Emma Gearon

Diabetes and risk of physical disability in adults: a systematic review and meta-analysis

BACKGROUND 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. METHODS 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]). RESULTS 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). INTERPRETATION 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. FUNDING Monash University, Baker IDI Bright Sparks Foundation, Australian Postgraduate Award, VicHealth, National Health and Medical Research Council, Australian Research Council, Victorian Government.

The association between socio-economic position and diet quality in Australian adults

OBJECTIVE We aimed to investigate the association between multiple measures of socio-economic position (SEP) and diet quality, using a diet quality index representing current national dietary guidelines, in the Australian adult population. DESIGN Cross-sectional study. Linear regression analyses were used to estimate the association between indicators of SEP (educational attainment, level of income and area-level disadvantage) and diet quality (measured using the Dietary Guideline Index (DGI)) in the total sample and stratified by sex and age (≤55 years and >55 years). SETTING A large randomly selected sample of the Australian adult population. SUBJECTS Australian adults (n 9296; aged ≥25 years) from the Australian Diabetes, Obesity and Lifestyle Study. RESULTS A higher level of educational attainment and income and a lower level of area-level disadvantage were significantly associated with a higher DGI score, across the gradient of SEP. The association between indicators of SEP and DGI score was consistently stronger among those aged ≤55 years compared with their older counterparts. The most disadvantaged group had a DGI score between 2 and 5 units lower (depending on the marker of SEP) compared with the group with the least disadvantage. CONCLUSIONS A higher level of SEP was consistently associated with a higher level of diet quality for all indicators of SEP examined. In order to reduce socio-economic inequalities in diet quality, healthy eating initiatives need to act across the gradient of socio-economic disadvantage with a proportionate focus on those with greater socio-economic disadvantage.

Prevalence of class-I, class-II and class-III obesity in Australian adults between 1995 and 2011-12

OBJECTIVE To compare the prevalence of class-I, II and III obesity in Australian adults between 1995, 2007-08 and 2011-12. METHODS Prevalence data for adults (aged 18+ years) were sourced from customised data from the nationally representative National Nutrition Survey (1995), the National Health Survey (2007-08), and the Australian Health Survey (2011-12) conducted by the Australian Bureau of Statistics. Obesity classifications were based on measured height and weight (class-I body mass index: 30.0-34.9 kg/m(2), class-II: 35.0-39.9 kg/m(2) and class-III: ≥ 40.0 kg/m(2)). Severe obesity was defined as class-II or class-III obesity. RESULTS Between 1995 and 2011-12, the prevalence of obesity (all classes combined) increased from 19.1% to 27.2%. During this 17 year period, relative increases in class I, II and III obesity were 1.3, 1.7 and 2.2-fold respectively. In 2011-12, the prevalence of class I, II and III obesity was 19.4, 5.9 and 2.0 per cent respectively in men, and 16.1, 6.9 and 4.2 per cent respectively in women. One in every ten people was severely obese, increasing from one in twenty in 1995, and women were disproportionally represented in this population. Obesity prevalence increased with increasing levels of area-level socioeconomic disadvantage, particularly for the more severely obese classes. Severe obesity affected 6.2% and 13.4% in the least and most disadvantaged quintiles respectively. CONCLUSION Over the last two decades, there have been substantial increases in the prevalence of obesity, particularly the more severe levels of obesity. This study highlights high risk groups who warrant targeted weight gain prevention interventions.

Letter to the Editor: Incidence of childhood obesity in the United States

To the Editor: Cunningham et al. (Jan. 30 issue)1 do not provide important information about weight change from kindergarten through eighth grade. It is obvious that children who have the highest body-mass index in kindergarten or at birth would be at highest risk for the subsequent development of obesity by eighth grade. They would have to gain the same or even less weight between kindergarten and eighth grade to cross the arbitrary threshold for obesity. Thus, if every child gained the same amount of weight from kindergarten onward or, for that matter, from birth onward, those who had the highest weights at birth or in kindergarten would ultimately have the highest risk of being overweight or obese. Over time, however, if children who were not overweight in kindergarten gained more weight per year, they would catch up and would have a similar or higher prevalence of obesity.2-5 Studies of obesity in children must include measures of velocity of weight change over time. It is likely that the velocity of weight change may be a more important determinant of health than body-mass index at earlier ages.

A systematic review of the effectiveness of whole-of-community interventions by socioeconomic position

Whole‐of‐community (WOC) interventions have led to modest reductions in population weight gain. Whether they exhibit differential effectiveness by socioeconomic position (SEP) remains unknown. We aimed to summarize evidence of differential effectiveness of WOC interventions by SEP. Electronic databases and grey literature were searched to identify studies that evaluated the effectiveness of a WOC intervention on behavioural change measures, energy balance behaviours and/or anthropometric outcomes according to any measure of SEP. Interventions were assessed for the following characteristics: structural changes to the environment, number of settings the intervention acted in, presence of community engagement and whether equity was considered in its design. Ten studies were included. Nine reported a greater or equal effect among low SEP groups compared with high SEP groups. These studies commonly featured interventions that incorporated structural changes to the environment, acted across more than three settings and/or employed community engagement. Conclusions did not change when excluding low‐quality studies (n = 4). WOC interventions represent an effective and equitable approach for the reduction of population weight. Structural components, a larger number of settings and community engagement were common in equitable WOC interventions and should be considered in the design of future WOC interventions.

Trends in body mass index according to educational attainment for urban Australian adults between 1980 and 2007

Background:We have previously demonstrated that between the years 1980 and 2000, the mean body mass index (BMI) of the urban Australian population increased, with greater increases observed with increasing BMI. The current study aimed to quantify trends over time in BMI according to level of education between 1980 and 2007.Methods:We compared data from the 1980, 1983 and 1989 National Heart Foundation Risk Factor Prevalence Studies, 1995 National Nutrition Survey, 2000 Australian Diabetes, Obesity and Lifestyle Study and the 2007 National Health Survey. For survey comparability, analyses were restricted to urban Australian residents aged 25–64 years. BMI was calculated from measured height and weight. The education variable was dichotomised at completion of secondary school. Four age-standardised BMI indicators were compared over time by sex and education: mean BMI, mean BMI of the top 5% of the BMI distribution, prevalence of obesity (BMI⩾30 kg m−2), prevalence of class II+ obesity (BMI⩾35 kg m−2).Results:Between 1980 and 2007, the mean BMI among men increased by 2.5 and 1.7 kg m−2 for those with low and high education levels, respectively, corresponding to increases in obesity prevalence of 20 (from 12–32%) and 11 (10–21%) %-points. Among women, mean BMI increased by 2.9 and 2.4 kg m−2 for those with low and high education levels, respectively, corresponding to increases in obesity prevalence of 16 (12–28%) and 12 (7–19%) %-points. The prevalence of class II+ obesity among men increased by 9 (1–10%) and 4 (1–5%) %-points for those with low and high education levels, and among women increased by 8 (4–12%) and 4 (2–6%) %-points. Absolute and relative differences between education groups generally increased over time.Conclusions:Educational differences in BMI have persisted among urban Australian adults since 1980 without improvement. Obesity prevention policies will need to be effective in those with greatest socio-economic disadvantage if we are to equitably and effectively address the population burden of obesity and its corollaries.

Age-specific changes in BMI and BMI distribution among Australian adults using cross-sectional surveys from 1980 to 2008

Background:Research efforts have focused mainly on trends in obesity among populations, or changes in mean body mass index (BMI), without consideration of changes in BMI across the BMI spectrum. Examination of age-specific changes in BMI distribution may reveal patterns that are relevant to targeting of interventions.Methods:Using a synthetic cohort approach (which matches members of cross-sectional surveys by birth year) we estimated population representative annual BMI change across two time periods (1980 to 1989 and 1995 to 2008) by age, sex, socioeconomic position and quantiles of BMI. Our study population was a total of 27 349 participants from four nationally representative Australian health surveys; Risk Factor Prevalence Study surveys (1980 and 1989), the 1995 National Nutrition Survey and the 2007/8 National Health Survey.Results:We found greater mean BMI increases in younger people, in those already overweight and in those with lower education. For men, age-specific mean annual BMI change was very similar in the 1980s and the early 2000s (P=0.39), but there was a recent slowing down of annual BMI gain for older women in the 2000s compared with their same-age counterparts in the 1980s (P<0.05). BMI change was not uniform across the BMI distribution, with different patterns by age and sex in different periods. Young adults had much greater BMI gain at higher BMI quantiles, thus adding to the increased right skew in BMI, whereas BMI gain for older populations was more even across the BMI distribution.Conclusions:The synthetic cohort technique provided useful information from serial cross-sectional survey data. The quantification of annual BMI change has contributed to an understanding of the epidemiology of obesity progression and identified key target groups for policy attention—young adults, those who are already overweight and those of lower socioeconomic status.

Trends in the skewness of the body mass index distribution among urban Australian adults, 1980 to 2007

PURPOSE We analyzed the changes in the body mass index (BMI) distribution for urban Australian adults between 1980 and 2007. METHODS We used data from participants of six consecutive Australian nation-wide surveys with measured weight and height between 1980 and 2007. We used quantile regression to estimate mean BMI (for percentiles of BMI) and prevalence of severe obesity, modeled by natural splines in age, date of birth, and survey date. RESULTS Since 1980, the right skew in the BMI distribution for Australian adults has increased greatly for men and women, driven by increases in skew associated with age and birth cohort/period. Between 1980 and 2007, the average 5-year increase in BMI was 1 kg/m(2) (0.8) for the 95th percentile of BMI in women (men). The increase in the median was about a third of this, and for the 10th percentile, a fifth of this. We estimated that for the cohort born in 1960 around 31% of men and women were obese by age 50 years compared with 11% of the 1930 birth cohort. CONCLUSIONS There have been large increases in the right skew of the BMI distribution for urban Australian adults between 1980 and 2007, and birth cohort effects suggests similar increases are likely to continue.

A systematic review and meta-analysis of diabetes and risk of physical disability and functional impairment - protocol

BackgroundDiabetes and increased age are known risk factors for physical disability. With the increasing prevalence of diabetes within our aging population, the future burden of disability is expected to increase. To date, there has not been a pooled estimate of the risk for disability associated with diabetes or its precursor states, impaired glucose tolerance and impaired fasting glucose. We aim to conduct a systematic review and meta-analysis of the association between prediabetes and diabetes with disability, and quantify the risk of association.Methods/designWe will search for relevant studies in Medline via Pubmed, Embase, Cochrane library and Cumulative Index to Nursing and Allied Health Literature (CINAHL), as well as scan reference lists from relevant reviews and publications included in our review. We will review all publications that include studies on human adults (18 years and older) where information is included on diabetes status and at least one measure of disability (Activities of Daily Living (ADL), Instrumental ADL (IADL) or functional/mobility limitation), and where a risk association is available for the relationship between diabetes and/or prediabetes with disability, with reference to those without diabetes.We will further conduct a meta-analysis to pool estimates of the risk of disability associated with prediabetes and diabetes. Sensitivity analysis will be conducted to assess for publication bias and study quality.Findings from this systematic review and meta-analysis will be widely disseminated through discussions with stake-holders, publication in a peer-reviewed journal and conference presentation.

Are low-to-middle-income households experiencing food insecurity in Victoria, Australia? An examination of the Victorian Population Health Survey, 2006?2009.

Food insecurity affects health and wellbeing. Little is known about the relationship between food insecurity across income levels. This study aims to investigate the prevalence and frequency of food insecurity in low-to-middle-income Victorian households over time and identify factors associated with food insecurity in these households. Prevalence and frequency of food insecurity was analysed across household income levels using data from the cross-sectional 2006-09 Victorian Population Health Surveys (VPHS). Respondents were categorised as food insecure, if in the last 12 months they had run out of food and were unable to afford to buy more. Multivariable logistic regression was used to describe factors associated with food insecurity in low-to-middle-income households (A