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HARP: HPV Assessment of Risk Profile. Susan Sotardi Mentor: Dr. Mark H. Einstein, MD, MS Department of Obstetrics & Gynecology and Women’s Health Division of Gynecologic Oncology Albert Einstein College of Medicine and Montefiore Medical Center.
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HARP: HPV Assessment of Risk Profile Susan Sotardi Mentor: Dr. Mark H. Einstein, MD, MS Department of Obstetrics & Gynecology and Women’s Health Division of Gynecologic Oncology Albert Einstein College of Medicine and Montefiore Medical Center
Biology of Human Papillomavirus (HPV) Infections Normal Cervix HPV Infection (CIN* 2) (CIN* 3) Cervical Cancer (Invasive) Infectious Viral Particles Perinuclear Clearing (Koilocytosis) Episome Basal cell layer *CIN = cervical intraepithelial neoplasia; ICC = invasive cervical cancer 1. Goodman A, Wilbur DC. N Engl J Med. 2003;349:1555–1564. Adapted with permission from the Massachusetts Medical Society.2.Doorbar J. J Clin Virol. 2005;32(suppl):S7–S15. 3. Bonnez W. In: Richman DD, Whitley RJ, Hayden FJ,eds. Washington, DC: American Society for Microbiology Press; 2002:557–596.
HPV Vaccine Characteristics • Recombinant vaccine • Most effective in preventing infections with specific HPV subtypes: • Quadrivalent: 6 / 11 / 16 / 18 • Bivalent: 16 / 18 • Three doses, administered at: 0 / 1-2 months / 6 months
HPV Vaccine & the ‘Catch-up’ Population • Recommended routinely for all 11 and 12 year olds • Can be given as young as age 9 • ‘catch-up’ population ages 13-26 • Coverage for payment of HPV vaccines • ≤ 18 years old - covered by the Vaccines for Children program. • 19-26 years old – covered by >99% of insurance companies including Medicaid • However, nearly1/3rd of women between 19-30 are uninsured. • Ideally, administer prior to onset of sexual activity
HPV Vaccine: Prophylactic Efficacy vs. Effectiveness • Prophylactic efficacy refers to prevention of endpoint in unexposed individuals • In HPV Vaccine clinical trials: PPE, ATP • Type-specific disease endpoint • Effectiveness refers to impact in defined populations with a mixture of exposed and unexposed subjects • Intention to treat: ITT • Different ages/exposures • Lesions of all HPV types Adopted from J. Cuzick, Eurogin 2008. Nice, France
Defining ‘Benefit’ for prophylactic HPV Vaccines: Prophylactic Efficacy vs. Effectiveness HPV DNA (active infection) + - + Serology (Past infection) -
Defining ‘Benefit’ for prophylactic HPV Vaccines: Efficacy of Quadrivalent Vaccine in Prespecified Outcomes 1Koutsky, 2007 2Ault, 2007 3Garland, 2007 4Joura, 2007 Modified from Burk, Lancet, 2007
Specific Aims • Assess previously validated risk factors associated with HPV in vaccine eligible women over age 18 presenting for HPV vaccination at the Montefiore adult HPV vaccination clinic. • Model HPV exposure and prevalence in Montefiore vaccine clinic based on these risk factors. • Use ITT data to predict vaccine effectivenss in Bronx population at various levels of uptake and HPV exposure. • End Goal: Create a model that predicts benefit derived from vaccination for individual patient inputs.
Risk Factors for HPV • Lifestyle factors: • Smoking • Drug Use • Relationship factors • Monogamous • Duration • Partner factors • Number of previous partners • Partner STD history • Demographics: • Age • Race • Education • Marital Status • Sexual history • Pap test history • STD history • Lifetime # partners • Number of new partners in 1 yr. • Pregnancy • Contraception
Smoking and Cervical Neoplasia are Strongly Associated • Proposed Mechanisms: • Increase Risk of Acquiring HPV • Confounded by link between smoking and sexual behavior 1, 2 • Prolong Duration of Infection • Varies by study design 3, 4 • Epigenetics • Remodeling of cervical epithelium after onset of sexual activity is accelerated by smoking 5 1 Appleby 2006 2 Ho 1998 3Giuliano2002 4Richardson 2005 5 Hwang 2009
HARP and Differences from Quadrivalent Efficacy Trials 1 Garland NEJM 2007 2 Koutsky NEJM 2002 3 Villa BJC 2006
Hypotheses • We hypothesize that women presenting to the Montefiore vaccination clinic are at higher risk for HPV exposure than previously published clinical trials. • As a result of higher exposure risk, HPV vaccine efficacy will be significantly lower in this population.
Possible Approaches to Estimating HPV Prevalence/Exposure • Directly: • Swabs + DNA test • Already done in 10’s of 1000’s of women in clinical trials • Surrogate Outcome: • History of abnormal Pap test (Persistent HPV) • Modeling: • Using previously validated risk factors we can model the exposure in our population and validate this model using our data and previously published studies.
Data Collection • Inclusion: • Women ages 18-26 presenting to Montefiore HPV vaccine clinic • Computer-based survey attached to medical record • based on previously validated risk factors from well-known prospective studies. • Power analysis based on pilot data • Plan to recruit until 150 surveys completed.
Risk Factors for HPV exposure: Demographics 1, 3 Kjaer 2007 2 Ho 1998 4, 6. 7 Manhart 2006 5Winer 2003
Risk Factors for HPV exposure: Gynecological History 1Sarias et al 2, 3, 4 Kjaer 2007 5Winer 2006 6Kjaer 2007
Risk Factors for HPV exposure:Cervical Lesion History 11Manhart 2006
Risk Factors for HPV exposure: Sexual History 1, 3 Kjaer 2007 2, 6Winer 2003 4, 8 Manhart 2006 5Vaccarella 2006 7Sarias et al.
Risk Factors for HPV exposure: Sexual History (continued) 1, 3 Kjaer 20072, 6Winer 20034, 8 Manhart 20065Vaccarella 2006 7Sarias et al.
Systems Dynamic Modeling of HPV Risk Factors and Prevalence System Dynamics Models (SDM) simulate incidence and prevalence of disease within a population. System Dynamics is a mathematical modeling methodology that can be used to represent associations between known risk factors and HPV exposure, over time. Key issue: What data do you have to inform the model? Published clinical trials, Registries, Key informants (experts), results of studies (such as our clinic survey)
Systems Dynamic Modeling: HPV Prevalence • Risk factor categories: • Socioeconomic status • Prior exposure to HPV • Risk related behaviors • Create causal loop diagrams to identify feedback structures • Build model and simulate behavior over time • Compare simulated and empirical outputs to determine if base model effectively reproduces trends in HPV prevalence
Final Goal: Create a model that predicts prior HPV exposure and vaccine benefit • Clinicians will input key risk factors into a calculator and retrieve prior exposure probability. • Can base decision to vaccinate on established risk factors. • Similar to the Gail Model.
Gail Model for Predicting Breast Cancer 1. Ever diagnosed with ductal carcinoma in situ (DCIS) or lobular carcinoma in situ (LCIS)? 2. Age? 3. Age at first menstrual period? 4. Age at the time of her first live birth of a child? 5. How many first-degree relatives with breast cancer? 6. Ever had a breast biopsy? 7. Race/ethnicity? Based on answers, the chance of being diagnosed with breast cancer is estimated to be: - within lifetime (to age 90). - within 5yrs, 10 yrs, 20yrs, 30 yrs.
Thank you! • Dr. Mark Einstein • Dr. Nicolas Schlecht • Dr. David Lounsbury • Dr. Hayley Thompson • Dr. Clyde Schechter • Montefiore vaccine clinic staff
Factors Influencing Prevalence to Inform Our Systems Dynamic Modeling Early onset of sexual activity Regression HPV Prevalence 6/11/16/18 Smoking/Drug Use Lifetime Number of Partners Partner STD History Prior exposure to HPV Partner lifetime number partners Prior Pregnancy Partner High Risk Behavior Patient High Risk Behavior Contraception Prior STD Socioeconomic Status Marital Status Perceived Risk Education
Vaccine Efficacy Modeling • Summary Relative Risk = RRax RRbx RRcx…x RRn • Absolute Risk calculated as polynomial formula • Linear equations estimate any missing values
Steps to development of Federal Advisory Committee on Immunization Practices (ACIP) Recommendations Pre-clinical Development Clinical Development Phase I/II/III trials Epidemiology FDA Licensure and labeling Acceptability Implementation Cost-effectiveness Adoption of recommendations by stake-holding organizations ACIP Recommendation Vaccines for children program Adopted from Society of Gynecologic Oncologists (SGO) Cervical Cancer Forum, Chicago, IL, Sept. 2008
HARP and Differences from Bivalent Efficacy Trials 1 Harper Lancet 2004 2 Hildesheim JAMA 2007 3 Paavonen Lancet 2009
Vaccine Models • Models are as good as their assumptions and input 1 • Input values into models that affect disease prevalence: • Age at vaccination- what is a ‘vaccinated’ female? • HPV type-specific prevalence, persistence, and clearance (Goldie, Brisson, many other ‘modelers’) 2 • Missing the male factor in most natural history studies- so impossible to determine HPV transmission efficacy and calculate ‘herd immunity’ 1 Garnett T. JID 2005. 2 Goldie S. IJC 2003
ICC ICC ICC ~70% ICC ICC ICC ICC ICC HSIL HSIL ~50% ~60% HSIL HSIL LSIL LSIL ~20% ~35% LSIL ASCUS Potential Impact of the Quadrivalent HPV Vaccine Potential reduction due to 16/18 and 6/11 ICC Decade(s) Years Months HSIL LSIL ASCUS ~20% ~35%
HPV Associated Lesions Quadrivalent Low Grade CIN 1 High Grade CIN 2/3 Cervical Cancer ASCUS HPV 6/11/16/18 Other HPV Subtypes Risk Factors