• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8439-8443
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• 2016

Background: Breast cancer is the most common malignancy in women worldwide and its incidence is generally increasing. In 2012, it was the second most common cancer in the world. It is necessary to obtain information on incidence and mortality for health planning. This study aimed to investigate the relationship between the human development index (HDI), and the incidence and mortality rates of breast cancer in the world in 2012. Materials and Methods: This ecologic study concerns incidence rate and standardized mortality rates of the cancer from GLOBOCAN in 2012, and HDI and its components extracted from the global bank site. Data were analyzed using correlation tests and regression with SPSS software (version 15). Results: Among the six regions of WHO, the highest breast cancer incidence rate (67.6) was observed in the PAHO, and the lowest incidence rate was 27.8 for SEARO. There was a direct, strong, and meaningful correlation between the standardized incidence rate and HDI (r=0.725, $p{\leq}0.001$). Pearson correlation test showed that there was a significant correlation between age-specific incidence rate (ASIR) and components of the HDI (life expectancy at birth, mean years of schooling, and GNP). On the other, a non-significant relationship was observed between ASIR and HDI overall (r=0.091, p=0.241). In total, a significant relationship was not found between age-specific mortality rate (ASMR) and components of HDI. Conclusions: Significant positive correlations exist between ASIR and components of the HDI. Socioeconomic status is directly related to the stage of the cancer and patient's survival. With increasing the incidence rate of the cancer, mortality rate from the cancer does not necessariloy increase. This may be due to more early detection and treatment in developed that developing countries. It is necessary to increase awareness of risk factors and early detection in the latter.

• Korea Journal of Hospital Management
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• v.25 no.4
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• pp.1-12
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• 2020

During the COVID-19 pandemic, most of the western countries with advanced medical technology failed to contain coronavirus. This fact triggered our research question of what factors influence the clinical outcomes like infection rates and case mortality rates. This study aims to identify the determinants of COVID-19 related infection rates and case mortality rates. We considered three sets of independent variables: 1) socio-demographic characteristics; 2) cultural characteristics; 3) healthcare system characteristics. For the analysis, we created an international dataset from diverse sources like World Bank, Worldometers, Hofstede Insight, GHS index etc. The COVID-19 related statistics were retrieved from Aug. 1. Total cases are from 95 countries. We used hierarchical regression method to examine the linear relationship among variables. We found that obesity, uncertainty avoidance, hospital beds per 1,000 made a significant influence on the standardized COVID-19 infection rates. The countries with higher BMI score or higher uncertainty avoidance showed higher infection rates. The standardized COVID-19 infection rates were inversely related to hospital beds per 1,000. In the analysis on the standardized COVID-19 case mortality rates, we found that two cultural characteristics(e.g., individualism, uncertainty avoidance) showed statistically significant influence on the case mortality rates. The healthcare system characteristics did not show any statistically significant relationship with the case mortality rates. The cultural characteristics turn out to be significant factors influencing the clinical outcomes during COVID-19 pandemic. The results imply that the persuasive communication is important to trigger the public commitment to follow preventive measures. The strategy to keep the hospital surge capacity needs to be developed.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.89-92
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• 2008

The extreme heat watch warning system(EHWWS) that Korea Meterological Administration carried out a preliminary from July 1, 2007, considered both daily maximum temperature and daily maximum heat index simultaneously. It was requested revision of the standard level of EHWWS to solve the difficulty of forecasting occurred when we were considering two parameters simultaneously and we did not considering heat index according to areas. For this, we established three type standard, such as type 1 that considered both daily maximum temperature and daily maximum heat index, Under the extreme heat day that daily minimum temperature was more than $25^{\circ}C$, type 2 that considered daily maximum temperature and type 3 that considered only daily maximum heat index and then analyzed whether these 3 types satisfies the excess mortality of the extreme heat warning or not. As a results, type 1 and 2 were more explain away excess mortality each warning step than type 3. type 2 could also apply case of not to consider heat index according to areas and had a merit for extreme heat forecasting easily because the standard was simple. Therefore we think type 2 is more suitable and reasonable standard for Korea extreme heat watch warning system(KEHWWS) than type 1. In addition, we need to develop model that exactly predicts the excess mortality will be take place during the extreme heat warning and construct KEHWWS.

• Journal of Clinical Nutrition
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• v.10 no.2
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• pp.51-55
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• 2018

Purpose: This study was conducted to assess how extreme obesity affects 30-day mortality in this patient group. Methods: A total of 802 patients who underwent emergency gastrointestinal surgery from January 2007 to December 2017 were retrospectively reviewed. Patients were divided into three groups according to their body mass index (BMI): group 1, normal weight (BMI: $18.5{\sim}22.9kg/m^2$); group 2, overweight (BMI: $23.0{\sim}29.9kg/m^2$ ); and group 3, obesity ($BMI{\geq}30kg/m^2$). Patients with a BMI under 18.5 were excluded from the analysis. Chi-squared test, Fisher's exact test, Kaplan-Meier survival analysis, and the log-rank test were used to assess and compare 30-day mortality rates between groups. Results: The mortality rates of group 1, group 2, and group 3 were 11.3%, 9.0%, and 26.9%, respectively (P<0.017). The mortality rate did not differ significantly between group 1 and 2 (11.3% vs. 9.0%; P=0.341), but group 1 and 2 showed better survival rates than group 3 (11.3% vs. 26.9%; P=0.028, 9.0% vs. 26.9%; P=0.011). Kaplan-Meier survival analysis revealed that group 3 had higher mortality than the other two groups (P=0.001). Conclusion: Obesity ($BMI{\geq}30kg/m^2$) was one of the risk factors influencing critically ill patients who underwent emergency surgery.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.245-257
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• 2015

This study was to develop the predictive model for severity-adjusted mortality of inpatients with multiple chronic conditions and analyse the factors on the variation of hospital standardized mortality ratio(HSMR) to propose the plan to reduce the variation. We collect the data "Korean National Hospital Discharge In-depth Injury Survey" from 2008 to 2010 and select the final 110,700 objects of study who have chronic diseases for principal diagnosis and who are over the age of 30 with more than 2 chronic diseases including principal diagnosis. We designed a severity-adjusted mortality predictive model with using data-mining methods (logistic regression analysis, decision tree and neural network method). In this study, we used the predictive model for severity-adjusted mortality ratio by the decision tree using Elixhauser comorbidity index. As the result of the hospital standardized mortality ratio(HSMR) of inpatients with multiple chronic conditions, there were statistically significant differences in HSMR by the insurance type, bed number of hospital, and the location of hospital. We should find the method based on the result of this study to manage mortality ratio of inpatients with multiple chronic conditions efficiently as the national level. So we should make an effort to increase the quality of medical treatment for inpatients with multiple chronic diseases and to reduce growing medical expenses.

• Journal of Trauma and Injury
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• v.32 no.1
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• pp.17-25
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• 2019

Purpose: We investigated how prehospital, emergency room (ER), and delta shock indices (SI) correlate with outcomes including mortality in patients with polytrauma. Methods: We retrospectively reviewed the medical records of 1,275 patients who visited the emergency department from January 2015 to April 2018. A total of 628 patients were enrolled in the study. Patients were divided into survivor and non-survivor groups, and logistic regression analysis was used to investigate independent risk factors for death. Pearson coefficient analysis and chi-square test were used to examine the significant relationship between SI and clinical progression markers. Results: Of 628 enrolled patients, 608 survived and 27 died. Multivariate logistic regression analysis reveals "age" (p<0.001; OR, 1.068), "pre-hospital SI >0.9" (p<0.001; OR, 11.629), and "delta SI ${\geq}0.3$" (p<0.001; OR, 12.869) as independent risk factors for mortality. Prehospital and ER SIs showed a significant correlation with hospital and intensive care unit length of stay and transfusion amount. Higher prehospital and ER SIs (>0.9) were associated with poor clinical progression. Conclusions: SI and delta SI are significant predictors of mortality in patients with polytrauma. Moreover, both prehospital and ER SIs can be used as predictive markers of clinical progression in these patients.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.6081-6087
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• 2015

Purpose: This study aimed to investigate the incidence and mortality of breast cancer, and its relationship with human development index (HDI) and its components in Asia in 2012. Materials and Methods: This study was an ecologic study in Asia for assessment of the correlation between age-specific incidence rate (ASIR) and age-specific mortality rate (ASMR) with HDI and its details that include: life expectancy at birth, mean years of schooling and gross national income (GNI) per capita. Data about SIR and SMR for every Asian country for the year 2012 were obtained from the global cancer project. We used a bivariate method for assessment of the correlation between SIR and SMR and HDI and its individual components. Statistical significance was assumed if P<0.05. All reported P-values are two-sided. Statistical analyses were performed using SPSS (Version 15.0, SPSS Inc.). Results: In 2012, 639,824 cases of breast cancer were recorded in Asian countries. Countries with the highest standardized incidence rate (ASIR) (per 100,000) were Israel (80.5), Lebanon (78.7), Armenia (74.1) and the highest standard mortality rate (ASMR) was observed in Pakistan (25.2), Armenia (24.2), and Lebanon (24). There was a positive correlation between the ASIR of breast cancer and HDI (r = 0.556, p <0.001), whereas there was a negative correlation between the ASMR of breast cancer and HDI (r = -0.051). Conclusions: Breast cancer incidence in countries with higher development is greater, while mortality is greatest in countries with less development. There was a positive and significant relationship between the ASIR of breast cancer and HDI and its components. Also there was a negative but non significant relationship between the ASMR of breast cancer and HDI.

• Korean Journal of Community Nutrition
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• v.21 no.4
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• pp.354-365
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• 2016

Objectives: Studies that reported the association between diet quality/nutritional intake status and mortality have rarely used long-term follow-up data in Asian countries, including Korea. This study investigated the association between the risk of mortality (all-cause and cause-specific) and the diet quality/nutritional intake status using follow-up 12-year mortality data from a nationally representative sample of South Koreans. Methods: 8,941 individuals who participated in 1998 and 2001 Korea Health and Nutrition Examination Surveys were linked to mortality data from death certificates. Of those individuals, 1,083 (12.1%) had died as of December, 2012. Cox proportional hazard models were used to estimate the relative risks of mortality according to the level of diet quality and intakes of major nutrients. Indicators for diet quality index and nutritional intake status were assessed using MAR (mean adequacy ratio) and energy and protein intake level compared with the 2010 Korean DRI. Results: Higher diet quality/nutritional intake status were associated with lower mortality; the mortality risk (95% confidence interval) from all-cause of lowest MAR group vs highest was 1.66 (1.27 to 2.18) among ${\geq}30$ year old, and 1.98 (1.36 to 2.86) among 30~64 year old individuals. Those with below 75% of energy and protein intake of Korean DRI had higher mortality risks of all-cause mortality compared to the reference group. Diet quality/nutritional intake status was inversely associated with mortality from cardiovascular diseases and cancer. Conclusions: Poor Diet quality/nutritional intake status were associated with a higher risk of mortality from all-cause and mortality from cardiovascular diseases and cancer among South Korean adults.

• Clinical and Experimental Pediatrics
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• v.51 no.11
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• pp.1158-1164
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• 2008

Purpose : To investigate the discriminative ability of pediatric index of mortality 2 (PIM2) and pediatric risk of mortality III (PRISM III) in predicting mortality in children admitted into the intensive care unit (ICU). Methods : We retrospectively analyzed variables of PIM2 and PRISM III based on medical records with children cared for in a single hospital ICU from January 2003 to December 2007. Exclusions were children who died within 2 h of admission into ICU or hopeless discharge. We used Students t test and ANOVA for general characteristics and for correlation between survivors and non-survivors for variables of PIM2 and PRISM III. In addition, we performed multiple logistic regression analysis for Hosmer-Lemeshow goodness-of-fit, receiver operating characteristic curve (ROC) for discrimination, and calculated standardized mortality ratio (SMR) for estimation of prediction. Results : We collected 193 medical records but analyzed 190 events because three children died within 2 h of ICU admission. The variables of PIM2 correlated with survival, except for the presence of post-procedure and low risk. In PRISM III, there was a significant correlation for cardiovascular/neurologic signs, arterial blood gas analysis but not for biochemical and hematologic data. Discriminatory performance by ROC showed an area under the curve 0.858 (95% confidence interval; 0.779-0.938) for PIM2, 0.798 (95% CI; 0.686-0.891) for PRISM III, respectively. Further, SMR was calculated approximately as 1 for the 2 systems, and multiple logistic regression analysis showed ${\chi}^2(13)=14.986$, P=0.308 for PIM2, ${\chi}^2(13)=12.899$, P=0.456 for PRISM III in Hosmer-Lemeshow goodness-of-fit. However, PIM2 was significant for PRISM III in the likelihood ratio test (${\chi}^2(4)=55.3$, P<0.01). Conclusion : We identified two acceptable scoring systems (PRISM III, PIM2) for the prediction of mortality in children admitted into the ICU. PIM2 was more accurate and had a better fit than PRISM III on the model tested.

• 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.