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Antimicrobial Resistance 101: The Global Problem of Antimicrobial Resistance

Foreword


Antibiotics were a major breakthrough in science. Microorganisms such as viruses, bacteria, fungi, and more can be treated by antibiotics relatively simply. Upon the introduction of antibiotics, previously deadly illnesses could now be treated with incredible efficacy. However, the pathogens targeted by the first antibiotics soon started to become resistant.


Antimicrobial resistance is one of the biggest problems in modern medicine. There is evidence of bacterial infections evolving to become resistant to the complete arsenal of antibiotics. As a result, people are increasingly dying from simple infections that would not have been dangerous a few years ago. Because the situation is only going to get worse in the coming decades, it is time to think about alternatives.


The Antimicrobial Resistance 101 series will be mainly divided into the following chapters:




3. Antimicrobial Resistance 101: The Global Problem of Antimicrobial Resistance


4. Antimicrobial Resistance 101: The Clinical Burden of Resistant Microorganisms


5. Antimicrobial Resistance 101: Solutions to Antimicrobial Resistance


6. Antimicrobial Resistance 101: Antimicrobial Resistance in the Future


Antimicrobial Resistance 101: The Global Problem of Antimicrobial Resistance


In recent years, it has become evident that the way in which pathogens become resistant is more complex than previously thought. The antibiotic carbapenem is exclusively used on humans. It is a last-resort antibiotic, which means that it may only be used in case all other alternatives have failed (Temkin et al., 2014). Legislation is strict with this drug because it is so potent. If pathogens were to become resistant on a large scale, no suitable alternative would be available. Although legislation and control are strict, specifically in the European Union, in 2022 pigs in Italy were found to carry bacteria resistant to this family of drugs (Bonardi et al., 2022). Earlier, it was found that animals that live near urbanised areas, especially those that live close to hospitals, host more resistant pathogens. This makes sense given that these animals, and therefore the bacteria that they host, simply come into contact with more drugs, promoting resistance against them. It seems now that the networks are much more complex than was once thought. The significance of these resistant bacteria showing up in animals is serious, given that these can cause human infections, which in turn are difficult to treat on account of the microbes' abilities to resist drugs.


The concept of One Health was explained in the introductory article of this series. Broadly speaking, One Health is the idea that everything that lives is connected and therefore influences each other. Although this sounds rather vague, it has some concrete implications and examples. Specifically, One Health focuses to connect the environment with animals and with humans. When it comes to antimicrobial resistance, the “One Health perspective” is easy to understand. Antimicrobials are used heavily in human and animal medicine. The wastewater from cities as well as farms, therefore, contains traces of these antimicrobials, which may render microbes resistant to such antimicrobial drugs. These microbes may multiply and spread into environments such as sewage water. These pathogens may then cause disease in humans and animals, drawing a full circle between the interactions of humans, animals and the environment (Collignon & McEwen, 2019).


Figure 1: A One Health overview of antimicrobial resistance. Antimicrobials used in urbanised areas, agriculture and industrialised areas cause accelerated resistance of microbes everywhere (White & Hughes, 2019).

Antimicrobial resistance is a true One Health problem and therefore requires a One Health solution. Medical doctors around the world arguing to be more prudent in the prescription of antimicrobials changes nothing if the bioindustry and farmers continue to give their cattle these drugs. Resistant microbes may still emerge, causing human and animal disease. Instead, a complex, multidisciplinary and One Health approach is required, influencing all the factors that cause and contribute to the problem of antimicrobial resistance (Collignon & McEwen, 2019). When resistance, and specifically that to antibiotics, was recognised as a real problem, the main influential factor was thought to be overuse in human medicine. Although this surely is one of the causes, research shows a lack of correlation between the number of antibiotics used in human health and the frequency of resistant pathogens, which has sparked interest in other factors as (partial) causes. One of these is antimicrobial use, most significantly its overuse in agriculture. (Collignon & McEwen, 2019).


The number of antimicrobials used in the world is incredibly large. Though many people seem to think the majority is used in human medicine, this is not the case. Instead, the overwhelming majority of antibiotics are used in agriculture (Emes et al., 2022). This is the case for all continents except Europe, because the European Union has very strict regulations when it comes to antimicrobials in agriculture. This regulation is significantly less strict in other parts of the world. Poultry and cattle around the world are fed an incredible number of antimicrobials as prophylaxis (Velazquez-Meza et al., 2022). Prophylaxis, from the Greek “phylax”, loosely translated to “guard” or “protect”, is the treatment of an individual without being certain the individual has a disease. It is therefore mostly meant as a preventative measure to a disease, in contrast to a therapeutic measure, or treatment. In a farm of tens of thousands of birds or pigs, it would take too much effort to test all animals for the presence of a bacterium or virus. Instead, farmers treat all their animals with antimicrobials as prophylaxis, by putting large amounts of these drugs in the animal feed. As a consequence of this “unguided” method of administering antibiotics, bacteria that live within these animals are exposed to large amounts of antibiotics, increasing the frequency of resistance (Collignon & McEwen, 2019).

Figure 2: The use of antimicrobials in the United States per sector. The amount used in agriculture, or rather, in livestock, heavily outweighs the use in human medicine (Hart, 2014).

Another rather worrisome element contributing to the problem of antimicrobial resistance is the overlap between drugs used in animal and human health (Collignon & McEwen, 2019). Historically, antibiotics were not used exclusively for human medicine. As a consequence, microbes emerged that were resistant to practically all antimicrobials. In modern medicine, however, there are some drugs that are meant to be used exclusively in human or animal medicine. One of the classes of antibiotics that may only be used to treat humans is carbapenem. As described earlier, however, carbapenem-resistant pathogens have emerged. This threatening development has led scientists to realise that the network through which resistance emerges is highly complex. Traces of antibiotics used in a clinical setting may be secreted by humans in their urine or stool, after which it gets into sewage water, which in turn may get in contact with the environment (Collignon & McEwen, 2019). Nonetheless, to not accelerate the frequency at which pathogens already grow resistant to the therapeutic arsenal of antimicrobials, new drugs, when developed, should be separated for exclusive animal use or human use.


The complexity of this network of factors and how they influence each other was highlighted in a fascinating 2019 study, in which Bhawna Malik and team tried to build a mathematical model of how antibiotic use and socioeconomic factors may be correlated with resistance emergence (Malik & Bhattacharyya, 2019). The idea that these factors are linked has been proposed in scientific literature for many years. In the European Union and North America, increasingly more money and research are being invested in antimicrobial resistance, and potential ways to solve the problem, which has led to somewhat of a stagnation in the frequency of emergence of such pathogens (Malik & Bhattacharyya, 2019). Their emergence is steadily rising in lower- and middle-income countries (LMICs), however. This is partially because these governments tend to spend less money on surveillance programmes, or on healthcare altogether. Surveillance systems are computerised pipelines that analyse large amounts of data, with the goal of detecting and preparing for outbreaks of diseases. Other factors could be that, in such countries, self-medication is much more common because patients do not wish to be faced with high medical costs. Additionally, education in LMICs tends to be less developed, which may be an important factor leading to the overuse and misuse of drugs. The model proposed by Malik et al is not a complete representation of reality. By definition, models are simplified, artificial systems mirroring a complex, and real-life system or network. In other words, a model is a simplified representation of reality (Zwietering & den Besten, 2011). The same is true in this case. While this model has its limitations, it paints a clear picture concerning the relation of socioeconomic factors with the emergence of resistance.


Figure 3: The prevalence of resistant microbes over the world. Asia looks the most colourful, signifying the largest prevalence. Do not, however, that the data lacking from Africa is rather limited (Roope et al., 2019).

Although Malik demonstrated that there is a correlation between socioeconomic factors and the presence of resistant microbes, the most fascinating conclusion of the study came from drawing a parallel with the concept of a self-reinforcing process from the realm of physics. A self-reinforcing process is known to be a process that once started, continues to grow and sustain itself, similar to the snowball effect. This concept has been applied to many contexts and is mostly used in economics. Malik proposed that the emergence of resistant pathogens is also a self-reinforcing process. In other words, over time, the severity of the problem of antimicrobial resistance will not significantly decrease. On the contrary, it will only become worse in the LMICs. To disrupt this self-reinforcing system, large amounts of financial aid is required, sooner rather than later. If people do not act now, the monetary resources that it will take to tackle resistance in the future will increase dramatically (Malik & Bhattacharyya, 2019).


Time and, again, studies find that LMICs suffer the heaviest from complex problems. In the context of climate change, these countries will likely not be able to spend large sums of money on climate adaptation, meaning that they will likely be unprepared for severe droughts, rising sea levels, or intense hurricanes (World Economic Forum, 2023). Similarly, these regions suffer the most from antimicrobial resistance. At this point, LMICs already experience the largest burden, mirrored in the large death toll in these regions compared to industrialised countries (Murray et al., 2022). Given that the healthcare systems and the pharmaceutical industry in these areas are underdeveloped, it makes sense that these areas show less resilience to the ever-growing problem of antimicrobial resistance. A very important note is, however, that the data from the LMICs on antimicrobial resistance is rather sparse. The conclusions drawn could therefore be based on biased data. The fact that these areas act as "data gaps" is a concern. (Murray et al., 2022). The higher-income countries invest enormous resources in the development of proper surveillance systems. This is done most notably by the Centre for Disease Control (CDC) and the European Centre for Disease Prevention and Control, although the WHO does collaborate with these two agencies. However good these systems are, if resistant pathogens still come in from other countries that do not have any systematic surveillance of the resistant microbes going around antimicrobial resistance will continue to be a problem (Murray et al., 2022).

Figure 4: The foundation of the mathematical model, linking socioeconomic factors to the emergence of resistance (Malik & Bhattacharyya, 2019).

One of the approaches that could make a difference in the realm of antibiotic resistance is surveillance and monitoring. Early detection of resistant strains improves the timing and accuracy of the response. Additionally, it is essential to make suitable policies for specific strains. Moreover, by keeping a close eye on such situations, more will be learned about the complex epidemiology of resistance (Queenan et al., 2016). Currently, there are large international discrepancies and data gaps for numerous reasons. The main reason is a lack of money. Money should be spent on developing a surveillance and monitoring system that can be expanded all over the world. A requirement of this is that the detection of bacteria, viruses, fungi and other microbes is standardised. Currently, cultures are made, antibodies are detected, or genes are sequenced to identify a strain. Testing for antibodies is an indirect way of detection, as the response of the body’s immune system is tested, rather than the pathogen itself. Because the sequencing and identification of a pathogen’s genes directly show the presence of the pathogen itself, such procedures are direct. As the standard way of doing so differs per region, there is no real standard method of determining which strain is responsible for an infection. As such, a scaled surveillance programme would not work well, because each region would function differently. The WHO has proposed many standardised ways of detecting specific pathogens (Queenan et al., 2016). One suggestion is that a proper first pipeline should be built upon the ESKAPE group. This group comprises six pathogens that are the main cause of concern, as they constitute the overwhelming majority of resistant bacterial infections. These bacteria are Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter species (Mancuso et al., 2021). Additionally, a surveillance programme should be implemented in agriculture. Surveillance in agriculture will be crucial in the future as more and more resistant pathogens are found in cattle and even wild animals. (Queenan et al., 2016).


This article lists multiple reasons and factors that cause and influence antimicrobial resistance worldwide. Although these differ per region and tend to have more severe effects in LMICs, the implications of antimicrobial resistance are global. In the near future, serious investment is needed to improve surveillance and monitoring of resistant pathogens worldwide, not only in developed countries. Additionally, prevention and control measures should be developed and ideally standardised internationally. Perhaps the most important thing, however, is that people need to be educated and policy needs to be adjusted. Currently, the vast majority of antibiotics are used in agriculture, for prophylactic reasons, which is simply irresponsible. Thus, farmers need to learn that antibiotics should only be used in case an animal is sick, to prevent overuse. This can also be realised by the adjustments of international policy, similar to that implemented by the European Union. Ultimately, while antibiotic resistance is chiefly a concern in LMICs, the threat of infections growing increasingly more difficult to treat affects us all.


Bibliographical References

Bonardi, S., Cabassi, C. S., Manfreda, G., Parisi, A., Fiaccadori, E., Sabatino, A., Cavirani, S., Bacci, C., Rega, M., Spadini, C., Iannarelli, M., Crippa, C., Ruocco, F., & Pasquali, F. (2022). Survey on Carbapenem-Resistant Bacteria in Pigs at Slaughter and Comparison with Human Clinical Isolates in Italy. Antibiotics, 11(6), 777. https://doi.org/10.3390/antibiotics11060777


Collignon, P. J., & McEwen, S. A. (2019). One health-its importance in helping to better control antimicrobial resistance. In Tropical Medicine and Infectious Disease (Vol. 4, Issue 1). MDPI AG. https://doi.org/10.3390/tropicalmed4010022


Emes, D., Naylor, N., Waage, J., & Knight, G. (2022). Quantifying the Relationship between Antibiotic Use in Food-Producing Animals and Antibiotic Resistance in Humans. Antibiotics, 11(1). https://doi.org/10.3390/antibiotics11010066


Malik, B., & Bhattacharyya, S. (2019). Antibiotic drug-resistance as a complex system driven by socio-economic growth and antibiotic misuse. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-46078-y


Murray, C. J., Ikuta, K. S., Sharara, F., Swetschinski, L., Robles Aguilar, G., Gray, A., Han, C., Bisignano, C., Rao, P., Wool, E., Johnson, S. C., Browne, A. J., Chipeta, M. G., Fell, F., Hackett, S., Haines-Woodhouse, G., Kashef Hamadani, B. H., Kumaran, E. A. P., McManigal, B., … Naghavi, M. (2022). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet, 399(10325), 629–655. https://doi.org/10.1016/S0140-6736(21)02724-0


Queenan, K., Häsler, B., & Rushton, J. (2016). A One Health approach to antimicrobial resistance surveillance: is there a business case for it? International Journal of Antimicrobial Agents, 48(4), 422–427. https://doi.org/10.1016/j.ijantimicag.2016.06.014


Temkin, E., Adler, A., Lerner, A., & Carmeli, Y. (2014). Carbapenem-resistant Enterobacteriaceae: biology, epidemiology, and management. Annals of the New York Academy of Sciences, 1323(1), 22–42. https://doi.org/10.1111/nyas.12537


Velazquez-Meza, M. E., Galarde-López, M., Carrillo-Quiróz, B., & Alpuche-Aranda, C. M. (2022). Antimicrobial resistance: One Health approach. In Veterinary World (Vol. 15, Issue 3, pp. 743–749). Veterinary World. https://doi.org/10.14202/vetworld.2022.743-749


World Economic Forum. (2023). The Climate Crisis Disproportionally Hits the Poor. How Can We Protect Them?. https://www.weforum.org/agenda/2023/01/climate-crisis-poor-davos2023/


Zwietering, M. H., & den Besten, H. M. W. (2011). Modelling: one word for many activities and uses. Food Microbiology, 28(4), 818–822. https://doi.org/10.1016/j.fm.2010.04.015

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