Identifying lifestyle patterns among Filipinos using cluster and latent class analysis - Adversario, Ma. Penafrancia L.
Background: A latent variable like lifestyle can be modeled statitically by applying two techniques for clustering individuals into groups when group membership is indirectly observale, cluster analysis and latent class analysis. Objectives: 1) To discover patterns of lifestyle based on thee following behavioral indices: tobacco use, alcohol intake, physical activity, and diet; 2) To compare lifestyle patterns formed by cluster analysis and those formed by latent class analysis; and 3) To determine factors predictive of a lifestyle pattern. Method: Cluster analysis using PAM and K modes algorithm and latent class analysis was done in R v2.12.0 The optimum number of groups was determined usinginternal validity measures for PAM, information criteria and normed fit index for LCA, the optimum number of groups derived from LCA for K modes and interpretability for all three methods. Cluster and latent class analysis were compared through test of equality of the lifestyle indicators and consistency in group assignments. Latent class regression analysis was carried out on the three-class solution to determin e covariates predictive of latent class membership. Latent class and latent class regression analysis were compared using statistical indices, classification quality and consistency in class assignments. Results: A seven-cluster solution was obtained inPAM while a three-group solution as recovered in K modes and latent class analysis. Utilizing a three-group solution for comparison, lifestyle behavior profile was relatively similar among the three methods for the healthy group, between latent class and PAM for the moderately healthy group and between latent class and K modes for the unhealthy group. There were significant differences though, in the proportion of non-smokers, non-drinkers, those with adequate exercise and good diet between latent class and K modes and between latent class and PAM for the healthy, the moderately healthy and the unhealthy group. Consistency is group assignments between the two clustering methods and latent class was 73% for the healthy group. The probability of belonging to the unhealthy class relative to the healthy class was 60 times higher for males than females, three to four times higher in the middle-aged group than the younger age group and two to three times more among professionals/administrative jobs, sales and service professions, farmers, laborers and the unemployed compared to housewives. It was 35 times more for those living in NCR and Region 8 compared to those in ARMM and it was twice more among those with a negative attitude than those with a positive attitude towards a lifestyle behavior. The lagtent class regression model gave a lower AIC, BIC and log-likelihood and higher average posterior probabilities than the latent class model. Consistency in class assignments between the two methods was 78% for the moderately healthy and 95% for unhealthy class. Conclusion: Based on the 2001 Behavioral Risk Factor Surveillance Survey three Filipino lifestyle patterns were iodentified: the healthy, moderately healthy, and unhealthy using cluster and latent class analysis. There was average consistency in group assignments between cluster analysis and latent class analysis. However, there were significant differences in the proportion of lifestyle indicagtors among the three methods. Sex, age group, marital status, education, occupation, region perceived severity of hypertension, perceived health status and attitude were significant predictors ofclass membership. The latent class regression model showed better fit, better classification quality and a more reasonable consistency in class assignments compared to the basic latent class model.
Thesis (MS Public Health, Biostatistics)--University of the Philippines Manila, 2011
Keywords: Lifestyle patterns