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Analytic Project--> Table 1: Sociodemographic and Behavioral Characteristics of Persons Brought in Dead at UTH, 2019 to 2024
Find a draft of my preliminary results for my analytic project. A total of 15,652 persons brought in dead (BID) at the University Teaching Hospital (UTH) were included in the analysis. Of these, 3,572 (23%) deaths were attributed to cardiovascular disease (CVD), while 12,080 (77%) were due to non-CVD causes. The median age at death was significantly higher among those with CVD-related deaths (60 years, IQR: 42–75) compared to non-CVD deaths (47 years, IQR: 35–67; p < 0.001). A higher proportion of males died from CVD compared to females (59% vs 41%; p = 0.017). In terms of marital status, widowhood and being married were more common among CVD deaths, while being never married was more common among non-CVD deaths (p < 0.001). Regarding educational attainment, individuals with no formal education were more likely to die from CVD compared to those with secondary or higher education (p < 0.001). Proportion of hypertension (BP) was markedly higher among CVD deaths (49%) compared to non-CVD deaths (27%; p < 0.001), reinforcing its known role as a major CVD risk factor. No significant difference was observed between groups in alcohol consumption (p = 0.077), though tobacco use and smoking were significantly more common among CVD deaths (p = 0.002 and p = 0.004, respectively). Unemployment was high across both groups, with no statistically significant difference (p = 0.077).
Trabajo integrador 2
Grupo N.º 5 (Acosta, Albornoz, Paradi)
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Painel de Sífilis Adquirida
Notificações de Sífilis Adquirida na Região Norte do Distrito Federal
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CASO 25 DIAGRAMAS DE DISPERSION
CASO 25 DIAGRAMAS DE DISPERSION
Painel de Sífilis Gestacional e Congênita
Painel feito para região norte do Distrito Federal
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LFS vs Census shares
Annual Greenhouse Gas Emissions Including Land Use
In this study, I investigate the global evolution of annual greenhouse gas emissions from 1850 to the present, including emissions resulting from changes in land use. This trip is motivated by a profound interest in how many nations contribute to the climate crisis and how land use, economic development, and industrialization have influenced the atmosphere of our world. To make these intricate patterns come to life, I use interactive visualization tools in R like Plotly and gganimate on free datasets that were curated by Jones et al. (2024) and processed by Our World in Data.