IMS Health 2011

Strengths and Limitations of Market Intelligence Data for Pharmaceutical Policy Analysis in LMIC

ICIUM 2011 - Third International Conference for Improving Use of Medicines

Antalya, November 2011

Agenda

• Introduction to:

– Volume data– Medical Data

• Sampling and projection• Data quality process• Summary

What do we collect, why do we collect it in that way & where we collect it

3

Volume data

4

Volume data: Collected from different parts of the supply chain, depending on the country

5

Consumers

Retail drugstores type A & B

Health centers & private clinics

Non drugstore outlets

72%

1%

Wholesalers6%

Manufacturers

Results are based on information provided by 27 manufacturers that represent 30% of the market share.

Updated 2010

Status 2009

Agents/distributors Own distributor

5%

Special hospitals

General hospitals

18%

3% 1%

71%

70%

2%

Market covered by TLPI Market covered by TLHI

ThailandChannels of distribution

6

Ecuador – multi-source sampling

Data sources: MF data (unprojected) Distributor data (unprojectable) Wholesaler data (projectable) Chain pharmacies (unprojectable) Independent pharmacies (projectable)

Data points Information typically captured (volume data)

Wholesaler Pharmacy Hospitals

Segment (not released)

(not released)

(aggregated)

Location - (aggregated)

(aggregated)

Pack details

Quantity

Price ? ? ?

Derived characteristics(EPhMRA ATC, Manufacturer, Corporation, Molecule, Salt,

Launch, Brand/Unbranded, Volume (Units, SU or Kg)

9

Medical data

10

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

Information captured in medical data

No in-patient

Country Doctors in period (Q, T, S)

Argentina 470

Brazil 1,315

Colombia 385

Indonesia 450

Lebanon 248

Mexico 1,050

Pakistan 540

Peru 565

Philippines 350

Poland 565

South Africa 385

Thailand 440

Turkey 705

Venezuela 500

Medical Data Availability – Low and Middle Income

+ 29 HI

12

Sampling and Projection

13

The right balance determines the relevance of our measurements

Sampling and projectionKey elements of sampling concepts

14

Sampling and projectionSample design stratification

Weighting variables

+ Geospatial

15

Data quality

16

Random Error:Unviewed ≠ sample

• Sample size• Stratification• Selection

Systematic Error=data collection

• Non-response• Incomplete reporting

• Reporting time• Reporting quality

Data quality – sampling error components

17

Data quality: Sample design

Key stats factors Description

Data sources 417 wholesalers/chains; 130 pharmacies

Sample type Multi-source panel

Sampling ratio 99%

Data availability Monthly

Shortcomings Some local MF only captured through panelNon-retail channel incomplete

Brazil

18

Data quality: Minimising systematic error

19

• Since 1964, in collaboration with industry associations (EPhMRA, BPIRG), we conduct annual comparisons with our customers, contrasting IMS data estimates with actual industry sales.

• These ‘validation studies’ are carried out in more than 60 markets with ~ 2,200 pharmaceutical companies, covering more than 70,000 product forms.

• The results are published once a year in the IMS Annual Report on Quality Assurance – ACTS.

• All validation studies follow the same uniform procedure and reporting is standardized in order to allow cross-country comparisons and easy reading.

IMS Annual Validation Studies (for sales data)

20

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%

21

Precision Index (only for sales data)Example of Precision

Index Precision

Total1.4751.3751.2751.1751.0750.9750.8750.7750.6750.5750.475from

1.5251.4751.3751.2751.1751.0750.9750.8750.7750.6750.575

to

2,280 5 25 45 100 410 770 590 230 55 35 15

No. ofR-Values

R-Value Class

0

100

200

300

400

500

600

700

800R-Value Distribution

Σ = 2,070

90.8% 100 *2,2802,070

Index Precision

R-Valuesinside ±22.5%deviation rangeR-Values in total

2,070

2,280

22

Share of total volume used in

validation comparison (2009)

Latvia = 27%Malaysia = 29%Mexico = 36%Turkey = 66%

Venezuela = 72%

23

Limitations of data utilization

• Prices– Collected only at one point in supply chain– Generally list prices– Discounting not always known or able to be taken into account

• Coverage– Not all channels, and samples of channels– Often combines public and private in same audit

• Accuracy varies by product size for sample-based data– Almost all audits are sample based

• Inpatient prescribing not available– Cross country comparisons using medical data needs to bear in mind

specialty mix

24

• Reimbursement policy assessment and impact• Generic market evolution

• Generics policies and impact

• Pricing policy impact on volume

• Potential savings (using country own price data)

• Medicines shortages

• Quality of care initiatives assessment and impact• Unwarranted variations in volumes

• Pharmaceutical “gaps”

• Usage by indication

• Exposure studies

• Adherence to guidelines• WHO/National Essential Drug List• Therapy area formularies e.g. antibiotics

25

THANK YOU!

26

IMS Institute for Healthcare InformaticsGlobal Health Research Program

Murray Aitken, Executive Director, IMS Institute for Healthcare Informatics

27

IMS Institute for Healthcare InformaticsGlobal Health Research Program

• Objective• Elements of the program

• Access to IMS Health data and support• Training and education• Coordination and alignment of activities• Terms and conditions of support

• Program operation• External Advisory Council• Research agenda priorities• Research proposal criteria