Australian National Health Survey Research Articles

Utilising causal inference methods to estimate effects and strategise interventions in observational health data.

Randomised controlled trials (RCTs) are the gold standard for evaluating health interventions but often face ethical and practical challenges. When RCTs are not feasible, large observational data sets emerge as a pivotal resource, though these data sets may be subject to bias and unmeasured confounding. Traditional statistical (or non-causal) learning methods, while useful, face limitations in fully uncovering causal effects, i.e., determining if an intervention truly has a direct impact on the outcome. This gap is bridged by the latest advancements in causal inference methods, building upon machine learning-based approaches to investigate not only population-level effects but also the heterogeneous effects of interventions across population subgroups. We demonstrate a causality approach that utilises causal trees and forests, enhanced by weighting mechanisms to adjust for confounding covariates. This method does more than just predict the overall effect of an intervention on the whole population; it also gives a clear picture of how it works differently in various subgroups. Finally, this method excels in strategising and optimising interventions, by suggesting precise and explainable approaches to targeting the intervention, to maximise overall population health outcomes. These capabilities are crucial for health researchers, offering new insights into existing data and assisting in the decision-making process for future interventions. Using observational data from the 2017-18 Australian National Health Survey, our study demonstrates the power of causal trees in estimating the impact of exercise on BMI levels, understanding how this impact varies across subgroups, and assessing the effectiveness of various intervention targeting strategies for enhanced health benefits.

An equity indicator for assessing mental healthcare access: a national population case study.

Achieving equitable healthcare access is a global challenge. Improving whole-population mental health and reducing the global burden of mental disorders is a key recommendation of the 2018 Lancet Global Mental Health Commission, which proposed monitoring national indicators, including the proportion of people with severe mental disorders who are service-users. This study aims to derive an equity indicator from national datasets integrating need, service utilisation and socioeconomic status, and demonstrate its utility in identifying gaps in mental health service use amongst those with the greatest need, thereby guiding equitable healthcare delivery. We present a case study of a universal health insurance scheme (Medicare) in Australia. We developed the equity indicator using three national datasets. Geographic areas were linked to an area-based socioeconomic deprivation quintile (Census 2016). Per geographic area, we estimated the number with a mental healthcare need using scores ≥30 on the Kessler-10 (Australian National Health Surveys 2015 and 2018), and obtained the number of services used, defined as mental health-related contacts with general practitioners and mental health professionals (Medicare administrative data 2015-2019). We divided the number of services by the population with an estimated mental healthcare need and averaged these use-rates across each socioeconomic deprivation quintile. The equity indicator is the ratio of the use-rates in the least versus most deprived quintiles. Those estimated to have the greatest need for mental healthcare in 2019 ranged between 8.2% in the most disadvantaged area quintile (Q1) and 2.4% in the least (Q5), corresponding to a proportional increase of 27.7% in Q1 and 19.5% in Q5 since 2015. Equity-indicator-adjusted service rates of 4.2 (3.8-4.6) and 23.9 (22.4-25.4) showed that individuals with the highest need for care residing in Q1 areas received a stark 6 times fewer services compared to their Q5 counterparts, producing an equity indicator of 6. As the global prevalence of common mental disorders may be increasing, it is crucial to calculate robust indicators evaluating the equity of mental health service use. In this Australian case study, we developed an equity indicator enabling the direct comparison of geographic areas with different need profiles. The results revealed striking inequities that persisted despite publicly-funded universal healthcare, recent service reforms and being a high-income country. This study demonstrates the importance and feasibility of generating such an indicator to inform and empower communities, healthcare providers and policymakers to pursue equitable service provision.

The prevalence of psychological distress and its associated sociodemographic factors in the Australian adults aged 18–64 years during COVID-19: Data from the Australian National Health Survey

ObjectiveCOVID-19 significantly contributed to an increase in psychological distress and may have disproportionally impacted certain sociodemographic groups. This study aimed to assess the prevalence of psychological distress among Australian adults aged 18–64 years and identify sociodemographic factors associated with psychological distress during the COVID-19 pandemic. MethodsSecondary analyses were conducted using data from the National Health Survey 2020–21 to calculate weighted national estimates of the prevalence of psychological distress using the Kessler Psychological Distress scale (K10). The association between sociodemographic factors and psychological distress was analysed using multivariable logistic regression, with adjustments for complex survey design. ResultsThe average of K10 score was 16.94, with a prevalence of combined high/very high distress reported at 21.13 % among Australian adults. Young women aged 18–25 reported the highest average scores (M = 20.44) across all sex and age groups. Risk factors associated with psychological distress included lower personal income, marital status (never married; widowed/divorced/separated) and being ‘born in Australia’. Protective factors included homeownership and having children in the household. Additionally, among women, being aged ≥56 was significantly associated with lower odds of psychological distress. LimitationsData collection relied on self-completed online form, and a cross-sectional design limits the inference of a causal relationship. DiscussionDuring COVID-19 pandemic, lower personal income and being female have consistently been identified as risk factors for psychological distress. Targeted and tailored interventions based on age, sex and disadvantage are warranted, particularly focusing on younger women, to alleviate the adverse effect of the pandemic on the population's mental health.

Measuring health literacy to inform actions to address health inequities: a cluster analysis approach based on the Australian national health literacy survey.

Measuring health literacy can inform interventions to address health inequities. This study used cluster analysis to examine health literacy data to determine if it can provide more insightful information than standard descriptive analysis to better inform intervention development. Using data from the Australian National Health Survey (2018), this study compared descriptive analysis and cluster analysis results of two states-New South Wales (NSW) and Victoria-generated from the Health Literacy Questionnaire (HLQ). Based on the nine scale scores of the HLQ, a hierarchical cluster analysis using Ward's method for linkage was undertaken. The number of NSW and Victoria respondents was 1018 and 923, respectively. The nine HLQ scale full sample mean scores from both states were similar. However, the cluster analyses identified 11 clusters for NSW and 12 clusters for Victoria. While six clusters from each state presented similar health literacy patterns, five and six clusters from NSW and Victoria, respectively, displayed unique health literacy patterns. The results demonstrate that descriptive analysis only provides an overview and may lead to one-size-fits-all interventions. The varying health literacy patterns among subgroups resulting from the cluster analysis pave the way to inform tailored actions to improve health equity.

A Cross-Sectional Nationwide Study of Australians' Health: Are There Differences in Health-Related Behaviors and Psychological Distress Between Teachers and Other Occupations?

Teachers' health behaviors and mental well-being are important for their chronic disease risk and reducing burnout. This study investigates the health-related behaviors and psychological distress of Australian teachers compared with other occupations. Data from the nationally representative Australian National Health Survey (NHS) were analyzed. Employed adults (20-64 years) from 2014/2015 (n = 8455), 2017/2018 (n = 9130), and 2020/2021 (n = 5753) survey waves were included. Logistic regression, adjusted for demographics, compared health-related behaviors (eg, physical activity, diet), and psychological distress among teachers, nonteacher professionals (NTPs; eg, accountants, doctors), and other general occupations (eg, laborers, beauticians). After adjusting for demographics and survey wave, teachers had similar odds as NTPs for meeting physical activity (OR: 1.12, 95% CI: 0.90-1.41), vegetable intake (OR: 0.72, 95% CI: 0.52-1.00), and alcohol consumption (OR: 1.01, 95% CI: 0.83-1.22) guidelines. However, teachers exhibited higher psychological distress (OR: 1.35, 95% CI: 1.01-1.75). Elevated psychological distress levels were observed in all groups in 2020/2021, compared with pre-COVID-19. Compared with NTPs, teachers showed similar or better health-related behaviors, although teachers had higher levels of psychological distress. School executives and policymakers need to implement sustainable measures to prioritise teachers' mental wellbeing.

Trend estimation of sub-national level daily smoking prevalence by age and sex in Australia.

Despite that the smoking prevalence has considerably declined in Australia after successful public health strategies over many decades, smoking is still the leading cause of preventable diseases and death in Australia. These declines have not occurred consistently across all geographical-demographic domains. In order to provide an evidence base for monitoring the trend towards the goal of reducing smoking across all domains in Australia, this study aims to estimate trends of smoking prevalence for small domains cross-classified by seven age groups (18-24, 25-29, 30-39, 40-49, 50-59, 60-69, and ≥70 years), two genders, and eight states and territories over twenty years (2001-2021). Direct estimates of smoking prevalence for the target small domains were calculated from the micro-data of the Australian National Health Surveys conducted in seven rounds during 2001-2021. The obtained direct estimates were then used as input for developing time-series models expressed in a hierarchical Bayesian structure as a form of small-area estimation. The developed models borrow cross-sectional, temporal, and spatial strength in such a way that they can interpolate smoking levels in the non-survey years for all detailed level small domains. Smoothed trends of smoking prevalence for higher aggregation levels are obtained by aggregation of the detailed level trend predictions. Model-based small area estimators provide consistent and reasonable smoothed trends at both detailed and higher aggregation levels. Results show that the national-level trend exhibits a steeper linear decline over the study period, from 24% in 2001 to 12% in 2021, with a considerable gender difference of around 5% over the period, with males reporting a higher prevalence. Improved model-based estimates at the state level and by age also show steady declines in trends except for the Northern Territory (still above 20%) and older age groups 60-69 and ≥70 years (declining trends remain stable after 2012). The findings of the study identify the geographical-demographic groups that had poor improvement over the period 2001-2021, and are still behind the target of achieving lower smoking prevalence. These, in turn, will help health researchers and policymakers deliver targeted programs to the most vulnerable, enabling the nation to meet its health goals in a timely way.

The health and wellbeing of Australian social housing tenants compared to people living in other types of housing

BackgroundAlthough social housing provides access to safe and affordable housing, recent studies have found that social housing tenants consistently have lower levels of health and well-being compared to other people. Given this, there is a need to examine multimorbidity for social housing tenants.MethodsSecondary data analysis of the 2017-18 Australian National Health Survey (n = 14,327) compared the health of adults residing in social housing compared to people in other housing types (private rentals, homeowners, and homeowners/mortgagees).ResultsMost health factors examined were more prevalent in social housing tenants compared to those living in other housing types. Individual health problems identified as more highly prevalent in social housing tenants compared to all other housing types included mental health issues (43%), arthritis (36%), back problems (32%), hypertension (25%), asthma (22%) and COPD (11%). 24% of social housing tenants reported five or more health factors compared to 3–6% of people in other housing types.ConclusionsAlthough these findings are not unexpected, they provide more detailed evidence that social housing providers and policy makers should consider when planning future initiatives.

How Does the Health Literacy of Adults Residing in Social Housing Compare with That of Those Living in Other Housing Tenures in Australia? A Secondary Analysis of the Australian National Health Survey 2017-2018 Dataset.

Social housing tenants have poorer health outcomes than homeowners or those renting privately. Health literacy is associated with access to care and health outcomes. This study aimed to examine the health literacy of Australian adults residing in social housing compared with that of people living in other housing types. A secondary analysis of the Australian National Health Survey 2017-2018 dataset was undertaken. A total of 5275 respondents were included in the sample and completed the Health Literacy Questionnaire (HLQ). Respondents were categorised according to their housing tenure: 163 (3.1%) respondents were living in social housing, 873 (17%) were living in private rentals, 2085 (40%) were homeowners, and 2154 (41%) were homeowners/mortgages. Mean scores were calculated for each of the nine health literacy domains in the HLQ and compared across housing tenure using linear regression models. Social housing tenants had lower mean domain scores than either homeowners, owner mortgagees, or private renters on six of the nine health literacy domains. This included 'having sufficient information to manage my health', 'social support for health', 'ability to engage with healthcare providers', 'navigating the healthcare system' 'ability to find good health information', and being able to 'understand health information enough to know what to do'. However, the differences in mean scores were small. Increasing health literacy may be an important part of multicomponent interventions seeking to improve the health and wellbeing of social housing tenants.

Accuracy of self-reported private health insurance coverage.

Studies on health insurance coverage often rely on measures self-reported by respondents, but the accuracy of such measures has not been thoroughly validated. This paper is the first to use linked Australian National Health Survey and administrative population tax data to explore the accuracy of self-reported private health insurance (PHI) coverage in survey data. We find that 11.86% of individuals misreport their PHI coverage status, with 11.57% of true PHI holders reporting that they are uninsured and 12.37% of true non-insured persons self-identifying as insured. Our results show reporting errors are systematically correlated with individual and household characteristics. Our evidence on the determinants of errors is supportive of common reasons for misreporting. We directly investigate biases in the determinants of PHI enrollment using survey data. We find that, as compared to administrative data, survey data depict a quantitatively different picture of PHI enrollment determinants, especially those capturing age, gender, language proficiency, labor force status, disability status, number of children in the household, or household income. We also show that PHI coverage misreporting is subsequently associated with misreporting of reasons for purchasing PHI, type of cover and length of cover.

Oral Health Inequities among CALD and Non-CALD Older Australians: A Decomposition Analysis.

Among Australia's older population, the burden of oral disease is disproportionality borne by culturally and linguistically diverse (CALD) communities. This study aims to examine changes in untreated decay surfaces (DS) between 2004-2006 and 2017-2018 among older CALD and non-CALD Australians. Data were sourced Australian national oral health surveys conducted in 2004-2006 and 2017-2018. An Oaxaca-Blinder decomposition analysis was used to assess the contribution of socio-demographics and dental behaviours. A total of 246 CALD and 2853 non-CALD dentate participants aged 60+ years took part in 2004-2006, and 363 and 4278 in 2017-2018, respectively. There were increases in mean DS for both CALD (0.74 to 1.42) and non-CALD (0.72 to 1.50) groups between 2004-2006 and 2017-2018. The decomposition model showed that, for CALD participants in 2004-2006 with untreated DS, 40% of the contribution was from not having dental insurance; nearly three-quarters of the contribution was from last dental visit being over one year ago (72.9%) in 2017-2018. Among non-CALD participants in 2017-2018 with untreated DS, 42.5% of the contribution was from the last dental visit being over one year ago. Our findings suggest that social determinants, including increased access to dental insurance, could mitigate the oral health inequities observed.

Health status and mental distress in people with cancer and comorbid conditions: The Australian National Health Survey analysis.

Data on the impact of specific comorbidities on health outcomes is limited. We compared health status and mental distress between individuals with and without cancer according to comorbidity type. A cross-sectional analysis using data from the Australian National Health Survey 2017-18 including all respondents aged ≥25 years with and without a history of cancer. The odds of poor health and mental distress were reported according to cancer status, and specific individual and cluster of comorbidities. There were 1982 individuals (52% female) with cancer and 12,635 (51% female) without cancer. Individuals with cancer were older, and more likely to have a comorbidity compared with those without cancer. They were more likely to report poor health than those without cancer for each specific comorbidity; except for skin conditions and infectious diseases; with the adjusted odds ratio (aOR) ranging from 1.34 (95% CI = 1.01-1.79) for digestive disorders to 2.93 (95% CI = 1.62-5.29) for blood conditions. The strongest association with poor health (aOR 2.79, 95% CI = 2.27-3.43) and mental distress (aOR 9.01, 95% CI = 7.25-11.20) was observed for those with a comorbid mental illness. Exploratory cluster analysis identified four distinct comorbidity clusters: low comorbidity, musculoskeletal, respiratory and cardiometabolic; cancer survivors in the cardiometabolic cluster had a higher odds of reporting poor health (aOR 3.50, 95% CI = 2.48-4.92) and mental distress (aOR 2.33, 95% CI = 1.53-3.55) than those with a low comorbidity. Comorbidities in cancer survivors were common and associated with inferior health status, although the magnitude of the effect varied by comorbidity type. Risk assessment and management of comorbidities should be an important priority for cancer care and research.

Area-level associations of travel behaviour metrics with waist circumference: findings from linkage of travel and health surveys

Individual-level analyses have consistently shown associations of travel behaviours with obesity-related measures. However, transport planning policies often target areas rather than individuals. To better inform transport-related policies and initiatives for obesity prevention, area-level relationships need to be investigated. This study linked data from two travel surveys with data from the Australian National Health Survey at the level of Population Health Areas (PHAs) and examined to what extent area-level travel behaviours metrics (prevalence of active travel, mixed travel and sedentary travel, diversity of travel modes) were associated with the rate of high waist circumference. Data from 51,987 travel survey participants were aggregated into 327 PHAs. Bayesian conditional autoregressive models were used to account for spatial autocorrelation. It was found that statistically replacing participants who relied on cars for travel (without walking/cycling) with those engaging in 30+ min/d of walking/cycling (without car use) was associated with a lower rate of high waist circumference. Areas with greater diversity of travel modes (mix of walking/cycling, car use, public transport use) also had lower prevalence of high waist circumference. This data-linkage study suggests that area-level transport planning strategies addressing car dependency, shifting car use to walking/cycling over 30 min/d, may help to reduce obesity.

Offering a lifestyle intervention to women of premenopausal age as primary prevention for cardiovascular disease? – assessing its cost-effectiveness

BackgroundThere is limited evidence of cost-effective primary prevention interventions for cardiovascular disease (CVD) in young women. This study aimed to assess the value for money of primary prevention of CVD in this population.MethodsA Markov microsimulation model consisting of both first-ever and recurrent CVD events was developed to simulate the lifetime intervention impact on cost and health outcomes in women of premenopausal age (30 to 54 years) from the Australian healthcare system perspective. The latest wave of the Australian National Health Survey defined the modelled population’s characteristics. The intervention effectiveness of a lifestyle modification program involving changes in diet and physical activity demonstrated to be effective in this population was sourced from a systematic review and meta-analysis. The first-ever and recurrent CVD probabilities were derived from the CVD risk calculators accounting for socio-demographic and clinical characteristics. Costs and utility weights associated with CVD events and long-term management post-CVD were informed by national statistics/published literature. Sensitivity analyses were undertaken to examine the robustness of base case results.ResultsThe lifestyle modification program was associated with both higher costs and benefits (in terms of quality-adjusted life years, QALYs) as a primary prevention measure of CVD in premenopausal women, with an ICER of $96,377/QALY or $130,469/LY. The intervention led to fewer first-ever (N = −19) and recurrent CVD events (N = -23) per 10,000 women over the modelled life horizon. The avoided cost due to reduced hospitalisations (−$24) and management (−$164) of CVD could partially offset the cost associated with the intervention ($1560). Sensitivity analysis indicated that time horizon, starting age of the intervention, discount rate, and intervention effectiveness were the key drivers of the results. If the intervention was scaled up to the national level (N = 502,095 at-risk premenopausal women), the total intervention cost would be $794 million with $95 million in healthcare cost-savings.ConclusionOffering a lifestyle modification program to premenopausal women in Australia as primary prevention of CVD is not cost-effective from a healthcare system perspective. We should continue to search for new or adapt/optimise existing effective and cost-effective primary prevention measures of CVD for women.

Factors related to under‐treatment of secondary cardiovascular risk, including primary healthcare: Australian National Health Survey linked data analysis

ObjectiveTo inform national evidence gaps on cardiovascular disease (CVD) preventive medication use and factors relating to under‐treatment ‐ including primary healthcare engagement ‐ among CVD survivors in Australia. MethodsData from 884 participants with self‐reported CVD from the 2014–15 National Health Survey were linked to primary care and pharmaceutical dispensing data for 2016 through the Multi‐Agency Data Integration Project. Logistic regression quantified the relation of combined blood pressure‐ and lipid‐lowering medication use to participant characteristics. ResultsOverall, 94.8% had visited a general practitioner (GP) and 40.0% were on both blood pressure‐ and lipid‐lowering medications. Medication use was least likely in: women versus men (OR=0.49[95%CI:0.37‐0.65]), younger participants (e.g. 45–64y versus 65–85y: OR=0.58[0.42–0.79])and current versus never‐smokers (OR=0.73[0.44–1.20]). Treatment was more likely in those with ≥9 versus ≤4 conditions (OR=2.15[1.39–3.31]), with ≥11 versus 0–2 GP visits/year (OR=2.62[1.53–4.48]) and with individual CVD risk factors (e.g. high blood pressure OR=3.13 [2.34–4.19]) versus without); the latter even accounting for GP service‐use frequency. ConclusionsYounger people, smokers, those with infrequent GP visits or without CVD risk factors were the least likely to be on medication. Implications for public healthSubstantial under‐treatment, even among those using GP services, indicates opportunities to prevent further CVD events in primary care.

Association between arthritis and cardiovascular risk factors in community-based adults: an opportunity to target cardiovascular risk

BackgroundUndertreated risk factors are major contributors to the burden of cardiovascular disease (CVD). Those with arthritis have an increased prevalence of CVD risk factors. CVD risk factors are often asymptomatic, which may be a barrier their treatment. Arthritis causes pain and immobility, and is a common reason for individuals to seek healthcare. Our aims were to (1) examine the relationship between arthritis and CVD risk factors in Australian adults, and (2) calculate the proportion of CVD risk factors that could be reduced if individuals with arthritis were targeted.MethodsThis cross-sectional study uses data from the 2017–18 Australian National Health Survey which included 13,776 participants, categorised into young (18–39 years), middle aged (40–64 years) and older (≥ 65 years) adults. Hypertension, height and weight were measured. Arthritis, dyslipidemia and diabetes were self-reported. The associations between arthritis and CVD risk factors were examined using logistic regression, and the population attributable fraction (PAF) of arthritis for each CVD risk factor was calculated.ResultsArthritis was reported by 4.0% of young adults, 28.8% of middle-aged adults and 54.5% of older adults. Those with arthritis were at increased odds of obesity (2.07 fold in young, 1.75 fold in middle-aged and 1.89 fold in older adults), increased odds of diabetes (5.70 fold in young, 1.64 fold in middle-aged and 1.37 fold in older adults), increased odds of hypertension (2.72 fold in young, 1.78 fold in middle-aged and 1.48 fold in older adults) and an increased odds of dyslipidaemia (4.64 fold in young, 2.14 fold in middle-aged and 1.22 fold in older adults) compared to those without arthritis. This elevated chance remained significant even after adjusting for obesity, with the exception of diabetes in the older population. This elevated chance remained significant even after adjusting for obesity, with the exception of diabetes in the older population. The PAF of the presence of arthritis for having at least one CVD risk factor was 30.7% in middle-aged adults and 70.4% in older adults.ConclusionAustralian adults of all ages with arthritis are at increased odds of having CVD risk factors. For young and middle-aged adults, this increased odds remains significant even when adjusted for obesity. Presentation to healthcare practitioners with arthritis is an opportunity to screen for asymptomatic CVD risk factors with the potential of improving outcomes for both diseases. By adopting an approach of managing arthritis and CVD risk factors in parallel, rather than in silos, we could reduce the burden of CVD risk factors by 20–30%.

Association of dietary risks, behavioural and lifestyle factors, and the magnitude of disability burden among Australian cancer patients: An observational epidemiology study

BackgroundCancer patients are confronted with a variety of other health-related issues, including physical disability, poor quality of life, and psychological challenges. This study aims to quantify the association of dietary, behavioural and lifestyle risk factors and comorbidities on the magnitude and distribution of disability burden among cancer patients in Australia. MethodsThis study comprised a sample of 2283 cancer patients drawn from the latest nationwide Australian National Health Survey conducted in 2017–18. Negative binomial regression models were used to estimate the incidence rate ratio (IRR) of the number of disabilities and its associations. ResultsForty-five percent of cancer patients experienced at least one disability. The magnitude of disability was significantly associated with sugar-sweetened drink consumption ≥ 3 days per week (IRR= 1.12, 95% CI: 1.02–1.26), a lack of physical activity (IRR = 1.69, 1.38–2.07), frequent or regular alcohol consumption (IRR = 1.95, 1.84–2.08), poor health status (IRR = 1.99, 1.78–2.24) and the presence of five or more chronic comorbid conditions (IRR = 3.59, 2.90–4.46). Cancer patients who consumed vegetables at least two or more times per day had a 10% lower risk of disability burden (IRR = 0.90, 0.82–0.99). ConclusionsThis study shows the association of diet, behavioural, and lifestyle risk factors on the degree of disability burden among cancer patients, highlighting the need for bold and effective policies. The findings will inform the implementation of evidence-based lifestyle interventions and offer a foundation for evaluating their influence on cancer survivors’ health.

Modelling the potential health and economic benefits of reducing population sitting time in Australia

BackgroundStrong evidence indicates that excessive time spent sitting (sedentary behaviour) is detrimentally associated with multiple chronic diseases. Sedentary behaviour is prevalent among adults in Australia and has increased during the COVID-19 pandemic. Estimating the potential health benefits and healthcare cost saving associated with reductions in population sitting time could be useful for the development of public health initiatives.MethodsA sedentary behaviour model was developed and incorporated into an existing proportional, multi-state, life table Markov model (ACE-Obesity Policy model). This model simulates the 2019 Australian population (age 18 years and above) and estimates the incidence, prevalence and mortality of five diseases associated with sedentary behaviour (type 2 diabetes, stroke, endometrial, breast and colorectal cancer). Key model inputs included population sitting time estimates from the Australian National Health Survey 2014–2015, healthcare cost data from the Australian Institute of Health and Welfare (2015) and relative risk estimates assessed by conducting literature reviews and meta-analyses. Scenario analyses estimated the potential change in disease incidence as a result of changes in population sitting time. This, in turn, resulted in estimated improvements in long term health outcomes (Health-adjusted life years (HALYs)) and healthcare cost-savings.ResultsAccording to the model, if all Australian adults sat no more than 4 h per day, the total HALYs gained would be approximately 17,211 with health care cost savings of approximately A$185 million over one year. Under a more feasible scenario, where sitting time was reduced in adults who sit 4 or more hours per day by approximately 36 min per person per day (based on the results of the Stand Up Victoria randomised controlled trial), potential HALYs gained were estimated to be 3,670 and healthcare cost saving could reach A$39 million over one year.ConclusionsExcessive sedentary time results in considerable population health burden in Australia. This paper describes the development of the first Australian sedentary behaviour model that can be used to predict the long term consequences of interventions targeted at reducing sedentary behaviour through reductions in sitting time. These estimates may be used by decision makers when prioritising healthcare resources and investing in preventative public health initiatives.

Cost-sharing reduction and health service utilisation, health-related lifestyles, and obesity: evidence from the Australian health concession card policy.

The Australian Health Concession Card (HCC) policy aims to improve access to affordable health services and medicines by providing reduced patient cost-sharing. This study explored the association between HCC holding and health service utilisation, as well as health-related lifestyles and obesity in Australian adults. The Australian National Health Survey data were used to derive three sets of study outcomes. Multivariable generalised linear regression models were used to estimate the association between concessional status and study outcomes after controlling for socio-demographic factors. HCC holders, compared to their non-concessional counterparts, had more visits to GPs (1.01, 95% CI: 0.74-1.30), specialists (0.23, 95% CI: 0.02-0.44), outpatient clinics (0.19, 95% CI: 0.06-0.31), and had more hospital admissions (0.04, 95% CI: -0.05-0.91). There was no significant difference in visits to doctors and health professionals about mental health problems among HCC holders and their counterparts. Moreover, less HCC holders consumed alcohol on ≥3days a week (OR: 0.75, 95% CI: 0.59-0.93) and had less of an alcohol risk that exceeded the 2001 alcohol guidelines (0.83, 95% CI: 0.69-0.99). This study revealed evidence that reduced patientcost-sharing provided by HCC policy was positively correlated with more health service utilisation after adjusting for socio-demographic characteristics. Strategies to prioritise resources are necessary.

Assessing the suitability of general practice electronic health records for clinical prediction model development: a data quality assessment

BackgroundThe use of general practice electronic health records (EHRs) for research purposes is in its infancy in Australia. Given these data were collected for clinical purposes, questions remain around data quality and whether these data are suitable for use in prediction model development. In this study we assess the quality of data recorded in 201,462 patient EHRs from 483 Australian general practices to determine its usefulness in the development of a clinical prediction model for total knee replacement (TKR) surgery in patients with osteoarthritis (OA).MethodsVariables to be used in model development were assessed for completeness and plausibility. Accuracy for the outcome and competing risk were assessed through record level linkage with two gold standard national registries, Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) and National Death Index (NDI). The validity of the EHR data was tested using participant characteristics from the 2014–15 Australian National Health Survey (NHS).ResultsThere were substantial missing data for body mass index and weight gain between early adulthood and middle age. TKR and death were recorded with good accuracy, however, year of TKR, year of death and side of TKR were poorly recorded. Patient characteristics recorded in the EHR were comparable to participant characteristics from the NHS, except for OA medication and metastatic solid tumour.ConclusionsIn this study, data relating to the outcome, competing risk and two predictors were unfit for prediction model development. This study highlights the need for more accurate and complete recording of patient data within EHRs if these data are to be used to develop clinical prediction models. Data linkage with other gold standard data sets/registries may in the meantime help overcome some of the current data quality challenges in general practice EHRs when developing prediction models.

Associations between indicators of diet quality and psychological distress, depression and anxiety in emerging adults: Results from a nationally representative observational sample

BackgroundDepression, anxiety and their related symptoms are a leading cause of global disability and economic burden. Three quarters of all mental disorders occur by the age of 24 years making the identification of modifiable risk factors during these years imperative for prevention. This study examined associations between diet and psychological distress, depression and anxiety in a nationally representative sample of Australians during emerging adulthood. MethodsCross-sectional data (N= 1,340) from the 2017-18 Australian National Health Survey were analysed including fruit, vegetable and sugar sweetened beverage consumption. The primary outcome was psychological distress measured by the Kesler Psychological Distress Scale. Self-reported depression and anxiety were also assessed. Ordinal logistic regression analyses were fitted and adjusted for potential sociodemographic and lifestyle confounders. ResultsUnadjusted odds of high psychological distress were 45% greater among participants not meeting daily fruit intake guidelines (OR 1.45; 95% CI 1.17–1.81). Failure to meet fruit intake guidelines was associated with 66% and 48% increased odds for depression (OR 1.66; 95% CI 1.06–2.60) and anxiety (OR 1.48; 95% CI 1.02–2.15), respectively. Adjustment for lifestyle factors attenuated results across all outcomes. LimitationsThis study is limited by reliance on cross-sectional, self-reported data, limited statistical power for some exposures, and use of rudimentary diet proxies. ConclusionsThe findings suggested an association between some dietary indicators and mental health amongst emerging adults, however this association may be explained by other sociodemographic and lifestyle factors. Further longitudinal studies in this age group are warranted to investigate the directionality of this association and the relative effects of other lifestyle factors that cluster with diet.