Richard W. Grant

Correlates of health-related quality of life in type 2 diabetes

Aims/hypothesisWe assessed the impact of medical comorbidities, depression, and treatment intensity on quality of life in a large primary care cohort of patients with type 2 diabetes.MethodsWe used the Health Utilities Index-III, an instrument that measures health-related quality of life based on community preferences in units of health utility (scaled from 0=death to 1.0=perfect health), in 909 primary care patients with type 2 diabetes. Demographic and clinical correlates of health-related quality of life were assessed.ResultsThe median health utility score for this population was 0.70 (interquartile range 0.39–0.88). In univariate analyses, older age, female sex, low socioeconomic status, cardiovascular disease, microvascular complications, congestive heart failure, peripheral vascular disease, chronic lung disease, depression, insulin use and number of medications correlated with decreased quality of life, while obesity, hypertension and hypercholesterolaemia did not. In multiple regression analyses, microvascular complications, heart failure and depression were most strongly related to decreased health-related quality of life, independently of duration of diabetes; in these models, diabetes patients with depression had a utility of 0.59, while patients without symptomatic comorbidities did not have a significantly reduced quality of life. Treatment intensity remained a significant negative correlate of quality of life in multivariable models.Conclusions/interpretationPatients with type 2 diabetes have a substantially decreased quality of life in association with symptomatic complications. The data suggest that treatment of depression and prevention of complications have the greatest potential to improve health-related quality of life in type 2 diabetes.

Clinical inertia in the management of Type 2 diabetes metabolic risk factors.

Aims  Delays in the initiation and intensification of medical therapy may be one reason patients with diabetes do not reach evidence‐based goals for metabolic control. We assessed intensification of medical therapy over time, comparing the management of hyperglycaemia, hypertension, and hyperlipidaemia.

Barriers to the treatment of hepatitis C : Patient, provider, and system factors

BACKGROUND: Hepatitis C virus (HCV) infection is both prevalent and undertreated. OBJECTIVE: To identify barriers to HCV treatment in primary care practice. DESIGN: Cross-sectional study. SETTING AND PARTICIPANTS: A cohort of 208 HCV-infected patients under the care of a primary care physician (PCP) between December 2001 and April 2004 at a single academically affiliated community health center. MEASUREMENTS: Data were collected from the electronic medical record (EMR), the hospital clinical data repository, and interviews with PCPs. MAIN RESULTS: Our cohort consisted of 208 viremic patients with HCV infection. The mean age was 47.6 (±9.7) years, 56% were male, and 79% were white. Fifty-seven patients (27.4% of the cohort) had undergone HCV treatment. Independent predictors of not being treated included: unmarried status (adjusted odds ratio [aOR] for treatment 0.36, P=.02), female gender (aOR 0.31, P=.01), current alcohol abuse (aOR 0.08, P=.0008), and a higher ratio of no-shows to total visits (aOR 0.005 per change of 1.0 in the ratio of no-shows to total visits, P=.002). The major PCP-identified reasons not to treat included: substance abuse (22.5%), patient preference (16%), psychiatric comorbidity (15%), and a delay in specialist input (12%). For 13% of the untreated patients, no reason was identified. CONCLUSIONS: HCV treatment was infrequent in our cohort of outpatients. Barriers to treatment included patient factors (patient preference, alcohol use, missed appointments), provider factors (reluctance to treat past substance abusers), and system factors (referral-associated delays). Multimodal interventions may be required to increase HCV treatment rates.

Association of diabetes‐related emotional distress with diabetes treatment in primary care patients with Type 2 diabetes

Aims  To characterize the determinants of diabetes‐related emotional distress by treatment modality (diet only, oral medication only, or insulin).

Genetic Risk Reclassification for Type 2 Diabetes by Age Below or Above 50 Years Using 40 Type 2 Diabetes Risk Single Nucleotide Polymorphisms

OBJECTIVE To test if knowledge of type 2 diabetes genetic variants improves disease prediction. RESEARCH DESIGN AND METHODS We tested 40 single nucleotide polymorphisms (SNPs) associated with diabetes in 3,471 Framingham Offspring Study subjects followed over 34 years using pooled logistic regression models stratified by age (<50 years, diabetes cases = 144; or ≥50 years, diabetes cases = 302). Models included clinical risk factors and a 40-SNP weighted genetic risk score. RESULTS In people <50 years of age, the clinical risk factors model C-statistic was 0.908; the 40-SNP score increased it to 0.911 (P = 0.3; net reclassification improvement (NRI): 10.2%, P = 0.001). In people ≥50 years of age, the C-statistics without and with the score were 0.883 and 0.884 (P = 0.2; NRI: 0.4%). The risk per risk allele was higher in people <50 than ≥50 years of age (24 vs. 11%; P value for age interaction = 0.02). CONCLUSIONS Knowledge of common genetic variation appropriately reclassifies younger people for type 2 diabetes risk beyond clinical risk factors but not older people.

Patient–Physician Connectedness and Quality of Primary Care

Context Continuity of care is a basic tenet of high-quality primary care, but the relationship between quality of care and the connection between patient and physician has not been rigorously studied. Contribution The researchers defined whether 155590 adults in a primary care network received most of their care from a specific physician, practice, or neither. Patients who were connected to a particular physician were more likely to have received recommended care than patients who were connected to a practice but not a physician. Caution The study involved only 1 network, which is one of many potential definitions of continuity, and selected quality measures. The Editors Persistent deficiencies exist in the quality of health care in the United States (14). Because primary care physicians are the first source of health care for most patients to receive preventive and chronic illness care, efforts to measure and improve quality of care have often focused on these physicians (57). In practice, however, many patients receive episodic care from different physicians (812). Patients without a regular source of care are less likely to receive care consistent with guidelines (1320). Continuity of care is a shared responsibility between physicians and patients. Even if physicians or practices treated all patients similarly, patients vary in their ability and willingness to adhere to recommendations. Performance measures originally designed for use in large populations are increasingly used to assess the quality of practices and individual physicians. One concern with this approach is that physicians who care for patients who are less willing or able to adhere to recommendations will seem to perform less well. To investigate this possibility, we developed the concept of physicianpatient connectedness. We use the term connectedness to describe the closeness of the relationship between a patient and an individual physician on the basis of a model predicting how likely a physician is to identify a patient as my patient. We hypothesized that patients highly connected to a specific physician would be more likely to receive care consistent with guidelines, according to common performance measures. We further hypothesized that differences in connectedness may contribute to health care disparities to the extent that connectedness is correlated with race or ethnicity and insurance status. We investigated these hypotheses in a network of primary care physicians affiliated with a large teaching hospital. We used a previously developed and validated algorithm (21, 22) to determine the connectedness of more than 150000 patients with a specific physician. The algorithm used the designated primary care physician field from the practice registration system along with patient age, time since most recent visit, and in-state residence. We then examined variation in the proportion of connected patients among practices and the association of connectedness with the performance of commonly used measures of health care quality. Methods Study Setting and Sample The Massachusetts General Hospital (Boston, Massachusetts) adult primary care network includes 181 primary care physicians working in 13 clinically and demographically diverse practices (4 community health centers and 9 hospital-affiliated practices). The practices use the same electronic billing and scheduling systems, and physicians have the same compensation plan and staffing resources. Patients must designate a primary care physician when registering for care. We identified all patients with a visit to 1 of these practices from 1 January 2003 to 31 December 2005 by using electronic billing records. During this time, 169024 unique patients were seen for 994431 visits. We excluded patients if they were younger than 18 years (n= 1924), had died (determined on the basis of review of social security records) (n= 2817), or were registered as having a primary care physician outside of the Massachusetts General Hospital network (n= 8693). The Massachusetts General Hospital institutional review board approved the study. Connecting Patients With Primary Care Physicians and Practices Figure 1 shows the process used to connect patients with a specific physician or practice. We previously developed and validated an algorithm to connect patients with a specific physician by having 18 primary care physicians review a list of all patients seen over 3 years (mean, 1029 patients per physician; range, 226 to 2372 patients per physician) and designate which patients they considered to be my patient (21, 22). The algorithm primarily uses the primary care physician designee field from the hospital registration system. However, as a stand-alone variable, its specificity (84.9%) would result in too many patients on a list being incorrectly identified as being connected to that physician (21). As a result, the final algorithm combined the primary care physician designee field with a logistic regression model that included patient age, time since most recent visit, in-state residence, and physician practice style (21). We defined the physician practice style variable according to the proportion of all visits by patients registered to the physician. Thus, physicians who were the registered provider for at least 70% of the patients they saw were categorized as following a solo-practice style, whereas physicians who were the registered provider for fewer than 70% of the patients they saw were designated as having a collaborative-practice style. The model variables were designed to provide a highly specific list of patients for a given physician (overall specificity, 93.7%; positive predictive value, 96.5% [range, 90.1% to 100%]) (21). Figure 1. Method of connecting patients with specific primary care physicians or practices. MGH = Massachusetts General Hospital; PCP = primary care physician. The square boxes represent the patient population seen in the MGH primary care network and their initial assessment based on listed provider. The hexagonal boxes represent the algorithms that connect patients to a specific physician or practice. The rounded boxes represent the disposition of the primary care population based on patientphysician connectedness. * Patients younger than 18 years and those who were deceased are also included in this category. Patients who could not be connected to a specific physician were connected to the primary care practice in which they received most of their care. Patients were not connected to a specific physician because they had a primary care physician in a given practice but did not meet threshold criteria (using the patientphysician connectedness algorithm), were only seen by physicians other than their registered primary care physician, were followed by a resident physician, or received care in a given practice but were not registered with a primary care physician in that practice. Patients who were followed by a resident physician were assigned to the practice in which the resident provided care. We developed criteria for connecting patients to individual practices by consensus in collaboration with physician practice representatives (Table 1). Patients who could not be assigned to either a physician or a practice with these methods were designated as unconnected. Table 1. Criteria Used to Define Whether Patients Not Connected to a Specific Physician Were Connected to a Specific Primary Care Practice Patient and Provider Characteristics and Performance Measures We obtained data from an electronic record repository for Massachusetts General Hospital and affiliated institutions (23). Available patient characteristics included date of birth, sex, race or ethnicity, primary language spoken, insurance status, number of outpatient office visits during the previous 3 years, and months since most recent outpatient visit. We obtained physician characteristics (age, sex, practice location, and years since medical school graduation) from the hospital registrar database. Physician performance measures focused on cancer screening and chronic disease management. Cancer screening measures were mammography for women age 42 to 69 years in the previous 2 years and without previous bilateral mastectomy; Papanicolaou cervical screening in the previous 3 years for women age 21 to 64 years without hysterectomy; and colonoscopy within 10 years, sigmoidoscopy or double-contrast barium enema within 5 years, or home fecal occult blood testing within 1 year for patients age 52 to 69 years without total colectomy. For patients with diabetes, we assessed 2 measures: hemoglobin A1c (HbA1c) and low-density lipoprotein cholesterol measured in the previous year (24). For patients with coronary artery disease, we assessed low-density lipoprotein cholesterol measured in the previous year (25). For persons who had HbA1c and low-density lipoprotein cholesterol testing, we also assessed the most recent value available and categorized HbA1c level as less than 8.0% or not and low-density lipoprotein cholesterol level as less than 2.59 mmol/L (<100 mg/dL) or not (26). We extracted data for these measures from electronic laboratory and imaging reports or billing data within the Partners Healthcare System on the basis of Healthcare Effectiveness Data and Information Set criteria (27). Statistical Analysis We first grouped patients by connectedness status and compared characteristics of physician-connected, practice-connected, and unconnected patients. To account for the repeated measures of patients from the same physician, we used generalized estimating equations techniques with compound symmetry correlation structure (PROC GENMOD [SAS, version 9.1.3, SAS Institute, Cary, North Carolina]) (28) in all statistical analyses for clustering effects. The physician was considered as the unit of cluster for physician-connected patients, and each patient was considered as an individual cluster for practice-connected patients. Becaus

Symptoms of Depression Prospectively Predict Poorer Self-Care in Patients with Type 2 Diabetes

Aims  To examine prospectively the association of depression symptoms with subsequent self‐care and medication adherence in patients with Type 2 diabetes mellitus.

Comparison of hyperglycemia, hypertension, and hypercholesterolemia management in patients with type 2 diabetes

PURPOSE Cardiovascular disease is the leading cause of death in patients with type 2 diabetes. We compared hyperglycemia management with the management of the cardiovascular disease risk factors hypertension and hypercholesterolemia in a cohort of type 2 diabetes patients. SUBJECTS AND METHODS We randomly selected 601 patients with type 2 diabetes seen at the outpatient practices of an academic medical center and assessed the care they received during an 18-month period. We compared proportions of patients who had hemoglobin A(1c) (HbA(1c)) levels, blood pressure, or total cholesterol levels measured; who had been prescribed any drug therapy if HbA(1c) levels, systolic blood pressure, or low-density lipoprotein (LDL) cholesterol levels exceeded recommended treatment goals; and who had been prescribed greater-than-starting-dose therapy if these values were above those of treatment goals. RESULTS Patients were less likely to have cholesterol levels (76%, n = 455) measured than HbA(1c) (92%, n = 552) levels or blood pressure (99%, n = 595; P <0.0001 for either comparison). The proportion of patients that received any drug therapy was greater for above-goal HbA(1c) (92%, n = 348) than for above-goal systolic blood pressure (78%, n = 274) or LDL cholesterol (38%, n = 82; P <0.0001 for each comparison). Similarly, patients whose HbA(1c) levels were above the treatment goal (80%, n = 302) were more likely to receive greater-than-starting-dose therapy, compared with those who had above-goal systolic blood pressure (62%, n = 218) and LDL cholesterol levels (13%, n = 28; P <0.0001). CONCLUSION In this cohort, hypercholesterolemia and hypertension were managed less aggressively than was hyperglycemia. Given the prevalence of cardiovascular disease in patients with type 2 diabetes, increased screening for hypercholesterolemia and more aggressive drug therapy for hypercholesterolemia and hypertension are needed.

Defining Patient Complexity From the Primary Care Physician's Perspective: A Cohort Study

BACKGROUND Patients with complex health needs are increasingly the focus of health system redesign. OBJECTIVE To characterize complex patients, as defined by their primary care physicians (PCPs), and to compare this definition with other commonly used algorithms. DESIGN Cohort study. SETTING 1 hospital-based practice, 4 community health centers, and 7 private practices in a primary care network in the United States. PARTICIPANTS 40 physicians who reviewed a random sample of 120 of their own patients. MEASUREMENTS After excluding patients for whom they were not directly responsible, PCPs indicated which of their patients they considered complex. These patients were characterized, independent predictors of complexity were identified, and PCP-defined complexity was compared with 3 comorbidity-based methods (Charlson score, Higashi score, and a proprietary Centers for Medicare & Medicaid Services algorithm). RESULTS Physicians identified 1126 of their 4302 eligible patients (26.2%) as complex and assigned a mean of 2.2 domains of complexity per patient (median, 2.0 [interquartile range, 1 to 3]). Mental health and substance use were identified as major issues in younger complex patients, whereas medical decision making and care coordination predominated in older patients (P<0.001 for trends by decade). Major independent predictors of PCP-defined complexity (P<0.001) included age (probability of complexity increased from 14.8% to 19.8% with age increasing from 55 to 65 years), poorly controlled diabetes (from 12.7% to 47.6% if hemoglobin A1c level≥9%), use of antipsychotics (from 12.7% to 31.8%), alcohol-related diagnoses (from 12.9% to 27.4%), and inadequate insurance (from 12.5% to 19.2%). Classification agreement for complex patients ranged from 26.2% to 56.0% when PCP assignment was compared with each of the other methods. LIMITATION Results may not be generalizable to other primary care settings. CONCLUSION Primary care physicians identified approximately one quarter of their patients as complex. Medical, social, and behavioral factors all contributed to PCP-defined complexity. Physician-defined complexity had only modest agreement with 3 comorbidity-based algorithms. PRIMARY FUNDING SOURCE Partners Community Healthcare, Inc.

Personalized Genetic Risk Counseling to Motivate Diabetes Prevention A randomized trial

OBJECTIVE To examine whether diabetes genetic risk testing and counseling can improve diabetes prevention behaviors. RESEARCH DESIGN AND METHODS We conducted a randomized trial of diabetes genetic risk counseling among overweight patients at increased phenotypic risk for type 2 diabetes. Participants were randomly allocated to genetic testing versus no testing. Genetic risk was calculated by summing 36 single nucleotide polymorphisms associated with type 2 diabetes. Participants in the top and bottom score quartiles received individual genetic counseling before being enrolled with untested control participants in a 12-week, validated, diabetes prevention program. Middle-risk quartile participants were not studied further. We examined the effect of this genetic counseling intervention on patient self-reported attitudes, program attendance, and weight loss, separately comparing higher-risk and lower-risk result recipients with control participants. RESULTS The 108 participants enrolled in the diabetes prevention program included 42 participants at higher diabetes genetic risk, 32 at lower diabetes genetic risk, and 34 untested control subjects. Mean age was 57.9 ± 10.6 years, 61% were men, and average BMI was 34.8 kg/m2, with no differences among randomization groups. Participants attended 6.8 ± 4.3 group sessions and lost 8.5 ± 10.1 pounds, with 33 of 108 (30.6%) losing ≥5% body weight. There were few statistically significant differences in self-reported motivation, program attendance, or mean weight loss when higher-risk recipients and lower-risk recipients were compared with control subjects (P > 0.05 for all but one comparison). CONCLUSIONS Diabetes genetic risk counseling with currently available variants does not significantly alter self-reported motivation or prevention program adherence for overweight individuals at risk for diabetes.