BackgroundThere 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%).MethodsLogistic 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.ResultsPrevalence 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.ConclusionsThese 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.

BackgroundLifestyle risk behaviors are responsible for a large proportion of disease burden worldwide. Behavioral risk factors, such as smoking, poor diet, and physical inactivity, tend to cluster within populations and may have synergistic effects on health. As evidence continues to accumulate on emerging lifestyle risk factors, such as prolonged sitting and unhealthy sleep patterns, incorporating these new risk factors will provide clinically relevant information on combinations of lifestyle risk factors.Methods and FindingsUsing data from a large Australian cohort of middle-aged and older adults, this is the first study to our knowledge to examine a lifestyle risk index incorporating sedentary behavior and sleep in relation to all-cause mortality. Baseline data (February 2006– April 2009) were linked to mortality registration data until June 15, 2014. Smoking, high alcohol intake, poor diet, physical inactivity, prolonged sitting, and unhealthy (short/long) sleep duration were measured by questionnaires and summed into an index score. Cox proportional hazards analysis was used with the index score and each unique risk combination as exposure variables, adjusted for socio-demographic characteristics.During 6 y of follow-up of 231,048 participants for 1,409,591 person-years, 15,635 deaths were registered. Of all participants, 31.2%, 36.9%, 21.4%, and 10.6% reported 0, 1, 2, and 3+ risk factors, respectively. There was a strong relationship between the lifestyle risk index score and all-cause mortality. The index score had good predictive validity (c index = 0.763), and the partial population attributable risk was 31.3%. Out of all 96 possible risk combinations, the 30 most commonly occurring combinations accounted for more than 90% of the participants. Among those, combinations involving physical inactivity, prolonged sitting, and/or long sleep duration and combinations involving smoking and high alcohol intake had the strongest associations with all-cause mortality. Limitations of the study include self-reported and under-specified measures, dichotomized risk scores, lack of long-term patterns of lifestyle behaviors, and lack of cause-specific mortality data.ConclusionsAdherence to healthy lifestyle behaviors could reduce the risk for death from all causes. Specific combinations of lifestyle risk behaviors may be more harmful than others, suggesting synergistic relationships among risk factors.

Objective: Food marketing is linked to childhood obesity through its influence on children's food preferences, purchase requests and food consumption. We aimed to describe the volume and nature of outdoor food advertisements and factors associated with outdoor food advertising in the area surrounding Australian primary schools. Methods: Forty primary schools in Sydney and Wollongong were selected using random sampling within population density and socio‐economic strata. The area within a 500m radius of each school was scanned and advertisements coded according to pre‐defined criteria, including: food or non‐food product advertisement, distance from the school, size and location. Food advertisements were further categorised as core foods, non‐core foods and miscellaneous drinks (tea and coffee). Results: The number of advertisements identified was 9,151, of which 2,286 (25%) were for food. The number of non‐core food advertisements was 1,834, this accounted for 80% of food advertisements. Soft drinks and alcoholic beverages were the food products most commonly advertised around primary schools (24% and 22% of food advertisements, respectively). Non‐core food products were twice as likely to be advertised close to a primary school (95 non‐core food advertisements per km2 within 250 m vs. 46 advertisements per km2 within 250–500 m). Conclusions: The density of non‐core food advertisements within 500 m of primary schools, and the potential for repeated exposure of children to soft drink and alcoholic beverage advertisements in particular, highlights the need for outdoor food marketing policy intervention. Implications: Outdoor advertising is an important food marketing tool that should be considered in future debates on regulation of food marketing to children.

ACTRN12616000661471; Results.

Background : Body mass index (BMI) is an important measure of adiposity. While BMI derived from self‐reported data generally agrees well with that derived from measured values, evidence from Australia is limited, particularly for the elderly. Methods : We compared self‐reported with measured height and weight in a random sample of 608 individuals aged ≥45 from the 45 and Up Study, an Australian population‐based cohort study. We assessed degree of agreement and correlation between measures, and calculated sensitivity and specificity to quantify BMI category misclassification. Results : On average, in males and females respectively, height was overestimated by 1.24cm (95% CI: 0.75–1.72) and 0.59cm (0.26–0.92); weight was underestimated by 1.68kg (–1.99– ‐1.36) and 1.02kg (–1.24– ‐0.80); and BMI based on self‐reported measures was underestimated by 0.90kg/m2 (–1.09– ‐0.70) and 0.60 kg/m2 (–0.75– ‐0.45). Underestimation increased with increasing measured BMI. There were strong correlations between self‐reported and measured height, weight and BMI (r=0.95, 0.99 and 0.95, respectively, p<0.001). While there was excellent agreement between BMI categories from self‐reported and measured data (kappa=0.80), obesity prevalence was underestimated. Findings did not differ substantially between middle‐aged and elderly participants. Conclusions : Self‐reported data on height and weight quantify body size appropriately in middle‐aged and elderly individuals for relative measures, such as quantiles of BMI. However, caution is necessary when reporting on absolute BMI and standard BMI categories, based on self‐reported data, particularly since use of such data is likely to result in underestimation of the prevalence of obesity.

BACKGROUNDThe role of supine positioning after acute stroke in improving cerebral blood flow and the countervailing risk of aspiration pneumonia have led to variation in head positioning in clinical practice. We wanted to determine whether outcomes in patients with acute ischemic stroke could be improved by positioning the patient to be lying flat (i.e., fully supine with the back horizontal and the face upwards) during treatment to increase cerebral perfusion. METHODSIn a pragmatic, cluster-randomized, crossover trial conducted in nine countries, we assigned 11,093 patients with acute stroke (85% of the strokes were ischemic) to receive care in either a lying-flat position or a sitting-up position with the head elevated to at least 30 degrees, according to the randomization assignment of the hospital to which they were admitted; the designated position was initiated soon after hospital admission and was maintained for 24 hours. The primary outcome was degree of disability at 90 days, as assessed with the use of the modified Rankin scale (scores range from 0 to 6, with higher scores indicating greater disability and a score of 6 indicating death). RESULTSThe median interval between the onset of stroke symptoms and the initiation of the assigned position was 14 hours (interquartile range, 5 to 35). Patients in the lying-flat group were less likely than patients in the sitting-up group to maintain the position for 24 hours (87% vs. 95%, P<0.001). In a proportional-odds model, there was no significant shift in the distribution of 90-day disability outcomes on the global modified Rankin scale between patients in the lying-flat group and patients in the sitting-up group (unadjusted odds ratio for a difference in the distribution of scores on the modified Rankin scale in the lying-flat group, 1.01; 95% confidence interval, 0.92 to 1.10; P = 0.84). Mortality within 90 days was 7.3% among the patients in the lying-flat group and 7.4% among the patients in the sitting-up group (P = 0.83). There were no significant betweengroup differences in the rates of serious adverse events, including pneumonia. CONCLUSIONSDisability outcomes after acute stroke did not differ significantly between patients assigned to a lying-flat position for 24 hours and patients assigned to a sitting-up position with the head elevated to at least 30 degrees for 24 hours. (Funded by the

BackgroundSedentary behaviour, sleeping, and physical activity are thought to be independently associated with health outcomes but it is unclear whether these associations are due to the direct physiological effects of each behaviour or because, across a finite 24-hour day, engagement in one behavior requires displacement of another. The aim of this study was to examine the replacement effects of sedentary behaviour (total sitting, television/computer screen time combined), sleeping, standing, walking, and moderate-to-vigorous physical activity on all-cause mortality using isotemporal substitution modelling.MethodsLongitudinal analysis (4.22 ± 0 · 9 years follow-up/849,369 person-years) of 201,129 participants of the 45 and Up study aged ≥45 years from New South Wales, Australia.ResultsSeven thousand four hundred and sixty deaths occurred over follow-up. There were beneficial associations for replacing total sitting time with standing (per-hour HR: 95 % CI: 0.95, 0.94–0.96), walking (0.86, 0.81–0.90), moderate-to-vigorous physical activity (0.88, 0.85–0.90), and sleeping in those sleeping ≤ 7 h/day (0.94, 0.90–0.98). Similar associations were noted for replacing screen time. Replacing one hour of walking or moderate-to-vigorous physical activity with any other activity class was associated with an increased mortality risk by 7–18 %. Excluding deaths in the first 24 months of the follow up and restricting analyses to those who were healthy at baseline did not materially change the above observations.ConclusionAlthough replacing sedentary behaviour with walking and moderate-to-vigorous physical activity are associated with the lowest mortality risk, replacements with equal amounts of standing and sleeping (in low sleepers only) are also linked to substantial mortality risk reductions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12966-015-0280-7) contains supplementary material, which is available to authorized users.

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