Comparative Analysis of Sociodemographic and Health Indicators in Normal, Prediabetes, and Diabetes Groups: Insights from Multivariable Logistic Regression Models

• 발행기관: East Asian Health Association
• 저자명: Dandan Zheng Jeong-Kyo Suh
• 간행물 정보: 『Journal of East Asian Health』Vol.1 No.2, 1~16쪽, 전체 16쪽
• 주제분류: 의약학 > 기타의약학
• 파일형태: PDF
• 발행일자: 2024.09.30

국문 초록

Purpose - This study aimed to examine the relationships between sociodemographic characteristics, health behaviors, subjective health awareness, and the risk of diabetes and prediabetes. Design/Methodology/Approach - Using data from the 2022 Korea National Health and Nutrition Examination Survey (KNHANES), chi-square tests, independent sample t-tests, and multiple logistic regression analyses were conducted to compare differences among normal, prediabetic, and diabetic groups. The diabetic group was used as the reference to evaluate the impact of various variables on diabetes risk. Findings - Agricultural, forestry, and fishery workers were more likely to maintain normal blood glucose levels compared to the unemployed (OR = 3.126, p < 0.05). Each one-unit increase in BMI was associated with a 15.1% higher risk of prediabetes (p < 0.01). Higher fasting blood glucose and HbA1c levels were significantly associated with increased diabetes risk (p < 0.001). Additionally, higher subjective health awareness was related to an increased likelihood of being in the normal group (OR = 0.703, p < 0.01) or prediabetic group (OR = 0.786, p < 0.05) compared to the diabetic group. Research Implications - Occupational type, health behaviors, clinical indicators, and subjective health awareness significantly influence the risk of diabetes and prediabetes. Health intervention strategies tailored to specific occupational groups, along with efforts to enhance public awareness of personal health, are recommended to reduce the incidence of diabetes.

목차

Ⅰ. Introduction

Ⅱ. Research Methods

Ⅲ. Results

Ⅳ. Discussion

Ⅴ. Conclusion

References

키워드

Diabetes Mellitus Health Indicators Multivariable Logistic Regression Models Prediabetes