Skip to main content

Cognitive impairment indicator for the neuropsychological test batteries in the Canadian Longitudinal Study on Aging: definition and evidence for validity



Prevalence of overall cognitive impairment based on each participant’s performance across a neuropsychological battery is challenging; consequently, we define and validate a dichotomous cognitive impairment/no cognitive indicator (CII) using a neuropsychological battery administered in a population-based study. This CII approximates the clinical practice of interpretation across a neuropsychological battery and can be applied to any neuropsychological dataset.


Using data from participants aged 45–85 in the Canadian Longitudinal Study on Aging receiving a telephone-administered neuropsychological battery (Tracking, N = 21,241) or a longer in-person battery (Comprehensive, N = 30,097), impairment was determined for each neuropsychological test based on comparison with normative data. We adjusted for the joint probability of abnormally low scores on multiple neuropsychological tests using baserates of low scores demonstrated in the normative samples and created a dichotomous CII (i.e., cognitive impairment vs no cognitive impairment). Convergent and discriminant validity of the CII were assessed with logistic regression analyses.


Using the CII, the prevalence of cognitive impairment was 4.3% in the Tracking and 5.0% in the Comprehensive cohorts. The CII demonstrated strong convergent and discriminant validity.


The approach for the CII is a feasible method to identify participants who demonstrate cognitive impairment on a battery of tests. These methods can be applied in other epidemiological studies that use neuropsychological batteries.

The Canadian Longitudinal Study on Aging is a population-based study of adults aged 45–85 at study entry [1, 2]. A major strength of the CLSA is the use of neuropsychological batteries to measure cognition. Unlike cognitive screening tests, neuropsychological batteries do not provide overall summaries of cognitive status and cannot be easily combined. Other longitudinal studies [3] have used clinician assessments to determine evidence for overall cognitive impairment from participants’ performance on a neuropsychological battery. Cut-offs for composite scores created from neuropsychological batteries have been developed for the Health and Retirement Study [4, 5] including a recent algorithm using machine learning approaches [6], but these approaches were trained on a subsample who received a clinician diagnosis in the Aging, Demographic, and Memory Study [7]. A clinician assessment was not undertaken in the CLSA. We present an approach to determine cognitive impairment based on participant performance across the CLSA neuropsychological battery and present evidence for its validity.

Determination of abnormally low performance on each neuropsychological test relies on comparison with a normative sample and setting a cut-off for impairment. Determining a person’s overall cognitive impairment based on performance across multiple tests in a neuropsychological battery is complex. A person can perform within normal limits or perform well below average (i.e., abnormally low performance) on any number of the neuropsychological tests. If a person obtains some abnormally low test scores, does this reflect overall cognitive impairment? If the cut-off for abnormally low scores is the 5th percentile relative to a comparison with normative data, every person has a 5% chance of their performance on a given test being recorded as abnormally low, even in the absence of true cognitive impairment. With several tests in a battery, the probability of at least one test falling below the 5th percentile in the absence of cognitive impairment is greater than 5%. The greater the number of tests in the battery, the higher the probability of a false conclusion of ‘impairment’ [8,9,10]. Ignoring this inflated probability of spuriously impaired scores results in an overestimation of cognitive impairment [8, 11]. Correcting for this inflated probability of impaired scores across a battery of neuropsychological tests when making a determination of overall cognitive impairment is imperative to good clinical neuropsychological practice and can be applied using a baserate approach for research, such as with the CLSA and other epidemiological studies. A baserate approach involves algorithms that estimate the expected number of very low scores [12], facilitating the interpretation of a neuropsychological battery [13]. The baserate approach to determine spurious low scores using Crawford et al.’s [12] algorithm has evidence for its validity [9, 10].

Normative comparison standards for the CLSA were created for each of the four neuropsychological tests used in the Tracking cohort [14] and for seven of the eight tests used in the Comprehensive cohort.

The CII is derived from the participant’s performance on each test in the battery, where each test score is compared with normative data from the CLSA [14]. Crawford and colleagues’ [12] baserate algorithm was used to adjust for the probability of spuriously low scores before classifying the person’s overall performance. To assess the convergent and discriminant validity of the CII, we used the participants’ responses to a series of questions about physician-diagnosed chronic conditions. Based on the literature, we had three sets of hypotheses. We hypothesized (1) that the CII would be associated with neurological conditions that can cause cognitive impairment such as dementia or Alzheimer’s disease [15], stroke [16], transient ischemic attack [17], multiple sclerosis [18], Parkinson’s disease [19], and epilepsy [20]; (2) that the CII would be strongly associated with physician-diagnosed memory problems; (3) that medical conditions that are risk factors for cognitive impairment would be associated with the CII, but to a lesser degree. The chronic conditions that are risk factors for cognitive impairment include diabetes [21], hypertension [22], cardiac diseases [23], major depressive disorder [24], peripheral vascular disease [25], kidney disease [26], and thyroid dysfunction [27]. We did not expect the CII to be associated with allergies [28], arthritis [29], migraines [30], osteoporosis [31], history of cancer [32], ulcers, or back pain. We had no a priori hypotheses for bowel or urinary incontinence because these are features of advanced neurological conditions, and the cognitive consequences for these as stand-alone conditions are not well-studied.


Aim, design, and setting

The aim is to develop and validate a cognitive impairment indicator that summarizes cognitive performance across a neuropsychological battery. Prospective cohort design, but cross-sectional analyses for the current project. The setting is community-based.


CLSA participants have been described elsewhere [1]. Briefly, an age-stratified random sample from the Canadian population between the age of 45 and 85 years was selected for the Tracking cohort, and random samples of participants residing near one of eleven data collection sites across Canada were selected for the Comprehensive cohort. The Tracking cohort (N = 21,241) was administered questionnaires over the telephone; including yes/no questions about having been diagnosed by a physician as having a chronic condition (34 conditions), four neuropsychological tests (see [33] for a description of the data collection protocol and tools). Participants in the Comprehensive cohort (N = 30,097) were assessed in. The descriptions of the two cohorts are shown in Table 1. Although the CLSA is an ongoing longitudinal study, the data for the current project were cross-sectional from the baseline data collection phase.

Table 1 Description of the Tracking and Comprehensive cohorts of the CLSA at baseline


Neuropsychological tests

The neuropsychological tests used in CLSA are described in more detail elsewhere [34, 35], but included the following tests administered by telephone to the Tracking cohort: Rey Auditory Verbal Learning Test immediate recall (REY I) and 5-min delayed recall (REY II), the Mental Alternation Test (MAT), and Animal Fluency (AF; we used AF2 scores that are consistent with scoring rules used clinically [14]).

The Comprehensive Cohort completed testing in-person, including the above four tests, as well as the Controlled Oral Word Association Test (total score was used for the letters FAS) and a modified version of the Victoria Stroop Test (Stroop Interference—interference ratio time it took to complete the task on the “color” task divided by performance on the “dot” task). For all but the Stroop test, higher scores indicated better cognitive performance; for the Stroop Interference score, lower scores indicated less interference. Summaries of the raw performances on these tests (i.e., not normed) for the whole sample in both cohorts are shown in the top of Table 2. To create normatively corrected scores Stroop Interference scores were reversed, making interpretation of these scores consistent with the other neuropsychological tests, and for all analyses the norm-corrected Stroop scores were used. Stroop errors were not used for the analyses due to the extreme skewness in this variable (i.e., most participants did not make errors). The Comprehensive cohort also received a choice reaction time and two prospective memory tasks, but these tests were not used in the current study due to problems in administration or highly skewed distributions.

Table 2 Performance on neuropsychological battery

Chronic conditions

Participants were asked to respond yes/no to the question: “Has a doctor ever told you that you have (the chronic condition)?” The list of conditions, and the number of participants who responded yes to each, is shown in Table 3. We examined each condition separately for its association with the CII, and we created three groupings based on our a priori hypotheses. One group labeled “Neurological” included participants who reported having a physician diagnosis of dementia or Alzheimer’s disease, memory problems, stroke, transient ischemic attack, multiple sclerosis, parkinsonism or Parkinson’s disease, or epilepsy; versus those who denied any neurological condition. A second group labeled “Risk Neurological” included participants with a self-reported physician diagnosis of at least one known risk factor for cognitive impairment: diabetes, hypertension, cardiac diseases, major depressive disorder, peripheral vascular disease, kidney disease, or thyroid dysfunction versus those reporting none of these conditions. To provide support for the CII with divergent validity, a third group was created with participants who had self-reported conditions for which we did not expect to see an increased likelihood of cognitive impairment: allergies, arthritis, migraines, osteoporosis, history of cancer, ulcers, or back pain. We were unable to create a comparison group of persons who reported none of these conditions because too many participants in each cohort had at least one of these conditions. Consequently, the third group condition was modified to include arthritis, migraines, osteoporosis, history of cancer, or ulcers with the comparison group comprised of those reporting none of these conditions.

Table 3 Frequency of chronic conditions by cohort

Analytic approach

Derivation of the cognitive impairment indicator (CII)

For each cohort, the derivation of the CII involved three steps. First, on each neuropsychological test, each participant’s raw score was transformed to a normed score based on comparisons with the neurologically healthy normative sample [14], with regression-based norms correcting for the participant’s age, sex, and education within each language group (referred to hereafter as “normed scores”). In the second step, the participant’s normed score was used to obtain their low score indicator (impaired versus within normal limits) for each neuropsychological test by comparing the participant’s normed score to the cut-off point for abnormally low scores. The cut-off point was the mean from a bootstrapped distribution of scores from the normative sample corresponding to the 5th percentile for each test score. In the third step, the CII was determined for each participant based on her/his performance across the battery of neuropsychological tests. This classification into overall impaired versus non-impaired for the CII incorporated a baserate of low scores. In particular, baserates of the expected proportions of a cognitively healthy population estimated to demonstrate cognitive impairment on any given test were determined using the algorithm created by Crawford and colleagues [12]. The Crawford et al. [12] algorithm uses a Monte Carlo-based method to estimate the probability of obtaining a given number of abnormally low scores. The probability of abnormally low scores increases as the number of tests in the battery increases and is dependent on the test scores’ intercorrelations. This baserate algorithm is based on the intercorrelations of the neuropsychological tests in the cognitively healthy sub-sample (i.e., the normative sample). The likelihood of low scores also depends on the cut-off used, and for the algorithm we selected as the 5th percentile. The algorithm estimates the baserate for the frequency of test scores falling in the abnormally low range that would be expected to occur in a cognitively healthy population.

We used Crawford et al.’s [12] algorithm in our neurologically healthy norming samples to determine how frequently abnormally low scores would occur on the neuropsychological battery of four (Tracking) or six (Comprehensive) intercorrelated normally distributed tests, separately for French- and English-speaking subsamples. Additionally, we completed this for the four tests given to both the Comprehensive cohort and the Tracking cohort to allow for more direct comparisons across the two. Abnormally low scores were defined as equal to or lower than the 5th percentile because these indicate relatively rare outcomes. For the CLSA Tracking cohort, the algorithm by Crawford and colleagues [12] estimated the percentage of a cognitively healthy population presenting with at least one abnormally low score on the four-test battery to be 15.9% of the English-speaking and 15.7% of the French-speaking subsamples, which in a clinical setting represents a relatively common outcome. In contrast, only 3.7% of the cognitively healthy population based on the English-speaking subsample and 3.8% of the cognitively healthy population based on the French-speaking subsample were estimated to present with at least two abnormally low scores. We propose that the probably of one abnormally low test was too high (over 15%) and the probability of two or more tests was a sufficiently rare baserate likely indicative of cognitive impairment so we used a cutoff of impairment on two or more tests for the CII.

For the Comprehensive cohort, the baserate for at least one abnormally low score on the four-test battery was estimated at 15.6% of the English-speaking and 15.9% for the French-speaking subsample, again a relatively common occurrence, whereas at least two abnormally low scores would be expected to occur with a baserate of 3.5% for both the French- and English-speaking subsamples. We determined that two of the four tests presenting as abnormally low was sufficiently rare to indicate cognitive impairment for the four-test CII in the Comprehensive cohort.

For the six-test Comprehensive battery, the estimated percentage of the population presenting with at least one abnormally low score was 22.6% (22.56% in English and 22.60% in French), whereas 5.8% were estimated to present with two or more low scores (5.81% in English and 5.78% in French) and only 1.4% of the population were estimated to present with three or more abnormally low scores. One abnormally low score was too common (over 22%), but low scores for three or more tests were estimated to occur in less than 2% of the population which was too rare, so we chose to use the cut-off of two or more tests as indicative of cognitive impairment.Footnote 1

In summary, for both Tracking and Comprehensive cohorts, participants who obtained two or more abnormally low test scores, whether in the four-test or the six-test battery, were classified as overall cognitively impaired (CII = 1); otherwise, they were classified as not cognitively impaired (CII = 0). The CII was created for all participants in the CLSA who had complete cognitive data (i.e., four test scores in the Tracking cohort and six test scores in the Comprehensive cohort) and for whom normative comparisons were possible (i.e., they had complete data for age, sex, education level, and language of administration).

Concurrent and discriminant validity of the CII

To explore the validity of the CII, we used logistic regression analyses to assess whether individual or groups of chronic medical conditions were associated with CII as posited by our a priori hypotheses (see the “Chronic conditions” section for the groupings). Groups of chronic conditions were created to mitigate concerns about small cell sizes for some of the chronic conditions. In the analyses for groups of chronic conditions, we used sampling weights (version 1.2) [36] that were adjusted for the Canadian population to explore if this impacted the associations with the CII. Sampling weights inflate the observations in the sample to the level of the population to minimize the sampling bias, allowing observations within the sample to be extrapolated to the population of origin.

For the odds ratio (OR) estimates from the logistic regressions, we used the descriptors of magnitude of OR provided by Chen et al. [37], based on a rate of cognitive impairment of 4% in a cognitively healthy group for the 4-test CII: OR = 1.0 to 1.49 as trivial to 1.5 as small, 1.6 to 2.7 as medium, and 2.8 to 5.0 as large (the six-test CII had a higher baserate of cognitive impairment, so OR = 1.5 was classified as small, OR = 2.7 was medium, OR = 4.6 was large). Finally, we calculated the prevalence of cognitive impairment in the Tracking and Comprehensive cohorts with and without sampling weights [36] using the CII based on the same four tests.


The prevalence of cognitive impairment in the CLSA was 4.3% (4.1% before applying the sampling weights) in the Tracking and 4.3% (3.1% before applying sampling weights) in the Comprehensive cohorts (see Table 2). Table 4 shows the estimated ORs estimates, and their 95% confidence intervals, from the logistic regression analyses for each chronic condition associated with an increased odds of cognitive impairment as indicated by the CII. Dementia or Alzheimer’s disease was associated with a large increased odds of cognitive impairment, and memory problems were associated with medium magnitude OR across both cohorts. Stroke had a medium magnitude OR in the Tracking and a small magnitude OR in the Comprehensive cohort. The OR associated with parkinsonism/Parkinson’s disease and multiple sclerosis was of medium magnitude in Tracking and small magnitude for the 4-test CII Comprehensive, but the OR’s confidence interval included zero when the CII was based on six tests. The Comprehensive cohort received additional health-related questions, including details about traumatic brain injury (TBI) and the location of cancer in the central nervous system (CNS). The CI of the ORs for self-reported residual symptoms from a TBI did not include zero (expB 2.147; CI 1.654–2.787 for the 4 test CII and 1.843; CI 1.472–2.308 for the 6 test CII), but the ORs for cancer of the CNS were not significant likely due to only 36 of the sampling repotting this type of cancer. Bowel incontinence had a medium magnitude OR in the Tracking cohort and in the Comprehensive cohort a small magnitude OR for the 4-test CII and a trivial magnitude OR for the 6-test CII. Peripheral vascular disease, mood disorders, anxiety disorders, epilepsy, and intestinal or stomach ulcers had a small magnitude OR in the Tracking and small to trivial ORs in Comprehensive. Vision impairment had a small magnitude OR and hearing impairment a trivial to small magnitude OR. Some medical conditions presented with statistically significant OR across both cohorts, but the magnitude of the OR was trivial such as for urinary incontinence, diabetes, stomach ulcers, transient ischemic attacks (TIAs), and rheumatoid arthritis. Where remaining conditions were statistically significant inconsistently across cohorts, significant ORs were trivial in magnitude.

Table 4 Results of logistic regression with each individual chronic conditions as predictors of overall cognitive impairmenta

The grouped variables (see method under Chronic conditions) of neurological conditions (n = 1694 in Tracking; n = 2312 in Comprehensive) presented with a small to medium increased odds of cognitive impairment (see Fig. 1); this finding was expected because these conditions were used as exclusionary criteria for the normative subsample. Figure 1 also shows the trivial magnitude of increased odds of cognitive impairment in conditions that are risk factors for cognitive impairment (n = 13,181 in Tracking; n = 18,975 in Comprehensive). Finally, there was no evidence of association for cognitive impairment with the grouped variable we did not expect to be associated with the CII (n = 11,164 in Tracking; n = 15,378 in Comprehensive).

Fig. 1
figure 1

Odds ratios and confidence intervals for Tracking (TR) and Comprehensive (CO) cohorts (4 test CO4 and 6 test CO6 for groups with neurological conditions (neurological), conditions that are risk factors for cognitive impairment (risk neurological), and conditions that we would not expect to be linked to cognition (not neurological)


Logistic regression analyses provided evidence for validity of the CII, which suggested the prevalence of cognitive impairment in the CLSA was 4.3% in the Tracking and 4.3% in the Comprehensive cohorts. The approach presented here for identifying cognitive impairment and creating a new CII variable in the CLSA is an approach common in clinical neuropsychology practice [9, 10], but newer in its application to epidemiological aging studies. Understanding the baserates of low scores for neuropsychological tests helps deepen the understanding of neurological conditions such as dementia in epidemiological studies. Kiselica and colleagues [38] studied the Uniform Data Set 3.0 Neuropsychological Battery and found that abnormally low scores were common, and the use of baserates analyses to adjust for the expected number of abnormally low scores displayed in a cognitively healthy subsample helped to predict dementia status. Holdnack and colleagues [39] used baserates of low scores with the National Institutes of Health cognition toolbox and found these agreed well with the reference standard of diagnosed severe traumatic brain injury. Tallying the number of impaired test scores in the neuropsychological battery has been associated with diagnostic criteria for mild cognitive impairment and dementia in the Alzheimer’s Disease Neuroimaging Initiative dataset [40].

Increased odds of cognitive impairment are associated with medical conditions that we would expect to be associated with cognitive impairment, namely diagnosis by a health professional of memory problems, dementia or Alzheimer’s disease, or stroke. This finding was expected because the CII was derived from normed scores and corrected for baserates of low scores expected in the cognitively healthy subsample, which excluded persons with neurological conditions such as dementia, memory problems, and stroke. Although the normative subsample also excluded persons with transient ischemic attacks, Parkinson’s disease (or parkinsonism), multiple sclerosis, and epilepsy, these conditions were not necessarily associated with increased odds of cognitive impairment in the present study. This finding likely reflects the heterogeneity in cognitive status presented by patients diagnosed with these conditions — some but not all individuals patients with these neurological conditions present with cognitive impairment [16].

Diagnosis of mood disorder was associated with a trivial to small increased likelihood of cognitive impairment, consistent with meta-analyses demonstrating a small magnitude of association between depression and cognition [41]. Similarly, sensory loss had a trivial to small association with cognitive impairment, consistent with associations reported between cognition and sensory function [42]. Bowel incontinence was associated with an increased risk of cognitive impairment, potentially due to comorbidities of bowel incontinence with some neurological conditions (e.g., more advanced dementia); alternatively, this finding could be related to a possible link between bowel disorders and cognition via the vagus nerve [43].

In this sample the prevalence of cognitive impairment was relatively low: 4.3% in the Tracking and 5.0% in the Comprehensive cohorts. It is likely that the CLSA sampling procedures led to a low prevalence of cognitive impairment. CLSA participants had to be able to consent without the need for proxy consent procedures at study entry [1], effectively excluding persons with overt cognitive impairment from the study. Nevertheless, the frequency of cognitive impairment in the CLSA appears similar to that reported by Hänninen et al. [44], who identified cognitive impairment (but no dementia) in 5.3% of participants in a population-based study of people aged 60–76. Larrieu and colleagues [45] identified cognitive impairment in 2.8% of a community-based sample. In contrast, other studies have found higher rates of cognitive impairment [46,47,48]. These varied rates reflect the fact that different ways of conceptualizing cognitive impairment and varied recruitment methods (i.e., were persons with overt cognitive impairment excluded from study entry in the CLSA), which together impact the prevalence in epidemiological studies [49].


The use of self-reported neurological and other medical conditions in this manuscript is a major limitation to these data, and these are the only data available with CLSA at this time. The medical conditions also lacked important details related to cognition. We had insufficient TBI detail to categorize persons as mild, moderate, or severe; no detail on cancer severity, treatment, or how distal the cancer was, we had no detail on the type of MS or whether persons with PD had dementia due to PD. Some of the self-reported conditions, for example, dementia included few people, widening the confidence intervals. Ideally, the validity of the current CII would comprise classification accuracy relative to a clinical evaluation as a gold standard reference. Another limitation of the CII is the limited nature of the tests used none were motor or speed of processing tests, which are the tests we would expect to show deficits for some persons with MS or PD, for example. Another limitation of the CII is the fact that it was only computed if all neuropsychological tests were completed; consequently, the CII was only able to be calculated on 77% of the Tracking cohort, and 83% (for the 6-item) 90% for the 4-item for the Comprehensive cohort due to missing values. The comparability of the findings despite differences in missing values for the different indicators is reassuring, nevertheless, missing data are a limitation and likely reflect missing data from those who have cognitive impairment. Another limitation of the CII is the use of all neuropsychological tests versus only using those that are most sensitive to cognitive impairment, for example, memory tests for Alzheimer’s disease. The chronic conditions reported here would be more or less likely to be associated with impairments on memory or executive function tests, but future work could derive a different CII with tests most sensitive to a condition of interest. In this modified version of the Victoria Stroop Test administered to the comprehensive cohort in CLSA errors were tabulated but not corrected during the task. With this modification, the errors were not captured in the interference score, as it would typically be in the original version of the Victoria Stroop Test. The CII did not include Stroop errors due to extreme skew in this variable and did not include the experimental prospective memory tests that were administered to the Comprehensive cohort in CLSA for this same reason. This CII, therefore, does not use all of the possible neuropsychological tests available in the Comprehensive cohort. The CII is based on one conceptualization of impairment, which was at the 5th percentile. Different conceptualizations of impairment will yield different findings. Finally, the CII is limited by the lack of a premorbid estimate of general cognitive ability, which could be important if the CII were used to help classify persons with suspected cognitive decline.


The CII appears to have good evidence for convergent and discriminant validity. The baserate approach that is core to the derivation of the CII approximates best practices in clinical neuropsychology, and this approach can be applied to any epidemiological database that includes a battery of neuropsychological tests. It does, however, miss procedural or process approach data, a key aspect of clinical neuropsychology practice that is impossible to detail from summary scores (i.e., raw test scores do not convey how participants approached the task). The CII can be used in future studies using the CLSA data, and the approach we used to create the CII can be applied to other epidemiological studies that use neuropsychological batteries.

Availability of data and materials

These data are available publicly through an application to CLSA — The CII is available as a derived variable or by contacting the first author for the SPSS syntax.


  1. Although not described here, a CII based on three or more tests abnormally low as a cut off are available as a derived variable from CLSA for researchers who wish to use the stricter criterion.



Animal Fluency


Canadian Institutes of Health Research


Cognitive impairment indicator


Canadian Longitudinal Study on Aging


Central nervous system


The Mental Alternation Test


Odds ratio


Rey Auditory Verbal Learning Test immediate recall


Rey Auditory Verbal Learning Test 5-min delayed recall


Transient ischemic attack


Traumatic brain injury


  1. Raina PS, Wolfson C, Kirkland SA, Griffith LE, Oremus M, Patterson C, et al. The Canadian longitudinal study on aging (CLSA). Canadian Journal on Aging/ La Revue canadienne du vieillissement. 2009;28(3):221–9.

    Article  PubMed  Google Scholar 

  2. Raina P, Wolfson C, Kirkland S, Griffith LE, Balion C, Cossette B, et al. Cohort Profile: The Canadian Longitudinal Study on Aging (CLSA). Int J Epidemiol. 2019;48(6):1752–3.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kawas C, Gray S, Brookmeyer R, Fozard J, Zonderman A. Age-specific incidence rates of Alzheimer’s disease The Baltimore Longitudinal Study of Aging. Neurology. 2000;54(11):2072–7.

    Article  CAS  PubMed  Google Scholar 

  4. Crimmins EM, Kim JK, Langa KM, Weir DR. Assessment of cognition using surveys and neuropsychological assessment: the health and retirement study and the aging, demographics, and memory study. J Gerontol B Psychol Sci Soc Sci. 2011;66 suppl_1(suppl_1):i162–71.

    Article  PubMed  Google Scholar 

  5. Herzog AR, Wallace RB. Measures of cognitive functioning in the AHEAD study. J Gerontol B Psychol Sci Soc Sci. 1997;52 Spec No:37–48.

    Article  CAS  PubMed  Google Scholar 

  6. Gianattasio KZ, Ciarleglio A, Power MC. Development of algorithmic dementia ascertainment for racial/ethnic disparities research in the US health and retirement study. Epidemiology. 2020;31(1):126–33.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Langa KM, Plassman BL, Wallace RB, Herzog AR, Heeringa SG, Ofstedal MB, et al. The aging, demographics, and memory study: study design and methods. Neuroepidemiology. 2005;25(4):181–91.

    Article  PubMed  Google Scholar 

  8. Binder LM, Iverson GL, Brooks BL. To err is human: “Abnormal” neuropsychological scores and variability are common in healthy adults. Arch Clin Neuropsychol. 2009;24(1):31–46.

    Article  PubMed  Google Scholar 

  9. Decker SL, Schneider WJ, Hale JB. Estimating base rates of impairment in neuropsychological test batteries: a comparison of quantitative models. Arch Clin Neuropsychol. 2012;27(1):69–84.

    Article  PubMed  Google Scholar 

  10. Schretlen DJ, Testa SM, Winicki JM, Pearlson GD, Gordon B. Frequency and bases of abnormal performance by healthy adults on neuropsychological testing. J Int Neuropsychol Soc. 2008;14(3):436–45.

    Article  PubMed  Google Scholar 

  11. Gisslén M, Price RW, Nilsson S. The definition of HIV-associated neurocognitive disorders: are we overestimating the real prevalence? BMC Infect Dis [Internet]. 2011 2011; 11:[356 p.]. Available from:

  12. Crawford JR, Garthwaite PH, Gault CB. Estimating the percentage of the population with abnormally low scores (or abnormally large score differences) on standardized neuropsychological test batteries: a generic method with applications. Neuropsychology. 2007;21(4):419.

    Article  PubMed  Google Scholar 

  13. Karr JE, Garcia-Barrera MA, Holdnack JA, Iverson GL. Advanced clinical interpretation of the Delis-Kaplan Executive Function System: multivariate base rates of low scores. Clin Neuropsychol. 2018;32(1):42–53.

    Article  PubMed  Google Scholar 

  14. O’Connell ME, Kadlec H, Maimon G, Taler V, Simard M, Griffith L, Tuokko H, Voll S, Wolfson C, Kirkland S, Raina P. Methodological considerations when establishing reliable and valid normative data: Canadian Longitudinal Study on Aging (CLSA) neuropsychological battery. Clin Neuropsychol. 2021.

  15. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Washington, DC: American Psychiatric Association; 2013.

    Book  Google Scholar 

  16. Vasquez BP, Zakzanis KK. The neuropsychological profile of vascular cognitive impairment not demented: a meta-analysis. J Neuropsychol. 2015;9(1):109–36.

    Article  PubMed  Google Scholar 

  17. Moran GM, Fletcher B, Feltham MG, Calvert M, Sackley C, Marshall T. Fatigue, psychological and cognitive impairment following transient ischaemic attack and minor stroke: a systematic review. Eur J Neurol. 2014;21(10):1258–67.

    Article  CAS  PubMed  Google Scholar 

  18. Santangelo G, Altieri M, Enzinger C, Gallo A, Trojano L. Cognitive reserve and neuropsychological performance in multiple sclerosis: a meta-analysis. Neuropsychology. 2019;33:379–90.

    Article  PubMed  Google Scholar 

  19. Curtis AF, Masellis M, Camicioli R, Davidson H, Tierney MC. Cognitive profile of non-demented Parkinson’s disease: meta-analysis of domain and sex-specific deficits. Parkinsonism Relat Disord. 2019;60:32–42.

    Article  PubMed  Google Scholar 

  20. Liguori C, Costa C, Franchini F, Izzi F, Spanetta M, Cesarini EN, et al. Cognitive performances in patients affected by late-onset epilepsy with unknown etiology: a 12-month follow-up study. Epilepsy Behav. 2019;101: 106592.

    Article  PubMed  Google Scholar 

  21. van Duinkerken E, Ryan CM. Diabetes mellitus in the young and the old: effects on cognitive functioning across the life span. Neurobiol Dis. 2020;134: 104608.

    Article  PubMed  Google Scholar 

  22. Zamora Z, Williamson JD. The effects of lower BP goals on cognitive function in the elderly. Curr Cardiol Rep. 2020;22(8):1–6.

    Article  Google Scholar 

  23. Sumida H, Yasunaga Y, Takasawa K, Tanaka A, Ida S, Saito T, et al. Cognitive function in post-cardiac intensive care: patient characteristics and impact of multidisciplinary cardiac rehabilitation. Heart vessels. 2020;35(7):946–56.

    Article  PubMed  Google Scholar 

  24. Semkovska M, Quinlivan L, O’Grady T, Johnson R, Collins A, O’Connor J, et al. Cognitive function following a major depressive episode: a systematic review and meta-analysis. The Lancet Psychiatry. 2019;6(10):851–61.

    Article  PubMed  Google Scholar 

  25. Csipo T, Lipecz A, Fulop GA, Hand RA, Ngo B-TN, Dzialendzik M, et al. Age-related decline in peripheral vascular health predicts cognitive impairment. Geroscience. 2019;41(2):125–36.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Weiner DE, Seliger SL. Cognitive and physical function in chronic kidney disease. Curr Opin Nephrol Hypertens. 2014;23(3):291.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Ritchie M, Yeap BB. Thyroid hormone: Influences on mood and cognition in adults. Maturitas. 2015;81(2):266–75.

    Article  CAS  PubMed  Google Scholar 

  28. Bensnes SS. You sneeze, you lose: the impact of pollen exposure on cognitive performance during high-stakes high school exams. J Health Econ. 2016;49:1–13.

    Article  PubMed  Google Scholar 

  29. Baker NA, Barbour KE, Helmick CG, Zack M, Al SS. Arthritis and cognitive impairment in older adults. Rheumatol Int. 2017;37(6):955–61.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Martins IP. Cognitive performance in chronic migraine. Arq Neuropsiquiatr. 2020;78(3):131–2.

    Article  PubMed  Google Scholar 

  31. Kang HG, Park HY, Ryu HU, Suk S-H. Bone mineral loss and cognitive impairment: the PRESENT project. Medicine. 2018;97(41):e12755.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Cullen B, Smith D, Deary I, Evans J, Pell J. The ‘cognitive footprint’of psychiatric and neurological conditions: cross-sectional study in the UK Biobank cohort. Acta Psychiatr Scand. 2017;135(6):593–605.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. CLSA/ÉLCV Data Collection. [Accessed 4 November 2021].

  34. Tuokko H, Griffith LE, Simard M, Taler V, O’Connell ME, Voll S, et al. The Canadian longitudinal study on aging as a platform for exploring cognition in an aging population. Clin Neuropsychol. 2020;34(1):174–203.

    Article  PubMed  Google Scholar 

  35. Tuokko H, Griffith LE, Simard M, Taler V. Cognitive measures in the Canadian Longitudinal Study on Aging. Clin Neuropsychol. 2017;31(1):233–50.

    Article  PubMed  Google Scholar 

  36. CLSA Methodology Working Group. Sampling and computation of response rates and sample weights for the tracking (telephone interview) participants and comprehensive participants v1.2. Hamilton ON: CLSA; 2020.

    Google Scholar 

  37. Chen H, Cohen P, Chen S. How big is a big odds ratio? Interpreting the magnitudes of odds ratios in epidemiological studies. Communications in Statistics - Simulation and Computation. 2010;39(4):860–4.

    Article  Google Scholar 

  38. Kiselica AM, Webber TA, Benge JF. Using multivariate base rates of low scores to understand early cognitive declines on the uniform data set 3.0 Neuropsychological Battery. Neuropsychology. 2020;34(6):629–40.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Holdnack JA, Iverson GL, Silverberg ND, Tulsky DS, Heinemann AW. NIH toolbox cognition tests following traumatic brain injury: Frequency of low scores. Rehabil Psychol. 2017;62(4):474–84.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Oltra-Cucarella J, Sánchez-SanSegundo M, Rubio-Aparicio M, Arango-Lasprilla JC, Ferrer-Cascales R. The association between the number of neuropsychological measures and the base rate of low scores. Assessment. 2019;28(3):955–63.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Rock P, Roiser J, Riedel W, Blackwell A. Cognitive impairment in depression: a systematic review and meta-analysis. Psychol Med. 2014;44:2029–40 Psychological medicine. 2013;44:1–12.

    Article  CAS  PubMed  Google Scholar 

  42. Mick PT, Hämäläinen A, Kolisang L, Pichora-Fuller MK, Phillips N, Guthrie D, et al. The prevalence of hearing, vision, and dual sensory loss in older Canadians: an analysis of data from the Canadian Longitudinal Study on Aging. Canadian Journal on Aging / La Revue canadienne du vieillissement. 2021;40(1):1–22.

    Article  PubMed  Google Scholar 

  43. Chen C-H, Lin C-L, Kao C-H. Irritable bowel syndrome is associated with an increased risk of dementia: a nationwide population-based study. PLoS ONE. 2016;11(1): e0144589.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Hänninen T, Hallikainen M, Tuomainen S, Vanhanen M, Soininen H. Prevalence of mild cognitive impairment: a population-based study in elderly subjects. Acta Neurol Scand. 2002;106(3):148–54.

    Article  PubMed  Google Scholar 

  45. Larrieu S, Letenneur L, Orgogozo JM, Fabrigoule C, Amieva H, Le Carret N, et al. Incidence and outcome of mild cognitive impairment in a population-based prospective cohort. Neurology. 2002;59(10):1594–9.

    Article  CAS  PubMed  Google Scholar 

  46. Plassman BL, Langa KM, Fisher GG, Heeringa SG, Weir DR, Ofstedal MB, et al. Prevalence of cognitive impairment without dementia in the United States. Ann Intern Med. 2008;148(6):427–34.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Klich-Rączka A, Piotrowicz K, Mossakowska M, Skalska A, Wizner B, Broczek K, et al. The assessment of cognitive impairment suspected of dementia in Polish elderly people: results of the population-based PolSenior Study. Exp Gerontol. 2014;57:233–42.

    Article  PubMed  Google Scholar 

  48. Zhang Y, Guan Y, Shi Z, Yue W, Liu S, Liu S, et al. Sex differences in the prevalence of and risk factors for cognitive impairment no dementia among the elderly in a rural area of Northern China: a population-based cross-sectional study. Neuroepidemiology. 2019;52(1–2):25–31.

    Article  PubMed  Google Scholar 

  49. Ganguli M, Chang CCH, Snitz BE, Saxton JA, Vanderbilt J, Lee CW. Prevalence of mild cognitive impairment by multiple classifications: the Monongahela-Youghiogheny Healthy Aging Team (MYHAT) Project. Am J Geriatr Psychiatry. 2010;18(8):674–83.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


This research has been conducted using the CLSA dataset [Baseline Tracking Data set version 3.5 & Baseline Comprehensive Data set version 4.1], under Application Number [190237]. The opinions expressed in this manuscript are the authors’ own and do not reflect the views of the Canadian Longitudinal Study on Aging.


This research was made possible using the data collected by the Canadian Longitudinal Study on Aging. Funding for the Canadian Longitudinal Study on Aging is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference LSA 94473 and the Canada Foundation for Innovation, as well as the following provinces, Newfoundland, Nova Scotia, Quebec, Ontario, Manitoba, Alberta, and British Columbia. Support for this work on comparison standards was provided by a grant from CIHR to a team led by MEO. Support was provided for LG by the McLaughlin Foundation Professorship in Population and Public Health. PR holds Tier 1 Canada Research Chair in Geroscience and Labarge Chair in Optimal Aging.

Author information

Authors and Affiliations



MEO conceived this work, wrote most of the first draft of the manuscript, and guided analyses. HK did most of the analyses with MEO helping and wrote some of the manuscript. LEG, CW, and GM provided feedback on earlier analytic approaches and guided re-analyses. MEO, HK, LEG, CW, GM, VT, SK, and PR helped revise drafts of the manuscript with MEO, HK, and VT providing comments/revisions on numerous iterations of the manuscript.

Corresponding author

Correspondence to Megan E. O’Connell.

Ethics declarations

Ethics approval and consent to participate

Ethical approval for secondary analysis of the ethically approved CLSA was obtained from the Behavoural REB at the University of Saskatchewan. The CLSA was conducted in accordance with the ethics in the Declaration of Helsinki, and all participants provided research consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

O’Connell, M., Kadlec, H., Griffith, L. et al. Cognitive impairment indicator for the neuropsychological test batteries in the Canadian Longitudinal Study on Aging: definition and evidence for validity. Alz Res Therapy 15, 167 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: