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Kelan lääkemääräyspalautteen vaikutus lääkemääräämiseen

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Iiro Ahomäki: Kelan lääkemääräyspalautteen vaikutus lääkemääräämiseen. Esitys Kelan tutkimuksen Voiko lääkkeiden käyttöön ja kustannuksiin vaikuttaa? Säädös- ja informaatio-ohjaus lääkepolitiikassa -seminaarissa 3.6.2019.

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Kelan lääkemääräyspalautteen vaikutus lääkemääräämiseen

  1. 1. Kelan lääkemääräyspalautteen vaikutus lääkemääräämiseen Iiro Ahomäki, Visa Pitkänen, Aarni Soppi, Leena Saastamoinen JYU. Since 1863. 1 3.6.2019
  2. 2. JYU. Since 1863. 2 Background • Opioid crisis in U.S.: estimated 49 000 people died of opioid overdose in U.S. in 2017 (National Institute on Drug Abuse 2018) • High rates of opioid prescriptions are associated with higher rates of overdose deaths (Schnell and Currie 2017) • In 2017 paracetamol-codeine combinations were used by 257 000 people in Finland • Starting patients treatment with large package of codeine may expose them to long-term use and adverse effects related to long-term use 3.6.2019
  3. 3. JYU. Since 1863. 3 Information letter • Topic of the Kela’s 2017 prescription feedback letter was prescribing drugs containing codeine as large packages • Patients who had no paracetamol-codeine purchases in years 2013-2015, but had a purchase in year 2016 were retrieved from Kela’s Prescription register • From these patients those with prescription of 100 tablets, or more, were chosen. • The letter was sent in May 29 2017 to 4 535 doctors and 14 dentists, who prescribed these large packages to new patients 3.6.2019
  4. 4. JYU. Since 1863. 43.6.2019
  5. 5. JYU. Since 1863. 5 Data • Prescription register: all purchases of paracetamol-codeine combinations between 1.1.2013-30.6.2018. Additionally, all tramadol (mild opioid) and oxycodone (strong opioid) purchases from the same time period. • From these we retrieved patients whose first purchase (no purchases in previous 3 years) was between January 2016 and June 2018 We use information about quantity of purchased tablets and time of purchase • From Population register Patients age, sex and native language Physicians age, sex, native language and specialty 3.6.2019
  6. 6. JYU. Since 1863. 6 Descriptive statistics 3.6.2019 A Patients No Letter Letter Variable Obs Mean Std. Dev. Obs Mean Std. Dev. Tablets per purchase 5 380 32.6 20.1 3 793 48.0 30.0 Purchase of at least 100 tablets, share 5 380 0.04 0.19 3 793 0.17 0.37 Age, years 5 380 49.8 18.8 3 793 52.4 19.1 Non-Finnish speaker, share 5 380 0.11 0.31 3 793 0.09 0.28 Male, share 5 380 0.49 0.50 3 793 0.51 0.50 B Physicians No Letter Letter Prescribed tablets per patient (mean) 2 942 33.6 20.6 1 570 49.0 30.4 Share of prescriptions of at least 100 tablets 2 942 0.05 0.21 1 570 0.18 0.34 Age, years 2 790 42.6 12.0 1 570 45.1 12.2 Non-Finnish speaker, share 2 942 0.15 0.36 1 570 0.13 0.34 Male, share 2 790 0.47 0.50 1 570 0.61 0.49 Specialist, share 2 942 0.38 0.48 1 570 0.47 0.50 Notes: In Panel B, variables Prescribed tablets per patient (mean) and Share of prescriptions of at least 100 tablets are calculated from data in which all first purchases in May 2017 are aggregated to physician-level.
  7. 7. JYU. Since 1863. Graphical evidence 73.6.2019 Figure 1 Monthly paracetamol-codeine purchases made by new patients, from January 2016 to June 2018
  8. 8. JYU. Since 1863. Graphical evidence 83.6.2019 Figure 2 Monthly averages of tablets per first paracetamol-codeine purchases
  9. 9. JYU. Since 1863. 9 Method • Difference-in-differences & event study • Treatment group: patients whose paracetamol-codeine purchase was prescribed by a doctor who received the letter • Control group: patients whose paracetamol-codeine purchase was prescribed by a doctor who did not receive the letter 3.6.2019 𝑦𝑖𝑝𝑡 = α + γ0 𝐴𝑓𝑡𝑒𝑟𝑡 + γ1 𝐿𝑒𝑡𝑡𝑒𝑟𝑖 + δ𝐴𝑓𝑡𝑒𝑟𝑡 ∙ 𝐿𝑒𝑡𝑡𝑒𝑟𝑖 + 𝜷′𝑿𝒊𝒑𝒕 + 𝑢𝑖𝑝𝑡 𝑦𝑖𝑝𝑡 = 𝛼 + 𝛾1 𝐿𝑒𝑡𝑡𝑒𝑟𝑖 + 𝑡=2017𝑚1 2018𝑚6 𝛿𝑡 𝑀𝑜𝑛𝑡ℎ 𝑡 ∗ 𝐿𝑒𝑡𝑡𝑒𝑟𝑝 + 𝜷′𝑿𝒊𝒑𝒕 + 𝑢𝑖𝑝𝑡
  10. 10. JYU. Since 1863. Preliminary results 103.6.2019 (1) (2) (3) (4) (5) (6) (7) (8) Dependent variable: Tablets Tablets Tablets Tablets Large Large Large Large After x Letter -5.874*** -5.875*** -5.743*** -5.974*** -0.0591*** -0.0591*** -0.0580*** -0.0609*** (0.449) (0.449) (0.439) (0.441) (0.00562) (0.00562) (0.00554) (0.00557) After -1.342*** -1.134*** -1.269*** -1.006** -0.00701*** -0.00490 -0.00627 -0.00234 (0.202) (0.425) (0.416) (0.421) (0.00157) (0.00426) (0.00421) (0.00432) Letter 15.67*** 15.67*** 15.08*** 14.99*** 0.130*** 0.130*** 0.126*** 0.126*** (0.595) (0.595) (0.579) (0.568) (0.00697) (0.00697) (0.00687) (0.00660) Time effects Yes Yes Yes Yes Yes Yes Patient controls Yes Yes Yes Yes Physician controls Yes Yes Observations 146178 146178 146158 141415 146178 146178 146158 141415 R squared 0.0715 0.0718 0.0957 0.104 0.0393 0.0394 0.0498 0.0540 Notes: Standard errors are clustered at the doctor level and presented in parentheses. Columns 1-4 present estimates from OLS regressions, where dependent variable is quantity of tablets purchased. Columns 5-8 present estimates from linear probability models, where dependent variable is a dummy taking the value of 1 if the quantity of tablets purchased was at least 100. All models are estimated using data on patients’ first paracetamol- codeine purchases between January 2017 and June 2018. Columns 3,4,7 and 8 include patients' age squared as a control variable. ** p<0.05 *** p<0.01.
  11. 11. JYU. Since 1863. 11 Preliminary results 3.6.2019 Notes: Every dot represents point estimate, on interaction of month dummy and treatment group, from regression estimated using equation (2). Vertical lines are 95 % confidence intervals. Model is estimated using data on patients’ first paracetamol-codeine purchases between January 2017 and June 2018.
  12. 12. JYU. Since 1863. 12 Preliminary results 3.6.2019 (1) (2) (3) (4) Specialists Non-Specialists Specialists Non-Specialists Dependent variable: Tablets Tablets Large Large After x Letter -6.072*** -6.010*** -0.0732*** -0.0479*** (0.671) (0.549) (0.00900) (0.00603) Time effects Yes Yes Yes Yes Patient controls Yes Yes Yes Yes Physician controls Yes Yes Yes Yes Observations 62460 78955 62460 78955 R squared 0.109 0.0991 0.0726 0.0351 Notes: Standard errors are clustered at the doctor level and presented in parentheses. All regressions are based on equation (1). Regression estimates in columns (1) and (3) are based on data on specialists. Estimates in columns (2) and (4) are based on data on non-specialists. All models are estimated using data on patients’ first paracetamol-codeine purchases between January 2017 and June 2018. ** p<0.05 *** p<0.01.
  13. 13. JYU. Since 1863. 13 Preliminary results 3.6.2019 (1) (2) (3) (4) (5) (6) Urban Semi-urban Rural Urban Semi-urban Rural Dependent variable: Tablets Tablets Tablets Large Large Large After x Letter -5.640*** -6.171*** -7.255*** -0.0574*** -0.0565*** -0.0817*** (0.512) (0.803) (0.961) (0.00642) (0.00965) (0.0117) Time effects Yes Yes Yes Yes Yes Yes Patient controls Yes Yes Yes Yes Yes Yes Physician controls Yes Yes Yes Yes Yes Yes Observations 101651 21829 17590 101651 21829 17590 R squared 0.107 0.0960 0.0922 0.0539 0.0507 0.0578 Notes: Standard errors are clustered at the doctor level and presented in parentheses. All regressions are based on equation (1). Columns (1) - (3) present estimates from OLS regressions, where dependent variable is quantity of tablets purchased. Columns (4)-(6) present estimates from Linear Probability Models, where dependent variable is a dummy taking the value of 1 if the quantity of tablets purchased was at least 100. All models are estimated using sub-samples, based on whether patients' municipality of residence is classified as urban (Columns 1 and 4), semi-urban (Columns 2 and 5) or rural (Columns 3 and 6), on patients first paracetamol-codeine purchases between January 2017 and June 2018. ** p<0.05 *** p<0.01. Table 4 Effect on paracetamol-codeine purchases, by degree of urbanization
  14. 14. JYU. Since 1863. 14 Second purchases • Treatment group - March – May 2017: mean: 57,8 tablets - June – August 2017: mean: 54,9 tablets • Control group - March – May 2017: mean: 45,5 - June – August 2017: mean: 45,3 3.6.2019
  15. 15. JYU. Since 1863. 15 Preliminary results 3.6.2019 (1) (2) Dependent variable: Tablets Large After x Letter -2.936*** -0.0316*** (0.792) (0.00961) Time effects Yes Yes Patients controls Yes Yes Physician controls Yes Yes Observations 36876 36876 R squared 0.0296 0.0283 Notes: Standard errors are clustered at the doctor level and presented in parentheses. All regressions are based on equation (1). Column 1 presents estimates from OLS regression, where dependent variable is quantity of tablets purchased. Column 2 presents estimates from linear probability model, where dependent variable is a dummy taking the value of 1 if the quantity of tablets purchased was at least 100. All models are estimated using data on patients' first tramadol purchases between January 2017 and June 2018. ** p<0.05 *** p<0.01. Table 5 Effect of the information letter on tramadol purchases
  16. 16. JYU. Since 1863. 16 Preliminary results 3.6.2019 (1) (2) Dependent variable: Tablets Large After * Letter 1.590 0.00846 (1.441) (0.00525) Time effects Yes Yes Patients controls Yes Yes Physician controls Yes Yes Observations 11450 11450 R squared 0.0237 0.00378 Notes: Standard errors are clustered at the doctor level and presented in parentheses. All regressions are based on equation (1). Column 1 presents estimates from OLS regression, in which dependent variable is quantity of tablets purchased. Column 2 presents estimates from linear probability model, in which dependent variable is a dummy taking the value of 1 if the quantity of tablets purchased was at least 100. All models are estimated using data on patients' first oxycodone purchases between January 2017 and June 2018. ** p<0.05 *** p<0.01. Table 6 Effect of the information letter on oxycodone purchases
  17. 17. JYU. Since 1863. 17 Preliminary results 3.6.2019
  18. 18. JYU. Since 1863. 18 Previous research • Written educational information has small or no effect on physicians prescribing practices (Arnold and Straus 2005) • There is some evidence that feedback based on physicians own previous behavior can have an effect on prescribing (e.g. Doctor et al. 2018) • However, Sacarny et al. (2016) found that feedback among potential overprescribers did not have an effect on opioid prescribing. 3.6.2019
  19. 19. JYU. Since 1863. 19 Summary • Our preliminary results suggest that the information letter decreased the average purchase size by 12,5 percent an the probability of prescribing a large package of paracetamol-codeine about 6 percent. This estimate is in line with some of the previous research (Doctor et al. 2018). • The letter reduced the probability of prescribing a large package more in rural areas compared to urban and semi-urban areas. • We also find that the letter had no impact on prescribing of tramadol or oxycodone 3.6.2019
  20. 20. JYU. Since 1863. 20 References Arnold S, Straus S. Interventions to improve antibiotic prescribing practices in ambulatory care. Cochrane Database of Systematic Reviews 2005, Issue 4. Art. No.: CD003539. DOI: 10.1002/14651858.CD003539.pub2 Doctor, J. N., Nguyen, A., Lev, R., Lucas, J., Knight, T., Zhao, H., & Menchine, M. Opioid prescribing decreases after learning of a patient’s fatal overdose. Science, 361(6402), 588-590. 2018 Overdose Death Rates. National Institute on Drug Abuse. 2018. < https://www.drugabuse.gov/related-topics/trends- statistics/overdose-death-rates > Schnell M, Currie J. Addressing the opioid epidemic: is there a role for physician education? Working Paper 23645, National bureau of economic research 2017 3.6.2019

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