Modelling and forecasting antimicrobial resistance and its dynamic relationship to antimicrobial use: a time series analysis

Long-Term Clinical and Ecological Impact of an Antimicrobial Stewardship Program on the Incidence of Carbapenem-Resistant Klebsiella pneumoniae Infections in a High-Endemic Hospital

Carbapenem-resistant Klebsiella pneumoniae (CR-Kp) is currently a serious global concern. Antimicrobial stewardship programs (ASPs) are one of the key strategies to overcome this resistance. However, evidence about the long-term clinical and ecological impacts of ASPs is scarce. A multidisciplinary team conducted a multifaceted intervention in a CR-Kp endemic hospital over a 6-year period. We assessed the monthly long-term impacts of ASPs on carbapenem use, incidence density (ID), and crude death rates of hospital-acquired CR-Kp infections. Other variables potentially related to CR-Kp incidence and healthcare activity indicators were monitored. Carbapenem use showed a sustained reduction over the long term, with a difference of -66.19% (95% CI -87.03 to -45.34) between the expected pre-intervention trend consumption value and that obtained six years after starting the program. The ID of CR-Kp also decreased significantly and was maintained over the long term, with a relative reduction of -88.14% (95% CI; -100.4 to -75.85) at the end of the study period. The crude death rate of CR-Kp at 14 and 28 days decreased significantly after the intervention and remained steady after six years. Infection control indicator trends remained stable. This mixed ASP contributed to reducing the high incidence of infections and mortality rates of CR-Kp, achieving a sustained ecological and clinical effect.

Forecasting antimicrobial resistance evolution

Antimicrobial resistance (AMR) is a major global health issue. Current measures for tackling it comprise mainly the prudent use of drugs, the development of new drugs, and rapid diagnostics. Relatively little attention has been given to forecasting the evolution of resistance. Here, we argue that forecasting has the potential to be a great asset in our arsenal of measures to tackle AMR. We argue that, if successfully implemented, forecasting resistance will help to resolve the antibiotic crisis in three ways: it will (i) guide a more sustainable use (and therefore lifespan) of antibiotics and incentivize investment in drug development, (ii) reduce the spread of AMR genes and pathogenic microbes in the environment and between patients, and (iii) allow more efficient treatment of persistent infections, reducing the continued evolution of resistance. We identify two important challenges that need to be addressed for the successful establishment of forecasting: (i) the development of bespoke technology that allows stakeholders to empirically assess the risks of resistance evolving during the process of drug development and therapeutic/preventive use, and (ii) the transformative shift in mindset from the current praxis of mostly addressing the problem of antibiotic resistance a posteriori to a concept of a priori estimating, and acting on, the risks of resistance.

Usefulness of dynamic regression time series models for studying the relationship between antimicrobial consumption and bacterial antimicrobial resistance in hospitals: a systematic review

BACKGROUNG

Antimicrobial resistance (AMR) is on the rise worldwide. Tools such as dynamic regression (DR) models can correlate antimicrobial consumption (AMC) with AMR and predict future trends to help implement antimicrobial stewardship programs (ASPs).

MAIN BODY

We carried out a systematic review of the literature up to 2023/05/31, searching in PubMed, ScienceDirect and Web of Science. We screened 641 articles and finally included 28 studies using a DR model to study the correlation between AMC and AMR at a hospital scale, published in English or French. Country, bacterial species, type of sampling, antimicrobials, study duration and correlations between AMC and AMR were collected. The use of β-lactams was correlated with cephalosporin resistance, especially in Pseudomonas aeruginosa and Enterobacterales. Carbapenem consumption was correlated with carbapenem resistance, particularly in Pseudomonas aeruginosa, Klebsiella pneumoniae and Acinetobacter baumannii. Fluoroquinolone use was correlated with fluoroquinolone resistance in Gram-negative bacilli and methicillin resistance in Staphylococcus aureus. Multivariate DR models highlited that AMC explained from 19 to 96% of AMR variation, with a lag time between AMC and AMR variation of 2 to 4 months. Few studies have investigated the predictive capacity of DR models, which appear to be limited.

CONCLUSION

Despite their statistical robustness, DR models are not widely used. They confirmed the important role of fluoroquinolones, cephalosporins and carbapenems in the emergence of AMR. However, further studies are needed to assess their predictive capacity and usefulness for ASPs.

Predicting antimicrobial resistance of bacterial pathogens using time series analysis

Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens (Escherichia coli, Streptococcus suis, Salmonella sp., Pasteurella multocida, and Bordetella bronchiseptica). We found that Seasonal Auto-Regressive Integrated Moving Average (SARIMA) outperformed five baselines, including Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA). We hope this study provides valuable tools to predict the AMR burden not only of the pathogens assessed in this study but also of other bacterial pathogens.

Systematic review of time lag between antibiotic use and rise of resistant pathogens among hospitalized adults in Europe

Background

Antimicrobial resistance (AMR) causes substantial health and economic burden to individuals, healthcare systems and societies globally. Understanding the temporal relationship between antibiotic consumption and antibiotic resistance in hospitalized patients can better inform antibiotic stewardship activities and the time frame for their evaluation.

Objectives

This systematic review examined the temporal relationship between antibiotic use and development of antibiotic resistance for 42 pre-defined antibiotic and pathogen combinations in hospitalized adults in Europe.

Methods

Searches in MEDLINE, Embase, Cochrane Library and NIHR Centre for Reviews and Dissemination were undertaken from 2000 to August 2021. Pathogens of interest were Escherichia coli, Klebsiella pneumoniae, Streptococcus pneumoniae, Staphylococcus aureus, Enterococcus faecium, CoNS, Pseudomonas aeruginosa and Acinetobacter baumannii complex.

Results

Twenty-eight ecological studies and one individual-level study were included. Ecological studies were predominantly retrospective in design (19 studies) and of reasonable (20 studies) to high (8 studies) methodological quality. Of the eight pathogens of interest, no relevant data were identified for S. pneumoniae and CoNS. Across all pathogens, the time-lag data from the 28 ecological studies showed a similar pattern, with the majority of studies reporting lags ranging from 0 to 6 months.

Conclusions

Development of antibiotic resistance for the investigated antibiotic/pathogen combinations tends to occur over 0 to 6 months following exposure within European hospitals. This information could inform planning of antibiotic stewardship activities in hospital settings.

Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app

Background

Antimicrobial resistance has emerged as one of the foremost public health troubles of the 21st century. This has ended in a public health disaster of the global situation, which threatens the exercise of present-day remedy. There is an urgent requirement for a cost-effective strategy to reduce antimicrobial resistance. Infectious disease control researchers most often analyze and predict antimicrobial resistance rate data that includes zeros or ones. Commonly used time-series analysis such as autoregressive moving average model is inappropriate for such data and may arrive at biased results.

Objective

This study aims to propose a time-series model for continuous rates or proportions when the interval of series includes zeros or ones and compares the model with existing models.

Data

The Escherichia coli, isolated from blood cultures showing variable susceptibility results to different antimicrobial agents, has been obtained from a clinical microbiology laboratory of a tertiary care hospital, Udupi district, Karnataka, during the years between 2011 and 2019.

Methodology

We proposed a Degenerate Beta Autoregressive model which is a mixture of continuous and discrete distributions with probability mass at zero or one. The proposed model includes autoregressive terms along with explanatory variables. The estimation is done using maximum likelihood with a non-linear optimization algorithm. An R shiny app has been provided for the same.

Results

The proposed Degenerate Beta Autoregressive model performed well compared to the existing autoregressive moving average models. The forecasted antimicrobial resistance rate has been obtained for the next 6 months.

Conclusion

The findings of this article could be beneficial to the infectious disease researchers to use an appropriate time-series model to forecast the resistance rate for the future and to have better or advance public health policies to control the rise in resistance rate.

Resistance to extended-spectrum cephalosporins in Escherichia coli and Salmonella enterica isolated from food-producing animals: Ecological study from selected national surveillance programs

Extended-spectrum cephalosporins (ESC) are categorized by World Health Organization as critically important antimicrobials with limited therapeutic alternatives for the treatment of severe bacterial infections in humans. Preserving the effectiveness of ESC requires continuous monitoring of resistance and comparison of associated data across national surveillance programs in the face of globalization. In this ecological study, we compared ESC resistance in Escherichia coli and Salmonella enterica isolated from food-producing animals from 2003 to 2019 between nine countries (Canada, Denmark, Finland, Japan, Netherlands, Norway, Sweden, United Kingdom, and the United States). Using the beta-regression model, compared to Canada, non-selective ESC-R Salmonella enterica was less likely isolated from food producing animals in other eight countries (Odds ratio range: 0.07-0.76). We observed an interaction between the country and the year with a significantly decreased proportion (P < 0.05) of non-selective ESC-R Escherichia coli from the Netherlands, the United Kingdom, and the United States compared to Canada over the years. There was a linear correlation between non-selective ESC-R Escherichia coli and ESC use from Netherlands (Spearman's ρ = 0.91, P < 0.0001). For the six European countries, the interaction between the country and year showed a significant decrease in the proportion of selective ESC-R Escherichia coli over the years for the Netherlands compared to Denmark (P = 0.002). While there were variations in the proportion of beta-lactamase genes reported over the years, bla_CTX-M and bla_CMY-2 genes were commonly detected among the selective ESC-R Escherichia coli. This study reveals variability in the recovery of ESC-resistant bacteria among the countries that seems likely influenced by the individual country policy on the use of critically important antimicrobials and resistance surveillance programs. However, there is a need for harmonization and consistency in food animal sources of bacterial isolates used in surveillance programs within and between the countries for easy comparability.

Identifying Targets for Antibiotic Use for the Management of Carbapenem-Resistant Acinetobacter baumannii (CRAb) in Hospitals-A Multi-Centre Nonlinear Time-Series Study

Solutions are needed to inform antimicrobial stewardship (AMS) regarding balancing the access to effective antimicrobials with the need to control antimicrobial resistance. Theoretical and mathematical models suggest a non-linear relationship between antibiotic use and resistance, indicating the existence of thresholds of antibiotic use beyond which resistance would be triggered. It is anticipated that thresholds may vary across populations depending on host, environment, and organism factors. Further research is needed to evaluate thresholds in antibiotic use for a specific pathogen across different settings. The objective of this study is to identify thresholds of population antibiotic use associated with the incidence of carbapenem-resistant Acinetobacter baumannii (CRAb) across six hospital sites in Oman. The study was an ecological, multi-centre evaluation that involved collecting historical antibiotic use and CRAb incidence over the period from January 2015 to December 2019. By using non-linear time-series analysis, we identified different thresholds in the use of third-generation cephalosporins, piperacillin-tazobactam, aminoglycoside, and fluoroquinolones across participating hospitals. The identification of different thresholds emphasises the need for tailored analysis based on modelling data from each hospital. The determined thresholds can be used to set targets for each hospital AMS, providing a balance between access to these antibiotics versus controlling CRAb incidence.

Quantifying the effect of in-hospital antimicrobial use on the development of colistin-resistant Acinetobacter baumannii strains: a time series analysis

OBJECTIVES

Colistin is currently considered an essential therapeutic option for the treatment of hospital-acquired infections caused by resistant isolates of Acinetobacter baumannii. In this study, conducted in a tertiary care hospital, the effect of previous detection rates and antimicrobial use on colistin-resistant A. baumannii strains was investigated.

METHODS

Susceptibility data for A. baumannii isolates and colistin use information were collected for 48 consecutive months (January 2014 to December 2017). ARIMA models were used to explore the time series of colistin use and resistance to A. baumannii. In addition, dynamic regression models were used to study the relationships between the use of antimicrobials (colistin, imipenem, meropenem, cefepime, ciprofloxacin) and colistin resistance.

RESULTS

The results of the univariate model showed a statistically significant positive association between colistin use and the detection rate of colistin-resistant A. baumannii (p=0.003). Moreover, a multivariate model confirmed the positive association of colistin use with the detection rate of colistin-resistant A. baumannii, also demonstrating statistically significant negative associations with imipenem (p=0.004) and meropenem use (p=0.005).

CONCLUSIONS

This study quantifies the effect of colistin use on the development of resistant strains. These findings can assist antimicrobial stewardship teams to elaborate their plans and predict the effect of their interventions.

A user-friendly decision support tool to assist one-health risk assessors

One-Health risk assessments are integral to developing efficient responses to disease threats, including global pandemics. However, short timeframes, inadequate disease-specific information and an insufficient skill-base make it hard for inexperienced assessors to distinguish between a large portfolio of approaches. The wrong choice can detract from the disease response. Here, we present an interactive decision support tool to help with this choice. A workshop with participants from diverse professional backgrounds provided six themes that should be considered when deciding on the best approach. Questions based on these themes were then developed to populate a decision tree which guides users to their most appropriate approach. One-Health risk assessment tools and literature were used as examples of the different approaches. The tool provides links to these examples and short descriptions of the approaches. Answers are easily changed, facilitating exploration though different approaches. The simple data structure of the tool means it is easy to update with more resources and approaches. It provides a valuable source of guidance and information for less experienced risk assessors.

Antimicrobial use and antimicrobial resistance in Enterobacterales and Enterococcus faecium: a time series analysis

BACKGROUND

Irish and European antimicrobial resistance (AMR) surveillance data have highlighted increasing AMR in Enterobacterales and vancomycin resistance in Enterococcus faecium (VRE). Antimicrobial consumption (AC) in Irish hospital settings is also increasing.

METHODS

A retrospective time series analysis (TSA) was conducted to evaluate the trends and possible relationship between AC of selected antimicrobials and AMR in Enterobacterales and vancomycin resistance in E. faecium, from January 2017 to December 2020.

RESULTS

Increased AC was seen with ceftriaxone (p= 0.0006), piperacillin/tazobactam (p = 0.03) and meropenem (p = 0.05), while ciprofloxacin and gentamicin use trended downwards. AMR rates in E. coli, K. pneumoniae and other Enterobacterales were largely stable, an increase in ertapenem resistance in the latter from 0.58% in 2017 to 5.19% in 2020 (p= 0.003) being the main concern. The proportion of E. faecium that was VRE did not changed significantly (64% in 2017; 53% in 2020, p = 0.1). TSA identified a correlation between piperacillin/tazobactam use and the decreasing rate of ceftriaxone resistance in E. coli.

CONCLUSION

Our data suggest that hospital antimicrobial stewardship programme is largely containing, but not reducing AMR in key nosocomial pathogens. An increase in AC following the COVID-19 pandemic appears as yet to have had no impact on AMR rates.

Consumption of anti-meticillin-resistant Staphylococcus aureus antibiotics in Swiss hospitals is associated with antibiotic stewardship measures

BACKGROUND

Consumption of antibiotics active against meticillin-resistant Staphylococcus aureus (MRSA) has been described in numerous European studies. However, the underlying predictors of consumption are still poorly understood.

AIM

To describe the consumption of anti-MRSA antibiotics (daptomycin, intravenous glycopeptides, linezolid) in Switzerland over time and to identify underlying predictor variables.

METHODS

A retrospective observational multi-centre study was conducted in 21 Swiss hospitals over a period of 11 years (2009-2019). Multiple linear regression models were built to identify regional and hospital-specific predictor variables affecting the consumption of anti-MRSA antibiotics.

FINDINGS

Consumption of anti-MRSA antibiotics increased between 2009 and 2019 from 12.7 to 24.5 defined daily doses per 1000 bed-days (+93%). In the first model presented, which includes data of the whole study period, the following variables were associated with higher anti-MRSA antibiotic consumption: number of MRSA cases (P < 0.01), year (P < 0.01), hospital type (tertiary care university hospitals vs others, P < 0.01), hospital department (intensive care unit vs others, P < 0.01) and linguistic region (French vs German and German vs Italian, P < 0.01). In a second model including data from a query on hospital policies in place in 2019, the presence of an antibiotic stewardship group (P < 0.01) and prescription restrictions (P < 0.01) were associated with consumption of anti-MRSA antibiotics.

CONCLUSION

Our study shows that both the presence of an antibiotic stewardship group and the implementation of prescription restrictions, i.e. factors that can be controlled by the hospital itself, were associated with a lower consumption of anti-MRSA antibiotics.

Impact of an antimicrobial stewardship programme on reducing broad-spectrum antibiotic use and its effect on carbapenem-resistant Acinetobacter baumannii (CRAb) in hospitals in Jordan

OBJECTIVES

To evaluate the impact of an antimicrobial stewardship programme (ASP) on reducing broad-spectrum antibiotic use and its effect on carbapenem-resistant Acinetobacter baumannii (CRAb) in hospitalized patients.

METHODS

The study was a retrospective, ecological assessment in a tertiary teaching hospital over 6 years (January 2014 to December 2019). The intervention involved the implementation of an ASP in February 2018, which remains in effect today. This ASP consists of several components, including education, antibiotic guidelines, antibiotic restriction policy with prior approval, audit of compliance to the restriction policy and feedback. Restricted antibiotics were imipenem/cilastatin, ertapenem, meropenem, vancomycin, teicoplanin, tigecycline, colistin, amikacin, piperacillin/tazobactam, levofloxacin and ciprofloxacin. The intervention was evaluated by time-series methods.

RESULTS

Statistically significant decreases in the level of antibiotic use, after the introduction of the ASP, were observed for the following antibiotics: imipenem/cilastatin (P = 0.0008), all carbapenems (P = 0.0001), vancomycin (P = 0.0006), colistin (P = 0.0016) and third-generation cephalosporins (P = 0.0004). A statistically significant decrease in the slope, after the introduction of the ASP, for ertapenem (P = 0.0044) and ciprofloxacin (P = 0.0117) was observed. For piperacillin/tazobactam, there was a significant increasing trend (P = 0.0208) before the introduction of the ASP. However, this increased trend was halted post-introduction of the ASP (P = 0.4574). The introduction of the ASP was associated with a significant impact on reducing the levels of CRAb (P = 0.0237).

CONCLUSIONS

The introduced antimicrobial stewardship interventions contributed to a reduction in the use of several broad-spectrum antibiotics, reversed the trends of increasing use of other antibiotics and were associated with a significant reduction in CRAb.

Identification of thresholds in relationships between specific antibiotic use and carbapenem-resistant Acinetobacter baumannii (CRAb) incidence rates in hospitalized patients in Jordan

BACKGROUND

Antibiotic resistance is a major threat to public health worldwide. The relationship between the intensity of antibiotic use and resistance might not be linear, suggesting that there might be a threshold of antibiotic use, beyond which resistance would be triggered.

OBJECTIVES

To identify thresholds in antibiotic use, below which specific antibiotic classes have no significant measurable impact on the incidence of carbapenem-resistant Acinetobacter baumannii (CRAb), but above which their use correlates with an increase in the incidence of CRAb.

METHODS

The study took place at a tertiary teaching hospital in Jordan. The study was ecological in nature and was carried out retrospectively over the period January 2014 to December 2019. The outcome time series for this study was CRAb cases. The primary explanatory variables were monthly use of antibiotics and the use of alcohol-based hand rub (ABHR). Non-linear time-series methods were used to identify thresholds in antibiotic use.

RESULTS

Non-linear time-series analysis determined a threshold in third-generation cephalosporin and carbapenem use, where the maximum use of third-generation cephalosporins and carbapenems should not exceed 8 DDD/100 occupied bed days (OBD) and 10 DDD/100 OBD, respectively. ABHR had a significant reducing effect on CRAb cases even at lower usage quantities (0.92 L/100 OBD) and had the most significant effect when ABHR exceeded 3.4 L/100 OBD.

CONCLUSIONS

The identification of thresholds, utilizing non-linear time-series methods, can provide a valuable tool to inform hospital antibiotic policies through identifying quantitative targets that balance access to effective therapies with control of resistance. Further studies are needed to validate the identified thresholds, through being prospectively adopted as a target for antimicrobial stewardship programmes, and then to evaluate the impact on reducing CRAb incidence.

Trends, seasonality and the association between outpatient antibiotic use and antimicrobial resistance among urinary bacteria in the Netherlands

OBJECTIVES

To determine trends, seasonality and the association between community antibiotic use and antimicrobial resistance (AMR) in Escherichia coli and Klebsiella pneumoniae in urinary tract infections.

METHODS

We analysed Dutch national databases from January 2008 to December 2016 regarding antibiotic use and AMR for nitrofurantoin, trimethoprim, fosfomycin and ciprofloxacin. Antibiotic use was expressed as DDD/1000 inhabitant-days (DID) and AMR was expressed as the percentage of resistance from total tested isolates. Temporal trends and seasonality were analysed with autoregressive integrated moving average (ARIMA) models. Each antibiotic use-resistance combination was cross-correlated with a linear regression of the ARIMA residuals.

RESULTS

The trends of DID increased for ciprofloxacin, fosfomycin and nitrofurantoin, but decreased for trimethoprim. Similar trends were found in E. coli and K. pneumoniae resistance to the same antibiotics, except for K. pneumoniae resistance to ciprofloxacin, which decreased. Resistance levels peaked in winter/spring, whereas antibiotic use peaked in summer/autumn. In univariate analysis, the strongest and most significant cross-correlations were approximately 0.20, and had a time delay of 3-6 months between changes in antibiotic use and changes in resistance. In multivariate analysis, significant effects of nitrofurantoin use and ciprofloxacin use on resistance to these antibiotics were found in E. coli and K. pneumoniae, respectively. There was a significant association of nitrofurantoin use with trimethoprim resistance in K. pneumoniae after adjusting for trimethoprim use.

CONCLUSIONS

We found a relatively low use of antibiotics and resistance levels over a 9 year period. Although the correlations were weak, variations in antibiotic use for these four antibiotics were associated with subsequent variations in AMR in urinary pathogens.

Impact of an Antimicrobial Stewardship Program on the Incidence of Carbapenem Resistant Gram-Negative Bacilli: An Interrupted Time-Series Analysis

Carbapenem-resistant Gram-negative bacilli (CR-GNB) are a critical public health threat, and carbapenem use contributes to their spread. Antimicrobial stewardship programs (ASPs) have proven successful in reducing antimicrobial use. However, evidence on the impact of carbapenem resistance remains unclear. We evaluated the impact of a multifaceted ASP on carbapenem use and incidence of CR-GNB in a high-endemic hospital. An interrupted time-series analysis was conducted one year before and two years after starting the ASP to assess carbapenem consumption, CR-GNB incidence, death rates of sentinel events, and other variables potentially related to CR-GNB incidence. An intense reduction in carbapenem consumption occurred after starting the intervention and was sustained two years later (relative effect -83.51%; 95% CI -87.23 to -79.79). The incidence density of CR-GNB decreased by -0.915 cases per 1000 occupied bed days (95% CI -1.743 to -0.087). This effect was especially marked in CR-Klebsiella pneumoniae and CR-Escherichia coli, reversing the pre-intervention upward trend and leading to a relative reduction of -91.15% (95% CI -105.53 to -76.76) and -89.93% (95% CI -107.03 to -72.83), respectively, two years after starting the program. Death rates did not change. This ASP contributed to decreasing CR-GNB incidence through a sustained reduction in antibiotic use without increasing mortality rates.

Prospective correlational time-series analysis of the influence of weather and air pollution on joint pain in chronic rheumatic diseases

OBJECTIVES

The primary objective was to evaluate the association between weather variables and joint pain in patients with chronic rheumatic diseases (CRD: rheumatoid arthritis (RA), osteoarthritis (OA), and spondyloarthritis (SpA)). A secondary objective was to study the impact of air pollution indicators on CRD pain.

METHOD

The study is prospective, correlational, with time-series analysis. Patients with CRD, living in a predefined catchment area, filled their level of pain daily using a 0-10 numerical scale (NS), for 1 year. Weather (temperature, relative humidity (H), atmospheric pressure (P)) and air pollution indicators (particulate matters (PM₁₀, PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃)) were recorded daily using monitoring systems positioned in the same area. Association between pain and weather and air pollution indicators was studied using Pearson's correlation. Time-series analysis methodology was applied to determine the temporal relationship between pain and indicators.

RESULTS

The study included 94 patients, 82% reported they were weather-sensitive. Pain variation was similar across diseases over a year. Pain was associated negatively with temperature, H, and O_3, and positively with P and NO₂. However, the strength of correlation was moderate; temperature explained 22% of pain variance. A drop of 10°C in temperature corresponded to an increase of 0.5 points in pain NS. Also, there was a significant interaction among environmental factors. In time-series analysis, temperature and NO₂ remained independently associated with pain.

CONCLUSIONS

The perception of joint pain in patients with CRD was correlated with weather and air pollution. The strength of association was moderate and independent of underlying disease. Key Points •Weather variation was moderately correlated with joint pain in chronic rheumatic diseases, with an inverse association with temperature, humidity, and O₃. • Air pollution indicators, mainly nitrogen dioxide and ozone, were correlated with joint pain; particulate matters were also correlated but to a lesser extent. • The influence of these environmental factors was independent of the type of rheumatic disease, thus raising the hypothesis of their impact on pain perception mechanisms.

Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R² > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

ICU Patients' Antibiotic Exposure and Triazole-Resistance in Invasive Candidiasis: Parallel Analysis of Aggregated and Individual Data

Background: The relationship between antibiotic use and the incidence of triazole-resistant phenotypes of invasive candidiasis (IC) in critically ill patients is unclear. Different methodologies on determining this relationship may yield different results.

Methods: A retrospective multicenter observational analysis was conducted to investigate exposure to antibiotics and the incidence of non-duplicate clinical isolates of Candida spp. resistant to fluconazole, voriconazole, or both during November 2013 to April 2018, using two different methodologies: group-level (time-series analysis) and individual-patient-level (regression analysis and propensity-score adjusting).

Results: Of 393 identified Candida spp. from 388 critically ill patients, there were three phenotypes of IC identified: fluconazole-resistance (FR, 63, 16.0%); voriconazole-resistance (VR, 46, 11.7%); and cross-resistance between fluconazole and voriconazole (CR, 32, 8.1%). Exposure to several antibacterial agents with activity against the anaerobic gastrointestinal flora, especially third-generation cefalosporins (mainly cefoperazone/sulbactam and ceftriaxone), but not triazoles, have an immediate effect (time lag = 0) on subsequent ICU-acquired triazole-resistant IC in the group-level (p < 0.05). When the same patient database was analyzed at the individual-patient-level, we found that exposure to many antifungal agents was significantly associated with triazole-resistance (fluconazole [adjusted odds ratio (aOR) = 2.73] or caspofungin [aOR = 11.32] on FR, voriconazole [aOR = 2.87] on CR). Compared to the mono-triazole-resistant phenotype, CR IC has worse clinical outcomes (14-days mortality) and a higher level of resistance.

Conclusion: Group-level and individual-patient-level analyses of antibiotic-use-versus-resistance relations yielded distinct but valuable results. Antibacterials with antianaerobic activity and antifungals might have “indirect” and “direct” effect on triazole-resistant IC, respectively.

Association of Antibiotic Use with the Resistance Epidemiology of Pseudomonas aeruginosa in a Hospital Setting: A Four-Year Retrospective Time Series Analysis

Background: Even though, Pseudomonas aeruginosa is a common cause of hospital-acquired infections, treatment is challenging because of decreasing rates of susceptibility to many broad-spectrum antibiotics. Methods: Consumption data of eight broad spectrum antimicrobial agents and resistance rates of P. aeruginosa were collected for 48 consecutive months. Autoregressive integrated moving average (ARIMA) and transfer functions models were used to develop relationships between antibiotic use and resistance. Results: Positive correlations between P. aeruginosa resistance and uses of ciprofloxacin (p < 0.001), meropenem (p < 0.001), and cefepime (p = 0.005) were identified. Transfer function models showed the quantified effect of each of these antibiotics on resistance. Regarding levofloxacin, ceftazidime, piperacillin/tazobactam and imipenem, no significant relationships were found. For ceftazidime and levofloxacin, this was probably due to their low consumption, while for imipenem the reason can possibly be ascribed to the already high established P. aeruginosa resistance in the hospital. Conclusion: In the hospital setting, the effect of antimicrobial agents’ consumption on the susceptibility epidemiology of P. aeruginosa differs significantly for each one of them. In this study, the role of precedent use of meropenem, cefepime and ciprofloxacin was quantified in the development of P. aeruginosa resistance.

Impact of antibiotic prescription on the resistance of Klebsiella pneumoniae at a tertiary hospital in China, 2012-2019

BACKGROUND

The incidence of Klebsiella pneumoniae (KP) infections is worrisome. Previous studies have shown that increased antibiotic treatment might affect the resistance profile of this organism. The objective of this study was to describe the resistance profile of KP strains and to correlate it with antibiotics consumption.

METHODS

A retrospective observational analysis was performed to examine exposure to antibiotics and resistant profile, comparing the results of different measuring outcomes of resistance (the incidence and the percentage of resistant KP) during January 2012 to June 2019 by using the autoregressive integrated moving average and transfer function model.

RESULTS

During the study period, the use of some third-generation cephalosporins and carbapenems continued to increase and a total of 5,519 KP isolates were collected. There were positive relationships between amikacin-resistant KP, ciprofloxacin-resistant KP, and corresponding antibiotic use in the transfer function models; both for the incidence rate and the resistant rate (time lag = 0) (P < .05).

CONCLUSIONS

The present study confirms that the history of amikacin or ciprofloxacin use influences the susceptibility of these agents against KP with no delay. Similar results were obtained with different measuring outcomes of resistance.

Linking antimicrobial resistance surveillance to antibiotic policy in healthcare settings: the COMBACTE-Magnet EPI-Net COACH project

OBJECTIVES

To systematically summarize the evidence on how to collect, analyse and report antimicrobial resistance (AMR) surveillance data to inform antimicrobial stewardship (AMS) teams providing guidance on empirical antibiotic treatment in healthcare settings.

METHODS

The research group identified 10 key questions about the link between AMR surveillance and AMS using a checklist of 9 elements for good practice in health research priority settings and a modified 3D combined approach matrix, and conducted a systematic review of published original studies and guidelines on the link between AMR surveillance and AMS.

RESULTS

The questions identified focused on AMS team composition; minimum infrastructure requirements for AMR surveillance; organisms, samples and susceptibility patterns to report; data stratification strategies; reporting frequency; resistance thresholds to drive empirical therapy; surveillance in high-risk hospital units, long-term care, outpatient and veterinary settings; and surveillance data from other countries. Twenty guidelines and seven original studies on the implementation of AMR surveillance as part of an AMS programme were included in the literature review.

CONCLUSIONS

The evidence summarized in this review provides a useful basis for a more integrated process of developing procedures to report AMR surveillance data to drive AMS interventions. These procedures should be extended to settings outside the acute-care institutions, such as long-term care, outpatient and veterinary. Without proper AMR surveillance, implementation of AMS policies cannot contribute effectively to the fight against MDR pathogens and may even worsen the burden of adverse events from such interventions.

COVID-19 Pandemic: ARIMA and Regression Model-Based Worldwide Death Cases Predictions

COVID-19 has now taken a frightening form. As the days pass, it is becoming more and more widespread and now it has become an epidemic. The death rate, which was earlier in the hundreds, changed to thousands and then progressed to millions. If the same situation persists over time, the day is not far when the humanity of all the countries on the globe will be endangered and we yearn for breath. From January 2020 till now, many scientists, researchers and doctors have been trying to solve this complex problem so that proper arrangements can be made by the governments in the hospitals and the death rate can be reduced. The presented research article shows the estimated mortality rate by the ARIMA model and the regression model. This dataset has been collected precisely from DataHub-Novel Coronavirus 2019-Dataset from 22nd January to 29th June 2020. To show the current mortality rate of the entire subject, the correlation coefficients of attributes (MAE, MSE, RMSE and MAPE) were used, where the average absolute percentage error validated the model by 99.09%. The ARIMA model is used to generate auto_arima SARIMAX results, auto_arima residual plots, ARIMA model results, and corresponding prediction plots on the training dataset. These data indicate a continuous decline in death cases. By applying a regression model, the coefficients generated by the regression model are estimated, and the actual death cases and expected death cases are compared and analyzed. It is found that the predicted mortality rate has decreased after May 2, 2020. It will help the government and doctors prepare for the forthcoming plans. Based on short-period predictions, these methods can be used to forecast the mortality rate for a long period.

Predicting the future distribution of antibiotic resistance using time series forecasting and geospatial modelling

Background: Increasing antibiotic resistance in a location may be mitigated by changes in treatment policy, or interventions to limit transmission of resistant bacteria. Therefore, accurate forecasting of the distribution of antibiotic resistance could be advantageous. Two previously published studies addressed this, but neither study compared alternative forecasting algorithms or considered spatial patterns of resistance spread. Methods: We analysed data describing the annual prevalence of antibiotic resistance per country in Europe from 2012 – 2016, and the quarterly prevalence of antibiotic resistance per clinical commissioning group in England from 2015 – 2018. We combined these with data on rates of possible covariates of resistance. These data were used to compare the previously published forecasting models, with other commonly used forecasting models, including one geospatial model. Covariates were incorporated into the geospatial model to assess their relationship with antibiotic resistance. Results: For the European data, which was recorded on a coarse spatiotemporal scale, a naïve forecasting model was consistently the most accurate of any of the forecasting models tested. The geospatial model did not improve on this accuracy. However, it did provide some evidence that antibiotic consumption can partially explain the distribution of resistance. The English data were aggregated at a finer scale, and expected-trend-seasonal (ETS) forecasts were the most accurate. The geospatial model did not significantly improve upon the median accuracy of the ETS model, but it appeared to be less sensitive to noise in the data, and provided evidence that rates of antibiotic prescription and bacteraemia are correlated with resistance. Conclusion: Annual, national-level surveillance data appears to be insufficient for fitting accurate antibiotic resistance forecasting models, but there is evidence that data collected at a finer spatiotemporal scale could be used to improve forecast accuracy. Additionally, incorporating antibiotic prescription or consumption data into the model could improve the predictive accuracy.

The influence of hospital antimicrobial use on carbapenem-non-susceptible Enterobacterales incidence rates according to their mechanism of resistance: a time-series analysis

BACKGROUND

Carbapenem non-susceptible Enterobacterales (CNSE) can be broadly divided into those that produce carbapenemases (carbapenemase-producing Enterobacterales (CPE)), and those that harbour other mechanisms of resistance (non-carbapenemase-producing CNSE (NCP-CNSE)).

AIM

To determine the predictors of CNSE nosocomial incidence rates according to their mechanism of resistance.

METHODS

A time-series analysis was conducted (July 2013 to December 2018) to evaluate the relationship in time between hospital antibiotic use and the percentage of adherence to hand hygiene with the CNSE rates.

FINDINGS

In all, 20,641 non-duplicated Enterobacterales isolates were identified; 2.2% were CNSE. Of these, 48.1% and 51.9% were CPE and NCP-CNSE, respectively. Of the CPE, 78.3% possessed a bla_OXA-232 gene. A transfer function model was identified for CNSE, CPE, and OXA-232 CPE that explained 20.8%, 19.3%, and 24.2% of their variation, respectively. According to the CNSE and CPE models, an increase in piperacillin-tazobactam (TZP) use of 1 defined daily dose (DDD) per 100 hospital patient-days (HPD) would lead to an increase of 0.69 and 0.49 CNSE and CPE cases per 10,000 HPD, respectively. The OXA-232 CPE model estimates that an increase of 1 DDD per 100 HPD of TZP use would lead to an increase of 0.43 OXA-232 CPE cases per 10,000 HPD. A transfer function model was not identified for NCP-CNSE, nor was there an association between the adherence to handhygiene and the CNSE rates.

CONCLUSION

The use of TZP is related in time with the CPE nosocomial rates, mostly explained by its effect on OXA-232 CPE.

Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction

Antimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, specifically Time Series Forecasting techniques, for predicting future outbreaks of Methicillin-resistant Staphylococcus aureus (MRSA). Infection incidence forecasting is approached as a Feature Selection based Time Series Forecasting problem using multivariate time series composed of incidence of Staphylococcus aureus Methicillin-sensible and MRSA infections, influenza incidence and total days of therapy of both of Levofloxacin and Oseltamivir antimicrobials. Data were collected from the University Hospital of Getafe (Spain) from January 2009 to January 2018, using months as time granularity. The main contributions of the work are the following: the applications of wrapper feature selection methods where the search strategy is based on multi-objective evolutionary algorithms (MOEA) along with evaluators based on the most powerful state-of-the-art regression algorithms. The performance of the feature selection methods has been measured using the root mean square error (RMSE) and mean absolute error (MAE) performance metrics. A novel multi-criteria decision-making process is proposed in order to select the most satisfactory forecasting model, using the metrics previously mentioned, as well as the slopes of model prediction lines in the 1, 2 and 3 steps-ahead predictions. The multi-criteria decision-making process is applied to the best models resulting from a ranking of databases and regression algorithms obtained through multiple statistical tests. Finally, to the best of our knowledge, this is the first time that a feature selection based multivariate time series methodology is proposed for antibiotic resistance forecasting. Final results show that the best model according to the proposed multi-criteria decision making process provides a RMSE = (0.1349, 0.1304, 0.1325) and a MAE = (0.1003, 0.096, 0.0987) for 1, 2, and 3 steps-ahead predictions.

Appropriateness of antimicrobial use among septic patients managed by the critical care response team: an opportunity for improvement through de-escalation

Background

Most septic patients managed by critical care response teams (CCRT) are prescribed antimicrobials. Nevertheless, data evaluating their appropriateness are lacking both locally and internationally. The objective was to assess antimicrobial use among septic and non-septic patients managed by CCRT.

Setting

Case-control design was used to compare septic (cases) and non-septic (controls) CCRT patients at tertiary care setting. The frequency of antimicrobial use was assessed before and after CCRT activation. The appropriateness of antimicrobial use was assessed at day four post-CCRT, based on standard recommendations, clinical assessment, and culture results.

Main results

A total of 157 cases and 158 controls were included. The average age was 61.1 ± 20.4 years, and 54.6% were males, with minor differences between groups. The use of any antimicrobial was 100.0% in cases and 87.3% in controls (p < 0.001). The use of meropenem (68.2% versus 34.8%, p < 0.001) and vancomycin (56.7% versus 25.9%, p < 0.001) were markedly higher in cases than controls. The overall appropriateness was significantly lower in cases than controls (50.7% versus 59.6%, p = 0.047). Individual appropriateness was lowest with meropenem (16.7%) and imipenem (25.0%), and highest with piperacillin/tazobactam (87.1%) and colistin (78.3%). Only 48.5% of antimicrobials prescribed by CCRT were de-escalated by a primary team within four days. Individual appropriateness and de-escalations were not different between groups.

Conclusions

Empiric use and inadequate de-escalation of broad-spectrum antimicrobials were major causes for inappropriate antimicrobial use in CCRT patients. Our findings highlight the necessity of urgent implementation of an antimicrobial stewardship program, including training and auditing of antimicrobial prescriptions.

Quantifying uncertainty about future antimicrobial resistance: Comparing structured expert judgment and statistical forecasting methods

The increase of multidrug resistance and resistance to last-line antibiotics is a major global public health threat. Although surveillance programs provide useful current and historical information on the scale of the problem, the future emergence and spread of antibiotic resistance is uncertain, and quantifying this uncertainty is crucial for guiding decisions about investment in antibiotics and resistance control strategies. Mathematical and statistical models capable of projecting future rates are challenged by the paucity of data and the complexity of the emergence and spread of resistance, but experts have relevant knowledge. We use the Classical Model of structured expert judgment to elicit projections with uncertainty bounds of resistance rates through 2026 for nine pathogen-antibiotic pairs in four European countries and empirically validate the assessments against data on a set of calibration questions. The performance-weighted combination of experts in France, Spain, and the United Kingdom projected that resistance for five pairs on the World Health Organization’s priority pathogens list (E. coli and K. pneumoniae resistant to third-generation cephalosporins and carbapenems and MRSA) would remain below 50% in 2026. In Italy, although upper bounds of 90% credible ranges exceed 50% resistance for some pairs, the medians suggest Italy will sustain or improve its current rates. We compare these expert projections to statistical forecasts based on historical data from the European Antimicrobial Resistance Surveillance Network (EARS-Net). Results from the statistical models differ from each other and from the judgmental forecasts in many cases. The judgmental forecasts include information from the experts about the impact of current and future shifts in infection control, antibiotic usage, and other factors that cannot be easily captured in statistical forecasts, demonstrating the potential of structured expert judgment as a tool for better understanding the uncertainty about future antibiotic resistance.

Influence of primary care antibiotic prescribing on incidence rates of multidrug-resistant Gram-negative bacteria in hospitalised patients

PURPOSE

Use of antibiotics can give rise to the selection of resistant bacteria. It remains unclear whether antibiotic use in primary care can influence bacterial resistance incidence in patients when hospitalised. The aim of this study is to explore the impact of prior community antibiotic usage on hospital-detected multidrug-resistant Gram-negative (MRGN) incidence rate.

METHODS

This pharmacoepidemiological study was case-control in design, and was carried out in the Antrim Area Hospital (N. Ireland) in two phases. In phase 1, the controls were matched according to: age, gender, admission ward, date of admission, and age-adjusted Charlson co-morbidity index score. During the second phase, controls were selected randomly from the total population of admissions to the hospital over the 2-year study period.

RESULTS

In phase 1, multivariate analysis revealed that prior exposure to the second- and third-generation cephalosporins (p = 0.004) and fluoroquinolones (p = 0.023) in primary care was associated with an increased likelihood of MRGN detection in inpatients. In phase 2, an independent relationship between an increased risk of identification of MRGN, while hospitalised was associated with: prolonged hospitalisation (p < 0.001), being elderly (p < 0.001), being female (p = 0.007), and having genitourinary disease (p < 0.001).

CONCLUSION

This study provides clear evidence which supports the need to optimise antibiotic use in primary care to help reduce MRGN incidence in hospitalised patients.

Measuring Appropriate Antibiotic Prescribing in Acute Hospitals: Development of a National Audit Tool Through a Delphi Consensus

This study developed a patient-level audit tool to assess the appropriateness of antibiotic prescribing in acute National Health Service (NHS) hospitals in the UK. A modified Delphi process was used to evaluate variables identified from published literature that could be used to support an assessment of appropriateness of antibiotic use. At a national workshop, 22 infection experts reached a consensus to define appropriate prescribing and agree upon an initial draft audit tool. Following this, a national multidisciplinary panel of 19 infection experts, of whom only one was part of the workshop, was convened to evaluate and validate variables using questionnaires to confirm the relevance of each variable in assessing appropriate prescribing. The initial evidence synthesis of published literature identified 25 variables that could be used to support an assessment of appropriateness of antibiotic use. All the panel members reviewed the variables for the first round of the Delphi; the panel accepted 23 out of 25 variables. Following review by the project team, one of the two rejected variables was rephrased, and the second neutral variable was re-scored. The panel accepted both these variables in round two with a 68% response rate. Accepted variables were used to develop an audit tool to determine the extent of appropriateness of antibiotic prescribing at the individual patient level in acute NHS hospitals through infection expert consensus based on the results of a Delphi process.

A nonlinear time-series analysis approach to identify thresholds in associations between population antibiotic use and rates of resistance

Balancing access to antibiotics with the control of antibiotic resistance is a global public health priority. At present, antibiotic stewardship is informed by a 'use it and lose it' principle, in which antibiotic use by the population is linearly related to resistance rates. However, theoretical and mathematical models suggest that use-resistance relationships are nonlinear. One explanation for this is that resistance genes are commonly associated with 'fitness costs' that impair the replication or transmissibility of the pathogen. Therefore, resistant genes and pathogens may only gain a survival advantage where antibiotic selection pressures exceed critical thresholds. These thresholds may provide quantitative targets for stewardship-optimizing the control of resistance while avoiding over-restriction of antibiotics. Here, we evaluated the generalizability of a nonlinear time-series analysis approach for identifying thresholds using historical prescribing and microbiological data from five populations in Europe. We identified minimum thresholds in temporal relationships between the use of selected antibiotics and incidence rates of carbapenem-resistant Acinetobacter baumannii (Hungary), extended-spectrum β-lactamase-producing Escherichia coli (Spain), cefepime-resistant E. coli (Spain), gentamicin-resistant Pseudomonas aeruginosa (France) and methicillin-resistant Staphylococcus aureus (Northern Ireland) in different epidemiological phases. Using routinely generated data, our approach can identify context-specific quantitative targets for rationalizing population antibiotic use and controlling resistance. Prospective intervention studies that restrict antibiotic consumption are needed to validate these thresholds.

Antimicrobial consumption in five adult intensive care units: a 33-month surveillance study

Background

Estimating the baseline antimicrobial consumption is extremely important to monitor the impact of antimicrobial stewardship activities that aim to reduce the burden and cost of antimicrobial consumption.

Objectives

To quantify service-specific antimicrobial consumption using different metrics.

Methods

A surveillance study was conducted at King Abdulaziz Medical City, Riyadh, Saudi Arabia, between October 2012 and June 2015 in five adult intensive care units (ICUs). Consumption data were collected manually on a daily basis by infection control practitioners. Data were presented as defined daily dose (DDD), days of therapy (DOT) per 1000 patient days, and frequency of daily consumption.

Results

A total of 43,970 DDDs and 46,940 DOTs were monitored during 54,116 patient-days. For the most frequently consumed antimicrobials, the consumption of carbapenems, piperacillin/tazobactam, vancomycin, and colistin (respectively) in all ICUs combined were 255.9, 134.3, 98.2, and 13.6 DDDs per 1000 patient-days and 235.7, 145.9, 129.5, and 117.5 DOTs per 1000 patient-days. For the frequency of daily consumption, carbapenems were the most frequently consumed antimicrobial group in medical/surgical, burn, and step-down ICUs while piperacillin/tazobactam was the most frequently consumed antimicrobial in neuro-surgical and cardio-thoracic ICUs.

Conclusion

High consumption of broad-spectrum antimicrobial agents such as meropenem and piperacillin/tazobactam is observed in multiple ICUs in a tertiary care hospital. Meropenem consumption is considerably higher than similar ICUs internationally. Future studies focusing on concurrent monitoring of antimicrobial resistance and identifying patient and physician characteristics associated with specific prescription patterns may help in improving judicious antimicrobial consumption.

Utilization of Vector Autoregressive and Linear Transfer Models to Follow Up the Antibiotic Resistance Spiral in Gram-negative Bacteria From Cephalosporin Consumption to Colistin Resistance

Background

Increasing antibiotic resistance may reciprocally affect consumption and lead to use of broader-spectrum alternatives; a vicious cycle that may gradually limit therapeutic options. Our aim in this study was to demonstrate this vicious cycle in gram-negative bacteria and show the utility of vector autoregressive (VAR) models for time-series analysis in explanatory and dependent roles simultaneously.

Methods

Monthly drug consumption data in defined daily doses per 100 bed-days and incidence densities of gram-negative bacteria (Escherichia coli, Klebsiella spp., Pseudomonas aeruginosa, and Acinetobacter baumannii) resistant to cephalosporins or to carbapenems were analyzed using VAR models. These were compared to linear transfer models used earlier.

Results

In case of all gram-negative bacteria, cephalosporin consumption led to increasing cephalosporin resistance, which provoked carbapenem use and consequent carbapenem resistance and finally increased colistin consumption, exemplifying the vicious cycle. Different species were involved in different ways. For example, cephalosporin-resistant Klebsiella spp. provoked carbapenem use less than E. coli, and the association between carbapenem resistance of P. aeruginosa and colistin use was weaker than that of A. baumannii. Colistin use led to decreased carbapenem use and decreased carbapenem resistance of P. aeruginosa but not of A. baumannii.

Conclusions

VAR models allow analysis of consumption and resistance series in a bidirectional manner. The reconstructed resistance spiral involved cephalosporin use augmenting cephalosporin resistance primarily in E. coli. This led to increased carbapenem use, provoking spread of carbapenem-resistant A. baumannii and consequent colistin use. Emergence of panresistance is fueled by such antibiotic-resistance spirals.

Antimicrobial resistance in bacteria isolated from mastitis in dairy cattle in France, 2006-2016

In dairy cattle, mastitis is the most frequent bacterial disease, and the routine use of antibiotics for treatment and prevention can drive antimicrobial resistance (AMR). The aim of our study was to estimate the levels of AMR of the 3 main bacteria isolated from dairy cattle with mastitis in France (Streptococcus uberis, Escherichia coli, and coagulase-positive staphylococci) and to investigate their changes over time. Data collected between 2006 and 2016 by the French surveillance network for AMR in pathogenic bacteria of animal origin (called RESAPATH) were analyzed. The proportions of mono- and multidrug resistance were calculated and the trends were investigated using nonlinear analyses applied to time series. Over the whole period, the lowest proportions of resistance in S. uberis isolates were observed for oxacillin (2.2%) and gentamicin (2.4%) and most resistance levels were below 20%. The trends in resistance showed some significant variation, mainly for S. uberis, but without a common pattern across the various antibiotics examined. For only 2 combinations of bacteria-antibiotic the trend in resistance showed a continuous increase from 2006 to 2016: tetracycline resistance in S. uberis isolates and third-generation cephalosporin resistance in E. coli isolates. In E. coli, the highest proportions of resistance were observed for amoxicillin (28.1%) and tetracycline (23.1%). Resistance to third-generation cephalosporins in E. coli from dairy cattle was almost nil in 2006, but reached 2.4% in December 2016. This increase is particularly concerning because these antibiotics constitute one of the latest therapeutic alternatives to fight severe infectious diseases in humans. Except for penicillin (33.9%), the proportions of resistance in coagulase-positive staphylococci were below 11% during the whole study period. Multidrug resistance (isolates with acquired resistance to at least one antibiotic in 3 or more antibiotic classes) ranged from 2.4% for coagulase-positive staphylococci to 9.9% for S. uberis. These findings can serve as guidelines for practitioners in the choice of the most appropriate antibiotic according to the prevailing epidemiological context. Ultimately, our results contribute to risk assessment of AMR and provide a baseline for setting up and evaluating control measures and designing strategies to limit AMR.

Time series analysis of bovine venereal diseases in La Pampa, Argentina

The venereal diseases bovine trichomoniasis (BT) and bovine genital campylobacteriosis (BGC) cause economic losses in endemic areas like La Pampa province in Argentina where beef cattle are usually extensively managed. This study used data compiled between 2007 and 2014 by a Provincial Program for the Control and Eradication of venereal diseases in order to develop and analyze retrospective models of time series for BT and BGC. Seasonality and long-term trend were explored with decomposition and simple regression methods. Autoregressive Integrated Moving Average models (ARIMA) were used to fit univariate models for the prevalence and persistence of BT and BGC. Autoregressive Integrated Moving Average with Explanatory Variable models (ARIMAX) were used to analyze the association between different time series, replacement entries and herd samplings. The prevalence and persistence of BT and BGC have decreased from 2007 to 2014. All the BT and BGC time series are seasonal and their long-term trend is decreasing. Seasonality of BT and BGC is similar, with higher rates of detection in autumn-winter than is spring-summer. Prevalence and persistence time series are correlated, indicating their changes are synchronic and follow a similar time pattern. Prevalence of BT and BGC showed the best fitting with the ARIMA (0,0,1)(0,1,1)12 model. While for persistence of BT and BGC, the best adjustment was with the same model with no seasonal difference where the current number of cases depends on the moving averages of the month and the previous season. Including covariates improve the fitting of univariate models, in addition, estimations using ARIMAX models are more precise than using ARIMA models. The time distribution of the samplings could be increasing the false negative ratio. According to the obtained results, the ARIMA and ARIMAX models can be considered an option to predict the BT and BGC prevalence and persistence in La Pampa (Argentina).

Comparative epidemiology of E. coli resistance to third-generation cephalosporins in diseased food-producing animals

Categorized by WHO as critically important antibiotics, third-generation cephalosporins (3GCs) are one of the latest therapeutic alternatives to fight severe infectious diseases in humans. Some antibiotics belonging to this class are prescribed to treat food-producing animals in specific pathological contexts. Preserving the effectiveness of 3GCs requires characterization and careful monitoring of 3GCs resistance and the identification and implementation of measures that can limit this antimicrobial resistance (AMR). Here, we characterized the 3GCs resistance in Escherichia coli isolated from diseased animals. Using data collected from broilers, hens, calves, piglets, sows, turkeys and ducks between 2006 and 2016 by the French surveillance network of AMR in pathogenic bacteria of animal origin (called RESAPATH), we investigated the dynamics of resistance to 3GCs. Our non-linear analysis applied to time series showed that the evolution of E. coli resistance to 3GCs is specific to each animal category. From 2006 to 2010, resistance to 3GCs increased for most animal categories. We observed peaks of high-level of resistance for hens (21.5% in 2010) and broilers (26.7% in 2011), whereas trends stayed below 10% for the other animal categories throughout the study period. Resistance later decreased and, since 2014, 3GCs resistance has dropped below 10% for all animal categories. The parallel between trends and measures to limit AMR over the period shed lights on the impact of practices changes, public policies (EcoAntibio Plan) and sector-led initiatives (moratorium in swine sector). Finally, they highlight the usefulness and importance of AMR surveillance networks in animal health, such as RESAPATH.

Colistin resistance in carbapenemase-producing Klebsiella pneumoniae bloodstream isolates: Evolution over 15 years and temporal association with colistin use by time series analysis

Colistin is often the only available treatment option against infections caused by carbapenemase-producing Klebsiella pneumoniae (CP-Kp). In this study, the evolution of colistin resistance among CP-Kp and its relationship with colistin use in a tertiary-care hospital in Athens, Greece, was investigated. All CP-Kp blood isolates recovered between January 2002 and June 2016 were tested for susceptibility to colistin by agar dilution and broth microdilution methods. Data on colistin use were collected from the pharmacy database. Time series of colistin use and resistance were analysed using the Box and Jenkins method. A transfer function model was built to quantify the dynamic relationship between colistin use and resistance. Overall, 313 CP-Kp isolates were identified. The percentage colistin resistance increased from 0% in 2002 to 26.9% in 2016 (R² = 0.5, P < 0.01). A temporal association between colistin use and resistance was observed; an increase in colistin use by 1 DDD/100 patient-days led to a 0.05 increase in the incidence rate of colistin resistance. The time lag between the effect of colistin use on subsequent variations in colistin resistance was 3 months. Colistin use and prior levels of colistin resistance could explain 69% of colistin resistance; in the remaining 31%, other factors might have played a role. The results presented here demonstrate a significant temporal association between colistin use and colistin resistance. These findings have important implications in implementing strategies to contain colistin resistance.

Coupled, multi-strain epidemic models of mutating pathogens

We introduce and analyze coupled, multi-strain epidemic models designed to simulate the emergence and dissemination of mutant (e.g. drug-resistant) pathogen strains. In particular, we investigate the mathematical and biological properties of a general class of multi-strain epidemic models in which the infectious compartments of each strain are coupled together in a general manner. We derive explicit expressions for the basic reproduction number of each strain and highlight their importance in regulating the system dynamics (e.g. the potential for an epidemic outbreak) and the existence of nonnegative endemic solutions. Importantly, we find that the basic reproduction number of each strain is independent of the mutation rates between the strains - even under quite general assumptions for the form of the infectious compartment coupling. Moreover, we verify that the coupling term promotes strain coexistence (as an extension of the competitive exclusion principle) and demonstrate that the strain with the greatest reproductive capacity is not necessarily the most prevalent. Finally, we briefly discuss the implications of our results for public health policy and planning.

An evolutionary model to predict the frequency of antibiotic resistance under seasonal antibiotic use, and an application to Streptococcus pneumoniae

The frequency of resistance to antibiotics in Streptococcus pneumoniae has been stable over recent decades. For example, penicillin non-susceptibility in Europe has fluctuated between 12% and 16% without any major time trend. In spite of long-term stability, resistance fluctuates over short time scales, presumably in part due to seasonal fluctuations in antibiotic prescriptions. Here, we develop a model that describes the evolution of antibiotic resistance under selection by multiple antibiotics prescribed at seasonally changing rates. This model was inspired by, and fitted to, published data on monthly antibiotics prescriptions and frequency of resistance in two communities in Israel over 5 years. Seasonal fluctuations in antibiotic usage translate into small fluctuations of the frequency of resistance around the average value. We describe these dynamics using a perturbation approach that encapsulates all ecological and evolutionary forces into a generic model, whose parameters quantify a force stabilizing the frequency of resistance around the equilibrium and the sensitivity of the population to antibiotic selection. Fitting the model to the data revealed a strong stabilizing force, typically two to five times stronger than direct selection due to antibiotics. The strong stabilizing force explains that resistance fluctuates in phase with usage, as antibiotic selection alone would result in resistance fluctuating behind usage with a lag of three months when antibiotic use is seasonal. While most antibiotics selected for increased resistance, intriguingly, cephalosporins selected for decreased resistance to penicillins and macrolides, an effect consistent in the two communities. One extra monthly prescription of cephalosporins per 1000 children decreased the frequency of penicillin-resistant strains by 1.7%. This model emerges under minimal assumptions, quantifies the forces acting on resistance and explains up to 43% of the temporal variation in resistance.

Trend and seasonality of community-acquired Escherichia coli antimicrobial resistance and its dynamic relationship with antimicrobial use assessed by ARIMA models

INTRODUCTION

We studied the trend and seasonality of community-acquired Escherichia coli resistance and quantified its correlation with the previous use of certain antibiotics.

METHODS

A time series study of resistant community-acquired E. coli isolates and their association with antibiotic use was conducted in a Primary Health Care Area from 2008 to 2012. A Poisson regression model was constructed to estimate the trend and seasonality of E. coli resistance.

RESULTS

A significant increasing trend in mean E. coli resistance to cephalosporins, aminoglycosides and nitrofurantoin was observed. Seasonal resistance to ciprofloxacin and amoxicillin-clavulanic acid was significantly higher in autumn-winter. There was a delay of 7, 10 and 12 months between the use of cotrimoxazole (P<0.038), fosfomycin (P<0.024) and amoxicillin-clavulanic acid (P<0.015), respectively, and the occurrence of E. coli resistance.

CONCLUSIONS

An average delay of 10 months between the previous use of amoxicillin-clavulanic acid, cotrimoxazole and fosfomycin and the appearance of resistant community-acquired E. coli strains was detected.

Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis

BACKGROUND

Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical.

METHOD

In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes.

RESULTS

This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted.

CONCLUSION

This tool will allow anyone with historic test volume data to model future demand.

Methicillin-ResistantStaphylococcus aureusin German Intensive Care Units During 2000-2003: Data from Project SARI (Surveillance of Antimicrobial Use and Antimicrobial Resistance in Intensive Care Units)

Objectives.

The objective of this study was to analyze methicillin-resistantStaphylococcus aureus(MRSA) percentages (defined as the percentage ofS. aureusisolates that are resistant to methicillin) and antimicrobial consumption in intensive care units (ICUs) participating in Project SARI (Surveillance of Antimicrobial Use and Antimicrobial Resistance in Intensive Care Units), to look for temporal changes in MRSA percentages and antimicrobial consumption in individual ICUs as an indicator of the impact of an active surveillance system, and to investigate the differences between ICUs with increased MRSA percentages versus those with decreased percentages during a period of 3 years (2001-2003).

Methods.

This was a prospective, ICU-based and laboratory-based surveillance study involving 38 German ICUs during 2000-2003. Antimicrobial use was reported in terms of defined daily doses (DDDs) per 1,000 patient-days. Temporal changes in the MRSA percentage and antimicrobial use in individual ICUs were calculated by means of the Wilcoxon signed rank test. The incidence density of nosocomial MRSA infection was defined as the number of nosocomial MRSA infections per 1,000 patient-days.

Results.

From February 2000 through December 2003, a total of 38 ICUs reported data on 499,694 patient-days and 9,552S. aureusisolates, including 2,249 MRSA isolates and 660,029 DDDs of antimicrobials. Cumulative MRSA percentages ranged from 0% to 64.4%, with a mean of 23.6%. The MRSA incidence density ranged from 0 to 38.2 isolates per 1,000 patient-days, with a mean of 2.77 isolates per 1,000 patient-days. There was a positive correlation between MRSA percentage and imipenem and ciprofloxacin use (P<.05). Overall, comparison of data from 2001 with data from 2003 showed that MRSA percentages increased in 18 ICUs (median increase, 13.2% [range, 1.6%-38.4%]) and decreased in 14 ICUs (median decrease, 12% [range, 1.0%-48.4%]). Increased use of third-generation cephalosporins, glycopeptides, or aminoglycosides correlated significantly with an increase in the MRSA percentage (P<.05). The cumulative nosocomial MRSA infection incidence density for 141 ICUs that did not participate in SARI and, therefore, did not receive feedback increased from 0.26 to 0.35 infections per 1,000 patient-days during a 3-year period, whereas the rate in SARI ICUs decreased from 0.63 to 0.40 infections per 1,000 patient-days.

Conclusion.

The MRSA situation in German ICUs is still heterogeneous. Because MRSA percentages range from 0% to 64.4%, further studies are required to confirm findings that no change in the MRSA percentage and a decrease in the nosocomial MRSA infection incidence density in SARI ICUs reflect the impact of an active surveillance system.

Antimicrobial Consumption Data From Pharmacy and Nursing Records: How Good Are They?

Objective:

To determine whether randomly selected intravenous (IV) antimicrobial doses dispensed from an inpatient pharmacy were administered.

Design:

This was a prospective, cross-sectional study in which dose administration was confirmed by direct observation and by assessment of the medication administration record (MAR). A retrospective analysis of the return rate of unused IV antimicrobial doses was performed subsequently.

Setting:

Medical and surgical intensive care units (ICUs) and non-ICUs of a 550-bed urban public teaching hospital.

Participants:

Hospitalized patients with an order in the pharmacy database for an IV antimicrobial during 9 non-consecutive weekdays in June 1999.

Results:

Of 397 doses, 221 (55.7%) assessed by bedside observation and 238 (59.9%) assessed by MAR review were classified as administered; 139 doses (35.0%) were dispensed but changes in the drug order or the patient's status prevented their administration. In the subsequent assessment, of 745 IV antimicrobial doses dispensed during 24 hours, 322 (43.2%) were returned to the pharmacy unused; 423 (56.8%) of the doses—consistent with our prior observations—were presumably administered.

Conclusions:

Because computerized pharmacy data may overestimate actual antimicrobial consumption, such data should be validated when used in studies of hospital antimicrobial use. Dispense-return analysis offers a simple validation method.

Seasonality of antibiotic prescriptions for outpatients and resistance genes in sewers and wastewater treatment plant outflow

To test the hypothesis of a seasonal relationship of antibiotic prescriptions for outpatients and the abundance of antibiotic resistance genes (ARGs) in the wastewater, we investigated the distribution of prescriptions and different ARGs in the Dresden sewer system and wastewater treatment plant during a two-year sampling campaign. Based on quantitative PCR (qPCR), our results show a clear seasonal pattern for relative ARGs abundances. The higher ARGs levels in autumn and winter coincide with the higher rates of overall antibiotic prescriptions. While no significant differences of relative abundances were observed before and after the wastewater treatment for most of the relative ARGs, the treatment clearly influenced the microbial community composition and abundance. This indicates that the ARGs are probably not part of the dominant bacterial taxa, which are mainly influenced by the wastewater treatment processes, or that plasmid carrying bacteria remain constant, while plasmid free bacteria decrease. An exception was vancomycin (vanA), showing higher relative abundance in treated wastewater. It is likely that a positive selection or community changes during wastewater treatment lead to an enrichment of vanA. Our results demonstrate that in a medium-term study the combination of qPCR and next generation sequencing corroborated by drug-related health data is a suitable approach to characterize seasonal changes of ARGs in wastewater and treated wastewater.

Antibiotic consumption in non-teaching Lebanese hospitals: A cross-sectional study

The rising threat of antibiotic resistance is linked to patterns of antibiotic use in hospital settings where global efforts are undertaken to encourage reporting and benchmarking antibiotic consumption in an attempt to improve prescription regimens. In Lebanon, where data concerning the level of antibiotic consumption in hospitals is scarce, the aim of our paper is to track the intensity of antibiotic consumption in order to identify potential evidence of antibiotic misuse or abuse. The study is conducted in 2012 for a period of 12-month using data from pharmacy records in 27 non-teaching Lebanese hospitals according to the Anatomical, Therapeutic and chemical classification system and Defined Daily Dose (ATC/DDD) recommended by the World Health Organization and compiling data on ABC Calc software version 3.1. Results show that the average antibiotic consumption excluding pediatric cases is 72.56 Defined Daily Dose per 100 Bed-Days (DDD/100BD). Total broad spectrum antibiotic consumption is 12.14 DDD/100BD with no significant difference found between public and private hospitals (p>0.05 for all). The most commonly used antibiotics were Amoxycillin/Clavulanic acid, Ceftriaxone, Amoxycillin and Cefuroxime for parenteral use. Consumption of beta-lactams, Cephalosporins, Carbapenems, Monobactams and quinolones did not vary significantly by region, occupancy rate, number of beds including the number of intensive care unit beds. Our data findings provides baseline information on patterns of antibiotic consumption in Lebanon and the issue calls for concerted efforts to encourage data reporting on national basis and to correlate future findings with results of antibiotic susceptibility testing which can provide insights and tools needed to assess the public health consequences of antimicrobial misuse and to evaluate the impact of antibiotic resistance containment interventions.

Impact of antifungal prescription on relative distribution and susceptibility of Candida spp. - Trends over 10 years

INTRODUCTION

The incidence of Candida spp. infections is worrisome, particularly in critically ill patients. Previous reports suggested that increasing use of antifungal therapy might affect resistance profiles of invasive strains. The study objective was to describe the distribution resistance profile of Candida spp. strains, and to correlate it with antifungal consumptions within one ICU.

METHOD

Antifungal drug consumption was measured as the number of defined daily doses per 1000 hospital days. The distribution of Candida spp. over a 10 year period 2004-2013 and the MICs of antifungal drugs over 2007-2013 were determined. Time series analyses were performed.

RESULTS

Of 2403 identified Candida spp. from 5360 patients, Candida albicans predominated (53.1%), followed by Candida glabrata (16.2%), Candida parapsilosis (7.9%) and Candida tropicalis (7.5%). C. parapsilosis increased from 5.7% in 2004 to 8.4% in 2013 (P = 0.02). The increase in caspofungin use is correlated with the increase in caspofungin MICs of C. parapsilosis (P = 0.01), C. glabrata (P = 0.001) and C. albicans (P = 0.02). Polyenes consumption correlated with an increase in amphotericin B MICs of C. glabrata (P = 0.04).

CONCLUSION

Previous history of antifungal prescription within an ICU influences Candida species distribution and susceptibility profile to antifungal agents. The significant selective pressure exerted by caspofungin and amphotericin B on C. glabrata is a concern.