title: Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty.
abstract: BACKGROUND
  Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases.
  
  
  METHODS
  Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000.
  
  
  RESULTS
  Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank.
  
  
  CONCLUSIONS
  Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences.
authors.affiliation: Expertise Centre for Methodology and Information Services, National Institute for Public Health and the Environment Antonie van Leeuwenhoeklaan, The Netherlands. vanbaal@bmg.eur.nl
authors.forename: Pieter H
authors.initials: PH
authors.lastname: van Baal
authors.forename: Peter M
authors.initials: PM
authors.lastname: Engelfriet
authors.forename: Rudolf T
authors.initials: RT
authors.lastname: Hoogenveen
authors.forename: Marinus J
authors.initials: MJ
authors.lastname: Poos
authors.forename: Catharina
authors.initials: C
authors.lastname: van den Dungen
authors.forename: Hendriek C
authors.initials: HC
authors.lastname: Boshuizen
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D002908:Chronic Disease / Q000453:epidemiology*; D005260:Female; D058006:General Practice; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D019451:Information Management / Q000379:methods; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D009426:Netherlands / Q000453:epidemiology; D015995:Prevalence; D012042:Registries / Q000706:statistics & numerical data*; D035501:Uncertainty; D055815:Young Adult