Changes in HIV preexposure prophylaxis prescribing in Australian clinical services following COVID-19 restrictions
Bibliographic record
Abstract
The first case of COVID-19 in Australia was diagnosed on 25 January 2020 [1]. In response, the Australian Federal and State governments implemented staged restrictions, including international and state border closures and physical distancing requirements in public spaces. Between 23 and 26 March, nonessential services, including gyms, restaurants and places of worship were closed, and, on 29 March, the government urged Australians to stay at home other than for essential reasons (i.e. care giving, exercise, and to access healthcare, food and supplies). Data from one large sexual health clinic in Melbourne showed a rapid decline in postexposure prophylaxis (PEP) dispensing following implementation of restrictions [2]. A survey of gay and bisexual men (GBM) accessing preexposure prophylaxis (PrEP) from the same clinic found that among 178 GBM reporting daily PrEP use in January to February 2020, 23% subsequently reported PrEP cessation in May (during restrictions) and 5% switched to on-demand PrEP, with participants reporting no longer engaging in casual sex and reduced number of sexual partners [3]. Although these data suggest that some GBM have reduced their PrEP use, findings are limited by self-report and it is not yet known whether these data reflect broader community trends beyond this single site. We extracted PrEP prescribing data from 42 primary care and sexual health services across Australia participating in the Australian Collaboration for Coordinated Enhanced Sentinel Surveillance of Blood-borne Viruses and Sexually Transmitted Infections (ACCESS) [4]. Specialized data extraction software installed at participating services extracts and links patient data across clinics over time [5]. We compared trends in weekly PrEP prescribing before (1 January 2019 to 31 March 2020) and after (1 April 2020 to 30 June 2020) the implementation of restrictions using segmented linear regression. We estimated the immediate drop in PrEP prescriptions following restrictions by comparing the predicted number of weekly PrEP prescriptions in the week starting 1 April 2020 (first week in the time-series following implementation of all restrictions) based on prerestrictions and during-restrictions trends. The ACCESS study was approved by the Alfred Hospital Ethics Committee (project 248/17). Between 1 January 2019 and 30 June 2020, 52 596 PrEP prescriptions among 19 876 individuals (96.3% male individuals) were recorded at ACCESS clinics. Between 1 January 2019 and 31 March 2020 (prerestrictions period), there was an average of 718 PrEP prescriptions per week across the network. During this period, the weekly number of PrEP prescriptions was stable, with an estimated decline of 0.2 prescriptions per week (P = 0.734). PrEP prescriptions declined by an estimated 236 at the week following implementation of restrictions, representing an immediate 33.3% decline in prescriptions (P < 0.001). Between 1 April 2020 and 30 June 2020 (during-restrictions period), the average number of PrEP prescriptions per week was 543 (a 24.4% decline compared with the prerestrictions period overall). We then observed a nonsignificant increase of 10.6 prescriptions per week during the restrictions period (P = 0.178) (Fig. 1).Fig. 1: Weekly preexposure prophylaxis prescriptions across 42 Australian services from January 2019 to June 2020, with segmented linear regression trends for prerestrictions (1 January 2019 to 31 March 2020)∗ and during-restrictions (1 April 2020 to 30 June 2020) periods.In New South Wales and Victoria (representing 77% of PrEP prescriptions in the study), the largest absolute declines in PrEP prescribing were observed. In Victoria, estimated weekly PrEP prescriptions fell from 294 to 188 (36% decline; P < 0.001); in New South Wales, estimated weekly prescriptions fell from 250 to 165 (33.9% decline; P = 0.002). Declines were also observed in the Australian Capital Territory (32--17; 46.9% reduction; P = 0.001), South Australia (50--35; 30.5% reduction; P = 0.005), and Tasmania (17--9; 47.1% reduction; P = 0.002) with no change in Western Australia (P = 0.806) and Queensland (P = 0.404). Declines in PrEP prescribing may be due to decreased sexual activity among PrEP users or decreased attendance at clinical services, although seeking medical care was exempt from COVID-19 restrictions and many clinics provided telehealth consultations. A recent online survey found that among 940 Australian GBM, the mean number of sexual partners decreased more than 12-fold after participants first reported becoming 'concerned' about COVID-19. Further, only 16% of respondents who reported having casual sex prior to COVID-19 continued to do so following the implementation of restrictions [6]. Rapid changes in PrEP use among GBM, alongside changes in sexual behaviour mediated by the implementation of social restrictions, may have salient implications for the transmission of HIV and other sexually transmitted infections. Although we did not detect a continuing decline in PrEP prescribing during restrictions, ongoing community transmission of COVID-19 across multiple Australian states suggests sustained and potentially additional restrictions are likely, with the state of Victoria already returning to lockdown status for the second time in late July. Reduced sexual activity may help interrupt community transmission of HIV and STIs. However, if sexual activity begins to return to pre-COVID-19 levels without a congruous and timely rebound in testing and PrEP use, this may create potential for increased transmission. Ongoing behavioural and epidemiological surveillance during the COVID-19 pandemic will be important in monitoring the effects of COVID-19 on HIV and STI diagnoses. Acknowledgements The authors acknowledge the contribution of the ACCESS team members who are not co-authors of this article; Jason Asselin, Burnet Institute; Lisa Bastian, WA Health; Deborah Bateson, Family Planning NSW; Scott Bowden, Doherty Institute; Mark Boyd, University of Adelaide; Denton Callander, Kirby Institute, UNSW Sydney; Allison Carter, Kirby Institute, UNSW Sydney; Aaron Cogle, National Association of People with HIV Australia; Jane Costello, Positive Life NSW; Wayne Dimech, NRL; Jennifer Dittmer, Burnet Institute; Basil Donovan, Kirby Institute, UNSW Sydney; Carol El-Hayek, Burnet Institute; Jeanne Ellard, Australian Federation of AIDS Organisations; Christopher Fairley, Melbourne Sexual Health Centre; Lucinda Franklin, Victorian Department of Health; Jane Hocking, University of Melbourne; Jules Kim, Scarlet Alliance; Scott McGill, Australasian Society for HIV Medicine; David Nolan, Royal Perth Hospital; Stella Pendle, Australian Clinical Laboratories; Victoria Polkinghorne, Burnet Institute; Long Nguyen, Burnet Institute; Thi Nguyen, Burnet Institute; Catherine O'Connor, Kirby Institute, UNSW Sydney; Philip Reed, Kirkton Road Centre; Norman Roth, Prahran Market Clinic; Nathan Ryder, NSW Sexual Health Service Directors; Christine Selvey, NSW Ministry of Health; Toby Vickers, Kirby Institute, UNSW Sydney; Melanie Walker, Australian Injecting and Illicit Drug Users League; Lucy Watchirs-Smith, Kirby Institute, UNSW Sydney; Michael West, Victorian Department of Health. The authors also acknowledge all clinics participating in ACCESS. Funding/support: ACCESS receives core funding from the Australian Department of Health. Funding for particular outcomes is also provided by the Blood Borne Virus & STI Research, Intervention and Strategic Evaluation Program (BRISE), an NHMRC Project Grant (APP1082336), a NHMRC Partnership Grant (GNT1092852), and the Prevention Research Support Program, funded by the New South Wales Ministry of Health. Author contributions: M.W.T. conducted the data analysis and lead the initial draft. P.P. contributed to data curation and analysis. R.G., M.E.H., and M.A.S. coordinate the Australian Collaboration for Coordinated Enhanced Sentinel Surveillance (ACCESS). Role of the funders/sponsors: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. Conflicts of interest M.W.T. has received speaker's fees from Gilead Sciences. M.A.S. received grants from the Australian Department of Health. Burnet Institute receives unrelated investigator-initiated research grants from Gilead Sciences, AbbVie, Merck/MSD, and Bristol Myers Squibb.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.004 |
| 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".