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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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| Published in: | Journal of clinical psychology in medical settings 2014-03, Vol.21 (1), p.10-18 |
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| container_title | Journal of clinical psychology in medical settings |
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| creator | Funderburk, Jennifer S. Maisto, Stephen A. Labbe, Allison K. |
| description | 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. |
| doi_str_mv | 10.1007/s10880-013-9376-x |
| format | article |
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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.</description><identifier>ISSN: 1068-9583</identifier><identifier>EISSN: 1573-3572</identifier><identifier>DOI: 10.1007/s10880-013-9376-x</identifier><identifier>PMID: 24158242</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>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</subject><ispartof>Journal of clinical psychology in medical settings, 2014-03, Vol.21 (1), p.10-18</ispartof><rights>Springer Science+Business Media New York (outside the USA) 2013</rights><rights>Springer Science+Business Media New York (outside the USA) 2013.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c357t-6a7dd9c795cf8d4779586b9d0d2ead6fd69bd89b5b21cb15cd0db70d5e43489a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24158242$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Funderburk, Jennifer S.</creatorcontrib><creatorcontrib>Maisto, Stephen A.</creatorcontrib><creatorcontrib>Labbe, Allison K.</creatorcontrib><title>Health-Related Outcomes Associated with Patterns of Risk Factors in Primary Care Patients</title><title>Journal of clinical psychology in medical settings</title><addtitle>J Clin Psychol Med Settings</addtitle><addtitle>J Clin Psychol Med Settings</addtitle><description>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. 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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.</abstract><cop>Boston</cop><pub>Springer US</pub><pmid>24158242</pmid><doi>10.1007/s10880-013-9376-x</doi><tpages>9</tpages></addata></record> |
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| 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 |
| title | Health-Related Outcomes Associated with Patterns of Risk Factors in Primary Care Patients |
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