Page 130 - Effective healthcare cost containment policies Using the Netherlands as a case study - Niek W. Stadhouders

Chapter 6
otherwise10 (van der Geest and Varkevisser, 2008). Success rates are published with a one- year delay, which means that patients and insurers are expected to base their choice for the clinic in year t on quality data of year t-1 and earlier. We test the first hypothesis by employing a fixed effects panel regression:
1)
Where is the number of treatments in clinic in year . The success rate is given by , while (demographics) is the number of women between 30 and 40 in the province. As a robustness check, we test different lag structures.
The coefficient contains the combined effect of two contradicting mechanisms: increased quality primarily reduces the number of secondary treatments, while as a secondary effect it potentially increases the number of new patients through patient choice and/or active purchasing by insurance companies. In order to test whether quality improvements attract new patients, the secondary mechanism needs to be isolated. However, our database does not contain information on individual patients. Therefore, on a patient level no distinction can be made between first and secondary treatments. However, we are able to calculate the effect of improved success rates on the number of secondary treatments by assuming that the dropout rate after an unsuccessful attempt is fixed. In that case, the number of new treatments is equal to:
Where is the dropout rate. We use a fixed dropout rate of 50%. In Germany dropout rates between 40% and 50% are reported (Schröder et al., 2004; Viardot-Foucault et al., 2015). For sensitivity analysis, we range the dropout rate between 30% and 70%. To disregard general trends in the number of patients, we calculate market shares :
The regression specification is:
2)
3)
10 The competitive region is defined as the densely populated Randstad area (roughly the provinces Noord- Holland, Zuid-Holland and Utrecht). In the Appendix alternative definitions are used as robustness checks.
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To test the influence of travel distance, we interact the coefficient with a competitive region dummy ( ) (van der Geest and Varkevisser, 2008). To test the effect of managed competition, we add interaction coefficient dummy , which is 1 after the reform. Next, we estimate the effects of success rate on market share of new patients: