For univariate analysis, the coefficient of determination (R) derived from linear regression models was calculated for variables expected to explain the variability in drug expenditure.
It is apparent that the adjustment for ACGs reduces the variability in mean expenditure.
Prediction of the optimal energy target is relatively difficult in critically ill patients because of the high variability in resting energy expenditure during the course of severe illness as a result of alterations induced by shock, sedation, fever, reduction of lean body mass, surgical procedures, etc.
There is, however, a large variability in expenditure per DDD in the group of cheap molecules (the ACE), resulting that some molecules from that group have higher expenditure than some of the molecules from the expensive group (the sartans).
In subjects with prescriptions, the variability in expenditure explained by ACG accounts for 35.4% in adults and 22.4% in children.
More than half of the variability in prescription drug expenditure still remains to be explained.
FFM is recognised as the main predictor of the interindividual variability in resting energy expenditure.
In our setting, case-mix, measured with the ACG system, is the variable studied that explains more variability in prescription drug expenditure in adults.
Using Texas Medicaid (the third largest in the USA) claims data, we examined the variability in expenditures, sociodemographics, comorbidities, and persistence across seven levels of ED utilization/year (i.e., 1, 2, 3 4, 5 6, 7 9, 10 14, and ≥ 15 visits).
The objective was to examine the variability in health care expenditures, demographic characteristics, health conditions, and comorbidities across seven levels of ED utilization (i.e., 1, 2, 3 4, 5 6, 7 9, 10 14, and 15 or more outpatient visits).
