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10.1016/j.ijid.2025.108003 http://scihub22266oqcxt.onion/10.1016/j.ijid.2025.108003 40784588!ä!40784588 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=40784588&cmd=llinks): Failed to open stream: HTTP request failed! 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Impact of age-stratified latent tuberculosis treatment on disease burden of active tuberculosis: A mathematical modeling study in an aging country with a high disease burden |pdf=|usr=}}{{40784588}} gab.com Text Impact of age-stratified latent tuberculosis treatment on disease burden of active tuberculosis: A mathematical modeling study in an aging country with a high disease burden JO: Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=40784588 #FOAvip BACKGROUND: The Republic of Korea has the highest tuberculosis (TB) incidence among OECD countries, with older adults at elevated risk of reactivation. However, latent TB infection (LTBI) control strategies often exclude individuals over 65 due to potential side effects, such as hepatotoxicity. Identifying optimal age groups for intervention is critical. METHODS: We developed an age-structured dynamic transmission model to simulate TB and LTBI progression in Korea. The model was calibrated using TB case data (2011-2018) from the Korea Disease Control and Prevention Agency and the Health Insurance Review and Assessment Service. We projected TB cases averted over 30 years by evaluating LTBI treatment strategies with varying coverage and success rates across age groups. RESULTS: Targeting LTBI treatment in adults aged 35-64 resulted in the greatest reduction in TB incidence. A four-fold increase in the LTBI treatment rate in this group averted 32,814 cases-compared to 11,564 and 5,689 cases in the 19-34 and >/=65 age groups, respectively. Increasing the probability of treatment success had a smaller but similar effect. CONCLUSION: Prioritizing LTBI treatment in the 35-64 age group may substantially reduce TB burden and supports age-stratified strategies for national TB control. Twit Text FOAVip Impact of age-stratified latent tuberculosis treatment on disease burden of active tuberculosis: A mathematical modeling study in an aging country with a high disease burden ????Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=40784588 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=40784588 #FOAvip English Wikipedia <ref>{{Cite pmid|40784588}}</ref> Impact of age-stratified latent tuberculosis treatment on disease burden of active tuberculosis: A mathematical modeling study in an aging country with a high disease burden #MMPMID40784588 Seong H; Lee Y; Suh J; Lee J; Song JY Int J Infect Dis 2025[Aug]; ä (ä): 108003 PMID40784588show ga BACKGROUND: The Republic of Korea has the highest tuberculosis (TB) incidence among OECD countries, with older adults at elevated risk of reactivation. However, latent TB infection (LTBI) control strategies often exclude individuals over 65 due to potential side effects, such as hepatotoxicity. Identifying optimal age groups for intervention is critical. METHODS: We developed an age-structured dynamic transmission model to simulate TB and LTBI progression in Korea. The model was calibrated using TB case data (2011-2018) from the Korea Disease Control and Prevention Agency and the Health Insurance Review and Assessment Service. We projected TB cases averted over 30 years by evaluating LTBI treatment strategies with varying coverage and success rates across age groups. RESULTS: Targeting LTBI treatment in adults aged 35-64 resulted in the greatest reduction in TB incidence. A four-fold increase in the LTBI treatment rate in this group averted 32,814 cases-compared to 11,564 and 5,689 cases in the 19-34 and >/=65 age groups, respectively. Increasing the probability of treatment success had a smaller but similar effect. CONCLUSION: Prioritizing LTBI treatment in the 35-64 age group may substantially reduce TB burden and supports age-stratified strategies for national TB control. äImpact of age-stratified latent tuberculosis treatment on disease burd(epmc).pdf DeepDyve Pubget Overpricing