Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2024 Jul 20.
Published in final edited form as: Appl Neuropsychol Adult. 2023 Jan 20;32(1):216–224. doi: 10.1080/23279095.2022.2164197

Differences in rates of impairment in adults who use methamphetamine using two sets of demographically corrected norms

Kate Shirley a,b, Maya O’Neil a,b,c, Stephen Boyd d, Jennifer M Loftis a,b,e
PMCID: PMC10356906  NIHMSID: NIHMS1868982  PMID: 36668907

Abstract

Neuropsychologists can expect to meet with increasing rates of patients who use methamphetamine (MA), as MA use is on the rise, often comorbid with other substance use disorders, and frequently accompanied by changes in cognitive functioning. To detect impairment, neuropsychologists must apply the appropriate normative data according to important demographic factors such as age, sex, and education. This study involved 241 adults with and without MA dependence who were administered the Neuropsychological Assessment Battery. Given the high rates of polysubstance use among adults who use MA, we included adults with mono-dependence and poly-dependence on MA and at least one other substance. We compared the rates of adults with and without previous MA dependence classified as impaired on neurocognitive testing when using norms corrected for age, education, and sex versus norms corrected only for age. Norms corrected for age, education, and sex resulted in less frequent identification of impairment compared to norms corrected only for age, but both sets of norms appeared sufficient and similar enough to warrant their use with this population. It may be appropriate to explore the possible implications of discrepancies between education-corrected and non-education corrected sets of scores when assessing impairment in individuals who use MA.

Keywords: Cognition, neuropsychology, normative data, substance use disorder, stimulant

Introduction

Methamphetamine (MA) use is a global health problem with high rates of morbidity, mortality, and other severe health consequences. In 2018, an estimated 1.1 million Americans met criteria for a MA use disorder (Substance Abuse and Mental Health Services Administration, 2018). The 12-month prevalence of MA use in individuals age 12 or older increased by 195% from 2010 to 2018 (Paulus & Stewart, 2020). In the U.S., MA related overdose deaths, law enforcement interventions, and treatment admissions have increased substantially in recent years (Gladden et al., 2019; Han et al., 2021; Jones et al., 2020). In addition, chronic MA use has been associated with profound medical consequences (e.g., cardiovascular disease, immune dysregulation, insomnia) and significant long-term cognitive and neurologic impairments (Chen et al., 2020; Gooden et al., 2021; Huckans et al., 2015, 2021; Loftis et al., 2011).

MA use is on the rise nationally with evidence that it has outpaced the opioid crisis in some states and localities (Artigiani et al., 2018; Jones et al., 2020). However, while the MA and opioid epidemics were previously considered distinct and affecting different populations, new patterns suggest they are beginning to overlap as increasing numbers of people use both substances (Jones et al., 2022; Lancet, 2018; Winkelman et al., 2018). An analysis of recent data from the National Surveys on Drug Use and Health showed that past month MA use increased from 9% in 2015 to 44% in 2019 among people with past month opioid use (Strickland et al., 2021). Substance use treatment admissions also provide evidence of a relationship between MA and opioid co-use, with past month MA use increasing from 18. 8% in 2011 to 34.2% in 2017 in adults entering treatment for opioid use disorder (Ellis et al., 2018). Given the rising rates of adults who use both methamphetamine and opioids, there is an urgent need to include individuals with polysubstance use in research samples to address gaps in our understanding of the evolving polysubstance use epidemic.

Previous research has demonstrated that chronic MA use can contribute to cognitive deficits in a range of domains, including attention, memory, executive functions, social cognition, and language (Basterfield et al., 2019; Hoffman et al., 2006; Huckans et al., 2015; Loftis et al., 2011; Potvin et al., 2018; Proebstl et al., 2018; Scott et al., 2007). The specific type, severity, and chronicity of the dysfunction vary across studies. Part of this variation is likely due to factors related to study design such as how study groups are defined, what neuropsychological measures are included, and when subjects are tested (i.e., inconsistency in time points across studies). In addition, as will be explored in this paper, the selection of normative data (i.e., norms) used to draw inferences about cognitive functioning can also impact one’s understanding of impairment in the context of chronic MA use.

Normative data in neuropsychological assessment provides information about the expected test performance of individuals within a particular group, which is often stratified based on age, sex, or level of education (Kendall et al., 1999). Norms allow for the detection of deficits across a wide range of cognitive domains by comparing how the individual performed relative to others with similar attributes, thereby accounting for demographic factors and ensuring that results are interpreted in the context of performance compared to similar peer groups. Corrective norms represent incomplete proxies for a host of important variables (e.g., race, socioeconomic status) but are applied with the goal of improving diagnostic accuracy.

Level of education is considered an important sociodemographic variable associated with neuropsychological performance that contributes to normative data differences (Lynn et al., 2018). Individuals with higher levels of education tend to perform better on most neuropsychological tests than those with lower levels of education (Lezak et al., 2012). However, this relies on the assumption that quality of education does not vary across schools and that individuals with the same quantity of education share the same ability status. Research has demonstrated that quality of education is highly variable due to differences in individual educational experiences, such as those arising from school quality, student-teacher ratios, teaching methods, peer characteristics, and disparities related to racial, ethnic, and socioeconomic statuses (Dotson et al., 2009; McLaughlin et al., 2020). Thus, two individuals can have the same quantity of education but vastly different quality of education, which could lead to significant discrepancies in scores if the normative reference group only takes into account one’s years of education. Even though level of education is frequently used as a normative reference group, norms adjusted for years of education may decrease accuracy in assigning standardized scores and lead to incorrect assessments of impairment if the individual patient does not match the normative sample.

Previous studies have demonstrated associations between use of MA and opioids and reduced educational attainment (Dean et al., 2013, 2018; Ellis et al., 2020; Martins et al., 2015; Schepis et al., 2018). In a study of 78 adults with and without MA use disorder, Dean et al. (2012) found that individuals who used MA attained less education compared to individuals who did not use MA, both in years of education as well as the quality of the education. In terms of quantity of education, age of first MA use was positively correlated with years of education, indicating that individuals who first used MA at a younger age achieved fewer years of education compared to individuals who began using MA later in life. When compared to a normative model of educational attainment, years of education of individuals who used MA was significantly lower (~1 year less) than predicted when compared to their current cognitive functioning and demographic characteristics. Furthermore, the earlier an individual’s first use, the larger the discrepancy between actual and predicted educational attainment. Quality of educational exposure was assessed using a test of vocabulary knowledge to estimate academic learning. They found that years of education attained prior to first MA use was a better predictor of vocabulary knowledge than the total years of education completed. Their findings suggest that individuals who use MA may have the baseline cognitive abilities necessary to achieve higher levels of education, but due to the influence of other factors (e.g., MA use, environmental and social difficulties), the potential for further education is often underrealized. Similar findings have been reported for adults who use opioids, as opioid use can negatively impact a person’s ability to achieve education due to hindrances such a decreased intrinsic motivation and interruptions to schooling due to use (Ellis et al., 2020). Thus, years of education may underestimate actual ability of individuals who use MA and/or opioids, which could lead to artificially inflated education-corrected standardized scores in this population.

Guided by the literature, this study compared the rates of adults with and without previous MA use who were classified as impaired on neurocognitive testing when using age, education, and sex-adjusted norms versus using only age-adjusted norms. While previous studies have included groups of either adults who were solely dependent on MA or adults who were dependent on MA and at least one other substance, we uniquely compare these two types of groups to see if rates of impairment differ across group. We hypothesized that a larger number of adults who have used MA will be classified as impaired when using norms adjusted for age compared to norms adjusted for age, education, and sex, whereas a similar number of adults who have not used MA will be classified as impaired when using norms adjusted for age compared to norms adjusted for age, education, and sex. Given that polysubstance use is common among individuals who use MA and the cognitive deficits associated with MA likely persist even when other substances are used, we hypothesized that group differences in impairment rates between polysubstance use versus use of MA only would be non-significant (p > .05) or rare (difference <5 participants between POLY and MA-only groups by domain).

Method

Participants

A total of 241 adults (≥21 years old) were recruited from substance use treatment centers in an urban area in the Pacific Northwest and from the community through study advertisements from 2013 to 2017. Participants were enrolled into one of five study groups: (1) MA-active (MA-ACT) group (n = 44) defined as adults actively using MA and currently meeting criteria for MA dependence; (2) MA-remission (MA-REM) group (n = 55) defined as adults in early remission from MA dependence; (3) polysubstance active (POLY-ACT) group (n = 24) defined as adults actively using MA and at least one other substance; (4) polysubstance remission (POLY-REM) group (n = 55) defined as adults in early remission from MA dependence and at least one other substance; and 5) control (CTL) group (n = 63) defined as adults with no lifetime history of dependence on any substance. Inclusion and exclusion criteria are defined by group in Table 1. In this study, what are currently referred to as substance use disorders in the Diagnostic and Statistical Manual of Mental Disorders—Fifth Edition (DSM-5) (APA, 2013) were previously termed substance dependence, and this term is used when referring to research participants described in this paper.

Table 1.

Study eligibility criteria.

Group Criteria
MA-ACT Inclusion criteria:
• Participant met criteria for dependence on MA but no other substances (except nicotine or caffeine) based on Diagnostic and Statistical Manual of Mental Disorders—Fourth Edition (DSM-IV) (APA, 2000) criteria and confirmed by the Mini International Neuropsychiatric Interview questionnaire (MINI) (Sheehan et al., 1998).
• MA use was more than 2 days per week for more than 1 year.
• Last MA use was less than 2 weeks ago.
• Participant did not test positive for any substances other than MA on urine drug screen.
MA-REM Inclusion criteria:
• Participant met criteria for dependence on MA but no other substances (except nicotine or caffeine) based on DSM-IV (APA, 2000) with confirmation by the MINI (Sheehan et al., 1998).
• MA use was more than 2 days per week for more than 1 year during the year prior to remission.
• Last use of MA was greater than 1 month and less than 6 months ago.
• Participant did not test positive for any substances on urine drug screen during day of study visit.
POLY-ACT Inclusion criteria:
• Participant met criteria for dependence on MA and at least one other substance (other than caffeine or nicotine) based on DSM-IV (APA, 2000) with confirmation by the MINI (Sheehan et al., 1998).
• MA use was more than 2 days per week for more than 1 year.
• Last MA use was less than 2 weeks ago.
POLY-REM Inclusion criteria:
• Participant met criteria for dependence on MA and at least one other substance (except nicotine or caffeine) based on DSM-IV (APA, 2000) with confirmation by the MINI (Sheehan et al., 1998).
• MA use was more than 2 days per week for more than 1 year during the year prior to remission.
• Last use of MA and other substances was greater than 1 month and less than 6 months ago.
• Participant did not test positive for any substances on urine drug screen during day of study visit.
CTL Inclusion criteria:
• Participant has never met criteria for past substance dependence (except nicotine or caffeine) based on DSM-IV (APA, 2000) and MINI (Sheehan et al., 1998).
• Participant did not test positive for any substance on urine drug screen.
All Groups Exclusion criteria:
• Participant was visibly intoxicated or had impaired capacity to understand study risks and benefits or otherwise provide informed consent.
• Participant reported medical conditions or use of current medications likely to impact immunological or central nervous system function (e.g., HIV, cancer, lupus, stroke, neurodegenerative disease, hepatic encephalopathy, multiple sclerosis, traumatic brain injury, immunosuppressants, antivirals, benzodiazepines, opiates, stimulants, antipsychotics, anticholinergics, antiparkinsonian agents).
• Participant met criteria for past or current manic episode, schizophrenia, schizoaffective disorder, or other psychotic disorder a based on the DSM-IV (APA, 2000) with confirmation by the MINI (Sheehan et al., 1998).
• Participant reported heavy alcohol use as defined by the National Institute on Alcohol Abuse and Alcoholism (Women: average alcohol use > 7 drinks per week for > one year. Men: average alcohol use > 14 drinks per week for > one year) (NIAAA, 2019).
Open in a new tab

Note. MA-ACT=Adults actively using methamphetamine; MA-REM=Adults in early remission from MA dependence; POLY-ACT=Adults actively using MA and at least one other substance; POLY-REM=Adults in early remission from MA and at least one other substance; CTL=Adults with no lifetime history of dependence on any substance.

a

A history of temporary substance-induced psychosis was acceptable for participants in the substance use groups, as long as they did not currently meet criteria for a psychotic disorder, and they did not meet criteria for a psychotic disorder prior to active substance use.

In total, 1226 potential participants were screened, 788 of whom were found ineligible (178 for medical exclusions, 39 for mental health exclusions, 411 for substance use exclusions, and 160 due to other or unknown reasons). The remaining 438 eligible participants were scheduled for enrollment, declined to participate, or were lost to follow-up.

Procedures

The study protocol is consistent with the ethical guidelines of the 1975 Declaration of Helsinki (6th revision, 2008) and was approved by the Institutional Review Boards (IRBs) at the VA Portland Health Care System (VAPORHCS) and Oregon Health & Science University (OHSU). Research participants gave informed consent after the procedures had been fully explained. Participants were compensated with grocery store vouchers of $125 for completing study procedures. Participants completed a clinical interview, urine drug screen, neuropsychiatric interview, and neuropsychological assessments.

The Neuropsychological Assessment Battery (NAB) (Stern & White, 2003a) is a comprehensive battery of 33 neuropsychological tests that assess cognitive functioning. The NAB has exhibited strong psychometric properties in reliability studies including internal consistency, test-retest reliability, equivalent form reliability, and interrater reliability (Sachs et al., 2016; White & Stern, 2003; Yochim et al., 2009; Zgaljardic & Temple, 2010). Clinical validation studies, confirmatory factor analyses, and criterion validity investigations have supported the validity of the battery (Grohman & Fals-Stewart, 2004; White & Stern, 2003). This study used the full battery, which includes the Attention, Language, Memory, Spatial, and Executive Functions modules. All NAB tests were standardized on the same sample of 1,448 healthy adults who ranged in age from 18 to 97 years old (White & Stern, 2003). The standardization sample was used to create demographically corrected norms, which allow for interpretation of an examinee’s scores while controlling for the potential influences of age, sex, and education. A subsample of the 950 adults from the total standardization sample were selected to create age-based, U.S. Census-matched norms, which were developed to reflect U.S. Census data in terms of sex, ethnicity, education, and geographic region. Either set of norms can be used to interpret an examinee’s test results so the examiner must decide which set of norms will provide the most accurate interpretation of test results based on the goals of the evaluation. In most cases, demographically corrected norms are recommended for clinical diagnostic purposes because they are considered the best estimate of premorbid level (Heaton et al., 2009; Stern & White, 2003b).

Following published procedures and norms (Stern & White, 2003b), raw subtest scores were converted into two sets of t-scores, one set that was demographically corrected on the variables of age, sex, and education (demographically corrected norms), and one set that was only corrected for age (U.S. Census-matched norms). Individual subtest scores were used to create summary index scores for each of the modules, as well as a total index score for the entire test battery.

Neuropsychological tests were administered and scored according to published standardization procedures by one of three psychometrists (one with a Ph.D. in psychology, one with an M.A. in counseling, and one with a B.S. in psychology) who were trained and supervised by a licensed psychologist and neuropsychologist. All tests were scored and re-scored by separate psychometrists. All data were entered into a database that was double-checked by a separate psychometrist prior to analysis. When scoring or data entry discrepancies were identified, the original psychometrist would review the score and either agree with the second psychometrist’s feedback and change the score or after consultation with the principal investigator maintain the original score.

Statistical analyses

Chi-square tests of independence and analyses of variance (ANOVA) procedures were conducted to examine potential differences between the groups on key demographic and substance use variables including age, age of first MA use, years of education, parental years of education, sex, and race.

To compare the rates of adults with and without previous MA use who were classified as impaired on neurocognitive testing when using the two sets of norms, each participant was classified as impaired or unimpaired within each cognitive domain according to the NAB’s impairment cutoff, which is a standard score below 85 (Stern & White, 2003b). This cutoff score corresponds to one standard deviation (SD = 15) below the mean score of 100 and is routinely used in clinical practice and in research. Normed t-scores were calculated for each participant within each cognitive domain to classify impairment status using the two sets of norms. McNemar’s tests were conducted for each of the groups (i.e. MA-ACT, MA-REM, POLY-ACT, POLY-REM, and CTL) to evaluate if there was a change in the rates of adults considered impaired when using the two sets of norms (i.e., age, education, and sex-adjusted norms versus norms adjusted only for age). We examined how the rates of impairment change for each group when using each of the norm types. A p-value of .05 was used as the level of significance for all analyses.

Results

Demographic and substance use characteristics

As described in Table 2, groups significantly (p < .05) differed on several demographic and clinical characteristics. Remission groups had significantly more males than active user or control groups. Active users were older than the remission and control groups, but all groups had a mean age in the 30’s or 40’s. The total sample was predominately White (86%), and groups did not significantly differ in terms of race. Groups did not significantly differ in terms of parents’ level of education, but there were significant differences between groups in participants’ years of education. Years of education ranged from 10 to 16 years (mean ± SD, 12.73 ± 1.37 years) for the MA-ACT group, 7 to 16 years (mean ± SD, 11.93 ± 1.41 years) for the MA-REM group, 5–14 years (mean ± SD, 12.21 ± 1. 69 years) for the POLY-ACT group, 8 to 16 years (mean ± SD, 12.25 ± 1.48 years) for the POLY-REM group, and 8 to 18 years (mean ± SD, 13.25 ± 1.48 years) for the CTL group. However, the group means only ranged from 11.93 to 13.25 (some high school to some college). Age of first use of MA did not differ between groups with a history of use. The polysubstance use groups did not significantly differ from each other in terms of rates of other substance dependence except for alcohol dependence with more participants in the POLY-REM group meeting criteria for alcohol dependence compared to the POLY-ACT group. Opioid dependence was the most common comorbid substance dependence among the POLY groups.

Table 2.

Group differences on demographic and substance use characteristics.

Total Group
MA-ACT MA-REM POLY-ACT POLY-REM CTL X 2 P
N 241 44 55 24 55 63
Sex (% Male) 154 (64%) 22 (50%)a 44 (80%)b 12 (50%)a 46 (84%)b 30 (48%)a 28.402 .000
Race (% White) 207 (86%) 38 (86%)a 50 (91%)a 23 (96%)a 43 (78%)a 53 (84%)a 5.968 .202
Opioid Dep (%)* 30 (38%) – – 9 (38%) 21 (38%) – 0.423 .809
Alcohol Dep (%)* 23 (29%) – – 3 (13%) 20 (36%) – 4.611 .032
Marijuana Dep (%)* 28 (35%) – – 9 (38%) 19 (35%) – 0.283 .868
Cocaine Dep (%)* 5 (6%) – – 1 (4%) 4 (7%) – 0.665 .717
Sedative Dep (%)* 6 (8%) – – 2 (8%) 4 (7%) – 0.287 .866
Stimulant Dep (%)* 1 (1%) – – 0 (0%) 1 (2%) – 0.780 .677
Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) F p
Age 38.50 (11.00) 43.55 (9.57)a 36.16 (10.37)b 43.13 (10.46)a 38.65 (9.98)a,b 35.13 (11.90)b 5.92 <.0001
Education, years 12.52 (1.54) 12.73 (1.37)a,b 11.93 (1.41)c 12.21 (1.69)a,c 12.25 (1.48)a,c 13.25 (1.48)b 7.12 <.0001
Parent Educ, years 13.41 (2.92) 12.71 (3.36)a,b 13.10 (2.78)a,b 12.21 (2.70)a 13.56 (2.54)a,b 14.44 (2.83)b 3.87 .424
Age of MA onset 20.77 (8.46) 20.32 (8.36)a 20.40 (8.31)a 23.50 (10.34)a 20.30 (7.77)a – 0.97 .410
Open in a new tab

Note. For non-continuous variables, data is reported as number (%), and results reflect chi square tests. For continuous variables, data is reported as mean (SD), and results reflect analysis of variance (ANOVA) procedures followed by post hoc comparisons with Bonferroni corrections for multiple comparisons. Superscript letters (a, b and c) denote study groups whose proportions or means do not differ significantly at the .05 level; study groups with different superscript letters have proportions or means that significantly differ at the .05 level.

*

Group differences in opioid, alcohol, marijuana, cocaine, sedative, and stimulant dependence (Dep) include polysubstance use groups only (POLY-ACT & POLY-REM). MA-ACT=Adults with active MA dependency; MA-REM=Adults in early remission from MA dependence; POLY-ACT=Adults actively using MA and at least one other substance; POLY-REM=Adults in early remission from MA dependence and at least one other substance; CTL=Adults without any history of substance use disorder; Education (years) = Total number of years of education; Parent Educ (years) = Total number of parent’s years of education; Age of MA onset=Age of first MA use.

Rates of impairment when comparing the two sets of norms

McNemar’s tests were conducted for each of the groups (i.e., MA-ACT, MA-REM, POLY-ACT, POLY-REM, and CTL) to determine whether there was a change in the number of adults considered impaired when using norms adjusted for age, education, and sex (demographically corrected) versus norms adjusted only for age (census-based). As shown in Table 3, norms adjusted solely for age identified impairment more frequently than norms adjusted for age, sex, and education in the MA and POLY groups. While not significant, the control group had reduced rates of impairment on the Language, Memory, Spatial, Executive Functions, and Total Score indexes when using age-only corrected norms. Norms adjusted only for age classified significantly more individuals in the MA-REM group as impaired on the Attention (p = .031), Memory (p = .021), and Total Score (p = .031) indexes than the norms adjusted for age, education, and sex. The greatest discrepancy in the norms was observed on the MA-REM’s Memory Index, as 19 participants were considered impaired using age, sex, and education corrected norms versus 27 considered impaired using norms corrected only for age. Impairment rates were similar for all other groups across indexes (p > .05).

Table 3.

McNemar’s tests comparing differences in the number of participants classified as impaired.

Number classified as impaired
Index Group n Age, education, and sex corrected Age-only corrected Change p
Attention MA-ACT 38 5 6 +1 1.00
MA-REM 49 11 17 +6 .031*
POLY-ACT 16 4 3 −1 1.00
POLY-REM 49 17 17 0 1.00
Control 61 10 11 +1 1.00
Language MA-ACT 38 5 6 +1 .250
MA-REM 49 7 9 +2 .625
POLY-ACT 16 6 5 −1 1.00
POLY-REM 49 9 11 +2 .500
Control 61 8 7 −1 1.00
Memory MA-ACT 38 10 10 0 1.00
MA-REM 49 19 27 +8 .021*
POLY-ACT 16 7 8 +1 1.00
POLY-REM 49 25 28 +3 .375
Control 61 8 6 −2 .500
Spatial MA-ACT 38 3 4 +1 1.00
MA-REM 49 5 4 −1 1.00
POLY-ACT 16 6 7 +1 1.00
POLY-REM 49 7 6 −1 1.00
Control 61 8 6 −2 .500
Executive Functions MA-ACT 38 1 0 −1 1.00
MA-REM 49 5 4 −1 .250
POLY-ACT 16 3 4 +1 1.00
POLY-REM 49 9 8 −1 1.00
Control 61 2 1 −1 1.00
Total Score MA-ACT 38 4 5 +1 1.00
MA-REM 49 7 13 +6 .031*
POLY-ACT 16 6 6 0 1.00
POLY-REM 49 13 15 +2 .687
Control 61 6 5 −1 1.00
Open in a new tab

Note. Asterisks denote significant difference based on McNemar’s tests. A p-value of .05 was used as the level of significance for all analyses. Change column identifies loss or gain in number of participants classified as impaired using the age-only corrected norms from the age, education, and sex corrected norms.

Discussion

Examination of results from the McNemar’s tests (Table 3) reveal that while the age-only versus age, education, and sex-adjusted norms did not differ statistically for most groups in terms of how many participants were classified as impaired, several participants were classified differently by the two sets of norms. Norms corrected only for age identified impairment more frequently than age, education, and sex-adjusted norms. While the POLY remission group did not produce significant differences in impairment status across the two sets of norms, significant differences were observed in the MA remission group, with more participants classified as impaired on the Attention, Memory, and Total Score indexes when using age-adjusted norms.

The observed differences in impairment status based on which set of norms were used has significant implications for both patients and neuropsychologists since false positives or missed detection of cognitive impairment may lead to poorer treatment outcomes regarding both recovery from substance use and everyday cognitive functioning. This is particularly relevant for individuals who use MA since cognitive impairment has been shown to interfere with a person’s ability to engage in treatment, impair everyday functioning abilities, and increase rates of relapse after completion of treatment (Henry et al., 2010; Kalechstein et al., 2003; Paulus et al., 2005; Potvin et al., 2018; Sommers et al., 2006). While scores adjusted for education aim to improve diagnostic accuracy by controlling for variance associated with a premorbid demographic factor, educational attainment for individuals who use MA is heavily influenced by environmental and societal factors. For example, MA use is often accompanied by external stressors that may impact a person’s ability to attain education, such as family dysfunction, physical and sexual abuse, living in high crime areas, low socioeconomic status, and inadequate school supports (Brecht et al., 2004; Gooden et al., 2021; Karriker-Jaffe, 2013; King et al., 2019; Russell et al., 2008). The interaction between MA use, educational attainment, and neurocognitive test performance is complex and remains to be elucidated, but as demonstrated in this study, viewing the relationship between education and test performance in isolation without considering additional contributing factors related to MA use may lead to an inaccurate assessment of a person’s cognitive functioning.

Given that factors such as age, sex, and education impact neuropsychological test performance, it makes sense to use demographically corrected norms to try to interpret results that will accurately reflect ability and performance rather than demographic or cultural factors (Heaton et al., 2009; Mungas et al., 2009). However, researchers have suggested that education-corrected norms may be inappropriate for individuals who use MA because they may result in inflated standardized scores that mask impairments (Dean et al., 2012, 2013, 2018). As demonstrated in our data, years of education as a demographic correction may lead to less frequent identification of impairment. While both sets of norms appeared sufficient and similar enough in their results to warrant the use of either in future studies or clinical work with this population, it is important for neuropsychologists to be aware of the potential for over-correction based on education when assessing individuals who use MA.

Limitations and future directions

While this study has important clinical implications, this study includes several limitations that may be addressed in future research. Given recruitment and enrollment occurred between 2013 to 2017, future research is needed to better understand the changing demographic and substance use patterns of adults who use MA. The sample was drawn pre-dominantly from White middle-aged men who resided in one Northwest metropolitan area so the results may not be generalizable to more diverse populations. The MA and polysubstance remission groups had significantly more males than the active user or control groups. It is unclear to what extent the lower percentage of females in the remission groups may have impacted results. Females typically report higher levels of impairment and side effects from stimulants, so it is possible that the unequal representation of males in our MA remission and polysubstance remission groups impacted group differences (Dluzen & Liu, 2008; Mayo et al., 2019). Additionally, females attend and complete college at a higher rate than males in Oregon and nationally, which may have impacted our findings (Oregon Higher Education Coordinating Commission, 2019; National Center for Education Statistics, 2021). Since this study was initiated prior to the publication of the DSM-5 (APA, 2013), substance dependence was determined through the administration of the MINI (Sheehan et al., 1998), which relies on DSM-IV (APA, 2000) criteria to classify substance use disorders. The DSM-5 reduced the threshold for a substance use disorder diagnosis and removed tolerance and withdrawal symptoms as diagnostic criteria among patients using substances under medical supervision, which is particularly relevant to adults who are prescribed amphetamines and/or opioids by their medical providers. It is unknown how results may have differed if DSM-5 diagnostic criteria were used to identify substance use disorders, but it is possible that DSM-5 criteria could have identified mild cases of SUDs in our participants who were deemed ineligible based off of DSM-IV criteria for substance dependence. This study utilized the two sets of norms provided by the NAB, which includes demographically corrected scores adjusted for age, sex, and education and U.S. Census corrected scores adjusted only for age. Since we were interested in the impact of education corrections, it would have been ideal if the U.S. Census based scores adjusted for age and sex; it is unclear how this may have impacted our findings. Furthermore, age, sex, and education corrected norms only adjust for years of education and do not adjust for quality of education; future studies may want to examine if quality of education is a more appropriate metric when assessing educational attainment in individuals who use MA. To further examine whether years of education may underestimate actual ability of individuals who use MA, future research could examine whether years of education commensurate with age at first MA use could more accurately identify cognitive impairment in individuals who use MA. To improve statistical power and focus, this paper did not include analyses of NAB subtest scores; future papers or studies could expand analyses to address subtest specific differences in performance using the two sets of norms. While this study is unable to say which set of norms is most appropriate to use when assessing adults who have used MA, this study uniquely adds to the field by calling attention to the possibility of over-correction based on education, potentially leading to less frequent identification of impairment in individuals who use MA.

Conclusion

With the increasing rates of MA use in the U.S. population, clinicians and researchers will continue to be called upon to perform evaluations to answer diagnostic issues and questions concerning cognitive rehabilitation, functional capacity, and other psychological outcomes. The accuracy and utility of their evaluations will depend on the capacity of the evaluator to skillfully choose appropriate norms and interpret test results within the context of available information. Although clinicians and researchers might like to know which norms are the most appropriate for this population, the present study and the current state of the literature do not provide a definite answer. Instead, there are strengths and weaknesses to using either set of norms, and examining both may be the best option depending on the aims of the assessment. When using education-corrected norms, the neuropsychologist should consider how similar the individual patient matches the normative sample and whether their years of education adequately reflects their quantity and quality of education. Furthermore, neuropsychology as a profession has an obligation to continue to identify sociodemographic influences and refine normative data sets to more accurately reflect the factors that impact cognition.

Acknowledgements

We thank Marilyn Huckans, Ph.D. and our participants for their contributions to this project. This material is the result of work supported with resources and the use of facilities at the VA Portland Health Care System (VAPORHCS) and Oregon Health & Science University (OHSU), Portland, Oregon. The corresponding author (K.S.) declares she had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors are or were Veterans Affairs employees (Kate Shirley, MA, Research Assistant, VAPORHCS; Maya O’Neil, PhD, Neuropsychologist, VAPORHCS; Stephen Boyd, PhD, Clinical Psychologist, VAPORHCS; Jennifer M. Loftis, PhD, Research Scientist, VAPORHCS). The contents do not represent the views of the United States Department of Veterans Affairs or the United States Government.

Funding

This work was funded by National Institute on Drug Abuse grant #1U18DA052351–01.

Footnotes

Disclosure statement

No potential conflict of interest was reported by the author(s).

References

  1. APA. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). APA. 10.1176/appi.books.9780890423349 [DOI] [Google Scholar]
  2. APA. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). APA. 10.1176/appi.books.9780890425596 [DOI] [Google Scholar]
  3. Artigiani EE, Hsu MH, McCandlish D, & Wish ED (2018). Methamphetamine: A regional drug crisis. National Drug Early Warning System. [Google Scholar]
  4. Basterfield C, Hester R, & Bowden SC (2019). A meta-analysis of the relationship between abstinence and neuropsychological functioning in methamphetamine use disorder. Neuropsychology, 33(5), 739–753. 10.1037/neu0000552 [DOI] [PubMed] [Google Scholar]
  5. Brecht M-L, O’Brien A, von Mayrhauser C, & Anglin MD (2004). Methamphetamine use behaviors and gender differences. Addictive Behaviors, 29(1), 89–106. 10.1016/S03064603(03)00082-0 [DOI] [PubMed] [Google Scholar]
  6. Chen G, Wei X, Xu X, Yu G, Yong Z, Su R, & Tao L (2020). Methamphetamine inhibits long-term memory acquisition and synaptic plasticity by evoking endoplasmic reticulum stress. Frontiers in Neuroscience, 14, 630713. 10.3389/fnins.2020.630713 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Dean AC, Hellemann G, Sugar CA, & London ED (2012). Educational attainment is not a good proxy for cognitive function in methamphetamine dependence. Drug and Alcohol Dependence, 123(1–3), 249–254. 10.1016/j.drugalcdep.2011.11.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Dean AC, Groman SM, Morales AM, & London ED (2013). An evaluation of the evidence that methamphetamine abuse causes cognitive decline in humans. Neuropsychopharmacology, 38(2), 259–274. 10.1038/npp.2012.179 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dean AC, Morales AM, Hellemann G, & London ED (2018). Cognitive deficit in methamphetamine users relative to childhood academic performance: Link to cortical thickness. Neuropsychopharmacology, 43(8), 1745–1752. 10.1038/s41386-018-0065-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Dluzen DE, & Liu B (2008). Gender differences in methamphetamine use and responses: A review. Gender Medicine, 5(1), 24–35. 10.1016/s1550-8579(08)80005-8 [DOI] [PubMed] [Google Scholar]
  11. Dotson VM, Kitner-Triolo MH, Evans MK, & Zonderman AB (2009). Effects of race and socioeconomic status on the relative influence of education and literacy on cognitive functioning. Journal of the International Neuropsychological Society, 15(4), 580–589. 10.1017/S1355617709090821 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Ellis MS, Kasper ZA, & Cicero TJ (2018). Twin epidemics: The surging rise of methamphetamine use in chronic opioid users. Drug and Alcohol Dependence, 193, 14–20. 10.1016/j.drugalcdep.2018.08.029 [DOI] [PubMed] [Google Scholar]
  13. Ellis MS, Kasper ZA, & Cicero TJ (2020). The impact of opioid use disorder on levels of educational attainment: Perceived benefits and consequences. Drug and Alcohol Dependence, 206, 107618–107616. 10.1016/j.drugalcdep.2019.107618 [DOI] [PubMed] [Google Scholar]
  14. Gladden RM, O’Donnell J, Mattson CL, & Seth P (2019). Changes in opioid-involved overdose deaths by opioid type and presence of benzodiazepines, cocaine, and methamphetamine – 25 states July–December 2017 to January–June 2018. Morbidity and Mortality Weekly Report, 68(34), 737–744. 10.15585/mmwr.mm6834a2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gooden JR, Cox CA, Petersen V, Curtis A, Manning V, & Lubman DI (2021). Characterisation of presentations to a community-based specialist addiction neuropsychology service: Cognitive profiles, diagnoses and comorbidities. Drug and Alcohol Review, 40(1), 83–92. 10.1111/dar.13135 [DOI] [PubMed] [Google Scholar]
  16. Grohman K, & Fals-Stewart W (2004). The detection of cognitive impairment among substance-abusing patients: The accuracy of the neuropsychological assessment battery-screening module. Experimental and Clinical Psychopharmacology, 12(3), 200–207. 10.1037/1064-1297.12.3.200 [DOI] [PubMed] [Google Scholar]
  17. Han B, Cotto J, Etz K, Einstein EB, Compton WM, & Volkow ND (2021). Methamphetamine overdose deaths in the US by sex and race and ethnicity. JAMA Psychiatry, 78(5), 564–567. 10.1001/jamapsychiatry.2020.4321 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Heaton RK, Ryan L, & Grant I (2009). Demographic influences and use of demographically corrected norms in neuropsychological assessment. In Grant I & Adams K (Eds.), Neuropsychological assessment of neuropsychiatric and neuromedical disorders (pp. 127–155). Oxford University Press. [Google Scholar]
  19. Henry BL, Minassian A, & Perry W (2010). Effect of methamphetamine dependence on everyday functional ability. Addictive Behaviors, 35(6), 593–598. 10.1016/j.addbeh.2010.01.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hoffman WF, Moore M, Templin R, McFarland B, Hitzemann RJ, & Mitchell SH (2006). Neuropsychological function and delay discounting in methamphetamine-dependent individuals. Psychopharmacology, 188(2), 162–170. 10.1007/s00213-006-0494-0 [DOI] [PubMed] [Google Scholar]
  21. Huckans M, Fuller BE, Chalker AL, Adams M, & Loftis JM (2015). Plasma inflammatory factors are associated with anxiety, depression, and cognitive problems in adults with and without methamphetamine dependence: An exploratory protein array study. Frontiers in Psychiatry, 6, 178. 10.3389/fpsyt.2015.00178 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Huckans M, Boyd S, Moncrief G, Hantke N, Winters B, Shirley K, Sano E, McCready H, Dennis L, Kohno M, Hoffman W, & Loftis JM (2021). Cognition during active methamphetamine use versus remission. Journal of Clinical and Experimental Neuropsychology, 43(6), 599–610. 10.1080/13803395.2021.1976734 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Jones CM, Underwood N, & Compton WM (2020). Increases in methamphetamine use among heroin treatment admissions in the United States, 2008–17. Addiction, 115(2), 347–353. 10.1111/add.14812 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Jones CM, Compton WM, & Mustaquim D (2020). Patterns and characteristics of methamphetamine use among adults – United States, 2015–2018. Morbidity and Mortality Weekly Report, 69(12), 317–323. 10.15585/mmwr.mm6912a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Jones CM, Houry D, Han B, Baldwin G, Vivolo-Kantor A, & Compton WM (2022). Methamphetamine use in the United States: Epidemiological update and implications for prevention, treatment, and harm reduction. Annals of the New York Academy of Sciences, 1508(1), 3–22. 10.1111/nyas.14688 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kalechstein AD, Newton TF, & Green M (2003). Methamphetamine dependence is associated with neurocognitive impairment in the initial phases of abstinence. The Journal of Neuropsychiatry and Clinical Neurosciences, 15(2), 215–220. 10.1176/jnp.15.2.215 [DOI] [PubMed] [Google Scholar]
  27. Karriker-Jaffe KJ (2013). Neighborhood socioeconomic status and substance use by U.S. adults. Drug and Alcohol Dependence, 133(1), 212–221. 10.1016/j.drugalcdep.2013.04.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kendall PC, Marrs-Garcia A, Nath SR, & Sheldrick RC (1999). Normative comparisons for the evaluation of clinical significance. Journal of Consulting and Clinical Psychology, 67(3), 285–299. 10.1037/0022-006x.67.3.285 [DOI] [PubMed] [Google Scholar]
  29. King KA, Vidourek RA, & Yockey RA (2019). Social determinants to lifetime methamphetamine use among a national sample of adults. Journal of Substance Use, 24(4), 388–393. 10.1080/14659891.2019.1581285 [DOI] [Google Scholar]
  30. Lancet. (2018). Opioids and methamphetamine: A tale of two crises. The Lancet, 391(10122), 713. 10.1016/s0140-6736(18)30319-2 [DOI] [PubMed] [Google Scholar]
  31. Lezak MD, Howieson DB, Bigler ED, & Tranel D (2012). Neuropsychological assessment (5th ed.). Oxford University Press. [Google Scholar]
  32. Loftis JM, Choi D, Hoffman W, & Huckans MS (2011). Methamphetamine causes persistent immune dysregulation: A cross-species, translational report. Neurotoxicity Research, 20(1), 59–68. 10.1007/s12640-010-9223-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Lynn R, Fuerst J, & Kirkegaard EO (2018). Regional differences in intelligence in 22 countries and their economic, social and demographic correlates: A review. Intelligence, 69, 24–36. 10.1016/j.intell.2018.04.004 [DOI] [Google Scholar]
  34. Martins SS, Kim JH, Chen LY, Levin D, Keyes KM, Cerdá M, & Storr CL (2015). Nonmedical prescription drug use among US young adults by educational attainment. Social Psychiatry and Psychiatric Epidemiology, 50(5), 713–724. 10.1007/s00127-014-0980-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Mayo LM, Paul E, DeArcangelis J, Van Hedger K, & de Wit H (2019). Gender differences in the behavioral and subjective effects of methamphetamine in healthy humans. Psychopharmacology, 236(8), 2413–2423. 10.1007/s00213-019-05276-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. McLaughlin SJ, Kim S, Li LW, & Zhang J (2020). Educational differences in trajectories and determinants of healthy ageing in midlife and older Americans. Maturitas, 134, 21–28. 10.1016/j.maturitas.2020.01.002 [DOI] [PubMed] [Google Scholar]
  37. Mungas D, Reed BR, Farias ST, & DeCarli C (2009). Age and education effects on relationships of cognitive test scores with brain structure in demographically diverse older persons. Psychology and Aging, 24(1), 116–128. 10.1037/a0013421 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. National Center for Education Statistics. (2021). Digest of education statistics. Retrieved from https://nces.ed.gov/programs/digest/current_tables.asp
  39. NIAAA. (2019). Drinking levels defined. Retrieved from https://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/moderate-binge-drinking
  40. Oregon Higher Education Coordinating Commission. (2019). Statewide higher education snapshots. Retrieved from https://www.oregon.gov/highered/research/Documents/Snapshots/Statewide-Snapshot.pdf
  41. Paulus MP, Tapert SF, & Schuckit MA (2005). Neural activation patterns of methamphetamine-dependent subjects during decision making predict relapse. Archives of General Psychiatry, 62(7), 761–768. 10.1001/archpsyc.62.7.761 [DOI] [PubMed] [Google Scholar]
  42. Paulus MP, & Stewart JL (2020). Neurobiology, clinical presentation, and treatment of methamphetamine use disorder. JAMA Psychiatry, 77(9), 959. 10.1001/jamapsychiatry.2020.0246 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Potvin S, Pelletier J, Grot S, Hebert C, Barr AM, & Lecomte T (2018). Cognitive deficits in individuals with methamphetamine use disorder: A meta-analysis. Addictive Behaviors, 80, 154–160. 10.1016/j.addbeh.2018.01.021 [DOI] [PubMed] [Google Scholar]
  44. Proebstl L, Kamp F, Koller G, & Soyka M (2018). Cognitive deficits in methamphetamine users: How strong is the evidence? Pharmacopsychiatry, 51(6), 243–250. 10.1055/s-0043-123471 [DOI] [PubMed] [Google Scholar]
  45. Russell K, Dryden DM, Liang Y, Friesen C, O’Gorman K, Durec T, Wild TC, & Klassen TP (2008). Risk factors for methamphetamine use in youth: A systematic review. BMC Pediatrics, 8, 48–58. 10.1186/1471-2431-8-48 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Sachs BC, Rush BK, & Pedraza O (2016). Validity and reliability of the NAB Naming Test. The Clinical Neuropsychologist, 30(4), 629–638. 10.1080/13854046.2016.1149618 [DOI] [PubMed] [Google Scholar]
  47. Scott JC, Woods SP, Matt GE, Meyer RA, Heaton RK, Atkinson JH, & Grant I (2007). Neurocognitive effects of methamphetamine: A critical review and meta-analysis. Neuropsychology Review, 17(3), 275–297. 10.1007/s11065-007-9031-0 [DOI] [PubMed] [Google Scholar]
  48. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, & Dunbar GC (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry, 59(Suppl 20), 22–33. quiz 34–57. [PubMed] [Google Scholar]
  49. Schepis TS, Teter CJ, & McCabe SE (2018). Prescription drug use, misuse and related substance use disorder symptoms vary by educational status and attainment in U.S. adolescents and young adults. Drug and Alcohol Dependence, 189, 172–177. 10.1016/j.drugalcdep.2018.05.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Sommers I, Baskin D, & Baskin-Sommers A (2006). Methamphetamine use among young adults: Health and social consequences. Addictive Behaviors, 31(8), 1469–1476. 10.1016/j.addbeh.2005.10.004 [DOI] [PubMed] [Google Scholar]
  51. Stern R, & White T (2003a). Neuropsychological assessment battery. Psychological Assessment Resources. [Google Scholar]
  52. Stern RA, & White T (2003b). Neuropsychological assessment battery: Administration, scoring, and interpretation manual. Psychological Assessment Resources. [Google Scholar]
  53. Strickland JC, Stoops WW, Dunn KE, Smith KE, & Havens JR (2021). The continued rise of methamphetamine use among people who use heroin in the United States. Drug and Alcohol Dependence, 225, 108750. 10.1016/j.drugalcdep.2021.108750 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Substance Abuse and Mental Health Services Administration. (2018). Key substance use and mental health indicators in the United States: Results from the 2017 National Survey on Drug Use and Health (HHS Publication No. SMA 18–5068, NSDUH Series H-53). Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. Retrieved from https://www.samhsa.gov/data/ [Google Scholar]
  55. White T, & Stern RA (2003). Neuropsychological Assessment Battery: Psychometric and technical manual. Psychological Assessment Resources. [Google Scholar]
  56. Winkelman TN, Admon LK, Jennings L, Shippee ND, Richardson CR, & Bart G (2018). Evaluation of amphetamine-related hospitalizations and associated clinical outcomes and costs in the United States. JAMA Network Open, 1(6), e183758. 10.1001/jamanetworkopen.2018.3758 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Yochim BP, Kane KD, & Mueller AE (2009). Naming test of the Neuropsychological Assessment Battery: Convergent and discriminant validity. Archives of Clinical Neuropsychology, 24(6), 575–583. 10.1093/arclin/acp053 [DOI] [PubMed] [Google Scholar]
  58. Zgaljardic DJ, & Temple RO (2010). Reliability and validity of the Neuropsychological Assessment Battery-Screening Module (NABSM) in a sample of patients with moderate-to-severe acquired brain injury. Applied Neuropsychology, 17(1), 27–36. 10.1080/09084280903297909 [DOI] [PubMed] [Google Scholar]

RESOURCES