• Journal of Preventive Medicine and Public Health
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• v.27 no.2 s.46
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• pp.175-185
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• 1994

In order to anticipate disease pattern and health problems of Koreans in the 1st part of 21st century (by the year 2020), transition of population characteristics, mortality and morbidity data during the last 30 years Koreans have experienced were reviewed. On the actual basis of epidemiologic transition process that has undergone during last 30 years since 1960 along with socioeconomic development and successful implementation of selective national health policies (family planning, medical insurance and etc.), following changes can be expected in the 21st century in Korea, under the assumption that the current rate of progress is maintained. The population of South Korea alone will be doubled the population of 1960 by the year 2013 : aged Population older than 65 years will be increased from 3.3% in 1960 to 11.4% in 2020 with increased average age of the population from 23.6 year in 1970 to 39.2 year in 2020; urban population from 28% in 1960 to 83% in 2005. GNP/capita has increased tremendously from U.S. $120 in 1970 to $6,749 in 1992, and the government estimated it would be 519,350 in 2010 and $29,460 in 2020. Growth and developmental indices of children, educational achievement and social status of women also showed a remarkable improvement and anticipated to make futher progress. Leading causes of mortality and morbidity have shown a striking change during the last 30 years, from infectious diseases to chronic degenerative diseases and man-made injuries. Occurrence of communicable diseases may become minimal although viral hepatitis, venereal diseases Including AIDS, and well adapted herpes virus infections will maintain their endemic level. Newly evolving infectious agents, however, should be carefully monitored because of rapidly changing environments and human behaviours. Tuberculosis may increase up to the epidemic level when AIDS prevails. Ischemic heart diseases may increase steadily with increasing occurrence of hypertension and diabetes mellitus whereas cerebrovascular diseases may be decreased slowly. Musculaskeletal diseases which contribute a lot to the disability of aged people may be a major health problems due to increased aged population. Mental diseases, particularly that caused by alcohol and drug abuse, and senile dementia may become a prominent health problem. On the other hand injuries caused by traffic and industrial accidents that have shown most striking increase till now may be decreased considerably by intensive intervention. The health policies in the 21st century will be oriented to the health promotion for good quality life rather than life-savings.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3870-3878
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• 2009

The purpose of this study was to investigate 119 rescue party's violence experiences and to analyze their responses of violence experiences based on the pre-hospital emergency medical field. Data were collected from the self-administered survey of 226 rescue party in Jeollabuk-do area in Korea from January 1 through March 2, 2009. The data were analyzed by descriptive statistics and logistic regression analysis using SPSS Win 12.0. The results were as follows: first, all of 119 rescue unit in the pre-hospital emergency medical field reported that they got violence experiences more than once a week, and 62% of rescue team were exposed to physical violence. Patient's factors that caused violence were drug abuse or alcohol(56.2%), on the other hand rescue party's factors were shortage of human resources. Second, 42% of the respondents replied that they did not have any educational experiences for prevention and coping methods about violence within 5 years. 77.4% of those surveyed indicated that they wanted to receive specific educations which can prevent and cope with violence. Third, the mean value of total violent response scores was 2.53, and emotional response score was 3.2, social response score was 2.22, and physical response score was 2.17. Fourth, violent response score related to general characteristics proposed that physical(t=-2.08, p=o.38), emotional(t=-7.13, p=.006), and total responses(t=-4.764, p=.000) were statistically significant. And emotional(t=4.257, p=.000) and total responses(t=8.1330, p=.000) related to age were also statistically significant. Finally, among current tenure qualification scores, social response(t=9.987, p=.012) and total score(t=8.130, p=.000) were statistically significant. Between fire departments, violent response score suggested that physical(t=9.987, p=.000), emotional(t=2.433, p=.012), social(t=6.738, p=.000), and total score(t=5.943, p=.000) were statistically significant.

• Journal of Preventive Medicine and Public Health
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• v.24 no.2 s.34
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• pp.195-210
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• 1991

Fatty liver is caused by derangement of fat metabolism and can be reversed by removal of contributing factors. The contributing factors of fatty liver is known to be overweight, chronic alcoholism, diabetes mellitus, malnutrition, and drug abuse such as tetracycline. This study was carried out on 1335 persons who visited 'Soon Chun Hyang Human Dock Center' from March to June 1990. In analysis of the data, prevalence of fatty liver diagnosed by ultrasonogram by age and sex, laboratory finding between fatty liver group and normal group, and odds ratio of known contributing factors, were compared. The results obtained are as following ; 1) The prevalence rate of fatty liver diagnosed by ultrasonogram is 29.6% in male and 11.5% in female. 2) Age groups with high prevalences are $40{\sim}50's$ in male (32.0%) and 50's in female (24.5%). 3) The fatty liver shows significant association with style (p<0.05), whereas not with hepatitis B-virus surface antigen (p>0.05). 4) All laboratory values except alkaline phosphatase and bilirubin are elevated significantly in accordance with the degree of fatty liver (p<0.01). 5) Fatty liver diagnosed by ultrasonogram showed so strong associations with body index, triglycerides and gamma-glutamyl transferase for males, and body index and fasting blood sugar for females that these factors may be used as supplementary data in establishing diagnosis of fatty liver. 6) Odds ratio of contributing factors are as follows ; If the odds ratio of below 29 year of age is 1.0 then that of $30{\sim}39$ is 1.74 (p=0.33), $40{\sim}49$ is 2.47 (p=0.10), $50{\sim}59$ is 2.86 (p=0.0570), over 60 is 1.81 (p=0.34). If the odds ratio of female is 1.0 then that of male is 5.67 (p<0.01). If the odds ratio of body index below zero is 1.0 then that of $0{\sim}9$ is 5.08 (p<0.01), $10{\sim}19$ is 12.37 (p<0.01), $20{\sim}29$ is 29.19 (p<0.01), 30 above is 154.02 (p<0.01). If the odds ratio of below 99 mg/dl FBS is 1.0 then that of $100{\sim}120$ is 106 (p=0.76), over 120 is 1.91 (p=0.02). If the odds ratio of below $29{\mu}/1{\gamma}-GT$ is 1.0 then that of $30{\sim}s59$ is 2.11 (p<0.01), $60{\sim}90$ is 1.87 (p<0.05), 90 above is 1.69 (p=0.15). If the odds ratio of below 149 mg/dl TG is 1.0 then $150{\sim}199$ is 1.49 (p=0.05), $200{\sim}250$ is 1.09 (P=0.77), 250 above is 2.53 (p<0.01). In summary, early diagnosis of fatty liver could be made by ultrasonogram supplemented with body index and nm triglyceride. The fatty liver could be preventive by avoiding contributing factors such as obesity, alcohol intake, high blood sugar appropriately.

• Korean Journal of Health Education and Promotion
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• v.2 no.1
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• pp.31-61
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• 2000

Introduction. Despite the fact that half of premature deaths are caused by unhealthy lifestyles such as smoking tobacco, sedentary lifestyle, alcohol and drug abuse and poor nutrition, there are no theoretical models which accurately explain these health promotion related behaviors. This study tests a new model of health behavior called the Model of Health Promotion Behavior. This model draws on elements and frameworks suggested by the Health Belief Model, Social Cognitive Theory, the Theory of Planned Action and the Health Promotion Model. This model is intended as a general model of behavior but this first test of the model uses amount of exercise as the outcome behavior. Design. This study utilized a cross sectional mail-out, mail-back survey design to determine the elements within the model that best explained intentions to exercise and those that best explained amount of exercise. A follow-up questionnaire was mailed to all respondents to the first questionnaire about 10 months after the initial survey. A pretest was conducted to refine the questionnaire and a pilot study to test the protocols and assumptions used to calculate the required sample size. Sample. The sample was drawn from 2000 eligible participants at two blue collar (utility company and part of a hospital) and two white collar (bank and pharmaceutical) companies located in Southeastern Michigan. Both white collar site had employee fitness centers and all four sites offered health promotion programs. In the first survey, 982 responses were received (49.1％) after two mailings to non-respondents and one additional mailing to secure answers to missing data, with 845 usable cases for the analyzing current intentions and 918 usable cases for the explaining of amount of current exercise analysis. In the follow-up survey, questionnaires were mailed to the 982 employees who responded to the initial survey. After one follow-up mailing to non-respondents, and one mailing to secure answers to missing data, 697 (71.0％) responses were received, with 627 (63.8％) usable cases to predict intentions and 673 (68.5％) usable cases to predict amount of exercise. Measures. The questionnaire in the initial survey had 15 scales and 134 items; these scales measured each of the variables in the model. Thirteen of the scales were drawn from the literature, all had Cronbach's alpha scores above .74 and all but three had scores above .80. The questionnaire in the second mailing had only 10 items, and measured only outcome variables. Analysis. The analysis included calculation of scale scores, Cronbach's alpha, zero order correlations, and factor analysis, ordinary least square analysis, hierarchical tests of interaction terms and path analysis, and comparisons of results based on a random split of the data and splits based on gender and employer site. The power of the regression analysis was .99 at the .01 significance level for the model as a whole. Results. Self efficacy and Non-Health Benefits emerged as the most powerful predictors of Intentions to exercise, together explaining approximately 19％ of the variance in future Intentions. Intentions, and the interaction of Intentions with Barriers, with Support of Friends, and with Self Efficacy were the most consistent predictors of amount of future exercise, together explaining 38％ of the variance. With the inclusion of Prior Exercise History the model explained 52％ of the variance in amount of exercise 10 months later. There were very few differences in the variables that emerged as important predictors of intentions or exercise in the different employer sites or between males and females. Discussion. This new model is viable in predicting intentions to exercise and amount of exercise, both in absolute terms and when compared to existing models.