Patient complexity in quality comparisons for glycemic control: an observational study

Clinical pharmacist practitioners on primary care teams play an important role in caring for complex patients with diabetes

PURPOSE

To evaluate whether clinical pharmacist practitioners (CPPs) are being utilized to care for patients with complex medication regimens and multiple chronic illnesses, we compared the clinical complexity of diabetes patients referred to CPPs in team primary care and those in care by other team providers (OTPs).

METHODS

In this cross-sectional comparison of patients with diabetes in the US Department of Veterans Affairs (VA) healthcare system in the 2017-2019 period, patient complexity was based on clinical factors likely to indicate need for more time and resources in medication and disease state management. These factors include insulin prescriptions; use of 3 or more other diabetes medication classes; use of 6 or more other medication classes; 5 or more vascular complications; metabolic complications; 8 or more other complex chronic conditions; chronic kidney disease stage 3b or higher; glycated hemoglobin level of ≥10%; and medication regime nonadherence.

RESULTS

Patients with diabetes referred to one of 110 CPPs for care (n = 12,728) scored substantially higher (P < 0.001) than patients with diabetes in care with one of 544 OTPs (n = 81,183) on every complexity measure, even after adjustment for age, sex, race, and marital status. Based on composite summary scores, the likelihood of complexity was 3.42 (interquartile range, 3.25-3.60) times higher for those in ongoing CPP care (ie, those with 2 or more visits) versus OTP care. Patients in CPP care also were, on average, younger, more obese, and had more prior outpatient visits and hospital stays.

CONCLUSION

The greater complexity of patients with diabetes seen by CPPs in primary care suggests that CPPs are providing valuable services in comprehensive medication and disease management of complex patients.

The Effect of Age and Comorbidities on the Association Between the Medicare STAR Oral Antihyperglycemic Adherence Metric and Glycemic Control

BACKGROUND

The Medicare STAR program for Medicare Advantage Plans that include drug benefits provides monetary incentives for health plans to achieve good adherence to oral antihyperglycemic (OAH) agents but does not account for differential case mix that could affect the ability of health plans to achieve the required quality metrics.

OBJECTIVE

To determine whether OAH adherence varies by age and comorbidities among patients aged 65 years or older and the extent to which adherence affects glycemic control across age and comorbidity strata.

METHODS

We studied 54,480 patients with diabetes aged > 65 years from the Colorado, Northwest, and Northern California regions of Kaiser Permanente who received OAH agents but not insulin in 2010. We calculated adherence using the proportion of days covered (PDC) method. Per the STAR program, hemoglobin A1c < 8% defined good glycemic control. We also defined poor control as A1c > 9%. We used modified Poisson regression to identify predictors of adherence and to determine its effects on A1c across age and comorbidity strata, adjusting for sociodemographics and medication-related variables.

RESULTS

The risk of being adherent to OAH declined moderately with an increasing number of comorbidities (risk ratio [RR] = 0.99, 95% CI = 0.98-1.00 for 1 comorbidity and RR = 0.90, 95% CI = 0.88-0.91 for 4 or more comorbidities). Adherence to OAH agents was associated with a 0%-3% increased risk of A1c < 8% across age and comorbidity categories, as well as a large decreased risk (RR = 0.55-0.73) of A1c > 9% for patients aged < 80 years or with < 3 comorbidities.

CONCLUSIONS

Among patients with diabetes aged > 65 years, having multiple comorbidities affects adherence. Adherence reduces the risk of poor A1c control among patients aged 65-79 years or with 2 or fewer comorbidities. Our results suggest that health plan case mix minimally influenced the Medicare STAR OAH adherence metric, but it may affect glycemic control quality measures, especially if a HEDIS-like measure of poor control were adopted.

DISCLOSURES

This study was supported by grant number 1R21DK103146-01A1 from the National Institute of Diabetes and Digestive and Kidney Disorders. Nichols currently receives grant funding from Boehringer-Ingelheim, Sanofi, Amarin Pharma, and Janssen Pharmaceuticals for other unrelated research projects. The other authors declare no conflicts of interest. This study was presented at the American Diabetes Association's 77th Scientific Sessions; June 9-13, 2017; San Diego, CA.

Risk-adjustment of diabetes health outcomes improves the accuracy of performance benchmarking

Benchmarking clinical performance by comparing diabetes health outcomes across healthcare providers drives quality improvement. Non-care related patient risk factors are likely to confound clinical performance, but few studies have tested this. This cross-sectional study is the first Australian investigation to analyse the effect of risk-adjustment for non-care related patient factors on benchmarking. Data from 4,670 patients with type 2 (n = 3,496) or type 1 (n = 1,174) were analysed across 49 diabetes centres. Diabetes health outcomes (HbA1c levels, LDL-cholesterol levels, systolic blood pressure and rates of severe hypoglycaemia) were risk-adjusted for non-care related patient factors using multivariate stepwise linear and logistic regression models. Unadjusted and risk-adjusted funnel plots were constructed for each outcome to identify low-performing and high-performing outliers. Unadjusted funnel plots identified 27 low-performing outliers and 15 high-performing outliers across all diabetes health outcomes. After risk-adjustment, 22 (81%) low-performing outliers and 13 (87%) high-performing outliers became inliers. Additionally, one inlier became a low-performing outlier. Risk-adjustment of diabetes health outcomes significantly reduced false positives and false negatives for outlier performance, hence providing more accurate information to guide quality improvement activity.

Longitudinal Analysis of Quality of Diabetes Care and Relational Climate in Primary Care

OBJECTIVE

To assess the influence of relational climate on quality of diabetes care.

DATA SOURCES/STUDY SETTING

The study was conducted at the Department of Veterans Affairs (VA). The VA All Employee Survey (AES) was used to measure relational climate. Patient and facility characteristics were gathered from VA administrative datasets.

STUDY DESIGN

Multilevel panel data (2008-2012) with patients nested into clinics.

DATA COLLECTION/EXTRACTION METHODS

Diabetic patients were identified using ICD-9 codes and assigned to the clinic with the highest frequency of primary care visits. Multiple quality indicators were used, including an all-or-none process measure capturing guideline compliance, the actual number of tests and procedures, and three intermediate continuous outcomes (cholesterol, glycated hemoglobin, and blood pressure).

PRINCIPAL FINDINGS

The study sample included 327,805 patients, 212 primary care clinics, and 101 parent facilities in 2010. Across all study years, there were 1,568,180 observations. Clinics with the highest relational climate were 25 percent more likely to provide guideline-compliant care than those with the lowest relational climate (OR for a 1-unit increase: 1.02, p-value <.001). Among insulin-dependent diabetic veterans, this effect was twice as large. Contrary to that expected, relational climate did not influence intermediate outcomes.

CONCLUSIONS

Relational climate is positively associated with tests and procedures provision, but not with intermediate outcomes of diabetes care.

Diabetes care quality is highly correlated with patient panel characteristics

INTRODUCTION

Health care reimbursement is increasingly based on quality. Little is known about how clinic-level patient characteristics affect quality, particularly in community health centers (CHCs).

METHODS

Using data from electronic health records for 4019 diabetic patients from 23 primary care CHCs in the OCHIN practice-based research network, we calculated correlations between a clinic's patient panel characteristics and rates of delivery of diabetes preventive services in 2007. Using regression models, we estimated the proportion of variability in clinics' preventive services rates associated with the variability in the clinics' patient panel characteristics. We also explored whether clinics' performance rates were affected by how patient panel denominators were defined.

RESULTS

Clinic rates of hemoglobin testing, influenza immunizations, and lipid screening were positively associated with the percentage of patients with continuous health insurance coverage and negatively associated with the percentage of uninsured patients. Microalbumin screening rates were positively associated with the percentage of racial minorities in a clinic's panel. Associations remained consistent with different panel denominators.

CONCLUSIONS

Clinic variability in delivery rates of preventive services correlates with differences in clinics' patient panel characteristics, particularly the percentage of patients with continuous insurance coverage. Quality scores that do not account for these differences could create disincentives to clinics providing diabetes care for vulnerable patients.

Clinical complexity and the effectiveness of an intervention for depressed diabetes patients

OBJECTIVES

In a trial completed in 2010, US patients with diabetes and depression were randomized to usual care or telephone cognitive behavioural therapy that emphasized physical activity. Twelve-month intervention effects were observed for blood pressure, depression, and pedometer-measured step-counts. This study examined variation in intervention effects across patient subgroups defined by a measure of clinical complexity.

METHODS

Three groups of patients were identified at baseline using the Vector Model of Complexity that recognizes socioeconomic, biological, behavioural, and other determinants of treatment response. Complexity-by-intervention interactions were examined using regression models.

RESULTS

Intervention effects for blood pressure, depression, and step-counts differed across complexity levels (each p < 0.01). Effects on Beck Depression Inventory scores were greater in the low-complexity group (-8.8) than in the medium- (-3.2) or high-complexity groups (-2.7). Physical activity effects also were greatest in the low-complexity group (increase of 1498 steps per day). In contrast, systolic blood pressure effects were greater among intervention patients with high complexity (-8.5 mmHg).

CONCLUSIONS

This intervention had varying impacts on physical and mental health depending on patients' clinical complexity. Physical activity and depressive symptom gains may be more likely among less complex patients, although more complex patients may achieve cardiovascular benefits through decreased blood pressures.

Patient complexity and diabetes quality of care in rural settings

PURPOSE

Even though pay-for-performance programs are being rapidly implemented, little is known about how patient complexity affects practice-level performance assessment in rural settings. We sought to determine the association between patient complexity and practice-level performance in the rural United States.

BASIC PROCEDURES

Using baseline data from a trial aimed at improving diabetes care, we determined factors associated with a practice's proportion of patients having controlled diabetes (hemoglobin A1c<or=7%): patient socioeconomic factors, clinical factors, difficulty with self-testing of blood glucose, and difficulty with keeping appointments. We used linear regression to adjust the practice-level proportion with A1c controlled for these factors. We compared practice rankings using observed and expected performance and classified practices into hypothetical pay-for-performance categories.

MAIN FINDINGS

Rural primary care practices (n=135) in 11 southeastern states provided information for 1641 patients with diabetes. For practices in the best quartile of observed control, 76.1% of patients had controlled diabetes vs 19.3% of patients in the worst quartile. After controlling for other variables, proportions of diabetes control were 10% lower in those practices whose patients had the greatest difficulty with either self testing or appointment keeping (p<.05 for both). Practice rankings based on observed and expected proportion of A1c control showed only moderate agreement in pay-for-performance categories (kappa=0.47; 95% confidence interval, 0.32-0.56; p<001).

PRINCIPAL CONCLUSIONS

Basing public reporting and resource allocation on quality assessment that does not account for patient characteristics may further harm this vulnerable group of patients and physicians.