- Global variation of COVID-19 mortality rates in the initial phase
-
Saman Hasan Siddiqui, Azza Sarfraz, Arjumand Rizvi, Fariha Shaheen, Mohammad Tahir Yousafzai, Syed Asad Ali
-
Osong Public Health Res Perspect. 2021;12(2):64-72. Published online April 29, 2021
-
DOI: https://doi.org/10.24171/j.phrp.2021.12.2.03
-
-
7,636
View
-
157
Download
-
8
Web of Science
-
10
Crossref
-
 Abstract
 PDF Supplementary Material
- Objectives
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused devastation in over 200 countries. Italy, Spain, and the United States (US) were most severely affected by the first wave of the pandemic. The reasons why some countries were more strongly affected than others remain unknown. We identified the most-affected and less-affected countries and states and explored environmental, host, and infrastructure risk factors that may explain differences in the SARS-CoV-2 mortality burden.
Methods
We identified the top 10 countries/US states with the highest deaths per population until May 2020. For each of these 10 case countries/states, we identified 6 control countries/states with a similar population size and at least 3 times fewer deaths per population. We extracted data for 30 risk factors from publicly available, trusted sources. We compared case and control countries/states using the non-parametric Wilcoxon rank-sum test, and conducted a secondary cluster analysis to explore the relationship between the number of cases per population and the number of deaths per population using a scalable EM (expectation–maximization) clustering algorithm.
Results
Statistically significant differences were found in 16 of 30 investigated risk factors, the most important of which were temperature, neonatal and under-5 mortality rates, the percentage of under-5 deaths due to acute respiratory infections (ARIs) and diarrhea, and tuberculosis incidence (p<0.05)
Conclusion
Countries with a higher burden of baseline pediatric mortality rates, higher pediatric mortality from preventable diseases like diarrhea and ARI, and higher tuberculosis incidence had lower rates of coronavirus disease 2019-associated mortality, supporting the hygiene hypothesis.
-
Citations
Citations to this article as recorded by  - Prediction models of COVID-19 fatality in nine Peruvian provinces: A secondary analysis of the national epidemiological surveillance system
Wendy Nieto-Gutierrez, Jaid Campos-Chambergo, Enrique Gonzalez-Ayala, Oswaldo Oyola-Garcia, Alberti Alejandro-Mora, Eliana Luis-Aguirre, Roly Pasquel-Santillan, Juan Leiva-Aguirre, Cesar Ugarte-Gil, Steev Loyola, Sizulu Moyo
PLOS Global Public Health.2024; 4(1): e0002854. CrossRef - The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis
Laura Houweling, Anke-Hilse Maitland-Van der Zee, Judith C.S. Holtjer, Somayeh Bazdar, Roel C.H. Vermeulen, George S. Downward, Lizan D. Bloemsma
Environmental Research.2024; 240: 117351. CrossRef - Demographic Characteristics and Status of Vaccinated Individuals with a History of COVID-19 Infection Pre- or Post-Vaccination: A Descriptive Study of a Nationally Representative Sample in Saudi Arabia
Yazed AlRuthia, Haya F. Al-Salloum, Omar A. Almohammed, Amani S. Alqahtani, Hana A. Al-Abdulkarim, Yousef M. Alsofayan, Sami S. Almudarra, Sara H. AlQahtani, Abdullah Almutlaq, Khaled Alabdulkareem, Bander Balkhi, Hamoud T. Almutairi, Abdullah S. Alanazi,
Vaccines.2022; 10(2): 323. CrossRef - Temporal variation, socioeconomic status, and out‐of‐hospital deaths as factors that influence mortality rates among hospitalized COVID‐19 patients receiving ACEIs/ARBs
Owais M. Aftab, Anurag Modak, Jai C. Patel
The Journal of Clinical Hypertension.2022; 24(4): 519. CrossRef - Coinfection of leptospirosis and coronavirus disease 2019: A retrospective case series from a coastal region in South India
Nitin Gupta, William Wilson, Prithvishree Ravindra, Roshini Raghu, Kavitha Saravu
Journal of Medical Virology.2022; 94(9): 4508. CrossRef - Dietary, comorbidity, and geo-economic data fusion for explainable COVID-19 mortality prediction
Milena Trajanoska, Risto Trajanov, Tome Eftimov
Expert Systems with Applications.2022; 209: 118377. CrossRef - Paraoxonase 1 rs662 polymorphism, its related variables, and COVID-19 intensity: Considering gender and post-COVID complications
Zohreh-Al-Sadat Ghoreshi, Mojtaba Abbasi-jorjandi, Gholamreza Asadikaram, Mohsen Sharif-zak, Fatemeh Seyedi, Mohammad Khaksari Haddad, Mohammadreza Zangouey
Experimental Biology and Medicine.2022; : 153537022211285. CrossRef - Clinical Effect of Q192R Paraoxonase 1 Polymorphism and its Related Variables on the COVID-19 Intensity Considering Gender and Post-COVID Complications
Zohreh-al-sadat Ghoreshi, Mojtaba abasi, Gholamreza Asadikaram, Mohsen sharif-zak, Mitra Rezazadeh-Jabalbarzi, Hamidreza rashidinejad, Mohammadreza Zangouey
SSRN Electronic Journal .2022;[Epub] CrossRef - Risk Factors and a Novel Score (CARI-65) Predicting Mortality in COVID-19 Patients
Fayaz Ahmad Sofi, Umar Hafiz Khan, Sonaullah Shah, Nazia Mehfooz, Farhana Siraj, Afshan Shabir, Tajamul Hussain Shah, Muzaffar Bindroo, Mushtaq Ahmad, Rafi Ahmed Jan, Asma Shah, Faizan Wani
Indian Journal of Respiratory Care.2022; 11(2): 154. CrossRef - Variances in BCG protection against COVID-19 mortality: A global assessment
Zouina Sarfraz, Azza Sarfraz, Krunal Pandav, Sarabjot Singh Makkar, Saman Hasan Siddiqui, Gaurav Patel, Tania Platero-Portillo, Bishnu Mohan Singh, Mohamed Iburahim Haja Maideen, Deepika Sarvepalli, Muzna Sarfraz, Jose Cardona-Guzman, Marcos A. Sanchez-Go
Journal of Clinical Tuberculosis and Other Mycobac.2021; 24: 100249. CrossRef
|
