• 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.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.79-85
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• 2002

In this paper, multilevel models are adopted to identify interactions among household members in trip making behaviors. The multilevel approach is a proper methodology to handle samples, which are extracted from a hierarchical structure universe. PSTP dataset is used in developing models and understand proportion of variations among individuals and household. The results of this study show that for activity participation and travel behavior household level variance is more than 1/4 of person level variance and therefore not negligible. The results confirm the importance of multilevel model in travel behavior analysis.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.7
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• pp.599-608
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• 2017

This study investigated factors that are associated with companion animal ownership among urban elderly persons. This study was based on data from 2014 Seoul Survey, and a total of 4,717 people aged 60 years or older were selected for the analyses. Community level data were from 2014 Seoul statistics and they included park area per person, number of elderly recreational facilities, and number of public sports facilities based on 25 districts of Seoul Metropolitan City. This study examined differences between companion animal owners and non-owners concerning individual level factors and community level factors. Factors that are associated with companion animal ownership were examined using multilevel logistic regression analysis. Among individual level variables, companion animal ownership was associated with gender, marital status, income, number of household members, and housing type. Among community level variables, park area per person was a significant factor that is associated with companion animal ownership. Study findings can be used Based on study findings, implications of the study and directions for future research are discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.6
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• pp.835-841
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• 2018

In this study, we analyzed public health data analysis using the hadoop-based spack in the cloud environment using the data of the Community Health Survey from 2012 to 2014, and the factors affecting the quality of life and quality of life. In the proposed paper, we constructed a cloud manager for parallel processing support using Hadoop - based Spack for open medical big data analysis. And we analyzed the factors affecting the "quality of life" of the individual among open medical big data quickly without restriction of hardware. The effects of public health data on health - related quality of life were classified into personal characteristics and community characteristics. And multiple-level regression analysis (ANOVA, t-test). As a result of the experiment, the factors affecting the quality of life were 73.8 points for men and 70.0 points for women, indicating that men had higher health - related quality of life than women.

• The Journal of the Korea Contents Association
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• v.13 no.5
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• pp.342-353
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• 2013

Objectives: It attempted to analyze influencing factors on the utilization of outpatient services which were adopted to predisposing, enabling, and need factors in Anderson model. Methods: The current study analyzed "2007 Korean National Health Nutrition Survey" data, which selected 3,335 people nationwide by proportional systematic sampling. This study analyzed data of persons who used outpatient services in two weeks. It adopted Anderson Model to control contextual factors including socioeconomic factors. The study compared means and fitted logistic regression models and multilevel model. Results: The logistic regression model showed that persons purchased private medical insurance were less likely to use outpatient services than the persons did not purchase private medical insurance. Persons with hypertension and diabetes mellitus, overweight, and problem drinkers were more likely to use outpatient services. Persons with high school graduates or higher in education level and experience of accidents or intoxications were more likely to use outpatient services according to the multilevel analysis of mixed model which treated region as random effect. Conclusion: Higher level of perceived stress increased the probability to use outpatient service than lower level of perceived stress. As number of days a person had exercised increased, the probability to use outpatient service decreased. Overweight and problem alcohol drinking increased the probability of outpatient service use. Further research should be conducted to find more factors influencing outpatient service use.

• Korean Journal of Clinical Pharmacy
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• v.22 no.1
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• pp.13-20
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• 2012

서론: 주사제 사용을 줄이기 위한 정책의 일환으로 건강보험심사평가원은 의료기관별 주사제 처방률을 통보하고 있으나, 여전히 주사제 처방은 높은 실정이다. 따라서 본 연구에서는 주사제에 대한 환자의 인식수준을 살펴보고, 환자의 주사제 요청에 영향을 미치는 요인을 파악하고자 하였다. 연구방법: 본 연구는 최근 6개월 이내에 병의원을 방문한 전국의 20세 이상의 남녀를 대상으로 전화조사한 단면설계 연구다. 환자의 일반적 특성과 주사제에 대한 태도, 인식을 조사하였고, 환자가 주사제를 요청하는데 영향을 미치는 특성을 규명하기 위해 일반적 특성(성, 연령, 결혼여부, 보험형태, 지역규모, 질환, 교육, 소득 등), 주사제에 대한 인식, 태도를 독립변수로 하고, 주사제 처방 요청여부를 종속변수로 하며, 16개 행정지역을 무작위 효과로 층화한 다수 준 분석을 실시하였다.결과: 연구대상에 포함된 응답자는 997명이었고(응답률 82.2%), 응답자 중 24%가 병의원 방문 당시 주사제를 요구했다고 응답했고, 58%가 한번 이상 주사제를 맞은 경험이 있다고 보고했다. 92%가 주사제에 대해 잘못된 인식을 갖고 있었고, 15%는 의사가 부적절하게 주사제를 처방한다고 응답했다. 다수준 로지스틱 회귀분석 결과, 남성의 경우(Odds ratio(OR) 0.71, 95% confidence interval(CI) 0.52-0.99), 고졸이상자(OR 0.63, 95% CI 0.41-0.96), 기혼자(OR 1.72, 95% CI 1.01-2.92)가 주사제를 더 요구하는 것으로 나타났고, 대도시에 비해 농촌지역 환자가(OR 2.12, 95% CI 1.24-3.63), 호흡기계 질환으로 방문한 경우(OR 1.48, 95% CI 1.03-2.12), 주사제를 처방하면 경구제에 비해 신뢰감이 생긴다는 응답자의 경우(OR 1.91, 95% CI 1.33-2.73) 주사제를 더 요구하는 것으로 나타났다. 결론: 본 연구결과 여성, 기혼자, 농촌 거주자, 호흡기계 질환으로 방문한 환자의 경우와 주사를 맞으면 신뢰감이 생긴다는 잘못된 태도를 가진 환자가 주사제를 요구하는 것으로 나타났고, 이러한 환자특성을 고려하여 주사제 사용을 감소시키기 위한 정책을 실시하는 것이 필요하겠다.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.499-510
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• 2016

It is very important to select relevant variables in regression models for survival analysis. In this paper, we introduce a penalized variable-selection procedure in multi-level frailty models based on the "frailtyHL" R package (Ha et al., 2012). Here, the estimation procedure of models is based on the penalized hierarchical likelihood, and three penalty functions (LASSO, SCAD and HL) are considered. The proposed methods are illustrated with multi-country/multi-center bladder cancer survival data from the EORTC in Belgium. We compare the results of three variable-selection methods and discuss their advantages and disadvantages. In particular, the results of data analysis showed that the SCAD and HL methods select well important variables than in the LASSO method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.4
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• pp.571-579
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• 2022

This research empirically analyzed the factors associated with the energy consumption of tourism hotels in Busan, Ulsan, and the Kyoungnam region of Korea based on their individual and locational characteristics. The study adopted a comprehensive modeling approach involving multi-level regression analyses that allowed for improved accuracy by considering the hierarchical structures of the hotels and their locational characteristics. The results indicated that the majority of energy consumption can be explained by the hotels'individual characteristics, including the size of building structure and the services, while their effects vary by region with statistical significance. Furthermore, the proximity to central commercial districts and hotel clusters had a significant influence on the variability in their energy consumption, indicating that locational factors are also important determinants. The findings here suggest the need for regional energy policies and solutions at various urban scales along with conventional energy policies at the building level and highlight regional responsibilities when attempting to create sustainable tourism industries.

• The Korean Journal of Health Service Management
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• v.9 no.2
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• pp.81-88
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• 2015

The purpose of this study was to compare the risk-adjusted in-hospital mortality for craniotomies between logistic regression and multilevel analysis. By using patient sample data from the Health Insurance Review & Assessment Service, in-patients with a craniotomy were selected as the survey target. The sample data were collected from a total number of 2,335 patients from 90 hospitals. The sample data were analyzed with SAS 9.3. From the results of the existing logistic regression analysis and multilevel analysis, the values from the multilevel analysis represented a better model than that of logistic regression. The intra-class correlation (ICC) was 18.0%. It was found that risk-adjusted in-hospital mortality for craniotomies may vary in every hospital. The agreement by kappa coefficient between the two methods was good for the risk-adjusted in-hospital mortality for craniotomies, but the factors influencing the outcome for that were different.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1169-1182
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• 2016

Cancer is a typical cause of death in Korea that becomes a major issue in health care. According to Cause of Death Statistics (2014) by National Statistical Office, SMRs (standardized mortality rates) in Busan were counted as the highest among all cities. In this paper, we used data of Busan Regional Cancer Center to estimate the extent of the cancer incidence rate and cancer mortality rate. The data are considered in small areas of administrative units such as Gu/Dong from years 2003 to 2009. All cancer including four major cancers (stomach cancer, colorectal cancer, lung cancer, liver cancer) have been analyzed. We carried out model selection and parameter estimation using spatial multi-level model incorporating a spatial correlation. For the spatial effects, CAR (conditional autoregressive model) has been assumed.