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. Author manuscript; available in PMC: 2017 May 1.
Published in final edited form as: HIV Med. 2015 Sep 6;17(5):327–339. doi: 10.1111/hiv.12312

Hospitalisation rates and associated factors in community-based cohorts of HIV- infected and - uninfected gay and bisexual men

Cecilia L Moore 1,§, Andrew E Grulich 1, Garrett Prestage 1,4, Heather F Gidding 2, Fengyi Jin 1, Limin Mao 3, Kathy Petoumenos 1, Iryna B Zablotska 1, I Mary Poynten 1, Matthew G Law 1, Janaki Amin 1
PMCID: PMC4779743  NIHMSID: NIHMS712881  PMID: 26344061

Abstract

Objectives

There is evidence that HIV-positive (HIV+ve) patients are suffering from a greater burden of morbidity as they age due to non-AIDS-related complications. To- date it has been difficult to determine what part of this excess risk is due to the health effects of HIV, its treatment, or to lifestyle factors common to gay and bisexual men (GBM). We calculated overall and cause-specific hospitalisation rates and risk factors for hospitalisations in HIV-negative (HIV-ve) and HIV+ve cohorts of GBM and compare these with rates in the general male population.

Methods

We conducted a record linkage study, linking two cohorts of HIV-ve (n=1325) and HIV+ve (n=557) GBM recruited in Sydney, New South Wales (NSW), Australia with the NSW hospital discharge data register. We compared rates of hospitalisation in the two cohorts and risk factors for hospitalisation using random-effects Poisson regression methods. Hospitalisation rates for each cohort were further compared with those in the general male population using indirect standardisation.

Results

We observed 2,032 hospitalisations in the HIV-ve cohort during 13,016 person-years (PYs) [crude rate:15.6/100PYs (95%CI:14.9-16.3)] and 2,130 hospitalisations in the HIV+ve cohort during 5,571 PYs [crude rate:38.2/100PYs (95%CI:36.6-39.9)]. HIV+ve individuals had an increased risk of hospitalisation compared with the HIV-ve individuals [adjusted-IRR:2.34(95%CI:1.91-2.86)] and the general population [SHR:1.45(95%CI:1.33-1.59)].Hospitalisation rates were lower in the HIV-ve cohort compared with the general population [SHR:0.72(95%CI:0.67-0.78)]. The primary causes of hospitalisation differed between groups.

Conclusions

HIV+ve GBM continue to experience excess morbidity compared with HIV-ve GBM men and the general population. HIV-ve GBM had lower morbidity compared with the general male population suggesting that GBM identity does not confer excess risk.

Keywords: HIV, hospitalization, homosexuality, cohort studies, medical record linkage

Introduction

Attention to gay and bisexual men's (GBM) health was heightened by the advent of the HIV/AIDS epidemic, firstly as a key population identified as being at risk of infection (1) and then as a major force in the HIV/AIDS response (2). GBM continue to shoulder a disproportionate amount of HIV disease burden in high income countries including Australia. While only around 1.6% of Australian males identify as gay or homosexual and 0.9% as bisexual (3), they make up 88% of people with diagnosed new HIV infection(4). The widespread use of combination antiretroviral therapy (cART) in Australia and other industrialized countries since the mid-1990s has resulted in a marked reduction in morbidity and mortality of HIV positive (HIV+ve) people, including GBM (5). However, there is evidence that as HIV+ve patients age and have extended exposure to cART they are suffering from a greater burden of morbidity due to non-AIDS-related complications (6). Despite attention to GBM's health in relation to HIV transmission, acquisition, infection and disease progression, there is a dearth of systematically collected and detailed information on the health of GBM. From the available information on GBM's health related behaviour, there are indications that GBM could be at risk for worse health outcomes compared with the general population. While country specific, there is evidence that they experience significant perceptions of stigma, discrimination and homophobia (7), have high rates of smoking, alcohol, and drug use (8, 9) and are exposed to a range of sexual-behaviour related health risks (9, 10). Although the long-term implications of these factors are not well understood, they have been reported to include increased risk of psychiatric morbidity and suicide(11, 12), increased self-reported physical disability and chronic conditions (13, 14), increased health service utilisation (15), sexually transmitted infections (STIs) and their sequelae (16, 17), cancer (18, 19) and mortality (20, 21). However in most studies of HIV+ve GBM's health it is difficult to discriminate between effects arising from men's health related behaviours and those related to HIV infection.

In this study, we aimed to assess overall and cause-specific hospitalisation rates in HIV-ve and HIV+ve cohorts of GBM and compare these to each other and with rates in the general male population. We hypothesised that rates would be significantly higher in the HIV+ve cohort than the HIV-ve cohort and that rates in both cohorts would be higher than rates in the general male population. We also hypothesised that the primary causes of hospitalisation would differ between the two groups.

Methods

Study design

Our study cohort included participants recruited to the Health in Men (HIM) (HIV-ve) and Positive Health (pH) (HIV+ve) studies who provided informed consent for their study data to be used for data linkage. Both studies have been described in detail elsewhere (22, 23). Briefly, men were recruited from Sydney, New South Wales (NSW), Australia using similar community-based methods. The majority of participants in both studies were recruited through gay community events and venues. Other sources of recruitment included direct recruitment from participants in other relevant studies, personal networking, ‘snowballing’ through friends and acquaintances, direct referrals from medical practitioners, gay press and HIV-positive publications (24, 25). Participants were interviewed face-to-face annually. Enrolment in HIM occurred from 2001 to 2004 and active follow-up ceased in 2007. Enrolment in pH occurred from 1998 to 2006 and follow up ceased in 2007. The serostatus of participants in both cohorts was confirmed by serological testing at intake and in HIV-ve participants through annual testing thereafter. All participants in both studies either had sexual contact with at least one man during the previous 5 years or self-identified as gay, homosexual, queer or bisexual. In both studies the majority of participants identified as gay, homosexual or queer (24, 26). Data collected common to both studies included demographics, sexual and drug use behaviour, STIs and STI testing, gay community involvement, general self-reported health and use of health care services. In the HIV+ve cohort, information on the use of cART, CD4+ T-cell count (CD4) and viral load (VL) as well as frequency of VL and CD4 testing were also collected.

Individual consent for data linkage was optional and was collected in addition to consent to participate in the study. Only data from participants who consented to data linkage were included in this analysis (93% of HIM and 74% of pH participants). Ethics approval was granted by the University of New South Wales (NSW) and the NSW Population and Health Services Research Ethics Committee.

Data sources

Data linkage was performed on all consenting participants. Probabilistic linkage methods (27) were used to link individuals to the data sources described below.

The NSW Admitted Patient Data Collection (APDC) includes all inpatient admissions (episodes of care) from all public (including psychiatric), private and repatriation hospitals, private day procedure centres and public nursing homes in NSW, Australia. Diagnosis fields are coded according to the 10th revision of the International Classification of Disease-Australian Modification (ICD-10-AM). Patient name has only been recorded since 1 July 2000, so we restricted analysis to admissions from 1 July 2000 to the most recent available data at time of analysis (30 June 2012).

The Registry of Births, Deaths and Marriages (RBDM) which reports fact of death was used to censor person-years of observation. Fact of death was available from 01 January 1998 to 30 June 2013.

The HIV administrative database is a register of HIV, notified to the NSW department of Health by laboratories, hospitals, and medical practitioners. In addition to annual serological testing in the HIM cohort, seroconversions were identified through linkage of participants to the HIV registry. HIV/AIDS notifications were available from 01 January 1993 to 31 December 2012.

First name, surname, address, postcode, date of birth and date of last contact were used to probabilistically link participants from the study cohorts to the APDC and RBDM registries using ChoiceMaker software (ChoiceMaker Technologies Inc., New York, US). Deterministic linkage was used to link participants to the HIV/AIDS notifications using two-character surname and given name codes, date of birth, sex and postcode. Linkage was conducted by the NSW Centre for Health Record Linkage, independent of the study investigators. Full details of the linkage process are outlined at (http://www.cherel.org.au/how-record-linkage-works).

Outcomes

Hospital admission was defined as an episode of care ending with hospital discharge, death or transfer to another type of care. All-cause and cause specific hospital admission rates were compared between the two cohorts and to the general male NSW population. Hospitalisation rates for the general male NSW population from the NSW APDC were obtained from the NSW Ministry of Health website (http://www.healthstats.nsw.gov.au). The ICD-10-AM chapter heading for the primary diagnosis field was used to describe the principal reason for admission. Full details of categorisation are outlined at (http://apps.who.int/classifications/icd10/browse/2015/en). AIDS-defining illnesses were categorised according to the Centers for Disease Control and Prevention (CDC) (28) definition and included candidiasis, coccidioidomycosis, cryptococcosis, cryptosporidiosis, cytomegalovirus, encephalopathy, herpes simplex, histoplasmosis, isosporiasis, Kaposi's Sarcoma, Burkitt's lymphoma, immunoblastic lymphoma, primary lymphoma of the brain, Mycobacterium tuberculosis, other mycobacterial diseases, penicilliosis, Pneumocystis jiroveci pneumonia (PJP), recurrent bacterial pneumonia, progressive multifocal leukoencephalopathy, salmonella septicaemia, toxoplasmosis, and HIV wasting syndrome. Due to the extremely high frequency of admissions with a principal diagnostic code of extracorporeal dialysis (Z49.1) we excluded them from analysis for the two cohorts (n admissions=396). Further, we excluded duplicate and nested admissions (admissions within the date range of another admission) in the two cohorts so that there was only one principal diagnostic code for each admission (n admissions=55). No data cleaning was undertaken for hospital admissions for the NSW male population however duplicate and nested admissions have been previously shown by Gidding, H.F. (2012) to be a small overall percentage of total admissions (0.22% duplicates; 0.09% nested admissions)(29).

Statistical Methods

Time at risk commenced at entry into the study cohort or opening of database for hospital admissions (1 July 2000), whichever was latest. Incidence rates of events were determined using person-years (PYs) methods with data right censored at death or the close of database (30 June 2012). Data from HIV-ve participants who seroconverted (n=51) were excluded from analysis.

Age- and year-adjusted incidence rate ratios (IRR) for hospitalisation were calculated to compare HIV+ve and HIV-ve cohorts using random-effects Poisson regression methods to take into account within-person variation for repeated measures (30). The incidence of hospital admissions in the HIV-ve cohort and the HIV+ve cohort were compared with the incidence of hospital admissions in the general NSW male population by calculating standardised hospitalisation ratios (SHRs). The number of hospitalisations was compared with expected number using rates for the NSW male population by 10 year age group and by year to adjust for age and year of admission. To account for correlation between hospitalisations in the same individual, 95% confidence intervals (CIs) for the SHRs were calculated using the method by Stukel et al. (31). Participants who attended the hospital for the same primary diagnosis greater than 20 times during the course of observation contributed only one hospitalisation for this primary diagnosis in the calculation of SHRs to enable better interpretation and reduce the influence of outliers. These exclusions are summarized in the footnotes for Table 2.

Table 2. Comparing rates of hospitalisation by primary reason for admission in 1325 HIV-ve and 557 HIV+ve gay and bisexual men recruited in Sydney NSW, Australia, 2000-2012.

HIV-ve HIV+ve HIV+ve v. HIV-ve Cohort v. NSW Male Population
N Rate p. 100 PYs (95%CI) N Rate p. 100 PYs (95%CI) IRR (95% CI)+ Adjusted IRR (95%CI)# Adjusted P-value# HIV-ve v. NSW Population SHR (95% CI)+ HIV+ve v. NSW Population SHR (95% CI)+
Total 2032 15.6 (14.9-16.3) 2130 38.2 (36.6-39.9) 2.81 (2.39-3.32) 2.34 (1.91-2.86) <0.0001 0.72 (0.67-0.78) 1.45 (1.33-1.59)
Infectious diseases (Non-AIDS defining)ˆ 48 0.4 (0.3-0.5) 94 1.7 (1.4-2.1) 4.08 (2.63-6.36) 4.29 (2.42-7.60) <0.0001 1.30 (0.69-2.47) 5.54 (3.50-8.76)
–AIDS-defining Illnessº 4 0.0 (0.0-0.1) 91 1.6 (1.3-2) 89.99 (28.01-289.1) 33.91 (7.00-164.29) <0.0001
Malignant cancers (Non-AIDS defining)* 64 0.5 (0.4-0.6) 109 2.0 (1.6-2.4) 7.92 (3.96-15.81) 3.99 (1.70-9.32) 0.0002 0.51 (0.3-0.85) 1.28 (0.66-2.49)
–AIDS-defining Illnessº% 0 8 0.1 (0.1-0.3)
Other cancers& 43 0.3 (0.2-0.4) 44 0.8 (0.6-1.1) 1.92 (1.07-3.43) 1.19 (0.56-2.56) 0.6475 0.69 (0.1-4.68) 1.16 (0.57-2.35)
Blood & immune diseases 7 0.1 (0-0.1) 30 0.5 (0.4-0.8) 13.71 (4.43-42.41) 26.06 (3.90-173.94) 0.0008 0.28 (0.11-0.58) 2.18 (0.39-12.09)
Endocrine diseases 68 0.5 (0.4-0.7) 35 0.6 (0.5-0.9) 1.73 (0.67-4.42) 3.65 (0.94-14.21) 0.0623 1.58 (0.99-2.52) 1.44 (0.72-2.88)
Mental disorders*ˆ 104 0.8 (0.7-1) 130 2.3 (2-2.8) 3.16 (1.67-5.98) 1.49 (0.65-3.44) 0.3459 0.43 (0.29-0.64) 1.33 (0.84-2.11)
Nervous & sense disorders (Non-AIDS defining)*ˆ 118 0.9 (0.8-1.1) 103 1.8 (1.5-2.2) 1.78 (1.20-2.63) 1.77 (1.06-2.96) 0.0297 0.73 (0.45-1.17) 1.10 (0.62-1.97)
–AIDS-defining Illnessº% 0 3 0.1 (0-0.2)
Cardiovascular diseases 154 1.2 (1-1.4) 143 2.6 (2.2-3) 1.63 (1.12-2.37) 2.28 (1.39-3.76) 0.0012 0.68 (0.45-1.02) 0.99 (0.65-1.49)
Respiratory diseases (Non-AIDS defining) 81 0.6 (0.5-0.8) 112 2.0 (1.7-2.4) 3.02 (1.96-4.64) 2.83 (1.58-5.11) 0.0005 0.72 (0.38-1.38) 2.06 (1.3-3.26)
AIDS-defining Illnessº% 0 4 0.1 (0-0.2)
Digestive system diseases 463 3.6 (3.2-3.9) 357 6.4 (5.8-7.1) 1.60 (1.31-1.95) 1.56 (1.21-2.02) 0.0007 0.99 (0.81-1.21) 1.50 (1.15-1.95)
Skin diseases 44 0.3 (0.3-0.5) 41 0.7 (0.5-1) 2.22 (1.29-3.82) 2.05 (1.03-4.07) 0.0401 0.64 (0.3-1.36) 1.35 (0.52-3.51)
Musculoskeletal diseasesˆ 134 1.0 (0.9-1.2) 108 1.9 (1.6-2.3) 1.28 (0.87-1.91) 1.42 (0.78-2.60) 0.2585 0.56 (0.38-0.81) 0.88 (0.48-1.6)
Genitourinary diseases 113 0.9 (0.7-1) 106 1.9 (1.6-2.3) 1.96 (1.24-3.09) 1.46 (0.79-2.67) 0.2531 0.88 (0.62-1.24) 1.47 (0.94-2.3)
Symptoms & abnormal findings 173 1.3 (1.1-1.5) 236 4.2 (3.7-4.8) 2.71 (2.06-3.55) 2.80 (1.95-4.02) <0.0001 0.75 (0.53-1.08) 1.91 (1.15-3.17)
Injury & poisoning 208 1.6 (1.4-1.8) 148 2.7 (2.3-3.1) 1.67 (1.19-2.32) 1.68 (1.09-2.58) 0.0184 0.66 (0.41-1.06) 1.17 (0.70-1.94)
Other factors infl. Health׈ 203 1.6 (1.4-1.8) 224 4.0 (3.5-4.6) 2.82 (1.95-4.09) 2.53 (1.53-4.21) 0.0003 0.65 (0.3-1.38) 1.24 (0.81-1.92)
Missing 3 4
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*

3 HIV-ve high frequency individuals (those with >20 hospital admissions for same primary diagnosis) only contributed 1 hospitalisation to observed #

ˆ

4 HIV+ve high frequency individuals (those with >20 hospital admissions for same primary diagnosis) only contributed 1 hospitalisation to observed #

×

Hospital admissions with a principal diagnostic code of extracorporeal dialysis (n=396) were excluded

&

Other cancers include in situ neoplasms, benign neoplasms and neoplasms of uncertain or unknown behaviour

º

AIDS-defining illnesses included candidiasis, coccidioidomycosis, cryptococcosis, cryptosporidiosis, cytomegalovirus, encephalopathy, herpes simplex, histoplasmosis, isosporiasis, Kaposi's Sarcoma, Burkitt's lymphoma, immunoblastic lymphoma, primary lymphoma of the brain, Mycobacterium tuberculosis, other mycobacterial diseases, penicilliosis, Pneumocystis jiroveci pneumonia (PJP), recurrent bacterial pneumonia, progressive multifocal leukoencephalopathy, salmonella septicaemia, toxoplasmosis, and HIV wasting syndrome

%

Could not compare cohorts as too few cases

+

Adjusted for age and year of diagnosis

#

Adjusted for age, year of diagnosis, ethnicity, country of birth, level of education, injected drug use, recreational drug use, smoking, number of drinks regularly consumed, unprotected anal intercourse and hepatitis C status

Abbreviations: N=Number; PYs=Person-years; SHR=Standardised Hospitalisation Ratio; IRR=Incidence Rate Ratio; 95%CI=95% Confidence Interval; Infl.=Influencing; NSW=New South Wales

Risk factors for hospitalisation within each cohort were assessed using random-effects Poisson regression methods. The following covariates were considered as fixed effects (reported at entry into the cohort): country of birth, ethnicity, highest level of education, occupation, employment, income, ever having had an STI (excluding HIV), presence of antibodies to hepatitis C, self-reported general health and use of mental health counselling services, Kessler 6 score of psychological distress, frequency of exercise (only in HIV-ve), smoking and alcohol consumption, recreational and injecting drug use, number of male partners, reported unprotected anal intercourse, experiences of discrimination and harassment and gay community involvement (32). Recreational drug use included use of cannabis; amyl nitrate; Viagra or other erection medications; cocaine; amphetamines or methamphetamines; MDMA or other forms of MDA; psychedelics or hallucinogens (lysergic acid diethylamide(LSD), mescaline, or phencyclidine(PCP)); downers (barbiturates, tranquilisers or sedatives), rohypnol (flunitrazepam) or ketamine; and heroin or other opiates (including methadone). Participants reported how frequently they used each drug and an aggregate measure was generated which categorised drug use as occasionally (1-24 times/year), more frequently (25-48 times/year), often (49-72 times/year) and very often (more than 73 times/year). Age and year were included as time-dependent covariates in the models. Antiretroviral use, and self-reported CD4, VL and frequency of CD4 and VL testing were also evaluated as fixed covariates in the HIV+ve cohort. Covariates were entered into a multivariate model if they had a p-value of less than 0.10 in the univariate analyses. The multivariate model was determined using a backwards step-wise approach with a two-sided statistical significance (p<0.05) with a priori inclusion of age and year. The log-likelihood ratio statistic was used to assess contribution to the model. Missing data were excluded in tests for trend for ordinal categorical covariates or tests for homogeneity for nominal categorical covariates.

Cox proportional hazard models were used to calculate age- and year-adjusted HR for mortality between the two cohorts. Age- and year-specific mortality rates within the cohorts were compared to those in the general male population of NSW and summarised as standardised mortality ratios (SMRs). The number of deaths was compared with expected number using rates for the NSW male population by 5 year age group and by year.

Analyses were performed using STATA (version 13; StataCorp LP, College Station, Texas, USA) and SAS (version 9.3; SAS Institute INC., North Carolina, USA).

Results

The study population included 1,325 HIV-ve GBM and 557 HIV+ve GBM (Table 1). Compared with the HIV-ve cohort, the HIV+ve cohort was older, was more likely to be receiving disability pensions or to be unemployed, to have lower incomes, have been exposed to hepatitis C, to be smokers and injecting drug users and to self-report poorer health. HIV+ve participants were less likely to have university-level education.

Table 1. Baseline characteristics of the 1325 HIV-ve and 557 HIV+ve gay and bisexual men recruited in Sydney NSW, Australia.

HIV-ve HIV+ve
N % N %
Number of participants 1325 - 557 -
Total follow-up (years) 13016 - 5571 -
Mean follow-up (years) 9.8 - 10 -
Linked to at least 1 Hospital Record 666 50.30% 396 71.10%
Number of Hospitalisations 2032 - 2130 -
Number of Deaths 14 1.10% 46 8.30%
Age (years), median (IQR) 35.3 29.6 to 42.0 40.9 36.0 to 46.7
Ethnicity
Anglo-Australian/Anglo-Celtic 985 74.30% 424 76.10%
Country of birth
Australia 913 68.90% 392 70.40%
Education
university or postgraduate 688 51.90% 200 35.90%
Employment
unemployed/disability pension 83 6.30% 158 28.40%
Income
<500 per week/<26,000 per year 266 20.10% 267 47.90%
Exposure to Hepatitis C
Yes 52 3.90% 69 12.40%
Self-Reported Health
poor/fair 113 8.50% 140 25.10%
Gay community involvement
low 393 29.70% - -
Daily Smoker
yes 386 29.10% 277 49.10%
Number of drinks per day
5 to 8 242 18.30% 68 12.20%
>9 62 4.70% 31 5.60%
Illicit Drug Use (past 6 months)*
Yes 1061 80.10% 481 86.40%
Injected drugs in last 6months
yes 50 3.80% 89 16.00%
Any unprotected anal intercourse (past 6 months)
Yes 832 62.80% 141 25.30%
HIV viral load (log copies/ml), median (IQR) - - 4.5 3.6 to 5.9
Last CD4 cell count
less than 100 - - 26 4.70%
100 to 200 - - 28 5.00%
201 to 350 - - 91 16.00%
>350 - - 354 63.60%
Antiretroviral Therapy History
never taken - - 87 15.60%
currently taking - - 413 74.20%
past but now stopped - - 47 8.40%
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*

includes amyl nitrate, cannabis, cocaine, meth/amphetamines, MDMA or other MDA, psychedelics, hallucinogens, downers (barbiturates, tranquilisers or sedatives), Rohypnol or ketamine, heroin or other opiates and GHB.

Abbreviations: N, Number; IQR, Interquartile Range; ml, millilitres;

Comparison of hospitalisations in the HIV+ve and HIV-ve cohorts

We observed 2,032 hospitalisations in the HIV-ve cohort during 13,025 PYs [crude rate: 15.6/100 PYs (95% CI 14.9-16.3)], and 2,130 hospitalisations in the HIV+ve cohort during 5,580 PYs [crude rate: 38.2/100 PYs (95%CI 36.6-39.9)] (Table 2). HIV+ve individuals had an increased risk of hospitalisation compared with the HIV-ve individuals [adjusted IRR: 2.34 (95%CI 1.91-2.86); p-value<0.0001].

Of the major diagnostic groups, HIV+ve compared to HIV-ve GBM were more likely to be hospitalised for all diagnostic groupings apart from endocrine disorders and musculoskeletal disease for which there was no difference between the two groups (Table 2). After adjusting for other risk factors (including socio-demographic and risk behaviour), there were no differences seen between HIV+ve and HIV-ve GBM for hospitalisations due to mental disorders, genitourinary diseases and other cancers (including in situ, benign or uncertain neoplasm). After adjusting for other risk factors, hospitalisations due to non-AIDS-defining infectious diseases, malignant non-AIDS-defining cancers, blood and immune diseases, nervous and sense disorders, cardiovascular diseases, non-AIDS-defining respiratory diseases, digestive system diseases, skin diseases, symptoms and abnormal findings, injuries and poisonings and other factors influencing health were all significantly higher in the HIV+ve group compared to the HIV-ve group.

Comparison of hospitalisations in the HIV+ve cohort and Australian male population

After adjusting for age and year, hospital admission rates were 45.1% higher in the HIV+ve cohort [SHR 1.45 (95% CI 1.33-1.59)] compared with the general male population (Table 2). The greatest excess was in hospitalisations for non-AIDS defining infectious diseases [SHR 5.54 (95% CI 3.50-8.76)], non-AIDS defining respiratory diseases [SHR 2.06 (95%CI 1.30-3.26)], digestive system diseases [SHR 1.50 (95% CI 1.15-1.95)] and symptoms and abnormal findings [SHR 1.91 (95% CI 1.15-3.17)]. The most frequent primary diagnosis in symptoms and abnormal findings was for pain in throat and chest [N=48] and abdominal and pelvic pain [N=41] (Appendix A).

Risk factors for hospitalisation in the HIV+ve cohort included having lower than a university-level education [tertiary diploma/trade certificate: IRR 1.84 (95% CI 1.32-2.54); completed high school: 1.60 (1.10-2.31); < 10 years of high school: 2.18 (1.37-3.46)] (Table 3). Self-reporting excellent or good (compared with poor) health was associated with a decreased risk of hospitalisation [IRR 0.41 (95% CI 0.20-0.83); 0.43 (0.22-0.86); respectively]. Using recreational drugs often and very often (compared with never) was associated with a decreased risk of hospitalisation [IRR 0.38 (95% CI 0.22-0.64); 0.37 (0.16-0.82); respectively], however weekly use of 2 or more drugs was associated with an increased risk of hospitalisation [IRR 2.60 (95% CI 1.19-5.69)]. Having a recent CD4 count of 100-200, 351-500, 501-750 and over 750 (compared with <100) was associated with a decreased risk of hospitalisation [IRR 0.33 (95%CI 0.15-0.74); 0.28 (0.15-0.54); 0.36 (0.19-0.68); 0.30 (0.15-0.58); respectively].

Table 3. Predictors of hospitalisation among 557 HIV+ve gay and bisexual men recruited in Sydney NSW, Australia, 2000-2012.

Risk Factor Person Years N Hospitalisations Incidence rate p. 100 PYs (95%CI) Multivariate adjusted IRRc 95%CI p-value p-value for homogeneity or trend
Agea
18-34 426 168 39.5 (33.9 to 45.9) 1 <0.001b
35-44 1893 566 29.9 (27.5 to 32.5) 0.66 0.52 to 0.85 <0.01
45-54 2245 947 42.6 (39.9 to 45.4) 0.73 0.55 to 0.98 0.03
55-64 827 497 60.1 (55.0 to 65.6) 0.95 0.68 to 1.33 0.76
65+ 209 100 47.9 (39.4 to 58.3) 0.93 0.59 to 1.49 0.77
Highest level of education
University 2014 575 28.5 (26.3 to 31.0) 1 0.001
Tertiary diploma/Trade Certificate 1311 625 47.7 (44.1 to 51.6) 1.84 1.32 to 2.54 <0.01
Completed high school 940 374 39.8 (36.0 to 44.0) 1.6 1.10 to 2.31 0.01
10 years of high school 797 342 42.9 (38.6 to 47.7) 1.42 0.96 to 2.12 0.08
< 10 years of high school 518 362 70 (63.0 to 77.4) 2.18 1.37 to 3.46 <0.01
Self-Reported General Health
poor 162 96 59.1 (48.4 to 72.2) 1 0.044
fair 1205 602 50 (46.1 to 54.1) 0.55 0.27 to 1.11 0.1
good 2891 1137 39.3 (37.1 to 41.7) 0.43 0.22 to 0.86 0.02
excellent 1270 395 31.1 (28.2 to 34.3) 0.41 0.20 to 0.83 0.01
missing 51 48 93.7 (70.6 to 124.4) 1.98 0.54 to 7.29 0.3
Use of recreational drugs
never 738 417 56.5 (51.3 to 62.2) 1 0.001
occasionally 1182 539 45.6 (41.9 to 49.6) 0.84 0.55 to 1.28 0.74
regularly 2591 1047 40.4 (38.0 to 42.9) 0.8 0.52 to 1.22 0.21
often 873 235 26.9 (23.7 to 30.6) 0.38 0.22 to 0.64 <0.01
very often 195 40 20.5 (15.0 to 27.9) 0.37 0.16 to 0.82 0.05
Weekly drug use
None 3615 1582 43.8 (41.7 to 46.0) 1 0.018
1 drug 1784 609 34.1 (31.5 to 36.9) 0.86 0.62 to 1.19 0.36
2 drugs or more 180 87 48.4 (39.2 to 59.7) 2.6 1.19 to 5.69 0.02
Recent CD4 count
less than 100 233 157 67.5 (57.7 to 79.0) 1 0.001
100-200 297 121 40.7 (34.1 to 48.7) 0.33 0.15 to 0.74 0.01
201-350 875 554 63.3 (58.3 to 68.8) 0.59 0.30 to 1.14 0.12
351-500 1042 353 33.8 (30.5 to 37.6) 0.28 0.15 to 0.54 <0.01
501-750 1360 529 38.9 (35.7 to 42.3) 0.36 0.19 to 0.68 <0.01
over 750 1183 355 30 (27.1 to 33.3) 0.3 0.15 to 0.58 <0.01
missing 590 209 35.4 (30.9 to 40.6) 0.29 0.14 to 0.58 <0.01
Financial Yeara
July 2000-Jul 2003 1020 475 46.6 (42.6 to 51.0) 1 0.006b
Aug 2003-July 2006 1428 522 36.4 (33.5 to 39.8) 0.95 0.83 to 1.09 0.48
Aug 2006-July 2009 1586 624 39.3 (36.4 to 42.5) 1.11 0.96 to 1.27 0.15
July 2009-July 2012 1545 657 42.5 (39.4 to 45.9) 1.21 1.04 to 1.41 0.02
Open in a new tab
a

Time-dependent covariate

b

P value for test for trend

c

Age and financial year were an a priori inclusion

Abbreviations: N=number; PYs=person years; CI=Confidence Interval; IRR=incidence rate ratio

Comparison of hospitalisations in the HIV-ve cohort and Australian male population

After adjusting for age and year, hospital admission rates were 27.6% lower in the HIV-ve cohort [SHR 0.72 (95%CI 0.67-0.78)] than in the general male population (Table 2). The greatest discrepancy was seen in hospitalisations for non-AIDS defining malignant cancers [SHR 0.51 (95%CI 0.30-0.85)], blood and immune diseases [SHR 0.28 (95% CI 0.11-0.58)], mental disorders [SHR 0.43 (95% CI 0.29-0.64)] and musculoskeletal diseases [SHR 0.56 (95% CI 0.38-0.81)].

Risk factors for hospitalisation in the HIV-ve cohort included receiving a pension or social security benefit (compared with full-time employment) [IRR 4.99 (95%CI 2.88-8.64)], having previously had a STI [IRR 1.23 (95%CI 1.01-1.51)] and having injected drugs in the past 6 months [IRR 2.61 (95%CI 1.64-4.17)] (Table 4). Increasing involvement in the gay community was associated with an increased risk of hospitalisation [p-value for trend<0.001]. Being born in Central or South America or the Caribbean or Asia (compared with Australia) was associated with a decreased risk of hospitalisation [IRR 0.48 (95% CI 0.23-1.01); 0.41 (0.26-0.65), respectively], as was reporting health as excellent, very good or good (compared to poor/fair) [IRR 0.46 (95%CI 0.34-0.64); 0.38 (0.28-0.52); 0.42 (0.30-0.59), respectively].

Table 4. Predictors of hospitalisation among 1325 HIV-ve gay and bisexual men recruited in Sydney NSW, Australia, 2000-2012.

Risk Factor Person Years N Hospitalisations Incidence rate p.100 Pys (95% CI) Multivariate adjusted IRR c 95%CI p-value p-value for homogeneity or trend
Agea
18-34 3703 421 11.4 (10.3 to 12.5) 1 <0.001b
35-44 5057 700 13.8 (12.9 to 14.9) 1.45 1.22 to 1.73 <0.01
45-54 2952 539 18.3 (16.8 to 19.9) 1.4 1.12 to 1.73 <0.01
55-64 1064 338 31.8 (28.6 to 35.3) 1.78 1.37 to 2.31 <0.01
65+ 249 209 83.9 (73.3 to 96.1) 3.02 2.06 to 4.44 <0.01
Country of birth
Australia 9016 1681 18.6 (17.8 to 19.6) 1 0.001
Europe/Russia/Middle East 1629 223 13.7 (12.0 to 15.6) 0.8 0.61 to 1.05 0.11
New Zealand/Pacific Islands/New Guinea 837 95 11.3 (9.3 to 13.9) 0.7 0.48 to 1.03 0.07
Canada/USA 366 102 27.9 (23.0 to 33.9) 1.27 0.78 to 2.09 0.34
Central/South America, Caribbean 225 19 8.4 (5.4 to 13.2) 0.48 0.23 to 1.01 0.05
Asia 724 47 6.5 (4.9 to 8.6) 0.41 0.26 to 0.65 <0.01
Africa 188 29 15.4 (10.7 to 22.2) 1.2 0.59 to 2.44 0.61
Other, DK, NA, Missing 40 8 20.2 (10.1 to 40.3) 1.34 0.30 to 5.95 0.7
Employment
Full-time 9853 1563 15.9 (15.1 to 16.7) 1 <0.001
Part-Time 1472 211 14.3 (12.5 to 16.4) 0.99 0.74 to 1.31 0.93
Unemployed 523 59 11.3 (8.7 to 14.6) 0.79 0.5 to 1.26 0.33
Student 537 60 11.2 (8.7 to 14.4) 0.98 0.61 to 1.58 0.94
Pension/Social security benefits 277 254 91.8 (81.2 to 103.9) 4.99 2.88 to 8.64 <0.01
Sex work 29 2 6.9 (1.7 to 27.7) 0.62 0.07 to 5.8 0.67
Other (including missing) 334 58 17.4 (13.4 to 22.5) 0.8 0.46 to 1.4 0.44
Self-Reported General Health
poor/fair 1126 372 33.3 (29.8 to 36.6) 1 <0.001
good 4120 770 18.7 (17.4 to 20.1) 0.46 0.34 to 0.64 <0.01
very good 5264 731 13.9 (12.9 to 14.9) 0.38 0.28 to 0.52 <0.01
excellent 2503 334 13.3 (12.0 to 14.9) 0.42 0.3 to 0.59 <0.01
Ever reported as having had any STI (other than HIV)
No 4336 591 13.6 (12.6 to 14.8) 1 0.042
Yes 8689 1616 18.6 (17.7 to 19.5) 1.23 1.01 to 1.51 0.04
Injected drugs in last 6months
Never 12554 2018 16.1 (15.4 to 16.8) 1 0.001
Yes 442 180 40.7 (35.1 to 47.1) 2.61 1.64 to 4.17 <0.01
Missing 28 9 32.4 (16.9 to 62.3) 0.97 0.8 to 1.17 0.73
Gay community involvement
low 3850 622 16.2 (14.9 to 17.5) 1 <0.001b
moderate 3783 621 16.4 (15.2 to 17.8) 1.1 0.87 to 1.38 0.43
high 2948 405 13.7 (12.5 to 15.1) 1.19 0.93 to 1.53 0.17
very high 2444 559 22.9 (21.1 to 24.9) 1.63 1.26 to 2.12 <0.01
Financial Yeara
July 2000-Jul 2003 1253 216 17.2 (15.1 to 19.7) 1 <0.001
Aug 2003-July 2006 3866 543 14 (12.9 to 15.3) 0.81 0.69 to 0.95 0.01
Aug 2006-July 2009 3965 675 17 (15.8 to 18.4) 0.94 0.8 to 1.1 0.46
July 2009-July 2012 3941 773 19.6 (18.3 to 21.0) 1.05 0.89 to 1.23 0.58
Open in a new tab
a

Time-dependent covariate

b

P value for test for trend

c

Age and financial year were an a priori inclusion

Abbreviations: N= number; PYs=person years; CI=Confidence Interval; IRR=incidence rate ratio; STI=Sexual Transmitted Infection; NA=Not Applicable; DK=Doesn't Know

Mortality

A total of 14 deaths were observed in 13,025 PYs in the HIV-ve cohort [crude rate of 0.11/100 PYs (95% CI 0.06-0.18)]. There was no difference observed in terms of mortality between the HIV-ve cohort and the general population [SMR 0.61 (95% CI 0.33-1.02)). 46 deaths were observed in 5,580 PYs in the HIV+ve cohort [crude rate of 0.82/100 PYs (95% CI 0.62-1.10)]. The mortality rate in the HIV+ve cohort was three times higher than the general population [SMR 3.07 (95%CI 2.25-4.09)]. HIV+ve individuals had an increased risk of mortality compared with the HIV-ve individuals [adjusted RR of 6.54 (95%CI 3.43-12.47)].

Discussion

We found higher rates of hospitalisation in the HIV+ve cohort of GBM compared with the HIV-ve cohort and the general male population. After adjusting for other risk factors, our HIV+ve participants were almost two and a half times as likely to be hospitalised as their HIV-ve counterparts, and hospitalisation rates in the HIV+ve cohort were 45% higher than in the general population. In the HIV+ve cohort, rates of hospitalisation for non-AIDS-defining infectious diseases, non-AIDS-defining respiratory diseases, and digestive system diseases were all higher compared with the HIV-ve cohort and compared with the general male population. In the HIV+ve cohort rates of hospitalisation for non-AIDS-defining cancers, blood and immune diseases, cardiovascular diseases and injuries and poisonings were all significantly higher compared with the HIV-ve cohort but not when compared with the general population. This emphasizes the need for data that directly compares morbidity in HIV infected and uninfected gay and bisexual men.

Admission rates in the HIV+ve cohort (41 (95%CI 39-43)) were slightly lower compared with those previously described by Falster et al. (59 (57-61)) in a predominantly male homosexual clinic-based HIV+ve cohort, the Australian HIV Observational Database (AHOD) (33). We also found the SMR in the HIV+ve cohort (3.1 (95%CI 2.3-4.1)) was slightly lower than that described in the AHOD cohort by McManus et al. (3.5 (3.0-4.0)) (34). It is likely that this discrepancy is due to the different recruitment methods employed by the different cohorts, that is community- versus clinic-based respectively. We also found many hospitalisations to be for non-AIDS-related diseases in the HIV+ve cohort. This accords with previous studies conducted in Australia (33) and elsewhere (35, 36).

Our finding of lower rates of hospitalisation in the HIV-ve cohort is somewhat surprising. However, recent studies examining mortality by sexual orientation are in support of the conclusion of no excess morbidity in HIV-ve GBM compared to the general male population. Studies conducted by Frisch et al. (21) and Cochran and Mays (20, 37) demonstrated all-cause mortality risk in men who have sex with men (MSM) was similar to that of their heterosexual counterparts, contradicting the belief that minority sexual orientation shortens lives. Furthermore, Cochran and Mays (20) also found no increase in suicide-related mortality in MSM consistent with our findings of lower rates of mental health-related hospitalisation in HIV-ve GBM. While our HIV-ve cohort demonstrated higher levels of smoking, alcohol and recreational drug use than found in the comparable general male population(38), this cohort was also more likely to have favourable socioeconomic indicators, such as higher income and education levels, which have been shown to be health protective and prevalent in other cohorts of GBM (39).

Unsurprisingly, poorer socioeconomic indicators in both cohorts were shown to be significantly associated with an increased likelihood of hospitalisation, as was poorer self-reported health. In the HIV+ve cohort, indicators of more advanced HIV at baseline were found to be associated with an increased likelihood of hospitalisation, consistent with previous findings (33). Contrary to expectation, ‘often’ and ‘very often’ recreational drug use was found to be protective in the HIV+ve cohort. However it is likely that problematic drug use is not being reflected in this measure, as indicators of very heavy drug use, such as using 2 or more drugs weekly in the HIV+ve cohort were found to be associated with an increased likelihood of hospitalisation. Being born in Central or South America or the Caribbean or Asia (compared with Australia) was associated with a decreased risk of hospitalisation. This is consistent with previous findings in the general population which have shown that overseas born immigrants have tend to have lower hospitalisation rates for most diagnoses in Australia(40). These inequalities have previously largely been explained by the ‘healthy migrant effect’, which ensures that, for the most part, only those migrants in good health migrate to Australia.(40, 41)

Our study had some limitations that should be considered when interpreting our results. With regards to the HIV+ve cohort, CD4 and VL data were only available as baseline measures and may have change substantially over the course of observation. We unfortunately did not have access to cause of death information for our cohorts which would have been of interest and sample size precluded us from undertaking a more detailed analysis of individual primary diagnoses. Furthermore we were unable to examine visits to HIV specialists in addition to hospitalisation in the HIV+ve cohort and thus could not enumerate pre-hospitalisation medical intervention that may be substantial in this group. It is also possible that differences in access to care could have impacted rates of hospitalisation seen in this study. Certainly it is likely that the HIV+ve cohort would be more greatly integrated into medical follow-up in the general practice setting, which was on post-hoc examination supported by our data (22% of HIV-ve GBM had no regular doctor vs. 2% of HIV+ve GBM). Whether or not this would impact participants' likelihood of seeking care in a hospital setting is unknown in this study. All residents in Australia have access to Medicare which enables them to receive hospital care free of charge; however discrimination and stigma and poorer health seeking behaviour could have contributed to lower hospital rates seen in the HIV-ve cohort.

An additional limitation could have arisen from behaviours which predisposed HIV+ve men to becoming HIV-infected contributing to higher hospitalisation rates among HIV+ve compared to HIV-ve men. However, the availability of rich behavioural and demographic data collected in our study cohorts enabled us to adjust for some of the potential confounding, as well as being able to investigate behavioural and health covariates as predictors of outcomes.

Consistent with other registry linkage studies, error could have arisen from participant migration outside of the registry region. Unfortunately it is impossible to estimate the impact of this on missing linkages as relevant linkage validation subsets with known outcomes were not available. Further, we were unable to exclude gay and bisexual men from the general population estimates. However, the proportion of men identifying as gay or bisexual is low in the general Australian male population (1.6 and 0.9% respectively) and would only have biased our relative estimates towards the null. While method of recruitment in both cohorts was similar and is a strength of the study, the representativeness of the cohort to the wider HIV+ve and HIV-ve homosexual population is unknown. Representative samples of gay and other homosexually active men are impossible to attain as the population cannot be enumerated (24). Despite these limitations, investigation of baseline characteristics in both cohorts showed similarity to those described in other cohorts of HIV+ve and HIV-ve GBM in Australia (39).

Conclusions

In summary, our findings show that while HIV+ve GBM still experience excess morbidity, excess risk is not being contributed by sexual orientation per se. Further, our data suggest that concerns regarding sexually-oriented morbidity in self-identifying GBM may be unfounded.

Acknowledgments

The authors would like to thank the participants, the dedicated pH and HIM study teams and the participating doctors and clinics for their contribution to the HIM and pH studies. The authors would also like to acknowledge the assistance of the New South Wales Centre for Health Record Linkage in the conduct of this study.

The Kirby Institute and the Centre for Social Research in Health are funded by the Australian Government Department of Health and Ageing. The Health in Men Cohort study was funded by the National Institutes of Health, a component of the USA Department of Health and Human Services (NIH/NIAID/DAIDS: HVDDT Award N01-AI-05395), the National Health and Medical Research Council in Australia (Project grant #400944), the Australian Government Department of Health and Ageing (Canberra) and the New South Wales Health Department (Sydney). The Positive Health Cohort study was funded by the Australian Government Department of Health and Ageing (Canberra) and the New South Wales Health Department (Sydney). The content of this publication is solely the responsibility of the authors and does not necessarily represent the view of any of the institutions mentioned above.

Appendix A

Most frequent primary diagnoses by ICD-10 Chapter heading in 1325 HIV-ve and 557 HIV+ve gay and bisexual men recruited in Sydney, Australia, 2000-2012.

HIV-ve HIV+ve
Primary Diagnosis N Hospitalisations % of hospitalisations for chapter Primary Diagnosis N Hospitalisations % of hospitalisations for chapter
Infectious diseases (Non-AIDS defining) Anogenital (venereal) warts 15 31.25 Other gastroenteritis and colitis of infectious and unspecified origin 29 30.85
Viral and other specified intestinal infections 9 18.75 Anogenital (venereal) warts 14 14.89
Gastroenteritis and colitis of unspecified origin 8 16.67 Zoster [herpes zoster] 9 9.57
Malignant cancers (Non-AIDS defining) Other malignant neoplasms of skin 18 28.13 Other malignant neoplasms of skin 25 22.94
Malignant neoplasm of prostate 18 28.13 Multiple myeloma and malignant plasma cell neoplasms 23 21.10
Malignant melanoma of skin 5 7.81 Hodgkin lymphoma 9 8.26
Malignant neoplasm of testis 5 7.81 Malignant neoplasm of anus and anal canal 7 6.42
Other cancers Benign neoplasm of colon, rectum, anus and anal canal 27 62.79 Benign neoplasm of colon, rectum, anus and anal canal 20 45.45
Benign lipomatous neoplasm 5 11.63 Benign neoplasm of colon, rectum, anus and anal canal 8 18.18
Melanoma in situ 3 6.98 Carcinoma in situ of skin 6 13.64
Benign lipomatous neoplasm 6 13.64
Blood & immune diseases Agranulocytosis 5 71.43 Other anaemias 10 33.33
Purpura and other haemorrhagic conditions 8 26.67
Iron deficiency anaemia 7 23.33
Endocrine diseases Type 1 diabetes mellitus 22 32.35 Testicular dysfunction 6 17.14
Disorders of porphyrin and bilirubin metabolism 11 16.18 Other disorders of fluid, electrolyte and acid-base balance 6 17.14
Type 2 diabetes mellitus 10 14.71 Type 1 diabetes mellitus 5 14.29
Mental disorders Mental and behavioural disorders due to use of alcohol 27 25.96 Bipolar affective disorder 35 26.92
Mental and behavioural disorders due to multiple drug use and use of other psychoactive substances 15 14.42 Mental and behavioural disorders due to use of alcohol 17 13.08
Schizophrenia 15 14.42 Depressive episode 12 9.23
Nervous & sense disorders (Non-AIDS defining) Sleep disorders 33 27.97 Sleep disorders 18 17.48
Other cataract 19 16.10 Epilepsy 9 8.74
Mononeuropathies of upper limb 10 8.47 Other polyneuropathies 9 8.74
Cardiovascular diseases Haemorrhoids 51 33.12 Acute myocardial infarction 21 14.69
Atrial fibrillation and flutter 27 17.53 Haemorrhoids 19 13.29
Chronic ischaemic heart disease 14 9.09 Oesophageal varices 16 11.19
Respiratory diseases (Non-AIDS defining) Other disorders of nose and nasal sinuses 19 23.46 Pneumonia, organism unspecified 34 30.36
Chronic sinusitis 11 13.58 Other chronic obstructive pulmonary disease 15 13.39
Pneumonia, organism unspecified 9 11.11 Asthma 9 8.04
Digestive system diseases Inguinal hernia 50 10.80 Other specified diseases of anus and rectum e.g. Proctitis NOS 30 8.40
Gastro-oesophageal reflux disease 43 9.29 Other noninfective gastroenteritis and colitis 28 7.84
Acute appendicitis 32 6.91 Gastro-oesophageal reflux disease 24 6.72
Skin diseases Cellulitis 18 40.91 Cellulitis 15 36.59
Follicular cysts of skin and subcutaneous tissue 5 11.36 Cutaneous abscess, furuncle and carbuncle 6 14.63
Follicular cysts of skin and subcutaneous tissue 4 9.76
Musculoskeletal diseases Internal derangement of knee 28 20.90 Dorsalgia 18 16.67
Dorsalgia 15 11.19 Osteoporosis without pathological fracture 15 13.89
Gonarthrosis [arthrosis of knee] 9 6.72 Osteonecrosis 12 11.11
Genitourinary diseases Calculus of kidney and ureter 25 22.12 Other disorders of urinary system 22 20.75
Other disorders of bladder 13 11.50 Calculus of kidney and ureter 16 15.09
Hyperplasia of prostate 11 9.73 Urethral stricture 15 14.15
Symptoms & abnormal findings Pain in throat and chest 50 28.90 Pain in throat and chest 48 20.34
Abdominal and pelvic pain 45 26.01 Abdominal and pelvic pain 41 17.37
Headache 13 7.51 Headache 22 9.32
Injury & poisoning Poisoning by antiepileptic, sedative-hypnotic and antiparkinsonism drugs (predominantly benzodiazepines) 21 10.10 Poisoning by antiepileptic, sedative-hypnotic and antiparkinsonism drugs (predominantly benzodiazepines) 14 9.46
Complications of procedures, not elsewhere classified 21 10.10 Complications of procedures, not elsewhere classified 12 8.11
Fracture of lower leg, including ankle 11 5.29 Fracture of forearm 11 7.43
Poisoning by psychotropic drugs, not elsewhere classified 11 5.29
Other factors infl. health Care involving use of rehabilitation procedures 63 31.03 Other medical care (predominantly chemotherapy session for neoplasm) 60 26.79
Family history of malignant neoplasm 39 19.21 Family history of malignant neoplasm 26 11.61
Follow-up examination after treatment for conditions other than malignant neoplasms 26 12.81 Care involving use of rehabilitation procedures 25 11.16
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Abbreviations: N=Number; Infl.=Influencing

Footnotes

Conflicts of Interest: The authors have no financial, consultant, institutional or other relationships that might lead to bias or conflict of interest for this manuscript.

Authors' contributions: All authors made a substantial contribution to the conception and design of the study. CM, JA, FJ and HG analysed the data. All authors contributed to the interpretation of the data. All authors were involved in drafting and revising the manuscript and have read and approved the final manuscript.

Contributor Information

Andrew E. Grulich, Email: agrulich@kirby.unsw.edu.au.

Garrett Prestage, Email: gprestage@kirby.unsw.edu.au.

Heather F. Gidding, Email: hgidding@unsw.edu.au.

Fengyi Jin, Email: jjin@kirby.unsw.edu.au.

Limin Mao, Email: limin.mao@unsw.edu.au.

Kathy Petoumenos, Email: kpetoumenos@kirby.unsw.edu.au.

Iryna B. Zablotska, Email: iazblotska@kirby.unsw.edu.au.

I. Mary Poynten, Email: mpoynten@kirby.unsw.edu.au.

Matthew G. Law, Email: mlaw@kirby.unsw.edu.au.

Janaki Amin, Email: jamin@kirby.unsw.edu.au.

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