Cognitive performance profiles by latent classes of drug use

Abstract

Background and Objectives The relationship between substance use and cognitive deficits is complex and requires innovative methods to enhance understanding. The present study is the first to use LCA to examine associations of drug use patterns with cognitive performance. Methods Cocaine/heroin users (N=552) completed questionnaires, and cognitive measures. LCA identified classes based on past-month drug use and adjusted for probabilities of group membership when examining cognitive performance. Latent indicators were: alcohol (ALC), cigarettes (CIG), marijuana (MJ), crack smoking (CS), nasal heroin (NH), injection cocaine (IC), injection heroin (IH), and injection speedball (IS). Age and education were included as covariates in model creation. Results Bootstrap Likelihood Ratio Test (BLRT) supported a 5-class model. Prevalent indicators (estimated probability of over 50%) for each class are as follows: “Older Nasal Heroin/Crack Smokers” (ONH/CS, n=166.9): ALC, CIG, NH, CS; “Older, Less Educated Polysubstance” (OLEP, n=54.8): ALC, CIG, CS, IH, IC, and IS; “Younger Multi-Injectors” (MI, n=128.7): ALC, CIG, MJ, IH, IC, and IS; “Less Educated Heroin Injectors” (LEHI, n=87.4): CIG, IH; and “More Educated Nasal Heroin” users (MENH, n = ALC, CIG, NH). In general, all classes performed worse than established norms and older, less educated classes performed worse, with the exception that MENH demonstrated worse cognitive flexibility than YMI. Discussion and Conclusions This study demonstrated novel applications of a methodology for examining complicated relationships between polysubstance use and cognitive performance. Scientific Significance Education and/or nasal heroin use are associated with reduced cognitive flexibility in this sample of inner city drug users.

Cannabis use and nonuse in patients with first-episode psychosis: A systematic review and meta-analysis of studies comparing neurocognitive functioning

“…Cross-sectional and longitudinal studies were included in the systematic review when they met the following criteria: (1) diagnosis of FEP according to the Diagnostic and Statistical Manual of Mental Disorders (patients with psychotic symptoms who could have received antipsychotic treatment for less than 12 weeks); (2) comparison between CU with FEP and NU with FEP; (3) cannabis abuse or dependence with no other comorbid substance use disorder (except for the common mixture of tobacco and cannabis in the same cigarette when patients did not report independent tobacco use); (4) assessment of neuropsychological functioning based on valid and reliable tests commonly used in clinical practice; and (5) sufficient statistical data for transformation into effect sizes from the original researchers. The reasons for exclusion were as follows: (1) diagnosis of a category other than FEP within the psychosis spectrum (e.g., schizophrenia, substance-induced psychotic disorders, schizoaffective disorders); (2) studies on the effects of individual components of cannabis on cognitive functioning; (3) studies in which participants had poly-substance use disorders, even if there was a preferential use toward cannabis, given that other substances of abuse (e.g., alcohol, cocaine, and stimulants) are associated with altered cognitive performance [42,43]; (4) studies whose main neuropsychological outcomes required MRI-based assessment; (5) available data on cannabis use classified according to more than two different levels of use (e.g., NU plus 2 or more cannabis use pathways).…”

Pattern and related factors of cognitive impairment among chronic methamphetamine users

“…Methamphetamine use has markedly increased in the past decades and caused many adverse health outcomes, including neurocognitive impairment and harmful neurobehavioral symptoms . Cognitive impairment has been linked to demographic characteristics (eg, gender and education), the severity of MA use, and adverse health outcomes …”

Latent transition analysis of instrumental activities of daily living in Chinese elderly: based on the 2014–2018 wave of the Chinese Longitudinal Healthy Longevity Survey

“…Lower values of the Akaike information criterion (AIC) [ 29 ], the Bayesian information criterion (BIC) [ 30 ], and the Sample-adjusted Bayesian Information Criterion (aBIC) indicate a better-fitting model [ 31 ]. Lower values of the Vuong-Lo-Mendell-Rubin (VLMR) and Bootstrapped Likelihood Ratio Test (BLRT) indicate that the K-profile model is better than the K-1 profile model [ 32 ]. Greater entropy indicates higher classification accuracy, with a value greater than 0.8 indicates that the classification accuracy is greater than 90% [ 33 ].…”