Literature DB >> 26456482

The adherence-outcome relationship is not altered by diary-driven adjustments of microelectronic monitor data.

Jessica Eby1, Jennifer Chapman1, Tafireyi Marukutira2, Gabriel Anabwani2,3, Ontibile Tshume2, Omphile Lepodisi2, Tebo Dipotso2, Keboletse Mokete2, Robert Gross4, Elizabeth Lowenthal1,5.   

Abstract

PURPOSE: The purpose of this study was to determine whether diary-driven adjustment of Medication Event Monitoring System (MEMS) data based on Supporting Information strengthens the relationship between measured antiretroviral medication adherence and plasma HIV viral load (VL).
METHODS: HIV+ adolescents on antiretroviral treatment were monitored with MEMS for 30 days preceding a VL measurement. The primary outcome was VL ≥ 400 copies/mL. Handwritten diaries were used to comprehensively record deviations from recommended use (bottle opened but dose not taken or bottle not opened and dose taken). Data were adjusted ("cleaned") based on diary events. Data were "capped" at the prescribed number of doses/day. Receiver operator characteristic analysis compared the relationships between (i) raw MEMS data, (ii) diary-cleaned, (iii) capped, or (iv) cleaned and capped MEMS data and VL.
RESULTS: Over 30 days preceding VL measurements, 273 adolescents had 465 diary events. Capping resulted in fewer patients classified as 95% adherent (65.2%) compared with raw data (71.4%), p < 0.001. Adherence was highly associated with VL (OR 1.05, p < 0.001). The area under the receiver operating characteristic curve for continuous adherence compared with VL was 0.89 (95%CI: 0.82-0.95). Neither diary-cleaning, capping, nor cleaning and capping MEMS data significantly altered the association between adherence and VL (p = 0.14, 0.40, and 0.19, respectively).
CONCLUSION: Medication Event Monitoring System data-cleaning based on diary entries did not affect the adherence-VL relationship.
Copyright © 2015 John Wiley & Sons, Ltd.

Entities:  

Keywords:  HIV; MEMS, self-report; adherence; electronic monitoring; pharmacoepidemiology

Mesh:

Substances:

Year:  2015        PMID: 26456482      PMCID: PMC4715738          DOI: 10.1002/pds.3887

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


  25 in total

1.  Antiretroviral therapy adherence and viral suppression in HIV-infected drug users: comparison of self-report and electronic monitoring.

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Journal:  Clin Infect Dis       Date:  2001-09-05       Impact factor: 9.079

2.  Understanding interobserver agreement: the kappa statistic.

Authors:  Anthony J Viera; Joanne M Garrett
Journal:  Fam Med       Date:  2005-05       Impact factor: 1.756

3.  Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

Authors:  Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde
Journal:  J Biomed Inform       Date:  2008-09-30       Impact factor: 6.317

4.  How often is medication taken as prescribed? A novel assessment technique.

Authors:  J A Cramer; R H Mattson; M L Prevey; R D Scheyer; V L Ouellette
Journal:  JAMA       Date:  1989-06-09       Impact factor: 56.272

5.  Perceptions about the acceptability of assessments of HIV medication adherence in Lilongwe, Malawi and Chennai, India.

Authors:  Steven A Safren; N Kumarasamy; Mina Hosseinipour; Meaghan M Harwood; Irving Hoffman; Marybeth McCauley; Allan Jumbe; Christina Nyirenda; Matthew J Mimiaga; Suniti Solomon; David Celentano; Kenneth H Mayer
Journal:  AIDS Behav       Date:  2006-07

6.  Social desirability response tendencies in psychiatric inpatient children.

Authors:  P A Mabe; F A Treiber
Journal:  J Clin Psychol       Date:  1989-03

7.  Treatment interruptions predict resistance in HIV-positive individuals purchasing fixed-dose combination antiretroviral therapy in Kampala, Uganda.

Authors:  Jessica H Oyugi; Jayne Byakika-Tusiime; Kathleen Ragland; Oliver Laeyendecker; Roy Mugerwa; Cissy Kityo; Peter Mugyenyi; Thomas C Quinn; David R Bangsberg
Journal:  AIDS       Date:  2007-05-11       Impact factor: 4.177

8.  Age differences in social desirability.

Authors:  T S Mwamwenda
Journal:  Psychol Rep       Date:  1995-06

9.  Adherence to Antiretroviral Therapy Among People Living with HIV.

Authors:  Basavaprabhu Achappa; Deepak Madi; Unnikrishnan Bhaskaran; John T Ramapuram; Satish Rao; Soundarya Mahalingam
Journal:  N Am J Med Sci       Date:  2013-03

10.  Not all missed doses are the same: sustained NNRTI treatment interruptions predict HIV rebound at low-to-moderate adherence levels.

Authors:  Jean-Jacques Parienti; Moupali Das-Douglas; Véronique Massari; David Guzman; Steven G Deeks; Renaud Verdon; David R Bangsberg
Journal:  PLoS One       Date:  2008-07-30       Impact factor: 3.240

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  3 in total

1.  Distinctive barriers to antiretroviral therapy adherence among non-adherent adolescents living with HIV in Botswana.

Authors:  Elizabeth Yang; Seipone Mphele; Neo Moshashane; Boineelo Bula; Jennifer Chapman; Harriet Okatch; Ed Pettitt; Ontibile Tshume; Tafireyi Marukutira; Gabriel Anabwani; Elizabeth Lowenthal
Journal:  AIDS Care       Date:  2017-06-23

2.  Adherence to antiretroviral therapy in a clinical cohort of HIV-infected children in East Africa.

Authors:  Rachel C Vreeman; Samuel O Ayaya; Beverly S Musick; Constantin T Yiannoutsos; Craig R Cohen; Denis Nash; Deo Wabwire; Kara Wools-Kaloustian; Sarah E Wiehe
Journal:  PLoS One       Date:  2018-02-21       Impact factor: 3.240

Review 3.  Outcome measures for adherence data from a medication event monitoring system: A literature review.

Authors:  Linda Hartman; Willem F Lems; Maarten Boers
Journal:  J Clin Pharm Ther       Date:  2018-09-01       Impact factor: 2.512

  3 in total

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