Benefits of Smoking Cessation for Longevity

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Abstract

This study determined the life extension obtained from stopping smoking at various ages. We estimated the relation between smoking and mortality among 877,243 respondents to the Cancer Prevention Study II. These estimates were applied to the 1990 US census population to examine the longevity benefits of smoking cessation. Life expectancy among smokers who quit at age 35 exceeded that of continuing smokers by 6.9 to 8.5 years for men and 6.1 to 7.7 years for women. Smokers who quit at younger ages realized greater life extensions. However, even those who quit much later in life gained some benefits: among smokers who quit at age 65 years, men gained 1.4 to 2.0 years of life, and women gained 2.7 to 3.7 years. Stopping smoking as early as possible is important, but cessation at any age provides meaningful life extensions.

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Most cited references 13

Record: found
Abstract: not found
Article: not found

Smoking, smoking cessation, and lung cancer in the UK since 1950: combination of national statistics with two case-control studies

R. Peto (2000)

0 comments Cited 124 times – based on 0 reviews      Review now

Record: found
Abstract: not found
Article: not found

Actual causes of death in the United States

J McGinnis (1993)

0 comments Cited 70 times – based on 0 reviews      Review now

Record: found
Abstract: found
Article: not found

Forecasting coronary heart disease incidence, mortality, and cost: the Coronary Heart Disease Policy Model.

M Weinstein, P Coxson, L Williams … (1987)

A computer simulation model was developed to project the future mortality, morbidity, and cost of coronary heart disease (CHD) in the United States population. The model contains a demographic-epidemiologic (DE) submodel, which stimulates the distribution of coronary risk factors and the conditional incidence of CHD in a demographically evolving population; a "bridge" submodel, which determines the outcome of the initial CHD event; and a disease history (DH) submodel, which simulates subsequent events in persons with a previous CHD event. The user of the model may simulate the effects of interventions, either preventive (i.e., risk factor modification) or therapeutic, upon mortality, morbidity, and cost for up to a 30-year period. If there were no future changes in risk factors or the efficacy of therapies after 1980, baseline projections indicate that the aging of the population, and especially the maturation of the post-World War II baby-boom generation, would increase CHD prevalence and annual incidence, mortality, and costs by about 40-50 per cent by the year 2010. Unprecedented reductions in risk factors would be required to offset these demographic effects on the absolute incidence of CHD. The specific forecasts could be inaccurate, however, as a consequence of erroneous assumptions or misestimated baseline data, and the model awaits validation based on actual future data.