Volume 24, Issue 2 (7-2021)                   jha 2021, 24(2): 20-32 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Asaadi M, Daliri H. Evaluating the Effect of Poverty and Economic Inequality on the Corona Pandemic in Iran and the World. jha 2021; 24 (2) :20-32
URL: http://jha.iums.ac.ir/article-1-3550-en.html
1- Assistant Professor, Department of Management and Economics, Faculty of Humanities and Social Science, Golestan University, Gorgan, Iran. , m.asaadi@gu.ac.ir
2- Assistant Professor, Department of Management and Economics, Faculty of Humanities and Social Science, Golestan University, Gorgan, Iran.
Abstract:   (3811 Views)
Introduction: The identification of factors affecting the prevalence of Covid-19 is vital in order to control the pandemic. Poverty and economic inequality are among the most important variables explaining the spread of coronavirus. This study evaluates the impact of poverty and economic inequality on the prevalence of COVID-19 worldwide.
Methods: This is an applied study which uses descriptive-analytical method. Also, the current study employs cross-sectional data and stable regression method to evaluate the effect of poverty and economic inequality on the prevalence and mortality rate of COVID-19 among 145 countries. The statistical population of this research study includes COVID-19 aggregate data extracted from Oxford-Martin Research Program and the World Bank in 2020-2021. According to the World Bank classification, we classified the surveyed countries, including Iran, in terms of their income levels into four income groups. Then, the impact of poverty and inequality indicators on the prevalence of COVID-19 has been analyzed among them. Also, econometrics and data analysis were performed using MATLAB software.
Results: Estimates confirm the increasing effect of undernourishment and population aging on the spread of coronavirus for all income groups worldwide. In contrast, increasing stringency index reduces the morbidity rate of COVID-19. Significant variables in the prevalence of corona for Iran and countries within the upper middle-income group have also confirmed the effect of aging and stringency index. Finally, the variables explaining the increase in COVID-19 mortality rate in Iran and the upper middle-income group includes GINI coefficient, aging, and undernourishment.
Conclusion: Results of this study suggest that adequate nutrition in line with health expenditures and stringency index can play an effective role in reducing COVID-19 morbidity and mortality. Also, employing appropriate policies in order to increase economic equality will play a significant role in reducing COVID-19 mortality rate.
Full-Text [PDF 1191 kb]   (1881 Downloads)    
Type of Study: Research | Subject: Health Economics
Received: 2021/04/1 | Accepted: 2021/06/19 | Published: 2021/08/1

References
1. Baker SR, Bloom N, Davis SJ, Terry SJ. COVID-induced economic uncertainty. Working Paper No: 26983 [Internet]. Cambridge, MA: National Bureau of Economic Research; 2020 Apr [cited 2021 Mar 31]. Available from: https://www.nber.org/papers/w26983 [DOI:10.3386/w26983]
2. Ray D, Subramanian S. India's lockdown: An interim report. Working Paper No: 27282 [Internet]. Cambridge, MA: National Bureau of Economic Research; 2020 May [cited 2021 Mar 31]. Available from: https://www.nber.org/papers/w27282 [DOI:10.3386/w27282]
3. Noy I, Doan N, Ferrarini B, Park D. Measuring the economic risk of Covid-19. Glob Policy. 2020 Sep; 11(4): 413-23. [DOI:10.1111/1758-5899.12851]
4. Grima S, Kizilkaya M, Rupeika-Apoga R, Romānova I, Dalli Gonzi R, Jakovljevic M. A country pandemic risk exposure measurement model. Risk Manag Healthc Policy. 2020; 13: 2067-77. [DOI:10.2147/RMHP.S270553]
5. Bigio S, Zhang M, Zilberman E. Transfers vs credit policy: Macroeconomic policy trade-offs during COVID-19. Working Paper No: 27118 [Internet]. Cambridge, MA: National Bureau of Economic Research; 2020 May [cited 2021 Mar 31]. Available from: https://www.nber.org/papers/w27118 [DOI:10.3386/w27118]
6. Starfield B. Effects of poverty on health status. Bull N Y Acad Med. 1992; 68(1):17-24.
7. Worku EB, Woldesenbet SA. Poverty and inequality - but of what - as social determinants of health in Africa? Afr Health Sci. 2015 Dec;15(4):1330-8. [DOI:10.4314/ahs.v15i4.36]
8. Kondo N, Sembajwe G, Kawachi I, van Dam RM, Subramanian SV, Yamagata Z, et al. Income inequality, mortality, and self rated health: Meta-analysis of multilevel studies. BMJ. 2009; 339(b4471): 1-9. [DOI:10.1136/bmj.b4471]
9. Aleem Z. The US needs a lot more hospital beds to prepare for a spike in coronavirus cases [Internet]. Washington: Vox Media; 2020 Mar 14; [cited 2021 Mar 31]. Available from: https://www.vox.com/science-and-health/2020/3/14/21179714/coronavirus-covid-19-hospital-beds-china.
10. Murphy SC. Malaria and global infectious diseases: Why should we care? Virtual Mentor. 2006 Apr; 8(4), 245-50. [DOI:10.1001/virtualmentor.2006.8.4.msoc1-0604]
11. Deaton A. Health, inequality, and economic development. J Econ Lit. 2003;41(1):113-158. [DOI:10.1257/jel.41.1.113]
12. Adams-Prassl A, Boneva T, Golin M, Rauh C. Inequality in the impact of the coronavirus shock: Evidence from real time surveys. J Publ Econ. 2020;189:104245. [DOI:10.1016/j.jpubeco.2020.104245]
13. Bartik AW, Bertrand M, Cullen ZB, Glaeser EL, Luca M, Stanton CT. How are small businesses adjusting to covid-19? Early evidence from a survey. Working Paper No: 26989 [Internet]. Cambridge, MA: National Bureau of Economic Research; 2020 Apr [cited 2021 Mar 31]. Available from: https://www.nber.org/papers/w26989 [DOI:10.3386/w26989]
14. Bottan N, Hoffmann B, Vera-Cossio D. The unequal impact of the coronavirus pandemic: Evidence from seventeen developing countries. PLoS One. 2020 Oct 7;15(10): e0239797 [DOI:10.1371/journal.pone.0239797]
15. Emadzadeh M, Samadi S, Paknejad S. Effects of unequal distribution of income on health status in the selection of the member. Health Information Management. 2011; 8(3): 306-14. [In Persian]
16. Wilkinson RG. Socioeconomic determinants of health. Health inequalities: Relative or absolute material standards? BMJ. 1997; 314(7080): 591-5. [DOI:10.1136/bmj.314.7080.591]
17. Hajizadeh M. Investigating justice in financing the health sector of Iran through the hosehold budget using the kakwani index in 1996-2001 [master's thesis on the Internet] Tehran: Iran University of Medical Sciences; 2002 [cited 2021 Mar 31]. Available from: https://ganj-old.irandoc.ac.ir/articles/109592. [In Persian]
18. Wilkinson RG. Income distribution and life expectancy. BMJ. 1992; 304(6820): 165-8. [DOI:10.1136/bmj.304.6820.165]
19. Wilkinson RG. Class mortality differentials, income distribution and trends in poverty 1921-1981. J Soc Policy. 1989; 18(3): 307-35. [DOI:10.1017/S0047279400017591]
20. Wilkinson RG,Pickett KE. Income inequality and population health: A review and explanation of the evidence. Soc Sci Med. 2006; 62(7): 1768-84. [DOI:10.1016/j.socscimed.2005.08.036]
21. Judge K, Mulligan JA, Benzeval M. Income inequality and population health. Soc Sci Med. 1998; 46(4-5): 567-79. [DOI:10.1016/S0277-9536(97)00204-9]
22. Rodgers GB. Income and inequality as determinants of mortality: An international cross-section analysis. Pop Stud. 1979; 33(2): 343-51. [DOI:10.1080/00324728.1979.10410449]
23. Gravelle H, Wildman J, Sutton M. Income, income inequality and health: What can we learn from aggregate data? Soc Sci Med. 2002; 54(4): 577-89. [DOI:10.1016/S0277-9536(01)00053-3]
24. Babones SJ. Income inequality and population health: Correlation and causality. Soc Sci Med. 2008; 66(7): 1614-26. [DOI:10.1016/j.socscimed.2007.12.012]
25. Biggs B, King L, Basu S,Stuckler D. Is wealthier always healthier? The impact of national income level, inequality, and poverty on public health in Latin America. Soc Sci Med. 2010; 71(2): 266-73. [DOI:10.1016/j.socscimed.2010.04.002]
26. Suwanprasert W. COVID-19 and endogenous public avoidance: Insights from an economic model. Discussion Papers No: 128 [Internet]. Bankok: Puey Ungphakorn Institute for Economic Research; 2020 Mar [cited 2021 Mar 31]. Available from: https://ideas.repec.org/p/pui/dpaper/128.html [DOI:10.2139/ssrn.3565564]
27. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet. 2020; 395(10229): 1054-62. [DOI:10.1016/S0140-6736(20)30566-3]
28. Evans D, Werker E. What a population's age structure means for COVID-19's impact in low-income countries [Internet]. Washington DC: Center for Global Development; 2020 Mar [cited 2021 Mar 31]. Available from: https://www.cgdev.org/blog/what-populations-age-structure-means-covid-19s-impact-low-income-countries [DOI:10.21820/23987073.2021.6.4]
29. Ritchie H, Ortiz-Ospina E, Beltekian D, Mathieu E, Hasell J, Macdonald B, et al. Coronavirus pandemic (COVID-19) [Internet]. Oxford, UK: OurWorldInData.org; 2020 [cited 2021 Mar 21]. Available from: https://ourworldindata.org/coronavirus
30. The World Bank [Internet]. Washington DC, USA: The World Bank Group; 2021 [cited 2021 Mar 21]. Available from: https://databank.worldbank.org/databases
31. Rousseeuw PJ, Leroy A M. Robust regression and outlier detection. New York: Wiley; 1987. [DOI:10.1002/0471725382]
32. Yohai VJ. High breakdown-point and high efficiency robust estimates for regression. Ann Stat. 1987; 15(2): 642-56. [DOI:10.1214/aos/1176350366]
33. Verardi V, Croux Ch. Robust regression in stata. The Stata Journal. 2009; 9(3): 439-53. [DOI:10.1177/1536867X0900900306]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Journal of Health Administration

Designed & Developed by : Yektaweb