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Open Access Highly Accessed Research article

Documentation of body mass index and control of associated risk factors in a large primary care network

Stephanie A Rose1*, Alexander Turchin2, Richard W Grant3 and James B Meigs4

Author Affiliations

1 Division of General Internal Medicine, University of Kentucky, 740 South Limestone Street, Kentucky Clinic K507, Lexington, KY 40536, USA

2 Clinical Informatics Research and Development, Harvard University, and Division of Endocrinology, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA, 02115, USA

3 General Medicine Division, Massachusetts General Hospital, 50 Staniford Street, Boston, MA, 02114, USA

4 General Medicine Division, Massachusetts General Hospital, 50 Staniford Street, Boston, MA, 02114, USA

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BMC Health Services Research 2009, 9:236  doi:10.1186/1472-6963-9-236

Published: 16 December 2009

Abstract

Background

Body mass index (BMI) will be a reportable health measure in the United States (US) through implementation of Healthcare Effectiveness Data and Information Set (HEDIS) guidelines. We evaluated current documentation of BMI, and documentation and control of associated risk factors by BMI category, based on electronic health records from a 12-clinic primary care network.

Methods

We conducted a cross-sectional analysis of 79,947 active network patients greater than 18 years of age seen between 7/05 - 12/06. We defined BMI category as normal weight (NW, 18-24.9 kg/m2), overweight (OW, 25-29.9), and obese (OB, ≥ 30). We measured documentation (yes/no) and control (above/below) of the following three risk factors: blood pressure (BP) ≤130/≤85 mmHg, low-density lipoprotein (LDL) ≤130 mg/dL (3.367 mmol/L), and fasting glucose <100 mg/dL (5.55 mmol/L) or casual glucose <200 mg/dL (11.1 mmol/L).

Results

BMI was documented in 48,376 patients (61%, range 34-94%), distributed as 30% OB, 34% OW, and 36% NW. Documentation of all three risk factors was higher in obesity (OB = 58%, OW = 54%, NW = 41%, p for trend <0.0001), but control of all three was lower (OB = 44%, OW = 49%, NW = 62%, p = 0.0001). The presence of cardiovascular disease (CVD) or diabetes modified some associations with obesity, and OB patients with CVD or diabetes had low rates of control of all three risk factors (CVD: OB = 49%, OW = 50%, NW = 56%; diabetes: OB = 42%, OW = 47%, NW = 48%, p < 0.0001 for adiposity-CVD or diabetes interaction).

Conclusions

In a large primary care network BMI documentation has been incomplete and for patients with BMI measured, risk factor control has been poorer in obese patients compared with NW, even in those with obesity and CVD or diabetes. Better knowledge of BMI could provide an opportunity for improved quality in obesity care.