scholarly journals Assessing measurement error in surveys using latent class analysis: application to self-reported illicit drug use in data from the Iranian Mental Health Survey

2016 ◽  
Vol 38 ◽  
pp. e2016013
Author(s):  
Kazem Khalagi ◽  
Mohammad Ali Mansournia ◽  
Afarin Rahimi-Movaghar ◽  
Keramat Nourijelyani ◽  
Masoumeh Amin-Esmaeili ◽  
...  
Keyword(s):  
Mental Health ◽  
Measurement Error ◽  
Drug Use ◽  
Latent Class Analysis ◽  
Health Survey ◽  
Latent Class ◽  
Illicit Drug ◽  
Class Analysis ◽  
Analysis Application ◽  
Mental Health Survey

Typologies of illicit drug use in mid‐adulthood: a quasi‐longitudinal latent class analysis in a community‐based sample of twins

Addiction ◽  
2020 ◽  
Author(s):  
Genevieve F. Dash ◽  
Nicholas G. Martin ◽  
Arpana Agrawal ◽  
Michael T. Lynskey ◽  
Wendy S. Slutske
Keyword(s):  
Drug Use ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Illicit Drug ◽  
Illicit Drug Use ◽  
Class Analysis ◽  
Community Based

Epidemiology of illicit drug use disorders in Iran: prevalence, correlates, comorbidity and service utilization results from the Iranian Mental Health Survey

Addiction ◽  
2016 ◽  
Vol 111 (10) ◽  
pp. 1836-1847 ◽  
Author(s):  
Masoumeh Amin-Esmaeili ◽  
Afarin Rahimi-Movaghar ◽  
Vandad Sharifi ◽  
Ahmad Hajebi ◽  
Reza Radgoodarzi ◽  
...  
Keyword(s):  
Mental Health ◽  
Drug Use ◽  
Service Utilization ◽  
Health Survey ◽  
Illicit Drug ◽  
Illicit Drug Use ◽  
Drug Use Disorders ◽  
Mental Health Survey

An ad hoc method for dual adjusting for measurement errors and nonresponse bias for estimating prevalence in survey data: Application to Iranian mental health survey on any illicit drug use

2017 ◽  
Vol 27 (10) ◽  
pp. 3062-3076 ◽  
Author(s):  
Kazem Khalagi ◽  
Mohammad Ali Mansournia ◽  
Seyed-Abbas Motevalian ◽  
Keramat Nourijelyani ◽  
Afarin Rahimi-Movaghar ◽  
...  
Keyword(s):  
Mental Health ◽  
Drug Use ◽  
Measurement Errors ◽  
Latent Class ◽  
Illicit Drug ◽  
Illicit Drug Use ◽  
Nonresponse Bias ◽  
Missing Not At Random ◽  
Mental Health Survey ◽  
Log Linear

Purpose The prevalence estimates of binary variables in sample surveys are often subject to two systematic errors: measurement error and nonresponse bias. A multiple-bias analysis is essential to adjust for both biases. Methods In this paper, we linked the latent class log-linear and proxy pattern-mixture models to adjust jointly for measurement errors and nonresponse bias with missing not at random mechanism. These methods were employed to estimate the prevalence of any illicit drug use based on Iranian Mental Health Survey data. Results After jointly adjusting for measurement errors and nonresponse bias in this data, the prevalence (95% confidence interval) estimate of any illicit drug use changed from 3.41 (3.00, 3.81)% to 27.03 (9.02, 38.76)%, 27.42 (9.04, 38.91)%, and 27.18 (9.03, 38.82)% under “missing at random,” “missing not at random,” and an intermediate mode, respectively. Conclusions Under certain assumptions, a combination of the latent class log-linear and binary-outcome proxy pattern-mixture models can be used to jointly adjust for both measurement errors and nonresponse bias in the prevalence estimation of binary variables in surveys.

scholarly journals Subtypes of Illicit Drug Users: A Latent Class Analysis of Data From an Australian Twin Sample

2006 ◽  
Vol 9 (4) ◽  
pp. 523-530 ◽  
Author(s):  
Michael T. Lynskey ◽  
Arpana Agrawal ◽  
Kathleen K. Bucholz ◽  
Elliot C. Nelson ◽  
Pamela A. F. Madden ◽  
...  
Keyword(s):  
Drug Use ◽  
Latent Class Analysis ◽  
Drug Users ◽  
Psychiatric Comorbidity ◽  
Latent Class ◽  
Illicit Drug ◽  
Illicit Drug Use ◽  
Self Report ◽  
Class Analysis ◽  
Illicit Drug Users

AbstractThis article applies methods of latent class analysis (LCA) to data on lifetime illicit drug use in order to determine whether qualitatively distinct classes of illicit drug users can be identified. Self-report data on lifetime illicit drug use (cannabis, stimulants, hallucinogens, sedatives, inhalants, cocaine, opioids and solvents) collected from a sample of 6265 Australian twins (average age 30 years) were analyzed using LCA. Rates of childhood sexual and physical abuse, lifetime alcohol and tobacco dependence, symptoms of illicit drug abuse/dependence and psychiatric comorbidity were compared across classes using multinomial logistic regression. LCA identified a 5-class model: Class 1 (68.5%) had low risks of the use of all drugs except cannabis; Class 2 (17.8%) had moderate risks of the use of all drugs; Class 3 (6.6%) had high rates of cocaine, other stimulant and hallucinogen use but lower risks for the use of sedatives or opioids. Conversely, Class 4 (3.0%) had relatively low risks of cocaine, other stimulant or hallucinogen use but high rates of sedative and opioid use. Finally, Class 5 (4.2%) had uniformly high probabilities for the use of all drugs. Rates of psychiatric comorbidity were highest in the polydrug class although the sedative/opioid class had elevated rates of depression/suicidal behaviors and exposure to childhood abuse. Aggregation of population-level data may obscure important subgroup differences in patterns of illicit drug use and psychiatric comorbidity. Further exploration of a ‘self-medicating’ subgroup is needed.

scholarly journals Patterns of drug use and abuse among aging adults with and without HIV: A latent class analysis of a US Veteran cohort

2010 ◽  
Vol 110 (3) ◽  
pp. 208-220 ◽  
Author(s):  
Traci C. Green ◽  
Trace Kershaw ◽  
Haiqun Lin ◽  
Robert Heimer ◽  
Joseph L. Goulet ◽  
...  
Keyword(s):  
Drug Use ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Aging Adults
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