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Background: The emergence of antimicrobial resistance across populations is a global threat to public health. Surveillance programs often monitor human and animal populations to evaluate trends of emergence in these populations. Many national level antibiotic resistance surveillance programs quantify the proportion of resistant bacteria as a means of monitoring emergence and control measures. The reason for monitoring these different populations are many, including interest in similar changes in resistance which might provide insight into emergence and control options.
Methods: In this research, we developed a method to quantify the correlation in antimicrobial resistance across populations, for the conventionally unnoticed mean shift of the susceptible bacteria. With the proposed Bayesian latent class mixture model with censoring and multivariate normal hierarchy, we address several challenges associated with analyzing the minimum inhibitory concentration data.
Results: Application of this approach to the surveillance data from National Antimicrobial Resistance Monitoring System led to a detection of positive correlation in the central tendency of azithromycin resistance of the susceptible populations from Salmonella serotype Typhimurium across food animal and human populations.
Conclusions: Our proposed approach has been shown to be accurate and superior to the naïve frequentist estimation by simulation studies. Further implementation of this Bayesian model could serve as a useful tool to indicate the co-existence of antimicrobial resistance, and potentially a need of clinical intervention.
Keywords
Correlation, Antimicrobial resistance, Minimum inhibitory concentration, Bayesian latent class model, NARMS
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Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 22 Oct, 2020
No community comments so far
Editorial decision: Major revision
On 01 May, 2021
Review #2 received
Received 28 Apr, 2021
Reviewer #2 agreed
On 06 Mar, 2021
Review #1 received
Received 27 Dec, 2020
Reviewer #1 agreed
On 19 Dec, 2020
Reviewers invited
Invitations sent on 28 Nov, 2020
Editor assigned
On 22 Oct, 2020
Submission checks complete
On 18 Oct, 2020
Editor invited
On 16 Oct, 2020
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A new preprint design is coming!
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This preprint is under consideration at BMC Medical Research Methodology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 22 Oct, 2020
No community comments so far
Editorial decision: Major revision
On 01 May, 2021
Review #2 received
Received 28 Apr, 2021
Reviewer #2 agreed
On 06 Mar, 2021
Review #1 received
Received 27 Dec, 2020
Reviewer #1 agreed
On 19 Dec, 2020
Reviewers invited
Invitations sent on 28 Nov, 2020
Editor assigned
On 22 Oct, 2020
Submission checks complete
On 18 Oct, 2020
Editor invited
On 16 Oct, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: The emergence of antimicrobial resistance across populations is a global threat to public health. Surveillance programs often monitor human and animal populations to evaluate trends of emergence in these populations. Many national level antibiotic resistance surveillance programs quantify the proportion of resistant bacteria as a means of monitoring emergence and control measures. The reason for monitoring these different populations are many, including interest in similar changes in resistance which might provide insight into emergence and control options.
Methods: In this research, we developed a method to quantify the correlation in antimicrobial resistance across populations, for the conventionally unnoticed mean shift of the susceptible bacteria. With the proposed Bayesian latent class mixture model with censoring and multivariate normal hierarchy, we address several challenges associated with analyzing the minimum inhibitory concentration data.
Results: Application of this approach to the surveillance data from National Antimicrobial Resistance Monitoring System led to a detection of positive correlation in the central tendency of azithromycin resistance of the susceptible populations from Salmonella serotype Typhimurium across food animal and human populations.
Conclusions: Our proposed approach has been shown to be accurate and superior to the naïve frequentist estimation by simulation studies. Further implementation of this Bayesian model could serve as a useful tool to indicate the co-existence of antimicrobial resistance, and potentially a need of clinical intervention.
Figure 1
Figure 2
The full text of this article is available to read as a PDF.
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