Literature DB >> 11377123

Derivation of adherence metrics from electronic dosing records.

P W Choo1, C S Rand, T S Inui, M T Lee, C Canning, R Platt.   

Abstract

Numerous adherence variables have been created from electronic dosing records hindering synthesis of the vast body of adherence research. To elucidate the mathematical foundation for electronic adherence monitoring and to understand how diverse electronic adherence metrics are related to each other and the underlying construct of adherence behavior. Several representative adherence metrics are derived mathematically and their relationship to the underlying consumption (or dosing event) rate analyzed. Data from a 3-month study of 286 individuals on single-drug antihypertensive therapy are then used to empirically study the statistical properties of several of these electronic adherence metrics. As suggested by their common link to the consumption (or dosing event) rate, the analyzed electronic adherence metrics were generally strongly correlated (r <- .6 and > .4). The lowest correlation (r = .15) involved the ratio of the observed number of doses to the recommended number (called average adherence), which tended to emphasize quantity consumed, and the ratio of the observed to maximum mean squared rate deviation (MSRD ratio), which focused more on dose timing. Despite their different formulations, electronic adherence variables are generally closely correlated. Adherence metrics that average the consumption rate over multiple doses (by summing up the number of doses and dividing by the monitored time) may be less sensitive to short-term fluctuations in medication intake. Metrics that are more sensitive to timing variability may thus be preferable when timing as well as quantity of dosing are of interest.

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Year:  2001        PMID: 11377123     DOI: 10.1016/s0895-4356(00)00348-6

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  2 in total

1.  Association between adherence measurements of metoprolol and health care utilization in older patients with heart failure.

Authors:  Wanzhu Tu; Andrew B Morris; Jingjin Li; Jingwei Wu; James Young; D Craig Brater; Michael D Murray
Journal:  Clin Pharmacol Ther       Date:  2005-03       Impact factor: 6.875

2.  Dynamic logistic regression model and population attributable fraction to investigate the association between adherence, missed visits and mortality: a study of HIV-infected adults surviving the first year of ART.

Authors:  Sylvia Kiwuwa-Muyingo; Hannu Oja; Ann Walker; Pauliina Ilmonen; Jonathan Levin; Andrew Reid; Peter Mugyenyi; Jim Todd
Journal:  BMC Infect Dis       Date:  2013-08-27       Impact factor: 3.090

  2 in total

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