Modeling the impact of interventions against Acinetobacter baumannii transmission in intensive care units

Evidence-Based Interventions to Reduce the Incidence of Common Multidrug-Resistant Gram-Negative Bacteria in an Adult Intensive Care Unit

Background Multidrug-resistant Gram-negative bacteria (MDR-GNB) present a significant and escalating hazard to healthcare globally. Context-specific interventions have been implemented for the prevention and control of MDR-GNB in several healthcare facilities. The objective of this study was to implement and evaluate the effectiveness of evidence-based interventions in the incidence and dissemination of MDR-GNB. Methods This was a pre-and post-intervention study conducted in three phases at King Abdulaziz Medical City Jeddah, Saudi Arabia. During Phase-1, the data on each of the four MDR-GNB (Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Escherichia coli) were collected prospectively. Genomic fingerprinting was performed on isolates using enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR) to determine clonality and establish a link between different strains within and between the hospital wards/units. In the second phase, targeted interventions were implemented in the adult intensive care unit (ICU) based on previously determined risk factors and included the education of healthcare workers on hand hygiene, disinfection of patients' surrounding, daily chlorhexidine baths, and disinfection rooms on discharge with hydrogen peroxide fogging after MDR-GNB patients were discharged. An antibiotic restriction protocol was simultaneously implemented as part of the hospital antibiotic stewardship program. In the third phase, the effectiveness of the interventions was evaluated by comparing the incidence rate and clonality (using ERIC-PCR genetic fingerprints) of MDR-GNB before and after the intervention. Results A significant reduction of MDR-GNB was observed in Phase-2 and Phase-3 compared with Phase-1. The mean incidence rate of MDR-GNB per 1000 patient days in Phase-1 (pre-intervention) was 11.08/1000, followed by 6.07 and 3.54/1000 in Phase-2 and Phase-3, respectively. A statistically significant reduction was observed in the incidence rate of MDR-GNB in the adult ICU (P=0.007), whereas no statistically significant decrease (P=0.419) was observed in areas other than the adult ICU. Two A. baumannii strains appear to be circulating within the ICU environment with reduced frequency in Phase-2 and Phase-3 compared to Phase-1. Conclusion  There was a significant reduction in the incidence of MDR-GNB in the adult ICU due to the successful implementation of both infection control and stewardship interventions, albeit challenging to ascertain the relative contribution of each.

Individual hand hygiene improvements and effects on healthcare-associated infections: A long-term follow-up study using an electronic hand hygiene monitoring system

BACKGROUND

Obtaining detailed insights into people's unique hand hygiene behaviour could play an important role in developing the most effective long-term hand hygiene compliance (HHC) interventions.

AIM

To investigate the effect of two feedback interventions provided by an electronic hand hygiene monitoring system (EHHMS) on sustained HHC improvement, individual responsiveness, and prevention of hospital-acquired bloodstream infections (HABSI) and urinary tract infections (HAUTI).

METHODS

The study included two two-year cohorts (exposed and unexposed to EHHMS) observed over four years in an internal medicine department with 142 caregivers and 39 doctors. Healthcare workers (HCWs) were stratified into four groups based on their baseline performance to assess predicted responsiveness to the interventions.

FINDINGS

All healthcare workers increased their HHC independently from their performance during baseline, except a few in the low-performance groups with constantly low HHC. The two low-performance groups at baseline were most responsive to group feedback (weekly change in HHC of 4.4% and 3.1%) compared to individual feedback (weekly change in HHC of 1.0% and 2.2%). The number of HABSI cases was significantly reduced during the intervention period (P=0.01), with the highest effect on Staphylococcus aureus. No significant change was observed in HAUTI.

CONCLUSION

The EHHMS interventions successfully sustained the HHC improvements and reduced the number of HABSI cases. All HCWs, except a few, responded to the interventions. The two low-performance groups during baseline never reached the same HHC levels as those in the high-performance groups, indicating a potential for further improvement and the need for intensified individualized interventions.

Check your assumptions: Further scrutiny of basic model frameworks of antimicrobial resistance

Since the mid-1990s, growing concerns over antimicrobial resistant (AMR) organisms has led to an increase in the use of mathematical models to explore the inter-host transmission of such infections. Previous work reviewing such models categorised them into generic frameworks based on their underlying assumptions. These assumptions dictated the coexistence between AMR and antimicrobial sensitive strains. We add to this work performing stability analyses of the frameworks, along with simulating them deterministically and stochastically. Stability analyses found that many of these assumptions lead to models having the same equilibria, but showed differences in the equilibria's stability between models. Deterministic simulations reveal that assuming replacement of one infecting strain by another leads to an unusual antimicrobial treatment threshold. Increasing beyond this threshold causes a discontinuous increase in disease burden. The cost of AMR to pathogen fitness (lowered transmission) dictates both the threshold of treatment that causes the discontinuous increase in disease burden and the size of that increase. It was also shown that Superinfection states can be biased against resident strains and so favour coexistence of both strains. Stochastic simulations demonstrated that differing scenario starting conditions can guide models to converge upon equilibria that they may not have under deterministic simulation. These findings highlight the importance of checking assumptions when modelling AMR and strain competition more widely.

Hand hygiene improvement of individual healthcare workers: results of the multicentre PROHIBIT study

BACKGROUND

Traditionally, hand hygiene (HH) interventions do not identify the observed healthcare workers (HWCs) and therefore, reflect HH compliance only at population level. Intensive care units (ICUs) in seven European hospitals participating in the "Prevention of Hospital Infections by Intervention and Training" (PROHIBIT) study provided individual HH compliance levels. We analysed these to understand the determinants and dynamics of individual change in relation to the overall intervention effect.

METHODS

We included HCWs who contributed at least two observation sessions before and after intervention. Improving, non-changing, and worsening HCWs were defined with a threshold of 20% compliance change. We used multivariable linear regression and spearman's rank correlation to estimate determinants for the individual response to the intervention and correlation to overall change. Swarm graphs visualized ICU-specific patterns.

RESULTS

In total 280 HCWs contributed 17,748 HH opportunities during 2677 observation sessions. Overall, pooled HH compliance increased from 43.1 to 58.7%. The proportion of improving HCWs ranged from 33 to 95% among ICUs. The median HH increase per improving HCW ranged from 16 to 34 percentage points. ICU wide improvement correlated significantly with both the proportion of improving HCWs (ρ = 0.82 [95% CI 0.18-0.97], and their median HH increase (ρ = 0.79 [0.08-0.97]). Multilevel regression demonstrated that individual improvement was significantly associated with nurse profession, lower activity index, higher nurse-to-patient ratio, and lower baseline compliance.

CONCLUSIONS

Both the proportion of improving HCWs and their median individual improvement differed substantially among ICUs but correlated with the ICUs' overall HH improvement. With comparable overall means the range in individual HH varied considerably between some hospitals, implying different transmission risks. Greater insight into improvement dynamics might help to design more effective HH interventions in the future.

Modelling of the transmission dynamics of carbapenem-resistant Klebsiella pneumoniae in hospitals and design of control strategies

Carbapenem-resistant Klebsiella pneumoniae (CRKP) has emerged as a major threat to global public health. Epidemiological and infection controls associated with CRKP are challenging because of several potential elements involved in a complicated cycle of transmission. Here, we proposed a comprehensive mathematical model to investigate the transmission dynamics of CRKP, determine factors affecting the prevalence, and evaluate the impact of interventions on transmission. The model includes the essential compartments, which are uncolonized, asymptomatic colonized, symptomatic colonized, and relapsed patients. Additionally, symptomatic colonized and relapsed patients were further classified into subpopulations according to their number of treatment failures or relapses. We found that the admission of colonized patients and use of antibiotics significantly impacted the endemic transmission in health care units. Thus, we introduced the treatment efficacy, defined by combining the treatment duration and probability of successful treatment, to characterize and describe the effects of antibiotic treatment on transmission. We showed that a high antibiotic treatment efficacy results in a significantly reduced likelihood of patient readmission in the health care unit. Additionally, our findings demonstrate that CRKP transmission with different epidemiological characteristics must be controlled using distinct interventions.

Improving mathematical modeling of interventions to prevent healthcare-associated infections by interrupting transmission or pathogens: How common modeling assumptions about colonized individuals impact intervention effectiveness estimates

Mathematical models are used to gauge the impact of interventions for healthcare-associated infections. As with any analytic method, such models require many assumptions. Two common assumptions are that asymptomatically colonized individuals are more likely to be hospitalized and that they spend longer in the hospital per admission because of their colonization status. These assumptions have no biological basis and could impact the estimated effects of interventions in unintended ways. Therefore, we developed a model of methicillin-resistant Staphylococcus aureus transmission to explicitly evaluate the impact of these assumptions. We found that assuming that asymptomatically colonized individuals were more likely to be admitted to the hospital or spend longer in the hospital than uncolonized individuals biased results compared to a more realistic model that did not make either assumption. Results were heavily biased when estimating the impact of an intervention that directly reduced transmission in a hospital. In contrast, results were moderately biased when estimating the impact of an intervention that decolonized hospital patients. Our findings can inform choices modelers face when constructing models of healthcare-associated infection interventions and thereby improve their validity.

Control and management of multidrug resistant Acinetobacter baumannii: A review of the evidence and proposal of novel approaches

Hospital-acquired infections are on the rise and are a substantial cause of clinical and financial burden for healthcare systems. While infection control plays a major role in curtailing the spread of outbreak organisms, it is not always successful. One organism of particular concern is Acinetobacter baumannii, due to both its persistence in the hospital setting and its ability to acquire antibiotic resistance. A. baumannii has emerged as a nosocomial pathogen that exhibits high levels of resistance to antibiotics, and remains resilient against traditional cleaning measures with resistance to Colistin increasingly reported. Given the magnitude and costs associated with hospital acquired infections, and the increase in multidrug-resistant organisms, it is worth re-evaluating our current approaches and looking for alternatives or adjuncts to traditional antibiotics therapies. The aims of this review are to look at how this organism is spread within the hospital setting, discuss current treatment modalities, and propose alternative methods of outbreak management.

How Should the Value Attributes of Novel Antibiotics Be Considered in Reimbursement Decision Making?

Antibiotics have revolutionized the treatment of bacterial infections. However, it is widely held that there is underinvestment in antibiotics research and development relative to the socially optimal level for a number of reasons. In this article, we discuss whether existing health technology assessment procedures recognize the full economic and societal value of new antibiotics to patients and society when making reimbursement decisions. We present three recommendations for modelling the unique attributes of value that are specific to novel antibiotics. We find, based on a review of the literature, that some of the value elements proposed by our framework have previously been discussed qualitatively by health technology assessment bodies when evaluating antibiotics, but are not yet formally captured via modelling. We present a worked example to show how it may be possible to capture these dimensions of value in a more quantitative manner. We conclude by answering the question of the title as follows: the unique attributes of novel antibiotics should be considered in reimbursement decision making, in a way that captures the full range of benefits these important technologies bring to patients, health care systems, and society.

Population-level mathematical modeling of antimicrobial resistance: a systematic review

BACKGROUND

Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work.

METHODS

We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens.

RESULTS

We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals.

CONCLUSIONS

The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

The natural environment as a reservoir of pathogenic and non-pathogenic Acinetobacter species

Acinetobacter is a genus of Gram-negative bacteria, which are oxidase-negative, exhibiting a twitching motility under a magnifying lens. Besides being important soil microorganisms, due to their contribution to the soil fertility, Acinetobacter species, particularly A. baumannii, hold a prominent place within the genus because, it is the most virulent among the other species, causing varying degrees of human infections in clinical environments. However, results of different research have shown that Acinetobacter species can be isolated from such natural environments as surface water, wastewater and sewage, healthy human skin, plant, animal and food material as well as domestic appliances. The presence of some other Acinetobacter species in the natural environment has been associated with beneficial roles including soil improvement, detoxification of oil spillages and as microflora in human and plant bodies. In this paper, we carried out an overview of various natural ecological niches as reservoirs of pathogenic and non-pathogenic Acinetobacter species.

Modeling the seasonality of Methicillin-resistant Staphylococcus aureus infections in hospitals with environmental contamination

A deterministic mathematical model with periodic antibiotic prescribing rate is constructed to study the seasonality of Methicillin-resistant Staphylococcus aureus (MRSA) infections taking antibiotic exposure and environmental contamination into consideration. The basic reproduction number R0 for the periodic model is calculated under the assumption that there are only uncolonized patients with antibiotic exposure at admission. Sensitivity analysis of R0 with respect to some essential parameters is performed. It is shown that the infection would go to extinction if the basic reproduction number is less than unity and would persist if it is greater than unity. Numerical simulations indicate that environmental cleaning is the most important intervention to control the infection, which emphasizes the effect of environmental contamination in MRSA infections. It is also important to highlight the importance of effective antimicrobial stewardship programmes, increase active screening at admission and subsequent isolation of positive cases, and treat patients quickly and efficiently.

A unified inter-host and in-host model of antibiotic resistance and infection spread in a hospital ward

As the battle continues against hospital-acquired infections and the concurrent rise in antibiotic resistance among many of the major causative pathogens, there is a dire need to conduct controlled experiments, in order to compare proposed control strategies. However, cost, time, and ethical considerations make this evaluation strategy either impractical or impossible to implement with living patients. This paper presents a multi-scale model that offers promise as the basis for a tool to simulate these (and other) controlled experiments. This is a "unified" model in two important ways: (i) It combines inter-host and in-host dynamics into a single model, and (ii) it links two very different modeling approaches - agent-based modeling and differential equations - into a single model. The potential of this model as an instrument to combat antibiotic resistance in hospitals is demonstrated with numerical examples.

A stochastic model for MRSA transmission within a hospital ward incorporating environmental contamination

Methicillin-resistant Staphylococcus aureus (MRSA) transmission in hospital wards is associated with adverse outcomes for patients and increased costs for hospitals. The transmission process is inherently stochastic and the randomness emphasized by the small population sizes involved. As such, a stochastic model was proposed to describe the MRSA transmission process, taking into account the related contribution and modelling of the associated microbiological environmental contamination. The model was used to evaluate the performance of five common interventions and their combinations on six potential outcome measures of interest under two hypothetical disease burden settings. The model showed that the optimal intervention combination varied depending on the outcome measure and burden setting. In particular, it was found that certain outcomes only required a small subset of targeted interventions to control the outcome measure, while other outcomes still reported reduction in the outcome distribution with up to all five interventions included. This study describes a new stochastic model for MRSA transmission within a ward and highlights the use of the generalized Mann–Whitney statistic to compare the distribution of the outcome measures under different intervention combinations to assist in planning future interventions in hospital wards under different potential outcome measures and disease burden.