Osteoporosis in the home health care setting: A window of opportunity?

✍ Scribed by Curtis, Jeffrey R. ;Kim, Young ;Bryant, Tara ;Allison, Jeroan ;Scott, Dana ;Saag, Kenneth G.

• Publisher: John Wiley and Sons
• Year: 2006
• Tongue: English
• Weight: 62 KB
• Volume: 55
• Category: Article
• ISSN: 0004-3591
• DOI: 10.1002/art.22349

✦ Synopsis

Patients and Methods

Role of the study sponsor. Eli Lilly had no role in the study design, data collection, analysis, or preparation or review of the manuscript, or the decision to submit the manuscript for publication.

Patient population.

Following institutional review board approval, we used the electronic databases of a large home health care agency to identify patients receiving home health services during 2003-2004 who were at high risk for fracture. This agency provides care to ϳ7,000 patients annually throughout a single southeastern US state. We defined high-risk patients as those with a history of fracture, a physician diagnosis of osteoporosis, current systemic glucocorticoid use (any dose), and a history of stroke. The electronic database used for the study was the primary record of the patient's clinical encounter with the trained nurses providing home health services. The database captured demographic data and a comprehensive list of medical diagnoses, prescription drugs, and over-thecounter medications and supplements relevant to each home health visit. The index developed by Charlson et al was used to quantify the burden of medical comorbidity for each patient (10). This index assigns an integer value to a number of medical conditions based on the conditions' previously validated association with adverse outcomes. Each patient in the cohort was assigned an individualized score based on the summed values of their specific medical conditions recorded in the electronic database. Patients receiving hospice services were excluded from analysis, although these individuals were included if they received home health services prior to hospice referral.

Descriptive statistics were used to characterize the patient cohort and examine rates of prescription and overthe-counter osteoporosis treatments. Multivariable logistic regression was used to examine factors associated with