van Baal et al. BMC Public Health 2011, 11:163 http://www.biomedcentral.com/1471-2458/11/163 Table 2 parameters in fitted regression models Page 6 of 11 Incidence f(age, age^2,age^3,age^4,sex) Prevalence f(age, age^2,age^3,age^4,age^5,age^6,sex,sex*age, sex*age^2) f(age, age^2,sex,sex*age, sex*age^2) f(age, age^2,age^3,sex,sex*age) f(age, age^2,age^3,age^4,sex,sex*age, sex*age^2) f(age, age^2,age^3,age^4, age^5, age^6,age^7,age^8, sex,sex*age, sex*age^2 sex*age^3 sex*age^4) Coronary heart disease f(age, age^2,sex,sex*age, sex*age^2, sex*age^3) Disease name Diabetes Heart failure COPD Stroke Dementia Schizophrenia Parkinson Multiple sclerosis Epilepsy Glaucoma Cataracts Hearing impairments Inflammatory bowel diseases Osteoporosis Rheumatoid arthritis Arthritis Macular degeneration f(age,sex) f(age, sex) f(age, age^2,age^3,sex) f(age, age^2,sex) f(age, age^2,sex) f(age, age^2,sex) f(age, age^2,sex) f(age, age^2,sex) f(age, age^2,age^3,sex) f(age, age^2,age^3,sex) f(age, age^2,age^3,sex) f(age, sex,sex*age, sex*age^2) f(age, age^2,sex) f(age, age^2,sex) f(age, age^2,age^3,sex) f(age, age^2,sex,sex*age, sex*age^2) f(age, age^2,age^3,sex) f(age, age^2,age^3,sex,sex*age) f(age, age^2,age^3,age^4,sex) f(age, age^2,sex) f(age, age^2,age^3,age^4,age^5,sex) f(age,sex) f(age, age^2,age^3,sex) f(age, age^2,age^3,age^4,sex) f(age, age^2,age^3,age^4,age^5,sex,sex*age) f(age, age^2,age^3,age^4,sex) f(age, sex,sex*age, sex*age^2,sex*age^3) f(age, age^2,sex,sex*age) f(age, age^2,sex,sex*age,age^2, sex*age^2,sex*age^3,sex*age^4) disease incidence. Differences in the incidence and pre- valence ranking can partly be explained by differences in lethality of the disease. For example, coronary heart is, due to its detrimental effects on life expectancy, ranked lower in terms prevalence than in terms of incidence. From both the ranking of incidence and prevalence we can see that if uncertainty margins overlap the uncer- tainty interval of one disease may influence the stochas- tic ranking of other diseases. Discussion and conclusions In this study we showed how routinely collected data from several general practitioners registration networks can be combined to estimate incidence and prevalence for 18 chronic diseases. Uncertainty around estimates of incidence and prevalence was looked at from different angles. First, we estimated confidence intervals both with and without the between GPRN variance. Next, we calculated a stochastic ranking of diseases in terms of incidences and prevalences. This was done using Monte Carlo simulation, which consisted of randomly drawing from the estimated probability distributions of the dis- ease prevalences and incidences, each time obtaining a particular ordering. After repeating this process many times, a distribution of rankings emerges that provides insight into the likelihood of orderings different from the “standard” ordering based on the point estimates. Our simulations showed that the presence of wide confidence intervals for some individual diseases also influences the rank of other diseases with smaller confi- dence intervals. In this respect, the results of our study showed that league tables of diseases should be inter- preted with caution as the standard (point estimates) ordering may not reflect the “real” ordering accurately. These findings obviously have relevance for public health policy and the monitoring of chronic diseases. Furthermore, our findings beg the need for effective tools to communicate uncertainty to policy makers. It should be noted that in this study we only presented a ranking of a small set of diseases. Increasing the num- ber of diseases would probably imply more overlap in confidence intervals and, therefore, more uncertainty in the ranking. Furthermore, in constructing the stochastic ranking we assumed independence between diseases. However, some diseases are causally related (e.g. diabetes and coronary heart disease) or share common risk factors such as smoking and body mass index. If the dependency between diseases could be quantified the stochastic rank- ing of diseases might again turn out to be different. A drawback of our study is that we only used second- ary data at the GPRN level that did not include demo- graphic characteristics besides age and sex and that we could not assess disease classifications errors within our data set [26-28]. We did not have data at the GP prac- tice level and could not verify how accurately GP’s establish their diagnoses and code them into their