Legacy Prescribing: A population based study demonstrating this is a global system issue driving unnecessary medication use
Bibliographic record
Abstract
CONTEXT Polypharmacy is a key clinical challenge for us in primary care. Legacy prescribing is defined as drugs that are usually prescribed for an intermediate term (>3 months, but not indefinitely), that are not discontinued after this usual effective/recommended period. This type of prescribing could represent a straightforward focus for reducing unnecessary polypharmacy OBJECTIVE We set out to identify and describe the proportion of legacy prescribing in NZ using 3 drug classes as exemplars STUDY DESIGN AND METHODS Our retrospective cohort study used prospectively collected data from the Pharmaceutical Collection dataset from 2013-2022 covering Canterbury (Kaikōura to Ashburton population 666, 300). This dataset includes medication and patient demographic information for patients >20 years old for analysis. We used Anatomic Therapeutic Chemical codes to identify the medications in three drug classes as examples of drugs for physical, psychological and risk factor condition management: antidepressants, bisphosphonates, and proton pump inhibitors (PPIs). We identified legacy prescriptions by calculating dispensing durations for each drug class from first prescription. Our definitions for legacy status were conservative (e.g. 15 months for PPIs and antidepressants) RESULTS Over a third of patients overall (88,478/ 240,444) who were started on one (or more) of these medications had a legacy length prescription, and of these 16% had legacy prescriptions for both antidepressants and PPIs. Individual legacy prescription proportions were 42%, 30% and 14% for antidepressants, PPIs and bisphosphonates respectively. Over three quarters (77%) of patients with legacy prescription had current active prescriptions. Legacy prescribing increased with age. A greater proportion of women had legacy prescriptions for antidepressants (44% vs 37% for men) while for bisphosphonates and PPIs, the proportion of patients with legacy prescriptions were similar. Europeans had the highest proportion of legacy prescriptions (40% vs Māori (27%), Pasifika (20%)) CONCLUSIONS These data are very similar to Canadian data first describing legacy prescribing. Replication confirms this is a global system issue. Well developed prescribing and patient communication systems around starting drugs are not matched by systems around stopping. Audit and feedback add manual work to already burdened clinician. System level changes are needed to mitigate this without increasing clinician workload.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".