LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Crump, RE; Medley, GF
Publisher: BioMed Central
Journal: Parasites & Vectors
Languages: English
Types: Article
Subjects: Leprosy, Hansen’s disease, RC, Back-calculation, Thailand, Infectious Diseases, Forecast, Surveillance, Elimination, Research, Parasitology
Background:\ud The number of new leprosy cases reported annually is falling worldwide, but remains relatively high in some populations. Because of the long and variable periods between infection, onset of disease, and diagnosis, the recently detected cases are a reflection of infection many years earlier. Estimation of the numbers of sub-clinical and clinical infections would be useful for management of elimination programmes. Back-calculation is a methodology that could provide estimates of prevalence of undiagnosed infections, future diagnoses and the effectiveness of control.\ud \ud Methods:\ud A basic back-calculation model to investigate the infection dynamics of leprosy has been developed using Markov Chain Monte Carlo in a Bayesian context. The incidence of infection and the detection delay both vary with calendar time. Public data from Thailand are used to demonstrate the results that are obtained as the incidence of diagnosed cases falls.\ud \ud Results:\ud The results show that the underlying burden of infection and short-term future predictions of cases can be estimated with a simple model. The downward trend in new leprosy cases in Thailand is expected to continue. In 2015 the predicted total number of undiagnosed sub-clinical and clinical infections is 1,168 (846–1,546) of which 466 (381–563) are expected to be clinical infections.\ud \ud Conclusions:\ud Bayesian back-calculation has great potential to provide estimates of numbers of individuals in health/infection states that are as yet unobserved. Predictions of future cases provides a quantitative measure of understanding for programme managers and evaluators. We will continue to develop the approach, and suggest that it might be useful for other NTD in which incidence of diagnosis is not an immediate measure of infection.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Rodrigues LC, Lockwood DNJ. Leprosy now: Epidemiology, progress, challenges, and research gaps. Lancet Infect Dis. 2011;11:464-70.
    • 2. World Health Organisation. Global leprosy update, 2013; reducing disease burden. Wkly Epidemiol Rec. 2014;89(36):389-400.
    • 3. Bratschi MW, Steinmann P, Wickenden A, Gillis TP. Current knowledge on Mycobacterium leprae transmission: A systematic literature review. Lepr Rev. 2015;86(2):142-55.
    • 4. World Health Assembly. Leprosy resolution WHA 44.9 : Forty-fourth world health assembly, 13 may 1991. http://www.who.int/neglected_diseases/ mediacentre/WHA_44.9_Eng.pdf; 1991.
    • 5. World Health Organisation. Accelerating work to overcome the global impact of neglected tropical diseases. a roadmap for implementation. 2012. http://www.who.int/neglected_diseases/ NTD_RoadMap_2012_Fullversion.pdf
    • 6. Brook CE, Beauclair R, Ngwenya O, Worden L, Ndeffo-Mbah M, Lietman TM, Satpathy SK, Galvani AP, Porco TC. Spatial heterogeneity in projected leprosy trends in India. Parasit Vectors. 2015;doi:10.1186/s13071-015-1124-7.
    • 7. Merle CS, Cunha SS, Rodrigues LC. BCG vaccination and leprosy protection: Review of current evidence and status of BCG in leprosy control. Expert Rev Vaccines. 2010;9(2):209-22.
    • 8. Cox DR, Medley GF. A process of events with notification delay and the forecasting of AIDS. Philos Trans R Soc Lond, B, Biol Sci. 1989;325(1226):135-45.
    • 9. Blok DJ, de Vlas SJ, Richardus JH. Global elimination of leprosy by 2020: Are we on track? Parasit Vectors. 2015;doi:10.1186/s13071-015-1143-4.
    • 10. Brookmeyer R, Gail MH. A method for obtaining short-term projections and lower bounds on the size of the AIDS epidemic. J Am Stat Assoc. 1988;83(402):301-8.
    • 11. Birrell PJ, Gill ON, Delpech VC, Brown AE, Desai S, Chadborn TR, et al. HIV incidence in men who have sex with men in England and Wales 2001-10: a nationwide population study. Lancet Infect Dis. 2013;13:313-8.
    • 12. Plummer M. JAGS Version 3.4.0 user manual. 2013. http://mcmc-jags.sourceforge.net/
    • 13. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2015. http://www.Rproject.org/
    • 14. Brubaker ML, Binford CH, Trautman JR. Occurrence of leprosy in U.S. veterans after service in endemic areas abroad. Public Health Rep. 1969;84(12):1051-8.
    • 15. Hasseltine HE. Leprosy in men who served in United States military service. Int J Lepr Other Mycobact Dis. 1940;8:501-8.
    • 16. Doyle JO. Case of leprosy seen in a venereal disease clinic in Britain. BMJ. 1953;2:261-2.
    • 17. Rogers J, Adamson DG. Leprosy: Report on four cases. BMJ. 1953;2(4830):259-60.
    • 18. Medford FE. Leprosy in Vietnam veterans. Arch Intern Med. 1974;134:373.
    • 19. Rose HD. Leprosy in Vietnam returnees. J Am Med Assoc. 1974;230(10):1388.
    • 20. Brickell K, Frith R, Ellis-Pegler R. Leprosy in a backpacker. J Travel Med. 2005;12(3):161-3.
    • 21. World Health Organisation. Leprosy control in Thailand: Trends in case detection, 1965-2005. Wkly Epidemiol Rec. 2007;82(30):261-71.
    • 22. World Health Organisation. Global leprosy situation, 2007. Wkly Epidemiol Rec. 2007;82(25):225-32.
    • 23. World Health Organisation. Global leprosy situation, beginning of 2008. Wkly Epidemiol Rec. 2008;83(33):293-300.
    • 24. World Health Organisation. Global leprosy situation, 2009. Wkly Epidemiol Rec. 2009;84(33):333-40.
    • 25. World Health Organisation. Global leprosy situation, 2010. Wkly Epidemiol Rec. 2010;85(35):337-48.
    • 26. World Health Organisation. Leprosy update, 2011. Wkly Epidemiol Rec. 2011;86(36):389-400.
    • 27. World Health Organisation. Global leprosy situation, 2012. Wkly Epidemiol Rec. 2012;87(34):317-28.
    • 28. World Health Organisation. Global leprosy: Update on the 2012 situation. Wkly Epidemiol Rec. 2013;88(35):365-79.
    • 29. Santos VS, de Mendonça Neto PT, Falcaõ Raposo OF, Fakhouri R, Prado Reis F, Corrêa Feitosa VL. Evaluation of agreement between clinical and histopathological data for classifying leprosy. Int J Infect Dis. 2013;17:e189-92.
    • 30. van Veen NH, Meima A, Richardus JH. The relationship between detection delay and impairment in leprosy control: A comparison of patient cohorts from bangladesh and ethiopia. Lepr Rev. 2006;77(4):356-65.
    • 31. Plummer M. Cuts in Bayesian graphical models. Statistical Computation. 2015;25:37-43.
    • 32. Birrell PJ, Chadborn TR, Gill ON, Delpech VC, Angelis DD. Estimating trends in incidence, time-to-diagnosis and undiagnosed prevalence using a CD4- based Bayesian back-calculation. Stat Commun Infect Dis. 2012;4(1):Article 6.
    • 33. United Nations. Probabilistic population projections based on the world population prospects: The 2015 revision [Internet]. Population Division, DESA; 2015. http://esa.un.org/unpd/ppp/. Accessed 17 Aug 2015.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article