Jan J Barendregt1, Alewijn Ott. 1. School of Population Health, University of Queensland, Herston Road, Herston, OLD 4006, Australia. j.barendregt@sph.uq.edu.au
Abstract
BACKGROUND: The epidemiology of a disease describes numbers of people becoming incident, being prevalent, recovering, surviving, and dying from the disease or from other causes. As a matter of accounting principle, the inflow, stock, and outflows must be compatible, and if we could observe completely every person involved, the epidemiologic estimates describing the disease would be consistent. Lack of consistency is an indicator for possible measurement error. METHODS: We examined the consistency of estimates of incidence, prevalence, and excess mortality of dementia from the Rotterdam Study. We used the incidence and excess mortality estimates to calculate with a mathematical disease model a predicted prevalence, and compared the predicted to the observed prevalence. RESULTS: Predicted prevalence is in most age groups lower than observed, and the difference between them is significant for some age groups. CONCLUSIONS: The observed discrepancy could be due to overestimates of prevalence or excess mortality, or an underestimate of incidence, or a combination of all three. We conclude from an analysis of possible causes that it is not possible to say which contributes most to the discrepancy. Estimating dementia incidence in an aging cohort presents a dilemma: with a short follow-up border-line incident cases are easily missed, and with longer follow-up measurement problems increase due to the associated aging of the cohort. Checking for consistency is a useful strategy to signal possible measurement error, but some sources of error may be impossible to avoid.
BACKGROUND: The epidemiology of a disease describes numbers of people becoming incident, being prevalent, recovering, surviving, and dying from the disease or from other causes. As a matter of accounting principle, the inflow, stock, and outflows must be compatible, and if we could observe completely every person involved, the epidemiologic estimates describing the disease would be consistent. Lack of consistency is an indicator for possible measurement error. METHODS: We examined the consistency of estimates of incidence, prevalence, and excess mortality of dementia from the Rotterdam Study. We used the incidence and excess mortality estimates to calculate with a mathematical disease model a predicted prevalence, and compared the predicted to the observed prevalence. RESULTS: Predicted prevalence is in most age groups lower than observed, and the difference between them is significant for some age groups. CONCLUSIONS: The observed discrepancy could be due to overestimates of prevalence or excess mortality, or an underestimate of incidence, or a combination of all three. We conclude from an analysis of possible causes that it is not possible to say which contributes most to the discrepancy. Estimating dementia incidence in an aging cohort presents a dilemma: with a short follow-up border-line incident cases are easily missed, and with longer follow-up measurement problems increase due to the associated aging of the cohort. Checking for consistency is a useful strategy to signal possible measurement error, but some sources of error may be impossible to avoid.
Authors: Alejandro Arias-Vásquez; Yurii S Aulchenko; Aaron Isaacs; Andy van Oosterhout; Kristels Sleegers; Albert Hofman; Christine van Broeckhoven; Ben A Oostra; Monique Breteler; Cornelia M van Duijn Journal: J Neurol Date: 2008-03-20 Impact factor: 4.849
Authors: Alejandro Arias-Vásquez; Aaron Isaacs; Yurii S Aulchenko; Albert Hofman; Ben A Oostra; Monique Breteler; Cornelia M van Duijn Journal: Neurogenetics Date: 2007-05-15 Impact factor: 2.660
Authors: Stefan K Lhachimi; Wilma J Nusselder; Henriette A Smit; Paolo Baili; Kathleen Bennett; Esteve Fernández; Margarete C Kulik; Tim Lobstein; Joceline Pomerleau; Hendriek C Boshuizen; Johan P Mackenbach Journal: BMC Public Health Date: 2016-08-05 Impact factor: 3.295