OBJECTIVE: Care remains suboptimal for a substantial proportion of the more than 17 million patients in the United States with diabetes. This review examines strategies for improving the quality of care for adult type 2 diabetic patients, through changes in provider behavior and modifications to the organization of care. SEARCH STRATEGY AND
INCLUSION CRITERIA: The researchers searched the MEDLINE® database, the Cochrane Collaboration's Effective Practice and Organisation of Care (EPOC) registry, article bibliographies, and relevant journals for experimental evaluations of quality improvement (QI) interventions involving outpatient care for adults with type 2 diabetes mellitus. The investigators included randomized or quasi-randomized controlled trials, controlled before-after studies, and interrupted time series in which at least one reported outcome involved changes in serum hemoglobin A1c or a measure of provider adherence to a recommended process of care.
DATA COLLECTION AND ANALYSIS: Two reviewers independently abstracted relevant data, including classifying the components of each QI intervention as provider education, provider reminders, facilitated relay of clinical information, patient education, promotion of self-management, patient reminders, audit and feedback, organizational change, or financial incentives. Certain categories were further subdivided into major subtypes (e.g., professional meetings for provider education and disease management for organizational change). The investigators also assessed the impact of clinical information systems as a mediator for interventions of all types. They compared different QI strategies in terms of the median effects achieved for glycemic control and for a generalized measure of clinician adherence. In addition, linear regression analyses were performed using methodologic features and QI types as predictors, taking into account baseline groups differences and study size.
MAIN RESULTS: Fifty-eight articles reporting a total of 66 trials met the established inclusion criteria. The most common interventions employed were organizational change in 40 trials, patient education in 28 trials, and provider education in 24 trials. Fifty-two trials involved interventions employing more than one QI strategy, with a median of 2 strategies per trial and a maximum of 5. The included trials reported a median absolute reduction in HbA1c of 0.48% (interquartile range: 0.20%, 1.38%), and a median improvement in clinician adherence of 4.9% (interquartile range: 3.8%, 15.0%). Trials in the lower 2 quartiles of sample size reported substantially larger effect sizes, as did non-randomized trials, strongly suggesting the presence of publication bias, with publication of smaller non-randomized trials occurring more often when reported improvements are large. Multifaceted trials reported a median reduction in HbA1c of 0.60% (interquartile range: 0.30%, 1.40%), compared to a median reduction of 0.0% (interquartile range: -0.08%, 0.16%) for trials of a single intervention (p=0.01). The benefit of employing more than one QI strategy appeared to persist among larger, randomized trials, but the small numbers of studies limits the reliability of this impression. The investigators did not find any specific type of QI strategy to confer unambiguous benefit. Provider education and disease management were the only strategies to approach statistical significance, compared with interventions absent these strategies.
CONCLUSION: The authors' analysis of quality improvement strategies for diabetes care showed no particular type of QI to have an advantage over others, but suggested that employing at least two strategies provides a greater chance of success than single-faceted interventions, in terms of improving glycemic control or provider adherence. These conclusions are limited by probable publication bias favoring smaller trials and non-randomized trials, and the confounding presence of multiple QI strategies in a given intervention, as well as important patient and provider factors, and organizational characteristics.

Background: Television viewing is associated with childhood obesity. Eating during viewing and eating highly advertised foods are 2 of the hypothesized mechanisms through which television is thought to affect children's weight.

Objectives: Our objectives were to describe the amounts and types of foods that children consume while watching television, compare those types with the types consumed at other times of the day, and examine the associations between children's body mass index (BMI) and the amounts and types of foods consumed during television viewing.

Design: Data were collected from 2 samples. The first sample consisted of ethnically diverse third-grade children, and the second consisted predominantly of Latino fifth-grade children. Three nonconsecutive 24-h dietary recalls were collected from each child. For each eating episode reported, children were asked whether they had been watching television. Height and weight were measured by using standard methods and were used to calculate BMI.

Results: On weekdays and weekend days, 17-18% and approximately 26% of total daily energy, respectively, were consumed during television viewing in the 2 samples. Although the fat content of the foods consumed during television viewing did not differ significantly from that of the foods consumed with the television off, less soda, fast food, fruit, and vegetables were consumed with the television on. The amount of food consumed during television viewing was not associated with children's BMI, but in the third-grade sample, the fat content of foods consumed during television viewing was associated with BMI.

Conclusions: A significant proportion of children's daily energy intake is consumed during television viewing, and the consumption of high-fat foods on weekends may be associated with BMI in younger children.

Objective: Hyperinsulinemia/insulin resistance is a risk factor for future type 2 diabetes. Fasting insulin and blood lipids serve as direct indicators of subsequent risk and as biochemical markers of metabolically significant adiposity. We examined the feasibility of obtaining fasting blood samples and report correlates of these biochemical markers in an understudied population sample.

Research Methods and Procedures: Fasting samples were requested from African-American girls, 8.00 to 10.99 years of age, for insulin, glucose, and lipid concentrations. Indices of insulin sensitivity and secretion were calculated and correlated with anthropometric, dietary, physical activity, and body composition data.

Results: Samples were obtained from 119 of 210 (57%) girls, varying from 5% to 86% across the four field centers. Glucose ranged from 71 to 104 mg/dL. Eleven percent had insulin concentrations >20 mU/liter. One girl had a triglyceride concentration >130 mg/dL. Thirteen percent had total cholesterol >200 mg/dL, whereas all subjects had high-density lipoprotein (HDL)-cholesterol of > or =35 mg/dL. Fourteen percent had low-density lipoprotein levels >130 mg/dL. Insulin concentrations showed consistently strong associations with measures of body weight (rs = 0.54 to 0.67); glucose, HDL, and LDL showed weaker correlations (rs = -0.11 to 0.22). Insulin concentration was highly correlated with indices of both insulin secretion and resistance (rs = 0.99).

Discussion: Fasting blood samples in young African-American girls were obtained with reasonable cooperation in three of the four field centers involved in this community-based study. Fasting insulin, glucose, LDL, and HDL concentrations may help evaluate future diabetes and cardiovascular risk in children of this age.

Objective: Television viewing has been associated with childhood obesity, although the mechanisms that link television viewing to higher BMI have not been established. Therefore, our objectives, in this report, were to describe the amount and types of foods that African-American girls consume while watching television and to examine the associations between African-American girls' BMI and the food they consume while watching television.

Research Methods and Procedures: Data were collected from 210 8- to 10-year-old African-American girls at four field centers by trained and certified nutritionists. Two nonconsecutive 24-hour dietary recalls were collected from each girl. For each eating episode reported, the girls were asked if they had been watching television while eating. Height and weight were collected using standard methods and used to calculate BMI.

Results: The data were analyzed separately by field center. The proportion of average daily energy intake that the girls consumed while watching television ranged from 26.9% to 35.0%. At all field centers, 40% to 50% of evening meals were consumed while watching television. None of the Spearman correlations between girls' BMI and the amount and type of foods consumed while watching television or at other times during the day were statistically significant (p > 0.05).

Discussion: This research revealed that a significant proportion of African-American girls' daily energy intake is consumed while watching television. Interventions that target reductions in food consumption while watching television or reducing television viewing may be effective strategies to decrease children's energy intakes. These results support a need for research to test the efficacy of these approaches.

Objective:

Care remains suboptimal for a substantial proportion of the more than 17 million

patients in the United States with diabetes. This review examines strategies for improving the quality of care for adult type 2 diabetic patients, through changes in provider behavior and modifications to the organization of care.

Search Strategy and Inclusion Criteria: The researchers searched the MEDLINE® database, the Cochrane Collaboration's Effective Practice and Organisation of Care (EPOC) registry, article bibliographies, and relevant journals for experimental evaluations of quality improvement (QI) interventions involving outpatient care for adults with type 2 diabetes mellitus. The investigators included randomized or quasi-randomized controlled trials, controlled before/after studies, and interrupted time series in which at least one reported outcome involved changes in serum hemoglobin A1c or a measure of provider adherence to a recommended process of care.

Data Collection and Analysis:

Two reviewers independently abstracted relevant data, including classifying the components of each QI intervention as provider education, provider reminders, facilitated relay of clinical information, patient education, promotion of self management,patient reminders, audit and feedback, organizational change, or financial incentives. Certain categories were further subdivided into major subtypes (e.g., professional meetings for provider education and disease management for organizational change). The investigators also assessed the impact of clinical information systems as a mediator for interventions of all types. They compared different QI strategies in terms of the median effects achieved for glycemic control and for a generalized measure of clinician adherence. In addition, linear regression analyses were performed using methodologic features and QI types as predictors, taking into account baseline groups differences and study size.

Main Results:

Fifty-eight articles reporting a total of 66 trials met the established inclusion criteria. The most common interventions employed were organizational change in 40 trials, patient education in 28 trials, and provider education in 24 trials. Fifty-two trials involved interventions employing more than one QI strategy, with a median of 2 strategies per trial and a maximum of 5. The included trials reported a median absolute reduction in HbA1c of 0.48% interquartile range: 0.20%, 1.38%), and a median improvement in clinician adherence of 4.9% (interquartile range: 3.8%, 15.0%). Trials in the lower 2 quartiles of sample size reported substantially larger effect sizes, as did non-randomized trials, strongly suggesting the presence of publication bias, with publication of smaller non-randomized trials occurring more often when reported improvements are large. Multifaceted trials reported a median reduction in HbA1c of 0.60% (interquartile range: 0.30%, 1.40%), compared to a median reduction of 0.0% (interquartile range: -0.08%, 0.16%) for trials of a single intervention (p=0.01). The benefit of employing more than one QI strategy appeared to persist among larger, randomized trials, but the small numbers of studies limits the reliability of this impression. The investigators did not find any specific type of QI strategy to confer unambiguous benefit. Provider education and disease management were the only strategies to approach statistical significance, compared with interventions absent these strategies.

Conclusion:

The authors' analysis of quality improvement strategies for diabetes care showed no particular type of QI to have an advantage over others, but suggested that employing at least two strategies provides a greater chance of success than single-faceted interventions, in terms of improving glycemic control or provider adherence. These conclusions are limited by probable publication bias favoring smaller trials and non-randomized trials, and the confounding presence of multiple QI strategies in a given intervention, as well as important patient and provider factors, and organizational characteristics.

OBJECTIVES: To determine (1) whether commercial health plans' coverage criteria for a costly technology-based medical intervention are consistent with recent clinical effectiveness evidence, (2) whether medical directors adhere to planwide coverage criteria when making coverage determinations for individual patients, and (3) if any organizational characteristics are associated with having more stringent coverage criteria or making more frequent coverage denials.

STUDY DESIGN: Case-based survey of medical directors of US commercial health plans.

METHODS: A close-ended survey was mailed to 346 medical directors meeting eligibility criteria, asking about the criteria specified in their plans' coverage policies for electrical bone growth stimulation (EBGS) and whether they would cover this intervention for a hypothetical patient with abnormal union of long-bone fracture.

RESULTS: Responses from 228 (66%) of the 346 directors indicated that approximately 72% of plans have a formal coverage policy for EBGS for long-bone fractures. More than 30% of plans specify that longer than 4 months must elapse before EBGS is attempted, although clinical studies do not support absolute waiting times. Directors of approximately 61% of plans with policies requiring extended waiting periods would nevertheless authorize EBGS for patients who did not meet this criterion.

CONCLUSIONS: Health plans apply varied criteria in coverage policies for technology-based treatments such as EBGS, but do not always adhere to stated criteria when determining coverage for individual patients. For-profit status, accreditation status, geographic location, and size of plan are not associated with being more or less likely to authorize EBGS.

Objective: To determine whether health maintenance organizations (HMOs) attract enrollees who use relatively few medical resources and whether a simple risk-adjustment system could mitigate or eliminate the inefficiency associated with risk selection.

Data Sources: The first and second rounds of the Community Tracking Study Household Survey (CTSHS), a national panel data set of households in 60 different markets in the United States.

Study Design: We use regression analysis to examine medical expenditures in the first round of the survey between enrollees who switched plan types (i.e., from a non-HMO plan to an HMO plan, or vice versa) between the first and second rounds of the survey versus enrollees who remained in their original plan. The dependent variable is an enrollee's medical resource use, measured in dollars, and the independent variables include gender, age, self-reported health status, and other demographic variables.

Data Collection Methods: We restrict our analysis to the 6,235 non-elderly persons who were surveyed in both rounds of the CTSHS, received health insurance from their employer or the employer of a household member in both years of the survey, and were offered a choice of an HMO and a non-HMO plan in both years.

Principal Findings: We find that people who switched from a non-HMO to an HMO plan used 11 percent fewer medical services in the period prior to switching than people who remained in a non-HMO plan, and that this relatively low use persisted once they enrolled in an HMO. Furthermore, people who switched from an HMO to a non-HMO plan used 18 percent more medical services in the period prior to switching than those who remained in an HMO plan.

Conclusions: HMOs are experiencing favorable risk selection and would most likely continue to do so even if employers adjusted health plan payments based on enrollees' gender and age because the selection is based on enrollee characteristics that are difficult to observe, such as preferences for medical care and health status.

We all have a stake in the size of the physician workforce. With too few physicians, access to care will be compromised; with too many, there will be strong pressures to overconsume health services. Increasing the production of U.S.-trained physicians by expanding physical resources of medical schools and creating new residency and fellowship positions will be costly and will have delayed, long-lasting effects on the supply of physicians' services. According to those who believe that physicians increase the demand for their own services, every additional physician would generate added health care costs for the length of a career, which now averages about 30 years. These increased expenditures would dwarf the short-term costs of expanding our capacity to train physicians.

Because new graduates are a small fraction of the total physician workforce, the supply of physicians would change little in the short run, even if it were possible to expand the number of training positions instantly. In an article in this issue (1), Richard Cooper forcefully argues that this delay is an important reason to take immediate action to increase the production of physicians. He projects that the United States will have 200 000 fewer physicians than we need in 2020. We agree that demographic and economic trends could increase the demand for physician services in the coming years, but we also believe that his forecast contains far too many uncertainties to serve as the basis for taking immediate action. We think that Cooper's analysis does not take account of important factors that could change the need for large increases in physician supply. In this commentary, we discuss the potential roles of a healthier aging population, changes in government policy, new technology, physician-induced demand for health care, and changes in the price of health care.

Purpose:

Little is known about how well clinicians are aware of their own adherence to clinical guidelines, an important indicator of quality. We compared clinicians' beliefs about their adherence to hypertension guidelines with data on their actual performance.

Methods:

We surveyed 139 primary care clinicians at three Veterans Affairs medical centers, asking them to assess their own adherence to hypertension guidelines. We then extracted data from the centers' clinical databases on guideline-concordant medication use and blood pressure control for patients cared for by these providers during a 6-month period. Data were collected for patients with hypertension and diabetes, hypertension and coronary disease, or hypertension with neither of these comorbid conditions.

Results:

Eighty-six clinicians (62%) completed the survey. Each clinician saw a median of 94 patients with hypertension (mean age, 65 years). Patients were treated with an average of 1.6 antihypertensive medications. Overall, clinicians overestimated the proportion of their patients who were prescribed guideline-concordant medications (75% perceived vs. 67% actual, P 0.001) and who had blood pressure levels 140/90 mm Hg on their last visit (68% perceived vs. 43% actual, P 0.001). Among individual clinicians, there were no significant correlations between perceived and actual guideline adherence (r = 0.18 for medications, r = 0.14 for blood pressure control; P 0.10 for both). Clinicians with relatively low actual guideline performance were most likely to overestimate their adherence to medication recommendations and blood pressure targets.

Conclusion:

Clinicians appear to overestimate their adherence to hypertension guidelines, particularly with regards to the proportion of their patients with controlled blood pressure. This limited awareness may represent a barrier to successful implementation of guidelines, and could be addressed through the use of provider profiles and point-of-service feedback to clinicians.