Grace Joshy

Body-mass index and all-cause mortality: Individual-participant-data meta-analysis of 239 prospective studies in four continents.

Summary Background Overweight and obesity are increasing worldwide. To help assess their relevance to mortality in different populations we conducted individual-participant data meta-analyses of prospective studies of body-mass index (BMI), limiting confounding and reverse causality by restricting analyses to never-smokers and excluding pre-existing disease and the first 5 years of follow-up. Methods Of 10 625 411 participants in Asia, Australia and New Zealand, Europe, and North America from 239 prospective studies (median follow-up 13·7 years, IQR 11·4–14·7), 3 951 455 people in 189 studies were never-smokers without chronic diseases at recruitment who survived 5 years, of whom 385 879 died. The primary analyses are of these deaths, and study, age, and sex adjusted hazard ratios (HRs), relative to BMI 22·5–<25·0 kg/m2. Findings All-cause mortality was minimal at 20·0–25·0 kg/m2 (HR 1·00, 95% CI 0·98–1·02 for BMI 20·0–<22·5 kg/m2; 1·00, 0·99–1·01 for BMI 22·5–<25·0 kg/m2), and increased significantly both just below this range (1·13, 1·09–1·17 for BMI 18·5–<20·0 kg/m2; 1·51, 1·43–1·59 for BMI 15·0–<18·5) and throughout the overweight range (1·07, 1·07–1·08 for BMI 25·0–<27·5 kg/m2; 1·20, 1·18–1·22 for BMI 27·5–<30·0 kg/m2). The HR for obesity grade 1 (BMI 30·0–<35·0 kg/m2) was 1·45, 95% CI 1·41–1·48; the HR for obesity grade 2 (35·0–<40·0 kg/m2) was 1·94, 1·87–2·01; and the HR for obesity grade 3 (40·0–<60·0 kg/m2) was 2·76, 2·60–2·92. For BMI over 25·0 kg/m2, mortality increased approximately log-linearly with BMI; the HR per 5 kg/m2 units higher BMI was 1·39 (1·34–1·43) in Europe, 1·29 (1·26–1·32) in North America, 1·39 (1·34–1·44) in east Asia, and 1·31 (1·27–1·35) in Australia and New Zealand. This HR per 5 kg/m2 units higher BMI (for BMI over 25 kg/m2) was greater in younger than older people (1·52, 95% CI 1·47–1·56, for BMI measured at 35–49 years vs 1·21, 1·17–1·25, for BMI measured at 70–89 years; pheterogeneity<0·0001), greater in men than women (1·51, 1·46–1·56, vs 1·30, 1·26–1·33; pheterogeneity<0·0001), but similar in studies with self-reported and measured BMI. Interpretation The associations of both overweight and obesity with higher all-cause mortality were broadly consistent in four continents. This finding supports strategies to combat the entire spectrum of excess adiposity in many populations. Funding UK Medical Research Council, British Heart Foundation, National Institute for Health Research, US National Institutes of Health.

Erectile dysfunction severity as a risk marker for cardiovascular disease hospitalisation and all-cause mortality: a prospective cohort study.

In a prospective Australian population-based study linking questionnaire data from 2006–2009 with hospitalisation and death data to June 2010 for 95,038 men aged ≥45 years, Banks and colleagues found that more severe erectile dysfunction was associated with higher risk of cardiovascular disease.

Inequalities in bariatric surgery in Australia : findings from 49 364 obese participants in a prospective cohort study

Objectives: To investigate variation, and quantify socioeconomic inequalities, in the uptake of primary bariatric surgery in an obese population.

Body mass index and incident hospitalisation for cardiovascular disease in 158 546 participants from the 45 and Up Study.

Objective:To investigate the relationship between fine gradations in body mass index (BMI) and risk of hospitalisation for different types of cardiovascular disease (CVD).Design, Subjects and Methods:The 45 and Up Study is a large-scale Australian cohort study initiated in 2006. Self-reported data from 158 546 individuals with no history of CVD were linked prospectively to hospitalisation and mortality data. Hazard ratios (HRs) of incident hospitalisation for specific CVD diagnoses in relation to baseline BMI categories were estimated using Cox regression, adjusting for age, sex, region of residence, income, education, smoking, alcohol intake and health insurance status.Results:There were 9594 incident CVD admissions over 583 100 person-years among people with BMI⩾20 kg m−2, including 3096 for ischaemic heart disease (IHD), 1373 for stroke, 411 for peripheral vascular disease (PVD) and 320 for heart failure. The adjusted HR of hospitalisation for all CVD diagnoses combined increased significantly with increasing BMI (P(trend) <0.0001)). The HR of IHD hospitalisation increased by 23% (95% confidence interval (95% CI): 18–27%) per 5 kg m−2 increase in BMI (compared to BMI 20.0–22.49 kg m−2, HR (95% CI) for BMI categories were: 22.5–24.99=1.25 (1.08–1.44); 25–27.49=1.43 (1.24–1.65); 27.5–29.99=1.64 (1.42–1.90); 30–32.49=1.63 (1.39–1.91) and 32.5–50=2.10 (1.79–2.45)). The risk of hospitalisation for heart failure showed a significant, but nonlinear, increase with increasing BMI. No significant increase was seen with above-normal BMI for stroke or PVD. For other specific classifications of CVD, HRs of hospitalisation increased significantly with increasing BMI for: hypertension; angina; acute myocardial infarction; chronic IHD; pulmonary embolism; non-rheumatic aortic valve disorders; atrioventricular and left bundle-branch block; atrial fibrillation and flutter; aortic aneurysm; and phlebitis and thrombophlebitis.Conclusion:The risk of hospitalisation for a wide range of CVD subtypes increases with relatively fine increments in BMI. Obesity prevention strategies are likely to benefit from focusing on bringing down the mean BMI at the population level, in addition to targeting those with a high BMI.

High risk prescribing in older adults: prevalence, clinical and economic implications and potential for intervention at the population level

BackgroundHigh risk prescribing can compromise independent wellbeing and quality of life in older adults. The aims of this project are to determine the prevalence, risk factors, clinical consequences, and costs of high risk prescribing, and to assess the impact of interventions on high risk prescribing in older people.MethodsThe proposed project will utilise data from the 45 and Up Study, a large scale cohort of 267,153 men and women aged 45 and over recruited during 2006–2009 from the state of New South Wales, Australia linked to a range of administrative health datasets. High risk prescribing will be assessed using three indicators: polypharmacy (use of five or more medicines); Beers Criteria (an explicit measure of potentially inappropriate medication use); and Drug Burden Index (a pharmacologic dose-dependent measure of cumulative exposure to anticholinergic and sedative medicines). Individual risk factors from the 45 and Up Study questionnaire, and health system characteristics from health datasets that are associated with the likelihood of high risk prescribing will be identified. The main outcome measures will include hospitalisation (first admission to hospital, total days in hospital, cause-specific hospitalisation); admission to institutionalised care; all-cause mortality, and, where possible, cause-specific mortality. Economic costs to the health care system and implications of high risk prescribing will be also investigated. In addition, changes in high risk prescribing will be evaluated in relation to certain routine medicines-related interventions. The statistical analysis will be conducted using standard pharmaco-epidemiological methods including descriptive analysis, univariate and multivariate regression analysis, controlling for relevant confounding factors, using a number of different approaches.DiscussionThe availability of large-scale data is useful to identify opportunities for improving prescribing, and health in older adults. The size of the 45 and Up Study, along with linkage to health databases provides an important opportunity to investigate the relationship between high risk prescribing and adverse outcomes in a real-world population of older adults.

Prevalence of diabetes in New Zealand general practice: the influence of ethnicity and social deprivation

Background: The prevalence of diagnosed diabetes among different ethnic groups and the influence of deprivation on the prevalence of diabetes among Māori and New Zealand Europeans was investigated. Methods: This was a cross-sectional survey on all patients registered with 10 practices in the Rotorua General Practice Group on 1 July 2007. Patients diagnosed with diabetes were identified though diagnostic codes for diabetes, prescriptions for diabetes medications and laboratory tests for glycosylated haemoglobin (HbA1c). Prevalence of diabetes by ethnicity, age group, gender and NZDep2001 quintiles was calculated. Adjusted ORs for the risk of diabetes were obtained from logistic regression analysis. Results: Of the 45 500 patients registered, 1819 had been diagnosed with diabetes mellitus. In the 40+ age groups, the prevalence of diabetes in Māori and Pacific people was around three times that in Europeans. With increasing deprivation, the age-standardised prevalence of diagnosed diabetes increased among European males (2.7–5.0%) and females (2.1–3.1%). However, the prevalence of diabetes was highest among the least deprived Māori (males 9.7%, females 6.2%). The adjusted risk of diabetes for the most deprived Māori is not significantly different from that for the least deprived Māori. The most deprived Europeans had nearly twice the risk of having diabetes than the least deprived Europeans. Conclusions: Although the rising prevalence of diabetes with increasing deprivation among Europeans shows a similar trend to results from national and international studies, the trend among Māori seems to be different because the least deprived are equally at risk of diabetes. Diabetes interventions aimed at Māori should be tailor-made to include the least deprived groups.

Investigation of methodological factors potentially underlying the apparently paradoxical findings on body mass index and all-cause mortality.

Introduction Findings regarding the association between overweight and all-cause mortality range from significantly lower to higher risk, compared with body-mass-index (BMI) within the “normal” range. Methods We examined empirically potential methodological explanations for these apparently conflicting results using questionnaire and linked mortality data from 246,314 individuals aged ≥45 years in the Australian 45 and Up Study (11,127 deaths; median follow-up 3.9 years). Hazard ratios (HR) for all-cause mortality associated with BMI were modelled according to different methods of accounting for illness at baseline, finer versus broader gradations of BMI and choice of reference group, adjusting for potential confounders. Results In analyses using the broad World Health Organization (WHO) categories, the all-cause mortality HR was significantly lower in the overweight category (25.0–29.99 kg/m2), than the normal weight (18.5–24.99 kg/m2) category. However, in analyses accounting for baseline illness, which excluded those with pre-existing illness at baseline, ever-smokers and the first 2 years of follow up, absolute age-standardised mortality rates varied up to two-fold between finer BMI categories within the WHO normal weight category; rates were lowest at 22.5–24.99 kg/m2 and mortality HRs increased steadily for BMI above (ptrend<0.02) and below (ptrend<0.003) this reference category. Hence, the breadth of the BMI categories used and whether or not baseline illness is accounted for explain the apparent discrepancies between reported BMI-mortality associations. Conclusion Using fine BMI categories and the category with the lowest absolute rates as the reference group and accounting for the potential confounding effects of baseline illness is likely to yield the most reliable risk estimates for establishing the independent relationship of BMI to all-cause mortality. These results and those of other studies indicate that a BMI of 22.5–24.99 kg/m2, not the broad “overweight” category of 25–29.99 kg/m2, was associated with the most favourable mortality risk.

Measuring psychological distress in older Aboriginal and Torres Strait Islanders Australians: a comparison of the K-10 and K-5.

Objectives: To assess the cross‐cultural validity of two Kessler psychological distress scales (K‐10 and K‐5) by examining their measurement properties among older Aboriginal and Torres Strait Islanders and comparing them to those in non‐Aboriginal individuals from NSW Australia.

The relationship between body mass index and hospitalisation rates, days in hospital and costs : findings from a large prospective linked data study

Background Internationally there is limited empirical evidence on the impact of overweight and obesity on health service use and costs. We estimate the burden of hospitalisation—admissions, days and costs—associated with above-normal BMI. Methods Population-based prospective cohort study involving 224,254 adults aged ≥45y in Australia (45 and Up Study). Baseline questionnaire data (2006-2009) were linked to hospitalisation and death records (median follow-up 3.42y) and hospital cost data. The relationships between BMI and hospital admissions and days were modelled using zero-inflated negative binomial regression; generalised gamma models were used to model costs. Analyses were stratified by sex and age (45-64, 65-79, ≥80y), and adjusted for age, area of residence, education, income, smoking, alcohol-intake and private health insurance status. Population attributable fractions were also calculated. Results There were 459,346 admissions (0.55/person-year) and 1,483,523 hospital days (1.76/person-year) during follow-up. For ages 45-64y and 65-79y, rates of admissions, days and costs increased progressively with increments of above-normal BMI. Compared to BMI 22.5-<25kg/m2, rates of admissions and days were 1.64-2.54 times higher for BMI 40-50kg/m2; costs were 1.14-1.24 times higher for BMI 27.5-<30kg/m2, rising to 1.77-2.15 times for BMI 40-50kg/m2. The BMI-hospitalisation relationship was less clear for ≥80y. We estimated that among Australians 45-79y, around 1 in every 8 admissions are attributable to overweight and obesity (2% to overweight, 11% to obesity), as are 1 in every 6 days in hospital (2%, 16%) and 1 in every 6 dollars spent on hospitalisation (3%, 14%). Conclusions The dose-response relationship between BMI and hospital use and costs in mid-age and older Australians in the above-normal BMI range suggests even small downward shifts in BMI among these people could result in considerable reductions in their annual health care costs; whether this would result in long-term savings to the health care system is not known from this study.

Prevalence of gestational diabetes mellitus among Indigenous women and comparison with non-Indigenous Australian women: 1990-2009

Evidence on long‐term trends in gestational diabetes mellitus (GDM) prevalence in Australia is lacking.