# Presentation: 252974

## Monitoring New Drugs for Safety in Insurance Claims 

- Alexander M. Walker
- Sr VP Epidemiology
- I3 Magnifi

## 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

## 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

## 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

## 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

## 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

## 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

## 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

## Time

**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 conducted**
**in all sets of matched cohorts**

**Dynamic comparator matching**

## 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

## 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

## 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