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Osteoporosis action: design of the healthy bones project trial
Contemporary Clinical Trials 26 (2005) 78 – 94 www.elsevier.com/locate/conclintrial
Osteoporosis action: design of the healthy bones project trial Daniel H. Solomona,b,*, M. Alan Brookharta, Jennifer Polinskia, Jeffrey N. Katzb,c, Danielle Cabrala, Andrea Licaria, Jerry Avorna a
Division of Pharmacoepidemiology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, United States b Division of Rheumatology, Immunology, and Allergy, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, United States c Department of Orthopedic Surgery, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, United States Received 28 May 2004; accepted 17 November 2004
Abstract Although osteoporosis is common in older adults, it is often under-diagnosed and under-treated. We developed community-based patient- and physician-directed interventions for fracture prevention and compared them in a 22 factorial randomized controlled trial. The study population included older adults who were enrolled in a state-run pharmacy benefits program (The Pharmaceutical Assistance Contract for the Elderly in Pennsylvania) for Medicare beneficiaries. We randomly assigned 826 primary care physicians and their 31,715 patients to one of four trial arms—no patient and no physician intervention, patient but no physician intervention, physician but no patient intervention, both patient and physician interventions. The patient intervention consisted of targeted communication about fall and fracture prevention and osteoporosis diagnosis and treatment. It was delivered through several mailings. The physician intervention entailed one-on-one academic detailing encounters covering the same topics. The composite primary endpoint consisted of use of osteoporosis medication or a bone mineral density test. Other endpoints included patient’s knowledge and attitudes towards fractures and osteoporosis, use of lower extremity strengthening to prevent falls, and the occurrence of fractures.
* Corresponding author. Division of Pharmacoepidemiology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, United States. Tel.: +1 617 278 0930; fax: +1 617 232 8602. E-mail address: [email protected] (D.H. Solomon). 1551-7144/$ - see front matter D 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.cct.2004.11.012
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All outcomes will be analyzed using random effects models accounting for clustering of subjects within physicians’ practices. D 2004 Elsevier Inc. All rights reserved. Keywords: Osteoporosis; Cluster randomized trial; Factorial study design; Physician education; Patient education
1. Introduction Osteoporosis is an important public health problem that results in substantial morbidity, mortality, and medical costs [1]. Several effective measures exist for preventing osteoporotic fractures. Medications, strength and gait training, home safety modifications, and other lifestyle modifications have all been shown in well-conducted trials to reduce the risk of fracture or of falls that lead to fractures [2–4]. Unfortunately, these interventions are under-utilized. Behavioral interventions are under-utilized and there are convincing data that medications for osteoporosis are not being adequately prescribed to high risk patients [5]. Since osteoporosis affects such a large and growing proportion of the population, it is imperative to develop practical public health strategies to bring these proven interventions to widespread use in typical at-risk populations. One of the research priorities described by the 2000 NIH Consensus Development Conference on Osteoporosis was the bneed to study the most effective method of educating the public and health care professionals about the diagnosis and treatment of osteoporosisQ [6]. Many osteoporotic fractures are associated with minimally traumatic falls. Such falls are associated with impairments in toilet-transfer skills, gait impairment, muscle weakness, visual impairment, and use of psychoactive drugs [7–11]. Based on these epidemiological insights, several fall intervention programs have been tested and found successful. Tinetti et al. [3] demonstrated that a multi-factorial intervention focusing on medications, gait and transfer instructions, and strength training reduced falls by 31% over a 1-year follow-up period. Another randomized trial compared a home safety evaluation with or without fall prevention education and found that the group receiving the more intensive intervention had a 24% reduction in falls over the study period [4]. However, to our knowledge, such fall prevention programs have not been studied on a large scale concurrently with interventions to improve the use of medications to reduce fracture risk. We have recently reviewed prescribing for osteoporosis and found that among subjects who have a prior fracture, only 11% subsequently receive any treatment, including calcium and vitamin D [12-15]. Based on the observation that management of osteoporosis and fall prevention is sub-optimal, we set out to develop and test patient and physician education interventions. We used a program of targeted communication to intervene with patients. Such communication programs aim to breach a more precisely defined populationQ [16]. The advent of information technology facilitates the integration of patient characteristics into focused health education messages being delivered directly to patients [17]. Targeted personalized communication has been found effective in a number of clinical settings, including smoking cessation, breast cancer screening, and dietary intake [18–20]. With regard to interventions directed at physicians, a large body of literature has documented the minimal effectiveness of traditional passive continuing medical education (CME). At least two major review articles have concluded that most CME has little impact on practice [21,22]]. However, interventions that are multi-faceted and give physicians patient-specific information have been shown to
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be more effective at changing behavior [21,22]. Many have found between a 10% and 40% improvement in physicians’ practice after receiving such information. One-on-one education, bacademic detailingQ, has been shown to be a successful method for improving the clinical management of a variety of conditions [23–25]. Initially, Avorn and Soumerai [23] hypothesized that an outreach program to physicians, similar to the bdetailingQ done by pharmaceutical representatives, could provide evidence-based information to practicing physicians in an interactive manner which would improve their prescribing practices. The detailing encounter had been tested in the pharmaceutical industry through many years of sales experience. In its academic adaptation, the detailer establishes his or her credibility by referring to important research performed concerning the medication or behavior in question. The encounter uses principles of twosided communication, always acknowledging the credibility of other competing practice behaviors. Well-designed graphic materials clearly lay out the bpreferredQ management strategy. As well, the detailer must be selected for his/her communication skills and credibility. This approach has been shown to be effective in a wide variety of settings and has been found to be cost-effective [23,24,26]. This paper describes the design of a clustered factorial randomized controlled trial comparing targeted patient education for osteoporosis with academic detailing for primary care physicians.
2. Aims and hypotheses The following aims and hypotheses guided the trial design. Aim 1: To compare the effects of patient and physician targeted educational interventions for improving the rates of osteoporosis treatment among high-risk individuals. Hypothesis 1. (a) Both the patient and physician interventions will be associated with higher rates of osteoporosis treatment than neither. (b) The physician intervention will produce greater improvement than the patient intervention. (c) Combining both patient and physician interventions will produce the greatest improvement. Aim 2: To compare the effects of patient and physician targeted educational interventions on other process measures, including bone densitometry and lower extremity physical therapy for fall reduction. Hypothesis 2. The rate of bone densitometry and preventive physical therapy will increase in a manner similar to that seen for medication use. Aim 3: To compare the effects of these interventions on fracture rates. Hypothesis 3. Fracture rates will be reduced in proportion to the increase in the preventive measures studied, including medication use. Aim 4: To compare control and intervention patients’ attitudes, knowledge, beliefs, and behaviors with respect to osteoporosis and fall prevention. Hypothesis 4. Compared to the control group, patients in the intervention group will have enhanced knowledge and self-confidence to carry out osteoporosis and fall prevention measures. They will also be more likely to engage in prevention behaviors.
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Aim 5: To study correlates of completed academic detailing visits and characteristics of these encounters. Hypothesis. The proportion of successful encounters will vary significantly across detailers, such that some detailers will only have successful visits with few doctors and others with many.
3. Interventions 3.1. Patient-directed education: development and pilot testing In this component of the study, we sought to determine the effectiveness of patient education for osteoporosis and fall prevention. A growing number of health messages regarding everything from cholesterol-lowering to anti-arthritis treatments increasingly target patients directly. Osteoporosis and fall prevention seemed ideal candidates for targeting messages to patients since these are common medical issues that may have already personally affected them or their family. We had determined that a mailed educational intervention was most feasible and able to be generalized. Through Medicare and prescription drug claims, we had certain information regarding each patient available to us. However, we determined that the letters should not be personally tailored lest we run the risk of invading patients’ privacy. Thus, we chose not to personalize (btailorQ) the letters for each person; rather, we mailed similar letters to all men and similar letters to all women. To develop effective patient-directed health communication, it is necessary to assess typical patient’s attitude toward the desired behavior and to understand which messages will be understood and may move people towards action [27]. Such messages can best be developed in the context of established health communications framework(s). We used the Stages of Change model and Witte’s Extended Parallel Processing model. The Stages of Change model describes that patients move through several stages towards a new health behavior, from pre-contemplation and contemplation to action and maintenance [28]. The Extended Parallel Processing model proposes that patients are more likely to engage in new behaviors when they perceive susceptibility to a serious health outcome and feel confident that they can act in ways that mitigate the perceived threat [29]. While a small literature suggests that many older adults are unaware of the impact of osteoporosis [30,31], little is known about patient’s readiness to change or their perceived susceptibility to osteoporosis and fractures. To develop and pilot test our patient education materials, we conducted exploratory interviews, then tested materials and messages in focus groups, and then refined and retested messages with more interviews. Briefly, 12 older adults similar to our target population were interviewed by a behavioral scientist and a Masters student in public health. These interviews were semistructured and tested patient’s stage of change, perceived susceptibility, and confidence to take on behaviors to prevent osteoporosis, falls, and fractures in terms of the existing behavioral science models. The interviews were audiotaped and transcribed. We tested several a priori hypotheses about patient’s attitudes and beliefs. These hypotheses included: ! patients have little baseline knowledge of what exactly osteoporosis means; ! the association between osteoporosis and fractures is not universally understood; ! patients have little appreciation that osteoporosis is without symptoms in many persons;
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! patients knew little about how to reduce their risk of falling; ! methods for testing and treating osteoporosis were not widely known; and ! few patients ever discuss osteoporosis or fractures with their physicians. These interviews led to development of two patient mailings that contained a wallet-size card with reminders that could be brought to a physician’s appointment. These materials were next formally tested in three focus groups in which each participant received the materials and was given an opportunity to respond negatively or positively. Based on the focus groups, the materials were refined and re-tested in further interviews. The final patient intervention (all available upon request from DHS) consisted of two letters, patient vignettes, and a wallet-size reminder card. These were mailed out over 2 months. The reminder card fit in a wallet and could be brought to a physician’s visit. It outlined the most important actions to prevent falls and fractures and had room for patients to write a list of current medications. 3.2. Physician academic detailing Our research group has developed and tested one-on-one educational outreach to improve physicians’ prescribing of numerous drug categories, including antibiotics, tranquilizers, and analgesics [32–34]. The educators in such an intervention are trained in the pharmacology of the drugs of interest, as well as in effective behavior change methods. When they meet with physicians, they are equipped with graphically accessible health education messages and attempt to engage the prescribing physician in an interactive conversation that focuses on methods for improvement. This method of quality improvement has been termed bacademic detailingQ since it takes an academic evidence-based approach to pharmaceutical detailing. While we and others have shown that this approach improves physician’s prescribing, we were interested in testing: (a) whether it would be effective for improving management of osteoporosis, fall prevention, and fracture prevention; (b) how the magnitude of its effect would compare with patient education; (c) how effective a combined approach with academic detailing and patient education would be; and (d) the feasibility of mounting a large-scale program of academic detailing in current medical practice. 3.3. Selection and training of academic detailers Academic detailing was performed by pharmacists or nurses. They were recruited by contacting schools of pharmacy in Pennsylvania, departments of pharmacy at local hospitals, and networking among pharmacists. Candidates were screened by phone and selected based on academic background, work experience, and level of interest. The principal investigator (DHS) and project director (JMP) conducted three different 6-h trainings. Twelve of the 16 academic detailers attended these sessions. The remaining 4 academic detailers were trained via teleconference. Trainings were standardized and included instruction on: the epidemiology, diagnosis and treatment of osteoporosis; the epidemiology of falls and fall prevention; and the physiology of fractures and fracture prevention. The training sessions also focused on the methods of academic detailing, including role-playing physician encounters. We provided very specific strategies for contacting physicians’ offices, making appointments, and conducting visits. At the conclusion of all training, detailers were
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assigned physicians in the intervention groups based on geographic location and schedule. For example, we recruited and trained one detailer from Erie Pennsylvania who was assigned all of the physicians in this region. We recruited detailers so that there would be adequate geographic coverage for all targeted counties in Pennsylvania. 3.4. Physician education materials Members of the study team also developed a set of evidence-based materials for use in the physician detailing encounters. These materials included graphically engaging two-page handouts on: (1) patient risk factors for osteoporosis and fractures; (2) osteoporosis diagnosis and bone mineral density testing; (3) drug treatments for osteoporosis; and (4) fall prevention. Four case vignettes highlighting common patient scenarios were developed to help exemplify key concepts during encounters. A pocket-sized laminated card that described medication options and a management algorithm was also provided. The physicians were given materials they could hand out to patients, including: (1) a fall prevention handout (in English and Spanish); (2) lay-language brochures on home safety, calcium options, and bone mineral density testing; and (3) a pad of patient recommendations for fracture prevention that resembles a prescription pad. Finally, bright bpost-itQ notes defining the characteristics of at-risk patients were given to the office staff to help flag patient charts prior to a visit. These materials were approved by the Department of Continuing Medical Education at Harvard Medical School for 1 h of credit to physicians who submitted a correctly completed post-visit questionnaire. The CME credit was paid for by the study investigators through grant support. 3.5. Scheduling physician appointments We initially contacted physicians through mailing an introductory letter explaining the program and offering 1 h of free CME credit for meeting with a detailer. Each academic detailer notified the project director weekly of which of their assigned physicians should be sent letters so that he/she could focus his efforts in a systematic way. Following the mailing of the introductory letter, the detailers used a variety of methods to schedule appointments for the academic detailing sessions, including telephone calls, facsimiles, and unscheduled visits. At a detailer’s request, we sent additional letters on the principal investigator’s letterhead if a physician was unresponsive to the initial contact. Detailers were encouraged to leave a sample of the project materials and/or coffee mugs imprinted with study name and telephone number for the staff. Detailers were discouraged from buying food and beverages for the office staff, but did do so if explicitly requested. If a physician appointment could not be scheduled, detailers were encouraged to meet with a nurse, physician’s assistant, or other allied health professional in the physician’s office. Four monthly conference calls lasting 1 h each were held among academic detailers, investigators, and project staff to develop and share strategies for scheduling physician appointments. In addition, biweekly bulletins were e-mailed to detailers to share new information, group and individual progress, and suggestions for detailing. Weekly e-mail and/or phone contact was initiated by the project director to determine detailers’ progress and additional contact with academic detailers was made as needed. Academic detailers were invited and encouraged to contact the project staff at any time for assistance.
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4. Study design 4.1. 22 factorial trial To assess the effectiveness of patient and physician education interventions and their possible synergy, we designed a 22 factorial trial (see Fig. 1). This design allows for direct comparison of all four groups—no patient or physician intervention (group A), patient but no physician intervention (group B), physician but no patient intervention (group C), patient and physician intervention (group D). This design also allows for efficient comparison of two interventions that may be additive. A recent systematic review of factorial trials suggested that such trials can be analyzed using the marginal results when the interventions are independent, or using interaction terms when the effects of one intervention may be dependent on the presence of the other [35]. Marginal results will combine the two similar intervention groups in analyses. For the patient intervention, this would mean treating groups B and D as similar. We suspect that the patient and physician interventions are dependent, and thus the primary analysis will assume an interaction. 4.2. Randomized controlled cluster trials The management of a given patient constitutes the endpoint of this trial, but patients are treated by particular physicians. This results in a natural clustering of patients within a given physician’s practice. As well, since both patients and physicians would receive interventions in some practices, we chose to randomize each physician and all of his/her patients together into one of the four arms of the 22 factorial trial (see Fig. 2). This design prevented the contamination of patients within a given physician’s practice that would have occurred if some patients received the education and some did not. However, there still could be some contamination if two physicians practiced in the same office space. Post-hoc analysis of physicians by their office addresses suggested that this occurred in 172 of the 826 physicians randomized.
5. Study population: patients and physicians 5.1. Patients Subjects eligible for this study were all Medicare beneficiaries also enrolled in a state-run pharmacy benefits program, the Pennsylvania Pharmaceutical Assistance Contract for the Elderly (PACE). The PACE program provides a pharmaceutical benefit to Pennsylvania residents who have annual incomes too high to qualify for the state’s Medicaid program but below US$20,000 (the exact figure threshold varies from year to year). Beneficiaries receive prescription medications for a co-payment of less than
Fig. 1. 22 factorial trial design.
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Fig. 2. Study cohort assembly and randomization.
US$10 per prescription. There is no restricted list of permissible medications (a dformularyT) and in general no prior authorization is required before drugs can be obtained. However, pharmacists intercede when dosage limits are exceeded or two drugs from the same drug category are prescribed, such as two benzodiazepines. Collaboration with the PACE program made it possible to ignore the problem of affordability of osteoporosis prescription medications, since access to drugs was provided with minimal financial barrier. To improve the efficiency of the intervention, we targeted eligible beneficiaries identified as being at high risk of osteoporosis and future fractures. Health care claims provided the primary source of data for this trial (see below), allowing us to make use of demographics, diagnosis and procedure codes, and medication information to identify high-risk groups. We focused on three groups of high-risk beneficiaries: (1) all women 65 years or older; (2) all men and women with evidence for a prior fragility (hip, wrist, humerus, and spine) fracture; and (3) all men and women with evidence for oral glucocorticoid use for at least 3 months. While other groups could have been targeted, these groups were chosen since there is wide agreement on the importance of screening and appropriate treatment for them [36,37]. Based on algorithms including health care and medication claims, we identified these high-risk groups from the total source population of eligible beneficiaries.
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5.2. Primary care physicians Since our intervention targeted both high-risk subjects and their primary care physicians, and the physicians were the unit of randomization, we developed an algorithm for identifying each eligible beneficiary’s primary care physician. This was important because many patients, especially those over 65, may be seen by and receive prescriptions from several physicians. Doctors eligible to be considered primary care physicians included family physicians, general practitioners, physicians practicing internal medicine or one of its subspecialties, and gynecologists. We then compiled a list of eligible physicians who had written prescriptions for each patient during the baseline year. From this list, the physician prescribing at least 50% of all medicines for a given patients was defined as the primary care physician. We excluded 18% of patients for whom no single physician prescribed at least 50% of medicines. To further improve the efficiency of the academic detailing, we focused on physicians practicing in non-rural counties with large numbers of PACE beneficiaries in their practice. Of the 67 counties in Pennsylvania, 28 were deemed urban or suburban; the 826 physicians practicing in these counties were eligible to be included in the intervention. We then assessed how many PACE beneficiaries would be considered a primary care patient for each physician (based on the algorithm described above). Physician who had at least 25 such patients were included in the intervention.
6. Recruitment 6.1. Institutional review board oversight We were granted an exemption from the Partners Healthcare Institutional Review Board oversight based on part 46 of the Code of Federal Regulations that exempts research testing the effectiveness of a quality improvement program in public benefits programs, such as Medicare and PACE. Thus, we were not required to obtain consent from patients or their physicians. However, the research protocol was reviewed by the Institutional Review Board to ensure compliance with standards of human research. In addition, the research posed minimal risk to patients and physicians and each was able to opt-out of any aspect of the study. 6.2. Opt-out process for patients A total of 31,715 eligible beneficiaries were identified as being in at least 1 of the 3 high-risk groups who were seen by physicians in 1 of the 28 counties who saw at least 25 PACE beneficiaries. Each of them was randomized to either the intervention group or the control group through a random number generator. In both intervention and control groups, beneficiaries were sent a personalized introductory letter. If the beneficiary had a known power of attorney, the introductory letter was sent to the power of attorney instead of to the beneficiary. In the intervention group, the introductory letter informed beneficiaries that PACE and physicians at Harvard Medical School were collaborating on an educational program about osteoporosis and that in the coming weeks, they would receive program materials by mail. The letter stated that if a beneficiary did not want to receive these materials, the beneficiary could check the refusal box at the bottom of the letter and
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return it to the program in a pre-paid envelope. The content of the letter to the control group differed only slightly. This letter explained that PACE was considering collaborating with physicians at Harvard and asked beneficiaries to return the letter to PACE if they would not want to receive materials from such a program if it indeed were launched. Beneficiaries who returned either letter were removed from the study. We also removed beneficiaries whose relatives informed us that they had died. Of the 15,684 eligible patients in the 2 patient intervention groups (groups B and D), 2322 (12.9%) declined participation. In the 2 patient control groups (groups A and C), 4373 of 16,031 (27.2%) declined. Overall, 6695 (21.1%) eligible subjects declined participation. Participants and nonparticipants had very similar sociodemographic characteristics (see Table 1). 6.3. Baseline characteristics of patients We have compared the baseline characteristics of the intervention and control subjects (see Table 2). The two groups were quite similar with respect to sociodemographic, general health care utilization, and osteoporosis related characteristics. 6.4. Opt-out process for physicians There were 414 physicians who were randomized to the physician-intervention groups. When these physicians were approached to schedule a visit with an academic detailer, the type and frequency of contacts with the physician and his/her staff were recorded. The outcome of these contact attempts were also recorded and assigned to one of the categories listed below. After removing, the doctors that were no longer eligible or we were unable to locate, 356 primary care physicians remained. 1. No longer eligible. The physician had changed his/her practice type, was no longer practicing medicine, had moved out of state, or was deceased; 2. Unable to locate. The physician had moved and a current address was not available from the Pennsylvania Medical Society, the American Medical Association, the Pennsylvania Department of Table 1 Characteristics of participants and non-participants, by patient treatment assignment Participant (n=25,020)
Non-participant (n=6695)
N (%) or mean (FS.D.) Age Female Race, white Marital status Married Divorced Widowed Others Annual income (USD)
81F7 24,844 (99.3) 24,178 (96.6)
83F7 6620 (98.9) 6550 (97.8)
1707 (6.8) 1000 (4.0) 21,445 (85.7) 868 (3.5) 10,966F3530
360 (5.4) 210 (3.1) 5875 (87.8) 250 (3.7) 10,667F3972
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Table 2 Baseline patient characteristics by patient treatment assignment Patient intervention (groups B and D)
Patient control (groups A and C)
N (%) or mean (FS.D.) Age Female Race White Black Other Marital status Married Divorced Widowed Other Annual income Diagnosis of osteoporosis Hip, wrist or humerus fracture Bone mineral density test Medications for osteoporosis Alendronate Calcitonin HRT Raloxifene Risedronate Comorbid illnesses Nursing home residence, any Acute care hospitalization, any Number of physician visits Number of different medicines
81.5F6.9 13,257 (99.2)
81.0F6.8 11,587 (99.4)
12,848 (96.2) 435 (3.3) 79 (0.6)
11,330 (97.2) 258 (2.2) 70 (0.6)
873 (6.5) 564 (4.2) 11,420 (85.5) 505 (3.8)
834 (7.2) 436 (3.7) 10,025 (86.0) 363 (3.1)
2971 (22.2) 862 (6.5) 648 (4.9)
2719 (23.3) 749 (6.4) 665 (5.7)
1318 (9.9) 611 (4.6) 580 (4.3) 463 (3.5) 377 (2.8) 1.8F2.2 1499 (11.2) 3911 (29.3) 6.5F6.3 8.0F6.2
1281 (11.0) 540 (4.6) 527 (4.5) 358 (3.1) 362 (3.1) 1.8F2.0 1115 (9.6) 3205 (27.5) 7.2F6.3 8.4F5.9
See Fig. 1 for an explanation of group assignments. Variables were assessed during 2002.
3. 4. 5. 6. 7.
State Bureau of Professional and Occupational Affairs License Verification database, or the Yellow Pages; Physician detailed. The physician met with the detailer in a one-on-one visit; Allied health professional detailed. The physician did not meet with the detailer, but an allied health professional in his/her office did meet with the detailer in a one-on-one visit; Both physician and allied health professional detailed. The detailer met with both the physician and an allied health professional; Physician refused. The detailer was verbally told that the physician refused to schedule a one-on-one visit; Physician did not respond. The physician, despite repeated contacts, never agreed to a visit by the detailer.
6.5. Characteristics of physicians who were successfully detailed The academic detailers were able to successfully visit 148 of the 356 (41.6%) primary care doctors targeted. We have analyzed the characteristics of doctors who were successfully detailed compared with
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Table 3 Primary care physician characteristics in the doctor intervention (groups C and D) Successful educational encounter (n=148)
Refused educational encounter (n=208)
N (%) or meanFS.D. Male gender Race, white Years since medical school Training Internal medicine Family practice General practice Practice location Urban Suburban Rural or semi-rural Study patients
137 (92.6) 86 (86.9) 19F9.2
187 (89.9) 127 (82.5) 13F8.4
78 (52.7) 57 (38.5) 13 (8.8)
113 (54.3) 84 (40.4) 11 (5.3)
69 (46.6) 62 (41.9) 17 (11.5) 39F20
77 (37.0) 95 (45.7) 36 (17.3) 40F19
the ones who refused a visit (see Table 3). There were no important differences between doctors who were successfully detailed and those who refused.
7. Data sources and study outcomes 7.1. Data sources We used data from several sources to design this trial. Data from Medicare Part A (hospital) and Part B (ambulatory) were linked with drug dispensing information from PACE. This information was obtained using appropriate Data Use Agreements from both the Centers for Medicare and Medicaid Services and PACE. Medicare data provided diagnoses and procedures regarding a patient’s relevant medical history, including prior fractures, other osteoporosis risk factors, recent hospitalizations, nursing home status, use of physical therapy, and bone densitometry. Prescription dispensing data from PACE allowed us to assess patients’ use of medications for osteoporosis, use of drugs that may cause metabolic bone disease (glucocorticoids, anti-convulsants, cyclosporin), as well as information on the total number of drugs that patients used. PACE data include names of medications, dispensing dates, dosages, and days supply. 7.2. Primary and secondary endpoints The trial’s primary and secondary endpoints are outlined in Table 4. The primary endpoint will be a composite of either filling a prescription for an osteoporosis medication or undergoing a bone mineral density test. This was chosen as the primary endpoint because they are both part of standard guidelines for managing osteoporosis in high risk patients [36,37] and medication use in appropriate patients has been clearly linked with a reduction in fractures. Because fractures are relatively uncommon, we will assess any possible differences in secondary analyses. The primary analysis will examine persons without evidence for filling a medicine in the 6 months prior to the intervention and no bone mineral density test in the 2 years prior to determine whether they
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Table 4 Trial endpoints Endpoints Primary Medication start or BMD examination*
Secondary—medications New medication start
Any medication use Medication adherence among all patients
Medication adherence among patients who filled at least one prescription
Subgroups
Secondary—bone mineral density testing New BMD examination BMD examination Subgroups Secondary—other Physical therapy Home visit Fractures Patient attitudes, beliefs, knowledge and self-reported behaviors Subgroups
Description In patients without evidence of recent management of osteoporosis, filling a prescription for one of the specified medications or undergoing a BMD examination.
In patients without evidence of recent management of osteoporosis, filling a prescription for one of the specified medications during the one year follow-up period. This endpoint includes all patients, not requiring that patients be without evidence of recent management. Includes all patients as the denominator and requires that patients have z80% of days with medicine after their first filling. Days in acute care are subtracted out. Includes patients filling at least one prescription for an osteoporosis medication as the denominator and requires that these patients have z80% of days with medicine after their first filling. Days in acute care are subtracted out. Relevant patient subgroups, i.e., gender, race, age, comorbidities, non-nursing home and with a physician visit during follow up, as well as physician subgroups, i.e., gender, age, urban/rural practice, # of PACE beneficiaries, specialty, completer.
In patients without evidence of recent BMD examination or medication, a procedure code for a test. This endpoint includes all patients, not requiring that patients be without evidence of recent management. As above.
Utilization services for lower extremity strengthening physical Among all patients, any evidence for a home visit by a health Diagnosis or procedure codes consistent with a fracture of the humerus or hip. Based on a patient survey distributed approximately 6 months patient education was disseminated. As above.
therapy. professional. wrist, after the
undergo testing or receive treatment after the intervention. Two years was chosen since this is the interval at which Medicare will reimburse for bone mineral density testing. Medicines of interest include all bisphosphonates, calcitonin, hormone replacement therapy, raloxifene, teriparatide, and vitamin D analogs. Bone mineral density tests will be detected by examining the Medicare claims for specific procedure codes, including CPT 76070 (computerized axial tomography bone density study), CPT 76075-76076 (dual energy X-ray absorptiometry, bone density study), CPT 76078 (radiographic absorptiometry), and CPT 76977 (ultrasound bone density). Secondary analyses will examine medicines
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and bone mineral density testing as separate endpoints. Subsequent subgroup analyses will focus on: medicines and bone mineral density testing in the total group, not only patients without prior management, and these endpoints in specific subgroups of patients, such as men versus women, different age groups, and those with prior fracture versus no fracture. In addition to the above secondary endpoints, we will assess several other outcomes. Lower extremity strengthening exercises to improve balance and reduce falls were part of the written recommendations for patients, and home safety evaluations were also suggested. Thus, we will compare the number of rehabilitation visits for lower extremity diagnoses and home visits by nurses and physical therapists. Reducing fracture is the ultimate goal of the intervention. We will assess the fracture rates in the study subjects over the 24 months after the intervention, 2004–2005. The fractures of interest include hip, wrist, and humerus fractures. We chose to focus on these fractures because they are accompanied by procedures (surgical or casting) that will allow for precise dating of their occurrence. The frequency of fractures that do not require procedures such as vertebral compression fractures or pelvic fractures cannot be estimated since they often are carried over as historical diagnoses. Fracture data are available through Medicare claims in the form of diagnoses and procedure codes. Two secondary endpoints for which data will be readily accessible concern the detailing process and changes in patient’s knowledge, attitudes, and beliefs. We have already collected data on the proportion of academic detailing visits that were successfully completed. This information will help us to gauge the proportion of physicians who were bcompletersQ of the intervention, even though the parent study will be evaluated primarily on an intention to treat basis. A patient survey will be mailed out to persons in the intervention and control groups. This will allow us to compare whether the educational material had any impact on patient’s knowledge, attitudes and beliefs regarding osteoporosis.
8. Analytic plan Our trial is organized using a cluster randomized design with the physician as the unit of randomization. The use of cluster randomized designs is common in health services research; however, there is not a yet a consensus on the most appropriate statistical analysis of such experiments [38]. Many argue that the analysis must occur at the level of the unit of randomization; in this trial, the physician. However, such an analysis would preclude the possibility of incorporating patient-level covariates, which are known to be strong risk factors for inappropriate clinical management of osteoporosis [39]. The creation of a patient-level statistical model of the trial data has the potential to increase the efficiency of the estimated intervention effects through adjusting for baseline covariates and will also facilitate analysis of potential interactions between treatment assignment and patient- and physician-level attributes. However, any patient-level analysis of a study randomized at the physician-level must adequately account for the correlation of outcomes between patients sharing the same physician [40]. Failure to account for this clustering effect will not bias parameter estimates, but can lead to invalid standard errors [41]. To state the problem simply, two patients from the same physician practice do not contribute as much information as two patients from different practices. Our primary analytical approach will be based on a generalized linear random effects model of patient-level outcomes. The effect of patient characteristics (e.g., age, race, gender, co-morbid conditions, fracture history, medication use history), physician characteristics (e.g., age, specialty, practice type) and treatment arm assignment on the outcome under study will be captured through fixed
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effects modeling. By contrast, a physician-level random effect model will capture the tendency of patients within a physician’s practice to experience similar outcomes. By accounting for this correlation, valid statistical inference can be made on patient-level fixed effects. Formally, we will let Yi,j,k denote the outcome observed on the ith patient of the jth physician in the kth treatment arm and P i,j,k denote observed patient characteristics and MDj,k denote observed physician characteristics. All primary outcomes considered in this trial will be dichotomous, so the basic statistical model will be a logistic mixed effects model, expressed as: Pr½Yi; j; k ¼ 1jPi; j; k ; MDj; k ¼ 1 þ exp dj þ bT Pi; j; k þ aT MDj; k þ ej; k
1
;
where e j,k is the physician-specific random effect that we will assume is distributed as a mean zero Gaussian random variable with unknown variance. The vectors of fixed effects d, b, and a estimate the effects of treatment arm assignment, patient, and physician characteristics, respectively, on the outcome under consideration. This model will be fit using glmmPQL function in the R language [42]. This software fits a generalized linear mixed model using the penalized quasi likelihood technique [43]. Results from this fit will be validated using the NLMIXED procedure in SAS [44].
9. Discussion In an aging society, osteoporosis and fractures represent growing public health problems. Data consistently show sub-optimal rates of screening, prevention, and treatment of osteoporosis and preventable fracture risk factors. Prior interventions have been few and mostly small-scale nonrandomized trials. We have developed and are testing a large-scale public health educational program regarding osteoporosis and fracture prevention that targeted patients and their primary care physicians. These interventions will be evaluated as a 22 factorial randomized controlled cluster trial. The interventions aim to improve the use of bone densitometry and anti-osteoporosis medications, as well as to increase the use of fall prevention strategies by patients and physicians. Patient recruitment has been relatively successful, with approximately 80% of eligible subjects participating. Non-participants did not differ in any important way from participants, and the baseline characteristics of participants in the intervention and control arms were quite similar. These preliminary results suggest that our full trial data will be both internally and externally valid. Patient retention and long-term data collection were minor issues since all of the participants were enrollees in Medicare and the state pharmaceutical benefits program, PACE. There is almost no attrition in either of these programs. As well, collection of claims data are routine since pharmacy, physician, and hospital services are reimbursed based on claim submissions. We are currently awaiting the end of the observation period to analyze the trial endpoints.
Acknowledgment We would like to thank Thomas Snedden, Director of the Pharmaceutical Assistance Contract, for the Elderly in the Department of Aging in Pennsylvania. His colleagues Margaret Glessner, Theresa Brown,
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and Debra Heller have also contributed substantially to developing this trial. We would also like to thank the anonymous referee who provided valuable suggestions. Support: Arthritis Foundation (Atlanta, GA) and National Institutes of Health (AR K23 48616). References [1] Center for Disease Control. Healthy People 2010. http://www.health.gov/healthypeople/volume1/02Arthritis. [2] Altkorn D, Vokes T. Treatment of postmenopausal osteoporosis. JAMA 2001;285:1415 – 8. [3] Tinetti ME, Baker DI, McAvay G, et al. A multifactorial intervention to reduce the risk of falling among elderly people living in the community. N Engl J Med 1994;331:821 – 7. [4] Hornbrook MC, Stevens VJ, Wingfield DJ, Hollis JF, Greenlick MR, Ory MG. Preventing falls among communitydwelling older persons: results from a randomized trial. Gerontologist 1994;34:16 – 23. [5] Solomon DH, Finkelstein JS, Katz JN, Mogun H, Avorn J. Underuse of osteoporosis medications in elderly patients with fractures. Am J Med 2003;115:398 – 400. [6] Consensus Development Panel J. National institutes of health consensus development conference statement: osteoporosis prevention, diagnosis, and therapy. Rockville, MD7 National Institutes of Health, 2000. p. 27. [7] Sattin RW. Falls among older persons: a public health perspective. Annu Rev Public Health 1992;13:489 – 508. [8] Tinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community. N Engl J Med 1988;319:1701 – 7. [9] Nevitt MC, Cummings SR, Kidd S, Black D. Risk factors for recurrent nonsyncopal falls: a prospective study. JAMA 1989;261:2663 – 8. [10] Sherrington C, Lord SR. Increased prevalence of fall risk factors in older people following hip fracture. Gerontology 1998;44:340 – 4. [11] Lord SR, Clark RD, Webster IW. Visual acuity and contrast sensitivity in relation to falls in an elderly population. Age Ageing 1991;20:175 – 81. [12] Keating NL, Cleary PD, Rossi AS, Zaslavsky AM, Ayanian JZ. Use of hormone replacement therapy by postmenopausal women in the United States. Ann Intern Med 1999;130:545 – 53. [13] Leveille SQ, LaCroix AZ, Newton KM, Keenan NL. Older women and hormone replacement therapy: factors influencing late life initiation. J Am Geriat Soc 1997;45:1496 – 500. [14] Truter I, Serfontein JHP. A survey of the treatment of female patients with osteoporosis using drug utilization consumption parameters. J Clin Pharm Ther 1999;24:209 – 17. [15] Morris CM, Cheng H, Cabral D, Solomon DH. Predictors of osteoporosis management. Endocrinologist 2004;14:70 – 5. [16] Kreuter MW, Strecher VJ, Glassman B. One size does not fit all: the case for tailoring print materials. Ann Behav Med 1999;21:276 – 83. [17] Rakowski W. The potential variances of tailoring in health behavior interventions. Ann Behav Med 1999;21:284 – 9. [18] Morgan GD, Noll EL, Orleans CT, Rimer BK, Amfoh K, Bonney G. Reaching midlife and older smokers: tailored interventions for routine medical care. Prev Med 1996;25:346 – 54. [19] Meldrum P, Turnbull D, Dobson HM, et al. Tailored written invitations for second round breast cancer screening: a randomized controlled trial. J Med Screen 1994;1:245 – 8. [20] Brug J, Steenhuis I, Van Assema P, De Vries H. The impact of a computer-tailored nutrition intervention. Prev Med 1996;25:236 – 42. [21] Davis D, O’Brien MAT, Freemantle N, Wolf FM, Mamanian P, Taylor-Vaisey A. Impact of formal continuing medical education: do conferences, workshops, rounds, and other traditional continuing education activities change physician behavior or health outcomes? JAMA 1999;282:867 – 74. [22] Oxman AD, Thomson MA, David DA, Haynes RB. No magic bullets: a systematic review of 102 trials of interventions to improve professional practice. Can Med Assoc J 1995;153:1423 – 31. [23] Avorn J, Soumerai SB. Improving drug-therapy decisions through educational outreach. A randomized controlled trial of academically based bdetailingQ. N Engl J Med 1983;308:1457 – 63. [24] Soumerai SB, Avorn J. Principles of educational outreach (dacademic detailingT) to improve clinical decision making. JAMA 1990;263:549 – 56.
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[25] Ray WA, Schaffner W, Federspiel DF. Persistence of improvement in antibiotic prescribing in office practice. JAMA 1985;253:1774 – 6. [26] Soumerai SB, Avorn J. Economic and policy analysis of university-based drug bdetailingQ. Med Care 1986;24:313 – 31. [27] Atkins C, Wallack L. Mass communication and public health: complexities and conflicts. California7 Sage Publications; 1990. [28] Prochaska JO, DiClements CC, Norcross JC. In search of how people change, application to addictive behavior. Am Psychol 2002;47:1102 – 14. [29] Witte K, Meyer G, Martell D. Effective health risk messages: a step-by step guide. California7 Sage Publications; 2001. [30] Ailinger RL, Emerson J. Women’s knowledge of osteoporosis. Appl Nurs Res 1998;11:111 – 4. [31] Sutterfield T, Johnson S, Slovic P, Neil N, Schein J. Perceived risks and reported behaviors associated with osteoporosis and its treatment. Women Health 2002;31:21 – 40. [32] Avorn J, Soumerai SB. Improving drug-therapy decisions through educational outreach. A randomized controlled trial of academically based bdetailingQ. N Engl J Med 1983;308:1457 – 63. [33] Avorn J, Soumerai SB, Everitt DE, Ross-Degnan D, Beers MH, Sherman D, et al. A randomized trial of a program to reduce the use of psychoactive drugs in nursing homes. N Engl J Med 1992;327:168 – 73. [34] Solomon DH, Van Houten L, Glynn RJ, et al. Academic detailing to improve use of broad spectrum antibiotics at an academic medical center. Arch Intern Med 2001;161:1897 – 902. [35] McAlister FA, Strause SE, Sackett DL, Altman DG. Analysis and reporting of factorial trials. JAMA 2003;289:2545 – 53. [36] National Osteoporosis Foundation. Physician’s Guide to the prevention and treatment of osteoporosis. Available at http://www.nof.org. Accessed on 18 May 2004. [37] American Association of Clinical Endocrinologists. American association of clinical endocrinologists 2001 medical guidelines for clinical practice for the prevention and management of postmenopausal osteoporosis. Endocr Pract 2001;7:294 – 312. [38] Murray DM. Design and analysis of group-randomized trials. Oxford University Press; 1998. [39] Solomon DH, Brookhart MA, Gandhi T, et al. Predictors of non-adherence with osteoporosis guidelines. Am J Med 2004 [in press]. [40] Rumana OZ, Thompson SG. Analysis of a cluster randomized trial with binary outcome data using a multilevel model. Stat Med 2000;19:2675 – 88. [41] Diggle PJ, Liang KY, Zeger SL. Analysis of longitudinal data. Oxford University Press; 1994. [42] R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria7 R Foundation for Statistical Computing; 2003. [43] Breslow NE, Clayton DG. Approximate inference in generalized linear mixed models. J Am Stat Assoc 1993;88:9 – 25. [44] SAS Institute. SAS/STAT User’s Guide, Version 8. 2nd edition. SAS Institute, Cary, NC: SAS Institute.
Osteoporosis action: design of the healthy bones project trial Daniel H. Solomona,b,*, M. Alan Brookharta, Jennifer Polinskia, Jeffrey N. Katzb,c, Danielle Cabrala, Andrea Licaria, Jerry Avorna a
Division of Pharmacoepidemiology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, United States b Division of Rheumatology, Immunology, and Allergy, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, United States c Department of Orthopedic Surgery, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, United States Received 28 May 2004; accepted 17 November 2004
Abstract Although osteoporosis is common in older adults, it is often under-diagnosed and under-treated. We developed community-based patient- and physician-directed interventions for fracture prevention and compared them in a 22 factorial randomized controlled trial. The study population included older adults who were enrolled in a state-run pharmacy benefits program (The Pharmaceutical Assistance Contract for the Elderly in Pennsylvania) for Medicare beneficiaries. We randomly assigned 826 primary care physicians and their 31,715 patients to one of four trial arms—no patient and no physician intervention, patient but no physician intervention, physician but no patient intervention, both patient and physician interventions. The patient intervention consisted of targeted communication about fall and fracture prevention and osteoporosis diagnosis and treatment. It was delivered through several mailings. The physician intervention entailed one-on-one academic detailing encounters covering the same topics. The composite primary endpoint consisted of use of osteoporosis medication or a bone mineral density test. Other endpoints included patient’s knowledge and attitudes towards fractures and osteoporosis, use of lower extremity strengthening to prevent falls, and the occurrence of fractures.
* Corresponding author. Division of Pharmacoepidemiology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, United States. Tel.: +1 617 278 0930; fax: +1 617 232 8602. E-mail address: [email protected] (D.H. Solomon). 1551-7144/$ - see front matter D 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.cct.2004.11.012
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All outcomes will be analyzed using random effects models accounting for clustering of subjects within physicians’ practices. D 2004 Elsevier Inc. All rights reserved. Keywords: Osteoporosis; Cluster randomized trial; Factorial study design; Physician education; Patient education
1. Introduction Osteoporosis is an important public health problem that results in substantial morbidity, mortality, and medical costs [1]. Several effective measures exist for preventing osteoporotic fractures. Medications, strength and gait training, home safety modifications, and other lifestyle modifications have all been shown in well-conducted trials to reduce the risk of fracture or of falls that lead to fractures [2–4]. Unfortunately, these interventions are under-utilized. Behavioral interventions are under-utilized and there are convincing data that medications for osteoporosis are not being adequately prescribed to high risk patients [5]. Since osteoporosis affects such a large and growing proportion of the population, it is imperative to develop practical public health strategies to bring these proven interventions to widespread use in typical at-risk populations. One of the research priorities described by the 2000 NIH Consensus Development Conference on Osteoporosis was the bneed to study the most effective method of educating the public and health care professionals about the diagnosis and treatment of osteoporosisQ [6]. Many osteoporotic fractures are associated with minimally traumatic falls. Such falls are associated with impairments in toilet-transfer skills, gait impairment, muscle weakness, visual impairment, and use of psychoactive drugs [7–11]. Based on these epidemiological insights, several fall intervention programs have been tested and found successful. Tinetti et al. [3] demonstrated that a multi-factorial intervention focusing on medications, gait and transfer instructions, and strength training reduced falls by 31% over a 1-year follow-up period. Another randomized trial compared a home safety evaluation with or without fall prevention education and found that the group receiving the more intensive intervention had a 24% reduction in falls over the study period [4]. However, to our knowledge, such fall prevention programs have not been studied on a large scale concurrently with interventions to improve the use of medications to reduce fracture risk. We have recently reviewed prescribing for osteoporosis and found that among subjects who have a prior fracture, only 11% subsequently receive any treatment, including calcium and vitamin D [12-15]. Based on the observation that management of osteoporosis and fall prevention is sub-optimal, we set out to develop and test patient and physician education interventions. We used a program of targeted communication to intervene with patients. Such communication programs aim to breach a more precisely defined populationQ [16]. The advent of information technology facilitates the integration of patient characteristics into focused health education messages being delivered directly to patients [17]. Targeted personalized communication has been found effective in a number of clinical settings, including smoking cessation, breast cancer screening, and dietary intake [18–20]. With regard to interventions directed at physicians, a large body of literature has documented the minimal effectiveness of traditional passive continuing medical education (CME). At least two major review articles have concluded that most CME has little impact on practice [21,22]]. However, interventions that are multi-faceted and give physicians patient-specific information have been shown to
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be more effective at changing behavior [21,22]. Many have found between a 10% and 40% improvement in physicians’ practice after receiving such information. One-on-one education, bacademic detailingQ, has been shown to be a successful method for improving the clinical management of a variety of conditions [23–25]. Initially, Avorn and Soumerai [23] hypothesized that an outreach program to physicians, similar to the bdetailingQ done by pharmaceutical representatives, could provide evidence-based information to practicing physicians in an interactive manner which would improve their prescribing practices. The detailing encounter had been tested in the pharmaceutical industry through many years of sales experience. In its academic adaptation, the detailer establishes his or her credibility by referring to important research performed concerning the medication or behavior in question. The encounter uses principles of twosided communication, always acknowledging the credibility of other competing practice behaviors. Well-designed graphic materials clearly lay out the bpreferredQ management strategy. As well, the detailer must be selected for his/her communication skills and credibility. This approach has been shown to be effective in a wide variety of settings and has been found to be cost-effective [23,24,26]. This paper describes the design of a clustered factorial randomized controlled trial comparing targeted patient education for osteoporosis with academic detailing for primary care physicians.
2. Aims and hypotheses The following aims and hypotheses guided the trial design. Aim 1: To compare the effects of patient and physician targeted educational interventions for improving the rates of osteoporosis treatment among high-risk individuals. Hypothesis 1. (a) Both the patient and physician interventions will be associated with higher rates of osteoporosis treatment than neither. (b) The physician intervention will produce greater improvement than the patient intervention. (c) Combining both patient and physician interventions will produce the greatest improvement. Aim 2: To compare the effects of patient and physician targeted educational interventions on other process measures, including bone densitometry and lower extremity physical therapy for fall reduction. Hypothesis 2. The rate of bone densitometry and preventive physical therapy will increase in a manner similar to that seen for medication use. Aim 3: To compare the effects of these interventions on fracture rates. Hypothesis 3. Fracture rates will be reduced in proportion to the increase in the preventive measures studied, including medication use. Aim 4: To compare control and intervention patients’ attitudes, knowledge, beliefs, and behaviors with respect to osteoporosis and fall prevention. Hypothesis 4. Compared to the control group, patients in the intervention group will have enhanced knowledge and self-confidence to carry out osteoporosis and fall prevention measures. They will also be more likely to engage in prevention behaviors.
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Aim 5: To study correlates of completed academic detailing visits and characteristics of these encounters. Hypothesis. The proportion of successful encounters will vary significantly across detailers, such that some detailers will only have successful visits with few doctors and others with many.
3. Interventions 3.1. Patient-directed education: development and pilot testing In this component of the study, we sought to determine the effectiveness of patient education for osteoporosis and fall prevention. A growing number of health messages regarding everything from cholesterol-lowering to anti-arthritis treatments increasingly target patients directly. Osteoporosis and fall prevention seemed ideal candidates for targeting messages to patients since these are common medical issues that may have already personally affected them or their family. We had determined that a mailed educational intervention was most feasible and able to be generalized. Through Medicare and prescription drug claims, we had certain information regarding each patient available to us. However, we determined that the letters should not be personally tailored lest we run the risk of invading patients’ privacy. Thus, we chose not to personalize (btailorQ) the letters for each person; rather, we mailed similar letters to all men and similar letters to all women. To develop effective patient-directed health communication, it is necessary to assess typical patient’s attitude toward the desired behavior and to understand which messages will be understood and may move people towards action [27]. Such messages can best be developed in the context of established health communications framework(s). We used the Stages of Change model and Witte’s Extended Parallel Processing model. The Stages of Change model describes that patients move through several stages towards a new health behavior, from pre-contemplation and contemplation to action and maintenance [28]. The Extended Parallel Processing model proposes that patients are more likely to engage in new behaviors when they perceive susceptibility to a serious health outcome and feel confident that they can act in ways that mitigate the perceived threat [29]. While a small literature suggests that many older adults are unaware of the impact of osteoporosis [30,31], little is known about patient’s readiness to change or their perceived susceptibility to osteoporosis and fractures. To develop and pilot test our patient education materials, we conducted exploratory interviews, then tested materials and messages in focus groups, and then refined and retested messages with more interviews. Briefly, 12 older adults similar to our target population were interviewed by a behavioral scientist and a Masters student in public health. These interviews were semistructured and tested patient’s stage of change, perceived susceptibility, and confidence to take on behaviors to prevent osteoporosis, falls, and fractures in terms of the existing behavioral science models. The interviews were audiotaped and transcribed. We tested several a priori hypotheses about patient’s attitudes and beliefs. These hypotheses included: ! patients have little baseline knowledge of what exactly osteoporosis means; ! the association between osteoporosis and fractures is not universally understood; ! patients have little appreciation that osteoporosis is without symptoms in many persons;
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! patients knew little about how to reduce their risk of falling; ! methods for testing and treating osteoporosis were not widely known; and ! few patients ever discuss osteoporosis or fractures with their physicians. These interviews led to development of two patient mailings that contained a wallet-size card with reminders that could be brought to a physician’s appointment. These materials were next formally tested in three focus groups in which each participant received the materials and was given an opportunity to respond negatively or positively. Based on the focus groups, the materials were refined and re-tested in further interviews. The final patient intervention (all available upon request from DHS) consisted of two letters, patient vignettes, and a wallet-size reminder card. These were mailed out over 2 months. The reminder card fit in a wallet and could be brought to a physician’s visit. It outlined the most important actions to prevent falls and fractures and had room for patients to write a list of current medications. 3.2. Physician academic detailing Our research group has developed and tested one-on-one educational outreach to improve physicians’ prescribing of numerous drug categories, including antibiotics, tranquilizers, and analgesics [32–34]. The educators in such an intervention are trained in the pharmacology of the drugs of interest, as well as in effective behavior change methods. When they meet with physicians, they are equipped with graphically accessible health education messages and attempt to engage the prescribing physician in an interactive conversation that focuses on methods for improvement. This method of quality improvement has been termed bacademic detailingQ since it takes an academic evidence-based approach to pharmaceutical detailing. While we and others have shown that this approach improves physician’s prescribing, we were interested in testing: (a) whether it would be effective for improving management of osteoporosis, fall prevention, and fracture prevention; (b) how the magnitude of its effect would compare with patient education; (c) how effective a combined approach with academic detailing and patient education would be; and (d) the feasibility of mounting a large-scale program of academic detailing in current medical practice. 3.3. Selection and training of academic detailers Academic detailing was performed by pharmacists or nurses. They were recruited by contacting schools of pharmacy in Pennsylvania, departments of pharmacy at local hospitals, and networking among pharmacists. Candidates were screened by phone and selected based on academic background, work experience, and level of interest. The principal investigator (DHS) and project director (JMP) conducted three different 6-h trainings. Twelve of the 16 academic detailers attended these sessions. The remaining 4 academic detailers were trained via teleconference. Trainings were standardized and included instruction on: the epidemiology, diagnosis and treatment of osteoporosis; the epidemiology of falls and fall prevention; and the physiology of fractures and fracture prevention. The training sessions also focused on the methods of academic detailing, including role-playing physician encounters. We provided very specific strategies for contacting physicians’ offices, making appointments, and conducting visits. At the conclusion of all training, detailers were
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assigned physicians in the intervention groups based on geographic location and schedule. For example, we recruited and trained one detailer from Erie Pennsylvania who was assigned all of the physicians in this region. We recruited detailers so that there would be adequate geographic coverage for all targeted counties in Pennsylvania. 3.4. Physician education materials Members of the study team also developed a set of evidence-based materials for use in the physician detailing encounters. These materials included graphically engaging two-page handouts on: (1) patient risk factors for osteoporosis and fractures; (2) osteoporosis diagnosis and bone mineral density testing; (3) drug treatments for osteoporosis; and (4) fall prevention. Four case vignettes highlighting common patient scenarios were developed to help exemplify key concepts during encounters. A pocket-sized laminated card that described medication options and a management algorithm was also provided. The physicians were given materials they could hand out to patients, including: (1) a fall prevention handout (in English and Spanish); (2) lay-language brochures on home safety, calcium options, and bone mineral density testing; and (3) a pad of patient recommendations for fracture prevention that resembles a prescription pad. Finally, bright bpost-itQ notes defining the characteristics of at-risk patients were given to the office staff to help flag patient charts prior to a visit. These materials were approved by the Department of Continuing Medical Education at Harvard Medical School for 1 h of credit to physicians who submitted a correctly completed post-visit questionnaire. The CME credit was paid for by the study investigators through grant support. 3.5. Scheduling physician appointments We initially contacted physicians through mailing an introductory letter explaining the program and offering 1 h of free CME credit for meeting with a detailer. Each academic detailer notified the project director weekly of which of their assigned physicians should be sent letters so that he/she could focus his efforts in a systematic way. Following the mailing of the introductory letter, the detailers used a variety of methods to schedule appointments for the academic detailing sessions, including telephone calls, facsimiles, and unscheduled visits. At a detailer’s request, we sent additional letters on the principal investigator’s letterhead if a physician was unresponsive to the initial contact. Detailers were encouraged to leave a sample of the project materials and/or coffee mugs imprinted with study name and telephone number for the staff. Detailers were discouraged from buying food and beverages for the office staff, but did do so if explicitly requested. If a physician appointment could not be scheduled, detailers were encouraged to meet with a nurse, physician’s assistant, or other allied health professional in the physician’s office. Four monthly conference calls lasting 1 h each were held among academic detailers, investigators, and project staff to develop and share strategies for scheduling physician appointments. In addition, biweekly bulletins were e-mailed to detailers to share new information, group and individual progress, and suggestions for detailing. Weekly e-mail and/or phone contact was initiated by the project director to determine detailers’ progress and additional contact with academic detailers was made as needed. Academic detailers were invited and encouraged to contact the project staff at any time for assistance.
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4. Study design 4.1. 22 factorial trial To assess the effectiveness of patient and physician education interventions and their possible synergy, we designed a 22 factorial trial (see Fig. 1). This design allows for direct comparison of all four groups—no patient or physician intervention (group A), patient but no physician intervention (group B), physician but no patient intervention (group C), patient and physician intervention (group D). This design also allows for efficient comparison of two interventions that may be additive. A recent systematic review of factorial trials suggested that such trials can be analyzed using the marginal results when the interventions are independent, or using interaction terms when the effects of one intervention may be dependent on the presence of the other [35]. Marginal results will combine the two similar intervention groups in analyses. For the patient intervention, this would mean treating groups B and D as similar. We suspect that the patient and physician interventions are dependent, and thus the primary analysis will assume an interaction. 4.2. Randomized controlled cluster trials The management of a given patient constitutes the endpoint of this trial, but patients are treated by particular physicians. This results in a natural clustering of patients within a given physician’s practice. As well, since both patients and physicians would receive interventions in some practices, we chose to randomize each physician and all of his/her patients together into one of the four arms of the 22 factorial trial (see Fig. 2). This design prevented the contamination of patients within a given physician’s practice that would have occurred if some patients received the education and some did not. However, there still could be some contamination if two physicians practiced in the same office space. Post-hoc analysis of physicians by their office addresses suggested that this occurred in 172 of the 826 physicians randomized.
5. Study population: patients and physicians 5.1. Patients Subjects eligible for this study were all Medicare beneficiaries also enrolled in a state-run pharmacy benefits program, the Pennsylvania Pharmaceutical Assistance Contract for the Elderly (PACE). The PACE program provides a pharmaceutical benefit to Pennsylvania residents who have annual incomes too high to qualify for the state’s Medicaid program but below US$20,000 (the exact figure threshold varies from year to year). Beneficiaries receive prescription medications for a co-payment of less than
Fig. 1. 22 factorial trial design.
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Fig. 2. Study cohort assembly and randomization.
US$10 per prescription. There is no restricted list of permissible medications (a dformularyT) and in general no prior authorization is required before drugs can be obtained. However, pharmacists intercede when dosage limits are exceeded or two drugs from the same drug category are prescribed, such as two benzodiazepines. Collaboration with the PACE program made it possible to ignore the problem of affordability of osteoporosis prescription medications, since access to drugs was provided with minimal financial barrier. To improve the efficiency of the intervention, we targeted eligible beneficiaries identified as being at high risk of osteoporosis and future fractures. Health care claims provided the primary source of data for this trial (see below), allowing us to make use of demographics, diagnosis and procedure codes, and medication information to identify high-risk groups. We focused on three groups of high-risk beneficiaries: (1) all women 65 years or older; (2) all men and women with evidence for a prior fragility (hip, wrist, humerus, and spine) fracture; and (3) all men and women with evidence for oral glucocorticoid use for at least 3 months. While other groups could have been targeted, these groups were chosen since there is wide agreement on the importance of screening and appropriate treatment for them [36,37]. Based on algorithms including health care and medication claims, we identified these high-risk groups from the total source population of eligible beneficiaries.
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5.2. Primary care physicians Since our intervention targeted both high-risk subjects and their primary care physicians, and the physicians were the unit of randomization, we developed an algorithm for identifying each eligible beneficiary’s primary care physician. This was important because many patients, especially those over 65, may be seen by and receive prescriptions from several physicians. Doctors eligible to be considered primary care physicians included family physicians, general practitioners, physicians practicing internal medicine or one of its subspecialties, and gynecologists. We then compiled a list of eligible physicians who had written prescriptions for each patient during the baseline year. From this list, the physician prescribing at least 50% of all medicines for a given patients was defined as the primary care physician. We excluded 18% of patients for whom no single physician prescribed at least 50% of medicines. To further improve the efficiency of the academic detailing, we focused on physicians practicing in non-rural counties with large numbers of PACE beneficiaries in their practice. Of the 67 counties in Pennsylvania, 28 were deemed urban or suburban; the 826 physicians practicing in these counties were eligible to be included in the intervention. We then assessed how many PACE beneficiaries would be considered a primary care patient for each physician (based on the algorithm described above). Physician who had at least 25 such patients were included in the intervention.
6. Recruitment 6.1. Institutional review board oversight We were granted an exemption from the Partners Healthcare Institutional Review Board oversight based on part 46 of the Code of Federal Regulations that exempts research testing the effectiveness of a quality improvement program in public benefits programs, such as Medicare and PACE. Thus, we were not required to obtain consent from patients or their physicians. However, the research protocol was reviewed by the Institutional Review Board to ensure compliance with standards of human research. In addition, the research posed minimal risk to patients and physicians and each was able to opt-out of any aspect of the study. 6.2. Opt-out process for patients A total of 31,715 eligible beneficiaries were identified as being in at least 1 of the 3 high-risk groups who were seen by physicians in 1 of the 28 counties who saw at least 25 PACE beneficiaries. Each of them was randomized to either the intervention group or the control group through a random number generator. In both intervention and control groups, beneficiaries were sent a personalized introductory letter. If the beneficiary had a known power of attorney, the introductory letter was sent to the power of attorney instead of to the beneficiary. In the intervention group, the introductory letter informed beneficiaries that PACE and physicians at Harvard Medical School were collaborating on an educational program about osteoporosis and that in the coming weeks, they would receive program materials by mail. The letter stated that if a beneficiary did not want to receive these materials, the beneficiary could check the refusal box at the bottom of the letter and
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return it to the program in a pre-paid envelope. The content of the letter to the control group differed only slightly. This letter explained that PACE was considering collaborating with physicians at Harvard and asked beneficiaries to return the letter to PACE if they would not want to receive materials from such a program if it indeed were launched. Beneficiaries who returned either letter were removed from the study. We also removed beneficiaries whose relatives informed us that they had died. Of the 15,684 eligible patients in the 2 patient intervention groups (groups B and D), 2322 (12.9%) declined participation. In the 2 patient control groups (groups A and C), 4373 of 16,031 (27.2%) declined. Overall, 6695 (21.1%) eligible subjects declined participation. Participants and nonparticipants had very similar sociodemographic characteristics (see Table 1). 6.3. Baseline characteristics of patients We have compared the baseline characteristics of the intervention and control subjects (see Table 2). The two groups were quite similar with respect to sociodemographic, general health care utilization, and osteoporosis related characteristics. 6.4. Opt-out process for physicians There were 414 physicians who were randomized to the physician-intervention groups. When these physicians were approached to schedule a visit with an academic detailer, the type and frequency of contacts with the physician and his/her staff were recorded. The outcome of these contact attempts were also recorded and assigned to one of the categories listed below. After removing, the doctors that were no longer eligible or we were unable to locate, 356 primary care physicians remained. 1. No longer eligible. The physician had changed his/her practice type, was no longer practicing medicine, had moved out of state, or was deceased; 2. Unable to locate. The physician had moved and a current address was not available from the Pennsylvania Medical Society, the American Medical Association, the Pennsylvania Department of Table 1 Characteristics of participants and non-participants, by patient treatment assignment Participant (n=25,020)
Non-participant (n=6695)
N (%) or mean (FS.D.) Age Female Race, white Marital status Married Divorced Widowed Others Annual income (USD)
81F7 24,844 (99.3) 24,178 (96.6)
83F7 6620 (98.9) 6550 (97.8)
1707 (6.8) 1000 (4.0) 21,445 (85.7) 868 (3.5) 10,966F3530
360 (5.4) 210 (3.1) 5875 (87.8) 250 (3.7) 10,667F3972
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Table 2 Baseline patient characteristics by patient treatment assignment Patient intervention (groups B and D)
Patient control (groups A and C)
N (%) or mean (FS.D.) Age Female Race White Black Other Marital status Married Divorced Widowed Other Annual income Diagnosis of osteoporosis Hip, wrist or humerus fracture Bone mineral density test Medications for osteoporosis Alendronate Calcitonin HRT Raloxifene Risedronate Comorbid illnesses Nursing home residence, any Acute care hospitalization, any Number of physician visits Number of different medicines
81.5F6.9 13,257 (99.2)
81.0F6.8 11,587 (99.4)
12,848 (96.2) 435 (3.3) 79 (0.6)
11,330 (97.2) 258 (2.2) 70 (0.6)
873 (6.5) 564 (4.2) 11,420 (85.5) 505 (3.8)
834 (7.2) 436 (3.7) 10,025 (86.0) 363 (3.1)
2971 (22.2) 862 (6.5) 648 (4.9)
2719 (23.3) 749 (6.4) 665 (5.7)
1318 (9.9) 611 (4.6) 580 (4.3) 463 (3.5) 377 (2.8) 1.8F2.2 1499 (11.2) 3911 (29.3) 6.5F6.3 8.0F6.2
1281 (11.0) 540 (4.6) 527 (4.5) 358 (3.1) 362 (3.1) 1.8F2.0 1115 (9.6) 3205 (27.5) 7.2F6.3 8.4F5.9
See Fig. 1 for an explanation of group assignments. Variables were assessed during 2002.
3. 4. 5. 6. 7.
State Bureau of Professional and Occupational Affairs License Verification database, or the Yellow Pages; Physician detailed. The physician met with the detailer in a one-on-one visit; Allied health professional detailed. The physician did not meet with the detailer, but an allied health professional in his/her office did meet with the detailer in a one-on-one visit; Both physician and allied health professional detailed. The detailer met with both the physician and an allied health professional; Physician refused. The detailer was verbally told that the physician refused to schedule a one-on-one visit; Physician did not respond. The physician, despite repeated contacts, never agreed to a visit by the detailer.
6.5. Characteristics of physicians who were successfully detailed The academic detailers were able to successfully visit 148 of the 356 (41.6%) primary care doctors targeted. We have analyzed the characteristics of doctors who were successfully detailed compared with
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Table 3 Primary care physician characteristics in the doctor intervention (groups C and D) Successful educational encounter (n=148)
Refused educational encounter (n=208)
N (%) or meanFS.D. Male gender Race, white Years since medical school Training Internal medicine Family practice General practice Practice location Urban Suburban Rural or semi-rural Study patients
137 (92.6) 86 (86.9) 19F9.2
187 (89.9) 127 (82.5) 13F8.4
78 (52.7) 57 (38.5) 13 (8.8)
113 (54.3) 84 (40.4) 11 (5.3)
69 (46.6) 62 (41.9) 17 (11.5) 39F20
77 (37.0) 95 (45.7) 36 (17.3) 40F19
the ones who refused a visit (see Table 3). There were no important differences between doctors who were successfully detailed and those who refused.
7. Data sources and study outcomes 7.1. Data sources We used data from several sources to design this trial. Data from Medicare Part A (hospital) and Part B (ambulatory) were linked with drug dispensing information from PACE. This information was obtained using appropriate Data Use Agreements from both the Centers for Medicare and Medicaid Services and PACE. Medicare data provided diagnoses and procedures regarding a patient’s relevant medical history, including prior fractures, other osteoporosis risk factors, recent hospitalizations, nursing home status, use of physical therapy, and bone densitometry. Prescription dispensing data from PACE allowed us to assess patients’ use of medications for osteoporosis, use of drugs that may cause metabolic bone disease (glucocorticoids, anti-convulsants, cyclosporin), as well as information on the total number of drugs that patients used. PACE data include names of medications, dispensing dates, dosages, and days supply. 7.2. Primary and secondary endpoints The trial’s primary and secondary endpoints are outlined in Table 4. The primary endpoint will be a composite of either filling a prescription for an osteoporosis medication or undergoing a bone mineral density test. This was chosen as the primary endpoint because they are both part of standard guidelines for managing osteoporosis in high risk patients [36,37] and medication use in appropriate patients has been clearly linked with a reduction in fractures. Because fractures are relatively uncommon, we will assess any possible differences in secondary analyses. The primary analysis will examine persons without evidence for filling a medicine in the 6 months prior to the intervention and no bone mineral density test in the 2 years prior to determine whether they
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Table 4 Trial endpoints Endpoints Primary Medication start or BMD examination*
Secondary—medications New medication start
Any medication use Medication adherence among all patients
Medication adherence among patients who filled at least one prescription
Subgroups
Secondary—bone mineral density testing New BMD examination BMD examination Subgroups Secondary—other Physical therapy Home visit Fractures Patient attitudes, beliefs, knowledge and self-reported behaviors Subgroups
Description In patients without evidence of recent management of osteoporosis, filling a prescription for one of the specified medications or undergoing a BMD examination.
In patients without evidence of recent management of osteoporosis, filling a prescription for one of the specified medications during the one year follow-up period. This endpoint includes all patients, not requiring that patients be without evidence of recent management. Includes all patients as the denominator and requires that patients have z80% of days with medicine after their first filling. Days in acute care are subtracted out. Includes patients filling at least one prescription for an osteoporosis medication as the denominator and requires that these patients have z80% of days with medicine after their first filling. Days in acute care are subtracted out. Relevant patient subgroups, i.e., gender, race, age, comorbidities, non-nursing home and with a physician visit during follow up, as well as physician subgroups, i.e., gender, age, urban/rural practice, # of PACE beneficiaries, specialty, completer.
In patients without evidence of recent BMD examination or medication, a procedure code for a test. This endpoint includes all patients, not requiring that patients be without evidence of recent management. As above.
Utilization services for lower extremity strengthening physical Among all patients, any evidence for a home visit by a health Diagnosis or procedure codes consistent with a fracture of the humerus or hip. Based on a patient survey distributed approximately 6 months patient education was disseminated. As above.
therapy. professional. wrist, after the
undergo testing or receive treatment after the intervention. Two years was chosen since this is the interval at which Medicare will reimburse for bone mineral density testing. Medicines of interest include all bisphosphonates, calcitonin, hormone replacement therapy, raloxifene, teriparatide, and vitamin D analogs. Bone mineral density tests will be detected by examining the Medicare claims for specific procedure codes, including CPT 76070 (computerized axial tomography bone density study), CPT 76075-76076 (dual energy X-ray absorptiometry, bone density study), CPT 76078 (radiographic absorptiometry), and CPT 76977 (ultrasound bone density). Secondary analyses will examine medicines
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and bone mineral density testing as separate endpoints. Subsequent subgroup analyses will focus on: medicines and bone mineral density testing in the total group, not only patients without prior management, and these endpoints in specific subgroups of patients, such as men versus women, different age groups, and those with prior fracture versus no fracture. In addition to the above secondary endpoints, we will assess several other outcomes. Lower extremity strengthening exercises to improve balance and reduce falls were part of the written recommendations for patients, and home safety evaluations were also suggested. Thus, we will compare the number of rehabilitation visits for lower extremity diagnoses and home visits by nurses and physical therapists. Reducing fracture is the ultimate goal of the intervention. We will assess the fracture rates in the study subjects over the 24 months after the intervention, 2004–2005. The fractures of interest include hip, wrist, and humerus fractures. We chose to focus on these fractures because they are accompanied by procedures (surgical or casting) that will allow for precise dating of their occurrence. The frequency of fractures that do not require procedures such as vertebral compression fractures or pelvic fractures cannot be estimated since they often are carried over as historical diagnoses. Fracture data are available through Medicare claims in the form of diagnoses and procedure codes. Two secondary endpoints for which data will be readily accessible concern the detailing process and changes in patient’s knowledge, attitudes, and beliefs. We have already collected data on the proportion of academic detailing visits that were successfully completed. This information will help us to gauge the proportion of physicians who were bcompletersQ of the intervention, even though the parent study will be evaluated primarily on an intention to treat basis. A patient survey will be mailed out to persons in the intervention and control groups. This will allow us to compare whether the educational material had any impact on patient’s knowledge, attitudes and beliefs regarding osteoporosis.
8. Analytic plan Our trial is organized using a cluster randomized design with the physician as the unit of randomization. The use of cluster randomized designs is common in health services research; however, there is not a yet a consensus on the most appropriate statistical analysis of such experiments [38]. Many argue that the analysis must occur at the level of the unit of randomization; in this trial, the physician. However, such an analysis would preclude the possibility of incorporating patient-level covariates, which are known to be strong risk factors for inappropriate clinical management of osteoporosis [39]. The creation of a patient-level statistical model of the trial data has the potential to increase the efficiency of the estimated intervention effects through adjusting for baseline covariates and will also facilitate analysis of potential interactions between treatment assignment and patient- and physician-level attributes. However, any patient-level analysis of a study randomized at the physician-level must adequately account for the correlation of outcomes between patients sharing the same physician [40]. Failure to account for this clustering effect will not bias parameter estimates, but can lead to invalid standard errors [41]. To state the problem simply, two patients from the same physician practice do not contribute as much information as two patients from different practices. Our primary analytical approach will be based on a generalized linear random effects model of patient-level outcomes. The effect of patient characteristics (e.g., age, race, gender, co-morbid conditions, fracture history, medication use history), physician characteristics (e.g., age, specialty, practice type) and treatment arm assignment on the outcome under study will be captured through fixed
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effects modeling. By contrast, a physician-level random effect model will capture the tendency of patients within a physician’s practice to experience similar outcomes. By accounting for this correlation, valid statistical inference can be made on patient-level fixed effects. Formally, we will let Yi,j,k denote the outcome observed on the ith patient of the jth physician in the kth treatment arm and P i,j,k denote observed patient characteristics and MDj,k denote observed physician characteristics. All primary outcomes considered in this trial will be dichotomous, so the basic statistical model will be a logistic mixed effects model, expressed as: Pr½Yi; j; k ¼ 1jPi; j; k ; MDj; k ¼ 1 þ exp dj þ bT Pi; j; k þ aT MDj; k þ ej; k
1
;
where e j,k is the physician-specific random effect that we will assume is distributed as a mean zero Gaussian random variable with unknown variance. The vectors of fixed effects d, b, and a estimate the effects of treatment arm assignment, patient, and physician characteristics, respectively, on the outcome under consideration. This model will be fit using glmmPQL function in the R language [42]. This software fits a generalized linear mixed model using the penalized quasi likelihood technique [43]. Results from this fit will be validated using the NLMIXED procedure in SAS [44].
9. Discussion In an aging society, osteoporosis and fractures represent growing public health problems. Data consistently show sub-optimal rates of screening, prevention, and treatment of osteoporosis and preventable fracture risk factors. Prior interventions have been few and mostly small-scale nonrandomized trials. We have developed and are testing a large-scale public health educational program regarding osteoporosis and fracture prevention that targeted patients and their primary care physicians. These interventions will be evaluated as a 22 factorial randomized controlled cluster trial. The interventions aim to improve the use of bone densitometry and anti-osteoporosis medications, as well as to increase the use of fall prevention strategies by patients and physicians. Patient recruitment has been relatively successful, with approximately 80% of eligible subjects participating. Non-participants did not differ in any important way from participants, and the baseline characteristics of participants in the intervention and control arms were quite similar. These preliminary results suggest that our full trial data will be both internally and externally valid. Patient retention and long-term data collection were minor issues since all of the participants were enrollees in Medicare and the state pharmaceutical benefits program, PACE. There is almost no attrition in either of these programs. As well, collection of claims data are routine since pharmacy, physician, and hospital services are reimbursed based on claim submissions. We are currently awaiting the end of the observation period to analyze the trial endpoints.
Acknowledgment We would like to thank Thomas Snedden, Director of the Pharmaceutical Assistance Contract, for the Elderly in the Department of Aging in Pennsylvania. His colleagues Margaret Glessner, Theresa Brown,
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and Debra Heller have also contributed substantially to developing this trial. We would also like to thank the anonymous referee who provided valuable suggestions. Support: Arthritis Foundation (Atlanta, GA) and National Institutes of Health (AR K23 48616). References [1] Center for Disease Control. Healthy People 2010. http://www.health.gov/healthypeople/volume1/02Arthritis. [2] Altkorn D, Vokes T. Treatment of postmenopausal osteoporosis. JAMA 2001;285:1415 – 8. [3] Tinetti ME, Baker DI, McAvay G, et al. A multifactorial intervention to reduce the risk of falling among elderly people living in the community. N Engl J Med 1994;331:821 – 7. [4] Hornbrook MC, Stevens VJ, Wingfield DJ, Hollis JF, Greenlick MR, Ory MG. Preventing falls among communitydwelling older persons: results from a randomized trial. Gerontologist 1994;34:16 – 23. [5] Solomon DH, Finkelstein JS, Katz JN, Mogun H, Avorn J. Underuse of osteoporosis medications in elderly patients with fractures. Am J Med 2003;115:398 – 400. [6] Consensus Development Panel J. National institutes of health consensus development conference statement: osteoporosis prevention, diagnosis, and therapy. Rockville, MD7 National Institutes of Health, 2000. p. 27. [7] Sattin RW. Falls among older persons: a public health perspective. Annu Rev Public Health 1992;13:489 – 508. [8] Tinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community. N Engl J Med 1988;319:1701 – 7. [9] Nevitt MC, Cummings SR, Kidd S, Black D. Risk factors for recurrent nonsyncopal falls: a prospective study. JAMA 1989;261:2663 – 8. [10] Sherrington C, Lord SR. Increased prevalence of fall risk factors in older people following hip fracture. Gerontology 1998;44:340 – 4. [11] Lord SR, Clark RD, Webster IW. Visual acuity and contrast sensitivity in relation to falls in an elderly population. Age Ageing 1991;20:175 – 81. [12] Keating NL, Cleary PD, Rossi AS, Zaslavsky AM, Ayanian JZ. Use of hormone replacement therapy by postmenopausal women in the United States. Ann Intern Med 1999;130:545 – 53. [13] Leveille SQ, LaCroix AZ, Newton KM, Keenan NL. Older women and hormone replacement therapy: factors influencing late life initiation. J Am Geriat Soc 1997;45:1496 – 500. [14] Truter I, Serfontein JHP. A survey of the treatment of female patients with osteoporosis using drug utilization consumption parameters. J Clin Pharm Ther 1999;24:209 – 17. [15] Morris CM, Cheng H, Cabral D, Solomon DH. Predictors of osteoporosis management. Endocrinologist 2004;14:70 – 5. [16] Kreuter MW, Strecher VJ, Glassman B. One size does not fit all: the case for tailoring print materials. Ann Behav Med 1999;21:276 – 83. [17] Rakowski W. The potential variances of tailoring in health behavior interventions. Ann Behav Med 1999;21:284 – 9. [18] Morgan GD, Noll EL, Orleans CT, Rimer BK, Amfoh K, Bonney G. Reaching midlife and older smokers: tailored interventions for routine medical care. Prev Med 1996;25:346 – 54. [19] Meldrum P, Turnbull D, Dobson HM, et al. Tailored written invitations for second round breast cancer screening: a randomized controlled trial. J Med Screen 1994;1:245 – 8. [20] Brug J, Steenhuis I, Van Assema P, De Vries H. The impact of a computer-tailored nutrition intervention. Prev Med 1996;25:236 – 42. [21] Davis D, O’Brien MAT, Freemantle N, Wolf FM, Mamanian P, Taylor-Vaisey A. Impact of formal continuing medical education: do conferences, workshops, rounds, and other traditional continuing education activities change physician behavior or health outcomes? JAMA 1999;282:867 – 74. [22] Oxman AD, Thomson MA, David DA, Haynes RB. No magic bullets: a systematic review of 102 trials of interventions to improve professional practice. Can Med Assoc J 1995;153:1423 – 31. [23] Avorn J, Soumerai SB. Improving drug-therapy decisions through educational outreach. A randomized controlled trial of academically based bdetailingQ. N Engl J Med 1983;308:1457 – 63. [24] Soumerai SB, Avorn J. Principles of educational outreach (dacademic detailingT) to improve clinical decision making. JAMA 1990;263:549 – 56.
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