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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".