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Longitudinal Analysis of Market Utilization / Pharmaceutical Sales Data

Longitudinal Analysis of Market Utilization / Pharmaceutical Sales Data. Christine Lu Harvard Medical School and Harvard Pilgrim Health Care Institute WHO Collaborating Center in Pharmaceutical Policy ICIUM 2011 ( Poster 878 ). WHO Collaborating Center in Pharmaceutical Policy .

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Longitudinal Analysis of Market Utilization / Pharmaceutical Sales Data

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  1. Longitudinal Analysis of Market Utilization / Pharmaceutical Sales Data Christine Lu Harvard Medical School and Harvard Pilgrim Health Care InstituteWHO Collaborating Center in Pharmaceutical Policy ICIUM 2011 (Poster 878) WHO Collaborating Center in Pharmaceutical Policy

  2. Acknowledgements • Co-investigators: Dennis Ross-Degnan, Anita Wagner, Bao Liu, Peter Stephens • IMS Health for providing the data • Location of work: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute • Conflict of interest: None

  3. Overview • Background / Context • The data • Challenges • Opportunities

  4. Background – China • Country: China • Most medicines sold in hospitals (80%) • Hospitals rely on profits from pharmaceutical sales to cover operating costs • Policy of interest: Medicine price regulations for antidiabetic products • Set product’s maximum retail price

  5. Study aim To examine the effects of two targeted price regulations on purchasing of insulin and oral hypoglycemics in Chinese hospitals • Dec 2001 • Dec 2006

  6. IMS data • Longitudinal, quarterly data • 1999-2004 • 2004-2009 • Volume data: standard units sold (e.g. one tablet) • Individual product-level, can be rolled up to • Drug level e.g. metformin • Drug class level e.g. biguanides • Categories: price regulated vs. non-regulated • Adjust for population • Calculate percentage market share

  7. IMS data: Data elements Further details see “IMS MIDAS Quantum Data Elements, Measures and Statistics”

  8. Interrupted Time Series • Prior to using this method: • Understanding the policy • Sufficient data points before and after the time of the policy • Segmented regression: • Estimate changes in level and trend (slope) post-policy • Control for pre-existing level and trend • Wagner AK et al. J ClinPharmTher2002

  9. Organizing data for time series plot

  10. Organizing data for segmented regression

  11. Some results Increase in trend of sales volume (0.18 standard units sold/1000 people/quarter) Price regulation

  12. Summary of the study • China’s price regulations for antidiabetics were associated with: • Increase in utilization of antidiabetics • No meaningful change in market share of price-regulated antidiabetic products • ? Impact of price regulation on medicine costs for • Patients • Hospitals • The system

  13. Challenges • IMS data-related • Only capture hospital data in China BUT most medicines are sold in hospitals • Come from a sample of Chinese hospitals (≥100 beds) • Volume data more reliable than pricing data • Non IMS data-related • Relative drug prices

  14. Opportunities • Clean, well-structured data from IMS Health • Data specifics well documented • Longitudinal data so can use ITS method • A large sample of hospitals across the country • Volume data available at the individual-product level • Small data file size manageable in MS Excel (& SAS) • Building a good collaboration

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