OBJECTIVE -To determine whether using the chronic care model (CCM) in an underserved community leads to improved clinical and behavioral outcomes for people with diabetes. RESEARCH DESIGN AND METHODS-This multilevel, cluster-design, randomized controlled trial examined the effectiveness of a CCM-based intervention in an underserved urban community. Eleven primary care practices, along with their patients, were randomized to three groups: CCM intervention (n ϭ 30 patients), provider education only (PROV group) (n ϭ 38), and usual care (UC group) (n ϭ 51).RESULTS -A marked decline in HbA 1c was observed in the CCM group (Ϫ0.6%, P ϭ 0.008) but not in the other groups. The magnitude of the association remained strong after adjustment for clustering (P ϭ 0.01). The same pattern was observed for a decline in non-HDL cholesterol and for the proportion of participants who self-monitor blood glucose in the CCM group (non-HDL cholesterol: Ϫ10.4 mg/dl, P ϭ 0.24; self-monitor blood glucose: ϩ22.2%, P Ͻ 0.0001), with statistically significant between-group differences in improvement (non-HDL cholesterol: P ϭ 0.05; self-monitor blood glucose: P ϭ 0.03) after adjustment. The CCM group also showed improvement in HDL cholesterol (ϩ5.5 mg/dl, P ϭ 0.0004), diabetes knowledge test scores (ϩ6.7%, P ϭ 0.07), and empowerment scores (ϩ2, P ϭ 0.02).CONCLUSIONS -These results suggest that implementing the CCM in the community is effective in improving clinical and behavioral outcomes in patients with diabetes. Diabetes Care 29:811-817, 2006D iabetes affects ϳ7% of the U.S. population and has reached epidemic proportions (1). Diabetes represents a significant public health burden worldwide by decreasing quality of life and causing death and disability at great economic cost (2). Though quality diabetes care is essential to prevent long-term complications, care often falls below recommended standards regardless of health care setting or patient population, emphasizing the necessity for system change (3-6).The chronic care model (CCM) (3,4,7,8) is a multifaceted framework for enhancing health care delivery. The model is based on a paradigm shift from the current model of dealing with acute care issues to a system that is prevention based (3,5,(7)(8)(9). The premise of the model is that quality diabetes care is not delivered in isolation and can be enhanced by community resources, selfmanagement support, delivery system redesign, decision support, clinical information systems, and organizational support working in tandem to enhance patient-provider interactions (3,4,7-13). Currently, few efforts exist to implement multifaceted approaches to improve quality of care in diabetes despite studies that demonstrate their proven effectiveness (3,4,11,14,15).The objective of the current study was to determine the effectiveness of an intervention based on the CCM in primary care settings. We hypothesized that patient clinical (glycemic, blood pressure, and lipid control), behavioral (selfmonitoring of blood glucose), psychological/psychosocial (qualit...

This prospective study with extended follow-up of a large cohort of elderly women has identified crash risk factors that can be measured in the clinical setting. Further study is needed to determine if interventions aimed at these risk factors can decrease the risk of motor vehicle crashes.

Existing knowledge from the literature demonstrates that primary prevention of diabetes complications is possible. However, patient-, provider-, and health system-based barriers may prevent implementation of evidence-based practices. This article explores barriers to diabetes care that are external to the patient and that may require particular attention when attempting to translate primary prevention strategies into clinical care in the community.Results from landmark clinical studies demonstrate that the complications of diabetes can be prevented or delayed by controlling risk factors such as hyperglycemia, 1,2 hypertension, 3,4 and hyperlipidemia. 5,6 Despite knowledge gained through these studies, adherence to established evidence-based treatment guidelines aimed at controlling these risk factors and utilization of preventive care services by both providers and patients are low. This ultimately may result in patient outcomes that are less than optimal. The solution to the lack of adherence by both providers and patients is not to lay blame, but rather to investigate the factors or barriers underlying nonadherence.Several factors are hypothesized to contribute to nonadherence, including patient-, provider-, and health care system-based issues. These factors are outlined in Figure 1.Preventive health care is the cornerstone of primary and secondary prevention of diabetes complications.

The beneficial effect of physical activity in the general population is well known, but, to the authors' knowledge, has not been reported for persons with insulin-dependent diabetes mellitus. In a cohort of 548 diabetes patients followed as part of the Pittsburgh Insulin-dependent Diabetes Mellitus Morbidity and Mortality Study, physical activity was ascertained by survey in 1981, and mortality was ascertained through January 1, 1988. Cases were also compared with non-diabetic sibling controls. Activity level among cases varied inversely with the occurrence of diabetic complications. Overall activity level was inversely related to mortality risk. Sedentary males (< 1,000 kcal/week) were three times more likely to die than active males (> 2,000 kcal/week). A similar, but statistically nonsignificant, relation was seen in females. Cox proportional hazards analysis controlling for potential confounders (age, body mass index, insulin dose, reported diabetes complications, cigarette smoking, and current alcohol drinking) similarly revealed that activity level was inversely associated with mortality risk. Comparison of cases with non-diabetic sibling controls identified similar activity levels for the two groups. The results suggest that activity is not detrimental with regard to mortality, and may in fact provide a beneficial effect in terms of longevity in diabetes patients.

Both individual medical conditions and comorbidity influence driving patterns in older drivers. Because it is common for older people to have several medical conditions simultaneously, comorbidity might be a more comprehensive measure of medical impact on driving.