Overseas- and locally acquired sexually transmissible infections in Australia, 2017–23
Bibliographic record
Abstract
INTRODUCTION: International travel is a significant contributor to the acquisition of sexually transmissible infections (STIs). Despite the high volume of outbound travel from Australia, peaking at 10.8 million travellers in 2023, limited data exist on the burden of overseas-acquired STIs. This study aims to investigate the burden and trends of overseas- and locally acquired STIs in Australia. METHODS: We analysed STI cases notified to Australia's National Notifiable Diseases Surveillance System (NNDSS) from January 2017 to December 2023. A comparative analysis was conducted by place of acquisition (i.e. overseas versus local), with the geographical origins of overseas-acquired cases mapped using ArcMap and temporal trends assessed across pre-COVID-19, pandemic and post-pandemic periods. RESULTS: A total of 967 193 records were obtained from NNDSS, of which 188 788 STI cases (11 782 overseas- and 177 006 locally acquired) were included in the analysis. Males were the most affected group (63% of overseas- and 60% of locally acquired), and young adults aged 20-24 years represented a quarter of cases (24.6% of overseas- and 25.9% of locally acquired). The incidence of overseas-acquired STI cases rose nearly threefold, from 12.8 per 100 000 travellers in 2017 to 35.0 per 100 00 travellers in 2019, and then declined during the COVID-19 pandemic due to Australia's travel restrictions to 16.4 per 100 000 travellers in 2020. A surge was observed in 2021, with 46.5 per 100 000 travellers. The most common regions of acquisition were Southeast Asia (n = 2390, 44.6%), North and South America (n = 663, 12.4%) and Northwest Europe (n = 580, 10.8%). CONCLUSIONS: This study highlights the patterns of overseas- and locally acquired STIs in Australia, with chlamydia remaining the most prevalent (but declining since 2021), while gonorrhoea has been increasing, among overseas-acquired cases. Variations in the region of acquisition and demographic factors highlight the critical need for tailored safer-sex advice during pre-travel consultations, particularly for males and young adults travelling to high-prevalence destinations.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".