Drug Characteristics Associated with Medication Adherence Across Eight Disease States PAWLOSKI

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Chronic Illness and Multimorbidity

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Drug Characteristics Associated with Medication Adherence Across Eight Disease States PAWLOSKI

  1. 1. DRUG CHARACTERISTICS ASSOCIATEDWITH MEDICATION ADHERENCEACROSS EIGHT DISEASE STATESPamala A. Pawloski, PharmD1,2; Richard J Bruzek, PharmD2; Brita Hedblom1;Steve Asche, MA1; Dana Meier, PharmD3; Cheri Rolnick, PhD, MPH11 HealthPartners Research Foundation, 2HealthPartners Pharmacy Services, 3Novartis PharmaceuticalCorporationHMORN Annual Meeting Wednesday, May 2, 2012, Seattle, WA
  2. 2. Background Medication non-adherence shown to be universally suboptimal and believed to be a key factor in achieving therapeutic goals Non-adherence represents a multifaceted challenge among patients and providers: leading to poor outcomes, compromised health and serious economic consequences Determinants of non-adherence previously described as health system, social/economic, therapy-related, condition-related, and patient-related To date, the impact of drug characteristics on medication adherence has not been studied across multiple disease states
  3. 3. Study Objective To describe medication adherence by drug characteristics across 8 different disease states:  drug class,  generic utilization,  average out-of-pocket cost/prescription To identify potential drug-specific factors by disease that may serve as targets for future intervention
  4. 4. Methods Retrospective analysis of patients with a single diagnosis and oral medication for the following conditions: •Asthma/COPD •Cancer •Depression •Diabetes •Hypercholesterolemia •Hypertension •Multiple sclerosis (MS) •Osteoporosis
  5. 5. Methods This study was conducted by HealthPartners Research Foundation  Integrated health system including 3 owned hospitals and 27 owned clinics  15 in-clinic pharmacies and a mail order pharmacy  Serve >750,000 members Included all patients > 18 years with 1 of 8 medical conditions described above
  6. 6. Methods Patient records were identified by ICD-9 DM for diagnosis and GPI code for 128 medications of interest through claims occurring between January 1, 2007 and March 31, 2009 • Diagnosis of interest must have occurred within 24 months of the most recent prescription fill • A minimum of 2 prescription medication fills for at least a 28-day supply
  7. 7. Methods Adherence was calculated by the Medication Possession Ratio (MPR) for each patient based on prescription fills within a 12-month window during the study period Binary adherence (MPR >80%) by drug characteristics within condition were tested with contingency tables and chi-square tests
  8. 8. Methods Analysis was adjusted for sex, race, age, proportion of adults with high school education, and median income Logistic regression analysis was conducted to examine predictors of adherence
  9. 9. Patient characteristics for patients with onecondition and one medication (n=14,875) Total % N=14,875 Female 60.9 Race/ethnicity White 82.8 African Am. 5.6 Asian 2.8 Hispanic 1.6 Am. Indian 0.5 Other 0.7 No answer 6.0 Age 18-49 30.4 50-59 26.2 60-69 20.7 70+ 22.7 Proportion of adults in living area with a high school education <88% 25.6 88-<93% 27.1 93-<96% 21.9 96%+ 25.5 Median=92.7% Median income of families in living area $64,500
  10. 10. Drug characteristics  within condition Asth/ Total Hyperten Depress Hyperlipid Diabetes Osteo Cancer MS COPD N=14,875 N=5,440 N=4,349 N=2,731 N=590 N=521 N=156 N=81 N=1,006 Onformulary 98.3 98.9 98.2 98.6 97.7 99.3 91.8 96.8 96.3 drug (%) On generic 75.7 91.6 74.6 66.7 22.8 85.6 78.9 41.7 0drug (%)Mean member amount paid per 30 days 0-$5 32.8 49.0 22.8 33.9 12.7 17.3 8.5 7.1 8.6 >$5-$12 39.1 38.2 46.4 38.0 16.2 52.4 31.5 27.6 8.6>$12-$22 15.2 7.9 19.1 13.8 24.4 23.2 38.2 27.6 8.6>$22-$50 11.2 4.5 10.6 12.6 39.6 6.1 18.8 31.4 45.7 >$50 1.6 0.4 1.2 1.5 7.2 1.0 3.1 6.4 28.4
  11. 11. Binary drug adherence (MPR >80%) by drug characteristics, within condition Asth/ Total Hyperten Depress Hyperlipid Diabetes Osteo Cancer MS COPD adherent adherent adherent adherent adherent adherent adherent adherent adherent N=14,875 N=5,440 N=4,349 N=2,731 N=590 N=521 N=156 N=81 N=1,007Overall (%) 69.7 77.6 62.4 78.3 32.6 59.7 76.6 90.4 84.0Generic vs.brand (%) Generic 70.9*** 77.3 60.6*** 78.8 18.8*** 59.0 79.6** 87.7 - Brand 65.9 81.1 67.6 77.3 36.6 63.5 64.5 92.3 84.0 Mean member amount paid per 30 days 0-$5 72.3*** 78.3*** 55.4*** 82.0*** 30.5 51.0*** 52.3*** - - >$5-$12 72.7 78.5 66.0 78.1 35.0 68.3 88.4 92.6 - >$12-$22 64.1 70.7 60.0 77.5 36.7 51.8 77.4 90.7 81.0 >$22-$50 61.5 77.3 66.4 71.3 30.7 42.9 67.5 88.1 85.0 >$50 50.0 50.0 56.9 66.7 26.4 - - - -* p<.05 **p<.01 ***p<.001 Pearson chi-square. N=20/row min for reporting.
  12. 12. Logistic regression: predicting adherence within condition Hyperten Depress Hyperlipid Asth/COPD Diabetes Osteo % adherent % adherent % adherent % adherent % adherent % adherent N=5,078 N=4,025 N=2,521 N=916 N=521 N=482 Brand vs. 2.82*** 1.61*** 1.56** 3.79*** 3.13** 0.67 generic (1.86-4.27) (1.34-1.94) (1.14-2.13) (2.47-5.81) (1.48-6.66) (0.37-1.21) Mean member amount paid per 30 days 0-$5 Ref Ref Ref Ref Ref Ref >$5-$12 0.92*** 1.45*** 0.61*** 1.52*** 2.01*** 5.75*** (0.79-1.06) (1.23-1.71) (0.58-0.78) 0.86-2.68 (1.19-3.41) (2.20-15.02) >$12-$22 0.53 1.04 0.46 1.02 0.95 2.10 (0.40-0.69) (0.85-1.27) (0.31-0.67) 0.61-1.7 (0.53-1.70) (0.88-5.02) >$22-$50 0.38 1.14 0.32 0.65 0.22 1.24 (0.23-0.62) (0.86-1.51) (0.21-0.49) 0.40-1.08 (0.08-0.64) (0.49-3.08) >$50 0.16 0.59 0.22 0.38 (0.06-0.42) (0.32-1.09) (0.10-0.47) 0.18-0.80 C statistic 0.65 0.61 0.69 0.67 0.70 0.74Odds ratio and 95%CI for odds ratio reported in tableAdjusted for sex, race (white/non-white), age, proportion of adults in living area with high school education, median income of families in the living area.* p<.05 **p<.01 ***p<.001 - p values are associated with entire contrast rather than individual contrasts
  13. 13. Binary drug adherence (MPR >=80%) by drugclass, within condition # adherent patients / Condition, Drug class # patients taking the Unadjusted % medication Hypertension*** Calcium channel antagonists 391/477 82.0 Angiotensin II receptor antagonists 196/242 81.0 β-blockers, α/β-blockers 1315/1659 79.3 Angiotensin converting enzyme inhibitors 777/992 78.3 Antihypertensive combinations (non-diuretic) 574/734 78.2 Antihypertensive combinations (non-diuretic) 334/444 75.2 Diuretics 574/806 71.2 Peripherally acting anti-adrenergic agents 60/86 69.8 Depression** SNRI1 359/506 71.0 Tricyclic antidepressants 118/172 68.6 SSRI2 1725-2775 62.2 Miscellaneous antidepressants 323/524 61.6 Miscellaneous antianxiety agents 33/63 52.4 Modified cyclic antidepressants 155/309 50.2
  14. 14. Binary drug adherence (MPR >=80%) by drug class, within condition # adherent patients/ Unadjusted Condition, Drug class (cont) # patients taking the % medicationHyperlipidemia*** HMG-CoA reductase inhibitors 1841/2311 79.7Combination antihyperlipidemics 106/142 74.7 Intestinal cholesterol absorption inhibitors 67/90 74.4 Fibric acid derivatives 112/165 67.9 Nicotinic acid derivatives 12/23 52.2Asthma/COPD*** Leukotriene modulators 93/140 66.4 Bronchodilators 34/52 65.4 Steroids 14/37 37.8 Adrenergics / combinations 139/431 32.3 Steroid inhalants 23/124 18.6 Nasally administered agents 25/223 11.2
  15. 15. Binary drug adherence (MPR >=80%) by drug class, within condition # adherent patients/ Unadjusted Condition, Drug class (cont) # patients taking the % medicationDiabetes Thiazolidinediones 41/56 73.2 Sulfonylureas 107/180 59.4 Biguanides 188/319 58.9 Antidiabetic combinations 8/15 53.3 GLP-1 receptor antagonists 8/20 40.0Osteoporosis** Bone density regulators 373/478 78.0 Hormone receptor modulators 15/20 75.0 Calcium 11/23 47.8Cancer* Antineoplastic hormonal agents/hormone 117/126 92.9receptor modulators Antineoplastic agents 24/30 80.0
  16. 16. Limitations Limited population to one diagnosis/medication to identify to evaluate least-confounded population Small sample size occurred in some cells (across member costs and some disease states) Generalizability may be limited due to the study population occurring within an integrated health system
  17. 17. Conclusions Variation in adherence rates by drug class underscores the need to study adherence and outcomes by both disease state and drug class Future steps include a continuous analysis of adherence to determine changes across the adherence spectrum by disease and drug class  Correlating adherence to clinical outcomes is needed to fully understand the meaning of adherence measures
  18. 18. Questions?
  19. 19. Study Population
  20. 20. MPRPatient MPR = [(Σ Days Supplied – Days Supply ofLast Fill) / (LAST Fill Date – FIRST Fill Date)] * 100 Mean MPR = (Σ Patients’ MPR / Number of patientsin the analysis) * 100

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