Since the late 1990s, there have been substantial increases in HIV diagnoses among men who have sex with men (MSM) worldwide [1–4] accompanied by increases in other sexually transmissible infections (STIs), including gonorrhoea, chlamydia and infectious syphilis . Trends in HIV-risk behaviours may help clarify whether increasing trends in HIV diagnoses are likely to reflect increases in HIV incidence. Theoretically, they are likely to precede increases in HIV incidence, and thus provide an early warning sign of an expanding epidemic, and indicate the need for swift public health action.
In predominantly heterosexual HIV epidemics, behavioural surveillance trends may provide a poor predictor of future HIV trends because of the difficulties in collecting valid and reliable data about risk behaviours [6–10]. Among MSM, systematic longitudinal data collections including HIV incidence and risky sexual practices are rare [3,6,11]. Self-reported unprotected anal intercourse with casual sex partners (UAIC) has been used as the most relevant HIV-risk behaviour for surveillance purposes . Australia has maintained a consistent surveillance of new HIV diagnoses and sexual practices among homosexual men, and we examine trends in HIV diagnoses and risky sexual practices over 1994–2008.
Per capita rates of HIV diagnoses per 100 000 men were obtained from the Australian National HIV Registry [13–17]. We included all cases of new HIV diagnoses among men which were associated with MSM or unspecified presumed MSM exposure (in Australia, ∼80% of all reported cases are transmitted through MSM contact). Behavioural data were derived from the repeated cross-sectional Australian Gay Community Periodic Surveys (GCPS) which were conducted since 1996 in New South Wales (NSW) and since 1998 elsewhere . GCPS annually collect self-reported information about sexual practices among gay-community engaged MSM. We reviewed available behavioural indicators  and present here the prevalence of UAIC as the best predictor of HIV diagnoses. UAIC was measured among men who were not HIV-positive and had such partners in the 6 months before the survey. This indicator was derived from a set of six questions about sexual practices as described by Zablotska et al.  previously. Both indicators (i.e. HIV diagnoses and UAIC) were directly age-standardized to the national male population in 5-year age groups using Australian Bureau of Statistics mid-year estimates of the adult male population size.
We present data from NSW, Victoria and Queensland, comprising three Australian states with the largest gay communities . In 2008, they contributed 86% of 837 new HIV diagnoses in Australian MSM (37%, 29% and 20%, respectively) . We used data from 37 820 behavioural questionnaires (in 2008; 1255, 1208 and 800, respectively). Annual age-standardized HIV diagnosis rates and UAIC prevalence are presented in overlay, for each state, for the period from 1994 to 2008 when available (Fig. 1). In Victoria and Queensland, there were continuing increases in both UAIC and HIV diagnosis rates during 1998–2008. In NSW, a markedly different pattern was observed. In 1996–2001, there were divergent trends: an increase in risk behaviour and a decline in HIV diagnoses. Since 2001, risk behaviour stabilized (2001–2005) and then declined, and the rate of HIV diagnoses was roughly stable.
To test whether the changes in UAIC may predict future changes in HIV diagnoses, we explored the relative percentage change in the trends in both indicators, and assessed the association between their values over all states and years with no delay and a delay of 1, 2 or 3 years. The change in UAIC was found not to be significantly associated with HIV diagnoses in the same or the following year (Spearman's r = 0.07, P = 0.71 and r = 0.10, P = 0.62), but was strongly associated with HIV diagnoses 2 years later (r = 0.62, P = 0.001), and also 3 years later (r = 0.48, P = 0.03).
The analysis demonstrates that the changes in UAIC predicted similar changes in HIV diagnoses 2 years later in Australian homosexual men during 2000–2008. Such a lag time is intuitive considering that the build-up of risk behaviour would likely facilitate subsequent increases in HIV transmission which would be detected in the following 1–2 years (based on HIV-testing rates in Australia) .
In contrast to the findings after 2000, during the late 1990s, the proportion of homosexual men reporting UAIC increased quite rapidly, whereas HIV diagnoses were decreasing. In part, this could be due to a delay between infection and diagnosis and the incidence may in fact have been increasing coincidently with UAIC trends. We also hypothesize that substantial and rapid decreases in viral load among Australian MSM in the late 1990s  was associated with declining infectivity. Stabilization in viral load may be an underlying reason for the alignment of behavioural and HIV notification trends since 2000.
In Australia, the usefulness of UAIC as a warning system has been noted [23–25]. When UAIC prevalence was increasing in all Australian states during the late 1990s, NSW mounted a quick and determined response [23–25] to reinforce condom use. This appeared to have had the intended outcome: a decline in UAIC after 2001, with a stabilization of HIV diagnoses following. The lack of similar investment in HIV prevention in other Australian states made the NSW experience almost unique .
We acknowledge the typical limitations of our ecological analyses of aggregated data. However, the findings clearly indicate a strong relationship between UAIC and HIV diagnoses rates on the population level and the temporal nature of this relationship. Furthermore, Australian experience suggests that HIV transmission can be contained by reducing UAIC in gay communities.
We also recognize methodological difficulties in measuring behaviours and the issue of consistency in data collection . Many countries have followed the UNAIDS/WHO recommendations and tailored HIV/STI behavioural surveillance systems , but few were able to maintain uninterrupted data collection and cover key communities at risk .
The Australian experience suggests that HIV prevention can benefit from investing in behavioural surveillance and preserving consistent methodology. Furthermore, the indicator of UAIC can be used as a behavioural ‘warning system’ for HIV prevention.
The authors would like to acknowledge:
1. The National BBV and STI Surveillance Committee who coordinate National HIV/AIDS Surveillance. In particular, we would like to thank the following members for collecting HIV/AIDS data: Riemke Kampen, Kate Ward, Jiunn-yih Su, Jo Murray, Tess Davey, David Coleman, Carol El-Hayek and Carolien Giele.
2. Our key community partners – the Australian Federation of AIDS Organisations (AFAO), the National Association of People Living with HIV/AIDS (NAPWA), the AIDS Councils of New South Wales (ACON), Victoria and Queensland and People Living with HIV/AIDS in all three states for being instrumental in the establishment of the behavioural surveillance in Australia and being a part of the partnership in HIV response.
3. Study participants for sharing their life experiences with the research team.
4. The New South Wales State Department of Health, the Victoria Department of Human Services and the Queensland Health for the ongoing work in HIV prevention and their support of the behavioural surveillance in Australia.
Funding: The Gay Community Periodic Surveys in NSW were funded by the New South Wales Health Department, in Victoria by Victoria Department of Human Services, and in Queensland by Queensland Health. The National Centre in HIV Epidemiology and Clinical Research is funded by the Australian Government Department of Health and Ageing. The views in this publication do not necessarily represent the position of the Australian Government.
Contributors: Iryna B. Zablotska contributed to formulating the research issue and the design of this analysis, and assumes principal responsibility for the data analysis and preparation of the paper. Garrett Prestage and Andrew E Grulich contributed to the study design, data collection and assisted with the interpretation of results and the preparation of the paper. David Wilson and Melanie Middleton provided HIV surveillance data and their statistical analysis and contributed to the interpretation of the results. All authors have seen and approved the final version of this paper.
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