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BACKGROUND: We quantified the individual and joint contribution of contemporaneous causal behavioural exposures on the future burden of oesophageal and stomach cancers and their subtypes and assessed whether these burdens differ between population groups in Australia, as such estimates are currently lacking. METHODS: We combined hazard ratios from seven pooled Australian cohorts (N = 367,058) linked to national cancer and death registries with exposure prevalence from the 2017-2018 National Health Survey to estimate Population Attributable Fractions (PAFs) with 95% confidence intervals (CIs), accounting for competing risk of death. RESULTS: Current and past smoking explain 35.2% (95% CI = 11.7-52.4%), current alcohol consumption exceeding three drinks/day 15.7% (95% CI = 0.9-28.4%), and these exposures jointly 41.4% (95% CI = 19.8-57.3%) of oesophageal squamous cell carcinomas in Australia. Current and past smoking contribute 38.2% (95% CI = 9.4-57.9%), obesity 27.0% (95% CI = 0.6-46.4%), and these exposures jointly 54.4% (95% CI = 25.3-72.1%) of oesophageal adenocarcinomas. Overweight and obesity explain 36.1% (95% CI = 9.1-55.1%), current and past smoking 24.2% (95% CI = 4.2-40.0%), and these exposures jointly 51.2% (95% CI = 26.3-67.8%) of stomach cardia cancers. Several population groups had a significantly higher smoking-attributable oesophageal cancer burden, including men and those consuming excessive alcohol. CONCLUSIONS: Smoking is the leading preventable behavioural cause of oesophageal cancers and overweight/obesity of stomach cancers.

OBJECTIVE: This study sought to assess whether changes in depressive symptoms, general health, and area-level socio-economic status (SES) were associated to changes over time in waist circumference and body mass index (BMI). METHODS: A total of 2871 adults (18 years or older), living in Adelaide (South Australia), were observed across three waves of data collection spanning ten years, with clinical measures of waist circumference, height and weight. Participants completed the Centre for Epidemiologic Studies Depression (CES-D) and Short Form 36 health questionnaires (SF-36 general health domain). An area-level SES measure, relative location factor, was derived from hedonic regression models using residential property features but blind to location. Growth curve models with latent variables were fitted to data. RESULTS: Waist circumference, BMI and depressive symptoms increased over time. General health and relative location factor decreased. Worsening general health and depressive symptoms predicted worsening waist circumference and BMI trajectories in covariate-adjusted models. Diminishing relative location factor was negatively associated with waist circumference and BMI trajectories in unadjusted models only. CONCLUSIONS: Worsening depressive symptoms and general health predict increasing adiposity and suggest the development of unhealthful adiposity might be prevented by attention to negative changes in mental health and overall general health.

Presumed pathways from environments to cardiometabolic risk largely implicate health behaviour although mental health may play a role. Few studies assess relationships between these factors. This study estimated associations between area socioeconomic status (SES), mental health, diet, physical activity, and 10-year change in glycosylated haemoglobin (HbA1c), comparing two proposed path structures: 1) mental health and behaviour functioning as parallel mediators between area SES and HbA1c; and 2) a sequential structure where mental health influences behaviour and consequently HbA1c. Three waves (10 years) of population-based biomedical cohort data were spatially linked to census data based on participant residential address. Area SES was expressed at baseline using an established index (SEIFA-IEO). Individual behavioural and mental health information (Wave 2) included diet (fruit and vegetable servings per day), physical activity (meets/does not meet recommendations), and the mental health component score of the 36-item Short Form Health Survey. HbA1c was measured at each wave. Latent variable growth models with a structural equation modelling approach estimated associations within both parallel and sequential path structures. Models were adjusted for age, sex, employment status, marital status, education, and smoking. The sequential path model best fit the data. HbA1c worsened over time. Greater area SES was statistically significantly associated with greater fruit intake, meeting physical activity recommendations, and had a protective effect against increasing HbA1c directly and indirectly through physical activity behaviour. Positive mental health was statistically significantly associated with greater fruit and vegetable intakes and was indirectly protective against increasing HbA1c through physical activity. Greater SES was protective against increasing HbA1c. This relationship was partially mediated by physical activity but not diet. A protective effect of men

Residential areas may shape health, yet few studies are longitudinal or concurrently test relationships between multiple residential features and health. This longitudinal study concurrently assessed the contributions of multiple environmental features to 10-year change in clinically measured body mass index (BMI) and waist circumference (WC). Longitudinal data for adults (18+ years of age, n = 2253) from the north-west of Adelaide, Australia were linked to built environment measures representing the physical activity and food environment (expressed for residence-based 1600 m road-network buffers) and area education. Associations were concurrently estimated using latent growth models. In models including all environmental exposure measures, area education was associated with change in BMI and WC (protective effects). Dwelling density was associated with worsening BMI and WC but also highly correlated with area education and moderately correlated with count of fast food outlets. Public open space (POS) area was associated with worsening WC. Intersection density, land use mix, greenness, and a retail food environment index were not associated with change in BMI or WC. This study found greater dwelling density and POS area exacerbated increases in BMI and WC. Greater area education was protective against worsening body size. Interventions should consider dwelling density and POS, and target areas with low SES.

OBJECTIVE: To estimate the burden of pancreatic cancer in Australia attributable to modifiable exposures, particularly smoking. DESIGN: Prospective pooled cohort study. SETTING, PARTICIPANTS: Seven prospective Australian study cohorts (total sample size, 365 084 adults); participant data linked to national registries to identify cases of pancreatic cancer and deaths. MAIN OUTCOME MEASURES: Associations between exposures and incidence of pancreatic cancer, estimated in a proportional hazards model, adjusted for age, sex, study, and other exposures; future burden of pancreatic cancer avoidable by changes in exposure estimated as population attributable fractions (PAFs) for whole population and for specific population subgroups with a method accounting for competing risk of death. RESULTS: There were 604 incident cases of pancreatic cancer during the first 10 years of follow-up. Current and recent smoking explained 21.7% (95% CI, 13.8-28.9%) and current smoking alone explained 15.3% (95% CI, 8.6-22.6%) of future pancreatic cancer burden. This proportion of the burden would be avoidable over 25 years were current smokers to quit and there were no new smokers. The burden attributable to current smoking is greater for men (23.9%; 95% CI, 13.3-33.3%) than for women (7.2%; 95% CI, -0.4% to 14.2%; P = 0.007) and for those under 65 (19.0%; 95% CI, 8.1-28.6%) than for older people (6.6%; 95% CI, 1.9-11.1%; P = 0.030). There were no independent relationships between body mass index or alcohol consumption and pancreatic cancer. CONCLUSIONS: Strategies that reduce the uptake of smoking and encourage current smokers to quit could substantially reduce the future incidence of pancreatic cancer in Australia, particularly among men.

Estimates of the future breast cancer burden preventable through modifications to current behaviours are lacking. We assessed the effect of individual and joint behaviour modifications on breast cancer burden for premenopausal and postmenopausal Australian women, and whether effects differed between population subgroups. We linked pooled data from six Australian cohort studies (n = 214,536) to national cancer and death registries, and estimated the strength of the associations between behaviours causally related to cancer incidence and death using adjusted proportional hazards models. We estimated exposure prevalence from representative health surveys. We combined these estimates to calculate Population Attributable Fractions (PAFs) with 95% confidence intervals (CIs), and compared PAFs for population subgroups. During the first 10 years follow-up, there were 640 incident breast cancers for premenopausal women, 2,632 for postmenopausal women, and 8,761 deaths from any cause. Of future breast cancers for premenopausal women, any regular alcohol consumption explains 12.6% (CI = 4.3-20.2%), current use of oral contraceptives for ≥5 years 7.1% (CI = 0.3-13.5%), and these factors combined 18.8% (CI = 9.1-27.4%). Of future breast cancers for postmenopausal women, overweight or obesity (BMI ≥25 kg/m2 ) explains 12.8% (CI = 7.8-17.5%), current use of menopausal hormone therapy (MHT) 6.9% (CI = 4.8-8.9%), any regular alcohol consumption 6.6% (CI = 1.5-11.4%), and these factors combined 24.2% (CI = 17.6-30.3%). The MHT-related postmenopausal breast cancer burden varied by body fatness, alcohol consumption and socio-economic status, the body fatness-related postmenopausal breast cancer burden by alcohol consumption and educational attainment, and the alcohol-related postmenopausal breast cancer burden by breast feeding history. Our results provide evidence to support targeted and population-level cancer control activities.

Background: Perceptions of neighbourhood attributes such as proximity of food retailers that are discordant with objective measures of the same are associated with poor health behaviours and weight gain. Factors associated with discordant perceptions are likely relevant to planning more effective interventions to improve health. Purpose: Analysis of cross-sectional relationships between individual and neighbourhood factors and overestimations of walking distances to local fruit/vegetable retailers (FVR). Methods: Perceived walking times, converted to distances, between participant residences and FVR were compared with objectively-assessed road network distances calculated with a Geographic Information System for n = 1305 adults residing in Adelaide, South Australia. Differences between perceived and objective distances were expressed as 'overestimated' distances and were analysed relative to perceptions consistent with objective distances. Cross-sectional associations were evaluated between individual socio-demographic, health, and area-level characteristics and overestimated distances to FVR using multilevel logistic regression. Results: Agreement between objective and perceived distances between participants' residence and the nearest FVR was only fair (weighted kappa = 0.22). Overestimated distances to FVR were positively associated with mental well-being, and were negatively associated with household income, physical functioning, sense of community, and objective distances to greengrocers. Conclusions: Individual characteristics and features of neighbourhoods were related to overestimated distances to FVR. Sense of connectivity and shared identity may shape more accurate understandings of local resource access, and offer a focal point for tailored public health initiatives that bring people together to achieve improved health behaviour.

BackgroundPrevious estimates of the colorectal cancer (CRC) burden attributed to behaviors have not considered joint effects, competing risk, or population subgroup differences.MethodsWe pooled data from seven prospective Australian cohort studies (n = 367 058) and linked them to national registries to identify CRCs and deaths. We estimated the strength of the associations between behaviors and CRC risk using a parametric piecewise constant hazards model, adjusting for age, sex, study, and other behaviors. Exposure prevalence was estimated from contemporary National Health Surveys. We calculated population attributable fractions for CRC preventable by changes to current behaviors, accounting for competing risk of death and risk factor interdependence. Statistical tests were two-sided.ResultsDuring the first 10 years of follow-up, there were 3471 incident CRCs. Overweight or obesity explained 11.1%, ever smoking explained 10.7% (current smoking 3.9%), and drinking more than two compared with two or fewer alcoholic drinks per day explained 5.8% of the CRC burden. Jointly, these factors were responsible for 24.9% (95% confidence interval [CI] = 19.7% to 29.9%) of the burden, higher for men (36.7%) than women (13.2%, Pdifference < .001). The burden attributed to these factors was also higher for those born in Australia (28.7%) than elsewhere (16.8%, Pdifference = .047). We observed modification of the smoking-attributable burden by alcohol consumption and educational attainment, and modification of the obesity-attributable burden by age group and birthplace.ConclusionsWe produced up-to-date estimates of the future CRC burden attributed to modifiable behaviors. We revealed novel differences between men and women, and other high–CRC burden subgroups that could potentially benefit most from programs that support behavioral change and early detection.

Much research has considered the relationship between neighbourhood crime and physical activity, but few studies have assessed clinical outcomes consequent to behaviour, such as cardiometabolic risk. Fewer still have simultaneously assessed perceived and objective measures of crime. Perceptions of crime and actual victimisation vary according to gender; thus, this study sought to assess: 1) correspondence between perceived and objective neighbourhood crime; and 2) gender-specific associations between perceived and reported crime and metabolic syndrome, representing cardiometabolic risk. The indirect effect of neighbourhood crime on metabolic syndrome via walking was additionally evaluated. An Australian population-based biomedical cohort study (2004-2007) collected biomedical, socio-demographic, and neighbourhood perceptions data from n = 1,172 urban-dwelling, adults. Area-level reported crime rates were standardised and linked to individual data based on participants' residential location. Correspondence between actual and perceived crime measures was assessed using Pearson correlation coefficients. Cross-sectional associations between crime and metabolic syndrome were analysed using generalised estimating equations regression models accounting for socio-demographic factors and area-level income. Correspondence between perceived and objective crime was small to medium among men and women (r = 0.17 to 0.33). Among men, metabolic syndrome was related to rates of violent (OR = 1.21, 95% CI 1.08-1.35) and total crime (OR = 1.17, 95% CI 1.04-1.32), after accounting for perceived crime. Among women, metabolic syndrome was related to perceived crime (OR = 1.35, 95% CI 1.14-1.60) after accounting for total reported crime. Among women, there were indirect effects of perceived crime and property crime on metabolic syndrome through walking. Results indicate that crime, an adverse social exposure, is linked to clinical health status. Crime rates, and perceptions of crime and s

(1) Background: In clinical trials, dietary magnesium use can improve insomnia symptoms. However, little is known about the association between dietary magnesium consumption and sleep disorder symptoms including daytime falling asleep, sleepiness and snoring at the population level. (2) Methods: We used data from 1487 adults aged 20 and above attending the Jiangsu Nutrition Study. At baseline in 2002, dietary magnesium was assessed by 3-day weighed food records. At follow-up in 2007, sleep disorder symptoms, including daytime falling asleep, sleepiness and snoring at night, were gathered using a sleep questionnaire. (3) Results: The mean intake of magnesium was 332.5 mg/day. In total, 5.3%, 13.2% and 35.7% of the subjects reported daytime falling asleep, daytime sleepiness, and snoring during sleep, respectively. Compared with the lowest quartile of magnesium intake, the highest quartile was associated with decreased likelihood of falling asleep (odds ratio (OR) 0.12 (0.02, 0.57)) in women but not in men after adjusting for demographic, anthropometric, lifestyle factors, hypertension, and overall dietary patterns. No associations were found between dietary magnesium intake and daytime sleepiness nor night snoring in either gender. (4) Conclusions: Dietary magnesium intake may have long-term benefits in reducing the likelihood of daytime falling asleep in women.

Perceived and objective measures of neighbourhood features have shown limited correspondence. Few studies have examined whether discordance between objective measures and individual perceptions of neighbourhood environments relates to individual health. Individuals with mismatched perceptions may benefit from initiatives to improve understandings of resource availability. This study utilised data from n = 1491 adult participants in a biomedical cohort to evaluate cross-sectional associations between measures of access (perceived, objective, and perceived-objective mismatch) to fruit and vegetable retailers (FVR) and public open space (POS), and clinically-measured metabolic syndrome and its component risk factors: central obesity, dyslipidaemia, hypertension and pre-diabetes/diabetes. Access measures included perceived distances from home to the nearest FVR and POS, corresponding objectively-assessed road network distances, and the discordance between perceived and objective distances (overestimated (i.e., mismatched) distances versus matched perceived-objective distances). Individual and neighbourhood measures were spatially joined using a geographic information system. Associations were evaluated using multilevel logistic regression, accounting for individual and area-level covariates. Hypertension was positively associated with perceived distances to FVR (odds ratio (OR) = 1.14, 95% confidence interval (CI) = 1.02, 1.28) and POS (OR = 1.19, 95% CI = 1.05, 1.34), after accounting for covariates and objective distances. Hypertension was positively associated with overestimating distances to FVR (OR = 1.36, 95% CI = 1.02, 1.80). Overestimating distances to POS was positively associated with both hypertension (OR = 1.42, 95% CI = 1.11, 1.83) and dyslipidaemia (OR = 1.25, 95% CI = 1.00, 1.57). Results provide new evidence for specific associations between perceived and overestimated distances from home to nearby resources and cardiometabolic risk factors.

BACKGROUND: Descriptive norms (what other people do) relate to individual-level dietary behaviour and health outcome including overweight and obesity. Descriptive norms vary across residential areas but the impact of spatial variation in norms on individual-level diet and health is poorly understood. This study assessed spatial associations between local descriptive norms for overweight/obesity and insufficient fruit intake (spatially-specific local prevalence), and individual-level dietary intakes (fruit, vegetable and sugary drinks) and 10-year change in body mass index (BMI) and glycosylated haemoglobin (HbA1c). METHODS: HbA1c and BMI were clinically measured three times over 10 years for a population-based adult cohort (n = 4056) in Adelaide, South Australia. Local descriptive norms for both overweight/obesity and insufficient fruit intake specific to each cohort participant were calculated as the prevalence of these factors, constructed from geocoded population surveillance data aggregated for 1600 m road-network buffers centred on cohort participants' residential addresses. Latent growth models estimated the effect of local descriptive norms on dietary behaviours and change in HbA1c and BMI, accounting for spatial clustering and covariates (individual-level age, sex, smoking status, employment and education, and area-level median household income). RESULTS: Local descriptive overweight/obesity norms were associated with individual-level fruit intake (inversely) and sugary drink consumption (positively), and worsening HbA1c and BMI. Spatially-specific local norms for insufficient fruit intake were associated with individual-level fruit intake (inversely) and sugary drink consumption (positively) and worsening HbA1c but not change in BMI. Individual-level fruit and vegetable intakes were not associated with change in HbA1c or BMI. Sugary drink consumption was also not associated with change in HbA1c but rather with increasing BMI. CONCLUSION: Adverse local descr

Purpose To estimate the Australian cancer burden attributable to lifestyle-related risk factors and their combinations using a novel population attributable fraction (PAF) method that accounts for competing risk of death, risk factor interdependence and statistical uncertainty. Participants 365 173 adults from seven Australian cohort studies. We linked pooled harmonised individual participant cohort data with population-based cancer and death registries to estimate exposure-cancer and exposure-death associations. Current Australian exposure prevalence was estimated from representative external sources. To illustrate the utility of the new PAF method, we calculated fractions of cancers causally related to body fatness or both tobacco and alcohol consumption avoidable in the next 10 years by risk factor modifications, comparing them with fractions produced by traditional PAF methods. Findings to date Over 10 years of follow-up, we observed 27 483 incident cancers and 22 078 deaths. Of cancers related to body fatness (n=9258), 13% (95% CI 11% to 16%) could be avoided if those currently overweight or obese had body mass index of 18.5-24.9 kg/m 2. Of cancers causally related to both tobacco and alcohol (n=4283), current or former smoking explains 13% (11% to 16%) and consuming more than two alcoholic drinks per day explains 6% (5% to 8%). The two factors combined explain 16% (13% to 19%): 26% (21% to 30%) in men and 8% (4% to 11%) in women. Corresponding estimates using the traditional PAF method were 20%, 31% and 10%. Our PAF estimates translate to 74 000 avoidable body fatness-related cancers and 40 000 avoidable tobacco-and alcohol-related cancers in Australia over the next 10 years (2017-2026). Traditional PAF methods not accounting for competing risk of death and interdependence of risk factors may overestimate PAFs and avoidable cancers. Future plans We will rank the most important causal factors and their combinations for a spectrum of cancers and inform cancer co

PURPOSE: To examine the association of individual and combined indicators of diabetes control with diabetic retinopathy and diabetic macular edema. MATERIALS AND METHODS: In this clinical, cross-sectional study, 613 adults with type 2 diabetes (372 any diabetic retinopathy; 183 any diabetic macular edema) were examined. Diabetic retinopathy was assessed from fundus photographs; diabetic macular edema from Ocular Coherence Tomography scans; and HbA1c and serum lipid values from fasting blood samples. Poor glucose control was defined as HbA1c≥7%; poor blood pressure control as SBP≥130/DBP≥80; and poor lipid control as total cholesterol:HDL ratio≥4.0. The association of poor glucose control, poor blood pressure control and poor lipid control alone and in combination (poor glucose & blood pressure control; poor glucose & lipid control; poor blood pressure & lipid control; and poor glucose, blood pressure & lipid control) with diabetic retinopathy/diabetic macular edema was examined using multiple logistic regression models. RESULTS: Patients' mean±standard deviation age was 64.9±11.6 years (57% male). In adjusted models, compared to those with good control of all indicators (n = 99, 18.3%), the odds ratio (95% Confidence Interval) of having any diabetic retinopathy was 2.44 (1.34-4.46), 3.75 (1.75-8.07), 4.64 (2.13-10.12) and 2.28 (1.01-5.16) for poor glucose control only; poor glucose & blood pressure control; poor glucose & lipid control; and poor glucose, blood pressure & lipid control, respectively. Correspondingly for diabetic macular edema, they were 3.19 (1.55-6.59); 3.60 (1.58-8.22); 2.76 (1.18-6.44); and 3.01 (1.18-7.67), respectively. Odds were not significantly increased for other indicators. DISCUSSION: Compared to individual indicators of poor diabetes control, risk of diabetic retinopathy and diabetic macular edema increased three to fourfold with a combination of these indicators. Targeting combined diabetes control indicators is important to reduce risk

BACKGROUND: Individual-level health outcomes are shaped by environmental risk conditions. Norms figure prominently in socio-behavioural theories yet spatial variations in health-related norms have rarely been investigated as environmental risk conditions. This study assessed: 1) the contributions of local descriptive norms for overweight/obesity and dietary behaviour to 10-year change in glycosylated haemoglobin (HbA1c), accounting for food resource availability; and 2) whether associations between local descriptive norms and HbA1c were moderated by food resource availability. METHODS: HbA1c, representing cardiometabolic risk, was measured three times over 10 years for a population-based biomedical cohort of adults in Adelaide, South Australia. Residential environmental exposures were defined using 1600 m participant-centred road-network buffers. Local descriptive norms for overweight/obesity and insufficient fruit intake (proportion of residents with BMI ≥ 25 kg/m2 [n = 1890] or fruit intake of <2 serves/day [n = 1945], respectively) were aggregated from responses to a separate geocoded population survey. Fast-food and healthful food resource availability (counts) were extracted from a retail database. Separate sets of multilevel models included different predictors, one local descriptive norm and either fast-food or healthful food resource availability, with area-level education and individual-level covariates (age, sex, employment status, education, marital status, and smoking status). Interactions between local descriptive norms and food resource availability were tested. RESULTS: HbA1c concentration rose over time. Local descriptive norms for overweight/obesity and insufficient fruit intake predicted greater rates of increase in HbA1c. Neither fast-food nor healthful food resource availability were associated with change in HbA1c. Greater healthful food resource availability reduced the rate of increase in HbA1c concentration attributed to the overweight/obesit

Associations between local-area residential features and glycosylated hemoglobin (HbA1c) may be mediated by individual-level health behaviors. Such indirect effects have rarely been tested. This study assessed whether individual-level self-reported physical activity mediated the influence of local-area descriptive norms and objectively expressed walkability on 10-year change in HbA1c. HbA1c was assessed three times for adults in a 10-year population-based biomedical cohort (n = 4056). Local-area norms specific to each participant were calculated, aggregating responses from a separate statewide surveillance survey for 1600 m road-network buffers centered on participant addresses (local prevalence of overweight/obesity (body mass index ≥25 kg/m²) and physical inactivity (<150 min/week)). Separate latent growth models estimated direct and indirect (through physical activity) effects of local-area exposures on change in HbA1c, accounting for spatial clustering and covariates (individual-level age, sex, smoking status, marital status, employment and education, and area-level median household income). HbA1c worsened over time. Local-area norms directly and indirectly predicted worsening HbA1c trajectories. Walkability was directly and indirectly protective of worsening HbA1c. Local-area descriptive norms and walkability influence cardiometabolic risk trajectory through individual-level physical activity. Efforts to reduce population cardiometabolic risk should consider the extent of local-area unhealthful behavioral norms and walkability in tailoring strategies to improve physical activity.

BACKGROUND: macronutrient intake has been found to affect sleep parameters including obstructive sleep apnoea (OSA) in experimental studies, but there is uncertainty at the population level in adults. METHODS: cross-sectional analysis was conducted of participants in the Men Androgen Inflammation Lifestyle Environment and Stress cohort (n = 784, age 35-80 years). Dietary intake was measured by a validated food frequency questionnaire. Self-reported poor sleep quality and daytime sleepiness were measured by questionnaires. Overnight in-home polysomnography (PSG) was conducted among participants with without previously diagnosed OSA. RESULTS: after adjusting for demographic, lifestyle factors, and chronic diseases, the highest quartile of fat intake was positively associated with excessive daytime sleepiness (relative risk ratio (RRR) = 1.78, 95% CI 1.10, 2.89) and apnoea-hypopnoea index (AHI) ≥20, (RRR = 2.98, 95% CI 1.20-7.38). Body mass index mediated the association between fat intake and AHI (30%), but not daytime sleepiness. There were no associations between other intake of macronutrient and sleep outcomes. CONCLUSION: high fat is associated with daytime sleepiness and AHI. Sleep outcomes are generally not assessed in studies investigating the effects of varying macronutrient diets on weight loss. The current result highlights the potential public health significance of doing so.

Understanding how health outcomes are spatially distributed represents a first step in investigating the scale and nature of environmental influences on health and has important implications for statistical power and analytic efficiency. Using Australian and French cohort data, this study aimed to describe and compare the extent of geographic variation, and the implications for analytic efficiency, across geographic units, countries and a range of cardiometabolic parameters (Body Mass Index (BMI) waist circumference, blood pressure, resting heart rate, triglycerides, cholesterol, glucose, HbA1c). Geographic clustering was assessed using Intra-Class Correlation (ICC) coefficients in biomedical cohorts from Adelaide (Australia, n = 3893) and Paris (France, n = 6430) for eight geographic administrative units. The median ICC was 0.01 suggesting 1% of risk factor variance attributable to variation between geographic units. Clustering differed by cardiometabolic parameters, administrative units and countries and was greatest for BMI and resting heart rate in the French sample, HbA1c in the Australian sample, and for smaller geographic units. Analytic inefficiency due to clustering was greatest for geographic units in which participants were nested in fewer, larger geographic units. Differences observed in geographic clustering across risk factors have implications for choice of geographic unit in sampling and analysis, and highlight potential cross-country differences in the distribution, or role, of environmental features related to cardiometabolic health.

This study examines the relationships between area-level socioeconomic position (SEP) and the prevalence and trajectories of metabolic syndrome (MetS) and the count of its constituents (i.e., disturbed glucose and insulin metabolism, abdominal obesity, dyslipidemia, and hypertension). A cohort of 4,056 men and women aged 18+ living in Adelaide, Australia was established in 2000-2003. MetS was ascertained at baseline, four and eight years via clinical examinations. Baseline area-level median household income, percentage of residents with a high school education, and unemployment rate were derived from the 2001 population Census. Three-level random-intercepts logistic and Poisson regression models were performed to estimate the standardized odds ratio (SOR), prevalence risk ratio (SRR), ratio of SORs/SRRs, and (95% confidence interval (CI)). Interaction between area- and individual-level SEP variables was also tested. The odds of having MetS and the count of its constituents increased over time. This increase did not vary according to baseline area-level SEP (ratios of SORs/SRRs ≈ 1; p ≥ 0.42). However, at baseline, after adjustment for individual SEP and health behaviours, median household income (inversely) and unemployment rate (positively) were significantly associated with MetS prevalence (SOR (95%CI) = 0.76 (0.63-0.90), and 1.48 (1.26-1.74), respectively), and the count of its constituents (SRR (95%CI) = 0.96 (0.93-0.99), and 1.06 (1.04-1.09), respectively). The inverse association with area-level education was statistically significant only in participants with less than post high school education (SOR (95%CI) = 0.58 (0.45-0.73), and SRR (95%CI) = 0.91 (0.88-0.94)). Area-level SEP does not predict an elevated trajectory to developing MetS or an elevated count of its constituents. However, at baseline, area-level SEP was inversely associated with prevalence of MetS and the count of its constituents, with the association of area-level education being modified by

BACKGROUND: Indicators of cardiometabolic risk typically include non-clinical factors (e.g., smoking). While the incorporation of non-clinical factors can improve absolute risk prediction, it is impossible to study the contribution of non-clinical factors when they are both predictors and part of the outcome measure. Metabolic syndrome, incorporating only clinical measures, seems a solution yet provides no information on risk severity. The aims of this study were: 1) to construct two continuous clinical indices of cardiometabolic risk (cCICRs), and assess their accuracy in predicting 10-year incident cardiovascular disease and/or type 2 diabetes; and 2) to compare the predictive accuracies of these cCICRs with existing risk indicators that incorporate non-clinical factors (Framingham Risk Scores). METHODS: Data from a population-based biomedical cohort (n = 4056) were used to construct two cCICRs from waist circumference, mean arteriole pressure, fasting glucose, triglycerides and high density lipoprotein: 1) the mean of standardised risk factors (cCICR-Z); and 2) the weighted mean of the two first principal components from principal component analysis (cCICR-PCA). The predictive accuracies of the two cCICRs and the Framingham Risk Scores were assessed and compared using ROC curves. RESULTS: Both cCICRs demonstrated moderate accuracy (AUCs 0.72 - 0.76) in predicting incident cardiovascular disease and/or type 2 diabetes, among men and women. There were no significant differences between the predictive accuracies of the cCICRs and the Framingham Risk Scores. CONCLUSIONS: cCICRs may be useful in research investigating associations between non-clinical factors and health by providing suitable alternatives to current risk indicators which include non-clinical factors.

OBJECTIVES: Cardiovascular (CVD) mortality disparities between rural/regional and urban-dwelling residents of Australia are persistent. Unavailability of biomedical CVD risk factor data has, until now, limited efforts to understand the causes of the disparity. This study aimed to further investigate such disparities. DESIGN: Comparison of (1) CVD risk measures between a regional (Greater Green Triangle Risk Factor Study (GGT RFS, cross-sectional study, 2004-2006) and an urban population (North West Adelaide Health Study (NWAHS, longitudinal cohort study, 2004-2006); (2) Australian Bureau of Statistics (ABS) CVD mortality rates between these and other Australian regions; and (3) ABS CVD mortality rates by an area-level indicator of socioeconomic status, the Index of Relative Socioeconomic Disadvantage (IRSD). SETTING: Greater Green Triangle (GGT, Limestone Coast, Wimmera and Corangamite Shires) of South-Western Victoria and North-West Adelaide (NWA). PARTICIPANTS: 1563 GGT RFS and 3036 NWAHS stage 2 participants (aged 25-74) provided some information (self-administered questionnaire +/- anthropometric and biomedical measurements). PRIMARY AND SECONDARY OUTCOME MEASURES: Age-group specific measures of absolute CVD risk, ABS CVD mortality rates by study group and Australian Standard Geographical Classification (ASGC) region. RESULTS: Few significant differences in CVD risk between the study regions, with mean absolute CVD risk ranging from approximately 1% in the age group 35-39 years to 14% in the age group 70-74 years. [corrected]. Similar mean 2003-2007 (crude) mortality rates in GGT (98, 95% CI 87 to 111), NWA (103, 95% CI 96 to 110) and regional Australia (92, 95% CI 91 to 94). NWA mortality rates exceeded that of other city areas (70, 95% CI 69 to 71). Lower measures of socioeconomic status were associated with worse CVD outcomes regardless of geographic location. CONCLUSIONS: Metropolitan areas do not always have better CVD risk factor profiles and outcomes than

Socioeconomic status is a strong predictor of cardiovascular disease. Pulse pressure, the difference between systolic and diastolic blood pressure, has been identified as an important predictor of cardiovascular risk even after accounting for absolute measures of blood pressure. However, little is known about the social determinants of pulse pressure. The aim of this study was to examine individual- and area-level socioeconomic gradients of pulse pressure in a sample of 2,789 Australian adults. Using data from the North West Adelaide Health Study we estimated the association between pulse pressure and three indices of socioeconomic status (education, income and employment status) at the area and individual level for hypertensive and normotensive participants, using Generalized Estimating Equations. In normotensive individuals, area-level education (estimate: -0.106; 95% CI: -0.172, -0.041) and individual-level income (estimate: -1.204; 95% CI: -2.357, -0.050) and employment status (estimate: -1.971; 95% CI: -2.894, -1.048) were significant predictors of pulse pressure, even after accounting for the use of medication and lifestyle behaviors. In hypertensive individuals, only individual-level measures of socioeconomic status were significant predictors of pulse pressure (education estimate: -2.618; 95% CI: -4.878, -0.357; income estimate: -1.683, 95% CI: -3.743, 0.377; employment estimate: -2.023; 95% CI: -3.721, -0.326). Further research is needed to better understand how individual- and area-level socioeconomic status influences pulse pressure in normotensive and hypertensive individuals.