Multimorbidity and health-related quality of life amongst Indigenous Australians: A longitudinal analysis

doi:10.21203/rs.3.rs-1997851/v1

Background

The burden of multimorbidity has been observed worldwide, and it has significant consequences on health outcomes. In Australia, health-related quality of life (HRQoL) is comparatively low amongst Aboriginal and/or Torres Strait Islanders. Yet no studies have examined the association between multimorbidity and HRQoL within this at-risk population. This is the aim of the present study.

Methods

Longitudinal data were derived from three waves (9, 13 and 17) of the Household, Income and Labour Dynamics in Australia Survey. A total of 1,007 person-year observations from 592 Aboriginal and/or Torres Strait Islanders aged 15 years and above were included. HRQoL was captured using the 36-item Short-Form Health Survey (SF-36), and multimorbidity was defined as having two or more chronic health conditions. A random effects tobit model was used to assess the associations between multimorbidity and HRQoL.

Results

Approximately 21% of Indigenous Australians were classified as experiencing multimorbidity. Respondents with multimorbidity reported lower scores than their counterparts without multimorbidity on the SF-36 sub-scales, summary measures and health utility index. Multimorbidity had pronounced adverse effects on overall HRQoL, including on the SF-36 physical component scale (β = −3.232; 95% CI: −4.734 to − 1.730), mental component scale (β = −5.625; 95% CI: −7.435 to − 3.815) and short-form six-dimension scale (β = −0.061; 95% CI: −0.082 to − 0.041).

Conclusion

This study demonstrated that having two or more chronic diseases is significantly associated with lower HRQoL amongst Indigenous Australians. Advanced health strategies addressing foreseeable challenges imposed on multimorbid adults should be implemented to improve the HRQoL of Indigenous Australians.

Australia

health-related quality of life

Indigenous Australians

multimorbidity

race

Multimorbidity is a leading public-health burden and a source of significant challenges to healthcare management. In the literature, the term multimorbidity’ is used interchangeably with the term ‘comorbidity’, and can be defined as the presence of two or more chronic diseases within an individual at the same time [1]. These co-occurring diseases may or may not be connected by a causal relationship. In Australia, multimorbidity impacts almost one-fifth of the population and 80% of those over 65 years of age [2, 3]. The growing prevalence of multimorbidity is a substantial contributor to individuals’ health-related quality of life (HRQoL), which refers to individuals’ overall physical health and well-being throughout their lifespan [1].

The potential impact of multimorbidity on HRQoL has received substantial academic attention over the last decade [4–6]. This body of evidence has revealed that a person’s life can be significantly affected by multimorbidity, including through physical-functioning limitations, psychological problems and financial burdens [7–9]. In other words, people’s social life is affected by multiple diseases’ altering their physical and psychological capacities, ultimately reducing their quality of life [10, 11]. Indeed, numerous studies from different settings have empirically confirmed that individuals with multimorbidity experience adverse outcomes in their quality of life [8, 11–13]. For example, they usually experience loneliness, social isolation, stress, anxiety and lower life satisfaction [14–16]. This reality applies also to Australia, the country in which the present study is based [1, 2, 11, 17].

In Australia, Indigenous people experience a disproportionately high disease burden [2, 17], including substantially greater prevalence of multimorbidity than non-Indigenous Australians [17, 18]. For instance, Indigenous Australians have a 4.6-fold higher age-standardised burden of cardiovascular disease than non-Indigenous individuals, and diabetes rates are also disproportionately high [19]. Thus, health disparities between Indigenous and non-Indigenous Australians may be partly driven by an excessive multimorbidity burden amongst Indigenous people [19]. Amongst Indigenous Australians, individuals of all age groups face impediments to accessing health care [20, 21]. Besides, poor health outcomes are observed amongst Indigenous adults due to intergenerational trauma, discrimination and racism [20–22]. Thus, Indigenous Australians may be particularly vulnerable to multimorbidity and its deleterious effects on HRQoL.

To the authors’ best knowledge, however, no studies have investigated the relationship between multimorbidity and HRQoL amongst Indigenous Australians. To fill this gap in the literature, the present study aims to analyse the impact of multimorbidity on the HRQoL of Indigenous Australians using longitudinal data. Given the preponderance of multimorbidity and poor HRQoL amongst the Indigenous population, our findings have substantial policy implications for developing interventions that redress this situation. Besides, they contribute to future economic analyses by identifying the utility value associated with multimorbidity.

Data Source and Sample Selection

The Household, Income and Labour Dynamics in Australia (HILDA) Survey, a household-based panel survey, was used as the basis for the current study. The HILDA Survey is an annual nationwide representative longitudinal survey initiated in 2001 that collects data on family relationships, health, wealth, income, labour market, employment and education through a combination of face-to-face interviews and self-completion questionnaires [23]. The HILDA project received approval from the University of Melbourne Human Research Ethics Committee.

The HILDA Survey features a representative sample of Australian households residing in private dwellings, identified through a multi-stage sampling approach. The initial survey wave collected data from 13,000 Australian adults from more than 7,000 households, and new respondents have joined the sample by marrying or beginning to live with existing sample members [24]. Furthermore, booster samples have been added to the survey since 2011 to maintain representativeness. Further details on the sampling procedure can be found elsewhere [25].

The subsample used in the current study was constructed from three waves (9, 13 and 17) of the HILDA Survey covering years 2009, 2013 and 2017. The analyses were restricted to those three data points because they are the only ones containing the required information on chronic conditions. The criteria for participants to be included in the sample were: (i) being aged 15 years and above, (ii) identifying as Indigenous Australians; and (iii) having valid information on the outcome and main variables of interest. Applying this inclusion criteria yielded an unbalanced panel consisting of 1,007 observations from 592 Indigenous Australians. Figure 1 describes the sample selection procedure and missing-data analysis.

Outcome Variable

HRQoL is the primary outcome variable of the present study and was approximated through the use of the by 36-item Short-Form Health Survey (SF-36). The SF-36 is the most extensively used and accepted health scale to assess individuals’ physical and mental functioning through a standard questionnaire [26]. The questionnaire includes 36 items measuring eight different health aspects: physical functioning (PF), role physical functioning (RP), role emotional functioning (RE), social functioning (SF), mental health (MH), vitality (VT), bodily pain (BP) and general health (GH). The converted score scale ranges between 0 (worst health) and 100 (best health) for all eight dimensions of SF-36.

Two summary measures are typically calculated using the SF-36 data: the physical component summary (PCS) and the mental component summary (MCS). Four subscales (PF, RP, BP and GH) are combined to generate the PCS and the remaining four subscales (RE, SF, MH and VT) are combined to generate the MCS. The PCS and MCS are standardised by linear z-score transformations, where the mean and deviation are set at 50 and 10, respectively. The MCS ranges from − 1.21 to 76.19 and the PCS ranges from 4.54 to 76.09, with higher scores implying a better health state [27].

Another important HRQoL measure that can be derived from the SF-36 is the health state utility index known as the Short-Form Six-Dimension (SF-6D) [28]. The SF-6D uses information from six SF-36 subscales (PF, RP, RE, SF, VT and BP), with the resulting index ranging between 0.29 (worst health) and 1 (best health) [28]. This index is used as a global measure of HRQoL.

Exposure Variable

Participants’ self-reported chronic-condition data were used to measure the key exposure variable in this study. HILDA Survey respondents were asked the question: ‘Have you ever been told by a medical practitioner that you have been diagnosed with a serious illness or medical condition?’, with a list of conditions being presented in a showcard for the respondent to choose from. The present study considered eleven long-term states (hypertension, heart disease, type 1 diabetes, type 2 diabetes, chronic bronchitis/emphysema, cancer, asthma, arthritis/osteoporosis, anxiety/depression, other mental health conditions, and circulatory disease) to define the state of multimorbidity. Participants who simultaneously had two or more of these chronic health conditions were considered to experience multimorbidity. The exposure variable is categorised into: no morbidity, single chronic condition, and multimorbidity.

Other Covariates

Following the extant literature [6, 10, 29–32], several socio-demographic and behavioural characteristics of individuals were included in the multivariable analyses as potential confounders. The following socio-demographic variables were considered: age (15–29, 30–44, 45–59 and ≥ 60 years), gender (male and female), partnership status (single and partnered), highest education level attained (year 12 and below, certificate courses and university degrees), annual household income (quintile 1 to 5), employment status (employed, and unemployed or not in the labour force [NILF]) and geographic location of residence (major city and regional or remote). Smoking status (never smoked, ex-smoker and current smoker), alcohol drinking (never drunk, ex-drinker, and current drinker) and physical activity (less than the recommended level and recommended level) were included to capture lifestyle factors, and BMI was included as a health-related factor (underweight, healthy weight, overweight, and obese).

Estimation Strategy

As explained before, the data comprised an unbalanced panel with 1,007 person-year observations of 592 unique individuals observed at a maximum of three time points (2009, 2013 and 2017). At the initial stage, descriptive analyses were used to summarise the characteristics of the pooled dataset. This univariate section presents means and standard deviations (SDs) for continuous variables and percentages (with 95% Confidence Intervals, CIs) for categorical variables. The HRQoL status of the study participants according to their multimorbidity status was examined through graphical means.

The multivariable analyses involved a series of random-effects Tobit regression models to investigate the association between multimorbidity and HRQoL. The functional form of the model is written below:

$${\text{H}\text{R}\text{Q}\text{o}\text{L}}_{it}=\alpha +{{\beta }_{1}M}_{it}+{{{\rm Y}}_{i}\varSigma X}_{it}+{{\mu }}_{\text{i}\text{t}}+{{\epsilon }}_{\text{i}\text{t}}$$

1

In Eq. 1, subscripts i and t denote individual and time periods, respectively; $HRQoL$ denotes the different measures of the SF-36, including its eight dimensions, summary measures (PCS and MCS), and health utility index (SF-6D); M is a categorical explanatory variable capturing the presence of chronic conditions or multimorbidity; X refers to the vector of control variables. $\alpha$ is the model’s grand intercept, ${\mu }$ represents person-specific random effects, and ${\epsilon }$ is the usual random error term. $\beta$ and ${\rm Y}$ are the model parameters of interest to be estimated.

Eleven regression models were fitted, one for each of the outcome variable of interests: PCS, MCS, SF-6D, PF, RP, RE, SF, MH, VT, BP and GH. A random effects tobit regression model was adopted in this study to measure the effects of multimorbidity on HRQoL.

The model results were reported as unstandardized regression coefficients (β) with 95% confidence intervals (CIs). P-values of < 0.001, < 0.01 and < 0.05 were set to determine the statistical significance of the explanatory variables. The statistical analyses were executed using Stata version 17.0.

Descriptive Analyses

Table 2 shows the study sample’s baseline, end, and pooled socio-demographic and health-related characteristics (persons = 592; observations = 1,007). Nearly half (49 percent) of the participants were between the ages of 15–29 years, 58 percent were female, and 53 percent were single. Only 9 percent of those surveyed had university degrees, 49 percent were employed, 46 percent lived in major cities, 40 percent currently smoke, 76 percent currently consume alcohol, 71 percent do not perform the recommended level of physical activities, and 30 percent were obese (pooled in all waves). The statistics are consistent with those reported elsewhere and reflect the severe levels of socio-economic exclusions experiences by Indigenous Australians.

Table 2

Distribution of the analytic sample (socio-demographic, lifestyle): Baseline, final and all waves pooled (persons = 592; observations = 1,007)
Characteristics	Baseline wave (2009)		Final wave (2017)		Pooled in all waves (2009, 2013, & 2017)
Characteristics	n	%	n	%	n	%
Socio-demographic characteristics
Age group
15–29 years	105	46.05	199	48.66	492	48.86
30–44 years	59	25.88	100	24.45	253	25.12
45–59 years	47	20.61	76	18.58	185	18.37
≥ 60 years	17	7.46	34	8.31	77	7.65
Sex
Male	88	38.60	175	42.79	425	42.20
Female	140	61.40	234	57.21	582	57.80
Marital status
Single	127	55.70	201	49.14	531	52.73
Partnered	101	44.30	208	50.86	476	47.27
Highest education level attained
Year 12 and below	156	68.42	227	55.50	602	59.78
Certificate courses	54	23.68	144	35.21	312	30.98
University degrees	18	7.89	38	9.29	93	9.24
Annual household income
Lowest quintile (Poorest)	46	20.18	82	20.05	202	20.06
Second quintile	46	20.18	82	20.05	201	19.96
Middle quintile	45	19.74	83	20.29	202	20.06
Fourth quintile	46	20.18	81	19.80	201	19.96
Highest quintile (Richest)	45	19.74	81	19.80	201	19.96
Labor-market status
Employed	116	50.88	196	47.92	490	48.66
Unemployed or NILF	112	49.12	213	52.08	517	51.34
Area of residence
Major city	105	46.05	180	44.01	463	45.98
Regional/Remote area	123	53.95	229	55.99	544	54.02
Lifestyle factors
Smoking status
Never smoked	81	35.53	145	35.45	362	35.95
Ex-smoker	51	22.37	100	24.45	240	23.83
Current smoker	96	42.11	164	40.10	405	40.22
Alcohol drinking
Never drunk	17	7.46	45	11.1	97	9.63
Used to drink	26	11.40	72	17.60	149	14.80
Currently drinks	185	81.14	292	71.39	761	75.57
Physical exercise
Less than the recommended level	158	69.30	300	73.35	716	71.10
Recommended level	70	30.70	109	26.65	291	28.90
Health-related factors
BMI category
Underweight	37	16.23	60	14.67	153	15.19
Healthy weight	69	30.26	99	24.21	268	26.61
Overweight	65	28.51	112	27.38	280	27.81
Obese	57	25.00	138	33.74	306	30.39

Table 3 shows the distribution of HRQoL scores, as well as multimorbidity, among the analytic sample. The mean PCS, MCS, and SF-6D scores of the study participants were 48.80, 45.81, and 0.73, respectively. The average score on four of the SF-36’s eight dimensions was less than 76 points: MH (68.78), VT (56.70), BP (70.94), and GH (62.82). About a fifth (21%) of respondents experienced multimorbidity (across all pooled waves).

Table 3

Distribution of subjective health scores, BMI, and chronic conditions: Baseline, final and pooled in all waves (persons = 592; observations = 1,007)
Variables	Baseline wave (2009)		Final wave (2017)		Pooled in all waves (2009, 2013, & 2017)
Variables	n	Mean (SD)	n	Mean (SD)	n	Mean (SD)
SF-36 domain scores
Physical functioning	228	83.09 (24.23)	409	78.39 (26.31)	1,007	80.03 (25.44)
Role physical	228	76.31 (38.32)	409	77.56 (35.79)	1,007	77.77 (36.60)
Role emotional	228	80.26 (36.15)	409	77.26 (36.92)	1,007	78.33 (36.58)
Social functioning	228	76.75 (26.80)	409	75.45 (25.56)	1,007	76.30 (25.58)
Mental health	228	71.59 (18.71)	409	67.04 (19.30)	1,007	68.78 (19.28)
Vitality	228	59.09 (21.48)	409	55.26 (20.09)	1,007	56.70 (20.75)
Bodily pain	228	74.03 (24.87)	409	69.15 (25.68)	1,007	70.94 (25.65)
General health	228	65.46 (21.97)	409	60.71 (21.62)	1,007	62.82 (21.81)
SF-36 component summary scores
PCS	228	49.39 (10.56)	409	48.33 (10.17)	1,007	48.80 (10.26)
MCS	228	46.87 (11.61)	409	45.11 (11.60)	1,007	45.81 (11.58)
SF-6D	228	0.75 (0.13)	409	0.72 (0.13)	1,007	0.73 (0.13)
Number of chronic conditions (% observations)
0 (No morbidity)	127	55.7	198	48.41	522	51.84
1 (Single chronic condition)	65	28.51	115	28.12	271	26.91
≥ 2 (Multimorbidity)	36	15.79	96	23.47	214	21.25

Figure 3 illustrates the PCS, MCS, and SF-6D utility ratings by the values of the (multi)morbidity exposure variable. Across survey waves, Indigenous Australians with multimorbidity had lower PCS, MCS, and SF-6D utility scores. For example, in wave 17 (year 2017), they had significantly lower mean PCS, MCS, and SF-6D utility ratings (41.81, 39.50, and 0.63, respectively) than those with no chronic diseases (50.33, 46.83, and 0.75, respectively), and those with only one chronic condition.

Figure 4 depicts the mean score of the SF-36’s eight dimensions by the (multi)morbidity exposure variable. Again, Indigenous Australians with multimorbidity exhibited significantly lower values in all dimensions of SF-36 than the other groups. For example, in 2017, the mean PF, RP, RE, SF, MH, VT, BP and GH scores among the participants having multimorbidity (83.01, 83.81, 84.45, 80.47, 69.65, 58.26, 74.00, and 65.75) were substantially lower than those of their counterparts with no chronic conditions (63.37, 57.20, 53.82, 59.11, 58.57, 45.52, 53.35, and 44.30,) or only one chronic condition.

Regression Models

Table 4 shows abridged results of the random effects tobit regression models.

According to the findings in models 1 and 2, respondents with multimorbidity had PCS (b = − 3.232) and MCS (b = − 5.625) ratings that were 3.232 and 5.625 units lower than respondents without multimorbidity. Furthermore, as compared to peers without multimorbidity, multimorbidity scored six percentage points lower on the SF-6D scale (b = -0.061).

Table 4

The relationship between multimorbidity and the SF-36 component summary scores and SF-6D utility score, random-effects tobit model
Variables	Model 1: PCS	Model 2: MCS	Model 3: SF-6D
Variables	β (95% CI), P-value	β (95% CI), P-value	β (95% CI), P-value
Number of chronic conditions
0 (No morbidity) (ref)
1 (Single chronic condition)	-2.25 (-3.534, -0.965), 0.001	-2.889 (-4.389, -1.389), < 0.001	-0.044 (-0.061, -0.026), < 0.001
≥ 2 (Multimorbidity)	-6.096 (-7.72, -4.472), < 0.001	-8.065 (-10.003, -6.128), < 0.001	-0.105 (-0.127, -0.083), < 0.001

*** p < 0.001, ** p < 0.01, * p < 0.05; Abbreviations: ref, reference category; PCS, Physical Component Summary; MCS, Mental Component Summary; SF-6D, Short-Form Six-Dimension health index.

Models (1 to 3) were adjusted for age, sex, marital status, highest attained education level, annual household income, labour market status, geographic residency, smoking status, alcohol drinking, physical exercise, and BMI.

Values in bold are statistically significant.

The results of the random-effects tobit regression for multimorbidity and the dimensions affecting the SF-36 subscales are summarised in Table 5. Morbid obesity was linked with significantly lower scores on all eight SF-36 dimensions: PF (-8.04 units), RP (-65.96 units), RE (-72.94 units), SF (-17.95 units), MH (-10.36 units), VT (-8.07 units), BP (-12.02 units), and GH (-12.26 units).

Table 5

The relationship between multimorbidity and the dimensions of the SF-36, random-effects Tobit model
Variables	Model 1: PF	Model 2: RP	Model 3: RE	Model 4: SF	Model 5: MH	Model 6: VT	Model 7: BP	Model 8: GH
Variables	β (95% CI), P-value	β (95% CI), P-value	β (95% CI), P-value	β (95% CI), P-value	β (95% CI), P-value	β (95% CI), P-value	β (95% CI), P-value	β (95% CI), P-value
Number of chronic conditions
0 (No morbidity) (ref)
1 (Single chronic condition)	-9.02 (-14.19, -3.84), 0.001	-40.77 (-65.49, -16.05), 0.001	-38.2 (-69.01, -7.4), 0.02	-10.95 (-16.23, -5.67), < 0.001	-6.04 (-8.61, -3.47), < 0.001	-5.83 (-8.53, -3.13), < 0.001	-12.35 (-16.93, -7.77), < 0.001	-6.68 (-9.41, -3.94), < 0.001
≥ 2 (Multimorbidity)	-16.92 (-23.19, -10.65), < 0.001	-110.92 (-141.26, -80.57), < 0.001	-113.18 (-151.45, -74.9), < 0.001	-28.81 (-35.32, -22.3), < 0.001	-15.2 (-18.51, -11.9), < 0.001	-13.44 (-16.93, -9.95), < 0.001	-23.37 (-29.05, -17.69), < 0.001	-18.95 (-22.49, -15.41), < 0.001

Abbreviations: ref, reference category; PF, Physical Functioning; RP, Role Physical; RE, Role Emotional; SF, Social Functioning; MH, Mental Health; V, Vitality; BP, Bodily Pain; GH, General Health

Models (1 to 8) were adjusted for age, sex, marital status, highest attained education level, annual household income, labour market status, geographic residency, smoking status, alcohol drinking, physical exercise, and BMI.

Values in bold are statistically significant.

The current study is the first nationally representative longitudinal work to investigate the impact of multimorbidity on HRQoL amongst the Indigenous Australians. The study utilised three waves from the HILDA survey covering years 2009, 013 and 2017. Approximately 21% of the Indigenous Australians were identified to have at least two or more long-term diseases (multimorbidity).

An association between multimorbidity and HRQoL (PCS, MCS and SF-6D) was estimated through the random-effects tobit model. Here, multiple pertinent confounders were controlled, including age, gender, civil status, education, household income, labour force status, place of living, smoking status, alcohol consumption and physical activity. The findings of this study indicated that adults with multimorbidity showed a decrease in PCS, MCS and SF-6D scores amongst the Indigenous Australians. This result is similar to previous studies conducted in high- and low-income counties, where the authors found that multimorbidity was inversely associated with overall HRQoL [6,29,30]. Existing literature also reported that PCS and MCS scores of quality of life were lower for adults with multimorbidity in countries such as India, Iran and the US [10,31,32].

Moreover, the findings from the present study showed that the reduction of the eight-dimension scores of SF-36 was significantly associated with multimorbidity amongst the Indigenous Australians. Diverse studies measuring the relationship between multimorbidity and quality of life obtained identical findings [33–35]. GH is one of the eight dimensions that is also adversely affected by multimorbidity, which essentially reduces the quality of life of adults. Fortin et al. likewise generalised poor scores of GH in their research [30]. There are multiple probable reasons behind the study results that could be related to the disease burden of multimorbidity. For example, arthritis/chronic back pain and vascular disease or psychological problems, vascular disease and metabolic disorders are common co-morbidity combinations [36].

Furthermore, these complications might be responsible for limited physical activity because higher health service usage, treatment costs, workplace absenteeism and presenteeism are immensely general, resulting in lower quality of life [37–39]. Therefore, impairment of body functioning may restrict patients’ personal-care ability and social versatility, thus leading to changes in lifestyle patterns [10]. Another possible indicator of HRQoL depletion can be explained by synergistic effects (interaction of two or more drugs) that control multimorbid individuals’ treatment adherence capability for multiple diseases [40]. Moreover, MH comorbid conditions reduce patients’ life quality, possibly due to chronic care shortage, social impairment, stress, depression, anxiety and suicidal attempts [41,42].

The significant inverse associations between multimorbidity and HRQoL demonstrated in the present study have several implications for designing health strategies to improve the life quality of the Indigenous Australians. The multiple coexisting chronic conditions require increased focus on disease interaction, treatment tolerance and quality of care. This research suggests that developing health programmes for comprehensive care involving medical specialists, occupational therapists, physiotherapists, psychologists and social workers is needed for multimorbid patients. Therefore, multidimensional interventions targeting daily activities, functional mobility and psychological distress should enforce the cluster of long-term disease complications. Further investigation is required to determine the assessment of drug application or health technology improvement according to multiple chronic disease combinations for multimorbid patients.

There are significant strengths of the current study. The first advantage of the study was that it utilised nationally representative longitudinal data, and the sampling procedure followed a randomised technique. Secondly, the health utility index of SF-36 is the universally accepted and well-tested measure of HRQoL [26]. Thus, the application of SF-36 to estimate the score of HRQoL, including a summary measure (PCS and MCS), utility index (SF-6D) and eight dimensions, increased the validation and reliability of the existing findings. Thirdly, the generation of multimorbidity exposure based on two or more chronic conditions was deemed a precise measurement for this study. However, measuring multimorbidity by 2+ chronic conditions is more suitable than 3+ chronic conditions for a larger age-scope population with limited diseases [1,43].

Although this study has several strengths, it also has some limitations. Firstly, the unbalanced longitudinal nature of the study restricts the ability to draw causal inferences for the Indigenous Australians. Secondly, the chronic disease information from participants in the HILDA survey was collected through a self-reported process that can result in reporting bias. Thirdly, due to the unavailability of potential confounders for reducing the quality of life amongst patients with multimorbidity, such as disease severity, disease knowledge, disease duration and drug adherence, these essential variables were not added to the study. This deficiency might substantially influence the identification of the association between multimorbidity and HRQoL.

This study was the first to demonstrate a large, negative association between multimorbidity and HRQoL amongst Indigenous Australians. The findings revealed that multimorbidity was associated with lower HRQoL across all dimensions and summary measures of the SF-36. Health promotion strategies and policies that target adults experiencing multimorbidity are urgently required to improve the quality of life of Indigenous Australians.

HILDA	Household, Income and Labour Dynamics in Australia
HRQoL	Health-related quality of life
PCS	Physical component scale
MCS	Mental component scale
SF-6D	Short-Form Six-Dimension
SF-36	36-Item Short-Form Health Survey

Acknowledgements

The authors are grateful to the Melbourne Institute of Applied Economic and Social Research for providing BDHS data access for conducting the study. This paper uses unit record data from the HILDA survey guided by the Australian Government’s DSS. However, the findings and views reported in this paper are those of the authors and should not be attributed to the Australian Government, DSS or any contractors or partners of DSS. DOI: 10.26193/OFRKRH, ADA Dataverse, V2.

Ethics Approval

This study used data from de-identified existing unit records from the HILDA survey, so that ethical approval is not required. As the source of the data was secondary, the authors completed and signed the Confidentiality Deed Poll. Then, they sent it to NCLD ([email protected]) and ADA ([email protected]) before the approval of data application. Therefore, the datasets analysed and/or generated during the current study are subject to the signed confidentiality deed.

Availability of Data and Materials

The data were obtained from the Melbourne Institute of Applied Economic and Social Research (https://melbourneinstitute.unimelb.edu.au/). Though the information is not available openly, one can access the data after accepting their guidelines and restrictions. The contact address is Melbourne Institute of Applied Economic and Social Research, the University of Melbourne, VIC 3010, Australia.

Conflict of Interest

The authors have no conflicts of interest to declare.

Funding

This research did not receive any specific grant from funding agencies in public, commercial or not-for-profit sectors.

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Table 1 is not available with this version

Multimorbidity and health-related quality of life amongst Indigenous Australians: A longitudinal analysis

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

Data Source and Sample Selection

Outcome Variable

Exposure Variable

Other Covariates

Estimation Strategy

Results

Descriptive Analyses

Regression Models

Discussion

Conclusion

Abbreviations

Declarations

References

Table 1

Status:

Version 1