Discretion in decision to receive COVID-19 vaccines and associated socio-economic inequalities in rates of uptake: a whole-of-population data linkage study from Australia.

OBJECTIVE: In Australia, first and second compared to third dose of a COVID-19 vaccine were implemented under different policies and contexts, resulting in greater discretion in decisions to receive a third compared to first and second dose. We quantified socio-economic inequalities in first and third dose to understand how discretion is associated with differences in uptake. STUDY DESIGN: Whole-of-population cohort study. METHODS: Linked immunisation, census, death and migration data were used to estimate weekly proportions who received first and third doses of a COVID-19 vaccine until 31 August 2022 for those with low (no formal qualification) compared to high (university degree) education, stratified by 10-year age group (from 30 to 89 years). We estimated relative rates using Cox regression, including adjustment for sociodemographic factors. RESULTS: Among 13.1 million people in our study population, 94% had received a first and 80% a third dose by 31 August 2022. Rates of uptake of first and third dose were around 50% lower for people with low compared to high education. Gaps were small in absolute terms for first dose, and at the end of the study period ranged from 1 to 11 percentage points across age groups. However, gaps were substantial for third dose, particularly at younger ages where the socio-economic gap was as wide as 32 percentage-points. CONCLUSION: Education-related inequalities in uptake were larger where discretion in decisions was larger. Policies that limited discretion in decisions to receive vaccines may have contributed to achieving the dual aims of maximising uptake and minimising inequalities.

Relative rates of cancers and deaths in Australian communities with PFAS environmental contamination associated with firefighting foams: A cohort study using linked data.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are environmental contaminants that are potentially harmful to health. We examined if rates of selected cancers and causes of deaths were elevated in three Australian communities with local environmental contamination caused by firefighting foams containing PFAS. The affected Australian communities were Katherine in Northern Territory, Oakey in Queensland and Williamtown in New South Wales. METHODS: All residents identified in the Medicare Enrolment File (1983-2019)-a consumer directory for Australia's universal healthcare-who ever lived in an exposure area (Katherine, Oakey and Williamtown), and a sample of those who ever lived in selected comparison areas, were linked to the Australian Cancer Database (1982-2017) and National Death Index (1980-2019). We estimated standardised incidence ratios (SIRs) for 23 cancer outcomes, four causes of death and three control outcomes, adjusting for sex, age and calendar time of diagnosis. FINDINGS: We observed higher rates of prostate cancer (SIR=1·76, 95 % confidence interval (CI) 1·36-2·24) in Katherine; laryngeal cancer (SIR=2·71, 95 % CI 1·30-4·98), kidney cancer (SIR=1·82, 95 % CI 1·04-2·96) and coronary heart disease (CHD) mortality (SIR=1·81, 95 % CI 1·46-2·33) in Oakey; and lung cancer (SIR=1·83, 95 % CI 1·39-2·38) and CHD mortality (SIR=1·22, 95 % CI 1·01-1·47) in Williamtown. We also saw elevated SIRs for control outcomes. SIRs for all other outcomes and overall cancer were similar across exposure and comparison areas. INTERPRETATION: There was limited evidence to support an association between living in a PFAS exposure area and risks of cancers or cause-specific deaths.

Inequalities in life expectancy in Australia according to education level: a whole-of-population record linkage study.

BACKGROUND: Life expectancy in Australia is amongst the highest globally, but national estimates mask within-country inequalities. To monitor socioeconomic inequalities in health, many high-income countries routinely report life expectancy by education level. However in Australia, education-related gaps in life expectancy are not routinely reported because, until recently, the data required to produce these estimates have not been available. Using newly linked, whole-of-population data, we estimated education-related inequalities in adult life expectancy in Australia. METHODS: Using data from 2016 Australian Census linked to 2016-17 Death Registrations, we estimated age-sex-education-specific mortality rates and used standard life table methodology to calculate life expectancy. For men and women separately, we estimated absolute (in years) and relative (ratios) differences in life expectancy at ages 25, 45, 65 and 85 years according to education level (measured in five categories, from university qualification [highest] to no formal qualifications [lowest]). RESULTS: Data came from 14,565,910 Australian residents aged 25 years and older. At each age, those with lower levels of education had lower life expectancies. For men, the gap (highest vs. lowest level of education) was 9.1 (95 %CI: 8.8, 9.4) years at age 25, 7.3 (7.1, 7.5) years at age 45, 4.9 (4.7, 5.1) years at age 65 and 1.9 (1.8, 2.1) years at age 85. For women, the gap was 5.5 (5.1, 5.9) years at age 25, 4.7 (4.4, 5.0) years at age 45, 3.3 (3.1, 3.5) years at 65 and 1.6 (1.4, 1.8) years at age 85. Relative differences (comparing highest education level with each of the other levels) were larger for men than women and increased with age, but overall, revealed a 10-25 % reduction in life expectancy for those with the lowest compared to the highest education level. CONCLUSIONS: Education-related inequalities in life expectancy from age 25 years in Australia are substantial, particularly for men. Those with the lowest education level have a life expectancy equivalent to the national average 15-20 years ago. These vast gaps indicate large potential for further gains in life expectancy at the national level and continuing opportunities to improve health equity.

Identifying long-term psychological distress from single measures: evidence from a nationally representative longitudinal survey of the Australian population.

BACKGROUND: Single time-point assessments of psychological distress are often used to indicate chronic mental health problems, but the validity of this approach is unclear. The aims of this study were to investigate how a single assessment of distress relates to longer-term assessment and quantify misclassification from using single measures to indicate chronic distress. METHODS: Data came from the Household, Income and Labour Dynamics in Australia Survey, a nationally representative study of Australian adults. Psychological distress, measured with the Kessler10 and categorised into low (scores:10- < 12), mild (12- < 16), moderate (16- < 22) and high (22-50), has been assessed in the Survey biennially since wave 7. Among respondents who were aged ≥25 years and participated in all waves in which distress was measured, we describe agreement in distress categories, and using a mixed linear model adjusting for age and sex we estimate change in scores, over a two-, four-, six- and eight-year follow-up period. We applied weights, benchmarked to the Australian population, to all analyses. RESULTS: Two-years following initial assessment, proportions within identical categories of distress were 66.0% for low, 54.5% for mild, 44.0% for moderate and 50.3% for high, while 94.1% of those with low distress initially had low/mild distress and 81.4% with high distress initially had moderate/high distress. These patterns did not change materially as follow-up time increased. Over the full eight-year period, 77.3% of individuals with high distress initially reported high distress on ≥1 follow-up occasion. Age-and sex- adjusted change in K10 scores over a two-year period was 1.1, 0.5, - 0.7 and - 4.9 for low, mild, moderate and high distress, respectively, and also did not change materially as follow-up time increased. CONCLUSION: In the absence of repeated measures, single assessments are useful proxies for chronic distress. Our estimates could be used in bias analyses to quantify the magnitude of the bias resulting from use of single assessments to indicate chronic distress.

How weight change is modelled in population studies can affect research findings: empirical results from a large-scale cohort study.

OBJECTIVES: To investigate how results of the association between education and weight change vary when weight change is defined and modelled in different ways. DESIGN: Longitudinal cohort study. PARTICIPANTS: 60 404 men and women participating in the Social, Environmental and Economic Factors (SEEF) subcomponent of the 45 and Up Study-a population-based cohort study of people aged 45 years or older, residing in New South Wales, Australia. OUTCOME MEASURES: The main exposure was self-reported education, categorised into four groups. The outcome was annual weight change, based on change in self-reported weight between the 45 and Up Study baseline questionnaire and SEEF questionnaire (completed an average of 3.3 years later). Weight change was modelled in four different ways: absolute change (kg) modelled as (1) a continuous variable and (2) a categorical variable (loss, maintenance and gain), and relative (%) change modelled as (3) a continuous variable and (4) a categorical variable. Different cut-points for defining weight-change categories were also tested. RESULTS: When weight change was measured categorically, people with higher levels of education (compared with no school certificate) were less likely to lose or to gain weight. When weight change was measured as the average of a continuous measure, a null relationship between education and annual weight change was observed. No material differences in the education and weight-change relationship were found when comparing weight change defined as an absolute (kg) versus a relative (%) measure. Results of the logistic regression were sensitive to different cut-points for defining weight-change categories. CONCLUSIONS: Using average weight change can obscure important directional relationship information and, where possible, categorical outcome measurements should be included in analyses.

Body mass index and incident hospitalisation for cardiovascular disease in 158 546 participants from the 45 and Up Study.

OBJECTIVE: To investigate the relationship between fine gradations in body mass index (BMI) and risk of hospitalisation for different types of cardiovascular disease (CVD). DESIGN, SUBJECTS AND METHODS: The 45 and Up Study is a large-scale Australian cohort study initiated in 2006. Self-reported data from 158 546 individuals with no history of CVD were linked prospectively to hospitalisation and mortality data. Hazard ratios (HRs) of incident hospitalisation for specific CVD diagnoses in relation to baseline BMI categories were estimated using Cox regression, adjusting for age, sex, region of residence, income, education, smoking, alcohol intake and health insurance status. RESULTS: There were 9594 incident CVD admissions over 583 100 person-years among people with BMI≥20 kg m(-2), including 3096 for ischaemic heart disease (IHD), 1373 for stroke, 411 for peripheral vascular disease (PVD) and 320 for heart failure. The adjusted HR of hospitalisation for all CVD diagnoses combined increased significantly with increasing BMI (P(trend) <0.0001)). The HR of IHD hospitalisation increased by 23% (95% confidence interval (95% CI): 18-27%) per 5 kg m(-2) increase in BMI (compared to BMI 20.0-22.49 kg m(-2), HR (95% CI) for BMI categories were: 22.5-24.99=1.25 (1.08-1.44); 25-27.49=1.43 (1.24-1.65); 27.5-29.99=1.64 (1.42-1.90); 30-32.49=1.63 (1.39-1.91) and 32.5-50=2.10 (1.79-2.45)). The risk of hospitalisation for heart failure showed a significant, but nonlinear, increase with increasing BMI. No significant increase was seen with above-normal BMI for stroke or PVD. For other specific classifications of CVD, HRs of hospitalisation increased significantly with increasing BMI for: hypertension; angina; acute myocardial infarction; chronic IHD; pulmonary embolism; non-rheumatic aortic valve disorders; atrioventricular and left bundle-branch block; atrial fibrillation and flutter; aortic aneurysm; and phlebitis and thrombophlebitis. CONCLUSION: The risk of hospitalisation for a wide range of CVD subtypes increases with relatively fine increments in BMI. Obesity prevention strategies are likely to benefit from focusing on bringing down the mean BMI at the population level, in addition to targeting those with a high BMI.

Prospective cohort study of body mass index and the risk of hospitalisation: findings from 246361 participants in the 45 and Up Study.

OBJECTIVE: To quantify the risk of hospital admission in relation to fine increments in body mass index (BMI). DESIGN, SETTING, AND PARTICIPANTS: Population-based prospective cohort study of 246,361 individuals aged greater than or equal to 45 years, from New South Wales, Australia, recruited from 2006-2009. Self-reported data on BMI and potential confounding/mediating factors were linked to hospital admission and death data. MAIN OUTCOMES: Cox-models were used to estimate the relative risk (RR) of incident all-cause and diagnosis-specific hospital admission (excluding same day) in relation to BMI. RESULTS: There were 61,583 incident hospitalisations over 479,769 person-years (py) of observation. In men, hospitalisation rates were lowest for BMI 20-<25 kg m(-2) (age-standardised rate: 120/1000 py) and in women for BMI 18.5-<25 kg m(-2) (102/1000 py); above these levels, rates increased steadily with increasing BMI; rates were 203 and 183/1000 py, for men and women with BMI 35-50 kg m(-2), respectively. This pattern was observed regardless of baseline health status, smoking status and physical activity levels. After adjustment, the RRs (95% confidence interval) per 1 kg m(-2) increase in BMI from ≥ 20 kg m(-2) were 1.04(1.03-1.04) for men and 1.04(1.04-1.05) for women aged 45-64; corresponding RRs for ages 65-79 were 1.03(1.02-1.03) and 1.03(1.03-1.04); and for ages ≥ 80 years, 1.01(1.00-1.01) and 1.01(1.01-1.02). Hospitalisation risks were elevated for a large range of diagnoses, including a number of circulatory, digestive, musculoskeletal and respiratory diseases, while being protective for just two-fracture and hernia. CONCLUSIONS: Above normal BMI, the RR of hospitalisation increases with even small increases in BMI, less so in the elderly. Even a small downward shift in BMI, among those who are overweight not just those who are obese, could result in a substantial reduction in the risk of hospitalisation.

Effect of healthcare on mortality: trends in avoidable mortality in Australia and comparisons with Western Europe.

OBJECTIVES: Using the concept of avoidable mortality, international studies suggest that healthcare has been effective in reducing mortality. This paper provides an analysis of avoidable mortality in Australia and compares trends with those of Western Europe. METHODS: Using unit-record mortality data, we calculated avoidable mortality rates in Australia for 1968-2001. We partitioned avoidable causes into three categories: those amenable to medical care; those mainly responsive to health policy; and ischaemic heart disease. We used Poisson regression to model the trends. We compared trends with those of nine European countries using published data. RESULTS: Total avoidable death rates fell by 68% in females and 72% in males. The corresponding non-avoidable death rates fell by 35 and 33%. The annual declines in avoidable mortality rates were: 3.47% [95% confidence intervals (CI) 3.44-3.50%] in males and 3.89% (95% CI 3.86-3.91%) in females. For non-avoidable mortality rates, the annual declines were 1.09% (95% CI 1.05-1.13%) and 0.95% (95% CI 0.92-0.98%), respectively. In females, declines in death rates from causes amenable to medical care contributed 54% to the decline in avoidable mortality rates, ischaemic heart disease contributed 45%, and causes responsive to health policy intervention contributed 1%. In males, the corresponding contributions were 32, 57 and 11%. These rates, and the declines between 1980 and 1998, were comparable with selected European countries, with Australia's ranking improving over the period. CONCLUSION: Trends in avoidable mortality in Australia suggest that the Australian healthcare system has been effective in improving population health. Australia's experience compares favourably with that of Europe.

Attention deficits in stroke patients with aphasia.

Attentional capacity and sustained attention were investigated in 21 aphasic stroke patients and 21 non-brain-damaged patients. Attentional capacity was assessed using a series of reaction time (RT) tasks. The aphasic patients demonstrated impaired attentional capacity as shown by slower processing speed than the non-brain-damaged group (p < .01) and greater increases in RT with increased processing load (p < .05). Similar patterns were found for both verbal and spatial material. There was no significant relationship between severity of auditory comprehension deficits and attentional capacity. Sustained attention was assessed using a cognitive vigilance task requiring identification of a target letter presented infrequently over 32 minutes. Both the aphasic and the non-brain-damaged group demonstrated a decline in performance with time on task as shown by a steady increase in RTs (p < .0001), but the decline was equivalent across the groups. Thus, the aphasic group did not show a specific deficit in the ability to sustain attention.