Latent class analysis (LCA) is a method of assessing and correcting measurement error in surveys. The local independence assumption in LCA assumes that indicators are independent from each other condition on the latent variable. Violation of this assumption leads to unreliable results. We explored this issue by using LCA to estimate the prevalence of illicit drug use in the Iranian Mental Health Survey. The following three indicators were included in the LCA models: five or more instances of using any illicit drug in the past 12 months (indicator A), any use of any illicit drug in the past 12 months (indicator B), and the self-perceived need of treatment services or having received treatment for a substance use disorder in the past 12 months (indicator C). Gender was also used in all LCA models as a grouping variable. One LCA model using indicators A and B, as well as 10 different LCA models using indicators A, B, and C, were fitted to the data. The three models that had the best fit to the data included the following correlations between indicators: (AC and AB), (AC), and (AC, BC, and AB). The estimated prevalence of illicit drug use based on these three models was 28.9%, 6.2% and 42.2%, respectively. None of these models completely controlled for violation of the local independence assumption. In order to perform unbiased estimations using the LCA approach, the factors violating the local independence assumption (behaviorally correlated error, bivocality, and latent heterogeneity) should be completely taken into account in all models using well-known methods.
Bias (Epidemiology) ; Mental Health* ; Methods ; Prevalence ; Self Report ; Substance-Related Disorders ; Surveys and Questionnaires

Objectives: This cross-sectional study, conducted as part of the Bushehr Elderly Health program stage II in Bushehr, Iran, aimed to evaluate health-related quality of life (HR-QoL) in individuals aged ≥ 60 with osteo sarcopenia, a condition characterized by the co-occurrence of osteopenia/osteoporosis and sarcopenia. Given the increasing elderly population worldwide, understanding the HR-QoL of this demographic is crucial, with osteosarcopenia being a significant factor. Methods: The study enrolled 2369 participants aged ≥ 60 and collected demographic and anthropometric data.Various questionnaires, including the Short Form 12, Patient Health Questionnaire-9, Activities of Daily Living, and Instrumental Activities of Daily Living, were administered. Comparisons were made between individuals with and without osteosarcopenia. Regression models were employed to identify variables associated with HRQoL in those with osteosarcopenia. Results: Key findings revealed that 22.5% of participants had osteosarcopenia. Significantly different HR-QoL measures were observed between the 2 groups, especially in physical functioning and physical component summary scores. Male gender, advanced age, and chronic illnesses were linked to lower physical and mental HRQoL scores among those with osteosarcopenia. In female participants, a history of fractures and physical disability were associated with reduced quality of life. Conclusions This study underscores the negative impact of osteosarcopenia on HR-QoL, particularly in male participants, with a focus on physical aspects. It also highlights age and chronic disease as contributing factors to diminished HR-QoL in individuals with osteosarcopenia. These findings emphasize the importance of addressing osteosarcopenia in the elderly population to improve their overall well-being.