• Journal of Preventive Medicine and Public Health
• /
• 제57권2호
• /
• pp.108-119
• /
• 2024

Objectives: Abrupt changes in air pollution levels associated with the coronavirus disease 2019 (COVID-19) outbreak present a unique opportunity to evaluate the effects of air pollution on influenza risk, at a time when emission sources were less active and personal hygiene practices were more rigorous. Methods: This time-series study examined the relationship between influenza cases (n=22 874) and air pollutant concentrations from 2018 to 2021, comparing the timeframes before and during the COVID-19 pandemic in and around Thailand's Khon Kaen province. Poisson generalized additive modeling was employed to estimate the relative risk of hospitalization for influenza associated with air pollutant levels. Results: Before the COVID-19 outbreak, both the average daily number of influenza hospitalizations and particulate matter with an aerodynamic diameter of 2.5 ㎛ or less (PM_2.5) concentration exceeded those later observed during the pandemic (p<0.001). In single-pollutant models, a 10 ㎍/m³ increase in PM_2.5 before COVID-19 was significantly associated with increased influenza risk upon exposure to cumulative-day lags, specifically lags 0-5 and 0-6 (p<0.01). After adjustment for co-pollutants, PM_2.5 demonstrated the strongest effects at lags 0 and 4, with elevated risk found across all cumulative-day lags (0-1, 0-2, 0-3, 0-4, 0-5, and 0-6) and significantly greater risk in the winter and summer at lag 0-5 (p<0.01). However, the PM_2.5 level was not significantly associated with influenza risk during the COVID-19 outbreak. Conclusions: Lockdown measures implemented during the COVID-19 pandemic could mitigate the risk of PM_2.5-induced influenza. Effective regulatory actions in the context of COVID-19 may decrease PM_2.5 emissions and improve hygiene practices, thereby reducing influenza hospitalizations.

• Proceedings of the National Institute of Ecology of the Republic of Korea
• /
• 제1권1호
• /
• pp.74-82
• /
• 2020

Predictions of suitable habitat areas can provide important information pertaining to the risk assessment and management of alien plants at early stage of their establishment. Here, we predict the invasion potential of Muhlenbergia capillaris (pink muhly) in South Korea using five bioclimatic variables. We adopt four models (generalized linear model, generalized additive model, random forest (RF), and artificial neural network) for projection based on 630 presence and 600 pseudo-absence data points. The RF model yielded the highest performance. The presence probability of M. capillaris was highest within an annual temperature range of 12 to 24℃ and with precipitation from 800 to 1,300 mm. The occurrence of M. capillaris was positively associated with the precipitation of the driest quarter. The projection map showed that suitable areas for M. capillaris are mainly concentrated in the southern coastal regions of South Korea, where temperatures and precipitation are higher than in other regions, especially in the winter season. We can conclude that M. capillaris is not considered to be invasive based on a habitat suitability map. However, there is a possibility that rising temperatures and increasing precipitation levels in winter can accelerate the expansion of this plant on the Korean Peninsula.

• 한국통계학회:학술대회논문집
• /
• 한국통계학회 2003년도 추계 학술발표회 논문집
• /
• pp.179-182
• /
• 2003

This study describes a new graphical method for assessing and characterizing effect modification by a matching covariate in matched case-control studies. This method to understand effect modification is based on a semiparametric model using a varying coefficient model. The method allows for nonparametric relationships between effect modification and other covariates, or can be useful in suggesting parametric models. This method can be applied to examining effect modification by any ordered categorical or continuous covariates for which cases have been matched with controls. The method applies to effect modification when causality might be reasonably assumed. An example from veterinary medicine is used to demonstrate our approach. The simulation results show that this method, when based on linear, quadratic and nonparametric effect modification, can be more powerful than both a parametric multiplicative model fit and a fully nonparametric generalized additive model fit.

• Journal of Preventive Medicine and Public Health
• /
• 제37권4호
• /
• pp.300-305
• /
• 2004

Objectives : The exposure to particulate air pollution during the pregnancy has reported to result in adverse pregnancy outcome such as low birth weight, preterm birth, still birth, and intrauterine growth retardation (IUGR). We aim to assess whether prenatal exposure of particulate matter less than 10 (m in diameter ($PM_{10}$) is associated with preterm birth in Seoul, South Korea. Methods : We included 382,100 women who delivered a singleton at 25-42 weeks of gestation between 1998 and 2000. We calculated the average PM10 exposures for each trimester period and month of pregnancy, from the first to the ninth months, based on the birth date and gestational age. We used three different models to evaluate the effect of air pollution on preterm birth; the logistic regression model, the generalized additive logistic regression model, and the proportional hazard model. Results : The monthly analysis using logistic regression model suggested that the risks of preterm birth increase with PM10 exposure between the sixth and ninth months of pregnancy and the highest risk was observed in the seventh month (adjusted odds ratio=1.07, 95% CI=1.01-1.14). We also found the similar results using generalized additive model. In the proportional hazard model, the adjusted odds ratio for preterm births due to PM10 exposure of third trimester was 1.04 (95% CI=0.96-1.13) and PM10 exposure between the seventh month and ninth months of pregnancy was associated with the preterm births. Conclusions : We found that there were consistent results when we applied the three different models. These findings suggest that air pollution exposure during the third trimester pregnancy has an adverse effect on preterm birth in South Korea.

• Journal of Radiation Protection and Research
• /
• 제46권3호
• /
• pp.83-97
• /
• 2021

Background: The lifetime risk of lung cancer incidence due to radiation for nonsmokers is overestimated because of the use of the average cancer baseline risk among a mixed population, including smokers. In recent years, the generalized multiplicative (GM)-excess relative risk (ERR) model has been developed in the life span study of atomic bomb survivors to consider the joint effect of radiation and smoking. Based on this background, this paper discusses the issues of radiation risk assessment considering smoking in two parts. Materials and Methods: In Part 1, we proposed a simple method of estimating the baseline risk for nonsmokers using current smoking data. We performed sensitivity analysis on baseline risk estimation to discuss the birth cohort effects. In Part 2, we applied the GM-ERR model for Japanese smokers to calculate lifetime attributable risk (LAR). We also performed a sensitivity analysis using other ERR models (e.g., simple additive (SA)-ERR model). Results and Discussion: In Part 1, the lifetime baseline risk from mixed population including smokers to nonsmokers decreased by 54% (44%-60%) for males and 24% (18%-29%) for females. In Part 2, comparison of LAR between SA- and GM-ERR models showed that if the radiation dose was ≤200 mGy or less, the difference between these ERR models was within the standard deviation of LAR due to the uncertainty of smoking information. Conclusion: The use of mixed population for baseline risk assessment overestimates the risk for lung cancer due to low-dose radiation exposure in Japanese males.

• 한국환경복원기술학회지
• /
• 제25권2호
• /
• pp.55-67
• /
• 2022

Land use is a critical factor that affects the hydrological characteristics of watersheds, thereby determining the biological condition of streams. This study analyzes the effects of land uses in the watersheds on biological indicators of streams across the Han River basin using a linear model (LM) and generalized additive model (GAM). LULC and biological monitoring data of streams were obtained from the Korean Ministry of Environment. The proportions of urban, agricultural, and forest areas in the watersheds were regressed to the three biological indicators, including diatom, benthic macroinvertebrate, and fish of streams. The estimated LM and GAM models for the biological indicators were then compared, using regression determination R² and AIC values. The results revealed that GAM models performed better than the LM models in explaining the variances of biological indicators of streams, indicating the non-linear relationships between biological indicators and land uses in watersheds. Also, the results suggested that the indicator of macroinvertebrates was the most sensitive indicator to land uses in watersheds. Although non-linear relationships between watershed land uses and biological indicators of streams could vary among biological indicators, it was consistent that streams' biological integrity significantly deteriorated by a relatively low percentage of urban areas. Meanwhile, biological indicators of streams were negatively affected by the relatively high percentage of agricultural areas. The results of this study can be integrated into effective quantitative criteria for the watershed management and land use plans to enhance the biological integrity of streams. In specific, land uses management plans in watersheds may need more close attention to urban land use changes than agricultural land uses to sustain the biological integrity of streams.

• Genomics & Informatics
• /
• 제21권4호
• /
• pp.50.1-50.9
• /
• 2023

Vaccine development is one of the key efforts to control the spread of coronavirus disease 2019 (COVID-19). However, it has become apparent that the immunity acquired through vaccination is not permanent, known as the waning effect. Therefore, monitoring the proportion of the population with immunity is essential to improve the forecasting of future waves of the pandemic. Despite this, the impact of the waning effect on forecasting accuracies has not been extensively studied. We proposed a method for the estimation of the effective immunity (EI) rate which represents the waning effect by integrating the second and booster doses of COVID-19 vaccines. The EI rate, with different periods to the onset of the waning effect, was incorporated into three statistical models and two machine learning models. Stringency Index, omicron variant BA.5 rate (BA.5 rate), booster shot rate (BSR), and the EI rate were used as covariates and the best covariate combination was selected using prediction error. Among the prediction results, Generalized Additive Model showed the best improvement (decreasing 86% test error) with the EI rate. Furthermore, we confirmed that South Korea's decision to recommend booster shots after 90 days is reasonable since the waning effect onsets 90 days after the last dose of vaccine which improves the prediction of confirmed cases and deaths. Substituting BSR with EI rate in statistical models not only results in better predictions but also makes it possible to forecast a potential wave and help the local community react proactively to a rapid increase in confirmed cases.

• Journal of Preventive Medicine and Public Health
• /
• 제34권2호
• /
• pp.119-126
• /
• 2001

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.

• Safety and Health at Work
• /
• 제3권4호
• /
• pp.294-297
• /
• 2012

Previous studies on the vast increase in suicide mortality in Southeast Asia have indicated that suicide rates increase in parallel with a rise in unemployment or during periods of economic recession. This paper examines the effects of economic recession on suicidal rates amongst agriculture, fisheries, and forestry workers in Korea. Monthly time-series gross domestic product (GDP) data were linked with suicidal rates gathered from the cause of death records between1993-2008. Data were analyzed using generalized additive models to analyze trends, while a polynomial lag model was used to assess the unconstrained time lag effects of changes in GDP on suicidal rate. We found that there were significant inverse correlations between changes in GDP and suicide for a time lag of one to four months after the occurrence of economic event. Furthermore, it was evident that the overall relative risks of suicide were high enough to bring about social concern.

• 말소리와 음성과학
• /
• 제10권4호
• /
• pp.31-37
• /
• 2018

Pitch trajectories reflect a continuous variation of vocal fold movements over time. This study examined the pitch trajectories of English vowels produced by 139 American English speakers, statistically analyzing their trajectories using the Generalized Additive Mixed Models (GAMMs). First, Praat was used to read the sound data of Hillenbrand et al. (1995). A pitch analysis script was then prepared, and six pitch values at the corresponding time points within each vowel segment were collected and checked. The results showed that the group of men produced the lowest pitch trajectories, followed by the groups of women, boys, then girls. The density line showed a bimodal distribution. The pitch values at the six corresponding time points formed a single dip, which changed gradually across the vowel segment from 204 to 193 to 196 Hz. The normality tests performed on the pitch data rejected the null hypothesis. Nonparametric tests were therefore conducted to discover the significant differences in the values among the four groups. The GAMMs, which analyzed all the pitch data, produced significant results among the pitch values at the six corresponding time points but not between the two groups of boys and girls. The GAMMs also revealed that the two groups were significantly different only at the first and second time points. Accordingly, the methodology of this study and its findings may be applicable to future studies comparing curvilinear data sets elicited by experimental conditions.