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Population Based Surveillance for Pre-Invasive Cervical Cancer through Cancer Registries

This article discusses the potential of using cancer registries for population-based surveillance of pre-invasive cervical cancer, specifically in monitoring the effectiveness of the HPV vaccine. It explores the barriers to implementation, the role of public health in vaccine-related surveillance, and the potential return on investment. The article also provides insights from a CDC pilot feasibility project and lessons learned from three central cancer registries.

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Population Based Surveillance for Pre-Invasive Cervical Cancer through Cancer Registries

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  1. Population Based Surveillance for Pre-Invasive Cervical Cancer through Cancer Registries Novel Usefulness in HPV Vaccine Monitoring Deblina Datta, MD Division of STD Prevention, CDC NAACCR June 18, 2009

  2. HPV Vaccine • Vaccine licensed June 2006 • Protects against new infection with HPV • HPV infection causes cervical cancer • Routine recommendation for girls aged 11-12 yrs • Anticipated reductions in cervical cancers, other anogenital cancers • Expensive: $360 for three dose series

  3. Highly Efficacious VaccineClinical Trial Data • Over 95% efficacy against cervical cancer outcomes • CIN 2/3 • Very high efficacy against vulvar/vaginal cancer outcomes • Almost 100% efficacy against genital warts

  4. What about “real world” effectiveness of this vaccine? • Barriers to implementation • Cost-?disparities in access • Stigma against sexual transmission of HPV • Success of adolescent vaccine platform • Controversies over school mandates, vaccine safety • Anti-vaccine sentiment • Need for surveillance systems • Nordic registries • What about US-based surveillance systems? • Public health role in vaccine related surveillance • Investment by healthcare system • What is return on this investment? (comparison with screening programs) • Effectiveness vs. efficacy

  5. Timeline of HPV Related Endpoints t0=HPV infection (Days) t3=Cervical Cancer (3-4 Decades) t2=Cervical Cancer Precursors (Months to Decades) t1=Genital Warts (Weeks to Months) Adolescence 5th to 6th Decades of Life

  6. Cervical cancer ages 15-29 CIN 2/3 ages 12-99 CIN 2/3 ages 15-29 Cervical cancer ages 12-99 Source: Chesson & Markowitz, International Society for Sexually Transmitted Diseases Research, London, 2009

  7. CDC Pilot Feasibility Project • Funded pilot in three central cancer registries • Michigan Central Cancer Registry (NPCR and SEER, geographically distinct sites) • Louisiana Tumor Registry (NPCR and SEER) • Kentucky Central Cancer Registry (NPCR and SEER) • Leverage infrastructure • Experienced tumor registrars • Data streams from pathology labs (and other sources) • Apply data standards for completeness, accuracy, timeliness

  8. CIN 3 and AIS • CIN 3 surveillance endpoint of choice • Most immediate precursor of invasive cervical cancer • Most likely to progress to cancer • Experience in registries with in situ cancers • Consideration for volume of cases • AIS

  9. Registry Background in Pre-invasive Cervical Cancer Surveillance • Previous experience collecting cervical carcinoma in situ (CIS/CIN 3) in NPCR and SEER • Decision to discontinue collection of CIS data in 1996 based on concerns over new nomenclature and misclassification of cases • More time has passed since nomenclature introduced • However now a new era due to HPV vaccine, more need to collect the data • Michigan Central Cancer Registry has continued to collect CIS data • value to cancer control program

  10. Protocol • Common protocol agreed upon • Which endpoint(s) to collect? • How to address issues around nomenclature and misclassification? • Most efficient methods • Rapid case ascertainment • Non research determination by CDC • Pre-pilot period (Sept-Dec 2008) • Data collection Jan 2009-Dec 2009

  11. Data Elements • Not a full abstract • Data limited to 24 variables (most are NAACCR or SEER variables) • Demographics • Name, age, race, ethnicity, address, sex, birthdate • Data Source • Reporting facility, facility type • “Tumor”-related data • Site, behavior code, histology code, histology terminology code, sequence code

  12. KY Cancer Registry Methods • AIM E-Pathology (E-Path) software in 21 labs. • 13 labs uses their own computer systems and send them directly to KCR • 9 hospital registrars forward reports directly to KCR (don’t abstract any cases) • 5 small hospitals, ( monthly or bi-monthly) by the KCR regional abstractors • Project coordinator contacts each reporting facility that is not using the AIM system monthly to ensure that they are sending all eligible case.

  13. Reports by Month January 124 February 134 March 116 April 100 May 5 TOTAL 479 (Updated May 28, 2009)

  14. Histology Coding 8010 – Carcinoma, NOS 10 8070 – Squamous Cell CIS 48 8077 – Squamous CIN III 407 8140 – Adenocarcinoma in situ 14 TOTAL 479 (Updated May 28, 2009)

  15. Histologic Terminology AIS 14 CIN III 259 CIS 58 Severe Dysplasia 148 TOTAL 479 (Updated May 28, 2009)

  16. Overall Reports from 3 Registries *Data known to be incomplete for this trial period except for Michigan **Data updated through May 2009 except Michigan data through June 2009

  17. Lessons Learned • Must be a funded activity for registries interested in this activity • CTR expertise very valuable • Accurate coding of pathologic diagnoses • Challenge with CIN3 • Case finding and consolidation, auditing • Electronic reporting very successful • Difficulty accessing race and ethnicity data • More difficult with electronic reporting • Difficulty collecting tumor site data • Not well documented on path reports

  18. Future Directions • Expand to other registries (more typing of specimens) • Expand electronic reporting using CDC E-PATH • Address legal authority to collect these data • Need input from NPCR, SEER, NAACCR • Evaluation of coding practices • Linkage of data between cancer registry and immunization registry to capture HPV vaccine history

  19. Michigan Cancer Surveillance Program Glenn Copeland Won Silva Jeanne Whitlock Louisiana Tumor Registry Vivien Chen Ed Peters Tara Ruhlen Kentucky Cancer Registry Mary Jane Byrne Tom Tucker CDC Umed Ajani Mona Saraiya Beth Unger Meg Watson Hillard Weinstock Macro International Aliza Fink Qiming He Don McMaster Benita O’Colmain Kevin Zhang Acknowledgements

  20. Thank You Deblina Datta, MD ddatta@cdc.gov 404-639-8424 Centers for Disease Control and Prevention Division of STD Prevention 1600 Clifton Rd MS E-02 Atlanta, GA 30333 Disclaimer: The findings and conclusions in this presentation are those of the author and do not necessarily represent the views of the Centers for Disease Control and Prevention.

  21. Cervical Intraepithelial Neoplasia (CIN) • Histologic diagnosis (biopsy) • Spectrum of intraepithelial changes of cervix • Graded on basis of thickness of abnormality • Epithelial layer is divided into thirds

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