Kathryn Ziegler-Graham

Bio: Kathryn Ziegler-Graham is an academic researcher from St. Olaf College. The author has contributed to research in topics: Incidence (epidemiology) & Mortality rate. The author has an hindex of 6, co-authored 10 publications receiving 4918 citations. Previous affiliations of Kathryn Ziegler-Graham include National Science Foundation.

Forecasting the global burden of Alzheimer’s disease

Abstract: 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.

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

Abstract: 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.

Modeling the effect of Alzheimer's disease on mortality.

Abstract: 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.

A Model for an Interdisciplinary Undergraduate Research Program

Abstract: In the May 2009 issue of The American Statistician, Brown and Kass (BK) offered thought-provoking answers to the question “What is Statistics?” which have direct implications for statistics education. For five years, St. Olaf College’s Center for Interdisciplinary Research’s (CIR) activities have aligned with BK in both philosophy and practice. We describe the program’s motivation and design, how we recruit students and find faculty collaborators with suitable projects, and how the teams of faculty and students work together. A research skills seminar series parallels the research process and prepares students for working on teams. Inevitably, administrative issues arose which we identify and address. Landes (2009) identified significant issues related to recruiting. Our model of undergraduate education has proved to be fruitful on this front. Sending nearly 50 students to graduate school in five years from a college of fewer than 3000 speaks to the program’s efficacy. Here we present a program based on a...

Vascular Contributions to Cognitive Impairment and Dementia A Statement for Healthcare Professionals From the American Heart Association/American Stroke Association

Abstract: Background and Purpose—This scientific statement provides an overview of the evidence on vascular contributions to cognitive impairment and dementia. Vascular contributions to cognitive impairment ...

The projected effect of risk factor reduction on Alzheimer's disease prevalence

Abstract: At present, about 33·9 million people worldwide have Alzheimer's disease (AD), and prevalence is expected to triple over the next 40 years. The aim of this Review was to summarise the evidence regarding seven potentially modifiable risk factors for AD: diabetes, midlife hypertension, midlife obesity, smoking, depression, cognitive inactivity or low educational attainment, and physical inactivity. Additionally, we projected the effect of risk factor reduction on AD prevalence by calculating population attributable risks (the percent of cases attributable to a given factor) and the number of AD cases that might be prevented by risk factor reductions of 10% and 25% worldwide and in the USA. Together, up to half of AD cases worldwide (17·2 million) and in the USA (2·9 million) are potentially attributable to these factors. A 10-25% reduction in all seven risk factors could potentially prevent as many as 1·1-3·0 million AD cases worldwide and 184,000-492,000 cases in the USA.

Potential for primary prevention of Alzheimer's disease: an analysis of population-based data

Abstract: Summary Background Recent estimates suggesting that over half of Alzheimer's disease burden worldwide might be attributed to potentially modifiable risk factors do not take into account risk-factor non-independence. We aimed to provide specific estimates of preventive potential by accounting for the association between risk factors. Methods Using relative risks from existing meta-analyses, we estimated the population-attributable risk (PAR) of Alzheimer's disease worldwide and in the USA, Europe, and the UK for seven potentially modifiable risk factors that have consistent evidence of an association with the disease (diabetes, midlife hypertension, midlife obesity, physical inactivity, depression, smoking, and low educational attainment). The combined PAR associated with the risk factors was calculated using data from the Health Survey for England 2006 to estimate and adjust for the association between risk factors. The potential of risk factor reduction was assessed by examining the combined effect of relative reductions of 10% and 20% per decade for each of the seven risk factors on projections for Alzheimer's disease cases to 2050. Findings Worldwide, the highest estimated PAR was for low educational attainment (19·1%, 95% CI 12·3–25·6). The highest estimated PAR was for physical inactivity in the USA (21·0%, 95% CI 5·8–36·6), Europe (20·3%, 5·6–35·6), and the UK (21·8%, 6·1–37·7). Assuming independence, the combined worldwide PAR for the seven risk factors was 49·4% (95% CI 25·7–68·4), which equates to 16·8 million attributable cases (95% CI 8·7–23·2 million) of 33·9 million cases. However, after adjustment for the association between the risk factors, the estimate reduced to 28·2% (95% CI 14·2–41·5), which equates to 9·6 million attributable cases (95% CI 4·8–14·1 million) of 33·9 million cases. Combined PAR estimates were about 30% for the USA, Europe, and the UK. Assuming a causal relation and intervention at the correct age for prevention, relative reductions of 10% per decade in the prevalence of each of the seven risk factors could reduce the prevalence of Alzheimer's disease in 2050 by 8·3% worldwide. Interpretation After accounting for non-independence between risk factors, around a third of Alzheimer's diseases cases worldwide might be attributable to potentially modifiable risk factors. Alzheimer's disease incidence might be reduced through improved access to education and use of effective methods targeted at reducing the prevalence of vascular risk factors (eg, physical inactivity, smoking, midlife hypertension, midlife obesity, and diabetes) and depression. Funding National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care for Cambridgeshire and Peterborough.