ArticlePharmacoepidemiology and drug safety2026
Prescription Intervals of Medications for Chronic Use: A Cohort Study.
Article in Pharmacoepidemiology and drug safety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Prescription Intervals of Medications for Chronic Use: A Cohort Study.Pharmacoepidemiology and drug safety · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
purposeIn deprescribing studies, a prescription-free gap is typically used to determine if patients discontinued their treatment. An appropriate gap depends on the typical time between prescriptions during continued use. This work aims to characterise the interval between prescriptions of chronic drugs (long-term use) using different methods for a cohort of older people in primary care in Ireland.
methodsThe prescription interval was analysed for 38 154 patients over 2011-2020 for the 20 most common drug classes in the dataset and the association between covariates and the interval was analysed using a multi-level model. Estimates were also compared with those obtained from the parametric waiting time distribution (pWTD) approach.
resultsPrescription and demographic factors had consistent relationships with prescription intervals across drug classes. Each additional prescription issue was associated with increases in the interval by 5.0 (NSAIDs) to 19.7 days ('other antidepressants'). Full public health cover was associated with a -29.0 day (inhaled adrenergics) to -11.0 day (opioids) change relative to partial cover, whilst other/private health cover had a -17.9 day (benzodiazepines and associated drugs) to -7.1 day (SSRI and SNRIs) change relative to partial cover. The pWTD produced consistent estimates of the population interval for most drugs.
conclusionsThe prescription interval varied substantially within drug classes, due to patient, practice and unmodelled factors. Variation between practices was effectively explained, with residual variation between patients and within patients. The pWTD approach is useful for describing complex distributions of intervals, and may be more appropriate for inferring a gap than summarising truncated data.
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