Health-Related Outcomes Associated with Patterns of Risk Factors in Primary Care Patients

It is important to find ways to identify prevalent co-occurring health risk factors to help facilitate treatment programming. One method is to use electronic medical record (EMR) data. Funderburk et al. (J Behav Med 31:525–535, 2008 ) used such data and latent class analysis to identify three classe...

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Bibliographic Details
Published in:Journal of clinical psychology in medical settings 2014-03, Vol.21 (1), p.10-18
Main Authors: Funderburk, Jennifer S., Maisto, Stephen A., Labbe, Allison K.
Format: Article
Language:English
Subjects:
Alcohol use
Alcoholism - epidemiology
Behavior modification
Blood Pressure
Body Mass Index
Cardiovascular Diseases - epidemiology
Causality
Comorbidity
Depressive Disorder
Diabetes Mellitus - epidemiology
Electronic Health Records - statistics & numerical data
Emergency medical care
Emergency Service, Hospital - statistics & numerical data
Family Medicine
Female
Follow-Up Studies
General Practice
Health Behavior
Health Psychology
Health risk assessment
Health risks
Health services utilization
Health Status
Humans
Hypertension - epidemiology
Intervention
Latent class analysis
Male
Medicine
Medicine & Public Health
Mental depression
Mental Disorders - epidemiology
Middle Aged
Patients
Post traumatic stress disorder
Primary care
Primary Health Care - methods
Primary Health Care - statistics & numerical data
Questionnaires
Risk Factors
Smoking - epidemiology
Smoking cessation
Stress Disorders, Post-Traumatic - epidemiology
United States - epidemiology
United States Department of Veterans Affairs
Veterans
Veterans - statistics & numerical data
Citations: Items that this one cites
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Summary:It is important to find ways to identify prevalent co-occurring health risk factors to help facilitate treatment programming. One method is to use electronic medical record (EMR) data. Funderburk et al. (J Behav Med 31:525–535, 2008 ) used such data and latent class analysis to identify three classes of individuals based on standard health screens administered in Veterans Affairs primary care clinics. The present study extended these results by examining the health-related outcomes for each of these identified classes. Follow-up data were collected from a subgroup of the original sample ( N  = 4,132). Analyses showed that class assignment predicted number of diagnoses associated with the diseases that the health screens target and number of primary care behavioral health, and emergency room encounters. The findings illustrate one way an EMR can be used to identify clusters of individuals presenting with multiple health risk factors and where the healthcare system comes in contact with them.
ISSN:1068-9583
1573-3572
DOI:10.1007/s10880-013-9376-x