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
Top Related