Preventable smoking and exercise-related hospital admissions. A model based on the NHEFS

The NHANES I Epidemiologic Follow-Up Study (NHEFS), a longitudinal study of a representative sample of U.S. adults, makes it possible for the first time to develop a simulation model relating hospital admissions to baseline clinical risk factors for the general adult population. The model is presented here and used to project the impact on hospital admissions of changes in smoking behavior and sedentary lifestyle.

Three kinds of projections were calculated for the cohort of adults aged 45 to 74 at baseline: projections of hospital admissions in the absence of the risk factor; projections that reflect a 10-year lag between behavior change and full health benefit; and projections that reflect both lag and incomplete adherence to behavior change. For incomplete adherence we assumed that only 10% of the at-risk population changed their behavior.

Tests of the simulation model showed that it agreed with a Cox analysis of the hospital data and accurately projected observed hospital admissions over the study period. The projections showed that eliminating smoking would reduce annual rates of all-cause hospitalization among older adults by 8.9% 20 years after baseline. Eliminating inactivity would reduce them 4.6%. Introducing a lag of 10 years between behavior modification and full benefit delayed the impact on hospitalization rates but the effect at 20 years was the same. When only 10% of the population at risk stopped smoking or became physically active, a percentage that reflects the effectiveness of current interventions, annual hospitalization rates at 20 years fell by 0.9% and 0.5%, respectively.

Substantial reductions in hospital admissions can be achieved by interventions to prevent smoking, help smokers quit, or encourage inactive persons to become active. Improving adherence can markedly improve the impact on hospitalizations. The costs of these efforts would be rewarded not only by better health, but by lower expenditures for hospitalization.