title: Age-specific and age-adjusted penetrance as poison center outcome measures.
abstract: BACKGROUND
  The American Association of Poison Control Centers defines penetrance as the number of exposures per 1000 residents of a population during one year. This metric fails to account for confounding by age group variability. We demonstrate the error caused by using raw penetrance and present two alternate methods of calculation, age-specific penetrance and age-adjusted penetrance.
  
  
  METHODS
  Data from the toxic exposure surveillance system were collected from calls to our 41 county regional poison centers in 1998. Age-specific penetrance (ASP) was calculated by dividing the number of exposures (E) in age interval "i" in county "A" by 1000 population in age interval "Pi" in county "A" or ASP = Ei/Pi. AAP is the summation of the weighted age-specific penetrance. Weights (w(si)) represent the relative age distribution of a standard population, in this case the 1998 US population. AAP = sigma(i) w(si) * Ei/Pi.
  
  
  RESULTS
  [table: see text] County C has low raw penetrance and is known to have relatively fewer toddlers and presumably a lower incidence of poisoning. This demonstrates that raw penetrance misrepresents populations with small proportions of children and should not be used to compare promotion or prevention activities between populations.
  
  
  CONCLUSIONS
  We recommend poison centers and the American Association of Poison Control Centers replace raw penetrance with age-adjusted penetrance as one measure of the effectiveness of a poison center's awareness efforts.
authors.affiliation: Central Ohio Poison Center, Children's Hospital, Ohio State University, Columbus, USA. wolowicb@chi.osu.edu
authors.forename: W R
authors.initials: WR
authors.lastname: Wolowich
authors.forename: M J
authors.initials: MJ
authors.lastname: Casavant
authors.forename: C A
authors.initials: CA
authors.lastname: Fisher
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D011039:Poison Control Centers / Q000706:statistics & numerical data*; D011041:Poisoning / Q000453:epidemiology* / Q000628:therapy; D011153:Population; D016896:Treatment Outcome