• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1245-1245
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145-154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1152-1152
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145 154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.20
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• pp.8801-8807
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• 2014

Purpose: The aim of the study was to evaluate knowledge, attitudes and behaviors of Turkish women over 20 years old on cervix cancer. Materials and Methods: This descriptive study was performed at a primary care center covering 246 women using a questionnaire composed of 3 sections and 38 questions. The data were analyzed using descriptive statistics, chi-square test in univariate analysis and multivariate hierarchical logistic regression analysis. Results: Of the 93.7% women who knew about cervical cancer, 68.0% of them had heard pap smear test and 46.1% had actually undergone a Pap smear once or more throughout their lives. According to the results of the hierarchical logistic regression analysis about factors affecting the Pap smear test; in Model 1, increase in age and education levels, in Model 2 and Model 3 increase in age and cervical cancer information points were determined. The most important information source for cervical cancer was TV-radio/media (59.9%) and health care workers (62.8%) for pap smear test. Conclusions: Although most women have heard of cervical cancer, knowledge about cervical cancer and also Pap smear screening rate are significantly lower. Having Pap smear test for women was affected by age and knowledge level about cervical cancer. Informing women about cervical cancer would be an important intervention.

• Journal of Preventive Medicine and Public Health
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• v.43 no.6
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• pp.513-522
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• 2010

Objectives: The purpose of this study was to define the association between the medical utilization of osteoarthritis patient and its related factors. Methods: We used the 2005 Korean National Health and Nutrition Survey data and we enrolled 2833 participants who were forty or older and who were diagnosed as having osteoarthritis by a doctor within 1 year and who had suffered from osteoarthritis for more than 3 months. The Andersen behavioral model was used as the analytic framework, and the variables were categorized into predisposing, enabling, and need factors. To determine the influence of each variable on the medical utilization of osteoarthritis patient, we applied hierarchical logistic regression analysis with two stages: the first stage included the predisposing and enabling factors and the second stage included the need factors. Results: On the hierarchical logistic analysis, the variables of personal income, the type of medical security, the duration of arthritis related symptoms within 1 month, the subjective health status and the duration of osteoarthritis showed a statistically significant association with medical utilization in men. And the variables of age, limitation activity due to osteoarthritis, arthritis related symptoms within 1 month, and the subjective health status had a statistically significant association with medical utilization in women. Conclusions: The patients who tend to receive less care are those who suffer less from symptoms of osteoarthritis, those who are within the initial phase, or those with a low-level severity of osteoarthritis. It is necessary to encourage patients to receive the treatment in the initial phase.

• Journal of the Korea Safety Management & Science
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• v.16 no.3
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• pp.257-266
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• 2014

This study was planned to investigate the effect of the exposure to hazard factors on work environment satisfaction. Existing researches about job satisfaction have focused on the general working conditions, such as working hours, wage, human relationship, job task and so on. Korean Working Conditions Survey was used for this study because that relevant questions were included. The effect of the exposure to hazard factors on work environment satisfaction may be produced by hierarchical regression analysis because of comparison with existing model for work environment satisfaction. The exposure to hazards factors were statistically significant effect on work environment satisfaction after adjusting other confounding variables, such as gender, age, educational level, job security, work hour, work load, work autonomy, social support, etc. This study has some limitation because that KWCS was cross sectional survey. Some researches about the causal effect and its mechanism may be suggested as future study.

• Journal of the Korea Safety Management & Science
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• v.15 no.4
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• pp.145-152
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• 2013

The purpose of this study was to investigate the effect of risk awareness on injury experience in quick delivery service workers. Risk awareness has complicate characteristics such as its level of worker and worker's decision about the level of other's risk perception. Data were collected by interview survey with structured questionnaire about injury experience, risk perception, work characteristics, and socio-demographic characteristics of quick delivery service workers by cross sectional survey design in 2012. The sample size was 120 respondent of quick delivery service workers. Statistical method for this study was hierarchical logistic regression method with 3 different models using socio-demographic characteristics and work characteristics and risk perception, etc. The difference between the level of risk perception of quick delivery service and other's was statistically significant effect on the experience of injury. Especially the higher the level of risk perception of quick delivery service workers is than other's, the lower the injury experience of quick delivery service worker is. The limitation of this study can be found in survey design. The future study for investigation of mechanism of the combined effect of risk perception of quick delivery service workers and others on injury experience.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.279-291
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• 2018

This study is designed to demonstrate risk factors of unmet medical care for people with cerebrovascular disease. To do this, statistical analysis was performed by using hierarchical logistic regression analysis with SPSS/WIN24.0 program using Korean Medical Panel data in 2014. In the final model of the hierarchical logistic regression analysis, which is based on Anderson's Model, adjusted for the factors of the predisposing and enabling factors, the explanatory variables affecting the unmet medical development are gender, economic activity, income level, the experience of lying in a sickbed, restriction on activity, subjective health condition, and the number of chronic diseases. Based on the results of this study, the practical and policy implications for the effective management and treatment of cerebrovascular disease should be included in the countermeasures for cerebrovascular disease, a strategy to reduce the unmet medical incidence of cerebrovascular disease, in order to meet the medical needs, the necessity of comprehensive measures considering various dimensions of variables and the influential variables of unmet medical emergence have been suggested for the necessity of making a detailed service manual that can improve accessibility to medical services.

• Journal of Korean Medicine Rehabilitation
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• v.29 no.4
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• pp.89-100
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• 2019

Objectives We are going to analyze patient's medical and Korean medicine use trends after lumbar surgery, and examine the percentage of use of Korean medicine after surgery and its relevance to the medical care outcome after lumbar surgery. Methods Using 3% patients' sample data of the Health Insurance Review and Assessment Service, two groups were compared the treatment progress of the Korean Medicine treatment group and the untreated group after lumbar surgery by hierarchical logistic regression analysis. After hierarchical logistic regression analysis(including propensity scores), two groups were compared after lumbar surgery, the Korean Medicine treatment group within 50days and untreated group within 50days. Results Lumbar surgery was performed in 2750 patients in 2015. It was 3.72 that the risk(odds ratio) of finished treatment of patients treated without Korean Medicine, compared to patients with Korean Medicine. It was 0.12 that the risk of continuing treatment(odds ratio) of patients treated with Korean Medicine within 50 days, compared to patients treatment more than 50 days. Conclusions The ratio of Korean Medicine treatment after lumbar surgery was 14.8%. The group that did not have Korean Medicine showed a higher possibility of treatment termination than the group who did not. Among the groups treated with Korean Medicine, the early treatment group was more likely to end treatment than the late treatment group. Considering various situations in the medical environment, further studies such as prospective studies and long-term data analysis are considered to be necessary.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.203-213
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• 2014

Sickness absence is one of the most important indicators for worker's health and occupational safety and health performance. Sickness absence is primarily depended upon sickness but psycho-social factors in workplace may moderate sickness absence. Even though worker is falling into illness, sickness absence can be prevented by job satisfaction. In Korea it is very difficult to find research output about the association of sickness absence with job satisfaction. This study is planned to investigate the effect of job satisfaction on sickness absence. The third Korean Working Conditions Survey done by Occupational Safety and Health Research Institute in 2011 was used to analyze by logistic regression analysis. The result has shown that job satisfaction has statistically significant effect on sickness absence and simultaneously diminish the effect of symptoms experience on sickness absence. The effect of job satisfaction is greater in short term sickness absence than in long term sickness absence. This study has some limitation because of the cross sectional data of Korean Working Conditions Survey. In future, sophisticated statistical analysis may be done with modelling.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.89-102
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• 2014

In this study, we analyze the effect on the smoking status in the Seoul Metropolitan area using a multilevel logistic model with Community Health Survey data from the Korea Centers for Disease Control and Prevention. Intraclass correlation coefficient (ICC), profiling analysis and two types of predicted value were used to determine the appropriate multilevel analysis level. Sensitivity, specificity, percentage of correctly classified observations (PCC) and ROC curve evaluated model performance. We showed the applicability for multilevel analysis allowed for the possibility that different factors contribute to within group and between group variability using survey data.