Sample
We conducted a cross-sectional analysis of the Household, Income and Labour Dynamics in Australia (HILDA) Survey from 2018 (Wave 17). The HILDA Survey is an annual, nationally representative household-based panel study of Australian residents, designed to collect respondents’ information on economic and personal well-being, household dynamics and labour market participation (22). Having started in 2001, the HILDA survey is completed via interviews with all survey household members over the age of 15 years, with the interviews then completed yearly from the same sample. Exempt from the HILDA Survey are Australian overseas residents, diplomatic personnel, members of non-Australian defence forces and people living in very remote areas. Further detailed descriptions of the survey are described elsewhere (23).
Ethics Approval was granted by the Melbourne School of Population and Global Health’s Human Ethics Advisory Group at the University of Melbourne, 27th May 2019 (reference number 1954211.1).
Wave 17 had a total of 17, 571 respondents, with a response rate of 96.4%. For this study, we included those respondents aged 18 years and over (n=16,833) and removed respondents with missing values in the dependent or independent variables (0.5% of the sample), leaving a total of 16,749 respondents. A flow chart is included in the Supplementary materials (Appendix Figure 1).
Variables
Multimorbidity
The predicting variable was the number of NCDs, self-reported by respondents in answer to the question “Have you been told by a doctor or nurse that you have any of these conditions?”. The HILDA Survey accounts for 12 NCDs; 9 possible physical health conditions (arthritis/osteoporosis, asthma, cancer, chronic bronchitis/emphysema, type 1 diabetes, type 2 diabetes, heart diseases, high blood pressure/hypertension, any other serious circulatory condition), and 3 mental health conditions (depression, anxiety, other mental illness). We examined the number of physical health conditions (0-9) to quantify the number of reported NCDs, as well as recording the presence of any of the three mental health conditions. Respondents were defined as experiencing multimorbidity if they reported two or more of any of these physical or psychological conditions.
We also examine (1) prevalence of physical health condition only multimorbidity (PHM), and (2) prevalence of co-existing physical and mental multimorbidity (PMM).
Outcome variables
Health service use was measured over the previous 12 months, including primary or secondary health service use, as well as medication use. Primary health service use was measured through general practitioner (GP) visits (both any and total number), while secondary health service use was captured through any visit to medical specialists, inpatient hospitalisation(s) and length of stay, and day hospital appointment(s). Medication use was measured as number of medications, as well as presence of polypharmacy (defined as ≥5 prescription medications).
Work productivity loss was measured through reduction in labour force participation, any reduced working hours, and days of sick leave or unpaid leave taken.
Health status was assessed through self-reported health (Poor/Fair vs Good/Very Good/Excellent), SF6D score, self-reported disability, and derived psychological distress risk (very high/high vs moderate/low risk of psychological distress), as well as receiving a disability support payment (Disability Support Pension). Unadjusted population level differences in outcomes can be found in Supplementary Materials (Appendix Table 1).
Stratification by Indigenous Status
All respondents were stratified by Indigenous status. Respondents were asked “Are you of Aboriginal and Torres Strait Islander origin?”, with those who identified as Aboriginal, Torres Strait Islander, or Aboriginal and Torres Strait Islander determined to be Aboriginal and all other responses considered Non-Indigenous.
Covariates
Covariates included Indigenous status (Aboriginal, Non-Indigenous), sex (Male/Female), age categories (18-29, 30-39, 40-49, 50-59, 60-69, 70+ years), education level (<Year 12 Schooling, Year 12 to Diploma, Bachelors or higher), household income (Q1-Q4), employment status (employed full time, employed part time, not employed but looking for work, not in workforce), marital status (married/de facto, never married/single, formerly married/formerly de facto) location by Australian state or territory (NSW, VIC, SA, WA, TAS, NT, ACT), region (urban, regional, remote), country of birth (Australia, Other), and private health insurance (yes/no).
Statistical analysis
We summarised the sample characteristics. We also presented the prevalence of the most common NCD combinations, and prevalence of PHM and PMM, stratifying by Indigenous status.
We applied multivariable negative binomial, linear and logistic regression models to determine the association between multimorbidity and outcomes (Supplementary Materials - Appendix Table 2). Multivariable logistic regression models were applied to examine the association between multimorbidity and binary outcomes, such as any GP visit, specialist visit or hospital/day hospital admission, as well as unemployment, and self-reported health. Multivariable negative binomial regression models were performed for outcomes modelled on count data such as number of medications, number of days leave, or number of health service interactions (GP/Hospital/Day hospital), given the skewed nature of the count data. A multivariable linear regression model was applied to examine changes in function as reported on the SF6D.
Two-way interaction terms were used in each regression model to examine the difference in association between multimorbidity and outcome differed, between Aboriginal and non-Indigenous Australians. Results are presented for the pooled sample and stratified by Indigenous status.
Results were weighted to account for the multi-stage sampling design of the HILDA Survey. Multivariable regression models were adjusted for covariates listed above. All analyses were performed using Stata 15 (Stata Corp.), sample weights were applied, and the level of statistical significance was set at 5%.