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Latent class analysis (LCA) is a solution for assessing and correcting measurement error. "Local independence" assumption in LCA expresses that indicators are independent from each other condition on the latent variable. This assumption may be violated in surveys due to: "behaviorally correlated error", "bivocality", and "latent heterogeneity". As an example, we used LCA for estimating prevalence of any illicit drug use in "Iranian Mental Health Survey".The three indicators included in LCA models were: indicator A (≥ 5 times use of any illicit drug in the past 12 months), indicator B (any use of any illicit drug in the past 12 months) and indicator D (need for using treatment services or using them for substance use disorders in the past 12 months). Gender is also used in all LCA models as a grouping variable. One LCA model using two A and B indicators, and ten different LCA models using three A, B and D indicators were fitted to the data.The three models which have the best fitness to the data were those including the following correlations between indicators: (AD and AB), (AD), and (AD, BD and AB). The prevalence estimate of any illicit drug use based on these three models were 28.9%, 6.2% and 42.2%, respectively. None of the models have controlled bivocality and latent heterogeneity completely.To achieve the unbiased estimations using LCA approach, the three factors violating local independence assumption should be taken into account completely in models through well-known methods.

epidemiology and health

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