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Socio-economic, demographic and health determinants of the coronavirus pandemic: Analysis of data from OECD countries

Year 2020, , 1 - 13, 06.12.2020
https://doi.org/10.20518/tjph.721921

Abstract

Objective: The main objective of this study was to examine the socio-economic, demographic and health determinants affecting the number of coronavirus cases, the number of coronavirus deaths, case fatality rates per 1,000 cases and coronavirus deaths per 1,000,000 population in OECD countries. Methods: The study was undertaken using 20 different independent variables and 4 different dependent variables, which were all obtained from the OECD and WHO databases. The study utilizes multiple linear regression statistical techniques to reveal the socio-economic, demographic and health determinants of the coronavirus pandemic. Results: The findings of the study show that higher shares of current expenditure on health in GDP, higher prevalence of obesity among adults, higher percentage of raised blood glucose levels among adults and the stringency index (which indicates the extent of the measures taken by the government related with the coronavirus outbreak) are influential on both the number of coronavirus cases and deaths in OECD countries. Increased case fatality rates seem to be closely related to the stringency index, higher share of current expenditure on health in GDP and higher percentage of tobacco users among adults in OECD countries. On the other hand, factors such as the stringency index, higher life expectancy at birth, higher use of tobacco and higher share of current expenditure on health in GDP are effective on the coronavirus death rate per 1,000,000 population. Conclusions: The demographic, economic, political and health factors that determine the current number of coronavirus cases and deaths indicate that the pandemic as a public health problem cannot be eliminated only with health interventions and that multi-dimensional policies are needed.

References

  • Atkeson, A. (2020). What will be the economic impact of COVID-19 in the US? Rough estimates of disease scenarios. NBER Working Paper 26867.
  • Bignami, S., ve Ghio, D. (2020). A demographic adjustment to improve measurement of COVID- 19 severity at the developing stage of the pandemic. medRxiv, https://doi.org/10.1101/2020.03.23.20040998.
  • Borjas, G. J. (2020). Demographic Determinants of Testing Incidence and COVID-19 Infections in New York City Neighborhoods (No. 13115). Institute of Labor Economics (IZA).
  • Chen, T. M., Rui, J., Wang, Q. P., Zhao, Z. Y., Cui, J. A., & Yin, L. (2020). A mathematical model for simulating the phase-based transmissibility of a novel coronavirus. Infectious Diseases of Poverty. 9(1), 1-8.
  • David B, Xiaolong Q, Karin N.S, Didier M, Léo Pr, Guillaume F. (2020). Real estimates of mortality following COVID-19 infection. The Lancet Infectious Diseases. https://doi.org/10.1016/S1473-3099(20)30195-X.
  • Dudel, C, Riffe, T. Acosta, E, van Raalte, AA. And Myrskyla, M. (2020) Monitoring trends and differences in COVID-19 case fatality rates using decomposition methods: Contributions of age structure and age-specific fatality, Max Planck Institute for Demographic Research, Germany. https://osf.io/dtmve/ (Accessed on 12/04/2010).
  • Jordan, R. E., Adab, P., & Cheng, K. K. (2020). Covid-19: risk factors for severe disease and death. Britich Medical Journal, 368 doi: https://doi.org/10.1136/bmj.m1198
  • Marzia L, ve Giovanni P. (2020). COVID-19 in Italy: momentous decisions and many uncertainties. The Lancet Global Health. https://doi.org/10.1016/S2214-109X(20)30110-8.
  • Qun L, Xuhua G, Peng W, Xiaoye W, Lei Z, Yeqing T, Ruiqi R, Kathy S.M. Leung, Eric H.Y. L, Jessica Y. W, Xuesen X, et al (2020). Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. New England Journal of Medicine 382: 1199-1207.
  • OECD (2020) Evaluating the initial impact of COVID-19 containment measures on economic activity, Tackling Coronavirus Contributing to Global Effort, OECD. http://www.cica.net/wp-content/uploads/2020/04/200331_OECD_evaluating-initial-impact-of-Covid-19.pdf (Accessed on 12/04/2010).
  • Onder, G, Rezza, G, Brusaferro, S. (2020). Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. Journal of the American Medical Association. https://doi.org/10.1001/jama.2020.4683
  • OECD (2020) Health Statistics, 2019: Frequently Requested Data. https://www.oecd.org/health/health-statistics.htm. (Accessed on 15/04/2010).
  • Stock, J. H. (2020). Data gaps and the policy response to the novel coronavirus (No. w26902). National Bureau of Economic Research.
  • WHO (2020a) Coronavirus (Covid-19) Updates. https://covid19.who.int/ (Accessed on 15/04/2010).
  • WHO (2020b) The Global Health Observatory: Non-Communicable Diseases. https://www.who.int/data/gho/data/indicators. (Accessed on 15/04/2010).
  • Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet. 2020. https://doi.org/10.1016/S0140-6736(20)30260-9.

Koronavirüs salgınının sosyo-ekonomik, demografik ve sağlık belirleyicileri: OECD ülkeleri verilerinin analizi

Year 2020, , 1 - 13, 06.12.2020
https://doi.org/10.20518/tjph.721921

Abstract

Amaç: Çalışmanın temel amacı, Ekonomik Kalkınma ve İşbirliği Örgütü (OECD) ülkelerinde koronavirüs vaka sayısı, koronavirüs ölüm sayısı, 1,000 vakaya düşen ölüm sayısı ve 1,000,000 nüfusa düşen koronavirüs ölüm sayısı üzerinde etkili olan sosyo-ekonomik, demografik ve sağlık değişkenlerini ortaya koymaktır. Yöntem: Çalışma, OECD ve Dünya Sağlık Örgütü (WHO) veri tabanlarından elde edilen 20’si bağımsız; 4’ü de bağımlı değişken kullanılarak gerçekleştirilmiştir. Çalışmada koronavirüs salgını üzerinde etkili olan sosyo-ekonomik, demografik ve sağlık belirleyicilerini ortaya koymak için çoklu doğrusal regresyon yöntemi kullanılmıştır. Bulgular: Çalışmanın bulguları, OECD ülkelerinde koronavirüs vaka ve ölüm sayılarının artışında yurt içi milli hasıla içinde sağlık harcamalarının payının, kilolu ve obez olanların oranının, yüksek kan şekeri bulunanların oranının yüksek olmasının ve ülkelerin koronavirüs salgınına ilişkin aldıkları tedbirlerin derecesini gösteren sıkılık endeksinin etkili olduğunu göstermiştir. OECD ülkelerindeki vaka ölüm hızının artışında, sıkılık endeksi yanında yurt içi milli hasıla içinde sağlık harcamalarının payının ve tütün kullananların oranının yüksek olmasının etkili olduğu görülmüştür. Bir milyon nüfus başına düşen ölüm hızının üzerinde ise, sıkılık endeksinin, doğumda yaşam beklentisinin, tütün kullananların oranının ve yurt içi milli hasıla içinde sağlık harcamalarının payının yüksek olmasının etkili olduğu gözlenmiştir. Sonuç: Koronavirüs salgınındaki mevcut vaka ve ölüm sayılarını belirleyen faktörlerin demografik, ekonomik, politik ve sağlık gibi farklı alanlara işaret etmesi, bir halk sağlığı sorunu olan salgının sadece sağlık müdahaleleri ile yok edilemeyeceğini, salgınla başetmek için çok boyutlu politikalara gereksinim olduğunu işaret etmiştir.

References

  • Atkeson, A. (2020). What will be the economic impact of COVID-19 in the US? Rough estimates of disease scenarios. NBER Working Paper 26867.
  • Bignami, S., ve Ghio, D. (2020). A demographic adjustment to improve measurement of COVID- 19 severity at the developing stage of the pandemic. medRxiv, https://doi.org/10.1101/2020.03.23.20040998.
  • Borjas, G. J. (2020). Demographic Determinants of Testing Incidence and COVID-19 Infections in New York City Neighborhoods (No. 13115). Institute of Labor Economics (IZA).
  • Chen, T. M., Rui, J., Wang, Q. P., Zhao, Z. Y., Cui, J. A., & Yin, L. (2020). A mathematical model for simulating the phase-based transmissibility of a novel coronavirus. Infectious Diseases of Poverty. 9(1), 1-8.
  • David B, Xiaolong Q, Karin N.S, Didier M, Léo Pr, Guillaume F. (2020). Real estimates of mortality following COVID-19 infection. The Lancet Infectious Diseases. https://doi.org/10.1016/S1473-3099(20)30195-X.
  • Dudel, C, Riffe, T. Acosta, E, van Raalte, AA. And Myrskyla, M. (2020) Monitoring trends and differences in COVID-19 case fatality rates using decomposition methods: Contributions of age structure and age-specific fatality, Max Planck Institute for Demographic Research, Germany. https://osf.io/dtmve/ (Accessed on 12/04/2010).
  • Jordan, R. E., Adab, P., & Cheng, K. K. (2020). Covid-19: risk factors for severe disease and death. Britich Medical Journal, 368 doi: https://doi.org/10.1136/bmj.m1198
  • Marzia L, ve Giovanni P. (2020). COVID-19 in Italy: momentous decisions and many uncertainties. The Lancet Global Health. https://doi.org/10.1016/S2214-109X(20)30110-8.
  • Qun L, Xuhua G, Peng W, Xiaoye W, Lei Z, Yeqing T, Ruiqi R, Kathy S.M. Leung, Eric H.Y. L, Jessica Y. W, Xuesen X, et al (2020). Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. New England Journal of Medicine 382: 1199-1207.
  • OECD (2020) Evaluating the initial impact of COVID-19 containment measures on economic activity, Tackling Coronavirus Contributing to Global Effort, OECD. http://www.cica.net/wp-content/uploads/2020/04/200331_OECD_evaluating-initial-impact-of-Covid-19.pdf (Accessed on 12/04/2010).
  • Onder, G, Rezza, G, Brusaferro, S. (2020). Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. Journal of the American Medical Association. https://doi.org/10.1001/jama.2020.4683
  • OECD (2020) Health Statistics, 2019: Frequently Requested Data. https://www.oecd.org/health/health-statistics.htm. (Accessed on 15/04/2010).
  • Stock, J. H. (2020). Data gaps and the policy response to the novel coronavirus (No. w26902). National Bureau of Economic Research.
  • WHO (2020a) Coronavirus (Covid-19) Updates. https://covid19.who.int/ (Accessed on 15/04/2010).
  • WHO (2020b) The Global Health Observatory: Non-Communicable Diseases. https://www.who.int/data/gho/data/indicators. (Accessed on 15/04/2010).
  • Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet. 2020. https://doi.org/10.1016/S0140-6736(20)30260-9.
There are 16 citations in total.

Details

Primary Language English
Subjects Health Care Administration
Journal Section Original Research
Authors

İsmet Koç 0000-0003-2283-6514

Melike Saraç 0000-0003-1076-9473

Publication Date December 6, 2020
Submission Date April 17, 2020
Acceptance Date July 17, 2020
Published in Issue Year 2020

Cite

APA Koç, İ., & Saraç, M. (2020). Socio-economic, demographic and health determinants of the coronavirus pandemic: Analysis of data from OECD countries. Turkish Journal of Public Health, 18(COVID-19 Special), 1-13. https://doi.org/10.20518/tjph.721921
AMA Koç İ, Saraç M. Socio-economic, demographic and health determinants of the coronavirus pandemic: Analysis of data from OECD countries. TJPH. December 2020;18(COVID-19 Special):1-13. doi:10.20518/tjph.721921
Chicago Koç, İsmet, and Melike Saraç. “Socio-Economic, Demographic and Health Determinants of the Coronavirus Pandemic: Analysis of Data from OECD Countries”. Turkish Journal of Public Health 18, no. COVID-19 Special (December 2020): 1-13. https://doi.org/10.20518/tjph.721921.
EndNote Koç İ, Saraç M (December 1, 2020) Socio-economic, demographic and health determinants of the coronavirus pandemic: Analysis of data from OECD countries. Turkish Journal of Public Health 18 COVID-19 Special 1–13.
IEEE İ. Koç and M. Saraç, “Socio-economic, demographic and health determinants of the coronavirus pandemic: Analysis of data from OECD countries”, TJPH, vol. 18, no. COVID-19 Special, pp. 1–13, 2020, doi: 10.20518/tjph.721921.
ISNAD Koç, İsmet - Saraç, Melike. “Socio-Economic, Demographic and Health Determinants of the Coronavirus Pandemic: Analysis of Data from OECD Countries”. Turkish Journal of Public Health 18/COVID-19 Special (December 2020), 1-13. https://doi.org/10.20518/tjph.721921.
JAMA Koç İ, Saraç M. Socio-economic, demographic and health determinants of the coronavirus pandemic: Analysis of data from OECD countries. TJPH. 2020;18:1–13.
MLA Koç, İsmet and Melike Saraç. “Socio-Economic, Demographic and Health Determinants of the Coronavirus Pandemic: Analysis of Data from OECD Countries”. Turkish Journal of Public Health, vol. 18, no. COVID-19 Special, 2020, pp. 1-13, doi:10.20518/tjph.721921.
Vancouver Koç İ, Saraç M. Socio-economic, demographic and health determinants of the coronavirus pandemic: Analysis of data from OECD countries. TJPH. 2020;18(COVID-19 Special):1-13.

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