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A DRG-based case-mix model was developed to measure hospital performance and to guide the hospital clusters' baseline budget adjustment.
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Among patients ( n = 264) for whom data were available for both periods, the changes in percentages of time in the target range were similar in the intervention cluster (baseline: 47.7%; follow-up 55.6%) and in the control cluster (baseline: 49.1%; follow-up: 52.3%; intervention effect: 5%; 95% confidence interval: -5% to 14%; P = 0.32).
The estimated curves of the different clusters are visualized in Figure 5. Analysis of the differences in cluster baseline characteristics and outcomes is shown in Table 1.
Medication use calculation adjusted for clustering, baseline medication use, and corresponding baseline biomedical measure (glycated haemoglobin (HbA1c) for oral hypoglycaemic agents; total cholesterol to high density lipoprotein cholesterol ratio for lipid lowering drugs; systolic blood pressure for antihypertensive drugs).
A few important differences existed between intervention and control clusters at baseline (table 1).
We excluded five clusters at baseline that did not meet eligibility criteria.
Finally, each patient's postbaseline cluster assignment was determined based on their closest Euclidean distance to each of the clusters at baseline.
The majority of patients (77%) belonged to either the "best" (n = 503) or the "second best" (n = 992) clusters at baseline.
In part two of our analyses, differences between clusters in baseline characteristics (mean [SD] or proportion) were tested with an ANOVA with post hoc Bonferroni or χ tests.
Change over time was ascertained by shifts in clusters from baseline to each postbaseline visit (end of year 1, 2, and 3).
Only after this decision had been made was the distribution of such clusters by baseline hormonal exposures examined by using the χ2 test.
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