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7 "Logistic Models"
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Research Papers
Influence of Social Capital on Depression of Older Adults Living in Rural Area: A Cross-Sectional Study Using the 2019 Korea Community Health Survey
Jung, Minho , Kim, Jinhyun
J Korean Acad Nurs 2022;52(2):144-156.   Published online April 30, 2022
DOI: https://doi.org/10.4040/jkan.21239
AbstractAbstract PDF
Purpose
This study aimed to investigate the influence of social capital on the depression of older adults living in rural areas.
Methods
Data sets were obtained from the 2019 Korea Community Health Survey. The participants were 39,390 older adults over 65 years old living in rural areas. Indicators of social capital included trust, reciprocity, network, and social participation. Depression—the dependent variable—was measured using the Patient Health Questionnaire-9 (PHQ-9). Hierarchical ordinal logistic regression was conducted to identify factors associated with depression after adjusting the data numbers to 102,601 by applying the Synthetic Minority Oversampling Technique (SMOTE).
Results
The independent variables—indicators of social capital—exhibited significant association with the depression of older adults. The odds ratios of depression were higher in groups without social capital variables.
Conclusion
To reduce depression, we recommend increasing social capital. Factors identified in this study need to be considered in older adult depression intervention programs and policies.
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Development of a Diabetic Foot Ulceration Prediction Model and Nomogram
Lee, Eun Joo , Jeong, Ihn Sook , Woo, Seung Hun , Jung, Hyuk Jae , Han, Eun Jin , Kang, Chang Wan , Hyun, Sookyung
J Korean Acad Nurs 2021;51(3):280-293.   Published online June 30, 2021
DOI: https://doi.org/10.4040/jkan.20257
AbstractAbstract PDF
Purpose
This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM).
Methods
This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients’ electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow–Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples.
Results
The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ2 = 5.85, p = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years.
Conclusion
The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM.
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Comparison of the Prediction Model of Adolescents’ Suicide Attempt Using Logistic Regression and Decision Tree: Secondary Data Analysis of the 2019 Youth Health Risk Behavior Web-Based Survey
Lee, Yoonju , Kim, Heejin , Lee, Yesul , Jeong, Hyesun
J Korean Acad Nurs 2021;51(1):40-53.   Published online February 28, 2021
DOI: https://doi.org/10.4040/jkan.20207
AbstractAbstract PDF
Purpose
The purpose of this study was to develop and compare the prediction model for suicide attempts by Korean adolescents using logistic regression and decision tree analysis. Methods: This study utilized secondary data drawn from the 2019 Youth Health Risk Behavior web-based survey. A total of 20 items were selected as the explanatory variables (5 of sociodemographic characteristics, 10 of health-related behaviors, and 5 of psychosocial characteristics). For data analysis, descriptive statistics and logistic regression with complex samples and decision tree analysis were performed using IBM SPSS ver. 25.0 and Stata ver. 16.0.
Results
A total of 1,731 participants (3.0%) out of 57,303 responded that they had attempted suicide. The most significant predictors of suicide attempts as determined using the logistic regression model were experience of sadness and hopelessness, substance abuse, and violent victimization. Girls who have experience of sadness and hopelessness, and experience of substance abuse have been identified as the most vulnerable group in suicide attempts in the decision tree model.
Conclusion
Experiences of sadness and hopelessness, experiences of substance abuse, and experiences of violent victimization are the common major predictors of suicide attempts in both logistic regression and decision tree models, and the predict rates of both models were similar. We suggest to provide programs considering combination of high-risk predictors for adolescents to prevent suicide attempt.
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Original Articles
Prediction Models of Mild Cognitive Impairment Using the Korea Longitudinal Study of Ageing
Park, Hyojin , Ha, Juyoung
J Korean Acad Nurs 2020;50(2):191-199.   Published online April 30, 2020
DOI: https://doi.org/10.4040/jkan.2020.50.2.191
AbstractAbstract PDF
Purpose
The purpose of this study was to compare sociodemographic characteristics of a normal cognitive group and mild cognitive impairment group, and establish prediction models of Mild Cognitive Impairment (MCI).
Methods
This study was a secondary data analysis research using data from “the 4th Korea Longitudinal Study of Ageing” of the Korea Employment Information Service. A total of 6,405 individuals, including 1,329 individuals with MCI and 5,076 individuals with normal cognitive abilities, were part of the study. Based on the panel survey items, the research used 28 variables. The methods of analysis included a c2-test, logistic regression analysis, decision tree analysis, predicted error rate, and an ROC curve calculated using SPSS 23.0 and SAS 13.2.
Results
In the MCI group, the mean age was 71.4 and 65.8% of the participants was women. There were statistically significant differences in gender, age, and education in both groups. Predictors of MCI determined by using a logistic regression analysis were gender, age, education, instrumental activity of daily living (IADL), perceived health status, participation group, cultural activities, and life satisfaction. Decision tree analysis of predictors of MCI identified education, age, life satisfaction, and IADL as predictors.
Conclusion
The accuracy of logistic regression model for MCI is slightly higher than that of decision tree model. The implementation of the prediction model for MCI established in this study may be utilized to identify middle-aged and elderly people with risks of MCI. Therefore, this study may contribute to the prevention and reduction of dementia.
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Factors Influencing Adolescent Lifetime Smoking and Current Smoking in South Korea: Using data from the 10th (2014) Korea Youth Risk Behavior Web-Based Survey
Seok Hyun Gwon, Suyong Jeong
J Korean Acad Nurs 2016;46(4):552-561.   Published online August 31, 2016
DOI: https://doi.org/10.4040/jkan.2016.46.4.552
AbstractAbstract PDF
Purpose

The purpose of this study was to investigate factors influencing lifetime smoking and current smoking among adolescents in South Korea.

Methods

Hierarchical logistic regression was conducted based on complex sample analysis using statistics from the 10th (2014) Korea Youth Risk Behavior Web-Based Survey. The study sample comprised 72,060 adolescents aged 12 to 18.

Results

The significant factors influencing adolescent lifetime smoking were female gender, older age, higher stress, higher weekly allowance, lower economic status, living apart from parents, parental smoking, sibling smoking, peer smoking, observation of school personnel smoking, and coed school compared to boys' school. The significant factors influencing adolescent current smoking were female gender, older age, higher stress, higher weekly allowance, both higher and lower economic status compared to middle economic status, living apart from parents, parental smoking, sibling smoking, peer smoking, observation of school personnel smoking, and coed school compared to boys' school.

Conclusion

Factors identified in this study need to be considered in programs directed at prevention of adolescent smoking and smoking cessation programs, as well as policies.

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Maternal and Hospital Factors Impacting the Utilization of Rooming-in Care in South Korea: Secondary Analysis of National Health Data
Yunmi Kim, Eun-Young Kim
J Korean Acad Nurs 2011;41(5):593-602.   Published online October 31, 2011
DOI: https://doi.org/10.4040/jkan.2011.41.5.593
AbstractAbstract PDF
Purpose

Purpose: In this study analysis was done of utilization of rooming-in care in South Korean hospitals in order to examine the factors related to mothers and hospitals that affect rooming-in care.

Methods

With the involvement of 254,414 mothers who gave birth across 953 hospitals, the analysis used the health insurance qualification data of the National Health Insurance Corporations and Health Insurance Review and Assessment Service (2006). Factors associated with rooming-in care were analyzed using a GEE logistic regression analysis to consider factors related to both mothers and hospitals.

Results

Only 45.1% of the mothers used rooming-in care. The results of the regression analysis revealed that individual factors of the mothers were not associated with rooming-in care, whereas group factors of the hospitals were. Rooming-in care use was primarily related to small hospital, location of hospital, and higher nurse staffing level.

Conclusion

The findings of this study indicate that the utilization of rooming-in care is not associated with factors an individual mother, but rather with the group factors of the hospitals. Thus, a policy-based approach considering both of these types of factors is required to enhance the utilization of rooming-in care.

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Predictive Bayesian Network Model Using Electronic Patient Records for Prevention of Hospital-Acquired Pressure Ulcers
In Sook Cho, Eunja Chung
J Korean Acad Nurs 2011;41(3):423-431.   Published online June 13, 2011
DOI: https://doi.org/10.4040/jkan.2011.41.3.423
AbstractAbstract PDF
Purpose

The study was designed to determine the discriminating ability of a Bayesian network (BN) for predicting risk for pressure ulcers.

Methods

Analysis was done using a retrospective cohort, nursing records representing 21,114 hospital days, 3,348 patients at risk for ulcers, admitted to the intensive care unit of a tertiary teaching hospital between January 2004 and January 2007. A BN model and two logistic regression (LR) versions, model-I and -II, were compared, varying the nature, number and quality of input variables. Classification competence and case coverage of the models were tested and compared using a threefold cross validation method.

Results

Average incidence of ulcers was 6.12%. Of the two LR models, model-I demonstrated better indexes of statistical model fits. The BN model had a sensitivity of 81.95%, specificity of 75.63%, positive and negative predictive values of 35.62% and 96.22% respectively. The area under the receiver operating characteristic (AUROC) was 85.01% implying moderate to good overall performance, which was similar to LR model-I. However, regarding case coverage, the BN model was 100% compared to 15.88% of LR.

Conclusion

Discriminating ability of the BN model was found to be acceptable and case coverage proved to be excellent for clinical use.

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