Literature DB >> 25257199

Evaluating the incident user design in the HIV population: incident use versus naive?

Emily S Brouwer1, Daniela C Moga, Joseph J Eron, Sonia Napravnik.   

Abstract

INTRODUCTION: The incident user design is the preferred study design in comparative effectiveness (CER) research. Usually, 180-365 days of exposure free time is adequate to remove biases associated with inclusion of prevalent users. In HIV research, the use of antiretrovirals (ARVs) at any time in the past may influence future treatment choices and CER results; thus, identifying naive as opposed to incident users is of importance. We examined misclassification of antiretroviral naive status based on Medicaid administrative data through linkage to the UNC CFAR HIV Clinical Cohort (UCHCC).
METHODS: We identified Medicaid patients with incident exposure to common first-line ARV regimens between 2002 and 2008 that were also patients enrolled in the UCHCC. We calculated the proportion of antiretroviral naive patients based on the UCHCC, among patients identified as having incident exposure in Medicaid and examined factors associated with being antiretroviral naive in both data sources using logistic regression to generate prevalence odds ratios and associated 95% confidence intervals.
RESULTS: Of the 3500 Medicaid patients with incident antiretroviral (ARV) exposure, 1344 were also enrolled in the UCHCC. In this sample, 34% were antiretroviral naive at the time of first exposure in the Medicaid data based on the UCHCC. In multivariable models, higher CD4 cell counts and log HIV RNA values were associated with being antiretroviral naive in both data sources.
CONCLUSIONS: Administrative data are an important source of information related to HIV treatment. As the construction of a durable and long-lasting HIV treatment plan involves knowledge of current and past antiretroviral therapy, augmentation of this data with comprehensive clinical cohort information is necessary.
Copyright © 2014 John Wiley & Sons, Ltd.

Entities:  

Keywords:  HIV; antiretroviral; comparative effectiveness research; incident user design; pharmacoepidemiology

Mesh:

Substances:

Year:  2014        PMID: 25257199      PMCID: PMC4426192          DOI: 10.1002/pds.3705

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  10 in total

Review 1.  The incident user design in comparative effectiveness research.

Authors:  Eric S Johnson; Barbara A Bartman; Becky A Briesacher; Neil S Fleming; Tobias Gerhard; Cynthia J Kornegay; Parivash Nourjah; Brian Sauer; Glen T Schumock; Art Sedrakyan; Til Stürmer; Suzanne L West; Sebastian Schneeweiss
Journal:  Pharmacoepidemiol Drug Saf       Date:  2012-10-01       Impact factor: 2.890

2.  Factors associated with fewer visits for HIV primary care at a tertiary care center in the Southeastern U.S.

Authors:  Sonia Napravnik; Joseph J Eron; Rosemary G McKaig; Amy D Heine; Prema Menezes; Evelyn Quinlivan
Journal:  AIDS Care       Date:  2006

3.  Evidence of the depletion of susceptibles effect in non-experimental pharmacoepidemiologic research.

Authors:  Y Moride; L Abenhaim; M Yola; A Lucein
Journal:  J Clin Epidemiol       Date:  1994-07       Impact factor: 6.437

4.  Validation of an algorithm to identify antiretroviral-naïve status at time of entry into a large, observational cohort of HIV-infected patients.

Authors:  Neel R Gandhi; Janet P Tate; Maria C Rodriguez-Barradas; David Rimland; Matthew Bidwell Goetz; Cynthia Gibert; Sheldon T Brown; Kristin Mattocks; Amy C Justice
Journal:  Pharmacoepidemiol Drug Saf       Date:  2013-07-09       Impact factor: 2.890

Review 5.  The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection.

Authors:  Edward M Gardner; Margaret P McLees; John F Steiner; Carlos Del Rio; William J Burman
Journal:  Clin Infect Dis       Date:  2011-03-15       Impact factor: 9.079

6.  The effect of insurance coverage changes on drug utilization in HIV disease.

Authors:  S R Smith; D M Kirking
Journal:  J Acquir Immune Defic Syndr       Date:  2001-10-01       Impact factor: 3.731

7.  Spectrum of heart disease and risk factors in a black urban population in South Africa (the Heart of Soweto Study): a cohort study.

Authors:  Karen Sliwa; David Wilkinson; Craig Hansen; Lucas Ntyintyane; Kemi Tibazarwa; Anthony Becker; Simon Stewart
Journal:  Lancet       Date:  2008-03-15       Impact factor: 79.321

8.  The link between public and private insurance and HIV-related mortality.

Authors:  Jayanta Bhattacharya; Dana Goldman; Neeraj Sood
Journal:  J Health Econ       Date:  2003-11       Impact factor: 3.883

9.  Evaluating medication effects outside of clinical trials: new-user designs.

Authors:  Wayne A Ray
Journal:  Am J Epidemiol       Date:  2003-11-01       Impact factor: 4.897

10.  Impact of discontinuity in health insurance on resource utilization.

Authors:  Ritesh Banerjee; Jeanette Y Ziegenfuss; Nilay D Shah
Journal:  BMC Health Serv Res       Date:  2010-07-06       Impact factor: 2.655

  10 in total

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