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Original Articles
- Correlations Between the Incidence of National Notifiable Infectious Diseases and Public Open Data, Including Meteorological Factors and Medical Facility Resources
- Jin-Hwa Jang, Ji-Hae Lee, Mi-Kyung Je, Myeong-Ji Cho, Young Mee Bae, Hyeon Seok Son, Insung Ahn
- J Prev Med Public Health. 2015;48(4):203-215. Published online July 27, 2015
- DOI: https://doi.org/10.3961/jpmph.14.057
- 11,349 View
- 133 Download
- 7 Crossref
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Abstract
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Supplementary Material - Objectives
This study was performed to investigate the relationship between the incidence of national notifiable infectious diseases (NNIDs) and meteorological factors, air pollution levels, and hospital resources in Korea.
Methods
We collected and stored 660 000 pieces of publicly available data associated with infectious diseases from public data portals and the Diseases Web Statistics System of Korea. We analyzed correlations between the monthly incidence of these diseases and monthly average temperatures and monthly average relative humidity, as well as vaccination rates, number of hospitals, and number of hospital beds by district in Seoul.
Results
Of the 34 NNIDs, malaria showed the most significant correlation with temperature (r=0.949, p<0.01) and concentration of nitrogen dioxide (r=-0.884, p<0.01). We also found a strong correlation between the incidence of NNIDs and the number of hospital beds in 25 districts in Seoul (r=0.606, p<0.01). In particular, Geumcheon-gu was found to have the lowest incidence rate of NNIDs and the highest number of hospital beds per patient.
Conclusions
In this study, we conducted a correlational analysis of public data from Korean government portals that can be used as parameters to forecast the spread of outbreaks. -
Summary
Citations
Citations to this article as recorded by
- Association between short-term exposure to PM2.5 and its components and mumps incidence in Lanzhou, China
Zixuan Zou, Zhenjuan Li, Donghua Li, Tingrong Wang, Rui Li, Tianshan Shi, Xiaowei Ren
Environmental Pollution.2025; 372: 126041. CrossRef - Whether Urbanization Has Intensified the Spread of Infectious Diseases—Renewed Question by the COVID-19 Pandemic
Dongsheng Yu, Xiaoping Li, Juanjuan Yu, Xunpeng Shi, Pei Liu, Pu Tian
Frontiers in Public Health.2021;[Epub] CrossRef - Research Trends in Agenda-setting for Climate Change Adaptation Policy in the Public Health Sector in Korea
Su-Mi Chae, Daeeun Kim
Journal of Preventive Medicine and Public Health.2020; 53(1): 3. CrossRef - Current State of Research on the Risk of Morbidity and Mortality Associated with Air Pollution in Korea
Sanghyuk Bae, Ho-jang Kwon
Yonsei Medical Journal.2019; 60(3): 243. CrossRef - Climate and air pollution alter incidence of tuberculosis in Beijing, China
Chun Yan Zhang, Ang Zhang
Annals of Epidemiology.2019; 37: 71. CrossRef - Mathematical Modeling for Scrub Typhus and Its Implications for Disease Control
Kyung-Duk Min, Sung-il Cho
Journal of Korean Medical Science.2018;[Epub] CrossRef - Is short-term exposure to ambient fine particles associated with measles incidence in China? A multi-city study
Gongbo Chen, Wenyi Zhang, Shanshan Li, Gail Williams, Chao Liu, Geoffrey G. Morgan, Jouni J.K. Jaakkola, Yuming Guo
Environmental Research.2017; 156: 306. CrossRef
- Characteristics of health lifestyle patterns by the quantification method.
- Soon Young Lee, Seon Woo Kim
- Korean J Prev Med. 1998;31(1):72-81.
- 2,444 View
- 28 Download
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Abstract
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- The purpose of this study was to investigate the relation between health behavior patterns and demographic, socio-economic characteristics, health status, health information in Korea. The quantification method through canonical correlation analysis was conducted to the data from Korea National Health Survey in 1995, which consisted of 5,805 persons. The health lifestyle patterns were quantified as good diet lifestyle, passive lifestyle to the negative direction and drinker lifestyle, smoker lifestyle, hedonic lifestyle and fitness lifestyle to the positive direction. The covariate were related to health lifestyle patterns in the order of sex, age, marital status, occupation, health information, economic status, level of physical labour, health status. Characteristics of male, age below 50, married, blue colored worker, no health information, low in economic status, heavy level of physical labour, and poor in health status were positively related to drinker lifestyle, smoker lifestyle, hedonic lifestyle, fitness lifestyle sequentially.
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Summary
- Lead Level in Blood, Scalp Hair and Toenail of Elementary Schoolchildren.
- Jae Uk Kim, Jung Jeung Lee, Chang Yoon Kim, Jong Hak Chung
- Korean J Prev Med. 1995;28(1):73-84.
- 2,251 View
- 20 Download
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Abstract
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- This study was conducted to measure the lead level in the blood, scalp hair and toenail of the elementary schoolchildren and assess the relationship among those samples. Lead concentration of the blood, scalp hair and toenail was measured for l00(male 50, female 50) fourth grade elementary schoolchildren in Taegu city. The mean lead level in the blood, scalp hair and toenail was 6.00+/-2.44 microgram/dl, 6.28+/-3.54 microgram/dl 6. 68 and 7.33+/-3.18 microgram/g. The mean lead level in the blood of schoolboys was 6.43+/-2.77 microgram/dl and that of schoolgirls was 5.59+/-2.01 microgram/dl. The mean lead level in the scalp hair of schoolboys was 7.66+/-2.97 microgram/dl and that of schoolgirls was 6.88+/-3.54 microgram/g. The mean lead level in the toenail of schoolboys was 8.19+/-3.5 microgram/g and that of schoolgirls was 6.47+/-2.52 microgram/g and their difference was statistically significant. In schoolboys, the correlation coefficient between the lead level in the blood and scalp hair was 0.4909, and the data were fitted best by the regression equation Y=0.5255X+4.2810, where Y and X are scalp hair and blood concentration. In schoolgirls the correlation coefficient between the lead level in the blood and scalp hair was 0.3778, and the data were fitted best by the regression equation Y=0.6655X+2.9632, where Y and X are scalp hair and blood concentration. In schoolboys. the correlation coefficient between the lead level in the blood and in the toenail was 0.5533, and the data were fitted best by the regression equation Y=0.7076X+3.6472, where Y and X are toenail and blood concentration. In schoolgirls the correlation coefficient between the lead level in the blood and in the toenail was 0.2738, and the data were fitted best by the regression equation Y=0.3431X+4.5570 where Y and X are toenail and blood concentration. In schoolboys, the correlation coefficient between the lead level in the scalp hair and in the toenail, in the schoolboys was 0.4148, and the data were fitted best by the regression equation Y=0.4956X+4.3986, where Y and X are toenail and scalp hair concentration. In schoolgirls the correlation coefficient between the lead level in the scalp hair and in the toenail 0.1159, and the data were fitted best by the regression equation Y=0.0825X+5.9214 here Y and X are toenail and scalp hair concentration. Correlation among lead concentration in the blood, scalp hair and toenail of schoolchildren were statistically significant except between scalp hair and toenail in schoolgirls. These finding suggest that blood, scalp hair and toenail can be used substitutive samples between each others.
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