title: Consistency of epidemiologic estimates.
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.
authors.affiliation: School of Population Health, University of Queensland, Herston Road, Herston, OLD 4006, Australia. j.barendregt@sph.uq.edu.au
authors.forename: Jan J
authors.initials: JJ
authors.lastname: Barendregt
authors.forename: Alewijn
authors.initials: A
authors.lastname: Ott
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003704:Dementia / Q000453:epidemiology* / Q000401:mortality; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009426:Netherlands / Q000453:epidemiology; D016012:Poisson Distribution; D015995:Prevalence