Process of Care Compliance Is Associated With Fewer Diabetes Complications
Published Online: January 10, 2014
Felicia J. Bayer, PhD; Deron Galusha, MS; Martin Slade, MPH; Isabella M. Chu, MPH; Oyebode Taiwo, MBBS, MPH; and Mark R. Cullen, MD
In 2010, the estimated prevalence of diabetes among adults ranged from 5% to 13% across the United States and its territories,1 accounting for substantial morbidity, mortality, and associated economic costs.2,3 Type 2 diabetes mellitus often develops 9 to 12 years before diagnosis4 and more than 50% of patients have at least 1 complication by that time.5 A significant body of research has established that effective treatments can significantly decrease the development and/or progression of complications.6-17
However, diabetes care remains suboptimal and varied in the United States.18-22 In an effort to improve outcomes, The National Diabetes Quality Improvement Project (1997) developed a comprehensive set of process and intermediate outcome measures to assess quality of care for diabetes patients that are now considered standards of care.20,23 These measures include annual screenings for lipids and microalbuminuria, at least 2 measurements of glycated hemoglobin (A1C) at least 30 days apart, and annual flu vaccinations, dilated eye exams, and foot examinations.23
A number of studies1,7,18,20-32 have used these performance measures to assess care provided to patients with diabetes. These studies provide evidence of the substantial gaps between national performance measures for diabetes care and actual care received by persons with diabetes in the United States.26
The present study examines 3 process measures of diabetes care: A1C tests, lipid tests, and urine screening tests for microalbuminuria; and their association with 4 subsequent complications of diabetes: coronary artery disease (CAD), stroke, heart failure (HF), and renal disease (RD); in a cohort of employees of a large national manufacturing company. This cohort provides an opportunity to study this issue in a geographically, ethnically, and socioeconomically diverse population with rich and uniform healthcare benefits. This study contributes to the literature by identifying systematic reasons for differences in care among privately insured Americans and examining whether adherence to recommended processes of care is associated with better outcomes.26,33
The study setting was a large (approximately 36,000 employees) US-based manufacturing company with uniform healthcare benefits in 22 states that generated claims data during the years of the study. We examined the relationships between the 3 measures of the quality of diabetes care and the risk of major diabetes complications to determine whether employees with diabetes who received all 3 quality-of-care measures in a baseline year (2003) demonstrated a lower risk for developing any of 4 complications over the 6-year observation period (2004-2009). Baseline characteristics suspected to impact outcome risk and/or likelihood of receiving optimal care included in the multivariate model were age, sex, ethnicity, marital status, income, insulin use, comorbidities, and smoking. Due to data limitations, we were unable to assess the possible confounding roles of obesity and duration of diabetes. Data on annual flu vaccinations, dilated eye exams, and foot examinations were not available; thus, these elements were not included in our model.
This was a retrospective cohort study using administrative data on employees collected between January 1, 2001, and December 31, 2009. The majority of the employees held hourly manufacturing jobs; minorities and women were well represented. Although employees could select from a menu of health benefits, only a single preferred provider organization (PPO) network was available to the entire population. Employees who did not opt for this PPO were excluded (3%). The panel was restricted to those employed by the manufacturer for some part of 2001 and 2002, and for all of 2003, the year during which quality of care measures were assessed. Health outcomes were assessed from claims between January 1, 2004, and December 31, 2009.
During the “lead-in” period (2001-2002), diabetes status was ascertained from the claims data based on International Classification of Diseases, Ninth Revision (ICD-9) codes for the condition on a submitted medical claim for at least 1 of the following: a hospitalization, emergency department visit, 2 office visits, or 1 prescription for a diabetes-specific medication. Participants were included in the final data set if they were between 18 and 64 years old and employed throughout 2003 and for at least 1 month in the observation period (2004-2009). Employees with diabetes who had any 1 of the 4 end point complications coded in a medical claim in 2001 or 2002 and women with a claim containing an ICD-9 code for gestational diabetes were excluded. Those with claims for 1 of the 4 complications in the baseline year (2003) were included in the panel, but were excluded from the analysis for that complication. A total of 1797 subjects were available for analysis.
Process measures of diabetes care (ie, 2 A1C tests at least 30 days apart and at least 1 test each for lipids and microalbuminuria) were assessed in the baseline year (2003).34 Each employee with diabetes was categorized into 1 of 2 mutually exclusive groups: those who received all 3 measures of care in 2003 and those who did not. Cox proportional hazard regression models were used to assess the associations between diabetes care and time to each of the 4 complications (CAD, stroke, HF, and RD) or any of the 4 complications.
The databases available for analysis have been described in detail in previous publications.35 Briefly, health data were obtained from the health insurance plan, extraction of onsite occupational health medical records available for about 40% of the locations, and other linked employer-managed administrative databases. Data included ICD-9 codes from the Chronic Condition Data Warehouse list36 for disease diagnosis and National Drug Codes37 for prescription information. End point complications were identified based on ICD-9 codes during a face-to-face exam or hospitalization. For end point data, the subset of ICD-9 codes from the Chronic Condition Data Warehouse list for causes of kidney disease and heart disease unrelated to diabetes (eg, infectious or genetic etiology) were excluded. Demographic and health behavior data were obtained from the health medical record and human resources databases.
Vision claims were not available. Therefore, claims for routine eye exams and screening for diabetic retinopathy were not usable as measures of quality of diabetes care.
Sightlines DxCG Risk Solutions software (Verisk Analytics, Jersey City, New Jersey) was used to assign risk scores to the cohort during the baseline assessment and capture potential illness-related influences on treatment decisions. This actuarial program uses medical and pharmacy data with proprietary risk-adjustment algorithms to predict future health spending, and its risk scores are comparable to similar scores used by the Centers for Medicare & Medicaid Services and other insurance bodies for global risk estimates. A score of 1 in 2003 implies an actuarial prediction that an individual would consume the median amount of healthcare or the population in 2004. Databases were linked by an encrypted unique identifier to ensure human subject privacy. This study was approved by the authors’ institutional review boards.
Hazard ratios for each outcome (CAD, stroke, HF, RD, and any of the 4 conditions) were calculated for quality of diabetes care (exposures) and covariates. Time to event, defined as the number of months from the start of the observation period (January 1, 2004) to the first medical claim for a complication, was used for all primary analyses. Employees with diabetes who no longer received employer-provided health benefits were censored at the time of their last month of coverage. Records (n = 901) were censored during the observation period for the following reasons: retirement, layoff or termination (n = 647), change from PPO to health maintenance organization or Consolidated Omnibus Budget Reconciliation Act (COBRA) coverage (n = 77), and death or long-term disability (n = 30). For 119 subjects, the reason for loss of coverage could not be definitely established from the available records. Differences between the baseline characteristics of censored and uncensored employees were assessed with x2 tests. The Kaplan-Meier method was used to identify crude time-to-event models for the associations between the exposure and outcome measures.
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