The case for action on socioeconomic differences in overweight and obesity among Australian adults: modelling the disease burden and healthcare co

Objective: We aimed to quantify the extent to which socioeconomic differences in body mass index (BMI) drive avoidable deaths, incident disease cases and healthcare costs. Methods: We used population attributable fractions to quantify the annual burden of disease attributable to socioeconomic differences in BMI for Australian adults aged 20 to <85 years in 2016, stratified by quintiles of an area-level indicator of socioeconomic disadvantage (SocioEconomic Index For Areas Indicator of Relative Socioeconomic Disadvantage; SEIFA) and BMI (normal weight, overweight, obese). We estimated direct healthcare costs using annual estimates per person per BMI category. Results: We attributed $AU1.06 billion in direct healthcare costs to socioeconomic differences in BMI in 2016. The greatest number (proportion) of cases and deaths attributable to socioeconomic differences in BMI was observed for type 2 diabetes among women (8,602 total cases [16%], with 3,471 cases [22%] in the most disadvantaged quintile [SEIFA 1]) and all-cause mortality among men (2027 total deaths [4%], with 815 deaths [6%] in SEIFA 1). Conclusions: Socioeconomic differences in BMI substantially contribute to avoidable deaths, disease cases and direct healthcare costs in Australia. Implications for public health: Population-level policies to reduce socioeconomic differences in overweight and obesity must be identified and implemented

Adult obesity and the burden of disability throughout life

OBJECTIVE: To analyze the prevalence of disability throughout life and life expectancy free of disability, associated with obesity at ages 30 to 49 years. RESEARCH METHODS AND PROCEDURES: We used 46 and 20 years of mortality follow-up, respectively, for 3521 Original and 3013 Offspring Framingham Heart Study participants 30 to 49 years and classified as normal weight, overweight, or obese at baseline. Disability measures were available between 36 and 46 years of follow-up for 1352 Original participants and at 20 years of follow-up for 2268 Offspring participants. We measured the odds of disability in the Original cohort after 46 years follow-up, and we estimated life expectancy with and without disability from age 50. Two disability measures were used, one representing limitations with mobility only and the second representing limitations with activities of daily living (ADL). RESULTS: Obesity at ages 30 to 49 years was associated with a 2.01-fold increase in the odds of ADL limitations 46 years later. Nonsmoking adults who were obese between 30 and 49 years lived 5.70 (95% confidence interval, 4.11 to 7.35) (men) and 5.02 (95% confidence interval, 3.36 to 6.61) (women) fewer years free of ADL limitations from age 50 than their normal-weight counterparts. There was no significant difference in the total number of years lived with disability throughout life between those obese or normal weight, due to both higher disability prevalence and higher mortality in the obese population. DISCUSSION: Obesity in adulthood is associated with an increased risk of disability throughout life and a reduction in the length of time spent free of disability, but no substantial change in the length of time spent with disability

The relation between non-occupational physical activity and years lived with and without disability

Objectives: The effects of non-occupational physical activity were assessed on the number of years lived with and without disability between age 50 and 80 years. Methods: Using the GLOBE study and the Longitudinal Study of Aging, multi-state life tables were constructed yielding the number of years with and without disability between age 50 and 80 years. To obtain life tables by level of physical activity (low, moderate, high), hazard ratios were derived for different physical activity levels per transition (non-disabled to disabled, non-disabled to death, disabled to non-disabled, disabled to death) adjusted for age, sex and confounders. Results: M

Physical activity and life expectancy with and without diabetes: life table analysis of the Framingham Heart Study

OBJECTIVE: Physical activity is associated with a reduced risk of developing diabetes and with reduced mortality among diabetic patients. However, the effects of physical activity on the number of years lived with and without diabetes are unclear. Our aim is to calculate the differences in life expectancy with and without type 2 diabetes associated with different levels of physical activity. RESEARCH DESIGN AND METHODS: Using data from the Framingham Heart Study, we constructed multistate life tables starting at age 50 years for men and women. Transition rates by level of physical activity were derived for three transitions: nondiabetic to death, nondiabetic to diabetes, and diabetes to death. We used hazard ratios associated with different physical activity levels after adjustment for age, sex, and potential confounders. RESULTS: For men and women with moderate physical activity, life expectancy without diabetes at age 50 years was 2.3 (95% CI 1.2-3.4) years longer than for subjects in the low physical activity group. For men and women with high physical activity, these differences were 4.2 (2.9-5.5) and 4.0 (2.8-5.1) years, respectively. Life expectancy with diabetes was 0.5 (-1.0 to 0.0) and 0.6 (-1.1 to -0.1) years less for moderately active men and women compared with their sedentary counterparts. For high activity, these differences were 0.1 (-0.7 to 0.5) and 0.2 (-0.8 to 0.3) years, respectively. CONCLUSIONS: Moderately and highly active people have a longer total life expectancy and live more years free of diabetes than their sedentary counterparts but do not spend more years with diabetes

Rising Educational Levels Contribute to Compression of Morbidity. A Multi-State Life Table Analysis of the Netherlands 1995-2015

Objective: This paper assesses whether the future rise in educational levels of theelderly may not only increase life expectancy (LE) but also at the same timecontribute to a reduction in life expectancy with disability (LED).Methods: For each educational level, LE and LED were estimated from multi-statelife tables with a disabled and non-disabled state. Basic transition rates wereestimated from regression analysis of data of a Dutch longitudinal study. The resultsper educational level were aggregated to the total population for the years 1995,2005 and 2015.Results: In 1995, men in the highest educational level had a 0.9 years longerLE and a 5.4 years shorter LED than men in the lowest level. Differences amongwomen were larger (2.0 and 8.3 years). Due to rising educational levels between1995 and 2015, LE for the total male population would increase by 0.2 years whileLED would decrease by 0.5 years. A larger effect was observed for women(0.2 and 1.5 years).Conclusion: Rising educational levels of the elderly are likely to contribute to acompression of morbidity over the next decades, especially among women

A hyperalgebraic proof of the isomorphism and isogeny theorems for reductive groups

textabstractWe examined whether specific input data and assumptions explain outcome differences in otherwise comparable health impact assessment models. Seven population health models estimating the impact of salt reduction on morbidity and mortality in western populations were compared on four sets of key features, their underlying assumptions and input data. Next, assumptions and input data were varied one by one in a default approach (the DYNAMO-HIA model) to examine how it influences the estimated health impact. Major differences in outcome were related to the size and shape of the dose-response relation between salt and blood pressure and blood pressure and disease. Modifying the effect sizes in the salt to health association resulted in the largest change in health impact estimates (33% lower), whereas other changes had less influence. Differences in health impact assessment model structure and input data may affect the health impact estimate. Therefore, clearly defined assumptions and transparent reporting for different models is crucial. However, the estimated impact of salt reduction was substantial in all of the models used, emphasizing the need for public health actions