Identification of unique ecosystem service bundles in farmland - A case study in the Huang-Huai-Hai Plain of China.

Ecosystem services (ESs) are essential for human well-being and are relevant to the region's sustainable development. Most studies have emphasized the importance of high ecosystem services areas for entire regions. However, some locations with particular contributions to a region's ecosystem services are still overlooked. Using the InVEST model, this study analyzed three ESs: annual water yield (WY), carbon storage (CS), and soil conservation (SC) in the farmland of the Huang-Huai-Hai Plain of China (HHHP) from 2005 to 2019. Combining climate regulation (NDVI) and food production (FP), this research calculated the city level of the diversity of ecosystem services supply (alpha-multifunctionality) and the unique contribution to the region in each city (beta-multifunctionality) from 2005 to 2019. The alpha-multifunctionality combines the number of ecosystem services and their supplies of ecosystem services. At the same time, the beta-multifunctionality assesses the average dissimilarity between the city and all other cities within that region. Furthermore, this study used Spearman correlation and self-organizing map (SOM) to analyze the relationships between these five ecosystem services and identify ecosystem service bundles. Finally, this study used random forests to analyze drivers of ecosystem service multifunctionality. Our results showed that food production in the Huang-Huai-Hai Plain increased significantly by 37.20% over time, annual water yield decreased significantly by 29.59%, and climate regulation decreased significantly by 6.09%. This may be because the Huang-Huai-Hai Plain mainly shifted from monoculture to crop rotation, and the increase in crops required more irrigation, which led to a significant decline in water yield. Furthermore, the area of grain crops in the HHHP was reduced in 2019 compared to 2005, which explains the significant decline in climate regulation. SOM found that cities with a higher beta-multifunctionality were mainly concentrated in the northern and southwest parts of HHHP. Bundles with a high alpha-multifunctionality were mainly in the southern and southeast parts of the HHHP. In addition, this research showed that farmers' per capita disposable income was the most important driver of ecosystem service multifunctionality, followed by annual average precipitation and temperature. In conclusion, this study suggests that policymakers should strengthen the protection of some high ecological value but low economic value farmlands, which are crucial for regional ecological security. Meanwhile, policymakers should introduce strict ecological protection policies for farmland to reduce the decline of ecological services caused by farmers' pursuit of economic income.

Can probiotics trigger a paradigm shift for cleaning healthcare environments? A narrative review.

BACKGROUND: The environment of healthcare institutions plays a major role in the transmission of multidrug resistant organisms (MDRO) and likely in subsequent healthcare-associated infections (HAIs). Probiotic cleaning products are a novel option for environmental cleaning. They represent a sustainable and biodegradable alternative to conventional chemical disinfectants for controlling microbial bioburden, and preventing pathogen transmission in hospital environments. High-quality studies including randomized clinical trials (RCT) triggered a summary with expert recommendations until further studies allow a critical review and meta-analysis of the data. METHODS: Infection control experts from five European countries summarized available data as of June 2023. Authors presented their published RCTs, reviewed the existing literature on probiotic cleaning, summarized the results and identified knowledge gaps and subsequent research needs. RESULTS: Probiotic cleaning was similarly effective for reducing HAI-related pathogens, enveloped viruses such as SARS-CoV-2 and MDRO in environmental samples compared to conventional chemical disinfectants. More importantly, probiotic cleaning was non-inferior to disinfectants in terms of preventing HAI in a large RCT. In addition, probiotic cleaning has also been shown to reduce antimicrobial resistance genes (ARG), costs and antimicrobial consumption in other hospital trials. They are biodegradable, do not require any protection for chemical hazards, and are compliant with occupational health. A paradigm shift, however, requires a very strong evidence to justify for such a change. In the past, this evidence was limited by the heterogeneity of study design, products, protocols, and few studies on clinical outcomes used in the trials. Furthermore, the regulatory, safety, and quality aspects of probiotic cleaning products are not, yet, completely defined and require clearing by authorities. CONCLUSION: To date, probiotic cleaning is a breakthrough technology and a biological alternative for chemical disinfectant when treating hospital environment. It may also have a positive effect on MDRO transmission. However, the different compositions of probiotic products will require standardization, and more robust data should be generated to support these promising results on different compositions. This may trigger a paradigm shift in cleaning of healthcare institutions from chemical to biological control of the hospital environment.

Device associated healthcare associated infection (DA-HAI): a detailed analysis of risk factors and outcomes in a university hospital in Rome, Italy.

Introduction: This study investigates the impact of invasive procedures on healthcare-associated infections (HAI) at Policlinico Universitario Tor Vergata in Rome, Italy, aiming to understand their role in device-associated HAI and to inform prevention strategies. Methods: A retrospective cohort analysis was conducted, examining mandatory discharge records and microbiology data from 2018 across all departments. The study focused on adult patients, analysing the correlation between invasive procedures and HAI through univariate and multivariate logistic regression. Results: Of the 12,066 patients reviewed, 1,214 (10.1%) experienced HAI. Univariate analysis indicated an association between invasive procedures and HAI (OR = 1.81, P < 0.001), which was not observed in multivariable analysis. Specific procedures significantly raised HAI risks: temporary tracheostomy (AOR = 22.69, P <0.001), central venous pressure monitoring (AOR = 6.74, P <0.001) prolonged invasive mechanical ventilation (AOR = 4.44, P <0.001), and venous catheterisation (AOR = 1.58, P <0.05). Aggregated high-risk procedures had an increased likelihood of HAI in multivariable analysis (OR = 2.51, P < 0.001). High-risk departments were also notably associated with HAI (OR = 6.13, P < 0.001). Conclusions: This study suggests that specific invasive procedures, such as temporary tracheostomy, significantly increase HAI risks. The results highlighting the need for targeted infection prevention and control procedures and supports the need for innovative methods such as record-linkage in policymaking to address HAI. These findings inform clinical practice and healthcare policy to improve patient safety and care quality.

Complex Infection-Control Measures with Disinfectant Switch Help the Successful Early Control of Carbapenem-Resistant Acinetobacter baumannii Outbreak in Intensive Care Unit.

A carbapenem-resistant Acinetobacter baumannii (CRAB) outbreak in an intensive care unit (ICU) was contained by an improved infection-control measure that included a disinfectant policy. In our retrospective cohort study, we describe the epidemiological investigations and infection-control measures during this outbreak. Descriptive analysis was used to summarize patient demographics, neurological diseases, surgical treatment, underlying diseases, infection, and outcomes. In December 2023, two CARB-positive patients were observed in the ICU, and four more patients became CRAB-positive in January. During this outbreak, there was an overlap of hospitalization periods among the CRAB-positive patients, and CRAB was isolated from the environment; the isolated CRAB strain was identical. Infection-control measures, including hand hygiene, contact precautions and isolation, surveillance, decolonization, environmental cleaning, and disinfection, were reviewed and modified. The aim of this study was to examine the molecular background of the effectiveness of the disinfectant shift used during successful outbreak control. Experiments were carried out to study the phenotypic sensitivity and genetic background of different disinfectant agents. A thorough analysis of the detected CRAB strain included whole-genome sequencing (WGS), investigation of the qacE and qacEΔ1 genes' relative expression by qPCR after exposure to different disinfectant solutions, as well as an analysis of biofilm formation. WGS analysis of the CRAB strain identified that an ST2 high-risk clone was responsible for the outbreak, which produced OXA-83 and ADC-30 beta-lactamases; in addition, qacE and qacEΔ1 genes were also detected, which confer resistance to disinfectants containing quaternary ammonium compounds (QACs). A qPCR analysis demonstrated that after exposure to different disinfectants, the gene expression levels of qacE and qacEΔ1 increased and correlated with concentrations of QACs of disinfectants. During the outbreak, the standard-of-care QAC-based disinfectant was changed to a mainly alcohol-based agent in the ICU, which contributed to the successful control of this outbreak, and no additional patients were identified with CRAB. We conclude that continuous surveillance and hand hygiene training combined with fast identification and reaction to new cases, as well as an in-depth analysis of multidrug-resistant outbreak strains and investigation of their disinfectant tolerance/resistance during an outbreak, are essential to effectively control the spread of nosocomial pathogens. The smart policy of disinfectant agent selection played a crucial role in controlling the outbreak and ensuring patient safety in the ICU.

Monitoring the Influence of Hand, Foot, and Mouth Disease: New Guidelines on Patient Care during the 2011-2012 Multiwaves and Multivariant Outbreak in Hai Phong City, Vietnam.

From 2011 to 2012, Northern Vietnam suffered its first large-scale hand, foot, and mouth disease (HFMD) epidemic. Two sets of official guidelines were issued during the outbreak to handle the HFMD crisis. The city of Hai Phong was used as a model to analyze the impact of the released guidelines. A total of 9621 HFMD cases were reported in Hai Phong city from April 2011 to December 2012. Three distinct waves of HFMD occurred. Enterovirus A71 and Coxsackievirus A16 were successively associated with the epidemics. Two periods, before and after the guidelines' release, could be distinguished and characterized by different patient patterns. The time to admission and severity changed notably. Guideline publications help the health system refocus on the 0.5-3 years age group with the highest incidence of the disease. The three waves showed different special distribution, but the main routes of infection were rivers and local secondary roads, most likely through local trade and occupational movements of people.

Unraveling the complex interactions between ozone pollution and agricultural productivity in China's main winter wheat region using an interpretable machine learning framework.

Surface ozone has become a significant atmospheric pollutant in China, exerting a profound impact on crop production and posing a serious threat to food security. Previous studies have extensively explored the physiological mechanisms of ozone damage to plants. However, the effects of ozone interactions with other environmental factors, such as climate change, on agricultural productivity at the regional scale, particularly under natural conditions, remain insufficiently understood. In this study, we employed an interpretable machine learning framework, specifically the eXtreme Gradient Boosting (XGBoost) algorithm enhanced by SHapley Additive exPlanations (SHAP), to investigate the influence of ozone and its interactions with environmental factors on crop production in China's primary winter wheat region. Additionally, a structural equation model was developed to elucidate the mechanisms driving these interactions. Our findings demonstrate that ozone pollution exerts a significant negative effect on winter wheat productivity (r = -0.47, P < 0.001), with productivity losses escalating from -12.28 % to -22.09 % as ozone levels increase. Notably, the impact of ozone is spatially heterogeneous, with western Shandong province identified as a hotspot for ozone-induced damage. Furthermore, our results confirm the complexity of the relationship between ozone pollution and agricultural productivity, which is influenced by multiple interacting environmental factors. Specifically, we found that severe ozone pollution, when combined with high aerosol concentrations or elevated temperatures, significantly exacerbates crop productivity losses, although drought conditions can partially mitigate these adverse effects. Our study highlights the importance of incorporating the interactive effects of air pollution and climate change into future crop models. The comprehensive framework developed in this study, which integrates statistical modeling with explainable machine learning, provides a valuable methodological reference for quantitatively assessing the impact of air pollution on crop productivity at a regional scale.

Prevalence of Healthcare-Associated Infections in a Tertiary Hospital in Casablanca, Morocco, 2021.

BACKGROUND: During the COVID-19 pandemic, healthcare professionals experienced an increased workload, which may have affected infection prevention and control (IPC) programs and consequently healthcare-associated infection (HAI) rates. The objective of this study was to estimate the prevalence of HAI in Ibn Rochd University Hospital Center (IRUHC) and identify associated factors. METHODS: A survey was conducted on November 30, 2021 at IRUHC, including all patients hospitalized for at least 48 hours. Data was collected using a questionnaire, and analyzed using SPSS IBM software version 16. The significance level was set at 0.05. RESULTS: Among 887 patients, the prevalence of HAI was 9.7% (7.7%; 11.6%). The highest prevalence was observed in intensive care units (ICUs) (44.2%). Nosocomial pneumonia was the most common site (26.8%). The main isolated microorganisms were Acinetobacter baumannii (18.0%) and Escherichia coli (16.0%). All Acinetobacter baumannii isolated strains were imipenem-resistant. The presence of HAI was significantly associated with the presence of an invasive medical device (p<0.001), a higher physical status score of American Society of Anesthesiologists (ASA) (p<0.001), and a longer hospital stay (p<0.001). Conclusion : The emergence of imipenem-resistant Acinetobacter baumannii (IRAB) represents a serious therapeutic and epidemiological problem requiring the establishment of a system for monitoring the microbial environment and the application of strict hygiene measures.

Medico-legal management of healthcare-associated infections: a cost-effectiveness analysis in an Italian tertiary hospital.

Introduction: Healthcare-associated infections are the main reported adverse event in healthcare, with significant economic costs that include those caused by medical malpractice claims. In Italy, there is a fault-based compensation system, but in this specific field, the burden of proof on the hospitals is particularly heavy. Hence, we aimed to verify the economic impact of the inclusion of experts in hospital infection surveillance into internal committees for claims assessment and to evaluate what would have been the economic impact of a mandatory no-fault system rather than the current system. Materials and methods: We compared two 4-year periods (T1: 2015-2018 and T2: 2019-2022), investigating the medical malpractice claims related to healthcare-associated infections in a large tertiary public hospital in Florence, Italy. Decisions of the internal committee, evolutions of the claims after the decision, and conclusions of the claims were registered. No-fault system simulations were used to evaluate the cost-effectiveness of the model. Results: We observed a decrease in the number of claims after the implementation of infection prevention and control (IPC) experts into the committee (a 24% decrease in rejections and a 19% increase in admissions). We found a 6806.98 euros difference (not statistically significant) in compensations in T1 and T2. Moreover, our simulations found that a no-fault compensation system - if alternative to the traditional fault-based approach - could lead to gains or losses for the plaintiffs depending on the approach chosen. (We observed a 52% mean decrease in compensations with a 150000 euros maximal indemnity and a 134% mean increase with an indemnity tailored considering also life expectancy). Discussion: Introducing experts in IPC into hospital committees for medico-legal claims management has proven to be cost-effective, offering a no-fault compensation system as an alternative to the traditional fault-based approach, supported by a properly evaluated maximal indemnity. Due to the limitations of our models, multicentric studies are recommended to verify our results.

Organizational and infrastructural risk factors for health care-associated Clostridioides difficile infections or methicillin-resistant Staphylococcus aureus in hospitals.

BACKGROUND: This study explores the infrastructural and organizational risk factors for health care-associated (HCA) Clostridioides difficile infections (CDIs) and methicillin-resistant Staphylococcus aureus (MRSA) in hospitals. METHODS: This is a retrospective observational study involving all eligible inpatient units from 12 hospitals in British Columbia, Canada, from April 1, 2020 to September 16, 2021. The outcomes were the average HCA CDI or MRSA rates. Covariates included, but were not limited to, infection control factors (eg, hand hygiene rate), infrastructural factors (eg, unit age), and organizational factors (eg, hallway bed utilization). Multivariable regression was performed to identify statistically significant risk factors. RESULTS: Older units were associated with higher HCA CDI rates (adjusted relative risk [aRR]: 0.012; 95% confidence interval (CI) [0.004, 0.020]). Higher HCA MRSA rates were associated with decreased hand hygiene rate (aRR: -0.035; 95% CI [-0.063, -0.008]), higher MRSA bioburden (aRR: 9.008; 95% CI [5.586, 12.429]), increased utilization of hallway beds (aRR: 0.680; 95% CI [0.094, 1.267]), increased nursing overtime rate (aRR: 5.018; 95% CI [1.210, 8.826]), and not keeping the clean supply room door closed (aRR: -0.283; 95% CI [-0.536, -0.03]). CONCLUSIONS: The study confirmed the multifaceted nature of infection prevention and emphasized the importance of interdepartmental collaboration to improve patient safety.

Modeling the Dynamic of Multiwave Diseases: The Model of Hand, Foot and Mouth Disease.

An HFMD outbreak spread over the city of Hai Phòng from summer 2011 to autumn 2012. This epidemic was chosen because it was the very first HFMD epidemic in North Vietnam, eliminating thus interferences with previous outbreaks. This epidemic displayed three separate waves. A complete dataset was collected for more than 9500 patients during this period, which enabled us to analyze this epidemic at different scales. Access to the healthcare system was crucial during this period, which was possible due to a reorganization of the system in February-March 2012. An analysis at the commune level enabled us to track the epidemic along certain communication routes. The three-waves structure reveals a wide disparity at the district level. We developed a mathematical model showing high accuracy at the adjustment of data for both the total number of cases and for the number of cases per week. As a consequence, the model was able to accurately determine the dates of the beginning and end of each wave and to show that they overlapped. Using mathematical functions associated with this model, it was possible to calculate the probability for a patient to belong to a specific wave.

The role of necroscopy and autopsy investigations in the diagno-sis and risk management of infections related to public health care: forensic and medico legal implications.

Background: Healthcare-associated infections (HAIs) represent the most frequent adverse event in healthcare systems around the world. From a forensic point of view, HAIs show various legal implications. Therefore, it is essential in cases of death or injury from a suspected nosocomial infection that the infection itself, the source and the method of contamination are correctly diagnosed in order to evaluate any profiles of professional liability. Methods: This study combined a minireview of the scientific literature using the Pubmed search engine, the website of the Higher Institute of Health and the member states information sessions on infection prevention and control (IPC). Discussion: Despite the significant impact that HAIs have on healthcare systems, their severity is often not fully understood by healthcare professionals, leading to insufficient responses. In the autopsy setting, the diagnosis of these infections is not always simple due to the risk of post-mortem contamination determined by the endogenous bacterial flora. In the forensic field, the medical examiner during the autopsy can use various diagnostic techniques and investigative tools to identify the infection. Some usefulpp approaches include: 1) Macroscopic examination of the organs; 2) Histopathological investiga-tions; 3) Microbiological analyzes with the performance of swabs; 4) Immunofluorescence tests for the detection of antigens or antibodies on biological liquids; 5) Molecular tests. The choice of methods will depend on the nature of the suspected infection and the availability of diagnostic resources.

Assessment of the level and risk of radioactive hazards in coastal sediments in northern Vietnam.

Radioactivity in coastal sediments in northern Vietnam was examined using data from five sediment cores to assess radioactivity concentrations and radiation risk indices. Radiation risk indices included radium equivalent activity (Raeq), the absorbed dose rate (ADR), the annual effective dose equivalent (AEDE), the activity utilization index (AUI), the external hazard index (Hex), the representative level gamma index (Iγr), and the annual gonadal effective dose rate (AGDE). The radioactivity concentrations of 40K, 232Th, 226Ra, 238U, and 137Cs were 567, 56.1, 35.1, 37.9, and 1.18 Bq/kg, respectively. The average concentrations of 40K, 232Th, 226Ra, and 238U were above the global average at five sites, except for 137Cs, which was low. The Raeq, Hex, and AUI indices were below the recommended values, while the AEDE, ADR, AGDE, and Iγr indices were above the recommended values. Moreover, 40K, 232Th, 226Ra, and 238U had significant impacts on the radiation hazard indices Raeq, ADR, AEDE, Iγr, AUI, Hex, and AGDE. There are three coastal sediment groups on the northern coast of Vietnam: Group 1 has a higher radioactivity and radiation risk index than Group 2 but a lower value than Group 3. Group 3 had the highest radioactivity and radiation risk index. The values of 40K, 232Th, 226Ra, and 238U and the ADR, AUI, Iγr, and AGDE indices in the sediment threaten the living environment.

Pre-existing immunity to influenza aids ferrets in developing stronger and broader H3 vaccine-induced antibody responses.

Influenza seasons occur annually, building immune history for individuals, but the influence of this history on subsequent influenza vaccine protection remains unclear. We extracted data from an animal trial to study its potential impact. The trial involved 80 ferrets, each receiving either one type of infection or a placebo before vaccination. We quantified the vaccine protection by evaluating hemagglutination inhibition (HAI) antibody titer responses. We tested whether hosts with different infection histories exhibited similar level of responses when receiving the same vaccine for all homologous and heterologous outcomes. We observed that different pre-existing immunities were generally beneficial to vaccine induced responses, but varied in magnitude. Without pre-immunity, post-vaccination HAI titers after the 1st dose of the vaccine were less likely to be above 1:40, and a booster shot was needed. Our study suggests that pre-existing immunity may strengthen and extend the homologous and heterologous vaccine responses.

Analysis of the contentious relating to medical liability for healthcare-associated infections (HAI) in a Sicilian hospital.

Background: Since 2012, the Sicilian regional government, in view of the increase in malpractice claims, has adopted a "self-insurance system" The claims management activities have been delegated to the Claims Management Committees (CMCs), the importance of which was also emphasized by Law No. 24/2017. This study aims to describe the experience of Sicilian Hospital CMC and analyze the claims' features, especially for contentious HAIs. Healthcare-associated infections (HAIs) continue to be a major public health concern. The contraction of infection during hospitalization generally results in a significant worsening of the patient's quality of life and prolongation of his or her stay. Still, it is also responsible for an increase in costs that burden the hospital and the entire Health System. Material and Methods: The study investigates the analysis of claims received by a Messina Hospital Company between January 2015 and December 2023 even though for events that occurred in earlier years. From the database, cases in which the Company was sued for HAIs were extrapolated and analyzed, distinguishing them by year and by Department. The data collected were statistically processed with the Epi Info 7.1.5 program (CDC - Atlanta - USA). Conclusion: The CMC experience highlighted a statistically significant increase in complaints especially for those relating to HAI, without differences by Department. In most cases, the CMC admitted the hospital's liability, and an attempt at conciliation was promoted and moreover risk management initiatives were adopted. This is important when considering the recent ruling 6386/2023 of March 3, 2023.

Estimating evapotranspiration and drought dynamics of winter wheat under climate change: A case study in Huang-Huai-Hai region, China.

Drought is one of the vital meteorological disasters that influence crop growth. Timely and accurately estimating the drought dynamics of crops is valuable for decision-maker to formulate scientific management measures of agricultural drought risk. In this study, the evapotranspiration and drought dynamics of winter wheat from 1981 to 2020 in the Huang-Huai-Hai (HHH) region of China were evaluated based on long-term multi-source observation data. Four key developmental stages of winter wheat were given attentions: growth before winter stage, overwintering stage, stage of greening-heading, and stage of filling-maturity. The crop water deficit index (CWDI) on a daily scale was established for quantitatively appraising the impacts of drought on winter wheat. Our results indicated that interannual variation in reference crop evapotranspiration (ET0) during the growth season of winter wheat from 1981 to 2020 in the HHH region showed a slight increase trend, with an average of 602.4 mm and obvious spatial differences of decreasing from the Northeast to the Southwest. Over the past forty years, the winter wheat in the HHH region was most severely affected by severe drought, followed by moderate drought, and finally mild drought. In addition, the impacts of drought on winter wheat at different critical growth stages varied greatly. For the growth before winter stage, the winter wheat was mainly threatened by mild, moderate, and severe droughts. For the overwintering stage, the winter wheat was mainly threatened by moderate, severe, and extreme droughts. For the greening-heading stage, the winter wheat was mainly threatened by mild, moderate, severe, and extreme droughts. For the filling-maturity stage, the winter wheat was mainly threatened by mild and moderate droughts. Finally, the impacts of drought on winter wheat during 1981-2020 in the HHH region were revealed to differ extraordinarily in space. In particular, the areas of winter wheat affected by severe drought significantly decreased. However, the areas of winter wheat affected by moderate drought clearly expanded. Our findings provide new insights for further improving climate change impact studies and agricultural drought defense capabilities adapting to continuous environmental change.

An Exploratory Analysis of the Association between Healthcare Associated Infections & Hospital Financial Performance.

Purpose: Healthcare-associated infections (HAIs) place a significant financial burden on United States hospitals. HAI treatments extend hospital lengths of stay and increase hospital operational costs while significantly reducing hospital profit margins. Given these challenges, the research aim of this study was to explore the association between HAIs and hospital financial performance. A better understanding of this relationship can assist hospital leaders in optimizing the use of scarce financial resources to reduce HAI prevalence. Methods: Data for calendar year 2022 for active short-term acute care hospitals (n = 1454) in the US were analyzed using multiple linear regression analysis. We explored two derived dependent variables, operating expense per staffed bed and operating expense per discharge. The independent variables included four healthcare-associated infection rates: methicillin-resistant Staphylococcus aureus (MRSA) infection rate, Clostridium difficile (C. diff) infection rate, Catheter-Associated Urinary Tract Infection (CAUTI) rate, and Central Line Associated Blood Stream Infections (CLABSI). Appropriate organizational and market-level variables that may independently influence hospital financial performance were included as control variables. Results: The results revealed that C. diff (ß: 0.037, p < 0.05) and CAUTI (ß: 0.031, p < 0.05) rates were positively associated with an increase in operating expense per staffed bed, while increases in MRSA (ß: 0.042, p < 0.001), C. diff (ß: 0.062, p < 0.001), and CAUTI rates (ß: 0.039, p < 0.001) were correlated with increased operating expenses per discharge. Conclusions: This study demonstrates that specific HAIs may be associated with increased hospital expenses. Proactively targeting these infections through tailored interventions may lead to reduced hospital costs, improved financial performance, and economic stability.

Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods.

BACKGROUND: Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) systems are constructed in each hospital; while these data are only used as real-time surveillance but fail to realize the prediction and early warning function. Study is to screen effective predictors from the routine NIS data, through integrating the multiple risk factors and Machine learning (ML) methods, and eventually realize the trend prediction and risk threshold of Incidence of Nosocomial infection (INI). METHODS: We selected two representative hospitals in southern and northern China, and collected NIS data from 2014 to 2021. Thirty-nine factors including hospital operation volume, nosocomial infection, antibacterial drug use and outdoor temperature data, etc. Five ML methods were used to fit the INI prediction model respectively, and to evaluate and compare their performance. RESULTS: Compared with other models, Random Forest showed the best performance (5-fold AUC = 0.983) in both hospitals, followed by Support Vector Machine. Among all the factors, 12 indicators were significantly different between high-risk and low-risk groups for INI (P < 0.05). After screening the effective predictors through importance analysis, prediction model of the time trend was successfully constructed (R2 = 0.473 and 0.780, BIC = -1.537 and -0.731). CONCLUSIONS: The number of surgeries, antibiotics use density, critical disease rate and unreasonable prescription rate and other key indicators could be fitted to be the threshold predictions of INI and quantitative early warning.

Training in infection prevention and control: survey on the volume and on the learning demands of healthcare-associated infections control figures in the Emilia-Romagna Region (Northern Italy).

Background: In the context of Infections Prevention and Control activities, the training of healthcare-associated infection control figures is crucial; the COVID-19 pandemic further emphasized the necessity of ensuring a widespread and stable level of skills over time for such professionals. The present work aims to identify the number and training needs of the personnel working in the Emilia-Romagna Region's healthcare facilities as "healthcare-associated infection control figures". Methods: Data were collected through a survey created by experts from the Regional Group "Training in the prevention and control of antibiotic resistance". The questionnaire explored the number, professional and educational background, and training requirements of Healthcare-associated infections control figures in Emilia-Romagna. Results: With 73 figures dedicated to Healthcare-associated infections control, the Emilia-Romagna Region appears to be in line with the European standard ratio (1 professional every 125 beds). Professionals with a nursing background, over 50 years old and of female sex prevail in the group, while the training needs expressed include both theoretical and practical aspects. Conclusions: Healthcare assistants and nursing staff represent a fundamental resource for the implementation of infection prevention and control programs in our healthcare facilities; continuous, multidisciplinary and targeted training of these professionals is confirmed as necessary.

Improving Risk Prediction of Methicillin-Resistant Staphylococcus aureus Using Machine Learning Methods With Network Features: Retrospective Development Study.

BACKGROUND: Health care-associated infections due to multidrug-resistant organisms (MDROs), such as methicillin-resistant Staphylococcus aureus (MRSA) and Clostridioides difficile (CDI), place a significant burden on our health care infrastructure. OBJECTIVE: Screening for MDROs is an important mechanism for preventing spread but is resource intensive. The objective of this study was to develop automated tools that can predict colonization or infection risk using electronic health record (EHR) data, provide useful information to aid infection control, and guide empiric antibiotic coverage. METHODS: We retrospectively developed a machine learning model to detect MRSA colonization and infection in undifferentiated patients at the time of sample collection from hospitalized patients at the University of Virginia Hospital. We used clinical and nonclinical features derived from on-admission and throughout-stay information from the patient's EHR data to build the model. In addition, we used a class of features derived from contact networks in EHR data; these network features can capture patients' contacts with providers and other patients, improving model interpretability and accuracy for predicting the outcome of surveillance tests for MRSA. Finally, we explored heterogeneous models for different patient subpopulations, for example, those admitted to an intensive care unit or emergency department or those with specific testing histories, which perform better. RESULTS: We found that the penalized logistic regression performs better than other methods, and this model's performance measured in terms of its receiver operating characteristics-area under the curve score improves by nearly 11% when we use polynomial (second-degree) transformation of the features. Some significant features in predicting MDRO risk include antibiotic use, surgery, use of devices, dialysis, patient's comorbidity conditions, and network features. Among these, network features add the most value and improve the model's performance by at least 15%. The penalized logistic regression model with the same transformation of features also performs better than other models for specific patient subpopulations. CONCLUSIONS: Our study shows that MRSA risk prediction can be conducted quite effectively by machine learning methods using clinical and nonclinical features derived from EHR data. Network features are the most predictive and provide significant improvement over prior methods. Furthermore, heterogeneous prediction models for different patient subpopulations enhance the model's performance.