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Original Articles
National trends and projection of chronic kidney disease incidence according to etiology from 1990 to 2030 in Iran: a Bayesian age-period-cohort modeling study
Fatemeh Shahbazi, Amin Doosti-Irani, Alireza Soltanian, Jalal Poorolajal
Epidemiol Health. 2023;45:e2023027.   Published online February 17, 2023
DOI: https://doi.org/10.4178/epih.e2023027
  • 3,225 View
  • 153 Download
  • 1 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
Chronic kidney disease (CKD) is a major public health problem worldwide. Predicting CKD incidence rates and case numbers at the national and global levels is vital for planning CKD prevention programs.
METHODS
Data on CKD incidence rates and case numbers in Iran from 1990 to 2019 were extracted from the Global Burden of Disease online database. The average annual percentage change was computed to determine the temporal trends in CKD age-standardized incidence rates from 1990 to 2019. A Bayesian age-period-cohort model was used to predict the CKD incidence rate and case numbers through 2030.
RESULTS
Nationally, CKD cases increased from 97,300 in 1990 to 315,500 in 2019. The age-specific CKD incidence rate increased from 168.52 per 100,000 to 382.98 per 100,000 during the same period. Between 2020 and 2030, the number of CKD cases is projected to rise to 423,300. The age-specific CKD incidence rate is projected to increase to 469.04 in 2030 (95% credible interval, 399.20 to 538.87). In all age groups and etiological categories, the CKD incidence rate is forecasted to increase by 2030.
CONCLUSIONS
CKD case numbers and incidence rates are anticipated to increase in Iran through 2030. The high level of CKD incidence in people with diabetes mellitus, hypertension, and glomerulonephritis, as well as in older people, suggests a deficiency of attention to these populations in current prevention plans and highlights their importance in future programs for the national control of CKD.
Summary
Key Message
Based on our findings, it is predicted that the number of chronic kidney patients in Iran will reach 423,300 people by 2030. Additionally, the age-specific incidence rate of chronic kidney disease (CKD) is projected to increase to 469.04 in the same year. The CKD incidence rate is forecasted to increase by 2030 in all age groups and etiological categories, including type 1 diabetes mellitus, type 2 diabetes mellitus, hypertension, glomerulonephritis, and other causes.

Citations

Citations to this article as recorded by  
  • Supporting Caregivers of Hemodialysis Patients: Applying the 5-A Self-management Model to Alleviate Caregiver Burden
    Zahra Zarmohammadi, Marzieh Khatooni, Mehdi Ranjbaran, Seyedeh Zahra Hosseinigolafshani
    Journal of Nursing and Midwifery Sciences.2024;[Epub]     CrossRef
Prediction of cancer survivors’ mortality risk in Korea: a 25-year nationwide prospective cohort study
Yeun Soo Yang, Heejin Kimm, Keum Ji Jung, Seulji Moon, Sunmi Lee, Sun Ha Jee
Epidemiol Health. 2022;44:e2022075.   Published online September 13, 2022
DOI: https://doi.org/10.4178/epih.e2022075
  • 4,610 View
  • 142 Download
  • 1 Web of Science
  • 2 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
This study aimed to investigate the factors affecting cancer survival and develop a mortality prediction model for Korean cancer survivors. Our study identified lifestyle and mortality risk factors and attempted to determine whether health-promoting lifestyles affect mortality.
METHODS
Among the 1,637,287 participants in the Korean Cancer Prevention Study (KCPS) cohort, 200,834 cancer survivors who were alive after cancer diagnosis were analyzed. Discrimination and calibration for predicting the 10-year mortality risk were evaluated. A prediction model was derived using the Cox model coefficients, mean risk factor values, and mean mortality from the cancer survivors in the KCPS cohort.
RESULTS
During the 21.6-year follow-up, the all-cause mortality rates of cancer survivors were 57.2% and 39.4% in men and women, respectively. Men, older age, current smoking, and a history of diabetes were high-risk factors for mortality, while exercise habits and a family history of cancer were associated with reduced risk. The prediction model discrimination in the validation dataset for both KCPS all-cause mortality and KCPS cancer mortality was shown by C-statistics of 0.69 and 0.68, respectively. Based on the constructed prediction models, when we modified exercise status and smoking status, as modifiable factors, the cancer survivors’ risk of mortality decreased linearly.
CONCLUSIONS
A mortality prediction model for cancer survivors was developed that may be helpful in supporting a healthy life. Lifestyle modifications in cancer survivors may affect their risk of mortality in the future.
Summary
Korean summary
현재 암 생존자의 사망 위험을 평가하는 데 유용한 한국형 암 생존자 사망률 예측 모델은 없습니다. 본 연구에서는 고령, 남성, 현재 흡연, 당뇨병 병력을 포함한 생활양식 요인이 사망의 고위험 요인인 반면, 운동 습관 및 암의 가족력은 사망 위험을 감소시키는 것으로 나타났습니다. 현재 흡연과 운동 습관은 사망 위험에 영향을 미치는 수정 가능한 두 가지 요소로써, 이러한 생활습관 요인으로 구성된 예측모형은 생활습관 교정을 통해 우리나라 암 생존자의 사망률을 낮출 수 있음을 시사합니다.
Key Message
Currently, there is no Korean mortality prediction model for cancer survivors that would be useful in evaluating their risk of mortality. The present study showed that lifestyle factors, including older age, male sex, current smoking, and history of diabetes were high-risk factors for mortality, while exercise habits and a family history of cancer reduced the risk of mortality. Current smoking and exercise habits are the two modifiable factors that affected the risk of mortality. The prediction model comprising these lifestyle factors implies that the risk of mortality of cancer survivors in Korea can be reduced through lifestyle modification.

Citations

Citations to this article as recorded by  
  • COVID-19 Mortality and Severity in Cancer Patients and Cancer Survivors
    Jae-Min Park, Hye Yeon Koo, Jae-ryun Lee, Hyejin Lee, Jin Yong Lee
    Journal of Korean Medical Science.2024;[Epub]     CrossRef
  • Weight maintenance and gain were significantly associated with lower risk of all-cause and cancer-related mortality in Korean adults who were newly diagnosed with cancer based on the Korean NHIS-HEALS cohort
    Yong-June Kim, Seung Park, Won Tae Kim, Yoon-Jong Bae, Yonghwan Kim, Hee-Taik Kang
    Medicine.2023; 102(47): e36184.     CrossRef
COVID-19: Methods
Individual-based simulation model for COVID-19 transmission in Daegu, Korea
Woo-Sik Son, RISEWIDs Team
Epidemiol Health. 2020;42:e2020042.   Published online June 15, 2020
DOI: https://doi.org/10.4178/epih.e2020042
  • 11,978 View
  • 287 Download
  • 7 Web of Science
  • 5 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
The aims of this study were to obtain insights into the current coronavirus disease 2019 (COVID-19) epidemic in the city of Daegu, which accounted for 6,482 of the 9,241 confirmed cases in Korea as of March 26, 2020, to predict the future spread, and to analyze the impact of school opening.
METHODS
Using an individual-based model, we simulated the spread of COVID-19 in Daegu. An individual can be infected through close contact with infected people in a household, at work/school, and at religious and social gatherings. We created a synthetic population from census sample data. Then, 9,000 people were randomly selected from the entire population of Daegu and set as members of the Shincheonji Church. We did not take into account population movements to and from other regions in Korea.
RESULTS
Using the individual-based model, the cumulative confirmed cases in Daegu through March 26, 2020, were reproduced, and it was confirmed that the hotspot, i.e., the Shincheonji Church had a different probability of infection than non-hotspot, i.e., the Daegu community. For 3 scenarios (I: school closing, II: school opening after April 6, III: school opening after April 6 and the mean period from symptom onset to hospitalization increasing to 4.3 days), we predicted future changes in the pattern of COVID-19 spread in Daegu.
CONCLUSIONS
Compared to scenario I, it was found that in scenario III, the cumulative number of patients would increase by 107 and the date of occurrence of the last patient would be delayed by 92 days.
Summary
Korean summary
신천지 교인 집단이 hotspot이 되어 지역사회로 전파된 대구의 COVID-19 확산을 시뮬레이션하였다. Individual based model을 이용하여 신천지 교인 집단, 즉 hotspot과 non-hotspot이 서로 다른 감염 확률을 갖고 있음을 확인하였으며, 4월 6일로 예정된 개학이 대구 지역 COVID-19 확산에 어떤 영향을 미칠지 분석하였다.

Citations

Citations to this article as recorded by  
  • Using simulation modelling and systems science to help contain COVID‐19: A systematic review
    Weiwei Zhang, Shiyong Liu, Nathaniel Osgood, Hongli Zhu, Ying Qian, Peng Jia
    Systems Research and Behavioral Science.2023; 40(1): 207.     CrossRef
  • Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature
    Hyojung Lee, Sol Kim, Minyoung Jeong, Eunseo Choi, Hyeonjeong Ahn, Jeehyun Lee
    Yonsei Medical Journal.2023; 64(1): 1.     CrossRef
  • Transition from growth to decay of an epidemic due to lockdown
    Hamid Khataee, Jack Kibble, Istvan Scheuring, Andras Czirok, Zoltan Neufeld
    Biophysical Journal.2021; 120(14): 2872.     CrossRef
  • A Full-Scale Agent-Based Model to Hypothetically Explore the Impact of Lockdown, Social Distancing, and Vaccination During the COVID-19 Pandemic in Lombardy, Italy: Model Development
    Giuseppe Giacopelli
    JMIRx Med.2021; 2(3): e24630.     CrossRef
  • Comparison of Psychosocial Distress in Areas With Different COVID-19 Prevalence in Korea
    Mina Kim, In-Hoo Park, Young-Shin Kang, Honey Kim, Min Jhon, Ju-Wan Kim, Seunghyong Ryu, Ju-Yeon Lee, Jae-Min Kim, Jonghun Lee, Sung-Wan Kim
    Frontiers in Psychiatry.2020;[Epub]     CrossRef
COVID-19: Original Article
Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes in Korea
Soyoung Kim, Yu Bin Seo, Eunok Jung
Epidemiol Health. 2020;42:e2020026.   Published online April 13, 2020
DOI: https://doi.org/10.4178/epih.e2020026
  • 20,588 View
  • 1,294 Download
  • 46 Web of Science
  • 46 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
Since the report of the first confirmed case in Daegu on February 18, 2020, local transmission of coronavirus disease 2019 (COVID-19) in Korea has continued. In this study, we aimed to identify the pattern of local transmission of COVID-19 using mathematical modeling and predict the epidemic size and the timing of the end of the spread.
METHODS
We modeled the COVID-19 outbreak in Korea by applying a mathematical model of transmission that factors in behavioral changes. We used the Korea Centers for Disease Control and Prevention data of daily confirmed cases in the country to estimate the nationwide and Daegu/Gyeongbuk area-specific transmission rates as well as behavioral change parameters using a least-squares method.
RESULTS
The number of transmissions per infected patient was estimated to be about 10 times higher in the Daegu/Gyeongbuk area than the average of nationwide. Using these estimated parameters, our models predicts that about 13,800 cases will occur nationwide and 11,400 cases in the Daegu/Gyeongbuk area until mid-June.
CONCLUSIONS
We mathematically demonstrate that the relatively high per-capita rate of transmission and the low rate of changes in behavior have caused a large-scale transmission of COVID-19 in the Daegu/Gyeongbuk area in Korea. Since the outbreak is expected to continue until May, non-pharmaceutical interventions that can be sustained over the long term are required.
Summary
Korean summary
본 논문은 행동변화를 고려한 수학적 모델을 이용하여 코로나바이러스병-19의 유행 양상을 분석하고 총 환자수와 유행기간을 예측하고자 한다. 질병관리본부 확진자 데이터를 이용하여 전국과 대구·경북 지역의 감염전파율과 행동변화율을 추정하였다. 3월 10일까지의 데이터를 기준으로 전국적으로 6월 중순까지 약 13,000명, 대구·경북지역의 경우 5월 말까지 약 11,000명의 환자가 발생할 것으로 예측된다.

Citations

Citations to this article as recorded by  
  • Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature
    Hyojung Lee, Sol Kim, Minyoung Jeong, Eunseo Choi, Hyeonjeong Ahn, Jeehyun Lee
    Yonsei Medical Journal.2023; 64(1): 1.     CrossRef
  • Numerical Scheme for Compartmental Models: New Matlab Software Codes for Numerical Simulation
    Samuel Okyere, Joseph Ackora-Prah, Ebenezer Bonyah, Samuel Akwasi Adarkwa
    F1000Research.2023; 12: 445.     CrossRef
  • Factors Influencing Infection Anxiety in Korean Male Firefighters Due to COVID-19 Infection Status
    Seung-Woo Han, Hyun-Ok Jung
    Healthcare.2023; 11(11): 1623.     CrossRef
  • Neural network method and multiscale modeling of the COVID-19 epidemic in Korea
    Ziqian Li, Jiwei Jia, Guidong Liao, Young Ju Lee, Siyu Liu
    The European Physical Journal Plus.2023;[Epub]     CrossRef
  • Numerical Scheme for Compartmental Models: New Matlab Software Codes for Numerical Simulation
    Samuel Okyere, Joseph Ackora-Prah, Ebenezer Bonyah, Samuel Akwasi Adarkwa
    F1000Research.2023; 12: 445.     CrossRef
  • Studying the Effect of Particulate Matter as SARS-CoV-2 Transmitters
    Abdulrahim R. Hakami, Gasim Dobie
    Journal of Public Health Research.2022; 11(1): jphr.2021.2521.     CrossRef
  • Managing the resource allocation for the COVID-19 pandemic in healthcare institutions: a pluralistic perspective
    Manimuthu Arunmozhi, Jinil Persis, V. Raja Sreedharan, Ayon Chakraborty, Tarik Zouadi, Hanane Khamlichi
    International Journal of Quality & Reliability Management.2022; 39(9): 2184.     CrossRef
  • Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic
    Ming Guan
    Journal of Epidemiology and Global Health.2022; 12(1): 40.     CrossRef
  • Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics
    Ali AlArjani, Md Taufiq Nasseef, Sanaa M. Kamal, B. V. Subba Rao, Mufti Mahmud, Md Sharif Uddin
    Arabian Journal for Science and Engineering.2022; 47(8): 10163.     CrossRef
  • Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges
    Jinxing Guan, Yang Zhao, Yongyue Wei, Sipeng Shen, Dongfang You, Ruyang Zhang, Theis Lange, Feng Chen
    Medical Review.2022; 2(1): 89.     CrossRef
  • Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019
    Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra
    ISA Transactions.2022; 124: 57.     CrossRef
  • Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model
    Hadeel AlQadi, Majid Bani-Yaghoub, Yury E. Khudyakov
    PLOS ONE.2022; 17(4): e0265815.     CrossRef
  • A Mathematical Model of Transmission Dynamics of SARS-CoV-2 (COVID-19) with an Underlying Condition of Diabetes
    Samuel Okyere, Joseph Ackora-Prah, Shih Pin Chen
    International Journal of Mathematics and Mathematical Sciences.2022; 2022: 1.     CrossRef
  • Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore
    Murali Krishna Gurram, Min Xian Wang, Yi-Chen Wang, Junxiong Pang
    Scientific Reports.2022;[Epub]     CrossRef
  • Compartmental structures used in modeling COVID-19: a scoping review
    Lingcai Kong, Mengwei Duan, Jin Shi, Jie Hong, Zhaorui Chang, Zhijie Zhang
    Infectious Diseases of Poverty.2022;[Epub]     CrossRef
  • Multi-Faceted Analysis of COVID-19 Epidemic in Korea Considering Omicron Variant: Mathematical Modeling-Based Study
    Youngsuk Ko, Victoria May Mendoza, Renier Mendoza, Yubin Seo, Jacob Lee, Jonggul Lee, Donghyok Kwon, Eunok Jung
    Journal of Korean Medical Science.2022;[Epub]     CrossRef
  • Investigating Online Learning Process in Business School: Case Study from Business School in Jakarta, Indonesia
    Sekar W. Prasetyaningtyas, Agustian B. Prasetya
    International Journal of Information and Education Technology.2022; 12(9): 964.     CrossRef
  • Evolution and consequences of individual responses during the COVID-19 outbreak
    Wasim Abbas, Masud M. A., Anna Park, Sajida Parveen, Sangil Kim, Siew Ann Cheong
    PLOS ONE.2022; 17(9): e0273964.     CrossRef
  • Managing bed capacity and timing of interventions: a COVID-19 model considering behavior and underreporting
    Victoria May P. Mendoza, Renier Mendoza, Youngsuk Ko, Jongmin Lee, Eunok Jung
    AIMS Mathematics.2022; 8(1): 2201.     CrossRef
  • Model-informed COVID-19 exit strategy with projections of SARS-CoV-2 infections generated by variants in the Republic of Korea
    Sung-mok Jung, Kyungmin Huh, Munkhzul Radnaabaatar, Jaehun Jung
    BMC Public Health.2022;[Epub]     CrossRef
  • Contribution to COVID-19 spread modelling: a physical phenomenological dissipative formalism
    Oualid Limam, Mohamed Limam
    Biomechanics and Modeling in Mechanobiology.2021; 20(1): 379.     CrossRef
  • Computational Approach For Real-Time Interval Type-2 Fuzzy Kalman Filtering and Forecasting via Unobservable Spectral Components of Experimental Data
    Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra
    Journal of Control, Automation and Electrical Systems.2021; 32(2): 326.     CrossRef
  • Non-pharmaceutical interventions during the COVID-19 pandemic: A review
    Nicola Perra
    Physics Reports.2021; 913: 1.     CrossRef
  • Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation
    Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra
    IEEE Journal of Biomedical and Health Informatics.2021; 25(3): 615.     CrossRef
  • Graphs in the COVID-19 news: a mathematics audit of newspapers in Korea
    Oh Nam Kwon, Chaereen Han, Changsuk Lee, Kyungwon Lee, Kyeongjun Kim, Gyeongha Jo, Gangwon Yoon
    Educational Studies in Mathematics.2021; 108(1-2): 183.     CrossRef
  • Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model
    Gregory L. Watson, Di Xiong, Lu Zhang, Joseph A. Zoller, John Shamshoian, Phillip Sundin, Teresa Bufford, Anne W. Rimoin, Marc A. Suchard, Christina M. Ramirez, Virginia E. Pitzer
    PLOS Computational Biology.2021; 17(3): e1008837.     CrossRef
  • Using the Weibull distribution to model COVID-19 epidemic data
    Vitor Hugo Moreau
    Model Assisted Statistics and Applications.2021; 16(1): 5.     CrossRef
  • Segmentation and shielding of the most vulnerable members of the population as elements of an exit strategy from COVID-19 lockdown
    Bram A. D. van Bunnik, Alex L. K. Morgan, Paul R. Bessell, Giles Calder-Gerver, Feifei Zhang, Samuel Haynes, Jordan Ashworth, Shengyuan Zhao, Roo Nicola Rose Cave, Meghan R. Perry, Hannah C. Lepper, Lu Lu, Paul Kellam, Aziz Sheikh, Graham F. Medley, Mark
    Philosophical Transactions of the Royal Society B: Biological Sciences.2021;[Epub]     CrossRef
  • A model and predictions for COVID-19 considering population behavior and vaccination
    Thomas Usherwood, Zachary LaJoie, Vikas Srivastava
    Scientific Reports.2021;[Epub]     CrossRef
  • Behavioral Risk Modeling for Pandemics: Overcoming Challenges and Advancing the Science
    Ellen P. Carlin, Koya C. Allen, Jeffrey J. Morgan, Jean-Paul Chretien, Suzan Murray, Deborah Winslow, Dawn Zimmerman
    Health Security.2021; 19(4): 447.     CrossRef
  • Structure of epidemic models: toward further applications in economics
    Toshikazu Kuniya
    The Japanese Economic Review.2021; 72(4): 581.     CrossRef
  • How Important Is Behavioral Change during the Early Stages of the COVID-19 Pandemic? A Mathematical Modeling Study
    Jongmin Lee, Seok-Min Lee, Eunok Jung
    International Journal of Environmental Research and Public Health.2021; 18(18): 9855.     CrossRef
  • Factors shaping the COVID-19 epidemic curve: a multi-country analysis
    Su Yeon Jang, Laith Hussain-Alkhateeb, Tatiana Rivera Ramirez, Ahmed Asa’ad Al-Aghbari, Dhia Joseph Chackalackal, Rocio Cardenas-Sanchez, Maria Angelica Carrillo, In-Hwan Oh, Eduardo Andrés Alfonso-Sierra, Pia Oechsner, Brian Kibiwott Kirui, Martin Anto,
    BMC Infectious Diseases.2021;[Epub]     CrossRef
  • A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries
    Theyazn H. H. Aldhyani, Hasan Alkahtani
    Life.2021; 11(11): 1118.     CrossRef
  • Health and pathology: a brief history of the biopolitics of US mathematics education
    Ryan Ziols, Kathryn L. Kirchgasler
    Educational Studies in Mathematics.2021; 108(1-2): 123.     CrossRef
  • Robust optimal parameter estimation for the susceptible-unidentified infected-confirmed model
    Chaeyoung Lee, Soobin Kwak, Sangkwon Kim, Youngjin Hwang, Yongho Choi, Junseok Kim
    Chaos, Solitons & Fractals.2021; 153: 111556.     CrossRef
  • Risk Perceptions, Knowledge and Behaviors of General and High-Risk Adult Populations Towards COVID-19: A Systematic Scoping Review
    Nathalie Clavel, Janine Badr, Lara Gautier, Mélanie Lavoie-Tremblay, Jesseca Paquette
    Public Health Reviews.2021;[Epub]     CrossRef
  • Modeling COVID-19 Transmission Dynamics With Self-Learning Population Behavioral Change
    Tsz-Lik Chan, Hsiang-Yu Yuan, Wing-Cheong Lo
    Frontiers in Public Health.2021;[Epub]     CrossRef
  • The first year of covid-19 in croatia - a mathematical model
    Tibor Rodiger, Edgar Glavaš, Ivan Kovač
    Croatian Regional Development Journal.2021; 2(2): 32.     CrossRef
  • Covid-19 Predictions Using a Gauss Model, Based on Data from April 2
    Janik Schüttler, Reinhard Schlickeiser, Frank Schlickeiser, Martin Kröger
    Physics.2020; 2(2): 197.     CrossRef
  • Preliminary Clinical and Epidemiological Analysis of the First 1,000 Pediatric COVID-19 Cases in Moscow Region
    Elena R. Meskina
    Journal of microbiology, epidemiology and immunobiology.2020; 97(3): 202.     CrossRef
  • Forecast predictions for the COVID-19 pandemic in Brazil by statistical modeling using the Weibull distribution for daily new cases and deaths
    Vitor Hugo Moreau
    Brazilian Journal of Microbiology.2020; 51(3): 1109.     CrossRef
  • Mathematical modeling for infectious viral disease: The COVID‐19 perspective
    Hafeez Aderinsayo Adekola, Ibrahim Ayoade Adekunle, Haneefat Olabimpe Egberongbe, Sefiu Adekunle Onitilo, Idris Nasir Abdullahi
    Journal of Public Affairs.2020;[Epub]     CrossRef
  • Estimating the Transmission Risk of COVID-19 in Nigeria: A Mathematical Modelling Approach
    Irany FA, Akwafuo SE, Abah T, Mikler AR
    Journal of Health Care and Research.2020; 1(3): 135.     CrossRef
  • Underlying Principles of a Covid-19 Behavioral Vaccine for a Sustainable Cultural Change
    Kalliu Carvalho Couto, Flora Moura Lorenzo, Marco Tagliabue, Marcelo Borges Henriques, Roberta Freitas Lemos
    International Journal of Environmental Research and Public Health.2020; 17(23): 9066.     CrossRef
  • Understanding South Korea’s Response to the COVID-19 Outbreak: A Real-Time Analysis
    Eunsun Jeong, Munire Hagose, Hyungul Jung, Moran Ki, Antoine Flahault
    International Journal of Environmental Research and Public Health.2020; 17(24): 9571.     CrossRef
Original Articles
Estimation of the size of the iatrogenic Creutzfeldt-Jakob disease outbreak associated with cadaveric dura mater grafts in Korea
Byoung-Hak Jeon, Jinseob Kim, Ganghyun Kim, Soochul Park, SangYun Kim, Hae-Kwan Cheong
Epidemiol Health. 2016;38:e2016059.   Published online December 19, 2016
DOI: https://doi.org/10.4178/epih.e2016059
  • 12,626 View
  • 210 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract AbstractSummary PDF
Abstract
OBJECTIVES
This study estimated the overall incidence of iatrogenic Creutzfeldt-Jakob disease (iCJD) based on dura graft cases in Korea using a mathematical model.
METHODS
We estimated the number of annual dura grafts performed between 1980 and 1995 by applying the proportion of dura grafts recorded by the Health Insurance Review Agency claim dataset in Korea to the number of nationwide neurosurgery cases. The distribution of the incubation period was assumed to fall under a Weibull distribution with density function or a log-logistic distribution with density function.
RESULTS
The total number of neurosurgery procedures performed from 1980 to 1995 was estimated to be 263,945, and among those operations, 37% used dura graft products. Between the years of 1980 and 2020, our model predicted that the total number of iCJD cases would be between 14.9 and 33.2 (95% confidence interval [CI], 13.4 to 50.9). Notably, we estimated that the cumulative number of iCJD cases caused by dura grafts between 1980 and 2011 was approximately 13.3 to 27.3 (95% CI, 12.2 to 40.6).
CONCLUSIONS
Based on our model, we postulate that the incidence of iCJD will sharply decline from 2012 to 2020. However, additional new cases are still expected, which necessitates a strong national surveillance system.
Summary
Korean summary
국내 경막유래 의인성 크로이츠펠트-증후군 환자의 발생규모 추정 본 연구는 국내 가용한 모든 자료원을 활용하여 국내 의인성 CJD 위험인구를 추산하고 이를기반으로 발생 가능한 의인성 CJD 환자의 규모를 수학적 모형을 통해 추산하는 것을 목적으로 시행하였으며, 예측모형 결과 2020년까지 가파르게 감소할것으로 보이나, 추가 발생에 대비한 국가감시체계의 강화가 필요할것으로 사료된다.

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    Matt Stevenson, Lesley Uttley, Jeremy E Oakley, Christopher Carroll, Stephen E Chick, Ruth Wong
    Health Technology Assessment.2020; 24(11): 1.     CrossRef
Evaluation of risk prediction model for stroke risk based on Cox's and Weibull model in Korea.
Youn Nam Kim, Ur Rin Cho, Byung Ho Nam, Il Soo Park, Sun Ha Jee
Korean J Epidemiol. 2008;30(1):41-48.   Published online June 30, 2008
DOI: https://doi.org/10.4178/kje.2008.30.1.41
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  • 45 Download
AbstractAbstract PDF
Abstract
OBJECTIVE: The objective was to compare Cox proportional hazards model and Weibull model for predicting long-term probabilities for stroke risk in the Korean Cancer Prevention Study(KCPS).
METHODS
The subjects comprised of 385,279 Korean aged 55 to 64 years who received health insurance from the National Health Insurance Corporation and who had medical examinations in 1992 and 1995. 70% of the subjects were used for model building and the rest for model evaluation. The final prediction model for stroke includes age, systolic blood pressure, diabetes, total cholesterol and smoking. Subjects were follow-up for identification of incident stroke cases between 1993 and 2005. Comparisons included predicted coefficients of stroke risk factors, incidence probabilities over 10 years, and the area under a receiver operating characteristics (ROC) curve for both Cox's proportional hazard model and Weibull model.
RESULTS
The average age of study population was 55.5 years in men and 56.3 years in women, respectively. Percentage of men and women in study population were 58.0% and 42.0%, respectively. The study findings satisfied proportionality according to the two models. There was no significant difference in coefficients between the two models of prediction models in men and in women. Moreover, there was no difference in incidence probabilities of stroke and c-statistics. C-statistics were 0.68 for men as same as for women.
CONCLUSION
There was no difference for the prediction of the stroke risk in the Korean population using Cox's proportional hazard model and Weibull model, thus the two models were found to be efficient for this purpose.
Summary
Epidemiol Health : Epidemiology and Health
© Korean Society of Epidemiology.