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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
HIV Med. Author manuscript; available in PMC 2017 May 1.
Published in final edited form as:
Published online 2015 September 6. doi:  10.1111/hiv.12312
PMCID: PMC4779743
NIHMSID: NIHMS712881

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

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

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

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

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

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-veHIV+ve
Primary DiagnosisN Hospitalisations% of hospitalisations for chapterPrimary DiagnosisN Hospitalisations% of hospitalisations for chapter


Infectious diseases (Non-AIDS defining)Anogenital (venereal) warts1531.25Other gastroenteritis and colitis of infectious and unspecified origin2930.85
Viral and other specified intestinal infections918.75Anogenital (venereal) warts1414.89
Gastroenteritis and colitis of unspecified origin816.67Zoster [herpes zoster]99.57


Malignant cancers (Non-AIDS defining)Other malignant neoplasms of skin1828.13Other malignant neoplasms of skin2522.94
Malignant neoplasm of prostate1828.13Multiple myeloma and malignant plasma cell neoplasms2321.10
Malignant melanoma of skin57.81Hodgkin lymphoma98.26
Malignant neoplasm of testis57.81Malignant neoplasm of anus and anal canal76.42


Other cancersBenign neoplasm of colon, rectum, anus and anal canal2762.79Benign neoplasm of colon, rectum, anus and anal canal2045.45
Benign lipomatous neoplasm511.63Benign neoplasm of colon, rectum, anus and anal canal818.18
Melanoma in situ36.98Carcinoma in situ of skin613.64
Benign lipomatous neoplasm613.64


Blood & immune diseasesAgranulocytosis571.43Other anaemias1033.33
Purpura and other haemorrhagic conditions826.67
Iron deficiency anaemia723.33


Endocrine diseasesType 1 diabetes mellitus2232.35Testicular dysfunction617.14
Disorders of porphyrin and bilirubin metabolism1116.18Other disorders of fluid, electrolyte and acid-base balance617.14
Type 2 diabetes mellitus1014.71Type 1 diabetes mellitus514.29


Mental disordersMental and behavioural disorders due to use of alcohol2725.96Bipolar affective disorder3526.92
Mental and behavioural disorders due to multiple drug use and use of other psychoactive substances1514.42Mental and behavioural disorders due to use of alcohol1713.08
Schizophrenia1514.42Depressive episode129.23


Nervous & sense disorders (Non-AIDS defining)Sleep disorders3327.97Sleep disorders1817.48
Other cataract1916.10Epilepsy98.74
Mononeuropathies of upper limb108.47Other polyneuropathies98.74


Cardiovascular diseasesHaemorrhoids5133.12Acute myocardial infarction2114.69
Atrial fibrillation and flutter2717.53Haemorrhoids1913.29
Chronic ischaemic heart disease149.09Oesophageal varices1611.19


Respiratory diseases (Non-AIDS defining)Other disorders of nose and nasal sinuses1923.46Pneumonia, organism unspecified3430.36
Chronic sinusitis1113.58Other chronic obstructive pulmonary disease1513.39
Pneumonia, organism unspecified911.11Asthma98.04


Digestive system diseasesInguinal hernia5010.80Other specified diseases of anus and rectum e.g. Proctitis NOS308.40
Gastro-oesophageal reflux disease439.29Other noninfective gastroenteritis and colitis287.84
Acute appendicitis326.91Gastro-oesophageal reflux disease246.72


Skin diseasesCellulitis1840.91Cellulitis1536.59
Follicular cysts of skin and subcutaneous tissue511.36Cutaneous abscess, furuncle and carbuncle614.63
Follicular cysts of skin and subcutaneous tissue49.76


Musculoskeletal diseasesInternal derangement of knee2820.90Dorsalgia1816.67
Dorsalgia1511.19Osteoporosis without pathological fracture1513.89
Gonarthrosis [arthrosis of knee]96.72Osteonecrosis1211.11


Genitourinary diseasesCalculus of kidney and ureter2522.12Other disorders of urinary system2220.75
Other disorders of bladder1311.50Calculus of kidney and ureter1615.09
Hyperplasia of prostate119.73Urethral stricture1514.15


Symptoms & abnormal findingsPain in throat and chest5028.90Pain in throat and chest4820.34
Abdominal and pelvic pain4526.01Abdominal and pelvic pain4117.37
Headache137.51Headache229.32


Injury & poisoningPoisoning by antiepileptic, sedative-hypnotic and antiparkinsonism drugs (predominantly benzodiazepines)2110.10Poisoning by antiepileptic, sedative-hypnotic and antiparkinsonism drugs (predominantly benzodiazepines)149.46
Complications of procedures, not elsewhere classified2110.10Complications of procedures, not elsewhere classified128.11
Fracture of lower leg, including ankle115.29Fracture of forearm117.43
Poisoning by psychotropic drugs, not elsewhere classified115.29


Other factors infl. healthCare involving use of rehabilitation procedures6331.03Other medical care (predominantly chemotherapy session for neoplasm)6026.79
Family history of malignant neoplasm3919.21Family history of malignant neoplasm2611.61
Follow-up examination after treatment for conditions other than malignant neoplasms2612.81Care involving use of rehabilitation procedures2511.16

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.

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