Factors associated with early nonpersistence among patients experiencing side effects from a new medication
Bibliographic record
Abstract
BACKGROUND: Drug discontinuation (i.e., nonpersistence) is often attributed to the emergence of adverse effects. However, it is not known whether other factors increase the risk of nonpersistence when adverse effects occur. OBJECTIVES: To identify factors associated with early nonpersistence among patients experiencing adverse effects from newly prescribed medications. METHODS: A questionnaire was mailed to new users of antihypertensive, antihyperglycemic, and lipid-lowering medications in Saskatchewan, Canada, between 2019 and 2020. Only respondents experiencing adverse effects were included. Responses were compared between the nonpersistent group (i.e., people who had discontinued their medication) and the persistent group (i.e., those who were taking their medication at the time of the survey). Statistically significant factors were tested in multivariable logistic regression models. Odds ratios (ORs) and 95% CIs were reported. RESULTS: Of the 3973 returned questionnaires, 813 respondents experienced adverse -effects from their new medication and were included in the study. Of these, 143 respondents (17.5%) had stopped their medication at the time of survey completion; most discontinuations (72.1%) occurred within 1 month of the first dose. Nonpersistent patients were older, had lower income, and were less likely to be taking an antihyperglycemic medication. After covariate adjustment, 6 factors were independently associated with nonpersistence: age less than 65 years (OR 1.56 [95% CI 1.01-2.41]), female sex (1.67 [1.08-2.59]), health condition not considered dangerous (2.09 [1.25-3.51]), medication not considered important for health (6.90 [4.40-10.84]), failure to expect adverse effects before starting medication (2.67 [1.74-4.10]), and taking 2 or more medications (0.45 [0.27-0.73]). CONCLUSION: Despite the strong link between the emergence of adverse effects and early nonpersistence, our findings confirm that this association is highly influenced by several factors external to the physical experiences caused by the new medication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".