臺灣博碩士論文加值系統

Background: As new variants keep emerging, it is essential for governments to monitor pandemic parameters and navigate proper COVID-19 control solutions effectively and rapidly.
Objectives: Our study aimed to compare COVID-19 average weekly infection fatality rate (AWIFR) and investigate factors related to COVID-19 AWIFR during periods of Delta and Omicron variant dominances.
Methods: This was an ecological study of 110 countries throughout the first 12 weeks of Delta and Omicron periods. Multivariable linear mixed-effect models and multivariate linear regression models were employed to determine the factors associated with AWIFR.
Conclusions: In Delta period, countries with high levels of vaccination and government effectiveness indices showed lower AWIFR. Higher burden of cardiovascular diseases was positively linked to AWIFR. In Omicron period, while years lived with disability (YLDs) caused by metabolism disorders, proportion of the population aged over 65 were positively associated with poorer AWIFR, high proportion of the population vaccinated with a booster dose was linked with the better outcome. Over two periods, the increase in government effectiveness index was associated with decrease of AWIFR, higher death rates caused by diabetes and kidney and percentage of population aged over 65 were associated with a significant increase in AWIFR.
Conclusion: The COVID-19 infection fatality rates was associated with vaccination coverage rate, government effectiveness index, and health burden related to chronic diseases. Therefore, governments should implement appropriate policies to significant alleviate the burden of COVID-19.
Keywords: COVID-19, Variants, Delta, Omicron, Vaccine, NPIs.
 

Background: As new variants keep emerging, it is essential for governments to monitor pandemic parameters and navigate proper COVID-19 control solutions effectively and rapidly.
Objectives: Our study aimed to compare COVID-19 average weekly infection fatality rate (AWIFR) and investigate factors related to COVID-19 AWIFR during periods of Delta and Omicron variant dominances.
Methods: This was an ecological study of 110 countries throughout the first 12 weeks of Delta and Omicron periods. Multivariable linear mixed-effect models and multivariate linear regression models were employed to determine the factors associated with AWIFR.
Conclusions: In Delta period, countries with high levels of vaccination and government effectiveness indices showed lower AWIFR. Higher burden of cardiovascular diseases was positively linked to AWIFR. In Omicron period, while years lived with disability (YLDs) caused by metabolism disorders, proportion of the population aged over 65 were positively associated with poorer AWIFR, high proportion of the population vaccinated with a booster dose was linked with the better outcome. Over two periods, the increase in government effectiveness index was associated with decrease of AWIFR, higher death rates caused by diabetes and kidney and percentage of population aged over 65 were associated with a significant increase in AWIFR.
Conclusion: The COVID-19 infection fatality rates was associated with vaccination coverage rate, government effectiveness index, and health burden related to chronic diseases. Therefore, governments should implement appropriate policies to significant alleviate the burden of COVID-19.
Keywords: COVID-19, Variants, Delta, Omicron, Vaccine, NPIs.
 

Acknowledgements i
Abstract ii
TABLE OF CONTENT iii
List of Tables v
List of Figures vi
CHAPTER 1. INTRODUCTION 1
1.1 Background 1
1.2 Statements of the problem 2
1.3 Objectives of the study and research questions 2
1.4 Significances of study 2
CHAPTER 2. LITERATURE REVIEW 3
2.1. Overview of the COVID-19 pandemic 3
2.2 The situation of infection fatality rate and its associated factors 4
2.3. The measures impacts on fighting against the COVID-19 pandemic 5
2.3.1 Non-pharmaceutical interventions 5
2.3.2 Vaccine 6
2.2.3 Other measures 7
CHAPTER 3. METHODS 8
3.1. Study design and study population 8
3.2. Conceptual framework of the study 8
3.3. Operational definition of variables and data sources 9
3.3.1. Outcome variables 9
3.3.2. Independent variables 9
3.4. Study periods 15
3.5. Data analysis 15
3.6. Ethical considerations 16
CHAPTER 4. RESULTS 17
4.1. Characteristics of selected countries 17
4.2. The factors associated with AWIFR during the Delta and Omicron periods 21
CHAPTER 5. DISCUSSION AND CONCLUSION 26
5.1. Discussion 26
5.2. Conclusions 28
REFERENCES 30

List of Tables

Table 1. The definition of variables and their data sources 10
Table 2. Vaccine and stringency index characteristics between two periods 17
Table 3. COVID-19 health-related outcomes of selected countries 19

List of Figures

Figure 1. The COVID-19 Situation by WHO Region (World Health Organization, 2022) 3
Figure 2. Study framework of interest outcomes and independent variables 8
Figure 3. Time lag effects were identified throughout the period of variant dominance 15
Figure 4. Vaccine and stringency index characteristics by regions and income groups 18
between two periods in each period of the Delta and Omicron 18
Figure 5. World map of AWIFR among countries during the Delta variant period 19
Figure 6. World map of AWIFR among countries during the Omicron variant period 20
Figure 7. COVID-19 outcomes by regions and country income classification 21
between two periods of the Delta and Omicron variants 21
Figure 8. Multivariable linear mixed-effects models of factors associated with the AWIFR 22
during the Delta and Omicron periods 22
Figure 9. The increase in stringency index, vaccination, and AWIFR by regions and classification of country income between two periods of the Delta and Omicron 23
Figure 10. Multivariable linear regression model of factors associated with the increase in AWIFR between Delta and Omicron periods 25

Abu-Raddad, L. J., Chemaitelly, H., Ayoub, H. H., AlMukdad, S., Yassine, H. M., Al-Khatib, H. A., Smatti, M. K., Tang, P., Hasan, M. R., Coyle, P., Al-Kanaani, Z., Al-Kuwari, E., Jeremijenko, A., Kaleeckal, A. H., Latif, A. N., Shaik, R. M., Abdul-Rahim, H. F., Nasrallah, G. K., Al-Kuwari, M. G., Butt, A. A., Al-Romaihi, H. E., Al-Thani, M. H., Al-Khal, A., & Bertollini, R. (2022). Effect of mRNA Vaccine Boosters against SARS-CoV-2 Omicron Infection in Qatar. New England Journal of Medicine, 386(19), 1804-1816. https://doi.org/10.1056/NEJMoa2200797
Ali, N. (2020). Role of vitamin D in preventing of COVID-19 infection, progression and severity. Journal of Infection and Public Health, 13(10), 1373-1380. https://doi.org/10.1016/j.jiph.2020.06.021
Andrews, N., Stowe, J., Kirsebom, F., Toffa, S., Rickeard, T., Gallagher, E., Gower, C., Kall, M., Groves, N., O’Connell, A.-M., Simons, D., Blomquist, P. B., Zaidi, A., Nash, S., Iwani Binti Abdul Aziz, N., Thelwall, S., Dabrera, G., Myers, R., Amirthalingam, G., Gharbia, S., Barrett, J. C., Elson, R., Ladhani, S. N., Ferguson, N., Zambon, M., Campbell, C. N. J., Brown, K., Hopkins, S., Chand, M., Ramsay, M., & Lopez Bernal, J. (2022). Covid-19 Vaccine Effectiveness against the Omicron (B.1.1.529) Variant. New England Journal of Medicine, 386(16), 1532-1546. https://doi.org/10.1056/NEJMoa2119451
Angelopoulos, A. N., Pathak, R., Varma, R., & Jordan, M. I. (2020). On Identifying and Mitigating Bias in the Estimation of the COVID-19 Case Fatality Rate. Harvard Data Science Review. https://doi.org/https://doi.org/10.1162/99608f92.f01ee285
Antony, S. J., Almaghlouth, N. K., & Heydemann, E. L. (2020). Are coinfections with COVID-19 and influenza low or underreported? An observational study examining current published literature including three new unpublished cases. Journal of Medical Virology, 92(11), 2489-2497. https://doi.org/10.1002/jmv.26167
Arceo-Gomez, E. O., Campos-Vazquez, R. M., Esquivel, G., Alcaraz, E., Martinez, L. A., & Lopez, N. G. (2022). The income gradient in COVID-19 mortality and hospitalisation: An observational study with social security administrative records in Mexico. The Lancet Regional Health – Americas, 6. https://doi.org/10.1016/j.lana.2021.100115
Asyary, A., & Veruswati, M. (2020). Sunlight exposure increased Covid-19 recovery rates: A study in the central pandemic area of Indonesia. Science the Total Environment, 729, 139016. https://doi.org/10.1016/j.scitotenv.2020.139016
Basu, A. (2020). Estimating The Infection Fatality Rate Among Symptomatic COVID-19 Cases In The United States. Health Affairs, 39(7), 1229-1236. https://doi.org/10.1377/hlthaff.2020.00455
Baum, A., & Schwartz, M. D. (2020). Admissions to Veterans Affairs Hospitals for Emergency Conditions During the COVID-19 Pandemic. JAMA, 324(1), 96-99. https://doi.org/10.1001/jama.2020.9972
Bo, Y., Guo, C., Lin, C., Zeng, Y., Li, H. B., Zhang, Y., Hossain, M. S., Chan, J. W. M., Yeung, D. W., Kwok, K. O., Wong, S. Y. S., Lau, A. K. H., & Lao, X. Q. (2021). Effectiveness of non-pharmaceutical interventions on COVID-19 transmission in 190 countries from 23 January to 13 April 2020. International Journal of Infectious Diseases, 102, 247-253. https://doi.org/https://doi.org/10.1016/j.ijid.2020.10.066
Burki, T. K. (2021). Challenges in the rollout of COVID-19 vaccines worldwide. Lancet Respiratory Medicine, 9(4), e42-e43. https://doi.org/10.1016/s2213-2600(21)00129-6
Cao, Y., Hiyoshi, A., & Montgomery, S. (2020). COVID-19 case-fatality rate and demographic and socioeconomic influencers: worldwide spatial regression analysis based on country-level data. BMJ Open, 10(11), e043560. https://doi.org/10.1136/bmjopen-2020-043560
Casari, I., Manfredi, M., Metharom, P., & Falasca, M. (2021). Dissecting lipid metabolism alterations in SARS-CoV-2. Progress in Lipid Research, 82, 101092. https://doi.org/10.1016/j.plipres.2021.101092
Chisadza, C., Clance, M., & Gupta, R. (2021). Government Effectiveness and the COVID-19 Pandemic. Sustainability, 13(6), 3042. https://www.mdpi.com/2071-1050/13/6/3042
Coccia, M. (2022). Preparedness of countries to face COVID-19 pandemic crisis: Strategic positioning and factors supporting effective strategies of prevention of pandemic threats. Environmental Research, 203, 111678. https://doi.org/10.1016/j.envres.2021.111678
COVID-19 Forecasting Team. (2022a). Variation in the COVID-19 infection-fatality ratio by age, time, and geography during the pre-vaccine era: a systematic analysis. Lancet, 399(10334), 1469-1488. https://doi.org/10.1016/s0140-6736(21)02867-1
COVID-19 Forecasting Team. (2022b). Variation in the COVID-19 infection fatality ratio by age, time, and geography during the pre-vaccine era: a systematic analysis. The Lancet, 399(10334), 1469-1488. https://doi.org/10.1016/S0140-6736(21)02867-1
Cucinotta, D., & Vanelli, M. (2020). WHO Declares COVID-19 a Pandemic. Acta Biomedica, 91(1), 157-160. https://doi.org/10.23750/abm.v91i1.9397
de Almeida-Pititto, B., Dualib, P. M., Zajdenverg, L., Dantas, J. R., de Souza, F. D., Rodacki, M., Bertoluci, M. C., & Brazilian Diabetes Society Study, G. (2020). Severity and mortality of COVID 19 in patients with diabetes, hypertension and cardiovascular disease: a meta-analysis. Diabetology & Metabolic Syndrome, 12(1), 75. https://doi.org/10.1186/s13098-020-00586-4
Decoster, A., Minten, T., & Spinnewijn, J. (2021). The Income Gradient in Mortality during the Covid-19 Crisis: Evidence from Belgium. The Journal of Economic Inequality, 19(3), 551-570. https://doi.org/10.1007/s10888-021-09505-7
Dessie, Z. G., & Zewotir, T. (2021). Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients. BMC Infectious Diseases, 21(1), 855. https://doi.org/10.1186/s12879-021-06536-3
Dyer, O. (2021). Covid-19: Omicron is causing more infections but fewer hospital admissions than delta, South African data show. BMJ, 375, n3104. https://doi.org/10.1136/bmj.n3104
Elbe, S., & Buckland-Merrett, G. (2017). Data, disease and diplomacy: GISAID's innovative contribution to global health. Global Challenges, 1(1), 33-46. https://doi.org/10.1002/gch2.1018
Ellen Nolte, & McKee, M. (2008). Caring-for-people-with-chronic-conditions. In.
European Center for Disease Prevention and Control. (2022). Covid-19-assessment-further-emergence-omicron-19th-risk-assessment-december-2021.
Hale, T., Angrist, N., Goldszmidt, R., Kira, B., Petherick, A., Phillips, T., Webster, S., Cameron-Blake, E., Hallas, L., Majumdar, S., & Tatlow, H. (2021). A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nature Human Behaviour, 5(4), 529-538. https://doi.org/10.1038/s41562-021-01079-8
Haug, N., Geyrhofer, L., Londei, A., Dervic, E., Desvars-Larrive, A., Loreto, V., Pinior, B., Thurner, S., & Klimek, P. (2020). Ranking the effectiveness of worldwide COVID-19 government interventions. Nature Human Behaviour, 4(12), 1303-1312. https://doi.org/10.1038/s41562-020-01009-0
Huy, L. D., Nguyen, N. T. H., Phuc, P. T., & Huang, C. C. (2022). The Effects of Non-Pharmaceutical Interventions on COVID-19 Epidemic Growth Rate during Pre- and Post-Vaccination Period in Asian Countries. International Journal of Environmental Research and Public Health, 19(3). https://doi.org/10.3390/ijerph19031139
Karim, S. S. A., & Karim, Q. A. (2021). Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic. Lancet, 398(10317), 2126-2128. https://doi.org/10.1016/s0140-6736(21)02758-6
Khajuria, A., Tomaszewski, W., Liu, Z., Chen, J. H., Mehdian, R., Fleming, S., Vig, S., & Crawford, M. J. (2021). Workplace factors associated with mental health of healthcare workers during the COVID-19 pandemic: an international cross-sectional study. BMC Health Services Research, 21(1), 262. https://doi.org/10.1186/s12913-021-06279-6
Kim, J., Hong, K., Yum, S., Gómez Gómez, R. E., Jang, J., Park, S. H., Choe, Y. J., Ryu, S., Park, D. W., Lee, Y. S., Lee, H., Kim, D. H., Kim, D.-H., & Chun, B. C. (2021). Factors associated with the difference between the incidence and case-fatality ratio of coronavirus disease 2019 by country. Scientific Reports, 11(1), 18938. https://doi.org/10.1038/s41598-021-98378-x
Lau, H., Khosrawipour, T., Kocbach, P., Ichii, H., Bania, J., & Khosrawipour, V. (2021). Evaluating the massive underreporting and undertesting of COVID-19 cases in multiple global epicenters. Pulmonology, 27(2), 110-115. https://doi.org/10.1016/j.pulmoe.2020.05.015
Li, H., Wang, L., Zhang, M., Lu, Y., & Wang, W. (2022). Effects of vaccination and non-pharmaceutical interventions and their lag times on the COVID-19 pandemic: Comparison of eight countries. PLOS Neglected Tropical Diseases, 16(1), e0010101. https://doi.org/10.1371/journal.pntd.0010101
Liang, L. L., Kao, C. T., Ho, H. J., & Wu, C. Y. (2021). COVID-19 case doubling time associated with non-pharmaceutical interventions and vaccination: A global experience. Journal of Global Health, 11, 05021. https://doi.org/10.7189/jogh.11.05021
Lin, T. Y., Liao, S. H., Lai, C. C., Paci, E., & Chuang, S. Y. (2021). Effectiveness of non-pharmaceutical interventions and vaccine for containing the spread of COVID-19: Three illustrations before and after vaccination periods. Journal of the Formosan Medical Association, 120 Suppl 1, S46-s56. https://doi.org/10.1016/j.jfma.2021.05.015
Liu, K., Chen, Y., Lin, R., & Han, K. (2020). Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients. Journal of Infection, 80(6), e14-e18. https://doi.org/10.1016/j.jinf.2020.03.005
Liu, Y., Morgenstern, C., Kelly, J., Lowe, R., & Jit, M. (2021). The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories. BMC Medine, 19(1), 40. https://doi.org/10.1186/s12916-020-01872-8
Mallapaty, S. (2020). The coronavirus is most deadly if you are older and male - new data reveal the risks. Nature, 585(7823), 16-17. https://doi.org/10.1038/d41586-020-02483-2
Mann, D. M., Chen, J., Chunara, R., Testa, P. A., & Nov, O. (2020). COVID-19 transforms health care through telemedicine: Evidence from the field. Journal of the American Medical Informatics Association, 27(7), 1132-1135. https://doi.org/10.1093/jamia/ocaa072
Maringe, C., Spicer, J., Morris, M., Purushotham, A., Nolte, E., Sullivan, R., Rachet, B., & Aggarwal, A. (2020). The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study. Lancet Oncology, 21(8), 1023-1034. https://doi.org/10.1016/s1470-2045(20)30388-0
Mathieu, E., Ritchie, H., Ortiz-Ospina, E., Roser, M., Hasell, J., Appel, C., Giattino, C., & Rodés-Guirao, L. (2021). A global database of COVID-19 vaccinations. Nature Human Behavior, 5(7), 947-953. https://doi.org/10.1038/s41562-021-01122-8
McDonald, H. I., Tessier, E., White, J. M., Woodruff, M., Knowles, C., Bates, C., Parry, J., Walker, J. L., Scott, J. A., Smeeth, L., Yarwood, J., Ramsay, M., & Edelstein, M. (2020). Early impact of the coronavirus disease (COVID-19) pandemic and physical distancing measures on routine childhood vaccinations in England, January to April 2020. Eurosurveillance, 25(19). https://doi.org/10.2807/1560-7917.Es.2020.25.19.2000848
Migaud, M., Gandotra, S., Chand, H. S., Gillespie, M. N., Thannickal, V. J., & Langley, R. J. (2020). Metabolomics to Predict Antiviral Drug Efficacy in COVID-19. American Journal of Respiratory Cell and Molecular Biology, 63(3), 396-398. https://doi.org/10.1165/rcmb.2020-0206LE
Molenberghs, G., Faes, C., Verbeeck, J., Deboosere, P., Abrams, S., Willem, L., Aerts, J., Theeten, H., Devleesschauwer, B., Bustos Sierra, N., Renard, F., Herzog, S., Lusyne, P., Van der Heyden, J., Van Oyen, H., Van Damme, P., & Hens, N. (2022). COVID-19 mortality, excess mortality, deaths per million and infection fatality ratio, Belgium, 9 March 2020 to 28 June 2020. Eurosurveillance, 27(7). https://doi.org/10.2807/1560-7917.Es.2022.27.7.2002060
Moynihan, R., Sanders, S., Michaleff, Z. A., Scott, A. M., Clark, J., To, E. J., Jones, M., Kitchener, E., Fox, M., Johansson, M., Lang, E., Duggan, A., Scott, I., & Albarqouni, L. (2021). Impact of COVID-19 pandemic on utilisation of healthcare services: a systematic review. BMJ Open, 11(3), e045343. https://doi.org/10.1136/bmjopen-2020-045343
Okonji, E. F., Okonji, O. C., Mukumbang, F. C., & Van Wyk, B. (2021). Understanding varying COVID-19 mortality rates reported in Africa compared to Europe, Americas and Asia. Tropical Medicine & International Health, 26(7), 716-719. https://doi.org/https://doi.org/10.1111/tmi.13575
Pan American Health Organization, C. f. D. C. a. P. C., & USAID,. (2020). Leadership during a pandemic: What Your Municipality Can Do. USAID.
Patel, M. D., Rosenstrom, E., Ivy, J. S., Mayorga, M. E., Keskinocak, P., Boyce, R. M., Hassmiller Lich, K., Smith, R. L., 3rd, Johnson, K. T., Delamater, P. L., & Swann, J. L. (2021). Association of Simulated COVID-19 Vaccination and Nonpharmaceutical Interventions With Infections, Hospitalizations, and Mortality. JAMA Network Open, 4(6), e2110782. https://doi.org/10.1001/jamanetworkopen.2021.10782
Rice, B. L., Annapragada, A., Baker, R. E., Bruijning, M., Dotse-Gborgbortsi, W., Mensah, K., Miller, I. F., Motaze, N. V., Raherinandrasana, A., Rajeev, M., Rakotonirina, J., Ramiadantsoa, T., Rasambainarivo, F., Yu, W., Grenfell, B. T., Tatem, A. J., & Metcalf, C. J. E. (2021). Variation in SARS-CoV-2 outbreaks across sub-Saharan Africa. Nature Medicine, 27(3), 447-453. https://doi.org/10.1038/s41591-021-01234-8
Staerk, C., Wistuba, T., & Mayr, A. (2021). Estimating effective infection fatality rates during the course of the COVID-19 pandemic in Germany. BMC Public Health, 21(1), 1073. https://doi.org/10.1186/s12889-021-11127-7
Taquet, M., Luciano, S., Geddes, J. R., & Harrison, P. J. (2021). Bidirectional associations between COVID-19 and psychiatric disorder: retrospective cohort studies of 62 354 COVID-19 cases in the USA. Lancet Psychiatry, 8(2), 130-140. https://doi.org/10.1016/s2215-0366(20)30462-4
Viswanath, K., Bekalu, M., Dhawan, D., Pinnamaneni, R., Lang, J., & McLoud, R. (2021). Individual and social determinants of COVID-19 vaccine uptake. BMC Public Health, 21(1), 818. https://doi.org/10.1186/s12889-021-10862-1
Vizheh, M., Qorbani, M., Arzaghi, S. M., Muhidin, S., Javanmard, Z., & Esmaeili, M. (2020). The mental health of healthcare workers in the COVID-19 pandemic: A systematic review. Journal of Diabetes & Metabolic Disorders, 19(2), 1967-1978. https://doi.org/10.1007/s40200-020-00643-9
Whittemore, P. B. (2020). COVID-19 fatalities, latitude, sunlight, and vitamin D. American Journal of Infection Control, 48(9), 1042-1044. https://doi.org/10.1016/j.ajic.2020.06.193
Yanez, N. D., Weiss, N. S., Romand, J.-A., & Treggiari, M. M. (2020). COVID-19 mortality risk for older men and women. BMC Public Health, 20(1), 1742. https://doi.org/10.1186/s12889-020-09826-8
Yao, H., Chen, J. H., & Xu, Y. F. (2020). Patients with mental health disorders in the COVID-19 epidemic. Lancet Psychiatry, 7(4), e21. https://doi.org/10.1016/s2215-0366(20)30090-0
Zhou, F., Hu, T.-J., Zhang, X.-Y., Lai, K., Chen, J.-H., & Zhou, X.-H. (2022). The association of intensity and duration of non-pharmacological interventions and implementation of vaccination with COVID-19 infection, death, and excess mortality: Natural experiment in 22 European countries. Journal of Infection and Public Health, 15(5), 499-507. https://doi.org/https://doi.org/10.1016/j.jiph.2022.03.011