Estimating the prevalence of limb loss in the United States: 2005 to 2050.

OBJECTIVE: To estimate the current prevalence of limb loss in the United States and project the future prevalence to the year 2050. DESIGN: Estimates were constructed using age-, sex-, and race-specific incidence rates for amputation combined with age-, sex-, and race-specific assumptions about mortality. Incidence rates were derived from the 1988 to 1999 Nationwide Inpatient Sample of the Healthcare Cost and Utilization Project, corrected for the likelihood of reamputation among those undergoing amputation for vascular disease. Incidence rates were assumed to remain constant over time and applied to historic mortality and population data along with the best available estimates of relative risk, future mortality, and future population projections. To investigate the sensitivity of our projections to increasing or decreasing incidence, we developed alternative sets of estimates of limb loss related to dysvascular conditions based on assumptions of a 10% or 25% increase or decrease in incidence of amputations for these conditions. SETTING: Community, nonfederal, short-term hospitals in the United States. PARTICIPANTS: Persons who were discharged from a hospital with a procedure code for upper-limb or lower-limb amputation or diagnosis code of traumatic amputation. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Prevalence of limb loss by age, sex, race, etiology, and level in 2005 and projections to the year 2050. RESULTS: In the year 2005, 1.6 million persons were living with the loss of a limb. Of these subjects, 42% were nonwhite and 38% had an amputation secondary to dysvascular disease with a comorbid diagnosis of diabetes mellitus. It is projected that the number of people living with the loss of a limb will more than double by the year 2050 to 3.6 million. If incidence rates secondary to dysvascular disease can be reduced by 10%, this number would be lowered by 225,000. CONCLUSIONS: One in 190 Americans is currently living with the loss of a limb. Unchecked, this number may double by the year 2050.

Worldwide variation in the doubling time of Alzheimer's disease incidence rates.

BACKGROUND: The doubling time is the number of chronological years for the age-specific incidence rate to double in magnitude. Doubling times describe the rate of increase of the risk of Alzheimer's disease (AD) with advancing age. Estimates of doubling times of AD assist in understanding disease etiology and forecasting future disease prevalence. The objective of this study was to investigate regional and gender differences in the doubling of AD age-specific incidence rates. METHODS: We identified all studies in the peer review literature that reported age-specific incidence rates for AD. We modeled the logarithm of the incidence rate as a linear function of age. We used both fixed effects models and random effects models to account for interstudy variation. RESULTS: AD incidence rates exponentially increase with increasing age. The overall estimate of the doubling time was 5.5 years. The doubling times from studies performed in North America and Europe were 6.0 and 5.8, respectively; whereas the doubling times in all other parts of the world were 5.0. There was no significant geographic differences in doubling times (P = .3). Although the doubling times were slightly longer for men (6.5 years) than for women (5.4 years), the difference was not significant (P = .3). CONCLUSIONS: Doubling times of AD incidence rates are not statistically significantly different among populations throughout the world. The risk of AD grows exponentially with age, doubling approximately every 5 to 6 years. Although the shapes of the incidence curves are similar, there is considerable variation in absolute incidence rates throughout the world. Currently, there are limited epidemiologic data at the oldest ages, and further study is needed to accurately define the incidence curve above age 90.

Forecasting the global burden of Alzheimer's disease.

BACKGROUND: Our goal was to forecast the global burden of Alzheimer's disease and evaluate the potential impact of interventions that delay disease onset or progression. METHODS: A stochastic, multistate model was used in conjunction with United Nations worldwide population forecasts and data from epidemiological studies of the risks of Alzheimer's disease. RESULTS: In 2006, the worldwide prevalence of Alzheimer's disease was 26.6 million. By 2050, the prevalence will quadruple, by which time 1 in 85 persons worldwide will be living with the disease. We estimate about 43% of prevalent cases need a high level of care, equivalent to that of a nursing home. If interventions could delay both disease onset and progression by a modest 1 year, there would be nearly 9.2 million fewer cases of the disease in 2050, with nearly the entire decline attributable to decreases in persons needing a high level of care. CONCLUSIONS: We face a looming global epidemic of Alzheimer's disease as the world's population ages. Modest advances in therapeutic and preventive strategies that lead to even small delays in the onset and progression of Alzheimer's disease can significantly reduce the global burden of this disease.

Modeling the effect of Alzheimer's disease on mortality.

Mortality rate ratios and the associated proportional hazards models have been used to summarize the effect of Alzheimer's disease on longevity. However, the mortality rate ratios vary by age and therefore do not provide a simple parsimonious summary of the effect of the disease on lifespan. Instead, we propose a new parameter that is defined by an additive multistate model. The proposed multistate model accounts for different stages of disease progression. The underlying assumption of the model is that the effect of disease on mortality is to add a constant amount to death rates once the disease progresses from an early to late stage. We explored the properties of the proposed model; in particular the behavior of the mortality rate ratio and median survival that is induced by the model. We combined information from several data sources to estimate the parameter in our model. We found that the effect of Alzheimer's disease on longevity is to increase the absolute annual risk of death by about 8% once a person progressed to late stage disease. Most importantly, we find that this additive effect is the same regardless of the patients' age or gender. Thus, the proposed additive multi-state model provides a parsimonious and clinically interpretable description of the effects of Alzheimer's disease on mortality.