High body mass index (BMI) is the second leading contributor to Australia's burden of disease and is particularly prevalent among Aboriginal peoples. This paper aims to provide insight into factors relating to obesity among Aboriginal adults and Aboriginal-non-Aboriginal differences.Cross-sectional analysis of data from the 45 and Up Study, comparing obesity (BMI ≥30 kg/m2) prevalence and risk factors among 1515 Aboriginal and 213 301 non-Aboriginal adults in New South Wales. Age-sex-adjusted prevalence ratios (PRs) for obesity by sociodemographic factors, health behaviours and health status were estimated (multivariable log-binomial regression) for Aboriginal and non-Aboriginal participants separately. We quantified the extent to which key factors (physical activity, screen time, education, remoteness, area-level disadvantage) accounted for any excess Aboriginal obesity prevalence.Obesity prevalence was 39% among Aboriginal and 22% among non-Aboriginal participants (PR=1.65, 95% CI 1.55 to 1.76). Risk factors for obesity were generally similar for Aboriginal and non-Aboriginal participants and included individual-level and area-level disadvantage, physical inactivity, and poor physical and mental health, with steeper gradients observed among non-Aboriginal participants for some factors (Pinteraction <0.05). Many risk factors were more common among Aboriginal versus non-Aboriginal participants; key factors accounted for >40% of the excess Aboriginal obesity prevalence.A substantial proportion of the excess obesity prevalence among Aboriginal versus non-Aboriginal participants was explained by physical activity, screen time, education, remoteness and area-level disadvantage. Socioeconomic and health behaviour factors are potential targets for promoting healthy BMI, but these must be considered within the context of upstream social and cultural factors. Adults with health needs and disability require particular attention.
To examine the effect of height, weight and body mass index (BMI) on the risk of hip and knee replacement in middle-aged women.In a prospective cohort study 490 532 women aged 50-69 yrs who were recruited in the UK in 1996-2001 were followed over 2.9 yrs for incident primary hip and knee replacements.Height, weight and BMI were all associated with the risk of hip and knee replacement. Comparing the tallest group (>or=170 cm) with the shortest (<155 cm) the relative risks were 1.90 (95%CI 1.55-2.32) for hip replacement and 1.55 (95%CI 1.19-2.00) for knee replacement. Comparing the heaviest group (>or=75 kg) with the lightest (<60 kg) the relative risks of hip and knee replacement were 2.37 (95%CI 2.04-2.75) and 9.71 (95%CI 7.39-12.77), respectively. Comparing obese women (BMI >or= 30 kg/m(2)) to women with a BMI < 22.5 kg/m(2), the relative risks for hip and knee replacement were 2.47 (95%CI 2.11-2.89) and 10.51 (95%CI 7.85-14.08), respectively. These effects did not vary according to age, education, alcohol and tobacco consumption, or with use of hormonal therapies. Currently, an estimated 27% of hip replacements and 69% of knee replacements in middle-aged women in the UK are attributable to obesity.In middle-aged women, the risk of having a hip or knee replacement increases with both increasing height and increasing BMI. From a clinical perspective, relatively small increases in average BMI among middle-aged women are likely to have a substantial impact on the already increasing rates of joint replacement in the UK.
Abstract Background Health surveys are commonly somewhat non-representative of their target population, potentially limiting the generalisability of prevalence estimates for health/behaviour characteristics and disease to the population. To reduce bias, weighting methods have been developed, though few studies have validated weighted survey estimates against generally accepted high-quality independent population benchmark estimates. Methods We applied post-stratification and raking methods to the Australian 45 and Up Study using Census data and compared the resulting prevalence of characteristics to accepted population benchmark estimates and separately, the incidence rates of lung, colorectal, breast and prostate cancer to whole-of-population estimates using Standardised Incidence Ratios (SIRs). Results The differences between 45 and Up Study and population benchmark estimates narrowed following sufficiently-informed raking, e.g. 13.6% unweighted prevalence of self-reported fair/poor overall health, compared to 17.0% after raking and 17.9% from a population benchmark estimate. Raking also improved generalisability of cancer incidence estimates. For example, unweighted 45 and Up Study versus whole-of-population SIRs were 0.700 (95%CI:0.574–0.848) for male lung cancer and 1.098 (95%CI:1.002–1.204) for prostate cancer, while estimated SIRs after sufficiently-informed raking were 0.828 (95%CI:0.684–0.998) and 1.019 (95%CI:0.926–1.121), respectively. Conclusion Raking may be a useful tool for improving the generalisability of exposure prevalence and disease incidence from surveys to the population.
There is little empirical evidence regarding the generalisability of relative risk estimates from studies which have relatively low response rates or are of limited representativeness. The aim of this study was to investigate variation in exposure-outcome relationships in studies of the same population with different response rates and designs by comparing estimates from the 45 and Up Study, a population-based cohort study (self-administered postal questionnaire, response rate 18%), and the New South Wales Population Health Survey (PHS) (computer-assisted telephone interview, response rate ~60%). Logistic regression analysis of questionnaire data from 45 and Up Study participants (n = 101,812) and 2006/2007 PHS participants (n = 14,796) was used to calculate prevalence estimates and odds ratios (ORs) for comparable variables, adjusting for age, sex and remoteness. ORs were compared using Wald tests modelling each study separately, with and without sampling weights. Prevalence of some outcomes (smoking, private health insurance, diabetes, hypertension, asthma) varied between the two studies. For highly comparable questionnaire items, exposure-outcome relationship patterns were almost identical between the studies and ORs for eight of the ten relationships examined did not differ significantly. For questionnaire items that were only moderately comparable, the nature of the observed relationships did not differ materially between the two studies, although many ORs differed significantly. These findings show that for a broad range of risk factors, two studies of the same population with varying response rate, sampling frame and mode of questionnaire administration yielded consistent estimates of exposure-outcome relationships. However, ORs varied between the studies where they did not use identical questionnaire items.
Data on the expected effectiveness of a formal cardiovascular risk screening program are needed Population-based screening programs for early disease detection are important for preventing morbidity, disability, and premature death. Australia has five structured population-based health screening programs for cancer and for newborn conditions.1 Australia's current guidelines for cardiovascular disease (CVD) prevention recommend risk assessment for the general population aged 45–74 years using a validated risk equation.2 Yet, recent data show that less than 50% of eligible Australians have relevant risk factor data recorded in primary care to enable risk assessment,3 and there are huge shortfalls and inequities in treatment for individuals at high risk.4-6 Although enhancement of chronic disease risk assessment is identified as a priority in the 2021 Australian National Preventive Health Strategy,7 no formal structured population screening programs are currently in place for CVD or related chronic diseases, such as chronic kidney disease (CKD) and diabetes. The Population Based Screening Framework sets out criteria to inform decision making on screening programs.1 We outline the evidence and data gaps for a formal CVD risk screening program in Australia, including elements relating to diabetes and CKD, against key criteria of the Framework (Box). CVD is a leading cause of death and morbidity in Australia and globally.8 In 2019, CVD accounted for a quarter of deaths in Australia9 and is estimated to cost the Australian economy around $5 billion annually.10 Around 80% of CVD events are preventable through early detection of risk and treatment.11, 12 CVD typically develops slowly over many decades before acute events occur. The risk factors for CVD, many of which are shared with diabetes and CKD, are well established and there is direct evidence that addressing these factors leads to a reduced probability of developing CVD. A range of predictive scores are available to quantify an individual's future risk of experiencing CVD events, including myocardial infarction, stroke, and death from CVD. These risk equations can be used in asymptomatic individuals. There are also measures of atherosclerosis for subclinical disease detection including coronary artery calcium scoring, intima-media thickness measurement, and ankle brachial index,13 but their validity varies and these measures are not broadly recommended in Australia.2 CVD risk can be assessed in primary care settings using predictive equations with information on risk factors, including age, sex, smoking, diabetes, blood pressure and cholesterol. The Framingham risk equation, recommended for use in Australia's soon to be updated 2012 guidelines,2 has been validated in several populations, including Australia.14 CVD risk assessment is non-invasive and considered safe and acceptable, although it may raise anxiety in some patients. In certain circumstances, additional testing with coronary calcium scoring may also be used to target preventive treatments. Sensitivity and specificity measures rely on being able to dichotomise outcomes based on people truly having or not having a disease and this being reflected in the screening test. Rather than diagnosing CVD, absolute CVD risk assessment quantifies the likelihood that an individual will experience a primary CVD event in given period of time. People above a specific threshold are considered at high risk and may be offered treatment. Risk treatment thresholds can change over time; if risk thresholds decrease (as has been typically observed around the world), then more people would be treated and more CVD events would be prevented. Thus, sensitivity and specificity are difficult to determine for absolute CVD risk assessment. In terms of the population that would be potentially treated, around 11.2% (95% CI, 10.2–12.2%) of the Australian population aged 45–74 years were estimated to be at high risk of a first time CVD event (> 15% risk over five years) in 2012.4 For comparison, 11% of women are recalled after a first mammogram as part of the Australian national BreastScreen program.15 The follow-up care of patients identified at high risk of CVD is embedded in Australian primary care and may include referral to allied health professionals and other specialists, further diagnostic testing and pharmacotherapy. Risk assessment usually occurs in primary care with general practitioners and practice nurses well equipped to conduct the screening activity and associated follow-up. Equity of access to medicines prescribed for the management of high CVD risk is through subsidy under the Pharmaceutical Benefits Scheme (PBS). Evidence-based guidelines for the assessment of CVD risk are available, and the Medicare Benefits Schedule currently supports this activity via items 699 and 177. Preventive treatments for those at high risk of developing CVD are cost-effective, safe, widely available, and acceptable. Evidence from large-scale randomised trials show that lipid- and blood pressure-lowering therapies reduce the risk of CVD events and all-cause mortality by around 25%.16, 17 Lipid- and blood pressure-lowering therapies are listed as a cost-effective intervention for preventing chronic disease in the population in both the Australian Assessing Cost-Effectiveness in Prevention (ACE-Prevention) study18 and the World Health Organization's "Best Buy" interventions.19 Treating CVD risk can also help tackle chronic diseases such as CKD, diabetes and dementia. Statins and blood pressure-lowering medications are readily available and subsidised through the PBS. Acceptability studies that have looked at patient preferences around statins found that people were more worried about clinical outcomes such as myocardial infarction and stroke than potential adverse effects of treatment.20 Like many conditions, including cancer, the disease processes underpinning CVD operate on a continuum, with atherosclerosis typically starting many years before CVD is diagnosed. CVD risk assessment involves using a combination of a person's observable risk factors to identify individuals most likely to have a future event, generally within five to ten years. CVD events will still occur in people assessed as low risk, but treating those identified as high risk is international best practice and more effective than treating individual risk factors, such as high blood pressure. Due to the imperfect nature of risk assessment and the long subclinical disease period, RCTs assessing the clinical impact of CVD risk assessment would need to be large-scale and long term to detect changes in CVD outcomes. A systematic review of systematic reviews found little evidence to support the clinical effectiveness of CVD risk assessment,21 although small reductions in individual risk factor levels (smoking, systolic blood pressure, and cholesterol)21 and predicted risk level22 have been found. Overall, studies have generally been of poor quality, with short follow-up periods (maximum 18 months), and have not assessed CVD events and mortality.21 Absolute CVD risk assessment and treatment meets three, and partly meets a further one, of the five key criteria for disease screening programs in Australia. The key evidence gap for supporting structured population CVD risk screening in Australia is a lack of RCT evidence on the effectiveness of screening programs in reducing CVD events and mortality. However, RCTs of CVD risk screening programs would need to be large-scale and long term to be sufficiently powered to detect a change in clinical outcomes. Other data need to be considered in absence of RCT data for this criterion. There is precedence for this: cervical cancer screening in Australia was recommended based on the effectiveness of the individual components of screening and prevention, despite lacking RCT data on the screening program. A formal CVD risk screening program is likely to reduce the burden of CVD across the population, but we currently lack data to support this. This evidence gap could be bridged with models that combine high quality, large-scale data on components of CVD risk assessment and prevention to assess the expected impact of population-wide screening. Similar modelling provided the evidence to underpin changes in bowel and cervical cancer screening.25, 26 Such models are lacking for CVD in Australia but are currently being developed. In the meantime, interventions that target chronic disease risk factors across the population, and improving systems for embedding CVD risk assessment, management and follow-up within primary care are crucial for continued prevention of CVD in Australia. This work was supported by an Ian Potter Foundation Public Research Project Grant (Ref. 31110715). Ellie Paige was supported by a Postdoctoral Fellowship (Ref. 102131) from the National Heart Foundation of Australia (2018–2022). Emily Banks is supported by the National Health and Medical Research Council of Australia (Ref. 1136128). The funders played no role in the planning or writing of this publication. Emma Lonsdale left her role as Executive Officer at the Australian Chronic Disease Prevention Alliance in October 2022. Open access publishing facilitated by Australian National University, as part of the Wiley - Australian National University agreement via the Council of Australian University Librarians. Natalie Raffoul receives speaker fees from Amgen and Novartis. Not commissioned; externally peer reviewed.
Objectives: We aimed to investigate antidepressant use, including the class of antidepressant, in mid-age and older Australians according to sociodemographic, lifestyle and physical and mental health-related factors. Methods: Baseline questionnaire data on 111,705 concession card holders aged ⩾45 years from the 45 and Up Study—a population-based cohort study from New South Wales, Australia—were linked to administrative pharmaceutical data. Current- and any-antidepressant users were those dispensed medications with Anatomical Therapeutic Chemical classification codes beginning N06A, within ⩽6 months and ⩽19 months before baseline, respectively; non-users had no antidepressants dispensed ⩽19 months before baseline. Multinomial logistic regression was used to calculate adjusted relative risk ratios (aRRRs) for predominantly self-reported factors in relation to antidepressant use. Results: Some 19% of the study population (15% of males and 23% of females) were dispensed at least one antidepressant during the study period; 40% of participants used selective serotonin reuptake inhibitors (SSRIs) only and 32% used tricyclic antidepressants (TCAs) only. Current antidepressant use was markedly higher in those reporting: severe versus no physical impairment (aRRR 3.86(95%CI 3.67–4.06)); fair/poor versus excellent/very good self-rated health (4.04(3.83–4.25)); high/very high versus low psychological distress (7.22(6.81–7.66)); ever- versus never-diagnosis of depression by a doctor (18.85(17.95–19.79)); low-dose antipsychotic use versus no antipsychotic use (12.26(9.85–15.27)); and dispensing of ⩾10 versus <5 other medications (5.97(5.62–6.34)). Sociodemographic and lifestyle factors were also associated with use, although to a lesser extent. Females, older people, those with lower education and those with poorer health were more likely to be current antidepressant users than non-users and were also more likely to use TCAs-only versus SSRIs-only. Conclusions: Use of antidepressants is substantially higher in those with physical ill-health and in those reporting a range of adverse mental health measures. In addition, sociodemographic factors, including sex, age and education were also associated with antidepressant use and the class of antidepressant used.
Correction to Banks E, Jorm L, Lujic S, Rogers K. Health, ageing and private health insurance: baseline results from the 45 and Up Study cohort. ANZ Health Policy 2009; 6: 16.
This study described the distribution of healthy body composition among Aboriginal adolescents in Australia aged 10-24 years and examined associations with health behaviours and self-rated health. Data were cross-sectional from the 'Next Generation: Youth Well-being study' baseline (N = 1294). We used robust Poisson regression to quantify associations of self-reported health behaviours (physical activity, screen time, sleep, consumption of vegetables, fruit, soft drinks and fast food, and tobacco smoking and alcohol) and self-rated health to healthy body mass index (BMI) and waist/height ratio (WHtR). Overall, 48% of participants had healthy BMI and 64% healthy WHtR, with healthy body composition more common among younger adolescents. Higher physical activity was associated with healthy body composition (5-7 days last week vs none; adjusted prevalence ratio (aPR) healthy BMI 1.31 [95% CI 1.05-1.64], and healthy WHtR 1.30 [1.10-1.54]), as was recommended sleep duration (vs not; aPR healthy BMI 1.56 [1.19-2.05], and healthy WHtR 1.37 [1.13-1.67]). There was a trend for higher proportion of healthy body composition with more frequent fast food consumption. Healthy body composition was also associated with higher self-rated health ('very good/excellent' vs 'poor/fair'; aPR healthy BMI 1.87 [1.45-2.42], and healthy WHtR 1.71 [1.40-2.10]). Culturally appropriate community health interventions with a focus on physical activity and sleep may hold promise for improving body composition among Aboriginal adolescents.
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