van Baal et al. BMC Public Health 2011, 11:163 http://www.biomedcentral.com/1471-2458/11/163 is defined as anyone in the registration who has been diagnosed previous to 1 January 2007. Table 1 displays the diseases selected for our exercise, the registrations for which we had data on prevalence and incidence counts for those diseases and the ICPC-1 and E-codes we used to classify cases (ICPC-2 codes were mapped into ICPC-1 codes). Furthermore, for each of these 5 registrations the fol- lowing data were available: population size, as of January 1 2007; numbers of person years (the years lived by the total population in the GP registration during the regis- tration period) for 2007. All data were grouped by sex and 18 age classes (0-4, 5-9, etc., 85+). For diseases that are only relevant at old age we discarded lower age categories. Regression modelling strategy To capture the hierarchical structure of the data inci- dence and prevalence were estimated using general- ized linear mixed models [23]. The possible systematic Table 1 selection of diseases Disease name General practitioner registration networks for which incidence data was available General practitioner registration networks for which incidence data was available Page 3 of 11 differences between the GPRNs registrations were taken into account by including a GPRN identifier as a random intercept. The random intercept in our model is meant to capture all of the differences that exist between GPRNs that may be the result of a range of different underlying reasons (differences in case definition, differences in socio-economic status, differences in GP practices, differences in GP compu- ter software). Incidence was estimated assuming a Poisson distribu- tion and using a log link function. Prevalence was esti- mated using a logit link function and a binomial distribution for the outcome variable. Age and sex were the only determinants ("fixed effects”) included in our dataset, so incidence and prevalence were estimated as a function of polynomials of age, sex and interactions between these variables. To reduce the risk of multi- collinearity we used orthogonal polynomials of age as explanatory variables. To select the ‘optimal’ model in terms of highest order polynomials in age and the E-codes ICPC1- codes Age range included in the analyses Rheumatoid arthritis CMR/RNH/RNUH-LEO/LINH. CMR/RNH/RNUH-LEO Arthritis CMR/RNH/LINH/RNUH-LEO RNH/RNUH-LEO CMR: Continuous Morbidity Registry Nijmegen; LINH: Netherlands Information Network of General Practice; RNH: Registration Network General Practices; RNUH-LEO: Registration Network of General Practitioners Associated with Leiden university; Transitie: Transition Project. 4061 + 4062 + L89 + L90 + 30-34, 35-39, etc., 85+ 4069 L91 Diabetes Heart failure COPD Stroke Coronary heart disease Dementia Schizophrenia Parkinson Multiple sclerosis Epilepsy Glaucoma Cataracts Hearing impairments Inflammatory bowel diseases Osteoporosis All 5 All 5 All 5 All 5 All 5 All 5 All 5 All 5 All 5 CMR/RNH/RNUH-LEO/ Transitie CMR/Transitie/RNH/RNUH- LEO CMR/Transitie/RNH CMR/Transitie/RNH/RNUH- LEO All 5 CMR/Transitie/RNUH-LEO/ LINH Macular degeneration Transitie/RNH/RNUH-LEO R91+R95 K74+K75 +K76 T90 K77 K90 P70 P72 N87 N86 N88 F84 F93 F92 D94 L95 L88 H84+H85 +H86 0-4, 5-9, etc., 85+ 50-54, 55-59, etc., 85+ 30-34, 35-39, etc., 85+ 25-29, 30-34, etc., 85+ 25-29, 30-34, etc., 85+ 50-54, 55-59, etc., 85+ 10-14, 15-19, etc., 85+ 30-34, 35-39, etc., 85+ 10-14, 15-19, etc., 85+ 0-4, 5-9, etc., 85+ 30-34, 35-39, etc., 85+ 25-29, 30-34, etc., 85+ 30-34, 35-39, etc., 85+ 0-4, 5-9, etc., 85+ 0-4, 5-9, etc., 85+ 25-29, 30-34, etc., 85+ 0-4, 5-9, etc., 85+ All 5 All 5 All 5 All 5 All 5 CMR/RNH/RNUH-LEO CMR/RNH/RNUH-LEO CMR/RNH/RNUH-LEO/ Transitie CMR/RNH/RNUH-LEO Transitie CMR/RNH/RNUH-LEO/ Transitie Transitie/RNH/RNUH-LEO CMR/Transitie/RNH/RNUH- LEO CMR//RNH CMR/RNH CMR/Transitie/RNUH-LEO CMR/Transitie/RNUH-LEO 2110+2120 0910 2131 2480 1559 1270 1250 1570 1560 1580 1800 1790 1890 2852 4154 4050