van Baal et al. BMC Public Health 2011, 11:163 http://www.biomedcentral.com/1471-2458/11/163 Page 7 of 11 Incident cases per 1,000 personyears Inflammatory bowel diseases Multiple sclerosis Schizophrenia Parkinson Epilepsy Glaucoma Macular degeneration Rheumatoid arthritis Dementia Osteoporosis Heart failure Stroke COPD Hearing impairments Arthritis Diabetes Cataracts Coronary heart disease 0.05 0.10 0.20 0.50 1.00 2.00 5.00 10.00 20.00 Figure 2 Incident cases per 1,000 (men plus women) in the Netherlands 2007. The black lines indicate prediction intervals using the fixed effects estimates only while the gray lines broken lines include the between GPRN variance. computerised files. We can only hope that they maintain their professional standards and adhere to the profes- sional standards as set forth in guidelines. Given these limitations regarding the crude data of our study, we employed methods of analysis that were chosen to take these potential sources of bias into account. The major challenge was to combine the data while doing justice to the heterogeneity between the different registries. This was achieved by modelling the data in a hierarchi- cal/multi-level fashion using generalized linear models, in which the registries were modelled as a random intercept. Since the goal of our study was explicitly to focus on the typical (in our case average) GPRN instead of average subject we have chosen a random effects model over a GEE model [29]. This assumes that there is such thing as an “average” practice, which can be used to extrapolate estimates valid for the population. An additional advantage of the random effects model is that it provided an estimate of the variability between GPRNs. We have presented this variability graphically and have incorporated it in our simulations and saw that especially for incidence the between GPRN variance