Hostname: page-component-848d4c4894-tn8tq Total loading time: 0 Render date: 2024-06-28T12:00:22.768Z Has data issue: false hasContentIssue false

Acquisition of carbapenem-resistant gram-negative bacilli among intensive care unit (ICU) patients with no previous use of carbapenems: Indirect population impact of antimicrobial use

Published online by Cambridge University Press:  14 February 2022

Juliana da Silva Oliveira
Affiliation:
Department of Infectious Diseases, Botucatu Medical School, São Paulo State University (UNESP), Botucatu, São Paulo, Brazil
Natalie Carlos Ferreira Melo Sampaio
Affiliation:
Department of Infectious Diseases, Botucatu Medical School, São Paulo State University (UNESP), Botucatu, São Paulo, Brazil
Gabriela Silveira Leite
Affiliation:
Department of Infectious Diseases, Botucatu Medical School, São Paulo State University (UNESP), Botucatu, São Paulo, Brazil
Milena Aparecida Del Masso Pereira
Affiliation:
Department of Infectious Diseases, Botucatu Medical School, São Paulo State University (UNESP), Botucatu, São Paulo, Brazil
Carlos Magno Castelo Branco Fortaleza*
Affiliation:
Department of Infectious Diseases, Botucatu Medical School, São Paulo State University (UNESP), Botucatu, São Paulo, Brazil
*
Author for correspondence: Carlos Magno Castelo Branco Fortaleza, E-mail: [email protected]

Abstract

Objective:

To measure the impact of exposure to patients using carbapenem on the acquisition of carbapenem-resistant gram-negative bacilli (CR-GNB) among patients not using carbapenems.

Design:

An ecological study and a cohort study.

Setting:

Two medical surgical intensive care units (ICUs) in inner Brazil.

Participants:

Patients admitted to 2 ICUs from 2013 through 2018 to whom carbapenem was not prescribed.

Methods:

In the ecologic study, the monthly use of carbapenems (days of therapy [DOT] per 1,000 patient days) was tested for linear correlation with the 2-month moving average of incidence CR-GNB among patients to whom carbapenem was not prescribed. In the cohort study, those patients were addressed individually for risk factors (demographics, invasive interventions, use of antimicrobials) for acquisition of CR-GNB, including time at risk and the “carbapenem pressure,” described as the aggregate DOT among other ICU patients during time at risk. The analysis was performed in univariate and multivariable Poisson regression models.

Results:

The linear regression model revealed an association of total carbapenem use and incidence of CR-GNB (coefficient, 0.04; 95% confidence interval [CI], 0.02–0.06; P = .001). In the cohort model, the adjusted rate ratio (RR) for carbapenem DOT was 1.009 (95% CI, 1.001–1.018; P = .03). Other significant risk factors were mechanical ventilation and the previous use of ceftazidime (with or without avibactam).

Conclusions:

Every additional DOT of total carbapenem use increased the risk of CR-GNB acquisition by patients not using carbapenems by nearly 1%. We found evidence for a population (“herd effect”-like) impact of antimicrobial use in the ICUs.

Type
Original Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Tacconelli, E. Antimicrobial use: risk driver of multidrug-resistant microorganisms in healthcare settings. Curr Opin Infect Dis 2009;22:352358.CrossRefGoogle ScholarPubMed
Harris, AD, Karchmer, TB, Carmeli, Y, Samore, MH. Methodological principles of case-control studies that analyzed risk factors for antibiotic resistance: a systematic review. Clin Infect Dis 2001;32:10551061.CrossRefGoogle ScholarPubMed
Weinstein, RA. Controlling antimicrobial resistance in hospitals: infection control and use of antibiotics. Emerg Infect Dis 2001;7:188192.CrossRefGoogle ScholarPubMed
Noyes, NR, Slizovskiy, IB, Singer, RS. Beyond antimicrobial use: a framework for prioritizing antimicrobial resistance interventions. Annu Rev Anim Biosci 2021;9:313332.CrossRefGoogle ScholarPubMed
Davey, P, Marwick, CA, Scott, CL, et al. Interventions to improve antibiotic prescribing practices for hospital inpatients. Cochrane Database Syst Rev 2017;2(2):CD003543.Google ScholarPubMed
Lipsitch, M, Samore, MH. Antimicrobial use and antimicrobial resistance: a population perspective. Emerg Infect Dis 2002;8:347354.CrossRefGoogle ScholarPubMed
International Classification of Disease, Tenth Revision (ICD-10), 2019. World Health Organization website. https://icd.who.int/browse10/2019/en. Published 2019. Accessed January 31, 2022.Google Scholar
Charlson, ME, Pompei, P, Ales, KL, MacKenzie, CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373383.CrossRefGoogle ScholarPubMed
Ribeiro, GS, Hamer, GL, Diallo, M, Kitron, U, Ko, AI, Weaver, SC. Influence of herd immunity in the cyclical nature of arboviruses. Curr Opin Virol 2020;40:110.CrossRefGoogle ScholarPubMed
Lahariya, C. Vaccine epidemiology: a review. J Fam Med Prim Care 2016;5:715.CrossRefGoogle ScholarPubMed
Fine, P, Eames, K, Heymann, DL. “Herd immunity”: a rough guide. Clin Infect Dis 2011;52:911916.CrossRefGoogle ScholarPubMed
Redwan, EM. COVID-19 pandemic and vaccination build herd immunity. Eur Rev Med Pharmacol Sci 2021;25:577579.Google ScholarPubMed
Tkachenko, AV, Maslov, S, Elbanna, A, Wong, GN, Weiner, ZJ, Goldenfeld, N. Time-dependent heterogeneity leads to transient suppression of the COVID-19 epidemic, not herd immunity. Proc Natl Acad Sci U S A 2021;118:e2015972118.CrossRefGoogle Scholar
Fuller, JA, Eisenberg, JN. Herd protection from drinking water, sanitation, and hygiene interventions. Am J Trop Med Hyg 2016;95:12011210.CrossRefGoogle ScholarPubMed
Call, DR, Davis, MA, Sawant, AA. Antimicrobial resistance in beef and dairy cattle production. Anim Health Res Rev 2008;9:159167.CrossRefGoogle ScholarPubMed
Labarca, JA, Salles, MJ, Seas, C, Guzmán-Blanco, M. Carbapenem resistance in Pseudomonas aeruginosa and Acinetobacter baumannii in the nosocomial setting in Latin America. Crit Rev Microbiol 2016;42:276292.Google ScholarPubMed
Sampaio, JL, Gales, AC. Antimicrobial resistance in Enterobacteriaceae in Brazil: focus on β-lactams and polymyxins. Braz J Microbiol 2016;47 suppl 1:3137.CrossRefGoogle Scholar
Braga, IA, Campos, PA, Batistão, DWDF, Gontijo Filho, PP, Ribas, RM. Using a point-prevalence survey to define burden of antimicrobial use among 35 adult intensive care units in Brazil. Infect Dis (Lond) 2019;51:459462.CrossRefGoogle ScholarPubMed
Morgenstern, H. Ecologic studies in epidemiology: concepts, principles, and methods. Ann Rev Public Health 1995;16:6181.CrossRefGoogle ScholarPubMed
Wakefield, J. Ecologic studies revisited. Ann Rev Public Health 2008;29:7590.Google ScholarPubMed
Ajao, AO, Harris, AD, Roghmann, MC, et al. Systematic review of measurement and adjustment for colonization pressure in studies of methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, and Clostridium difficile acquisition. Infect Control Hosp Epidemiol 2011;32:481489.CrossRefGoogle ScholarPubMed
Castelo Branco Fortaleza, CM, Moreira de Freitas, F, da Paz Lauterbach, G. Colonization pressure and risk factors for acquisition of imipenem-resistant Acinetobacter baumannii in a medical surgical intensive care unit in Brazil. Am J Infect Control 2013;41:263265.CrossRefGoogle Scholar
DalBen, MF, Basso, M, Garcia, CP, et al. Colonization pressure as a risk factor for colonization by multiresistant Acinetobacter spp and carbapenem-resistant Pseudomonas aeruginosa in an intensive care unit. Clinics (Sao Paulo) 2013;68:11281133.CrossRefGoogle Scholar
Causality, Pearl J.. Models, Reasoning and Inference, Second Edition. Cambridge, UK: Cambridge University Press, 2009.Google Scholar
Pearl, J. Causal diagrams for empirical research. Biometrika 1995;82:669688.CrossRefGoogle Scholar
Morgan, DJ, Rogawski, E, Thom, KA, et al. Transfer of multidrug-resistant bacteria to healthcare workers’ gloves and gowns after patient contact increases with environmental contamination. Crit Care Med 2012;40:10451051.CrossRefGoogle ScholarPubMed
Ajao, AO, Johnson, JK, Harris, AD, et al. Risk of acquiring extended-spectrum β-lactamase-producing Klebsiella species and Escherichia coli from prior room occupants in the intensive care unit. Infect Control Hosp Epidemiol 2013;34:453458.CrossRefGoogle ScholarPubMed
Fortaleza, CM, Caldeira, SM, Moreira, RG, et al. Tropical healthcare epidemiology: weather determinants of the etiology of bloodstream infections in a Brazilian hospital. Infect Control Hosp Epidemiol 2014;35:8588.CrossRefGoogle Scholar
Álvarez-Marín, R, López-Cerero, L, Guerrero-Sánchez, F, et al. Do specific antimicrobial stewardship interventions have an impact on carbapenem resistance in gram-negative bacilli? A multicentre quasi-experimental ecological study: time-trend analysis and characterization of carbapenemases. J Antimicrob Chemother 2021;76:19281936.CrossRefGoogle ScholarPubMed