hershkovitz, I., Donoghue, H. D., Minnikin, D. E., May, H., Lee, O. Y.-C., Feldman, M., Galili, E., Spigelman, M., Rothschild, B. M., & Bar‐Gal, G. K. (2015) · Tuberculosis · doi:10.1016/j.tube.2015.02.021 · Cited by 174
Ledesma, J. R., Ma, J., Vongpradith, A., Maddison, E. R., Novotney, A., Biehl, M. H., LeGrand, K. E., Ross, J. M., Jahagirdar, D., Bryazka, D., et al. (2021) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(21)00449-7 · Cited by 169
Ledesma, J. R., Ma, J., Zhang, M., Basting, A. V. L., Chu, H. T., Vongpradith, A., Novotney, A., LeGrand, K. E., Xu, Y. Y., Dai, X., et al. (2024) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(24)00007-0 · Cited by 146
Self-citationAge–period–cohort analysis of pulmonary tuberculosis reported incidence, China, 2006–2020
Dong, Z., Wang, Q., Yu, S., et al. (2022) · Infectious Diseases of Poverty · doi:10.1186/s40249-022-01009-4 · Cited by 130
Ending tuberculosis in China: health system challenges
Long, Q., Guo, L., Jiang, W., et al. (2021) · The Lancet Public Health · doi:10.1016/s2468-2667(21)00203-6 · Cited by 97
Cheng, J., Sun, Y., Zhang, C., Yu, Y., Tang, L., Peng, X., Peng, Y., Yao, Y., Hou, S.-Y., Li, J., et al. (2020) · Infectious Diseases of Poverty · doi:10.1186/s40249-019-0614-9 · Cited by 91
Self-citationGlobal prevalence and burden of multidrug-resistant tuberculosis from 1990 to 2019
Lv, H., Zhang, X., Zhang, X., Bai, J., You, S., Li, X., Li, S., Wang, Y., Zhang, W., & Xu, Y. (2024) · BMC Infectious Diseases · doi:10.1186/s12879-024-09079-5 · Cited by 90
Self-citationBootstrap confidence intervals for the sensitivity of a quantitative diagnostic test
Platt, R. W., Hanley, J. A., & Yang, H. (2000) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(20000215)19:3 · Cited by 83
Morishita, F., Garfin, A. M. C., Lew, W., Oh, K. H., Yadav, R. P. H., Reston, J. C., Infante, L. L., Acala, M. R. C., Palanca, D. L., Kim, H. J., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171310 · Cited by 66
Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018
Haeuser, E., Serfes, A. L., Cork, M. A., Yang, M., Abbastabar, H., Abhilash, E. S., Adabi, M., Adebayo, O., Adekanmbi, V., Adeyinka, D. A., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02639-z · Cited by 66
Saied, A. A., Metwally, A. A., Dhawan, M. K., Choudhary, O. P., & Aiash, H. (2022) · EMBO Molecular Medicine · doi:10.15252/emmm.202216287 · Cited by 60
Association of Tuberculosis With Household Catastrophic Expenditure in South India
Muniyandi, M., Thomas, B. E., Karikalan, N., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.20973 · Cited by 59
Chenciner, L., Annerstedt, K. S., Pescarini, J. M., & Wingfield, T. (2021) · The Lancet Global Health · doi:10.1016/s2214-109x(21)00300-4 · Cited by 51
Noviyani, A., Nopsopon, T., & Pongpirul, K. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258809 · Cited by 51
Institutional mistrust and child vaccination coverage in Africa
Stoop, N., Hirvonen, K., & Maystadt, J.-F. (2021) · BMJ Global Health · doi:10.1136/bmjgh-2020-004595 · Cited by 50
The Road to Healthy Ageing: What Has Indonesia Achieved So Far?
Basrowi, R. W., Rahayu, E. M., Khoe, L. C., Wasito, E., & Sundjaya, T. (2021) · Nutrients · doi:10.3390/nu13103441 · Cited by 48
Wei, X., Hicks, J. P., Zhang, Z., Haldane, V. E., Pasang, P., Li, L., Yin, T., Zhang, B., Li, Y., Pan, Q., et al. (2024) · The Lancet · doi:10.1016/s0140-6736(23)02270-5 · Cited by 48
Vaccine Delay and Its Association With Undervaccination in Children in Sub-Saharan Africa
Janusz, C. B., Frye, M., Mutua, M. K., et al. (2020) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2020.10.003 · Cited by 44
Risk factors for unsuccessful tuberculosis treatment outcomes in children
Hamid, M., Brooks, M. B., Madhani, F., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222776 · Cited by 35
The tuberculosis profile of the Philippines, 2003–2011: advancing DOTS and beyond
Vianzon, R., Garfin, A. M. C., Lagos, A. Q., et al. (2013) · Western Pacific surveillance response journal · doi:10.5365/wpsar.2012.3.4.022 · Cited by 35
Significant decline in the tuberculosis burden in the Philippines ten years after initiating DOTS.
Tupasi, T. E., Radhakrishna, S., Chua, J. A., Mangubat, N. V., Guilatco, R., Galipot, M., Ramos, G. S., Quelapio, M. I. D., Beltran, G., Legaspi, J., et al. (2009) · PubMed · Cited by 33
Tuberculosis in children, adolescents, and women
Marais, B. J., Amanullah, F., Gupta, A., Becerra, M. C., Snow, K. J., Ngadaya, E. S., Sharma, M., Hesseling, A. C., Chakaya, M., & Zumla, A. I. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30077-1 · Cited by 24
Ending TB in Southeast Asia: current resources are not enough
Bhatia, V., Srivastava, R., Reddy, K. S., et al. (2020) · BMJ Global Health · doi:10.1136/bmjgh-2019-002073 · Cited by 23
Tuberculosis among children, adolescents and young adults in the Philippines: a surveillance report
Snow, K. J., Yadav, R. P. H., Denholm, J. T., Sawyer, S. M., & Graham, S. M. (2018) · Western Pacific surveillance response journal · doi:10.5365/wpsar.2017.8.4.011 · Cited by 20
Nigeria's widening tuberculosis gap
Adepoju, P. (2019) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(19)30712-1 · Cited by 20
Fatima, R., Yaqoob, A., Qadeer, E., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0227186 · Cited by 18
The social determinants of tuberculosis in the Philippines
Flores, G. P., Alberto, I. R. I., Eala, M. A. B., et al. (2021) · The Lancet Global Health · doi:10.1016/s2214-109x(21)00516-7 · Cited by 18
Linguissi, L. S. G., Gwom, L. C., Nkenfou, C. N., et al. (2016) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2016.10.012 · Cited by 17
Tuberculosis and malaria in the age of COVID-19
Diseases, T. L. I. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30946-4 · Cited by 17
Tuberculosis treatment outcomes and patient support groups, southern India
Potty, R. S., Kumarasamy, K., Munjattu, J. F., et al. (2023) · Bulletin of the World Health Organization · doi:10.2471/blt.22.288237 · Cited by 17
Network meta‐analysis with integrated nested Laplace approximations
Sauter, R., & Held, L. (2015) · Biometrical Journal · doi:10.1002/bimj.201400163 · Cited by 15
Bayesian connective field modeling using a Markov Chain Monte Carlo approach
Invernizzi, A., Haak, K. V., Carvalho, J., Renken, R. J., & Cornelissen, F. W. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119688 · Cited by 11
Locally Adaptive Function Estimation for Binary Regression Models
Jerak, A., & Lang, S. (2005) · Biometrical Journal · doi:10.1002/bimj.200410095 · Cited by 7
Reversing the neglect of children and adolescents affected by tuberculosis
Deborggraeve, S., Casenghi, M., Hewison, C., et al. (2023) · The Lancet Child & Adolescent Health · doi:10.1016/s2352-4642(23)00217-1 · Cited by 6
Tuberculosis and undernutrition in India
Lall, D. (2023) · The Lancet Global Health · doi:10.1016/s2214-109x(23)00500-4 · Cited by 5
Shimizu, K., Checchi, F., & Warsame, A. (2022) · Healthcare · doi:10.3390/healthcare10020179 · Cited by 5
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“The incidence of head and neck TB is higher in developing countries, where TB continues to be a significant public health concern [3].”
Metadata
- Authors
- Lv, H., Wang, L., Zhang, X., Dang, C., Liu, F., Zhang, X., Bai, J., You, S., Chen, H., Zhang, W., & Xu, Y.
- Affiliations
- Chinese Center For Disease Control and Prevention (CN)
- Army Medical University (CN)
- Chinese People's Liberation Army (CN)
- Corresponding author
- Chen, Hui
- Zhang, Wenyi
- Xu, Yuanyong
- Venue
- Infectious Diseases of Poverty
- Published
- 2024-09-30
- Volume
- 13
- Issue
- 1
- Pages
- 70-70
- ISSN
- 2049-9957 (ISSN-L)
- PMC
- PMC11440896
- OpenAlex
- W4402956134
- Cited by
- 54
- References
- 45
- Citations by year
- 2026: 32 · 2025: 19 · 2024: 3
- FWCI
- 18.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Infectious Diseases of Poverty (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (12031010)
- Topics
- Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › HIV, TB, and STIs Epidemiology
- Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
- Keywords
- tuberculosis, tuberculosis incidence, high-burden countries, age-standardized incidence rate, age-period-cohort model, temporal trends, tuberculosis control, Global Burden of Disease 2021, India, Indonesia, Philippines, Pakistan, Nigeria, Bangladesh, Democratic Republic of the Congo
- MeSH terms
- Global Burden of Disease, Adolescent, Adult, Aged, Aged, 80 and over, Bangladesh, Bayes Theorem, Child, Child, Preschool, China, Female, Humans, India, Indonesia, Infant, Infant, Newborn, Male, Middle Aged, Pakistan, Philippines, Tuberculosis, Democratic Republic of the Congo, Incidence, Young Adult
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