Abstract
Objectives
To quantify geographic variation in anticoagulant use and explore what resident, nursing
home, and county characteristics were associated with anticoagulant use in a clinically
complex population.
Design
A repeated cross-sectional design was used to estimate current oral anticoagulant
use on December 31, 2014, 2015, and 2016.
Setting and Participants
Secondary data for United States nursing home residents during the period 2014-2016
were drawn from the Minimum Data Set 3.0 and Medicare Parts A and D. Nursing home
residents (≥65 years) with a diagnosis of atrial fibrillation and ≥6 months of Medicare
fee-for-service enrollment were eligible for inclusion. Residents in a coma or on
hospice were excluded.
Methods
Multilevel logistic models evaluated the extent to which variation in anticoagulant
use between counties could be explained by resident, nursing home, and county characteristics
and state of residence. Proportional changes in cluster variation (PCVs), intraclass
correlation coefficients (ICCs), and adjusted odds ratios (aORs) were estimated.
Results
Among 86,736 nursing home residents from 11,860 nursing homes and 1694 counties, 45%
used oral anticoagulants. The odds of oral anticoagulant use were 18% higher in 2016
than 2014 (aOR: 1.18; 95% confidence interval: 1.14-1.22). Most states had counties
in the highest (51.3-58.9%) and lowest (31.1%-41.4%) deciles of anticoagulant use.
Compared with the null model, adjustment for resident characteristics explained one-third
of the variation between counties (PCV: 34.8%). The full model explained 65.5% of
between-county variation. Within-county correlation was a small proportion (ICC < 2.2%)
of total variation.
Conclusions and Implications
In this older adult population at high risk for ischemic stroke, less than half of
the residents received treatment with anticoagulants. Variation in treatment across
counties was partially attributable to the characteristics of residents, nursing homes,
and counties. Comparative evidence and refinement of predictive algorithms specific
to the nursing home setting may be warranted.
Keywords
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Article info
Footnotes
The study was funded by the National Institute on Aging (R21AG060529-01). D.D.M.‘s time was also supported by R01HL126911, R01HL137734, R01HL137794, R01HL141434, R01HL136660, and U54HL143541 from the National Heart, Lung, and Blood Institute. The funders had no role in the study design, data collection, analysis, and interpretation, the writing of the report, or the decision to submit for publication.
D.D.M. has received research grant funding from Bristol-Myers Squibb, Boeringher-Ingelheim, Pfizer, Samsung, Philips Healthcare, Philips, Biotronik, and FlexCon and has received consultancy fees from Bristol-Myers Squibb, Pfizer, Flexcon, and Boston Biomedical Associates. Other authors have no conflicts of interest to disclose.
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