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Association between antibiotic resistance in intensive care unit (ICU)–acquired infections and excess resource utilization: Evidence from Spain, Italy, and Portugal

Published online by Cambridge University Press:  18 October 2021

Miquel Serra-Burriel*
Affiliation:
Centre for Research in Health and Economics, Pompeu Fabra University, Barcelona, Spain Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
Carlos Campillo-Artero
Affiliation:
Centre for Research in Health and Economics, Pompeu Fabra University, Barcelona, Spain Balearic Islands Health Service, Palma de Mallorca, Balearic Islands, Spain
Antonella Agodi
Affiliation:
Department of Medical and Surgical Sciences and Advanced Technologies “GF Ingrassia,” University of Catania, Catania, Italy GISIO-SItI: Italian Study Group of Hospital Hygien—Italian Society of Hygiene, Preventive Medicine and Public Health, Rome, Italy
Martina Barchitta
Affiliation:
Department of Medical and Surgical Sciences and Advanced Technologies “GF Ingrassia,” University of Catania, Catania, Italy GISIO-SItI: Italian Study Group of Hospital Hygien—Italian Society of Hygiene, Preventive Medicine and Public Health, Rome, Italy
Guillem López-Casasnovas
Affiliation:
Centre for Research in Health and Economics, Pompeu Fabra University, Barcelona, Spain
*
Author for correspondence: Miquel Serra-Burriel, E-mail: miquel.serraburriel@uzh.ch

Abstract

Background:

Intensive care unit (ICU)–acquired infections with antibiotic-resistant bacteria have been associated with substantial health and economic costs. Moreover, southern Europe has historically reported high levels of antimicrobial resistance.

Objectives:

We estimated the attributable economic burden of ICU-acquired infections due to resistant bacteria based upon hospital excess length of stay (LOS) in a selected sample of southern European countries.

Methods:

We studied a cohort of adult patients admitted to the ICU who developed an ICU-acquired infection related to an invasive procedure in a sample of Spanish, Italian, and Portuguese hospitals between 2008 and 2016, using data from The European Surveillance System (TESSy) released by the European Centers for Disease Control (ECDC). We analyzed the association between infections with selected antibiotic-resistant bacteria of public health importance and excess LOS using regression, matching, and time-to-event methods. We controlled for several confounding factors as well as time-dependent biases. We also computed the associated economic burden of excess resource utilization for each selected country.

Results:

In total, 13,441 patients with at least 1 ICU-acquired infection were included in the analysis: 4,106 patients (30.5%) were infected with antimicrobial-resistant bacteria, whereas 9,335 patients (69.5%) were infected with susceptible bacteria. The unadjusted association between resistance status and excess LOS was 7 days (95% CI, 6.13–7.87; P < .001). Fully adjusted models yielded significantly lower estimates: 2.76 days (95% CI, 1.98–3.54; P < .001) in the regression model, 2.60 days (95% CI, 1.66–3.55; P < .001) in the genetic matching model, and a hazard ratio of 1.15 (95% CI, 1.11–1.19; P < .001) in the adjusted Cox regression model. These estimates, alongside the prevalence of resistance, translated into direct hospitalization attributable costs per ICU-acquired infection of 5,224€ (95% CI, 3,691–6,757) for Spain, 4,461€ (95% CI, 1,948–6,974) for Portugal, and 4,320€ (95% CI, 1,662–6,977) for Italy.

Conclusions:

ICU-acquired infections associated with antibiotic-resistant bacteria are substantially associated with a 15% increase in excess LOS and resource utilization in 3 southern European countries. However, failure to appropriately control for significant confounders inflates estimates by ∼2.5-fold.

Type
Original Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

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References

Edwardson, S, Cairns, C. Nosocomial infections in the ICU. Anaesth Intensive Care Med 2019;20:1418.CrossRefGoogle Scholar
Li, Y, Cao, X, Ge, H, Jiang, Y, Zhou, H, Zheng, W. Targeted surveillance of nosocomial infection in intensive care units of 176 hospitals in Jiangsu Province, China. J Hosp Infect 2018;99:3641.Google ScholarPubMed
Ramsamy, Y, Hardcastle, TC, Muckart, DJJ. Surviving sepsis in the intensive care unit: the challenge of antimicrobial resistance and the trauma patient. World J Surg 2017;41:11651169.CrossRefGoogle ScholarPubMed
Roberts, RR, Hota, B, Ahmad, I, et al. Hospital and societal costs of antimicrobial-resistant infections in a Chicago teaching hospital: implications for antibiotic stewardship. Clin Infect Dis 2009;49:11751184.CrossRefGoogle Scholar
Micek, ST, Wunderink, RG, Kollef, MH, et al. An international multicenter retrospective study of Pseudomonas aeruginosa nosocomial pneumonia: impact of multidrug resistance. Crit Care 2015;19:219.Google ScholarPubMed
Gulen, TA, Guner, R, Celikbilek, N, Keske, S, Tasyaran, M. Clinical importance and cost of bacteremia caused by nosocomial multidrug-resistant Acinetobacter baumannii . Int J Infect Dis 2015;38:3235.CrossRefGoogle Scholar
Fernández-Barat, L, Ferrer, M, De Rosa, F, et al. Intensive care unit-acquired pneumonia due to Pseudomonas aeruginosa with and without multidrug resistance. J Infect 2017;74:142152.Google ScholarPubMed
Engler-Hüsch, S, Heister, T, Mutters, NT, Wolff, J, Kaier, K. In-hospital costs of community-acquired colonization with multidrug-resistant organisms at a German teaching hospital. BMC Health Serv Res 2018;18:737.Google Scholar
Adrie, C, Ibn, W, Schwebel, C, et al. Attributable mortality of ICU-acquired bloodstream infections: impact of the source, causative micro-organism, resistance profile and antimicrobial therapy. J Infect 2017;74:131141.Google ScholarPubMed
Lambert, ML, Suetens, C, Savey, A, et al. Clinical outcomes of healthcare-associated infections and antimicrobial resistance in patients admitted to European intensive care units: a cohort study. Lancet Infect Dis 2011;11:3088.CrossRefGoogle ScholarPubMed
Martin-Loeches, I, Torres, A, Rinaudo, M, et al. Resistance patterns and outcomes in intensive care unit (ICU)–acquired pneumonia. Validation of European Centre for Disease Prevention and Control (ECDC) and the Centers for Disease Control and Prevention (CDC) classification of multidrug-resistant organisms. J Infect 2015;70:213222.CrossRefGoogle ScholarPubMed
Pelz, RK, Lipsett, PA, Swoboda, SM, et al. Vancomycin-sensitive and vancomycin-resistant enterococcal infections in the ICU: attributable costs and outcomes. Intensive Care Med 2002;28:692697.CrossRefGoogle ScholarPubMed
Magira, EE, Islam, S, Niederman, MS. Multidrug-resistant organism infections in a medical ICU: association to clinical features and impact upon. Med Intensiva (English Ed) 2018;42:225234.Google Scholar
Vrijens, F, Hulstaert, F, Devriese, S, Van de Sande, S. Hospital-acquired infections in Belgian acute-care hospitals: an estimation of their global impact on mortality, length of stay and healthcare costs. Epidemiol Infect 2012;140:126136.CrossRefGoogle ScholarPubMed
Glance, LG, Stone, PW, Mukamel, DB, Dick, AW. Increases in mortality, length of stay, and cost associated with hospital-acquired infections in trauma patients. Arch Surg 2011;146:794801.CrossRefGoogle ScholarPubMed
Graves, N, Weinhold, D, Tong, E, et al. Effect of healthcare-acquired infection on length of hospital stay and cost. Infect Control Hosp Epidemiol 2007;28:280292.Google ScholarPubMed
Cassini, A, Högberg, LD, Plachouras, D, et al. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. Lancet Infect Dis 2019;19:5666.CrossRefGoogle ScholarPubMed
Serra-Burriel, M, Keys, M, Campillo-Artero, C, et al. Impact of multidrug-resistant bacteria on economic and clinical outcomes of healthcare-associated infections in adults: systematic review and meta-analysis. PLoS One 2020;15(1):e0227139.CrossRefGoogle ScholarPubMed
Nelson, RE, Samore, MH, Jones, M, et al. Reducing time-dependent bias in estimates of the attributable cost of healthcare-associated methicillin-resistant Staphylococcus aureus infections: a comparison of three estimation strategies. Med Care 2015;53:827834.CrossRefGoogle Scholar
Nelson, RE, Schweizer, ML, Perencevich, EN, et al. Costs and mortality associated with multidrug-resistant healthcare-associated Acinetobacter infections. Infect Control Hosp Epidemiol 2016;37:12121218.CrossRefGoogle ScholarPubMed
Nelson, RE, Stevens, VW, Jones, M, et al. Attributable cost and length of stay associated with nosocomial gram-negative bacterial cultures. Antimicrob Agents Chemother 2018;62(11):e0046218.CrossRefGoogle ScholarPubMed
Nelson, RE, Slayton, RB, Stevens, VW, et al. Attributable mortality of healthcare-associated infections due to multidrug-resistant gram-negative bacteria and methicillin-resistant Staphylococcus aureus . Infect Control Hosp Epidemiol 2017;38:848856.CrossRefGoogle ScholarPubMed
Dickerman, BA, García-Albéniz, X, Logan, RW, Denaxas, S, Hernán, MA. Avoidable flaws in observational analyses: an application to statins and cancer. Nat Med 2019;25:16011606.Google ScholarPubMed
Hernán, MA, Robins, JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol 2016;183:758764.Google Scholar
Harrell, FE Jr, Lee, KL, Mark, DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361387.3.0.CO;2-4>CrossRefGoogle ScholarPubMed
Dodd, S, Bassi, A, Bodger, K, Williamson, P. A comparison of multivariable regression models to analyse cost data. J Eval Clin Pract 2006;12:7686.CrossRefGoogle ScholarPubMed
Agodi, A, Auxilia, F, Barchitta, M, et al. Building a benchmark through active surveillance of intensive care unit-acquired infections: the Italian network SPIN-UTI. J Hosp Infect 2010;74:258265.CrossRefGoogle ScholarPubMed
Masia, MD, Barchitta, M, Liperi, G, et al. Validation of intensive care unit–acquired infection surveillance in the Italian SPIN-UTI network. J Hosp Infect 2010;76:139142.CrossRefGoogle ScholarPubMed
Agodi, A, Auxilia, F, Barchitta, M, et al. Trends, risk factors and outcomes of healthcare-associated infections within the Italian network SPIN-UTI. J Hosp Infect 2013;84:5258.CrossRefGoogle ScholarPubMed
Monnet, DL. Trends in antimicrobial resistance in Europe. Int J Infect Dis 2016;53:22.CrossRefGoogle Scholar
Hahn, PR, Carvalho, CM, Puelz, D, He, J. Regularization and confounding in linear regression for treatment effect estimation. Bayesian Anal 2018;13:163182.Google Scholar
Bithell, JF, Dutton, SJ, Neary, NM, Vincent, TJ. Controlling for socioeconomic confounding using regression methods. J Epidemiol Community Health 1995;49 suppl 2:S15S19.CrossRefGoogle Scholar
Diamond, A, Sekhon, JS. Genetic matching for estimating causal effects: a general multivariate matching method for achieving balance in observational studies. Rev Econ Stat 2013;95:932945.Google Scholar
Lin, DY, Ying, Z. Cox regression with incomplete covariate measurements. J Am Stat Assoc 1993;88:13411349.CrossRefGoogle Scholar
Abadie, A, Imbens, GW. Bias-corrected matching estimators for average treatment effects. J Bus Econ Stat 2011;29:111.Google Scholar
Oster, E. Unobservable selection and coefficient stability: theory and evidence. J Bus Econ Stat 2019;37:187204.CrossRefGoogle Scholar
van Walraven, C, Davis, D, Forster, AJ, Wells, GA. Time-dependent bias was common in survival analyses published in leading clinical journals. J Clin Epidemiol 2004;57:672682.CrossRefGoogle ScholarPubMed
Freedman, DA. On the so-called “Huber Sandwich Estimator” and “robust standard errors.” Am Stat 2006;60:299302.CrossRefGoogle Scholar
Rodrigues, J, Sousa, P. Economic and clinical impact of ventilator-associated pneumonia in intensive care units of a university hospital center. In: Cotrim T, et al, editors. Health and Social Care Systems of the Future: Demographic Changes, Digital Age and Human Factors. Advances in Intelligent Systems and Computing, vol 1012. New York: Springer; 2019.Google Scholar
Precios públicos SMS según BORM 28-febrero-2017. Formación y la Investigación Sanitaria website. http://www.ffis.es/investigacion/precios_pruebas.php. Published 2017. Accessed September 30, 2021.Google Scholar
Tan, SS, Bakker, J, Hoogendoorn, ME, et al. Direct cost analysis of intensive care unit stay in four European countries: applying a standardized costing methodology. Value Heal 2012;15:8186.CrossRefGoogle ScholarPubMed
Organisation for Economic Co-operation and Development. Healthcare prices. Health Care Financ Trends 2020;2:1520.Google Scholar
Team, RC. R: A Language and Environment for Statistical Computing. Vienna, Austria; 2019.Google Scholar
Kurth, T, Walker, AM, Glynn, RJ, et al. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol 2006;163:262270.Google ScholarPubMed
Vincent, J-L, Sakr, Y, Singer, M, et al. Prevalence and outcomes of infection among patients in intensive care units in 2017. JAMA 2020;323:14781487.Google ScholarPubMed
Agodi, A, Barchitta, M, Quattrocchi, A, et al. Preventable proportion of intubation-associated pneumonia: role of adherence to a care bundle. PLoS One 2017;12(9):e0181170.CrossRefGoogle ScholarPubMed
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