Adherence And Persistence To Antidepressant Medication During Pregnancy: Does It Differ By The Class Of Antidepressant Medication Prescribed?
Bibliographic record
Abstract
IntroductionPregnant women are often concerned about the impact of antidepressant medication use on their pregnancy, such as congenital abnormalities. This concern may vary in a way that depends on the class of antidepressant medication prescribed. Objectives and ApproachThis study examined the rate of adherence and persistence to antidepressants based on the class of antidepressants prescribed during pregnancy This is a retrospective cohort study using population-based administrative data in Alberta– linking delivery record, hospitalization data, physician claims data, emergency department data, and prescription medication data. The eligible study population included women with depression who gave birth between 2012-2015, and were adherent (medication possession ratio ≥80%) to ≥ 2 consecutive antidepressant prescriptions during the preconception year (n=1,865). The rates of adherence and persistence (prescription refill gap ≤30 days) were calculated by medication class and were compared using chi-square tests. ResultsDuring pregnancy, 834 (44.7%) women completely stopped taking antidepressants. Among those taking antidepressants, the overall rate of adherence was 62.6% (95% CI: 59.4%, 65.7%). The rate differed significantly by medication class (p<0.0001), with rate of 75.1% (95% CI: 68.3%, 80.9%) for serotonin-norepinephrine inhibitors, 60.9% (95% CI: 57.2%, 64.5%) for selective serotonin reuptake inhibitors, 42.9% (95% CI: 19.9%, 69.2%) for non-selective monoamine reuptake inhibitors, and 37.5% (95% CI: 22.4%, 55.4%) for the atypical antidepressants. Similarly, 40.7%, (95% CI: 37.5, 44.0) of women were persistent to antidepressants up to the full pregnancy period – similar to the adherence pattern, the rate differed significantly by medication class. Conclusion/ImplicationsAdherence to and persistence in using antidepressants is low during pregnancy and it varies by medication class, possessing to the worsening of depression symptoms. This could be improved by conducting more research on drug safety during pregnancy and translating research evidence into treatment decision and correcting mothers’ misperceptions towards antidepressants.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".