Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3856
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dc.contributor.authorCadosch, Dominique-
dc.contributor.authorGarcia, Victor-
dc.contributor.authorAlthaus, Christian L.-
dc.contributor.authorJensen, Jorgen Skov-
dc.contributor.authorLow, Nicola-
dc.date.accessioned2018-07-12T12:01:08Z-
dc.date.available2018-07-12T12:01:08Z-
dc.date.issued2018-05-24-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/7945-
dc.description.abstractThe rapid spread of antimicrobial resistance in sexually transmitted infections caused by Mycoplasma genitalium is a growing concern. It is not yet clear to what degree macrolide resistance in M. genitalium results from the emergence of de novo mutations or the transmission of resistant strains. We analysed epidemiological data and developed a compartmental model to investigate the contribution of de novo macrolide resistance mutations to the spread of antimicrobial resistant M. genitalium. We fitted the model to data from France, Sweden and Denmark and estimated treatment rates and the time point of azithromycin introduction. In a meta-analysis of six studies, we estimated that de novo resistance develops in 12% (95% CI 7-17%, I2 44%) of azithromycin treated M. genitalium infections. Our model shows that the high probability of de novo resistance accelerates the spread of antimicrobial resistant M. genitalium in comparison with lower probabilities. The estimated per capita treatment rate in France was lower than in Denmark and Sweden but confidence intervals for the three estimates overlap. The estimated dates of introduction of azithromycin in each country are consistent with published reports. We conclude that clinical management strategies for M. genitalium should seek to limit the unnecessary use of macrolides.de_CH
dc.format.extent25de_CH
dc.language.isoende_CH
dc.publisherCold Spring Harbor Laboratoryde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectMycoplasma genitaliumde_CH
dc.subjectMathematical modellingde_CH
dc.subjectSexually transmitted diseasesde_CH
dc.subject.ddc570: Biologiede_CH
dc.titleDe novo mutations drive the spread of macrolide resistant Mycoplasma genitalium : mathematical modelling studyde_CH
dc.typeWeiteres (textuelles Material)de_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.21256/zhaw-3856-
dc.identifier.doi10.1101/321216de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusdraftde_CH
zhaw.webfeedApplied Mathematical Biologyde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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Cadosch, D., Garcia, V., Althaus, C. L., Jensen, J. S., & Low, N. (2018). De novo mutations drive the spread of macrolide resistant Mycoplasma genitalium : mathematical modelling study. Cold Spring Harbor Laboratory. https://doi.org/10.21256/zhaw-3856
Cadosch, D. et al. (2018) De novo mutations drive the spread of macrolide resistant Mycoplasma genitalium : mathematical modelling study. Cold Spring Harbor Laboratory. Available at: https://doi.org/10.21256/zhaw-3856.
D. Cadosch, V. Garcia, C. L. Althaus, J. S. Jensen, and N. Low, De novo mutations drive the spread of macrolide resistant Mycoplasma genitalium : mathematical modelling study. Cold Spring Harbor Laboratory, 2018. doi: 10.21256/zhaw-3856.
CADOSCH, Dominique, Victor GARCIA, Christian L. ALTHAUS, Jorgen Skov JENSEN und Nicola LOW, 2018. De novo mutations drive the spread of macrolide resistant Mycoplasma genitalium : mathematical modelling study. Cold Spring Harbor Laboratory
Cadosch, Dominique, Victor Garcia, Christian L. Althaus, Jorgen Skov Jensen, and Nicola Low. 2018. De Novo Mutations Drive the Spread of Macrolide Resistant Mycoplasma Genitalium : Mathematical Modelling Study. Cold Spring Harbor Laboratory. https://doi.org/10.21256/zhaw-3856.
Cadosch, Dominique, et al. De Novo Mutations Drive the Spread of Macrolide Resistant Mycoplasma Genitalium : Mathematical Modelling Study. Cold Spring Harbor Laboratory, 2018, https://doi.org/10.21256/zhaw-3856.


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