Major risk factors for obstructive sleep apnea monitored in the home. A cross-sectional study

ABSTRACT BACKGROUND: Obstructive sleep apnea (OSA) is characterized by recurrent pharyngeal wall collapse during sleep caused by anatomical or functional changes associated with obesity or dislocation of maxillofacial structures. OBJECTIVE: To determine the major risk factors for obstructive sleep apnea monitored in the home. DESIGN AND SETTING: Cross-sectional study conducted in a private clinic in Fortaleza (CE), Brazil. METHODS: Between 2015 and 2018, 427 patients were screened for OSA with home-based monitoring, yielding 374 positives. Information was collected on age, sex, body mass index (BMI), hypertension, diabetes (DM), dyslipidemia, coronary artery disease (CAD), arrhythmia, peripheral artery occlusive disease (PAOD), heart failure (HF) and lung disease. The home sleep apnea test result was then compared with the clinical diagnosis. Lastly, parameters identified as significant in the univariate analysis were subjected to multivariate logistic regression. RESULTS: Male sex predominated, although not significantly. OSA was associated with hypertension, DM, dyslipidemia, age and BMI. The risk of OSA being associated with these parameters was 2.195 (hypertension), 11.14 (DM), 2.044 (dyslipidemia) and 5.71 (BMI). The association was also significant for BMI categories (normal, overweight or obese). No significant association was observed for CAD, arrhythmia, PAOD, HF or lung disease. After multivariate logistic analysis, only age and BMI (and its categories) remained significant. CONCLUSION: OSA was associated with hypertension, DM, dyslipidemia, age and BMI in univariate analyses, but only with age and BMI (and its categories) in multivariate logistic analysis.

Índice de vulnerabilidade à COVID-19: uma aplicação para a cidade de Fortaleza (CE), Brasil / Vulnerability index to COVID-19: Fortaleza, Brazil study case

RESUMO O estudo propôs a elaboração de um índice de vulnerabilidade à propagação da COVID-19 utilizando análise multivariada associada à análise geoespacial. O método empregado considerou a vulnerabilidade como uma combinação de três fatores: exposição, susceptibilidade e capacidade de resposta. A metodologia foi composta de seis etapas: seleção e agrupamento de variáveis; definição dos indicadores; normalização; atribuição de pesos via análise dos componentes principais; estimativa e normalização do índice; e classificação por meio das técnicas Jenks, Kmeans, quantis e clusterização hierárquica (Hclust). Foi realizada uma caracterização da exposição da cidade de Fortaleza, Brasil, à COVID-19 por meio da densidade populacional, da quantidade de habitações subnormais e precárias, da porcentagem de idosos por residência e da proximidade a terminais de ônibus. O estudo procurou sobrepor fatores socioeconômicos e índices de abastecimento público e de esgotamento sanitário, para a classificação de setores censitários em cinco níveis de vulnerabilidade. Estes apresentaram, em sua maioria, classe de alta (Jenks e quantis) e moderada (K-means e Hclust) vulnerabilidade. As regiões de maior vulnerabilidade estão localizadas no sul e no oeste da cidade, onde há maior concentração de aglomerados subnormais. Os resultados podem auxiliar no desenvolvimento de estratégias de enfrentamento direcionadas para os grupos mais expostos aos riscos associados à COVID-19, assim como na preparação para futuras crises de saúde pública. A metodologia pode ser replicada para outras cidades e serve como ferramenta para os gestores públicos.

ABSTRACT The study proposes an index of vulnerability to the spread of COVID-19 using multivariate and geospatial analyses. Vulnerability was considered as a combination of exposure, susceptibility, and responsiveness of the population. The methodology consisted of six steps: selection and grouping of variables; definition of indicators; normalization of the variables; weight calculation using principal component analysis; index calculation and normalization; and classification using Jenks, Kmeans, Quantile, and Hclust techniques. The exposure of Fortaleza, Brazil, to COVID-19 was assessed by means of population density, number of subnormal and precarious housing, percentage of aged people per residence and proximity to bus terminals. The study integrates factors related to socioeconomic characteristics, public water supply and sanitation for the classification of census tracts in five levels of vulnerability. Most of them present high (Jenks and Quantile) and moderate (K-means and Hclust) vulnerability. The most vulnerable regions are in the South and West Zones of the city, where many subnormal agglomerates are located. The results can assist the development of coping strategies for the groups most exposed to the risks associated with COVID-19, as well as in the preparation for future public health crises. The methodology can be replicated in other cities and can be useful for public managers.