Does use of GP and specialist services vary across areas and according to individual socioeconomic position? A multilevel analysis using linked data in Australia.

OBJECTIVE: Timely access to primary care and supporting specialist care relative to need is essential for health equity. However, use of services can vary according to an individual's socioeconomic circumstances or where they live. This study aimed to quantify individual socioeconomic variation in general practitioner (GP) and specialist use in New South Wales (NSW), accounting for area-level variation in use. DESIGN: Outcomes were GP use and quality-of-care and specialist use. Multilevel logistic regression was used to estimate: (1) median ORs (MORs) to quantify small area variation in outcomes, which gives the median increased risk of moving to an area of higher risk of an outcome, and (2) ORs to quantify associations between outcomes and individual education level, our main exposure variable. Analyses were adjusted for individual sociodemographic and health characteristics and performed separately by remoteness categories. SETTING: Baseline data (2006-2009) from the 45 and Up Study, NSW, Australia, linked to Medicare Benefits Schedule and death data (to December 2012). PARTICIPANTS: 267 153 adults aged 45 years and older. RESULTS: GP (MOR=1.32-1.35) and specialist use (1.16-1.18) varied between areas, accounting for individual characteristics. For a given level of need and accounting for area variation, low education-level individuals were more likely to be frequent users of GP services (no school certificate vs university, OR=1.63-1.91, depending on remoteness category) and have continuity of care (OR=1.14-1.24), but were less likely to see a specialist (OR=0.85-0.95). CONCLUSION: GP and specialist use varied across small areas in NSW, independent of individual characteristics. Use of GP care was equitable, but specialist care was not. Failure to address inequitable specialist use may undermine equity gains within the primary care system. Policies should also focus on local variation.

National estimates of occupation-related inequalities in all-cause mortality using linked Census-mortality data.

OBJECTIVE: This report aims to provide national estimates of occupation-related inequalities in all-cause mortality for Australian residents aged 25-64 years. METHOD: Data came from the 2016 Census linked to Deaths Registrations, available via the Multi-Agency Data Integration Project. Using negative binomial regression, we estimated age-adjusted relative and absolute inequalities in all-cause mortality rates in the 13 months following Census according to occupation, defined using the Australian and New Zealand Standard Classification of Occupations (eight major groups), using managers as the reference group. RESULTS: Among 10.8M people, there were 20,987 deaths. Age-adjusted mortality rates were lowest among managers and professionals and were generally highest for manual occupations, for example, among men, relative risks (RR) for labourers ranged across age groups from 1.44 (95% CI 1.19-1.75, age 54-64) to 2.99 (1.93-4.65, age 25-34); among women, the RR for machine operators and drivers were 3.95 (1.39-11.21 in age 25-24 and 2.73 (1.66-4.49) in age 45-54, but there was relatively little variation by occupation in women aged 35-44 and 55-64. Around one in five deaths (23% for men, 17% for women) were associated with being in an occupation other than manager. CONCLUSION: These findings highlight that there is benefit in documenting national mortality inequalities according to occupation in addition to other measures of socioeconomic position. They provide further insights into socioeconomic inequalities in mortality. IMPLICATIONS FOR PUBLIC HEALTH: Methods that aim to reduce mortality for those in manual occupations, particularly among young men, will reduce inequalities and improve population health.

Changes in general practice use and costs with COVID-19 and telehealth initiatives: analysis of Australian whole-population linked data.

BACKGROUND: In response to the COVID-19 pandemic, general practice in Australia underwent a rapid transition, including the roll-out of population-wide telehealth, with uncertain impacts on GP use and costs. AIM: To describe how use and costs of GP services changed in 2020 - following the COVID-19 pandemic and introduction of telehealth - compared with 2019, and how this varied across population subgroups. DESIGN AND SETTING: Linked-data analysis of whole-population data for Australia. METHOD: Multi-Agency Data Integration Project data for ∼19 million individuals from the 2016 census were linked to Medicare data for 2019-2020. Regression models were used to compare age- and sex-adjusted GP use and out-of-pocket costs over time, overall, and by sociodemographic characteristics. RESULTS: Of the population, 85.5% visited a GP in Q2-Q4 2020, compared with 89.5% in the same period of 2019. The mean number of face-to-face GP services per quarter declined, while telehealth services increased; overall use of GP services in Q4 2020 was similar to, or higher than, that of Q4 2019 for most groups. The proportion of total GP services by telehealth stabilised at 23.5% in Q4 2020. However, individuals aged 3-14 years, ≥70 years, and those with limited English proficiency used fewer GP services in 2020 compared with 2019, with a lower proportion by telehealth, compared with the rest of the population. Mean out-of-pocket costs per service were lower across all subgroups in 2020 compared with 2019. CONCLUSION: The introduction of widespread telehealth maintained the use of GP services during the COVID-19 pandemic and minimised out-of-pocket costs, but not for all population subgroups.

Association of individual-socioeconomic variation in quality-of-primary care with area-level service organisation: A multilevel analysis using linked data.

RATIONALE, AIMS AND OBJECTIVES: Ensuring equitable access to primary care (PC) contributes to reducing differences in health related to people's socioeconomic circumstances. However, there is limited data on system-level factors associated with equitable access to high-quality PC. We examine whether individual-level socioeconomic variation in general practitioner (GP) quality-of-care varies by area-level organisation of PC services. METHODS: Baseline data (2006-2009) from the Sax Institute's 45 and Up Study, involving 267,153 adults in New South Wales, Australia, were linked to Medicare Benefits Schedule claims and death data (to December 2012). Small area-level measures of PC service organisation were GPs per capita, bulk-billing (i.e., no copayment) rates, out-of-pocket costs (OPCs), rates of after-hours and chronic disease care planning/coordination services. Using multilevel logistic regression with cross-level interaction terms we quantified the relationship between area-level PC service characteristics and individual-level socioeconomic variation in need-adjusted quality-of-care (continuity-of-care, long-consultations, and care planning), separately by remoteness. RESULTS: In major cities, more bulk-billing and chronic disease services and fewer OPCs within areas were associated with an increased odds of continuity-of-care-more so among people of high- than low education (e.g., bulk-billing interaction with university vs. no school certificate 1.006 [1.000, 1.011]). While more bulk-billing, after-hours services and fewer OPCs were associated with long consultations and care planning across all education levels, in regional locations alone, more after-hours services were associated with larger increases in the odds of long consultations among people with low- than high education (0.970 [0.951, 0.989]). Area GP availability was not associated with outcomes. CONCLUSIONS: In major cities, PC initiatives at the local level, such as bulk-billing and after-hours access, were not associated with a relative benefit for low- compared with high-education individuals. In regional locations, policies supporting after-hours access may improve access to long consultations, more so for people with low- compared with high-education.

Education-related inequalities in cause-specific mortality: first estimates for Australia using individual-level linked census and mortality data.

BACKGROUND: Socioeconomic inequalities in mortality are evident in all high-income countries, and ongoing monitoring is recommended using linked census-mortality data. Using such data, we provide the first estimates of education-related inequalities in cause-specific mortality in Australia, suitable for international comparisons. METHODS: We used Australian Census (2016) linked to 13 months of Death Registrations (2016-17). We estimated relative rates (RR) and rate differences (RD, per 100 000 person-years), comparing rates in low (no qualifications) and intermediate (secondary school) with high (tertiary) education for individual causes of death (among those aged 25-84 years) and grouped according to preventability (25-74 years), separately by sex and age group, adjusting for age, using negative binomial regression. RESULTS: Among 13.9 M people contributing 14 452 732 person-years, 84 743 deaths occurred. All-cause mortality rates among men and women aged 25-84 years with low education were 2.76 [95% confidence interval (CI): 2.61-2.91] and 2.13 (2.01-2.26) times the rates of those with high education, respectively. We observed inequalities in most causes of death in each age-sex group. Among men aged 25-44 years, relative and absolute inequalities were largest for injuries, e.g. transport accidents [RR = 10.1 (5.4-18.7), RD = 21.2 (14.5-27.9)]). Among those aged 45-64 years, inequalities were greatest for chronic diseases, e.g. lung cancer [men RR = 6.6 (4.9-8.9), RD = 57.7 (49.7-65.8)] and ischaemic heart disease [women RR = 5.8 (3.7-9.1), RD = 20.2 (15.8-24.6)], with similar patterns for people aged 65-84 years. When grouped according to preventability, inequalities were large for causes amenable to behaviour change and medical intervention for all ages and causes amenable to injury prevention among young men. CONCLUSIONS: Australian education-related inequalities in mortality are substantial, generally higher than international estimates, and related to preventability. Findings highlight opportunities to reduce them and the potential to improve the health of the population.

Reflection on modern methods: statistical, policy and ethical implications of using age-standardized health indicators to quantify inequities.

Methods for calculating health indicators profoundly influence understanding of and action on population health and inequities. Age-standardization can be useful and is commonly applied to account for differences in age structures when comparing health indicators across groups. Age-standardized rates have well-acknowledged limitations, including that they are relative indices for comparison, and not accurate measures of actual rates where the age structures of groups diverge. This paper explores these limitations, and demonstrates alternative approaches through a case study quantifying mortality rates within the Aboriginal and Torres Strait Islander (Indigenous) population of Australia and inequities compared with the non-Indigenous population, over 2001-16. Applying the Australian Standard Population, the Aboriginal and Torres Strait Islander age-standardized mortality rate was more than double the crude mortality rate in 2001 and 2016, inflated through high weighting of older age groups. Despite divergent population age structures, age-standardized mortality rates remain a key policy metric for measuring progress in reducing Indigenous-non-Indigenous inequities in Australia. Focusing on outcomes age-standardized to the total population can obscure inequities, and denies Aboriginal and Torres Strait Islander peoples and communities valid, actionable information about their health and well-being. Age-specific statistics convey the true magnitude of health risks and highlight high-risk subgroups. When requiring standardization, standardizing to a population-specific standard (here, an Indigenous standard) generates metrics centred around and reflective of reality for the population of focus, supporting communities' self-determination to identify priorities and informing resource allocation and service delivery. The principles outlined here apply across populations, including Indigenous and other populations internationally.

Comparison of cardiovascular disease risk factors, assessment and management in men and women, including consideration of absolute risk: a nationally representative cross-sectional study.

OBJECTIVES: Cardiovascular disease (CVD) is highly preventable and optimal treatments based on absolute risk can halve risk of future events. Compared with women, men have higher risks of developing CVD. However, women can experience suboptimal treatment. We aimed to quantify sex differences in CVD risk, assessment and treatment in Australian adults. DESIGN, PARTICIPANTS, SETTING: Cross-sectional analysis of nationally representative data from interview, physical measures, medication review and blood and urine samples, from 2011 to 2012 Australian Health Survey participants aged 45-74 (n=11 518). OUTCOME MEASURES: CVD risk factors, absolute 5-year risk of a primary CVD event, blood pressure and cholesterol assessment in the previous 2 and 5 years and use of recommended CVD preventive medications were compared using Poisson regression to estimate age-adjusted male versus female prevalence ratios (PRs). RESULTS: Women had a generally more favourable CVD risk factor profile than men, including lower: current smoking prevalence (women=14.5%; men=18.4%, PR=0.78, 95% CI=0.70 to 0.88); body mass index (women (mean)=28.3 kg/m2; men (mean)=28.8 kg/m2, p<0.01); systolic and diastolic blood pressure (systolic: women (mean)=127.1 mm Hg; men (mean)=130.5 mm Hg, p<0.001); blood glucose (women (mean)=5.2 mmol/L; men (mean)=5.5 mmol/L); diabetes prevalence (women=6.8%; men=12.5%, PR=0.55, 95% CI=0.44 to 0.67); prior CVD (women=7.9%; men=11.3%) and absolute primary CVD risk (absolute 5-year CVD risk >15%: women=6.6%, 95% CI=5.4 to 7.8; men=15.4%, 95% CI=13.9% to 16.9%). Compared with men, women had higher low-density lipoprotein, high-density lipoprotein and total cholesterol and sedentary behaviour and lower physical activity. Blood pressure and cholesterol assessment were common in both sexes. Among those at high absolute risk, age-adjusted proportions receiving recommended CVD medications were low, without sex differences (women=21.3%; men=23.8%, PR=0.93, 95% CI=0.49 to 1.78). Fewer women than men with prior atherosclerotic CVD were receiving recommended treatment (women=21.8%, men=41.4%, PR=0.55, 95% CI=0.31 to 0.96). CONCLUSION: Women have a more favourable CVD risk factor profile than men. Preventive treatment is uncommon and women with prior atherosclerotic CVD are around half as likely as men to be receiving recommended treatment.

Education-related variation in coronary procedure rates and the contribution of private health care in Australia: a prospective cohort study.

BACKGROUND: Contemporary Australian evidence on socioeconomic variation in secondary cardiovascular disease (CVD) care, a possible contributor to inequalities in cardiovascular disease outcomes, is lacking. This study examined the relationship between education, an individual-level indicator of socioeconomic position, and receipt of angiography and revascularisation procedures following incident hospitalisation for acute myocardial infarction (AMI) or angina, and the role of private care in this relationship. METHODS: Participants aged ≥45 from the New South Wales population-based 45 and Up Study with no history of prior ischaemic heart disease hospitalised for AMI or angina were followed for receipt of angiography or revascularisation within 30 days of hospital admission, ascertained through linked hospital records. Education attainment, measured on baseline survey, was categorised as low (no school certificate/qualifications), intermediate (school certificate/trade/apprenticeship/diploma) and high (university degree). Cox regression estimated the association (hazard ratios [HRs]) between education and coronary procedure receipt, adjusting for demographic and health-related factors, and testing for linear trend. Private health insurance was investigated as a mediating variable. RESULTS: Among 4454 patients with AMI, 68.3% received angiography within 30 days of admission (crude rate: 25.8/person-year) and 48.8% received revascularisation (rate: 11.7/person-year); corresponding figures among 4348 angina patients were 59.7% (rate: 17.4/person-year) and 30.8% (rate: 5.3/person-year). Procedure rates decreased with decreasing levels of education. Comparing low to high education, angiography rates were 29% lower among AMI patients (adjusted HR = 0.71, 95% CI: 0.56-0.90) and 40% lower among angina patients (0.60, 0.47-0.76). Patterns were similar for revascularisation among those with angina (0.78, 0.61-0.99) but not AMI (0.93, 0.69-1.25). After adjustment for private health insurance status, the HRs were attenuated and there was little evidence of an association between education and angiography among those admitted for AMI. CONCLUSIONS: There is a socioeconomic gradient in coronary procedures with the most disadvantaged patients being less likely to receive angiography following hospital admission for AMI or angina, and revascularisation procedures for angina. Unequal access to private health care contributes to these differences. The extent to which the remaining variation is clinically appropriate, or whether angiography is being underused among people with low socioeconomic position or overused among those with higher socioeconomic position, is unclear.

Education inequalities in adult all-cause mortality: first national data for Australia using linked census and mortality data.

BACKGROUND: National linked mortality and census data have not previously been available for Australia. We estimated education-based mortality inequalities from linked census and mortality data that are suitable for international comparisons. METHODS: We used the Australian Bureau of Statistics Death Registrations to Census file, with data on deaths (2011-2012) linked probabilistically to census data (linkage rate 81%). To assess validity, we compared mortality rates by age group (25-44, 45-64, 65-84 years), sex and area-inequality measures to those based on complete death registration data. We used negative binomial regression to quantify inequalities in all-cause mortality in relation to five levels of education ['Bachelor degree or higher' (highest) to 'no Year 12 and no post-secondary qualification' (lowest)], separately by sex and age group, adjusting for single year of age and correcting for linkage bias and missing education data. RESULTS: Mortality rates and area-based inequality estimates were comparable to published national estimates. Men aged 25-84 years with the lowest education had age-adjusted mortality rates 2.20 [95% confidence interval (CI): 2.08‒2.33] times those of men with the highest education. Among women, the rate ratio was 1.64 (1.55‒1.74). Rate ratios were 3.87 (3.38‒4.44) in men and 2.57 (2.15‒3.07) in women aged 25-44 years, decreasing to 1.68 (1.60‒1.76) in men and 1.44 (1.36‒1.53) in women aged 65-84 years. Absolute education inequalities increased with age. One in three to four deaths (31%) was associated with less than Bachelor level education. CONCLUSIONS: These linked national data enabled valid estimates of education inequality in mortality suitable for international comparisons. The magnitude of relative inequality is substantial and similar to that reported for other high-income countries.

Socioeconomic variation in absolute cardiovascular disease risk and treatment in the Australian population.

Cardiovascular disease (CVD), preventable through appropriate management of absolute CVD risk, disproportionately affects socioeconomically disadvantaged individuals. The aim of this study was to estimate absolute and relative socioeconomic inequalities in absolute CVD risk and treatment in the Australian population using cross-sectional representative data on 4751 people aged 45-74 from the 2011-12 Australian Health Survey. Poisson regression was used to calculate prevalence differences (PD) and ratios (PR) for prior CVD, high 5-year absolute risk of a primary CVD event and guideline-recommended medication use, in relation to socioeconomic position (SEP, measured by education). After adjusting for age and sex, the prevalence of high absolute risk of a primary CVD event among those of low, intermediate and high SEP was 12.6%, 10.9% and 7.7% (PD, low vs. high = 5.0 [95% CI: 2.3, 7.7], PR = 1.6 [1.2, 2.2]) and for prior CVD was 10.7%, 9.1% and 6.7% (PD = 4.0 [1.4, 6.6], PR = 1.6 [1.1, 2.2]). The proportions using preventive medication use among those with high primary risk were 21.3%, 19.5% and 29.4% for low, intermediate and high SEP and for prior CVD, were 37.8%, 35.7% and 17.7% (PD = 20.1 [9.7, 30.5], PR = 2.1 [1.3, 3.5]). Proportions at high primary risk and not using medications among those of low, intermediate and high SEP were 10.6%, 8.8% and 4.7% and with prior CVD and not using medications were 8.5%, 6.3% and 4.1%. Findings indicate substantial potential to prevent CVD and reduce inequalities through appropriate management of high absolute risk in the population.

Burden of Clostridium difficile infection: Associated hospitalization in a cohort of middle-aged and older adults.

BACKGROUND: Clostridium difficile is the principal cause of infectious diarrhea in hospitalized patients. The aim of this study was to describe and compare length of stay (LOS), costs, and in-hospital deaths for C difficile infection (CDI) and non-CDI hospitalizations, in a cohort of middle-aged and older Australians. METHODS: We used survey data from the 45 and Up Study, linked to hospitalization and death data. We calculated the average LOS and costs per hospitalization, and the proportion of in-hospital deaths for CDI and non-CDI hospitalizations. We then compared hospitalizations with CDI as a secondary diagnosis to non-CDI hospitalizations by stratifying hospitalizations based on principal diagnosis and then using generalized linear models to compare LOS and in-hospital costs, and logistic regression for in-hospital deaths, adjusting for age and sex. RESULTS: There were 641 CDI hospitalizations during 2006-2012. The average LOS was 17 days; the average cost per hospitalization was AUD 12,704; and in 7.3% of admissions (47 out of 641) the patient died. After adjusting for age and sex, hospitalizations with CDI were associated with longer LOS, higher costs, and a greater proportion of in-hospital deaths compared with hospitalizations with similar principal diagnosis but without CDI. CONCLUSIONS: CDI places additional burden on the Australian hospital system, with CDI patients having relatively lengthy hospital stays and high costs.

Socioeconomic variation in incidence of primary and secondary major cardiovascular disease events: an Australian population-based prospective cohort study.

BACKGROUND: Cardiovascular disease (CVD) disproportionately affects disadvantaged people, but reliable quantitative evidence on socioeconomic variation in CVD incidence in Australia is lacking. This study aimed to quantify socioeconomic variation in rates of primary and secondary CVD events in mid-age and older Australians. METHODS: Baseline data (2006-2009) from the 45 and Up Study, an Australian cohort involving 267,153 men and women aged ≥ 45, were linked to hospital and death data (to December 2013). Outcomes comprised first event - death or hospital admission - for major CVD combined, as well as myocardial infarction and stroke, in those with and without prior CVD (secondary and primary events, respectively). Cox regression estimated hazard ratios (HRs) for each outcome in relation to education (and income and area-level disadvantage), separately by age group (45-64, 65-79, and ≥ 80 years), adjusting for age and sex, and additional sociodemographic factors. RESULTS: There were 18,207 primary major CVD events over 1,144,845 years of follow-up (15.9/1000 person-years), and 20,048 secondary events over 260,357 years (77.0/1000 person-years). For both primary and secondary events, incidence increased with decreasing education, with the absolute difference between education groups largest for secondary events. Age-sex adjusted hazard ratios were highest in the 45-64 years group: for major CVDs, HR (no qualifications vs university degree) = 1.62 (95% CI: 1.49-1.77) for primary events, and HR = 1.49 (1.34-1.65) for secondary events; myocardial infarction HR = 2.31 (1.87-2.85) and HR = 2.57 (1.90-3.47) respectively; stroke HR = 1.48 (1.16-1.87) and HR = 1.97 (1.42-2.74) respectively. Similar but attenuated results were seen in older age groups, and with income. For area-level disadvantage, CVD gradients were weak and non-significant in older people (> 64 years). CONCLUSIONS: Individual-level data are important for quantifying socioeconomic variation in CVD incidence, which is shown to be substantial among both those with and without prior CVD. Findings reinforce the opportunity for, and importance of, primary and secondary prevention and treatment in reducing socioeconomic variation in CVD and consequently the overall burden of CVD morbidity and mortality in Australia.

Absolute risk of cardiovascular disease events, and blood pressure- and lipid-lowering therapy in Australia.

OBJECTIVE: To quantify absolute cardiovascular disease (CVD) risk and treatment in Australian adults. DESIGN, PARTICIPANTS: Cross-sectional representative study of 9564 people aged 18 years or more who had participated in the 2011-12 Australian National Health Measures Survey (response rate for those aged 45-74 years: 46.5%). MAIN OUTCOME MEASURES: Prior CVD was ascertained and 5-year absolute risk of a primary CVD event calculated (using the Australian National Vascular Disease Prevention Alliance algorithm; categories: low [< 10%], moderate [10-15%], and high [> 15%] risk) on the basis of data on medical history, risk factors and medications, derived from interviews, physical measurements, and blood and urine samples. RESULTS: Absolute CVD risk increased with age and was higher among men than women. Overall, 19.9% (95% CI, 18.5-21.3%) of Australians aged 45-74 years had a high absolute risk of a future CVD event (an estimated 1 445 000 people): 8.7% (95% CI, 7.8-9.6%) had prior CVD (estimated 634 000 people) and 11.2% (95% CI, 10.2-12.2%) had high primary CVD risk (estimated 811 000 people). A further 8.6% (95% CI, 7.4-9.8%, estimated 625 000) were at moderate primary CVD risk. Among those with prior CVD, 44.2% (95% CI, 36.8-51.6%) were receiving blood pressure- and lipid-lowering medications, 35.4% (95% CI, 27.8-43.0%) were receiving only one of these, and 20.4% (95% CI, 13.9-26.9%) were receiving neither. Corresponding figures for high primary CVD risk were 24.3% (95% CI, 18.3-30.3%); 28.7% (95% CI, 22.7-34.7%); and 47.1% (95% CI, 39.9-54.3%). CONCLUSIONS: About one-fifth of the Australian population aged 45-74 years (about 1.4 million individuals) were estimated to have a high absolute risk of a future CVD event. Most (estimated 970 000) were not receiving currently recommended combination blood pressure- and lipid-lowering therapy, indicating substantial potential for health gains by increasing routine assessment and treatment according to absolute CVD risk.

The relationship between body mass index and hospitalisation rates, days in hospital and costs: findings from a large prospective linked data study.

BACKGROUND: Internationally there is limited empirical evidence on the impact of overweight and obesity on health service use and costs. We estimate the burden of hospitalisation-admissions, days and costs-associated with above-normal BMI. METHODS: Population-based prospective cohort study involving 224,254 adults aged ≥45y in Australia (45 and Up Study). Baseline questionnaire data (2006-2009) were linked to hospitalisation and death records (median follow-up 3.42y) and hospital cost data. The relationships between BMI and hospital admissions and days were modelled using zero-inflated negative binomial regression; generalised gamma models were used to model costs. Analyses were stratified by sex and age (45-64, 65-79, ≥80y), and adjusted for age, area of residence, education, income, smoking, alcohol-intake and private health insurance status. Population attributable fractions were also calculated. RESULTS: There were 459,346 admissions (0.55/person-year) and 1,483,523 hospital days (1.76/person-year) during follow-up. For ages 45-64y and 65-79y, rates of admissions, days and costs increased progressively with increments of above-normal BMI. Compared to BMI 22.5-<25kg/m2, rates of admissions and days were 1.64-2.54 times higher for BMI 40-50kg/m2; costs were 1.14-1.24 times higher for BMI 27.5-<30kg/m2, rising to 1.77-2.15 times for BMI 40-50kg/m2. The BMI-hospitalisation relationship was less clear for ≥80y. We estimated that among Australians 45-79y, around 1 in every 8 admissions are attributable to overweight and obesity (2% to overweight, 11% to obesity), as are 1 in every 6 days in hospital (2%, 16%) and 1 in every 6 dollars spent on hospitalisation (3%, 14%). CONCLUSIONS: The dose-response relationship between BMI and hospital use and costs in mid-age and older Australians in the above-normal BMI range suggests even small downward shifts in BMI among these people could result in considerable reductions in their annual health care costs; whether this would result in long-term savings to the health care system is not known from this study.

Income-related inequalities in chronic conditions, physical functioning and psychological distress among older people in Australia: cross-sectional findings from the 45 and up study.

BACKGROUND: The burden of chronic disease continues to rise as populations age. There is relatively little published on the socioeconomic distribution of this burden in older people. This study quantifies absolute and relative income-related inequalities in prevalence of chronic diseases, severe physical functioning limitation and high psychological distress in mid-age and older people in Australia. METHODS: Cross-sectional study of 208,450 participants in the 45 and Up Study, a population-based cohort of men and women aged 45-106 years from New South Wales, Australia. Chronic conditions included self-reported heart disease, diabetes, Parkinson's disease, cancer and osteoarthritis; physical functioning limitation (severe/not) was measured using Medical Outcomes Study measures and psychological distress (high/not) using the Kessler Psychological Distress Scale. For each outcome, prevalence was estimated in relation to annual household income (6 categories). Prevalence differences (PDs) and ratios (PRs) were generated, comparing the lowest income category (< $20,000) to the highest (≥ $70,000), using Poisson regression with robust standard errors, weighted for age, sex and region of residence. Analyses were stratified by age group (45-64, 65-79 and ≥ 80 years) and sex and adjusted for age and country of birth. RESULTS: With few exceptions, there were income gradients in the prevalence of chronic conditions among all age-sex groups, with prevalence decreasing with increasing income. Of the chronic diseases, PDs were highest for diabetes (ranging between 5.69% and 10.36% across age-sex groups) and in women, also for osteoarthritis (5.72% to 8.14%); PRs were highest for osteoarthritis in men aged 45-64 years (4.01), otherwise they were highest for diabetes (1.78 to 3.43). Inequalities were very high for both physical functioning limitation and psychological distress, particularly among those aged 45-64 (PDs between 18.67% and 29.23% and PRs between 4.63 and 16.51). Absolute and relative inequalities tended to decrease with age, but remained relatively high for diabetes and physical functioning in the elderly (≥ 80 years). CONCLUSIONS: Significant inequalities in the prevalence of chronic conditions, physical functioning and psychological distress persist into old age. The additional health burden placed on those who are already disadvantaged is likely to become an increasingly important issue in an ageing population.

Variation in health inequalities according to measures of socioeconomic status and age.

OBJECTIVE: To examine variation in the magnitude of socioeconomic inequalities in health and age-related variations in inequalities, according to the socioeconomic status (SES) measure used. METHODS: Cross-sectional study involving 205,709 participants in the 45 and Up Study. We used the Relative Index of Inequality (RII) to quantify health inequalities in relation to income, education and Socio-Economic Indexes for Areas (SEIFA). The outcomes used were heart disease and self-rated health. Analyses were stratified by age (45-64, 65-79, ≥80 years). RESULTS: RIIs were largest for income and smallest for SEIFA; they were generally largest in the youngest age group and smallest in the oldest group. Age-related differences in RIIs were particularly marked for income (e.g. for fair/poor health, RII=11.81, 95%CI 11.14-12.53 in the 45-64 age group and RII=2.42, 95%CI 2.10-2.78 in ≥80 group), and less marked for SEIFA (e.g. respectively, RII=2.68, 95%CI 2.53-2.84 and RII=1.32, 95%CI 1.22-1.44). CONCLUSIONS: The magnitude of socioeconomic inequality in health varies substantially according to the type of SES measure used and age. Income is the most sensitive measure. IMPLICATIONS: Researchers and policy makers should be aware of the extent to which SEIFA-based estimates underestimate the magnitude of health inequality compared to individual-level measures, especially in younger age groups.

Inequalities in bariatric surgery in Australia: findings from 49,364 obese participants in a prospective cohort study.

OBJECTIVES: To investigate variation, and quantify socioeconomic inequalities, in the uptake of primary bariatric surgery in an obese population. DESIGN, SETTING AND PARTICIPANTS: Prospective population-based cohort study of 49,364 individuals aged 45-74 years with body mass index (BMI)≥30 kg/m2. Data from questionnaires (distributed from 1 January 2006 to 31 December 2008) were linked to hospital and death data to 30 June 2010. The sample was drawn from the 45 and Up Study (approximately 10% of New South Wales population aged 45 included, response rate approximately 18%). MAIN OUTCOME MEASURES: Rates of bariatric surgery and adjusted rate ratios (RRs) in relation to health and sociodemographic characteristics. RESULTS: Over 111,757 person-years (py) of follow-up, 312 participants had bariatric surgery, a rate of 27.92 per 10,000 py (95% CI, 24.91-31.19). Rates were highest in women, those living in major cities and those with diabetes, and increased significantly with a higher BMI and number of chronic health conditions. Adjusted RRs were 5.27 (95% CI, 3.18-8.73) for those with annual household income≥ $70 000 versus those with household income<$20,000, and 4.01 (95% CI, 2.41-6.67) for those living in areas in the least disadvantaged quintile versus those in the most disadvantaged quintile. Having versus not having private health insurance (age- and sex-adjusted RR, 9.25; 95% CI, 5.70-15.00) partially explained the observed inequalities. CONCLUSIONS: Bariatric surgery has been shown to be cost-effective in treating severe obesity and associated illnesses. While bariatric surgery rates in Australia are higher in those with health problems, large socioeconomic inequalities are apparent. Our findings suggest these procedures are largely available to those who can afford private health insurance and associated out-of-pocket costs, with poor access in populations who are most in need. Continuing inequalities in access are likely to exacerbate existing inequalities in obesity and related health problems.

Chronic disease management items in general practice: a population-based study of variation in claims by claimant characteristics.

OBJECTIVE: To describe how Medical Benefits Schedule (MBS) chronic disease (CD) item claims vary by sociodemographic and health characteristics in people with heart disease, asthma or diabetes. DESIGN, SETTING AND PARTICIPANTS: A cross-sectional analysis of linked unit-level MBS and survey data from the first 102,934 participants enrolled in the 45 and Up Study, a large-scale cohort study in New South Wales, who completed the baseline survey between January 2006 and July 2008. MAIN OUTCOME MEASURE: Claim for any general practitioner CD item within 18 months before enrolment, ascertained from MBS records. RESULTS: The proportion of individuals making claims for MBS CD items was 18.5% for asthma, 22.3% for heart disease, and 44.9% for diabetes. Associations between participant characteristics and a claim for a CD item showed similar patterns across the three diseases. For heart disease and asthma, people most likely to claim a CD item were women, older, of low income and education levels, with multiple chronic conditions, fair or poor self-rated health, obesity and low physical activity levels. The pattern of claims was slightly different for participants with diabetes in that there was no significant association with number of chronic conditions, smoking or physical activity. CONCLUSIONS: Many individuals with self-reported CD do not claim CD items. People with diabetes and individuals with greatest need based on health, socioeconomic and lifestyle risk factors are the most likely to claim CD items.

Socioeconomic inequalities in the diffusion of health technology: Uptake of coronary procedures as an example.

This paper examines socioeconomic lags in the diffusion of high technology health care, focusing on the diffusion of coronary procedures in people with ischaemic heart disease. Using linked hospital and mortality data, we studied patients admitted to Western Australian hospitals with a first admission for acute myocardial infarction between 1989 and 2003 (n = 27,209). An outcome event was the receipt, within a year, of a coronary procedure-angiography, angioplasty and/or coronary artery bypass surgery (CABG). Socioeconomic status (SES) was assigned to each individual using the SEIFA Index of Disadvantage. Cox regression was used to model the association between SES and procedure rates in five consecutive three-year time periods. Angiography and CABG showed socioeconomic lags in diffusion, with rates peaking earlier in higher SES patients, such that the inequality patterns were consistent with the inverse equity hypothesis. The evidence for a lag in diffusion for angioplasty was weaker. Overall, that there is some evidence for a lag in diffusion of health technology indicates that it is essential to consider trends over time when examining the equity impact of health technologies.

Universal health care no guarantee of equity: comparison of socioeconomic inequalities in the receipt of coronary procedures in patients with acute myocardial infarction and angina.

BACKGROUND: In Australia there is a socioeconomic gradient in morbidity and mortality favouring socioeconomically advantaged people, much of which is accounted for by ischaemic heart disease. This study examines if Australia's universal health care system, with its mixed public/private funding and delivery model, may actually perpetuate this inequity. We do this by quantifying and comparing socioeconomic inequalities in the receipt of coronary procedures in patients with acute myocardial infarction (AMI) and patients with angina. METHODS: Using linked hospital and mortality data, we followed patients admitted to Western Australian hospitals with a first admission for AMI (n = 5539) or angina (n = 7401) in 2001-2003. An outcome event was the receipt, within a year, of a coronary procedure-angiography, angioplasty and/or coronary artery bypass surgery (CABG). Socioeconomic status was assigned to each individual using an area-based measure, the SEIFA Index of Disadvantage. Multivariable proportional hazards regression was used to model the association between socioeconomic status and procedure rates, allowing for censoring and adjustment of multiple covariates. Mediating models examined the effect of private health insurance. RESULTS: In the AMI patient cohort, socioeconomic gradients were not evident except that disadvantaged women were more likely than advantaged women to undergo CABG. In contrast, in the angina patient group there were clear socioeconomic gradients for all procedures, favouring more advantaged patients. Compared with patients in the most disadvantaged quintile of socioeconomic status, patients in the least disadvantaged quintile were 11% (1-21%) more likely to receive angiography, 52% (29-80%) more likely to undergo angioplasty and 30% (3-55%) more likely to undergo CABG. Private health insurance explained some of the socioeconomic variation in rates. CONCLUSIONS: Australia's universal health care system does not guarantee equity in the receipt of high technology health care for patients with ischaemic heart disease. While such a system might ensure equity for patients with AMI, where guidelines for treatment are relatively well established, this is not the case for angina patients, where health care may be less urgent and more discretionary.