P281424/phd_thesis
1
0
Fork 0
mirror of https://github.com/msberends/a_new_instrument_for_microbial_epidemiology.git synced 2025-07-27 16:17:50 +02:00
Code Releases Activity

chapter 2 and 3 finished

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends
2021-09-17 20:04:22 +02:00
parent e7a6189645
commit 03f22d8b19
61 changed files with 3931 additions and 2155 deletions
Download Patch File Download Diff File Expand all files Collapse all files

77
docs/introduction.html
View File

@ -32,7 +32,7 @@
<link rel="prev" href="contents.html"/>
<link rel="next" href="diagnostic-stewardship.html"/>
<script src="libs/header-attrs-2.9/header-attrs.js"></script>
<script src="libs/jquery-2.2.3/jquery.min.js"></script>
<link href="libs/gitbook-2.6.7/css/style.css" rel="stylesheet" />
@ -70,7 +70,7 @@
<nav role="navigation">
<ul class="summary">
<li><a href="./">A New Instrument for Microbial Epidemiology</a></li>
<li><a href="https://doi.org/10.33612/diss.177417131" target="blank"><img src="images/cover.jpg" style="width:90%; margin-left:5%;"></a></li>
<li class="divider"></li>
<li class="chapter" data-level="" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i>Preamble</a>
@ -94,8 +94,37 @@
<li class="chapter" data-level="1.5" data-path="introduction.html"><a href="introduction.html#aim-of-this-thesis-and-introduction-to-its-chapters"><i class="fa fa-check"></i><b>1.5</b> Aim of this thesis and introduction to its chapters</a></li>
<li class="chapter" data-level="" data-path="introduction.html"><a href="introduction.html#references"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="2" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html"><i class="fa fa-check"></i><b>2</b> Diagnostic Stewardship: Sense or Nonsense?!</a>
<ul>
<li class="chapter" data-level="" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#abstract"><i class="fa fa-check"></i>Abstract</a></li>
<li class="chapter" data-level="2.1" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#introduction-1"><i class="fa fa-check"></i><b>2.1</b> Introduction</a>
<ul>
<li class="chapter" data-level="2.1.1" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#case-1"><i class="fa fa-check"></i><b>2.1.1</b> Case 1</a></li>
<li class="chapter" data-level="2.1.2" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#case-2"><i class="fa fa-check"></i><b>2.1.2</b> Case 2</a></li>
</ul></li>
<li class="chapter" data-level="2.2" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#the-general-concept"><i class="fa fa-check"></i><b>2.2</b> The general concept</a>
<ul>
<li class="chapter" data-level="2.2.1" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#diagnostics"><i class="fa fa-check"></i><b>2.2.1</b> ‘Diagnostics’</a></li>
<li class="chapter" data-level="2.2.2" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#dsp-in-the-microbiological-laboratory"><i class="fa fa-check"></i><b>2.2.2</b> DSP in the microbiological laboratory</a></li>
<li class="chapter" data-level="2.2.3" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#dsp-as-process-optimisation"><i class="fa fa-check"></i><b>2.2.3</b> DSP as process optimisation</a></li>
<li class="chapter" data-level="2.2.4" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#multidisciplinary-aspects-of-dsp-and-infection-management"><i class="fa fa-check"></i><b>2.2.4</b> Multidisciplinary aspects of DSP and infection management</a></li>
</ul></li>
<li class="chapter" data-level="2.3" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#conclusion"><i class="fa fa-check"></i><b>2.3</b> Conclusion</a></li>
<li class="chapter" data-level="" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#financing"><i class="fa fa-check"></i>Financing</a></li>
<li class="chapter" data-level="" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#references-1"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="3" data-path="introducing-method.html"><a href="introducing-method.html"><i class="fa fa-check"></i><b>3</b> Introducing a New, Free, and Independent Method for Standardised, Reproducible and Reliable Analyses of Antimicrobial Resistance Data</a>
<ul>
<li class="chapter" data-level="" data-path="introducing-method.html"><a href="introducing-method.html#abstract-1"><i class="fa fa-check"></i>Abstract</a></li>
<li class="chapter" data-level="3.1" data-path="introducing-method.html"><a href="introducing-method.html#background"><i class="fa fa-check"></i><b>3.1</b> Background</a></li>
<li class="chapter" data-level="3.2" data-path="introducing-method.html"><a href="introducing-method.html#standardising-amr-data-analysis"><i class="fa fa-check"></i><b>3.2</b> Standardising AMR data analysis</a></li>
<li class="chapter" data-level="3.3" data-path="introducing-method.html"><a href="introducing-method.html#comparison-with-existing-software-methods"><i class="fa fa-check"></i><b>3.3</b> Comparison with existing software methods</a></li>
<li class="chapter" data-level="3.4" data-path="introducing-method.html"><a href="introducing-method.html#user-feedback"><i class="fa fa-check"></i><b>3.4</b> User feedback</a></li>
<li class="chapter" data-level="3.5" data-path="introducing-method.html"><a href="introducing-method.html#conclusion-1"><i class="fa fa-check"></i><b>3.5</b> Conclusion</a></li>
<li class="chapter" data-level="" data-path="introducing-method.html"><a href="introducing-method.html#references-2"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="divider"></li>
<li><a href="https://doi.org/10.33612/diss.177417131" target="blank"><img src="images/cover.jpg" style="width:90%; margin-left:5%;"></a></li>
<li class="copyright">© 2021 Matthijs Berends</li>
</ul>
@ -133,7 +162,7 @@
Figure 1.1: Visualisations of the ‘Broad Street cholera outbreak’ in London in 1854. Top: original map as drawn by John Snow. Bottom: Snow’s original map with a self-made heatmap visualisation overlay, based on the geographic position of the cases. The blue circles (n = 13) indicate the location of the water pumps.
</p>
</div>
<p>Spatial epidemiology is one example of the many different specialities in the field of epidemiology. Another example is the direct consequence of Snow’s work: infectious disease epidemiology, which has developed widely since the nineteenth century and has become the de facto standard for researching diseases and their health effects caused by pathogens (i.e., bacteria, viruses and fungi). Since this speciality concerns pathogens, it is a domain shared by the fields of epidemiology and clinical microbiology (Figure 2). Moreover, infectious disease epidemiology can be split into two subspecialties: clinical (infectious disease) epidemiology and microbial epidemiology. The former focuses on the properties of the disease (such as the burden of disease caused by infection, or the disease-related mental and financial costs), while the latter focuses on the properties of the pathogen (such as the credibility of its source, antimicrobial resistance and pathogenicity).</p>
<p>Spatial epidemiology is one example of the many different specialities in the field of epidemiology. Another example is the direct consequence of Snow’s work: infectious disease epidemiology, which has developed widely since the nineteenth century and has become the de facto standard for researching diseases and their health effects caused by pathogens (i.e., bacteria, viruses and fungi). Since this speciality concerns pathogens, it is a domain shared by the fields of epidemiology and clinical microbiology (Figure <a href="introduction.html#fig:fig1-2">1.2</a>). Moreover, infectious disease epidemiology can be split into two subspecialties: clinical (infectious disease) epidemiology and microbial epidemiology. The former focuses on the properties of the disease (such as the burden of disease caused by infection, or the disease-related mental and financial costs), while the latter focuses on the properties of the pathogen (such as the credibility of its source, antimicrobial resistance and pathogenicity).</p>
<p>Applying microbial epidemiology was barely possible in the days of John Snow, for the lack of scientific knowledge about pathogens and the lack of advancement in information technology. Antibiotics were not discovered yet, the cause of cholera was undetermined, and scientists had no clue about the infectivity and pathogenicity of different bacteria. However, what John Snow did in 1854 ‘clinical epidemiologically,’ is in essence quite equal to what we currently do on a large scale during the COVID-19 pandemic. Information technology required to attain this large scale has brought us not only the possibilities to look beyond regional, national and international borders but to observe, analyse and understand pandemics in real-time. Methods we develop and use today can be implemented on the other side of the world tomorrow. This is an important advantage in modern infectious disease epidemiology, as is also illustrated in this thesis.</p>
<p>Microbial epidemiology has an important focus on observing and analysing (1) the microorganisms that cause infections and the human site of origin, (2) the intrinsic or acquired antimicrobial resistance they manifest, and (3) their infectivity and pathogenicity. As any type of microorganism – bacteria, viruses and fungi (including yeasts) – can cause infections in humans, microbial epidemiology is not limited to a certain type of microorganism. Nonetheless, there tends to be a stronger focus on bacteria and fungi, which are more easily isolated at a clinical microbiology laboratory than viruses and can be tested for phenotypical antimicrobial resistance in a routine diagnostic setting. Based on these diagnostic findings, treatment guidelines are developed and evaluated. This in itself urges microbial epidemiology to be employed in a routine setting as well, to make sure that treatment guideline development continually has a solid epidemiological basis.</p>
<div class="figure" style="text-align: center"><span id="fig:fig1-2"></span>
@ -164,7 +193,7 @@ Exposure is a function of how the mode of administration, dose, dosing interval,
(according to EUCAST) I = Susceptible, increased exposure. A microorganism is categorised as such when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.</p></li>
</ul>
<p>For this interpretation of raw AMR test results, international guidelines exist. The most often applied guidelines are supplied by the Clinical and Laboratory Standards Institute (CLSI) and the European Committee on Antimicrobial Susceptibility Testing (EUCAST) <sup>[18,19]</sup>. In Europe, an increasing number of clinical laboratories apply EUCAST guidelines, as it was shown that the coverage of EUCAST guidelines among these laboratories was 73.2% in 2013, and only a few European countries did not use the EUCAST methodology in 2019 <sup>[20,21]</sup>. According to the World Health Organisation (WHO), guidelines from CLSI and EUCAST are adopted by 94% of all countries reporting AMR to the Global Antimicrobial Resistance Surveillance System (GLASS) of the WHO <sup>[22]</sup>.</p>
<p>Generally, AMR is defined as the proportion of resistant microorganisms (R) among all tested microorganisms of the same species (R + S + I). The CLSI and EUCAST guidelines define the interpretations for the most common combinations of pathogenic microorganisms and antimicrobial agents. For example, the EUCAST 2021 guideline considers ciprofloxacin against Escherichia coli to be susceptible when either the MIC is at most 0.25 mg/L or when a diffusion disk with 5 µg has a growth inhibition zone of at least 25 millimetres (Figure 3).</p>
<p>Generally, AMR is defined as the proportion of resistant microorganisms (R) among all tested microorganisms of the same species (R + S + I). The CLSI and EUCAST guidelines define the interpretations for the most common combinations of pathogenic microorganisms and antimicrobial agents. For example, the EUCAST 2021 guideline considers ciprofloxacin against Escherichia coli to be susceptible when either the MIC is at most 0.25 mg/L or when a diffusion disk with 5 µg has a growth inhibition zone of at least 25 millimetres (Figure <a href="introduction.html#fig:fig1-3">1.3</a>).</p>
<p>In 2017, EUCAST implemented the area of technical uncertainty (ATU) for certain microbial species/antibiotic combinations, to warn laboratory staff that the interpretation of routine susceptibility testing is uncertain <sup>[23]</sup>. For example, disk diffusion results from the combination of any species in the order of Enterobacterales with amoxicillin/clavulanic acid are considered unreliable for a zone diameter of 19-20 mm in the latest EUCAST interpretation guideline <sup>[24]</sup>. EUCAST advises to rerun the test, perform an additional test, or to report this uncertainty with a clear warning <sup>[23]</sup>.</p>
<div class="figure" style="text-align: center"><span id="fig:fig1-3"></span>
<img src="images/01-03.svg" alt="Interpretation of 100 random minimum inhibitory concentrations (top) and 100 random disk diffusion growth inhibition zones (bottom) of ciprofloxacin in *Escherichia coli*, interpreted using colours according to the EUCAST 2021 guideline. These plots were generated with the AMR package for R." width="100%" />
@ -174,7 +203,7 @@ Figure 1.3: Interpretation of 100 random minimum inhibitory concentrations (top)
</div>
<p>To mitigate the risks of laboratories reporting erroneous susceptibility results, CLSI and EUCAST guidelines are also provided as “expert rules” in the previously mentioned AST devices, which helps to ensure compliance with guidelines and standards, increasing the quality of AMR data <sup>[25]</sup>.</p>
<p>Analysing AMR data, such as raw MICs and antimicrobial interpretations (‘RSI’), is tedious and complex, especially when evaluating cumulative AMR reports <sup>[26]</sup>. Nonetheless, it is essential to monitor up-and-coming AMR trends at the local and regional level to support clinical decision-making, infection control interventions, and AMR containment strategies <sup>[27,28]</sup>. AMR data analysis has been challenged by poor comparability of antimicrobial susceptibility statistics between institutions because of the diversity of calculation methods <sup>[26]</sup>. Moreover, many laboratories have used simplistic calculation approaches, with a strong tendency to overestimate drug resistance rates <sup>[26]</sup>. In the first ten years of this century, it was shown that this was primarily attributed to the lack of correction for duplicate isolates <sup>[29–31]</sup>.</p>
<p>In an attempt to overcome this, CLSI started in 2002 with developing guidelines to recommend epidemiologically sound workflows for the analysis and presentation of AMR results and trends, with their fourth and currently latest version released in 2014 <sup>[32]</sup>. These guidelines comprise advice on the inclusion of a minimum number of isolates, the choice of antimicrobial agents to analyse, and the presenting of numbers and percentages of AMR. In 2007, Hindler et al. evaluated the then-latest version of this guideline <sup>[26]</sup>. They concluded that although CLSI provided a comprehensive collection of suggestions, only a few publications had implemented these practical recommendations. Nevertheless, it continuously provides a theoretical basis for microbial epidemiological analyses but lacks suggestions of how these theoretical recommendations can be implemented practically or what kind of software would be suitable to analyse AMR data and, more specific, AMR data about multi-drug resistant organisms.</p>
<p>In an attempt to overcome this, CLSI started in 2002 with developing guidelines to recommend epidemiologically sound workflows for the analysis and presentation of AMR results and trends, with their fourth and currently latest version released in 2014 <sup>[32]</sup>. These guidelines comprise advice on the inclusion of a minimum number of isolates, the choice of antimicrobial agents to analyse, and the presenting of numbers and percentages of AMR. In 2007, Hindler <em>et al.</em> evaluated the then-latest version of this guideline <sup>[26]</sup>. They concluded that although CLSI provided a comprehensive collection of suggestions, only a few publications had implemented these practical recommendations. Nevertheless, it continuously provides a theoretical basis for microbial epidemiological analyses but lacks suggestions of how these theoretical recommendations can be implemented practically or what kind of software would be suitable to analyse AMR data and, more specific, AMR data about multi-drug resistant organisms.</p>
</div>
<div id="multi-drug-resistant-organisms" class="section level3" number="1.2.3">
<h3><span class="header-section-number">1.2.3</span> Multi-drug resistant organisms</h3>
@ -184,11 +213,11 @@ Figure 1.3: Interpretation of 100 random minimum inhibitory concentrations (top)
<div id="surveillance-programs" class="section level3" number="1.2.4">
<h3><span class="header-section-number">1.2.4</span> Surveillance programs</h3>
<p>With the current WHO surveillance program GLASS, the overall coverage of AMR is continuously being monitored for most countries of the world <sup>[37]</sup>. For Europe, the prevalence of AMR on the country level is monitored by national surveillance programs that share their data with the European Centre for Disease Prevention and Control (ECDC), an agency of the European Union <sup>[38]</sup>. Their surveillance program European Antimicrobial Resistance Surveillance Network (EARS-Net) is the largest publicly funded system for AMR surveillance in Europe. Public access to descriptive data (maps, graphs and tables) are available through the ECDC Surveillance Atlas of Infectious Diseases <sup>[38]</sup>, which was also consulted for multiple studies in this thesis. While the ECDC estimated in 2009 that bacterial infections caused by MDROs were responsible for 25,000 extra deaths per year <sup>[39]</sup>, others found that there is a large discrepancy between the real count of deaths attributable to MDROs and the subsequent alarmist predictions, based on data from over 500 studies <sup>[35]</sup>.</p>
<p>Although surveillance programs allow for signalling significant differences and shifts in AMR rates, additional AMR data analyses and AMR surveillance studies are strict requirements to fully understand the continuous development in AMR rates as there is no “ideal” surveillance system covering all aspects <sup>[28]</sup>. Nonetheless, the desire to continuously monitor, analyse, model and predict AMR, has led to the increased development and use of local, regional, national and international surveillance systems <sup>[27]</sup>. Critchley et al. have inventoried the requirement set by different types of users (Table 1).</p>
<p>Although surveillance programs allow for signalling significant differences and shifts in AMR rates, additional AMR data analyses and AMR surveillance studies are strict requirements to fully understand the continuous development in AMR rates as there is no “ideal” surveillance system covering all aspects <sup>[28]</sup>. Nonetheless, the desire to continuously monitor, analyse, model and predict AMR, has led to the increased development and use of local, regional, national and international surveillance systems <sup>[27]</sup>. Critchley <em>et al.</em> have inventoried the requirement set by different types of users (Table 1).</p>
<p>On the local level, clinical microbiology laboratories should communicate AMR surveillance data to healthcare providers in an understandable manner. Since MDROs can migrate between healthcare institutions, countries and continents by migrating people, local healthcare providers should be aware of local, regional, national and international surveillance program implementations and their ensuing results on AMR. On the other hand, such surveillance program implementations should be well-designed, well-maintained, longitudinal, and involve an appropriate collaboration with local laboratories over time <sup>[27]</sup>.</p>
<p><tbl>Table 1. Uses of antibiotic resistance surveillance system data by hospitals, university researchers, pharmaceutical companies and governments.</tbl></p>
<p><img src="images/01-t01.svg" width="100%" style="display: block; margin: auto;" /></p>
<p>From Critchley et al., 2004 <sup>[27]</sup>.</p>
<p><img src="images/01-t01.svg" width="100%" style="display: block; margin: auto;" />
From Critchley <em>et al.</em>, 2004 <sup>[27]</sup>.</p>
<p>As an example, ISIS-AR (Infectious disease Surveillance Information System for Antibiotic Resistance) is a Dutch national surveillance program, for which a large number of the Dutch clinical microbiology laboratories provide anonymised data on AMR to the National Institute for Public Health and the Environment (Rijksinstituut voor Volksgezondheid en Milieu, RIVM) <sup>[40]</sup>. In Germany, ARS (Antibiotic Resistance Surveillance) is a similar laboratory-based national surveillance program, that attempts to enable differential statements according to structural characteristics of health care and regions <sup>[41,42]</sup>. Both these national surveillance programs provide data for EARS-Net and GLASS of the WHO <sup>[37,43]</sup>.</p>
</div>
</div>
@ -196,7 +225,7 @@ Figure 1.3: Interpretation of 100 random minimum inhibitory concentrations (top)
<h2><span class="header-section-number">1.3</span> Data analysis using R</h2>
<p>In academia, the free and open-source statistical language R is an increasingly popular tool for analysing study results and developing new scientific methods, especially in medical fields such as human genetics, health decision sciences, and proteomics <sup>[44–47]</sup>. Even more so, a new type of study seems to currently arise where researchers from different medical fields publish tutorials on how to acquire new insights using R as a programming language <sup>[48–50]</sup>. In 2020, R ranked 8th in the TIOBE index, a global initiative to measure the popularity of programming languages, while it ranked 73rd in 2008 <sup>[51]</sup>.</p>
<p>R was developed for statistical computing and graphics supported by the R Foundation for Statistical Computing <sup>[52,53]</sup>. It is freely available under the GNU General Public License v2, meaning that it may be used for both private and commercial purposes in any way, but not for patent purposes. As a statistical package, it is comparable to the proprietary software programs Stata, SAS and SPSS <sup>[54]</sup>. However, as opposed to these proprietary software programs, R has an open file format and can read data from any source, including files from other software programs, and websites. Moreover, the ‘base’ functions of R are extendible by users who develop so-called packages for R. The Comprehensive R Archive Network (CRAN) that hosts and maintains R through the R Foundation for Statistical Computing, accepts package submissions from users and subjects users to a peer-review submission process and a strict repository policy <sup>[53,55]</sup>. As of May 2021, the CRAN package repository features 17,671 available packages.</p>
<p>Not only the popularity of using R has increased over the last decade. The number of developed packages has also increased strongly over the last years, especially since 2016 (Figure 4). This is probably attributed to a rather new integrated desktop environment (IDE) to use R, called RStudio <sup>[56]</sup>. RStudio is also the name of the corporation that developed the RStudio IDE and authored the so-called tidyverse, a collection of R packages (such as dplyr and ggplot2) that are specifically designed to ease data importing, tidying, manipulating, visualising, and programming, as well as to improve code reading <sup>[57–59]</sup>. The tidyverse can be used for most data analytical tasks and has been the method of choice for numerous (clinical) studies, including those presented in this thesis.</p>
<p>Not only the popularity of using R has increased over the last decade. The number of developed packages has also increased strongly over the last years, especially since 2016 (Figure <a href="introduction.html#fig:fig1-4">1.4</a>). This is probably attributed to a rather new integrated desktop environment (IDE) to use R, called RStudio <sup>[56]</sup>. RStudio is also the name of the corporation that developed the RStudio IDE and authored the so-called tidyverse, a collection of R packages (such as dplyr and ggplot2) that are specifically designed to ease data importing, tidying, manipulating, visualising, and programming, as well as to improve code reading <sup>[57–59]</sup>. The tidyverse can be used for most data analytical tasks and has been the method of choice for numerous (clinical) studies, including those presented in this thesis.</p>
<div class="figure" style="text-align: center"><span id="fig:fig1-4"></span>
<img src="images/01-04.svg" alt="The number of R packages by date of the last update over the last ten years. Every bar represents one month. Every R package occurs once in this figure." width="100%" />
<p class="caption">
@ -207,9 +236,9 @@ Figure 1.4: The number of R packages by date of the last update over the last te
</div>
<div id="setting-for-this-thesis" class="section level2" number="1.4">
<h2><span class="header-section-number">1.4</span> Setting for this thesis</h2>
<p>Studies within this thesis were geographically organised or initiated in the Northern cross-border region of the Netherlands and Germany, Figure 5. According to the German philosopher Liessmann, there are only national borders defined by humans, but no natural borders <sup>[67]</sup>. He explained that borders as man-made conventions are never absolute, but that it is always possible to cross them. Despite the existing territorial border, there are many similarities in the Netherlands and Germany today, but just as many and clear differences, especially concerning the healthcare sector. A German patient can become a patient in the Netherlands just as quickly as a Dutch patient can in Germany. Since pathogens know no borders, patient protection and infection prevention must not stop at borders <sup>[68]</sup>. The Netherlands and Germany have, among many other matters, apparent differences within the healthcare system in general and in terms of AMR, especially concerning MDRO definitions and infection prevention guidelines. To study these differences, INTERREG programs enable cross-border, transnational and interregional cooperation. INTERREG is one of the central instruments in European cohesion and regional policy, with which the development differences between the European countries in the border regions should be reduced and economic cohesion strengthened. It aims to ensure that national borders are not an obstacle to the balanced development and integration of the European territory <sup>[69]</sup>. One of its programs, EurHealth-1Health, was a large research project that aimed to facilitate working together in battling AMR and MDROs and to empower sustainable collaborations across the border.</p>
<p>Studies within this thesis were geographically organised or initiated in the Northern cross-border region of the Netherlands and Germany, Figure <a href="introduction.html#fig:fig1-5">1.5</a>. According to the German philosopher Liessmann, there are only national borders defined by humans, but no natural borders <sup>[67]</sup>. He explained that borders as man-made conventions are never absolute, but that it is always possible to cross them. Despite the existing territorial border, there are many similarities in the Netherlands and Germany today, but just as many and clear differences, especially concerning the healthcare sector. A German patient can become a patient in the Netherlands just as quickly as a Dutch patient can in Germany. Since pathogens know no borders, patient protection and infection prevention must not stop at borders <sup>[68]</sup>. The Netherlands and Germany have, among many other matters, apparent differences within the healthcare system in general and in terms of AMR, especially concerning MDRO definitions and infection prevention guidelines. To study these differences, INTERREG programs enable cross-border, transnational and interregional cooperation. INTERREG is one of the central instruments in European cohesion and regional policy, with which the development differences between the European countries in the border regions should be reduced and economic cohesion strengthened. It aims to ensure that national borders are not an obstacle to the balanced development and integration of the European territory <sup>[69]</sup>. One of its programs, EurHealth-1Health, was a large research project that aimed to facilitate working together in battling AMR and MDROs and to empower sustainable collaborations across the border.</p>
<div class="figure" style="text-align: center"><span id="fig:fig1-5"></span>
<img src="images/01-05.svg" alt="Geographic overview of three Euregio’s that make up most of the Dutch-German cross-border region." width="100%" />
<img src="images/01-05.jpg" alt="Geographic overview of three Euregio’s that make up most of the Dutch-German cross-border region." width="100%" />
<p class="caption">
Figure 1.5: Geographic overview of three Euregio’s that make up most of the Dutch-German cross-border region.
</p>
@ -239,7 +268,7 @@ Figure 1.5: Geographic overview of three Euregio’s that make up most of the Du
<li>O’Neill J. Antimicrobial Resistance: Tackling a Crisis for the Health and Wealth of Nations. Rev Antimicrob Resist 2014:1–16.</li>
<li>de Kraker MEA, Stewardson AJ, Harbarth S. Will 10 Million People Die a Year due to Antimicrobial Resistance by 2050? PLOS Med 2016;13:e1002184. <a href="doi:10.1371/journal.pmed.1002184" class="uri">doi:10.1371/journal.pmed.1002184</a>.</li>
<li>Centers for Disease Control and Prevention (CDC). AR Threats Report: Antibiotic Resistance Threats In The United States. 2019.</li>
<li>Humphries RM, Kircher S, Ferrell A, Krause KM, Malherbe R, Hsiung A, et al. The Continued Value of Disk Diffusion for Assessing Antimicrobial Susceptibility in Clinical Laboratories: Report from the Clinical and Laboratory Standards Institute Methods Development and Standardization Working Group. J Clin Microbiol 2018;56. <a href="doi:10.1128/JCM.00437-18" class="uri">doi:10.1128/JCM.00437-18</a>.</li>
<li>Humphries RM, Kircher S, Ferrell A, Krause KM, Malherbe R, Hsiung A, <em>et al.</em> The Continued Value of Disk Diffusion for Assessing Antimicrobial Susceptibility in Clinical Laboratories: Report from the Clinical and Laboratory Standards Institute Methods Development and Standardization Working Group. J Clin Microbiol 2018;56. <a href="doi:10.1128/JCM.00437-18" class="uri">doi:10.1128/JCM.00437-18</a>.</li>
<li>Wheat PF. History and development of antimicrobial susceptibility testing methodology. J Antimicrob Chemother 2001;48:1–4. <a href="doi:10.1093/jac/48.suppl_1.1" class="uri">doi:10.1093/jac/48.suppl_1.1</a>.</li>
<li>Bauer AW, Kirby WMM, Sherris JC, Turck M. Antibiotic Susceptibility Testing by a Standardized Single Disk Method. Am J Clin Pathol 1966;45:493–6. <a href="doi:10.1093/ajcp/45.4_ts.493" class="uri">doi:10.1093/ajcp/45.4_ts.493</a>.</li>
<li>BAUER AW. Single-Disk Antibiotic-Sensitivity Testing of Staphylococci. AMA Arch Intern Med 1959;104:208. <a href="doi:10.1001/archinte.1959.00270080034004" class="uri">doi:10.1001/archinte.1959.00270080034004</a>.</li>
@ -248,7 +277,7 @@ Figure 1.5: Geographic overview of three Euregio’s that make up most of the Du
<li>Stürenburg E, Sobottka I, Feucht H-H, Mack D, Laufs R. Comparison of BDPhoenix and VITEK2 automated antimicrobial susceptibility test systems for extended-spectrum beta-lactamase detection in Escherichia coli and Klebsiella species clinical isolates. Diagn Microbiol Infect Dis 2003;45:29–34. <a href="doi:10.1016/S0732-8893(02)00481-9" class="uri">doi:10.1016/S0732-8893(02)00481-9</a>.</li>
<li>Clinical and Laboratory Standards Institute (CLSI) 2021. <a href="https://clsi.org" class="uri">https://clsi.org</a> (accessed March 21, 2021).</li>
<li>European Committee on Antimicrobial Susceptibility Testing (EUCAST) 2021. <a href="https://eucast.org" class="uri">https://eucast.org</a> (accessed March 21, 2021).</li>
<li>Brown D, Cantón R, Dubreuil L, Gatermann S, Giske C, MacGowan A, et al. Widespread implementation of EUCAST breakpoints for antibacterial susceptibility testing in Europe. Eurosurveillance 2015;20. <a href="doi:10.2807/1560-7917.ES2015.20.2.21008" class="uri">doi:10.2807/1560-7917.ES2015.20.2.21008</a>.</li>
<li>Brown D, Cantón R, Dubreuil L, Gatermann S, Giske C, MacGowan A, <em>et al.</em> Widespread implementation of EUCAST breakpoints for antibacterial susceptibility testing in Europe. Eurosurveillance 2015;20. <a href="doi:10.2807/1560-7917.ES2015.20.2.21008" class="uri">doi:10.2807/1560-7917.ES2015.20.2.21008</a>.</li>
<li>European Centre for Disease Prevention and Control. Antimicrobial resistance in the EU/EEA (EARS-Net): Annual Epidemiological Report for 2019. 2019.</li>
<li>World Health Organization. Global Antimicrobial Resistance Surveillance System (GLASS) Report: Early Implementation 2017-2018. 2018.</li>
<li>EUCAST. Area of Technical Uncertainty (ATU) in antimicrobial susceptibility testing 2019. <a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Disk_test_documents/ATU/Area_of_Technical_Uncertainty_-_guidance_2019.pdf" class="uri">https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Disk_test_documents/ATU/Area_of_Technical_Uncertainty_-_guidance_2019.pdf</a> (accessed July 7, 2021).</li>
@ -256,47 +285,47 @@ Figure 1.5: Geographic overview of three Euregio’s that make up most of the Du
<li>Clinical and Laboratory Standards Institute. Performance standards for antimicrobial susceptibility testing; approved standard - 28th ed M100. Wayne (Pennsylvania): 2018.</li>
<li>Hindler JF, Stelling J. Analysis and Presentation of Cumulative Antibiograms: A New Consensus Guideline from the Clinical and Laboratory Standards Institute. Clin Infect Dis 2007;44:867–73. <a href="doi:10.1086/511864" class="uri">doi:10.1086/511864</a>.</li>
<li>Critchley IA, Karlowsky JA. Optimal use of antibiotic resistance surveillance systems. Clin Microbiol Infect 2004;10:502–11. <a href="doi:10.1111/j.1469-0691.2004.00911.x" class="uri">doi:10.1111/j.1469-0691.2004.00911.x</a>.</li>
<li>Bax R, Bywater R, Cornaglia G, Goossens H, Hunter P, Isham V, et al. Surveillance of antimicrobial resistance — what, how and whither? Clin Microbiol Infect 2001;7:316–25. <a href="doi:10.1046/j.1198-743x.2001.00239.x" class="uri">doi:10.1046/j.1198-743x.2001.00239.x</a>.</li>
<li>Bax R, Bywater R, Cornaglia G, Goossens H, Hunter P, Isham V, <em>et al.</em> Surveillance of antimicrobial resistance — what, how and whither? Clin Microbiol Infect 2001;7:316–25. <a href="doi:10.1046/j.1198-743x.2001.00239.x" class="uri">doi:10.1046/j.1198-743x.2001.00239.x</a>.</li>
<li>Bosso JA, Mauldin PD, Steed LL. Consequences of Combining Cystic Fibrosis— and Non-Cystic Fibrosis-Derived Pseudomonas aeruginosa Antibiotic Susceptibility Results in Hospital Antibiograms. Ann Pharmacother 2006;40:1946–9. <a href="doi:10.1345/aph.1H377" class="uri">doi:10.1345/aph.1H377</a>.</li>
<li>Horvat RT, Klutman NE, Lacy MK, Grauer D, Wilson M. Effect of Duplicate Isolates of Methicillin-Susceptible and Methicillin-Resistant Staphylococcus aureus on Antibiogram Data. J Clin Microbiol 2003;41:4611–6. <a href="doi:10.1128/JCM.41.10.4611-4616.2003" class="uri">doi:10.1128/JCM.41.10.4611-4616.2003</a>.</li>
<li>Cebrián L, Rodríguez JC, Escribano I, Cascales E, López-Lozano JM, Royo G. Influence of various criteria for elimination of duplicates when calculating the prevalence and antibiotic susceptibility of microorganisms associated with urinary infections. Int J Antimicrob Agents 2005;25:173–6. <a href="doi:10.1016/j.ijantimicag.2004.09.017" class="uri">doi:10.1016/j.ijantimicag.2004.09.017</a>.</li>
<li>Clinical and Laboratory Standards Institute (CLSI). M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. 2014.</li>
<li>World Health Organization. Antimicrobial resistance 2021. <a href="https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance" class="uri">https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance</a> (accessed March 24, 2021).</li>
<li>Magiorakos A-P, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect 2012;18:268–81. <a href="doi:10.1111/j.1469-0691.2011.03570.x" class="uri">doi:10.1111/j.1469-0691.2011.03570.x</a>.</li>
<li>Magiorakos A-P, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, <em>et al.</em> Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect 2012;18:268–81. <a href="doi:10.1111/j.1469-0691.2011.03570.x" class="uri">doi:10.1111/j.1469-0691.2011.03570.x</a>.</li>
<li>Abat C, Fournier P-E, Jimeno M-T, Rolain J-M, Raoult D. Extremely and pandrug-resistant bacteria extra-deaths: myth or reality? Eur J Clin Microbiol Infect Dis 2018;37:1687–97. <a href="doi:10.1007/s10096-018-3300-0" class="uri">doi:10.1007/s10096-018-3300-0</a>.</li>
<li>World Health Organization. Antimicrobial resistance: global report on surveillance. 2014.</li>
<li>World Health Organization. Global Antimicrobial Resistance Surveillance System (GLASS) 2021. <a href="https://www.who.int/glass/en/" class="uri">https://www.who.int/glass/en/</a> (accessed March 25, 2021).</li>
<li>European Centre for Disease Prevention and Control. ECDC Surveillance Atlas of Infectious Diseases 2021. <a href="http://atlas.ecdc.europa.eu" class="uri">http://atlas.ecdc.europa.eu</a> (accessed March 24, 2021).</li>
<li>European Centre for Disease Prevention and Control, European Medicines Agency. The bacterial challenge: time to react (Joint Technical Report, 2009). 2009.</li>
<li>Rijksinstituut voor Volksgezondheid en Milieu. Infectieziekten Surveillance Informatie Systeem-Antibiotica Resistentie (ISIS-AR) 2021. <a href="https://www.rivm.nl/isis-ar" class="uri">https://www.rivm.nl/isis-ar</a> (accessed March 25, 2021).</li>
<li>Schweickert B, Noll I, Feig M, Claus H, Krause G, Velasco E, et al. MRSA-surveillance in Germany: data from the Antibiotic Resistance Surveillance System (ARS) and the mandatory surveillance of MRSA in blood. Eur J Clin Microbiol Infect Dis 2012;31:1855–65. <a href="doi:10.1007/s10096-011-1511-8" class="uri">doi:10.1007/s10096-011-1511-8</a>.</li>
<li>Schweickert B, Noll I, Feig M, Claus H, Krause G, Velasco E, <em>et al.</em> MRSA-surveillance in Germany: data from the Antibiotic Resistance Surveillance System (ARS) and the mandatory surveillance of MRSA in blood. Eur J Clin Microbiol Infect Dis 2012;31:1855–65. <a href="doi:10.1007/s10096-011-1511-8" class="uri">doi:10.1007/s10096-011-1511-8</a>.</li>
<li>Robert Koch Institut. Antibiotika-Resistenz-Surveillance (ARS) 2021. <a href="https://ars.rki.de" class="uri">https://ars.rki.de</a> (accessed March 25, 2021).</li>
<li>European Centre for Disease Prevention and Control. European Antimicrobial Resistance Surveillance Network (EARS-Net) 2021. <a href="https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/ears-net" class="uri">https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/ears-net</a> (accessed March 25, 2021).</li>
<li>Tippmann S. Programming tools: Adventures with R. Nature 2015;517:109–10. <a href="doi:10.1038/517109a" class="uri">doi:10.1038/517109a</a>.</li>
<li>Jalal H, Pechlivanoglou P, Krijkamp E, Alarid-Escudero F, Enns E, Hunink MGM. An Overview of R in Health Decision Sciences. Med Decis Mak 2017;37:735–46. <a href="doi:10.1177/0272989X16686559" class="uri">doi:10.1177/0272989X16686559</a>.</li>
<li>Gatto L, Christoforou A. Using R and Bioconductor for proteomics data analysis. Biochim Biophys Acta - Proteins Proteomics 2014;1844:42–51. <a href="doi:10.1016/j.bbapap.2013.04.032" class="uri">doi:10.1016/j.bbapap.2013.04.032</a>.</li>
<li>Chan BKC. Data Analysis Using R Programming. Adv. Exp. Med. Biol., vol. 1082, 2018, p. 47–122. <a href="doi:10.1007/978-3-319-93791-5_2" class="uri">doi:10.1007/978-3-319-93791-5_2</a>.</li>
<li>Herber R, Kaiser A, Grählert X, Range U, Raiskup F, Pillunat LE, et al. Statistische Auswertung korrelierter Messdaten in der Augenheilkunde. Der Ophthalmol 2020;117:27–35. <a href="doi:10.1007/s00347-019-0904-4" class="uri">doi:10.1007/s00347-019-0904-4</a>.</li>
<li>Herber R, Kaiser A, Grählert X, Range U, Raiskup F, Pillunat LE, <em>et al.</em> Statistische Auswertung korrelierter Messdaten in der Augenheilkunde. Der Ophthalmol 2020;117:27–35. <a href="doi:10.1007/s00347-019-0904-4" class="uri">doi:10.1007/s00347-019-0904-4</a>.</li>
<li>Balduzzi S, Rücker G, Schwarzer G. How to perform a meta-analysis with R: a practical tutorial. Evid Based Ment Heal 2019;22:153–60. <a href="doi:10.1136/ebmental-2019-300117" class="uri">doi:10.1136/ebmental-2019-300117</a>.</li>
<li>Muschelli J, Gherman A, Fortin J-P, Avants B, Whitcher B, Clayden JD, et al. Neuroconductor: an R platform for medical imaging analysis. Biostatistics 2019;20:218–39. <a href="doi:10.1093/biostatistics/kxx068" class="uri">doi:10.1093/biostatistics/kxx068</a>.</li>
<li>Muschelli J, Gherman A, Fortin J-P, Avants B, Whitcher B, Clayden JD, <em>et al.</em> Neuroconductor: an R platform for medical imaging analysis. Biostatistics 2019;20:218–39. <a href="doi:10.1093/biostatistics/kxx068" class="uri">doi:10.1093/biostatistics/kxx068</a>.</li>
<li>The Software Quality Company. TIOBE Index 2020. <a href="https://www.tiobe.com/tiobe-index/" class="uri">https://www.tiobe.com/tiobe-index/</a>.</li>
<li>The Comprehensive R Archive Network, Hornik K. What is the R Foundation? 2017. <a href="https://cran.r-project.org/doc/FAQ/R-FAQ.html#What-is-the-R-Foundation_003f" class="uri">https://cran.r-project.org/doc/FAQ/R-FAQ.html#What-is-the-R-Foundation_003f</a> (accessed August 6, 2018).</li>
<li>The Comprehensive R Archive Network, Hornik K. What is R? 2017. <a href="https://cran.r-project.org/doc/FAQ/R-FAQ.html#What-is-R_003f" class="uri">https://cran.r-project.org/doc/FAQ/R-FAQ.html#What-is-R_003f</a> (accessed August 6, 2018).</li>
<li>Burns P. R Relative to Statistical Packages: Comment 1 on Technical Report Number 1 (Version 1.0) Strategically using General Purpose Statistics Packages: A Look at Stata, SAS and SPSS. Stat Consult Gr UCLA Acad Technol Serv 2006.</li>
<li>The Comprehensive R Archive Network. CRAN Repository Policy 2021. <a href="https://cran.r-project.org/web/packages/policies.html" class="uri">https://cran.r-project.org/web/packages/policies.html</a> (accessed March 25, 2021).</li>
<li>RStudio PBC. RStudio 2021. <a href="https://www.rstudio.com/products/rstudio/" class="uri">https://www.rstudio.com/products/rstudio/</a> (accessed March 25, 2021).</li>
<li>Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. J Open Source Softw 2019;4:1686. <a href="doi:10.21105/joss.01686" class="uri">doi:10.21105/joss.01686</a>.</li>
<li>Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, <em>et al.</em> Welcome to the Tidyverse. J Open Source Softw 2019;4:1686. <a href="doi:10.21105/joss.01686" class="uri">doi:10.21105/joss.01686</a>.</li>
<li>Wickham H, François R, Henry L, Müller K. dplyr: A Grammar of Data Manipulation 2018.</li>
<li>Wickham H, Winston C, RStudio. ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. CRAN 2016.</li>
<li>Petzoldt T. antibioticR: Analysis of Antibiotic Resistance Data 2021. <a href="https://github.com/tpetzoldt/antibioticR" class="uri">https://github.com/tpetzoldt/antibioticR</a> (accessed April 19, 2021).</li>
<li>Kamvar ZN, Cai J, Pulliam JRC, Schumacher J, Jombart T. Epidemic curves made easy using the R package incidence. F1000Research 2019;8:139. <a href="doi:10.12688/f1000research.18002.1" class="uri">doi:10.12688/f1000research.18002.1</a>.</li>
<li>Jombart T, Kamvar ZN, Cai J, Pulliam J, Chisholm S, FitzJohn R, et al. reconhub/incidence: Incidence version 1.6.0 2019. <a href="doi:10.5281/zenodo.2584018" class="uri">doi:10.5281/zenodo.2584018</a>.</li>
<li>Jombart T, Kamvar ZN, Cai J, Pulliam J, Chisholm S, FitzJohn R, <em>et al.</em> reconhub/incidence: Incidence version 1.6.0 2019. <a href="doi:10.5281/zenodo.2584018" class="uri">doi:10.5281/zenodo.2584018</a>.</li>
<li>Nagraj VP, Jombart T, Randhawa N, Sudre B, Campbell F, Crellen T. epicontacts: Handling, Visualisation and Analysis of Epidemiological Contacts 2017.</li>
<li>Cori A. EpiEstim: Estimate Time Varying Reproduction Numbers from Epidemic Curves 2021.</li>
<li>Cori A, Ferguson NM, Fraser C, Cauchemez S. A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics. Am J Epidemiol 2013;178:1505–12. <a href="doi:10.1093/aje/kwt133" class="uri">doi:10.1093/aje/kwt133</a>.</li>
<li>Rijksinstituut voor Volksgezondheid en Milieu. Rekenmodellen openbaar en toegankelijk 2021. <a href="https://www.rivm.nl/coronavirus-covid-19/hoe-berekeningen-bijdragen-aan-bestrijding-van-virus/rekenmodellen" class="uri">https://www.rivm.nl/coronavirus-covid-19/hoe-berekeningen-bijdragen-aan-bestrijding-van-virus/rekenmodellen</a> (accessed March 25, 2021).</li>
<li>Link O. “Ohne Grenzen könnten wir nicht leben”: Konrad Paul Liessmann im Interview 2013. <a href="https://www.brandeins.de/magazine/brand-eins-wirtschaftsmagazin/2013/grenzen/ohne-grenzen-koennten-wir-nicht-leben" class="uri">https://www.brandeins.de/magazine/brand-eins-wirtschaftsmagazin/2013/grenzen/ohne-grenzen-koennten-wir-nicht-leben</a> (accessed April 19, 2021).</li>
<li>Glasner C, Rocker D, Köck R, Pulz M, Jurke A, Smollich M, et al. Deutschland – Niederlande: Grenzenloser Schutz der Gesundheit. Umweltmed - Hyg - Arbeitsmed 2017;33:313–23.</li>
<li>Glasner C, Rocker D, Köck R, Pulz M, Jurke A, Smollich M, <em>et al.</em> Deutschland – Niederlande: Grenzenloser Schutz der Gesundheit. Umweltmed - Hyg - Arbeitsmed 2017;33:313–23.</li>
<li>European Commission. Budget of the European Cohesion policy 2020. <a href="http://ec.europa.eu/regional_policy/en/funding/available-budget/" class="uri">http://ec.europa.eu/regional_policy/en/funding/available-budget/</a> (accessed March 31, 2021).</li>
<li>Statista. Anzahl der Krankenhäuser in Deutschland 2018. <a href="https://de.statista.com/statistik/daten/studie/2617/umfrage/anzahl-der-krankenhaeuser-in-deutschland-seit-2000/" class="uri">https://de.statista.com/statistik/daten/studie/2617/umfrage/anzahl-der-krankenhaeuser-in-deutschland-seit-2000/</a> (accessed March 31, 2021).</li>
<li>Statistisches Bundesamt. Bevölkerung nach Nationalität und Geschlecht 2018. <a href="https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Bevoelkerungsstand/Tabellen/zensus-geschlecht-staatsangehoerigkeit-2018.html" class="uri">https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Bevoelkerungsstand/Tabellen/zensus-geschlecht-staatsangehoerigkeit-2018.html</a> (accessed March 31, 2021).</li>
@ -311,8 +340,8 @@ Figure 1.5: Geographic overview of three Euregio’s that make up most of the Du
</div>
</div>
</div>
<a href="contents.html" class="navigation navigation-prev navigation-unique" aria-label="Previous page"><i class="fa fa-angle-left"></i></a>
<a href="contents.html" class="navigation navigation-prev " aria-label="Previous page"><i class="fa fa-angle-left"></i></a>
<a href="diagnostic-stewardship.html" class="navigation navigation-next " aria-label="Next page"><i class="fa fa-angle-right"></i></a>
</div>
</div>
<script src="libs/gitbook-2.6.7/js/app.min.js"></script>