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AnCompany.... Monitoring New Drugs for Safety in Insurance Claims Alexander M. Walker Sr VP Epidemiology I3 Magnifi
Monitoring New Drugs Copyright © 2005 i3page 1 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 Copyright © 2005 i3page 2 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 Copyright © 2005 i3page 3 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 Copyright © 2005 i3page 4 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 Copyright © 2005 i3page 5 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 Copyright © 2005 i3page 6 Treatment-emergent diagnoses Identify only Hospitalizations, MD visits, other services Occurring after dispensing Not sharing 1 st 3 digits of ICD with any service in the 6 months preceding 1 st dispensing Removes Progression of disease Concomitant illnesses
Monitoring New Drugs Copyright © 2005 i3page 7 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 Copyright © 2005 i3page 8 Treated Group Control Group P Drug Launch Time Follow-Up Treated Group Control Group P Follow-Up Treated Group Control Group P Follow-Up Treated Group Control Group P Follow-Up Outcome Follow-up conducted in all sets of matched cohorts Dynamic comparator matching
Monitoring New Drugs Copyright © 2005 i3page 9 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 Copyright © 2005 i3page 10 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 Copyright © 2005 i3page 11 Timeline Beta-version of four drugs available end of August –NMEComparator –Cialis Viagra –CymbaltaEffexor –SpirivaAtrovent –KetekBiaxin All NMEs with >1000 users since 2004 by end of 2005
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