• The Korean Journal of Pain
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• v.33 no.2
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• pp.153-165
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• 2020

Background: Well-validated risk prediction models help to identify individuals at high risk of diseases and suggest preventive measures. A recent systematic review reported lack of validated prediction models for low back pain (LBP). We aimed to develop prediction models to estimate the 8-year risk of developing LBP and its recurrence. Methods: A population based prospective cohort study using data from 435,968 participants in the National Health Insurance Service-National Sample Cohort enrolled from 2002 to 2010. We used Cox proportional hazards models. Results: During median follow-up period of 8.4 years, there were 143,396 (32.9%) first onset LBP cases. The prediction model of first onset consisted of age, sex, income grade, alcohol consumption, physical exercise, body mass index (BMI), total cholesterol, blood pressure, and medical history of diseases. The model of 5-year recurrence risk was comprised of age, sex, income grade, BMI, length of prescription, and medical history of diseases. The Harrell's C-statistic was 0.812 (95% confidence interval [CI], 0.804-0.820) and 0.916 (95% CI, 0.907-0.924) in validation cohorts of LBP onset and recurrence models, respectively. Age, disc degeneration, and sex conferred the highest risk points for onset, whereas age, spondylolisthesis, and disc degeneration conferred the highest risk for recurrence. Conclusions: LBP risk prediction models and simplified risk scores have been developed and validated using data from general medical practice. This study also offers an opportunity for external validation and updating of the models by incorporating other risk predictors in other settings, especially in this era of precision medicine.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.347-373
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• 2021

Over the past 50 years, explorative research on the nation's mortality decline patterns has showed a decrease in age-specific mortality rates in all age groups, but there were different improvement patterns in specific mortality rates depending on ages and periods. Greater distinct improvement was observed in mortality rates among women than men, and there was a noticeable improvement in mortality rates in certain groups especially in the more recent decades, revealing a structural change in the overall trends regarding death periods. In this paper, we compare various stochastic mortality models considering cohort effects for mortality projection using Korean female mortality data and further explore the uncertainty related to projection. It also created age-specific mortality rates and life expectancy for women until 2067 based on the results of the analysis, and compared them with future age-specific mortality rates and life expectancy provided by the national statistical office (KOISIS). The best optimal model could vary depending on data usage periods. however, considering the overall fit and predictability, the PLAT model would be regarded to have appropriate predictability in terms of the mortality rates of women in South Korea.

• Korea journal of population studies
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• v.31 no.1
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• pp.75-97
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• 2008

Recently Korea is expected with the decrease of population in working ages and also population structure, especially age structure, has changed as aging goes faster. This study focuses on the relationship between age structure and wage structure to analyzes the cohort size effect on the change of age-earnings profile. Our empirical analysis based on Wright(1991)'s model takes weighted OLS regression using the male worker's data of Ministry of Labor 'Wage Structure Survey'($1990{\sim}2006$). In pooled data, we take the conclusion that the cohort size effect was found in high school and college graduate workers, but the effect is different between them. The labor market entry effect of high school graduate workers is negative(-) and his persistent effect is positive(+). On the other hand, the cohort size effect of college graduate workers have appeared the opposite directions in contrary with the existing results of Welch(1979) and Wright(1991). This results are seen as the possibility that college graduate worker has the benefit of wage level by his relative cohort size in spite of high unemployment of young graduate. It will be the sign of need that we should interest in the change of age structure with balancing the labor supply side approach and the demand side study which the previous studies was mainly tended to focus on.

• The Journal of the Acoustical Society of Korea
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• v.19 no.5
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• pp.20-26
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• 2000

In speaker verification system the HMM(hidden Markov model) parameter updating using small amount of data and the priori threshold decision are crucial factor for dealing with long-term variability in people voices. In the paper we present the speaker model updating technique which can be adaptable to the session-to-intra speaker variability and the priori threshold determining technique. The proposed technique decreases verification error rates which the session-to-session intra-speaker variability can bring by adapting new speech data to speaker model parameter through Baum Welch re-estimation. And in this study the proposed priori threshold determining technique is decided by a hybrid score measurement which combines the world model based technique and the cohen model based technique together. The results show that the proposed technique can lead a better performance and the difference of performance is small between the posteriori threshold decision based approach and the proposed priori threshold decision based approach.

• Journal of Preventive Medicine and Public Health
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• v.39 no.2
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• pp.123-129
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• 2006

Objectives : The incidence of colorectal cancer increased greatly among the elderly in Korea, but the relationship between smoking and colon cancer remains controversial. Few studies have targeted Asian elderly people. We analyzed the smoking status, the amount smoked, and the smoking duration as risk factors of colorectal cancer to determine their association and causality. Methods: The cohort members (n=14, 103) consisted of 4,694 males and 9,409 females, and they were derived from the Korea Elderly Phamacepidemilogic Cohort (KEPEC), which was a population-based dynamic cohort. They were aged 65 years or more and they lived in Busan Metropolitan City between from 1993-1998; they were beneficiaries of the Korean Medical Insurance Corporation (KMIC). The baseline information was surveyed by a self-administered mailed questionnaire; after 8.7 person-years of mean follow up period, 100 cases of colorectal cancer occurred. The adjusted relative ratio (aRR) of smoking status, the smoking amount and the smoking duration were calculated from the Cox's proportional hazard model with the never-smokers as a reference group and the Cox model controlled for age, gender, precancerous lesions of CRC, medication history of NSAIDs & antibiotics, the alcohol drinking status and BMI. Results : Compared with the never smokers, the aRRs were 2.03 (95% CI=1.02-4.03) and 1.36 (95% CI=0.80-2.32) for the ex-smokers and current smokers, respectively. Statistical significant trends were not observed for the dose-relationship among the elderly, either for the mean daily amount smoked (p for trend=0.28) or for the total amount (p for trend=0.15). Still, the aRRs were 1.51 (95% CI=0.97-2.34) for the elderly who smoked less than 40 years and 2.35 (95% CI=1.16-4.74) for the elderly who had 40 years or more of smoking (p for trend=0.06). Smokers who started smoking before the age 20 had an increased aRR of 2.15 (95% CI=1.17-3.93) compared to the never smokers. Conclusions : After controlling for age, gender, precancerous lesion of CRC, medication history of NSAIDs & antibiotics, the alcohol drinking status and BMI, smoking increases the risk of colorectal cancer among elderly people. The age when starting smoking is also important.

• Health Policy and Management
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• v.25 no.4
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• pp.253-255
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• 2015

The concept of precision medicine-prevention and treatment strategies that take individual variability into account-is hot issue of US in the year 2015. Precision medicine is a new concept that approach patients individually by there characteristics, such as genome, life style, environmental exposure, etc. For developing the precision medicine, National Institute of Health of US has been prepared the Precision Medicine Initiative Cohort Program, at least 1 million people cohort. The US President Obama announced the Precision Medicine Initiative on 30th January 2015. He announced that he will pioneer a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients. Most medical treatments have been designed for the 'average patient.' As a result of this 'one-size-fits-all-approach,' treatments can be very successful for some patients but not for others. This is changing with the emergence of precision medicine, an innovative approach to disease prevention and treatment that takes into account individual differences in people's genes, environments, and lifestyles. Precision medicine gives clinicians tools to better understand the complex mechanisms underlying a patient's health, disease, or condition, and to better predict which treatments will be most effective. The healthcare researcher should prepare the new medicine era such as bio-information technology convergence, big data study.

• Journal of Ginseng Research
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• v.27 no.4
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• pp.158-164
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• 2003

Recently, there are much concerns about ginseng as disease therapeutics. There are no epidemiologic study on relationship between ginseng intake and all cause mortality based from general population Cohort. This study sought to examine relationships between ginseng intake and all cause mortality from Kangwha Cohort data. From March 1985 through December 1999, 2696 males and 3595 females who were aged 55 or over as of 1985 were followed up. We calculate the mortality rate, standardized mortality ratio and risk ratios by ginseng intake. Cox proportional hazard model was used to adjust various confounding factors. Ginseng intake group had the lower all cause mortality(Risk ratio(RR)=0.88, 95%Confidence Interval(CI)=0.79-0.97) among males. Increasing ginseng intake, lower all cause mortality(Low ginseng intake: RR=0.88, 95%CI=0.79-0.98; high ginseng intake : RR=0.87, 95%CI=0.75-1.00) among males. There is no statistically significant difference between ginseng intake and mortality among females. The results of this study suggests that ginseng intake may prolong the human life among males.

• The Journal of Industrial Distribution & Business
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• v.12 no.8
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• pp.33-39
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• 2021

Purpose: The influence of age on time preference is not identified in the usual cross-sectional analysis. This study aims to test whether age affects time preference after controlling for the effects of individual heterogeneity including cohort effects. Research design, data and methodology: Drawing on a nationally representative panel dataset of Indonesians, we estimate the effects of age on time preference after controlling for unobserved individual heterogeneity as well as potential cohort effects. We measure time preference exploiting information on two sets of multiple price lists: one for a one-year delay, and the other for a five-year delay. Results: When we controlled for time-invariant individual characteristics, including birth cohort effects in a fixed effects model, the older men and women were more patient in a linear fashion, particularly when the delay was longer. To highlight the importance of controlling for individual fixed effects, we repeated the specification without controlling for individual fixed effects in OLS or censored maximum likelihood regression; we found no relation between age and impatience in men or women and for a one or five-year delay. Conclusions: The older men and women are more patient, and time preferences are correlated with unobserved individual heterogeneity.

• Journal of Korean society of Dental Hygiene
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• v.19 no.3
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• pp.409-419
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• 2019

Objectives: The purpose of this study was to analyze the factors affecting antibiotic prescription in dental outpatients. Methods: The present study was conducted using data from the National Health Insurance Service - National Sample Cohort. We analyzed prescriptions issued in the dental outpatient department in 2015, for adults over 19 years of age. Antibiotic prescription rates and mean prescription days were analyzed by sex, age, insurance type, presence of diabetes mellitus and hypertension, season in treatment, type of dental institution, and location of dental institution. Multivariate logistic regression was also performed to analyze the factors affecting antibiotic prescription in dental outpatients. Results: A total of 257,038 prescriptions were analyzed. The mean prescription days of antibiotics in dental outpatients were $3.04{\pm}1.08days$, and the prescription rate was 93.0%. Two variables (presence of diabetes mellitus and insurance type) were excluded from the multivariate logistic regression analysis model because they did not significantly affect antibiotic prescription. The possibility of antibiotic prescription was higher in men ${\geq}61years$ of age and those with hypertension. Furthermore, antibiotics were most frequently prescribed in dental clinics rather than dental hospitals, and more frequently in Busan compared to other areas (p<0.001). Conclusions: Several factors were determined to affect antibiotic prescription, and detailed guidelines for consistent antibiotic prescription are needed.

• Genomics & Informatics
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• v.20 no.3
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• pp.34.1-34.11
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• 2022

Multilevel analysis is an appropriate and powerful tool for analyzing hierarchical structure data widely applied from public health to genomic data. In practice, however, we may lose the information on multiple nesting levels in the multilevel analysis since data may fail to capture all levels of hierarchy, or the top or intermediate levels of hierarchy are ignored in the analysis. In this study, we consider a multilevel linear mixed effect model (LMM) with single imputation that can involve all data hierarchy levels in the presence of missing top or intermediate-level clusters. We evaluate and compare the performance of a multilevel LMM with single imputation with other models ignoring the data hierarchy or missing intermediate-level clusters. To this end, we applied a multilevel LMM with single imputation and other models to hierarchically structured cohort data with some intermediate levels missing and to simulated data with various cluster sizes and missing rates of intermediate-level clusters. A thorough simulation study demonstrated that an LMM with single imputation estimates fixed coefficients and variance components of a multilevel model more accurately than other models ignoring data hierarchy or missing clusters in terms of mean squared error and coverage probability. In particular, when models ignoring data hierarchy or missing clusters were applied, the variance components of random effects were overestimated. We observed similar results from the analysis of hierarchically structured cohort data.