• The Journal of Korean Society for School & Community Health Education
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• v.13 no.2
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• pp.45-58
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• 2012

Purpose: The study was done to identify health behavior for cancer prevention in university students according to characteristics of the university students and other factors affecting health behavior for cancer prevention and to provide data to set up a strategy to reduce the cancer. Methods: Data were collected by questionnaires from 353 university students in G city. To analyze the sample survey data, descriptive statistics, t-test, ANOVA, Scheffe's test, and multiple regression analysis were performed with SPSS/WIN 15.0. Results: Significant factors that affect health behaviors for cancer prevention in university students were perception of health status, knowledge and attitudes about cancer, and smoking. These variables explained 21% of health behaviors for cancer prevention. Conclusion: The results of this study indicate that in order to improve the health behavior for cancer prevention in university students it is important to development health education programs that focus on positive perception of health status. This development could be enhanced with structured and on-going education about cancer.

• Health Policy and Management
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• v.25 no.3
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• pp.152-161
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• 2015

We analyze the determinants of obesity and the chronic diseases using the Korea Health Panel data. Also we analyze the effect of obesity and the chronic diseases on the health care expenditures. Through this study, to reduce the health care expenditures, we suggest the policy implication that might curb the obesity and the chronic diseases. We estimate the determinants of obesity, the chronic diseases, and the health care expenditures using 2SLS (two stage least squares) estimation method under the simultaneous equations framework. Result says that obesity and chronic diseases significantly have positive effects on the health care expenditures. Also the determinants of the health care expenditures that have positive effects are age, income and health care utilization variables.

• Health Policy and Management
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• v.26 no.1
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• pp.71-78
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• 2016

The value of using health insurance claim database is continuously rising in healthcare research. In studies where comorbidities act as a confounder, comorbidity adjustment holds importance. Yet researchers are faced with a myriad of options without sufficient information on how to appropriately adjust comorbidity. The purpose of this study is to assist in selecting an appropriate index, look back period, and data range for comorbidity adjustment. No consensus has been formed regarding the appropriate index, look back period and data range in comorbidity adjustment. This study recommends the Charlson comorbidity index be selected when predicting the outcome such as mortality, and the Elixhauser's comorbidity measures be selected when analyzing the relations between various comorbidities and outcomes. A longer look back period and inclusion of all diagnoses of both inpatient and outpatient data led to increased prevalence of comorbidities, but contributed little to model performance. Limited data range, such as the inclusion of primary diagnoses only, may complement limitations of the health insurance claim database, but could miss important comorbidities. This study suggests that all diagnoses of both inpatients and outpatients data, excluding rule-out diagnosis, be observed for at least 1 year look back period prior to the index date. The comorbidity index, look back period, and data range must be considered for comorbidity adjustment. To provide better guidance to researchers, follow-up studies should be conducted using the three factors based on specific diseases and surgeries.

• Environmental Analysis Health and Toxicology
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• v.28
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• pp.5.1-5.7
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• 2013

Objectives Despite increasing availability of global positioning system (GPS), no research has been conducted to analyze GPS data for exposure opportunities associated with time at indoor and outdoor microenvironments. We developed location-based and time-weighted spatial measures that incorporate indoor and outdoor time-location data collected by GPS. Methods Time-location data were drawn from 38 female subjects in California who wore a GPS device for seven days. Ambient standard deviational ellipse was determined based on outdoor locations and time duration, while indoor time weighted standard deviational ellipse (SDE) was developed to incorporate indoor and outdoor times and locations data into the ellipse measure. Results Our findings indicated that there was considerable difference in the sizes of exposure potential measures when indoor time was taken into consideration, and that they were associated with day type (weekday/weekend) and employment status. Conclusions This study provides evidence that time-location weighted measure may provide better accuracy in assessing exposure opportunities at different microenvironments. The use of GPS likely improves the geographical details and accuracy of time-location data, and further development of such location-time weighted spatial measure is encouraged.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1531-1538
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• 2014

The causes of cancer are diverse, complex, and only partially understood. Many factors including health behaviors, socioeconomic environments and geographical locations can directly damage genes or combine with existing genetic faults within cells to cause cancerous mutations. Collecting the cancer data and reporting the statistics, therefore, are important to help identify health trends and establish normal health changes in geographical areas. In this article, we analyzed cancer data and demon-strated how spatial patterns of the age-standardized rate and health indicators can be examined visually and simultaneously using linked micromap plots. As a result of data analysis, the age-standardized rate has positive correlativity with thyroid and breast cancer, but the rate has negative correlativity with smoking and drinking. In addition, the regions with high age-standardized rate are located in southwest and the areas of high population density while the standardized mortality ratio is higher in southwest and northeast where there are lots of rural areas.

• Health Policy and Management
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• v.33 no.2
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• pp.118-128
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• 2023

Background: Personal socio-economic abilities are crucial as it affects health inequalities. These multidimensional inequalities across the regions have been structured and fixed. This study aimed to analyze health vulnerabilities by regional cluster and identify regional health disparities of self-rated health, using nationally representative cross-sectional data. Methods: This study used personal and regional data. Data from the Community Health Survey 2021 were analyzed. K-means cluster analysis was applied to 250 si-gun-gu using administrative regional data. The clusters were based on three areas: physical environment, health-related behaviors and biological factors, and the psychosocial environment through the conceptual framework for action on the social determinants of health. And binary logistic regression analyses were conducted to examine the differences in self-rated health status by the regional clusters, controlling human biology, environment, lifestyle, and healthcare organization factors. Results: The most vulnerable group was group 3, the moderate vulnerable group was group 1, and the least vulnerable group was group 2. The group 2 was more likely to have high self-rated health status than the moderate vulnerable group (odds ratio [OR], 1.023; p<0.001). And the group 3 showed low self-rated health status than the moderate vulnerable group (OR, 0.775; p<0.001). However, the moderate vulnerable group had significantly higher self-rated health status than the most vulnerable group (group 2: OR, 1.023; p<0.001; group 3: OR, 0.775; p<0.001). Conclusion: These results demonstrate that community members' health status is influenced by regional determinants of health and individual levels. And these contribute to understanding the importance of specific and differentiated interventions like locally tailored support programs considering both individual and regional health determinants.

• International Journal of Contents
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• v.13 no.3
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• pp.49-58
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• 2017

This is a descriptive research study that classified Korean adults with obesity into those with Metabolically Healthy Obesity and those with Metabolically Unhealthy Obesity based on the data from the fifth and sixth South Korea's National Health and Nutrition Examination Surveys, designed due to the development of information and communication technology, to examine the impacts of obese adults' health behavior factors and mental health factors on their metabolic state. With respect to data analysis, the collected data were analyzed by complex sample statistics. The results of this study can be summarized as follows: Men who were smoking at the time of the survey had a 1.29 times higher probability of inclusion in the MUO group than in the MHO group. Women who had a high stress cognition rate had a 1.02 times higher probability of inclusion in the MUO group than in the MHO group. This study is significant as it provides the basic data for establishing strategies of nursing intervention for the promotion of obese adults' health, and it suggests that it is necessary to develop a program for the promotion of obese adults' health based on these results.

• Journal of Korean Public Health Nursing
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• v.11 no.1
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• pp.39-50
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• 1997

This study was designed to identify the relationship between health perception and health behavior in nursing students and provide basic data for structuring the strategies of health promotion. The targets in this study were the 191 nursing students in nursing department of one national university in Chonju city. The data were collected during the period from 10 to 25 in Nov. 1995 by means of a structured questionnaire. Health perception was measured by the health perception questionnaire developed by Ware and translated by You. Health behavior was measured by health promotion questionnaire developed by Cho. The data were analyzed by descriptive statistics, t-test, ANOVA and Pearson correlation using the $SPSS-PC^+$ program. The results of this study were as follows; 1. The mean health perception score of the subjects was 3.21; the level of health perception was relatively high. 2. The mean health behavior score of the subjects was 3.61; the level of health behavior was relatively high. 3. When health perception and health behavior was analyzed by Pearson correlation., it was found that the higher the degree of health perception, the better the reported health behavior(r=.1463, p=.022). 4. General characteristics related to health perception were attitude and school life(p<0.05). General characteristics related to health behavior were degree, religion, attitude and school life(p<0.05).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.387-395
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• 2013

It is important to collect reliable measured data for proper bridge health monitoring. However, in reality incomplete and unreliable data may be acquired due to sensor problems and environmental effects. In case of sensor malfunction, parts of measured data are missing and thus health monitoring cannot be carried out reliably. Due to environmental effects such as temperature variation, dynamic characteristics of natural frequencies may change as if the structure is damaged. The paper proposes a systematic procedure of data processing and data analysis for reliable structural health monitoring. Also, it applies the Mahalanobis distance as a health index computed statistically using revised data. The proposed procedure has been examined using numerically simulated data from a truss structure and then applied to a set of field data measured from Seohae cable-stayed bridge.

• The Korean Journal of Health Service Management
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• v.12 no.4
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• pp.203-217
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• 2018

Objectives : This study aimed to investigate the clinical practice ability and satisfaction of clinical training of health-medical information management major students. Methods : The data were collected from 68 persons from students finished clinical training at medical record (information) team using self administered questionnaires. The data were analyzed using t-test, ANOVA and correlation with SPSS 22.0 version. Results: Performance of data collection, data management, and data analysis were analyzed in three areas of the job area. In terms of academic characteristics and correlation, they were not related to the level of satisfaction with the practical experience. Conclusions : Research on a virtuous cycle clinical practice program that analyzes the factors by assessing the satisfaction level of clinical practice in each area of health care information management will be conducted continuously.