Web-based application to project the burden of Alzheimer's disease

Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
Alzheimer's & dementia: the journal of the Alzheimer's Association (Impact Factor: 12.41). 09/2010; 6(5):425-8. DOI: 10.1016/j.jalz.2010.01.014
Source: PubMed


Health care planning and research would benefit from tools that enable researchers to project the future burden of Alzheimer's disease (AD) and evaluate the effect of potential interventions.
We created a web-based application of the AD prevalence model developed by Brookmeyer et al (Am J Public Health 1998;88:1337-42; Alzheimers Dement 2007;3:186-91). The user defines the disease parameters and any interventions that may either reduce risk or slow disease progression. We expanded the parameters to include the cost and weights for disability-adjusted life years.
The secure, web-based application generates detailed AD projections for each calendar year to 2050, and allows users to create personal accounts for them to save, retrieve, and modify the input parameters. The flexibility of the application is illustrated with a forecast for the state of Maryland, USA.
The application generates AD burden projections, costs, and disability-adjusted life years, along with changes associated with potential interventions.

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Available from: H. Michael Arrighi, Mar 04, 2014
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    • "For example, knowing that age is associated with faster decline is not of practical, immediate significance because we cannot control whether or not people get older. Considering the enormous burden of AD (Alzheimer's Association, 2010), identifying modifiable risk factors for more precipitous decline would have substantial public health impact (Colantuoni et al., 2010). "
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    ABSTRACT: Several observational studies have suggested a link between health status and rate of decline among individuals with Alzheimer's disease (AD). We sought to quantify the relationship in a population-based study of incident AD, and to compare global comorbidity ratings to counts of comorbid conditions and medications as predictors of AD progression. This was a case-only cohort study arising from a population-based longitudinal study of memory and aging, in Cache County, Utah. Participants comprised 335 individuals with incident AD followed for up to 11 years. Patient descriptors included sex, age, education, dementia duration at baseline, and APOE genotype. Measures of health status made at each visit included the General Medical Health Rating (GMHR), number of comorbid medical conditions, and number of non-psychiatric medications. Dementia outcomes included the Mini-Mental State Examination (MMSE), Clinical Dementia Rating - sum of boxes (CDR-sb), and the Neuropsychiatric Inventory (NPI). Health status tended to fluctuate over time within individuals. None of the baseline medical variables (GMHR, comorbidities, and non-psychiatric medications) was associated with differences in rates of decline in longitudinal linear mixed effects models. Over time, low GMHR ratings, but not comorbidities or medications, were associated with poorer outcomes (MMSE: β = -1.07 p = 0.01; CDR-sb: β = 1.79 p < 0.001; NPI: β = 4.57 p = 0.01). Given that time-varying GMHR, but not baseline GMHR, was associated with the outcomes, it seems likely that there is a dynamic relationship between medical and cognitive health. GMHR is a more sensitive measure of health than simple counts of comorbidities or medications. Since health status is a potentially modifiable risk factor, further study is warranted.
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    • "The take-home message from these calculations is that even small delays in disease onset and progression can significantly reduce the global burden of disease. A web-based software application that implements the forward calculation methodology is available, which enables researchers to project the burden of AD, to investigate the sensitivity to input assumptions, and to evaluate the effect of potential interventions [14]. "
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    ABSTRACT: Several methods of estimating prevalence of dementia are presented in this article. For both Brookmeyer and the Chicago Health and Aging project (CHAP), the estimates of prevalence are derived statistically, forward calculating from incidence and survival figures. The choice of incidence rates on which to build the estimates may be critical. Brookmeyer used incidence rates from several published studies, whereas the CHAP investigators applied the incidence rates observed in their own cohort. The Aging, Demographics, and Memory Study (ADAMS) and the East Boston Senior Health Project (EBSHP) were sample surveys designed to ascertain the prevalence of Alzheimer's disease and dementia. ADAMS obtained direct estimates by relying on probability sampling nationwide. EBSHP relied on projection of localized prevalence estimates to the national population. The sampling techniques of ADAMS and EBSHP were rather similar, whereas their disease definitions were not. By contrast, EBSPH and CHAP have similar disease definitions internally, but use different calculation techniques, and yet arrive at similar prevalence estimates, which are considerably greater than those obtained by either Brookmeyer or ADAMS. Choice of disease definition may play the larger role in explaining differences in observed prevalence between these studies.
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