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The American Journal of Managed Care September 2012
Asthma Expenditures in the United States Comparing 2004 to 2006 and 1996 to 1998
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Impact of Clinical Complexity on the Quality of Diabetes Care
LeChauncy D. Woodard, MD, MPH; Cassie R. Landrum, MPH; Tracy H. Urech, MPH; Degang Wang, PhD; Salim S. Virani, MD; and Laura A. Petersen, MD, MPH
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Impact of Clinical Complexity on the Quality of Diabetes Care

LeChauncy D. Woodard, MD, MPH; Cassie R. Landrum, MPH; Tracy H. Urech, MPH; Degang Wang, PhD; Salim S. Virani, MD; and Laura A. Petersen, MD, MPH
We examined the impact of clinical complexity defined by comorbidity count and illness burden on comprehensive diabetes care, including blood pressure, glycemic, and lipid management.
Objectives: To assess the impact of clinical complexity on 3 dimensions of diabetes care.


Study Design: We identified 35,872 diabetic patients receiving care at 7 Veterans Affairs facilities between July 2007 and June 2008 using administrative and clinical data. We examined control at index and appropriate care (among uncontrolled patients) within 90 days, for blood pressure (<130/80 mm Hg), glycated hemoglobin (<7%), and low-density lipoprotein cholesterol (<100 mg/dL). We used ordered logistic regression to examine the impact of complexity, defined by comorbidities count and illness burden, on control at index and a combined measure of quality (control at index or appropriate follow-up care) for all 3 measures.


Results: There were 6260 (17.5%) patients controlled at index for all 3 quality indicators. Patients with >3 comorbidities (odds ratio [OR], 1.94; 95% confidence interval [CI], 1.67-2.26) and illness burden >2.00 (OR, 1.22; 95% CI, 1.13-1.32) were more likely than the least complex patients to be controlled for all 3 measures. Patients with >3 comorbidities (OR, 2.30; 95% CI, 2.07-2.54) and illness burden >2.00 (OR, 1.25; 95% CI, 1.18-1.33) were also more likely than the least complex patients to meet the combined quality indicator for all 3 measures.


Conclusions: Patients with greatest complexity received higher quality diabetes care compared with less complex patients, regardless of the definition of complexity chosen. Although providers may appropriately target complex patients for aggressive control, deficits in guideline achievement among all diabetic patients highlight the challenges of caring for chronically ill patients and the importance of structuring primary care to promote higher-quality, patient-centered care.


(Am J Manag Care. 2012;18(9):508-514)
Studies have shown that greater clinical complexity is associated with higher quality; however, it is unknown if these findings persist when using different complexity definitions.

  • We found that the most complex patients were more likely to meet blood pressure, glycemic, and lipid quality indicators than the least complex patients, regardless of complexity definitions chosen.

  • While providers may appropriately target complex patients for aggressive risk factor control, there is room for improvement for all diabetic patients.

  • These findings highlight the challenges of caring for chronically ill patients and the importance of implementing patient-centered approaches to chronic illness care.
The influence of clinical complexity on guideline adherence among chronically ill patients is an important consideration for quality-of-care assessments. This is particularly relevant for patients with diabetes, as approximately 80% of diabetic patients have at least 1 comorbid illness and 40% have 3 or more.1,2 The importance of comprehensive diabetes care, including glycemic, blood pressure (BP), and lipid control is widely acknowledged for most diabetic patients.3-7 Although improving decision support, clinical information systems, and self-management support has led to improvements in diabetes outcomes, studies consistently report suboptimal control across these dimensions.6-11 Because achieving guideline-recommended treatment goals may have the greatest benefit in preventing diabetes-related complications among the most complex and thus highest risk patients, assessing the magnitude of deficits in care for these patients is critical.

Recent studies examining the relationship between clinical complexity and quality of chronic illness care have generally found that greater complexity is associated with higher levels of quality.12-15 In our prior work, we found that patients with higher complexity, defined as having both diabetes concordant and discordant conditions, were more likely to receive guideline-recommended diabetes care.12 However, among diabetic patients, studies suggest that increasing the number, severity, and type of certain comorbidities predicts poorer self-management skills2,16 which may, in turn, result in poorer risk factor control. With the increasing number and complexity of comorbid conditions, both patients and healthcare providers may find risk factor control challenging. For example, compliance with clinical practice guidelines often requires that patients with multiple chronic conditions take numerous medications and make frequent visits for which adherence may be difficult.17 Further, healthcare providers are faced with time constraints and competing demands during office visits that may limit their ability to thoroughly address all clinical guidelines that pertain to an individual patient. Given these barriers, we sought to examine the relationship between 2 definitions of clinical complexity and quality of care for glycemic, BP, and lipid control among patients with diabetes.

METHODS

Study Population


We identified patients with diabetes who had a primary care visit between July 2007 and June 2008 at 7 midwestern Veterans Affairs (VA) facilities located in 3 states. We used the VA National Patient Care Database, VA fee-basis files, VA Decision Support System, and a VA network data warehouse which contains clinical and demographic information from patient medical records at the 7 facilities, to classify patients as having diabetes. We classified patients as having diabetes if they had the following: diagnoses codes indicating diabetes (2 outpatient codes or 1 inpatient code), filled prescriptions for diabetes medications (oral hypoglycemic agents or insulin), or at least 2 outpatient blood glucose readings >200 mg/dL recorded at least 1 day apart. Consistent with VA quality indicators,18 we excluded patients with documented limited life expectancy, including those receiving hospice care and those with metastatic cancer. To allow for equal opportunity for follow-up care, we also excluded patients who died during the study interval or follow-up period. We assigned each patient index dates based on the most recent reading for each measure (eg, the date of the last recorded BP reading for the hypertension measure). We also assessed each patient’s past engagement with the VA healthcare system, by identifying the number of primary care and specialty care visits in the prior year, anchored from the patient’s last primary care visit during the study interval. We included those specialty care clinics most likely to treat the comorbidities we studied and required the patient to have the coexisting condition appropriate to the clinic’s treating specialty (eg, depression and psychiatry).

Clinical Complexity Definitions

We defined clinical complexity using 2 different approaches. First, we used a count of 6 common comorbidities to define complexity: hypertension, ischemic heart disease, hyperlipidemia, depression, arthritis, and chronic obstructive pulmonary disease. Patients were categorized as having 0, 1, 2, or >3 of these coexisting conditions. Second, we used the Diagnostic Cost Group Relative Risk Score (DCG RRS), a measure of patient illness burden, to define clinical complexity.19 The DCG RRS is a ratio of the patient’s predicted cost to the average actual cost of the VA population. A score of 1.00 represents the cost of an “average” patient whereas a DCG RRS of <1.00 represents a lower-than-average cost (and illness burden), and a score of >1.00 represents a higher than- average illness burden. We categorized patients into 4 categories of increasing illness severity: DCG RRS <0.50, 0.50-0.99, 1.00-1.99, and >2.00.

Study Outcomes

We assessed the quality of diabetes care using the American Diabetes Association’s3 recommendations for BP (<130/80 mm Hg), glycemic (glycated hemoglobin [A1C] <7%), and low-density lipoprotein cholesterol (LDL-C <100 mg/dL) control using laboratory and vital sign readings obtained from the network data warehouse. Among those not meeting goals at the index visit or who did not have an index reading recorded, we examined a 90-day follow-up period from index to determine the receipt of appropriate follow-up care (ie, medication treatment intensification or controlled follow-up reading).12

Statistical Analyses

We determined the proportion of patients that were controlled at index and at the conclusion of a 90-day follow-up period for each of the 3 diabetes quality indicators. We used χ2 analyses to assess the difference in the proportions of patients controlled at each timepoint. To allow ample time for follow-up in response to uncontrolled readings, we also assessed the proportion of patients uncontrolled at index that received appropriate follow-up care (ie, medication treatment intensification or controlled reading) within 90 days of index for each quality indicator. Next, to examine a single longitudinal measure of quality, we combined patients who were controlled at index and those who received appropriate follow-up care. We then performed separate generalized ordered logistic regression analyses to examine the impact of each definition of clinical complexity on achieving control at index and the combined measure of quality. The models included a variable with 4 level-ordered values (0 = patient did not meet any of the quality indicators [ie, control at index or combined measure of quality]; 1 = patient met only 1 of the indicators; 2 = only 2 of the indicators; and 3 = all of the indicators). We adjusted all models for age, number of VA primary and specialty care visits in the prior year, and clustering of patients by facility. We controlled for visits to ensure that the study findings were not due solely to differences in healthcare utilization between the complexity groups.20 Also, because this analysis consisted of patients who received care in 7 different facilities, we adjusted for clustering to remove any potential facility-level variations.21 We conducted sensitivity analyses to determine the impact of a shorter (45 days) and longer (180 days) follow-up interval on the combined measure of quality (ie, control at index or appropriate followup care). We conducted the analyses using SAS v 9.2 (SAS Institute Inc, Cary, North Carolina) and Stata 10 (StataCorp LP, College Station, Texas). Institutional review boards at the Michael E. DeBakey VA Medical Center and Baylor College of Medicine, both in Houston, Texas, approved this study.

RESULTS

Of the 190,156 patients receiving care at the 7 VA facilities during the study period, 35,872 (18.9%) had diabetes and met the study inclusion criteria. Patient characteristics according to each clinical complexity definition are presented in Table 1. Mean age was lowest among those with no comorbid conditions (58.7 years). Patients with 3 or more comorbid conditions had higher levels of measured illness burden than patients with fewer conditions. The most complex patients, defined by DCG RRS >2.00, utilized VA primary and specialty care in the 1 year prior most often (7.1 and 5.4 visits, respectively).

Table 2 reports the number and proportion of patients that were controlled at index and 90 days following the patient’s last primary care visit for each quality indicator. We found that the proportion of patients controlled was significantly higher at 90 days compared with index for A1C and LDL-C (P <.001 for both comparisons). Similarly, when examining control for all 3 quality indicators, we found that 6260 patients (17.5%) were controlled at index and 6974 (19.4%) were controlled at 90 days (P <.001). We also examined the number and proportion of patients that were uncontrolled at index and that received appropriate follow-up care within 90 days for each clinical complexity group (Table 3). The proportion of patients that received appropriate follow-up care was different across groups for each quality indicator when measuring complexity by number of comorbidities (P <.001 for each comparison) and for BP and LDL-C when measuring complexity by DCG RRS (P <.001 for both comparisons).

In the ordered logistic regression analysis evaluating clinical complexity using number of comorbidities, patients with the highest number of comorbid conditions (>3 conditions) were more likely than those with no comorbid conditions to be controlled at index (odds ratio [OR] 1.94; 95% confidence interval [CI], 1.67-2.26) or to meet the combined measure of control at index or receipt of appropriate follow-up care for all 3 quality indicators (OR, 2.30; 95% CI, 2.07-2.54), adjusting for age, VA primary and specialty care visits, and clustering of patients at facilities. In addition, patients with the highest illness burden (DCG RRS >2.00) were more likely than those with the lowest illness burden (DCG RRS <0.50) to be controlled at index (OR, 1.22; 95% CI, 1.13-1.32) or to meet the combined measure of quality for all 3 quality indicators (OR, 1.25; 95% CI, 1.18-1.33) (Table 4). Our findings that patients with greater clinical complexity were more likely to receive high quality care across all 3 indicators persisted when we assessed a shorter (45 days) and longer (180 days) followup period.

DISCUSSION

 
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