• Health Policy and Management
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• v.25 no.1
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• pp.31-39
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• 2015

Background: The purpose of this study is comparison of the results between regression and multi-level analysis to find out factors influencing outcome indicators (in-hospital death, length of stay, and medical charges) of stroke patients. Methods: By using patient sample data of Health Insurance Review & Assessment Service, patients admitted with stroke were selected as survey target and 15,864 patients and 762 hospitals were surveyed. Results: For the results of existing regression analysis and multi-level analysis, models were assessed through model suitability index value and as a result, the value of results of multi-level analysis decreased compared to the results of regression, showing it is a better model. Conclusion: Factors influencing in-hospital death of stroke patients were analyzed and as a result, intra-class correlation (ICC) was 13.6%. In factors influencing length of stay, ICC was 11.4%, and medical charges, ICC was 17.7%. It was found that factors influencing the outcome indicators of stroke patients may vary in every hospital. This study could carry out more accurate analysis than existing research findings through analysis of reflecting structure at patient level and hospital level factors and analysis on random effect.

• Genomics & Informatics
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• v.20 no.4
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• pp.48.1-48.7
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• 2022

Penalized regression has been widely used in genome-wide association studies for joint analyses to find genetic associations. Among penalized regression models, the least absolute shrinkage and selection operator (Lasso) method effectively removes some coefficients from the model by shrinking them to zero. To handle group structures, such as genes and pathways, several modified Lasso penalties have been proposed, including group Lasso and sparse group Lasso. Group Lasso ensures sparsity at the level of pre-defined groups, eliminating unimportant groups. Sparse group Lasso performs group selection as in group Lasso, but also performs individual selection as in Lasso. While these sparse methods are useful in high-dimensional genetic studies, interpreting the results with many groups and coefficients is not straightforward. Lasso's results are often expressed as trace plots of regression coefficients. However, few studies have explored the systematic visualization of group information. In this study, we propose a multi-level polar Lasso (MP-Lasso) chart, which can effectively represent the results from group Lasso and sparse group Lasso analyses. An R package to draw MP-Lasso charts was developed. Through a real-world genetic data application, we demonstrated that our MP-Lasso chart package effectively visualizes the results of Lasso, group Lasso, and sparse group Lasso.

• Health Policy and Management
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• v.30 no.3
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• pp.355-365
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• 2020

Background: This study is aimed to verify individual and regional-level factors affecting the depression of Koreans and to develop social programs for improving the depressive status. Methods: This study used individual-level variables from the Korean Community Health Survey (2018) and used the e-regional index of the Korean Statistical Information Service as the regional-level variable. A multi-level logistic regression was executed to identify individual and regional-level variables that were expected to affect the extent of depressive symptoms and to draw the receiver operating characteristic curve to compare the volume of impact between variables from both levels. Results: The results of the multi-level logistic regression analysis in regards to individual-level factors showed that older age, female gender, a lower income level, a lower education level, not having a spouse, the practice of walking, the consumption of breakfast higher levels of stress, and having high blood pressure or diabetes were associated with a greater increase in depressive symptoms. In terms of regional factors, areas with fewer cultural facilities and fewer car registration had higher levels of depressive symptoms. The comparison of area under the curve showed that individual factors had a greater influence than regional factors. Conclusion: This study showed that while both, individual and regional-level factors affect depression, the influence of the latter was relatively weaker as compared to the first. In this sense, it is necessary to develop programs focused on the individual, such as social prescribing at the local or community-level, rather than the city and nation-level approach that are currently prevalent.

• Health Policy and Management
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• v.30 no.3
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• pp.399-408
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• 2020

Background: The purpose of this study was to examine the impact of the regional characteristics on the accessibility of emergency care and the impact of emergency medical accessibility on the patients' prognosis and the emergency medical expenditure. Methods: This study used the 13th beta version 1.6 annual data of Korea Health Panel and the statistics from the Korean Statistical Information Service. The sample included 8,119 patients who visited the emergency centers between year 2013 and 2017. The arrival time, which indicated medical access, was used as dependent variable for multi-level analysis. For ordinal logistic regression and multiple regression, the arrival time was used as independent variable while patients' prognosis and emergency medical expenditure were used as dependent variables. Results: The results for the multi-level analysis in both the individual and regional variables showed that as the number of emergency medical institutions per 100 km² area increased, the time required to reach emergency centers significantly decreased. Ordinal logistic regression and multiple regression results showed that as the arrival time increased, the patients' prognosis significantly worsened and the emergency medical expenses significantly increased. Conclusion: In conclusion, the access to emergency care was affected by regional characteristics and affected patient outcomes and emergency medical expenditure.

• Journal of the Korea Institute of Building Construction
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• v.4 no.4
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• pp.63-70
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• 2004

The objective of this study is to evaluate the compressive strength of recycled aggregate concrete by the non-destructive testing. Main experimental variables were the replacement level of recycled aggregate and blast-furnace slag, which were divided into two series according to recycled aggregate maximum size. Test results showed that a recycled aggregate had a significant influence on the non-destructive testing results, such as rebound number, Ultrasonic pulse velocity, and frequency. A prediction model of compressive strength considering the replacement level of recycled aggregate was suggested by multi-regression analysis and was compared with test results.

• 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.

• Korea Journal of Hospital Management
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• v.27 no.2
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• pp.44-52
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• 2022

Purposes: This study tried to extract variables affecting patient-experience satisfaction level in hospital situation, using a multiple-regression analysis and ISA(Revised Importance-Satisfaction Analysis), and to explore variables needed to be improved. Methodology: A mobile-based online patient-experience survey was conducted in eleven general hospitals in A city. To test the validity of this test, this data was compared with the data from Health-Insturance Review and Assessment Service. Then, the standardized regression coefficients extracted from a multiple-regression analysis were used as the importance scale to be used in ISA. Finding: Taken together, the areas with the highest contribution for the in-hospital patient-experience satisfaction level were medication and treatment process and hospital environment. In conclusion, the revised ISA which can show satisfaction and importance both with simultaneously and multi-axis way would be useful in hospital improvement activities. Practical Implications: This study tried to develop a mobile-based patient-experience survey, and to extract the major variables affecting patient-satisfaction level and to identify variables need to be improved. Finally, this should help hostipals to prepare the assessment process with various improvement activities.

• Water for future
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• v.27 no.3
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• pp.55-69
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• 1994

In order to enhance the short-term flood forecasting accuracy of the water level of the In-do Brdg., three statistical flood forecasting models are presented models are presented and the forecasting accuracies and stabilities of the models are studied. The presented statistical models are as follows: The multi-input model by the multi-regression analysis between the water level of the In-do Brdg. and the influence parameters(Model MM). The two-level multi parameter model according to the water level tendency(Model 2MP). Among the three models, the Model MM showed the lowest forecasting accuracy, the model 2MP showed the highest forecasting accuracy, although this model sometimes became unstable and diverged. The model MMP forecasted the flood less accurately than model 2MP, but it gave more stable forecasting results.

• Journal of Preventive Medicine and Public Health
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• v.55 no.2
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• pp.153-163
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• 2022

Objectives: This study divided the factors that affect participation in health screenings into individual, household, and regional levels and conducted a multi-level analysis to identify the factors related to participation in health screenings. Methods: Participants from the 2017 Community Health Survey were classified into 2 groups (under 40 and 40 or older). A multi-level logistic regression analysis was conducted to identify the factors that affected participation in health screenings. Results: The screening rate of the participants was 69.7%, and it was higher among participants aged 40 and older (80.3%) than it was among participants younger than 40 (49.8%). At the individual level, the factors that influenced participation in health screenings included age, economic activity, smoking status, physician-diagnosed hypertension, and a moderate or high physical activity level. At the household level, the odds ratio of participation in health screenings was high for participants who lived in single-person households, lived with a spouse, earned a high monthly household income, and were not beneficiaries of national basic livelihood security. At the regional level, the odds ratio at the 95% confidence interval level of participation in health screenings was high for participants who had trust in the local community and lived in an area with a proportionally high social welfare budget. Conclusions: This study analyzed nationalwide data and confirmed that individual, household, and regional characteristics affected participation in health screenings. Therefore, policies that prioritize the improvement of regional level factors and especially household level factors are likely to be the most effective for improving the screening rate.

• Journal of the Korean Regional Science Association
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• v.36 no.4
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• pp.57-67
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• 2020

This study aims to analyze influential factors of residents' housing instability in Korean large cities. The previous studies deal with low-income households' experiences with housing instability. However, this study empirically analyzed the impact of regional characteristics such as spatial openness and community characteristics on residents' housing instability. For this purpose, I analyzed various experiences as symptoms of residents' housing instability using data from the Ministry of Land, Infrastructure, and Transport's (MOLIT) Korean Housing survey through a multi-level logistic regression model. The study finds that regional factors as well as household characteristics influence their housing instability. This result implies that promoting spatial inclusivity alleviate residents' housing instability in metropolitan environments. In addition, this study calls for policy efforts such as a continuous supply of public rental housing and a greater variety of housing types to mitigate housing instability.