Het volume van chirurgische ingrepen en de impact ervan op ... - KCE

30 Volume Outcome KCE reports 113
As this experiment is practically infeasible, the case mix differences must be controlled
through various statistical means. Unfortunately, such approaches are never entirely
convincing to the sceptical reader. However, a decision must be made whether the
likely problems can fully explain the reported results. That is, can plausible biases due to
unexplained variables account for the observed results? 82
There are two general approaches to deal with differences in patient case mix among
hospitals. The first approach is to specify a procedure or a diagnosis as carefully and
narrowly as possible, resulting in a reasonably homogeneous group of patients. The
second approach, which can be combined with the first, is to include variables in the
analysis that may capture risk differences among patients included in the study.
The problem of improper case mix adjustment is crucial when the purpose is to
compare or to rank institutions (health care profiling). To some extend, the problem of
inadequate case mix adjustment is less troublesome if the focus is the pattern of
outcome rather than individual hospitals. In order for the case mix to compromise the
volume outcome relationship, there must be unmeasured differences in case severity
across hospitals, and they must be correlated with volume. Random errors will only
reduce one’s ability to detect a true relationship. Only if the omitted variables, such as
severity, are correlated with explanatory factors, such as volume, will the estimated
relationship be biased. 82
A complete discussion of proper case mix adjustment for health care outcome data is
beyond the scope of this chapter. For in-depth discussion we refer to the book by
Iezzoni. 95
Knowing that the method for adjustment and the variables elected as confounders can
influence the results, volume effects should always be presented with and without
adjustment for case mix, so that the reader can assess the magnitude and direction of
adjustment on the estimates.
3.6.1.3 The hierarchical nature of data
Health outcome data have a hierarchical structure by nature. This structure consists of
2 or 3 levels. Figure 3.5 shows the two level structure where several patients are
treated by 1 physician or provider. Figure 3.6 illustrates the 3 levels with several
patients treated by a physician or provider, and several providers belonging to the same
institution. The reality is often more complex, as physicians may perform in different
institutions (cross-classified model, Figure 3.5). Although specific models exist to analyze
cross-classified structures, their complexity and their lack of availability in standard
software prevented them to be used in volume outcome studies. Instead, physicians
performing in different institutions are usually considered in the analysis as different
physicians at each hospital. 94
Figure 3.5: An example of 2 level structure (level 1 patient, level 2 provider
or institution)