METHOD OF IDENTIFYING LOCATIONS EXPERIENCING ELEVATED LEVELS OF ABUSE OF OPIOID ANALGESIC DRUGS

• US 20080300902A1
• Filed: 11/14/2007
• Published: 12/04/2008
• Est. Priority Date: 11/15/2006
• Status: Abandoned Application

• Current Assignee: Purdue Pharma L.P.
• Original Assignee: Purdue Pharma L.P.
• Inventors: Wang, Jianmin, Smith, Meredith Y., Irish, William
• Application Number:
  US11/940,299
• Publication Number:
  US 20080300902A1
• Time in Patent Office: Days
• US Class Current: 705/1
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 50/80 for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

In the field of pharmacovigilance (monitoring of adverse events associated with the use of marketed prescription drugs), an improved method of detecting “signals” of abuse or diversion of pharmaceuticals is needed, in order to more reliably distinguish real anomalies in pharmaceutical consumption in particular locations from apparent anomalies which are statistical artifacts of high local concentrations of authorized pharmaceutical users. An improved method relies upon government statistics on adverse drug reactions and on census statistics, and employs a Poisson statistical model to adjust for both fixed effects and random effects, such as spatial relations between different locations. The more accurate “signals” provided by the improved method permit public health and law enforcement officials to allocate public resources more efficiently and effectively.

Claims

1. A method of identifying locations of anomalous levels of pharmaceutical abuse, by analyzing a set of data containing

a specific code for each prescription being monitored;

a time period datum;

a location code;

a count of actual adverse event reports (AER); and

a number of persons dispensed each drug in each location;

comprising the steps of;

fitting a Poisson 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 adverse event reports;

comparing each expected number of adverse event reports with said count of actual adverse event reports; and

flagging, as an outlier, each instance where said count of actual adverse event reports deviates from said expected number of adverse event reports.

2. The method of claim 1, wherein said regression model is a spatial mixed effect regression model.

3. The method of claim 1, further comprising generating a graphical representation of a plurality of geographic locations corresponding to said location codes, and

representing said outliers by at least one distinctive representation.

4. The method of claim 3, wherein said distinctive representation comprises at least one distinctive color.

5. The method of claim 4, wherein a plurality of colors represent respective ranges of numerical values.

6. The method of claim 3, wherein said distinctive representation comprises a simulated 3-dimensional peak, scaled to represent a ratio of said actual AER count to said expected AER count.

7. The method of claim 1, wherein said step of fitting a Poisson regression model to said set of data includes

deriving, for each drug code, a parameter (beta) representing a statistical relationship between said number of persons dispensed said drug and said count of actual adverse event reports.

8. The method of claim 7, wherein said deriving step includes:

preparing a series of model specification instructions, preparing a set of observed data, and preparing a set of starting values; and

processing said instructions and data using a computer program which performs Bayesian analysis using Markov Chain Monte Carlo (MCMC) techniques.

9. The method of claim 7, wherein said parameter deriving step comprises:

performing a regression analysis to determine how a count of Unique Recipients of a Dispensed Drug correlates with said count of actual adverse event reports.

10. The method of claim 1, wherein said regression model fitting step comprises:

loading a model into a computer,

loading starting data values,

compiling a computer program used to process said data values, executing said computer program, and

saving values resulting from said execution of said program.

11. The method of claim 1, wherein said confidence interval calculating step comprises:

deriving a value representing variability of data in said dataset, and dividing said variability value by a square root of how many data values are contained in said dataset.

12. The method of claim 1, wherein said step of calculating an expected number of AERs comprises:

inputting data which specify a conditional autoregressive time structure, inputting initial values of parameters, inputting data characterizing a local population and the number of Unique Recipients of each Dispensed Drug, and executing a computer program which makes Bayesian inferences based upon said inputted data.

13. The method of claim 1, wherein said step of flagging comprises:

providing a flag field in a data record which includes a location code associated with said actual number of adverse event reports,

determining whether or not said actual number of adverse event reports exceeds said expected number and, if said actual value does exceed said expected number,

recording a YES value in said flag field of said data record.

14. The method of claim 1, further comprising

outputting a list of locations where said actual number of AERs deviates from the expected number of AERs according to a predetermined statistical criterion.

15. The method of claim 14, wherein said statistical criterion is a relative report rate exceeding a predetermined threshold value.

16. The method of claim 14, wherein

said statistical criterion is that a probability, that a drug has a Relative Report Rate greater than 3, is greater than 0.95.

17. 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

Y_ijk 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,

Y_ijk˜

Poisson (exp (μ

<sub>φ

k</sub>)*N_ijk)

and the equation

Log (η

_ijk)=log(N_ijk)+β

₀ +β

_i(i=1, . . . ,

     8)D₁ β

_jTime+α

_iIncome+α

₂Age+α

₃Race+b_k+T_y

yields a statistically significant elevated value,

where D_i (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.

18. 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.

19. The method of claim 18, wherein said regression model is a spatial mixed effect regression model.