Method of identifying locations experiencing elevated levels of abuse of opioid analgesic drugs

A method of analyzing a set of data on abuse of pharmaceuticals, to identify locations where data values constitute a “signal” of anomalous abuse, comprising identifying locations for which

• Yijk represents data for drug i at time j in location k,
• N is a count of Unique Recipients of the Dispensed Drug (URDD) for drug i at time j in location k,
• Yijk˜Poisson (exp (μφk)*Nijk)
• and the equation
• Log (ηijk)=log(Nijk)+β0 +βi(i=1, . . . , 8)D1 βjTime+αiIncome+α2Age+α3Race+bk+Ty
• yields a statistically significant elevated value,
• where Di (i=1, . . . 8) are indicator variables for drug,
• Time is a continuous variable representing year-quarter, Race is the percent of white people in 3 DZ k, Age is the percent of individuals age 18 to 24 years in 3 DZ k, Income is the median household income (US $100,000) for 3 DZ k, bk is the 3 DZ random effect, and
• Tj is a year-quarter random effect.

A method of identifying locations of anomalous levels of pharmaceutical drug diversion, by analyzing a set of data containing

• a specific code for each prescription drug being monitored;
• a time period datum;
• a location code;
• a count of actual drug diversion reports;
• and
• a number of persons dispensed each prescription drug in each location;
• comprising the steps of:
• fitting a regression model to said set of data;
• calculating associated confidence intervals;
• calculating, for each combination of
• a location code, a drug code, and time period datum,
• a respective expected number of drug diversion reports;
• comparing each expected number of drug diversion reports with said count of actual drug diversion reports;
• and
• flagging, as a signal, each instance where said count of actual drug diversion reports exceeds said expected number of drug diversion reports.