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Forecasting trachoma: control, elimination, or eradication?

Forecasting trachoma: control, elimination, or eradication?. Thomas M. Lietman Kathryn J. Ray Travis C. Porco FI Proctor Foundation, UCSF December 2012. Trachoma. Leading infectious cause of blindness (WHO 2002) Causative agent Chlamydia trachomatis

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Forecasting trachoma: control, elimination, or eradication?

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  1. Forecasting trachoma: control, elimination, or eradication? Thomas M. Lietman Kathryn J. Ray Travis C. Porco FI Proctor Foundation, UCSF December 2012

  2. Trachoma • Leading infectious cause of blindness (WHO 2002) • Causative agent Chlamydia trachomatis • Repeated infection leads to progressive scarring of the eyelid and mechanical damage to the cornea • Infection in children leads to blindness later in life.

  3. Trachoma (2) • Progression from follicular and inflammatory disease • scarred eyelids • inturned eyelashes • secondary bacterial infections lead to corneal opacity

  4. Healthy eyelid

  5. Severe TF/TI

  6. Scarring

  7. Trichiasis, Corneal opacity

  8. How much less trachoma? • WHO: annual treatment of all inhabitants, reduce infection to level where blindness not a public health problem. • Or, should we try to actually reduce the prevalence of infection to zero?

  9. Important facts • Ocular infection by C. trachomatis is easily cured with single-dose azithromycin (95% efficacy). • Only humans are infected (there is no animal reservoir). • No vaccine is available. • Clinical signs are unreliable in detecting infection; laboratory tests are far too expensive and take far too long.

  10. Trachoma now • WHO plan to stamp it out as a public health problem • Surgery, antibiotics, face-washing, environment • The SAFE program • Mass distribution of azithromycin the cornerstone Schachter J, West SK, Mabey D, et al Lancet. 1999 Aug 21;354(9179):630-5

  11. Modeling? So if we know what causes trachoma… http://www.pacificu.edu/optometry/ce/courses/13036/antibacterialpg1.cfm

  12. …and we know what stops it… …?

  13. Mass administration • Why do we call this program a mass administration? • Because no effort is made to try to find out who actually has the infection and who does not--everybody gets the treatment, regardless.

  14. Has modeling helped? • Lietman et al 1999: Repeat mass treatment can theoretically eliminate the infection • Melese 2004 • Age, immunity, serotypes, households,etc.?

  15. Good data Practical implications

  16. Current issues • Assess ongoing efforts • Optimize current efforts • Minimize collateral damage • Consider biological questions • Predict future trends

  17. Assessment • We want to eliminate infection, but what should the epidemiology of trachoma look like during elimination? • Theoretical models suggest an approximately exponential quasistationary distribution (Nåsell; Lietman 2013 under review) • Thus, long tails are expected

  18. One of these is real data from a district in Nepal, during elimination

  19. …the others are simulated geometric random variables

  20. Chasing ghosts • Unpredictability of trachoma at the village level • Long tail of the distribution • Expect transient local hot spots • The presence of a local hot spot does NOT imply failure

  21. When can we stop? • Elementary models suggest as long as conditions favor transmission, disease returns unless all cases are eliminated. • Prevalencethresholdspossible Lietman, Epidemics, 2011

  22. Collateral damage • Mass administration is controversial because of macrolide resistant pneumococcus Maher et al, PLoS1, 2012

  23. Antibiotic minimization? • Targeting children only leads to reductions in prevalence, even among adults • Can treating children alone actually eliminate infection? • Maybe

  24. Other examples • Does repeat treatment seem to reduce the efficacy in future years? • Tanzania data (Kongwa region), PRET data (S. West) Liu et al, PLoS NTD, 2013

  25. Predict future trends

  26. A specific model

  27. TANA Trial • TANA trial: Trachoma Amelioration iN Amhara • Lietman Group U10 being conducted in Ethiopia • Community randomized trial with four primary specific aims

  28. Community-specific prevalence • Analysis of pooled prevalence from randomly chosen individuals yields a prevalence estimate for the community. • In the TANA trial, we regard whole communities as the units of the trial.

  29. Trial

  30. Trial

  31. TANA • May 2006 to March 2007 • 66,404 people in 48 subkebele • Annual: 50 state teams; 4,437 children • Biannual: 61 ST; 4,462 children • Children: 49 ST; 4,150 children • Delay: 57 ST; 5,166 children

  32. Stochastic epidemic • Many books now on stochastic models in epidemiology • Standard method used here, e.g. Bailey, Elements of Stochastic Processes, 1964

  33. State space • Given a village of size N, let Y be the number of infected individuals; Y ranges from 0, 1, … N-1, N. • Ignore adults for now (low prevalence) • We examined models with age structure, partial immunity

  34. State space (2) 0 1 2 N-1 N … Infection Recovery

  35. Model • Continuous time • P(Y(t)=i) = pi(t) • Assume population is fixed • Model period between treatments first

  36. Acknowledgments • Tom Lietman • Teshome Gebre, Berhan Ayele • Jenafir House, Nicole Stoller • Bruce Gaynor, Jeremy Keenan • Zhaoxia Zhou, Vicky Cevallos, Kevin Hong, Kathryn Ray, Jack Whitcher, Paul Emerson • Data and Safety Monitoring Committee (W. Barlow, D. Everett, L. Schwab, A. Reingold, S. Resnikoff) • Study participants

  37. Acknowledgments, cont’d Tadege Alemayehu Tesfaye Belay Azmeraw Adgo Melese Temesgen Gabeyehu Sibhat Abebe Mekonen Manalush Berihun Temesgen Demile Wosen Abebe Melkam Andwalem Mitsalal Aberahraney Banchu Gedamu Tessema Eneyew Muluken Gobezle

  38. Trachoma Projects in Ethiopia Deb Gill Melissa Neuwelt Nandini Gandhi Cyril Dalmon Nicolle Benitah Ying Pan Lauren Patty Vivian Schiedler Ali Zaidi Dwight Silvera Isabella Phan Chihori Wada David Lee Harsha Reddy Kathryn Ray Rachel May Alison Skalet Sara Haug Andi Hatch Jesse Biebesheimer Traci Brown Laura Cieslik Anita Gupta Susie Osaki-Holm Nazzy Pakpour Karen Shih Scott Shimotsu Kristine Vinup John Warren Yinghui Miao Mariko Bird Greg Schmidt Lynn Olinger Scott Lee Kevin Hong Jaya Chidambaram Allison Loh Deb Gill Larry Schwab Jeremy Keenan Vicky Cevallos Lauren Friedly Bruce Gaynor Tom Lietman Kevin Miller Tisha Prabriputaloong Michael Saidel John P. Whitcher Elizabeth Yi Michael Yoon John Warren Macdara Bodeker Muthiah Srinivasan Marilyn Whitcher Jenafir House Jon Yang Nicole Stoller Charles Lin Tina Rutar Colleen Halfpenny

  39. Funding That Man May See Bernard Osher Foundation Bodri Foundation Harper-Inglis Trust Peierls Foundation Jack and DeLoris Lange Foundation Research to Prevent Blindness International Trachoma Initiative/Pfizer NIAID: RO1-AI48789 NIAID: R21-AI55752 NEI: U10-EY016214 Bill and Melinda Gates Foundation With grateful acknowlegment

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