ancompany.... monitoring new drugs for safety in insurance claims alexander m. walker sr vp...
TRANSCRIPT
An Company
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Monitoring New Drugs for Safety in Insurance Claims
Alexander M. WalkerSr VP EpidemiologyI3 Magnifi
Monitoring New Drugs
page 2Copyright © 2005 i3
The Need
A routine, comprehensive system “All” drugs, “all” outcomes Capable of generating signals
– Verified elsewhere– Tested internally
Capable of testing signals from other sources Serving all stakeholders
– Patients– Doctors– Payers– Managed care– Regulators– Manufacturers
Monitoring New Drugs
page 3Copyright © 2005 i3
An i3 Response to the Need
• An active drug safety surveillance program
•Using the full data assets of United Health Group concerning 11 million individuals
• Pushing closer and closer to “real time” surveillance
Monitoring New Drugs
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Technical
Number of drugs ~20 NMEs introduced each year Bring into follow when there is a critical number of users
Data source ~11 million individuals, open formulary Growing constantly: Oxford, MAMSI, Americhoice, ..
Data quality These are insurance claims data, no better no worse Significant in-house experience in sorting, cleaning, extracting
medical data from these
Reflects real-life drug use
Monitoring New Drugs
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Three Filters for Effective Claims-Based Surveillance
•Treatment-emergent diagnoses Events associated with diagnoses not seen prior to drug initiations Filters out: continuation of disease
•Comparator groups Stat methods now exist for identifying comparison groups with
similar distributions on demographics, diagnoses, drug use, and health services. Focus on drug-comparator differences.
Filters out: “confounding by indication,” concomitant illnesses and their consequences
•Data mining Look for population and subgroups, as well as outcome
combinations that may not be apparent in the crude tables Filters out: deception by the typical result
Monitoring New Drugs
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Trade-offs in Filtering
•Advantage Systematic removal of confounding, background noise, and the
dominance of the whole reduces false positives Differences that emerge have greater scientific and rhetorical
power
•Disadvantage Loss of ability to detect AE’s that are caught in the filter
Monitoring New Drugs
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Treatment-emergent diagnoses
•Identify only Hospitalizations, MD visits, other services Occurring after dispensing Not sharing 1st 3 digits of ICD with any service in the 6
months preceding 1st dispensing
•Removes Progression of disease Concomitant illnesses
Monitoring New Drugs
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Comparator drug
Problem: False positives and noise Not all new Dx’s are drug AEs – “natural history” /
background incidence Claims far from perfect
Response: Similar drug, similar people for comparison Choose a standard drug (usually the most prescribed for
same indication) Choose users of that drug who resemble the users of
Registry Drug with respect to medical history, drugs, doctors, procedures, health services utilization
Statistically balanced for all elements available from claims
Monitoring New Drugs
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Treated Group
Control Group
P
Drug Launch Time
Follow-Up
Follow-Up
Treated Group
Control Group
P
Follow-Up
Follow-Up
Treated Group
Control Group
P
Follow-Up
Follow-Up
Treated Group
Control Group
P
Follow-Up
Follow-Up
Outcome Follow-up conductedin all sets of matched cohorts
Dynamic comparator matching
Monitoring New Drugs
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Data mining
Good comparisons, good data still not enough– Drug/comparator tables can still obscure important relations– So many outcomes to look at
Evaluate clinically meaningful subsets
Search for hidden multi-way associations
Monitoring New Drugs
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The program
Quarterly reports on all New Molecular Entities Treatment patterns New Diagnoses Emerging During Treatment Data-mining Interactive query Data feed
Annual print and web-based summaries
Monitoring New Drugs
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Timeline
Beta-version of four drugs available end of August– NME Comparator– Cialis Viagra– Cymbalta Effexor– Spiriva Atrovent– Ketek Biaxin
All NMEs with >1000 users since 2004 by end of 2005