10 datasets found
  1. Data_Sheet_1_Evaluating the impact of mobility in COVID-19 incidence and...

    • frontiersin.figshare.com
    • datasetcatalog.nlm.nih.gov
    docx
    Updated Jun 4, 2023
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    César Arturo Méndez-Lizárraga; MLucía Castañeda-Cediel; Guadalupe Delgado-Sánchez; Edith Elizabeth Ferreira-Guerrero; Leticia Ferreyra-Reyes; Sergio Canizales-Quintero; Norma Mongua-Rodríguez; Norma Tellez-Vázquez; María Eugenia Jiménez-Corona; Kathryn Bradford Vosburg; Omar Y. Bello-Chavolla; Lourdes García-García (2023). Data_Sheet_1_Evaluating the impact of mobility in COVID-19 incidence and mortality: A case study from four states of Mexico.docx [Dataset]. http://doi.org/10.3389/fpubh.2022.877800.s001
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    docxAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    César Arturo Méndez-Lizárraga; MLucía Castañeda-Cediel; Guadalupe Delgado-Sánchez; Edith Elizabeth Ferreira-Guerrero; Leticia Ferreyra-Reyes; Sergio Canizales-Quintero; Norma Mongua-Rodríguez; Norma Tellez-Vázquez; María Eugenia Jiménez-Corona; Kathryn Bradford Vosburg; Omar Y. Bello-Chavolla; Lourdes García-García
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Mexico
    Description

    IntroductionThe COVID-19 pandemic in Mexico began at the end of February 2020. An essential component of control strategies was to reduce mobility. We aimed to evaluate the impact of mobility on COVID- incidence and mortality rates during the initial months of the pandemic in selected states.MethodsCOVID-19 incidence data were obtained from the Open Data Epidemiology Resource provided by the Mexican government. Mobility data was obtained from the Observatory for COVID-19 in the Americas of the University of Miami. We selected four states according to their compliance with non-pharmaceutical interventions and mobility index. We constructed time series and analyzed change-points for mobility, incidence, and mortality rates. We correlated mobility with incidence and mortality rates for each time interval. Using mixed-effects Poisson models, we evaluated the impact of reductions in mobility on incidence and mortality rates, adjusting all models for medical services and the percentage of the population living in poverty.ResultsAfter the initial decline in mobility experienced in early April, a sustained increase in mobility followed during the rest of the country-wide suspension of non-essential activities and the return to other activities throughout mid-April and May. We identified that a 1% increase in mobility yielded a 5.2 and a 2.9% increase in the risk of COVID-19 incidence and mortality, respectively. Mobility was estimated to contribute 8.5 and 3.8% to the variability in incidence and mortality, respectively. In fully adjusted models, the contribution of mobility to positive COVID-19 incidence and mortality was sustained. When assessing the impact of mobility in each state compared to the state of Baja California, increased mobility conferred an increased risk of incident positive COVID-19 cases in Mexico City, Jalisco, and Nuevo León. However, for COVID-19 mortality, a differential impact of mobility was only observed with Jalisco and Nuevo León compared to Baja California.ConclusionMobility had heterogeneous impacts on COVID-19 rates in different regions of Mexico, indicating that sociodemographic characteristics and regional-level pandemic dynamics modified the impact of reductions in mobility during the COVID-19 pandemic. The implementation of non-pharmaceutical interventions should be regionalized based on local epidemiology for timely response against future pandemics.

  2. f

    Supplementary_Table_1_Association between mortality and cardiovascular...

    • figshare.com
    xlsx
    Updated May 31, 2023
    + more versions
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    Gerardo R. Padilla-Rivas; Juan Luis Delgado-Gallegos; Gerardo Garza-Treviño; Kame A. Galan-Huerta; Zuca G-Buentello; Jorge A. Roacho-Pérez; Michelle Giovana Santoyo-Suarez; Hector Franco-Villareal; Ahidée Leyva-Lopez; Ana E. Estrada-Rodriguez; Jorge E. Moreno-Cuevas; Javier Ramos-Jimenez; Ana M. Rivas-Estrilla; Elsa N. Garza-Treviño; Jose Francisco Islas (2023). Supplementary_Table_1_Association between mortality and cardiovascular diseases in the vulnerable Mexican population: A cross-sectional retrospective study of the COVID-19 pandemic.XLSX [Dataset]. http://doi.org/10.3389/fpubh.2022.1008565.s001
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    xlsxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Frontiers
    Authors
    Gerardo R. Padilla-Rivas; Juan Luis Delgado-Gallegos; Gerardo Garza-Treviño; Kame A. Galan-Huerta; Zuca G-Buentello; Jorge A. Roacho-Pérez; Michelle Giovana Santoyo-Suarez; Hector Franco-Villareal; Ahidée Leyva-Lopez; Ana E. Estrada-Rodriguez; Jorge E. Moreno-Cuevas; Javier Ramos-Jimenez; Ana M. Rivas-Estrilla; Elsa N. Garza-Treviño; Jose Francisco Islas
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Cardiovascular diseases (CVDs) continue to be the leading cause of death worldwide. Over the past couple of years and with the surge of the COVID-19 pandemic, mortality from CVDs has been slightly overshadowed by those due to COVID-19, although it was during the peak of the pandemic. In the present study, patients with CVDs (CVDs; n = 41,883) were analyzed to determine which comorbidities had the largest impact on overall patient mortality due to their association with both diseases (n = 3,637). Obesity, hypertension, and diabetes worsen health in patients diagnosed positive for COVID-19. Hence, they were included in the overview of all patients with CVD. Our findings showed that 1,697 deaths were attributable to diabetes (p < 0.001) and 987 deaths to obesity (p < 0.001). Lastly, 2,499 deaths were attributable to hypertension (p < 0.001). Using logistic regression modeling, we found that diabetes (OR: 1.744, p < 0.001) and hypertension (OR: 2.179, p < 0.001) significantly affected the mortality rate of patients. Hence, having a CVD diagnosis, with hypertension and/or diabetes, seems to increase the likelihood of complications, leading to death in patients diagnosed positive for COVID-19.

  3. COVID-19 cases and deaths in Colombia 2025

    • statista.com
    Updated Jun 5, 2025
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    Statista (2025). COVID-19 cases and deaths in Colombia 2025 [Dataset]. https://www.statista.com/statistics/1288290/colombia-covid-19-cases-deaths/
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    Dataset updated
    Jun 5, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 8, 2020 - May 11, 2025
    Area covered
    Colombia
    Description

    As of May 11, 2025, Colombia reached 6.4 million cases of COVID-19, and approximately 143,000 deaths caused by the disease. Within Latin America, Colombia is the fourth most affected country by number of cases, after Brazil, Argentina, and Mexico. The first positive case of COVID-19 in Colombia was registered on March 8, 2020, and the first reported deaths were confirmed on March 23, 2020.Find the most up-to-date information about the coronavirus pandemic in the world under Statista’s COVID-19 facts and figures site.

  4. Table_1_Coronavirus Disease-2019 Survival in Mexico: A Cohort Study on the...

    • frontiersin.figshare.com
    docx
    Updated May 30, 2023
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    Horacio Márquez-González; Jorge F. Méndez-Galván; Alfonso Reyes-López; Miguel Klünder-Klünder; Rodolfo Jiménez-Juárez; Juan Garduño-Espinosa; Fortino Solórzano-Santos (2023). Table_1_Coronavirus Disease-2019 Survival in Mexico: A Cohort Study on the Interaction of the Associated Factors.DOCX [Dataset]. http://doi.org/10.3389/fpubh.2021.660114.s001
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    docxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Horacio Márquez-González; Jorge F. Méndez-Galván; Alfonso Reyes-López; Miguel Klünder-Klünder; Rodolfo Jiménez-Juárez; Juan Garduño-Espinosa; Fortino Solórzano-Santos
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The pandemic caused by the new coronavirus Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is currently affecting more than 200 countries. The most lethal clinical presentation is respiratory insufficiency, requiring attention in intensive care units (ICU). The most susceptible people are over 60 years old with comorbidities. The health systems organization may represent a transcendental role in survival.Objective: To analyze the correlation of sociodemographic factors, comorbidities and health system organization variables with survival in cases infected by SARS-CoV-2 during the first 7 months of the pandemic in Mexico.Methods: The cohort study was performed in a health system public basis from March 1st to September 30th, 2020. The included subjects were positive for the SARS-CoV-2 test, and the target variable was mortality in 60 days. The risk variables studied were: age, sex, geographic distribution, comorbidities, health system, hospitalization, and access to ICU. Bivariate statistics (X2-test), calculation of fatality rates, survival analyses and adjustment of confusing variables with Cox proportional-hazards were performed.Results: A total of 753,090 subjects were analyzed, of which the 52% were men. There were 78,492 deaths (10.3% of general fatality and 43% inpatient). The variables associated with a higher risk of hospital mortality were age (from 60 years onwards), care in public sectors, geographic areas with higher numbers of infection and endotracheal intubation without management in the ICU.Conclusions: The variables associated with a lower survival in cases affected by SARS-CoV-2 were age, comorbidities, and respiratory insufficiency (with endotracheal intubation without care in the ICU). Additionally, an interaction was observed between the geographic location and health sector where they were treated.

  5. Mexico: quarterly distribution of tweets 2012-2023, by mood

    • statista.com
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    Statista, Mexico: quarterly distribution of tweets 2012-2023, by mood [Dataset]. https://www.statista.com/statistics/1063984/mood-twitter-users-mexico/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Mexico
    Description

    According to tweets collected and evaluated from users in Mexico, the mood or state of mind of users in the North American country has been ** percent tending to a positive tone. This is mostly the case for all the analyzed periods and the mood has been especially positive in the past years. However, the percentage of negative tweets was at its highest during the 2020 COVID-19 pandemic, with positive tweets only increasing again by the end of that year.

  6. Characteristics of prevalent cases and associated risks of having a positive...

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated Jun 4, 2023
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    Vanessa Dávila-Conn; Maribel Soto-Nava; Yanink N. Caro-Vega; Héctor E. Paz-Juárez; Pedro García-Esparza; Daniela Tapia-Trejo; Marissa Pérez-García; Pablo F. Belaunzarán-Zamudio; Gustavo Reyes-Terán; Juan G. Sierra-Madero; Arturo Galindo-Fraga; Santiago Ávila-Ríos (2023). Characteristics of prevalent cases and associated risks of having a positive serological test at baseline in healthcare workers of the two largest COVID-19 referral hospitals in Mexico City, October 2020-June 2021. [Dataset]. http://doi.org/10.1371/journal.pone.0264964.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Vanessa Dávila-Conn; Maribel Soto-Nava; Yanink N. Caro-Vega; Héctor E. Paz-Juárez; Pedro García-Esparza; Daniela Tapia-Trejo; Marissa Pérez-García; Pablo F. Belaunzarán-Zamudio; Gustavo Reyes-Terán; Juan G. Sierra-Madero; Arturo Galindo-Fraga; Santiago Ávila-Ríos
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Mexico, Mexico City
    Description

    Characteristics of prevalent cases and associated risks of having a positive serological test at baseline in healthcare workers of the two largest COVID-19 referral hospitals in Mexico City, October 2020-June 2021.

  7. Hazard ratios (95% confidence intervals) for mortality amongst those tested...

    • plos.figshare.com
    • figshare.com
    bin
    Updated Jun 15, 2023
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    Viridiana Ríos; Edgar Denova-Gutiérrez; Simón Barquera (2023). Hazard ratios (95% confidence intervals) for mortality amongst those tested positive for COVID-19. [Dataset]. http://doi.org/10.1371/journal.pone.0264137.t002
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    binAvailable download formats
    Dataset updated
    Jun 15, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Viridiana Ríos; Edgar Denova-Gutiérrez; Simón Barquera
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Hazard ratios (95% confidence intervals) for mortality amongst those tested positive for COVID-19.

  8. COVID-19 impact on tourism in Latin America and the Caribbean 2020

    • statista.com
    Updated Nov 28, 2025
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    Statista (2025). COVID-19 impact on tourism in Latin America and the Caribbean 2020 [Dataset]. https://www.statista.com/statistics/1121536/coronavirus-impact-tourism-gdp-latin-america-caribbean/
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    Dataset updated
    Nov 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2020
    Area covered
    Latin America, Caribbean, Americas
    Description

    In 2020, the gross domestic product (GDP) of Central and South America had suffered a contraction of more than 110 billion U.S. dollars due to the impact of the COVID-19 pandemic on tourism. Meanwhile, the global travel restrictions imposed due to the health crisis caused a GDP decline of roughly 33 billion U.S. dollars in the Caribbean. In consequence, tourism employment was also severely affected in those regions that year.

    Tourism contribution to GDP in Latin America and the Caribbean

    The gross domestic product (GDP) measures the value of all goods and services produced in a country or a region within a certain period. Excluding Mexico, the total contribution of the tourism sector to Latin America and the Caribbean’s GDP saw a moderate but overall positive trend during the past decade, surpassing 350 billion U.S. dollars in 2019. In Mexico alone, nearly two trillion Mexican pesos (more than 100 billion U.S. dollars at exchange rates of December 31, 2019) were added that year to the country’s GDP by tourism-related activities.

    COVID-19 impact on travel and tourism in Mexico

    Mexico is not only the leading country for tourism in Latin America but also one of the key players in the travel sector worldwide. For most of 2020, the Mexican government opted out of travel restrictions and lockdowns. This measure, however, did not rescue the country's tourism sector from the harsh impact of COVID-19. As of October of that year, Mexico was among the travel destinations most affected by the pandemic, with tourism revenue losses amounting to nearly 14 billion U.S. dollars.

  9. Baseline characteristics of healthcare personal of two of the largest...

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated Jun 14, 2023
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    Vanessa Dávila-Conn; Maribel Soto-Nava; Yanink N. Caro-Vega; Héctor E. Paz-Juárez; Pedro García-Esparza; Daniela Tapia-Trejo; Marissa Pérez-García; Pablo F. Belaunzarán-Zamudio; Gustavo Reyes-Terán; Juan G. Sierra-Madero; Arturo Galindo-Fraga; Santiago Ávila-Ríos (2023). Baseline characteristics of healthcare personal of two of the largest COVID-19 referral hospitals in Mexico City by study group, October 2020-June 2021. [Dataset]. http://doi.org/10.1371/journal.pone.0264964.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 14, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Vanessa Dávila-Conn; Maribel Soto-Nava; Yanink N. Caro-Vega; Héctor E. Paz-Juárez; Pedro García-Esparza; Daniela Tapia-Trejo; Marissa Pérez-García; Pablo F. Belaunzarán-Zamudio; Gustavo Reyes-Terán; Juan G. Sierra-Madero; Arturo Galindo-Fraga; Santiago Ávila-Ríos
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Mexico City, Mexico
    Description

    Baseline characteristics of healthcare personal of two of the largest COVID-19 referral hospitals in Mexico City by study group, October 2020-June 2021.

  10. Cox model on risks associated with being an incident case in healthcare...

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated May 30, 2023
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    Vanessa Dávila-Conn; Maribel Soto-Nava; Yanink N. Caro-Vega; Héctor E. Paz-Juárez; Pedro García-Esparza; Daniela Tapia-Trejo; Marissa Pérez-García; Pablo F. Belaunzarán-Zamudio; Gustavo Reyes-Terán; Juan G. Sierra-Madero; Arturo Galindo-Fraga; Santiago Ávila-Ríos (2023). Cox model on risks associated with being an incident case in healthcare workers of the two largest COVID-19 referral hospitals in Mexico City, October 2020-June 2021. [Dataset]. http://doi.org/10.1371/journal.pone.0264964.t003
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    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Vanessa Dávila-Conn; Maribel Soto-Nava; Yanink N. Caro-Vega; Héctor E. Paz-Juárez; Pedro García-Esparza; Daniela Tapia-Trejo; Marissa Pérez-García; Pablo F. Belaunzarán-Zamudio; Gustavo Reyes-Terán; Juan G. Sierra-Madero; Arturo Galindo-Fraga; Santiago Ávila-Ríos
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Mexico, Mexico City
    Description

    Cox model on risks associated with being an incident case in healthcare workers of the two largest COVID-19 referral hospitals in Mexico City, October 2020-June 2021.

  11. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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César Arturo Méndez-Lizárraga; MLucía Castañeda-Cediel; Guadalupe Delgado-Sánchez; Edith Elizabeth Ferreira-Guerrero; Leticia Ferreyra-Reyes; Sergio Canizales-Quintero; Norma Mongua-Rodríguez; Norma Tellez-Vázquez; María Eugenia Jiménez-Corona; Kathryn Bradford Vosburg; Omar Y. Bello-Chavolla; Lourdes García-García (2023). Data_Sheet_1_Evaluating the impact of mobility in COVID-19 incidence and mortality: A case study from four states of Mexico.docx [Dataset]. http://doi.org/10.3389/fpubh.2022.877800.s001
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Data_Sheet_1_Evaluating the impact of mobility in COVID-19 incidence and mortality: A case study from four states of Mexico.docx

Related Article
Explore at:
docxAvailable download formats
Dataset updated
Jun 4, 2023
Dataset provided by
Frontiers Mediahttp://www.frontiersin.org/
Authors
César Arturo Méndez-Lizárraga; MLucía Castañeda-Cediel; Guadalupe Delgado-Sánchez; Edith Elizabeth Ferreira-Guerrero; Leticia Ferreyra-Reyes; Sergio Canizales-Quintero; Norma Mongua-Rodríguez; Norma Tellez-Vázquez; María Eugenia Jiménez-Corona; Kathryn Bradford Vosburg; Omar Y. Bello-Chavolla; Lourdes García-García
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Mexico
Description

IntroductionThe COVID-19 pandemic in Mexico began at the end of February 2020. An essential component of control strategies was to reduce mobility. We aimed to evaluate the impact of mobility on COVID- incidence and mortality rates during the initial months of the pandemic in selected states.MethodsCOVID-19 incidence data were obtained from the Open Data Epidemiology Resource provided by the Mexican government. Mobility data was obtained from the Observatory for COVID-19 in the Americas of the University of Miami. We selected four states according to their compliance with non-pharmaceutical interventions and mobility index. We constructed time series and analyzed change-points for mobility, incidence, and mortality rates. We correlated mobility with incidence and mortality rates for each time interval. Using mixed-effects Poisson models, we evaluated the impact of reductions in mobility on incidence and mortality rates, adjusting all models for medical services and the percentage of the population living in poverty.ResultsAfter the initial decline in mobility experienced in early April, a sustained increase in mobility followed during the rest of the country-wide suspension of non-essential activities and the return to other activities throughout mid-April and May. We identified that a 1% increase in mobility yielded a 5.2 and a 2.9% increase in the risk of COVID-19 incidence and mortality, respectively. Mobility was estimated to contribute 8.5 and 3.8% to the variability in incidence and mortality, respectively. In fully adjusted models, the contribution of mobility to positive COVID-19 incidence and mortality was sustained. When assessing the impact of mobility in each state compared to the state of Baja California, increased mobility conferred an increased risk of incident positive COVID-19 cases in Mexico City, Jalisco, and Nuevo León. However, for COVID-19 mortality, a differential impact of mobility was only observed with Jalisco and Nuevo León compared to Baja California.ConclusionMobility had heterogeneous impacts on COVID-19 rates in different regions of Mexico, indicating that sociodemographic characteristics and regional-level pandemic dynamics modified the impact of reductions in mobility during the COVID-19 pandemic. The implementation of non-pharmaceutical interventions should be regionalized based on local epidemiology for timely response against future pandemics.

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