Toilet Paper, Minced Meat and Diabetes Medicines: Australian Panic Buying Induced by COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has impacted the management of non-communicable diseases in health systems around the world. This study aimed to understand the impact of COVID-19 on diabetes medicines dispensed in Australia. Publicly available data from Australia's government subsidised medicines program (Pharmaceutical Benefits Scheme), detailing prescriptions by month dispensed to patients, drug item code and patient category, was obtained from January 2016 to November 2020. This study focused on medicines used in diabetes care (Anatomical Therapeutical Chemical code level 2 = A10). Number of prescriptions dispensed were plotted by month at a total level, by insulins and non-insulins, and by patient category (general, concessional). Total number of prescriptions dispensed between January and November of each year were compared. A peak in prescriptions dispensed in March 2020 was identified, an increase of 35% on March 2019, compared to average growth of 7.2% in previous years. Prescriptions dispensed subsequently fell in April and May 2020 to levels below the corresponding months in 2019. These trends were observed across insulins, non-insulins, general and concessional patient categories. The peak and subsequent dip in demand have resulted in a small unexpected overall increase for the period January to November 2020, compared to declining growth for the same months in prior years. The observed change in consumer behaviour prompted by COVID-19 and the resulting public health measures is important to understand in order to improve management of medicines supply during potential future waves of COVID-19 and other pandemics.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".