Non-AIDS complexity amongst patients living with HIV in Sydney: risk factors and health outcomes

The Albion Centre is a publicly funded, ambulatory care clinic in metropolitan Sydney with in-house medical specialists, nurses, psychologists, social workers, dieticians and pharmacists providing same-visit HIV care and treatment. The patients are mostly homosexually-active men, the group most affected by Australia’s HIV epidemic.

A checklist was developed to enable a retrospective, non-consecutive audit of medical records for HIV patients presenting for medical care between 1 May–15 June 2014. The project was approved by South Eastern Sydney Human Research Ethics Committee (study number 16/215) adjust.

Data collected included; (1) patients’ demographic information, including gender, age, CALD or ATSI background, GP or specialist involvement in care; (2) clinical history; duration of HIV infection, recent HIV RNA viral load, current and nadir CD4 count, HIV genotypic resistance, ART adherence, past AIDS defining conditions, hospital admissions in past year, NACs (cardiac, hepatic, endocrine, renal, neurological, respiratory, neoplastic, musculoskeletal conditions), body mass index (BMI); and (3) psychosocial factors; such as mental health diagnoses, smoking, alcohol and (intravenous) drug use, employment status, housing, financial support and federally-funded Medicare eligibility (only Australian permanent residents and citizens are eligible for Medicare). Regarding categorisation where a particular condition could be reported under more than one NAC domain (e.g. haemochromatosis could be classified as liver, endocrine or cardiac disease) only one NAC was assigned. We did not adjust for frequency of attendance as this was a cross-sectional, non-consecutive analysis of patient records. The mental health diagnoses were made by the attending doctor with or without input from staff clinical psychologists. ATSI and CALD status were defined according to patient registration data including self-declaration of aboriginality, first language spoken at home and year of arrival in Australia if born overseas. The designation of ‘social isolation’ was derived from the patient’s psychosocial history and self-report of same as documented in the records. Refugees were patients who were referred by the Australian Immigration authorities for treatment and care.

Data were analysed using Stata software (StataCorp. 2011, v12: College Station, TX: StataCorp LP). DAG theory was used to determine confounders for each predictor-outcome association (Fig. 1). DAG theory minimises bias and identifies only necessary confounders to increase the power of the analysis (statistical efficiency) [4]. The adverse health outcomes identified in our study were: CD4 count, genotypic resistance, hospital admission, ART adherence and co-morbidity score (health scores are discussed below).

Figure 1 shows the pre-analysis DAG for HIV patients. Arrows in the DAGs in this paper (Figs. 1 and 2) represent a causal association as per syntax of causal inference used by Pearl [5]. This model is not strictly acyclic, as mental health diagnoses may cause social issues and vice versa. For these ‘bi-directional’ variables, the social issue score was considered a confounder for the mental health score and vice versa in the analysis. This produces conservative effect sizes when assessing causality but the probability rules of the model are maintained.

Associations between predictors with binary outcomes were calculated using logistic regression. The exception is ordinal regression, which is used to calculate the associations between the co-morbidity score and CD4 nadir, social issue score, Medicare eligibility, virological failure, BMI > 30 and CALD. Missing values were ignored (case-wise deletion) and multiple imputation was not performed because some variables in the dataset had more missing observations than others and were likely missing not at random (MNAR) making them unsuitable for multiple imputation.

We developed ‘health scores’ to facilitate quantitative analysis. These scores, although not validated for PLHIV, follow the principles of the validated Charlson score [6] and are simply a method for counting the total number or NACs, mental health diagnoses and social issues for each patient. The Comorbidity score was the number of NACs (zero to a maximum seven defined co-morbidities including Cardiac, Renal, Neurological, Liver, Endocrine, Respiratory, Metabolic Bone Disease, Malignancy). The mental health score was the sum of mental health diagnoses (zero to a maximum three diagnoses including anxiety, major depression, drug-dependency, alcohol dependency, personality disorder, schizophrenia and other psychoses). Lastly, the social issues score was the sum of social issues (zero to a maximum of three issues including: unemployment, disability support pension, socially isolated, refugee, retired, homeless, domestic violence).

211 files of 264 attendees had audit forms completed in the study period. 89.5% were male, 10% female and 0.5% transgender. Median age was 47.2 years (range 21–73). Many patients were CALD (41.8%) and 4.1% ATSI 95% were taking ART and one-third had HIV infection for 20 years or more. All patients are requested to have a GP as part of our shared-care model, yet only half did. The reasons patients reported for not having a GP included: fear of disclosure, limited GP appointments, cost or lack of multidisciplinary services, or perceived lack of HIV expertise. Half had a specialist managing their NACs. Variables from the DAG model and health scores are given in Table 1.

The patients were generally healthy: 94% were taking ART and 82.3% had undetectable viral load. 18% had poor ART adherence (as determined by doctor), 8.1% past genotypic resistance and 12.8% past virological failure. 12.4% were underweight (BMI < 20) and 12.9% were obese (BMI > 30). 17% had been hospitalised in the past 12 months (most not HIV-related) and 13% had a past AIDS-defining condition. Polypharmacy was common with 20% prescribed five or more non-ART medications. Approximately one-third had been living with HIV for 10–20 years and one-third more than 20 years.

43% of patients had no NAC. 28.1% had at least one NAC and some had multiple NACs. The prevalence of specific NACs (with predominant condition) was: cardiovascular disease (hypercholesterolaemia, hypertension) 26.8%; liver disease (hepatitis B or C co-infection, alcoholic liver disease) 22.1%; endocrinopathy (e.g.: type 2 diabetes, dyslipidemia) 21.7%; neurological disease (peripheral neuropathy, HIV-associated neurocognitive disorder) 12%; renal impairment (eGFR < 60 mL/min) 7.8%; osteoporosis 6%; respiratory disease (chronic obstructive pulmonary disease) 6.9%; and malignancy 4.6%.

There was a significant burden of mental health and social issues in this sample. 54% had at least one psychiatric diagnosis: predominantly anxiety disorder (one-third) and major depression (one-third), followed by drug dependence (12.9%), alcohol dependence (4.3%), personality disorder (3.7%), schizophrenia (3.1%), other psychosis, e.g. drug-induced (3.1%), post-traumatic stress disorder (2.4%), bipolar affective disorder (1.8%) and adjustment disorder (1.8%). Dual mental health diagnoses occurred in 20% of patients. 49% were past/current smokers, 14.3% were past or current intravenous drug users, and 1.8% were homeless. About one-fifth of patients were unemployed (22.1%), receiving disability support pension (22.6%) or were Medicare ineligible (25%).

We identified five predictors of poor health outcomes for our HIV patients adjusting for appropriate confounders as per DAG (Table 2).

Firstly, the comorbidity score was causally associated with hospitalisation in the previous year, odds ratio, OR 1.58; 95% CI 1.01–2.46; p = 0.044 (adjusting for Medicare eligibility, mental health score, social issue score, CALD, ART adherence, BMI > 30 as confounders). Interestingly, CALD status was causally associated with a lower risk of hospitalisation, OR 0.31; 95% CI 0.13–0.74; p = 0.008.

The mental health score was also causally associated with hospitalisation, OR 1.79; 95% CI 1.22–2.62; p = 0.003 (adjusting for CALD) and poor ART adherence, OR 2.34; 95% CI 1.52–3.59; p = 0.001 (adjusting for social issue score, CALD).

The social issue score was causally associated with: (1) hospitalisation, OR 1.72; 95% CI 1.1–2.7; p = 0.018 (adjusting for ART adherence, CALD, mental health score); (2) genotypic resistance, OR 2.61; 95% CI 1.48–4.59; p = 0.001 (adjusting for CALD); and (3) the co-morbidity score, OR 1.69; 95% CI 1.24–2.3; p = 0.001 (adjusting for CALD, mental health score).

The final DAG model shows the significant predictors of adverse health outcomes supported by our data (Fig. 2). Notably, it identifies potential specific roles for multidisciplinary management of health predictors in virologically stable patients, underpinning our current model of HIV care.