Abstract
Introduction
Many women at risk of fracture do not receive anti-osteoporosis medication (AOM), while others may be receiving unnecessary treatment.
Purpose
To examine the characteristics associated with AOM use among women at low and high risk of fracture.
Methods
The Global Longitudinal Study of Osteoporosis in Women (GLOW) is a prospective cohort study in which data were collected, via self-administered questionnaires, from 60,393 non-institutionalized women aged ≥55 years in 10 countries between October 1, 2006 and April 30, 2008. This is a cross-sectional analysis of baseline USA data, in which women were classified as having low fracture risk (<65 years; no FRAX risk factors) or high fracture risk (≥65 years; prior fracture or ≥2 other FRAX risk factors).
Results
Of 27,957 women, 3013 were at low risk of fracture and 3699 were at high risk. Only 35.7% of high-risk women reported AOM treatment, rising to 39.5% for those with self-reported osteopenia and 65.4% for those with self-reported osteoporosis. Conversely, 13.4% of low-risk women reported AOM, rising to 28.7% for osteopenia and 62.4% for osteoporosis. Characteristics associated with significantly higher AOM treatment rates among low-and high-risk women were: osteoporosis (odds ratios 75.3 and 18.1, respectively), osteopenia (17.9 and 6.3), concern about osteoporosis (2.0 and 1.8), higher perceived risk of fracture (2.3 and 1.6), and higher vitality score (1.7 and 1.6).
Conclusion
Use of AOM is frequently inconsistent with published guidelines in both high -and low-risk women. Characteristics other than FRAX fracture risk appear to influence this use, particularly the presence of self-reported osteoporosis.
Keywords: Anti-osteoporosis medication, Fracture risk, Postmenopausal osteoporosis, Women
Introduction
Expert groups have published treatment guidelines for preventing osteoporosis-related fractures based on clinical risks and bone mineral density (BMD) determinations [1–3]. Despite such guidance, multiple studies have demonstrated that many women at increased fracture risk remain undertreated [4–9]. There is also a suggestion that women at low risk are sometimes treated unnecessarily [10].
Within a large cohort of postmenopausal women, we identified subgroups of women at high and low risk of fracture using clinical risk factors specified in the FRAX prediction model [11]. We then investigated whether, after accounting for fracture risk, any additional characteristics were associated with anti-osteoporosis medication (AOM) use and whether these differed between the high-and low-risk women.
Material and methods
The Global Longitudinal Study of Osteoporosis in Women (GLOW) is a large multinational prospective cohort study involving 60,393 women aged ≥55 years in 10 countries (Australia, Belgium, Canada, Germany, Italy, Netherlands, Spain, France, the UK, and the USA) [12]. Women who are patients at primary care practices at 17 different sites were surveyed. Women were eligible for inclusion if they had visited their physician in the previous 24 months, were not institutionalized, and had no cognitive impairment, language barrier, or illness preventing them from completing the survey questionnaire. The questions covered seven domains: patient characteristics/risk factors, perceptions about fracture risk and osteoporosis, medication use, comorbid conditions, healthcare use and access, physical activity, and physical function/quality of life. The overall aim of the GLOW study is to identify patterns of risk as well as of management and treatment of osteoporosis and fractures in this multinational cohort [12].
This study is a cross-sectional analysis of the baseline GLOW data for the cohort of women in the USA. Women were placed into one of two risk categories: low-risk women were those aged <65 years with no FRAX risk factors; while high-risk women were those aged ≥65 years with a prior fracture or ≥2 other FRAX risk factors (parental hip fracture, current smoker, ≥3 alcoholic drinks/day, rheumatoid arthritis, current corticosteroid use, body mass index [BMI] <20 kg/m2, and secondary osteoporosis) [11]. Women who did not fit into either of these two categories were not included in the analysis, although data for the entire USA cohort is also included. BMD, which is included in the FRAX risk model, was not accounted for in this study, as BMD data were not collected in GLOW.
The characteristics considered for potential association with treatment were: education, age (within risk strata), number of comorbid conditions, prescription drug coverage, concern about osteoporosis, self-perception of fracture risk, self-assessed health status, short form 36 (SF-36) physical function, and vitality score [13]. Baseline data were used for all of the characteristics. Treatment rates were also analyzed by self-reported diagnosis of osteoporosis or osteopenia. The outcome of interest was use of AOM (current use at baseline), defined as any of the following: alendronate, calcitonin, raloxifene, risedronate, teriparatide, or zoledronic acid. Women who were members of the USA cohort, but who were taking medications that are not approved in the USA were excluded from analysis. Women on raloxifene who also reported having cancer were excluded from the analysis, as cancer can be an indication for such use. Women on estrogen were excluded from the analysis, as it was not possible to determine whether they were on estrogen for osteoporosis or another indication.
Statistical methods
Means and standard deviations (SDs) are reported for continuous variables; while numbers and percentages are reported for discrete variables. Chi-square tests for significance were used to detect differences in treatment rates between risk groups. A p value <0.05 was considered significant. Odds ratios (ORs) with 95% confidence intervals (CIs) for characteristics associated with AOM treatment were calculated using logistic regression models for both risk groups. Study site and age were adjusted for in the models. All analyses were performed using SAS 9.2 (SAS Institute, Cary, NC, USA).
Results
Characteristics of the USA GLOW cohort, overall and according to risk group, are listed in Table 1. There were 27,957 women in the USA cohort, including 3013 in the low-risk group and 3699 in the high-risk group. High-risk women had lower vitality and physical function scores than low-risk women (Table 1). Fewer high-risk women listed no comorbid conditions and fewer rated their own health as excellent, very good, or good (Table 1). The numbers of high-and low-risk women who were very or somewhat concerned about osteoporosis were similar, but high-risk women were more likely to rate their own fracture risk as higher than others (Table 1).
Table 1.
Baseline characteristics of women in the GLOW USA cohort and in the low -and high-risk fracture groups.
| GLOW USA cohort (n=27,957) | Low riska (n=3013) | High riskb (n=3699) | |
|---|---|---|---|
| Age (years) | 69 ± 9.1 | 60 ± 2.5 | 75 ± 7.0 |
| BMI (kg/m2) | 28 ± 6.4 | 29 ± 6.8 | 27 ± 5.9 |
| Vitality score | 61 ± 20.4 | 66 ± 18.7 | 59 ± 21.0 |
| Physical function score | 72 ± 27.7 | 85 ± 20.0 | 65 ± 28.2 |
| Number of co-morbidities | |||
| 0 | 4408 (19.3) | 1011 (34.2) | 435 (12.6) |
| ≥1 | 18,400 (80.7) | 1943 (65.8) | 3025 (87.4) |
| General health | |||
| Excellent, very good, or good | 23,275 (84.4) | 2805 (93.5) | 2995 (81.7) |
| Fair or poor | 4313 (15.6) | 195 (6.5) | 669 (18.3) |
| Prescription drug coverage | |||
| Yes | 25,591 (92.4) | 2841 (94.5) | 3411 (92.6) |
| No | 2094 (7.6) | 167 (5.5) | 274 (7.4) |
| Concern about osteoporosis | |||
| Very/somewhat | 23,604 (85.1) | 2603 (86.4) | 3228 (87.7) |
| Not at all | 4129 (14.9) | 408 (13.6) | 452 (12.3) |
| Perceived fracture risk | |||
| Same or lower than others | 21,702 (79.6) | 2597 (87.1) | 2474 (68.0) |
| Higher than others | 5558 (20.4) | 386 (12.9) | 1166 (32.0) |
| Education | |||
| High school or less | 9211 (33.5) | 441 (14.7) | 1363 (37.6) |
| More than high school | 18,246 (66.5) | 2564 (85.3) | 2265 (62.4) |
| Race | |||
| White | 23,925 (86.6) | 2696 (90.0) | 3372 (92.1) |
| Non-white | 3706 (13.4) | 299 (10.0) | 290 (7.9) |
Data are mean ± SD for continuous variables and number (%) for discrete variables.
BMI, body mass index; GLOW, Global Longitudinal study of Osteoporosis in Women; SD, standard deviation.
Low risk: age <65 years, no FRAX risk factors, cnot on estrogen.
High risk: age ≥65 years and prior fracture or ≥2 FRAX risk factors, cnot on estrogen.
FRAX risk factors: parental hip fracture, current smoker, prior fracture, ≥3 alcoholic drinks/day, rheumatoid arthritis, current corticosteroid use, BMI <20 kg/m2, secondary osteoporosis.
Table 2 shows AOM treatment rates at baseline by risk factors for fracture for the whole USA cohort. Women with rheumatoid arthritis, current corticosteroid use, and prior fracture had the highest treatment rates (33–36%).
Table 2.
AOM treatment rates at baseline by risk factors for fracture (n=27,957).
| Risk factors for fracture | With risk factor | Without risk factor | p value |
|---|---|---|---|
| Parental hip fracture | 27.5 | 21.5 | <0.0001 |
| Current smoker | 17.9 | 22.8 | <0.0001 |
| Prior fracture | 33.2 | 19.1 | <0.0001 |
| Alcohol ≥3 drinks/day | 18.1 | 22.5 | 0.31 |
| Rheumatoid arthritis | 35.8 | 22.5 | <0.0001 |
| Current corticosteroid use | 34.7 | 22.0 | <0.0001 |
| Secondary osteoporosis | 20.2 | 23.2 | <0.0001 |
| BMI <20 kg/m2 | 31.1 | 21.6 | <0.0001 |
Data are %.
AOM, anti-osteoporosis medication; BMI, body mass index.
Overall, 13.4% and 35.7% of low-and high-risk women, respectively, reported treatment with AOM. Table 3 shows AOM reported treatment rates among low -and high -risk women, by various baseline factors. Factors that significantly increased reported AOM use among both low-and high-risk women were: concern about osteoporosis, higher perceived fracture risk, higher vitality and physical function scores, and having prescription drug coverage (Table 3). However, having no comorbidities only significantly increased reported AOM usage among low-risk patients; while being white, being better educated, and having better general health only significantly increased reported AOM usage among high-risk patients (Table 3). Results from the whole GLOW USA cohort were generally similar to those in the low-and high-risk groups (Table 3).
Table 3.
Percentages of women receiving AOM by fracture risk factors
| GLOW USA cohort (n=27,957) | Low risk (n=3013) | High risk (n=3699) | ||||
|---|---|---|---|---|---|---|
|
| ||||||
| % | p-valuea | % | p-valuea | % | p-valuea | |
| Total | 22.4 | 13.4 | 35.7 | |||
| Education | 0.94 | 0.96 | 0.007 | |||
| High school or less | 22.4 | 13.4 | 32.8 | |||
| More than high school | 22.5 | 13.5 | 37.3 | |||
| Race | <0.0001 | 0.97 | <0.0001 | |||
| White | 23.5 | 13.5 | 36.6 | |||
| Non-white | 16.2 | 13.4 | 24.8 | |||
| Number of comorbidities | 0.57 | 0.01 | 0.26 | |||
| 0 | 22.9 | 15.7 | 38.2 | |||
| ≥1 | 22.5 | 12.1 | 35.4 | |||
| General health | 0.01 | 0.2 | 0.03 | |||
| Excellent/very good/good | 22.7 | 13.6 | 36.7 | |||
| Fair/poor | 21.0 | 10.5 | 32.1 | |||
| Prescription drug coverage | <0.0001 | <0.0001 | 0.004 | |||
| No | 13.4 | 2.5 | 27.5 | |||
| Yes | 23.2 | 14.0 | 36.5 | |||
| Concern about osteoporosis | <0.0001 | <0.0001 | <0.0001 | |||
| Very/somewhat | 25.2 | 15.2 | 39.0 | |||
| Not at all | 6.3 | 2.0 | 11.9 | |||
| Perceived fracture risk | <0.0001 | <0.0001 | <0.0001 | |||
| Same/lower than others | 17.3 | 9.6 | 27.5 | |||
| Higher than others | 43.5 | 40.5 | 53.6 | |||
| Vitality score | <0.0001 | <0.001 | 0.001 | |||
| ≤median (62.5) | 21.2 | 10.9 | 33.5 | |||
| >median (62.5) | 24.0 | 15.6 | 39.0 | |||
| Physical function score | 0.12 | 0.001 | 0.03 | |||
| ≤median (85) | 22.0 | 10.0 | 34.4 | |||
| >median (85) | 22.8 | 14.7 | 37.9 | |||
AOM, anti-osteoporosis medication; GLOW, Global Longitudinal study of Osteoporosis in Women.
p values are from chi-square tests of differences in percentage treated between rows within risk groups.
Table 4 shows the percentages of women reporting AOM use, further categorized by self-reported osteoporosis, osteopenia, or neither. Not surprisingly, reported AOM treatment rates were much higher among women with self-reported osteoporosis or osteopenia than among those with neither condition, in both the low-and high-risk groups (Table 4). However, less than two-thirds of high-risk women with osteoporosis were actually receiving AOM. Results by risk factors were generally similar among these groups to the overall results shown in Table 3.
Table 4.
Percentages of women receiving AOM by diagnosis (osteoporosis, osteopenia, or neither) and fracture risk factors.
| n | Low risk | High risk | |||||
|---|---|---|---|---|---|---|---|
|
| |||||||
| Osteopenia (n=745) | Osteoporosis (n=252) | Neither (n=2016) | Osteopenia (n=839) | Osteoporosis (n=1321) | Neither (n=1539) | ||
| Total | 6538 | 28.7 | 62.4 | 1.6 | 39.5 | 65.4 | 7.6 |
| Education | |||||||
| High school or less | 1751 | 28.9 | 51.0 | 2.4 | 35.4 | 60.5 | 8.5 |
| More than high school | 4711 | 28.7 | 65.3 | 1.4 | 40.7 | 68.3 | 6.9 |
| Race | |||||||
| White | 5920 | 28.5 | 62.7 | 1.3 | 39.8 | 66.4 | 7.4 |
| Non-white | 566 | 34.0 | 62.5 | 3.3 | 30.3 | 49.4 | 10.0 |
| Number of comorbidities | |||||||
| 0 | 1410 | 30.7 | 68.7 | 2.5 | 46.2 | 76.1 | 10.6 |
| ≥1 | 4849 | 27.6 | 58.7 | 1.2 | 38.1 | 64.7 | 7.2 |
| General health | |||||||
| Excellent/very good/good | 5649 | 29.4 | 61.4 | 1.6 | 40.3 | 69.7 | 7.6 |
| Fair/poor | 843 | 13.8 | 70.0 | 1.4 | 37.0 | 50.5 | 8.3 |
| Prescription drug coverage | |||||||
| No | 423 | 3.4 | 20.0 | 0.8 | 31.3 | 54.4 | 9.3 |
| Yes | 6096 | 29.8 | 64.3 | 1.6 | 39.9 | 66.4 | 7.5 |
| Concern about osteoporosis | |||||||
| Very/somewhat | 5683 | 28.9 | 63.3 | 1.8 | 39.9 | 65.4 | 9.1 |
| Not at all | 836 | 21.7 | 20.0 | 0.5 | 30.8 | 69.8 | 2.8 |
| Perceived fracture risk | |||||||
| Same/lower than others | 4944 | 24.8 | 56.0 | 1.5 | 37.8 | 61.0 | 7.3 |
| Higher than others | 1510 | 42.3 | 70.4 | 4.1 | 43.7 | 69.6 | 9.3 |
| Vitality score | |||||||
| ≤median (62.5) | 3578 | 23.3 | 54.5 | 1.1 | 36.0 | 61.3 | 6.2 |
| >median (62.5) | 2939 | 32.8 | 69.5 | 2.1 | 43.9 | 72.5 | 9.6 |
| Physical function score | |||||||
| ≤median (85) | 3087 | 23.7 | 51.6 | 1.9 | 37.1 | 62.9 | 7.5 |
| >median (85) | 3433 | 30.0 | 66.1 | 1.5 | 42.7 | 70.5 | 7.8 |
Data are %.
AOM, anti-osteoporosis medication.
Results of the multivariable analysis are listed in Table 5. The most important factors for reported AOM treatment were self-reported osteoporosis or osteopenia, with higher ORs among low-risk women (ORs 75.3 and 17.9, respectively) than among high-risk women (ORs 18.1 and 6.3, respectively) (Table 5). Prescription drug coverage was important among low-risk women (OR 5.6), but not among high-risk women (OR 1.0). Concern about osteoporosis and higher perceived fracture risk approximately doubled the chance of receiving AOM in both risk groups, as did an above median vitality score (Table 5).
Table 5.
Results from logistic regression: characteristics associated with treatment with AOM in low-and high-risk women (adjusted for study site)(n=6054)
| Low risk | High risk | |
|---|---|---|
| Increasing age (5-year increments) | 1.2 (0.9–1.6) | 1.2 (1.1–1.3) |
| Diagnosis (self-reported) of osteoporosis vs. neither | 75.3 (46.5–121.9) | 18.1 (14.0–23.5) |
| Diagnosis (self-reported) of osteopenia vs. neither | 17.9 (11.9–27.0) | 6.3 (4.8–8.2) |
| More than high school education vs. high school or less | 1.2 (0.8–1.8) | 1.2 (1.0–1.5) |
| 0 vs. ≥1 co-morbidities | 1.4 (1.1–1.9) | 1.5 (1.1–2.0) |
| Prescription drug coverage vs. not | 5.6 (1.9–16.4) | 1.0 (0.7–1.5) |
| Very/somewhat vs. not at all concerned about osteoporosis | 2.0 (0.9–4.3) | 1.8 (1.2–2.6) |
| Perceived higher vs. same/lower risk of fracture than others | 2.3 (1.7–3.2) | 1.6 (1.3–1.9) |
| > vs. ≤ median vitality | 1.7 (1.2–2.2) | 1.6 (1.3–1.9) |
| > vs. ≤ median physical function | 1.2 (0.8–1.7) | 1.2 (1.0–1.5) |
Data are OR (95% CI).
CI, confidence interval; OR, odds ratio.
Discussion
The results from this large cohort of postmenopausal women show that established clinical risk factors are associated with AOM treatment. There was a 2.7-fold higher rate of reported treatment among high-versus low-risk women. As expected, self-reported osteoporosis was associated with the strongest likelihood of treatment, with self-reported osteopenia also highly associated with AOM use. However, our results suggest that certain other characteristics are also driving treatment. Concern about osteoporosis, perception of having a higher fracture risk than others, having fewer comorbid conditions, and having greater vitality each increased the likelihood of being treated, by 1.4–2.3-fold, regardless of fracture risk.
As an example, AOM use was somewhat higher among low-risk women who were very/somewhat concerned about osteoporosis (15.2%) than in high-risk women who were not concerned (11.9%). Additionally, 42.3% of women in the low-risk group with self-reported osteopenia who perceived their fracture risk as higher than others were treated with AOM, compared with only 37.8% of high-risk women with self-reported osteopenia who did not perceive themselves to be at high risk. The tendency for higher treatment rates among women with higher vitality and physical function scores, fewer comorbid conditions, and better reported general health suggests that healthier, more energized individuals may be stronger advocates for treatment or may focus more on AOM if they have fewer competing health concerns.
We also found that reported rates of AOM use were much higher among low-risk women with versus without prescription drug coverage, but that this difference was less pronounced among high-risk women. In contrast, race had little impact on AOM treatment among low-risk patients, but being white significantly increased the rate of treatment among high-risk patients.
Overall, the AOM treatment rates in our population were low. The reported treatment rate among all high-risk women was only 35.7%, increasing to 39.5% in women with self-reported osteopenia and to 65.4% for those with self-reported osteoporosis, which, while improved, still falls below suggested standards. The National Osteoporosis Foundation and the American Academy of Clinical Endocrinologists recommend treatment for postmenopausal women aged ≥50 years with: a hip or vertebral fracture; a T-score ≤–2.5 at the femoral neck or spine; a T-score between −1.0 and −2.5 at the femoral neck or spine and a 10-year probability of a hip fracture ≥3%; or a 10-year probability of a major hip fracture ≥20%, based on the USA-adapted World Health Organization [1,3]. The American College of Physicians recommends offering treatment to those with a diagnosis of osteoporosis and those who have experienced a fragility fracture[2].
While our data indicate substantial under-treatment, we also found that 8.9% of low-risk women without self-reported osteoporosis reported receiving AOM treatment. The National Osteoporosis Foundation does not recommend pharmacologic treatment for osteopenia in the absence of other risk factors [1]. However, in the low-risk group (i.e. those with no FRAX risk factors), 28.7% of women with self-reported osteopenia reported receiving AOM treatment. Therefore, it appears that there is a substantial group of women who are being treated with AOM who are not likely to benefit from treatment.
These findings are reminders that active treatment involves both physician and patient participation. Doctors must recognize risk and prescribe appropriately, but patient attitudes may also contribute. Study subjects in both the high-and low-risk groups who perceived that their fracture risk was increased were significantly more likely to report taking AOMs. Women who reported concern about osteoporosis were also more likely to report AOM use in both risk groups.
Previous studies have focused on predictors of receiving AOM treatment. Bessette et al. reported significant predictors of treatment following a fracture to be: low BMD, fracture site (hip, femur, pelvis, or wrist), use of calcium and vitamin D consumption at the time of fracture, and age ≥60 years [14]. Hamel et al. reported that a finding of osteoporosis or osteopenia by first BMD testing influenced the prescribing of bisphosphonates, while a pretest history of fracture did not[15]. Asche et al. also examined predictors of oral bisphosphonate prescriptions in postmenopausal women, and found that fracture history, older age, low T-score (≤–2.5), and oral corticosteroid use were associated with treatment with an oral bisphosphonate [16]. Onder et al. studied patients with a diagnosis of osteoporosis who were discharged from hospital [17]. Admission for a hip or vertebral fracture and corticosteroid treatment were associated with a higher rate of treatment, while older age, male sex, a greater number of comorbid conditions, and a greater number of medications were associated with a lower likelihood of AOM [17]. Data from Greenspan et al., also using the GLOW cohort, demonstrated that self-reported osteoporosis and use of calcium were associated with a higher likelihood of being treated for osteoporosis among women who sustained incident fractures[18]. In line with our finding that there was a segment of the study population receiving treatment without a clear indication, Roux et al. found, in the Prospective Observational Study Investigating Bone Loss Experience in Europe (POSSIBLE EU) study, that 25% of patients were taking AOM despite having neither a previous fracture nor a dual energy X-ray absorptiometry (DXA) diagnosis [19]. They also found that nearly 25% of patients had no DXA diagnosis and no prior fracture, yet were taking AOM. This suggests that there are other factors driving treatment, such as clinical risk factors or other diagnostic modalities. None of this prior work considers the role that patient attitudes or reported health status may contribute to treatment.
Study limitations and strengths
As this is a cross-sectional study and the data are prevalence estimates, it remains uncertain whether characteristics such as “concern about osteoporosis” and “fracture risk” are predictive of treatment with AOM or whether they are the result of treatment.
These are self-reported data, which have not been verified by medical record review, so we do not know true doses or indications. The diagnoses of osteoporosis and osteopenia, for example, are presumably based on BMD testing, but lack numeric results. However, we do know that among those women who stated that they had osteoporosis, 94.6% also reported having had a BMD test, and among those who reported that they had osteopenia, 97.4% reported having had a BMD test. Moreover, it is not known whether women not reporting osteoporosis or osteopenia had normal BMD examinations or had not been tested. They may also have had appropriate indications for AOM that were not captured in our study (in particular unreported clinical risk factors for fractures). There are indications for AOM treatment other than FRAX risk factors, such as Paget’s disease and bone metastases. However, these indications are rare and are not likely to have had a significant effect on the results.
The strengths of this study include the large sample size and the uniform method of collecting data across study sites. Data were collected from patients of primary care physicians and there were few exclusion criteria. Physicians did not select specific patients for this study; they merely provided lists of active patients so the overall group to whom the questionnaires were sent initially should be representative of the practices.
Conclusions
The results of this study indicate low overall rates of treatment with AOM, but also reveal a number of women at low risk of fracture who were on AOM. Among the low -and high-risk women, rates of treatment were higher among healthier, higher-functioning women. Rates were also higher among women who felt they had a higher fracture risk than others and who were more concerned about osteoporosis regardless of actual fracture risk, suggesting that attitudes and beliefs of the women themselves, as well as FRAX risk factors, are affecting the likelihood of AOM treatment.
Acknowledgments
We thank the physicians and study coordinators participating in GLOW, the staff at the Center for Outcomes Research, Linda Chase for secretarial support, and Sophie Rushton-Smith, PhD, for coordinating revisions and providing editorial assistance including editing, checking content and language, formatting, and referencing.
Role of the funding source
Financial support for the GLOW study is provided by Warner Chilcott Co, LLC and sanofi-aventis to the Center for Outcomes Research, University of Massachusetts Medical School. Dr Boonen is senior clinical investigator of the Fund for Scientific Research, Flanders, Belgium (FWO-Vlaanderen) and holder of the Leuven University Chair in Metabolic Bone Diseases.
The sponsors had no involvement in the collection, analysis, or interpretation of data; in the writing of the submitted manuscript; or in the decision to submit the manuscript for publication. The design, conduct, and interpretation of the GLOW data are undertaken by an independent steering committee.
Abbreviations
- AOM
anti-osteoporosis medication
- BMD
bone mineral density
- BMI
body mass index
- CI
confidence interval
- DXA
dual energy X-ray absorptiometry
- GLOW
Global Longitudinal Study of Osteoporosis in Women
- OR
odds ratio
- POSSIBLE EU
Prospective Observational Study Investigating Bone Loss Experience in Europe
- SD
standard deviation
- SF-35
short-form 36
Footnotes
Disclosure statement
Pamela Guggina: Nothing to declare.
Julie Flahive: The Alliance for Better Bone Health (sanofi-aventis and Warner Chilcott).
Frederick H Hooven: The Alliance for Better Bone Health (sanofi-aventis and Warner Chilcott).
Nelson B Watts: Stock options/holdings, royalties, company owner, patent owner, official role: none. Honoraria for lectures in past year: Amgen, Novartis, Procter & Gamble, sanofi-aventis. Consulting in past year: Amgen, Baxter, InteKrin, Johnson & Johnson, MannKind, Novo Nordisk, NPS, Pfizer, Procter & Gamble, sanofi-aventis, Takeda Pharmaceuticals, Warner Chilcott. Research support (through University): Amgen, Eli Lilly, Merck, NPS.
Ethel S Siris: Consultant: Amgen, Lilly, Novartis, and the Alliance for Better Bone Health. Speakers’ Bureau: Amgen, Lilly.
Stuart Silverman: Research grants: Wyeth, Lilly, Novartis, Alliance. Speakers’ bureau: Lilly, Novartis, Pfizer, Procter & Gamble. Honoraria: Procter & Gamble. Consultant/Advisory Board: Lilly, Argen, Wyeth, Merck, Roche, Novartis.
Christian Roux: Honoraria and/or research grants: Alliance, Amgen, Lilly, Merck Sharp and Dohme, Novartis, Nycomed, Roche, GlaxoSmithKline, Servier, Wyeth.
Johannes Pfeilschifter: Research grant: AMGEN, Kyphon, Novartis, Roche. Other research support: Equipment: GE LUNAR. Speakers’ bureau: AMGEN, sanofi-aventis, GlaxoSmithKline, Roche, Lilly Deutschland, Orion Pharma, MerckSharp and Dohme, Merckle, Nycomed, Procter & Gamble. Advisory Board membership: Novartis, Roche, Procter & Gamble, TEVA.
Susan L Greenspan: Consultant/advisory board: Amgen, Lilly, Merck. Research grants: The Alliance for Better Bone Health (sanofi-aventis and Proctor & Gamble), Lilly.
Adolfo Díez-Pérez: Honoraria: Novartis, Eli Lilly, Amgen, Procter & Gamble, Roche. Expert Witness: Merck. Consultant/Advisory board: Novartis, Eli Lilly, Amgen, Procter & Gamble.
Cyrus Cooper: Consulting fees from and lectured for: AMGEN, The Alliance for Better Bone Health (sanofi-aventis and Warner Chilcott), Eli Lily, Merck Sharp and Dohme, Servier, Novartis, and Roche-GSK.
Juliet E Compston: Paid consultancy work: Servier, Shire, Nycomed, Novartis, Amgen, Procter & Gamble, Wyeth, Pfizer, Alliance for Better Bone Health, Roche, GlaxoSmithKline. Paid speaking engagements, reimbursement, and travel and accommodation: Servier, Procter & Gamble, Eli Lilly. Research grants: Servier R&D and Procter & Gamble. No stocks or shares in relevant companies.
Roland Chapurlat: French Ministry of Health, Merck, Servier, Lilly, Procter and Gamble. Honoraria: Amgen, Servier, Novartis, Lilly, Roche, sanofi-aventis. Consultant/Advisory Board: Amgen, Merck, Servier, Nycomed, Novartis.
Steven Boonen: Research grants: Amgen, Eli Lilly, Novartis, Pfizer, Procter & Gamble, sanofi-aventis, Roche, GlaxoSmithKline. Speakers’ bureau: Amgen, Eli Lilly, Merck, Novartis, Procter & Gamble, sanofi-aventis, Servier. Honoraria: Amgen, Eli Lilly, Merck, Novartis, Procter & Gamble, sanofi-aventis, Servier. Consultant/advisory board: Amgen, Eli Lilly, Merck, Novartis, Procter & Gamble, sanofi-aventis, Servier.
Steven Boonen: Research grants: Amgen, Eli Lilly, Novartis, Pfizer, Procter & Gamble, sanofi-aventis, Roche, GlaxoSmithKline. Speakers’ bureau: Amgen, Eli Lilly, Merck, Novartis, Procter & Gamble, sanofi-aventis, Servier. Honoraria: Amgen, Eli Lilly, Merck, Novartis, Procter & Gamble, sanofi-aventis, Servier. Consultant/Advisory Board: Amgen, Eli Lilly, Merck, Novartis, Procter & Gamble, sanofi-aventis, Servier.
Jonathan D Adachi: Consultant/Speaker: Amgen, Astra Zeneca, Eli Lilly, GlaxoSmithKline, Merck, Novartis, Nycomed, Pfizer, Procter & Gamble, Roche, sanofi-aventis, Servier, Wyeth, Bristol-Myers Squibb. Clinical trials for: Amgen, Eli Lilly, GlaxoSmithKline, Merck, Novartis, Pfizer, Procter & Gamble, Roche, sanofi-aventis, Wyeth and Bristol-Myers Squibb.
Frederick A Anderson: Research support from: sanofi-aventis, The Medicines Company, Procter & Gamble, Scios. Consultant for: sanofi-aventis, GlaxoSmithKline, and Millennium and Sage. Advisory board member: sanofi-aventis, The Medicines Company.
Stephen Gehlbach: The Alliance for Better Bone Health (sanofi-aventis and Warner Chilcott).
Contributor Information
Pamela Guggina, Email: phguggina@gmail.com.
Julie Flahive, Email: Julie.Flahive@umassmed.edu.
Frederick H. Hooven, Email: Fred.Hooven@umassmed.edu.
Nelson B. Watts, Email: nelson.watts@hotmail.com.
Ethel S. Siris, Email: es27@columbia.edu.
Stuart Silverman, Email: stuarts@omcresearch.org.
Christian Roux, Email: christian.roux@cch.ap-hop-paris.fr.
Johannes Pfeilschifter, Email: johannes.pfeilschifter@krupp-krankenhaus.de.
Susan L. Greenspan, Email: GreenspanS@dom.pitt.edu.
Adolfo Díez-Pérez, Email: ADiez@imas.imim.es.
Cyrus Cooper, Email: cc@mrc.soton.ac.uk.
Juliet E. Compston, Email: jec1001@cam.ac.uk.
Roland Chapurlat, Email: roland.chapurlat@inserm.fr.
Steven Boonen, Email: steven.boonen@uz.kuleuven.ac.be.
Jonathan D. Adachi, Email: jd.adachi@sympatico.ca.
Frederick A. Anderson, Jr, Email: Fred.Anderson@umassmed.edu.
Stephen Gehlbach, Email: gehlbach@schoolph.umass.edu.
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