Is guideline-adherent prescribing associated with quality of life in patients with type 2 diabetes?

Background

Guideline-adherent prescribing for treatment of multiple risk factors in type 2 diabetes (T2D) patients is expected to improve clinical outcomes. However, the relationship to Health-Related Quality of Life (HRQoL) is not straightforward since guideline-adherent prescribing can increase medication burden.

Objectives

To test whether guideline-adherent prescribing and disease-specific medication burden are associated with HRQoL in patients with T2D.

Methods

Cross-sectional study including 1,044 T2D patients from the e-VitaDM/ZODIAC study in 2012 in the Netherlands. Data from the diabetes visit, such as laboratory and physical examinations and prescribed medication, and from two HRQoL questionnaires, the EuroQol 5 Dimensions 3 Levels (EQ5D-3L) and the World Health Organization Well-Being Index (WHO-5) were collected. Twenty indicators assessing prescribing of recommended glucose lowering drugs, statins, antihypertensives and renin-angiotensin-aldosterone system (RAAS)-inhibitors and potentially inappropriate drugs from a validated diabetes indicator set were included. Disease-specific medication burden was assessed using a modified version of the Medication Regimen Complexity Index (MRCI). Associations were tested with regression models, adjusting for age, gender, diabetes duration, comorbidity, body mass index and smoking.

Results

The mean MRCI was 7.1, the median EQ5D-3L-score was 0.86 and the mean WHO-5 score was 72. Seven indicators included too few patients and were excluded from the analysis. The remaining thirteen indicators focusing on recommended start, intensification, current and preferred use of glucose lowering drugs, statins, antihypertensives, RAAS inhibitors, and on inappropriate prescribing of glibenclamide and dual RAAS blockade were not significantly associated with HRQoL. Finally, also the MRCI was not associated with HRQoL.

Conclusions

We found no evidence for associations between guideline-adherent prescribing or disease-specific medication burden and HRQoL in T2D patients. This gives no rise to refrain from prescribing intensive treatment in T2D patients as recommended, but the interpretation of these results is limited by the cross-sectional study design and the selection of patients included in some indicators.

Development and validation of prescribing quality indicators for patients with type 2 diabetes

AIM

Quality indicators are used to measure whether healthcare professionals act according to guidelines, but few indicators focus on the quality of pharmacotherapy for diabetes. The aim of this study was to develop and validate a set of prescribing quality indicators (PQIs) for type 2 diabetes in primary care, and to apply this set in practice. To take into account the stepwise treatment of chronic disease, clinical action indicators were specifically considered.

METHODS

Potential PQIs were derived from clinical practice guidelines and evaluated using the RAND/UCLA Appropriateness Method, a modified Delphi panel. Thereafter, the feasibility of calculating the PQIs was tested in two large Dutch primary care databases including >80 000 diabetes patients in 2012.

RESULTS

32 PQIs focusing on treatment with glucose, lipid, blood pressure and albuminuria lowering drugs, and on vaccination, medication safety and adherence were assessed by ten experts. After the Delphi panel, the final list of twenty PQIs was tested for feasibility. All PQIs definitions were feasible for measuring the quality of medication treatment using these databases. Indicator scores ranged from 18.8% to 90.8% for PQIs focusing on current medication use, clinical action and medication choice, and from 2.1% to 37.2% for PQIs focusing on medication safety.

DISCUSSION AND CONCLUSIONS

Twenty PQIs focusing on treatment with glucose, lipid, blood pressure and albuminuria lowering drugs, and on medication safety in type 2 diabetes were developed, considered valid and operationally feasible. Results showed room for improvement, especially in initiation and intensification of treatment as measured with clinical action indicators.

Dutch diabetes prevalence estimates (DUDE-1)

BACKGROUND

Recent decades have seen a constant upward projection in the prevalence of diabetes. Attempts to estimate diabetes prevalence rates based on relatively small population samples quite often result in underestimation. The aim of the present study was to investigate whether the Dutch diabetes prevalence estimate of 930 000 for 2013, based on a relatively small sample, still holds true when a larger population is studied using actual prevalence data.

METHODS

Data were collected from 92 primary care groups, including the total number of people with and without diabetes in 2013. Patients with diabetes were identified using the International Classification of Primary Care codes T90.02 (diabetes mellitus type 2; T2DM), T90.01 (diabetes mellitus type 1) and T90 (diabetes mellitus). Prevalence data were compared with previous estimates made in 2009. Diabetes prevalence was estimated using linear extrapolation.

RESULTS

Complete data were available from 67 (73%) care groups, which together provided care for 7 922 403 subjects; 431 396 patients were coded as having diabetes, of whom 406 183 were coded as having T2DM. Based on these results, the extrapolated Dutch diabetes prevalence was 914 387 (5.45%).

CONCLUSIONS

The results show that the previous estimate (reported in 2009), which was based on data collected in 2007, resulted in a <2% (~16 000) overestimation in diabetes prevalence in 2013 compared with the analysis presented. These results indicate that no upward adjustment in Dutch diabetes prevalence estimates is necessary. Repeated large-scale monitoring can help develop more accurate prevalence estimates and improve future prevalence predictions.