1. Consequences Of Non-Compliance To Osteoporosis Medication Among Osteoporotic Women Ankita Modi, Ph.D, M.D. 1, Jackson Tang, M.Sc. 2, Shuvayu Sen, Ph.D. 1 1 Health Outcomes, Merck & Co., NJ, 2 AsclepiusJT LLC, NY Consequences Of Non-Compliance To Osteoporosis Medication Among Osteoporotic Women Ankita Modi, Ph.D, M.D. 1, Jackson Tang, M.Sc. 2, Shuvayu Sen, Ph.D. 1 1 Health Outcomes, Merck & Co., NJ, 2 AsclepiusJT LLC, NY Table 1. Patient characteristics in the Baseline period Conclusions Rate of non-compliance was found to be high in a managed care population with osteoporosis. As a result, rate of osteoporotic fractures and healthcare utilization was substantially higher for patients who were non-compliant. The results emphasize the importance of good treatment compliance in order to achieve a treatment benefit and thereby reduce the burden that osteoporosis and associated fractures place on individuals and healthcare systems. Introduction More than 40 million people in the US have osteoporosis (OP) or are at risk for developing OP, placing them at greater risk of bone fractures. Poor compliance with osteoporosis treatment regimens is a well-recognized problem, and the risk of having osteoporotic-related fractures increases among patients with poor compliance. Higher medical costs is also a consequence of poor compliance. Objective To examine consequences associated with non-compliance to osteoporosis (OP) medication in terms of fracture rates and healthcare resource utilization Methods Study design A retrospective cohort study was conducted using i3 Invision Datamart; a large U.S. claims database records from January 1, 2001 to December 31, 2010 (study window). Index date: The date of initiation of first OP medication (alendronate, ibandronate, risedronate, zoledronate, raloxifene, calcitonin, teriparitide) during the study window. Compliance: Defined as having a medication possession ratio (MPR) > 0.8  MPR was defined as the total number of days’ supply of all OP mediation received in the compliance period, divided by 365 days. Study outcome: OP-related fractures and healthcare resource utilization  OP-related fractures: included non-traumatic fractures occurred at the hip, vertebral, and non-vertebral sites.  Healthcare resource utilization: Medical (Inpatient, Outpatient, ER, and Other) and Pharmacy Study period: Consisted of three consecutive, one-year time periods (Figure 1):  Baseline period: 1 year prior to the index date  Compliance period: Year 1 after index date, during which compliance with osteoporosis treatment was calculated  Study period: Year 2 after index date, during which study outcomes were measured Disease diagnoses and comorbidities were identified based on ICD-9 codes, medications were identified based on NDC codes. Study sample Inclusion:  Prescribed with > 1 OP mediation in the study window  Female aged > 55 as of index date  > 12 months pre- and > 24 months post-index continuous eligibility Exclusion:  Paget’s disease ever in the claims history  Diagnosis of malignant neoplasm during the study period Data analysis Baseline period: patient characteristics Study period:  OP-related fractures Fracture rates (% of patients with fractures), by fracture type  All-cause and OP-related health care resource  OP-related health care events  Multivariate analysis Key independent variable: non-compliance (MPR < 0.8) Covariates: Age, presence of GI symptoms at baseline, OP-related fractures at baseline, CCI, and presence of other comorbidities at baseline Fracture rates: Logistic regression All-cause resource utilization (Yes/No): Logistic regression All-cause number of resource use: Gamma regression Presented at The DIA 2013 49 th Annual Meeting, Boston, MA, June 22 – June 27, 2013. Poster Session I ID: 52370 MPR calculated Study period (12-months) Baseline period (12-months) Compliance period (12-months) OP-related fractures and resource utilization assessment Index Date Initiation of OP medication Naive to all osteoporosis treatment Figure 1. Study period Results Among 57,913 women who met eligibility criteria, 34,483 (59.5%) were non-compliant to osteoporosis therapy during year 1 (mean [SD] age, 64.2 [8.9] years). (Table 1) Non-compliant patients also experienced significantly more hospitalization events (12.3% vs. 9.6%), emergency room (ER) events (12.4% vs. 11.1%) and frequent OP-related hospital stays (0.03 vs. 0.02) during year 2 (all p<0.01). (Table 2) Compared to compliant patients (N=23,430 (40.5%)), a significantly higher proportion of non-compliant patients developed vertebral (0.79% vs. 0.48%), hip (0.42% vs. 0.25%) and non-vertebral (1.93% vs.1.52%) fractures during year 2. (Table 3) N = 686,505 Patients initiating OP treatment The initiation of the first BIS is defined as the Index Date N= 488,361 Female age > 55 as of index date N = 87,235 Patients with > 12-month continuous eligibility before and > 24-month of continuous eligibility after the index date N = 57,913 Exclude patients with malignant neoplasm or Paget’s disease of bone TOTAL SAMPLE = 57,913 Figure 2. Sample selection Compliant (N = 23,430) [40.5%] Non-compliant (N = 34,483) [59.5%]P-value BaselineN%N% Age at index date (mean, std)63.68.564.28.9<0.01 Charlson comorbidity index (mean, std) 0.440.90.561.0<0.01 Baseline fractures866 3.7%1,5664.5%<0.01 Vertebral (dorsal/lumbar)2841.2%5331.6%0.01 Hip1420.6%2540.7%0.06 Other5792.5%1,0213.0%<0.01 Baseline medication use Gastro-protective agents3,90616.7%6,84019.8%<0.01 Estrogen6,27126.8%7,76622.5%<0.01 NSAIDS5,47823.4%9,11226.4%<0.01 Table 3. Study Period Fracture Rates Compliant (N = 23,430) [40.5%] Non-compliant (N = 34,483) [59.5%]P-value Number of Patients with OP- related fracturesN%N% Vertebral (dorsal/lumbar)1130.482730.79<0.01 Hip580.251450.420.01 Other3561.526671.93<0.01 Table 2. Study Period Healthcare utilization Compliant (N = 23,430) [40.5%] Non-compliant (N = 34,483) [59.5%]P-value Number of patientsN%N% All-cause health care resource Inpatient 2,2569.6%4,23612.3% <0.01 Emergency room 2,61011.1%4,28212.4% <0.01 Outpatient services 22,52596.1%32,54894.4% <.001 Other medical services 16,04668.5%23,51368.2%0.45 OP-related health care resource Inpatient 4191.8%7132.1%0.017 Emergency room 1740.7%2800.8%0.35 Outpatient services 7,65632.7%9,30827.0% <0.01 Other medical services 1,5856.8%2,0836.0% <0.001 OP-related health care eventsmeanstdmeanstd Inpatient 0.020.180.030.22<0.01 Emergency room 0.010.230.010.170.45 Outpatient services 0.692.710.602.37 <0.01 Other medical services 0.131.630.130.970.83 Logistic regressionAny fractures Vertebral fractures (lumbar or dorsal) Hip fractures (femur neck) OR95% CIP-valueOR95% CIP-valueOR95% CIP-value Treatment non- compliance (MPR< 80%) 1.19 (1.06, 1.3) <0.011.43(1.14, 1.79)<0.011.47(1.08, 2.00)0.02 Logistic regressionInpatient useER useOutpatient use Other health care resource use OR95% CIP-valueOR95% CIP-valueOR95% CIP-valueOR95% CIP-value Treatment non- compliance (MPR< 80%) 1.16 (1.10, 1.23) <0.010.99 (0.93, 1.04) 0.740.61 (0.56, 0.67) <0.010.93 (0.90, 0.97) <0.01 Gamma regression Number of Inpatient events Number of ER events Number of Outpatient events Number of other types of resource utilization events IRR95% CIP-valueIRR95% CI P- value IRR95% CI P- value IRR95% CIP-value Treatment non- compliance (MPR< 80%) 1.27 (1.19, 1.33) <0.011.03 (0.95, 1.11) 0.430.97 (0.95, 0.98) <0.011.10 (1.06, 1.14) <0.01 Multivariate Results Non-compliance was associated with higher OP-related fractures (Table 4)  19%, 43%, and 47% more likely to experience any fractures, vertebral, and hip fractures, respectively. Non-compliance was associated with more frequent inpatient events  16% more likely to experience > 1 inpatient visit, 95% CI: [1.10, 1.23] (Table 5)  27% more likely to have an additional inpatient event, 95% CI: [1.19, 1.33] (Table 6) Table 4. Association of Fracture Rates and Treatment non-compliance Table 5. Association of healthcare resource and Treatment non-compliance Table 6. Association of healthcare events nd Treatment non-compliance Disclosure Author(s) of this presentation have the following to disclose concerning possible financial or personal relationships with commercial entities that may have a direct or indirect interest in the subject matter of this presentation: Ankita Modi: Employee – Merck & Co. Jackson Tang: Consultant – Received research funding from Merck & Co. Shuvayu Sen: Employee – Merck & Co.