Research Article - (2015) Volume 4, Issue 4

Examining Exposure Misclassification of Oral Bisphosphonate Therapy and the Associated Fracture Risk: A Cohort Study

Burden AM1,2*, Gruneir A3,4,5, Paterson JM3,5,6 and Cadarette SM1,3
1Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto ON, Canada
2Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Centre+, Maastricht, The Netherlands
3Institute for Clinical Evaluative Sciences, Toronto ON, Canada
4Women’s College Research Institute, Women’s College Hospital, Toronto ON, Canada
5Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto ON, Canada
6Department of Family Medicine, McMaster University, Hamilton ON, Canada
*Corresponding Author: Burden AM, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada, Tel: 416-978-7349, Fax: 416-978-8511 Email:

Abstract

Introduction: Using pharmacy claims data we previously identified exposure misclassification in pharmacy claims data that underestimated oral bisphosphonate compliance, particularly in long-term care (LTC). In this study we examined the impact of exposure misclassification in pharmacy claims data on estimates of drug effectiveness using osteoporosis pharmacotherapy and hip fractures as a case example.

Methods: We identified new users of oral bisphosphonates, aged 66 or more years, using Ontario claims data. Compliance was quantified by the proportion of days covered (PDC) and categorized into groups during a 365-day ascertainment period. PDC was calculated using observed and cleaned days supply values. Hip fracture rates were calculated using Cox proportional hazard models, adjusted for behavioral and fracture risk factors. Low compliance (PDC < 20%) was the referent. Analyses were completed overall and separately for patients in community and LTC settings.

Results: The rate of hip fracture was higher in LTC (2.4/100 patient-years) than in the community (1.0/100 patient-years). Following data cleaning, to adjust for exposure misclassification, the estimated benefit of high compliance (PDC ≥ 80%) on fracture prevention (HRobserved = 0.74, 95% CI = 0.66-0.83; HRcleaned = 0.65, 95% CI = 0.57-0.74) increased. Risk estimates were similar among community-dwelling patients (HRobserved = 0.68, 95% CI = 0.60–0.77; HRcleaned = 0.65, 95% CI = 0.56–0.75), yet differed substantially in LTC (HRobserved = 0.96, 95% CI = 0.73–1.26; HRcleaned = 0.64, 95% CI = 0.46–0.91).

Conclusion: Exposure misclassification can bias estimates of drug effectiveness. While minimal change was noted in the community setting where most studies are completed, large differences were noted in LTC where fracture risk was highest. These results highlight the importance of understanding and examining the potential for exposure misclassification prior to data analysis in pharmacoepidemiology, particularly when including LTC settings.

Keywords: Compliance; Exposure misclassification; Claims data; Osteoporosis; Hip fracture

Introduction

Osteoporosis is a major public health concern resulting in significant fracture morbidity and mortality [1-3]. Hip fractures are the most serious consequence of osteoporosis and result in significant morbidity, mortality, and social costs [4-6]. Several effective treatments exist to reduce a patient’s risk for fracture, yet 30% to 50% of patients will become non-compliant within the first year of treatment initiation [7-9]. Unfortunately, poor medication compliance is associated with a reduced clinical benefit and increase in hip fracture risk [10-14]. Previous studies have identified that high compliance with osteoporosis medications within the first year of initiation is associated with a significant reduction in fracture rates, [11] with up to a 60% reduction in hip fracture rates [14,15]. However, while there is consistent evidence that better medication compliance reduces fracture risk; the effect estimates reported are inconsistent.

One explanation for the inconsistent results may be differences in data source availability and analytic approaches. Pharmacy and medical claims data are the most commonly used data sources to estimate measures of medication compliance and health outcomes. While pharmacy data are considered reliable for exposure classification, we previously identified misclassification in the days supply reporting for extended dose osteoporosis medications that underestimated drug adherence, particularly in nursing home or long-term care (LTC) patients where compliance was underestimated by >25% [16,17]. Moreover, the risk for fracture, particularly hip fractures, is substantially higher among LTC patients when compared to community-dwelling patients [18]. Thus, with the increased fracture risk in LTC, failure to account for the differential exposure misclassification by site of patient residence may bias estimates of drug effects.

Thus, the objective of this manuscript was to investigate the impact of exposure misclassification on estimates of adherence to osteoporosis therapy when examining the relationship between compliance to anti-osteoporosis pharmacotherapy and hip fracture prevention.

Methods

Data source

We used Ontario healthcare claims data (medical and pharmacy) to identify all new users of oral bisphosphonates. In Canada, all medical services are provided through a universal healthcare insurance, and all Ontario residents aged 65 or more years receive full coverage for all prescriptions listed on the Ontario Drug Benefit (ODB) formulary. During the study observation, all oral bisphosphonates were listed on the formulary without restriction, thus permitting complete drug coverage.

The ODB database includes detailed information about each prescription dispensed to patients residing in community or nursing homes (LTC). This information, recorded by the pharmacy technician or pharmacist, includes the patient identifier, prescription date, drug identification number, dose, quantity supplied, days supply (i.e., estimate of prescription duration), and a flag indicating patient residence (community or LTC). All fields are mandatory, pharmacy reimbursement is determined based on the quantity of drug dispensed and the drug identification number, while early or late refills are identified using the days supply values.

Study cohort

We utilized a previously identified cohort of new oral bisphosphonate users aged 66 or more years in Ontario, Canada (April 2001–March 2010) [17]. The index date for cohort entry was the first dispensing for an eligible oral bisphosphonate: alendronate (10 mg and 70 mg), etidronate (400 mg etidronate and 500 mg calcium), or risedronate (5 mg, 35 mg, and 150 mg). New users of oral bisphosphonates were identified using pharmacy claims and defined as having no use of any osteoporosis medication (bisphosphonate, calcitonin, denosumab, raloxifene) in the year prior to the index date. To examine the effect of compliance on hip fracture, we excluded patients with a diagnosis for a condition that may impact bone quality and fracture risk [17].

Exposure measurement

We defined compliance to therapy as the proportion of days covered (PDC), [6] which was identified during a one-year ascertainment period that followed index date (Figure 1). PDC was calculated as the total number of days supplied in the one-year ascertainment, divided by the number of days in the ascertainment period (365-days maximum), and capped at 100% [6]. Since medications dispensed during inpatient hospitalizations are not captured in Ontario pharmacy data, we deducted hospitalization days from the denominator of compliance and from the total gap length when measuring persistence [19]. Early refills of the same drug and dose was considered additive (cumulative use), while a switch between drugs or dosing regimens was considered a complete switch, and no overlap in days supply was granted.

pharmacoepidemiology-drug-safety-Study-Design

Figure 1: Study Design.

Outcomes

Hip fractures were defined using validated diagnostic codes, with an estimated sensitivity and specific >90% [20]. Follow-up to identify fractures began one-year following the index date, and patient observation time ended at the first of: patient death, hip fracture, or end of the one-year follow-up.

Covariates

Patient demographics were determined on the date of the index prescription and health and medication related variables were identified in the year prior to the index date. We considered covariates that were related to hip fracture risk. These included demographic characteristics (e.g., age group, sex), osteoporosis related (e.g., prior fracture, osteoporosis diagnosis), disease comorbidities (e.g., diabetes, falls history, inflammatory arthritis), drug use (e.g., benzodiazepines, corticosteroids, narcotics), and indicators of health service utilization (e.g., prior hospitalizations). Additionally, we included variables that indicate health promotion (e.g., mammography or prostate exams, vaccinations) [12,21]. Hip fractures occurring during the one-year ascertainment period were included as a covariate in the outcome model.

Data cleaning

A complete description of data cleaning strategies has been described previously, including example data imputation scenarios [17]. In brief, we identified days supply values that did not match dosespecific expected values (e.g., 1 day supply for a monthly medication), and developed data cleaning algorithms to impute values that better reflected real-world utilization based on the medication, dose, quantity dispensed, and refill patterns (e.g., impute 30-days when 1-day observed for monthly medication) [17]. For example, if a 1-day supply was identified for a monthly medication a 30-day supply was imputed as the cleaned value. Data imputation was done in 10% of community prescription records and 41% of LTC prescription records [16,17]. Duplicate records were also removed prior to data analysis [17].

Statistical analysis

PDC was calculated using the two measures of days supply (observed and cleaned), [17] and was categorized into five groups: <20%, 20-39%, 40-59%, 60-79%, and ≥ 80%. In a secondary analysis, compliance was included as a dichotomous variable (PDC<80% and PDC ≥ 80%) and as a continuous variable.

Patient characteristics (demographic, comorbidities, health services use, and drug utilization) were summarized using means or proportions, as appropriate. Hip fracture rates were expressed as the number of events per 100 person-years. Cox proportional hazard models were used to compare event rates between compliance groups (<20% as referent), adjusting for covariates. A variable for calendar time (month and year) of the index prescription was included to adjust for trends in prescribing. We tested proportional hazard assumptions by including an interaction term between exposure and the log of time. No violations of the proportional hazard assumptions were identified. Analyses were calculated overall and separately for patients in community or LTC residence. All analyses were completed using SAS/STAT® software version 9.3 (SAS Institute, Inc., Cary, North Carolina) [22].

Results

We identified 279,343 eligible new users of oral bisphosphonates (n = 11,924 in LTC) (Figure 2). Following data cleaning, more patients were categorized as having high PDC (PDC ≥ 80%), yet little differences in patient characteristics across PDC categories were identified when using the observed or cleaned PDC (Table 1a and 1b). Compared to community-dwelling patients, LTC patients were older and had higher prevalence of comorbidities, drug utilization and prior fractures. Yet, they were less likely to have had a prior bone mineral density test, osteoporosis diagnosis or health promotion service.

pharmacoepidemiology-drug-safety-cohort-identification

Figure 2: Study three flow diagram of cohort identification, April 2001-March 2011.

  Observed days supply Cleaned days supply
Compliance (PDC), % <20 20-39 40-49 50-79 ≥80 <20 20-39 40-49   50-79 ≥80
N 17,535 32,271 27,742 34,005 155,866 10,985 28,937 25,552 34,491 167,454
Demographics                    
Female 81.5 81.6 81.7 82.1 81.9 81.9 81.8 81.0 82.0 81.9
Age, mean (SD) 75.8(7.2) 74.9(6.7) 74.9(6.7) 74.7(6.6) 75.0(6.6) 76.3(7.3) 74.9(6.6) 74.9(6.7) 74.6(6.6) 75.0(6.6)
Age category                    
65-69 24.6 26.7 26.8 28.1 25.6 23.2 26.9 27.0 28.3 25.6
70-74 23.5 25.6 25.7 25.9 26.3 22.0 25.6 25.6 26.1 26.3
75-79 21.2 22.4 22.1 22.0 23.0 21.3 22.3 22.3 21.9 22.9
80-84 17.3 15.8 15.5 15.1 15.6 18.4 15.8 15.4 15.1 15.6
85+ 13.4 9.5 9.9 9.0 9.4 15.2 9.3 9.6 8.7 9.6
Osteoporosis Variables                    
DXA test 63.2 67.1 66.6 69.2 70.6 62.5 66.6 65.6 69.2 70.5
Previous fracture 10.3 7.2 7.4 7.0 8.1 11.5 7.1 7.2 6.8 8.1
Osteoporosis diagnosis 36.2 37.5 37.8 39.5 41.0 36.9 37.4 37.4 39.3 40.8
Health Services Use                    
Hospitalization 16.9 13.1 13.5 12.8 14.0 18.7 12.8 13.1 12.5 14.1
Physician visits, mean (SD) 10.7(8.0) 10.4(7.8) 10.4(7.7) 10.3(7.6) 10.3(7.4) 10.7(8.1) 10.5(7.9) 10.4(7.8) 10.3(7.6) 10.3(7.4)
Colonoscopy 12.5 13.1 13.0 14.2 14.2 12.6 13.1 13.0 14.3 15.0
Mammography1 19.8 22.5 22.4 23.9 23.9 19.0 22.6 22.2 23.7 23.9
Prostate exam1 1.6 1.3 1.4 1.4 1.4 2.3 1.4 1.3 1.5 1.5
Vaccination 6.0 5.7 5.9 5.7 5.7 5.9 5.7 5.8 5.7 5.4
Comorbidities                    
Asthma/COPD/Emphysema 8.0 7.3 7.4 6.9 6.6 8.1 7.3 7.4 6.8 6.7
Alzheimer’s/other dementia 7.3 4.0 4.3 4.0 4.6 7.7 3.6 4.0 3.7 4.8
Depression 20.8 19.1 19.2 18.0 17.5 21.2 19.3 19.1 18.1 17.6
Diabetes 10.9 10.3 10.8 10.1 10.0 11.1 10.0 10.6 10.2 10.1
Falls/syncope/neurological 6.2 3.3 3.5 3.1 4.0 7.5 3.2 3.3 3.0 4.1
Hyperparathyroidism 0.9 0.8 0.8 0.8 0.8 0.8 0.9 0.8 0.8 0.8
Inflammatory arthritis 5.6 5.0 5.3 5.2 5.1 5.8 5.2 5.2 5.1 5.1
Inflammatory bowel disease 0.5 0.5 0.4 0.4 0.4 0.5 0.5 0.4 0.4 0.4
Liver disease 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.1
Parkinson’s disease 1.7 1.4 1.4 1.4 1.5 1.9 1.3 1.3 1.3 1.5
Stroke 4.1 3.3 3.3 3.3 3.4 4.3 3.2 3.1 3.2 3.5
Drug Utilization                    
No. drug classes, mean (SD) 7.7(5.7) 7.1(5.4) 7.2(5.4) 7.0(5.2) 7.0(5.1) 7.6(5.7) 7.1(5.4) 7.2(5.4) 6.9(5.2) 7.0(5.2)
Angiotensin II antagonist 8.2 7.3 7.4 7.6 8.0 9.0 7.5 7.4 7.4 8.0
Anticonvulsants/Epileptic 1.9 2.0 1.8 1.8 1.9 1.8 1.8 1.7 1.8 1.9
Aromatase inhibitors1 0.8 0.7 0.8 0.8 1.1 1.8 0.7 0.8 0.8 1.0
Benzodiazepines 24.4 23.1 22.7 21.7 21.0 25.0 23.3 22.6 21.6 21.2
Beta-blockers 12.6 12.5 12.7 12.5 12.7 12.5 12.5 12.4 12.4 12.7
Corticosteroids 14.1 13.5 14.3 13.5 13.1 13.8 13.9 14.1 13.3 13.2
Gastroprotective Agents 33.0 30.9 30.7 29.4 39.5 33.6 30.9 30.6 29.4 29.7
Glitazones 1.0 0.8 0.9 1.0 0.9 1.1 0.9 0.9 1.0 0.9
Narcotics 29.9 28.0 27.8 27.0 26.2 30.5 28.0 28.0 27.0 26.3
Nitrates 9.1 8.1 8.9 7.9 8.2 8.9 8.0 8.5 7.9 8.3
Non-SSRIs 12.6 10.2 10.3 10.1 10.0 12.5 10.0 10.2 10.0 10.2
NSAIDs 30.4 31.0 32.2 30.4 29.3 28.2 30.4 31.6 30.7 29.7
SSRIs 12.3 10.1 10.6 10.2 9.6 12.5 9.9 10.4 10.2 9.8
Statins 32.2 31.0 31.8 32.8 34.3 32.9 30.5 31.4 32.6 34.3
Thiazide diuretics 25.5 24.7 25.3 26.4 27.0 26.1 24.5 25.1 26.2 26.9
Thyroid medications 16.3 15.2 15.3 15.7 16.4 16.9 15.0 15.1 15.5 16.4
1Proportions for aromatase inhibitors and mammography testing among females, and proportion for prostate exam among males.
COPD: Chronic Obstructive Pulmonary Disease; SSRI: Selective Serotonin Reuptake Inhibitor; NSAID: Non-Steroidal Anti-Inflammatory Drug; PDC: Proportion of Days Covered

Table 1a: Demographic characteristics among patients residing in community (N = 267,419), stratified by observed and cleaned compliance groups.

  Observed Days Supply Cleaned Days Supply
Compliance (PDC), % <20 20-39 40-49 50-79 ≥80 <20 20-39 40-49  50-79 ≥80
N 3,773 939 766 640 5,806 1,313 485 392 558 9,176
Demographics
Female 81.8 83.7 80.8 84.8 83.5 82.7 83.9 77.8 83.3 83.0
Age, mean (SD)  84.3(6.9)  84.1(7.3) 83.4(7.4) 83.8(7.3) 83.9(7.1) 84.1(6.9) 84.5(6.9) 84.0(6.8) 84.0(7.3) 83.9(7.1)
Age category                    
65-69 3.3 3.2 5.0 4.5 3.5 3.6 2.7 2.3 5.9 3.5
70-74 5.9 8.2 7.6 6.7 7.1 5.9 6.2 7.4 5.7 7.0
75-79 14.0 13.8 15.4 15.0 15.0 14.9 14.6 14.5 13.3 14.7
80-84 24.7 25.1 26.8 26.1 25.4 24.8 24.5 26.8 26.2 25.3
85+ 53.7 52.0 49.3 50.2 51.0 52.8 52.9 49.7 53.1 49.5
Osteoporosis Variables
DXA test 10.4 12.0 10.4 12.2 12.3 11.3 11.1 9.4 12.4 11.6
Previous fracture 35.3 33.4 28.2 34.8 32.9 34.7 38.1 34.4 33.6 33.2
Osteoporosis diagnosis 14.3 17.7 12.1 17.8 14.9 14.9 18.6 11.5 14.7 14.9
Health Services Use
Hospitalization 52.1 50.5 46.9 54.4 51.1 52.7 53.0 55.1 50.9 50.8
Physician visits, mean (SD) 6.2(7.1) 6.6(7.6) 6.4(7.2) 7.0(7.3) 6.5(7.1) 6.9(7.4) 7.1(8.2)  6.8(7.4) 6.9(7.3) 6.3(7.0)
Colonoscopy 5.5 5.2 5.4 5.6 5.5 5.9    6.0 5.9 4.5 5.4
Mammography1 2.2 1.5 2.3 2.8 2.1 2.2 1.7 1.3 2.4 2.1
Prostate exam1 0.6 0.7 0.0 2.1 1.3 0.0 0.0 0.0 0.0 1.2
Vaccination 10.8 12.0 11.0 10.9 11.2 10.9 11.8 12.5 11.3 3.5
Comorbidities                    
Asthma/COPD/Emphysema 10.8 12.0 11.0 10.9 11.2 10.9 11.8 12.5 11.3 11.0
Alzheimer’s/other dementia 65.9 60.8 59.0 62.3 60.4 65.0 55.1 59.9 65.6 62.1
Depression 28.8 31.5 31.2 31.3 30.5 28.6 30.1 29.1 29.4 30.4
Diabetes 13.7 13.0 13.2 9.5 12.7 14.4 12.2 16.1 9.1 12.8
Falls/syncope/neurological 31.1 23.5 21.5 28.8 25.3 30.3 28.9 29.1 26.3 26.3
Hyperparathyroidism 0.3 1.2 1.0 0.6 0.7 0.2 0.4 0.5 0.5 0.7
Inflammatory arthritis 3.9 4.8 3.9 4.8 4.4 4.5 4.5 4.6 3.4 4.3
Inflammatory bowel disease 0.5 0.3 0.7 1.1 0.5 0.3 0.6 0.8 0.7 0.5
Liver disease 0.4 0.1 0.4 0.3 0.2 0.7 0.0 0.8 0.5 0.2
Parkinson’s disease 6.7 6.3 7.2 7.0 7.8 6.5 5.8 8.7 7.9 7.4
Stroke 16.8 18.2 19.1 15.5 16.2 17.8 15.7 17.1 16.5 16.6
Drug Utilization
No. drug classes, mean (SD) 11.2(6.3) 10.4(6.0) 10.9(6.0) 10.6(6.0) 10.8(6.1) 11.0(6.3) 10.7(6.2) 11.4(6.4) 10.8(5.6) 10.9(6.1)
Angiotensin II antagonist 6.2 5.5 3.7 4.1 5.3 6.3 5.8 5.4 3.8 5.4
Anticonvulsants/Epileptic 7.6 8.0 7.7 7.2 7.6 8.0 8.0 5.4 7.7 7.6
Aromatase inhibitors1 0.5 0.6 0.5 0.4 0.4 0.8 0.2 0.7 0.6 0.4
Benzodiazepines 38.6 38.2 40.5 42.2 41.3 38.6 34.0 39.8 42.8 40.6
Beta-blockers 13.8 11.4 14.8 12.5 13.3 13.8 12.2 16.6 12.0 13.3
Corticosteroids 16.5 17.0 19.7 16.6 16.8 14.9 17.1 17.9 18.1 17.0
Gastroprotective Agents 38.7 38.1 41.9 37.8 38.5 38.0 37.5 39.0 40.4 38.8
Glitazones 0.9 1.0 0.3 0.5 0.6 1.1 0.8 1.0 0.4 0.6
Narcotics 42.3 40.3 40.9 37.3 40.3 43.6 42.9 40.6 40.1 40.4
Nitrates 16.2 16.2 18.0 14.8 17.9 15.6 19.4 18.6 14.9 17.2
Non-SSRIs 45.6 40.4 42.6 44.1 43.6 42.7 39.8 46.9 47.1 44.0
NSAIDs 25.0 26.0 24.0 22.8 25.0 23.5 25.8 20.9 22.2 25.3
SSRIs 34.7 30.1 31.5 31.4 34.4 32.2 28.0 34.2 31.9 34.5
Statins 23.0 17.1 18.3 18.6 19.8 23.2 19.2 22.2 18.6 20.2
Thiazide diuretics 22.3 19.3 22.5 24.4 21.9 25.6 22.3 21.2 21.7 21.5
Thyroid medications 21.7 20.3 19.8 20.6 20.8 19.6 20.2 19.4 18.8 21.4
1Proportions for aromatase inhibitors and mammography testing among females, and proportion for prostate exam among males.
COPD: Chronic Obstructive Pulmonary Disease; SSRI: Selective Serotonin Reuptake Inhibitor; NSAID: Non-Steroidal Anti-Inflammatory Drug PDC: Proportion of Days Covered.

Table 1b: Demographic characteristics among patients residing in long-term care (N = 11,924), stratified by observed and cleaned compliance groups.

We identified 254 hip fractures among LTC patients (incidence rate = 2.40 fractures per 100 person-years), in comparison to 2,703 hip fractures among community-dwelling patients (incidence rate = 1.03 fractures per 100 person-years). Changes in fracture rates across PDC groups following data cleaning are presented in Table 2. We report on the adjusted hazard ratios (unadjusted Cox proportional hazard results provided in in Appendix 1). Notable differences in hazard ratio estimates were observed following data cleaning among patients in LTC. Using the observed PDC we identified that intermediate compliance (40- 59% PDC) was associated with a statistically significant reduction in hip fracture rate (HRobserved = 0.42, 95% CI = 0.20–0.92), while high compliance (PDC ≥ 80%) was associated with a non-significant 1% (HRobserved = 0.99, 95% CI = 0.75–1.31) reduction in hip fracture rate. Conversely, the cleaned analysis identified only high compliance was associated with a significant reduction in hip fracture risk (HRcleaned = 0.65, 95% CI = 0.46–0.91). We observed similar results whether PDC was a dichotomous or continuous variable in the adjusted models.

  Observed Days Supply Cleaned Days Supply
  N  Events (n) Rate1 HR 95% CI N Events (n) Rate1 HR 95% CI
Long Term Care
PDC Groups                    
<20% 3,773 87 2.61 1.00 Referent 1,313 40 3.44 1.00 Referent
20-39% 939 18 2.18 0.85 0.51–1.42 485 18 4.28 1.22 0.70–2.14
40-59% 766 8 1.16 0.42 0.20–0.92 392 6 1.74 0.44 0.17–1.11
60-79% 640 10 1.77 0.69 0.36–1.33 558 9 1.87 0.55 0.26–1.14
≥80% 5,806 131 2.54 0.99 0.75–1.31 9,176 181 2.22 0.65 0.46–0.91
PDC Groups                    
0-79% 6,118 123 2.27 1.00 Referent 2,748 73 3.03 1.00 Referent
80-100% 5,806 131 2.54 1.14 0.89–1.46 9,176 181 2.22 0.74 0.56–0.98
Continuous PDC                
Total 11,924 254 2.40 0.95 0.70–1.29 11,924 254 2.40 0.61 0.43–0.85
Community  
PDC Groups                    
<20% 17,494 276 1.64 1.00 Referent 10,952 192 1.82 1.00 Referent
20-39% 32,105 378 1.19 0.87 0.73–1.00 28,750 361 1.28 0.89 0.74–1.01
40-59% 27,828 277 1.02 0.74 0.62–0.87 25,676 254 1.01 0.71 0.57–0.83
60-79% 33,741 312 0.94 0.71 0.59–0.82 34,196 317 0.94 0.68 0.56–0.80
≥80% 156,251 1,460 0.95 0.69 0.60–0.78 167,845 1,579 0.96 0.66 0.56–0.76
PDC Groups                    
0-79% 111,168 1,243 1.16 1.00 Referent 99,574 1,124 1.17 1.00 Referent
80-100% 156,251 1,460 0.95 0.85 0.79–0.91 167,845 1,579 0.96 0.83 0.77–0.90
Continuous PDC
Total 267,419 2,703 1.03 0.70 0.63–0.79 267,419 2,703 1.03 0.67 0.59–0.75
All Patients  
PDC Groups                    
<20% 21,267 363 1.81 1.00 Referent 12,265 232 1.99 1.00 Referent
20-39% 33,044 396 1.21 0.92 0.80–1.07 29,235 379 1.25 0.90 0.76–1.06
40-59% 28,594 285 1.01 0.77 0.66–0.91 26,068 260 1.02 0.70 0.58–0.83
60-79% 34,381 322 0.99 0.75 0.65–0.88 34,754 326 0.99 0.68 0.57–0.80
≥80% 162,057 1,591 1.00 0.75 0.67–0.84 177,021 1,760 1.02 0.66 0.57–0.75
PDC Groups                    
0-79% 117,286 1,366 1.21 1.00 Referent 102,322 1,197 1.21 1.00 Referent
80-100% 162,057 1,591 1.00 0.87 0.81–0.94 177,021 1,760 1.02 0.83 0.77–0.89
Continuous PDC                  
Total 279,343 2,957 1.06 0.74 0.67–0.83 279,343 2,957 1.06 0.66 0.59–0.74
1Multivariate model adjusted for all variables listed in Table 1
2Hip fracture rate/100 person-years of observation
HR: Hazard Ratio; CI: Confidence Interval; PDC: Proportion of Days Covered

Table 2: Demographic characteristics among patients residing in long-term care (N = 11,924), stratified by observed and cleaned compliance groups.

Following data cleaning we identified that of the majority of patients in LTC were categorized in the high compliance (n = 9,176) or low compliance (n = 1,313) groups, with few in the moderate compliance categories (20% to 79%). Similarly, only 33 hip fractures were identified among patients in the moderate compliance categories. This resulted in some instability in estimates of drug effectiveness in these categories, with wide confidence intervals.

Among community-dwelling patients, we identified that higher compliance reduced the risk for hip fracture. Using the observed and cleaned PDC, we identified that high compliance was associated with a 31% (HRobserved = 0.69, 95% CI = 0.60–0.78) and 34% (HRcleaned = 0.66, 95% CI = 0.56–0.76) reduction in hip fracture rate, respectively. Little difference in HR estimates was apparent between the observed and cleaned analysis when PDC was included as a dichotomous or continuous variable.

In the observed PDC analysis, combining LTC and communitydwelling patients resulted in an underestimation of the association between compliance and risk (HRobserved = 0.75, 95% CI = 0.67–0.84). The cleaned analysis provided estimates more similar to those identified among community-dwelling patients (HRcleaned = 0.66, 95% CI = 0.57–0.75). Results were similar for dichotomous and continuous measures of PDC.

Discussion

Our results illustrate the potential influence of exposure misclassification in studies of osteoporosis drug effectiveness. In our study of Ontario seniors, misclassification of days supply values in pharmacy claims data resulted in an underestimation of the effect of drug compliance on fracture risk reduction, with the greatest influence seen in LTC. It is comforting to see minimal misclassification (<10%) and a minimal effect on drug effectiveness in the community setting, as this is where the majority of patients reside. However, while LTC patients represent only 5% of the study sample, combining these patients with community-dwelling patients may lead to biased estimates of drug benefit when using the observed days supply values to calculate PDC. The patients in LTC are sicker and more likely to have the outcome of interest. As we have shown in our findings, these patients were also more likely to be classified as non-compliant to their medications. Thus, our findings highlight the importance of data cleaning to correct for exposure misclassification.

These results build upon our previous work identifying the potential for days supply misclassification to underestimate medication compliance, [17] yet further add to the literature by providing a realworld application. In the area of osteoporosis pharmacotherapy, variation in risk estimates have made estimating the true relationship between bisphosphonate compliance and fracture risk reduction challenging [11,22]. While there are a number of factors that may influence the relationship between medication compliance and health outcomes, our results suggest that the accuracy of exposure and patient residence classification may play an important role.

We were able to stratify our results by residence status and as a result highlighted some key differences in both potential exposure misclassification and benefit of medication compliance, and we believe these warrant additional discussion as LTC patients are often not stratified from their community-dwelling counterparts in studies of drug outcomes. Compared to community-dwelling patients, patients in LTC were older, had more comorbidities increasing risk for fracture, had higher overall drug utilization, and a higher rate of prior hospitalization. It is also important to recognize the difference between the reasons for non-adherence among community-dwelling patients in comparison to LTC patients [23]. The decision to discontinue, or miss doses, among community-dwelling patients is likely an individual decision, except in some cases of physician directed discontinuation. Conversely, in the LTC setting such decisions would often be nurse, or family member, directed.

Finally, in regards to the potential for exposure misclassification, there are important differences in the billing restrictions placed upon medications dispensed to community-dwelling patients, as compared to LTC patients [24, 25]. For example, the capitated reimbursement in community pharmacies, that restricts pharmacies to two dispensing fees per month for a given medication, is not applied to LTC dispensed medications [24]. Thus, full reimbursement of frequent (e.g., daily or weekly) dispensing is possible to pharmacies with LTC contracts, and may partially explain the tendency for the short cycle dispensing observed. This may begin to shed some light on the higher proportion of misclassified days supply values identified in LTC compared to community in our analysis. It is important to understand why inaccurate days supply reporting may occur, particularly in the LTC setting. We expect that there may be a number of reasons that may influence data entry, including structure and process level factors that may facilitate data entry efficiency. For example, frequent dispensing may result from the billing structure in LTC pharmacies and as a means to avoid medication errors among patients with complex care. However, a thoughtful investigation is required to best inform future educational strategies aimed towards pharmacies to emphasize the importance of accurate data entry.

While our study has several important methodological and clinical messages, some limitations merit emphasis. First, we did not complete a validation study to confirm imputed days supply values with prescriptions, however, osteoporosis medications have fixed dosing intervals, and therefore the logical days supply can be inferred. In a sensitivity analysis (data not shown), we imputed a corrected days supply by simply multiplying the quantity dispensed by expected dose interval (e.g., 7 for weekly medications). While minimal differences in the final HR from our primary analysis were identified, we identified highly skewed days supply values (i.e., range: 0.1-16,000) suggesting additional errors in the quantity reported. Thus, we believe a more detailed approach is warranted; yet we recognize that additional validation may be required, particularly for medications with more complex dosing intervals.

Second, we used medical claims data to identify hip fractures and estimate confounders, and therefore there is the potential for missing data. While immeasurable confounders are a limitation when using claims data, the aim of this study was to examine the methodological impact of exposure misclassification in the days supply values on estimates of fracture risk. Thus, any unmeasured confounders would be constant in both the observed and cleaned days supply analyses. Similarly, in using Ontario pharmacy claims data we were unable to identify drug utilization prior to age 65. While we applied a oneyear look-back period to identify new users, it is possible that some new users at age 66 had received osteoporosis therapy prior to age 65. Another limitation to our study was that we identified fewer outcomes and person time in the intermediate PDC categories in LTC settings, thereby making estimates unstable. We believe this may be an indicator of prescribing practices in LTC facilities, where physicians may make fewer changes to medication use during a patient’s stay. Thus, we would expect early discontinuation (PDC<20% due to medication complications) or continued use for the duration of their time in LTC (PDC ≥ 80%), and this pattern was reflected following data cleaning.

Despite the noted limitations, our study had a number of strengths. The selection of osteoporosis pharmacotherapy and hip fracture outcome permitted an examination of the impact of exposure misclassification on estimates of bisphosphonate effectiveness. Oral bisphosphonates have scheduled dosing intervals, and we were previously able to identify the influence of days supply cleaning on quantifying compliance in this population of Ontario seniors [17]. Further, hip fractures are the most serious consequence of osteoporosis and are well captured in administrative data, thereby reducing the potential for outcome misclassification [20]. Second, we stratified by site of patient residence. Few studies have examined the association between compliance and fracture risk in community and LTC separately. We identified that patients’ in LTC facilities were older, with more comorbidities and drug utilization, and had a fracture risk that was more than double that of community-dwelling patients. With little available research on drug compliance and drug effectiveness in LTC facilities our results provide some evidence supporting the need for high medication compliance to osteoporosis pharmacotherapy in this patient population. While patients in LTC are expected to have poorer outcomes, we observed an increased benefit of bisphosphonate compliance, following data cleaning that was similar to communitydwelling patients. This may have important quality of life benefits for these patients, and therefore support the need for further investigation into the use of oral bisphosphonates in LTC settings.

From a methodological perspective, our study has some important strength. To our knowledge, we are the first to report the potential influence of cleaning days supply values on exposure misclassification and estimates of drug effectiveness. We found that data cleaning, particularly in LTC, produced estimates of drug effectiveness that are more consistent with results from clinical trials, thus providing some comfort to the accuracy of our data cleaning algorithm [25]. Second, our analysis was strengthened by the inclusion of three PDC definitions that identified how the definition of exposure can influence estimates of drug effects. This is unique to our study and provides some insight to the variation in estimates identified in the literature.

Conclusion

Using osteoporosis pharmacotherapy and hip fractures as the case example, the current manuscript highlights several of these methodological (exposure classification) and clinical (compliance benefit) implications. To our knowledge, no study to date has examined the impact of misclassification in a real-world drug effectiveness example. Our results add to the growing body of literature advancing methodological practice to address and overcome the inherent challenges when utilizing large administrative claims data to study patterns of drug utilization and examine drug safety and effectiveness in real-world databases [17,26,27].

We believe additional research is warranted to examine the impact of exposure misclassification in days supply values in safety research, and in other disease areas with extended-dose medications, before strong conclusions are drawn. Our results highlight the potential influence of misclassified days supply and serve as a signal to encourage researchers to examine and report on any data cleaning strategies. We posit that greater accuracy in exposure measurement and transparency in methodological reporting will lead to improved estimates of drug effectiveness used to inform policy decisions [28].

Acknowledgements

This research was supported by a research grant to Dr. Cadarette from the Ontario Ministry of Research and Innovation (OMRI, Early Researcher Award), and completed by AMB at the Institute for Clinical Evaluative Sciences (ICES). Dr. Suzanne Cadarette was supported by a Canadian Institutes of Health Research (CIHR) New Investigator Award (MSH-95364), and Dr. Andrea Burden was supported by Ontario Graduate Scholarships (OGS) for doctoral research (2011-2014). All analyses were completed at ICES, a non-profit research corporation funded by the Ontario Ministry of Health and Long-Term Care (MOHLTC). The Research Ethics Boards at Sunnybrook Health Sciences Centre and the University of Toronto approved the protocol for this study. The opinions, results, and conclusions herein are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred.

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Citation: Burden AM, Gruneir A, Paterson JM, Cadarette SM (2015) Examining Exposure Misclassification of Oral Bisphosphonate Therapy and the Associated Fracture Risk: A Cohort Study. Adv Pharmacoepidemiol Drug Saf 4:188.

Copyright: © 2015 Burden AM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.