<metaname="description"content="This is the integral PhD thesis ‘A New Instrument for Microbial Epidemiology’ (DOI 10.33612/diss.177417131) by Matthijs S. Berends, which was defended publicly at the University of Groningen, the Netherlands, on 25 August 2021."/>
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<metaproperty="og:description"content="This is the integral PhD thesis ‘A New Instrument for Microbial Epidemiology’ (DOI 10.33612/diss.177417131) by Matthijs S. Berends, which was defended publicly at the University of Groningen, the Netherlands, on 25 August 2021."/>
<metaname="twitter:description"content="This is the integral PhD thesis ‘A New Instrument for Microbial Epidemiology’ (DOI 10.33612/diss.177417131) by Matthijs S. Berends, which was defended publicly at the University of Groningen, the Netherlands, on 25 August 2021."/>
<liclass="chapter"data-level="1"data-path="ch01-introduction.html"><ahref="ch01-introduction.html"><iclass="fa fa-check"></i><b>1</b> General Introduction</a>
<liclass="chapter"data-level="1.2.2"data-path="ch01-introduction.html"><ahref="ch01-introduction.html#interpretation-of-raw-results"><iclass="fa fa-check"></i><b>1.2.2</b> Interpretation of raw results</a></li>
<liclass="chapter"data-level="1.3"data-path="ch01-introduction.html"><ahref="ch01-introduction.html#data-analysis-using-r"><iclass="fa fa-check"></i><b>1.3</b> Data analysis using R</a></li>
<liclass="chapter"data-level="1.4"data-path="ch01-introduction.html"><ahref="ch01-introduction.html#setting-for-this-thesis"><iclass="fa fa-check"></i><b>1.4</b> Setting for this thesis</a></li>
<liclass="chapter"data-level="1.5"data-path="ch01-introduction.html"><ahref="ch01-introduction.html#aim-of-this-thesis-and-introduction-to-its-chapters"><iclass="fa fa-check"></i><b>1.5</b> Aim of this thesis and introduction to its chapters</a></li>
<liclass="chapter"data-level="2"data-path="ch02-diagnostic-stewardship.html"><ahref="ch02-diagnostic-stewardship.html"><iclass="fa fa-check"></i><b>2</b> Diagnostic Stewardship: Sense or Nonsense?!</a>
<liclass="chapter"data-level="2.1.1"data-path="ch02-diagnostic-stewardship.html"><ahref="ch02-diagnostic-stewardship.html#case-1"><iclass="fa fa-check"></i><b>2.1.1</b> Case 1</a></li>
<liclass="chapter"data-level="2.1.2"data-path="ch02-diagnostic-stewardship.html"><ahref="ch02-diagnostic-stewardship.html#case-2"><iclass="fa fa-check"></i><b>2.1.2</b> Case 2</a></li>
<liclass="chapter"data-level="2.2"data-path="ch02-diagnostic-stewardship.html"><ahref="ch02-diagnostic-stewardship.html#the-general-concept"><iclass="fa fa-check"></i><b>2.2</b> The general concept</a>
<liclass="chapter"data-level="2.2.2"data-path="ch02-diagnostic-stewardship.html"><ahref="ch02-diagnostic-stewardship.html#dsp-in-the-microbiological-laboratory"><iclass="fa fa-check"></i><b>2.2.2</b> DSP in the microbiological laboratory</a></li>
<liclass="chapter"data-level="2.2.3"data-path="ch02-diagnostic-stewardship.html"><ahref="ch02-diagnostic-stewardship.html#dsp-as-process-optimisation"><iclass="fa fa-check"></i><b>2.2.3</b> DSP as process optimisation</a></li>
<liclass="chapter"data-level="2.2.4"data-path="ch02-diagnostic-stewardship.html"><ahref="ch02-diagnostic-stewardship.html#multidisciplinary-aspects-of-dsp-and-infection-management"><iclass="fa fa-check"></i><b>2.2.4</b> Multidisciplinary aspects of DSP and infection management</a></li>
<liclass="chapter"data-level="3"data-path="ch03-introducing-new-method.html"><ahref="ch03-introducing-new-method.html"><iclass="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>
<liclass="chapter"data-level="3.2"data-path="ch03-introducing-new-method.html"><ahref="ch03-introducing-new-method.html#standardising-amr-data-analysis"><iclass="fa fa-check"></i><b>3.2</b> Standardising AMR data analysis</a></li>
<liclass="chapter"data-level="3.3"data-path="ch03-introducing-new-method.html"><ahref="ch03-introducing-new-method.html#comparison-with-existing-software-methods"><iclass="fa fa-check"></i><b>3.3</b> Comparison with existing software methods</a></li>
<liclass="chapter"data-level="3.4"data-path="ch03-introducing-new-method.html"><ahref="ch03-introducing-new-method.html#user-feedback"><iclass="fa fa-check"></i><b>3.4</b> User feedback</a></li>
<liclass="chapter"data-level="4"data-path="ch04-amr-r-package.html"><ahref="ch04-amr-r-package.html"><iclass="fa fa-check"></i><b>4</b><code>AMR</code> - An <code>R</code> Package for Working with Antimicrobial Resistance Data</a>
<liclass="chapter"data-level="4.3.1"data-path="ch04-amr-r-package.html"><ahref="ch04-amr-r-package.html#working-with-taxonomically-valid-microorganism-names"><iclass="fa fa-check"></i><b>4.3.1</b> Working with taxonomically valid microorganism names</a></li>
<liclass="chapter"data-level="4.3.2"data-path="ch04-amr-r-package.html"><ahref="ch04-amr-r-package.html#working-with-antimicrobial-names-or-codes"><iclass="fa fa-check"></i><b>4.3.2</b> Working with antimicrobial names or codes</a></li>
<liclass="chapter"data-level="4.3.3"data-path="ch04-amr-r-package.html"><ahref="ch04-amr-r-package.html#working-with-antimicrobial-susceptibility-test-results"><iclass="fa fa-check"></i><b>4.3.3</b> Working with antimicrobial susceptibility test results</a></li>
<liclass="chapter"data-level="4.3.4"data-path="ch04-amr-r-package.html"><ahref="ch04-amr-r-package.html#interpretative-rules-by-eucast"><iclass="fa fa-check"></i><b>4.3.4</b> Interpretative rules by EUCAST</a></li>
<liclass="chapter"data-level="4.3.5"data-path="ch04-amr-r-package.html"><ahref="ch04-amr-r-package.html#working-with-defined-daily-doses-ddd"><iclass="fa fa-check"></i><b>4.3.5</b> Working with defined daily doses (DDD)</a></li>
<liclass="chapter"data-level="4.4.1"data-path="ch04-amr-r-package.html"><ahref="ch04-amr-r-package.html#determining-first-isolates"><iclass="fa fa-check"></i><b>4.4.1</b> Determining first isolates</a></li>
<liclass="chapter"data-level=""data-path="ch04-amr-r-package.html"><ahref="ch04-amr-r-package.html#appendix-a-included-data-sets"><iclass="fa fa-check"></i>Appendix A: Included Data Sets</a></li>
</ul></li>
<liclass="chapter"data-level="5"data-path="ch05-radar.html"><ahref="ch05-radar.html"><iclass="fa fa-check"></i><b>5</b> Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR): Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship</a>
<liclass="chapter"data-level="5.3.3"data-path="ch05-radar.html"><ahref="ch05-radar.html#development-process"><iclass="fa fa-check"></i><b>5.3.3</b> Development Process</a></li>
<liclass="chapter"data-level="5.4.1"data-path="ch05-radar.html"><ahref="ch05-radar.html#principal-findings"><iclass="fa fa-check"></i><b>5.4.1</b> Principal Findings</a></li>
<liclass="chapter"data-level=""data-path="ch05-radar.html"><ahref="ch05-radar.html#conflicts-of-interests"><iclass="fa fa-check"></i>Conflicts of interests</a></li>
<liclass="chapter"data-level="6"data-path="ch06-radar2.html"><ahref="ch06-radar2.html"><iclass="fa fa-check"></i><b>6</b> Better Antimicrobial Resistance Data Analysis and Reporting in Less Time</a>
<liclass="chapter"data-level="6.2.1"data-path="ch06-radar2.html"><ahref="ch06-radar2.html#study-setup"><iclass="fa fa-check"></i><b>6.2.1</b> Study setup</a></li>
<liclass="chapter"data-level="6.2.2"data-path="ch06-radar2.html"><ahref="ch06-radar2.html#amr-data"><iclass="fa fa-check"></i><b>6.2.2</b> AMR data</a></li>
<liclass="chapter"data-level="6.2.3"data-path="ch06-radar2.html"><ahref="ch06-radar2.html#amr-data-analysis-and-reporting"><iclass="fa fa-check"></i><b>6.2.3</b> AMR data analysis and reporting</a></li>
<liclass="chapter"data-level="6.2.4"data-path="ch06-radar2.html"><ahref="ch06-radar2.html#study-participants"><iclass="fa fa-check"></i><b>6.2.4</b> Study participants</a></li>
<liclass="chapter"data-level="6.2.5"data-path="ch06-radar2.html"><ahref="ch06-radar2.html#study-execution-and-data"><iclass="fa fa-check"></i><b>6.2.5</b> Study execution and data</a></li>
<liclass="chapter"data-level="6.2.6"data-path="ch06-radar2.html"><ahref="ch06-radar2.html#evaluation-and-study-data-analysis"><iclass="fa fa-check"></i><b>6.2.6</b> Evaluation and study data analysis</a></li>
<liclass="chapter"data-level="6.3.1"data-path="ch06-radar2.html"><ahref="ch06-radar2.html#study-participants-1"><iclass="fa fa-check"></i><b>6.3.1</b> Study participants</a></li>
<liclass="chapter"data-level="6.3.2"data-path="ch06-radar2.html"><ahref="ch06-radar2.html#effectiveness-and-accuracy"><iclass="fa fa-check"></i><b>6.3.2</b> Effectiveness and accuracy</a></li>
<liclass="chapter"data-level=""data-path="ch06-radar2.html"><ahref="ch06-radar2.html#conflict-of-interest"><iclass="fa fa-check"></i>Conflict of interest</a></li>
<liclass="chapter"data-level="7"data-path="ch07-cons.html"><ahref="ch07-cons.html"><iclass="fa fa-check"></i><b>7</b> Trends in Occurrence and Phenotypic Resistance of Coagulase-Negative Staphylococci (CoNS) Found in Blood in the Northern Netherlands between 2013 and 2019</a>
<liclass="chapter"data-level="7.3"data-path="ch07-cons.html"><ahref="ch07-cons.html#materials-methods"><iclass="fa fa-check"></i><b>7.3</b> Materials & methods</a>
<ul>
<liclass="chapter"data-level="7.3.1"data-path="ch07-cons.html"><ahref="ch07-cons.html#study-setting-and-patient-cohort"><iclass="fa fa-check"></i><b>7.3.1</b> Study setting and patient cohort</a></li>
<liclass="chapter"data-level="7.3.2"data-path="ch07-cons.html"><ahref="ch07-cons.html#microbiological-and-demographic-data"><iclass="fa fa-check"></i><b>7.3.2</b> Microbiological and demographic data</a></li>
<liclass="chapter"data-level="7.3.3"data-path="ch07-cons.html"><ahref="ch07-cons.html#species-determination-and-antibiotic-susceptibility-testing-ast"><iclass="fa fa-check"></i><b>7.3.3</b> Species determination and antibiotic susceptibility testing (AST)</a></li>
<liclass="chapter"data-level="7.3.4"data-path="ch07-cons.html"><ahref="ch07-cons.html#selection-of-bacterial-isolates"><iclass="fa fa-check"></i><b>7.3.4</b> Selection of bacterial isolates</a></li>
<liclass="chapter"data-level="7.3.5"data-path="ch07-cons.html"><ahref="ch07-cons.html#eucast-rules-and-antibiotic-resistance-analysis"><iclass="fa fa-check"></i><b>7.3.5</b> EUCAST rules and antibiotic resistance analysis</a></li>
<liclass="chapter"data-level="7.4.1"data-path="ch07-cons.html"><ahref="ch07-cons.html#patients-and-included-isolates"><iclass="fa fa-check"></i><b>7.4.1</b> Patients and included isolates</a></li>
<liclass="chapter"data-level="7.4.2"data-path="ch07-cons.html"><ahref="ch07-cons.html#occurrence-of-cons-species"><iclass="fa fa-check"></i><b>7.4.2</b> Occurrence of CoNS species</a></li>
<liclass="chapter"data-level="7.4.3"data-path="ch07-cons.html"><ahref="ch07-cons.html#definition-of-cons-persistence"><iclass="fa fa-check"></i><b>7.4.3</b> Definition of CoNS persistence</a></li>
<p>This is the integral PhD thesis ‘A New Instrument for Microbial Epidemiology’ (DOI <ahref="https://doi.org/10.33612/diss.177417131">10.33612/diss.177417131</a>) by <ahref="https://www.rug.nl/staff/m.s.berends">Matthijs S. Berends</a>, which was defended publicly at the University of Groningen, the Netherlands, on 25 August 2021.</p>
<p>All texts were copied from the printed version ‘as is’; no modifications were made.</p>
<p>Treating infectious diseases requires insights into the microorganisms causing infectious diseases. Antimicrobial resistance (AMR) in microorganisms limits treatment possibilities and poses an enormous healthcare problem worldwide. The spread and AMR patterns of microorganisms, risk factors for infection, and preventive and control measures of infectious disease are studied within the field of Microbial Epidemiology, a cross-over field between Epidemiology and Clinical Microbiology. For analysing the spread and AMR patterns of microorganisms, however, no standardised method previously existed. This thesis showcases the development and applied use of a new instrument to analyse AMR data: the AMR package for R. From multiple viewpoints, the AMR package and its advantages are put into perspective: from a technical viewpoint, from an infection management viewpoint and from a clinical viewpoint. These combined provide a common ground for comprehending what the AMR package could yield in the field and how it can set a new empowered starting point for future applications of microbial epidemiology, in clinical and research settings alike. This thesis subsequently elaborates on these multiple viewpoints by illustrating the use of this new instrument in epidemiological research projects in the Dutch-German cross-border region to better understand the occurrence and AMR patterns of microorganisms on a (eu)regional level. In conclusion, this thesis shows the added value of a consistent data-analytical instrument to prepare and analyse AMR data in a full-region approach, that can also be used in clinical settings to obtain novel insights on AMR patterns.</p>
