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Related Concept Videos

Development of Antibiotic Resistance01:30

Development of Antibiotic Resistance

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Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...
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Combined Effects of Drugs: Synergism01:27

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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
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Healthcare Associated Infections II: Preventive Measures01:22

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Essential infection prevention measures are based on the knowledge of the infection chain, the modes of transmission in healthcare settings, and the use of the best practices in all healthcare settings. Compulsory public reporting of healthcare-associated infection rates is needed to allow individuals and the community to make informed choices regarding selecting a healthcare facility.
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Calculating drug dosage and accumulation in multiple-dose regimens is crucial for achieving therapeutic efficacy while avoiding toxicity. This involves determining the plasma drug concentrations over time to optimize dosing schedules. The principle of superposition is fundamental in this process, allowing for the prediction of drug concentration in plasma following multiple doses based on single-dose data.The principle of superposition asserts that the plasma concentration-time curves from...
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Related Experiment Video

Updated: Nov 25, 2025

One-day Workflow Scheme for Bacterial Pathogen Detection and Antimicrobial Resistance Testing from Blood Cultures
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Code-Sharing in Cost-of-Illness Calculations: An Application to Antibiotic-Resistant Bloodstream Infections.

Nichola R Naylor1, Kazuto Yamashita2, Michiyo Iwami1,3

  • 1NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, United Kingdom.

Frontiers in Public Health
|December 17, 2020
PubMed
Summary
This summary is machine-generated.

Estimating the cost of antibiotic resistance is crucial. Sharing costing models efficiently increases evidence on the burden of antibiotic-resistant Staphylococcus aureus bloodstream infections, aiding decision-making.

Keywords:
Staphylococcus aureusantibiotic resistancecode-sharingcostlength of stay

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Area of Science:

  • Health economics
  • Infectious disease epidemiology
  • Health services research

Background:

  • Limited data-driven evidence exists on the economic burden of antibiotic resistance.
  • Japan and England possess extensive surveillance and administrative datasets suitable for such research.
  • Sharing costing models can reduce research duplication and enhance efficiency.

Purpose of the Study:

  • To estimate the economic burden of antibiotic-resistant Staphylococcus aureus bloodstream infections (BSIs) in Japan.
  • To adapt and utilize R software code developed for estimating antibiotic-resistant Escherichia coli BSIs in England.
  • To detail the process of code-sharing and application for future health economics research.

Main Methods:

  • Linked national administrative and voluntary surveillance data from Japan.
  • Adapted R software code, employing multistate models to estimate excess length of stay.
  • Applied unit costs to determine healthcare system burden in 2017 international dollars (I$).

Main Results:

  • Successful re-application of costing code from England to the Japanese setting was facilitated by clear documentation and open-access code.
  • An estimated excess cost of I$6,392 per Staphylococcus aureus BSI was identified from the Japanese healthcare system perspective.
  • Oxacillin resistance in Staphylococcus aureus was associated with an additional healthcare cost of I$8,155.

Conclusions:

  • Antibiotic resistance profiles in Staphylococcus aureus beyond methicillin resistance can significantly impact hospital costs.
  • Sharing costing models is an efficient strategy to generate more evidence on the burden of antibiotic resistance.
  • This approach enables rapid cost-of-illness estimations for policymakers with access to relevant data.