Optimal Management of Diabetes Among Overweight and Obese Adults | Page 1

Published Online: January 20, 2014
Denison S. Ryan, MPH; Karen J. Coleman, PhD, MS; Jean M. Lawrence, ScD, MPH, MSSA; Teresa N. Harrison, SM; and Kristi Reynolds, PhD, MPH
The prevalence of obesity in the United States has risen dramatically in the past 50 years and continues to rise. Currently, more than 30% of the American population is considered obese.1,2 Obese individuals have more comorbidities and a higher rate of healthcare utilization than non-obese individuals.1,3 There are well-documented associations between obesity and several chronic  conditions, including diabetes.4-6 Diabetes is associated with high rates of cardiovascular disease, retinopathy, peripheral vascular disease, neuropathy, and nephropathy.7 Many of these complications can be delayed or prevented with effective management; however, many people with diabetes are not meeting the American Diabetes Association (ADA) goals for risk factor control.8-11 An analysis of the National Health and Nutrition Examination Survey 2001-2002 found that the percentage of adults in the United States with diabetes who were not at goal was 50% for glycated hemoglobin (A1C), 53% for blood pressure (BP), 65% for low-density lipoprotein cholesterol (LDL-C), and 49% for triglycerides.10

Obese patients have reported feeling stigmatized by healthcare professionals12; those who feel stigmatized may be more likely to  delay or avoid seeking healthcare services.13 In addition, a number of studies have documented negative healthcare provider attitudes toward obese patients,12,14,15 perhaps making it less likely that healthcare professionals will work with their obese patients on behavioral changes. The extent of the impact of stigmatization and bias on quality of care in obese patients is not well   understood, especially for those patients with chronic health conditions such as diabetes. Most of the available research relies on self-report rather than using medical record data to assess actual utilization.16,17

The aim of this study was to examine whether there were weight-related disparities in risk factor screening and control rates based  upon the National Committee for Quality Assurance (NCQA) Healthcare Effectiveness Data and Information Set (HEDIS) performance measures for diabetes among members of a large, integrated managed healthcare organization. The HEDIS performance measures are widely used by health plans in the United States to measure performance on quality of care and services across a range of health conditions including diabetes.18



This study was conducted within Kaiser Permanente Southern California (KPSC), an integrated healthcare system that provides comprehensive health services for approximately 3.5 million residents of southern California. Members of KPSC are socioeconomically diverse and broadly representative of the general population of southern California.19

Study Population and Sample Selection

We identified all KPSC members aged 18 to 75 years continuously enrolled in the health plan between July 1, 2007, and June 30, 2008, with no more than one 45-day gap in enrollment and with a diagnosis of diabetes mellitus (N = 169,077). A diabetes diagnosis was based on the HEDIS specifications of at least 2 outpatient or 1 inpatient International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes of 250, 357.2, 362.0, 366.41, or 648.0, or having been dispensed insulin or an oral anti-hyperglycemic medication (excluding metformin alone) during the measurement period. We then excluded 3901 members (2.3%) who did not have at least 1 health encounter with a recorded weight and height measurement during the study period and 455 with a body mass index (BMI) of less than 18.5 kg/m2 (0.3%), resulting in a final study sample of 164,721 individuals. Information from KPSC electronic health records (EHRs) including encounter, pharmacy, and laboratory data were linked with administrative membership information for these analyses. The study protocol was reviewed and approved by the KPSC Institutional Review Board.

Main Measures

Body mass index was calculated by dividing weight in kilograms by height in meters squared and categorized according to World Health Organization guidelines: healthy weight was a BMI of 18.5 to 24.9 kg/m2, overweight was a BMI of 25.0 to 29.9 kg/m2, obese class I was a BMI of 30.0 to 34.9 kg/ m2, obese class II was a BMI of 35.0 to 39.9 kg/m2, and obese class III was a BMI of 40 kg/m2 or more.20 If more than 1 BMI measurement was recorded during the study period, the first recorded measurement was used.

Quality-of-care measures for comprehensive diabetes care specified by NCQA included 4 screening measures: A1C testing, eye (retinal) examination, LDL-C screening, and medical attention for nephropathy (microalbuminuria screening or evidence of  nephropathy), and 5 control measures: A1C control (≤9.0% [≤75 mmol/mol] and <7.0% [<53 mmol/mol]), LDL-C control (<2.6  mmol/L), and BP control (<130/80 mm Hg and <140/90 mm Hg) (Table 1).21 The ADA recommendations for patients with diabetes  are BP less than 130/80 mm Hg and A1C less than 7.0%.8 All recorded quality-of-care measures were performed in the HEDIS  measurement year between July 1, 2007, and June 30, 2008, with the exception of lipid screening and retinal examination, which  could be performed in the measurement year or the year prior to the measurement year. 

Age was calculated from date of birth to July 1, 2007; income was based on 2000 census data geocoded at the block level; and race/ethnicity and number of healthcare encounters during the study period were ascertained from administrative data. All persons of Hispanic ethnicity were coded as Hispanic, regardless of race, and then non-Hispanic persons were categorized based on their race. Individuals with no information on race or Hispanic ethnicity were categorized as unknown. As an overall measure of disease burden, the Deyo adaption of the Charlson Comorbidity Index was calculated using ICD-9 diagnosis codes from inpatient and outpatient encounters within 12 months prior to the measurement period.22

Statistical Analysis

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Issue: January 2014
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