| Title |
Development of an algorithm for analysing the electronic measurement of medication adherence in routine HIV care
|
|---|---|
| Published in |
International Journal of Clinical Pharmacy, July 2016
|
| DOI | 10.1007/s11096-016-0354-x |
| Pubmed ID | |
| Authors |
Aurélie Rotzinger, Matthias Cavassini, Olivier Bugnon, Marie Paule Schneider |
| Abstract |
Background Medication adherence is crucial for successful treatment. Various methods exist for measuring adherence, including electronic drug monitoring, pharmacy refills, pill count, and interviews. These methods are not equivalent, and no method can be considered as the gold standard. A combination of methods is therefore recommended. Objective To develop an algorithm for the management of routinely collected adherence data and to compare persistence and implementation curves using post-algorithm data (reconciled data) versus raw electronic drug monitoring data. Setting A community pharmacy located within a university medical outpatient clinic in Lausanne, Switzerland. Methods The algorithm was developed to take advantage of the strengths of each available adherence measurement method, with electronic drug monitoring as a cornerstone to capture the dynamics of patient behaviour, pill count as a complementary objective method to detect any discrepancy between the number of openings measured by electronic monitoring and the number of pills ingested per opening, and annotated interviews to interpret the discrepancy. The algorithm was tested using data from patients taking lopinavir/r and having participated in an adherence-enhancing programme for more than 3 months. Main outcome measure Adherence was calculated as the percentage of persistent patients (persistence) and the proportion of days with correct dosing over time (implementation) from inclusion to the end of the median follow-up period. Results A 10-step algorithm was established. Among 2041 analysed inter-visit periods, 496 (24 %) were classified as inaccurate, among which 372 (75 %) could be reconciled. The average implementation values were 85 % (raw data) and 91 % (reconciled data) (p < 0.0001). At day 544, persistence values were 68 % (raw) and 82 % (reconciled) (p = 0.11), and adherence values were 74 % (raw) and 82 % (reconciled) (p < 0.0001). Conclusion Combining electronic drug monitoring, pill count and patient interviews is possible within the setting of a medication adherence clinic. Electronic drug monitoring underestimates medication adherence, affecting subsequent analysis of routinely collected adherence data. To ensure a set of reliable electronic drug monitoring data, structured and timely electronic drug monitoring management should be reinforced. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 22 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 45% |
| Student > Master | 5 | 23% |
| Student > Doctoral Student | 2 | 9% |
| Other | 1 | 5% |
| Researcher | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 2 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 6 | 27% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 18% |
| Psychology | 3 | 14% |
| Business, Management and Accounting | 1 | 5% |
| Agricultural and Biological Sciences | 1 | 5% |
| Other | 3 | 14% |
| Unknown | 4 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 August 2016.
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#15,380,722
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Outputs from International Journal of Clinical Pharmacy
#784
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#236,520
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Outputs of similar age from International Journal of Clinical Pharmacy
#22
of 27 outputs
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