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Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases.
Bayraktar, Yüksel; Özyilmaz, Ayfer; Toprak, Metin; Isik, Esme; Büyükakin, Figen; Olgun, Mehmet Firat.
  • Bayraktar Y; Economics, Istanbul University, Istanbul, Turkey.
  • Özyilmaz A; Economics, Gümüshane University, Gümüshane, Turkey.
  • Toprak M; Economics, Istanbul Sabahattin Zaim University, Istanbul, Turkey.
  • Isik E; Optician, Turgut Özal University, Malatya, Turkey.
  • Büyükakin F; Economics, Kocaeli University, Kocaeli, Turkey.
  • Olgun MF; Lecturer, Kastamonu University, Kastamonu, Turkey.
Soc Work Public Health ; 36(2): 178-193, 2021 02 17.
Article in English | MEDLINE | ID: covidwho-990462
ABSTRACT
In the fight against Covid-19, developed countries and developing countries diverge in success. This drew attention to the discussion of how different health systems and different levels of health spending are effective in combating Covid-19. In this study, the role of the health system in the fight against Covid-19 is discussed. In this context, the number of hospital beds, the number of doctors, life expectancy at 60, universal health service and the share of health expenditures in GDP were used as health indicators. In the study, firstly 2020 data was estimated by using the Artificial Neural Networks simulation method and this year was used in the analysis. The model, with the data of 124 countries, was estimated using the cross-sectional OLS regression method. The estimation results show that the number of hospital beds, number of doctors and life expectancy at the age of 60 have statistically significant and positive effects on the ratio of Covid-19 recovered/cases. Universal health service and share of health expenditures in GDP are not significant statistically on the cases and recovered. Hospital bed capacity is the most effective variable on the recovered/case ratio.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Simulation / Global Health / Neural Networks, Computer / Delivery of Health Care / COVID-19 Type of study: Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Soc Work Public Health Journal subject: Public Health Year: 2021 Document Type: Article Affiliation country: 19371918.2020.1856750

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Simulation / Global Health / Neural Networks, Computer / Delivery of Health Care / COVID-19 Type of study: Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Soc Work Public Health Journal subject: Public Health Year: 2021 Document Type: Article Affiliation country: 19371918.2020.1856750