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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
- Miquel Serra-Burriel, Carlos Campillo-Artero, Antonella Agodi, Martina Barchitta, Guillem López-Casasnovas
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 43 / Issue 10 / October 2022
- Published online by Cambridge University Press:
- 18 October 2021, pp. 1360-1367
- Print publication:
- October 2022
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- Article
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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.
Ten - Inclusive economic growth for health equity: in search of the elusive evidence
- Edited by Christopher Deeming, Paul Smyth, The University of Melbourne
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- Book:
- Reframing Global Social Policy
- Published by:
- Bristol University Press
- Published online:
- 12 April 2022
- Print publication:
- 29 November 2017, pp 229-250
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- Chapter
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Summary
Introduction
It has to be true. Inclusive growth, less unequal wealth creation, betterdistributed incomes ‘must’ lead to more health and health equity (certainly the growing evidence is persuasive; see Wilkinson and Pickett, 2009). But as we will see in this chapter, to test this affirmative sequence, with all the assumed linkages, proves to be very difficult. This has to do with, on one hand, the issue of reverse causality (in identifying what is causing what) and, on the other, the heterogeneity of the observed sample and country diversity. In setting the details of the empirical framework of the analysis we try to show how complex this task is. By exploring some initial results, we hope to convince our readers and further researchers in this field that, despite all those caveats, ‘something’ is there waiting to be found under multiple data limitations.
The literature on health and economic growth is large and extensive. The socioeconomic determinants of health provide the hidden link for the studies (Marmot, 2013). However, the analysis of the impact of economic growth on health, and the impact of health on economic growth is still a very challenging issue in the health economics research portfolio (see López-Casasnovas et al, 2005). Many contributions attempt to model the effects of health on economic growth (Bhargava et al, 2001), while others focus on the reverse: how health changes as a result of economic growth (see López-Casasnovas and Soley-Bori, 2014). Although there exists some endogeneity problems in both approaches—particularly in the variable of economic growth, since this may be explained by, among other things, the level of a population's health—we pursue here a step further in the analysis of the effects of growth on health and particularly on health inequality, and vice versa.
In general, these relevant links may be tested at the macro level through the dynamics of health and income, in a cross-section study (for a specific country) or at the micro level by testing whether the poorest today, in a place of high income and inequality, is in better health than in the past. In addition, the relationship between income and health is likely to be non-lineal and may be differentially affected by short-run impacts. Moreover, the standard view is that health inequalities seem to relate mainly to poverty and not so much to income inequality (Leigh et al, 2009).