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Antibiotic use in different hospital administrative categories: an overview of 10 years of a statewide surveillance program in Brazil

Published online by Cambridge University Press:  13 January 2025

Filipe Piastrelli*
Affiliation:
Infection Control Department, Hospital Alemão Oswaldo Cruz, São Paulo, Brazil
Denise Brandão de Assis
Affiliation:
Divisao de Infeccoes Hospitalares, Centro de Vigilancia Epidemiologica “Prof. Alexandre Vranjac” Centro de Controle de Doencas, Secretaria de Estado da Saude, São Paulo, SP, BR, Brazil
Geraldine Madalosso
Affiliation:
Divisao de Infeccoes Hospitalares, Centro de Vigilancia Epidemiologica “Prof. Alexandre Vranjac” Centro de Controle de Doencas, Secretaria de Estado da Saude, São Paulo, SP, BR, Brazil
Ícaro Boszczowski
Affiliation:
Infection Control Department, Hospital Alemão Oswaldo Cruz and Infection Control Department Hospital das Clínicas, São Paulo, Brazil
*
Corresponding author: Filipe Piastrelli; Email: fpiastrelli@gmail.com

Abstract

Objective:

The present study aimed to describe ICU antibiotic use based on data reported from 2009 to 2018 to the Nosocomial Surveillance System (NSS) of the State Health Department in the State of Sao Paulo, Brazil.

Design:

Ecological study.

Setting:

Data obtained from hospitals located in the state of São Paulo, Brazil from 2009 to 2018.

Participants:

Intensive care units located at participant hospitals.

Methods:

Data on healthcare-associated infections, antibiotic usage, and bacterial identification were collected and reported monthly by hospitals. Antibiotic consumption was quantified as defined daily doses (DDD) per 1000 patient-days. The relationship between antibiotic use and bacterial resistance, categorized by hospital type and ICU complexity, was analyzed using statistical methods to assess correlations and significance.

Results:

Our findings reveal an escalating trend in antibiotic consumption over the study period, with a notable increase from 588.16 DDD per 1000 patient-days in the initial year to 943.12 DDD/1000 patient-days in the final year (p < 0.01). Cephalosporins emerged as the most frequently utilized class, accounting for 33.9% of total antibiotic consumption. Public hospitals exhibited significantly higher antibiotic use compared to private and philanthropic institutions, with a mean of 889.11 DDD/1000 patient-days in public hospitals compared to 849.07 DDD/1000 patient-days in private hospitals and 785.12 DDD/1000 patient-days in philanthropic hospitals (p < 0.05).

Conclusions:

The study provides critical insights into antibiotic use and resistance in different hospital settings, emphasizing the importance of tailored antimicrobial stewardship strategies.

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

Introduction

Antimicrobial resistance (AMR) is a global public health threat that reduces drug effectiveness and undermines efforts to prevent and treat infectious diseases. The emergence of resistance is closely linked to the overuse of antimicrobials across various sectors, including human health, animal husbandry, and agriculture.1

In human health, the overprescription of antibiotics emerges as a notable risk factor driving the escalation of antibiotic resistance, albeit establishing a direct causal effect can be challenging. Consequently, infections caused by resistant microorganisms are associated with prolonged hospitalizations, escalated healthcare expenditures, and elevated mortality rates.Reference Lai, Chu, Cheng, Huang and Hsueh2,Reference Jacoby, Kuchenbecker, Dos Santos, Magedanz, Guzatto and Moreira3 Despite hospitals’ antibiotic consumption representing a fraction of global usage, the intensive care unit (ICU) serves as a critical focal point for resistance emergence and dissemination due to the frequent and intense antibiotic usage.Reference Meyer, Gastmeier, Deja and Schwab4

Addressing AMR within the integrated framework of One Health stands as a paramount objective for the World Health Organization (WHO), as articulated in the Global Action Plan on Antimicrobial Resistance. The aim is to fortify monitoring and surveillance endeavors regarding antimicrobial utilization and AMR.1

A comprehensive understanding of antibiotic utilization is imperative for formulating effective interventions in antibiotic stewardship. However, surveillance in this context is intricate, with heterogeneous patterns of antibiotic usage observed even within the same country across different healthcare facilities.Reference Grau, Fondevilla and Mojal5Reference Plüss-Suard, Pannatier, Kronenberg, Mühlemann and Zanetti7

Brazil’s healthcare system is characterized by a complex structure featuring distinct financing models based on administrative categories: public, private, and philanthropic. Public hospitals can be government-administered or privately operated with public funding. Philanthropic hospitals operate as private entities but are nonprofit, while private hospitals operate for profit.Reference Pimentel, Barbosa, Machado, Adão and Reis8 These administrative nuances give rise to varying resource allocations and organizational structures, yet data elucidating disparities in antibiotic utilization are scarce.

Establishing a well-structured surveillance system is pivotal for comprehending antibiotic utilization and bacterial resistance patterns. Surveillance programs have been instituted to monitor antibiotic usage and resistance at various echelons. In Brazil, a national surveillance system has been monitoring antibiotic utilization in ICUs since 2014.9 Moreover, in 2017, Brazil entered the Global Antimicrobial Resistance Surveillance System (GLASS) and initiated the National Surveillance Program on Antimicrobial Resistance (BR-GLASS).Reference Pillonetto, Jordão and Andraus10

Over a decade ago, in 2004, a regional center for healthcare infection surveillance was established in Sao Paulo, Brazil’s most populous state. This initiative has substantially enhanced the monitoring of healthcare-associated infections (HAIs), AMR, and antibiotic utilization in ICUs. Initially, 457 hospitals reported data to the system, a figure that has since burgeoned to 724 institutions by 2018.Reference Padoveze, Assis and Freire11,Reference Assis, Madalosso, Ferreira and Yassuda12

The present study aimed to describe ICU antibiotic use based on data reported to the Nosocomial Surveillance System (NSS) of the State Health Department in the State of Sao Paulo.

Material and methods

Study design and data collection

This ecological study describes antibiotic utilization at an aggregated level in adult intensive care units reported to São Paulo SHD (State Health Department) from 2009 to 2018.

At the onset of each calendar year, hospitals across the State of São Paulo receive an Excel spreadsheet encompassing monthly data on HAIs, antibiotic usage, and bacterial identification from blood cultures. The nosocomial infection control department of each hospital is responsible for reporting the number of primary bloodstream infections (BSIs), bacterial identification, and resistance pattern, and the quantity of antimicrobial vials utilized in the ICU. The spreadsheet automatically converts the number of filled vials into defined daily doses (DDD) as stipulated by the WHO.13 Completed spreadsheets are transmitted monthly to the State Health Department.

We included hospitals that reported a minimum of 500 patient-days each year. Data were pooled and analyzed as described below.

Antibiotic use

Hospitals reported the utilization of the following antimicrobial agents: ampicillin-sulbactam, ciprofloxacin, moxifloxacin, levofloxacin, ceftriaxone, ceftazidime, cefotaxime, cefepime, piperacillin-tazobactam, ertapenem, imipenem, meropenem, polymyxin B, colistin, linezolid, vancomycin, and teicoplanin. Antibiotics were categorized into classes, and the data were presented as DDD per 1000 patient-days.

Intensive care units

ICUs were categorized according to hospital administrative classification (private, public, and philanthropic) and complexity. An ICU was designated as high complexity when the average mechanical ventilation rate (%MV) exceeded 50%, and low complexity when the average mechanical ventilation rate was below 50%.

Public hospitals were further subdivided into two administrative subcategories: Social Health Organization (SHO), which operates with private administration supplemented by governmental resources, and direct public administration (DPA).

Bacterial resistance and antibiotic use

Data regarding multidrug-resistant organisms (MDRO) isolated from blood cultures of ICU patients were reported in two formats throughout the study period: (1) from 2009 to 2011, both primary and secondary BSIs were documented; (2) from 2012 to 2018, only primary BSI cases were recorded. Primary and secondary BSIs adhere to the CDC criteria for central line-associated bloodstream infection (CLABSI) surveillance. Primary BSI is defined as “a laboratory-confirmed bloodstream infection not secondary to an infection at another body site,” while secondary BSI is described as “a bloodstream infection believed to have originated from a site-specific infection at another body site”.14

MDRO were categorized based on phenotypical characteristics, including methicillin-resistant Staphylococcus aureus (MRSA), third-generation cephalosporin (3GC)-resistant Enterobacterales, and carbapenem-resistant gram-negative bacilli (CR-GNB). Subsequently, the incidence and proportion of each MDRO group were calculated.

Incidence of resistance phenotypes and correlation with antibiotic usage were determined for MRSA and glycopeptide use, 3GC-resistant Enterobacterales and carbapenem use, and CR-GNB and polymyxin use.

Statistical analyses

The overall pooled mean was calculated for each therapeutic class across hospitals and subgroups. Given the non-normal data distribution, the Kruskal–Wallis test was used for group comparisons, with Mann–Whitney plus Bonferroni correction for pairwise comparisons. Spearman’s coefficient assessed antibiotic utilization-resistance correlation.

Data analysis was performed with IBM-SPSS for Windows software version 25.0. A P-value <0.05 was considered statistically significant.

Results

Setting and antibiotic use

The average number of hospitals reporting annual data on antibiotic use in the study period was 386 (332–420) per year, with private hospitals being the most frequent administrative category (46.7%) (Table 1).

Table 1. Number of hospitals reporting to the epidemiological surveillance system in the state of São Paulo, 2009–2018

SHO, Social Health Organization; DPA, Direct Public Administration.

Total antibiotic consumption in ICUs escalated from 588.16 DDD per 1000 patient-days in the initial year to 943.12 DDD/1000 patient-days in the final year (P < 0.01). Cephalosporins constituted the most frequently utilized antibiotic class (33.9%), with no significant variance observed over the study period. Following cephalosporins, glycopeptides (147.52 DDD/1000 patient-days) and carbapenems (140.76 DDD/1000 patient-days) ranked as the second and third most utilized groups, respectively (Figure 1).

Figure 1. Antimicrobial use by therapeutic class between 2009 and 2018 in DDD/1000 patient-days.

The DDD of antibiotics was higher in public hospitals (mean 889.11 DDD/1000 patients-day) than in private hospitals (mean 849.07 DDD/1000 patients-day) and philanthropic hospitals (mean 785.12 DDD/1000 patients-day) (P < 0.05), but there is no significant difference between private and philanthropic hospitals. Use in philanthropic institutions was significantly higher for cephalosporins and quinolones. Private hospitals presented greater use for linezolid, while public facilities showed higher use for carbapenems, polymyxins, and glycopeptides (Table 2).

Table 2. Antibiotic use by therapeutic class by administrative type in DDD/1000 patients-day

DDD, defined daily dose. a, Significant difference between all groups. b, No difference between groups private and public. c, No difference between groups philanthropic and private.

In public hospitals, total antibiotic use in the SHO subcategory was higher than in DPA (928.84 and 871.67 DDD/1000 patients-day, respectively – P < 0.001). Additionally, SHO exhibited higher consumption across all antibiotic classes except for cephalosporins and quinolones (Table 3).

Table 3. Antibiotic use in public hospitals by subgroup between 2009 and 2018 in DDD/1000 patients-day

SHO, Social Health Organization – private administration; DPA, direct public administration.

ICUs classified as high-complexity demonstrated greater utilization of carbapenems, polymyxins, and glycopeptides compared to lower complexity ICUs (Table 4).

Table 4. Antibiotic use according to ICU complexity from 2009 to 2018 in DDD/1000 patient-days

ICU, intensive care unit.

Correlation between antibiotic use and bacterial resistance

The incidence of all resistance phenotypes, except vancomycin-resistant Enterococcus sp. (VRE), showed significant variation between 2009 and 2018 because of the difference in the report method adopted in two periods: 2009–2011 and 2012–2018. The incidence of carbapenem-resistant Acinetobacter baumannii (CRAb) and MRSA decreased between 2012 and 2018 when only primary BSI was reported (P < 0.05). However, in this period, the proportion of CRAb increased significantly (P < 0.001). The incidence and proportion of carbapenem-resistant Klebsiella pneumoniae (CRKp) showed a substantial increase between 2012 and 2018 (Figure 2).

Figure 2. Incidence of resistant bacteria by phenotypic profile of resistance in the period 2009 to 2018. BSI, bloodstream infection; CRAb, carbapenem-resistant Acinetobacter baumannii; CRPa, carbapenem-resistant Pseudomonas aeruginosa; CRKp, carbapenem-resistant Klebsiella pneumoniae; ESBL, extended-spectrum beta-lactamase; MRSA, methicillin-resistant S.aureus; VRE, vancomycin-resistant Enterococcus sp.

The proportion of MDRO was higher in public hospitals compared to private and philanthropic institutions (P <0.05). However, only CRAb exhibited no difference when comparing public and private hospitals (Table 5). For public hospitals, MRSA prevalence was higher in SHO subgroup than in the DPA subgroup (P <0.05). No disparity was observed in the proportion of the other patterns assessed.

Positive correlations were identified between glycopeptide use and MRSA incidence, as well as polymyxin use and carbapenem-resistant gram-negative bacilli (CR-GNB) incidence. Additionally, a weak negative correlation was observed between carbapenem use and third-generation cephalosporin (3GC)-resistant Enterobacterales incidence (Table 6).

Table 5. Proportion of resistant bacteria by phenotypic profile of resistance and administration type in the period 2009 to 2018

CRAb, carbapenem-resistant Acinetobacter baumannii; CRPa, carbapenem-resistant Pseudomonas aeruginosa; CRKp, carbapenem-resistant Klebsiella pneumoniae; ESBL, extended-spectrum beta-lactamase; MRSA, methicillin-resistant S.aureus; VRE, vancomycin-resistant Enterococcus sp.

Table 6. Spearman’s correlation coefficient for antibiotic use and bacterial resistance incidence between 2009 a 2018

MRSA, methicillin-resistant Staphylococcus aureus; 3GC, third-generation cephalosporin-resistant Enterobacterales; CR-GNB, carbapenem-resistant gram-negative bacilli.

Discussion

Improving the use of antibiotics and the knowledge of bacterial resistance represent key objectives of the Global Action Plan on Antimicrobial Resistance, as presented by the WHO and endorsed in Brazil.1,15 A significant strategy involves investigating the relationship between antimicrobial use and resistance. This study aimed to explore the patterns of antibiotic utilization in the ICUs and their correlation with bacterial resistance in bloodstream isolates within the Health Surveillance System of a Brazilian state. Furthermore, we examined antibiotic use across different groups of hospitals categorized by administrative type – public, private, and philanthropic – a unique feature of Brazil’s healthcare sector.

Until 2015, antibiotic use data at the hospital level based on a systematic national surveillance system were restricted to a few countries,Reference Plüss-Suard, Pannatier, Kronenberg, Mühlemann and Zanetti7,Reference Dumartin, L’Hériteau and Péfau16Reference Qu, Yin and Sun18 while antibiotic use reports based on regional statisticsReference Grau, Fondevilla and Mojal5,Reference Domínguez, Rosales, Cabello, Bavestrello and Labarca17,Reference Versporten, Bolokhovets and Ghazaryan19 or restricted to local intensive care unitsReference Hanberger, Arman and Gill20Reference Meyer, Schwab, Schroeren-Boersch and Gastmeier22 were relatively more common. However, the establishment and expansion of GLASS have significantly increased surveillance of AMR and antibiotic use with national data from different regions of the globe. In 2021, 109 countries enrolled in GLASS.23 Our study presents BSIs data from an AMR surveillance period before BR-GLASS, describing antibiotic use in intensive care units in the State of São Paulo, whose SHD was a national pioneer in the composition of an information surveillance system for hospital infections.

The global use of antibiotics in the ICU found in our study is lower than previously reported in ICU by other studies: 916 DDD/1000 patients-day in Porto Alegre, Brazil, 1140 DDD/1000 patients-day in Germany, 1143 DDD/1000 patients-day in Switzerland, 1466 DDD/1000 patients-day in France and 1471 DDD/1000 patients-day in Catalonia, Spain.Reference Jacoby, Kuchenbecker, Dos Santos, Magedanz, Guzatto and Moreira3,Reference Grau, Fondevilla and Mojal5,Reference Plüss-Suard, Pannatier, Kronenberg, Mühlemann and Zanetti7,Reference Dumartin, L’Hériteau and Péfau16,Reference de With, Meyer, Steib-Bauert, Schwab, Daschner and Kern21 However, different drug surveillance methods may explain multinational antibiotic use discrepancies.

In our study, cephalosporins were the most used therapeutic class, which differs from the practice observed in European ICUs in which penicillins, especially using amoxicillin-clavulanate, represent the group of antibiotics most used in intensive care units.Reference Grau, Fondevilla and Mojal5,Reference Plüss-Suard, Pannatier, Kronenberg, Mühlemann and Zanetti7,Reference Dumartin, L’Hériteau and Péfau16,Reference Agodi, Auxilia and Barchitta24 A Brazilian point-prevalence study showed that ceftriaxone was the most prescribed antibiotic in the sample evaluated, mainly for treating respiratory and urinary infections.Reference Porto, Goossens, Versporten and Costa25 The reasons for these differences in prescription patterns, favoring either amoxicillin-clavulanate or ceftriaxone as the first choice, remain unclear and warrant further investigation, possibly involving cultural factors or cost considerations.

Otherwise, cephalosporins use exhibited a decreasing trend over the observed period, possibly associated with an increase in the proportion of gram-negative bacteria resistant to these drugs. Globally, there is a trend toward increasing MDRO gram-negative in intensive care units,Reference Mascarello, Simonetti and Knezevich26Reference Fowler and Lee28 which is generally associated with an increase in hospital length of stay, mortality, and hospital costs.Reference Chen, Lee, Su, Tang, Chang and Liu27 Our study also observed an increase in the proportion of CRAb and CRKp over the 10-year period.

Few studies have evaluated the difference in antibiotic use by administrative type. A French study,Reference Dumartin, L’Hériteau and Péfau16 showed a higher antibiotic use in private hospitals than in public institutions, a trend not observed in our study. Public hospitals exhibited the highest antibiotic use, while private institutions used more antibiotics than philanthropic hospitals. A previous Brazilian survey on community and hospital antibiotic use observed a greater burden of resistant bacteria in public hospitals, possibly attributable to patient overload in these institutions, which may affect healthcare quality.Reference Boszczowski, Neto and Blangiardo29

Moreover, among public hospitals, we observed higher use of carbapenems, glycopeptides, and polymyxins in SHO hospitals. Notably, there was a positive correlation between glycopeptide use and MRSA incidence in SHO hospitals but not in DPA hospitals. For both types of administration, there was a positive correlation between polymyxins and the incidence of CR-GNB and no correlation between the use of meropenem and the incidence of 3GC-resistant Enterobacterales. Because these are unique administration profiles in other countries, there are no comparative data for these subgroups. SHO represents the private administration of public institutions with governmental funding.Reference Rodrigues and Spagnuolo30,Reference Barbosa and Elias31 The correlation of antibiotic use with bacterial resistance incidence suggests more efficient resource utilization, which has significant implications for Brazilian public health policy. However, further studies are needed to evaluate this hypothesis.

Typically, comparisons of antibiotic utilization among ICU types are based on the specialty of care, such as cardiology, medical and surgical, and there are limited studies describing differences based on the level of care complexity.Reference Fridkin, Edwards and Pichette32 In our study, ICUs were stratified into high or low complexity based on the rate of mechanical ventilation. Although it is not a formally validated parameter for the definition of care complexity, the percentage of mechanical ventilation usage allows inference of patient severity in the unit and has been described in another study as a method of ICU stratification for data benchmarking.Reference Wetzker, Bunte-Schönberger, Walter, Schröder, Gastmeier and Reichardt33

We observed higher antibiotic utilization and incidence of MRSA, carbapenem-resistant gram-negative bacteria (CR-GNB), and third-generation cephalosporin (3GC)-resistant Enterobacterales in high-complexity ICUs. C Critically ill patients in the ICU, along with the associated risks of delayed therapy, contribute to a lower threshold for initiating antimicrobial treatment, and this hypothesis helps to explain the observed result.

We found a weak negative correlation between the proportion of 3GC-resistant Enterobacterales and carbapenems use. His finding was unexpected; however, one possible explanation is the concurrent administration of carbapenems in the treatment of CR-GNB,, which may be associated with the observed negative correlation between the proportion of 3GC-resistant Enterobacterales and CR-GNB.

Our study has several limitations. First, the ecological study design cannot establish the cause-and-effect relationship between antibiotic use and resistance. Second, during the period observed, there was a change in the notification criteria of bacteremias, and this does not allow for continuous analysis of the resistance incidence over the entire period. Third, although a significant difference in antibiotic use was found based on the complexity of the ICU, the criteria employed for stratification require validation by other studies. Fourth, significant differences in antibiotic use and MDRO incidence were observed in the different hospital administrative categories. However, the study design does not allow us to establish the causes related to these findings.

In summary, we found substantial variations in antibiotic utilization and MDRO incidence across different hospital administrative categories, highlighting the importance of tailored interventions based on healthcare facility characteristics. Notably, we observed a higher proportion of MDRO in public hospitals compared to private and philanthropic institutions, suggesting potential areas for targeted interventions to mitigate resistance emergence. Furthermore, our findings revealed an unexpected negative correlation between the proportion of third-generation cephalosporin (3GC)-resistant Enterobacterales and carbapenem use, underscoring the complexity of AMR dynamics in healthcare settings. Despite these insights, our study has inherent limitations, including its ecological design and the need for validation of ICU complexity stratification criteria. Future research efforts should focus on elucidating the causal relationships between antibiotic use and resistance patterns, thereby facilitating the development of evidence-based antimicrobial stewardship strategies tailored to diverse healthcare contexts.

Acknowledgments

Financial support

No financial support was provided relevant to this article.

Competing interests

All authors report no conflicts of interest relevant to this article.

References

World Health Organization. GLOBAL ACTION PLAN ON ANTIMICROBIAL RESISTANCE. https://www.who.int/publications/i/item/9789241509763.Google Scholar
Lai, CC, Chu, CC, Cheng, A, Huang, YT, Hsueh, PR. Correlation between antimicrobial consumption and incidence of health-care-associated infections due to methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci at a university hospital in Taiwan from 2000 to 2010. J Microbiol Immunol Infect 2015;48:431436. doi: 10.1016/j.jmii.2013.10.008 CrossRefGoogle Scholar
Jacoby, TS, Kuchenbecker, RS, Dos Santos, RP, Magedanz, L, Guzatto, P, Moreira, LB. Impact of hospital-wide infection rate, invasive procedures use and antimicrobial consumption on bacterial resistance inside an intensive care unit. J Hosp Infect 2010;75:2327. doi: 10.1016/j.jhin.2009.11.021 CrossRefGoogle ScholarPubMed
Meyer, E, Gastmeier, P, Deja, M, Schwab, F. Antibiotic consumption and resistance: data from Europe and Germany. Int J Med Microbiol 2013;303:388395. doi: 10.1016/j.ijmm.2013.04.004 CrossRefGoogle ScholarPubMed
Grau, S, Fondevilla, E, Mojal, S, et al. Antibiotic consumption at 46 VINCat hospitals from 2007 to 2009, stratified by hospital size and clinical services. Enferm Infecc Microbiol Clin 2012; 3:4351. doi: 10.1016/S0213-005X(12)70096-4 CrossRefGoogle Scholar
Nogueira Junior, C, Mello, DS de, Padoveze, MC, Boszczowski, I, Levin, AS, Lacerda, RA. Characterization of epidemiological surveillance systems for healthcare-associated infections (HAI) in the world and challenges for Brazil. Cad Saude Publica 2014;30:1120. doi: 10.1590/0102-311x00044113 CrossRefGoogle ScholarPubMed
Plüss-Suard, C, Pannatier, A, Kronenberg, A, Mühlemann, K, Zanetti, G. Hospital antibiotic consumption in Switzerland: comparison of a multicultural country with Europe. J Hosp Infect 2011;79:166171. doi: 10.1016/j.jhin.2011.05.028 CrossRefGoogle ScholarPubMed
Pimentel, VP, Barbosa, LM de LH, Machado, L, Adão, LF, Reis, C. Sistema de saúde brasileiro: gestão, institucionalidade e financiamento. https://web.bndes.gov.br/bib/jspui/handle/1408/14134.Google Scholar
Brasil AN de VS. NOTA TÉCNICA GVIMS/GGTES/Anvisa No 01/2021: Notificação Dos Indicadores Nacionais Das Infecções Relacionadas à Assistência à Saúde (IRAS) e Resistência Microbiana (RM) - 2021.; 2021.Google Scholar
Pillonetto, M, Jordão, RT de S, Andraus, GS, et al. The experience of implementing a national antimicrobial resistance surveillance system in Brazil. Front Public Health 2020;8:575536. doi: 10.3389/fpubh.2020.575536 CrossRefGoogle ScholarPubMed
Padoveze, MC, Assis, DB, Freire, MP, et al. Surveillance programme for healthcare associated infections in the state of São Paulo, Brazil. Implementation and the first three years’ results. J Hosp Infect 2010;76:311315. doi: 10.1016/j.jhin.2010.07.005 CrossRefGoogle Scholar
Assis, DB, Madalosso, G, Ferreira, SA, Yassuda, YY. Vigilância Das Infecções Hospitalares No Estado de São Paulo. Dados 2004-2008.; 2009. Accessed June 15, 2024. https://www.saude.sp.gov.br/resources/cve-centro-de-vigilancia-epidemiologica/areas-de-vigilancia/infeccao-hospitalar/outros/ih09_vih0408.pdf Google Scholar
World Health Organization. Guidelines for ATC Classification and DDD Assignment.; 2023. Accessed June 15, 2024. https://atcddd.fhi.no/atc_ddd_index_and_guidelines/guidelines/ Google Scholar
NHSN. National Healthcare Safety Network (NHSN) Patient Safety Component Manual.; 2021. Accessed February 5, 2021. https://www.cdc.gov/nhsn/pdfs/pscmanual/pcsmanual_current.pdf Google Scholar
Brasil M da S. Plano de Ação Nacional de Prevenção e Controle Da Resistência Aos Antimicrobianos No Âmbito Da Saúde Única 2018-2022.; 2017. Accessed June 12, 2021. https://portalarquivos2.saude.gov.br/images/pdf/2018/dezembro/20/af-pan-br-17dez18-20x28-csa.pdf Google Scholar
Dumartin, C, L’Hériteau, F, Péfau, M, et al. Antibiotic use in 530 French hospitals: results from a surveillance network at hospital and ward levels in 2007. J Antimicrob Chemother 2010;65:20282036. doi: 10.1093/jac/dkq228 CrossRefGoogle Scholar
Domínguez, I, Rosales, R, Cabello, Á, Bavestrello, L, Labarca, J. Evaluation of antimicrobial consumption en 15 Chilean hospitals: Results of a collaborative work, 2013. Rev Chilena Infectol 2016;33:307312. doi: 10.4067/S0716-10182016000300010 CrossRefGoogle ScholarPubMed
Qu, X, Yin, C, Sun, X, et al. Consumption of antibiotics in Chinese public general tertiary hospitals (2011-2014): Trends, pattern changes and regional differences. PLoS One 2018;13(5):e0196668. doi: 10.1371/journal.pone.0196668 CrossRefGoogle ScholarPubMed
Versporten, A, Bolokhovets, G, Ghazaryan, L, et al. Antibiotic use in eastern Europe: a cross-national database study in coordination with the WHO Regional Office for Europe. Lancet Infect Dis 2014;14:381387. doi: 10.1016/S1473-3099(14)70071-4 CrossRefGoogle ScholarPubMed
Hanberger, H, Arman, D, Gill, H, et al. Surveillance of microbial resistance in European intensive care units: a first report from the Care-ICU programme for improved infection control. Intensive Care Med 2009;35:91100. doi: 10.1007/s00134-008-1237-y CrossRefGoogle ScholarPubMed
de With, K, Meyer, E, Steib-Bauert, M, Schwab, F, Daschner, FD, Kern, W V. Antibiotic use in two cohorts of German intensive care units. J Hosp Infect 2006;64:231237. doi: 10.1016/j.jhin.2006.05.018 CrossRefGoogle ScholarPubMed
Meyer, E, Schwab, F, Schroeren-Boersch, B, Gastmeier, P. Surveillance of antibiotic use and bacterial resistance in intensive care units. Bundesgesundheitsblatt Gesundheitsforsch Gesundheitsschutz 2008;51:926935. doi: 10.1007/s00103-008-0614-6 CrossRefGoogle ScholarPubMed
World Health Organization. Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report 2021.; 2021. Accessed June 15, 2024. https://www.who.int/publications/i/item/9789240027336 Google Scholar
Agodi, A, Auxilia, F, Barchitta, M, et al. Antibiotic consumption and resistance: results of the SPIN-UTI project of the GISIO-SItI. Epidemiol Prev 2015;39:9498.Google ScholarPubMed
Porto, APM, Goossens, H, Versporten, A, Costa, SF, Brazilian Global-PPS Working Group. Global point prevalence survey of antimicrobial consumption in Brazilian hospitals. J Hosp Infect 2020;104:165171. doi: 10.1016/j.jhin.2019.10.016 CrossRefGoogle Scholar
Mascarello, M, Simonetti, O, Knezevich, A, et al. Correlation between antibiotic consumption and resistance of bloodstream bacteria in a University Hospital in North Eastern Italy, 2008-2014. Infection 2017;45:459467. doi: 10.1007/s15010-017-0998-z CrossRefGoogle Scholar
Chen, IL, Lee, CH, Su, LH, Tang, YF, Chang, SJ, Liu, JW. Antibiotic consumption and healthcare-associated infections caused by multidrug-resistant gram-negative bacilli at a large medical center in Taiwan from 2002 to 2009: implicating the importance of antibiotic stewardship. PLoS One 2013;8:e65621. doi: 10.1371/journal.pone.0065621 CrossRefGoogle Scholar
Fowler, LH, Lee, S. Antibiotic trends amid multidrug-resistant gram-negative infections in intensive care units. Crit Care Nurs Clin North Am 2017;29:111118. doi: 10.1016/j.cnc.2016.09.010 CrossRefGoogle ScholarPubMed
Boszczowski, Í, Neto, FC, Blangiardo, M, et al. Total antibiotic use in a state-wide area and resistance patterns in Brazilian hospitals: an ecologic study. Braz J Infect Dis 2020;24:479488. doi: 10.1016/j.bjid.2020.08.012 CrossRefGoogle Scholar
Rodrigues, CT, Spagnuolo, RS. Organizações Sociais de Saúde: potencialidades e limites na gestão pública. Rev Eletr Enferm 2014;16:549557. doi: 10.5216/ree.v16i3.22319 CrossRefGoogle Scholar
Barbosa, NB, Elias, PEM. As organizações sociais de saúde como forma de gestão público/privado. Cien Saude Colet 2010;15:24832495. doi: 10.1590/S1413-81232010000500023 CrossRefGoogle Scholar
Fridkin, SK, Edwards, JR, Pichette, SC, et al. Determinants of vancomycin use in adult intensive care units in 41 United States hospitals. Clin Infect Dis 1999;28:11191125. doi: 10.1086/514752 CrossRefGoogle ScholarPubMed
Wetzker, W, Bunte-Schönberger, K, Walter, J, Schröder, C, Gastmeier, P, Reichardt, C. Use of ventilator utilization ratio for stratifying alcohol-based hand-rub consumption data to improve surveillance on intensive care units. J Hosp Infect 2017;95:185188. doi: 10.1016/j.jhin.2016.10.020 CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Number of hospitals reporting to the epidemiological surveillance system in the state of São Paulo, 2009–2018

Figure 1

Figure 1. Antimicrobial use by therapeutic class between 2009 and 2018 in DDD/1000 patient-days.

Figure 2

Table 2. Antibiotic use by therapeutic class by administrative type in DDD/1000 patients-day

Figure 3

Table 3. Antibiotic use in public hospitals by subgroup between 2009 and 2018 in DDD/1000 patients-day

Figure 4

Table 4. Antibiotic use according to ICU complexity from 2009 to 2018 in DDD/1000 patient-days

Figure 5

Figure 2. Incidence of resistant bacteria by phenotypic profile of resistance in the period 2009 to 2018. BSI, bloodstream infection; CRAb, carbapenem-resistant Acinetobacter baumannii; CRPa, carbapenem-resistant Pseudomonas aeruginosa; CRKp, carbapenem-resistant Klebsiella pneumoniae; ESBL, extended-spectrum beta-lactamase; MRSA, methicillin-resistant S.aureus; VRE, vancomycin-resistant Enterococcus sp.

Figure 6

Table 5. Proportion of resistant bacteria by phenotypic profile of resistance and administration type in the period 2009 to 2018

Figure 7

Table 6. Spearman’s correlation coefficient for antibiotic use and bacterial resistance incidence between 2009 a 2018