Figure 1 presents the effect of 4 scenarios on diabetes prevalence over the remaining lifetime of the cohort of 51- and 52-year-old respondents. Under the status quo, diabetes prevalence was expected to rise as this population ages, peaking at 33.8% at 79 years of age. In the 10%, 25%, and 50% scenarios, peak diabetes prevalence would be reduced to 30.4%, 25.3%, and 16.2%, respectively. The 100% simulation was the best-case scenario, in which all diabetes would effectively be cured, affecting 43.0% of the cohort who would have developed it in their lifetime.
Prevalence of diabetes in a national cohort of adults aged 51 years, by status quo and 4 intervention scenarios.
We also modeled the effect of 4 hypertension scenarios (Figure A in Appendix B, available as an online supplement). With increasing age, hypertension prevalence in the cohort would rise, reaching 91% at 97 years of age. Under the 10%, 25% and 50% scenarios, peak hypertension prevalence was reduced to 78.9%, 64.5%, and 41.0%, respectively. In the 100% scenario, all hypertension would be cured, affecting the 84.8% of the cohort who would have developed it over their lifetime.
Figure 2 shows the effect of weight reduction in this population. Under the status quo, the prevalence of obesity would continue to rise until 65 years of age, peaking at 43.7%. Under the 10%, 25%, and 50% scenarios, peak obesity prevalence would be reduced to 39.3%, 33.3%, and 21.8%, respectively. Over the cohort's lifetime, 71.3% of the population would become obese, but all of these persons would be successfully treated immediately in the 100%, best-case scenario.
Prevalence of obesity (BMI≥30 kg/m2) in a national cohort of adults aged 51 years, by status quo and 4 intervention scenarios.
Smoking prevalence would decrease with age because of the excessive mortality risk of smokers, as well as smoking cessation at older ages (Figure B in Appendix B, available as an online supplement). The prevalence would be reduced to half at 65 years of age. In the 25% scenario, the prevalence would be reduced to half at 61 years of age. In the 50% scenario, the prevalence of smoking would be approximately half of the prevalence in the status quo. Over the cohort's lifetime, 28% of the population would smoke under the status quo, but they would all quit in the 100%, best-case scenario.
Table 1 and Table 2 summarize the health and cost implications of the 4 scenarios, relative to the status quo. Results averaged over the entire population are shown in Table 1. Under the status quo, a person aged 51 or 52 years in 2004 could expect to live 30.19 more years, corresponding to 15.53 discounted QALYs, and would incur $170013 in medical expenses (2004 dollars). A 10% effective treatment of diabetes control would increase average life expectancy for the entire population to 30.32 years (15.60 QALYs), a gain of 0.07 QALYs, and would reduce lifetime medical spending to $168836. A 25% effective treatment of diabetes would increase average life expectancy for the entire population to 30.53 years (15.71 QALYs), a gain of 0.18 QALYs, and would reduce lifetime medical spending to $166298.
The maximum life-extending benefit of diabetes prevention would come from a cure, which, when averaged over the entire cohort, would add 1.36 years, or 0.70 QALYs.
The population averages shown in Table 1, however, do not reveal the much bigger gains in persons who are successfully treated. Table 2 shows the effect of prevention for each successfully treated individual in the 100% effective treatment scenario (results were similar for the other scenarios). For example, we found that a person aged 51 or 52 years who was successfully treated for obesity would live 0.85 additional years; for hypertension, 2.05 years; and for diabetes, 3.17 years; quitting smoking would add 3.44 years. Despite living longer, those successfully treated would still accumulate less health care spending over their lifetime for obesity (savings of $7168), hypertension (savings of $13702), and diabetes (savings of $34483). Smoking cessation, on the other hand, would cost an additional $15959 over an individual's lifetime.
Thus, for persons treated with diabetes, hypertension, and obesity interventions, life extension could be achieved without increasing average lifetime medical spending. Although these estimates do not include the cost of implementing interventions, they are useful for calibrating the benefits of preventing and treating disease. For example, a 100% effective hypertension treatment would add 1.24 QALYs and would reduce lifetime medical spending by $13702. Valuing the life-year conservatively at $100000, we calculated an economic gain of approximately $137964 per treated 51- or 52-year-old person. On a population basis, this hypertension treatment would be worth $117015 per capita. A diabetes cure would be worth $85120. The benefits for obesity and smoking cures would be $36878 and $33287, respectively.
Am J Public Health. 2009;99(11):2096 © 2009 American Public Health Association
Cite this: The Benefits of Risk Factor Prevention in Americans Aged 51 Years and Older - Medscape - Nov 01, 2009.