New Measure of Adherence Adjusted for Prescription Patterns: The Case of Adults with Asthma Treated with Inhaled Corticosteroid Monotherapy
Bibliographic record
Abstract
BACKGROUND: Common measures of adherence to prescribed medications derived from administrative databases reflect both patients' and physicians' behavior, even if the measures are often interpreted as reflecting only the patient's adherence. Adherence to inhaled corticosteroids (ICSs) has been shown to be low among patients with asthma. OBJECTIVE: To develop a new measure of patients' adherence adjusted for prescription patterns and to evaluate the extent to which the low use of ICSs in asthma is due to patients' nonadherence or suboptimal prescribing practices. METHODS: The new measure of adherence, called the proportion of prescribed days covered (PPDC), is defined as the ratio of the total days' supply dispensed to the total days' supply prescribed during the study period. The PPDC is a modification of an existing adherence measure, the proportion of days covered (PDC). The PPDC and PDC for ICSs, therapy that should be prescribed for chronic daily use to patients with persistent asthma, were compared within a cohort of 4190 ICS-naïve patients with asthma aged 18-45 years derived from the administrative health databases of Quebec, Canada. We estimated the mean and the 95% confidence interval of the PPDC and PDC for ICSs over 1 year, and we calculated the part of nonadherence attributed to patients when measured with the PDC that can be attributed to nonoptimal prescribing of ICSs for chronic daily use with the following formula: [(1-PDC)-(1-PPDC)]/(1-PDC). RESULTS: The mean PPDC and PDC during the 1-year study were 52.6% (95% CI 51.6 to 53.6) and 19.1% (95% CI 18.6 to 19.6), respectively. Forty-one percent of nonadherence attributed to patients when measured with the PDC could be, in fact, attributed to nonprescribing of ICSs for chronic daily use. CONCLUSIONS: Our new adherence measure, the PPDC, may be considered as another way to assess patient adherence, taking into account differing prescribing patterns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".