Barriers to following dietary recommendations in Type 2 diabetes

Aims  To evaluate barriers to following dietary recommendations in patients with Type 2 diabetes. Methods  We conducted focus groups and surveys in urban and suburban VA and academic medical centres. For the written survey, a self-administered questionnaire was mailed to a random sample of 446 patients with diabetes. For the focus groups, six groups of patients with diabetes (three urban, three suburban) were conducted, with 6–12 participants in each group. The focus groups explored barriers across various types of diabetes self-management; we extracted all comments relevant to barriers that limited patients’ ability to follow a recommended diet. Results  The written survey measured the burden of diabetes therapies (on a seven-point rating scale). Moderate diet was seen as a greater burden than oral agents (median 1 vs. 0, P  = 0.001), but less of a burden than insulin (median 1 vs. 4, P  < 0.001). A strict diet aimed at weight loss was rated as being similarly burdensome to insulin (median 4 vs. 4, P  = NS). Despite this, self-reported adherence was much higher for both pills and insulin than it was for a moderate diet. In the focus groups, the most commonly identified barrier was the cost (14/14 reviews), followed by small portion sizes (13/14 reviews), support and family issues (13/14 reviews), and quality of life and lifestyle issues (12/14 reviews). Patients in the urban site, who were predominantly African-American, noted greater difficulties communicating with their provider about diet and social circumstances, and also that the rigid schedule of a diabetes diet was problematic. Conclusions  Barriers to adherence to dietary therapies are numerous, but some, such as cost, and in the urban setting, communication with providers, are potentially remediable. Interventions aimed at improving patients’ ability to modify their diet need to specifically address these areas. Furthermore, treatment guidelines need to consider patients’ preferences and barriers when setting goals for treatment. Diabet. Med. (2004)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73213/1/j.1464-5491.2004.01342.x.pd

Metabolic control in a nationally representative diabetic elderly sample in Costa Rica: patients at community health centers vs. patients at other health care settings

<p>Abstract</p> <p>Background</p> <p>Costa Rica, like other developing countries, is experiencing an increasing burden of chronic conditions such as diabetes mellitus (DM), especially among its elderly population. This article has two goals: (1) to assess the level of metabolic control among the diabetic population age ≥ 60 years old in Costa Rica, and (2) to test whether diabetic elderly patients of community health centers differ from patients in other health care settings in terms of the level of metabolic control.</p> <p>Methods</p> <p>Data come from the project CRELES, a nationally representative study of people aged 60 and over in Costa Rica. This article analyzes a subsample of 542 participants in CRELES with self-reported diagnosis of diabetes mellitus. Odds ratios of poor levels of metabolic control at different health care settings are computed using logistic regressions.</p> <p>Results</p> <p>Lack of metabolic control among elderly diabetic population in Costa Rica is described as follows: 37% have glycated hemoglobin ≥ 7%; 78% have systolic blood pressure ≥ 130 mmHg; 66% have diastolic blood pressure ≥ 80 mmHg; 48% have triglycerides ≥ 150 mg/dl; 78% have LDL ≥ 100 mg/dl; 70% have HDL ≤ 40 mg/dl. Elevated levels of triglycerides and LDL were higher in patients of community health centers than in patients of other clinical settings. There were no statistical differences in the other metabolic control indicators across health care settings.</p> <p>Conclusion</p> <p>Levels of metabolic control among elderly population with DM in Costa Rica are not that different from those observed in industrialized countries. Elevated levels of triglycerides and LDL at community health centers may indicate problems of dyslipidemia treatment among diabetic patients; these problems are not observed in other health care settings. The Costa Rican health care system should address this problem, given that community health centers constitute a means of democratizing access to primary health care to underserved and poor areas.</p

Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm

BACKGROUND: Effective population management of patients with diabetes requires timely recognition. Current case-finding algorithms can accurately detect patients with diabetes, but lack real-time identification. We sought to develop and validate an automated, real-time diabetes case-finding algorithm to identify patients with diabetes at the earliest possible date. METHODS: The source population included 160,872 unique patients from a large public hospital system between January 2009 and April 2011. A diabetes case-finding algorithm was iteratively derived using chart review and subsequently validated (n = 343) in a stratified random sample of patients, using data extracted from the electronic health records (EHR). A point-based algorithm using encounter diagnoses, clinical history, pharmacy data, and laboratory results was used to identify diabetes cases. The date when accumulated points reached a specified threshold equated to the diagnosis date. Physician chart review served as the gold standard. RESULTS: The electronic model had a sensitivity of 97%, specificity of 90%, positive predictive value of 90%, and negative predictive value of 96% for the identification of patients with diabetes. The kappa score for agreement between the model and physician for the diagnosis date allowing for a 3-month delay was 0.97, where 78.4% of cases had exact agreement on the precise date. CONCLUSIONS: A diabetes case-finding algorithm using data exclusively extracted from a comprehensive EHR can accurately identify patients with diabetes at the earliest possible date within a healthcare system. The real-time capability may enable proactive disease management