Prescription intervals of medications for chronic use: a cohort study
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Purpose
In 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 using different methods for a cohort of older people in primary care in Ireland.
Methods
The empirical prescription interval was analysed for 38,154 patients for the twenty most common drug classes and the association between covariates and the interval was analysed using a multi-level model. Estimates were also compared to those obtained from the parametric waiting time distribution (pWTD) approach.
Results
Available covariates had consistent relationships with prescription intervals across drug classes. For example, each additional prescription issue was associated with an increase 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, while other/private cover had a -17.9 day (benzodiazepines and associated drugs) to -7.1 day (SSRI and SNRIs) change relative to partial cover. The pWTD also produced consistent estimates of the population interval for most drugs.
Conclusions
The interval varied substantially within drug classes, due to a mixture of 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.
Plain language summary
The time between one drug prescription and the next was calculated for the 20 most commonly prescribed drug classes in a database of older people attending primary care in Ireland. The timing varied drastically among drugs, due to differences in patients and practices. We analysed these variables using two approaches. The first looked at how factors such as age, sex, and healthcare cover relate to the time between prescriptions. The second described the overall pattern of prescription intervals as a combination of simpler underlying patterns.
Key points
Prescription intervals are highly focussed around intervals of typical prescribed quantities, as expected, with modes of approximately 30, 60, and 90 days for all drug classes.
Heterogeneity in the prescription interval for specific drug classes was found to be mainly at the within-patient level. This may be due to prescription renewal behaviour changing over time for individuals.
Patient-specific covariates, such as age, sex and healthcare scheme cover had a similar effect on the prescription interval across the most frequent drug classes prescribed.
The parametric waiting time distribution, using a finite mixture model to account for unmodelled variation, was able to robustly explain the population average distribution of empirical intervals using a penalised likelihood procedure.
