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Original Article
Time-series comparison of COVID-19 case fatality rates across 21 countries with adjustment for multiple covariates
Yongmoon Kim, Bryan Inho Kim, Sangwoo Tak
Osong Public Health Res Perspect. 2022;13(6):424-434.   Published online November 28, 2022
DOI: https://doi.org/10.24171/j.phrp.2022.0212
  • 2,464 View
  • 110 Download
  • 1 Web of Science
  • 1 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDF
Objectives
Although it is widely used as a measure for mortality, the case fatality rate (CFR) ofcoronavirus disease 2019 (COVID-19) can vary over time and fluctuate for many reasons otherthan viral characteristics. To compare the CFRs of different countries in equal measure, weestimated comparable CFRs after adjusting for multiple covariates and examined the mainfactors that contributed to variability in the CFRs among 21 countries.Methods: For statistical analysis, time-series cross-sectional data were collected from OurWorld in Data, CoVariants.org, and GISAID. Biweekly CFRs of COVID-19 were estimated bypooled generalized linear squares regression models for the panel data. Covariates includedthe predominant virus variant, reproduction rate, vaccination, national economic status,hospital beds, diabetes prevalence, and population share of individuals older than age 65. Intotal, 21 countries were eligible for analysis.Results: Adjustment for covariates reduced variation in the CFRs of COVID-19 across countriesand over time. Regression results showed that the dominant spread of the Omicron variant,reproduction rate, and vaccination were associated with lower country-level CFRs, whereasage, the extreme poverty rate, and diabetes prevalence were associated with higher countrylevel CFRs.Conclusion: A direct comparison of crude CFRs among countries may be fallacious, especiallyin a cross-sectional analysis. Our study presents an adjusted comparison of CFRs over timefor a more proper comparison. In addition, our findings suggest that comparing CFRs amongdifferent countries without considering their context, such as the epidemic phase, medicalcapacity, surveillance strategy, and socio-demographic traits, should be avoided.

Citations

Citations to this article as recorded by  
  • Comments on the article "Time-series comparison of COVID-19 case fatality rates across 21 countries with adjustment for multiple covariates"
    Gaetano Perone
    Osong Public Health and Research Perspectives.2023; 14(2): 146.     CrossRef
Review Article
COVID-19 prediction models: a systematic literature review
Sheikh Muzaffar Shakeel, Nithya Sathya Kumar, Pranita Pandurang Madalli, Rashmi Srinivasaiah, Devappa Renuka Swamy
Osong Public Health Res Perspect. 2021;12(4):215-229.   Published online August 13, 2021
DOI: https://doi.org/10.24171/j.phrp.2021.0100
  • 7,507 View
  • 167 Download
  • 14 Web of Science
  • 11 Crossref
AbstractAbstract PDF
As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.

Citations

Citations to this article as recorded by  
  • The Telemedicine Demand Index and its Utility in Managing COVID-19 Case Surges
    Martin Yong Kwong Lee, Kie Beng Goh, Deanna Xiuting Koh, Si Jack Chong, Raymond Swee Boon Chua
    Telemedicine and e-Health.2024; 30(2): 545.     CrossRef
  • Vaccination compartmental epidemiological models for the delta and omicron SARS-CoV-2 variants
    J. Cuevas-Maraver, P.G. Kevrekidis, Q.Y. Chen, G.A. Kevrekidis, Y. Drossinos
    Mathematical Biosciences.2024; 367: 109109.     CrossRef
  • The reporting completeness and transparency of systematic reviews of prognostic prediction models for COVID-19 was poor: a methodological overview of systematic reviews
    Persefoni Talimtzi, Antonios Ntolkeras, Georgios Kostopoulos, Konstantinos I. Bougioukas, Eirini Pagkalidou, Andreas Ouranidis, Athanasia Pataka, Anna-Bettina Haidich
    Journal of Clinical Epidemiology.2024; 167: 111264.     CrossRef
  • Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan
    Berik Koichubekov, Aliya Takuadina, Ilya Korshukov, Anar Turmukhambetova, Marina Sorokina
    Healthcare.2023; 11(5): 752.     CrossRef
  • Early triage echocardiography to predict outcomes in patients admitted with COVID‐19: a multicenter study
    Daniel Peck, Andrea Beaton, Maria Carmo Nunes, Nicholas Ollberding, Allison Hays, Pranoti Hiremath, Federico Asch, Nitin Malik, Christopher Fung, Craig Sable, Bruno Nascimento
    Echocardiography.2023; 40(5): 388.     CrossRef
  • Static Seeding and Clustering of LSTM Embeddings to Learn From Loosely Time-Decoupled Events
    Christian G. Manasseh, Razvan Veliche, Jared Bennett, Hamilton Scott Clouse
    IEEE Access.2023; 11: 64219.     CrossRef
  • Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting
    Muhammad Usman Tariq, Shuhaida Binti Ismail, Muhammad Babar, Ashir Ahmad, Lin Wang
    PLOS ONE.2023; 18(7): e0287755.     CrossRef
  • Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study
    Riku Klén, Ivan A Huespe, Felipe Aníbal Gregalio, Antonio Lalueza Lalueza Blanco, Miguel Pedrera Jimenez, Noelia Garcia Barrio, Pascual Ruben Valdez, Matias A Mirofsky, Bruno Boietti, Ricardo Gómez-Huelgas, José Manuel Casas-Rojo, Juan Miguel Antón-Santos
    eLife.2023;[Epub]     CrossRef
  • Dynamic transmission modeling of COVID-19 to support decision-making in Brazil: A scoping review in the pre-vaccine era
    Gabriel Berg de Almeida, Lorena Mendes Simon, Ângela Maria Bagattini, Michelle Quarti Machado da Rosa, Marcelo Eduardo Borges, José Alexandre Felizola Diniz Filho, Ricardo de Souza Kuchenbecker, Roberto André Kraenkel, Cláudia Pio Ferreira, Suzi Alves Cam
    PLOS Global Public Health.2023; 3(12): e0002679.     CrossRef
  • Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic
    Jose M. Martin-Moreno, Antoni Alegre-Martinez, Victor Martin-Gorgojo, Jose Luis Alfonso-Sanchez, Ferran Torres, Vicente Pallares-Carratala
    International Journal of Environmental Research an.2022; 19(9): 5546.     CrossRef
  • Artificial intelligence and clinical deterioration
    James Malycha, Stephen Bacchi, Oliver Redfern
    Current Opinion in Critical Care.2022; 28(3): 315.     CrossRef
Original Articles
Factors Associated with Cesarean Section in Tehran, Iran using Multilevel Logistic Regression Model
Payam Amini, Maryam Mohammadi, Reza Omani-Samani, Amir Almasi-Hashiani, Saman Maroufizadeh
Osong Public Health Res Perspect. 2018;9(2):86-92.   Published online April 30, 2018
DOI: https://doi.org/10.24171/j.phrp.2018.9.2.08
  • 5,192 View
  • 61 Download
  • 11 Crossref
AbstractAbstract PDF
Objectives

Over the past few decades, the prevalence of cesarean sections (CS) have risen dramatically worldwide, particularly in Iran. The aim of this study was to determine the prevalence of CS in Tehran, and to examine the associated risk factors.

Methods

A cross-sectional study of 4,308 pregnant women with singleton live-births in Tehran, Iran, between July 6–21, 2015 was performed. Multilevel logistic regression analysis was performed using demographic and obstetrical variables at the first level, and hospitals as a variable at the second level.

Results

The incidence of CS was 72.0%. Multivariate analysis showed a significant relationship between CS and the mother’s age, socioeconomic status, body mass index, parity, type of pregnancy, preeclampsia, infant height, and baby’s head circumference. The intra-class correlation using the second level variable, the hospital was 0.292, indicating approximately 29.2% of the total variation in the response variable accounted for by the hospital.

Conclusion

The incidence of CS was substantially higher than other countries. Therefore, educational and psychological interventions are necessary to reduce CS rates amongst pregnant Iranian women.

Citations

Citations to this article as recorded by  
  • Determinants of cesarean mode of childbirth among Rwandan women of childbearing age: Evidence from the 2019–2020 Rwanda Demographic and Health Survey (RDHS)
    Nsereko Etienne, Uwase Aline, Mpinganzima Ornella, Usanzineza Henriette, Niyitegeka Jean Pierre, Turabayo Jean Léonard, Mwiseneza Marie Josee, Mugeni Girimpundu Candide, Moreland Patricia
    Public Health Challenges.2024;[Epub]     CrossRef
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    Halimeh Mohammadi, Javad Rasti, Elham Ebrahimi
    Anesthesiology and Pain Medicine.2023;[Epub]     CrossRef
  • The double burden of maternal overweight and short stature and the likelihood of cesarean deliveries in South Asia: An analysis of national datasets from Bangladesh, India, Maldives, Nepal, and Pakistan
    Mosiur Rahman, Syed Emdadul Haque, Md. Jahirul Islam, Nguyen Huu Chau, Izzeldin Fadl Adam, Md. Nuruzzaman Haque
    Birth.2022; 49(4): 661.     CrossRef
  • Geospatial analysis of cesarean section in Iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services
    Alireza Mohammadi, Elahe Pishgar, Zahra Salari, Behzad Kiani
    BMC Pregnancy and Childbirth.2022;[Epub]     CrossRef
  • Factors associated with cesarean delivery in Bangladesh: A multilevel modeling
    Md. Akhtarul Islam, Mst. Tanmin Nahar, Md. Ashfikur Rahman, Sutapa Dey Barna, S.M. Farhad Ibn Anik
    Sexual & Reproductive Healthcare.2022; 34: 100792.     CrossRef
  • The Birth Satisfaction Scale-Revised Indicator (BSS-RI): a validation study in Iranian mothers
    Reza Omani-Samani, Caroline J. Hollins Martin, Colin R. Martin, Saman Maroufizadeh, Azadeh Ghaheri, Behnaz Navid
    The Journal of Maternal-Fetal & Neonatal Medicine.2021; 34(11): 1827.     CrossRef
  • The effect of familiarization with preoperative care on anxiety and vital signs in the patient’s cesarean section: A randomized controlled trial
    Mehrnush Mostafayi, Behzad Imani, Shirdel Zandi, Faeze Jongi
    European Journal of Midwifery.2021; 5(June): 1.     CrossRef
  • Dynamic prediction of liver cirrhosis risk in chronic hepatitis B patients using longitudinal clinical data
    Ying Wang, Xiang-Yong Li, Li-Li Wu, Xiao-Yan Zheng, Yu Deng, Meng-Jie Li, Xu You, Yu-Tian Chong, Yuan-Tao Hao
    European Journal of Gastroenterology & Hepatology.2020; 32(1): 120.     CrossRef
  • Factors Contributing to Iranian Pregnant Women’s Tendency to Choice Cesarean Section
    Soraya Nouraei Motlagh, Zahra Asadi-piri, Razyeh Bajoulvand, Fatemeh Seyed Mohseni, Katayoun Bakhtiar, Mehdi Birjandi, Maryam Mansouri
    Medical - Surgical Nursing Journal.2020;[Epub]     CrossRef
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    Aliza K. C. Bhandari, Bibha Dhungel, Mahbubur Rahman
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Are There Spatial and Temporal Correlations in the Incidence Distribution of Scrub Typhus in Korea?
Maengseok Noh, Youngjo Lee, Chaeshin Chu, Jin Gwack, Seung-Ki Youn, Sun Huh
Osong Public Health Res Perspect. 2013;4(1):39-44.   Published online February 28, 2013
DOI: https://doi.org/10.1016/j.phrp.2013.01.002
  • 3,451 View
  • 21 Download
  • 10 Crossref
AbstractAbstract PDF
Objectives
A hierarchical generalized linear model (HGLM) was applied to estimate the transmission pattern of scrub typhus from 2001 to 2011 in the Republic of Korea, based on spatial and temporal correlation.
Methods
Based on the descriptive statistics of scrub typhus incidence from 2001 to 2011 reported to the Korean Centers for Disease Control and Prevention, the spatial and temporal correlations were estimated by HGLM. Incidences according to age, sex, and year were also estimated by the best-fit model out of nine HGLMs. A disease map was drawn to view the annual regional spread of the disease.
Results
The total number of scrub typhus cases reported from 2001 to 2011 was 51,136: male, 18,628 (36.4%); female, 32,508 (63.6%). The best-fit model selected was a combination of the spatial model (Markov random-field model) and temporal model (first order autoregressive model) of scrub typhus transmission. The peak incidence was 28.80 per 100,000 persons in early October and the peak incidence was 40.17 per 100,000 persons in those aged 63.3 years old by the best-fit HGLM. The disease map showed the spread of disease from the southern central area to a nationwide area, excepting Gangwon-do (province), Gyeongsangbuk-do (province), and Seoul.
Conclusion
In the transmission of scrub typhus in Korea, there was a correlation to the incidence of adjacent areas, as well as that of the previous year. According to the disease map, we are unlikely to see any decrease in the incidence in the near future, unless ongoing aggressive measures to prevent the exposure to the vector, chigger mites, in rural areas, are put into place.

Citations

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    Soojin Kim, In Yong Lee, Sezim Monoldorova, Jiro Kim, Jang Hoon Seo, Tai-Soon Yong, Bo Young Jeon
    Parasites, Hosts and Diseases.2023; 61(3): 263.     CrossRef
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    Sangho Choi, Do-Hwan Ahn, Min-Gyu Yoo, Hye-Ja Lee, Seong Beom Cho, Hee-Bin Park, Sung Soon Kim, Hyuk Chu
    The American Journal of Tropical Medicine and Hygi.2023; 108(2): 296.     CrossRef
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Article
Spatial and Temporal Distribution of Plasmodium vivax Malaria in Korea Estimated with a Hierarchical Generalized Linear Model
Maengseok Noh, Youngjo Lee, Seungyoung Oh, Chaeshin Chu, Jin Gwack, Seung-Ki Youn, Shin Hyeong Cho, Won Ja Lee, Sun Huh
Osong Public Health Res Perspect. 2012;3(4):192-198.   Published online December 31, 2012
DOI: https://doi.org/10.1016/j.phrp.2012.11.003
  • 3,265 View
  • 19 Download
  • 10 Crossref
AbstractAbstract PDF
Objectives
The spatial and temporal correlations were estimated to determine Plasmodium vivax malarial transmission pattern in Korea from 2001–2011 with the hierarchical generalized linear model.
Methods
Malaria cases reported to the Korea Centers for Disease Control and Prevention from 2001 to 2011 were analyzed with descriptive statistics and the incidence was estimated according to age, sex, and year by the hierarchical generalized linear model. Spatial and temporal correlation was estimated and the best model was selected from nine models. Results were presented as diseases map according to age and sex.
Results
The incidence according to age was highest in the 20–25-year-old group (244.52 infections/100,000). Mean ages of infected males and females were 31.0 years and 45.3 years with incidences 7.8 infections/100,000 and 7.1 infections/100,000 after estimation. The mean month for infection was mid-July with incidence 10.4 infections/100,000. The best-fit model showed that there was a spatial and temporal correlation in the malarial transmission. Incidence was very low or negligible in areas distant from the demilitarized zone between Republic of Korea and Democratic People’s Republic of Korea (North Korea) if the 20–29-year-old male group was omitted in the diseases map.
Conclusion
Malarial transmission in a region in Korea was influenced by the incidence in adjacent regions in recent years. Since malaria in Korea mainly originates from mosquitoes from North Korea, there will be continuous decrease if there is no further outbreak in North Korea.

Citations

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    Premakumara Jagath Dickella Gamaralalage, Sadhan Kumar Ghosh, Kazunobu Onogawa
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Invited Original Article
Incubation Period of Ebola Hemorrhagic Virus Subtype Zaire
Martin Eichner, Scott F. Dowell, Nina Firese
Osong Public Health Res Perspect. 2011;2(1):3-7.   Published online June 30, 2011
DOI: https://doi.org/10.1016/j.phrp.2011.04.001
  • 3,966 View
  • 15 Download
  • 45 Crossref
AbstractAbstract PDF
Objectives
Ebola hemorrhagic fever has killed over 1300 people, mostly in equatorial Africa. There is still uncertainty about the natural reservoir of the virus and about some of the factors involved in disease transmission. Until now, a maximum incubation period of 21 days has been assumed.
Methods
We analyzed data collected during the Ebola outbreak (subtype Zaire) in Kikwit, Democratic Republic of the Congo, in 1995 using maximum likelihood inference and assuming a log-normally distributed incubation period.
Results
The mean incubation period was estimated to be 12.7 days (standard deviation 4.31 days), indicating that about 4.1% of patients may have incubation periods longer than 21 days.
Conclusion
If the risk of new cases is to be reduced to 1% then 25 days should be used when investigating the source of an outbreak, when determining the duration of surveillance for contacts, and when declaring the end of an outbreak.

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PHRP : Osong Public Health and Research Perspectives