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

Abstract

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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Web-based application to project the burden of Alzheimer’s disease
Elizabeth Colantuoni*, Greg Surplus, Andre Hackman, H. Michael Arrighi, Ron Brookmeyer
Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
Abstract Background: Health care planning and research would benefit from tools that enable researchers to
projectthefutureburdenofAlzheimer’sdisease(AD)andevaluatetheeffectofpotentialinterventions.
Methods: 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.
Application: 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.
Conclusions: The application generates AD burden projections, costs, and disability-adjusted life
years, along with changes associated with potential interventions.
? 2010 The Alzheimer’s Association. All rights reserved.
Keywords:
Forecasting; Disease burden; Multi-stage disease model; Web-tool; Costs; Preventative therapy; Disability-
adjusted life years (DALYs)
1. Introduction
Projecting the future burden of Alzheimer’s disease (AD)
is critical for health care planning. Additionally, researchers
and policy makers are interested in evaluating the effect of
potential interventions that may reduce disease risk or slow
disease progression. Brookmeyer et al [1,2] developed and
implemented a multistage, incidence to prevalence disease
model to estimate and project the prevalence of AD. The
model allows for the introduction of potential interventions
that either reduce the risk of or slow progression of AD to
assess the effect of interventions on the burden of disease.
We have expanded the Brookmeyer et al [1,2] model to
incorporate alternative health burden measures (years of
life lost [YLL] and disability-adjusted life years [DALYs])
and the economic costs of disease. These measures can be
incorporated into decisions regarding resource allocation
[3] and in cost-effectiveness studies for new therapies [4,5].
We created a web-based application to implement the model,
allowing anyone to generate his/her own projections based
on his/her particular parameters. The user may specify inputs
related to AD incidence, disease progression, and the effect
ofADonmortality,costs,andDALYs.Theweb-basedappli-
cation generates annual projections of AD prevalence, costs,
YLL, DALYs by age, gender, and stage for each year, up to
2050. The application allows the user to easily generate mul-
tiple ‘‘what if’’ scenarios by varying the underlying assump-
tions, i.e., input parameters. In this report, we describe the
web-based application and illustrate its flexibility for making
both global and local forecasts.
2. Web-based application
We created a web-based application using the Brook-
meyeretal[1,2] model.Itislocatedonasecure-serverwithin
the Department of Biostatistics, Bloomberg School of Public
Health, Johns Hopkins University (available at: http://www.
biostat.jhsph.edu/project/globalAD).
Conflict of interest: H Michael Arrighi was an employee of and owns
stock in Elan and is currently an employee of JANSSEN Alzheimer Immu-
notherapy Research & Development, LLC.
*Corresponding author. Tel.: 443-844-8155; Fax: 410-955-0958.
E-mail address: ejohnson@jhsph.edu
1552-5260/$ – see front matter ? 2010 The Alzheimer’s Association. All rights reserved.
doi:10.1016/j.jalz.2010.01.014
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TheBrookmeyeretal[1,2]modelforecaststheprevalence
of disease by estimating annual age, gender, and stage-
specific prevalence rates and then applies these to population
projections. The annual prevalence rates are estimated using
a stochastic multistage discrete-time Markov model. The
model is general and may be used to project the burden of
AD globally, for the U.S., for a specific country or region
of interest.
2.1. Inputs
Global, by United Nations regions [6], and U.S [7] popu-
lation projections for the years 2000–2050 by age and gender
are provided in the web-based application. Alternatively, the
user may upload other population projections for any specific
country or region of interest.
Table 1 lists the required inputs for the web-based appli-
cation. The inputs include current and future background
mortality rates for the population, age, and gender-
specific incidence rates for AD, the annual transition prob-
ability from early to late stage disease, and the effect of AD
on mortality. For each of these inputs, the user may use ei-
ther the default value or enter their own value. For instance,
the user may specify the risk of progression from early to
late stage to evaluate different definitions of disease stages.
For the population background mortality rates, the user
may upload rates or change the default rates by a multiplier
(i.e., increase rates by 20%, multiplier 1.2). However, our
sensitivity investigations have revealed that the results
are relatively insensitive to the specification of that multi-
plier.
The user may evaluate the effect of potential interventions
that either delay the onset of disease (i.e., reduce the inci-
dence rate), or slow disease progression (i.e., reduce the
rate of transition from early to late stage disease). If the inter-
vention delays disease onset, the user specifies the relative
amount to which the intervention decreases the incidence
of disease (i.e., to delay the onset of disease by roughly 1
year on average, the default incidence rates would decrease
by 12%). If the intervention delays disease progression, the
user must specify the average number of years that the inter-
vention would delay the progression of disease to an ad-
vanced stage. The calendar year the interventions take
effect must be specified.
The application also calculates DALYs and costs associ-
ated with AD. DALYs are the sum of YLL and years lost
duetodisability and arecalculated accordingtomethods pro-
posed by Murray [14]. We provide default values for the dis-
ability weights for early and late stage disease [13], which
may be modified by the user.
Users may estimate cost associated with the prevalence
forecast. The user may input costs for direct or indirect
medical costs to treat or the hours spent by caregivers to
care for a patient with AD. Forecasts of costs may be in
current or future dollars by including an annual inflation
factor.
2.2. Outputs
Outputsofourweb-based applicationincludeage,gender,
stage, and calendar year counts of prevalent and incident
cases of AD, DALYs, and costs (Table 1). Age, gender,
and calendar year–specific prevalence rates are produced.
The web-based application produces an html page and sev-
eral figures displaying the results of the forecast (e.g., preva-
lence by gender, stage, and calendar year). In addition, a text
fileiscreatedwiththeresultswhichmaybeimportedintosta-
tisticalsoftware forfurther analysisor creation of customized
figures.
3. Illustration of the web-based application for local
forecasts: Maryland, USA
The flexibility of the web-based application is illustrated
by projecting the prevalence and cost of AD for the state of
Maryland (MD), USA. MD population projections by gender
and age (in single years of age from 60 to 84, and pooled for
those aged 85 years and older) for the calendar years 2004–
2030 were downloaded from the U.S. Census Bureau [15].
We forecast the formal costs of caring for persons with AD
in 2009 dollars, assuming that the costs are $23,160 for early
stage and $82,957 for late stage disease (adapted from Fox
[16]) and calculated into 2009 dollars using the Bureau of
Labor Statistics inflation calculator (www.bls.gov/data/
Table 1
Inputs and outputs of the web-based application
InputsOutputs
Population forecasts*
Background mortality ratesy
AD incidence ratesz
Annual transition probability
from stage 1 to 2 diseasex
Impact of AD on mortality{
Years during which an
intervention is introduced**
Effect of intervention on disease
incidence**
Effect of intervention on disease
progression**
Disability weightsyy
Costs of AD**
AD prevalencezz
AD incidencezz
Costszz
Disability Adjusted Life Years
(DALYs)zz
Prevalence ratexx
Median survivalxx
*GlobalpopulationprojectionsbyUNregionoftheworld[6]andUSpop-
ulation projections [7] are provided.
yDefault values provided by age (60–100), gender, and year (1959–2050)
based on historic and projected US mortality rates [8].
zDefault values provided based on meta analysis of worlds incidence rates
by age (60–100) [9].
xDefault value is 0.167, based on approximate mean duration of 6 years
living with AD before care equivalent to nursing home [10].
{Default values assumes no increase in mortality for stage 1 disease and
effect of mortality on stage 2 disease is to add 11% per year (additive model)
[12].
**User supplies (optional).
yyDefault values are 0.45 for stage 1 and 0.94 for stage 2 [13].
zzBy age (60–100), gender, stage for years 2000–2050.
xxBy age (60–100) and gender for years 2000–2050.
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inflation_calculator.htm). Formal costs include costs of med-
icalcare andsocialservices,andweassumethat personswith
early stage disease are similar to community residents with
AD and persons with late stage disease require institutional
care. We performed the following forecasts: (1) no interven-
tion, (2) preventative intervention introduced in 2015 that de-
lays disease onset by 1 year (multiply incidence rates by
0.88), and (3) preventative intervention introduced in 2015
that delays disease onset by 2 years (multiply incidence rates
by 0.75). The input files for the example with additional ex-
planations are available on-line (http://www.biostat.jhsph.
edu/project/globalAD/programs.htm#md).
We estimate that the current (2009) prevalence of AD in
MD is approximately 42,600 and the cost of formal care
for persons with AD in MD is approximately 2.1 billion dol-
lars (Fig. 1, Panel A: thick solid black line). Slightly more
than60%ofthecases are womenand nearly 40%ofthecases
are late stage cases regardless of calendar year (Fig. 1, Panel
B). We forecast that the prevalence of and the formal costs of
caring for persons with AD in MD will increase by nearly
75% by 2030 to 75,200 cases and 3.7 billion dollars, respec-
tively (Fig. 1, Panel A: solid black line).The effect ofthepre-
ventative interventions is to decrease the prevalence of AD in
2030 by 8500 (approximately 11%) or 16,400 (approxi-
mately22%)iftheinterventiondelaysdiseaseonsetbyanav-
erage of 1 or 2 years, respectively (Fig. 1, Panel A: thin solid
and dashed lines, respectively).
4. Discussion
Wehaveadaptedamultistageincidencetoprevalencedis-
ease model into a web-based application that allows users to
project the burden of AD globally or locally. We provide de-
fault inputs or the user may specify his/her own. Inputs, such
as population projections or incidence rates, may need to be
modified before using the application. The user may run the
application multiple times, varying one or two input param-
eters, to generatea variety ofexploratory ‘‘what if’’ scenarios
or sensitivity analyses.
Theburdenofdiseasehasbeenquantifiedusingprevalence,
incidence,costs,YLL,and DALYs.Costs may focusona par-
ticular aspect, asinthe example,whereADcostwas limitedto
the cost of formal care under the assumption that early stage is
equivalenttocommunitycareandlatestageisequivalenttoin-
stitutionalized care. Additionally, the effect of an intervention
on either incidence or progression may be assessed.
Acknowledgments
Fundingforthedevelopmentoftheweb-basedapplication
was provided from both Elan Pharmaceuticals, Inc. and
Wyeth Research.
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Fig. 1. Examples of graphical displays created from the output of the web-based application. Panel A displays the estimated prevalence (in 10,000s) and costs of
caring for persons with AD (in billions of 2009 dollars) for 2005–2030 for the state of Maryland, USA. The figure displays prevalence and cost assuming no
intervention (thick solid black line) and preventative interventions introduced in 2015 that delays onset of disease on average 1 year (thin solid black line) or
2years(thindashedblackline).PanelBdisplaystheestimatedprevalenceofAD (in10,000s)bygenderandstagefor 2005–2030 forthestate ofMaryland,USA.
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