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Item type:Publication, Influenza vaccination hesitancy in five countries of South America. Confidence, complacency and convenience as determinants of immunization rates(Public Library of Science, 2020-12-01)INTRODUCTION: Influenza morbidity and mortality are significant in the countries of South America, yet influenza vaccination is as low as 56.7% among pregnant women, reaching 76.7% of adults with chronic diseases. This article measures the relative values for the vaccination hesitancy indicators of confidence, complacency and convenience by risk-groups in urban areas of five countries of South America with contrasting vaccination rates, analyzing their association with sociodemographic variables and self-reported immunization status. METHODS: An exit survey was applied to 640 individuals per country in Brazil, Chile, Paraguay, Peru and Uruguay, distributed equally across risk groups of older adults, adults with risk factors, children ≤6 and pregnant women. Indicators were constructed for vaccine confidence, complacency and convenience. Analysis of variance and multiple logistic analysis was undertaken. RESULTS: Adults with risk factors are somewhat more confident of the influenza vaccine yet also more complacent. Convenience is higher for mothers of minors. Children and older adults report higher levels of vaccination. The 3Cs are more different across countries than across risk groups, with values for Chile higher for confidence and those for Uruguay the lowest. Complacency is lower in Brazil and higher in Uruguay. Results suggest that confidence and complacency affect vaccination rates across risk groups and countries. CONCLUSIONS: Influenza vaccine confidence, complacency and convenience have to be bolstered to improve effective coverage across all risk groups in the urban areas of the countries studied. The role played by country contextual and national vaccination programs has to be further researched in relation to effective coverage of influenza vaccine. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine learning application for predicting heart attacks in patients from Europe(Science and Information Organization, 2022-01-01)Even today, there are still a large number of people suffering from heart attacks, which have already claimed numerous lives worldwide. To examine the main components of this problem in an objective and timely manner, we chose to work with a methodology that relies on taking and learning from real and existing data for use in training and testing predictive models. This was carried out to obtain useful data for the present research work. There are in parallel different methodologies that do not quite fit the model of this work. Data was collected from the "Center for Machine Learning and Intelligent Systems" which in turn contains data from patients who have ever suffered a cardiovascular attack and from patients who never suffered the disease, all of them being patients selected from different medical institutions. With the corresponding information, it was subjected to different processes such as cleaning, preparation, and training with the data, to obtain a logistic regression type automatic learning model ready to predict whether or not a person may suffer a cardiovascular attack. Finally, a result of 87% accuracy was obtained for people who suffered a heart attack and an accuracy of 81% for people who would not suffer from this disease. This can greatly reduce the mortality rate due to infarction, by knowing the condition of a person who is unaware of his or her health situation and thus being able to take appropriate measures. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Gender inequality in time allocation across life stages: A comparative study in a Latin American country(Routledge, 2024-01-01)The study analyzes gender differences in time use in the Latin American context, from a comparative perspective (2010–2019) and across life stages. Particularly, the study presents evidence on the likelihood of women and men spending time on three types of activities: Household work, paid work activities, and personal activities. Logistic regression was applied using data from time use surveys conducted in Peru in 2010 and 2019. The results suggests that women were much more likely to spend additional time on household work, while men were more likely to spend additional time on paid work and personal activities. However, these differences became less pronounced in 2019. In terms of age groups, a U-shaped and an inverted U-shaped pattern were found for paid work activities and household chores respectively. In both cases, the largest gender gap was found in the 30–39 age group, possibly related to motherhood. The originality of the research lies in providing a comprehensive analysis of gender inequality in time use in a developing country that has one of the highest rates of female labor force participation in the region, and is also characterized by a patriarchal structure in which machismo and marianismo influence time distribution.