OBJECTIVE: to identify the prevalence of arterial hypertension and its association with cardiovascular risk factors among adults. METHOD: cross-sectional, population-based, descriptive study conducted with 408 adult individuals. Data were collected through a questionnaire and measurements of weight, height and waist circumference. Person's Chi-square and multiple logistic regression were used in the data analysis. RESULTS: 23.03% of the individuals reported hypertension with a higher prevalence among women. Odds Ratio indicated that smoking, body mass index, waist circumference, diabetes mellitus and dyslipidemia were positively associated with arterial hypertension. CONCLUSION: high self-reported hypertension and its association with other cardiovascular risk factors such as diabetes, obesity and dyslipidemia show the need for specific nursing interventions and the implementation of protocols focused on minimizing complications arising from hypertension, as well as to prevent the emergence of other cardiovascular diseases.

ResumoAssumir distribuições como a normal nas análises de dados é comum em diferentes áreas do conhecimento. Entretanto, pode-se fazer uso de outras que possuem capacidade de modelar também o parâmetro de assimetria, para as situações em que são necessários modelar dados com caudas mais pesadas que a normal. Este trabalho pretende apresentar alternativas à suposição de normalidade nos erros, dispondo também de distribuições assimétricas. Propõe-se uma abordagem Bayesiana para ajuste de modelos não-lineares quando os erros não são normais. Assim, adotam-se as distribuições t, skew-normal e skew-t. A metodologia visa aplicação em diferentes curvas de crescimento para dados de pesos de codornas. Verificou-se que os modelos de Gompertz com erros skew-normal e skew-t, respectivamente, para machos e fêmeas, são os que melhor se ajustam aos dados. Palavras-chave: Distribuições com erros assimétricos, inferência Bayesiana, MCMC, modelos de crescimento AbstractBayesian modeling growth curves for quail assuming skewness in errors -To assume normal distributions in the data analysis is common in different areas of the knowledge. However we can make use of the other distributions that are capable to model the skewness parameter in the situations that is needed to model data with tails heavier than the normal. This article intend to present alternatives to the assumption of the normality in the errors, adding asymmetric distributions. A Bayesian approach is proposed to fit nonlinear models when the errors are not normal, thus, the distributions t, skew-normal and skew-t are adopted. The methodology is intended to apply to different growth curves to the quail body weights. It was found that the Gompertz model assuming skew-normal errors and skew-t errors, respectively for male and female, were the best fitted to the data.

Highly Active Antiretroviral therapy (HAART) depends on optimal adherence to be effective. Pharmacotherapeutic follow-up can be used as a strategy for treatment fidelity. To provide pharmaceutical care for HAART patients, to assess adherence, to identify and resolve drug related problems (DRP). This is a prospective, interventional study aimed at people on HAART. Data was collected using the pharmacotherapeutic follow-up form and CEAT-VIH. There was a predominance of women (59.1%), older than 33 years (75%), mostly single (43,2%). Regarding adherence, (63.6%) had insufficient adherence at the start of the study, while (36.4%) had strict/ adequate adherence. After the pharmacotherapeutic follow-up, (70,4%) presented strict/adequate adherence. Regarding HAART, the relationship between adherence versus time of HAART and adherence versus regimen used was significant, considering that less time of therapy and regimen containing protease inhibitors are predictors for insufficient adherence. Regarding the DRP identified (f=77), missed pills (32.4%), untreated disease, incorrect management frequency, and undue self-medication (11.7%) were the most frequent. Pharmaceutical interventions (f=137) were predominantly advising related to specific pharmacological treatment (32.1%), nonpharmacological measures (19.7%), and medication suspension (8.7%). Pharmaceutical care was shown to be animportant strategy, within the multi professional team, to improve adherence, besides identifying and resolving DRP.

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