Skip to main content
Book cover

Tuberculosis Control in Migrating Population pp 191–214Cite as

New Technologies for TB Control in Migrating Population

Abstract

According to the 2017 Development Report of migrating Population in China, the migrating population in China reached 245 million in 2016, accounting for 18% of the total population. In the next decade or two, China is still in the rapid development stage of urbanization. According to the implementation of the National New Urbanization Planning, there are still more than 200 million migrating population in China by 2020. During the 13th Five-Year Plan period, the population continues to aggregate in the large delta areas, coastal areas, and major transportation areas, and the population of mega-cities and metropolis continues to grow. The migrating population is characterized by high mobility, poor awareness about health, high incidence of TB, and difficult treatment and management after the occurrence of TB. Therefore, the migrating population is vulnerable to TB epidemic. Since 2006, TB epidemic in migrating population has gained focused attention from Chinese government. Previous surveys have found that the new registry rates of TB and smear-positive TB in the migrating population in large cities of China are higher than those in the local population [1]. In Shenzhen, Guangzhou, Zhuhai, Shanghai, Suzhou, Wuxi, Nanjing, and other large cities, surveys found that the increase of patients with TB in the migrating population is the main reason for the high number of TB in these large cities [2]. There are also studies showing that the estimated incidence of active TB in China in 2014 is 64 per 0.1 million, while the estimated incidence of TB in the migrating population is 85 per 0.1 million. The proportion of trans-regionally managed patients with TB in the migrating population is much higher than that in the non-migrating population. Meanwhile, the percentage of unknown prognosis in patients with TB is 8–9% nationwide, and most of these patients are trans-regionally managed patients [3]. Migrating patients with TB have been a bridge for the spread of TB from high-prevalence areas to low-prevalence areas. The TB control in migrating population is one of the three challenges in TB control in China.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-32-9763-0_9
  • Chapter length: 24 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-981-32-9763-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Hardcover Book
USD   159.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 9.1
Fig. 9.2
Fig. 9.3

References

  1. Zhong D, Zhang YH, Fu YY, et al. Prevalence of TB in migrating population and advances in its control strategy. Mod Prev Med. 2011;4282(20):4277–8.

    Google Scholar 

  2. Wu LW. Treatment and management of patients with TB in migrating population in Shanghai and their effects. Shanghai: Fudan University; 2013.

    Google Scholar 

  3. Li T, Du X, Shewade HD, et al. What happens to migrant tuberculosis patients who are transferred out using a web-based system in China? PLoS One. 2018;13(11):e206580.

    Google Scholar 

  4. Li X, Jiang SW. TB control in migrating population of China and its strategy. Chin J Antituberculosis. 2009;2009(10):561–3.

    Google Scholar 

  5. Li T, Zhang H, Shewade HD, et al. Patient and health system delays before registration among migrant patients with tuberculosis who were transferred out in China. BMC Health Serv Res. 2018;18(1):786.

    PubMed  PubMed Central  Google Scholar 

  6. Yang YZ. Difficulties of TB control in migrating population and the countermeasures. Guangdong Med. 2010;2010(15):1912–3.

    Google Scholar 

  7. Zhou CH, Qin LX, Li BX, et al. Application of diagnostic imaging in TB control system. Chin J Antituberculosis. 2016;2016(11):925–8.

    Google Scholar 

  8. Wei JF, Jiang XQ, Zheng XL, et al. Problems of TB control in migrating population of China. China Pract Med. 2007;2007(31):155.

    Google Scholar 

  9. Gao CN, Tan SY, Wen WP, et al. Factor analysis on health needs of patients with TB in migrating population. China Health Educ. 2008;2008(6):449–50.

    Google Scholar 

  10. Guan HY, Yang YZ, Tan WG, et al. Factors influencing occurrence of drug resistant tuberculosis in migrating population in Shenzhen. Chin J Antituberculosis. 2013;2013(8):557–61.

    Google Scholar 

  11. Lin DW, Gong XL. Management of TB in remote mountain areas of China. Chin J Antituberculosis. 2007;29(5):472–3.

    Google Scholar 

  12. Guan HY, Yang YZ, Tan WG, et al. Needs analysis on treatment and management of initially treated patients with sputum smear positive TB in migrating population in Shenzhen. Chin J Antituberculosis. 2011;33(7):407–11.

    Google Scholar 

  13. Tu DH, Wan LY, Wang LX. Theories and practice of modern TB control. Beijing: Military Medical Science Press; 2013. p. 188.

    Google Scholar 

  14. Xie HB, Zhang H, Li X, et al. Analysis on human resources in TB control agencies nationwide. Chin J Antituberculosis. 2011;33(1):12–5.

    Google Scholar 

  15. Dorman S. Advances in the diagnosis of tuberculosis: current status and future prospects. Int J Tuberc Lung Dis. 2015;19(5):504–16.

    CAS  PubMed  Google Scholar 

  16. Jiang SW. Innovative management of patients with TB for better compliance to treatment. Chin J Antituberculosis. 2017;39(7):673–6.

    Google Scholar 

  17. Valencia S, Leon M, Losada I, et al. How do we measure adherence to anti-tuberculosis treatment? Expert Rev Anti-Infect Ther. 2017;15(2):157–65.

    CAS  PubMed  Google Scholar 

  18. Tu DH, Wan LY, Wang LX. Modern theories and practice in tuberculosis control. Beijing: Military Medical Science Press; 2013. p. 188.

    Google Scholar 

  19. Guan HY, Yang YZ, Tan WG, et al. Survey on demands for treatment and management of initially treated patients with smear positive TB in migrating population of Shenzhen. Chin J Antituberculosis. 2011;33(7):407–11.

    Google Scholar 

  20. Hu DY, Wang T, Liu XY, et al. Implementation and factors influencing DOT for patients with smear positive TB in Chongqing. China Healthcare Res. 2006;2006(5):219–21.

    Google Scholar 

  21. Zhong T, Liu SY, Zhu MM, et al. Electronic DOT for registered TB patients in Nanshan district of Shenzhen. China Chronic Dis Prev Control. 2016;24(5):332–6.

    Google Scholar 

  22. Li RL, Lin HY, Yang YZ, et al. Effects of volunteer implemented DOT on compliance to TB treatment in migrating population. Chin J Antituberculosis. 1999;1999(1):22–5.

    Google Scholar 

  23. Zhao J, Duan QH, Zuo T, et al. Factors influencing attitudes and needs towards DOT in 187 patients with TB in Wuhan. Chin J Antituberculosis. 2016;2016(12):1073–7.

    Google Scholar 

  24. Yang XR, Huang XP. Effects of family member implemented DOT on compliance to treatment in patients with TB in migrating population. J Nurs Manag. 2013;2013(4):288–9.

    Google Scholar 

  25. Li JJ, Zhang JL, Li GG, et al. Effects of family member implemented DOT on compliance to treatment in patients with TB in migrating population. J Med Pest Control. 2013;2013(12):1315–8.

    Google Scholar 

  26. Zhou J, Yuan LL, Zhong QH. Health care professionals and family member implemented DOT for patients with TB in migrating population in Foshan. J Clin Pneumol. 2012;2012(5):847–8.

    Google Scholar 

  27. Yang XR. Effects of long distance follow ups on compliance to treatment in initially treated patients with TB in migrating population. Mod Clin Nurs. 2012;2012(04):65–7.

    Google Scholar 

  28. Gao CN, Xu ZW, Tan QY, et al. Management model for patients with TB in migrating population in Guangzhou. Guangdong Med. 2010;2010(15):1920–2.

    Google Scholar 

  29. Wang J, Zhu MM, Lu JJ, et al. Intervention to trans-regional referral management in patients with TB in migrating population. Pract J Card Cereb Pneumol Vasc Dis. 2010;2010(07):891–2.

    Google Scholar 

  30. Lin DW, Qin LY, Lin M, et al. Exploration on a predicting system for compliance to treatment in patients with TB. Chin J Antituberculosis. 2016;2016(12):1066–72.

    Google Scholar 

  31. Guo XJ, Wang J, Zhong T, et al. Effects of real-time observation and medication reminder mode in eDOT system for TB. Chin J Antituberculosis. 2017;39(7):689–94.

    Google Scholar 

  32. Jiang L, Zhong Q, Li JW, et al. Detection, treatment and management of patients with TB in migrating population in Baoan district of Shenzhen. South China Prev Med. 2010;2010(03):47–9.

    Google Scholar 

  33. Liu SY, Tan WG, Yang YR, et al. Application of information technology in TB control. J Tuberc Lung Health. 2018;7(1):3–8.

    Google Scholar 

  34. Moulding TS. The unrealized potential of the medication monitor. Clin Pharmacol Ther. 1979;25(2):131–6.

    CAS  PubMed  Google Scholar 

  35. Moulding T, Onstad GD, Sbarbaro JA. Supervision of outpatient drug therapy with the medication monitor. Ann Intern Med. 1970;73(4):559–64.

    CAS  PubMed  Google Scholar 

  36. Peng H, Lu W, Zhu LM, et al. Consistency of electronic drug box based drug dispense and actual intake. Jiangsu Prev Med. 2013;24(1):13–6.

    Google Scholar 

  37. Xie YT, Du J, Luo P, et al. Preliminary report on application of APP for management of patients with TB in Tongzhou district of Beijing in 2016. Chin J Antituberculosis. 2017;39(7):708–12.

    Google Scholar 

  38. Holzman SB, Zenilman A, Shah M. Advancing patient-centered care in tuberculosis management: a mixed-methods appraisal of video directly observed therapy. Open Forum Infect Dis. 2018;5(4):y46.

    Google Scholar 

  39. Lai K, Han YT, Fang HX. Application of video directly observed treatment in management of patients with TB. Chin J Antituberculosis. 2017;2017(7):679–83.

    Google Scholar 

  40. Fang HX, Qin YB, Liu CW, et al. Application of internet plus mobile phone video in treatment and management of patients with TB. Chin J Antituberculosis. 2017;2017(7):684–8.

    Google Scholar 

  41. Macaraig M, Lobato MN, McGinnis PK, et al. A national survey on the use of electronic directly observed therapy for treatment of tuberculosis. J Public Health Manag Pract. 2018;24:567.

    PubMed  Google Scholar 

  42. Ren H, Yuan ZA, Gu ZR, et al. Development and application of spread trajectory analysis of latent respiratory infectious diseases. Chin J Prev Med. 2013;47(1):63–6.

    Google Scholar 

  43. Vazquez-Prokopec GM, Bisanzio D, Stoddard ST, et al. Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS One. 2013;8(4):e58802.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Touray K, Adetifa IM, Jallow A, et al. Spatial analysis of tuberculosis in an urban west African setting: is there evidence of clustering? Tropical Med Int Health. 2010;15(6):664–72.

    CAS  Google Scholar 

  45. Liu SY, Yang YR, Chen L, et al. Application of information technology in family quarantine of patients with smear positive TB. Chin J Antituberculosis. 2018;40(9):1012–8.

    Google Scholar 

  46. Yan BS. Further study on thick smear test for detection of Mycobacterium tuberculosis. Chin J Lab Med.1980;3(3):146–7.

    Google Scholar 

  47. Zhang L, Zhou HJ, Feng S. Application of florescent staining in detection of Mycobacterium tuberculosis. Shandong Med. 2014;2014(7):55–7.

    Google Scholar 

  48. Peng J, Liu WE, Li HL, et al. Evaluation of our self-designed nanometer silicon membrane sandwich cup system for diagnosing tuberculosis. Clin Respir J. 2016;10(5):647–52.

    CAS  PubMed  Google Scholar 

  49. Huang GQ, Cai CZ, He SQ, et al. Study on a new technology for detection of Mycobacterium tuberculosis. Hainan Med. 2016;2016(7):1048–50.

    Google Scholar 

  50. Dai ZC. History of anti-tuberculosis in China. Beijing: People’s Medical Publishing House; 2013.

    Google Scholar 

  51. Liu SY, Tan WG, Yang YR, et al. Application of information technology to promote TB prevention and control. J Tuberc Lung Health. 2018;7(1):3–8.

    Google Scholar 

  52. Dainton C, Chu CH. A review of electronic medical record keeping on mobile medical service trips in austere settings. Int J Med Inform. 2017;98:33–40.

    PubMed  Google Scholar 

  53. World Health Organization. Digital heath for the end TB strategy: an agenda for action. Geneva: World Health Organization; 2014.

    Google Scholar 

  54. Wade VA, Karnon J, Eliott JA, et al. Home videophones improve direct observation in tuberculosis treatment: a mixed methods evaluation. PLoS One. 2012;7(11):e50155.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Jiang SW. Application of innovative management to improve patients' compliance to treatment of TB. Chin J Antituberculosis. 2017;39(7):673–6.

    Google Scholar 

  56. Qin YB, Xie YG, Fang HX, et al. Application of text/voice message in management of urban patients with sputum smear positive TB. Chin J Antituberculosis. 2017;39(7):695–701.

    Google Scholar 

  57. Shi LJ, Jin JM. Effects of follow ups via telephone or text message on anti-tuberculosis treatment. China Mod Phys. 2013;51(1):119–20, 122.

    Google Scholar 

  58. Zhang T, Xi MX. Advances in supervision and management of TB patients. Pract Prev Med. 2017;24(3):382–4, Cover 3.

    Google Scholar 

  59. Zhong DL, Huang HA, Liu GF, et al. Effects of internet video based medication supervision on TB control. China Med Eng. 2012;20(4):143–5.

    Google Scholar 

  60. Miao YJ, Gao GZ. Research advances in development of mobile health care APP. Nurs Stud. 2018;2018(06):860–2.

    Google Scholar 

  61. Cao H, Chen J, Fang J. Effects of internet based health education about TB control in a university. China Sch Health. 2014;35(6):890–2.

    Google Scholar 

  62. Wu J, Zhao MW, Ning F, et al. Effects of Wechat public platform based health education about TB control in student volunteers from universities. Chin J Antituberculosis. 2016;38(7):588–91.

    Google Scholar 

  63. Liu PP, Lin WB, Zhong NR, et al. Internet Wechat platform based health education about TB control in migrating population. Chin J Antituberculosis. 2017;39(7):713–6.

    Google Scholar 

  64. Mirsaeidi M, Farshidpour M, Banks-Tripp D, et al. Video directly observed therapy for treatment of tuberculosis is patient-oriented and cost-effective. Eur Respir J. 2015;46(3):871–4.

    PubMed  PubMed Central  Google Scholar 

  65. Xie YT, Du J, Luo P, et al. Preliminary report on application of mobile APP for supervision and management of TB patients in Tongzhou district of Beijing in 2016. Chin J Antituberculosis. 2017;39(7):708–12.

    Google Scholar 

  66. Liu SY, Yang YR, Chen L, et al. Application of information technology in family quarantine of patients with sputum smear positive TB. Chin J Antituberculosis. 2018;40(9):1012–8.

    Google Scholar 

  67. Bain EE, Shafner L, Walling DP, et al. Use of a novel artificial intelligence platform on mobile devices to assess dosing compliance in a phase 2 clinical trial in subjects with schizophrenia. JMIR Mhealth Uhealth. 2017;5(2):e18.

    PubMed  PubMed Central  Google Scholar 

  68. Peng H, Lu W, Zhu LM, et al. Consistency of drug dispensing in electronic medicine box with actual medication in patients with TB in Jiangsu province. Jiangsu Prev Med. 2013;24(1):13–6.

    Google Scholar 

  69. Huan ST, Chen R, Liu XQ, et al. Feasibility of drug dispensing record in electronic medicine box for medication supervision in patients with TB. Chin J Antituberculosis. 2012;34(7):419–24.

    Google Scholar 

  70. Menzies D, Dion MJ, Francis D, et al. In closely monitored patients, adherence in the first month predicts completion of therapy for latent tuberculosis infection. Int J Tuberc Lung Dis. 2005;9(12):1343–8.

    CAS  PubMed  Google Scholar 

  71. Au-Yeung KY, DiCarlo L. Cost comparison of wirelessly vs. directly observed therapy for adherence confirmation in anti-tuberculosis treatment. Int J Tuberc Lung Dis. 2012;16(11):1498–504.

    CAS  PubMed  Google Scholar 

  72. Zheng M, Wang P. Presentation of suspected TB cases reported by non-TB control institutions in Zhengzhou during 2010 to 2014. Chin J Antituberculosis. 2016;38(3):228–9.

    Google Scholar 

  73. Xu XP. Effects of TB information management system on referral and tracking of TB patients. Public Health Prev Med. 2012;23(6):91–2.

    Google Scholar 

  74. Zhang LH, Fang AM, Guo CY. Large-scale data analysis on missed report and missed registry of TB. Public Health Prev Med. 2017;28(4):100–2.

    CAS  Google Scholar 

  75. An XD. Application of Internet technology in health care service and Internet resources about TB control. Chin J Antituberculosis. 2000;22(3):174–5.

    Google Scholar 

  76. Guo XJ, Wang J, Zhong T, et al. Effects of real-time supervision and reminder in electronic DOT management of TB. Chin J Antituberculosis. 2017;39(7):689–94.

    Google Scholar 

  77. Lin YH, Zhou L, Wu Y. Student health surveillance information system in Shenzhen. China Sch Health. 2014;35(8):1206–8.

    Google Scholar 

  78. Fu XS. Urban social management organization system: introduction of Shenzhen Social Work Committee and its network project. China Inst Reform Manag. 2013;2013(10):8–11.

    Google Scholar 

  79. Wang L, Zhang QB, Wu XM, et al. Design and application of TB information system based on GIS. China Digit Med. 2012;7(4):40–3.

    Google Scholar 

  80. Fan J, Rao HX, Wu P, et al. Spatial distribution of pulmonary TB during 2012 to 2014 in China. Chin J Epidemiol. 2017;38(7):926–30.

    CAS  Google Scholar 

  81. Rao HX, Yu J, Guo P, et al. Bartonella Species Detected in the plateau pikas (Ochotona curzoiae) from Qinghai Plateau in China. Biomed Environ Sci. 2015;28(9):674–8.

    PubMed  Google Scholar 

  82. Sun XL, Zhang H, Wang Y, et al. Application and prospects on Internet+ health care service. Health Care Equip. 2017;38(10):132–4.

    Google Scholar 

  83. Ghodbane R, Raoult D, Drancourt M. Dramatic reduction of culture time of Mycobacterium tuberculosis. Sci Rep. 2014;4:4236.

    PubMed  PubMed Central  Google Scholar 

  84. Drobniewski F, Nikolayevskyy V, Maxeiner H, et al. Rapid diagnostics of tuberculosis and drug resistance in the industrialized world: clinical and public health benefits and barriers to implementation. BMC Med. 2013;11:190.

    PubMed  PubMed Central  Google Scholar 

  85. Ou X, Song Y, Zhao B, et al. A multicenter study of cross-priming amplification for tuberculosis diagnosis at peripheral level in China. Tuberculosis (Edinb). 2014;94:428–33.

    CAS  Google Scholar 

  86. Centers for Disease Control and Prevention (CDC). Update: Nucleic acid amplification tests for tuberculosis. MMWR Morb Mortal Wkly Rep. 2000;49:593–4.

    Google Scholar 

  87. Ling DI, Flores LL, Riley LW, et al. Commercial nucleic-acid amplification tests for diagnosis of pulmonary tuberculosis in respiratory specimens: meta-analysis and meta-regression. PLoS One. 2008;3:e1536.

    PubMed  PubMed Central  Google Scholar 

  88. WHO guidelines approved by the Guidelines Review Committee. In Xpert MTB/RIF implementation manual: technical and operational ‘how-to’: practical considerations. Geneva: World Health Organization; 2014.

    Google Scholar 

  89. Catanzaro A, Rodwell TC, Catanzaro DG, et al. Performance comparison of three rapid tests for the diagnosis of drug-resistant tuberculosis. PLoS One. 2015;10:e0136861.

    PubMed  PubMed Central  Google Scholar 

  90. Bodmer T, Strohle A. Diagnosing pulmonary tuberculosis with the Xpert MTB/RIF test. J Vis Exp. 2012:e3547. https://doi.org/10.3791/3547.

  91. Koser CU, Ellington MJ, Peacock SJ. Whole-genome sequencing to control antimicrobial resistance. Trends Genet. 2014;30:401–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Banada PP, Sivasubramani SK, Blakemore R, et al. Containment of bioaerosol infection risk by the Xpert MTB/RIF assay and its applicability to point-of-care settings. J Clin Microbiol. 2010;48:3551–7.

    PubMed  PubMed Central  Google Scholar 

  93. Kim, Y.J., Park, M.Y., Kim, S.Y., et al. [Evaluation of the performances of AdvanSure TB/NTM real time PCR kit for detection of mycobacteria in respiratory specimens]. Korean J Lab Med. 2008;28:34–8.

    CAS  PubMed  Google Scholar 

  94. Fujita Y, Doi T, Maekura R, et al. Differences in serological responses to specific glycopeptidolipid-core and common lipid antigens in patients with pulmonary disease due to Mycobacterium tuberculosis and Mycobacterium avium complex. J Med Microbiol. 2006;55:189–99.

    CAS  PubMed  Google Scholar 

  95. Steingart KR, Ramsay A, Dowdy DW, et al. Serological tests for the diagnosis of active tuberculosis: relevance for India. Indian J Med Res. 2012;135:695–702.

    PubMed  PubMed Central  Google Scholar 

  96. Li Q, Dong HY, Pang Y, et al. Multicenter evaluation of the molecular line probe assay for multidrug resistant Mycobacterium Tuberculosis detection in China. Biomed Environ Sci. 2015;28:464–7.

    PubMed  Google Scholar 

  97. Viveiros M, Leandro C, Rodrigues L, et al. Direct application of the INNO-LiPA Rif.TB line-probe assay for rapid identification of Mycobacterium tuberculosis complex strains and detection of rifampin resistance in 360 smear-positive respiratory specimens from an area of high incidence of multidrug-resistant tuberculosis. J Clin Microbiol. 2005;43:4880–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Yadav RN, Singh BK, Sharma SK, et al. Comparative evaluation of GenoType MTBDRplus line probe assay with solid culture method in early diagnosis of multidrug resistant tuberculosis (MDR-TB) at a tertiary care centre in India. PLoS One. 2013;8:e72036.

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Lacoma A, Molina-Moya B, Prat C, et al. Pyrosequencing for rapid detection of Mycobacterium tuberculosis second-line drugs and ethambutol resistance. Diagn Microbiol Infect Dis. 2015;83:263–9.

    CAS  PubMed  Google Scholar 

  100. Lin SY, Rodwell TC, Victor TC, et al. Pyrosequencing for rapid detection of extensively drug-resistant Mycobacterium tuberculosis in clinical isolates and clinical specimens. J Clin Microbiol. 2014;52:475–82.

    PubMed  PubMed Central  Google Scholar 

  101. Mahendradhata Y, Probandari A, Widjanarko B, et al. Embedding operational research into national disease control programme: lessons from 10 years of experience in Indonesia. Glob Health Action. 2014;7:25412.

    PubMed  Google Scholar 

  102. Lawn SD. Advances in diagnostic assays for tuberculosis. Cold Spring Harb Perspect Med. 2015;5:a017806.

    PubMed  PubMed Central  Google Scholar 

  103. WHO. Global tuberculosis control: epidemiology, strategy, financing (WHO report 2018).

    Google Scholar 

  104. Jaeger S, Karargyris A, Candemir S, et al. Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging. 2014;33(2):233–45.

    PubMed  Google Scholar 

  105. Maduskar P, Muyoyeta M, Ayles H, et al. Detection of tuberculosis using digital chest radiography: automated reading vs. interpretation by clinical officers. Int J Tuberc Lung Dis. 2013;17(12):1613–20.

    CAS  PubMed  Google Scholar 

  106. Hogeweg L, Sanchez C, Maduskar P, et al. Automatic detection of tuberculosis in chest radiographs using a combination of textural, focal, and shape abnormality analysis. IEEE Trans Med Imaging. 2015;34:2429.

    PubMed  Google Scholar 

  107. Muyoyeta M, Maduskar P, Moyo MR, et al. The sensitivity and specificity of using a computer aided diagnosis program for automatically scoring chest X-rays of presumptive TB patients compared with Xpert MTB/RIF in Lusaka Zambia. PLoS One. 2014;9(4):e93757.

    PubMed  PubMed Central  Google Scholar 

  108. Folio, L, Sigelman, J, Wang, Y, et al. Automatic identification and classification of tuberculosis findings on chest radiographs for global surveillance programs. Annual Meeting of the American Roentgen Ray Society (ARRS).

    Google Scholar 

  109. C. Pangilinan, A. Divekar, G. Coetzee, et al. Application of stepwise binary decision classification for reduction of false positives in tuberculosis detection from smeared slides. Proceedings of the IASTED International Symposia on Imaging and Signal Processing in Healthcare and Technology, 16–18 May 2011, Washington, DC; https://doi.org/10.2316/P.2011.737-035.

  110. Ismail NA, Omar SV, Lewis JJ, et al. Performance of a novelalgorithm using automated digital microscopy for diagnosing tuberculosis. Am J Respir Crit Care Med. 2015;191(12):1443–9.

    PubMed  Google Scholar 

  111. Karargyris A, Folio L, Siegelman J, et al. Comparing the performance of man and machine for TB screening in chest radiographs. NIH Intramural Research Festival, Bethesda MD, 6–8 Nov 2013.

    Google Scholar 

  112. Lure F, Jaeger S, Antani S, et al. Application of automatic microscopy and digital chest radiography diagnostic system in screening of tuberculosis. Electron J Emerg Inf Dis. 2017;2(1):5–8.

    Google Scholar 

  113. Computer-aided TB Screening on Chest X-rays. https://lhncbc.nlm.nih.gov/project/computer-aided-tb-screening-chest-x-rays.

  114. Fleming Y. Lure, Stefan Jaeger, Sameer Antani, et al. Automated system for radiologic tuberculosis screening. Chinese Congress of Radiology (CCR) 2017, Shanghai, China, 12–15 Oct 2018.

    Google Scholar 

  115. Jaeger S, Candemir S, Antani SK, et al. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant Imaging Med Surg. 2014;4(6):475–7. https://doi.org/10.3978/j.issn.2223-4292.2014.11.20.

    CrossRef  PubMed  PubMed Central  Google Scholar 

  116. Aoki T, Oda N, Yamashita Y, et al. Usefulness of computerized method for lung nodule detection in digital chest radiographs using temporal subtraction images. Acad Radiol. 2011;18(8):1000–5.

    PubMed  Google Scholar 

  117. Kik SV, Denkinger CM, Casenghi M, Vadnais C, Pai M. Tuberculosis diagnostics: which target product profiles should be prioritised? Eur Respir J. 2014;44(2):537–40.

    PubMed  Google Scholar 

  118. James J Lewis, Bernard Fourie, Gerrit Coetzee, Fleming Y. M. Lure, Ajay Divekar, Gavin Churchyard, Susan Dorman. Computer aided detection (CAD) of TB on sputum smear microscope: comparing the performance of a microbiologist’s conventional reading with a microbiologist’s performance using CAD detection as an aid, in a high HIV prevalent setting. XIX International AIDS Conference (AIDS 2012), 22–27 July 2012, Washington, DC.

    Google Scholar 

  119. C. Pangilinan, A. Divekar, G. Coetzee, et al. Application of stepwise classification for detection of tuberculosis on smeared slides under microscope system. Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83153E (February 23, 2012); https://doi.org/10.1117/12.910484; SPIE Medical Imaging Conference, Newport Beach, CA, 4–9 Feb 2012.

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, The Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China

    Pu-Xuan Lu

  2. Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, Guangdong, China

    Ya-rui Yang & Sheng-yuan Liu

  3. Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China

    Li Xie

  4. College of Engineering, University of Texas, El Paso, TX, USA

    Fleming Lure

  5. Shenzhen Smart Imaging Healthcare Co. Ltd., Shenzhen, Guangdong, China

    Mu-Lan Li

Authors
  1. Pu-Xuan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ya-rui Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sheng-yuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fleming Lure
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mu-Lan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Tuberculosis, The Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China

    Prof. Wei-ye Yu

  2. Department of Radiology, The Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China

    Prof. Pu-Xuan Lu

  3. Department of Tuberculosis, The Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China

    Prof. Wei-guo Tan

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 People's Medical Publishing House, PR of China

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Lu, PX., Yang, Yr., Liu, Sy., Xie, L., Lure, F., Li, ML. (2020). New Technologies for TB Control in Migrating Population. In: Yu, Wy., Lu, PX., Tan, Wg. (eds) Tuberculosis Control in Migrating Population. Springer, Singapore. https://doi.org/10.1007/978-981-32-9763-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-32-9763-0_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9762-3

  • Online ISBN: 978-981-32-9763-0

  • eBook Packages: MedicineMedicine (R0)