Evidence map›Paper›PMID 39629640›Full record

ArticleJournal of the International AIDS Society2024

Applying population-specific incidence prevalence ratio benchmarks to monitor the Australian HIV epidemic: an epidemiological analysis.

Jonathan M King, Hamish McManus, Richard T Gray, Steven J Nigro, Jana Sisnowski, Timothy Dobbins, Benjamin R Bavinton, Andrew E Grulich, Kathy Petoumenos, Jane Costello and 1 more

Abstract read
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In one paragraph

Article in Journal of the International AIDS Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jonathan M KingThe Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0001-9086-5929
Hamish McManusThe Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-7264-8848
Richard T GrayThe Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-2885-0483
Steven J NigroHealth Protection NSW, NSW Health, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0001-9613-8829
Jana SisnowskiCommunicable Disease Control Branch, SA Health, Adelaide, South Australia, Australia.
Timothy DobbinsSchool of Population Health, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0003-1841-9056
Benjamin R BavintonThe Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0001-5834-8278
Andrew E GrulichThe Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia.
Kathy PetoumenosThe Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0001-7534-7154
Jane CostelloPositive Life NSW, Sydney, New South Wales, Australia.
Skye McGregorThe Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-8116-4319

Funding

Department of Health and Aged Care, Australian Government 4-E0AZC3O
6 · The paper itself

Abstract

introductionDue to a lack of robust population denominators, Australia is unable to accurately monitor changes in the HIV epidemic for some populations. The ratio of HIV transmission relative to the number of people with HIV (an incidence prevalence ratio, or IPR) can measure such changes. The IPR is measured against an IPR benchmark derived from post-HIV acquisition life expectancy, to indicate whether an HIV epidemic is shrinking or growing. Using IPRs and Australia-specific IPR benchmarks, this study aims to describe the Australian HIV epidemic among three groups: men with HIV attributed to male-to-male sex, women with HIV and people with HIV attributed to injection drug use.

methodsUsing mathematical modelling derived from HIV notifications, cohort and administrative data, IPRs were generated for each of the three groups. These IPRs were compared with IPR benchmarks derived from post-HIV acquisition mortality estimates using abridged life tables for men, women and people who inject drugs. The IPR benchmark for men was applied to people with HIV attributed to male-to-male sex. Trends in the IPR over time were described for each reported population from 2015 to 2022.

resultsOverall, the IPR fell by 80%, from 0.040 (range: 0.034-0.045) in 2015 to 0.008 (range: 0.003-0.013) in 2022 and fell below the benchmark (0.022) in 2020. Among people with HIV attributed to male-to-male sex, the IPR fell by 85%, from 0.041 (range: 0.034-0.047) in 2015 to 0.006 (range: 0.003-0.024) in 2022 and fell below the benchmark (0.022) in 2020. Among women with HIV, the IPR fell by 56%, from 0.032 (range: 0.026-0.039) in 2015 to 0.014 (range: 0.003-0.029) in 2022 and fell below the benchmark (0.022) in 2019. Among people with HIV attributed to injection drug use, the IPR fell by 61%, from 0.036 (range: 0.022-0.047) in 2015 to 0.014 (range: 0.002-0.057) in 2022 and fell below the benchmark (0.028) in 2019.

conclusionsAustralian IPRs in all populations examined have dropped below the level required to sustain the HIV epidemic at current levels. By applying this method in other contexts, the changing scale of HIV epidemics may be better described for populations lacking robust population denominators.

Indexed as

Epidemiological ModelsHIV InfectionsAustraliaBenchmarkingDrug UsersFemaleHumansIncidenceMalePrevalenceSexual and Gender MinoritiesHIV epidemiologyincidencekey and vulnerable populationsmen who have sex with menpeople who inject drugsprevalence

Identifiers

PMID39629640
PMCPMC11615647

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.