According to previous reports, the risk of disability as a result of diabetes varies from none to double. Disability is an important measure of health and an estimate of the risk of disability as a result of diabetes is crucial in view of the global diabetes epidemic. We did a systematic review and meta-analysis to estimate this risk. We searched Ovid, Medline, Embase, Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature up to Aug 8, 2012. We included studies of adults that compared the risk of disability-as measured by activities of daily living (ADL), instrumental activities of daily living (IADL), or mobility-in people with and without any type of diabetes. We excluded studies of subpopulations with specific illnesses or of people in nursing homes. From the studies, we recorded population characteristics, how diabetes was diagnosed (by doctor or self-reported), domain and definition of disability, and risk estimates for disability. We calculated pooled estimates by disability type and type of risk estimate (odds ratio [OR] or risk ratio [RR]). Our systematic review returned 3224 results, from which 26 studies were included in our meta-analyses. Diabetes increased the risk of mobility disability (15 studies; OR 1.71, 95% CI 1.53-1.91; RR 1.51, 95% CI 1.38-1.64), of IADL disability (ten studies; OR 1.65, 95% CI 1.55-1.74), and of ADL disability (16 studies; OR 1.82, 95% CI 1.63-2.04; RR 1.82, 95% CI 1.40-2.36). Diabetes is associated with a strong increase in the risk of physical disability. Efforts to promote healthy ageing should account for this risk through prevention and management of diabetes. Monash University, Baker IDI Bright Sparks Foundation, Australian Postgraduate Award, VicHealth, National Health and Medical Research Council, Australian Research Council, Victorian Government.

Controversy exists about the differences in air pollution exposure and inhalation dose between mode of transport. We aimed to review air pollution exposure and inhaled dose according to mode of transport and pollutant and their effect in terms of years of life expectancy (YLE). In this systematic review, we searched ten online databases from inception to April 13, 2016, without language or temporal restrictions, for cohort, cross-sectional, and experimental studies that compared exposure to carbon monoxide, black carbon, nitrogen dioxide, and fine and coarse particles in active commuters (pedestrian or cyclist) and commuters using motorised transport (car, motorcycle, bus, or massive motorised transport [MMT-ie, train, subway, or metro]). We excluded studies that measured air pollution exposure exclusively with biomarkers or on the basis of simulated data, reviews, comments, consensuses, editorials, guidelines, in-vitro studies, meta-analyses, ecological studies, and protocols. We extracted average exposure and commuting time per mode of transport and pollutant to calculate inhaled doses. We calculated exposure and inhaled dose ratios using active commuters as the reference and summarised them with medians and IQRs. We also calculated differences in YLE due to fine particle inhaled dose and physical activity. We identified 4037 studies, of which 39 were included in the systematic review. Overall, car commuters had higher exposure to all pollutants than did active commuters in 30 (71%) of 42 comparisons (median ratio 1·22 [IQR 0·90-1·76]), followed by those who commuted by bus in 57 (52%) of 109 (1·0 [0·79-1·41]), by motorcycle in 16 (50%) of 32 (0·99 [0·86-1·38]), by a car with controlled ventilation settings in 39 (45%) of 86 (0·95 [0·66-1·54]), and by MMT in 21 (38%) of 55 (0·67 [0·49-1·13]). Overall, active commuters had higher inhalation doses than did commuters using motorised transport (median ratio car with controlled ventilation settings 0·16 [0·10-0·28]; car 0·22 [0·15-0·30]; motorcycle 0·38 [0·26-0·78]; MMT 0·49 [0·34-0·81]; bus 0·72 [IQR 0·50-0·99]). Commuters using motorised transport lost up to 1 year in YLE more than did cyclists. Proximity to traffic and high air interchange increased the exposure to air pollution of commuters using motorised transport. Larger inhalation rates and commuting time increased inhaled dose among active commuters. Benefits of active commuting from physical activity are larger than the risk from an increased inhaled dose of fine particles. Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), National Health and Medical Research Council, Nestlé Nutrition (Nestec), Metagenics, and AXA.

Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Addressing social determinants of health (SDoH) may be the next forefront of reducing the enormous burden of CVD. SDoH can be defined as any social, economic, or environmental factor that influences a health outcome. Comprehensive evidence of the role of SDoH in CVD is lacking, nevertheless. This umbrella review aims to give a comprehensive overview of the role of SDoH in CVD. We searched systematic reviews (with or without meta-analyses) using 8 databases and included review reference lists. Four themes (economic circumstances, social/community context, early childhood development, and neighbourhood/built environment) and health literacy in the health/health care theme were considered. Seventy reviews were eligible. Despite the quality of the included reviews being low or critically low, there was consistent evidence that factors relating to economic circumstances and early childhood development themes were associated with an increased risk of CVD and CVD mortality. We also found evidence that factors in the social/community context and neighbourhood/built environment themes, such as social isolation, fewer social roles, loneliness, discrimination, ethnicity, neighborhood socioeconomic status, violence, and environmental attributes, had a role in CVD. SDoH factors without (or with minimal) evidence synthesis for CVD were also identified. In sum, this umbrella review offers evidence that SDoH, especially economic circumstance and early childhood development, play a significant role in CVD. This calls for the strengthening of nonmedical interventions that address multiple factors simultaneously and the inclusion of SDoH in future CVD risk prediction models. Registration URL: https://www.crd.york.ac.uk/prospero/; Unique identifier: CRD42022346994.

Background: Poor social health is associated with increased risk of cardiovascular disease (CVD). Recent research suggests that different social health domains should be considered separately as the implications for health and possible interventions may differ. Aim: To assess social isolation, low social support and loneliness as predictors of CVD. Methods: Secondary analysis of 11,486 community-dwelling, Australians, aged 70 years and over, free of CVD, dementia, or significant physical disability, from the ASPirin in Reducing Events in the Elderly (ASPREE) trial. Social isolation, social support (Revised Lubben Social Network Scale), and loneliness were assessed as predictors of CVD using Cox proportional-hazard regression. CVD events included fatal CVD, heart failure hospitalization, myocardial infarction and stroke. Analyses were adjusted for established CVD risk factors. Results: Individuals with poor social health were 42 % more likely to develop CVD (p = 0.01) and twice as likely to die from CVD (p = 0.02) over a median 4.5 years follow-up. Interaction effects indicated that poorer social health more strongly predicted CVD in smokers (HR 4.83, p = 0.001, p-interaction = 0.01), major city dwellers (HR 1.94, p < 0.001, p-interaction=0.03), and younger older adults (70-75 years; HR 2.12, p < 0.001, p-interaction = 0.01). Social isolation (HR 1.66, p = 0.04) and low social support (HR 2.05, p = 0.002), but not loneliness (HR 1.4, p = 0.1), predicted incident CVD. All measures of poor social health predicted ischemic stroke (HR 1.73 to 3.16). Conclusions: Among healthy older adults, social isolation and low social support may be more important than loneliness as cardiovascular risk factors. Social health domains should be considered in future CVD risk prediction models.

Self-reported anthropometric data are commonly used to estimate prevalence of obesity in population and community-based studies. We aim to: 1) Determine whether survey participants are able and willing to self-report height and weight; 2) Assess the accuracy of self-reported compared to measured anthropometric data in a community-based sample of young people. Participants (16–29 years) of a behaviour survey, recruited at a Melbourne music festival (January 2011), were asked to self-report height and weight; researchers independently weighed and measured a sub-sample. Body Mass Index was calculated and overweight/obesity classified as ≥25kg/m2. Differences between measured and self-reported values were assessed using paired t-test/Wilcoxon signed ranks test. Accurate report of height and weight were defined as <2cm and <2kg difference between self-report and measured values, respectively. Agreement between classification of overweight/obesity by self-report and measured values was assessed using McNemar’s test. Of 1405 survey participants, 82% of males and 72% of females self-reported their height and weight. Among 67 participants who were also independently measured, self-reported height and weight were significantly less than measured height (p=0.01) and weight (p<0.01) among females, but no differences were detected among males. Overall, 52% accurately self-reported height, 30% under-reported, and 18% over-reported; 34% accurately self-reported weight, 52% under-reported and 13% over-reported. More females (70%) than males (35%) under-reported weight (p=0.01). Prevalence of overweight/obesity was 33% based on self-report data and 39% based on measured data (p=0.16). Self-reported measurements may underestimate weight but accurately identified overweight/obesity in the majority of this sample of young people.