van Baal et al. BMC Public Health 2011, 11:163 http://www.biomedcentral.com/1471-2458/11/163 indirectly [9-14]. Even when suitable sources to estimate incidence and prevalence are available, the estimates obtained from them will be subject to uncertainty. Uncertainty surrounding point estimates of population incidence and prevalence may be due to limitations of sample sizes, differences in diagnostic criteria, and methods of case finding. Moreover, the impact of such differences may have different consequences for differ- ent diseases. Some sort of quantitative insight into the magnitude of the uncertainty of the estimates is essen- tial when interpreting the results. This should also be borne in mind when policy makers and epidemiologists rank chronic diseases in terms of incidence and preva- lence when formulating health policies. Uncertainty in estimates of incidence and prevalence may lead to dif- ferent orderings of diseases in a ‘league table or ranking’ when confidence intervals of different diseases overlap. As these rankings are actually used in practice it is important to appreciate the impact of such uncertainty margins on the hierarchical ordering of diseases. In this study we demonstrate how routinely collected data from general practice registration networks (GPRNs) can be used to estimate incidence and preva- lence figures for the most common chronic diseases. Furthermore, we also focus on the role of uncertainty. In the Netherlands, when patients seek medical care from a specialist they have to be referred by their GP, and after consultation the medical specialist reports back to the patient’s GP. As a result, GPs have contact with patients suffering from diseases in various stages of their disease and with all patient groups without selec- tion regarding age, gender, socio-economic status or ethnicity [15-18]. We will focus on the role of uncer- tainty by presenting different outcomes. First, we will present confidence intervals surrounding point estimates of incidence and prevalence. In doing so, we distinguish different levels of uncertainty assessed with multilevel regression modelling. Next, we will illustrate how uncer- tainty in the estimates affects the ranking of the various diseases in a “league table of morbidity”, i.e. a list of diseases in descending order of their prevalences (or incidences). Taking into account uncertainty, turns a league table into a “stochastic” league table. Presenting outcome measures in terms of stochastic league tables is common in economic evaluations [19,20], but has not yet been applied to the ranking of diseases. Methodology Data The data used for this study was collected during 2009 for the Dutch Public Health Forecasts Report that is published every 4 years [21]. That report describes public health on the basis of core indicators, including incidence and prevalence for a selection of more than Page 2 of 11 50 diseases. In this paper we will present estimates of incidences for the year 2007 and prevalences at 1 January 2007 of a subset of 18 chronic diseases using data from five different GPRNs which are non-overlapping in terms of GP practices: CMR (Continuous Morbidity Registry Nijmegen): this registration, which started in 1971, consists of 4 GP practices (11 GPs) delivering service to 12,000 persons located in the eastern part of the Netherlands; LINH (Netherlands Information Network of General Practice): this registration, which started in 1991, con- sists of 80 GP practices (160 GPs) delivering service to 350,000 persons. GP practices are located all over the Netherlands; RNH (Registration Network General Practices): this registration, which started in 1988, consists of 22 GP practices (65 GPs) delivering service to 88,000 persons located in the southern part of the Netherlands; RNUH-LEO (Registration Network of General Prac- titioners Associated with Leiden University): this regis- tration, which started in 1989, consists of 4 GP practices (20 GPs) delivering service to 30,000 persons located in the Western part of the Netherlands; Transitie (Transition Project): this registration, which started in 1985, consists of 5 GP practices (8 GPs) deli- vering service to 13,000 persons located in the Northern part of the Netherlands. Approval to use the data for the Dutch Public Health Forecasts Report and related publications was obtained from each GPRN (the data are anonymous and aggre- gated, requiring no ethics approval, is not publicly available). When using data from GPRNs for the estimation of incidence and prevalence it is important to realize the differences among GPRNs [22]. Two types of differences are especially relevant. First, several classification sys- tems are in use: three of the five GPRNs use ICPC-1 (International Classification of Primary Care 1) to clas- sify diseases, the Transition project uses ICPC-2 and CMR-N uses E-codes [22]. To account for these differ- ences in classification, we mapped out the codes between these registries, which in some cases required combining codes. For example, the code for diabetes in ICPC is T90, which corresponds with E-codes 0910, 0911 and 0919. Second, methods of data collection are episode-based in some registries, but problem-based in others. Problem-based data contain only information about health problems that are permanent, chronic (duration longer than 6 months) or recurrent. Episode- based data have information about all health problems, at least when a person seeks medical care. For the ana- lyses in this paper we only used incidence and preva- lence counts using a classification such that each person can only be an incident case once, and that prevalence