Abstract
Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).
Highlights
Asthma preventer medication non-adherence is strongly associated with poor asthma control
Electronic monitoring devices (EMDs) enable the real-time tracking of inhaler use, by means of a small electronic chip fitted onto an inhaler, which records the date and time of each dose taken
These clusters, representing poor, moderate, and good medication adherence, were best approximated in a decision tree using the measure of the percentage of prescribed doses that were taken during the study (84% accuracy in data unseen to the training model)
Summary
Asthma preventer medication non-adherence is strongly associated with poor asthma control. We demonstrate the use of adherence measures towards a three-group categorization of medication nonadherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data). Medication non-adherence, that is not taking a medication as prescribed, is very common and is a major factor associated with poor asthma control, leading to increased likelihood of asthma attacks[4,5,6,7,8]. EMDs can measure medication use more accurately than either self-report or indirect adherence measurement methods such as using prescribing or dispensing records, which makes their data a valuable resource for investigating patterns of adherence behaviors
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