Journal of Preventive Medicine and Public Health

English Abstract

Statistical Issues in Genomic Cohort Studies.: Sohee Park; J Prev Med Public Health. 2007;40(2):108-113.; DOI: https://doi.org/10.3961/jpmph.2007.40.2.108

Abstract PDF: When conducting large-scale cohort studies, numerous statistical issues arise from the range of study design, data collection, data analysis and interpretation. In genomic cohort studies, these statistical problems become more complicated, which need to be carefully dealt with. Rapid technical advances in genomic studies produce enormous amount of data to be analyzed and traditional statistical methods are no longer sufficient to handle these data. In this paper, we reviewed several important statistical issues that occur frequently in large-scale genomic cohort studies, including measurement error and its relevant correction methods, cost-efficient design strategy for main cohort and validation studies, inflated Type I error, gene-gene and gene-environment interaction and time-varying hazard ratios. It is very important to employ appropriate statistical methods in order to make the best use of valuable cohort data and produce valid and reliable study results.; Summary

Original Articles

Associations between Air Pollution and Asthma-related Hospital Admissions in Children in Seoul, Korea: A Case-crossover Study.: Jong Tae Lee; Korean J Prev Med. 2003;36(1):47-53.

Abstract PDF: OBJECTIVES
I used a case-crossover design to investigate the association between air pollution, and hospital admissions for asthmatic children under the age of 15 years in Seoul, Korea METHODS: I estimated the changes in the levels of hospitalization risk from theinterquartile (IQR) increase in each pollutant concentrations, using conditional logistic regression analyses, with controls for weather information. RESULTS: Using bidirectional control sampling, the results from a conditional logistic regression model, with controls for weather conditions, showed the estimated relative risk of hospitalization for asthma among children to be 1.04 (95% confidence interval (CI), 1.01-1.08) for particulate matter with an aerodynamic diameter less than or equal to 10m (IQR=40.4ug/m3) ; 1.05 (95% CI, 1.00-1.09) for nitrogen dioxide (IQR=14.6ppb) ; 1.02 (95% CI, 0.97-1.06) for sulfur dioxide (IQR=4.4ppb) ; 1.03 (95% CI, 0.99-1.08) for ozone (IQR=21.7ppb) ; and 1.03 (95% CI, 0.99-1.08) for carbon monoxide (IQR=1.0ppm). CONCLUSIONS: This empirical analysis indicates the bidirectional control sampling methods, by design, would successfully control the confounding factors due to the long-term time trends of air pollution. These findings also support the hypothesis that air pollution, at levels below the current ambient air quality standards of Korea, is harmful to sensitive subjects, such as asthmatic children.; Summary

Epidemiologic Methods and Study Designs for Investigating Adverse Health Effects of Ambient Air Pollution.: Jong Tae Lee, Ho Kim; Korean J Prev Med. 2001;34(2):119-126.

Abstract PDF: Air pollution epidemiologic studies are intrinsically difficult because the expected effect size at general environmental levels is small, exposure and misclassification of exposure are common, and exposure is not selective to a specific pollutant. In this review paper, epidemiologic study designs and analytic methods are described, and two nationwide projects on air pollution epidemiology are introduced. This paper also demonstrates that possible confounding issues in time-series analysis can be resolved and the impact on the use of data from ambient monitoring stations may not be critical. In this paper we provide a basic understanding of the types of air pollution epidemiologic study designs that be subdivided by the mode of air pollution effects on human health (acute or chronic). With the improvements in the area of air pollution epidemiologic studies, we should emphasize that elaborate models and statistical techniques cannot compensate for inadequate study design or poor data collection.; Summary

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