Identifying the pattern of unhealthy dietary habits among an Iranian population: A latent class analysis

Background: An unhealthy diet is one of the most important risk factors for chronic diseases. The goal of this study was to use the latent class analysis (LCA) modeling to define unhealthy diet habits among an Iranian population. Methods: This cross-sectional study was conducted within the framework of Amol (North of Iran) cohort health study (Phase 1). The participants aged 10 to 90 years. All participants provided written informed consent. Latent class analysis was used to classify the participants of the study. All analyses were conducted by PROC LCA in SAS 9.2 software. Significance level was set at 0.05. Results: The mean age of the participants was 42.58±17.23 years. Four classes of individuals with different diet habits were identified using LCA modeling: class 1: individuals with healthy diet patterns (92.6%); class 2: individuals with slightly unhealthy diet habits (6.3%); class 3: individuals with relatively unhealthy diet habits (0.8%); and class 4: individuals with unhealthy diet habits (0.2%). Being female and alcohol consumption increased the odds of membership in latent classes 2,3, and 4 compared to class 1. Physical activity decreased the odds of membership in classes 3 and 4 compared to class 1. Conclusion: Overall, almost more than 7.4% of all participants had some degree of unhealthy dietary habits, and some variables acted as risk factors for membership in risky classes. Therefore, focusing on these variables may help design and execute effective preventive interventions in groups with unhealthy dietary habits.