1. Cervical Dysplasia/HPV Surveillance in California
Erin Libby Whitney, MPH
Project Coordinator

2. Emerging Infections Program (EIP)/CDC Surveillance System
CA part of 4-site EIP/CDC network to monitor cervical cancer precursor lesions
Population-based
County-based (Alameda County, CA)
Cervical cancer precursor lesions not reportable in CA
Relationships & collaboration are essential
IRB issues need to be addressed
Challenges are not insurmountable!
Linda Niccolai has provided a fabulous overview of the EIP/CDC surveillance project and mentioned the variation among sites.
She also talked about CT’s approach which made CIN 2 and 3 reportable in their state. What if you’re thinking, “I don’t think that will happen in my state!?” CIN is not a reportable condition in CA and will likely not be in the near future, so while you’ll need to work a little harder in the beginning, nothing is insurmountable.

3. Opportunities and Strengths
CDC Funding
Interest in HPV vaccine and vaccine monitoring
Political and legislative climate
Strengths
Infrastructure of the EIP
Experience of investigators
Collaborations with Immunization and Cancer Control
I’d like to start by talking about some of the opportunities that jumpstarted this project and will hopefully keep the project sustainable:

4. Challenges CIN 2/3 not reportable in California
Identifying lab reporting sources
CIN 2/3 case ascertainment
Collecting representative, valid data

5. Challenge #1
Identify all laboratory reporting sources for cervical intraepithelial neoplasias (CIN) 2/3 and adenocarcinoma in-situ (AIS) used by providers serving a defined population
Our first challenge was to identify the laboratory reporting sources reviewing cervical histology and diagnosing CIN 2/3 and AIS

6. Identifying Labs Existing databases reviewed
Cancer registry
Billing databases
State lab survey
Internet & yellow pages
Telephone survey of lab and clinical providers
Interviewed lab directors
Surveyed OB/GYN doctors—where do they send cervical biopsies?
In California, to contrast with Connecticut’s approach as discussed by Linda Niccolai, we first created a list of possible labs by contacting the cancer registry for lists of facilities reporting cervical cancer; we reviewed the billing databases from the state family planning entitlement program; we also reviewed the labs included in the state lab survey; and finally, we looked for any missed labs or hospitals online.
We then conducted a telephone survey of lab directors and also asked them to identify any other labs that read cervical histology. Finally, we did a telephone survey on a convenience sample of clinical providers—ob/gyn doctors—and asked where they sent cervical biopsies.

7. Identifying Labs: Lessons Learned?
Small # of large labs do majority of testing
Managed care contracts determine where specimens sent
Labs may be in-state or out-of-state
Different labs for diagnostic vs. treatment biopsies
Lab characteristics
Mix of commercial and hospital labs
Labs in CA not required to report cancer
Licensing and certification databases don’t differentiate types of labs
Talk to lab directors and care providers
So we learned a lot by conducting such an exhaustive survey—you may find similar issues around labs in your own state.
First, we identified a small # of large labs that read most of the biopsies, but there were a large # of small labs that read only a few specimens. These, it turns out, are all throughout the state thanks to managed care contracts that determine where specimens are sent. And many go out of state to large reference labs.
In addition, we found that smaller hospitals mainly read treatment biopsies and not the initial diagnostic biopsies—these went to the larger, often commercial, labs.
And finally, there was a varied mix of commercial and hospital labs. In CA, commercial labs that don’t provide care, are not required to report cancer. I’ve noted this because we initially thought our surveillance system could be tagged to existing cancer reporting systems.
Also know that licensing and certification databases don’t differentiate types of labs, so there may not be a ready list of path labs. And finally, be sure to ask those ‘in-the-know’—the lab directors and care providers. We identified 4 additional labs through our surveys and Linda Niccolai discussed the utility of Connecticut’s provider survey.

8. Challenge #2 Identifying cases of CIN 2/3 and AIS using lab databases
Our second challenge turned out to be identifying CIN 2/3 and AIS cases at the labs!

9. Case Ascertainment in Lab Databases
Labs use different terminology
CIN 2/3
HGSIL
Moderate or severe dysplasia
Information in electronic databases not uniformly standard
Drop-down menus
Open text
We learned that labs use different terminology for the same diagnosis
In addition, the information in the labs’ databases are not uniformly standard. Some have drop-down menus to choose the diagnosis, while others have open fields or multiple choice options based on the pathologist’s preferred diagnosis terminology.

10. Case Ascertainment: Lessons Learned
Need to identify all diagnostic codes and terms
Create and test algorithms that are sensitive and specific to find cases
Determine who will identify cases—
Laboratory IT programmer
In-house public health programmer
Quality assurance (QA) system critical
In order to proceed, we learned that we’ll have to identify all diagnostic terms used at each lab and we’ll need to create and test sensitive and specific algorithms to correctly pull CIN 2/3 and AIS cases.
Now this could be done either at the lab by an IT programmer or by a data person at the department of public health. Cosette Wheeler has described her in-house data parsing in New Mexico. The capabilities of the lab may dictate how this will be done.
And finally, a QA system is critical to be sure you are identifying all cases. (May be able to refer to Karla or Cosette’s talk for their experience)

11. Challenge #3
How to collect high-quality, valid, and informative data on cases
The final challenge I’ll discuss today is how to collect high-quality and representative data for our cases. High quality data refers to data that is complete and valid.

12. Data Collection Pathway
Electronic Surveillance from Labs
CDPH
Fax pre-populated CRF to Clinical Provider office to complete
Rough schematic of our expected data collection pathway
Chart Review
COMPLETED BASIC CASE REPORT FORM

13. Data Collection Challenges
Data from labs
May need to link billing and clinical data
Not all variables may be in lab databases
Data from providers—unanswered questions
Provider cooperation to complete form?
Chart review—is this feasible?
Cost to collect?
Data about vaccine history
Patient interview
Reliable?
Resources required?
Vaccine registries?
Incomplete
Opportunity to collaborate
There are a number of issues that need to be addressed before we can get data of high quality. We don’t have answers to all of these questions, but we hope to investigate our options through this pilot project.
So let’s start with the labs. Labs don’t store information in the way we want to receive it and they may need to link their billing and clinical databases. They often don’t have all the variables we want.
We still have a number of unanswered questions swirling around data collection from providers--will medical providers will cooperate and complete the missing information on our forms? Otherwise, chart review requires intense resources. Will this be cost-efficient for a long-term surveillance system?
Finally, a key component when looking at vaccine impact is collecting data about the person’s vaccine history. Will this be available in the chart or will a patient interview be required? Again, we’ll have to consider the cost to collect this data for a long-term project and the validity of such data. Another option, vaccine registries, are, unfortunately, an unreliable source. As mentioned in earlier talks, this may be an opportunity to build relationships and advocate for building the capacity of state vaccine registries.

14. General Considerations and Conclusion
What attributes are important in your surveillance system?
Look for collaboration among branches
Programmatic issues to consider
Local or statewide surveillance
Sustainability
Cost-efficiency
Determine what variables are absolutely necessary
Using readily available data
Mandatory vs voluntary data reporting
Legislation
Collaboration
So when creating a surveillance system, you’ll want to determine what attributes are going to be important. Look at your objectives.
Programmatically, decide whether to create a local or statewide surveillance (issues of sustainability and cost-efficiency may come into play)
Determine what variables are absolutely necessary and readily available
And decide how you’ll want to proceed regarding mandatory or voluntary data reporting. Issues to consider here may include involving the legislature or public health authorities or working toward collaboration.