title: Use of Medicaid prescription data for syndromic surveillance--New York.
abstract: BACKGROUND
  This study explored the utility and value of Medicaid prescription data for statewide syndromic surveillance.
  
  
  METHODS
  Daily Medicaid claims forms are transmitted to the New York State Syndromic Surveillance Project as summary counts by ZIP Code, age category, sex, and 18 medication groups. The CUSUM statistic is used to analyze the data daily at the county level with a 7-10-day moving average baseline. The system was evaluated following an outbreak of pertussis in a small institutional setting in a rural county by comparing the county's CUSUM signals for prescriptions for macrolide antibiotics with the onset of the outbreak.
  
  
  RESULTS
  A case of pertussis was suspected on July 21, 2004, and was reported to the New York State Department of Health on July 22. Treatment and prophylaxis were initiated on July 22. CUSUM analysis flagged a county-wide increase in macrolide antibiotics on the day of treatment/prophylaxis for the first case and contacts in the outbreak. The following week, approximately 300 contacts received prophylaxis (not all of whom were Medicaid clients), resulting in CUSUM signals during the week (July 28 and 29).
  
  
  CONCLUSIONS
  Medicaid prescription data are routinely collected and readily available for syndromic surveillance. This data source has shown potential value as an indicator of disease activity, as in this case study, in an area where a high concentration of Medicaid recipients reside. However, for the surveillance system to be considered a useful early warning system, additional study is required to determine the best methods for selecting from the automatically generated CUSUM signals those that might be important for public health.
authors.affiliation: New York State Department of Health, Albany, New York, USA. jhc04@health.state.ny.us
authors.forename: Jian-Hua
authors.initials: JH
authors.lastname: Chen
authors.forename: K
authors.initials: K
authors.lastname: Schmit
authors.forename: H
authors.initials: H
authors.lastname: Chang
authors.forename: E
authors.initials: E
authors.lastname: Herlihy
authors.forename: J
authors.initials: J
authors.lastname: Miller
authors.forename: P
authors.initials: P
authors.lastname: Smith
mesh_terms: D004196:Disease Outbreaks / Q000517:prevention & control*; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D004363:Drug Utilization; D006801:Humans; D008484:Medicaid / Q000706:statistics & numerical data*; D009518:New York / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D040442:Public Health Informatics