A Prediction of Antibiotic Resistance with Regard to Urinary and Respiratory Tract Infections

Abstract: In this project we set out to find when the resistance level against first line antibiotics would reach 20%. This was executed by first defining relevant bacteria and antibiotics for urinary and respiratory tract infections (UTI's, RTI's). The data was collected from the European Center for Disease Control (ECDC) and the Center for Disease Dynamics, Economics & Policy (CDDEP). The data included the level of resistance for specific years for countries in Europe, as well as for the USA. A prediction model was made using the programming language R. A linear model was used to make a five and ten year prediction. The accuracy was tested. The results were then visualized using R and MATLAB. The results show a big variation between different bacteria and antibiotic combinations. For the two E. coli combinations the resistance is already near 20% for many countries and the resistance is increasing. For the three K. pneumoniae combinations the resistance is high in Southern Europe, meaning many countries have reached or are near 20%. For the two P. aeruginosa combinations there is also a higher resistance in Southern Europe but the resistance is decreasing in most countries. The resistance for E. faecalis is also decreasing and is generally very low in all of Europe. For the only RTI relevant combination, S. pneumoniae and penicillins, the resistance is low and many countries except for Sweden show a decrease in resistance. The USA did not have data for the same time span as Europe and was therefore analyzed separately. For many combinations the USA are near the 20% limit. Only for two combinations the USA showed a decrease in resistance level, and for one of those combinations the prediction is too uncertain to make any assumptions about. For the USA there were two more combinations for RTI than for Europe. For the S. pneumoniae and penicillins combination they have, just as most of Europe, a decreasing resistance. The two combinations with Acinetobacter spp. have a high resistance that is increasing. The main challenge during this project was finding relevant data with a long timespan and with high certainty. The data found is based on invasive isolates which means that the disease which the samples are taken from is not known. The timespan and the certainty of the data affected the accuracy of the prediction model and how long period that could be predicted. The prediction model generated 202 predictions that were visualized. An ethical analysis was made concerning both research ethics and general ethics on the topic of antibiotic resistance. This analysis is meant to acknowledge these questions since we believe they are important when discussing antibiotic resistance. The objective of this project turned out to be more difficult to attain than first believed. This was because of the lack of quality data. Even though we cannot give a clear answer when each country will reach a resistance of 20% this report gives a good understanding of how the situation looks for UTI and RTI relevant bacteria.