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.
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
Descriptive statistics were used to describe demographic characteristics (age, sex, race/ethnicity, and income) by the BMI categories. Means and standard deviations (SDs) were calculated for continuous variables, and percentages were calculated for categorical variables. Demographic characteristics were compared across BMI categories using the Cochran-Armitage trend test for categorical variables and analysis of variance for continuous variables.
Performance rates for each NCQA HEDIS measure were calculated as the percentage of individuals who achieved the measure overall and by BMI category. According to HEDIS specifications, patients without a BP measure or laboratory test result during the study period were counted as not being at goal for that measure.21 The Cochran-Armitage trend test was used to assess trends in performance rates with increasing BMI.
Multivariate logistic regression was used to examine the association between BMI and the individual HEDIS measures after adjusting for age, sex, race/ethnicity, income, number of comorbidities, and number of healthcare encounters during the study period. The presence of a linear trend across BMI categories was tested by including the median of each BMI category as a continuous independent variable in the model. All analyses were performed using SAS software version 9.2 (SAS Institute Inc, Cary, North Carolina).
Description of the Study Sample
The study sample was composed of 164,721 individuals with diabetes and a mean age of 56.9 (11.2) years. Overall, 12% of individuals were in the healthy-weight category, 30% were overweight, and 28%, 17%, and 14% were in obese categories I, II, and III, respectively (Table 2). About half of the individuals were male, approximately one-third were Hispanic, and slightly more than half resided in a census block with a household income of less than $50,000. The mean (SD) BMI was 32.4 kg/m2 (11.2 kg/m2), and BMI was inversely related to mean age. The proportion of males was lower at higher BMI. The proportions of whites and blacks and individuals in the lowest 2 income categories increased with increasing BMI (P for trend <.0001).
Screening and Control Rates
Nearly all (94%) individuals had an A1C test, 58% had a retinal examination, 93% had a lipid screening profile performed, and 95% had a documented screening for or evidence of nephropathy. In unadjusted analyses, there was a statistically significant trend toward an increase in performance rates for all screening measures with increasing BMI. Conversely, levels of control for A1C, LDL-C, and BP all decreased as BMI increased. Only 41% of all individuals achieved the optimal control target of <7% (<53 mmol/mol) for A1C. More than half (58%) achieved the LDL-C goal of less than 2.6 mmol/L, 82% achieved the BP goal of less than 140/90 mm Hg, and 54% achieved the BP goal of less than 130/80 mm Hg. The largest disparity across BMI categories was seen for BP less than 130/80 mm Hg, where control decreased across all BMI categories from 63% in healthy-weight individuals to 46% of individuals in obese class III (data not shown).
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