We arrived at the adjusted benefit in the intervention groups by accounting for potential savings or excess expenditures in the CG (adjusted benefit = savings — [costs CG at visit 1 X 2 – costs CG at visit 2 X 2]). The net benefit in the first year after intervention was calculated by subtracting the intervention costs from the adjusted benefit. Finally, the benefit-cost ratio was determined as the ratio of adjusted benefits and intervention costs. The same method was used to calculate the incremental savings and benefits of the educational activities including the IEP when compared to standard patient education.
Baseline demographic (sex, age) and baseline clinical variables (lung function) were compared between the groups using x2 tests for categorical variables and t tests for continuous variables. For comparison of study data with normal distribution, we used the Kolmogorov-Smirnov test. In case of normal distribution of continuous variables, we used the t test for group-specific before/after comparisons. Bonferroni tests were applied for the respective group comparisons. More info
If no normal distribution was assumed following the Kolmog-orov-Smirnov test, patient data for the baseline and follow-up periods were compared intraindividually using nonparametric analysis methods. We used the Wilcoxon signed-rank test for continuous variables. Possible differences between study groups were analyzed using the Mann-Whitney U test.
A two-tailed p value < 0.05 was regarded as statistically significant. In order to ensure comparability between groups, all health-care resource data were adjusted to the distribution of asthma severity degrees in the largest of the three study groups. The weighted average of asthma-severity specific rates (eg, costs), with weights taken from the SPMP group (which was defined as the “standard population,” as it is the largest one in patient numbers), provides for each other population (IEP and control group) a summary rate (eg, costs) that reflects the costs that would have been expected if the populations compared had had identical distributions of asthma severity degrees. We have chosen asthma severity as weighting factor as it has a strong impact on asthma-related costs. Similar approaches are applied in other disease areas, eg, in comparisons of cancer mortality rates over different decades with changing age structures of underlying populations.