Use of power-law analysis to predict abuse or diversion of prescribed medications: proof-of-concept mathematical exploration
Abstract Objective To conduct a proof-of-concept study comparing Lorenz-curve analysis (LCA) with power-law (exponential function) analysis (PLA), by applying segmented regression modeling to 1-year prescription claims data for three medications—alprazolam, opioids, and gabapentin—to predict abuse a...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2018-07-01
|
Series: | BMC Research Notes |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s13104-018-3632-y |
id |
doaj-019a728f76de492392a28f759a741819 |
---|---|
record_format |
Article |
spelling |
doaj-019a728f76de492392a28f759a7418192020-11-25T01:17:20ZengBMCBMC Research Notes1756-05002018-07-011111810.1186/s13104-018-3632-yUse of power-law analysis to predict abuse or diversion of prescribed medications: proof-of-concept mathematical explorationKathleen A. Fairman0Alyssa M. Peckham1Michael L. Rucker2Jonah H. Rucker3David A. Sclar4College of Pharmacy-Glendale, Midwestern UniversityNortheastern University School of Pharmacy and Massachusetts General HospitalWood Environment & Infrastructure SolutionsKathleen Fairman LTDCollege of Pharmacy-Glendale, Midwestern UniversityAbstract Objective To conduct a proof-of-concept study comparing Lorenz-curve analysis (LCA) with power-law (exponential function) analysis (PLA), by applying segmented regression modeling to 1-year prescription claims data for three medications—alprazolam, opioids, and gabapentin—to predict abuse and/or diversion using power-law zone (PLZ) classification. Results In 1-year baseline observation, patients classified into the top PLZ groups (PLGs) were demographically and diagnostically similar to those in Lorenz-1 (top 1% of utilizers) and Lorenz-25 (top 25%). For prediction of follow-up (6-month post-baseline) Lorenz-1 use of alprazolam and opioids (i.e., potential abuse/diversion), PLA had somewhat lower sensitivity compared with LCA (83.5–95.4% vs. 99.5–99.9%, respectively) but better specificity (98.2–98.8% vs. 75.5%) and much better positive predictive value (PPV; 34.5–45.3% vs. 4.0–4.6%). Of top-PLG alprazolam- and opioid-treated patients, respectively, 20.7 and 9.9% developed incident (new) Lorenz-1 in followup, compared with < 3% of Lorenz-25 patients. For gabapentin, neither PLA nor LCA predicted incident Lorenz-1 (PPV = 0.0–1.4%). For all three medications, PLA sensitivity for follow-up hospitalization was < 5%, but specificity was better for PLA (97.3–99.2%) than for LCA (74.3–75.4%). PLA better identified patients at risk of future controlled substance abuse/diversion than did LCA, but the technique needs refinement before widespread use.http://link.springer.com/article/10.1186/s13104-018-3632-yGabapentinAlprazolamOpioidsAbuseDiversionPower-law analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kathleen A. Fairman Alyssa M. Peckham Michael L. Rucker Jonah H. Rucker David A. Sclar |
spellingShingle |
Kathleen A. Fairman Alyssa M. Peckham Michael L. Rucker Jonah H. Rucker David A. Sclar Use of power-law analysis to predict abuse or diversion of prescribed medications: proof-of-concept mathematical exploration BMC Research Notes Gabapentin Alprazolam Opioids Abuse Diversion Power-law analysis |
author_facet |
Kathleen A. Fairman Alyssa M. Peckham Michael L. Rucker Jonah H. Rucker David A. Sclar |
author_sort |
Kathleen A. Fairman |
title |
Use of power-law analysis to predict abuse or diversion of prescribed medications: proof-of-concept mathematical exploration |
title_short |
Use of power-law analysis to predict abuse or diversion of prescribed medications: proof-of-concept mathematical exploration |
title_full |
Use of power-law analysis to predict abuse or diversion of prescribed medications: proof-of-concept mathematical exploration |
title_fullStr |
Use of power-law analysis to predict abuse or diversion of prescribed medications: proof-of-concept mathematical exploration |
title_full_unstemmed |
Use of power-law analysis to predict abuse or diversion of prescribed medications: proof-of-concept mathematical exploration |
title_sort |
use of power-law analysis to predict abuse or diversion of prescribed medications: proof-of-concept mathematical exploration |
publisher |
BMC |
series |
BMC Research Notes |
issn |
1756-0500 |
publishDate |
2018-07-01 |
description |
Abstract Objective To conduct a proof-of-concept study comparing Lorenz-curve analysis (LCA) with power-law (exponential function) analysis (PLA), by applying segmented regression modeling to 1-year prescription claims data for three medications—alprazolam, opioids, and gabapentin—to predict abuse and/or diversion using power-law zone (PLZ) classification. Results In 1-year baseline observation, patients classified into the top PLZ groups (PLGs) were demographically and diagnostically similar to those in Lorenz-1 (top 1% of utilizers) and Lorenz-25 (top 25%). For prediction of follow-up (6-month post-baseline) Lorenz-1 use of alprazolam and opioids (i.e., potential abuse/diversion), PLA had somewhat lower sensitivity compared with LCA (83.5–95.4% vs. 99.5–99.9%, respectively) but better specificity (98.2–98.8% vs. 75.5%) and much better positive predictive value (PPV; 34.5–45.3% vs. 4.0–4.6%). Of top-PLG alprazolam- and opioid-treated patients, respectively, 20.7 and 9.9% developed incident (new) Lorenz-1 in followup, compared with < 3% of Lorenz-25 patients. For gabapentin, neither PLA nor LCA predicted incident Lorenz-1 (PPV = 0.0–1.4%). For all three medications, PLA sensitivity for follow-up hospitalization was < 5%, but specificity was better for PLA (97.3–99.2%) than for LCA (74.3–75.4%). PLA better identified patients at risk of future controlled substance abuse/diversion than did LCA, but the technique needs refinement before widespread use. |
topic |
Gabapentin Alprazolam Opioids Abuse Diversion Power-law analysis |
url |
http://link.springer.com/article/10.1186/s13104-018-3632-y |
work_keys_str_mv |
AT kathleenafairman useofpowerlawanalysistopredictabuseordiversionofprescribedmedicationsproofofconceptmathematicalexploration AT alyssampeckham useofpowerlawanalysistopredictabuseordiversionofprescribedmedicationsproofofconceptmathematicalexploration AT michaellrucker useofpowerlawanalysistopredictabuseordiversionofprescribedmedicationsproofofconceptmathematicalexploration AT jonahhrucker useofpowerlawanalysistopredictabuseordiversionofprescribedmedicationsproofofconceptmathematicalexploration AT davidasclar useofpowerlawanalysistopredictabuseordiversionofprescribedmedicationsproofofconceptmathematicalexploration |
_version_ |
1725146520543559680 |