A Proactive Approach to Identify the Exposure Risk to COVID-19: Validation of the Pandemic Risk Exposure Measurement (PREM) Model Using Real-World Data
Simon Grima, 1 Ramona Rupeika-Apoga, 2 Murat Kizilkaya, 3 Inna Romānova, 2 Rebecca Dalli Gonzi, 4 Mihajlo Jakovljevic 5– 7 1Department of Insurance, Faculty of Economics, Management and Accountancy, University of Malta, Msida, Malta; 2Faculty of Business, Management and Economics, Univer...
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Dove Medical Press
2021
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oai:doaj.org-article:6e5999dc103040d19edae60d84daf8202021-11-28T19:13:10ZA Proactive Approach to Identify the Exposure Risk to COVID-19: Validation of the Pandemic Risk Exposure Measurement (PREM) Model Using Real-World Data1179-1594https://doaj.org/article/6e5999dc103040d19edae60d84daf8202021-11-01T00:00:00Zhttps://www.dovepress.com/a-proactive-approach-to-identify-the-exposure-risk-to-covid-19-validat-peer-reviewed-fulltext-article-RMHPhttps://doaj.org/toc/1179-1594Simon Grima, 1 Ramona Rupeika-Apoga, 2 Murat Kizilkaya, 3 Inna Romānova, 2 Rebecca Dalli Gonzi, 4 Mihajlo Jakovljevic 5– 7 1Department of Insurance, Faculty of Economics, Management and Accountancy, University of Malta, Msida, Malta; 2Faculty of Business, Management and Economics, University of Latvia, Riga, Latvia; 3Department of Economics, Faculty of Economics and Administrative Sciences, Ardahan University, Ardahan, Turkey; 4Department of Construction & Property Management, University of Malta, Msida, MSD, 2080, Malta; 5Institute of Comparative Economic Studies ICES, Faculty of Economics, Hosei University, Tokyo, Japan; 6Department of Global Health Economics and Policy, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia; 7Department of Public Health and Healthcare Named After N.A. Semashko, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, RussiaCorrespondence: Simon GrimaUniversity of Malta, Msida, MSD, 2080, MaltaTel +356 79 651 410Email simon.grima@um.edu.mtPurpose: To statistically validate the PREM (Pandemic Risk Exposure Measurement) model devised in a previous paper by the authors and determine the model’s relationship with the level of current COVID-19 cases (NLCC) and the level of current deaths related to COVID-19 (NLCD) based on the real country data.Methods: We used perceived variables proposed in a previous study by the same lead authors and applied the latest available real data values for 154 countries. Two endogenous real data variables (NLCC) and (NLCD) were added. Data were transformed to measurable values using a Likert scale of 1 to 5. The resulting data for each variable were entered into SPSS (Statistical Package for the Social Sciences) version 26 and Amos (Analysis of a Moment Structures) version 21 and subjected to statistical analysis, specifically exploratory factor analysis, Cronbach’s alpha and confirmatory factor analysis.Results: The results obtained confirmed a 4-factor structure and that the PREM model using real data is statistically reliable and valid. However, the variable Q14 – hospital beds available per capita (1000 inhabitants) had to be excluded from the analysis because it loaded under more than one factor and the difference between the factor common variance was less than 0.10. Moreover, its Factor 1 and Factor 3 with NLCC and Factor 1 with NLCD showed a statistically significant relationship.Conclusion: Therefore, the developed PREM model moves from a perception-based model to reality. By proposing a model that allows governments and policymakers to take a proactive approach, the negative impact of a pandemic on the functioning of a country can be reduced. The PREM model is useful for decision-makers to know what factors make the country more vulnerable to a pandemic and, if possible, to manage or set tolerances as part of a preventive measure.Keywords: COVID-19, pandemic risk exposure, PREM, proactive, vulnerabilityGrima SRupeika-Apoga RKizilkaya MRomānova IDalli Gonzi RJakovljevic MDove Medical Pressarticlecovid-19pandemic risk exposurepremproactivevulnerabilityPublic aspects of medicineRA1-1270ENRisk Management and Healthcare Policy, Vol Volume 14, Pp 4775-4787 (2021) |
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covid-19 pandemic risk exposure prem proactive vulnerability Public aspects of medicine RA1-1270 |
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covid-19 pandemic risk exposure prem proactive vulnerability Public aspects of medicine RA1-1270 Grima S Rupeika-Apoga R Kizilkaya M Romānova I Dalli Gonzi R Jakovljevic M A Proactive Approach to Identify the Exposure Risk to COVID-19: Validation of the Pandemic Risk Exposure Measurement (PREM) Model Using Real-World Data |
| description |
Simon Grima, 1 Ramona Rupeika-Apoga, 2 Murat Kizilkaya, 3 Inna Romānova, 2 Rebecca Dalli Gonzi, 4 Mihajlo Jakovljevic 5– 7 1Department of Insurance, Faculty of Economics, Management and Accountancy, University of Malta, Msida, Malta; 2Faculty of Business, Management and Economics, University of Latvia, Riga, Latvia; 3Department of Economics, Faculty of Economics and Administrative Sciences, Ardahan University, Ardahan, Turkey; 4Department of Construction & Property Management, University of Malta, Msida, MSD, 2080, Malta; 5Institute of Comparative Economic Studies ICES, Faculty of Economics, Hosei University, Tokyo, Japan; 6Department of Global Health Economics and Policy, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia; 7Department of Public Health and Healthcare Named After N.A. Semashko, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, RussiaCorrespondence: Simon GrimaUniversity of Malta, Msida, MSD, 2080, MaltaTel +356 79 651 410Email simon.grima@um.edu.mtPurpose: To statistically validate the PREM (Pandemic Risk Exposure Measurement) model devised in a previous paper by the authors and determine the model’s relationship with the level of current COVID-19 cases (NLCC) and the level of current deaths related to COVID-19 (NLCD) based on the real country data.Methods: We used perceived variables proposed in a previous study by the same lead authors and applied the latest available real data values for 154 countries. Two endogenous real data variables (NLCC) and (NLCD) were added. Data were transformed to measurable values using a Likert scale of 1 to 5. The resulting data for each variable were entered into SPSS (Statistical Package for the Social Sciences) version 26 and Amos (Analysis of a Moment Structures) version 21 and subjected to statistical analysis, specifically exploratory factor analysis, Cronbach’s alpha and confirmatory factor analysis.Results: The results obtained confirmed a 4-factor structure and that the PREM model using real data is statistically reliable and valid. However, the variable Q14 – hospital beds available per capita (1000 inhabitants) had to be excluded from the analysis because it loaded under more than one factor and the difference between the factor common variance was less than 0.10. Moreover, its Factor 1 and Factor 3 with NLCC and Factor 1 with NLCD showed a statistically significant relationship.Conclusion: Therefore, the developed PREM model moves from a perception-based model to reality. By proposing a model that allows governments and policymakers to take a proactive approach, the negative impact of a pandemic on the functioning of a country can be reduced. The PREM model is useful for decision-makers to know what factors make the country more vulnerable to a pandemic and, if possible, to manage or set tolerances as part of a preventive measure.Keywords: COVID-19, pandemic risk exposure, PREM, proactive, vulnerability |
| format |
article |
| author |
Grima S Rupeika-Apoga R Kizilkaya M Romānova I Dalli Gonzi R Jakovljevic M |
| author_facet |
Grima S Rupeika-Apoga R Kizilkaya M Romānova I Dalli Gonzi R Jakovljevic M |
| author_sort |
Grima S |
| title |
A Proactive Approach to Identify the Exposure Risk to COVID-19: Validation of the Pandemic Risk Exposure Measurement (PREM) Model Using Real-World Data |
| title_short |
A Proactive Approach to Identify the Exposure Risk to COVID-19: Validation of the Pandemic Risk Exposure Measurement (PREM) Model Using Real-World Data |
| title_full |
A Proactive Approach to Identify the Exposure Risk to COVID-19: Validation of the Pandemic Risk Exposure Measurement (PREM) Model Using Real-World Data |
| title_fullStr |
A Proactive Approach to Identify the Exposure Risk to COVID-19: Validation of the Pandemic Risk Exposure Measurement (PREM) Model Using Real-World Data |
| title_full_unstemmed |
A Proactive Approach to Identify the Exposure Risk to COVID-19: Validation of the Pandemic Risk Exposure Measurement (PREM) Model Using Real-World Data |
| title_sort |
proactive approach to identify the exposure risk to covid-19: validation of the pandemic risk exposure measurement (prem) model using real-world data |
| publisher |
Dove Medical Press |
| publishDate |
2021 |
| url |
https://doaj.org/article/6e5999dc103040d19edae60d84daf820 |
| work_keys_str_mv |
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