The burden of healthcare-associated infections in Brazil: multi-hospital point prevalence using a matched case-control study

ABSTRACT

Background:  Healthcare-associated infections (HAIs) have a significant impact on patient care worldwide and have serious implications for the Brazilian healthcare system.

Objectives:  This study aimed to describe the trends in HAIs in adult intensive care units (ICUs) using data from a national point-prevalence survey.

DESIGN AND SETTING:  A point-prevalence study was conducted in 2019 across adult intensive ICUs in large acute care hospitals in Brazil.

METHODS:  A matched case-control study was performed to assess the risk factors associated with the development of infection.

RESULTS:  A total of 386 patients from 15 hospitals were studied, of whom 102 (26.4%; 102/386) were infected, and 76.5% had at least one ICU-acquired infection. In clinical-surgical ICUs (CSU), the prevalence of infections acquired in the unit was 77.9%, whereas in Coronary ICUs (COU), it was 68.7%. There was a predominance of pneumonia (51.0%), mainly caused by Gram-negative non-fermenters, and bloodstream infections (34.4%), predominantly caused by coagulase-negative Staphylococcus (CoNS). In the risk factor analysis, cancer and general antimicrobial use were independently associated.

CONCLUSION:  This study found a high burden of HAIs in adult ICUs in Brazil, mainly associated with the high use of antibiotics for infections and a worse prognosis.

KEYWORDS (MeSH terms)
Infection control; Multicenter study; Intensive care units; Epidemiology

AUTHORS’ KEYWORDS:
Healthcare-associated infections; Point prevalence; Infections; High use of antibiotics

INTRODUCTION

Healthcare-associated infections (HAIs) and antimicrobial resistance are growing global public health concerns, with particular significance in intensive care units (ICUs).¹ In Brazilian ICUs, estimates of HAI prevalence in tertiary hospitals range from 44.3% to 79.4%.²,³,⁴

Comprehensive data on infection types, pathogenic microorganisms, risk factors, and antimicrobial use across Brazil are essential for developing policies that prioritize the prevention and treatment of HAIs. Such data can help optimize patient care and efficiently allocate financial resources.⁵,⁶ In 2016, a multicenter study was conducted in Brazil to determine the one-day point prevalence of infections in 28 ICUs in the state of Minas Gerais. The study included 303 patients, of whom 155 (51.2%) were found to be infected, and 123 (79.4%) had at least one infection acquired in the ICU.⁴

The current study was conducted in 2019 using a design similar to that previously described but including additional Brazilian states. We hypothesized that the prevalence of HAIs and associated microorganisms would vary significantly among geographical regions. To gain a broad understanding of the magnitude of HAIs in ICUs and the associated burden of disease in Brazil, we estimated the prevalence of HAIs using data from multicenter studies.

OBJECTIVE

This article aimed to describe trends in HAIs in adult intensive care units (ICUs) using data from a national point-prevalence survey.

METHODS

Survey design and participating hospitals

This matched case-control study was conducted in 17 clinical-surgical ICUs (CSUs) and five coronary ICUs (COUs) across the five main geographic regions of Brazil (North, Northeast, Midwest, Southeast, and South) in 2019. Fifteen hospitals, comprising both public and private institutions, participated in the study with the consent of their administrations. This study included 386 patients hospitalized in the ICUs on the corresponding day. The prevalence of HAIs and episodes of infection were determined for each region, along with the overall frequency of microorganisms diagnosed in each type of ICU. For the microorganisms evaluated in this study, data were provided by the respective hospitals at the time of microbiological diagnosis. This case-control study was based on a one-day point prevalence survey to determine the demographic characteristics and risk factors between groups. The analyzed variables included age, length of hospital stay, underlying diseases, hospital risk factors, and antimicrobial therapy. This study was approved on August 25, 2018, by the Research Ethics Committee of the Federal University of Uberlandia under the protocol number CAAE: 88387817.0.0000.5152.

The co-participating centers were randomly selected. A survey of hospitals with ICU beds was conducted in the main cities of each region, and the managers of these institutions were contacted to complete the project. The executing team applied the same methodology to all centers for data collection and surveys in the visited ICUs.

Definitions

HAIs were defined according to the guidelines of the Agência Nacional de Vigilância Sanitária (ANVISA, Brazil),⁷ which were largely based on definitions from the National Healthcare Safety Network (NHSN).⁸ However, ANVISA’s guidelines expand the definition of bloodstream infections (BSIs) to include patients with clinically defined sepsis without laboratory confirmation. Treatment administered during the period between the suspicion of infection and obtaining susceptibility results was defined as empirical.⁹

Selection of cases

Cases were defined as patients who had a confirmed HAI acquired in the ICU up to the corresponding day in each hospital, as defined by physicians according to the Diagnostic Criteria for Infection Related to Health Care established in each institution, according to ANVISA. At least one case was selected from each ICU included in the prevalence survey. Patient pairing was performed in a 1:1 ratio for patients who met the established criteria.

Selection of controls

Controls were patients without HAI who met the following predetermined criteria: they needed to be hospitalized in the same unit as the patient and should not have acquired an infection until the corresponding day. Controls were matched according to sex, age, reason for hospitalization (clinical, surgical, or traumatic), and the total length of hospital stay before infection (risk time).

To eliminate time bias, the total hospitalization time for controls until the corresponding day should be greater than or equal to the interval between the admission and infection dates of the cases.¹⁰ In addition, controls had to be in the same age group as the case patients, with a maximum age difference of ± 10 years.

Statistical analysis

Comparisons between groups were made using Student’s t-test for variables with a normal distribution (evaluated using the D’Agostino and Lilliefors tests) and the Mann-Whitney U test for variables with a non-normal distribution. Chi-squared and Fisher’s exact tests were used to assess the relationships between categorical variables. Multiple regression models were used for multivariate analysis. A significance level of 5% (P < 0.05) was considered significant. All analyses were performed using BioEstat 5.0 software (Instituto de Desenvolvimento Sustentável Mamirauá, Tefé, AM, Brazil).

RESULTS

This study included 15 hospitals located in the five regions of Brazil. The institutions had 4,204 beds, of which 337 (8.0%) were CSUs and 99 (2.3%) were COUs. Table 1 shows the prevalence of HAIs in ICUs on the day of the study. Among the CSUs, 86/300 (28.7%) patients had HAIs, and 77.9% of these infections were acquired within the unit. In addition, when the frequency was examined by region, the Southwestern (39.7%) and Northern (35.5%) regions had the highest rates. Regarding the prevalence in the COUs, 16/86 (18.6%) patients had at least one infectious episode, and 68.7% of these infections were acquired in the unit.

Table 1
Prevalence of healthcare-associated infections in adult clinical-surgical and coronary intensive care units in different regions in Brazil

Regions	Number of Hospitals	Patients admitted to the ICU*		Patients with HAI§(%)		Patients with HAI acquired in the ICU (%)
		CSU¥	COU◆	CSU	COU	CSU	COU
North	3	45	13	16 (35.5)	5 (38.5)	13 (81.2)	4 (80.0)
Northeast	5	99	24	19 (19.2)	3 (12.5)	13 (68.4)	2 (66.7)
Midwest	2	60	0	14 (23.3)	0	13 (92.8)	0
Southwest	4	83	49	33 (39.7)	8 (16.3)	24 (72.7)	5 (62.5)
South	1	13	0	4 (30.8)	0	4 (100.0)	0
Total	15	300	86	86 (28.7)	16 (18.6)	67 (77.9)	11 (68.7)

• ^*ICU = intensive care unit;
• ^§HAI = healthcare-associated infection;
• ^¥CSU = clinical-surgical unit;
• ^◆COU = coronary unit.

Table 2 shows the frequency of infectious episodes identified according to the site of infection in HAIs acquired exclusively in the ICUs. A total of 84 episodes were identified in 67 infected patients in CSUs, with 12 episodes observed in 11 infected patients in COUs. Pneumonia episodes were the most prevalent infections in both units, accounting for 50.0% of CSUs and 58.3% of COUs, followed by BSIs (34.5% and 33.3 %, respectively). The frequency of episodes by region followed the same pattern, except in the southeastern region, where pneumonia and BSIs had the same frequency.

Table 2
Prevalence of healthcare-associated infection episodes in adult clinical-surgical and coronary intensive care units in different regions of Brazil

Regions	Total number of HAI* episodes acquired in the ICU		Pneumonia (%)		Bloodstream infection (%)		Urinary tract infection (%)
	CSU§	COU¥	CSU	COU	CSU	COU	CSU	COU
North	17	5	8 (47.0)	4 (80.0)	6 (35.3)	0	2 (11.8)	0
Northeast	17	2	8 (47.0)	0	7 (41.2)	2 (100.0)	2 (11.8)	0
Midwest	14	0	9 (64.3)	0	5 (35.7)	0	0	0
Southwest	26	5	10 (38.5)	3 (60.0)	10 (38.5)	2 (40.0)	4 (15.4)	0
South	10	0	7 (70.0)	0	1 (10.0)	0	1	0
Total	84	12	42 (50.0)	7 (58.3)	29 (34.5)	4 (33.3)	9 (10.7)	0

• ^*HAI = healthcare-associated infection;
• ^§CSU = clinical-surgical unit;
• ^¥COU = coronary unit; Other infections – CSU = surgical site (2); skin (1).

The microorganisms responsible for HAIs are shown in Table 3. A total of 45 microorganisms were identified in 96 episodes (46.9%) of HAI acquired in the ICUs. The most common microorganisms were Gram-negative bacilli, accounting for 34.4% (n = 33/96 episodes) of the total documented pathogens. In CSUs, the most common bacteria were Pseudomonas aeruginosa, Coagulase-Negative Staphylococcus (CoNS), and Klebsiella pneumoniae, all having the same proportions (18.9%), followed by Acinetobacter baumannii (13.5%) and Enterobacter cloacae (10.8%). The most common agents causing pneumonia in these units were A. baumannii (22.2%) and P. aeruginosa (22.2%). In BSIs, the most prevalent microorganism was CoNS, accounting for approximately half of all cases. Furthermore, we observed a low frequency (48.8%; data not provided) of infections based on the microbiological diagnostic criteria.

Table 3
Microorganism frequency by common healthcare-associated infections acquired in adult clinical-surgical and coronary intensive care units in Brazil

	Total HAI*	Bloodstream infection	Pneumonia
Clinical-surgical ICU§
Patients with HAI; N	67	29	40
Number of microorganisms identified; n	41	14	21
Microorganisms (N; %)	Pseudomonas aeruginosa (7; 18.9%)	CoNS¥(6; 42.8%)	Acinetobacter baumannii (4; 22.2%)
	CoNS¥ (7; 18.9%)	Pseudomonas aeruginosa (2; 14.3%)	Pseudomonas aeruginosa (4; 22.2%)
	Klebsiella pneumoniae (7; 18.9%)	Klebsiella pneumoniae (2; 14.3%)	Klebsiella pneumoniae (3; 16.7%)
	Acinetobacter baumannii (5; 13.5%)	Enterobacter cloacae (1; 7.1%)	Enterobacter cloacae (2; 11.1%)
	Other Gram-negative bacilli◆ (5; 13.5%)	Klebsiella oxytoca (1; 7.1%)	Staphylococcus aureus (2; 11.1%)
	Enterobacter cloacae (4; 10.8%)	Pseudomonas stutzeri (1; 7.1%)	CoNS¥(1; 5.5%)
	Escherichia coli (2; 5.4%)	Escherichia coli (1; 7.1%)	Proteus mirabilis (1; 5.5%)
	Staphylococcus aureus (2; 5.4%)		Raoultella ornithinolytica (1; 5.5%)
	Candida albicans (1; 2.7%)		Citrobacter freundii complex (1; 5.5%)
	Streptococcus pneumoniae (1; 2.7%)		Candida tropicalis (1; 5.5%)
			Streptococcus pneumoniae (1; 5.5%)
Coronary ICU
Patients with HAI*; N	11	6	8
Number of microorganisms identified; n	4	2	2
Microorganisms (frequency)	Klebsiella pneumoniae (2; 50.0%)	Klebsiella pneumoniae (1; 50.0%)	Klebsiella pneumoniae (1; 50.0%)
	Staphylococcus aureus (1; 25.0%)	Acinetobacter baumannii (1; 50.0%)	Staphylococcus aureus (1; 50.0%)
	Acinetobacter baumannii (1; 25.0%)

• ^*HAI = healthcare-associated infection;
• ^§ICU = intensive care unit;
• ^¥CoNS = Coagulase-Negative Staphylococcus: Staphylococcus epidermidis (3), Staphylococcus hominis (2), Staphylococcus haemolyticus (1), Staphylococcus capitis (1);
• ^◆Pseudomonas stutzeri (1), Klebsiella oxytoca (1), Proteus mirabilis (1), Raoultella ornithinolytica (1), Citrobacter freundii complex (1).

Risk factor analysis was performed using a matched case-control study, and 50 pairs were selected for analysis. The matching criteria were selected to ensure that there were no significant differences between the groups. Success rates ranged from 56.0% to 86.0% (Table 4).

Table 4
Success rate of paired variables for risk factors in patients with healthcare-related infections through a case-control study paired in intensive care units in Brazil

Variables	Total of pairs; n	No. of pairs reached; n	Success achieved (%)	P value
Reason for hospitalization	50	41	82.0	0.3284*
Sex	50	43	86.0	0.6787*
Age	50	29	58.0	0.4969◆
Risk time	50	28	56.0	0.6439§

• ^*Chi-square test;
• ^§Student’s t-test;
• ^◆Mann-Whitney test.

In general, the patients were relatively young, with 57.0% younger than 65 years, and the majority (78.3%) used three or more invasive devices. Approximately 59.3% of the patients were treated with broad-spectrum antibiotics, such as β-lactams with inhibitors (24.1%) and carbapenems (19.0%) (data not shown). Among all patients, 59.8% used antimicrobials, whereas only 24% of the control patients received such treatment. Table 5 compares the cases and controls, revealing significant differences between patients with cancer and those using a central venous catheter, mechanical ventilation, tracheostomy, and enteral nutrition. Patients who used antimicrobials for initial empirical therapy, infection treatment, or prophylaxis exhibited significant differences between the groups, along with variations in the average number of prescribed antimicrobials.

Table 5
Characteristics and risk factors of matched case-control patients in the point-prevalence study of healthcare-associated infections in adult intensive care units in Brazil

Variables	Total patients	Total matched patients	Matched case	Matched control	Statistical analysis
	n = 386 (%)	n = 100 (%)	n = 50 (%)	n = 50 (%)	Univariate, P value	Multivariate, P value, OR (95%IC)
Age
18 64 years	210 (54.4)	57 (57.0)	28 (56.0)	29 (58.0)	1.0000
≥ 65 years	176 (45.6)	43 (43.0)	22 (44.0)	21 (42.0)
Length of hospital stay, mean days	14.5	19.6	22.9	16.3	0.0929
Underlying conditions
Heart disease	106 (27.5)	13 (13.0)	3 (6.0)	10 (20.0)	0.0744
Systemic arterial hypertension	187 (48.4)	38 (38.0)	21 (42.0)	17 (34.0)	0.5365
COPD*	40 (10.4)	7 (7.0)	4 (8.0)	3 (6.0)	0.7180
Diabetes	113 (29.3)	31 (31.0)	15 (30.0)	16 (32.0)	1.0000
Cancer	50 (12.9)	8 (8.0)	7 (14.0)	1 (2.0)	0.0326	0.0446, 13.9559 (1.07-182.80)
Stroke	52 (13.5)	16 (16.0)	7 (14.0)	9 (18.0)	0.7850
Kidney disease	52 (13.5)	13 (13.0)	7 (14.0)	6 (12.0)	1.0000
Liver disease	10 (2.6)	3 (3.0)	2 (4.0)	1 (2.0)	1.0000
Polytrauma	46 (11.9)	18 (18.0)	9 (18.0)	9 (18.0)	0.7948
HIV§	9 (2.3)	4 (4.0)	1 (2.0)	3 (6.0)	0.6173
Hospital and clinical risk factors
Surgery	169 (43.8)	42 (42.0)	18 (36.0)	24 (48.0)	0.3110
Central venous catheter	231 (59.8)	62 (62.0)	34 (68.0)	28 (56.0)	0.0027	0.4599, 1.4681 (0.53-4.07)
Peripheral venous catheter	49 (12.7)	7 (7.0)	2 (4.0)	5 (10.0)	0.4360
Urinary catheter	217 (56.2)	63 (63.0)	36 (72.0)	27 (54.0)	0.0975
Mechanical ventilation	202 (52.3)	64 (64.0)	39 (78.0)	25 (50.0)	0.0068	0.2180, 1.1968 (0.34-4.22)
Tracheostomy	72 (18.6)	27 (27.0)	19 (38.0)	8 (16.0)	0.0243	0.7801, 1.1968 (0.34-4.22)
Surgical drain	50 (12.9)	14 (14.0)	5 (10.0)	9 (18.0)	0.3873
Hemodialysis	34 (8.8)	7 (7.0)	4 (8.0)	3 (6.0)	0.7180
Enteral nutrition	199 (51.5)	58 (58.0)	35 (70.0)	23 (46.0)	0.0258	0.1422, 2.2363 (0.76-6.55)
Parenteral nutrition	8 (2.1)	0	0	0
Antimicrobial therapy
Use of antimicrobials	231 (59.8)	62 (62.0)	50 (100.0)	12 (24.0)	< 0.0001	N/A
Mean of antimicrobials prescribed	1.0	1.1	1.9	0.5	< 0.0001	N/A
Empirical use	125 (32.1)	28 (28.0)	28 (56.0)	0	< 0.0001	N/A
Infection treatment	55 (14.2)	21 (21.0)	21 (42.0)	0	< 0.0001	N/A
Prophylactic use	51 (13.2)	13 (13.0)	1 (2.0)	12 (24.0)	0.0029	0.0071, 0.0296 (0.00-0.38)

• *COPD = chronic obstructive pulmonary disease;
• §HIV = human immunodeficiency virus.

According to the logistic regression model presented in Table 5, cancer was identified as an independent risk factor for HAIs (odds ratio [OR] = 13.9559; 95% confidence interval [CI] = 1.07-182.80; P = 0.0446), whereas prophylactic use of antibiotics was identified as a protective factor in the control group (OR = 0.0296; 95%CI = 0.00-0.38; P = 0.0071).

DISCUSSION

The lack of surveillance data and comprehensive prevalence surveys in low- and middle-income countries is concerning.¹¹ While several countries are making efforts to quantify the burden and determinants of Healthcare-Associated Infections (HAIs), ensuring data adherence and reliability remains a significant challenge in developing countries.⁶,¹²,¹³

In this 24-hour point-prevalence study conducted at 15 participating centers in Brazil, the overall rate of suspected or confirmed HAIs was 26.4% (102/386). This rate exceeded those reported in previous studies conducted in Europe (ranging from 3.0% to 30.7%)¹³,¹⁴,¹⁵,¹⁶ and the United States (ranging from 4.0% to 11.9%).¹⁷,¹⁸ Our results suggest a remarkably high prevalence of HAIs acquired in Brazilian ICUs, regardless of geographic region, consistent with the findings reported by Braga et al.⁴ Although most participating centers were located in the Northern and Southeastern regions, the proportion of patients admitted to the ICU with HAI did not significantly differ across geographic regions.

Considering only the public hospitals evaluated in this study, an extremely high rate of HAIs was observed in adult CSUs (82.5%; data not shown). This finding aligns with previous studies reporting elevated rates of HAIs in Brazilian ICUs.⁴,¹⁹,²⁰ Furthermore, due to the COVID-19 pandemic, recent literature has indicated an increase in infection rates associated with healthcare in countries with limited resources, with a reported prevalence increase of up to 15.0%.²¹,²²

Another aspect analyzed in this study was the incidence of infection. Our findings confirm that pneumonia (50.0%) and BSI (34.5%) were the most prevalent infections, which is consistent with similar studies conducted in Brazil.³,⁴,²⁰,²³ These results reinforce the notion that infections at these anatomical sites are associated with a worse prognosis and higher mortality rates among hospitalized patients in low- and middle-income countries.²⁴,²⁵,²⁶ Notably, the etiology of infections, as determined by positive cultures, is an important finding. Our results are consistent with the current literature, with Gram-negative bacilli being the most frequently isolated pathogens in developing countries, including Brazil.²⁷,²⁸,²⁹

A concerning aspect to be emphasized in this regard is that only 48.8% of the infectious episodes had microbiological diagnostic criteria, which may contribute to inappropriately high consumption of antimicrobials, either due to the lack of medication de-escalation or the in vitro resistance of microorganisms to the administered antibiotic.³⁰ As a result of the absence or delay in microbiological diagnosis, we identified a high rate of initial empirical therapy (54.1%) among the total number of patients using antimicrobials, which are determinant factors for the increase in adverse events in the patient’s clinical course.³¹,³²

While multivariate analysis indicated that cancer was an independent risk factor for HAIs, traditional factors such as the use of central venous catheters, mechanical ventilation, tracheostomy, enteral nutrition, and general antimicrobial use were found to be more significant in the group of infected patients.

The excessive use of antimicrobials in Brazil is a healthcare issue that affects all critically hospitalized patients. Studies conducted by researchers in Latin America have reported high prevalence rates of antimicrobial use, particularly of broad-spectrum antibiotics, as we have also demonstrated.³³,³⁴,³⁵,³⁶ Two primary factors may be associated with this issue: delayed microbiological diagnosis, as previously mentioned, and a lack of diversity in available drugs.³⁷ Therefore, the indiscriminate use of antimicrobials is becoming increasingly concerning because it applies selective pressure on microorganisms that progressively restricts the existing therapeutic options through acquired resistance mechanisms.³⁷,³⁸,³⁹

Another aspect analyzed, in addition to the previously discussed CSUs, were the findings found in the COUs. Eight ICUs were included, revealing a prevalence of infection of 18.6% (n = 16/86), which is significantly higher than the rates found in developed countries, which typically range from 4.0% to 10.0%.⁴⁰,⁴¹,⁴² Thus, HAIs are a significant complication of cardiovascular procedures, with high morbidity and mortality in affected patients.⁴⁰,⁴³ Similar to data reported in the literature,⁴⁰,⁴²,⁴³ pneumonia was the most common infection in these units.

Acknowledging the limitations of this study in terms of design, time, and resource availability, we believe that the results are well represented and emphasize the importance of conducting similar studies to estimate the burden of HAIs in Brazil. This is particularly relevant given the restricted geographic coverage, data availability, and low participation of centers in this type of surveillance. It is worth noting that such studies can aid in planning and strengthening HAI prevention and control strategies, even in resource-limited settings, particularly for public health in Brazil.

CONCLUSION

This multicenter study of the prevalence of HAI revealed alarming rates across different regions of the country. Pneumonia and sepsis associated with Gram-negative bacilli are the most significant infections. This group of microorganisms poses a considerable challenge to public health authorities in terms of their content and control. Our findings suggest that the microbiological diagnosis of HAIs falls short of expectations, likely because of the high rates of antibiotic use and empirical treatments.

Acknowledgments

We thank all hospital directors, hospital infection control services, and intensive care units that voluntarily participated in the survey. Special recognition goes to Alexandre Marcio Boschiroli (Hospital Infection Control Service, Hospital Governador Celso Ramos, Brazil), as well as Maria Maryllya Ferreira Francisco and Raniella Ramos de Lima (Hospital Infection Control Service, Santa Casa de Misericórdia de Maceió, Brazil), for their valuable contributions.

REFERENCES

• ^1. Alp E, Damani N. Healthcare-associated infections in intensive care units: Epidemiology and infection control in low-to-middle income countries. J Infect Dev Ctries. 2015;9(10):1040-5. PMID: 26517477; https://doi.org/10.3855/jidc.6832.
  » https://doi.org/10.3855/jidc.6832
• ^2. Salomao R, Rosenthal VD, Grimberg G, et al. Device-associated infection rates in intensive care units of Brazilian hospitals: Findings of the International Nosocomial Infection Control Consortium. Rev Panam Salud Publica. 2008;24(3):195-202. PMID: 19115547; https://doi.org/10.1590/s1020-49892008000900006.
  » https://doi.org/10.1590/s1020-49892008000900006
• ^3. Silva E, Dalfior Junior L, Da Silveira Fernandes H, Moreno R, Vincent JL. Prevalence and outcomes of infections in Brazilian ICUs: a subanalysis of EPIC II study. Rev Bras Ter Intensiva. 2012;24(2):143-50. PMID: 23917761; https://doi.org/10.1590/S0103-507X2012000200008.
  » https://doi.org/10.1590/S0103-507X2012000200008
• ^4. Braga IA, Campos PA, Gontijo-Filho PP, Ribas RM. Multi-hospital point prevalence study of healthcare-associated infections in 28 adult intensive care units in Brazil. J Hosp Infect. 2018;99(3):318-24. PMID: 29522784; https://doi.org/10.1016/j.jhin.2018.03.003.
  » https://doi.org/10.1016/j.jhin.2018.03.003
• ^5. Nogueira-Jr C, Padoveze MC. Public policies on healthcare associated infections: a case study of three countries. Health Policy. 2018;122(9):991-1000. PMID: 30037567; https://doi.org/10.1016/j.healthpol.2018.07.006.
  » https://doi.org/10.1016/j.healthpol.2018.07.006
• ^6. Nogueira Junior C, Mello DS, Padoveze MC, et al. Characterization of epidemiological surveillance systems for healthcare-associated infections (HAI) in the world and challenges for Brazil. Cad Saude Publica. 2014;30(1):11-20. PMID: 24627009; https://doi.org/10.1590/0102-311x00044113.
  » https://doi.org/10.1590/0102-311x00044113
• ^7. Agência Nacional de Vigilância Sanitária. Critérios Diagnósticos de Infecção Relacionada à Assistência à Saúde. Segurança do Paciente e Qualidade em Serviços de Saúde. Brasilia: ANVISA; 2013. Available from: https://www.gov.br/anvisa/pt-br/centraisdeconteudo/publicacoes/servicosdesaude/publicacoes/caderno-2-criterios-diagnosticos-de-infeccao-relacionada-a-assistencia-a-saude.pdf/view Accessed in 2025 (Feb. 19).
  » https://www.gov.br/anvisa/pt-br/centraisdeconteudo/publicacoes/servicosdesaude/publicacoes/caderno-2-criterios-diagnosticos-de-infeccao-relacionada-a-assistencia-a-saude.pdf/view
• ^8. Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control. 2008;36(5):309-32. PMID: 18538699; https://doi.org/10.1016/j.ajic.2008.03.002.
  » https://doi.org/10.1016/j.ajic.2008.03.002
• ^9. Daikos GL, Tsaousi S, Tzouvelekis LS, et al. Carbapenemase-producing Klebsiella pneumoniae bloodstream infections: Lowering mortality by antibiotic combination schemes and the role of carbapenems. Antimicrob Agents Chemother. 2014;58(4):2322-8. PMID: 24514083; https://doi.org/10.1128/AAC.02166-13.
  » https://doi.org/10.1128/AAC.02166-13
• ^10. Lü Y, Cai MH, Cheng J, Zou K, Xiang Q, et al. A multi-center nested case-control study on hospitalization costs and length of stay due to healthcare-associated infection. Antimicrob Resist Infect Control. 2018;7:99. PMID: 30116526; https://doi.org/10.1186/s13756-018-0386-1.
  » https://doi.org/10.1186/s13756-018-0386-1
• ^11. World Health Organization. Report on the Burden of Endemic Health Care-Associated Infection Worldwide. Geneva: WHO; 2011. Available from: http://who.int/publications/i/item/report-on-the-burden-of-endemic-health-care-associated-infection-worldwide Accessed in 2025 (Feb. 19).
  » http://who.int/publications/i/item/report-on-the-burden-of-endemic-health-care-associated-infection-worldwide
• ^12. Allegranzi B, Bagheri Nejad S, Combescure C, Graafmans W, Attar H, et al. Burden of endemic health-care-associated infection in developing countries: systematic review and meta-analysis. Lancet. 2011;377(9761):228-41. PMID: 21146207; https://doi.org/10.1016/S0140-6736(10)61458-4.
  » https://doi.org/10.1016/S0140-6736(10)61458-4
• ^13. Smiddy MP, Murphy OM. The use of point prevalence surveys of healthcare-associated infection to identify risk factors and facilitate infection prevention and control planning. Healthc Infect. 2013;18(4):162-67. https://doi.org/10.1071/HI13022.
  » https://doi.org/10.1071/HI13022
• ^14. Suetens C, Latour K, Kärki T, et al. Prevalence of healthcare-associated infections, estimated incidence and composite antimicrobial resistance index in acute care hospitals and long-term care facilities: Results from two European point prevalence surveys, 2016 to 2017. Euro Surveill. 2018;23(46):1800516. PMID: 30458912. https://doi.org/10.2807/1560-7917.ES.2018.23.46.1800516.
  » https://doi.org/10.2807/1560-7917.ES.2018.23.46.1800516
• ^15. European Centre for Disease Prevention and Control. Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals – ECDC PPS validation protocol version 3.1.2. Stockholm: ECDC; 2019. Available from: https://www.ecdc.europa.eu/sites/default/files/documents/PPS-HAI-AMR-protocol.pdf Accessed in 2025 (Feb. 19).
  » https://www.ecdc.europa.eu/sites/default/files/documents/PPS-HAI-AMR-protocol.pdf
• ^16. Erdem H, Inan A, Altındis S, et al. Surveillance, control and management of infections in intensive care units in Southern Europe, Turkey and Iran - A prospective multicenter point prevalence study. J Infect. 2014;68(2):131-40. PMID: 24269951; https://doi.org/10.1016/j.jinf.2013.11.001.
  » https://doi.org/10.1016/j.jinf.2013.11.001
• ^17. Magill SS, Edwards JR, Bamberg W, et al. Multistate point-prevalence survey of health care-associated infections. N Engl J Med. 2014;370(13):1198-208. PMID: 24670166. https://doi.org/10.1056/NEJMoa1306801.
  » https://doi.org/10.1056/NEJMoa1306801
• ^18. Grohskopf LA, Sinkowitz-Cochran RL, Garrett DO, et al. A national point-prevalence survey of pediatric intensive care unit-acquired infections in the United States. J Pediatr. 2002;140(4):432-8. PMID: 12006957; https://doi.org/10.1067/mpd.2002.122499.
  » https://doi.org/10.1067/mpd.2002.122499
• ^19. Melo LSW, Estevão TM, Chaves JSC, et al. Success factors of a collaborative project to reduce healthcare-associated infections in intensive care units in Northeastern Brazil. Rev Bras Ter Intensiva. 2022;34(3):327-34. PMID: 36197370; https://doi.org/10.5935/0103-507x.20220070-pt.
  » https://doi.org/10.5935/0103-507x.20220070-pt
• ^20. Lisboa T, Faria M, Hoher JA, et al. The prevalence of nosocomial infection in intensive care units in the State of Rio Grande do Sul. Rev Bras Ter Intensiva. 2007;19(4):414-20. PMID: 25310156; https://doi.org/10.1590/S0103-507X2007000400002.
  » https://doi.org/10.1590/S0103-507X2007000400002
• ^21. Ghali H, Ben Cheikh A, Bhiri S, et al. Trends of healthcare-associated infections in a Tunisian University Hospital and impact of COVID-19 pandemic. Inquiry. 2021;58:469580211067930. PMID: 34910605; https://doi.org/10.1177/00469580211067930.
  » https://doi.org/10.1177/00469580211067930
• ^22. Rosenthal VD, Myatra SN, Divatia JV, et al. The impact of COVID-19 on health care–associated infections in intensive care units in low- and middle-income countries: International Nosocomial Infection Control Consortium (INICC) findings. Int J Infect Dis. 2022;118:83-8. PMID: 35218928; https://doi.org/10.1016/j.ijid.2022.02.041.
  » https://doi.org/10.1016/j.ijid.2022.02.041
• ^23. Fortaleza CMCB, Padoveze MC, Kiffer CRV, et al. Multi-state survey of healthcare-associated infections in acute care hospitals in Brazil. J Hosp Infect. 2017;96(2):139-44. PMID: 28433398; https://doi.org/10.1016/j.jhin.2017.03.024.
  » https://doi.org/10.1016/j.jhin.2017.03.024
• ^24. Vincent JL, Bihari DJ, Suter PM, et al. The prevalence of nosocomial infection in intensive care units in Europe: Results of the European prevalence of infection in intensive care (EPIC) study. JAMA. 1995;274(8):639-44. PMID: 7637145; https://doi.org/10.1001/jama.1995.03530080055041.
  » https://doi.org/10.1001/jama.1995.03530080055041
• ^25. Ponce de León-Rosales SP, Molinar-Ramos F, Domínguez-Cherit G, Rangel-Frausto MS, Vázquez-Ramos VG. Prevalence of infections in intensive care units in Mexico: A multicenter study. Crit Care Med. 2000;28(5):1316-21. PMID: 10834672; https://doi.org/10.1097/00003246-200005000-00010.
  » https://doi.org/10.1097/00003246-200005000-00010
• ^26. Cairns S, Reilly J, Booth M. Prevalence of healthcare-associated infection in Scottish intensive care units. J Hosp Infect. 2010;76(4):308-10. PMID: 20970880; https://doi.org/10.1016/j.jhin.2010.05.010.
  » https://doi.org/10.1016/j.jhin.2010.05.010
• ^27. Dias VMCH, Silva DMWD, Burger M, et al. Active surveillance of carbapenem-resistant Gram-negative healthcare-associated infections in a low-middle-income country city. Braz J Infect Dis. 2021;25(2):101540. PMID: 33592171; https://doi.org/10.1016/j.bjid.2021.101540.
  » https://doi.org/10.1016/j.bjid.2021.101540
• ^28. Iskandar K, Molinier L, Hallit S, et al. Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture. Antimicrob Resist Infect Control. 2021;10(1):63. PMID: 33789754; https://doi.org/10.1186/s13756-021-00931-w.
  » https://doi.org/10.1186/s13756-021-00931-w
• ^29. Sands K, Carvalho MJ, Portal E, et al. Characterization of antimicrobial-resistant Gram-negative bacteria that cause neonatal sepsis in seven low- and middle-income countries. Nat Microbiol. 2021;6(4):512-23. PMID: 33782558; https://doi.org/10.1038/s41564-021-00870-7.
  » https://doi.org/10.1038/s41564-021-00870-7
• ^30. Fair RJ, Tor Y. Antibiotics and bacterial resistance in the 21st Century. Perspect Medicin Chem. 2014;6:25-64. PMID: 25232278; https://doi.org/10.4137/PMC.S14459.
  » https://doi.org/10.4137/PMC.S14459
• ^31. Gupta A, Kapil A, Kabra SK, et al. Prospective study estimating healthcare associated infections in a paediatric hemato-oncology unit of a tertiary care hospital in north India. Indian J Med Res. 2013;138(6):944-9. PMID: 24521640.
• ^32. Rosa RG, Goldani LZ. Factors associated with hospital length of stay among cancer patients with febrile neutropenia. PLOS One. 2014;9(10):e108969. PMID: 25285790; https://doi.org/10.1371/journal.pone.0108969.
  » https://doi.org/10.1371/journal.pone.0108969
• ^33. Wirtz VJ, Dreser A, Gonzales R. Trends in antibiotic utilization in eight Latin American countries, 1997-2007. Rev Panam Salud Publica. 2010;27(3):219-25. PMID: 20414511; https://doi.org/10.1590/s1020-49892010000300009.
  » https://doi.org/10.1590/s1020-49892010000300009
• ^34. Wolff MJ. Use and misuse of antibiotics in Latin America. Clin Infect Dis. 1993;17(Supplement 2):S346-51. PMID: 8274599; https://doi.org/10.1093/clinids/17.supplement_2.s346.
  » https://doi.org/10.1093/clinids/17.supplement_2.s346
• ^35. Levy Hara G, Rojas-Cortés R, Molina León HF, et al. Point prevalence survey of antibiotic use in hospitals in Latin American countries. J Antimicrob Chemother. 2022;77(3):807-15. PMID: 34957520; https://doi.org/10.1093/jac/dkab459.
  » https://doi.org/10.1093/jac/dkab459
• ^36. Huerta-Gutiérrez R, Braga L, Camacho-Ortiz A, et al. One-day point prevalence of healthcare-associated infections and antimicrobial use in four countries in Latin America. Int J Infect Dis. 2019;86:157-66. PMID: 31229613; https://doi.org/10.1016/j.ijid.2019.06.016.
  » https://doi.org/10.1016/j.ijid.2019.06.016
• ^37. Prestinaci F, Pezzotti P, Pantosti A. Antimicrobial resistance: a global multifaceted phenomenon. Pathog Glob Health. 2015;109(7):309-18. PMID: 26343252; https://doi.org/10.1179/2047773215Y.0000000030.
  » https://doi.org/10.1179/2047773215Y.0000000030
• ^38. Levin ASS. Quais os princípios gerais da profilaxia antibiótica antes de intervenção cirúrgica? Rev Assoc Med Bras. 2002;48(4):282. https://doi.org/10.1590/S0104-42302002000400013.
  » https://doi.org/10.1590/S0104-42302002000400013
• ^39. Martinez-Sobalvarro JV, Júnior AAP, Pereira LB, et al. Antimicrobial stewardship for surgical antibiotic prophylaxis and surgical site infections: a systematic review. Int J Clin Pharm. 2022;44(2):301-19. PMID: 34843035; https://doi.org/10.1007/s11096-021-01358-4.
  » https://doi.org/10.1007/s11096-021-01358-4
• ^40. Michalopoulos A, Geroulanos S, Rosmarakis ES, Falagas ME. Frequency, characteristics, and predictors of microbiologically documented nosocomial infections after cardiac surgery. Eur J Cardiothorac Surg. 2006;29(4):456-60. PMID: 16481186; https://doi.org/10.1016/j.ejcts.2005.12.035.
  » https://doi.org/10.1016/j.ejcts.2005.12.035
• ^41. Bouza E, Hortal J, Muñoz P, et al. Postoperative infections after major heart surgery and prevention of ventilator-associated pneumonia: a one-day European prevalence study (ESGNI-008). J Hosp Infect. 2006;64(3):224-30. PMID: 16930769; https://doi.org/10.1016/j.jhin.2006.06.019.
  » https://doi.org/10.1016/j.jhin.2006.06.019
• ^42. Liu Z, Zhang X, Zhai Q. Clinical investigation of nosocomial infections in adult patients after cardiac surgery. Medicine. 2021;100(4):e24162. PMID: 33530207; https://doi.org/10.1097/MD.0000000000024162.
  » https://doi.org/10.1097/MD.0000000000024162
• ^43. De Santo LS, Bancone C, Santarpino G, et al. Microbiologically documented nosocomial infections after cardiac surgery: an 18-month prospective tertiary care centre report. Eur J Cardiothorac Surg. 2008;33(4):666-72. PMID: 18261917; https://doi.org/10.1016/j.ejcts.2007.12.046.
  » https://doi.org/10.1016/j.ejcts.2007.12.046

• Source of funding:
  This research was funded by public agencies dedicated to promoting scientific and technological research: Fundação de Amparo à Pesquisa do Estado de Minas Gerais/Programa de Pesquisa para o SUS (FAPEMIG/PPSUS, DECIT/SCTIE/MS, SES/MG) (APQ-04062-17), and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (428397/2018-2 and 306476/2018-5). Coordenação de Aperfeiçoamento para Pessoal de Nível Superior (CAPES)( PROEX 421/2021) for scholarship

Edited by

• Editor responsible for the evaluation process:
  Paulo Manuel Pêgo-Fernandes, MD, PhD

Publication Dates

• Publication in this collection
  28 Apr 2025
• Date of issue
  2025

History

• Received
  01 Sept 2023
• Reviewed
  07 June 2024
• Accepted
  03 July 2024