Modeling Antibiotic Resistance In Turkey With System Dynamics Approach

Hasgül Z., Gül N. N. , Geçici E.

40. Yöneylem Araştırması ve Endüstri Mühendisliği Kongresi, İstanbul, Turkey, 4 - 07 July 2021, pp.112

  • Publication Type: Conference Paper / Summary Text
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.112


Increasing antibiotic resistance is expected to be one of the most important health care problems of the modern world. Antibiotics are medicines used to prevent and treat bacterial infections. Antibiotic resistance occurs when bacteria evolve in response to the use of these medicines. It is the ability of bacteria to resist the effects of an antibiotic to which they were once sensitive. Due to antibiotic resistance, the effectiveness of antibiotics decreases. Accordingly, the duration of treatment is extended, the hospital stays are increased, and a decrease in recovery is observed. In addition to these, it causes undesired consequences on the economy and human health. It is known that one of the main reasons for antibiotic resistance is excessive antibiotic consumption. As most respiratory diseases result from non-bacterial infections, most antibiotics are used when they are not required. Unnecessary prescriptions can be reduced by educating the doctors and society on antimicrobial resistance, and making testing widely available to identify whether the infection is bacterial or nonbacterial. However antibiotic resistance is a complex problem with many nonlinear relationships furthermore, causes and effects are distant in time and space. Therefore, modeling approaches can help decision makers to understand the problem and take necessary actions. Many countries around the world suffer from the antibiotic resistance problem yet, the situation in Turkey is critic. Turkey is the country, where most of the antibiotics consumed in Europe. Moreover, Turkey is the second country with the highest antibiotic resistance. In this study, Turkey’s antibiotic resistance level is modeled by using the system dynamics approach. This model is simulated over the years, and several policies to reduce antibiotic resistance levels are analyzed. In policy analyses it is observed that the most effective interventions are achieved by combined policies.