• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.33-39
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• 1997

Tsutakawa(1988) proposed a mixed model for using empirical Bayes method to study the geographic variability in mortality rates of a disease. In particular cases of the analysis in mortality rate, we need to consider the effect of time. If observed data are collected annually for the time period, then time effect will be emphasized. Here, an extended model for estimating the geographic effect and the mortality rates of the disease with time effect is proposed.

• Proceedings of The Korean Society of Health Promotion Conference
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• 1998.07a
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• pp.11-12
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• 1998

The health status of a population is usually measured by mortality such as crude dealth rate, cause-specific mortality rate, infant mortality rate and life expectancy. However, these indices based upon mortality (i.e., quantity of life) are increasingly unsatisfactory to assess health status, especially in an aging society. In this presentation, I will discuss the followings as regards quality of life (QOL) in health science.

• Journal of Preventive Medicine and Public Health
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• v.38 no.3
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• pp.283-290
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• 2005

Objectives : The aim of this paper was to examine the relationship between the summertime (June to August) heat index, which quantifies the bioclimatic apparent temperature in sultry weather, and the daily disease-related mortality in Seoul for the period from 1991 to 2000. Methods : The daily maximum (or minimum) summertime heat indices, which show synergetic apparent temperatures, were calculated from the six hourly temperatures and real time humidity data for Seoul from 1991 to 2000. The disease-related daily mortality was extracted with respect to types of disease, age and sex, etc. and compared with the time series of the daily heat indices. Results : The summertime mortality in 1994 exceeded the normal by 626 persons. Specifically, blood circulation-related and cancer-related mortalities increased in 1994 by 29.7% (224 persons) and 15.4% (107 persons), respectively, compared with those in 1993. Elderly persons, those above 65 years, were shown to be highly susceptible to strong heat waves, whereas the other age and sex-based groups showed no significant difference in mortality. In particular, a heat wave episode on the 22nd of July 2004 ($>45^{\circ}C$ daily heat index) resulted in double the normal number of mortalities after a lag time of 3 days. Specifically, blood circulation-related mortalities, such as cerebral infraction, were predominant causes. Overall, a critical mortality threshold was reached when the heat index exceeded approximately $37^{\circ}C$, which corresponds to human body temperature. A linear regression model based on the heat indices above $37^{\circ}C$, with a 3 day lag time, accounted for 63% of the abnormally increased mortality (${\geq}+2$ standard deviations). Conclusions : This study revealed that elderly persons, those over 65 years old, are more vulnerable to mortality due to abnormal heat waves in Seoul, Korea. When the daily maximum heat index exceeds approximately $37^{\circ}C$, blood circulation-related mortality significantly increases. A linear regression model, with respect to lag-time, showed that the heat index based on a human model is a more dependable indicator for the prediction of hot weather-related mortality than the ambient air temperature.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.450-456
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• 2017

This study was conducted to develop estimated equation for mortality rates (volume of dead trees, %) on coniferous and broad-leaved forests, representative forest types of South Korea. There were 6 equation models applied for estimating mortality such as a exponential equation, a Hamilton equation and variables using were DBH, basal area, and site index. Raw data used for estimating mortality were $5^{th}$ and $6^{th}$ national forest inventory data, and mortality was calculated with the difference of stocks between lived trees and dead trees by each sample plots. The most applicable equation to describe mortality on coniferous forest and broad-leaved forest was indicated as $P=(1+e^{(a+b{\times}DBH+c{\times}BA+d{\times}no\_ha+e{\times}density)})^{-1}$ and their goodness of fit showed 34% and 51% respectively. Goodness of fit in both equations were not much high because there were various factors which affect the mortality such as topographic conditions, soil characteristic, climatic factors, site quality, and competition. Therefore, it is considered that explaining mortality in forest with only 2 or 3 variables like DBH, basal area used in this analysis could be very difficult facts. However, this study is certainly worth in that there is no useful information on mortality by each forest type throughout the country at the present, and we would make an effort to promote the fitness of estimated equation for mortality adding competition index, tree crown density etc.

• Journal of Preventive Medicine and Public Health
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• v.31 no.4 s.63
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• pp.604-615
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• 1998

Although previous studies revealed the association of physical activity with mortality rate, it is unclear whether there is a linear trend between physical activity and mortality rate. In this study, the association of physical activity with the risk of all-cause mortality was analysed using Cox's proportional hazard model for a cohort of 14,204 healthy Korean men aged 40-59 years followed up for 4 years(Jan. 1993-Dec. 1996). Physical activity and other life style were surveyed by a postal questionnaire in December 1992. Total of 14,204 subjects were grouped into quartiles by physical activity. Using death certificate data, 123 deaths were identified. The second most active quartile had a lowest mortality .ate with relative risk of 0.44(95% C.I. : 0.23-0.84) compared with most sedentary quartile, showing a J-shape pattern of physical activity-mortality curve. By examining the difference in proportion of cause of the death between most active quartile and the other quartiles, there was no significant difference of proportional mortality from cardiovascular deaths, cerebrovascular deaths or deaths from trauma. The covariates were stratified into two group between which the trend of RR was compared to test the effect modification. There was no remarkable effect modification by alcohol intake, smoking, body mass index, calorie consumption, percent fat consumption. In conclusion, moderate activity was found to have more protective effect on all-cause mortality than vigorous activity and that the J-shape pattern of physical activity-mortality curve was not due to the difference of mortality pattern or effect modification by alcohol intake, smoking, body mass index, calorie consumption and percent fat consumption.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5685-5689
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• 2014

Background: It is well known that smoking is a preventable factor for all-cause mortality; however, it is still questionable how many years after smoking cessation that people will have reduced risk for mortality, in particular in those with a high interest in their own health. We aimed to examine the association between time since quitting smoking and total mortality among past-smokers relative to current smokers. Materials and Methods: We enrolled 36,446 health examinees that voluntarily taken with diverse health check-up packages of high cost burden in 1995-2003 and followed them till death by 2004. The history of cigarette smoking consumption was collected using a self-administrative questionnaire at the first visit time. Mortality risk by smoking cessation years was analyzed using Cox's proportional hazard model. Results: Compared to non-smokers, male smokers over 15 pack-years had higher risk for total mortality (HR=1.60, 95%CI 1.23-2.14). The mortality risk in female smokers with same pack-years was more pronounced than that in male smokers (HR=2.83, 95%CI 1.17-7.04) despite a small number of cases. Compared to current smokers, a decrease of total mortality was observed among those who ceased smoking, and inverse dose-response was found with years after cessation: RR 0.98 (95%CI, 0.64-1.41) (<2 yrs), 0.60 (95%CI, 0.43-0.83) (3-9 yrs), and 0.58 (95%CI, 0.43-0.79) (${\geq}10$ yrs). Conclusions: A reduced risk of total mortality was observed after 3 years of smoking cessation. Our findings suggest that at least 3 years of smoking cessation may contribute to reduce premature mortality among Asian men.

• Korea journal of population studies
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• v.26 no.2
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• pp.33-62
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• 2003

The purpose of this study is to analyze the relative importance of three factor -socioeconomic development, public health development, egalitarian nature of socioeconomic development- affecting mortality declines. Infant mortality rate and life expectancy at birth are used as the mortality index, that is the dependent variables, while GNP is used as the indicator of socioeconomic development, primary school enrollment ratio of female as the indicator of egalitarian nature of socioeconomic development, population per hospital bed as the indicator of public health. The data of these variables are collected two time-periods -before 1970 and during 1970-1980- over 50 countries. The explanatory data analysis is used as the statistical technique. We can find whether the relationship between dependent variable and independent variables are linear or nonlinear, and which case is the influential case in our model. The main results of this study are followings. First, the association between infant mortality rates and four indices are not linear. The most important factor explaining the variation of infant mortality is GNP, while primary enrollment of female is the second and GINI is the third important factor. However, population per hospital bed does not have a significant effect on the infant mortality rates in this study. Second, life expectancy at birth is log-linearly related to GNP. Unlike infant mortality rates, the most important factor explaining the variation of life expectance at birth is women's education and the next important factor GNP, and then the third one GINI. But, still population per hospital bed is not significantly related to the variation of life expectance in this study.

• Health Policy and Management
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• v.17 no.3
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• pp.87-105
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• 2007

The purpose of this study was to analyze whether nonemergency, isolated coronary artery bypass graft (CABG) surgery for high- or low-risk patients biases the assessment of the risk-adjusted mortality rates of hospitals. This study used 2002 National Health Insurance claims data for tertiary hospitals in Korea. The study sample consisted of 1,959 patients from 23 tertiary hospitals. The risk-adjustment model used the patients' biological, admission, and comorbidity data identified in the claims. The subjects were classified into high- and low-risk groups based on predicted surgical risk. The crude mortality rates and risk-adjusted mortality rates for low-risk, high-risk, and all patients in a hospital were compared based on the rank and the four intervals defined by quartile. Also, the crude mortality rates of the three groups were compared with their 95% confidence intervals of predicted mortality rates. The C-statistic (0.83) and Hosmer-Lemeshow test ($X^2$=11.47, p=0.18) indicated that the risk-adjustment model performed well. Presenting crude mortality rates with their 95% confidence intervals of predicted rates showed higher agreements among the three groups than using the rank or intervals of mortality rates defined by quartile in the hospital performance assessment. The crude mortality rates for the low-risk patients in 21 of the 23 hospitals were located on the same side of their 95% confidence intervals compared to that for all patients. High-risk patients and all patients differed at only one hospital. In conclusion, the impact of risk selection by hospital on the assessment results was the smallest when comparing the crude inpatient mortality rates of CABG patients with the 95% confidence intervals of predicted mortality rates. Given the increasing importance of quality improvements in Korean health policy, it will be necessary to use the appropriate method of releasing the hospital performance data to the public to minimize any unwanted impact such as risk-based hospital selection.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.4
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• pp.2259-2263
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• 2013

Background: Public use National Health and Nutrition Examination Survey (NHANES III) and NHANES III linked mortality data were here applied to investigate the association between health insurance coverage and all cause and all cancer mortality in adults. Patients and Methods: NHANES III household adult, laboratory and mortality data were merged. Only patients examined in the mobile examination center (MEC) were included in this study. The sampling weight employed was WTPFEX6, SDPPSU6 being used for the probability sampling unit and SDPSTRA6 to designate the strata for the survey analysis. All cause and all cancer mortalities were used as binary outcomes. The effect of health insurance coverage status on all cause and all cancer mortalities were analyzed with potential socioeconomic, behavioral and health status confounders. Results: There were 2398 sample persons included in this study. The mean age was 40 years and the mean (S.E.) follow up was 171.85 (3.12) person months from the MEC examination. For all cause mortality, the odds ratios (significant p-values) of the covariates were: age, 1.0095 (0.000); no health insurance coverage (using subjects with health insurance), 1.71 (0.092); black race (using non-Hispanic white subjects as the reference group) 1.43, (0.083); Mexican-Americans, 0.60 (0.089); DMPPIR, 0.82, (0.000); and drinking hard liquor, 1.014 (0.007). For all cancer mortality, the odds ratio (significant p-values) of the covariates were: age, 1.0072 (0.00); no health insurance coverage, using with health coverage as the reference group, 2.91 (0.002); black race, using non-Hispanic whites as the reference group, 1.64 (0.047); Mexican Americans, 0.33 (0.008) and smoking, 1.017 (0.118). Conclusion: There was a 70% increase in risk of all cause death and almost 300% of all cancer death for people without any health insurance coverage.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5525-5528
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• 2012

Introduction: Breast cancer is the most common cancer in women. Improvements of early diagnosis modalities have led to longer survival rates. This study aimed to determine the 5, 10 and 15 year mortality rates of breast cancer patients compared to the normal female population. Materials and Methods: The follow up data of a cohort of 615 breast cancer patients referred to Iranian Breast Cancer Research Center (BCRC) from 1986 to 1996 was considered as reference breast cancer dataset. The dataset was divided into 5 year age groups and the 5, 10 and 15 year probability of death for each group was estimated. The annual mortality rate of Iranian women was obtained from the Death Registry system. Standardized mortality ratios (SMRs) of breast cancer patients were calculated using the ratio of the mortality rate in breast cancer patients over the general female population. Results: The mean age of breast cancer patients at diagnosis time was 45.9 (${\pm}10.5$) years ranging from 24-74. A total of 73, 32 and 2 deaths were recorded at 5, 10 and 15 years, respectively, after diagnosis. The SMRs for breast cancer patients at 5, 10 and 15 year intervals after diagnosis were 6.74 (95% CI, 5.5-8.2), 6.55 (95%CI, 5-8.1) and 1.26 (95%CI, 0.65-2.9), respectively. Conclusion: Results showed that the observed mortality rate of breast cancer patients after 15 years from diagnosis was very similar to expected rates in general female population. This finding would be useful for clinicians and health policy makers to adopt a beneficial strategy to improve breast cancer survival. Further follow-up time with larger sample size and a pooled analysis of survival rates of different centres may shed more light on mortality patterns of breast cancer.