ArticlePharmacoepidemiology and drug safety2023
"Take up to eight tablets per day": Incorporating free-text medication instructions into a transparent and reproducible process for preparing drug exposure data for pharmacoepidemiology.
Article in Pharmacoepidemiology and drug safety, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 17 citations in OpenAlex.
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- Comparative risk of delirium among opioid users for non-cancer pain: a retrospective cohort study.BMC medicine · 2026Article
- Development and evaluation of machine learning algorithms for the prediction of opioid-related deaths among UK patients with non-cancer pain.PLOS digital health · 2026Article
- Comparative risk of severe constipation in patients treated with opioids for non-cancer pain: a retrospective cohort study in Northwest England.BMC medicine · 2025Article
- On the use of natural language processing to implement the target trial framework using unstructured data from the electronic health record.Global epidemiology · 2025Article
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- Factors associated with long-term opioid use among patients with axial spondyloarthritis or psoriatic arthritis who initiate opioids.Rheumatology (Oxford, England) · 2025Article
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- A systematic review of machine learning applications in predicting opioid associated adverse events.NPJ digital medicine · 2025Article
- Structuring medication signeturs as a language regression task: comparison of zero- and few-shot GPT with fine-tuned models.JAMIA open · 2024Article
- Machine learning identifies risk factors associated with long-term opioid use in fibromyalgia patients newly initiated on an opioid.RMD open · 2024Article
- Article
- "Take up to eight tablets per day": Incorporating free-text medication instructions into a transparent and reproducible process for preparing drug exposure data for pharmacoepidemiology.Pharmacoepidemiology and drug safety · 2023Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeRoutinely collected prescription data provides drug exposure information for pharmacoepidemiology, informing start/stop dates and dosage. Prescribing information includes structured data and unstructured free-text instructions, which can include inherent variability, such as "one to two tablets up to four times a day". Preparing drug exposure data from raw prescriptions to a research ready dataset is rarely fully reported, yet assumptions have considerable implications for pharmacoepidemiology. This may have bigger consequences for "pro re nata" (PRN) drugs. Our aim was, using a worked example of opioids and fracture risk, to examine the impact of incorporating narrative prescribing instructions and subsequent drug preparation assumptions on adverse event rates.
methodsR-packages for extracting free-text medication prescription instructions in a structured form (doseminer) and an algorithm for transparently processing drug exposure information (drugprepr) were developed. Clinical Practice Research Datalink GOLD was used to define a cohort of adult new opioid users without prior cancer. A retrospective cohort study was performed using data between January 1, 2017 and July 31, 2018. We tested the impact of varying drug preparation assumptions by estimating the risk of opioids on fracture risk using Cox proportional hazards models.
resultsDuring the study window, 60 394 patients were identified with 190 754 opioid prescriptions. Free-text prescribing instruction variability, where there was flexibility in the number of tablets to be administered, was present in 42% prescriptions. Variations in the decisions made during preparing raw data for analysis led to marked differences impacting the event number (n = 303-415) and person years of drug exposure (5619-9832). The distribution of hazard ratios as a function of the decisions ranged from 2.71 (95% CI: 2.31, 3.18) to 3.24 (2.76, 3.82).
conclusionsAssumptions made during the drug preparation process, especially for those with variability in prescription instructions, can impact results of subsequent risk estimates. The developed R packages can improve transparency related to drug preparation assumptions, in line with best practice advocated by international pharmacoepidemiology guidelines.
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