Andrew R Zullo1, David D Dore2, Omar Galarraga1. 1. Department of Health Services, Policy, and Practice, Brown University, Providence, RI. 2. Department of Health Services, Policy, and Practice, Brown University, Providence, RI; Optum Epidemiology, Waltham, MA.
Abstract
OBJECTIVE: Personal prescription drug importation (PPDI) is prevalent in the United States (U.S.) because of the high cost of U.S. medicines and lower cost of foreign equivalents. The practice carries the risk of exposure to counterfeit, adulterated, and substandard medicines. No known tools are available for predicting person-level PPDI risk. The objective of this study was to develop and validate a predictive PPDI index for policymakers, researchers, and clinicians. METHODS: Using 2011 and 2012 National Health Interview Survey (NHIS) data as the development and validation cohorts respectively, we identified predictors, built multivariable logistic regression models, and validated the index by comparing predicted risk of PPDI in the development cohort to the observed risk in the validation cohort. We assessed calibration using the Hosmer-Lemeshow goodness-of-fit test and discrimination with C-statistics. The outcome measure was survey-reported PPDI (1=yes; 0=no). KEY FINDINGS: In the development cohort, prevalence of PPDI in respondents with 0-2, 3, 4, 5-6, or ≥7 risk factors were 0.32%, 0.57%, 1.09%, 2.95%, and 13.67% (C-statistic=0.78), and in the validation cohort, were 0.32%, 0.54%, 0.95%, 2.89%, and 10.80% (C-statistic=0.76). The Hosmer-Lemeshow test indicated absence of a gross lack of fit (P=0.58) in the validation cohort. On the basis of index performance in the validation cohort, if an intervention to reduce importation were applied to all patients with scores of ≥7, it would be applied to 31.1% of patients who engage in PPDI and 0.6% of the overall population. CONCLUSION: This predictive index accurately stratifies U.S. adults into groups at differential risk of PPDI and may provide value to those who are responsible for health policy and regulation of pharmaceutical importation.
OBJECTIVE: Personal prescription drug importation (PPDI) is prevalent in the United States (U.S.) because of the high cost of U.S. medicines and lower cost of foreign equivalents. The practice carries the risk of exposure to counterfeit, adulterated, and substandard medicines. No known tools are available for predicting person-level PPDI risk. The objective of this study was to develop and validate a predictive PPDI index for policymakers, researchers, and clinicians. METHODS: Using 2011 and 2012 National Health Interview Survey (NHIS) data as the development and validation cohorts respectively, we identified predictors, built multivariable logistic regression models, and validated the index by comparing predicted risk of PPDI in the development cohort to the observed risk in the validation cohort. We assessed calibration using the Hosmer-Lemeshow goodness-of-fit test and discrimination with C-statistics. The outcome measure was survey-reported PPDI (1=yes; 0=no). KEY FINDINGS: In the development cohort, prevalence of PPDI in respondents with 0-2, 3, 4, 5-6, or ≥7 risk factors were 0.32%, 0.57%, 1.09%, 2.95%, and 13.67% (C-statistic=0.78), and in the validation cohort, were 0.32%, 0.54%, 0.95%, 2.89%, and 10.80% (C-statistic=0.76). The Hosmer-Lemeshow test indicated absence of a gross lack of fit (P=0.58) in the validation cohort. On the basis of index performance in the validation cohort, if an intervention to reduce importation were applied to all patients with scores of ≥7, it would be applied to 31.1% of patients who engage in PPDI and 0.6% of the overall population. CONCLUSION: This predictive index accurately stratifies U.S. adults into groups at differential risk of PPDI and may provide value to those who are responsible for health policy and regulation of pharmaceutical importation.
Entities:
Keywords:
Health Policy; International; Modeling; Pharmaceutical HSR; Regulatory; Statistics
Authors: R B D'Agostino; M W Russell; D M Huse; R C Ellison; H Silbershatz; P W Wilson; S C Hartz Journal: Am Heart J Date: 2000-02 Impact factor: 4.749
Authors: Katherine Baicker; Sarah L Taubman; Heidi L Allen; Mira Bernstein; Jonathan H Gruber; Joseph P Newhouse; Eric C Schneider; Bill J Wright; Alan M Zaslavsky; Amy N Finkelstein Journal: N Engl J Med Date: 2013-05-02 Impact factor: 91.245