Determination of Risk Factors or Health Issues Utilizing Multiple Binary Logistic Regression Analysis
DOI:
https://doi.org/10.14456/dcj.2025.47Keywords:
Risk Factor, Health Issue, Logistic RegressionAbstract
The occurrence of diseases or health problems is influenced by multiple factors. Analyzing data without considering all relevant variables may lead to inaccurate conclusions, which can negatively affect the planning of effective interventions. Binary logistic regression is a widely used statistical method for analyzing data when the dependent variable is dichotomous, while independent variables may be categorical (with more than two levels) or continuous. When the dependent variable still has two categories, but the model includes two or more independent variables, it is referred to as multiple binary logistic regression analysis. The method aims to describe the relationship between independent and dependent variables, indicating the magnitude of risk or association, while controlling confounding factors. Accurate and reliable results from such analyses can provide valuable evidence to support the planning, promotion, prevention, and control of diseases.
Downloads
References
Nutbeam D. Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century. Health Promotion International. 2000;15(3):259-67.
Khowhit D, Kumvan W, Dana K, Kumsom C. Factors related to fall prevention behavior among older adults in rural communities, Yang Sisurat District, Maha Sarakham Province. Nursing Journal of the Ministry of Public Health. 2023;33(1):131-46. (in Thai)
Pothipim M, Ketbumroong V, Norkaew J, Udomporn T, Chumklang T. Factors associated with falls among older adults in Nakhon Ratchasima Province. Journal of Nursing and Health Care. 2025;43(2):1-13. (in Thai)
Baum F. The New Public Health. 2nd ed. Australia: Oxford University Press Australia; 2008.
Tinkhamrob B. Analysis of health science research data using logistic regression. Khon Kaen: Faculty of Public Health, Khon Kaen University; 2000. (in Thai)
Hosmer, DW, Lemeshow S. Applied logistic regression. 2nd ed. New York: McGraw-Hill; 2000.
Burns N, Grove SK. The Practice of Nursing Research: Conduct, Critique and Utilization. Philadelphia: W.B. Saunders Company; 1993.
Stevens J. Applied multivariate statistics for the social science. Mahwah, New Jersey: Lawrence Erlbaum Associate; 1996.
Hsieh FY, Block DA, Larsen MD. A simple method of sample size calculation for linear and logistic regression. Stat Med. 1998;
Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373-9.
Montgomery DC, Peck EA, G.G. V. Introduction to Linear Regression Analysis. 5th ed. New Jersey: John Wiley & Sons; 2012.
Kleinbaum DG, Kupper LL, Morgenstern H. Epidemiologic research: Principles and quantitative methods. Belmont, CA: Lifetime Learning Publications; 1982.
Greenland-s. Generalized mantel-haenszel estimators for k 2xj tables. Biometrics. 1989;45(1):183-91.
Fu Y, Li KB, Yang XC. A risk score model for predicting cardiac rupture after acute myocardial infarction. Chin Med J (Engl) [Internet]. 2010 [cited 2022 Apr 2];132(9):1039-44. Available from: https://mednexus.org/doi/full/10.1097/CM9.0000000000000175
Sarakarn P, Munpolsri P. The appropriate cut-off points for Receiver Operating Characteristic (ROC) curve analysis in developing health innovation tools: A case study using STATA program. Thai Bull Pharm Sci. 2021;16(1):93-108. (in Thai)
Kaiyawan Y. Principles and using logistic regression analysis for research. Rajamangala University of Technology Srivijaya Research Journal. 2012;4(1):1-12. (in Thai)
Zhang J, Yu KF, What’s the Relative Risk? A Method of Correcting the Odds Ratio in Cohort Studies of Common Outcomes. Am Med Assoc. 1998;17(14):1623-34.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Disease Control Journal
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the Disease Control Journal are considered as academic work, research or analysis of the personal opinion of the authors, not the opinion of the Thailand Department of Disease Control or editorial team. The authors must be responsible for their articles.
