• The Korean Journal of Health Service Management
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• v.8 no.4
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• pp.81-94
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• 2014

The purpose of this study is to investigate factors affecting cancer mortality inequality in Busan according to demographic characteristics identified based on the region's mortality data including cancer incidence and mortality rates, ultimately helping the region improve its existing health policies and establish a more effective cancer prevention policy. To achieve this purpose, this researcher surveyed data about all persons who died in Busan from 2006 to 2009. Data were analyzed with an SPSS 18.0 program using descriptive statistics, Chi-Square(${\chi}^2$), and Logistic Regression analysis. Findings of the study can be summarized as follows. First, in Busan, men were about two times higher in cancer mortality rate than women. Second, persons who died of cancer in Busan were significantly different from one another in terms of demographic characteristics, especially, age, marital status, and job. Third, factors affecting cancer mortality inequality in Busan included such demographic characteristics as gender, age at the time of death, marital status, educational background, and job.

• Health Policy and Management
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• v.28 no.1
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• pp.15-22
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• 2018

Background: Health is affected by various local factors. This study aims to investigate the age-standardized mortality variation of Seoul as well as the characteristics of the factors related to the mortality variation. Methods: The Korea Community Health Survey data, Seoul Survey data, Seoul statistics, and e-regional indicators of the National Statistical Office were used. To investigate the basic boroughs standardized mortality variation in Seoul, external quotient, coefficient of variation (CV), and systematic component of variation (SCV) values were suggested; correlation analysis and multiple regression analysis were conducted to investigate the characteristics related to standardized mortality rate. Results: The highest and the lowest standardized mortality rate of Seoul by boroughs had as much as 1.4 times difference; a low level of variation was shown in CV by 8.2; and was shown in SCV by 79. As a result of the multiple regression analysis of the factors that affect standardized mortality variation, the higher the rate of householders with college or higher, the lower the standardized mortality rate, and the higher the high-risk drinking rate, the higher the standardized mortality rate. Of the two, the rate of householder with a degree equivalent or higher than college was shown to have the biggest impact, followed by high-risk drinking rate. Conclusion: We found a variation in age-standardized mortality rate of boroughs in Seoul. The results suggest that policy makers should take into account socioeconomic environmental characteristics of community in developing community-based health promotion rather than focusing on lifestyle changes of residents.

• Health Policy and Management
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• v.30 no.3
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• pp.301-310
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• 2020

Background: In this study, we aimed to investigate the recent trends for health care indicators including maternal mortality ratio, infant mortality rate, under-five mortality rate, life expectancy, years of life lost, and healthcare resources in South Korea, North Korea, Germany, Russian Federation, Mongolia, Vietnam, China, Czech Republic, Poland, and Hungary. Methods: We used data from five sources: World Health Organization, Federal Institute for Population Research, World Bank, Organization for Economic Cooperation and Development health statistics, and national statistics. Results: In the early 1990s, health indicators continued to improve in countries that switched to the health insurance system, but the gap widened in North Korea as health indicators worsened. Conclusion: The establishment of a sustainable health care system after unification of the Korean peninsula requires substantial changes in the health care system and efforts to improve the health of North Koreans.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.177-190
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• 2006

The aim of this study is to propose a Bayesian model for fitting mortality rate of colon cancer. For the analysis of mortality rate of a disease, factors such as age classes of population and spatial characteristics of the location are very important. The model proposed in this study allows the age class to be a random effect in addition to its conventional role as the covariate of a linear regression, while the spatial factor being a random effect. The model is fitted using Metropolis-Hastings algorithm. Posterior expected predictive deviances, standardized residuals, and residual plots are used for comparison of models. It is found that the proposed model has smaller residuals and better predictive accuracy. Lastly, we described patterns in disease maps for colon cancer.

• Journal of Gastric Cancer
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• v.11 no.3
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• pp.135-140
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• 2011

Gastric cancer has been the most commonly diagnosed cancer in Korea although the age-standardized mortality and incidence has decreased gradually during last two decades. $Helicobacter$ $pylori$ infection and cigarette smoking are well-established risk factors, and the role of dietary factors, such as salted foods, fresh vegetables and fruits, soy foods, and processed or grilled meats on gastric carcinogenesis has been suggested. In this review, we review national and international gastric cancer statistics, studies on environmental risk factors conducted in the Korean population, and gastric cancer screening activities.

• Journal of Korean Academy of Nursing
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• v.35 no.1
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• pp.47-55
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• 2005

Purpose: This study was to evaluate the validity of the Pediatric Index of Mortality Ⅱ(PIM Ⅱ). Method: The first values on PIM Ⅱ variables following ICU admission were collected from the patient's charts of 548 admissions retrospectively in three ICUs(medical, surgical, and neurosurgical) at P University Hospital and a cardiac ICU at D University Hospital in Busan from January 1, 2002 to December 31, 2003. Data was analyzed with the SPSSWIN 10.0 program for the descriptive statistics, correlation coefficient, standardized mortality ratio(SMR), validity index(sensitivity, specificity, positive predictive value, negative predictive value), and AUC of ROC curve. Result: The mortality rate was 10.9% (60 cases) and the predicted death rate was 9.5%. The correlation coefficient(r) between observed and expected death rates was .929(p<.01) and SMR was 1.15. Se, Sp, pPv, nPv, and the correct classification rate were .80, .96, .70, .98, and 94.0% respectively. In addition, areas under the curve (AUC) of the receiver operating characteristic(ROC) was 0.954 (95% CI=0.919～0.989). According to demographic characteristics, mortality was underestimated in the medical group and overestimated in the surgical group. In addition, the AUCs of ROC curve were generally high in all subgroups. Conclusion: The PIM Ⅱ showed a good, so it can be utilized for the subject hospital. better.

• Quality Improvement in Health Care
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• v.10 no.2
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• pp.164-175
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• 2003

Objectives: To evaluate the performance of models to predict AMI patients death using severity adjustment measures in Korea. Methods: Medical records of 861 patients treated by AMI in 7 general hospitals during 1996 and 1997 were reviewed by trained nurses. We measured the severity of patients by APACHE III, MedisGroups, CSI and DS. Using each severity method a predictive mortality for each patient was calculated from a logistic regression model including the severity score. The statistical performance of each severity method model was evaluated by using c-statistics and R2. For each hospital, z scores compared actual and expected mortality rates. Results: The overall in-hospital mortality was 14.5%, ranged from 10.0% to 22.2%. The distributions of severity scores for each method was significantly different by hospitals. The four severity-adjusted models to predict AMI patients death varied in their statistical performance for discrimination power of patients death. Order of Severity-adjusted mortality rates and z scores by four severity measures was different. Conclusion: Severity-adjusted mortality rates of AMI patients might be applied as an indicator for hospital performance evaluation in Korea. Because different severity methods frequently produce different impressions about relative hospital performance, more studies has to be done to use it as quality indicator and more attention should be paid to select appropriate severity measures.

• Communications for Statistical Applications and Methods
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• v.20 no.1
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• pp.53-62
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• 2013

This study focuses on spatial/temporal relationship deaths caused by Human Immunodeficiency Virus (HIV) and Alcohol Use Disorder (AUD). Several studies have found links between these two diseases. By looking for clusters in mortality of Alcohol and HIV related deaths this study contributes to the field through the identification of exact spatial/temporal time of high and low occurrence risks based on the observed over the expected number of deaths. This study does not provide political or social interpretations of the data. It merely wants to show where clusters are found.

• The Journal of the Korean life insurance medical association
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• v.16
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• pp.123-144
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• 1997

• The Journal of the Korean life insurance medical association
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• v.16
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• pp.109-121
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• 1997