Publication
A set of four simple performance measures reflecting adherence to guidelines predicts hospitalization: a claims-based cohort study of patients with diabetes
Journal Paper/Review - Mar 1, 2016
Huber Carola A, Brändle Michael, Rapold Roland, Reich Oliver, Rosemann Thomas
Units
PubMed
Doi
Citation
Type
Journal
Publication Date
Issn Print
Pages
Brief description/objective
BACKGROUND
The link between guideline adherence and outcomes is a highly demanded issue in diabetes care. We aimed to assess the adherence to guidelines and its impact on hospitalization using a simple set of performance measures among patients with diabetes.
METHODS
We performed a retrospective cohort study, using health care claims data for adult patients with treated diabetes (2011-2013). Patients were categorized into three drug treatment groups (with oral antidiabetic agents [OAs] only, in combination with insulin, and insulin only). Performance measures were based on international established guidelines for diabetes care. Multivariate logistic regression models predicted the probability of hospitalization (2013) by adherence level (2011) among all treatment groups.
RESULTS
A total of 40,285 patients with diabetes were enrolled in 2011. Guideline adherence was quite low: about 70% of all patients received a biannual hemoglobin A1c measurement and 19.8% had undergone an annual low-density lipoprotein cholesterol test. Only 4.8% were exposed to full adherence including all performance measures (OAs: 3.7%; insulin: 7.7%; and in combination: 7.2%). Increased guideline adherence was associated with decreased probability of hospitalization. This effect was strongest in patients using OAs and insulin in combination.
CONCLUSION
Our study showed that measures to reflect physicians' guideline adherence in diabetes care can easily be calculated based on already available datasets. Furthermore, these measures are clearly linked with the probability of hospitalization suggesting that a better guideline adherence by physicians could help to prevent a large number of hospitalizations.