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The Danish Adult Diabetes Database (DADD) annually reports a quality indicator for lipid-lowering treatment of type 2 diabetes mellitus (T2DM) patients. This retrospective cohort study aims to A) investigate the reasons for inadequate or lacking lipid-lowering treatment and to B) assess the validity of the DADD indicator as a measure of quality of care.A) A pop-up questionnaire enquiring about reasons for lack of treatment was added to the clinicians' data entry tool in the Central Denmark Region. B) The DADD indicator was compared on a per-clinic basis with the achieved median low-density lipoprotein (LDL) cholesterol level and with an internationally widely used indicator of lipid-lowering treatment quality.A) A total of 3,491 patients were registered from 1 January 2013 to 28 February 2015. For 170 (62%) of 309 patients with an LDL level2.5 mmol/l who were not receiving lipid-lowering treatment, there was no "good" explanation for lacking treatment. Among 518 patients with an LDL level2.5 mmol/l despite lipid-lowering treatment, 259 (50%) did not receive high-intensity treatment. B) The DADD quality indicator was neither associated with the international quality indicator nor with the median per-clinic LDL level for T2DM patients.A) We found substantial potential for improvement of lipid management among T2DM patients in Denmark by initiating and/or intensifying lipid-lowering treatment. B) The current DADD indicator is not a valid measure of lipid-lowering quality of care.supported by the Rosa and Asta Jensen Foundation.not relevant.

Disease management programmes (DMPs) require a high degree of participation from general practitioners (GPs) in order to succeed. We aimed to describe the participation among Danish GPs in a DMP.A quality improvement project entitled the Chronic Care Compass (CCC) was introduced in 2010 by the Central Denmark Region. The project was based on DMPs targeting persons suffering from three chronic diseases (diabetes, chronic obstructive pulmonary disease and acute coronary syndrome). All GPs in the region were invited to participate. We obtained data from administrative registries and studied the participation and its association with characteristics of practices and patients. Differences in participation were assessed using binomial regression models.A total of 271 (69.1%) practices participated in the CCC. The participation was 28.9 percentage points (pp) (confidence interval (CI): 14.3; 43.6) lower among GPs who were older than 60 years versus younger than 50 years, 32.2 pp (CI: 19.1; 45.2) lower among GPs who provided few versus many chronic care consultations, 13.7 pp (CI: 1.7; 25.6) lower among GPs with lower versus medium practice gross income, and 16.9 pp (CI:6.1; 27.8) lower among GPs with a patient population with medium versus low degree of socio-economic deprivation.Participation in the CCC was lower among GPs who provided less chronic care, had a lower practice gross income and had a patient population with a higher degree of deprivation.The project was supported by the Research Unit for General Practice, Aarhus University, and the Lundbeck Foundation.not relevant.

Background An important prerequisite for the Chronic Care Model is to be able to identify, in a valid, simple and inexpensive way, the population with a chronic condition that needs proactive and planned care. We investigated if a set of administrative data could be used to identify patients with Chronic Obstructive Pulmonary Disease in a Danish population. Methods Seven general practices were asked to identify patients with known Chronic Obstructive Pulmonary Disease in their practices. For the 266 patients (population A), we used administrative data on hospital admissions for lung-related diagnoses, redeemed prescriptions for lung-diseases drugs and lung- function tests combined to develop an algorithm that identified the highest proportion of patients with Chronic Obstructive Pulmonary Disease with the fewest criteria involved. We tested nine different algorithms combining two to four criteria. The simplest algorithm with highest positive predictive value identified 532 patients (population B); with possible diagnosis of Chronic Obstructive Pulmonary Disease in five general practices. The doctors were asked to confirm the diagnosis. The same algorithm identified 2,895 patients whom were asked to confirm their diagnosis (population C). Results In population A the chosen algorithm had a positive predictive value of 72.2 % and three criteria: a) discharged patients with a chronic lung-disease diagnosis at least once during the preceding 5 years; or b) redeemed prescription of lung-medication at least twice during the preceding 12 months; or c) at least two spirometries performed at different dates during the preceding 12 months. In population B the positive predictive value was 65.0 % [60.8;69.1 %] and the sensitivity 44.8 % [41.3;48.4 %)] when the “uncertain” were added to where doctors agreed with the diagnosis. For the 1,984 respondents in population C, the positive predictive value was 72.9 % [70.8;74.8 %] and the sensitivity 29.7 % [28.4;31.0 %]. Conclusions An algorithm based on administrative data has been developed and validated with sufficient positive predictive value to be used as a tool for identifying patients with Chronic Obstructive Pulmonary Disease. Some of the identified patients had other chronic lung-diseases (asthma). The algorithm should mostly be regarded as a tool for identifying chronic lung-disease and further development of the algorithm is needed. Trial registration www.clinicaltrials.gov (NCT01228708)