AGENDA. Data Collection Methodologies. Sampling and Projection. Quality Control. Selected Countries

© IMS Health 2008

Use of IMS Data in Pharmaceutical Policy Analysis

Andreas Gieshoff

Utrecht, January 2010

Vice President

Statistics & Advanced Analytics, EMEA

AGENDA

•

Data Collection Methodologies

•

Sampling and Projection

•

Quality Control

Data Collection Methodologies

Elements-Data Collection Methodologies

-

Study

Objectives-Channel

Variations

Retail

Sales to Pharmacies

Pharmacy Sales to

Consumers

Hospital

Sales to Hospitals

Product usage within

hospitals

Data Collection Methodologies

-

Study

Objectives-Doctor-Patient

Interactions Variations

Doctor related Medical Data Index (MDI)

− Diagnosis-Treatment Relations

− Prescribing speciality

Patient related Anonymized Patient Level Data (APLD)_{− Prevalence}

− Disease Pathways

− Compliance

Longitudinal Prescription Data (LRx)

− Therapy switch

Data Collection Methodologies

-

Information typically captured (Sales

Data)-Wholesalers/

Distributors

Pharmacies

Hospitals

•

Product

•

Price / Value

•

Purchasing account / segment

•

Transaction quantity

•

Transaction type o Sale o Bonus o Return

•

Product

•

Price / Value

•

Sell-in / Sell-out quantity

•

Sell-out type

o

Rx

o

Cash

•

Product

•

Price/Value

•

Sell-in / Consumption quantity

•

Speciality ward

Diagnosis

• ICD10 codes

• Doctor wording

• Co-diagnoses

• Treated/untreated

Patient Demographics

• Age

• Sex

• Smoker/Non-smoker

• Insurance

Doctor Demographics

• Age, sex

• Speciality

• Year qualified

• University

• Region

Therapy

• Product prescribed

• Desired effect

• Co-prescription

• ATC, NDF

• Dosage data

Data Collection Methodolgies

-• Retail supply data available in all IMS countries (exception: China)

• Retail demand data available in EU5 and some smaller EU countries

• Hospital data is mostly supply data

• IMS covers most relevant channels, but not all

Medical Data

• 43 countries covered (18 Europe, 9 Americas, 8 APAC, 8 MENA).

• The specialty coverage differs by country. Cross-country analyses should consider this.

• Data covers physicians in private practice (exception: Belgium has also a hospital MDI).

Sales Data

Data Availability

Microsoft Office

Excel Worksheet Microsoft Office

Price Levels in selected Countries

Country Price Level Remarks Brazil Pharmacy Purchase Price List Price

China Hospital Purchase Price Weighted Average Price India Stockist Purchase Price List Price

Indonesia Pharmacy Purchase Price List Price Pakistan Pharmacy Purchase Price List Price

Peru Pharmacy Purchase Price Weighted Average Price Russia Pharmacy Purchase Price Weighted Average Price Egypt Public Price List Price

Jordan Pharmacy Purchase Price List Price Morocco Pharmacy Purchase Price List Price

Vietnam Ex-Manufacturer Price Weighted Average Price

• Price levels differ across countries

Sampling and Projection

-

Key elements of sampling

concepts-Study Objective

Practicality

Sample Design

Cost

Accuracy

Sampling and Projection

-

Sample Design

Stratification-Retail

Hospital

Doctor-Patient Data

• Region

• Size

(e.g. turnover)

• Type

(e.g. chains,

cooperatives)

• Region

• Ownership

• Specialization

• Size

• Region

• Doctor Speciality

• Practice Type

Sampling and Projection

-

Projection

Methodologies-• Projection factors are typically the quotient of universe

elements (N) by sample elements (n)

• There are variations of complexities

−

Weighting variables (e.g. turnover, size of hospitals) Ratio

estimators

−

Geo-spatial projections

−

Regression-based universe estimations

• The right choice is based on available universe information

a

n

N

Data Collection Methodologies

-

Data Source

Configurations-Data Source Data Source

Configuration Market Supply • Retail • Hospital • Pharmacies • Wholesalers • Distributors • Single source • Multi-source Market demand • Retail • Hospital • Pharmacies • Hospitals • Generally single-source Doctor-Patient Interactions

• Doctors for MDI • Doctors for APLD • Pharmacies for LRx

• Generally single-source

Random Error:

_{• Sample size}

• Stratification

• Selection

Systematic Error:

• Non-response

• Incomplete reporting

• Reporting time

• Reporting quality

Data Quality – Error Components

•Quality controls minimize the systematic error

Quality Controls

Input

Process

Output

• Data Formats • File Sizes • Duplications • Completeness • Statistical Outliers −Confidence intervals normal, gamma, poisson) −Expected volumes −Box-plot −… • Multivariate controls −Cluster analysis −Chi-sqare / Mahalanobis • Rank&volume correlations • Market trend • Annual Validations (Bias, Precision)

Bias (only for sales data)

Average over/underestimation of the real market performance:

Total IMS units of all validated product forms

Total real units of all validated product forms

Pack

IMS Units

A

1,000

B

1,200

C

4,000

Example:

D

6,500

E

7,200

Total

19,900

Real Units

900

1,500

3,800

7,000

7,400

20,600

R-Value

1.111

0.800

1.053

0.929

0.973

0.966

Bias

= -3.4%

Precision Index (only for sales data)

Example of Precision

Index Precision = Total 1.475 1.375 1.275 1.175 1.075 0.975 0.875 0.775 0.675 0.575 0.475 from 1.525 1.475 1.375 1.275 1.175 1.075 0.975 0.875 0.775 0.675 0.575 to 2,280 5 25 45 100 410 770 590 230 55 35 15 No. of R-Values R-Value Class 0 100 200 300 400 500 600 700 800 0.47 5 0.57 5 0.67 5 0.77 5 0.87 5 0.97 5 1.07 5 1.175 1.27 5 1.37 5 1.47 5 R-Value Distribution Σ = 2,070 90.8% 100 * 2,280 2,070 Index Precision = = R-Values inside ±22.5% deviation range R-Values in total 2,070 2,280

Data:

_{• Completeness control}

• Coding control

• Plausibility checks (contents)

• Univariate and multivariate outlier detection

Sample:

• Design fulfillment

• Weekly distribution diagram

• Reporting time

• Plausibility checks (volume)

Sampling Error Performance MDI

-

Morocco (n=5000 patient records, cluster

effect=30%)-0,0% 2,0% 4,0% 6,0% 8,0% 10,0% 12,0% 14,0% 16,0% 45,0% 40,0% 35,0% 30,0% 25,0% 20,0% 15,0% 10,0% 5,0% Measured Share (e.g. of a disease)

Sa m p lin g E rr o r

•The sampling error increases with increasing granularity of analysis

Estonia

Channels of Distribution

-Manufacturers Hospitals Wholesalers Retail Pharmacies 88% 12% Status 2008

Estonia

Retail Data

-Updated: Dec-09/mb Data Source Wholesalers/Pharmacies -Frequency Timeliness Performance Average DAP -Type 2008 2009 MIDAS 24 22 On-Site Database System 22 20 Printed 23 23 Printed Quarterly On-Site Database System Monthly Universe (2009) 528 WS-Coverage 48 % Pharmacies (Rx/LRx) n=227

Data Quality Measurement

Year Bias Precision 2007 3.1 83.0

Malaysia

Channel of Distribution

-Manufacturers / Agents / Distributors

Retail Dispensing Doctors 21% 26% 53% Othe rs Drugstores Pharmacies Hospitals Private Government Consumers 21% 1% 20% 5% 37% 16% Status 2009

Malaysia

Retail Data

-Updated: Dec-09/mb Data Source Retail Sample -Frequency Timeliness Performance Average DAP -Type 2008 2009 MIDAS 51 48 On-Site Database System 47 41 Printed 51 46 Printed Quarterly On-Site Database System Monthly Universe (2006) 9,030 Sample (2006) 211

Stratification Reg, Spec, Hosp

WS-Coverage 65 %

Design Fulfillment 90 %

Data Quality Measurement

Year Bias Precision 2006 -16.7 79.1

2007 -13.8 79.5

Report Information

Report Type Purchase

IMS Data for Pharmaceutical Policy Analysis

• IMS data offers a multitude of opportunities for

pharmaceutical policy analysis

– Collected sales data provide facts for pricing and reimbursement analysis

– IMS medical data measures changes in prescribing habits

– APLD data allow „cohorting“ of data to measure health outcomes – LRx data provide insights into therapy switches

–

• Country-specific differences in IMS data are relevant

− IMS covers most relevant channels but not all

− Measured prices differ across countries

IMS Data for Pharmaceutical Policy Analysis

-

Selected studies conducted by EMEA stats services

-• Effects of pharmaceutical innovation and demographic

change on the German oncology market

– Study showed effects of product age, innovation, health care reforms, and parallel imports on oncology prescriptions

– Combination of IMS data (IMS NPA, IMS LifeCycle) and public domain demographic data (GENESIS)

– Study used random and fixed effects models

• Factors influencing reimbursement and prescription

decisions on the RA-market in EU5

− Study showed effects of price and RA prevalence on RA-related prescriptions

− The study used IMS MIDAS data