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P APER
A BSTRACTS
P6
CRASH: A Brief Screening Tool to Identify High-Risk Older
Drivers.
M. E. Betz, 1 R. Schwartz, 2 J. Haukoos, 1,3 C. DiGuiseppi, 4 M. Valley, 1
R. Johnson, 1 S. Lowenstein. 1 1. Emergency Medicine, University of
Colorado School of Medicine, Aurora, CO; 2. Division of Geriatrics,
University of Colorado School of Medicine, Aurora, CO; 3.
Emergency Medicine, Denver Health Medical Center, Denver, CO; 4.
Epidemiology, Colorado School of Public Health, Aurora, CO.
Supported By: Funding: Emergency Medicine Foundation and John
A Hartford University of Colorado Denver Center of Excellence.
This study was also supported in part by Grant Number
R49/CCR811509 from the Centers for Disease Control and
Prevention. Its contents are solely the responsibility of the authors
and do not necessarily represent the official views of the CDC.
Background: Current older driver screening tools are impractical
for use in busy clinical settings. We sought to develop and internally
validate a brief questionnaire to identify drivers needing further
evaluation.
Methods: Cross-sectional study of patients aged 65+ years at a
geriatric clinic or emergency department (ED) who drove at least occasionally,
spoke English and had no significant cognitive impairment.
Study staff administered a confidential survey to 246 participants,
enrolled equally from the clinic and ED (participation rate:
43%). Logistic regression was used to identify characteristics associated
with an adverse driving event (ADE), defined as 1+ reported
crash or police stop while driving within the preceding 12 months.
Results: Median participant age was 76 years; half were women.
Most participants (82%, 95%CI 77-86%) reported daily or near-daily
driving; 15% (95%CI 11-19%) reported an ADE. Age and gender
were not associated with ADEs. The final model included five variables
associated with an ADE, to which the CRASH tool assigns one
point each (table): “C” ever feels confused or disoriented while driving;
“R” regular (daily or near-daily) driver; “A” avoids driving alone;
“S” has difficulty seeing the license plate in front while stopped; and
“H” reports that someone has recommended handing over the keys
in the past year. In internal validation, the CRASH tool maintained
its goodness-of-fit (average p=0.70) and had an averaged area under
the ROC curve of 0.72. A score of two or higher was 64% (95%CI 46-
79) sensitive and 70% (95%CI 64-77) specific for an ADE.
Conclusions: The simple, history-based CRASH screening tool
may be useful to identify older drivers who need additional evaluation.
Prospective testing is needed to validate the tool and determine
optimal cut-points for clinical use.
Paper Session
Plenary
Thursday, May 3
11:00 am – 12:00 pm
P7
Out of Pocket Spending in the Last 5 Years of Life.
A. S. Kelley, 1 K. McGarry, 2 J. S. Skinner. 3 1. Geriatrics and Palliative
Medicine, Mount Sinai School of Medicine, New York, NY; 2.
Department of Economics, University of California, Los Angeles, Los
Angeles, CA; 3. Department of Economics, Dartmouth College,
Hanover, NH.
Background: A key objective of the Medicare program was to
reduce the risk of financial catastrophe arising from out-of-pocket
(OOP) health-related expenditures among older adults. Yet little is
known about the financial risks faced by Medicare beneficiaries related
to the death of a household head or spouse. We aimed to measure
risks to financial security arising from OOP health-related expenditures
among a nationally representative cohort of adults over
age 65.
Methods: We included participants from the Health and Retirement
Study (HRS) aged 65 years or older, who died between 2003 -
2008 (N = 3,809). We used detailed HRS survey data for each subject
and spouse, when applicable, to examine total OOP health-related
expenditures in the 5 years preceding the subject’s death. We also
measured OOP spending by category of spending (e.g. nursing home,
insurance, and others) and examined OOP spending stratified by
cause of death and quartile of household wealth.
Results: Average OOP expenditures in the five years prior to
death were $34,497.99 (median, $20,876; 90th percentile, $77,910) for
individuals and $46,767 (median $36,874; 90th percentile, $87,081) for
married couples in which one spouse dies. Median expenditures were
84% of median net financial wealth. By cause of death, individuals’
average total spending ranged from $33,699 for those with infectious
disease to $59,314 for those with Alzheimer’s disease. Spending on
long-term care needs was substantial. For the entire sample, 24% of
spending was for nursing home care and 9% for helpers and other expenses
to retain independence at home. Spending differed sharply by
wealth; ranging from $20,241 in the lowest wealth quartile to $49,477
in the highest.
Conclusion: Despite nearly universal insurance coverage under
the Medicare program, older adults face considerable financial risk
from out-of-pocket medical expenses in the last 5 years of life. Longterm
care expenses appear large yet insurance coverage for these expenses
is limited.Wealth-related differences in the components of care
could exacerbate existing inequalities in well-being at the end of life.
P8 Encore Presentation
Geriatric versus General Medical Conditions have Opposite Effects
on Overall Quality of Ambulatory Care.
L. Min, 1,2 E. Kerr, 1,2 C. Blaum, 1,2 C. Cigolle, 1,2 D. Reuben, 4
N. Wenger. 4,3 1. Medicine, University of Michigan, Ann Arbor, MI; 2.
GRECC and Center for Clinical Management Research, VA
Healthcare Systems, Ann Arbor, MI; 3. RAND, Santa Monica, CA; 4.
UCLA, Los Angeles, CA.
Supported By: Agency for Healthcare Quality and Research (Min
and Blaum), VA Healthcare System Health Services Research
(Kerr) and the Geriatric Research Clinical Care Center (GRECC,
Min, Cigolle, and Blaum), Hartford Foundation (Min), RAND
(Wenger, Min), NIA-Pepper Center (Min), NIH-LRP (Min), NIH-
K08 (Cigolle).
Background: Contrary to expectations, patients with greater comorbidity
receive better - rather than worse - quality of care. We evaluated
whether time-consuming geriatric conditions differ from general
medical conditions in their effect on quality.
Sample: 644 older (age >=75) ambulatory care patients in the
Assessing the Care of Vulnerable Elders-2 (ACOVE-2) study.
Methods: Predictors: Condition counts, defined as general medical
(atrial fibrillation, coronary artery disease, heart failure, cerebrovascular
disease, diabetes, hypertension) vs geriatric (falls, dementia,
hearing impairment, incontinence, malnutrition, and
osteoporosis). Outcome: Overall quality of care (QOC) using 65
process-of-care quality indicators, calculated as a mean score across
preventive and eligible general medical, and geriatric-specific care
over 13 months. We used multivariable regression to test for relationships
between overall quality and both geriatric and general medical
condition counts, controlling for age, gender, functional status, and
number of primary care visits.
Results: General medical condition counts (mean 1.9, range 0-6)
were comparable to geriatric conditions (mean 1.6, range 0-4) but the
AGS 2012 ANNUAL MEETING
S3