Health-related outcomes associated with patterns of risk factors in primary care patients

Jennifer S. Funderburk, Stephen A. Maisto, Allison K. Labbe

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)10-18
Number of pages9
JournalJournal of Clinical Psychology in Medical Settings
Issue number1
StatePublished - Mar 2014


  • Latent class analysis
  • Multiple risk factors
  • Primary care
  • Regression
  • Veterans

ASJC Scopus subject areas

  • Clinical Psychology


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