5 resultados para Health Expectancy
em University of Queensland eSpace - Australia
Resumo:
Background There are substantial social inequalities in adult male mortality in many countries. Smoking is often more prevalent among men of lower social class, education, or income. The contribution of smoking to these social inequalities in mortality remains uncertain. Methods The contribution of smoking to adult mortality in a population can be estimated indirectly from disease-specific death rates in that population (using absolute lung cancer rates to indicate proportions due to smoking of mortality from certain other diseases). We applied these methods to 1996 death rates at ages 35-69 years in men in three different social strata in four countries, based on a total of 0.6 million deaths. The highest and lowest social strata were based on social class (professional vs unskilled manual) in England and Wales, neighbourhood income (top vs bottom quintile) in urban Canada, and completed years of education (more than vs less than 12 years) in the USA and Poland. Results In each country, there was about a two-fold difference between the highest and the lowest social strata in overall risks of dying among men aged 35-69 years (England and Wales 21% vs 43%, USA 20% vs 37%, Canada 21% vs 34%, Poland 26% vs 50%: four-country mean 22% vs 41%, four-country mean absolute difference 19%). More than half of this difference in mortality between the top and bottom social strata involved differences in risks of being killed at age 35-69 years by smoking (England and Wales 4% vs 19%, USA 4% vs 15%, Canada 6% vs 13%, Poland 5% vs 22%: four-country mean 5% vs 17%, four-country mean absolute difference 12%). Smoking-attributed mortality accounted for nearly half of total male mortality in the lowest social stratum of each country. Conclusion In these populations, most, but not all, of the substantial social inequalities in adult male mortality during the 1990s were due to the effects of smoking. Widespread cessation of smoking could eventually halve the absolute differences between these social strata in the risk of premature death.
Resumo:
Background Estimates of the disease burden due to multiple risk factors can show the potential gain from combined preventive measures. But few such investigations have been attempted, and none on a global scale. Our aim was to estimate the potential health benefits from removal of multiple major risk factors. Methods We assessed the burden of disease and injury attributable to the joint effects of 20 selected leading risk factors in 14 epidemiological subregions of the world. We estimated population attributable fractions, defined as the proportional reduction in disease or mortality that would occur if exposure to a risk factor were reduced to an alternative level, from data for risk factor prevalence and hazard size. For every disease, we estimated joint population attributable fractions, for multiple risk factors, by age and sex, from the direct contributions of individual risk factors. To obtain the direct hazards, we reviewed publications and re-analysed cohort data to account for that part of hazard that is mediated through other risks. Results Globally, an estimated 47% of premature deaths and 39% of total disease burden in 2000 resulted from the joint effects of the risk factors considered. These risks caused a substantial proportion of important diseases, including diarrhoea (92%-94%), lower respiratory infections (55-62%), lung cancer (72%), chronic obstructive pulmonary disease (60%), ischaemic heart disease (83-89%), and stroke (70-76%). Removal of these risks would have increased global healthy life expectancy by 9.3 years (17%) ranging from 4.4 years (6%) in the developed countries of the western Pacific to 16.1 years (43%) in parts of sub-Saharan Africa. Interpretation Removal of major risk factors would not only increase healthy life expectancy in every region, but also reduce some of the differences between regions, The potential for disease prevention and health gain from tackling major known risks simultaneously would be substantial.
Resumo:
This paper presents an economic model to explain the behavior of life expectancy of both sexes. It explicitly examines the relationship between the gender gap in life expectancy and the gender gap in pay. It shows that as the latter narrows over the course of economic development, the former may initially expand but will eventually shrink. Simulation results from our model accord with the behavior of life expectancy for both sexes since the 1940s in the United States. (C) 2003 Elsevier B.V. All rights reserved.
Resumo:
This paper develops an overlapping-generations model in which agents invest in health to prolong life in both working and retirement periods. It explores how unfunded social security with or without health subsidies affects life expectancy, economic growth, and welfare. In particular, by extending life at a possible cost of capital accumulation, health subsidies and a pay-as-you-go pension can improve welfare, especially in the short run.
Resumo:
Objective: To explore the use of epidemiological modelling for the estimation of health effects of behaviour change interventions, using the example of computer-tailored nutrition education aimed at fruit and vegetable consumption in The Netherlands. Design: The effects of the intervention on changes in consumption were obtained from an earlier evaluation study. The effect on health outcomes was estimated using an epidemiological multi-state life table model. input data for the model consisted of relative risk estimates for cardiovascular disease and cancers, data on disease occurrence and mortality, and survey data on the consumption of fruits and vegetables. Results: if the computer-tailored nutrition education reached the entire adult population and the effects were sustained, it could result in a mortality decrease of 0.4 to 0.7% and save 72 to 115 life-years per 100000 persons aged 25 years or older. Healthy life expectancy is estimated to increase by 32.7 days for men and 25.3 days for women. The true effect is likely to lie between this theoretical maximum and zero effect, depending mostly on durability of behaviour change and reach of the intervention. Conclusion: Epidemiological models can be used to estimate the health impact of health promotion interventions.