Öz
Dünya genelinde COVID-19 pandemisi yaygınlığı ile ilişkili faktörlere yönelik bir ekolojik çalışma
Özet
Amaç: COVID-19 salgınında ülkelerdeki vaka, ölüm ve test sayılarıyla ülkelerin Gini katsayıları, yaşlı nüfus oranları, ekvatora uzaklıkları ve küresel sağlık güvenliği endeksleri arasındaki ilişkilerin değerlendirilmesidir.
Gereç ve yöntem: Ağustos 2020 tarihinde yapılan ekolojik tipteki bu araştırmada ülkelerin COVID-19 salgını yaygınlığı ile ilgili Worldometers internet sitesinde raporlanan verileri kullanılmıştır. Ülkelerin COVID-19 ilişkili değişkenleri ile Gini katsayıları, yaşlı nüfus popülasyonları, ekvatora uzaklıkları ve küresel sağlık güvenliği endeksleri arasındaki ilişkiye bakılmıştır.
Bulgular: Araştırmada 215 ülke değerlendirmeye alınmıştır. Milyonda toplam vaka sayısının en fazla görüldüğü ülke Katar iken; milyonda toplam ölüm sayısı en fazla San Marino'da, milyonda toplam test sayısı en fazla Monako'dadır. Doğrusal regresyon analizi sonucunda ülkelerin Gini katsayıları milyonda toplam vaka sayısı ile; yaşlı nüfus oranları milyonda toplam ölüm sayısı ile; ekvatora uzaklıkları milyonda toplam test sayısı ile ilişkili bulunmuştur. Ülkelerin Gini katsayıları arttıkça milyonda toplam vaka sayıları (p=0,006); yaşlı nüfus oranları arttıkça milyonda ölüm sayıları (p=0,005); ekvatora uzaklıkları arttıkça milyonda test sayıları (p=0,015) artmaktadır.
Sonuç: Sonuç olarak gelir eşitsizliği, yaşlı nüfus, ekvatora uzaklık arttıkça salgından etkilenim artmaktadır.
Anahtar kelimeler: COVID-19, pandemi, Gini katsayısı, küresel sağlık güvenliği endeksi.
An ecological study of factors associated with the prevalence of the COVID-19 pandemic worldwide
Abstract
Purpose: The aim is to evaluate the relationships between the number of cases, deaths and tests in countries in the COVID-19 outbreak and the countries' Gini coefficients, elderly population rates, distances to the equator and global health security indexes.
Materials and methods: In this ecological study conducted in August 2020, the data reported on the Worldometers website on the prevalence of the COVID-19 outbreak were used. The relationship between COVID-19 related variables of countries and Gini coefficients, elderly population ratios, distance from the equator and global health security indexes were examined.
Results: 215 countries were evaluated in the study. Qatar is the country with the highest number of cases per million; San Marino has the highest number of deaths per million and Monaco has the highest number of tests per million. As a result of the linear regression analysis, the Gini coefficients of the countries were associated with the total number of cases per million, the elderly population ratios were associated with the total number of deaths per million, and distance to the equator was associated with the total number of tests per million. As the Gini coefficients of the countries increase, the total number of cases per million (p=0.006); as the elderly population rates increase, deaths per million (p=0.005); as the distance from the equator increases, the number of tests per million (p=0.015) increases.
Conclusion: As a result, as income inequality, elderly population and distance from the equator increase, the impact from the pandemic increases.
Keywords: COVID-19, pandemic, Gini coefficient, global health security index.
Anahtar Kelimeler
COVID-19 pandemi gini katsayısı küresel sağlık güvenliği endeksi
Teşekkür
Katkılarından dolayı Özgecan Elçi, Şebnem Erten, Mert Hamit Haliloğlu, Orçun Burak Karadağ, Merve Tuğana Keskin, Elifnaz Keskin, Gülşen Korkut, Ümmühan Betül Köseoğlu, Kübra Yıldız, Melike Köroğlu, Ebru Şen, Büşra Fışkın, İsmail Salih Alacalı, Rurkiye Elif Başın, Ferhat Perk, Emre Sağ, İrem Sarıca, Merve Satılmış, Şeyma Gülsüm Şencan, Neslihan Türk, Mukaddes Kübra Uçar ve Oğuzhan Yıldırım'a teşekkür ederiz.
Kaynakça
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Öz
Anahtar Kelimeler
COVİD-19 pandemic gini coefficient global health security index
Kaynakça
- 1. Klinik Mikrobiyoloji Uzmanlık Derneği, MERS virüsü hakkında bilgilendirme. Avaliable at: https://www.klimud.org/content/260/mers-virusu-hakkinda-bilgilendirme. Erişim tarihi: 16 Ağustos 2020.
- 2. T.C. Sağlık Bakanlığı Halk Sağlığı Genel Müdürlüğü, COVID-19 (SARS-COV-2 ENFEKSİYONU) REHBERİ, 2020.
- 3. A Chronicle on the SARS Epidemic. Chinese Law & Government 2003;36(4):12-15. https://doi.org/10.2753/CLG0009-4609360412
- 4. Middle East respiratory syndrome coronavirus (MERS-CoV). Avaliable at: https://www.who.int/news-room/fact-sheets/detail/middle-east-respiratory-syndrome-coronavirus-(mers-cov). Accessed August 16, 2020.
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- 6. Coronavirus disease 2019 (COVID-19) Situation Report – 94. Avaliable at: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200423-sitrep-94-covid-19.pdf?sfvrsn=b8304bf0_4#:~:text=humans%20since%202012.-,All%20available%20evidence%20for%20COVID%2D19%20suggests%20that%20SARS%2DCoV,to%20be%20handled%20by%20humans. Accessed August 16, 2020.
- 7. Kang SJ, Jung SI. Age-Related Morbidity and Mortality among Patients with COVID-19. Infect Chemother 2020;52(2):154-164. https://doi.org/10.3947/ic.2020.52.2.154
- 8. COVID-19 Coronavirus Pandemic. Avaliable at: https://www.worldometers.info/coronavirus/?. Accessed July 31,2020.
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- 10. Gini Coefficient By Country 2020. Avaliable at: https://worldpopulationreview.com/country-rankings/gini-coefficient-by-country. Accessed August 15, 2020.
- 11. The World Bank. Available at: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS. Accessed August 29, 2020.
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- 15. Our World in Data Coronavirus (COVID-19) Testing. Available at: https://ourworldindata.org/coronavirus-testing. Accessed August 29, 2020.
- 16. Duncan CJ, Scott S. What caused the Black Death? Postgraduate Medical Journal 2005;81:315-320. https://doi.org/10.1136/pgmj.2004.024075
- 17. Hiscott J, Alexandridi M, Muscolini M. et al. The global impact of the coronavirus pandemic. Cytokine & Growth Factor Reviews 2020;53:1–9. https://doi.org/10.1016/j.cytogfr.2020.05.010
- 18. Mikolai J, Keenan K, Kulu H. Intersecting household level health and socio-economic vulnerabilities and the COVID-19 crisis: An analysis from the UK. SSM – Population Health 2020; 100628. https://doi.org/10.1016/j.ssmph.2020.100628
- 19. Hewitt J, Carter B, Vilches-Moraga A. et al. The effect of frailty on survival in patients with COVID-19 (COPE): a multicentre, European, observational cohort study. The Lancet Public Health 2020;5(8):e444–e451. https://doi.org/10.1016/S2468-2667(20)30146-8
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- 21. Centers for Disease Control and Prevention. Available at: https://www.cdc.gov/nchs/nvss/vsrr/covid_weekly/index.htm#AgeAndSex. Accessed August 25, 2020.
- 22. Williamson EJ, Walker AJ, Bhaskaran K. et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584(7821):430-436. https://doi.org/10.1038/s41586-020-2521-4
- 23. World Health Organization. Estimating mortality from COVID-19. Available at: https://www.who.int/news-room/commentaries/detail/estimating-mortality-from-covid-19. Accessed August 25, 2020.
- 24. Chennakesavulu K, Reddy GR. (2020). The effect of latitude and PM2.5 on spreading of SARS-CoV-2 in tropical and temperate zone countries. Environ Pollut 2020. https://doi.org/10.1016/j.envpol.2020.115176
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Halk Sağlığı, Çevre Sağlığı |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Temmuz 2021 |
| Gönderilme Tarihi | 24 Eylül 2020 |
| Kabul Tarihi | 3 Mart 2021 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 14 Sayı: 3 |
Kaynak Göster
