AIM: To compare clinical-metabolic monitoring and coronary risk status in people with type 2 diabetes from Australia, France and Latin America. METHODS: Retrospective analysis of data collected at primary care (except ANDIAB--secondary care) [corrected] matched for age, gender and disease duration. Measurements included participants' characteristics, performance frequency of clinical-metabolic process indicators, and percentage of clinical-metabolic outcomes at recommended target values. RESULTS: The weighted mean of the percentage of process performance was within 68 to 81%; that of outcomes at target dropped to 29 to 45%. Although statistically significant, differences among groups were far from those in healthcare budgets, and probably only of marginal clinical impact. The percentage of patients with low, slight or high coronary risk was similar in the three groups, with most people at high or very high risk. CONCLUSIONS: Despite the high difference in health per capita investment and system characteristics among countries, the study populations had striking similarities regarding the low percentage of participants who achieved cardiovascular risk factor and diabetes treatment goals. Therefore, differences in health budget and system characteristics would not be the main drivers in care quality. Diabetes education at every level and quality care registries would contribute to improve this situation and assess such improvement.
AIM: To compare clinical-metabolic monitoring and coronary risk status in people with type 2 diabetes from Australia, France and Latin America. METHODS: Retrospective analysis of data collected at primary care (except ANDIAB--secondary care) [corrected] matched for age, gender and disease duration. Measurements included participants' characteristics, performance frequency of clinical-metabolic process indicators, and percentage of clinical-metabolic outcomes at recommended target values. RESULTS: The weighted mean of the percentage of process performance was within 68 to 81%; that of outcomes at target dropped to 29 to 45%. Although statistically significant, differences among groups were far from those in healthcare budgets, and probably only of marginal clinical impact. The percentage of patients with low, slight or high coronary risk was similar in the three groups, with most people at high or very high risk. CONCLUSIONS: Despite the high difference in health per capita investment and system characteristics among countries, the study populations had striking similarities regarding the low percentage of participants who achieved cardiovascular risk factor and diabetes treatment goals. Therefore, differences in health budget and system characteristics would not be the main drivers in care quality. Diabetes education at every level and quality care registries would contribute to improve this situation and assess such improvement.
Authors: Alberto Barcelo; Armando Arredondo; Amparo Gordillo-Tobar; Johanna Segovia; Anthony Qiang Journal: J Glob Health Date: 2017-12 Impact factor: 4.413
Authors: Pablo Miranda-Machado; Fernando Salcedo-Mejía; Justo Paz Wilches; Juan Fernandez-Mercado; Fernando De la Hoz-Restrepo; Nelson Alvis-Guzmán Journal: BMC Cardiovasc Disord Date: 2019-01-08 Impact factor: 2.298