Analysis of risk factors for multi-drug resistant bacterial infections and prevention and control based on logistic regression analysis

[1] A P R W, B D M L, C J A O, et al. (2016). Prevention and control of multi-drug-resistant Gram-negative bacteria: recommendations from a Joint Working Party. Journal of Hospital Infection, 92(Supplement 1), S1-S44. Search in Google Scholar

[2] Lixin, Sun, Suzhe, et al. (2019). Analysis of Risk Factors for Multiantibiotic-Resistant Infections Among Surgical Patients at a Children’s Hospital. Microbial Drug Resistance. Search in Google Scholar

[3] Ede H, Kili M. (2015). PP.29.17: THE EFFECT OF RISK FACTORS ON THE PREVALENCE, AWARENESS AND CONTROL OF HYPERTENSION: A POPULATION-BASED STUDY. Journal of Hypertension, 33. Search in Google Scholar

[4] Prevention and control of multi-drug-resistant Gram-negative bacteria: recommendations from a Joint Working Party. (2016). Journal of Hospital Infection. Search in Google Scholar

[5] Eichel V, Papan C, Boutin S, et al. (2019). Altering Antibiotic Regimen as Additional Control Measure in Suspected Multi-Drug-Resistant Enterobacter cloacae Outbreak in a Neonatal Intensive Care Unit. Journal of Hospital Infection, 104(2). Search in Google Scholar

[6] Nolff M C, Reese S, Fehr M, et al. (2016). Assessment of wound bio-burden and prevalence of multi- drug resistant bacteria during open wound management. Journal of Small Animal Practice, 57(5), 255-259. Search in Google Scholar

[7] Nguemeleu E T, Beogo I, Sia D, et al. (2020). Economic analysis of healthcare-associated infection prevention and control interventions in medical and surgical units: Systematic review using a discounting approach. Journal of Hospital Infection, 106(1). Search in Google Scholar

[8] Su L X, Wang X T, Pan P, et al. (2019). Infection management strategy based on prevention and control of nosocomial infections in intensive care units. Chinese Medical Journal, 132(1), 115-119. Search in Google Scholar

[9] Wang Y, Gao J, Duan W, et al. (2020). Inactivation of sulfonamide antibiotic resistant bacteria and control of intracellular antibiotic resistance transmission risk by sulfide-modified nanoscale zero-valent iron. Journal of Hazardous Materials, 400, 123226. Search in Google Scholar

[10] Madisi N, Gupta R, Manasia A, et al. (2015). A Novel Comprehensive Infection Control Protocol; Prevention and Eradication of Multiresistant Organisms Outbreak in the Intensive Care Unit Using a Multidisciplinary Approach. Chest, 148(4), 119A Search in Google Scholar

[11] Bernardo, G., S., et al. (2015). Prevention and control of healthcare-associated infection in Europe: a review of patients’ perspectives and existing differences. The Journal of Hospital Infection. Search in Google Scholar

[12] Wang, W., Zhang, Y. W., Hu, S. J., et al. (2021). Design, synthesis, and antibacterial evaluation of PFK-158 derivatives as potent agents against drug-resistant bacteria. Bioorganic & Medicinal Chemistry Letters, 31, 127980. Search in Google Scholar

[13] Pete, S., & Joe, W. (2015). The impact of pre-selected variance inflation factor thresholds on the stability and predictive power of logistic regression models in credit scoring. Ima Journal of Management Mathematics, 26(3), 309-330. Search in Google Scholar

[14] Min, H. L., Lee, G. A., Lee, S. H., et al. (2019). Effectiveness and Core Components of Infection Prevention and Control Programmes in Long-term Care Facilities: A Systematic Review. Journal of Hospital Infection, 102(4), 377-392. Search in Google Scholar

[15] Chikhungu, L. C., Amos, M., Kandala, N., et al. (2019). Married Women’s Experience of Domestic Violence in Malawi: New Evidence From a Cluster and Multinomial Logistic Regression Analysis. Journal of Interpersonal Violence, 34(10), 2068-2088. Search in Google Scholar

[16] Brendli, K. R., MD Broda, & Brown, R. (2021). Children With Intellectual Disability and Victimization: A Logistic Regression Analysis. Child Maltreatment, 26(4), 397-408. Search in Google Scholar

[17] Rongfan, M. A., & Yang, J. (2019). Research on the Identification Mechanism of University Young Teachers’ Political Institutional based on Binary Logistic Regression Analysis. REVISTA DE CERCETARE SI INTERVENTIE SOCIALA, 65, 248-259. Search in Google Scholar

[18] Enticott, J. C., I-Hao, et al. (2015). Emergency department mental health presentations by people born in refugee source countries: an epidemiological logistic regression study in a Medicare Local region in Australia. Australian Journal of Primary Health, 21(4), 405-411. Search in Google Scholar

[19] Karasmanaki, E., Galatsidas, S., & Tsantopoulos, G. (2019). An Investigation of Factors Affecting the Willingness to Invest in Renewables among Environmental Students: A Logistic Regression Approach. Sustainability, 11, 3602. Search in Google Scholar

[20] Hsieh, S. H., & Perri, P. F. (2020). A Logistic Regression Extension for the Randomized Response Simple and Crossed Models: Theoretical Results and Empirical Evidence. Sociological Methods & Research. Advance online publication. https://doi.org/10.1177/0049124120914950 Search in Google Scholar

[21] Kipkorir, P., Ngeno, V., Mose, J., et al. (2018). Marginal effects of determinants of smoking participation among young adults in Kenya: A by gender logistic regression analysis. Tobacco Induced Diseases, 16(3). Search in Google Scholar

[22] Wroniszewska, A., & Zwodziak, J. (2020). Odor Annoyance Assessment by Using Logistic Regression on an Example of the Municipal Sector. Sustainability, 12. Search in Google Scholar

[23] Alshehri, A. A., Jalal, Z., Cheema, E., et al. (2020). Impact of the pharmacist-led intervention on the control of medical cardiovascular risk factors for the primary prevention of cardiovascular disease in general practice: A systematic review and meta-analysis of randomised controlled trials. British Journal of Clinical Pharmacology, 86(1). Search in Google Scholar

[24] Lucia, C. D., Pazienza, P., & Bartlett, M. (2020). Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe. Sustainability, 12(13), 5317. Search in Google Scholar

[25] Tsegay, T., Mengistu, Y., & Nigussie, T. (2019). Application of Ordinal Logistic Regression Analysis in Determining Risk Factors of Active Trachoma among Rural Children of Aged 1-9 Years Old in Kaffa Zone, South West Ethiopia. Journal of Public Health, 4(1), 20-28. Search in Google Scholar