Ethical Decision Making in Clinical Neuropsychology
Shane S. Bush
Mild Traumatic Brain Injury and Postconcussion Syndrome
Michael A. McCrea
Understanding Somatization in the Practice of Clinical Neuropsychology
Greg J. Lamberty
Board Certification in Clinical Neuropsychology
Kira E. Armstrong, Dean W. Beebe, Robin C. Hilsabeck, and Michael W. Kirkwood
Adult Learning Disabilities and ADHD
Robert L. Mapou
The Business of Neuropsychology
Mark T. Barisa
Neuropsychology of Epilepsy and Epilepsy Surgery
Gregory P. Lee
Mild Cognitive Impairment and Dementia
Glenn E. Smith and Mark W. Bondi
Intellectual Disability: Civil and Criminal Forensic Issues
Michael Chafetz
Executive Functioning: A Comprehensive Guide for Clinical Practice
Yana Suchy
The Independent Neuropsychological Evaluation
Howard J. Oakes, David W. Lovejoy, and Shane S. Bush
A Practical Guide to Geriatric Neuropsychology
Susan McPherson and Deborah Koltai
A PRACTICAL GUIDE TO GERIATRIC NEUROPSYCHOLOGY
Susan McPherson, PhD, ABPP/CN
Deborah Koltai, PhD, ABPP/CN
1
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Library of Congress Cataloging-in-Publication Data
Names: McPherson, Susan, 1958– author. | Koltai, Deborah, author. | American Academy of Clinical Neuropsychology.
Title: A practical guide to geriatric neuropsychology / Susan McPherson, Deborah Koltai.
Description: New York, NY : Oxford University Press, 2018. | Series: Oxford workshop series | Includes bibliographical references and index. | Identifiers: LCCN 2017038152 (print) | LCCN 2017038941 (ebook) | ISBN 9780199988624 (UPDF) | ISBN 9780199988631 (EPUB) | ISBN 9780199988617 (paperback : alk. paper)
LC record available at https:// lccn.loc.gov/ 2017038152
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To James and Winifred McPherson, who taught me the value of persistence and hard work.
To Asenath LaRue and Jeffrey Cummings, who inspired me.
To Mary Lynn, for her ever-present love and support. SM
To my children, who are my joy and my light.
To my anchors, who bring me back to center.
To Susan, for sharing her venture with patience, determination, and cheer.
DK
Acknowledgments ix
Chapter 1 The Aging Population in Clinical Practice 1
Chapter 2 Normal Aging 7
Chapter 3 Factors Affecting Clinical Interaction and Performance 19
Chapter 4 The Clinical Interview 27
Chapter 5 Cognitive Screening 41
Chapter 6 Neuropsychological Assessment in Geriatric Settings 53
Chapter 7 Psychiatric Disorders 67
Chapter 8 Capacity 87
Chapter 9 Feedback and Intervention 105
Chapter 10 Older Adults in the Workforce 123
Chapter 11 The Future of Health Care 131
Notes 141 References 143 Index 189
Acknowledgments
Special thanks to Terry Barclay, PhD, for his unending patience in tracking down articles, and to my co-author, Deborah, for her guidance and support.
SM
A PRACTICAL GUIDE TO GERIATRIC NEUROPSYCHOLOGY
The Aging Population in Clinical Practice
The landscape of the world population is changing, and over recent decades in the United States the shifting demographics are manifest. Readiness, as indicated by awareness, policy and systems is developing. We appreciate that health care systems serving elders will be affected by these shifts, with optimal systems anticipating and creating triage and care systems that are responsive and effective. However, we are aware that much remains to be done to prepare for changes in referral patterns and care needs that will come. In this volume, we focus on dimensions of psychological aging associated with risk, namely the aging central nervous system and mental health of older adults. It is our hope to offer a general introduction to central matters of importance in the care of older adults at this point in the 21st century.
Census Indicators
According to the 2010 US Census, persons 65 years or older numbered 40.2 million, representing 13% of the US population, about one in every eight Americans. In 2010, more people were older than 65 years than in any prior Census, and this group represented the fastest growing segment of the population between 2000 and 2010 (15.1% to 9.7%) (Werner, 2011). By 2030, it is estimated that people 65 years and older will make up 19% of the population, compared with 13% in 2010 (Ortman, Velkoff & Hoga, 2014). The ethnic and racial diversity of older adults in the United States will also change, with older non-Hispanic whites accounting for only 6.9% of
the population older than 60 years, representing a decline of 9% from 2010 (Administration on Aging, 2016). Of the population older than 60 years, it is expected that, between 2010 and 2030, the proportion of older Hispanics will increase from 7% to 13%, the proportion of older Asian Americans will increase from 3.5% to 5.6%, and the proportion of older African Americans will increase from 8.6% to 10% (Administration on Aging, 2016).
It is well established that the leading cause of dementia in older adults is Alzheimer’s disease (AD). Of the estimated 5.2 million Americans with AD, the majority are older than 65 years (Hebert, Weuve, Scherr & Evans, 2013), with an estimated 13% between the ages of 65 and 74 years, 44% between ages 75 and 84 years, and 38% 85 years or older. The estimated incidence (new cases per year) of AD increases significantly with age, rising to as high as 231 new cases per 1,000 people 85 years and older (the “oldest old”) (Hebert, Beckett, Scherr & Evans, 2001).
Older adults are also not immune to mental health disorders. In 1999, an estimate one in four older adults had a significant mental health–related disorder. By the year 2030, the number of older adults with major psychiatric illnesses is projected to reach 15 million (Jeste et al., 1999). Bartels (2003) cited numerous studies indicating that older adults with mental illness are at increased risk for receiving inadequate and inappropriate care resulting in (1) impaired independent and community-based functioning, (2) compromised quality of life, (3) cognitive impairment, (4) increased caregiver stress, (5) significant disability, (6) increased mortality, (7) poor health outcomes, and (8) higher utilization and costs of health care services.
The Impact of Older Adults on the Health Care System
Defined by the Census, the term older adults generally refers to individuals who are 65 years and older. Subsequent to the increase in the numbers of older adults will be the increase in referrals for cognitive evaluation of neurologic disorders common to older people, specifically dementia, as well as referrals to psychologists for behavioral interventions to manage chronic health conditions (e.g., diabetes, cardiac disease) and the concomitant conditions that accompany many of those conditions (e.g., depression). Referrals for mental health care will also increase in this population given the aging of individuals with chronic psychiatric conditions.
Report of the impact of the aging population on Medicare and the “Medicare Crisis” that will ensue as the baby boomers age permeates the
A Practical Guide to Geriatric Neuropsychology
news. These reports are not without some merit given the increased risk for dementia as the population ages and the costs associated with care for individuals with dementia. Individuals with AD incur about 60% higher costs than non-AD patients in the Medicare program, and AD patients impose a substantial cost on Medicaid programs through nursing home use (Weimer & Sager, 2009). Individuals with dementia have a significantly higher rate of hospital admissions for all causes and for ambulatory care– sensitive conditions (ACSCs) for which proactive care may have prevented hospitalizations than persons without dementia (Phelan, Borson, Grothaus, Balch & Larson, 2012). Phelan et al. (2012) propose that proactively monitoring dementia patients for ACSCs, such as urinary tract infection or pneumonia, on an outpatient basis is likely to prevent the need for a hospital stay and thus result in lower health care costs. Such prevention requires the monitoring of mental status to detect dementia before the individual is hospitalized, increasing the role of the neuropsychologist in clinical care. As noted earlier, older adults with mental health disorders have higher utilization and costs of health care services (Bartels, 2003). Providing effective mental health services can result in cost offsets (Strain et al., 1991).
The Need for Mental Health Services for Older Adults
Mental health disorders are particularly common in older adults who are living in nursing homes. Data from 2005 indicated that among the 996,311 new admissions to nursing homes, 19% (n = 187,478) of patients were admitted with mental illnesses other than dementia, whereas 12% (n = 118,290) had dementia only (Fullerton, McGuire, Feng, Mor & Grabowski, 2009). Conditions such as major depression, schizophrenia and other psychotic disorders are becoming more common in the nursing home setting, yet access to psychiatric care is often not available or is inadequate (Bartels, 2003). 30% to 56% of persons living in assisted living facilities have a mental health diagnosis, but payment systems do not allow for care within a residential living facility (Becker, Stiles & Schonfeld, 2002). It has been estimated that approximately one- third of older adults in primary care have significant mental health symptoms (Lyness, Caine, King, Cox & Yoediono, 1999) and receive care from a primary care physician instead of a mental health professional (USDHHS, 1999a).
The Role of Psychology and Neuropsychology in Care
The rapidly increasing aging population and subsequent rise in cases of disorders such as dementia and chronic health conditions are producing a demand for services that can be provided by clinical psychologists and neuropsychologists. The need for cognitive evaluation and subsequent treatment of behavioral disorders, caregiving issues, and mood disorders poses an opportunity for neuropsychologists and psychologists to be on the “front lines” of treatment. As will be discussed in detail in Chapter 5, one of the potential roles for neuropsychology, whether in the institutional setting or private sector, is in training other professionals in the appropriate use of screening tools that might detect the earliest signs of cognitive change as well as help determine which patients require additional evaluation. While not diagnostic, the use of screening tools can assist all care providers in determining changes in cognition that might trigger a dementia diagnosis. Dementia caregivers, approximately 20% of whom are older adults, report higher levels of stress and depression compared with the general population (Pinquart & Sorensen, 2007). Older individuals caring for a loved one with dementia are also within the scope of practice of psychology as well as neuropsychology. As will be discussed in Chapter 9, feedback and intervention for individuals with dementia focus not only on the person with the disease but also on the environment and persons caring for the patient with dementia.
Scope of the Current Text
The increasing numbers of older adults in the population almost guarantees that practitioners who serve adult populations will begin to experience an increase in the number of older patients referred for services. Unfortunately, across professions, the geriatric mental health care workforce is not adequately trained to meet the health and mental health needs of the aging population (Institute of Medicine, 2012). While this text alone is not adequate in providing extensive training in geriatrics, it will provide a basis for the practitioner in understanding the cognitive changes that occur with normal aging (Chapter 2). We will focus on factors that affect interaction with an older adult, such as vision and hearing (Chapter 3) and the importance of gathering information from a collateral source during the clinical interview (Chapter 4). The importance of screening for cognitive changes in primary care will be addressed
(Chapter 5), as will the purpose and utility of more extensive cognitive evaluation (Chapter 6) and the evaluation and treatment of psychiatric disorders (Chapter 7). This text will also provide an overview on issues of capacity that can arise in the geriatric population and on how a variety of capacities are determined (Chapter 8). The importance of providing feedback and recommendations for treatment and intervention specific to geriatrics will be discussed (Chapter 9). An increasing number of older adults are remaining in the workforce past retirement, and we will focus on some of the challenges specific to older workers (Chapter 10). Finally, we will discuss the changes evolving in health care and the impact of those changes on practice (Chapter 11). While not exhaustive, our intent has been to provide an overview of the principles vital to the care of older adults focusing on psychological and neuropsychological health. We recognize the unique and overlapping expertise of neuropsychologists, geropsychologists, geriatric psychiatrists, neurologists, geriatricians, and behavioral- cognitive and behavioral health psychologists. We encourage all to work collaboratively and are delighted to participate in the care of our vital older adults.
Normal Aging
Ms. Pickens is a 68-year- old, married woman who recently retired from her nursing career. She has started to notice changes in her memory and states that she will “walk into a room and forget what it was I went in there to get.” She has no difficulty remembering conversations or remembering to takes medications and is not misplacing items or repeating herself. She has no difficulty with navigating while driving. She admits that she is “worried” because her mother developed Alzheimer’s disease at age 80. An interview with her husband does not reveal any significant changes in memory or other cognitive abilities. Testing does not reveal any significant deficits, and memory scores were above average. Ms. Pickens is provided feedback regarding the aging process and is reassured that her current test performance does not reveal any signs of dementia or mild cognitive impairment.
Normal Aging: Physiological, Cognitive, and Psychological
Aging is a term used to describe advancement through the life cycle from birth to death and is used by the general population to describe the process of getting older (Pankow & Solotoroff, 2007). Normal aging encompasses myriad changes involving physiological, psychological, cognitive, sociological and economic aspects. While all of these areas are important in understanding the aging process, a comprehensive review is outside the scope of this book. The present chapter will focus on the physiological, psychological
and cognitive aspects of normal aging given that those aspects of normal aging are most likely to be encountered by the clinician.
Optimal Versus Typical Aging
One caveat to the study of normal aging involves whether the study population of older adults includes individuals with “optimal” versus “typical” aging. In studies of optimal aging (also referred to as “successful” aging), individuals with common medical illnesses (e.g., diabetes, cardiac disease, chronic obstructive pulmonary disease) or those taking numerous medications are excluded from the study. As will be discussed in later chapters, the impact of medical illness needs to be considered when assessing mood, cognition and quality of life. Optimal aging individuals have often been described as “super normal” because they perform at the upper end of the normal distribution of cognitive and physical test scores. Studies of “typical” aging are motivated by the theory that diseases are to be expected as part of normal physiological aging. Studies of typical aging include individuals with common medical illnesses using the typical medications to treat those illnesses (i.e., antihypertensive medication) and tend to provide a less optimistic picture of normal aging than studies of optimal aging (Smith, Ivnik & Lucas, 2008). In drawing conclusions about normal aging, it is important to consider which group of individuals has been studied— those who are typical of the aging process, or those who have in many aspects “succeeded” in avoiding the typical process.
Physiological Aspects of Aging
Anatomical and imaging studies of the brain across the life span have revealed differences in brain structure, particularly between men and women. Cross-sectional studies of aging estimate the average rate of aging from correlations with age but cannot directly determine rates of change and individual differences, whereas many longitudinal studies have relied on small sample sizes. A five-year longitudinal study of brain regions in healthy adults revealed that longitudinal changes in brain volume are not uniform and that the magnitude of change varies across regions and individuals (Raz et al., 2005). In terms of brain regions, the greatest areas of shrinkage were found in the caudate, cerebellum, hippocampus and tertiary association cortices. Entorhinal cortex shrinkage was noted to be minimal, and stable volumes were noted in the primary visual cortex. Age-related differences were found for the hippocampus (memory) and prefrontal cortex (planning and
A Practical Guide to Geriatric Neuropsychology
problems solving). Changes in brain volume varied during adulthood across individuals, with reliable individual differences in change in a select group of healthy volunteers in all measured regions except the inferior parietal lobe. Significant differences in the entorhinal cortex were noted between the oldest adults studied, with no shrinkage noted in younger and middle-aged individuals (Raz et al., 2005).
Studies relying on larger sample sizes, such as the Framingham Heart Study, have shown that age explained 50% of total cerebral volume agerelated differences after age 50 years (DeCarli et al., 2005). The greatest volume loss attribute to age was noted in the frontal lobe (12%), with smaller difference found in the temporal lobe (9%) and “modest” occipital and parietal lobe changes. Men had significantly smaller brain volume in the frontal lobe compared with women, although other age-related gender differences were noted to be small. The presence of infarction on magnetic resonance imaging increased with age, was common after age 50 years and was associated with larger white-matter hyperintensity (WMH) volumes.
It is important for studies of anatomical aging to include individuals with common health conditions so as to portray changes in “typical” versus “optimal” aging. Using data from the Rotterdam Scan Study, Ikram et al. (2008) investigated how age, sex, small vessel disease and cardiovascular risk factors affected cerebrospinal fluid, gray matter, white matter and white-matter lesions. The study included 490 nondemented individuals between the ages of 60 and 90 years who had a history of hypertension (51%), had a history of diabetes mellitus (4.9%), were current smokers (17.8%) and were former smokers (54%). Decreases in total brain, normal white matter and total white matter decreased with increased age, whereas gray matter remained unchanged. White-matter lesions increased in both men and women, even when persons with evidence of infarctions (i.e., stroke) were excluded. Those individuals with larger amounts of small vessel disease had smaller brain volume and smaller normal white-matter volume. Other factors related to smaller brain volume included diastolic blood pressure, diabetes mellitus and current history of smoking.
Cognitive Aspects of Aging
While some cognitive decline due to aging is inevitable, not all older adults develop degenerative conditions as they age. It is the task of the geriatric clinician to determine whether the complaints and concerns of cognitive
change in the older adult reflect subjective worry or are indicative of a neurodegenerative disorder. This chapter explores the factors related to stability and decline in normal aging.
Cognitive Changes
It is well understood that along with normal age-related changes in brain morphology, there exist incremental declines in cognition in multiple cognitive domains (Drachman, 2006; Finch, 2009; Salthouse, 2009). These declines begin early, in the third and fourth decades of life (Salthouse, 2009), but are often not noticeable until late life. Compared with young adults, older adults show selective losses in functions related to speed and efficiency of information processing. Vulnerable systems are those involved with attention, memory recall, executive working memory and multitasking skills (Salthouse, 1996; van Hooren et al., 2007). While delayed free recall is less efficient, it is not the profound rapid forgetting deficit seen among those with Alzheimer’s disease (AD) (Welsh Butters, Hughes, Mohs, & Heyman, 1991, 1992), and retrieval with cues is typically preserved. The profile of amnestic disturbance in normal aging is primarily in the efficient accessing of stored information, rather than in the consolidation and storage of information (Welsh-Bohmer & Koltai Attix, 2014). Performance on measures of executive efficiency (e.g., Trail Making) and language retrieval (e.g., verbal fluency) also tend to be lower in older groups compared with their younger counterparts (Salthouse, 2010). Finally, normal older adults also show less efficient performances than younger groups on tests of visuoperceptual, visuospatial and constructional functions (Eslinger, Damasio, Benton, & Van Allen, 1985; Howieson, Holm, Kaye, Oken & Howieson, 1993; Park & Schwarz, 2000).
Theories of Normal Cognitive Aging
Most cognitive science theories of normal age-related cognitive decline support the idea of a broad explanatory mechanism for age-related cognitive change rather than unique and specific changes in specific domains and structures. These explanations are not mutually exclusive, but rather use difference vantage points to illustrate similar concepts. Perhaps the most popular theory focuses on changes in the speed of central processing (Finkel, Reynolds, McArdle & Pedersen, 2007; Salthouse, 2005). Another explanation focuses on the “fluid versus crystallized” constructs of decades past, with the novel problem-solving and flexible thought skills of fluid
A Practical Guide to Geriatric Neuropsychology
intelligence being more susceptible than well-rehearsed verbal crystallized skills (Botwinick, 1977; Horn, 1982). Support from neuroimaging and histopathological studies (Coffey et al., 1992; Gur, Gur, Obrist, Skolnick & Reivich, 1987; Haug et al., 1983; Tisserand, 2003) have led to a conceptualization of normal aging as a selective vulnerability in frontal-subcortical, dysexecutive processes (Daigneault & Braun, 1993). Other theories have focused more on failures in distributed brain networks across the age span (Finkel et al., 2007; Reuter-Lorenz & Park, 2013; Salthouse 2010).
In considering the findings of studies involving normal aging, we offer three basic cautions:
• Consider results in light of the definition for inclusion of “normal older adults” in each study. There is substantial variability, with some not screening objectively for nervous system disorders or strictly operationalizing their criteria for normal aging.
• When considering differences in age-related test results, in groups or individually, keep in mind that tests also have different inclusion criteria for their standardization sample (see test manuals) and that standard scores thus correct for age differentially across measures.
• “Normal” aging is not a unitary state. Story and Koltai Attix (2009) described the variability in normal, nonpathological aging in three (albeit arbitrary) groups: (1) optimal aging, (2) normal aging, and (3) suboptimal aging. Story and Koltai Attix proposed that if one were to follow longitudinally a normal group of older adults who have objectively met criteria indicating that they are free from pathology (e.g., normal neurological examination and neuroimaging, absence of major medical conditions and of history of CNS trauma), the performance curves of aging in that group of individuals would show normal variance, with some declining less and some declining more than others, regardless of the group (i.e., optimal, normal, suboptimal).
Identification of the Prodromal Stages of Neuropathological Aging
The ability to draw the line between normal and pathological aging is indeed imperfect. Nonetheless, researchers have defined with increasing accuracy the ability to detect the earliest signs of neurodegenerative
disease. Such efforts were launched to improve clinical treatment, care planning, and management and to characterize cohorts for early intervention in clinical trials.
In the early 1990s, researchers at Duke Medical Center characterized the neuropsychological hallmark of AD: the early amnestic pattern of impaired consolidation and rapid forgetting (Welsh et al., 1991, 1992), which remains the most affected area in most cases as other areas of cognition progressively become involved. Contemporaneously, the early selective involvement of the medial temporal lobe, followed by progression throughout the cortices, was illustrated in pathological stages by Braak and Braak (1991). In 1995, the identification of the elevated risk for amnestic disturbance related to AD with the presence of one or two copies of the apolipoprotein epsilon E4 allele (Roses et al., 1995) catalyzed AD research. More recently, cerebrospinal fluid biomarkers of AB peptide and tau levels, along with structural and functional neuroimaging studies, also have defined uses in diagnostically ambiguous cases (Albert et al., 2011; McKhann et al., 2011; Sperling et al., 2011).
Mild Cognitive Impairment
With the characterization of AD across stages with clinical, biological, genetic, imaging and pathological correlates well defined, there was also a new characterization of isolated memory disorders and the early or prodromal phases of AD. The late 1990s and 2000s saw the clinical characterization of mild cognitive impairment (MCI) and the subsequent refinement of single- and multiple- domain, amnestic and nonamnestic MCI definitions (Petersen et al., 1999; Petersen, 2004). Nomenclature relevant to early identification includes MCI due to AD, prodromal AD (used in Europe) and mild neurocognitive impairment due to Alzheimer’s disease (used by the American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders, fifth edition [DSM-5]) (Albert et al., 2011; Dubois et al., 2010).
Incidence and prevalence rates for MCI have been established. In the Chicago Health and Aging Project, 34% of a community sample was found to have MCI, whereas 13% had AD. Not surprisingly, prevalence and incidence rates of MCI and rates of conversion to dementia vary considerably depending on the definition applied (Busse et al., 2003). Gomar et al. (2011) investigated the utility of biomarkers and cognitive markers to predict conversion from MCI to AD in the Alzheimer’s Disease Neuroimaging Initiative
A Practical Guide to Geriatric Neuropsychology
(ADNI) study. They found that cognition at baseline predicted conversion over time better than most biomarkers. Here again, the utility of behavioral data is underscored not only in its obvious use to characterize and educate patients and families but also in its inclusion as a prognostic variable.
Studies have demonstrated conversion rates to dementia and risk factors for progression versus stability or improvement. Busse et al. (2006) conducted a six-year study of a community of adults 75 years and older who were dementia free. Based on their results they estimated that 60% to 65% of people with MCI will develop dementia during their life and that the progression to AD from MCI was time dependent, occurring in the first 2 to 3 years. Fischer et al. (2007) likewise followed a group of communitybased adults older than 75 years over a period of 30 months. They showed that conversion rates to AD were much higher for those who had amnestic MCI (48.7%) than for those that had nonamnestic MCI (26.8%). A host of studies characterized risk factors and probabilities for conversion to various states of disease. All of these longitudinal studies likewise demonstrated another important factor: that about 20% to 25% of those diagnosed with MCI did not progress to dementia, but rather improved or remained stable over time. This presumably is because cohorts with MCI include people with cognitive impairment due to treatable factors (e.g., depression, metabolic disorders) and potentially stable disorders (e.g., small vessel disease). The varied longitudinal trajectories of these samples resulted in the apt characterization of MCI as a “risk state” for dementia, rather than a preclinical diagnosis.
More recently, the National Institute on Aging– Alzheimer’s Association workgroups on diagnostic guidelines for AD detailed the diagnosis of MCI due to AD (Albert et al., 2011) and characterized the preclinical stages of AD (Sperling et al., 2011). Criteria for MCI due to AD include concern regarding a change in cognition, impairment in one or more cognitive domains, preservation of independence in functional skills, impairment of 1 to 1.5 standard deviations below age- and education-matched peers on culturally appropriate measures of cognition and an absence of dementia. The incorporation of biomarkers into research criteria is discussed, and the value of longitudinal cognitive evaluation is emphasized to establish the accuracy of MCI due to AD.
In MCI due to AD, patients often show the characteristic memory disorder of more fully expressed disease, but they also may show other mild deficits in executive function, language expression, visuoperception and
