Compliance with drug therapies for the treatment
and prevention of osteoporosis
Jeffrey S. McCombs
a,∗, Patrick Thiebaud
b, Connie McLaughlin-Miley
c, Jinhai Shi
c aDepartment of Pharmaceutical Economics and Policy, School of Pharmacy, University of Southern California, Los Angeles, CA, USAbDepartment of Economics, University of Southern California, Los Angeles, CA, USA cAmgen Inc., Thousand Oaks, CA, USA
Received 6 June 2003; received in revised form 14 January 2004; accepted 23 February 2004
Abstract
Objectives: This study used paid claims data from real-world treatment settings to investigate the impact of hormone replace-ment therapy (HRT), bisphosphonate and raloxifene on patients with a recorded diagnosis of osteoporosis. Methods: Data from a large health insurer were used to identify 58,109 osteoporosis patients who initiated drug therapy for osteoporosis. Multivariate statistical models were developed for duration of therapy, compliance at 1 year, time to discontinuation or a change in therapy, health care costs and risk of fracture over 1 year. Results: One-year compliance rates were below 25% for all osteoporosis ther-apies. The mean unadjusted duration of continuous therapy was 221 days for raloxifene, 245 days for bisphosphonate, 262 for estrogen-only and 292 days for estrogen plus progestin. Raloxifene patients were consistently less compliant than estrogen-only patients after adjusting for differences in patient characteristics. Estrogen plus progestin patients were generally more compli-ant while bisphosphonate did not differentiate from estrogen-only. Compliance reduced the risk of hip fracture (o.r.=0.382, P < 0.01) and vertebral fracture (o.r. = 0.601,P < 0.05). Compliant patients used fewer physicians services (−US$ 56, P < 0.0001), hospital outpatient services (−US$ 38,P <0.05) and hospital care (−US$ 155,P <0.01). Bisphosphonate patients were twice as likely as estrogen-only patients to experience vertebral, Colles and other fractures and experienced higher health care costs (+US$ 420,P < 0.01). The effectiveness of both raloxifene and bisphosphonate medications relative to estrogen-only improved significantly with the age of the patient. Conclusions: Compliance with drug therapies for osteoporosis over 1 year is poor leaving patients at risk for fractures and higher health care costs.
© 2004 Elsevier Ireland Ltd. All rights reserved. Keywords: Estrogen; Health care costs; Osteoporosis
∗Corresponding author. Tel.:+1-323-442-1465;
fax:+1-323-442-1462.
E-mail address: jmccombs@usc.edu (J.S. McCombs).
1. Background
Osteoporosis is a disease characterized by low bone mass density and micro-architectural deterioration of bone that increases the risk of bone fracture resulting in pain and deformity. Osteoporosis is considered a 0378-5122/$ – see front matter © 2004 Elsevier Ireland Ltd. All rights reserved.
significant public health concern that will be magnified in the future as the population ages in the developed world. The National Osteoporosis Foundation (NOF)
[1] estimates that low bone density affects about 44 million men and women in the US based on 2000 cen-sus data. This represents 55% of people 50 or older in the US, of which 10 million (8 million women and 2 million men) suffer from osteoporosis. The condition disproportionately affects postmenopausal women, es-pecially those who are Caucasian or Asian. Melton et al. [2] found that 45% of white women over age 50 in Minnesota had a bone density greater than 2 standard deviations below the mean of healthy young women.
The major consequence of osteoporosis is bone frac-ture due to bone fragility[3]. The NOF report[1] esti-mates that 1.5 million fractures are caused annually by osteoporosis, including 300,000 hip fractures, 700,000 vertebral fractures, and 250,000 wrist (Colles) frac-tures. Lifetime risk of fracture has been estimated at: 6% men, 18% women (hip); 5% men, 16% women (vertebral); 3% men, 18% women (wrist)[6]. In any year, fracture occurs in 1–2% of women around age 65 and 6–10% of women age 75[7].
Several studies have tackled the issues of excess mortality and morbidity caused by osteoporosis. The consequences of fracture depend on the type of frac-ture considered. Mortality increases substantially immediately after hip fracture and returns to normal after 2 years[6,8]. The pattern is reversed for verte-bral fractures: mortality diverges from normal rates at an increasing pace from the fracture date. No signif-icant difference was observed for forearm fractures. Hip fracture is associated with greater morbidity than vertebral or wrist fractures; it is particularly disabling and almost always requires hospital stays. Cooper
[6] reports that of those who are independent before the fracture, 74% remain independent, 18% become dependent, and 8% enter a nursing home. Of those already dependent before the fracture, 50% enter a nursing home.
A limited number of recent studies evaluate the cost of osteoporosis. In the US, the direct annual costs were estimated at US$ 14 billion in 1995[4]of which 62% was for hospital services [5]. However, these estimates ignore the effect of bone fragility and frac-tures on quality of life. Martin et al.[3] describe the cost on Medicaid and Medicare of osteoporosis-prone
Medicaid recipients. They evaluate the impact of fractures in women over 50 on hospital, physician, long-term care, drug use, and miscellaneous expen-ditures, controlling for patients’ prior health care utilization. They find an average cost increase of US$ 8986 per person-year for femur fracture and US$ 3326 per person-year for non-femur fracture. Johnell
[9], reviewing studies conducted in different coun-tries, observed that the direct cost of hip fracture is US$ 7000 in direct hospital cost and US$ 21,000 in total cost over 1 year after the occurrence of the fracture. Brainsky et al. [10] estimate the marginal cost of a hip fracture at between US$ 16,322 and US$ 18,727 while Cummings et al. [11] estimate that the number of hip fractures in the United States would increase from 261,000 in year 2000 to 512,000 in 2040. Hip fractures are by far the most expen-sive to treat as they almost always involve hospital-ization and long rehabilitation periods. In contrast, forearm fractures do not usually require inpatient stays.
Long-term compliance with osteoporosis treatment can be problematic. Kotzan et al.[12], in a study of postmenopausal Medicaid women, found that 54% of women remained on treatment for more than 29 months and 17% for more than 35 months. Cano
[13]assessed compliance in 331 menopausal women treated with hormone replacement therapy (HRT) over a period of 5 years and reports that 9% of women never filled their prescription, 15% interrupted their treatment, and 14% followed their treatment intermit-tently. Faulkner et al.[14] conducted a retrospective study among 29,000 women aged 40–59 who were new HRT users. Compliance was defined there as a medication possession ratio (MPR) greater than 0.75. After 1 year, 54.4% of women were non-compliant with HRT. Cole et al.[15]assessed compliance in a sample of 222 women between the ages of 23 and 83 who had received a bone mineral density measure-ment (BMD). Compliance over a 9-month follow-up period ranged between 60% for HRT to 95.5% for Vitamin D, with the rates for alendronate sodium, calcitonin, and calcium being 82, 87, and 85%, re-spectively. Kayser et al.[16]evaluated the differences in continuation among 1394 women age 60 or above initiating treatment with either raloxifene or estro-gen. At 24 months, 72% of the women who started on estrogen had discontinued their treatment versus
56% of raloxifene patients. Marwick [17] reported that 27% of 1586 women enrolled in the Harvard Community Health Plan who received a new pre-scription for HRT terminated therapy within 100 days and 40% stopped taking HRT within 1 year. Bjorn and Backsrom [18] report that negative side-effects were a major cause of non-compliance with HRT. Ettigenger et al. [19] found that nearly 35% of 812 women using alendronate to treat osteoporosis discon-tinued therapy at 6 months based on prescription refill data.
2. Objectives
This study is designed to investigate patient com-pliance with drug therapies use to prevent and treat osteoporosis in real-world treatment settings, and to estimate the cost-consequences associated with non-compliance. Particular attention will be paid to comparing the patient outcomes achieved using estrogen-only, estrogen plus progestin, raloxifene or bisphosphonate. This study is particularly important on three fronts. First, the cost-effectiveness of alterna-tive drug therapies to treat osteoporosis in real-world clinical practice settings requires patients to main-tain continuous drug therapy in excess of 10 years
[20]. Second, more information is needed on the rel-ative performance of newer alternrel-atives to HRT for treating osteoporosis and preventing adverse patient outcomes, such as fractures. Finally, a recent random-ized clinical trial of the long-term use of estrogen and progestin found increased risks for coronary heart dis-ease, stroke, breast cancer, and pulmonary embolism compared to placebo. These risks exceeded the bene-fits from reduced risks for colorectal and endometrial cancer, and hip fractures after an average exposure of 5.2 years[21].
3. Methods 3.1. Data
Data for this analysis were derived from the his-torical paid claims files for a large health insurance company located in California. Paid claims from the period January 1, 1998 to August 30, 2001 were
available for inclusion in the analysis. These data included claims for prescription drugs, hospitaliza-tions, physicians’ services, home health care, labo-ratory tests, emergency room visits, physical therapy and durable medical equipment. Patients were cov-ered under a variety of insurance plans including capitated health maintenance organizations (HMO), point-of-service and preferred provider options. The claims data included information on diagnosis, the amount paid by the plan, and patient co-payment obligations. However, data on patient height, weight and bone mineral density measurements were not available for study.
3.2. Inclusion and exclusion criteria
The unit of observation was the initial observed treatment episode for any of the osteoporosis drug therapies listed inTable 1. Given the common use of HRT therapies to treat the symptoms of menopause, female patients had to be over the age of 55 to be included. Furthermore, all patients had to have a di-agnosis of osteoporosis recorded on at least one paid
Table 1
Study medications
Medications Sample size
Single HRT 46,109
Estradiol 7,332
Estrogens, conjugated 37,720
Estrogens, conjugated, synthetic 21
Estrogens, esterified 1,886
Estropipate 1,629
Medroxyprogesterone acetatea 2,349
Norethindronea 25
Norethindrone acetatea 147
Two HRTs (estrogen plus. . .) 6,766 Medroxyprogesterone acetatea 6,491 Norethindronea 27 Norethindrone acetatea 124 Estrogen 124 Bisphosphonate 3,720 Alendronate sodium 3,584 Etidronate disodium 131 Risedronate sodium 5 Raloxifene 1,514 Total sample 58,109 a Progesterone.
claim during the study period to be included. To insure that the treatment episode was ‘new’, drug treatment episodes were included in the study only if the patient was covered by insurance for a minimum of 6 months prior to the initiation of drug therapy. Therefore, it is possible that some patient episodes selected for study are not the first attempt by the patient to seek treatment of osteoporosis. Similarly, patients were included in the study only if their initiation of drug therapy began prior to August 30, 2000 and they maintained their health insurance coverage for the full year following treatment.
The number of treatment episodes by the class of medication initially used by the patient are listed in
Table 1. For purposes of the analysis, all patients us-ing hormone replacement therapies were classified as filled either one or two HRT prescriptions on the first day of therapy. Over 98% of patients who filled two HRT-related prescriptions on the first day of therapy used a combination of estrogen and progestin. How-ever, it is unclear to what extent patients filling a single HRT prescription on the first day were also estrogen plus progestin patients. For example, patients filling a prescription for progestin only could have filled a prescription for estrogen on a later day. Similarly, pa-tients filling an initial prescription for estrogen may have filled a second prescription for progestin at a later date. Patients filling a single HRT prescription were designated as the comparison group in the multivari-ate analyses.
3.3. Outcome measures
In this analysis, prescription drug data were used calculate the number of days of uninterrupted therapy achieve by the patient on the initial medication used and across all osteoporosis-related medications. Du-ration of therapy was measured by the count of days of therapy without an interruption of drug purchases greater than 2 weeks. Specifically, a refill prescrip-tion was considered to have been purchased without a break in therapy if the cumulative days supply for all previous prescriptions plus 14 days was greater than or equal to the number of days between the refill prescription’s purchase date and the index date for the treatment episode. If the cumulative days supply plus 14 was less than the total days between the purchase date of the refill prescription and the index date, the
count of continuous days of therapy was terminated. Separate counts of continuous days of therapy were calculated for the initial study drug used by the patient and for each additional study drug used thereafter. The total days of uninterrupted days of therapy achieved by the patient was then calculated across all drugs so long as the there was no gap in excess of 14 days between the termination of continuous use for one medication and the start of the following medication. The time to a change in therapy by the patient was also recorded. Fi-nally, the total days of medication available during the first post-treatment year was calculated to capture re-turns to treatment or changes in therapy that occurred after a break in therapy in excess of 14 days. This latter outcome measure can be transformed into the medica-tion possession ratio used in many studies as a measure of compliance by simply dividing days of therapy by 360 days.
The costs associated with treating osteoporosis patients were investigated using the total cost of treating patients over the first year. Total costs were also broken down into the individual components of cost: drug costs, ambulatory care (physician services), hospital outpatient care, laboratory tests and hospital services.
The drug therapies listed in Table 1 are typically used to prevent future bone fractures caused by osteo-porosis. Three specific fractures are of particular inter-est in this study: fractures of the vertebrae, lower arm and wrist (Colles fractures), and the neck of the fe-mur (hip fractures). Patient data for the 6 months prior to the initiation of drug therapy were screened for an ICD-9 diagnostic code indicating that the patient expe-rienced a fracture of the vertebra (805.x, 806.x), lower arm (813.x, 814.x), hip (820.x), or any other fracture. Next, paid claims during the first post-treatment year were then screened for similar indicators of a ture. A dichotomous variable indicating a new frac-ture in the post-treatment period was created only if the patient was not identified as having a similar type of fracture in the 6 month pre-treatment period. However, it is possible fractures first reported in the post-treatment period existed prior to treatment but were either undetected or their diagnosis was not yet definitive. This problem of pre-existing but undiag-nosed fractures may be particularly troublesome for vertebral fractures that are often initially diagnosed as back pain.
3.4. Statistical methods
Any comparison of compliant and non-compliant patients, or patients treated with different drug thera-pies using data from real-world clinical settings must use multivariate statistical models designed to take into account baseline differences in the populations being compared. Several different multivariate mod-eling techniques were required in this analysis given the broad range of patient outcome variables studied. Simple dichotomous patient outcome measures, such as whether or not a patient completed a year of un-interrupted therapy, switched drugs, or experienced a fracture, were analyzed using logistic regression models. Outcome measures based on the time to these events were analyzed using Cox proportional hazards models. Continuous outcome measures, such as health care costs over the first year, were analyzed using ordinary least squares (OLS) regression models. For hospital costs that may have a large number of patients with zero values, the two step approach de-veloped by Duan et al.[22]was used. This approach first investigates the likelihood of being hospitalized using logistic regression models, then proceeds with an analysis of the hospital cost per hospitalized pa-tient. The measures of the number of continuous days of therapy achieved were capped at 360 days, necessitating the use of the maximum likelihood estimation techniques using the Newton–Raphson algorithm[23]. The adjusted R2‘goodness-of-fit’ val-ues were reported for all OLS regression models and can be interpreted as the percentage of the dependent variable’s variance explained by the model.
All multivariate statistical modeling approaches are only as good as the data available to control for differences between the patient groups of inter-est. This analysis used the following variables to characterize the baseline characteristics of the study population:
1. Patient age and gender;
2. Type of health insurance coverage (FFS, HMO, point-of-service, PPO);
3. Use of health care services (cost) by type of ser-vice in the 6 months prior to initiation of the drug therapy episode;
4. Diagnostic profile as determined by the ICD-9 codes recorded on paid claims during the prior
6 months and the month in which treatment was initiated;
5. Prior fractures (vertebral, hip, Colles, all others); 6. Prior use of the following classes of prescription
drugs: (a) NSAIDS;
(b) Arthritis medications; (c) Drugs to treat diabetes; (d) Vitamin D therapy; (e) Anti-coagulation therapy;
(f) Antidepressants; (g) Prescribed steroids.
The availability of these data elements should sig-nificantly reduce the likelihood of bias in the multi-variate statistical results due to unobserved factors that differ across patient groups and are correlated with patient outcomes. In particular, the diagnostic profile data were used to control for bias created by the pos-sible inclusion of patients using HRT for its presumed cardiovascular benefits. Unfortunately, data on height, weight and bone density were not available for use in the analysis, thus creating the possibility of missing variable bias if physicians selected their initial therapy based on these factors.
3.5. Sensitivity analyses
It is possible that female patients under the age of 55 may have used HRT primarily to relieve the acute symptoms of menopause, even after screening patients for a recorded diagnosis for osteoporosis. This is not likely to be the case for patients treated with bisphos-phonates and raloxifene, thus creating a potential bias in estimates of the relative impact of alternative drugs. Moreover, raloxifene has been associated with a return of menopausal symptoms in symptom-free, younger female patients, thus reducing its effectiveness in these patients [24,25]. In an effort to document the extent to which bias may have been introduced by having included patients using HRT to treat menopause, sen-sitivity analyses were conducted to investigate if the impact of raloxifene and bisphosphonates relative to HRT vary with age.
Cost data are often highly skewed, thus violating the normality assumptions of OLS regressions. Sensitivity analyses were also conducted using log-transform cost models to investigate the robustness of the OLS cost results.
4. Results
4.1. Descriptive statistics 4.1.1. Baseline characteristics
Table 2presents data for the baseline demographic characteristics of patients by initial drug therapy. As expected, there are statistically significant and im-portant differences across the four treatment groups under study. Based on this dataset, HRT is the dom-inant therapy among osteoporosis patients (91.0%). Table 2
Baseline demographic characteristic
Baseline characteristic Single HRT
(N=46,109) Two HRTs (N=6,766) Bisphosphonate (N=3,720) Raloxifene (N=1,514) Demographics
Age (mean, in years)∗∗∗ 58.0 57.0 69.1 62.4
Age categories (%)∗∗∗ 45–55 43.8 43.2 13.6 27.5 55–65 36.1 42.4 23.2 35.2 65–75 12.6 10.3 27.5 21.5 75–85 6.1 3.6 26.1 11.7 85+ 1.4 0.5 9.6 4.0 Gender (% male)∗∗∗ 0.2 0.1 7.3 0.2
Type of health insurance (%)∗∗∗
Fee-for-service 2.9 2.3 11.8 6.2
HMO 29.9 33.3 15.4 20.0
Point-of-service 14.0 16.1 9.0 12.5
Preferred provider 53.3 48.4 63.9 61.4
Diagnostic profile at baseline (%)
Endocrine disorders∗∗∗ 17.9 14.9 24.6 28.5 Blood disorders∗∗∗ 3.0 2.3 6.2 5.1 Nervous disorders∗∗∗ 18.3 14.2 34.2 22.9 Circulatory disorders∗∗∗ 20.2 14.6 36.7 27.4 Respiratory disorders∗∗∗ 19.3 16.3 25.6 21.6 Congenital disorders∗∗∗ 1.3 0.9 2.6 1.5 Digestive disorders∗∗∗ 11.1 8.2 16.5 14.0 Gentio-urinary disorders∗∗∗ 34.2 33.0 35.4 41.7 Infections∗∗∗ 5.3 4.9 6.9 6.4 Trauma/injury∗∗∗ 13.7 11.1 22.9 16.2 Mental disorders 5.1 4.7 4.7 4.5 Muscle disorders∗∗∗ 27.7 23.4 63.5 47.4 Neoplasms∗∗∗ 11.0 8.7 18.6 22.9 Skin disorders∗∗∗ 16.1 14.3 26.8 20.9
Fractures prior to treatment (%)
Vertebral∗∗∗ 0.12 0.04 1.02 0.33
Hip∗∗∗ 0.10 0.04 0.83 0.13
Colles∗∗∗ 0.16 0.16 1.05 0.59
Other∗∗∗ 0.98 0.81 3.71 1.25
∗∗∗P <0.0001.
Only 6.4% of the patients used bisphosphonate; and 2.6% used raloxifene. Patients initiating hormone re-placement therapy are younger than patients bisphos-phonate or raloxifene and more likely to be members of an HMO. Patients treated initially with an HRT are also significantly less likely to have a reported co-morbidity and less likely to have experienced a fracture in the 6 months prior to starting therapy.
Table 3presents baseline data on prior use of health care services. Not surprisingly, HRT patients are less costly in terms of the health care use relative to
Table 3
Prior use of health care services
Baseline characteristic Single HRT (N=46,109) Two HRTs (N=6,766) Bisphosphonate (N=3,720) Raloxifene (N=1,514) Prior use of health care (US$ per6 months)
Physicians services∗∗∗ 240 189 282 278
Outpatient department∗∗∗ 177 132 249 377
Used DME (%)∗∗∗ 6.6 5.2 12.8 8.9
Durable medical equipment∗∗∗ 15 10 41 18
Use emergency room (%)∗∗∗ 5.1 3.6 11.9 7.1
Emergency room∗∗ 31 13 50 21
Laboratory tests∗∗∗ 64 51 91 95
Used physical therapy (%)∗∗ 8.3 7.3 9.5 8.8
Physical therapy∗∗ 24 19 18 23
Other services 1 1 1 0
Used hospital services (%)∗∗∗ 6.0 4.2 10.1 5.3
Hospital care∗∗∗ 188 77 229 150
Nursing home care 0 0 0 0
Co-payments for medical care∗∗∗ 65 51 70 81
Prescription drugs
Plan payments∗∗∗ 112 73 218 298
Patient co-payments∗∗∗ 32 18 60 73
Total plan costs∗∗ 948 633 1309 1415
Total co-payments∗∗∗ 97 69 130 154
Total costs∗∗∗ 1045 702 1439 1569
Drug profile (%)
NSAIDS∗∗∗ 8.6 6.4 11.7 13.7
Arthritis medications∗∗∗ 4.7 4.2 6.1 5.5
Drugs for diabetes∗∗∗ 3.6 2.2 3.9 4.7
Anti-coagulation therapy∗∗∗ 1.2 0.7 4.1 2.0
Antidepressants∗∗∗ 12.3 10.6 10.0 13.3
Prescribed steroids∗∗∗ 5.4 4.4 11.3 9.1
∗∗ P <0.01. ∗∗∗ P <0.0001.
patients initiating therapy on either bisphosphonate or raloxifene. Patients treated initially with an HRT have the lowest rates of prior use of NSAIDS, prescribed steroids, antidepressants, anti-coagulation therapy and prescription drugs used to treat diabetes.
4.1.2. Patient outcomes
Table 4 presents descriptive data on patient out-comes across the four drug therapies under study. As before, patients treated with HRT continue to experi-ence the lowest levels of health care use in the first post-treatment year compared to either raloxifene and bisphosphonates. Patients using two HRTs as initial therapy have the longest duration of therapy (191 days) while bisphosphonate patients have the highest rates of switching of therapy to a second medication (37.3%).
Twenty-four percent of patients initiating therapy with bisphosphonates continue to use this therapy unin-terrupted for 1 year as compared to only 17.9% of raloxifene patients. Patients using two HRTs are the most likely to complete 1 year of uninterrupted ther-apy (30.7%).
The rate of fractures reported in the 1-year post-period are consistent with observed differences in pre-treatment rates across the four treatment groups. As a whole, HRT patients have the lowest rates of all types of fractures in the first post-treatment year, ranging from 0.13% for hip fractures patients using two initial HRTs to 0.57% for other fractures for sin-gle HRT patients. Raloxifene patients experience the next highest rates of fracture, ranging from 0.20% for Colles fractures to 0.46% for other fractures. Patients
Table 4
Patient outcomes over the first treatment year: health care costs, duration of therapy and number of fractures
Baseline characteristic Single HRT
(N=46,109) Two HRTs (N=6,766) Bisphosphonate (N=3,720) Raloxifene (N=1,514) Use of health care (US$ per 12 months post)
Physicians services 424 403 450 452
Outpatient department∗∗ 350 319 440 530
Durable medical equipment∗∗∗ 26 19 56 22
Emergency room 19 13 22 20
Laboratory tests∗∗ 132 119 148 133
Physical therapy 46 43 36 40
Other services 23 16 23 8
Hospital care∗∗∗ 289 199 738 256
Nursing home care∗ 7 4 20 4
Co-payments for medical care∗∗∗ 114 107 105 115
Prescription drugs (all Rx)
Plan payments∗∗∗ 823 736 1353 1195
Patient co-payments∗∗∗ 248 231 347 275
Total Plan Costs∗∗∗ 2501 2211 3737 3020
Total Co-Payments∗∗∗ 362 338 452 390
Total costs∗∗∗ 2863 2549 4189 3410
Use of osteoporosis drugs
Days of continuous therapy on first Rx:1 year∗∗∗ 168 191 170 141 Days of continuous therapy, all Rx: 1
year∗∗∗ (this row contributes little but may cause some confusions)
169 192 172 142
Total days of therapy, all Rx: 1 year∗∗∗ 262 292 245 221
Medication possession ratio∗∗∗ 0.73 0.81 0.68 0.61
Second therapy used-overall(%)∗∗∗ 34.5% 26.5% 37.3% 27.9%
Duration in days (initial therapy, %)∗∗∗
0–30 19.8 14.7 24.0 32.7 31−60 8.7 6.9 7.6 7.9 61−90 12.9 13.7 11.1 12.0 91−120 10.1 8.9 6.9 7.3 121−150 5.2 5.4 5.0 5.4 151−180 4.5 4.2 4.3 3.3 181−210 4.3 5.0 4.6 4.0 211−240 2.5 2.6 2.8 2.5 241−270 2.5 2.4 2.7 1.8 271−300 2.2 1.9 2.7 1.8 301−330 2.0 1.9 2.3 2.4 331−360 1.7 1.7 1.9 1.1 >360 23.5 30.7 24.2 17.9
Fracture in first post-treatment year
Vertebral∗∗∗ 81 (0.18%) 9 (0.13%) 48 (1.29%) 4 (0.26%) Hip∗∗∗ 62 (0.13%) 11 0.16%) 34 (0.91%) 6 (0.40%) Colles∗∗∗ 141 (0.31%) 14 (0.21%) 40 (1.08%) 3 (0.20%) Other∗∗∗ 265 (0.57%) 27 (0.40%) 80 (2.15%) 7 (0.46%) ∗P <0.05.. ∗∗ P <0.01. ∗∗∗P <0.0001.
treated initially with a bisphosphonate drug exhibit the highest rates of fractures in the post-treatment pe-riod, ranging from 0.91% for hip fractures to 2.15% for other fractures.
4.2. Multivariate statistical results 4.2.1. Duration of therapy
Three different types of analyses were used to inves-tigate the impact of the initial medication class used by the patient on drug therapy outcomes. First, ordi-nary least squares (OLS) regression models were es-timated for the number of days of continuous therapy achieved during the first year without a break in ther-apy either on the initial medication used or over all medications used. A third OLS model was estimated for the total days of therapy used during the first year regardless of breaks between prescriptions. Second, the likelihood that a patient would achieve 360 days of uninterrupted therapy was investigated using logis-tic regression models. Third, Cox proportional hazards models were estimated for time to break in therapy and time to the use of a second medication. This ap-proach allows for censoring of the data at the end of the data period and was not limited to events occur-ring in the first year. The results of all four analyses are presented inTable 5.
Bisphosphonate patients are 6.9% more likely to break therapy (P < 0.05) and used 17.7 fewer days of total drug therapy over 1 year (P < 0.0001) than patients treated initially with a single HRT. No other statistically significant differences in duration of un-interrupted therapy were found. Bisphosphonate pa-tients were four times more likely to change therapies than single HRT patients. In comparison, raloxifene patients are uniformly less compliant and more likely to change therapies than single HRT patients. Patients using two HRTs as initial therapy were found to be more compliant than single HRT patients in four of five analyses and were no more likely to change ther-apies.
Comments on several other results are warranted. Older patients were more compliant and more likely to change therapies than patients under 55 years of age. Prior hospital costs and having used durable med-ical equipment were associated with increased compli-ance while out of pocket drug costs in the prior period reduced compliance. The prior use of an emergency
room visit was negatively associated with compliance. A history of a prior fracture was generally not found to affect compliance with the exception of vertebral fractures. Patient covered under preferred provider, point-of-service and fee-for-service insurance plans appear to achieve significantly better compliance than HMO patients, but were also more likely to change therapies. The presence of co-morbid conditions had a mixed pattern of effects. The use of NSAIDS, steroids and drugs for diabetes was correlated with reduced compliance while the use of antidepressants signifi-cantly increased compliance.
4.2.2. Fractures
The results of the logistic analysis of the likelihood of a patient experiencing a hip, vertebral, Colles or other fracture in the first post-treatment year as a func-tion of initial therapy are presented inTable 6. These results indicate that patients treated with bisphospho-nates are twice as likely as single HRT patients to ex-perience a vertebral, Colles or other fracture in the first post-treatment year. This result adjusts for the patient’s fracture history and the other factors included in the analyses as independent variables. The risk of fracture was not found to differ between HRT and raloxifene patients.
Several additional results in the models summarized inTable 6are of interest. The risk of fracture increases with age, especially for hip fractures. For example, the likelihood of a hip fracture increased 22-fold for patients 65+years of age relative to patients younger than age 55. HMO patients are significantly less likely to experience a fracture in the post-treatment period despite HMO patients being found to be less compli-ant. A history of fractures is not generally predictive of future fractures with the exception of the relationship between Colles fractures and future other fractures, and between other fractures and future hip fractures.
The impacts of drug use patterns on the likelihood of fracture were also estimated using logistic regression models and these results are summarized in Table 7. While compliance with therapy for 1 year was found to reduce the likelihood of all types of fractures (o.r.
<1.00), the estimated impact is only statistically sig-nificant for hip fractures (o.r.=0.382,P <0.01) and vertebral fractures (o.r.=0.601,P <0.05). Switch-ing therapies durSwitch-ing the year was associated with an increased risk of all types of fractures except hip
Table 5
Impact of intial drud therapy on drug use patterns Baseline characteristic OLS: duration:
initial Rx
(R2=0.0376)
OLS: duration: all drugs
(R2=0.0383)
OLS: total days in first year R2=0.0583 Logistic: duratio(n > 360) Time to break in therapy: initial Rx Time to change in therapy Days of therapy Days of therapy Days of therapy Odds ratio Hazard ratio Hazard ratio Initial medication (wrt HRT)
Bisphosphonates −1.8 −1.9 −17.7∗∗∗ 1.001 1.069∗ 4.104∗∗∗
Raloxifene −29.0∗∗∗ −35.9∗∗∗ −34.1∗∗∗ 0.735∗∗∗ 1.218∗∗∗ 3.750∗∗∗
Two HRTs 23.4∗∗∗ 31.7 30.4∗∗∗ 1.437∗∗∗ 0.839∗∗∗ 1.072
Days of post data available – – 0.05∗∗∗ 1.001∗∗∗ 0.999∗∗∗ 1.0001∗∗
Gender (1: male) −8.7 –13.1 −20.0∗∗ 0.864 1.090 0.063∗∗∗
Age
55–65 11.1∗∗∗ 13.9∗∗∗ 15.9∗∗∗ 1.133∗∗∗ 0.911∗∗∗ 1.498∗∗∗
65+ 7.1∗∗∗ 9.2∗∗∗ 11.0∗∗∗ 1.106∗∗ 0.921∗∗∗ 2.137∗∗∗
Prior use of health care Ambulatory care (US$
per 6 months)
−0.0004 −0.0005 0.0004 0.999 1.000 1.001∗
Drugs (insurance, US$ per 6 months)
−0.0018 −0.0018 −0.006∗∗∗ 1.001∗ 0.999 1.001 Drugs (copay, US$ per
6 months)
−0.0304∗∗∗ −0.0398∗∗∗ −0.041∗∗∗ 0.999∗ 1.001∗∗∗ 0.999∗ Hospital costs (US$
per 6 months)
0.0014∗∗ 0.0018∗∗∗ 0.001∗∗∗ 1.001∗∗ 0.999∗∗ 0.999 Medical equipment (yes: 1) 11.2∗∗ 13.9∗∗∗ 6.7∗∗ 1.122∗∗ 0.931∗∗∗ 1.055 Emergency room (yes: 1) −8.6∗∗ −11.9∗∗ −6.9∗∗ 0.859∗∗ 1.069∗∗ 0.930
Home health care (yes: 1) −46.1 −56.8 −45.7 0.610 1.186 1.039
Physical therapy 0.3 0.3 −0.2 0.994 0.987 0.950
Prior fracture (1: yes)
Hip 1.9 2.1 −1.0 1.029 0.984 0.795
Vertebral 26.8∗ 34.4∗ 10.5 1.293 0.905 1.582∗
Colles −15.5 −20.1 −18.5∗ 0.859 1.131 0.936
Other −6.1 −7.5 −5.4 0.952 1.056 1.139
Health insurance (wrt HMO)
Fee-for-service 24.1∗∗∗ 30.0∗∗∗ 14.9∗∗∗ 1.192∗∗ 0.894∗∗∗ 1.065 Point-of-Service 17.4∗∗∗ 21.7∗∗∗ 14.3∗∗∗ 1.130∗∗ 0.926∗∗∗ 1.382∗∗∗ Preferred provider 19.8∗∗∗ 24.8∗∗∗ 16.9∗∗∗ 1.173∗∗∗ 0.905∗∗∗ 1.171∗∗ Diagnosis at baseline Digestive disorders −5.7∗∗ −7.3∗∗ −3.8∗∗ 0.928∗ 1.049∗∗ 1.088∗ Blood disorders −10.7∗∗ −14.5∗∗ −7.5∗∗ 0.847∗∗ 1.070∗∗ 1.161∗ Endocrine disorders 2.5 3.4 −1.1 1.059∗ 0.982 10.58 Circulatory disorders 6.2∗∗∗ 8.1∗∗∗ 3.7∗∗ 1.085∗∗ 0.950∗∗∗ 0.931∗ Muscle disorders −0.6 −0.3 4.4 1.016 1.003 1.339∗∗∗ Genitourinary disorders 2.0 2.9 −2.2∗ 1.029 0.980 1.294∗∗∗ Skin disorders 5.5∗∗ 7.5∗∗ 0.8 1.078∗∗ 0.952∗∗∗ 1.158∗∗∗ Trauma/injury −1.7 −2.1 −0.3 0.970 1.018 0.978 Drug profile NSAIDS −3.9∗ −4.6 −8.0∗∗∗ 0.983 1.022 0.968 Arthritis medications 2.6 4.2 4.3∗ 1.058 0.968 1.163∗∗
Drugs for diabetes −5.3 −6.5 −5.4∗ 0.936 1.055∗ 0.699∗∗∗
Anti-coagulation therapy 3.6 4.3 3.0 1.020 0.945 0.987 Antidepressants 4.0∗ 5.3∗ 6.0∗∗∗ 1.044 0.973 1.067 Prescribed steroids −8.6∗∗ −10.1∗∗ −8.0∗∗∗ 0.939 1.031 1.092 ∗P <0.05. ∗∗ P <0.01. ∗∗∗P <0.0001.
Table 6
Impact of initial drug therapy on the likelihood of fracutres in the post-treatment period
Baseline characteristic Hip fracture Vertebral fracture Colles fracture Other fractures
Bisphosphonates 1.550 2.364∗∗∗ 2.080∗∗ 2.073∗∗∗
Raloxifene 1.445 0.744 0.484 0.621
Two HRT 1.802 1.042 0.775 0.748
Days of post data available 0.999∗ 0.999∗ 1.000 1.001∗∗
Gender (1: male) 2.140 1.237 0.239 0.855
Age
55−65 3.092 2.625∗∗ 1.112 1.292
65+ 22.223∗∗∗ 5.740∗∗∗ 1.895∗∗ 1.615∗∗
Prior use of health care
Ambulatory care (US$ per 6 months) 1.000 1.000 0.999 0.999
Drugs (insurance, US$ per 6 months) 0.999 0.999 0.999 1.000
Drugs (copay, US$ per 6 months) 0.999 0.999 1.000 1.000
Hospital costs (US$ per 6 months) 1.000 1.000 0.999 1.000
Medical equipment (yes: 1) 2.093∗∗ 1.129 1.016 1.089
Emergency room (yes: 1) 1.172 0.975 1.085 1.123
Physical therapy 0.851 1.066 0.972 1.345
Prior fracture (1: yes)
Hip n.a. 1.089 1.625 1.351
Vertebral 0.966 n.a. 1.434 1.737
Colles 0.800 1.098 n.a. 17.637∗∗∗
Other 2.360∗ 0.807 1.048 n.a.
Health Insurance (wrt HMO)
Fee-for-service 2.818∗ 6.690∗∗ 2.712∗∗ 4.135∗∗∗ Point-of-Service 1.099 1.536 1.694 2.213∗∗ Preferred provider 1.879 2.993∗∗ 1.923∗ 2.695∗∗∗ Diagnosis at baseline Digestive disorders 0.520∗ 1.205 1.040 0.825 Blood disorders 1.757 2.146∗∗ 1.686 1.406 Mental disorders 0.321 0.559 0.663 1.015 Circulatory disorders 1.183 1.120 1.134 1.064 Congenital disorders 1.568 1.121 0.767 1.299 Genitourinary disorders 0.993 1.055 1.511∗∗ 1.267∗ Nervous disorders 1.707∗∗ 1.013 1.173 1.199 Trauma/injury 1.264 2.400∗∗∗ 1.901∗∗ 1.226 Drug profile NSAIDS 1.313 1.196 1.490 0.915 Arthritis medications 1.200 1.487 1.321 1.557∗
Drugs for diabetes 0.933 1.854 1.427 1.051
Anti-coagulation therapy 1.922 1.768 0.883 1.371 Antidepressants 1.371 1.285 1.252 1.118 Prescribed steroids 1.674 1.701∗ 0.996 0.894 ∗P <0.05. ∗∗ P <0.01. ∗∗∗ P <0.0001.
fractures. However, the temporal relationship between the fracture event and the date of switching cannot be clearly determined using these data, thus calling into question the validity of this result.
4.2.3. Health care costs
Three alternative models of health care cost were in-vestigated (Table 8). The first drug use patterns model used dichotomous variables indicating whether or not
Table 7
Effects of drug use patterns and initial drug therapy on the likelihood of fracture Odds ratios
Hip fractures Vertebral fractures Colles fractures Other fractures Drug use patterns: Model 1
One year of uninterrupted therapy 0.382∗∗ 0.601∗ 0.785 0.870
Switched/added second drug 1.200 2.158∗∗∗ 2.056∗∗ 1.732∗∗∗
Drug use patterns: Model 2
Total days of therapy in first year 0.999 0.998∗ 0.999 0.999
Switched/added second drug 1.208 2.146∗∗∗ 2.050∗∗∗ 1.722∗∗∗
Alternative drugs Bisphosphonate 1.550 2.364∗∗∗ 2.080∗∗ 2.073∗∗∗ Raloxifene 1.445 0.744 0.484 0.621 Two HRTs 1.802 1.042 0.775 0.748 ∗P <0.05. ∗∗ P <0.01. ∗∗∗P <0.0001.
the patient achieved 360 days of continuous therapy or switched therapies during the first treatment year. The second drug use patterns model used the count of days of therapy as its measure of compliance. The third cost model used independent variables for the initial therapy used by the patient with single HRT patients as the comparison group. Simple OLS mod-els of all cost except hospital costs for which a two part model was estimated[22]. The results by type of service are reported inTable 9.
The uninterrupted use of one or more medications for osteoporosis was not associated with lower total health care costs, inclusive of patient co-payments, in the year following the initiation of drug therapy. This result for total cost reflects the offsetting effects of a significant increase in drug costs of+US$ 266 (P < 0.0001) associated with continuous therapy against significant reductions in physician services (−US$ 56,
P <0.0001), hospital outpatient services (−US$ 38,
P <0.05), laboratory tests (−US$ 9,P <0.01) and hospital costs (−US$ 155,P <0.01) (seeTable 9).
The impact of compliance on total costs is signifi-cant and positive in Model 2 in which the total days of therapy used within 1 year is substituted for the dichotomous 1-year compliance variable. In this lat-ter case, the savings in physicians services, outpatient care, laboratory services and hospital care per day of therapy used (Table 9) were not sufficient to fully off-set the increased drug costs of US$ 1.58 per day of drug therapy. In both models, the switching of
osteo-porosis medications within 1 year was associated with significantly higher total costs due to higher drug costs and increased use if laboratory tests.
Patients treated with raloxifene experienced a re-duction in total costs in the year following treatment (−US$ 280) but this estimated effect was not statis-tically significant. However, raloxifene use was cor-related with significantly lower costs for physicians’ services (−US$ 92, P < 0.01) and laboratory tests (−US$ 21,P <0.05), and a reduction of 38% in the likelihood of a hospital admission (o.r.=0.622,P < 0.0001) (Table 9). Conversely, patients treated with a bisphosphonate medication experienced significantly higher total costs than single HRT patients (+US$ 420,P <0.01), due to higher drug costs (+US$ 188,
P < 0.0001) and hospital costs per patient (+US$ 266, P<0.01), the latter due to a significant increase in the cost per hospitalized patient (US$ 2917,P < 0.05) (Table 9).
The statistical results for other explanatory variables inTable 9warrant discussion. Surprisingly, total cost in the first post-treatment year appears to decrease with age over 65. This un-intuitive result may reflect the fact that the model controls for each patient’s profile of co-morbid disease states and baseline prescription drug use. Second, total cost is negatively correlated with hospital spending in the 6 months prior to the initiation of drug therapy. Third, a history of vertebral and Colles fractures are negatively correlated with fu-ture costs of between −US$ 1200 and −US$ 1500
Table 8
Factors affecting total costs of treatment over 1 year
Baseline characteristic Effects of drug use patterns: Model 1
Effects of drug use patterns: Model 2
Effect of alternative drugs
R2=0.1254 R2=0.1256 R2=0.1255
Drug use profile
One year of uninterrupted therapy 20 – –
Added/switched to second drug 279∗∗ – –
Total days of therapy in first year 0.99∗∗
Added/switched to second drug 293∗∗
Alternative drugs Bisphosphonate therapy (wrt HRT) – – 420∗∗ Raloxifene (wrt HRT) – – −280 Two HRTs – – 38 Gender(male: 1) 2380∗∗∗ 2410∗∗∗ 2085∗∗∗ Age 55–65 370∗∗∗ 354∗∗∗ 375∗∗∗ 65+ –225∗∗ −233∗∗ −236∗∗
Prior use of health care
Ambulatory care (US$ per 6 months) 0.97∗∗∗ 0.97∗∗∗ 0.97∗∗∗
Drugs (insurance, US$ per 6 months) 2.28∗∗∗ 2.29∗∗∗ 2.28∗∗∗
Drugs (copay, US$ per 6 months) 0.38 0.42 0.37
Hospital costs (US$ per 6 months) −0.20∗∗∗ −0.20∗∗∗ −0.20∗∗∗
Medical equipment (yes: 1) 450∗∗ 444∗∗ 449∗∗
Emergency room (yes: 1) −37 −30 −45
Physical therapy (yes: 1) −110 −111 −102
Home health (1: yes) −2123 −2080 −2096
Prior fracture
Hip fracture (yes: 1) 169 173 104
Vertebral (yes: 1) −1448 −1456∗ −1465∗
Colles (yes: 1) −1191∗ −1171 −1234∗
Other (yes: 1) −374 −368 −399
Health insurance (wrt HMO)
Fee-for-service −32 −44 −45 Point-of-Service 253∗∗ 239∗∗ 258∗∗ Preferred provider 328∗∗∗ 313∗∗ 334∗∗∗ Diagnosis at baseline Digestive disorders 422∗∗∗ 425∗∗∗ 426∗∗∗ Blood disorders 316 323 311 Mental disorders 660∗∗∗ 660∗∗∗ 659∗∗∗ Circulatory disorders 416∗∗∗ 201 416∗∗∗ Endocrine disorders 242∗∗ 242∗∗ 245∗∗ Muscle disorders 262∗∗ 263∗∗ 248∗∗ Nervous disorders 278∗∗ 277∗∗ 277∗∗ Infections 181 181 180 Congenital disorders 205 201 198 Respiratory disorders 656∗∗∗ 655∗∗∗ 658∗∗∗ Trauma/injury 279∗∗ 279∗∗ 277∗∗ Drug profile NSAIDS 287∗∗ 295∗∗ 289∗∗ Arthritis medications 663∗∗∗ 659∗∗∗ 669∗∗∗
Drugs for diabetes 1691∗∗∗ 1697∗∗∗ 1687∗∗∗
Anti-coagulation therapy 1866∗∗∗ 1863∗∗∗ 1854∗∗∗
Antidepressants 967∗∗∗ 960∗∗∗ 974∗∗∗
Prescribed steroids 744∗∗∗ 752∗∗∗ 738∗∗∗
Days of data in post period 0.71∗∗∗ 0.66∗∗∗ 0.72∗∗∗
∗P <0.05. ∗∗ P <0.01. ∗∗∗ P <0.0001.
Table 9
Effects of drug use patterns and initial drug therapy on treatment cost by type of service Drugs Physicians Hospital
outpatient
Laboratory Likelihood of admission
Hospital costs per admitted patient (N=3502)
Hospital costs (all patients) Drug use: Model 1
One year compliance 266∗∗∗ −56∗∗∗ −38∗ −9∗∗ 0.744∗∗∗ −1,385 −155∗∗
Switched 162∗∗∗ 23 34 26∗∗∗ 1.040 −306 2
Adjusted R2 0.3968 0.1186 0.2675 0.1716 0.0307 0.0059
Drug use: Model 2 Total days of therapy in first year 1.58∗∗∗ −0.14∗ −0.22∗∗ −0.02 0.999∗∗∗ −5.26 −0.55∗∗ Switched 176∗∗∗ 23 32 26∗∗∗ 1.039 −371 −1 Adjusted R2 0.4054 0.1185 0.2675 0.1715 0.0306 0.0059
Alternative drug ITT model
Bisphospahte 188∗∗∗ −49 14 2 0.895 2917∗ 266∗∗ Raloxifene 22 −92∗∗ 57 −21∗ 0.622∗∗∗ −800 −139 Two HRTs 24 24 18 −1 0.807∗∗ 114 −38 Adjusted R2 0.3887 0.1185 0.2674 0.1711 0.0314 0.0059 ∗P <0.05. ∗∗ P <0.01. ∗∗∗P <0.0001.
(P <0.05 for five of six estimates). Finally, patients covered by the point-of-service or preferred provider insurance options exhibited higher total costs relative to HMO patients, even after controlling for the drug use patterns achieved or the initial therapy used by the patient.
4.3. Sensitivity analyses
The results from the sensitivity analysis for age-dependent treatment effects are presented in
Table 10. The impact of alternative therapies to HRT on the patient outcomes displays significant age de-pendency. Consider first the results for raloxifene. The difference in duration of initial therapy improved from a significant deficit of 47 days relative to single HRT in the under 55 age group to a deficit of only 6 days for patients over 65, an improvement of 41 days (P < 0.01). Similar improvements are evident in duration on all therapies (−46 days to −5 days), likelihood of completing 1 year of continuous ther-apy, the likelihood of discontinuation of therapy and the likelihood of changing therapies within 1 year. Not surprisingly, the trend in health care costs show a similar pattern with total costs relative to single HRT patients being +US$ 295 for patient under 55 (not
significant) to a savings of−US$ 599 (P <0.05) for patients over 65.
The impacts of bisphosphonate medications rela-tive to single HRT patients were also found to be age-dependent. Duration of therapy improves from a differential of−20 days for patients under 55 to a dif-ference of+6 days for patients over 65, an improve-ment of 26 days (P < 0.01). Cost differentials also improve in older bisphosphonate patients, decreasing from+US$ 1263 in higher total costs for bisphospho-nate patients under 55 to a difference of+US$ 349 for patients over 65 (P <0.01 for the change).
The sensitivity models for age-dependent treatment effects were also estimated using the log-transfor-mation of total costs as the dependent variable. These results also display the pattern of improved performance of both raloxifene and bisphosphonate medications with age and were consistent with OLS results.
5. Discussion
Retrospective database analyses using paid claims data present an array of advantages and limitations relative to randomized clinical trials. First, clinicians
Table 10
Effects of initial drug therapy on patient outcomes by age group [HRT as comparison medication within each age group]
Patient outcome Raloxifene Bisphosphonates
<55 55−65a ≥65a <55 55−65a ≥65a
Drug use patterns
Continuous days of Rx: initial drug −47∗∗∗ −37 −6∗∗ −20∗∗ 1∗ 6∗∗ Continuous days of Rx: all drugs −46∗∗∗ −37 −5∗∗ −20∗ 2∗ 6∗∗ Total days of Rx in first year −40∗∗∗ −42 −20∗∗ −26∗∗∗ −15 −16 Likelihood of 1 year of therapy 0.581∗∗ 0.644 0.954∗∗ 0.847 1.035 1.037 Likelihood of discontinuation 1.372∗∗∗ 1.287 1.060∗∗∗ 1.134∗∗ 1.031 1.060 Likelihood of switching drugs 9.444∗∗∗ 4.248∗∗∗ 1.873∗∗∗ 10.671∗∗∗ 7.096∗∗∗ 2.412∗∗∗ One year health care costs
Prescription drugs 99∗ −46 5 317∗∗∗ 202∗ 146∗∗
Physician services −81 −18 −175 47 −58 −75
Hospital outpatient services 238∗∗ 7 −34∗ 66 30 −12
Laboratory tests −7 −11 −44 61∗∗∗ 30 −27∗∗∗
Hospital services (unconditional) 111 −277 −196 670∗∗∗ −190∗∗ 351
Likelihood of admission 0.663 0.616 0.611 1.073 0.889 0.876
Hospital cost per admitted patient 5217 −1718 −2267 11518∗∗ −3231∗∗ 3045∗
Total costs 295 −419 −599∗ 1263∗∗∗ 15∗∗ 349∗∗
Log-transform of total cost +10.1%∗ +5.0% −5.0%∗ +40.5%∗∗∗ +20.1%∗∗ +6.8%∗∗∗ a Difference with respect to estrogen-only within each age group. Significance levels for the hypothesis that the impact of raloxifene or bisphosphonates relative to estrogen-only changes with age with the under 55 age group as the comparison group.
∗P <0.05. ∗∗ P <0.01. ∗∗∗ P <0.0001.
require better data on how well alternative treatment options perform in unrestricted, real-world clinical set-tings. Compliance data from well controlled clinical trials does not correspond well to real-world practice as clinical trials are typically designed to minimize subject drop-out. It is also difficult to adequately mea-sure health care utilization patterns and the incidence of rare events over long periods of time in the clini-cal trial environment. Finally, the limited sample size available for study and the selection criteria applied in clinical trials make it difficult to study the impact of alternative therapies across a wide range of patient characteristics. Conversely, retrospective database re-search cannot establish causality primarily due to the fact that patients are not randomly assigned to the treatment alternatives under study. At best, the multi-variate statistical techniques used to estimate treatment effects adjusted for differences in patient characteris-tics only establish whether or not patient outcomes are correlated with the treatment option selected by the physician.
The extent to which the user of retrospective database research is convinced that reported correla-tions are valid can be enhanced by several factors. First, multivariate models are only as good as the independent variables available to control for differ-ences between treatment groups. While this analysis was careful to include as many clinically-relevant in-dependent variables as were available from a patient’s paid claim history, several important factors related to osteoporosis were not available, among them height, weight and BMD measurements. Second, patient out-comes should be measured using several alternative approaches and the estimated treatment effects should be robust across the multitude of analyses presented. This was the approach taken here as we analyzed six different dependent variables related to treatment pattern outcomes (Table 4), four outcomes related to fractures (Tables 5 and 6), and both total costs and the major components of total costs (Tables 7 and 8). Third, the multivariate models used to estimate treatment effects must display plausible results across
the independent variables included in the model. We have displayed these results for selected models for the reader’s analysis (Tables 3–5 and 7). Finally, sensitivity analyses can be conducted that test the extent to which the results are sensitive to important issues not fully addressed by either the criteria used to select patients or the multivariate statistical models used to estimate treatment effects. In this case, since HRT therapies may be used to treat the symptoms of menopause in younger female patients, additional analyses were conducted to test whether or not the impact of alternatives to HRT therapy improved with the age of the patient (Table 9). In this case, the performance of raloxifene and bisphosphonates did improve with the age of the patient, as expected.
Clinicians unfamiliar with retrospective database analyses may find it difficult to identify those results that are most relevant to clinical practice from the ar-ray of analyses presented here. We believe the critical take-home results for clinicians are:
1. Compliance with all of these therapies is intermit-tent, at best, and likely does not approach the 10 years of therapy estimated for these therapies to achieve cost-effectiveness[20].
2. Bisphosphonates and raloxifene do not offer any compliance advantage relative to HRT.
3. While duration of uninterrupted therapy does im-prove with age, age-related incremental increases in duration were limited to a few days rather than additional months or years of therapy.
4. Continuous therapy over 1 year significantly re-duced the risk of hip and vertebral fractures over 1 year and did not significantly increase total direct medical cost for compliant patients.
6. Limitaitons
While great care has been taken to adjust estimated results for the baseline clinical characteristics of the patient population, other unobserved factors may exist that are correlated with the patient outcome measures studied here. Of particular concern is the lack of data for height, weight and bone density that are likely to be correlated with both patient outcomes and the selection of an initial therapy. Moreover, this analysis considers only the initial osteoporosis drug therapy used by the
patient during the data period available for analysis. It is likely that many of these patients experienced prior treatment attempts using these medications and that these prior unobserved episodes of care would influ-ence duration and switching patterns. However, one would think that duration of therapy would increase with the second or third treatment attempt by the pa-tient. If so, the poor compliance reported here is even more discouraging.
Hormone replacement therapy may have been in-tended for short-term use to treat the adverse effects of menopause, even in this population of patients with a diagnosis of osteoporosis. Conversely, the use of a bis-phosphonate medication or raloxifene may have been intended for longer-term use to treat osteoporosis. If this is true, then one would expect to see longer dura-tion of therapy for the alternative therapies relative to HRT. This was not found to be the case. Patients using HRT may have also been at significantly lower risk of fractures even after controlling for patient character-istics such as age, gender, co-morbid conditions and baseline drug profile. If this is the case, the estimated effects of raloxifene and bisphosphonates on fracture rates and costs may be significantly under-estimated.
7. Conclusions
Taken as a whole, patients who achieve 1 year of uninterrupted drug therapy for osteoporosis achieve better patient outcomes than patients who terminate or interrupt therapy during the first year. Unfortunately, less than 25% of patients achieve in excess of 1 year without breaking therapy and approximately 30% of patients switch therapies. This is a mixed blessing. The Women’s Health Initiative study[21]found that the long-term use of estrogen and progestin combina-tion therapy (average 5.2 years) significantly increases a woman’s risk of breast cancer, coronary heart dis-ease, stroke and pulmonary embolisms to the point that these risks outweigh the benefits achieved. In this case, non-compliance with HRT in less than 1 year may be beneficial in the long term. However, therapies to replace HRT for the long-term treatment of osteo-porosis are needed. Much remains to be done to im-prove long-term patient compliance with bisphospho-nate medications and raloxifene before the full benefit of these therapies are achieved.
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