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Rapid Penetration of COX 2 Inhibitors in Non-Steroidal Antiinflammatory Drug Market:

Rapid Penetration of COX 2 Inhibitors in Non-Steroidal Antiinflammatory Drug Market: an Implication to Hospital Cost Containment Policy. Supon Limwattananon, MPHM, PhD * Chulaporn Limwattananon, MPharm, MSc, PhD * Supasit Pannarunothai, MD, PhD **

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Rapid Penetration of COX 2 Inhibitors in Non-Steroidal Antiinflammatory Drug Market:

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  1. Rapid Penetration of COX2 Inhibitors in Non-Steroidal Antiinflammatory Drug Market: an Implication to Hospital Cost Containment Policy Supon Limwattananon, MPHM, PhD * Chulaporn Limwattananon, MPharm, MSc, PhD * Supasit Pannarunothai, MD, PhD ** * Faculty of Pharmaceutical Sciences, Khon Kaen University ** Center for Health Equity Monitoring, Naresuan University - Thailand

  2. Cyclo-Oxygenase-2 (COX2) Inhibitors • In Thailand, • Celecoxib and Rofecoxib have been available since 1999, • each by a sole pharmaceutical company • “single-source product” • Report from MOPH-provincial hospitals(N =41*, Year 2002), • Spending for COX2 inhibitors • Total acquisition costs 42.9 million Baht • Share of top 50 high cost drugs 6.2% • Ranking • Celecoxib 1st (in secondary care hospitals*) • 3rd(in tertiary care hospitals*)

  3. Objectives 1.To examine variations in hospital NSAID expenditures as related to the use of COX2 inhibitors 2. To assess patterns of drug channeling for COX2 inhibitors

  4. Study Population • Settings: 18 provincial hospitals in 4 regions of Thailand • (secondary and tertiary acute care settings) • Sample: 1,558,633 prescriptions for oral NSAID solid forms • rendered to ambulatory patients in 4 health insurance schemes • Civil Servant Medical Benefit Scheme-CSMBS • Social Security Scheme-SSS • Low-Income Card & Universal Health Care • Coverage-LIC/UCschemes • Rest of the population-ROP • Time periods: Fiscal years 2000-2002

  5. Study Design & Analysis • Retrospective, secondary analysis of electronic databases • of hospital drug use • Statistical analysis * • For drug expenditures: a generalized linear model (GLM) • For propensity of drug use: logistic regression analysis • Control for the underlying differences in drug use patterns due to • patient demographics (age groups and sex) • years of drug use (and interaction with health insurance schemes) • hospital settings • (proxy for variations in prescribing practicestyles)

  6. Utilization and Expenditures All Types of NSAIDs Prescriptions Daily dosesThai Baht Year 2000484,452 4,944,28523,205,944 Year 2001549,366 5,658,36234,257,243 Annual growth from Year 2000 (13.4%) (14.4%)(47.6%) Year 2002538,517 5,260,40437,991,221 Annual growth from Year 2000 (11.2%) (6.4%)(63.7%)

  7. Daily Doses by Types of NSAID Days 5.2% 8.2% 10.0% 0.7% COX2 inhibitors Other NSAID-NED Meloxicam Other NSAID-ED

  8. Expenditures by types of NSAID Baht COX2 inhibitors 33.9% 52.1% 46.5% 6.5% Other NSAID-NED Meloxicam Other NSAID-ED

  9. Factors Affecting NSAID Expenditures per Capita(Competing Models) Model with interaction terms Main effect model Coefficienta P value Coefficienta P value COX2 inhibitors 2.486 < 0.001 2.488 < 0.001 Age 36 – 49 years b 0.368 < 0.001 0.370 < 0.001 Age 50+ years b 0.798 < 0.001 0.805 < 0.001 Male - 0.158 < 0.001 - 0.158 < 0.001 CSMBS c 0.864 < 0.001 0.847 < 0.001 LIC/UC c - 0.001 0.954 - 0.053 < 0.001 ROP c - 0.022 0.188 - 0.084 < 0.001 Year 2001 d - 0.035 0.065 0.038 < 0.001 Year 2002 d 0.186 < 0.001 0.025 0.002 CSMBS x Year 2001 0.083 0.002 CSMBS x Year 2002 - 0.093 < 0.001 LIC/UC x Year 2001 0.123 < 0.001 LIC/UC x Year 2002 - 0.205 < 0.001 ROP x Year2001 0.070 0.002 ROP x Year2002 - 0.249 < 0.001 a Based on generalized linear model (GLM) using log link, gamma distribution , adjusted for hospital indicators b Age of 18-35 years as the reference category c SSS as the reference category d Year 2000 as the reference category

  10. Effects on Difference in NSAID Expenditure % difference a 95% CI COX2 inhibitors 1,101.2% 1,056.5 to 1,147.6% vs. other NSAID Age 36-49 years 44.5% 42.3 to 46.7% vs. 18-35 years Age 50+ years 122.0% 118.5 to 125.6% vs. 18-35 years Male -14.6% - 15.7 to -13.5% vs. Female a % difference due to an indicator variable = exp(Coefficient) - 1

  11. Effects on Difference in NSAID Expenditure (Trends for Each Scheme) % difference a LIC/UC SSS ROP CSMBS Year 2001 vs. 9.2% -3.4% 3.5% 4.9% Year 2000 Year 2002 vs. -1.9% 20.4% -6.1% 9.7% Year 2000 a % difference due to an indicator variable = exp(Coefficient) - 1 Based on GLM with interaction of schemes and years

  12. Effects on Difference in NSAID Expenditure (Comparison between Schemes for Each Year) % difference a Year 2000 Year 2001 Year 2002 CSMBS vs. SSS 137.2% 157.7% 116.1% ROP vs. SSS -2.2% 4.9% -23.7% LIC/UC vs. SSS -0.1% 13.0% -18.6% a % difference due to an indicator variable = exp(Coefficient) - 1 Based on GLM with interaction of schemes and years

  13. Propensity to Receive COX2 Inhibitors(Competing Models) Model with interaction terms Main effect model Coefficienta P value Coefficienta P value Age 36 – 49 years b 0.619 < 0.001 0.617 < 0.001 Age 50+ years b 1.267 < 0.001 1.270 < 0.001 Male - 0.302 < 0.001 - 0.301 < 0.001 CSMBS c 2.279 < 0.001 2.434 < 0.001 LIC/UC c - 0.845 < 0.001 - 0.585 < 0.001 ROP c - 0.407 < 0.001 0.178 < 0.001 Year 2001 d 1.105 < 0.001 1.200 < 0.001 Year 2002 d 1.145 < 0.001 1.512 < 0.001 CSMBS x Year 2001 - 0.009 0.936 CSMBS x Year 2002 0.303 0.003 LIC/UC x Year 2001 0.367 0.009 LIC/UC x Year 2002 0.285 0.038 ROP x Year2001 0.461 < 0.001 ROP x Year2002 0.853 < 0.001 a Based on logistic regression analysis, adjusted for hospital indicators b Age of 18-35 years as the reference category c SSS as the reference category d Year 2000 as the reference category

  14. Odds Ratio of Receiving COX2 Inhibitors(Comparison between Schemes for Each Year) Odds Ratio a Year 2000 Year 2001 Year 2002 CSMBS vs. LIC/UC 22.74 15.62 23.14 CSMBS vs. SSS 9.77 9.68 13.22 ROP vs. LIC/UC 1.55 1.70 2.73 LIC/UC vs. SSS 0.43 0.62 0.57 a Based on logistic regression model with interaction of schemes and years

  15. Odds of Receiving COX2 Inhibitors CSMBS Odds* (in log scale) SSS ROP LIC/UC * Odds = exp(constant+bAge+bGender+bScheme+bYear+bSchemexYear+bHosp)

  16. Conclusion • Growth in NSAID expenditures was largely driven by • rapid penetration of the expensive COX2 inhibitors. • The prime target for the patent-protected, single-source drugs • was patients covered by fee-for-service scheme like CSMBS. • To contain hospital drug costs, a generic substitution for • COX2 inhibitors is unfeasible due to market exclusivity nature. • Therapeutic substitution with the multi-source NSAID is • a viable alternative in curbing the expenditure growth.

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