[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/jes/journl/y2022v13p185-207.html
   My bibliography  Save this article

Narrowing the gap in regional and age-specific excess mortality during the COVID-19 in Hungary

Author

Listed:
  • Csaba G. TÓTH

    (Institute of Economics, Centre for Economic and Regional Studies; Corvinus Institute for Advanced Studies, Hungary)

Abstract
In the first year and a half of the pandemic, the excess mortality in Hungary was 28,400, which was 1,700 lower than the official statistics on COVID-19 deaths. This discrepancy can be partly explained by the protective measures instated during the COVID-19 pandemic which decreased the intensity of the seasonal flu outbreak, which caused on average 3,000 deaths per year. Compared to the second wave of the COVID-19 pandemic, the third wave showed a reduction in the differences in excess mortality between age groups and regions. The excess mortality rate for people aged 75+ fell significantly in the third wave, presumably partly due to the vaccination schedule and the absence of a normal flu season. For people aged 40-74, the excess mortality rate rose slightly in the third wave. Between regions, excess mortality was highest in Northern Hungary and Western Transdanubia, and much lower in Central Hungary, where the capital is located. The excess mortality rate for men was almost twice as high as that for women in almost all age groups.

Suggested Citation

  • Csaba G. TÓTH, 2022. "Narrowing the gap in regional and age-specific excess mortality during the COVID-19 in Hungary," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 13, pages 185-207, June.
    • Handle: RePEc:jes:journl:y:2022:v:13:p:185-207
    DOI: https://doi.org/10.47743/ejes-2022-0109
    as

    Download full text from publisher

    File URL: https://ejes.uaic.ro/articles/EJES2022_1301_TOT.pdf
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.47743/ejes-2022-0109?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gergo Pinter & Imre Felde & Amir Mosavi & Pedram Ghamisi & Richard Gloaguen, 2020. "COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    2. Patrizio Vanella & Ugofilippo Basellini & Berit Lange, 2020. "Assessing Excess Mortality in Times of Pandemics Based on Principal Component Analysis of Weekly Mortality Data -- The Case of COVID-19," Working Papers axbhmxrs-o0viyh9z07m, French Institute for Demographic Studies.
    3. Magdolna Sass & János Köllő & Balázs Reizer, 2021. "The impact of the first wave of the COVID-19 pandemic on employment and firm revenues in Hungary," Acta Oeconomica, Akadémiai Kiadó, Hungary, vol. 71(supplemen), pages 93-117, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrés Rodríguez‐Pose & Chiara Burlina, 2021. "Institutions and the uneven geography of the first wave of the COVID‐19 pandemic," Journal of Regional Science, Wiley Blackwell, vol. 61(4), pages 728-752, September.
    2. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    3. Zoltán Tánczos & Borbála Bernadett Zala & Zsolt Szakály & László Tóth & József Bognár, 2022. "Home Office, Health Behavior and Workplace Health Promotion of Employees in the Telecommunications Sector during the Pandemic," IJERPH, MDPI, vol. 19(18), pages 1-11, September.
    4. Gabriel Sepulveda & Abraham J. Arenas & Gilberto González-Parra, 2023. "Mathematical Modeling of COVID-19 Dynamics under Two Vaccination Doses and Delay Effects," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
    5. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    6. Carmen-Alexandra Stoian & Chirața Caraiani & Ionuț Florin Anica-Popa & Cornelia Dascălu & Camelia Iuliana Lungu, 2022. "Telework Systematic Model Design for the Future of Work," Sustainability, MDPI, vol. 14(12), pages 1-22, June.
    7. Mustapha Kamal Benramdane & Elena Kornyshova & Samia Bouzefrane & Hubert Maupas, 2024. "Supervised Machine Learning for Matchmaking in Digital Business Ecosystems and Platforms," Information Systems Frontiers, Springer, vol. 26(4), pages 1331-1343, August.
    8. Abdelrahman E. E. Eltoukhy & Ibrahim Abdelfadeel Shaban & Felix T. S. Chan & Mohammad A. M. Abdel-Aal, 2020. "Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations," IJERPH, MDPI, vol. 17(19), pages 1-25, September.
    9. Haghighat, Fatemeh, 2021. "Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    10. Albertus J. Smit & Jennifer M. Fitchett & Francois A. Engelbrecht & Robert J. Scholes & Godfrey Dzhivhuho & Neville A. Sweijd, 2020. "Winter Is Coming: A Southern Hemisphere Perspective of the Environmental Drivers of SARS-CoV-2 and the Potential Seasonality of COVID-19," IJERPH, MDPI, vol. 17(16), pages 1-28, August.
    11. Amir Masoud Rahmani & Efat Yousefpoor & Mohammad Sadegh Yousefpoor & Zahid Mehmood & Amir Haider & Mehdi Hosseinzadeh & Rizwan Ali Naqvi, 2021. "Machine Learning (ML) in Medicine: Review, Applications, and Challenges," Mathematics, MDPI, vol. 9(22), pages 1-52, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jes:journl:y:2022:v:13:p:185-207. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Alupului Ciprian (email available below). General contact details of provider: https://edirc.repec.org/data/csjesro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.