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• Repeated infection leads to progressive

scarring of the eyelid and mechanical damage to the cornea

• Infection in children leads to blindness later in life.

Trachoma (2)

• Progression from follicular and inflammatory disease

• scarred eyelids

• inturned eyelashes

• secondary bacterial infections lead to corneal opacity

Healthy eyelid

Severe TF/TI

Scarring

Trichiasis, Corneal opacity

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?

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.

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.

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.

Models informing trials

• Suggesting and supporting hypotheses

• Interpretation

• Analysis

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

Children a core group?

• Why do we care?

• Children easier to treat

• Lowered antibiotic selection pressure at the community level

Children a core group?

• This was actually tested

Models informing trials

• Suggesting and supporting hypotheses

• Interpretation

• Analysis

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=

Extension

• Deterministic version

dY/dt=(0P + 1Y/N + 2 Y2/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

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

TANA Trial

• TANA trial: Trachoma Amelioration iN Amhara

• Lietman Group U10 being conducted in Ethiopia

• Community randomized trial with four primary specific aims

NIH TANA Trial, Ethiopia

Trial

Trial

But…

Stochastic epidemic

• Many books now on stochastic models in epidemiology

• Standard method used here, e.g. Bailey, Elements of Stochastic Processes, 1964

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

State space (2)

0 1 2 N-1 N…

Infection

Recovery

Model

• Continuous time

• P(Y(t)=i) = pi(t)

• Assume population is fixed

• Model period between treatments

Fit to data

• Not one community, but 24 communities

• Taken from TANA clinical trial

• Communities embedded in similarly treated communities to reduce contamination

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).

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)

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

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

Models informing trials

• Suggesting and supporting hypotheses

• Interpretation

• Analysis– Practicum

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

Acknowledgments, cont’d

Tadege AlemayehuTesfaye BelayAzmeraw AdgoMelese TemesgenGabeyehu SibhatAbebe MekonenManalush Berihun

Temesgen DemileWosen AbebeMelkam AndwalemMitsalal AberahraneyBanchu GedamuTessema EneyewMuluken Gobezle

Trachoma Projects in EthiopiaDeb GillMelissa NeuweltNandini GandhiCyril DalmonNicolle BenitahYing PanLauren PattyVivian SchiedlerAli ZaidiDwight SilveraIsabella PhanChihori WadaDavid LeeHarsha ReddyKathryn RayRachel MayAlison SkaletSara HaugAndi HatchJesse Biebesheimer

Traci BrownLaura CieslikAnita GuptaSusie Osaki-HolmNazzy PakpourKaren ShihScott ShimotsuKristine VinupJohn WarrenYinghui MiaoMariko BirdGreg SchmidtLynn OlingerScott LeeKevin HongJaya ChidambaramAllison LohDeb GillLarry SchwabJeremy Keenan

Vicky CevallosLauren FriedlyBruce GaynorTom LietmanKevin MillerTisha PrabriputaloongMichael SaidelJohn P. WhitcherElizabeth YiMichael YoonJohn WarrenMacdara BodekerMuthiah SrinivasanMarilyn WhitcherJenafir HouseJon YangNicole StollerCharles LinTina RutarColleen Halfpenny

FundingThat Man May SeeBernard Osher FoundationBodri FoundationHarper-Inglis TrustPeierls FoundationJack and DeLoris Lange FoundationResearch to Prevent BlindnessInternational Trachoma Initiative/PfizerNIAID: RO1-AI48789NIAID: R21-AI55752NEI: U10-EY016214Bill and Melinda Gates Foundation

With grateful acknowlegment