Page 136 - Effective healthcare cost containment policies Using the Netherlands as a case study - Niek W. Stadhouders

Chapter 6
with a low dropout rate, the effect is smaller. One can assume that if quality is higher, the dropout rate will be lower, suggesting that our estimates are conservative. However, the results on the number of treatments is unaffected by the dropout rate (specification one). We can therefore definitely conclude that higher success rates have no significant effect on the total number of treatments.
Dropout rate Dropout rate Dropout rate Dropout rate
Table 6.6: Dropout rate regression results
30% -14.883*** 40% -13.776*** 60% -10.705*** 70% Success rate in t-1 7.466 4.178 -.457 Success rate in t-2 3.545 2.968 2.430 Interaction with region 19.964*** 15.380** 8.127* Interaction with reform 8.628 4.883 -.121 22.267*** 17.469*** 9.834**
-9.618** -2.104 2.230 5.567 -1.904 7.138*
Success rate in t
Interaction with region and reform
Demographics 3.46e-06 8.07e-06 .000012
Time trend -.0595 -.0211 .0216
Constant 125.788 48.895 -36.646
N (i,t) 243 (13,19) 243 (13,19) 243 (13,19)
R2 (within, between) (0.1913, 0.0408) (0.1579, 0.0181) (0.1077, 0.0012) (0.0935, 0.0000)
Other authors have used the Randstad area as definition of a competitive region (van der Geest and Varkevisser, 2008). Other definitions are also possible, which may influence the results. We test two alternatives: one narrower definition, which focuses on four clinics which are very close to each other (within 15 minutes of travel time), one broader definition, which is defined as the Randstad area including Tilburg and Eindhoven (all have competitors within 35 minutes of travel time). Furthermore, we separately test the effect of travel time to the nearest competitor in minutes as effect modifier (specification 3). We find that the competitive effect is predominant in the four clinics that are closest to each other, but the size of the effect is very similar over the different definitions of competitive regions (table 6.7). The third specification confirms that travel time is a significant effect modifier: each additional minute of travel time to the nearest competitor reduces the effect of an increase in success rate on market share by 0.4 percentage points. For example, a 1% increase in quality increases market share by 1.6% when travel time to the nearest competitor is 10 minutes, while this is 0.3% if travel time is 40 minutes. This confirms the hypothesis that patients do choose on quality when the nearest alternative is close, but the effect is too small to compensate the reductions in secondary treatments due to the increase in success rates.
.0000135 .0367 -66.915 243 (13,19)
*p<.10, **p<.05, ***P<.01
Competitive region definition
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