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BackgroundPublic health is increasingly concerned with recognising factors that lead to sex differences in stroke. We conducted a study to determine the effect of sex on knowledge of stroke risk factors and warning signs, and how both are perceived, in a representative sample of adults.MethodsA representative sample of the population of Extremadura, Spain was selected using a double randomisation technique. Previously trained medical students carried out face-to-face interviews using a structured questionnaire.Results2409 subjects were interviewed [59.9 % women; mean age (SD) 49.0 (18.7) years]. Seventy-three percent of all subjects reported at least one correct warning sign of stroke (OR: 1.01; 95 % CI: 0.84–1.21). The most frequently mentioned warning signs were sudden weakness, dizziness, and headache. There were no sex differences regarding the types of warning symptoms that respondents listed. Women displayed better knowledge of risk factors than men (OR: 1.23; 95 % CI: 1.05–1.46). Women were more likely to name hypertension as a risk factor for stroke whereas men more frequently listed smoking, alcohol consumption and a sedentary lifestyle as risk factors. In response to stroke, women were significantly less likely than men to choose to call an ambulance or to go immediately to hospital (OR: 0.69; 95 % CI: 0.60–0.85).ConclusionsStroke knowledge is suboptimal in both men and women. We detected better knowledge of stroke risk factors in women, as well as differences in the type of risk factors listed by men and women. There were significant sex differences regarding response to stroke or to its warning signs.Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-015-1582-1) contains supplementary material, which is available to authorized users.

The extreme variability of temporary disability duration has a deep effect in public health. We tried to understand what factors duration of disability depends on. Through cohort study with data of temporary disabilities collected by Ibermutuamur from 2008 to 2012, we used statistical multivariate methods. The most reliable and convenient algorithm to predict duration was a categorical classification tree that distinguished between brief and long disabilities, taking into account both medical-biological and socioeconomic factors. The influence of socioeconomic factors in the disability process made numeric predictive models not accurate enough. Some of these socioeconomic factors were isolated and their influences were quantified. In particular, the one we named factor unemployment could explain a huge increase in duration for certain common diagnoses such as anxiety, low back pain, headache, and depression.