van Baal et al. BMC Public Health 2011, 11:163 http://www.biomedcentral.com/1471-2458/11/163 data, because a national system of data collection for pri- mary health care does not exist in most countries. Fortu- nately, several GPRNs have been started in the Netherlands over the past decades, which collect and share their morbidity data on a regular and structured basis. Secondly, diagnostic criteria and procedures are not always clear-cut and unambiguous, leaving room for dif- ferences in “case finding” and case ascertainment [28]. Under such conditions, differences between GPRNs (as captured in our models by the random intercept) can lead to big differences in estimates of prevalence and incidence. We hypothesize that for diseases for which uncertainty intervals are wide (such as arthritis), differences in case finding between GPRNs may offer an explanation for our findings [28]. Furthermore, differences in registration length between GPRNs might explain the between GPRN variance for some diseases because the longer the registra- tion period the lower the probability that a prevalent case is misclassified as incident [33]. Concluding, estimates of incidence and prevalence can be obtained by combining data from different GPRNs, but confidence intervals must be considered. Monte Carlo simulation techniques can be utilized to assess uncertainty in the relative rankings of diseases. League tables of diseases based on point estimates should be interpreted with caution. Acknowledgements This work was funded by the Dutch Ministry of Health, Welfare and Sports. We would like to thank Albert Wong for his statistical expertise. Author details 1Expertise Centre for Methodology and Information Services, National Institute for Public Health and the Environment Antonie van Leeuwenhoeklaan, 9 Bilthoven, 3720 BA, The Netherlands. 2Institute of Health Policy & Management/institute for Medical Technology Assessment, Erasmus University, Rotterdam, Burgemeester Oudlaan, 50 Rotterdam, 3000 DR, The Netherlands. 3Centre for Prevention and Health Services Research, National Institute for Public Health and the Environment, Antonie van Leeuwenhoeklaan, 9 Bilthoven, 3720 BA, The Netherlands. 4Centre for Public Health Forecasting, National Institute for Public Health and the Environment, Antonie van Leeuwenhoeklaan, 9 Bilthoven, 3720 BA, The Netherlands. Authors’ contributions PB did the analyses and drafted the initial manuscript. RH and HB contributed to the development of the methodology. All authors helped interpret the results and contributed to the writing of the manuscript. Competing interests The authors declare that they have no competing interests. Received: 22 April 2010 Accepted: 15 March 2011 Published: 15 March 2011 References 1. Michaud CM, Murray CJ, Bloom BR: Burden of disease–implications for future research. JAMA 2001, 285:535-9. 2. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ: Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet 2006, 367:1747-57. Page 10 of 11 3. Mathers CD, Vos ET, Stevenson CE, Begg SJ: The burden of disease and injury in Australia. Bull World Health Organ 2001, 79:1076-84. 4. Melse JM, Essink-Bot ML, Kramers PG, Hoeymans N: A national burden of disease calculation: Dutch disability-adjusted life-years. Dutch Burden of Disease Group. Am J Public Health 2000, 90:1241-7. 5. Hoogenveen RT, van Baal PH, Boshuizen HC: Chronic disease projections in heterogeneous ageing populations: approximating multi-state models of joint distributions by modelling marginal distributions. Math Med Biol 2009, 27(1):1-19. 6. Barendregt JJ, Bonneux L, van der Maas PJ: The health care costs of smoking. N Engl J Med 1997, 337:1052-7. 7. Wolfson MC: POHEM–a framework for understanding and modelling the health of human populations. World Health Stat Q 1994, 47:157-76. 8. Feenstra TL, van Genugten ML, Hoogenveen RT, Wouters EF, Rutten-van Molken MP: The impact of aging and smoking on the future burden of chronic obstructive pulmonary disease: a model analysis in the Netherlands. Am J Respir Crit Care Med 2001, 164:590-6. 9. Giampaoli S, Palmieri L, Capocaccia R, Pilotto L, Vanuzzo D: Estimating population-based incidence and prevalence of major coronary events. Int J Epidemiol 2001, 30(Suppl 1):S5-10. 10. Giampaoli S, Palmieri L, Pilotto L, Vanuzzo D: Incidence and prevalence of ischemic heart disease in Italy: estimates from the MIAMOD method. Ital Heart J 2001, 2:349-55. 11. De Angelis G, De Angelis R, Frova L, Verdecchia A: MIAMOD: a computer package to estimate chronic disease morbidity using mortality and survival data. Comput Methods Programs Biomed 1994, 44:99-107. 12. Kruijshaar ME, Barendregt JJ, Van De Poll-Franse LV: Estimating the prevalence of breast cancer using a disease model: data problems and trends. Popul Health Metr 2003, 1:5. 13. Kruijshaar ME, Barendregt JJ, Hoeymans N: The use of models in the estimation of disease epidemiology. Bull World Health Organ 2002, 80:622-8. 14. Houweling H, Heisterkamp SH, Wiessing LG, Coutinho RA, van Wijngaarden JK, Jager HJ: Methods for estimating HIV prevalence: A comparison of extrapolation from surveys on infection rate and risk behaviour with back- calculation for the Netherlands. Eur J Epidemiol 1998, 14:645-52. 15. Okkes IM, Polderman GO, Fryer GE, Yamada T, Bujak M, Oskam SK, Green LA, Lamberts H: The role of family practice in different health care systems: a comparison of reasons for encounter, diagnoses, and interventions in primary care populations in the Netherlands, Japan, Poland, and the United States. J Fam Pract 2002, 51:72-3. 16. Van Weel C: Validating long term morbidity recording. J Epidemiol Community Health 1995, 49(Suppl 1):29-32. 17. Westert GP, Schellevis FG, de Bakker DH, Groenewegen PP, Bensing JM, van der Zee J: Monitoring health inequalities through general practice: the Second Dutch National Survey of General Practice. Eur J Public Health 2005, 15:59-65. 18. Metsemakers JF, Knottnerus JA, van Schendel GJ, Kocken RJ, Limonard CB: Unlocking patients’ records in general practice for research, medical education and quality assurance: the Registration Network Family Practices. Int J Biomed Comput 1996, 42:43-50. 19. Claxton K, Sculpher M, McCabe C, Briggs A, Akehurst R, Buxton M, Brazier J, O’Hagan T: Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra. Health Econ 2005, 14:339-47. 20. Hutubessy RC, Baltussen RM, Evans DB, Barendregt JJ, Murray CJ: Stochastic league tables: communicating cost-effectiveness results to decision- makers. Health Econ 2001, 10:473-7. 21. de Hollander AE, Hoeymans N, Melse JM, van Oers JAM, Polder JJ, de Hollander AE, Hoeymans N, Melse JM, van Oers JAM, Polder JJ: Care for Health. The 2006 Dutch Public Health Status and Forecasts Report Bilthoven: Rijksinstituut voor Volksgezondheid en Milieu; 2007. 22. van den Dungen C, Hoeymans N, Gijsen R, van den Akker M, Boesten J, Brouwer H, Smeets H, van der Veen WJ, Verheij R, de Waal M, Schellevis F, Westert G: What factors explain the differences in morbidity estimations among general practice registration networks in the Netherlands? A first analysis. Eur J Gen Pract 2008, 14(Suppl 1):53-62. 23. Gelman A, Hill J: Data Analysis Using Regression and Multilevel/Hierarchical Models Cambridge University Press; 2006. 24. Schwarz G: Estimating the dimension of a model. Annals of Statistics 1978, 6:461-464.