Alzheimer’s & Dementia 9 (2013) 58–62

The economics of mild cognitive impairmentPei-Jung Lin*, Peter J. Neumann

Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA

Abstract Individuals with amnestic mild cognitive impairment (MCI) are at elevated risk of developing

*Corresponding A

E-mail address: pl

1552-5260/$ - see fro

http://dx.doi.org/10.10

Alzheimer’s disease (AD). Although the economic burden of AD itself is well recognized, little isknown about the direct and indirect costs associated with MCI before the onset of AD. Insufficientdata on the economic impact of MCI as well as other gaps in the knowledge base (such as estimatesofMCI progression rates and factors that driveMCI-related costs) present challenges to understandingthe burden of MCI and to modeling the cost-effectiveness of potential MCI interventions. Initiatingtreatment and care management in theMCI phase could improve the health and well-being of patientsand caregivers and possibly offset certain costs. Future economic analyses should incorporate newdata, as they become available, from patient registries and linked administrative claims and electronicmedical records to better characterize the cost consequences ofMCI detection andmanagement. Suchanalyses should help payers, providers, and policy makers make more informed decisions about thecosts and benefits of new tests, treatments, and other management strategies for the condition.� 2013 The Alzheimer’s Association. All rights reserved.

Keywords: Alzheimer’s disease; Mild cognitive impairment; Cost-effectiveness; Cost analysis

1. Introduction

Researchers have long recognized that Alzheimer’s dis-ease (AD) is a slowly progressive degenerative illness thatincludes a predementia period during which symptoms aremild or barely detectable [1]. Individuals in this prodromalstage have been identified as having mild cognitive impair-ment (MCI) [2], affecting 10% to 20% of individuals aged�65 years [3–6]. Although not all individuals with MCIprogress to full-blown AD, they are at an increased risk—as many as 15% develop AD or related dementias eachyear [7].

What is new and potentially paradigm shifting are ad-vances in neuroimaging and other diagnostics that promiseearly identification of AD, and the eventual prospect ofdisease-modifying treatments. These developments willhave profound economic as well as clinical implications. In-dividuals with MCI are often divided into two groups: thosewith amnestic MCI and those with nonamnestic MCI [2].Memory loss is the dominant symptom of amnestic MCI,and these patients are at elevated risk of developing AD

uthor: Tel.: 617-636-4616; Fax: 617-636-5560.

in@tuftsmedicalcenter.org

nt matter � 2013 The Alzheimer’s Association. All rights r

16/j.jalz.2012.05.2117

[4,6–8]. In this Perspective, we consider economic issuessurrounding the amnestic subtype of MCI, focusing on theUS population.

Many payers now use economic data in addition to safetyand efficacy measures for drug and medical reimbursementdecisions. It is important for payers, providers, and policymakers to understand the economic aspects of MCI, asthey will confront invariably decisions about the costs andbenefits of new tests, treatments, and other managementstrategies for the condition. Economic data can be used toimprove efficiencies and to inform management decisions,such as the identification of patients who would most benefitfrom early interventions. From a societal perspective, thegrowth of older populations increases the importance of un-derstanding the economics of MCI. Forecasting how earlytreatment may influence downstream costs would enablebetter targeting of health care services throughout the ADcontinuum.

2. Economics of MCI: State of the field

It is well established that individuals with AD incurhigher health care costs compared with thosewithout the dis-ease [9–15], despite great variation in the magnitude ofspecific estimates [16]. Moreover, AD imposes substantial

eserved.

P.-J. Lin and P.J. Neumann / Alzheimer’s & Dementia 9 (2013) 58–62 59

physical and psychological burden on caregivers, as well ashigh indirect costs (unpaid caregiver time, lost caregiver pro-ductivity) [10,17]. It is also well documented that costs ofAD increase with advanced stages in which patients haveworse Mini-Mental State Examination (MMSE) scores[18], more functional impairment [19], and behavioralsymptoms [20].

In contrast, the economic literature on MCI is relativelysparse [21–24]. This is in part because of the lack, untilrecently, of well-defined criteria for characterizing thissymptomatic transition phase between normal aging andAD [1]. Before their formal AD diagnosis, individualsmay begin using more health care services [23] and accruinghigher expenditures [22] compared with those without thecondition. One analysis found, for example, that the excessprimary care costs of incident AD cases in the year beforediagnosis were $1167 (85% higher compared with controlcases) for men and $239 (26% higher) for women amongMedicare enrollees in New York (New York) in 1996 [22].

Although studies have suggested poor cost-effectivenessof imaging tests, such as positron emission tomography, inthe diagnosis of AD (especially in the absence of effectivedisease-modifying agents for AD [25,26]), detection in theMCI phase could reflect reasonable value on cost-effectiveness grounds. A recent United Kingdom–basedsimulation model showed that although assessment ofearly-stage AD (which was assumed to include a visit toa general practitioner, two specialist visits, laboratory tests,and a magnetic resonance imaging [MRI] or computed to-mography scan) had significant up-front costs, identifyingpatients in this stage could produce downstream cost savings($3100 per patient, with an additional $5300 in savings at-tributable to reductions in caregiver time) and health benefitscompared with no early assessment [27]. These findings areconsistent with another recent simulation model of the po-tential benefits of early AD detection followed by treatmentin the United States [28], indicating that early assessmentmay reduce costs, particularly if it targets resources to thoseindividuals most likely to benefit from intensive workup andfollow-up.

3. Gaps in the knowledge base

3.1. Epidemiology of MCI

Epidemiological studies can provide information on MCIincidence and prevalence rates, which are needed for esti-mates of the economic burden at the population level. Sev-eral longitudinal studies have shown that individuals withamnestic MCI are at higher risk for developing AD [4,6–8], and that AD progresses more slowly in the early stagesof the condition [29,30]. However, even within therestrictive definition of (amnestic) MCI, there existsconsiderable heterogeneity in the prognosis and thedisease progression with respect to patient characteristics.Moreover, estimates about the course of MCI vary acrossstudies and populations [8]. This is in part because the

slow and uneven progression of the condition makes it chal-lenging to identify a threshold for conversion between nor-mal aging, MCI, and AD [31]. Currently, the diagnosis ofMCI cannot be made by a laboratory test, but requires thejudgment of a clinician based on symptoms defined by clin-ical, cognitive, and functional criteria. Yet, measurementsused to define an individual’s conversion from MCI to ADcan be subjective [32]. A survey of 420 American Academyof Neurology members showed that some respondents be-lieved MCI would be too difficult to diagnose accuratelyor reliably, and a diagnosis would cause unnecessary worryamong patients and families [33]. In addition, studies com-monly use measures of cognitive function to define the con-dition, but the rate of cognitive decline may be highlydependent on the precise tool used [34].

Although a variety of physiological and clinical measureshave been used to define MCI, practical markers for predict-ing progression to AD have yet to be identified. A multicen-ter longitudinal study using data from the Alzheimer’sDisease Neuroimaging Initiative (ADNI) [35] suggestedthat MCI patients who had abnormal results on both 18F-flu-orodeoxyglucose positron emission tomography and epi-sodic memory tests were 11.7 times more likely to developAD over a 2-year follow-up period, compared with subjectswho had normal results on both measures [36]. AnotherADNI-based analysis indicated that both cerebrospinal fluid(CSF) biomarkers and Spatial Pattern of Abnormalities forRecognition of Early AD at baseline were sensitive in pre-dicting conversion to AD; however, many MCI patientswho were nonconverters also had abnormal baseline CSFand Spatial Pattern of Abnormalities for Recognition ofEarly AD results. One study that included 148 outpatientswith MCI demonstrated that combining multiple diseasemarkers (informant report of functioning, olfactory identifi-cation, verbal memory, MRI hippocampal volume, and MRIentorhinal cortex volume) was more accurate (sensitivity:85%, specificity: 90%) in predicting the conversion fromMCI to AD during a 3-year follow-up period than the com-bination of age and MMSE (sensitivity: 39%, specificity:90%) [37]. To date, researchers have not validated the panelof early disease markers in large representative samples.Longer follow-up is also needed to inform the determinationof optimal predictors.

Even less is known about the progression from preclinicalpresymptomatic phases to MCI [38]. Although various bio-markers (e.g., CSF tau, brain imaging to detect b-amyloidplaques) are being tested for detecting AD in its preclinicalpresymptomatic stage, there are no established clinical diag-nostic criteria for this very early phase of disease [38]. Ad-vances in preclinical AD detection may enable earliermore effective treatment and eventually guide therapy be-fore the onset of symptoms. As knowledge accumulatesabout the biomarkers’ ability to predict the timing of declineor progression toMCI, researchers can incorporate the infor-mation into a framework that better characterizes the earlieststage of the disease [1,38].

P.-J. Lin and P.J. Neumann / Alzheimer’s & Dementia 9 (2013) 58–6260

3.2. Economic impact of MCI

During MCI, patients’ everyday activities and function-ing may not yet be compromised, although subtle changesin memory and thinking abilities are enough to be noticedand measured. These changes have economic consequences[1], but the role of memory and cognition in predicting theeconomic impact of MCI is not well understood. Previousstudies have used MMSE [39], Clinical Dementia Rating(CDR) [24], and Global Deterioration Scale [40] scoresto estimate costs for mild, moderate, and severe AD.More and better data are needed to determine the perfor-mance of these measures in predicting the cost associatedwith MCI.

Some research suggests that “patient dependence” maybe an important multidimensional predictor of cost in theMCI phase [41–43]. Patient dependence attempts tosummarize the level of care required, that is, the need ofa patient across domains, such as cognition, function, andbehavior. Because studies of patient dependence typicallyhave had small sample sizes and relatively brief follow-upperiods, it remains unclear whether and how this constructpredicts the costs associated with MCI.

“Indirect” costs associated with MCI patient and care-giver productivity are also largely understudied. As MCIadvances, the time caregivers spend to assist patientswith activities of daily living and instrumental activitiesof daily living increases, as does caregiver distress andcaregiver work time lost [44,45]. Recent estimates bythe Alzheimer’s Association reported 17.4 billion hoursof unpaid care provided for AD patients in 2011, valuedat .$210 billion [17]. Although indirect costs of MCIare likely much lower than comparable costs in AD, care-giving burden and the resulting productivity loss shouldbe considered as part of the condition’s economic burden[2,24].

3.3. Modeling the consequences of MCI interventions

One Swedish study showed that an increase of CDR scorefrom 0.5 (i.e., MCI) to 1.0 (i.e., mild AD) corresponded toSEK$54,000 (US$5700) in excess costs, suggesting that de-laying the transition to AD may result in considerable eco-nomic benefits [24]. However, because many uncertaintiesexist about the effectiveness of therapeutic agents for MCI[46], it is unclear whether any savings generated from earlydetection and treatment will offset costs associated withmore testing and therapies [24]. In addition, methodologicaldifferences, such as the study population (e.g., patients frommemory clinics, primary care, or community settings), typeof economic model, treatment evaluated, and assumptionsabout disease progression over time, warrant caution in in-terpreting the extent of economic benefits. Research is alsoneeded to determine howMCI treatment would impact man-agement of expensive comorbidities and downstream long-term care costs (such as effects on delaying nursing homeplacement).

Although there is currently no US Food and Drug Admin-istration–approved treatment for MCI, it is important to keeppatients and caregivers well informed about what resourcesand treatment options are available when symptoms becomeprominent. Nonpharmacological interventions such as coun-seling and education may promote advance care planningand reduce family stress and misunderstanding [28,33],and these nonhealth outcomes should be considered ineconomic models for MCI.

Over the years, researchers have developed and applieda number of decision-analytic models to characterize thecost-effectiveness of interventions for AD. However, thesemodels have certain limitations. Some are Markov models,which, although useful in characterizing how patients tran-sition among discrete health states (e.g., mild, moderate,and severe AD), represent relatively crude depictions ofa complex disease [47]. In addition, Markov models as-sume a “memoryless” property (i.e., transition probabili-ties between states are independent of the patient’shistory), which does not reflect actual experience. A sys-tematic review of AD decision-analytic models suggestedthat many of the models have defined states on the basisof cognitive impairment, ignoring potentially importantchanges in other dimensions, such as dependency and pro-ductivity [47].

Ideally, future economic models for MCI (as well as forfull-blown AD) will address these limitations. The modelswould have the flexibility to incorporate information onthe accuracy of early-detection diagnostics, as well as infor-mation on the progression ofMCI to AD, the effectiveness ofnew treatments, and impacts on a broad array of outcomes(cognition, function, quality of life, costs). It would be usefulif models had the granularity to reflect the pathophysiologyof MCI at a refined level of biological and clinical detailbased on trial data. An improved model would incorporateinformation about the impact of potential disease-modifying agents on various model parameters, and howagents affect subgroups such as patients with apolipoproteinE4 (APOE 34) and other factors [48]. Researchers can thenuse the model to project how early detection and treatmentimpact disease progression and health costs under a rangeof assumptions.

4. Incorporating new data

Studies on AD costs typically rely on administrativeclaims databases, and estimate the incremental expendituresof treating AD patients [9,11–13,15], leaving prediagnosiscosts largely unaddressed. Major challenges exist in usingadministrative data to determine the costs of MCI. Forexample, MCI and mild AD cases are typicallyunderdiagnosed and uncoded in claims files because ofdifficulties recognizing symptoms and resistance amongpatients and caregivers to a dementia diagnosis [10,49]. Inaddition, the US reimbursement system provides littlefinancial incentive for coding AD as the primary diagnosis

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and instead encourages hospitals and physicians to codecomorbidities, such as aspiration pneumonia, to enhancereimbursement. Therefore, additional data sources (e.g.,linked administrative claims and electronic medicalrecords) are needed to improve case ascertainment.Ideally, researchers can use results of memory, cognitive,functional, and neuropsychological tests documented inmedical records to identify MCI patients.

Data sets, such as the Health and RetirementStudy–Aging, Demographics, and Memory Study [50],linked with Medicare claims files will provide clinical de-tail (e.g., using instruments such as the NeuropsychiatricInventory and CDR) to help identify affected individualsbefore they have symptoms prominent enough so they ap-pear on claims records. Researchers can use these datasources to address questions such as the following: Howutilization components (inpatient stays, physician visits,medications) impact health costs in the MCI stage? Howdo cost drivers change as patients progress along the dis-ease continuum? Who bears the costs associated withMCI—Medicare, private insurance, or patients and familiesthemselves? For economic models of MCI treatment, ongo-ing patient registries (e.g., the National Alzheimer’s Coor-dinating Center Uniform Data Set [51] and the ADNI) cansupply disease progression rate estimates for patients oncurrent standard of care based on real-world data, and pro-vide a baseline for comparison when new treatments be-comes available.

5. Conclusion

Although the costs of caring for AD patients have beenstudied extensively, the economic literature on MCI is rela-tively sparse. During the MCI phase, patients may begin us-ing more health care and incurring higher costs, but thedirect and indirect costs of treatment for individuals withMCI as well as the rate at which patients progress to ADhave not been well characterized. Many uncertainties existabout the potential cost-effectiveness of early detectionand therapy for MCI. Economic analyses are needed to ac-count for the impact of interventions on increased time inthe MCI state, and consequences for patient and caregiverproductivity and the long-term cost. A new comprehensiveAD simulation model, which includes an MCI state, couldhelp researchers assess the economic impacts of early diag-nosis and treatment, and to inform coverage and reimburse-ment decisions of new interventions. The model also canhelp clinicians characterize various progression scenarios,which may assist patients and their families with diseaseplanning.

Acknowledgments

This work was supported by a grant from Novartis toTufts Medical Center. Publication was not contingent onNovartis’ approval. We thank Usha Mallya for helpful

comments on an earlier version of the manuscript and SarahBliss for her research assistance.

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