Immediate and Ongoing Impact of COVID-19 on Chlamydia Treatment in Australia
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has impacted the sexual health and well-being of individuals, directly through risk of contracting COVID-19, and indirectly through government lockdowns. Government restrictions were especially strict and long-lasting in Australia, they also varied by state, offering an interesting opportunity to study the impacts of varying restrictions. This study compares the impact of the COVID-19 pandemic and resulting restrictions on chlamydia treatment prescriptions during 2020, through to July 2021 between different states and demographic groups in Australia. METHODS: The rate of prescriptions per 100,000 population filled each month from January 2017 to July 2021 from Australia's Pharmaceutical Benefits Scheme for Azithromycin with a restricted indication to treat Chlamydia trachomatis was used to measure chlamydia treatment. The impact of COVID-19 lockdowns was modeled using an interrupted time-series Poisson regression model. RESULTS: The data included 520,025 prescriptions to treat chlamydia, averaging 37.5 prescriptions per month per 100,000 population. Prescriptions declined 26% in April to May 2020 when initial COVID-19 lockdowns began in Australia; prescriptions increased in the following months but remained on average 21% below prepandemic (2017-2019) levels through to July 2021. Prescriptions declined the most in 1 Australian state, Victoria, both in the initial lockdown and the following period; generally, states with more COVID-19 cases saw bigger reductions in prescriptions. CONCLUSIONS: This is the first study to examine how treatment for chlamydia in Australia was impacted by the COVID-19 pandemic and restrictions not only in the immediate-term, but also ongoing up to July 2021, providing important information for planning for sexual health services in future 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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".