inclusion of social risk factors into risk assessment
- From: MarilynMann <mannm@xxxxxxxxxxx>
- Date: Tue, 09 Oct 2007 18:03:37 -0700
New Prediction Model To Guide Prevention Of Heart Disease
Article Date: 09 Oct 2007 - 3:00 PDT
A University of Rochester Medical Center researcher has been awarded a
National Institutes of Health grant to study whether accounting for
social risk factors, in addition to traditional predictors, can be
useful in assessing patients' risk and ultimately preventing coronary
heart disease.
Kevin A. Fiscella, M.D., M.P.H., associate professor in the Department
of Family Medicine, was awarded $823,199 by the National Heart, Lung
and Blood Institute, part of the NIH, to demonstrate that inclusion of
social risk factors -- socioeconomic status, race, ethnicity and
marital status -- into overall risk assessment and preventive
treatment guidelines offers the potential for reducing disparities in
coronary heart disease (CHD).
Traditional tools to make predictions -- indicators such as age and
gender, whether a patient is a smoker, diabetic or has high blood
pressure -- have been used for decades by health care professionals to
calculate a patient's risk of heart disease, Fiscella said. Such
predictions ultimately drive treatment.
A large body of research shows that a socioeconomic component also
predicts risk in determining heart disease. Disparities in coronary
heart disease mortality by socioeconomic status, race and ethnicity,
have been extensively documented, but translating them into clinical
practice to reduce CHD disparities has proven challenging and the data
is not yet incorporated into formal health assessments, Fiscella
said.
"We're proposing to modify the risk tools to include socioeconomic
factors and calculate the impact on prediction. If you're less
educated or make less money, you have a much higher risk of heart
disease. An inaccurate estimate impacts what tests and therapies a
patient receives, and when." As a result, more intensive behavioral
interventions focusing on diet, exercise, smoking cessation and
medical adherence may be implemented.
Fiscella's three-year study will use a database containing more than
30,000 adult patient cases representative of the nation's population.
It will compare the performance of predicting risk using only existing
tools, to a formula that incorporates socioeconomic factors with
conventional tools. His team will determine whether persons of lower
economic position could be more appropriately treated using an
updated, supplemented risk model.
Risk scoring using traditional criteria is currently available
electronically. When a physician sees a patient, their risk factor is
determined with just a few clicks.
"If we're successful in validating our prediction model, in the future
physicians may see it available through a paper version or through a
handheld device," Fiscella said.
A handful of studies have been done in Europe regarding the issue of
social risk factors. Findings show that adding social class into the
equation of risk prediction models does help to identify those in a
lower social class who are at higher risk of CHD, even in the UK,
where there is universal health care access.
"We know intuitively that social risk factors have a significant
impact on the care of a patient but no one has said how can we apply
this to decision-making at the bedside."
.
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