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Models and trials

Models and trials. Lietman, Ray, Porco DAIDD Workshop 2012. Models informing trials. Suggesting and supporting hypotheses Interpretation Analysis. Trachoma. Leading infectious cause of blindness (WHO 2002) Causative agent Chlamydia trachomatis

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Models and trials

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  1. Models and trials Lietman, Ray, Porco DAIDD Workshop 2012

  2. Models informing trials Suggesting and supporting hypotheses Interpretation Analysis

  3. 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.

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

  5. Healthy eyelid

  6. Severe TF/TI

  7. Scarring

  8. Trichiasis, Corneal opacity

  9. 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?

  10. 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.

  11. Azithromycin Mass administration of azithromycin is cornerstone of public health measures* *Schachter J, West SK, Mabey D, et al Lancet. 1999 Aug 21; 354(9179):630-5.

  12. 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.

  13. Models informing trials Suggesting and supporting hypotheses Interpretation Analysis

  14. Children a core group? • Can we eliminate trachoma by mass treatment of children? • Models (Lietman 99) suggested that even though adults can reinfect children, eliminating infection in children through mass treatment might eliminate trachoma entirely

  15. Children a core group? • Why do we care? • Children easier to treat • Lowered antibiotic selection pressure at the community level

  16. Children a core group? • This was actually tested

  17. Models informing trials Suggesting and supporting hypotheses Interpretation Analysis

  18. Simple model • dX/dt = -XY/N + Y (susceptibles) • dY/dt = XY/N - Y (infectives) • Force of infection for each susceptible X is just proportional to the prevalence Y/N, with proportionality constant  • Recovery rate is constant, , for each individual • R0= 

  19. Extension • Deterministic version dY/dt=(0P + 1Y/N + 2Y2/N2) X - (0 + 1Y/N) Y P is prevalence in neighboring communities (term for exogenous infection) • Higher order terms added in prevalence (Y/N) • Can think of this model as possibly beginning to capture the behavior of more complex mechanistic models--left unspecified--which may have immune history or multiple strains

  20. Purpose • We are not trying to use the data to distinguish between all the possible processes that can generate a key feature of the data • Rather, we wish to ensure that a class of simple models has enough degrees of freedom to at least capture a relevant feature

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

  22. NIH TANA Trial, Ethiopia

  23. Trial

  24. Trial

  25. But…

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

  27. 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

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

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

  30. Fit to data • Not one community, but 24 communities • Taken from TANA clinical trial • Communities embedded in similarly treated communities to reduce contamination

  31. Starting the model.. • Stochastic SIS model: If we insist that p-1(t)=pN+1(t)=0 these apply to all i (from i=0 to N).

  32. Fit to TANA data • Remember, 24 different communities • 5 parameter choices leads to 25=32 models • We fit each of these and used the AIC as a basis for model selection (more specifically, a small-sample version of the AIC)

  33. Interpretation • Look at deterministic version dY/dt=(0P + 1Y/N + 2 Y2/N2) X - (0 + 1Y/N) Y where P is prevalence in neighboring communities

  34. Conclusion • These models imply trachoma is easier to reduce to zero prevalence than models which do not include the higher order terms. • Eradication of trachoma through treatment would be a major breakthrough--the first bacterial infection eliminated through public health treatment programs

  35. Models informing trials • Suggesting and supporting hypotheses • Interpretation • Analysis • Practicum

  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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