269044 Electronic medical records and identification of individuals with morbid obesity in a rural Appalachian population

Tuesday, October 30, 2012 : 5:00 PM - 5:15 PM

Sandipan Bhattacharjee, MS , Department of Pharmaceutical Systems & Policy, School of Pharmacy, West Virginia University, Morgantown, WV
Mayank Ajmera, MS , Pharmaceutical Systems and Policy, West Virginia University, Morgantown, WV, WV
Jeff Cox, BS , WV Clinical and Translational Science Institute, West Virginia University, Morgantown, WV
Michael Denney, MA , WV Clinical and Translational Science Institute, West Virginia University, Morgantown, WV
Niti Armistead, MD , School of Medicine, Ruby Memorial Hospital, West Virginia University, Morgantown, WV
Frank Briggs, PharmD , Center for Quality Outcomes, West Virginia University, Morgantown, WV
Michael Sweet, PharmD , Center for Quality Outcomes, West Virginia University, Morgantown, WV
S. Suresh Madhavan, PhD, MBA , Department of Pharmaceutical Systems & Policy, West Virginia University, Morgantown, WV
Usha Sambamoorthi, PhD , Pharmaceutical Systems and Policy, School of Pharmacy, Morgantown, WV
     
Background: Obesity is a major public health concern throughout United States (US); particularly in the Appalachian region the prevalence of obesity is higher than 30%. The significant increase in the number of morbidly obese population in this region is a reason of immense concern. Morbid obesity being independent risk factors of diabetes and cardiovascular diseases, tend to be associated with higher healthcare utilization and expenditure. Thus identification and management of morbid obesity is crucial. Electronic Medical Records (EMRs) offer an opportunity to identify individuals with morbid obesity and manage their care.

Objective: The current study evaluated the use of EMRs in capturing BMI values and identifying individuals with morbid obesity for various chronic diseases such as diabetes, hypertension, congestive heart failure (CHF) and acute conditions such as acute myocardial infarction (AMI).

Methods: We used a retrospective cross-sectional study design. The study population consisted of individuals (N = 45,904) with an outpatient visit in year 2010 to a tertiary academic center in Appalachian region. Obesity-related chronic conditions – CHF (n = 4,104), diabetes (n = 18,450), and hypertension (N = 39,067) and AMI (N = 11,974) were identified using ICD9 codes. Obesity data were captured through linked electronic medical records. Body mass index (BMI) values were calculated based on height and weight using the standard formula available from the Centers for Disease Control and Prevention. Individuals with BMI values greater than or equal to 40 were defined as having morbid obesity. Percentages of individuals with BMI values for each condition were compared to evaluate capture of BMI values through EMR. Percentages of individuals with morbid obesity were compared across different chronic conditions and AMI.

Results: Among individuals with any of the chronic conditions listed above, 90% had an electronically recorded BMI value. This rate was highest for those with AMI at 93%. Rates of morbid obesity ranged from 19% among individuals with diabetes, 17% among those with CHF, 14% among those with hypertension and 11% among those with AMI. Conclusion: EMRs were able to capture BMI values for an overwhelming majority of the study population. Morbid obesity was highly prevalent among individuals with CHF, diabetes, hypertension and AMI. Bio-Informatics efforts need to incorporate electronic reminders/pop-ups of BMI values over 40, so that providers can counsel patients with chronic illnesses and morbid obesity on proper nutrition and physical activities for wellness and secondary prevention of chronic illness complications.

Learning Areas:
Chronic disease management and prevention
Other professions or practice related to public health
Public health or related research

Learning Objectives:
Identify individuals with chronic condition using ICD-9-CM codes. Define individuals with morbid obesity using Center for Disease Control and Prevention (CDC) definition. Assess body mass index values using the EMR record. Evaluate whether patient characteristics are associated with capture of obesity in EMR records using bivariate and multivariate models. Describe individuals with morbid obesity using bivariate and multivariate techniques.

Keywords: Obesity, Rural Health

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a health services researcher with a Masters in Pharmacy Administration. I am interested in chronic complex illness and the lifestyle factors associated with it. In this project I wanted to assess whether obesity rates are captured accurately by Electronic Medical Records in rural Appalachian population. I believe APHA conference will give me a good platform to disseminate the knowledge from my research project.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

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