California PDMP Enhancement, Analysis and Response Initiative

Transcription

1 California PDMP Enhancement, Analysis and Response Initiative California Department of Justice University of California Davis Harold Rogers PDMP National Meeting August 15-16, 2016

2 Outline 1. Project overview 2. Evaluating de-duplication of PDMP data 3. Integrating PDMP and hospital / emergency department data 4. Integrating data from other states

3 CURES 2.0 California SB 809 (stats 2013, Chapter 400, DeSaulnier) was signed by the Governor on September 27, 2013: 1. Re-funded PDMP operations 2. Mandatory provider registration by July 1, Enabled CA DOJ to add advanced features: Updated user interface Automatic (unsolicited) alerts within PDMP Ability to send peer-to-peer messages within PDMP

4 California Harold Rogers Grant Partnership between California DOJ and UC Davis researchers Evaluate implementation and effect of CURES 2.0 on prescribing practices drug overdose rates Optimize PDMP utility by coordinating with public health, regulatory, and law enforcement agencies Design and test data-driven algorithms for identifying high-risk prescribing and dispensing patterns

5 De-duplication of PDMP data Purpose: to identify prescriptions for the same individual Challenges No unique patient identifier Variation in identifying data for an individual Hundreds of millions of records

6 Example First Name Last Name Sex DOB Address Zip Code Stephen Henry Male 05/11/ th Street Steven Henry Male 05/11/ th St Henry Stevens Male 11/05/ th St., Apt Steve Henry Male 05/11/ th Street Are these the same person?

7 Strategy: Compare record linkage programs CURES 2.0 custom built program SAS application The Link King: SAS application Link Plus: Microsoft Windows stand-alone application LinkSolv: Microsoft Access application

8 Determine for each program Blocking variables What matches to consider Cut-points for probabilistic matches How good does the match have to be Feasibility program run times, ease of use

9 Comparison procedure Stratify data by certainty of linkage From high to low probability of a match 4 reviewers will inspect a stratified random sample of matches Identity of the program and certainty of the match is withheld Truth determined by majority opinion

10 Statistics to compare Sensitivity: proportion of true matches identified by the program Specificity: proportion of true non-matches identified by the program Positive predictive value: proportion of identified matches that are true matches Negative predictive value: proportion of identified non-matches that are true nonmatches Area under ROC curve

11 Goals Determine which program is most accurate and feasible to use in production Inform best practices for PDMP record linkage

12 Integrating PDMP & outcome data

13 Analysis plan time series Track data from multiple sources PDMP registration & use (CURES) Prescribing patterns (CURES) Hospitalization & emergency department visits related to opioid-overdose (OSHPD) Overdose deaths (CDPH) Identify major events (e.g., mandatory PDMP registration) to estimate the effect on the indicators

14 Follow Counties Over Time Include county indicators into the time series. Explore each county s contribution to overlying trends.

15 Integrating data from other states Extend time series by including prescribing and overdose data from other states without the specific enhanced PDMP features of CURES 2.0 Compare trends in California counties (intervention group) with counties in other PBSS states (control group) Takes into account potential changes affecting both groups (e.g., national policy trends)

16 Analysis plan Estimate the effect CURES 2.0 enhancements on prescribing practices, fatal overdoses, and non-fatal overdose. Use data from PBSS, NSDUH, & CDC WONDER Use a difference-in-difference approach to isolate effects associated with CURES 2.0 enhancements.

17 Example 1: Time series analysis (single group) Intervention starts Apparent effect of the intervention Time Rate intervention group

18 Example 2: Time series analysis (with comparison group) Intervention starts No effect of the intervention. Something else could have happened at the same intervention period affecting both groups Time Rate intervention group Rate control group

19 Questions California Department of Justice Mike Small Tina Farales University of California Davis Stephen Henry Alvaro Castillo