Since the co-occurrence of several health-risk behaviors is associated with an increase in chronic diseases, the study of clustering is relevant. The aim of this study was to evaluate how seven types of general and oral health-risk behaviors, cluster among adolescents. A cross-sectional analysis was performed with a sample of high school students from state public schools in São Lourenco da Mata, State of Pernambuco, Brazil (n = 1154). Data were obtained using a self-reported questionnaire. To identify cluster solutions of seven general and oral health-risk behaviors, Hierarchical Agglomerative Cluster Analysis (HACA) was performed. Most of the adolescents participating in the study was female (54.3%) and aged <16 years old (77.1%). HACA indicated two broad stable clusters for the seven health-risk types of behaviors. The first cluster included following behaviors: smoking, drinking and less frequent tooth brushing. The second cluster reveals the combination of high bread, pasta and snack intake; high intake of sweets; high intake of soft drinks; low intake of fruits and vegetables. Results provided by HACA identified two groups of health-risk behaviors. The first cluster mainly shows risk (problematic) behaviors, whereas the second cluster denotes the non-adhesion of preventive behavior (non-healthy diet). Health-compromising behaviors are common among teens and occur in distinct clusters. These findings could be used by schools, health promotion authorities and other stakeholders to design and implement tailored preventive interventions in Pernambuco, Brazil. Therefore, clustering of several types of behavior has important implications for a comprehensive strategy in health promotion policies and practices.
Objetive To determine the percentage of correctness of the Orbital Index (OI) for estimation of sex, ancestry and age in Brazilian skulls. Methods Cross-sectional study of 183 human dry skulls from the southeastern Brazil. A total of 100 skeletons were males and 83 females; of which 36 were aged up to 39 years, 60 aged between 40 and 59 years, and 87 aged 60 years or older. As for ancestry, 103 were from white, 51 mixed race, and 29 black individuals. The OI was calculate by the formula = height/width x 100. The data were submitted to Student’s t test, F (ANOVA), Tukey and Kruskal Wallis tests as well as to discriminant analysis, with a 5% significance level. Results The sample was characterized as mesoseme, with a mean age of 56.62 (±19.97) years. No significant difference was observed (p=0.511) between the OI in females (right: 86.43 ± 6.58 and left: 86.70 ± 5.93) and males (right: 85.78 ± 6.69 and left: 86.37 ± 6.20). There were no significant differences between age, ancestry and the variables analyzed (p>0.05). The right and left orbital widths were significantly dimorphic between sexes (p<0.001). The percentage of correctness of the method for estimation of sex, age and ancestry was found to be 65.6%, 43.7%, and 43.6%, respectively. Conclusions The OI is not an appropriate method for estimation of sex, ancestry and estimation of age in this Brazilian sample. The methodology should be expanded to other population groups so that it can be improved.
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