title: Progress in understanding and using over-the-counter pharmaceuticals for syndromic surveillance.
abstract: BACKGROUND
  Public health researchers are increasingly interested in the potential use of monitoring data on over-the-counter (OTC) pharmaceutical sales as a source of timely information about community health. However, fundamental uncertainties persist, including how timely such information is and how best to aggregate information about hundreds of products.
  
  
  OBJECTIVE
  This analysis provides new information about OTC timeliness and illustrates a method of OTC product aggregation for surveillance purposes.
  
  
  METHODS
  Timeliness measurements were made by correlating pharmaceutical sales counts with counts of physician encounters, after adjustment to remove seasonal effects from both counts. OTC product aggregations were formed by a two-stage process. In the first stage, individual products were placed into small groups based on qualitative observations. In the second stage, a clustering algorithm was used to form supergroups (i.e., product group clusters) sharing similar sales histories.
  
  
  RESULTS
  Even after seasonal correction, OTC counts correlated with clinical measures of community illness. However, the lead time of nonseasonal fluctuations was substantially shorter than that for uncorrected data. The clustering approach produced 16 meaningful supergroups containing products that behaved approximately alike.
  
  
  CONCLUSIONS
  Measurements of OTC lead time sensitive to the timing of annual cyclic trends in the behavior of persons seeking health care do not reliably indicate the lead time observed for short-term (e. g. weekly or monthly) fluctuations in community health-care utilization.
authors.affiliation: Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, Maryland, 20723-6099, USA. steve.magruder@jhuapl.edu
authors.forename: Steven F
authors.initials: SF
authors.lastname: Magruder
authors.forename: S Happel
authors.initials: SH
authors.lastname: Lewis
authors.forename: A
authors.initials: A
authors.lastname: Najmi
authors.forename: E
authors.initials: E
authors.lastname: Florio
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003132:Commerce / Q000706:statistics & numerical data*; D004196:Disease Outbreaks / Q000517:prevention & control; D015438:Health Behavior; D006801:Humans; D004366:Nonprescription Drugs; D011159:Population Surveillance / Q000379:methods*; D013995:Time
chemical_list: D004366:Nonprescription Drugs