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Observational Study
. 2018 Jul 6;1(3):e180826.
doi: 10.1001/jamanetworkopen.2018.0826.

Association of Osteoporosis Medication Use After Hip Fracture With Prevention of Subsequent Nonvertebral Fractures: An Instrumental Variable Analysis

Affiliations
Observational Study

Association of Osteoporosis Medication Use After Hip Fracture With Prevention of Subsequent Nonvertebral Fractures: An Instrumental Variable Analysis

Rishi J Desai et al. JAMA Netw Open. .

Abstract

Importance: Osteoporosis medication treatment is recommended after hip fracture, yet contemporary estimates of rates of initiation and clinical benefit in the patient population receiving routine care are not well documented.

Objectives: To report osteoporosis treatment initiation rates between January 1, 2004, and September 30, 2015, and to estimate the risk reduction in subsequent nonvertebral fractures associated with treatment initiation in patients with hip fracture.

Design, setting, and participants: In this cohort study, data from a commercial insurance claims database from the United States were analyzed. Patients 50 years and older who had a hip fracture and were not receiving treatment with osteoporosis medications before their fracture were included.

Exposure: Prescription dispensing of an osteoporosis medication within 180 days of a hip fracture hospitalization.

Main outcomes and measures: Each initiation episode was matched with 10 nonuse episodes on person-time after the index hip fracture event to preclude immortal time bias and followed up for the outcome of nonvertebral fracture until change in exposure or a censoring event. An instrumental variable analysis using 2-stage residual inclusion method was conducted using calendar year, specialist access, geographical variation in prescribing patterns, and hospital preference.

Results: Among 97 169 patients with a hip fracture identified, the mean (SD) age was 80.2 (10.8) years, and 64 164 (66.0%) were women. A continuous decline over the study years was observed in osteoporosis medication initiation rates from 9.8% (95% CI, 9.0%-10.6%) in 2004 to 3.3% (95% CI, 2.9%-3.8%) in 2015. In the effectiveness analyses, the hospital preference instrumental variable had a stronger association with treatment (pseudo R2 = 0.20) than the other 3 instrumental variables (specialist access: pseudo R2 = 0.04; calendar year: pseudo R2 = 0.05; and geographic variation: pseudo R2 = 0.07). Instrumental variable analysis with hospital preference suggested a rate difference of 4.2 events (95% CI, 1.1-7.3) per 100 person-years in subsequent fractures associated with osteoporosis treatment initiation compared with nonuse in an additive hazard model.

Conclusions and relevance: Low rates of osteoporosis treatment initiation after a hip fracture in recent years were observed. Clinically meaningful reduction in subsequent nonvertebral fracture rates associated with treatment suggests that improving prescriber adherence to guidelines and patient adherence to prescribed regimens may result in notable public health benefit.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Desai reported receiving grants from Merck & Co during the conduct of the study. Dr Mavros reported being an employee and holds stocks of Merck & Co. Dr Kim reported receiving grants from Merck & Co during the conduct of the study and grants from Pfizer, Roche, and Bristol-Myers Squibb outside the submitted work. Dr Franklin reported receiving grants from Merck & Co during the conduct of the study. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Osteoporosis Treatment Initiation Over Time in Patients With Hip Fracture Hospitalizations
Data in this figure are from a total of 97 169 patients 50 years and older with hip fracture who were not taking any osteoporosis treatment prior to the hip fracture, of whom 6743 (6.9%) initiated treatment. Error bars indicate 95% confidence intervals.
Figure 2.
Figure 2.. Scaled Covariate Balance (Bias Component) by Levels of Treatment and Proposed Instrumental Variables
To demonstrate balance, we dichotomized the categorical instrumental variables (calendar year, regional variation, and hospital preference) based on extreme strata. However, this dichotomization is for illustrative purpose only, and in stage 1 of instrumental variable modeling, these variables were included as categorical variables to preserve maximum information. These plots aim to visually compare the absolute covariate prevalence difference by treatment and by the proposed instruments after scaling for instrument strength. An instrument with bias components closer to 0 is expected to account for unmeasured confounding more efficiently than instruments with large bias components.

Comment in

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