Efficient national surveillance for health-care-associated infections

Strategies to Prevent Healthcare-Associated Infections: A Narrative Overview

Healthcare-associated infections (HCAIs) are a major source of morbidity and mortality and are the second most prevalent cause of death. Furthermore, it has been reported that for every one-hundred patients admitted to hospital, seven patients in high-income economies and ten in emerging and low-income economies acquire at least one type of HCAI. Currently, almost all pathogenic microorganisms have developed antimicrobial resistance, and few new antimicrobials are being developed and brought to market. The literature search for this narrative review was performed by searching bibliographic databases (including Google Scholar and PubMed) using the search terms: "Strategies," "Prevention," and "Healthcare-Associated Infections," followed by snowballing references cited by critical articles. We found that although hand hygiene is a centuries-old concept, it is still the primary strategy used around the world to prevent HCAIs. It forms one of a bundle of approaches used to clean and maintain a safe hospital environment and to stop the transmission of contagious and infectious microorganisms, including multidrug-resistant microbes. Finally, antibiotic stewardship also has a crucial role in reducing the impact of HCAIs through conserving currently available antimicrobials.

Advanced Loss Eventuality Assessment and Technical Estimates: An Integrated Approach for Management of Healthcare-Associated Infections

BACKGROUND

Healthcare Associated Infections (HAIs) represent a crucial issue in health and patient safety management due to the persistent nature, economic impact and possible preventability of the phenomenon. Compensation claims for damages resulting from HAI could provide insights that can improve the understanding of suboptimal steps in the therapeutic process, enable an estimate of costs related to infectious complications, and guide the development of planning tools for implementation of the quality of care.

OBJECTIVE

This paper analyzes all the HAI claims received at the Umberto I General Hospital of Rome across a five-year period with the aim of outlining a methodological approach to the litigation management and of characterizing the economic impact of infections on health facilities resources.

METHODS

All claims received during the study period have been classified according to the International Classification for Patient Safety (ICPS) system. Subsequently, claims related to Healthcare Associated Infections were evaluated through an innovative tool for determination of the risk of loss, the Advanced Loss Eventuality Assessment (ALEA) score.

RESULTS

The results obtained demonstrate the relevance of a correct management of HAI claims in the administration of a health care system. Specifically, the cases examined during the study highlighted the significant impact of infectious diseases of a nosocomial nature in terms of frequency and economic exposure.

CONCLUSION

The proposed methodological approach allows a productive analysis of the internal processes, providing fundamental data for the refinement of the preventive strategies and for the rationalization of the resources through the expenditure forecasts. Article Highlights Box: Healthcare-Associated Infections represent an essential element to consider in the management of health facilities. • Many studies highlight the economic burden of Healthcare-Associated Infections in health policies. • Litigation management represents a useful resource in the prevention of Healthcare Associated Infections. • Appropriate clinical risk management policies in the field of Healthcare-Associated Infections allow the implementation of preventive measures, the reduction of the incidence of the phenomenon and the quality of care. • The costs of Healthcare-Associated Infections can be limited through a systematic methodological approach based on Advanced Loss Eventuality Assessment and technical estimate of the value of each case. • The application of a standardized system would be desirable in any health facility despite the potential methodological, technical, behavioral and financial issues.

Prevalence of health care-associated infections and antimicrobial resistance of the responsible pathogens in Ukraine: Results of a multicenter study (2014-2016)

BACKGROUND

The aim of this study was to obtain the first national estimates of the current prevalence and incidence and death of health care-associated infections (HAIs) of all types in acute care hospitals in Ukraine.

METHODS

Prospective surveillance was conducted from January 2014 to December 2016 in 17 hospitals. Surveillance case definitions were derived from the Centers for Disease Control and Prevention's National Healthcare Safety Network HAI case definitions. The identification and antimicrobial susceptibility of cultures were determined using a automated microbiology analyzer. Some antimicrobial susceptibility tests used Kirby-Bauer antibiotic testing.

RESULTS

Of 97,340 patients, 10,986 (11.3%) HAIs were observed. The most frequently reported HAI types were surgical site infections (60%), respiratory tract infections (pneumonia and lower respiratory tract, 18.4%), bloodstream infections (10.2%), and urinary tract infections (9.5%). Death during hospitalization was reported in 9.7% of HAI cases. The most common organism reported was Escherichia coli, accounting for 21.8% of all organisms, followed by Staphylococcus aureus (18.4%), Enterococcus spp (15.7%), and Pseudomonas aeruginosa (12.4%). Antimicrobial resistance among the isolates associated with HAIs showed that 42.1% and 3.6% of coagulase-negative Staphylococcus spp isolates were β-lactam (oxacillin)- and glycopeptide (teicoplanin)-resistant, respectively. Meticillin resistance was reported in 39.2% of S aureus isolates.

CONCLUSIONS

HAIs and increasing antimicrobial resistance present a significant burden to the Ukraine hospital system. Infection control priorities in hospitals should include preventing surgical site infections, respiratory tract infections (which also include PNEU and LRTI), bloodstream infections, and urinary tract infections, as well preventing infections due to antimicrobial-resistant pathogens.

Prevalence of healthcare-associated infections and antimicrobial resistance in acute care hospitals in Kyiv, Ukraine

BACKGROUND

Healthcare-associated infections (HAIs) are among the most common adverse events in patient care, and account for substantial morbidity and mortality.

AIM

To obtain the first estimates of the current prevalence of HAIs and antimicrobial resistance in acute care hospitals in Kyiv, Ukraine.

METHODS

Prospective surveillance was conducted from January 2014 to December 2016 in five acute care hospitals in Kyiv. Definitions of HAIs were adapted from the Centers for Disease Control and Prevention's National Healthcare Safety Network.

FINDINGS

Among 53,884 patients, 3753 (7%) HAIs were observed. The most frequently reported HAIs were respiratory tract infections (pneumonia 19.4%, lower respiratory tract infections 4.1%), surgical site infections (19.6%), urinary tract infections (17.5%) and bloodstream infections (10.6%). Death during hospitalization was reported in 7.2% cases of HAI. The micro-organisms most frequently isolated from HAIs were Escherichia coli (15.9%), Staphylococcus aureus (14.8%), Enterococcus spp. (10.2%), Pseudomonas aeruginosa (8.9%) and Klebsiella spp. (8.9%). Meticillin resistance was reported in 28.2% of S. aureus, and 14.2% of enterococci were resistant to vancomycin. Overall, 35.1% of all Enterobacteriaceae were resistant to third-generation cephalosporins, with the highest resistance rates seen in K. pneumoniae (53.8%) and E. coli (32.1%).

CONCLUSIONS

Infection control priorities in hospitals should include prevention of surgical site infections, pneumonia, bloodstream infections and urinary tract infections. These results may help to delineate the requirements for infection prevention and control in acute care hospitals.

Prospective surveillance of healthcare-associated infections and patterns of antimicrobial resistance of pathogens in an Italian intensive care unit

Background

The study aimed to evaluate the distribution of healthcare-associated infections (HAIs), the incidence rates and device utilization ratio (DUR) of device-associated infections (DAIs), as well as the distribution and patterns of antimicrobial resistance of the responsible pathogens.

Methods

Eligible patients who were admitted to an adult Intensive Care Unit (ICU) from May 1, 2013 to December 31, 2016 were included in the surveillance. Demographics, intrinsic and extrinsic risk factors, information regarding infection and isolated pathogens with antibiogram results were collected.

Results

One thousand two hundred eighty-three patients were included in the surveillance. One hundred forty-seven HAIs were detected with a cumulative incidence of 9.2 per 100 patients 4-year period and an incidence rate of 17.4 per 1000 patient days. Fifty-six out of 1283 patients were affected by at least one episode of ICU-acquired pneumonia, and 72.7% of these were associated with intubation. ICU-acquired bloodstream infections (BSIs) occurred in 4.4% of patients and 89.5% were catheter-related. ICU-acquired urinary tract infections (UTIs) occurred in 1% of patients, with 84.6% of the episodes being associated with the use of an urinary catheter. The pattern of antimicrobial-resistance in the isolates showed, among the Gram-positive bacteria, that 66.6% and 16.6% of Staphylococcus epidermidis were oxacillin and teicoplanin resistant, respectively. Among the Gram-negative bacteria, carbapenem resistance was found in 91.6% of Acinetobacter baumannii and 28.5% of Klebsiella pneumoniae isolates.

Conclusions

The majority of HAIs in the ICU studied were associated with the use of invasive devices. Since a significant proportion of these HAIs are considered preventable, reinforcement of the evidence-based preventive procedures are needed.

Electronic supplementary material

The online version of this article (10.1186/s13756-018-0337-x) contains supplementary material, which is available to authorized users.

Applications of network analysis to routinely collected health care data: a systematic review

Objective

To survey network analyses of datasets collected in the course of routine operations in health care settings and identify driving questions, methods, needs, and potential for future research.

Materials and Methods

A search strategy was designed to find studies that applied network analysis to routinely collected health care datasets and was adapted to 3 bibliographic databases. The results were grouped according to a thematic analysis of their settings, objectives, data, and methods. Each group received a methodological synthesis.

Results

The search found 189 distinct studies reported before August 2016. We manually partitioned the sample into 4 groups, which investigated institutional exchange, physician collaboration, clinical co-occurrence, and workplace interaction networks. Several robust and ongoing research programs were discerned within (and sometimes across) the groups. Little interaction was observed between these programs, despite conceptual and methodological similarities.

Discussion

We use the literature sample to inform a discussion of good practice at this methodological interface, including the concordance of motivations, study design, data, and tools and the validation and standardization of techniques. We then highlight instances of positive feedback between methodological development and knowledge domains and assess the overall cohesion of the sample.

Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data

PURPOSE OF REVIEW

Mathematical modeling approaches have brought important contributions to the study of pathogen spread in healthcare settings over the last 20 years. Here, we conduct a comprehensive systematic review of mathematical models of disease transmission in healthcare settings and assess the application of contact and patient transfer network data over time and their impact on our understanding of transmission dynamics of infections.

RECENT FINDINGS

Recently, with the increasing availability of data on the structure of interindividual and interinstitution networks, models incorporating this type of information have been proposed, with the aim of providing more realistic predictions of disease transmission in healthcare settings. Models incorporating realistic data on individual or facility networks often remain limited to a few settings and a few pathogens (mostly methicillin-resistant Staphylococcus aureus).

SUMMARY

To respond to the objectives of creating improved infection prevention and control measures and better understanding of healthcare-associated infections transmission dynamics, further innovations in data collection and parameter estimation in modeling is required.

Mathematical models used to inform study design or surveillance systems in infectious diseases: a systematic review

BACKGROUND

Mathematical models offer the possibility to investigate the infectious disease dynamics over time and may help in informing design of studies. A systematic review was performed in order to determine to what extent mathematical models have been incorporated into the process of planning studies and hence inform study design for infectious diseases transmitted between humans and/or animals.

METHODS

We searched Ovid Medline and two trial registry platforms (Cochrane, WHO) using search terms related to infection, mathematical model, and study design from the earliest dates to October 2016. Eligible publications and registered trials included mathematical models (compartmental, individual-based, or Markov) which were described and used to inform the design of infectious disease studies. We extracted information about the investigated infection, population, model characteristics, and study design.

RESULTS

We identified 28 unique publications but no registered trials. Focusing on compartmental and individual-based models we found 12 observational/surveillance studies and 11 clinical trials. Infections studied were equally animal and human infectious diseases for the observational/surveillance studies, while all but one between humans for clinical trials. The mathematical models were used to inform, amongst other things, the required sample size (n = 16), the statistical power (n = 9), the frequency at which samples should be taken (n = 6), and from whom (n = 6).

CONCLUSIONS

Despite the fact that mathematical models have been advocated to be used at the planning stage of studies or surveillance systems, they are used scarcely. With only one exception, the publications described theoretical studies, hence, not being utilised in real studies.

Measuring distance through dense weighted networks: The case of hospital-associated pathogens

Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014–2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult.

Shared patients can spread hospital-associated pathogens between hospitals, together forming a large network in which all hospitals are connected. We set out to measure the distance between hospitals in such a network, best reflecting the risk of a hospital-associated pathogen spreading from one to the other. The central problem is that this risk may not be a directly reflected by the weight of the direct connections between hospitals, because the pathogen could arrive through a longer indirect route, first causing a problem in an intermediate hospital. We determined the optimal balance between connection weights and path length, by testing different weighting factors between them against simulated spread of a pathogen. We found that while strong connections are important risk factor for a hospital’s direct neighbours, weak connections offer ideal indirect routes for hospital-associated pathogens to travel further faster. These routes should not be underestimated when designing control strategies.

Population genetic structuring of methicillin-resistant Staphylococcus aureus clone EMRSA-15 within UK reflects patient referral patterns

Antibiotic resistance forms a serious threat to the health of hospitalised patients, rendering otherwise treatable bacterial infections potentially life-threatening. A thorough understanding of the mechanisms by which resistance spreads between patients in different hospitals is required in order to design effective control strategies. We measured the differences between bacterial populations of 52 hospitals in the United Kingdom and Ireland, using whole-genome sequences from 1085 MRSA clonal complex 22 isolates collected between 1998 and 2012. The genetic differences between bacterial populations were compared with the number of patients transferred between hospitals and their regional structure. The MRSA populations within single hospitals, regions and countries were genetically distinct from the rest of the bacterial population at each of these levels. Hospitals from the same patient referral regions showed more similar MRSA populations, as did hospitals sharing many patients. Furthermore, the bacterial populations from different time-periods within the same hospital were generally more similar to each other than contemporaneous bacterial populations from different hospitals. We conclude that, while a large part of the dispersal and expansion of MRSA takes place among patients seeking care in single hospitals, inter-hospital spread of resistant bacteria is by no means a rare occurrence. Hospitals are exposed to constant introductions of MRSA on a number of levels: (1) most MRSA is received from hospitals that directly transfer large numbers of patients, while (2) fewer introductions happen between regions or (3) across national borders, reflecting lower numbers of transferred patients. A joint coordinated control effort between hospitals, is therefore paramount for the national control of MRSA, antibiotic-resistant bacteria and other hospital-associated pathogens.

Using national movement databases to help inform responses to swine disease outbreaks in Scotland: the impact of uncertainty around incursion time

Modelling is an important component of contingency planning and control of disease outbreaks. Dynamic network models are considered more useful than static models because they capture important dynamic patterns of farm behaviour as evidenced through animal movements. This study evaluates the usefulness of a dynamic network model of swine fever to predict pre-detection spread via movements of pigs, when there may be considerable uncertainty surrounding the time of incursion of infection. It explores the utility and limitations of animal movement data to inform such models and as such, provides some insight into the impact of improving traceability through real-time animal movement reporting and the use of electronic animal movement databases. The study concludes that the type of premises and uncertainty of the time of disease incursion will affect model accuracy and highlights the need for improvements in these areas.

Validation of a Sampling Method to Collect Exposure Data for Central-Line–Associated Bloodstream Infections

OBJECTIVE

Surveillance of central-line–associated bloodstream infections requires the labor-intensive counting of central-line days (CLDs). This workload could be reduced by sampling. Our objective was to evaluate the accuracy of various sampling strategies in the estimation of CLDs in intensive care units (ICUs) and to establish a set of rules to identify optimal sampling strategies depending on ICU characteristics.

DESIGN

Analyses of existing data collected according to the European protocol for patient-based surveillance of ICU-acquired infections in Belgium between 2004 and 2012.

SETTING AND PARTICIPANTS

CLD data were reported by 56 ICUs in 39 hospitals during 364 trimesters.

METHODS

We compared estimated CLD data obtained from weekly and monthly sampling schemes with the observed exhaustive CLD data over the trimester by assessing the CLD percentage error (ie, observed CLDs – estimated CLDs/observed CLDs). We identified predictors of improved accuracy using linear mixed models.

RESULTS

When sampling once per week or 3 times per month, 80% of ICU trimesters had a CLD percentage error within 10%. When sampling twice per week, this was >90% of ICU trimesters. Sampling on Tuesdays provided the best estimations. In the linear mixed model, the observed CLD count was the best predictor for a smaller percentage error. The following sampling strategies provided an estimate within 10% of the actual CLD for 97% of the ICU trimesters with 90% confidence: 3 times per month in an ICU with >650 CLDs per trimester or each Tuesday in an ICU with >480 CLDs per trimester.

CONCLUSION

Sampling of CLDs provides an acceptable alternative to daily collection of CLD data.

Infect Control Hosp Epidemiol 2016;37:549–554