Analysis of Osteoporosis Treatment Patterns with Bisphosphonates andOutcomes among Postmenopausal Veterans

J. LaFleur, S.L. DuVall, T. Willson, T. Ginter, O. Patterson, Y. Cheng,K. Knippenberg, C. Haroldsen, R.A. Adler, J.R. Curtis, I. Agodoa, R.E.Nelson

PII: S8756-3282(15)00135-0DOI: doi: 10.1016/j.bone.2015.04.022Reference: BON 10689

To appear in: Bone

Received date: 16 July 2014Revised date: 24 March 2015Accepted date: 14 April 2015

Please cite this article as: LaFleur J, DuVall SL, Willson T, Ginter T, Patterson O,Cheng Y, Knippenberg K, Haroldsen C, Adler RA, Curtis JR, Agodoa I, Nelson RE,Analysis of Osteoporosis Treatment Patterns with Bisphosphonates and Outcomes amongPostmenopausal Veterans, Bone (2015), doi: 10.1016/j.bone.2015.04.022

This is a PDF le of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.The manuscript will undergo copyediting, typesetting, and review of the resulting proofbefore it is published in its nal form. Please note that during the production processerrors may be discovered which could aect the content, and all legal disclaimers thatapply to the journal pertain.

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ANALYSIS OF OSTEOPOROSIS TREATMENT PATTERNS WITH BISPHOSPHONATES AND OUTCOMES AMONG POSTMENOPAUSAL VETERANS

LaFleur J1,2, DuVall SL1,2, Willson T1,2, Ginter T2, Patterson O2, Cheng Y1,2, Knippenberg K1, Haroldsen C2,3,

Adler RA4, Curtis JR5, Agodoa I6, Nelson RE2,3

1. Pharmacotherapy Outcomes Research Center, University of Utah, 30 South 2000 East, Salt Lake City, Utah 84112

2. VA Salt Lake City Heath Care System, 500 Foothill Drive, Salt Lake City, Utah 84148 3. Department of Internal Medicine, University of Utah, 30 North 1900 East, Salt Lake City, Utah

84132 4. Hunter Holmes McGuire Veterans Affairs Medical Center, 1201 Broad Rock Boulevard,

Richmond, Virginia 23224 5. Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, 1825

University Boulevard, Birmingham, Alabama 35294-2182 6. Amgen, Inc., 1 Amgen Center Drive, Thousand Oaks, California 91320

Correspondence and requests for reprints: Joanne LaFleur, PharmD, MSPH Department of Pharmacotherapy 30 South 2000 East, Room 4765 Salt Lake City, UT 84112 University of Utah Phone: 801-585-3794 Fax: 801-587-7923 Email: joanne.lafleur@pharm.utah.edu Abbreviations: BMD = bone mineral density; BMI = body mass index; CDW = corporate data warehouse; COPD = chronic obstructive pulmonary disease; CPT = Current Procedural Terminology; DSS = decision support system; GEE = generalized estimating equations; ICD-9 = 9th revision of the International Classification of Diseases; NLP = natural language processing; OLS = ordinary least squares; PCE = Personal Consumption Expenditures; PMO = postmenopausal osteoporosis; SSRIs = selective serotonin reuptake inhibitors; VA = Veterans Affairs; VHA = Veterans Health Administration; VINCI = Veterans INformatics and Computing Infrastructure; WHO FRAX = World Health Organization absolute Fracture Risk Assessment Tool.

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ABSTRACT Purpose: Adherence and persistence with bisphosphonates are frequently poor, and stopping, restarting, or switching bisphosphonates is common. We evaluated bisphosphonate change behaviors (switching, discontinuing, or reinitiating) over time, as well as fractures and costs, among a large, national cohort of postmenopausal veterans. Methods: Female veterans ages 50+ treated with bisphosphonates during 2003-2011 were identified in Veterans Health Administration (VHA) datasets. Bisphosphonate change behaviors were characterized using pharmacy refill records. Patients' baseline disease severity was characterized based on age, T-score, and prior fracture. Cox Proportional Hazard analysis was used to evaluate characteristics associated with discontinuation and the relationship between change behaviors and fracture outcomes. Generalized estimating equations were used to evaluate the relationship between change behaviors and cost outcomes. Results: A total of 35,650 patients met eligibility criteria. Over 6,800 patients (19.1%) were non-switchers. The remaining patients were in the change cohort; at least half displayed more than one change behavior over time. A strong, significant predictor of discontinuation was 5 healthcare visits in the prior year (11-23% more likely to discontinue), and discontinuation risk decreased with increasing age. No change behaviors were associated with increased fracture risk. Total costs were significantly higher in patients with change behaviors (4.7-19.7% higher). Change-behavior patients mostly had significantly lower osteoporosis-related costs than non-switchers (22%-118% lower). Conclusions: Most bisphosphonate patients discontinue treatment at some point, which did not significantly increase the risk of fracture in this majority non-high risk population. Bisphosphonate change behaviors were associated with significantly lower osteoporosis costs, but significantly higher total costs. Keywords: Osteoporosis, bisphosphonate, treatment patterns, fracture risk, cost, veterans

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INTRODUCTION

Poor patient adherence and persistence are common with bisphosphonates, which are considered first-line treatments for postmenopausal osteoporosis (PMO). Reasons for poor adherence and persistence include but are not limited to lack of perceived benefit, lack of understanding, side effects, and inconvenience.1 In observational studies, the proportions of patients that persisted with therapy for 1 year are low for United States (US) cohorts (21.0-56.7%) and non-US cohorts (21.9-74.8%).2-11 Some studies have reported an association between low adherence or persistence and fracture outcomes or healthcare costs (i.e., higher fracture rates or higher costs in patients with lower adherence).8,12-18 However, the effect of changing medications on osteoporosis health outcomes has not been well characterized. Several studies demonstrate improved persistence or adherence following a treatment switch,7,19,20 with only one to the contrary.16 We are aware of one study using a small regional cohort that examined the effect of medication switching on fracture risk. Briesacher and colleagues did not find a significant effect of medication switching on fracture risk in a small sample of patients who switched between once weekly and once monthly bisphosphonates.21 Given these controversies and inconsistencies in the literature, we sought to examine the effect of switching behaviors on fracture risk and cost outcomes in a large, national cohort of postmenopausal veterans.

Few studies have investigated the link between bisphosphonate medication-taking behavior and osteoporosis-related outcomes while controlling for baseline disease severity. One study restricted analysis to patients with a bone mineral density (BMD) T-score -2.5 and/or a prior vertebral fracture.14 However, no studies adjusted for baseline BMD in an effort to isolate the independent effect of adherence on outcomes in patients across a range of osteoporosis severity. This is, perhaps, because most studies that use administrative claims datasets typically do not contain clinical data.11,14-17,20 However, even studies conducted in datasets containing some clinical data8,13 still lacked the ability to capture BMD in structured data, making adjustment for baseline disease severity challenging.

To our knowledge, no research has been done to describe bisphosphonate use and outcomes in the female veteran PMO population while controlling for severity. Studies conducted in the Veterans Health Administration (VHA) are usually overwhelmingly male,22,23 and osteoporosis studies have been no exception.24,25 Thus, in the bisphosphonate-treated PMO population in the VHA, we sought to characterize bisphosphonate switching patterns; to identify patient and disease characteristics that were associated with switching or discontinuation behaviors; and to investigate the possible relationship between patient medication-taking behaviors and outcomes, including cost and fracture events. As a methodological improvement on past observational analyses and a unique feature of our investigation, we used natural language processing (NLP; a computerized algorithm with which certain data or concepts in an electronic text record are identified) to extract information on BMD and other clinical risk factors for fracture from radiology reports and clinic notes. This information was used to control for baseline disease severity.26-28

METHODS

Study design and datasets

In this cohort study, we used historical data from several VHA datasets hosted in the VINCI (Veterans INformatics and Computing Infrastructure) environment, linking across datasets with the VHAs unique scrambled social security number system. Datasets included the VHAs Decision Support System (DSS) dataset, from which we identified bisphosphonate fills, other medications, and cost information for

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pharmacy, inpatient, outpatient, radiology, and laboratory services; the VHAs Medical SAS dataset, from which we identified age, race/ethnicity, and fracture outcomes; the Corporate Data Warehouse (CDW) dataset, from which we identified vital signs (height and weight for body mass index [BMI] calculation) and narrative records (the clinician progress notes and radiology reports that we used to extract BMD T-scores and other clinical risk factors for fracture).

Natural language processing (NLP)

We used NLP technology to identify several risk factors from narrative text including radiology reports (for BMD) and clinic notes (for BMD, smoking, alcohol consumption, and family/maternal history of osteoporosis or fracture). Separate, narrow-focused NLP applications were built for each of these four variables.

Smoking status was extracted using a system previously developed and validated on the VA clinical notes.27

Applications to extract the other three variables were built following an iterative system development model, in which a system is built incrementally in phases called iterations. Each iteration consists of planning, development, and error analysis phases. For the current system at each iteration, a set of manually developed rules was built or expanded aiming to detect relevant concepts, their context, and relationships. Iterations involve rigorous error checking to compare system output to manual annotations. The error analysis findings inform the next iteration. The measure of the system performance improvement is estimated by relative decrease in the error rate between iterations. When the error rate improvement falls bellow 1% it is determined to have reached a plateau and the development cycle is determined to be complete.29

Final performance of these applications was manually validated on a randomly selected set of notes for each involved variable separately. For example, for the T-score application, a random sample of 1,000 instances was reviewed to assess the accuracy of the tool for extracting T-score, anatomy (e.g., anatomic sites mentioned in proximity to the T-score such as lumbar spine or femoral neck), and the association between anatomy and T-score (i.e., that a given site was related in the note to a particular anatomic site, such as a femoral neck BMD T-score versus a lumbar spine BMD T-score). The mean accuracy (number of correct extractions divided by the total number of extractions) of the BMD extraction tool was 82.8% for T-score, 92.6% for anatomic site, and 82.8% for the correct BMD being associated with the correct anatomic site. For smoking, alcohol use, and family/maternal history of osteoporosis or fracture, the mean accuracies were 83.4%, 75.9%, and 76.3%, respectively. 26,27

Previous approaches to acquiring variables for family/maternal history of osteoporosis and BMD scores have used administrative data, personal interviews or questionnaires, manual chart review, 30-33 and prospective measurement. 34 As such, they have been limited to smaller cohorts of patients. Several studies have used NLP to identify family history of other medical conditions or distinguish between family history and personal history, with accuracies ranging from 81.3% to 93.8%.35-37 Although this range is slightly higher than reported here, each of these studies used only 1 to 2 document types from 1 to 2 hospitals, in which document sections could be specifically identified. Data for our study came from thousands of document types from more than 1,400 points of care (medical centers, clinics, nursing homes, and long-care facilities) all across the US.

Alcohol consumption is also usually recorded through interviews, questionnaires, or manual chart reviews.38-40 Extraction of alcohol consumption status has been attempted using NLP before, with

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accuracy of 89.4%. 41 The authors admit that such a high level of accuracy is partially attributable to the high level of consistency in the clinical notes in the sample the same physician dictated them all.

Smoking status has been classified using NLP in several studies, ranging from 23% to 81%. 42 Our system performance is comparable to these previous systems.

Patients

We identified a national cohort of female veterans aged 50 years and older who had an outpatient encounter and who received an osteoporosis bisphosphonate prescription (oral alendronate, oral or injectable ibandronate, oral risedronate, or injectable zoledronic acid) during the study period (January 1, 2003-December 31, 2011). All patients with an outpatient encounter during the study period and then a bisphosphonate prescription at least 6 months later but still within the study period (i.e., the index prescription) were eligible for analysis and were classified as incident or prevalent bisphosphonate users. The index date was defined, not as the first bisphosphonate prescription filled during the study period, but as the first bisphosphonate prescription filled at least 6 months after the first VHA outpatient encounter in the study period. Prevalent bisphosphonate users were those with any bisphosphonate prescription between the beginning of the study period (January 1, 2003) and the index bisphosphonate prescription. Incident bisphosphonate users were those with no bisphosphonate use of any kind from the beginning of the study period (January 1, 2003) up to the index bisphosphonate prescription (the first prescription at least 6 months after the first outpatient encounter). For example, a patient may already be taking a bisphosphonate when the study period begins, and may continue on that bisphosphonate, but her index date/prescription would not occur until 6 months after her first VHA outpatient encounter during the study period. This patient would be a prevalent user. In the fracture analyses, patients were censored upon the first fracture after the index bisphosphonate or, for those who did not fracture, on their last encounter in the VA system.

Patients were excluded if they had one or more of the exclusionary diseases, defined as having at least 2 codes from the 9th revision of the International Classification of Diseases (ICD-9) for a condition on two separate occasions at any time prior to the index date. Exclusionary diseases included Pagets disease, osteogenesis imperfecta, hypercalcemia, malignant cancer, or human immunodeficiency virus infection (HIV; see Supplemental Table 1 for specific ICD-9 codes). To avoid selection bias (because of conditioning study inclusion on an event that occurs after the index date), patients who had a second exclusionary diagnosis after the index date contributed person-time only until the date of the second exclusionary diagnosis and then were censored. Patients were also excluded if their index bisphosphonate was for a dose or dosage form that is primarily used for Pagets disease (e.g., alendronate 40 mg daily or risedronate 30 mg daily).

This Health Insurance Portability and Accountability Act (HIPAA)-compliant study was approved by the University of Utah Institutional Review Board (IRB) and the Salt Lake City VHA Research and Development office.

Definitions

Baseline patient characteristics

Baseline patient characteristics in the 6 month period before the index prescription included demographics (age, race/ethnicity, BMI, marital status, smoking status, alcohol history, and healthcare visit frequency for prior year); osteoporosis disease characteristics (10-year hip fracture probability and 10-year major osteoporosis-related fracture probability based on the US-adapted World Health

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Organization [WHO] absolute Fracture Risk Assessment Tool [FRAX], an actionable score being >3% for hip fracture or >20% for any major fracture), BMD T-score, history of prior fracture, family/maternal history of osteoporosis or fracture, and index bisphosphonate use); comorbidities (diabetes, dementia, chronic obstructive pulmonary disease [COPD], and depression); and drug exposures (calcitonin, calcium, heparin, hormone replacement therapy, lithium, proton pump inhibitors, raloxifene, seizure medications, benzodiazepines, opioids, selective serotonin reuptake inhibitors [SSRIs], statins, teriparatide, thiazide diuretics, thyroid medications, and vitamin D). In the case of multiple readings of the same characteristic, we always used the one closest to the index date as "baseline." Patients were classified as high risk or low risk as described below (see the Baseline disease severity section).

Baseline disease severity

Patients baseline disease status was classified as high risk if they met any 2 of the following 3 conditions: (1) femoral neck BMD T-score -2.5,43,44 (2) age 70 and BMD T-score of the total hip, spine, or one-third radius -2.5;43-45 or (3) prior fracture at the hip, spine, forearm, humerus, pelvis, or tibia/fibula at any time.46 These particular fracture sites were chosen not only because the WHO FRAX uses them, but also because they are documented in the literature as the most common sites for osteoporotic fractures. 47 Some baseline patient characteristics were used as inputs for the WHO FRAX absolute fracture risk calculator,48 including race/ethnicity, age, weight, height, prior major fracture, family/maternal history of fracture, current smoking, corticosteroid use, rheumatoid arthritis diagnosis, a diagnosis related to secondary osteoporosis, and alcohol exposure. These fields were extracted from structured data or via NLP where structured data were not available (i.e., BMD, family/maternal history of osteoporosis or fracture, current smoking status as of the index date, and alcohol consumption).

Bisphosphonate change behavior definitions

Patient bisphosphonate change behaviors over time were characterized as non-switching, switching, discontinuing, and reinitiating. Non-switching was defined as continuing on the index bisphosphonate for the duration of follow-up (until censoring), switching was defined as switching from the index bisphosphonate to a different bisphosphonate (e.g., alendronate to risedronate), discontinuing was defined as having stopped treatment for a gap length of at least 90 days after the end of the prior days supply, and reinitiating was defined as restarting the index bisphosphonate after a prior discontinuation or switch. Zoledronic acid, administered once per calendar year, was also subject to the 90-day rule. We selected a 90-day gap length for classifying a discontinuation event to be conservative in classification; however, we evaluated alternate gap lengths of 30 and 60 days in sensitivity analyses. All patients were considered to be non-switchers on the index date and their change behavior status was updated every 90 days depending on their change behavior in the prior 90-day quarter. For patients with multiple change behaviors in the same quarter, the patients exposure was updated to correspond to the most recent change.

Fracture outcomes

We conducted analyses for two fracture outcomes (hip fracture and any major fracture, [i.e., hip, spine, forearm, or humerus]) and a composite outcome (fracture of the hip, forearm, clinical spine, or humerus) to occur during 2003-2011. Fractures were identified using ICD-9 codes (see Supplemental Table 1). Since a recent publication demonstrated a consensus among clinicians that most fractures in patients over age 50 are osteoporosis-related,47 we included all fractures of the above skeletal sites, regardless of whether they occurred in the context of a trauma code.

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Cost outcomes

We calculated total costs and osteoporosis-related costs during 2003-2011 by summing costs associated with inpatient and outpatient encounters from the DSS dataset. DSS contains records for office visits, pharmacy services, laboratory testing, and radiology. The definitions of inpatient and outpatient were based entirely on structured data. Inpatient costs were found in the Inpatient table of the DSS dataset, with separate variables for nursing, lab, radiology, surgery, prescription, and other costs. The Outpatient table of the DSS dataset contained outpatient costs. Osteoporosis costs were calculated by including only those cost observations that were associated with a primary ICD-9 code for osteoporosis or fracture, Current Procedural Terminology (CPT) codes for bone density scans, or a pharmacy record for an osteoporosis-related treatment (oral alendronate, oral risedronate, oral or injectable ibandronate, or injectable zoledronic acid; see Supplemental Table 1). All costs were converted to 2011 US dollars using the Personal Consumption Expenditures (PCE) Index from the Bureau of Economic Analysis.

Statistical analysis

Patient characteristics, overall and by change behavior

Descriptive statistics, including means and standard deviations (SD) for continuous variables and frequencies and proportions for categorical variables, were calculated. We calculated descriptive statistics for baseline characteristics for all patients, patients with bisphosphonate change behaviors, and patients without bisphosphonate change behaviors. Descriptive statistics were also used to characterize the proportions with change behaviors in the follow-up period.

Predicting the effect of patient characteristics on bisphosphonate discontinuation

To identify predictors of bisphosphonate discontinuation (defined as the occurrence of a 90-day gap following the end of a prior days supply), a multivariable Cox Proportional Hazards regression model was constructed to examine the relationship between baseline patient characteristics and discontinuation. To identify independent predictors of discontinuation, we used a backward stepwise selection procedure, eliminating variables one at a time if p0.1, which is the common p-value used in backwards stepwise selection. Patients were censored at the last encounter with the VHA system in the study period. Sensitivity analyses were conducted for alternate gap lengths of 30 and 60 days using the same methods.

Predicting the effect of bisphosphonate change behaviors on fracture, with sensitivity analyses

A series of multivariable Cox Proportional Hazards regression models was constructed to examine the relationship between bisphosphonate change behaviors and fracture events, one for overall and one each for high-risk and not high-risk patients. To identify the least biased effect measure for the association between each change behavior and fracture events, our regression models included as covariates all observable variables with a known or theoretical relationship to fracture and bone quality: patient characteristics (age, marital status, race), utilization traits (visits in the prior year), index bisphosphonate (alendronate, ibandronate, risedronate, zoledronic acid), and FRAX risk factors (alcohol use, smoking status, BMI, prior fracture by site, rheumatoid arthritis diagnosis, diagnosis of conditions related to secondary osteoporosis, and steroid exposure). We also adjusted for baseline diagnoses of COPD and depression and baseline drug exposures thought to be associated with fracture risk (calcium, vitamin D, calcitonin, heparin, hormone therapy, lithium, proton pump inhibitors, raloxifene, benzodiazepines, opioids, selective serotonin reuptake inhibitors, statins, and thyroid medications). The bisphosphonate change behavior variable was treated as time-varying and was the only time-varying

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independent variable included in the models. The unit of analysis for these regression models was a 90-day person-quarter. The exposure was lagged by one quarter to avoid the scenario in which a fracture event is attributed to a change that occurred later in the same quarter. However, an unlagged sensitivity analysis was also conducted.

In our primary analysis, we defined the medication change behavior variable in each quarter as a current, momentary point in time based on the most recent behavior (non-switching, switching, discontinuing, reinitiating) in the previous 90 days. However, because the effects of bisphosphonates are thought to take 1-2 years,49 we also performed 2 sensitivity analyses that varied how the bisphosphonate change behavior variable was defined. In the first sensitivity analysis, we treated the bisphosphonate exposures as cumulative rather than momentary; discontinuation was defined as the cumulative proportion of person-time during which the patient was classified as a discontinuer and updated each quarter, and switching was defined as the cumulative count of switches the patient had made since the index date, also updated quarterly. In the second sensitivity analysis, we used only patients with at least 1 year of observation and for whom there were no changes in the first year. In this model, follow-up started at the 1-year mark, so as to model the onset of effect of bisphosphonate therapy. In all models, the dependent variable was the time to a dichotomous fracture event. Multivariable Cox Proportional Hazards regression models were also used for sensitivity analyses.

Effect of change behaviors on cost

Many patients have no healthcare use while a few outliers have very high healthcare costs; therefore, the assumption required for ordinary least squares (OLS) regression to yield unbiased results is violated in the association between change behaviors and cost outcomes.50 Several alternative statistical models have been proposed to overcome this problem,50 and we used model fit tests to determine the most appropriate distribution for our data.51 To obtain the least biased estimate of the effect of change behaviors on cost, we adjusted for all the important covariates listed above, plus total cost in the prior 6 months, and ran three models: one for all patients and one each for high-risk and not high-risk patients. Because each patient in the analysis cohort had the potential for multiple observations (one for each quarter of data), we used generalized estimating equations (GEE) to adjust for the clustering of observations within patients.52 For these GEE models, we assumed a gamma distributed dependent variable with a log link. Similar to the fracture analyses, the dependent variables in the cost regressions were lagged forward one quarter to allow us to estimate the impact of a change behavior on expenditures in the subsequent quarter, all of which will have occurred after the change behavior occurred. 53,54 We ran separate models for each outcome: total costs, osteoporosis-related costs, and osteoporosis-related pharmacy costs (see Cost outcomes, above) for overall patients, high-risk patients, and not high-risk patients.

RESULTS

Patients

As shown in Figure 1, out of more than 1.6 million female veterans age 50+ with an outpatient encounter in 2003-2011, 43,543 (2.6%) had a prescription for a bisphosphonate and 35,650 (2.2%) met all eligibility criteria and were included in the study. The majority of included patients (90.7%) were incident bisphosphonate users; only a small minority (9.3%) comprised prevalent users. Characteristics of the cohort are summarized in Table 1. The mean (SD) age was 65.7 (12.5), BMI was 27.2 (6.1), and BMD T-score (at the hip, spine, femur, or 1/3 radius) was -1.58 (1.75). Only 63.2% of patients had T-scores available; of these, the majority was in the low bone mass range (T-score of -1.0 to -2.5; 48.3%).

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Among the 85% of patients in whom FRAX scores could be calculated, the majority exceeded the National Osteoporosis Foundation (NOF)-recommended FRAX risk thresholds of 3% for hip fracture (54.2% of the cohort) or 20% for any major fracture (69.6% of the cohort), for whom pharmacologic treatment is recommended. Overall, mean (SD) observation time until fracture events was 52.8 (27.9) months. The minimum and maximum were 0.03 and 102 months, respectively.

Bisphosphonate change behaviors

As shown in Figure 2, only 6,804 patients (19.1%) persisted on their index bisphosphonate and thus were counted as non-switchers over a median follow-up period of 4 years and 95 days (i.e., 1556 days). Among the 28,846 patients (80.9%) who exhibited change behaviors, 2,610 (7.3%) switched to a different bisphosphonate, 28,622 (80.3%) discontinued, and 14,452 (40.5%) reinitiated at least once during the follow-up period. At least half of all patients in the change behavior subcohort displayed more than one type of change over time. Among discontinuers, the median time to first discontinuation was 294 days and the median duration of the first discontinuation gap was 159 days from the end of the prior days supply.

Predictors of bisphosphonate discontinuation

Table 2 shows the patient characteristics that remained significant predictors of bisphosphonate discontinuation at p

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Cost outcomes

Total costs for the cohort over the study period were $1.48 billion, with inpatient costs totaling $395 million and outpatient costs accounting for $1.08 billion. Total osteoporosis-related costs over the study period were $44.7 million. Of this, inpatient costs were $11.6 million, outpatient costs were $12.7 million, and pharmacy costs were $20.4 million.

Accounting for variable patient follow-up time, mean annual total costs per patient were $10,291 (with outpatient costs being $7,070 and inpatient costs being $3,221) per patient. Of this, mean annual osteoporosis-related costs were $320, and of this, mean annual osteoporosis-related inpatient costs were $96 and mean annual osteoporosis-related outpatient costs were $83. The greatest contributor to osteoporosis-related costs was prescription drugs, with a mean annual cost of $140.

The adjusted quarterly total, osteoporosis-related, and osteoporosis-related pharmacy cost differences associated with change behaviors compared to the non-switching subcohort are shown in Table 3. The coefficients presented in this table represent the percentage change in quarterly cost associated with the change behavior relative to being a non-switcher; the quarterly differences are also given in dollars. The total costs were significantly higher for all change behavior subcohorts (p 0.02) except for the high-risk switchers. In terms of magnitude, the greatest differences in total cost can be seen in the reinitiating subcohort, which was 20% higher for high-risk, 16% higher for not high-risk, and 17% higher overall; these differences corresponded to quarterly total cost differences of $637, $343, and $394, respectively. Total cost differences for switchers ranged from 13-14%, a cost difference of $294-494; the total cost differences for discontinuers ranged from only 5-8%, a cost difference of $102-268.

In contrast to total costs, patients with change behaviors had significantly lower osteoporosis-related costs compared to non-switchers. In terms of magnitude, the greatest percentage differences were seen in the discontinuing cohort, which was 63% lower for high-risk and 118% lower for not high-risk switchers (106% lower overall). However, although the magnitudes of these differences were large (versus those seen for total costs), in terms of dollars, these differences were quite small: $76 lower for high-risk and $74 lower for not high-risk discontinuers ($77 overall). For switchers and re-initiators, these differences were even smaller: the percentage differences in osteoporosis-related costs among reinitiators ranged from 17-24%, which corresponded to a $15-21 lower quarterly cost. For switchers, percentage differences ranged from a 14% lower cost to a non-significant 30% higher cost, or a range from a $10 quarterly decrease to a non-significant $36 quarterly increase.

Like osteoporosis-related costs, the osteoporosis-related pharmacy costs were all significantly lower in the change behavior subcohorts compared to the non-switching subcohort. The discontinuing subcohort had between 220% and 236% lower osteoporosis-related pharmacy costs than the non-switching subcohort; this corresponded to a $75 lower quarterly osteoporosis pharmacy cost. Costs for switchers were between 61-67% lower compared to non-switchers, corresponding to a $21 lower quarterly osteoporosis pharmacy cost. For re-initiators, the quarterly cost differences ranged from 58-60%, or $19-20 lower cost per quarter.

DISCUSSION

In this study of veterans with PMO, we examined bisphosphonate change behaviors discontinuing, switching, and reinitiating bisphosphonates and their association with fracture and cost outcomes. Discontinuation was by far the most common of the change behaviors, with more than 80% having at least one discontinuation in the first 2.5 years of treatment. The strongest risk factors for discontinuation were Hispanic race and having a high number of healthcare visits in the prior year.

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However, these risk increases were modest, less than 23%. One recently published study by Yun et al.55 reported modestly increased risk (less than 15%) for Hispanic patients as well as patients with 10+ healthcare visits per year, among other risk factors, in a database cohort study of Medicaid patients. These differences in adherence associated with race/ethnicity may be the result of differences in socioeconomic characteristics of patients. Although the VAs coverage of prescription drugs is quite robust and consistent across the country, the low-cost copay for a 3-month supply might still be a hardship for people with very low incomes.

Notably, having zoledronic acid as an index bisphosphonate reduced the risk of discontinuation substantially compared to alendronate, suggesting that less frequent dosing dramatically improves persistence because patients are not at risk for discontinuation for much of the follow-up time. Curtis et al. estimate real-world zoledronic acid adherence to be 20% greater than for IV ibandronate, which is dosed quarterly, and also higher than other studies reporting adherence to weekly or monthly oral bisphosphonates.56

The subcohort with the highest incidence of both fracture types was the switching subcohort; however, after multivariable adjustment for potential confounders, the risk differences between the non-switching cohort and the change subcohorts were no longer significant. This suggests that most of the difference in risk is driven by patient disease severity, and that more severe patients are more likely to switch. The subcohort that most closely approached the trend of increased risk with inconsistent bisphosphonate use was patients at high risk for hip fracture. Switchers, discontinuers, and reinitiators with a baseline high-risk for hip fracture showed increased (adjusted) risks of 28%, 26%, and 44%, respectively (see Figure3b), though not statistically significantly so. Indeed, the 44% increased risk for reinitiators in the high-risk group may be a reassuring sign that clinicians are identifying high-risk patients and are pressuring them to restart their bisphosphonates after discontinuing them.

For cost outcomes, quarterly total healthcare costs were higher in most change behavior subcohorts compared to those who did not change, increases that ranged from $102 to $637 per patient per quarter. In contrast, and given that we did not find higher fracture-related risks and costs in the change subcohorts, osteoporosis-related total costs were all significantly lower in the change subcohorts; however, in terms of dollars, these savings were modest (ranging from $10-77 per patient per quarter). The savings in osteoporosis-related costs among change subcohorts appeared to be entirely driven by lower osteoporosis-related pharmacy costs; osteoporosis-related pharmacy costs were $19-75 lower per patient per quarter in the change subcohorts compared to non-switchers. Although the numbers seem small, particularly the mean annual osteoporosis-related inpatient costs, it is important to realize that these averages are over the whole cohort, including patients who did not have any inpatient costs at all. For example, out of 35,650 patients in the cohort, only 420 (1.2%) had osteoporosis-related inpatient costs; the average in just that subset was $8153. It should also be noted that the large number of discontinuers in the change cohort likely drives the lower osteoporosis-related pharmacy costs. However, when you look at the other change subcohorts (i.e., switchers and reinitiators), costs go down significantly only in patients who were not high-risk. This may be explained by the overlap of these two subcohorts with the discontinuing subcohort; in the clinical setting, there may be less urgency to keep patients who do not present as high-risk on bisphosphonates.

Our results confirm what many other researchers have found: patient adherence to bisphosphonates is poor. We found that 80% discontinued their bisphosphonate medications long before they had completed 5 years of treatment. Similar estimates of discontinuation after 1 year of therapy from other US studies have ranged from 43.3% to 83.8%.2-4,6 These results indicate that optimizing osteoporosis treatment adherence should be a high priority for the clinical community, and while it may result in

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increased pharmacy costs, it should be associated with lower total healthcare costs overall. Chronic conditions necessitating persistent preventive medication against future outcome are some of the most difficult conditions to treat, not least because patient compliance tends to drop off over time.57 Likely the preventive care nature of bisphosphonates reduces the patient's perception of its importance: if nothing happens (i.e., no fracture), then the bisphosphonate may be working, but the patient is unlikely to perceive such. This effect can be compounded if the patient suffers from one of the more immediate side effects of bisphosphonates, such as gastric distress.5

Although switching had a higher incidence of subsequent fracture compared to discontinuing in our data, it was not statistically significantly associated. Switching bisphosphonates was much less common than discontinuing altogether or restarting the index bisphosphonate. A finding of increased risk associated with switching relative to discontinuing does not align with published literature. Briesacher and colleagues similarly did not find a significant effect on fracture risk in patients who switched from once weekly to once monthly bisphosphonates,21 and others found that switching bisphosphonates improved persistence overall, which would be expected to reduce fracture risk.7,19,20 Our data also suggest that medication-taking patterns, even discontinuation, do not significantly impact fracture risk, which does not correspond to what others have found.8,12-18,58 We can propose reasons for our inability to detect a relationship between bisphosphonate discontinuations and fracture risk. One is the low proportion of patients who were non-switchers: only 19%. Considering that only a small percentage of these non-switchers fractured (4.2%), our study was likely underpowered for detecting differences. Relatedly, a significant number of fractures may have not been recorded because many fractures are treated outside the VA.59,60 Existing research is not illustrative, even stratifying by gap length in the definition of discontinuation (3 months to 7 days).8,12-15,18 Notably, most of these studies also used medication possession ratio (MPR), 8,12,14,15,18 which Curtis17 shows may inflate the difference between the adherent and non-adherent. Finally, most (approximately 85%) of women were not at high risk for fracture at baseline, which may also explain why adherence did not seem to reduce fractures. Our study covers a time period when many relatively young women with osteopenia being given bisphosphonates.

Study limitations

This study is subject to a few limitations. First, like all observational studies, our findings are potentially confounded by unmeasured characteristics, despite our best efforts to control for those that were available in our data. These unmeasured characteristics include subsequent incident events---such as new incident prescriptions, new incident diagnoses, or other medical or surgical interventions during the observation period---that might have affected bisphosphonate persistence. Given the long observation period, all these factors might eventually affect treatment behavior. Second, our inclusion of zoledronic acid, ensuring 100% persistence for 1 year per dose compared to daily, weekly, or monthly bisphosphonates, may have skewed annual persistence (though zoledronic acid is not shown to improve persistence over multiple years/doses 61,62) and reduced the sensitivity of our 90-day discontinuation gap length to detect changes in bisphosphonate use. However, the 30- and 60-day gap length sensitivity analyses both indicated that our results are robust. Third, there may be some misclassification of outcomes or exposures, particularly if patients received care outside the VHA system. We tried to minimize this by restricting the analysis to patients who showed a pattern of getting their routine care from within the VHA. For example, since some emergent fractures are more likely to be treated at the nearest facility, rather than at the patients primary facility, there is a high likelihood that many patients had fractures that were treated outside the VHA. Nelson et al. documented that only 13% of dual-eligible Medicare/VHA patients who fracture are treated in the VHA, based largely on distance from the VHA facility. 63 Veterans living in rural settings are reported to have lower healthcare utilization and quality of life, 64,65 which may in turn impact the baseline health state under which fracture events occur.

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However, a study by MacKenzie et al. reported mixed results in elderly veterans, suggesting socioeconomic factors rather than rurality as a greater driver of overall mortality. 66 It is unknown how VHA/Medicare utilization may or may not mediate fracture occurrence in our cohort. Our fracture incidence rates were also 1/3-2/3 lower than would be expected based on clinical trial data in an at-risk population,67 although the low fracture rate may also be explained by the fact that only 15% of all women in our study were classified as high-risk, and less than one-quarter were >80 years old. With regards to the exposure, the inexpensive prescriptions and mail-order prescription service in the VHA provide an incentive for veterans to receive their refills from VHA pharmacies. However, recent changes in the retail market with respect to inexpensive generics may promote non-system use. Fourth, we may also have inadequate characterization of patient baseline risk level. We only had BMD T-scores from notes and radiology reports in 63% of the sample. For patients who received their BMD screening at a non-VA facility, then that report may have been kept in the medical record as an image file, which was not available to us for processing. Thus, if the clinician did not subsequently mention DEXA results in his/her clinic note, then that would explain the high percentage of missing BMD T-scores.

Future work should examine the impact of Medicare eligibility on male osteoporosis screening and treatment patterns in the VHA. It should attempt to detect nuances among the various bisphosphonate users (e.g., the difference between patients who restart bisphosphonates after a discontinuation and patients who discontinue bisphosphonates altogether). It should also attempt to predict bisphosphonate usage patterns, with disease severity (i.e., high-risk and low-risk) as the main independent variable. Finally, it should also explore the reasons for changing bisphosphonates, which may be due in large part to adverse effects.

CONCLUSIONS

This study suggests that real-world variations in adherence and persistence during the long-term treatment of osteoporosis are common. Our data show that most patients who use bisphosphonates discontinue them within 30 months, many sooner. However, patients who discontinued or switched were not at significantly higher risk for fracture compared to non-switchers when controlling for disease severity, which may be explained by the low proportion of high-risk patients in our cohort. Therefore, results should be interpreted as applying primarily to low-risk patients. Total costs were higher in patients with change behaviors. While osteoporosis costs were lower in patients with change behaviors, the magnitudes of the reductions were small and appeared to be primarily driven by the smaller pharmacy costs. Furthermore, getting a patient on an individually optimal bisphosphonate, even if it results in switches and discontinuations, may result in lower osteoporosis-related costs in the long term.

ACKNOWLEDGMENTS

This material is the result of work supported with resources and the use of facilities at the George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, Utah. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government. This research was funded by a grant from Amgen Inc. The authors would like to thank the Agency for Healthcare Research and Quality (Grant #K08-HS018582-01) for their support of Dr. LaFleur during the writing of this manuscript. The authors would also like to thank Joice Huang and Brad Stolshek for their contributions to the initial research idea.

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FIGURE LEGENDS

Figure 1. Attrition summary for study sample

Figure 2. Percentages of patients classified as non-switchers, discontinuers, switchers, and reinitiators in each quarter following the index date

Figure 3. Unadjusted (a) and adjusted (b) risks of hip and any major fracture associated with time-varying quarterly bisphosphonate exposure and patient baseline risk

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Figure 1. Attrition summary for study sample

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Figure 2. Percentages of patients classified as non-switchers, discontinuers, switchers, and reinitiators in each quarter following the index date

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Figure 3. Unadjusted (a) and adjusted (b) risks of hip and any major fracture associated with time-varying quarterly bisphosphonate exposure and patient baseline risk

Key: SW = Switching; D = Discontinuing; RE = Reinitiating; NCE = Non-cumulative exposure (Ref: Non-switching); CCSW = Cumulative count of switches (Ref: None); CQNP = Cumulative quarters of discontinuation (Ref: None). *High-risk defined as 2 or more of the following: (1) femoral neck T-score -2.5; (2) age 70 and total hip, spine, or one-third radius T-score -2.5; (3) prior fracture of hip, spine, one-third radius, humerus, pelvis, or tibia/fibula.

1.17 1.08 1.16 0.96 1.01

1.08

1.12 1.03 0.93 1.01 1.14 1.08 1.2 0.94 1.01

1.22

1.11

1.38

0.9 1.01

1.69

1.46

1.6

0.91 1.02

0.82

0.94 1.21

0.83 1.01

0.00

0.50

1.00

1.50

2.00

2.50

3.00

SW D RE SW D RE SW D RE SW D RE SW D RE SW D RE

NCE CCSW CQNP NCE CCSW CQNP NCE CCSW CQNP NCE CCSW CQNP NCE CCSW CQNP NCE CCSW CQNP

OVERALL HIGH-RISK* NOT HIGH-RISK OVERALL HIGH-RISK* NOT HIGH-RISK

ANY MAJOR FRACTURE HIP FRACTURE

(a) Unadjusted

0.95 0.98 1.05 0.89 1.00 0.85 0.95 0.90 0.88 0.99 1.07 1.01 1.14 0.92 1.00

0.95

1.02

1.29

0.82 1.01

1.28

1.26

1.44

0.83 1.02

0.79

0.92

1.21

0.82 1.01

0.00

0.50

1.00

1.50

2.00

2.50

3.00

SW D RE SW D RE SW D RE SW D RE SW D RE SW D RE

NCE CCSW CQNP NCE CCSW CQNP NCE CCSW CQNP NCE CCSW CQNP NCE CCSW CQNP NCE CCSW CQNP

OVERALL HIGH-RISK* NOT HIGH-RISK OVERALL HIGH-RISK* NOT HIGH-RISK

ANY MAJOR FRACTURE HIP FRACTURE

(b) Adjusted

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Table 1. Frequencies and percentages for selected baseline patient characteristics in N=35,650 patients who met all inclusion/exclusion criteria

Overall

(N=35,650) Non-switching cohort

(N=6,804) Change cohort

(N=28,846)

N % N % N %

DEMOGRAPHICS

Age

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Table 1. Frequencies and percentages for selected baseline patient characteristics in N=35,650 patients who met all inclusion/exclusion criteria

Overall

(N=35,650) Non-switching cohort

(N=6,804) Change cohort

(N=28,846)

N % N % N %

DISEASE CHARACTERISTICS

High riska

Yes 5,478 15.4 1,079 15.9 4,399 15.2

No 30,172 84.6 5,725 84.1 24,447 84.8

FRAX score (hip)b

3% 19,337 54.2 3,265 48.0 16,072 55.7

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Table 2. Significant predictors of discontinuation at p

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Table 3. Unadjusted and adjusted percentage differences in quarterly total and osteoporosis-related costs associated with change behaviors compared to non-switchers

Univariate (unadjusted) Multivariable (adjusted)a

Stratum Percentage difference

95% CI Absolute

differenceb 95% CI

Percentage difference

95% CI Absolute

differenceb 95% CI

TOTAL COST DIFFERENCES

Overall (Reference = Non-switching)

Switching Discontinuing Reinitiating

0.14 0.02 0.10

0.06, 0.22 0.00, 0.05 0.07, 0.14

331.40 51.73

242.55

147.87, 514.3 -6.41, 109.87

158.35, 326.75

0.14 0.05 0.17

0.06, 0.21 0.03, 0.08 0.13, 0.20

317.55 123.13 393.62

140.05, 495.04 66.90, 179.37

311.71, 475.53

High-risk (Reference = Non-switching)

Switching Discontinuing Reinitiating

0.06 0.03 0.09

-0.14, 0.25 -0.04, 0.10 -0.01, 0.19

186.95 97.19

304.27

-476.60, 850.51 -142.19, 336.57 -31.42, 639.97

0.13 0.08 0.20

-0.07, 0.33 0.01, 0.15 0.10, 0.30

424.39 267.72 637.19

-223.60, 1072.38 33.56, 501.98

307.42, 966.95

Not high-risk (Reference = Non-switching)

Switching Discontinuing Reinitiating

0.16 0.02 0.10

0.08, 0.23 -0.00, 0.05 0.07, 0.14

343.71 46.79

221.45

170.23, 517.18 -6.72, 100.31

143.65, 299.25

0.14 0.05 0.16

0.06, 0.21 0.02, 0.07 0.12, 0.19

294.07 102.38 342.64

127.40, 460.73 50.96, 153.80

267.45, 417.83

OSTEOPOROSIS-RELATED COST DIFFERENCES

Overall (Reference = Non-switching)

Switching Discontinuing Reinitiating

-0.01 -0.92 -0.18

-0.18, 0.15 -1.01, -0.82 -0.32, -0.04

-1.05 -64.19 -12.82

-12.66, 10.56 -70.66, -57.71 -22.39, -3.26

-0.14 -1.06 -0.22

-0.29, 0.00 -1.14, -0.98 -0.32, -0.11

-10.42 -77.12 -15.62

-21.07, 0.22 -82.85, -71.40 -23.42, -7.83

High-risk (Reference = Non-switching)

Switching Discontinuing Reinitiating

0.30 -0.45 -0.19

-0.07, 0.67 -0.66, -0.24 -0.46, 0.08

35.28 -53.25 -22.38

-8.96, 79.53 -78.22, -28.29 -54.63, 9.87

0.30 -0.63 -0.17

-0.14, 0.73 -0.79, -0.47 -0.39, 0.04

36.07 -76.40 -21.25

-17.57, 89.71 -95.40, -57.40 -46.94, 4.44

Not high-risk (Reference = Non-switching)

Switching Discontinuing Reinitiating

-0.19 -1.08 -0.19

-0.34, -0.05 -1.18, -0.98 -0.35, -0.03

-11.91 -66.12 -11.60

-20.95, -2.88 -72.31, -59.94 -21.29, -1.90

-0.27 -1.18 -0.24

-0.40, -0.15 -1.26, -1.09 -0.36, -0.12

-17.22 -73.94 -14.82

-24.95, -9.48 -79.36, -68.52 -22.18, -7.46

OSTEOPOROSIS-RELATED PHARMACY COST DIFFERENCES

Overall (Reference = Non-switching)

Switching Discontinuing Reinitiating

-0.76 -2.35 -0.59

-0.88, -0.65 -2.38, -2.32 -0.61, -0.56

-24.48 -75.40 -18.78

-28.27, -20.69 -77.04, -73.76 -19.74, -17.83

-0.66 -2.34 -0.58

-0.78, -0.54 -2.37, -2.31 -0.61, -0.56

-21.35 -75.14 -18.80

-24.22, -17.48 -76.57, -73.71 -19.65, -17.95

High-risk (Reference = Non-switching)

Switching Discontinuing Reinitiating

-0.61 -2.22 -0.61

-1.05, -0.17 -2.31, -2.13 -0.68, -0.53

-20.75 -75.71 -20.62

-35.61, -5.89 -81.44, -69.98 -23.84, -17.40

-0.61 -2.20 -0.60

-0.98, -0.24 -2.28, -2.11 -0.66, -0.54

-20.73 -75.07 -20.39

-33.19, -8.27 -79.61, -70.53 -22.92, -17.87

Not high-risk (Reference = Non-switching)

Switching Discontinuing Reinitiating

-0.81 -2.38 -0.58

-0.89, -0.73 -2.41, -2.34 -0.61, -0.56

-25.59 -75.31 -18.47

-28.14, -23.04 -76.95, -73.68 -19.45, -17.49

-0.67 -2.36 -0.58

-0.79, -0.56 -2.40, -2.33 -0.61, -0.56

-21.46 -75.21 -18.54

-25.04, -17.89 -76.62, -73.79 -19.41, -17.67

a Adjusted for demographics (age, marital status, race, visits in the prior year), bisphosphonate, FRAX risk factors (alcohol exposure, BMI, prior fracture with site, rheumatoid arthritis, secondary osteoporosis, smoking status, and corticosteroid use), other drug exposures (calcitonin, calcium, heparin, hormone therapy, proton pump inhibitors, raloxifene, benzodiazepines, opioids, somnolence-causing drugs, selective serotonin reuptake inhibitors, statins, teriparatide, thiazide diuretics, thyroid medications, vitamin D), comorbid conditions (COPD and depression), and 6 months of cost prior to index date.

b In 2011 dollars.

ACCE

PTED

MAN

USCR

IPT

ACCEPTED MANUSCRIPT

26

HIGHLIGHTS

Persistence with bisphosphonates is frequently poor, and stopping, restarting, or switching bisphosphonates is common.

We evaluated bisphosphonate change behaviors, fractures and costs in a large, national cohort of postmenopausal veterans.

Most patients discontinue treatment at some point, which did not significantly increase fracture risk in the majority of patients.

Bisphosphonate change behaviors were associated with significantly lower osteoporosis costs, but significantly higher total costs.