An agent-based and spatially explicit model of pathogen dissemination in the intensive care unit
OBJECTIVE:To develop and disseminate a spatially explicit model of contact transmission of pathogens in the intensive care unit. DESIGN:A model simulating the spread of a pathogen transmitted by direct contact (such as methicillin-resistant Staphylococcus aureus or vancomycin-resistant Enterococcus)...
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| Veröffentlicht in: | Critical care medicine 2005-01, Vol.33 (1), p.168-176 |
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| creator | Hotchkiss, John R Strike, David G Simonson, Dana A Broccard, Alain F Crooke, Philip S |
| description | OBJECTIVE:To develop and disseminate a spatially explicit model of contact transmission of pathogens in the intensive care unit.
DESIGN:A model simulating the spread of a pathogen transmitted by direct contact (such as methicillin-resistant Staphylococcus aureus or vancomycin-resistant Enterococcus) was constructed. The modulation of pathogen dissemination attending changes in clinically relevant pathogen- and institution-specific factors was then systematically examined.
SETTING AND PATIENTS:The model was configured as a hypothetical 24-bed intensive care unit. The model can be parameterized with different pathogen transmissibilities, durations of caregiver and/or patient contamination, and caregiver allocation and flow patterns.
INTERVENTIONS:Pathogen- and institution-specific factors examined included pathogen transmissibility, duration of caregiver contamination, regional cohorting of contaminated or infected patients, delayed detection and isolation of newly contaminated patients, reduction of the number of caregiver visits, and alteration of caregiver allocation among patients.
MEASUREMENTS AND MAIN RESULTS:The model predicts the probability that a given fraction of the population will become contaminated or infected with the pathogen of interest under specified spatial, initial prevalence, and dynamic conditions. Per-encounter pathogen acquisition risk and the duration of caregiver pathogen carriage most strongly affect dissemination. Regional cohorting and rapid detection and isolation of contaminated patients each markedly diminish the likelihood of dissemination even absent other interventions. Strategies reducing “crossover” between caregiver domains diminish the likelihood of more widespread dissemination.
CONCLUSIONS:Spatially explicit discrete element models, such as the model presented, may prove useful for analyzing the transmission of pathogens within the intensive care unit. |
| doi_str_mv | 10.1097/01.CCM.0000150658.05831.D2 |
| format | Article |
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DESIGN:A model simulating the spread of a pathogen transmitted by direct contact (such as methicillin-resistant Staphylococcus aureus or vancomycin-resistant Enterococcus) was constructed. The modulation of pathogen dissemination attending changes in clinically relevant pathogen- and institution-specific factors was then systematically examined.
SETTING AND PATIENTS:The model was configured as a hypothetical 24-bed intensive care unit. The model can be parameterized with different pathogen transmissibilities, durations of caregiver and/or patient contamination, and caregiver allocation and flow patterns.
INTERVENTIONS:Pathogen- and institution-specific factors examined included pathogen transmissibility, duration of caregiver contamination, regional cohorting of contaminated or infected patients, delayed detection and isolation of newly contaminated patients, reduction of the number of caregiver visits, and alteration of caregiver allocation among patients.
MEASUREMENTS AND MAIN RESULTS:The model predicts the probability that a given fraction of the population will become contaminated or infected with the pathogen of interest under specified spatial, initial prevalence, and dynamic conditions. Per-encounter pathogen acquisition risk and the duration of caregiver pathogen carriage most strongly affect dissemination. Regional cohorting and rapid detection and isolation of contaminated patients each markedly diminish the likelihood of dissemination even absent other interventions. Strategies reducing “crossover” between caregiver domains diminish the likelihood of more widespread dissemination.
CONCLUSIONS:Spatially explicit discrete element models, such as the model presented, may prove useful for analyzing the transmission of pathogens within the intensive care unit.</description><identifier>ISSN: 0090-3493</identifier><identifier>EISSN: 1530-0293</identifier><identifier>DOI: 10.1097/01.CCM.0000150658.05831.D2</identifier><identifier>PMID: 15644665</identifier><identifier>CODEN: CCMDC7</identifier><language>eng</language><publisher>Hagerstown, MD: by the Society of Critical Care Medicine and Lippincott Williams & Wilkins</publisher><subject>Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy ; Biological and medical sciences ; Caregivers - statistics & numerical data ; Clinical death. Palliative care. Organ gift and preservation ; Cross Infection - prevention & control ; Cross Infection - transmission ; Enterococcus ; Gram-Positive Bacterial Infections - transmission ; Humans ; Infectious Disease Transmission, Patient-to-Professional - statistics & numerical data ; Infectious Disease Transmission, Professional-to-Patient - statistics & numerical data ; Intensive care medicine ; Intensive Care Units ; Investigative techniques, diagnostic techniques (general aspects) ; Likelihood Functions ; Medical sciences ; Medical Staff, Hospital ; Methicillin Resistance ; Models, Theoretical ; Nursing Staff, Hospital ; Personnel Staffing and Scheduling ; Probability ; Radiodiagnosis. Nmr imagery. Nmr spectrometry ; Referral and Consultation - statistics & numerical data ; Respiratory system ; Risk ; Staphylococcal Infections - transmission ; Vancomycin Resistance</subject><ispartof>Critical care medicine, 2005-01, Vol.33 (1), p.168-176</ispartof><rights>2005 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins</rights><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3225-9822b8e99be1db12bad9957f609175d99f32ebecb86bc2716c358a107f75e61b3</citedby><cites>FETCH-LOGICAL-c3225-9822b8e99be1db12bad9957f609175d99f32ebecb86bc2716c358a107f75e61b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4024,27923,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16407895$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15644665$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hotchkiss, John R</creatorcontrib><creatorcontrib>Strike, David G</creatorcontrib><creatorcontrib>Simonson, Dana A</creatorcontrib><creatorcontrib>Broccard, Alain F</creatorcontrib><creatorcontrib>Crooke, Philip S</creatorcontrib><title>An agent-based and spatially explicit model of pathogen dissemination in the intensive care unit</title><title>Critical care medicine</title><addtitle>Crit Care Med</addtitle><description>OBJECTIVE:To develop and disseminate a spatially explicit model of contact transmission of pathogens in the intensive care unit.
DESIGN:A model simulating the spread of a pathogen transmitted by direct contact (such as methicillin-resistant Staphylococcus aureus or vancomycin-resistant Enterococcus) was constructed. The modulation of pathogen dissemination attending changes in clinically relevant pathogen- and institution-specific factors was then systematically examined.
SETTING AND PATIENTS:The model was configured as a hypothetical 24-bed intensive care unit. The model can be parameterized with different pathogen transmissibilities, durations of caregiver and/or patient contamination, and caregiver allocation and flow patterns.
INTERVENTIONS:Pathogen- and institution-specific factors examined included pathogen transmissibility, duration of caregiver contamination, regional cohorting of contaminated or infected patients, delayed detection and isolation of newly contaminated patients, reduction of the number of caregiver visits, and alteration of caregiver allocation among patients.
MEASUREMENTS AND MAIN RESULTS:The model predicts the probability that a given fraction of the population will become contaminated or infected with the pathogen of interest under specified spatial, initial prevalence, and dynamic conditions. Per-encounter pathogen acquisition risk and the duration of caregiver pathogen carriage most strongly affect dissemination. Regional cohorting and rapid detection and isolation of contaminated patients each markedly diminish the likelihood of dissemination even absent other interventions. Strategies reducing “crossover” between caregiver domains diminish the likelihood of more widespread dissemination.
CONCLUSIONS:Spatially explicit discrete element models, such as the model presented, may prove useful for analyzing the transmission of pathogens within the intensive care unit.</description><subject>Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy</subject><subject>Biological and medical sciences</subject><subject>Caregivers - statistics & numerical data</subject><subject>Clinical death. Palliative care. Organ gift and preservation</subject><subject>Cross Infection - prevention & control</subject><subject>Cross Infection - transmission</subject><subject>Enterococcus</subject><subject>Gram-Positive Bacterial Infections - transmission</subject><subject>Humans</subject><subject>Infectious Disease Transmission, Patient-to-Professional - statistics & numerical data</subject><subject>Infectious Disease Transmission, Professional-to-Patient - statistics & numerical data</subject><subject>Intensive care medicine</subject><subject>Intensive Care Units</subject><subject>Investigative techniques, diagnostic techniques (general aspects)</subject><subject>Likelihood Functions</subject><subject>Medical sciences</subject><subject>Medical Staff, Hospital</subject><subject>Methicillin Resistance</subject><subject>Models, Theoretical</subject><subject>Nursing Staff, Hospital</subject><subject>Personnel Staffing and Scheduling</subject><subject>Probability</subject><subject>Radiodiagnosis. Nmr imagery. Nmr spectrometry</subject><subject>Referral and Consultation - statistics & numerical data</subject><subject>Respiratory system</subject><subject>Risk</subject><subject>Staphylococcal Infections - transmission</subject><subject>Vancomycin Resistance</subject><issn>0090-3493</issn><issn>1530-0293</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpFkFGP1CAQx4nReHurX8EQE31rHaDQ4ttlz1OTM77oMwKduiila2k979vL3W6yJGSG8PvPJD9CXjOoGej2HbB6t_tSQzlMgpJdDbITrL7mT8iGSQEVcC2ekg2Ahko0WlyQy5x_FbyRrXhOLphUTaOU3JAfV4nan5iWytmMPbWpp_lgl2BjvKf47xCDDwsdpx4jnQZavvZT4WkfcsYxpIJOiYZElz2WsmDK4S9Sb2ekawrLC_JssDHjy1Pdku83H77tPlW3Xz9-3l3dVl5wLivdce461Noh6x3jzvZay3ZQoFkrSz8Ijg6965TzvGXKC9lZBu3QSlTMiS15e5x7mKc_K-bFjCF7jNEmnNZsVCsUZ8XFlrw_gn6ecp5xMIc5jHa-NwzMg18DzBS_5uzXPPo117yEX522rG7E_hw9CS3AmxNgs7dxmG3yIZ851UDb6QeuOXJ3U1xwzr_jeoez2aONy_5xteCNqjiABFZeVblciv96sZO7</recordid><startdate>200501</startdate><enddate>200501</enddate><creator>Hotchkiss, John R</creator><creator>Strike, David G</creator><creator>Simonson, Dana A</creator><creator>Broccard, Alain F</creator><creator>Crooke, Philip S</creator><general>by the Society of Critical Care Medicine and Lippincott Williams & Wilkins</general><general>Lippincott</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>200501</creationdate><title>An agent-based and spatially explicit model of pathogen dissemination in the intensive care unit</title><author>Hotchkiss, John R ; Strike, David G ; Simonson, Dana A ; Broccard, Alain F ; Crooke, Philip S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3225-9822b8e99be1db12bad9957f609175d99f32ebecb86bc2716c358a107f75e61b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy</topic><topic>Biological and medical sciences</topic><topic>Caregivers - statistics & numerical data</topic><topic>Clinical death. Palliative care. Organ gift and preservation</topic><topic>Cross Infection - prevention & control</topic><topic>Cross Infection - transmission</topic><topic>Enterococcus</topic><topic>Gram-Positive Bacterial Infections - transmission</topic><topic>Humans</topic><topic>Infectious Disease Transmission, Patient-to-Professional - statistics & numerical data</topic><topic>Infectious Disease Transmission, Professional-to-Patient - statistics & numerical data</topic><topic>Intensive care medicine</topic><topic>Intensive Care Units</topic><topic>Investigative techniques, diagnostic techniques (general aspects)</topic><topic>Likelihood Functions</topic><topic>Medical sciences</topic><topic>Medical Staff, Hospital</topic><topic>Methicillin Resistance</topic><topic>Models, Theoretical</topic><topic>Nursing Staff, Hospital</topic><topic>Personnel Staffing and Scheduling</topic><topic>Probability</topic><topic>Radiodiagnosis. Nmr imagery. Nmr spectrometry</topic><topic>Referral and Consultation - statistics & numerical data</topic><topic>Respiratory system</topic><topic>Risk</topic><topic>Staphylococcal Infections - transmission</topic><topic>Vancomycin Resistance</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hotchkiss, John R</creatorcontrib><creatorcontrib>Strike, David G</creatorcontrib><creatorcontrib>Simonson, Dana A</creatorcontrib><creatorcontrib>Broccard, Alain F</creatorcontrib><creatorcontrib>Crooke, Philip S</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Critical care medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hotchkiss, John R</au><au>Strike, David G</au><au>Simonson, Dana A</au><au>Broccard, Alain F</au><au>Crooke, Philip S</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An agent-based and spatially explicit model of pathogen dissemination in the intensive care unit</atitle><jtitle>Critical care medicine</jtitle><addtitle>Crit Care Med</addtitle><date>2005-01</date><risdate>2005</risdate><volume>33</volume><issue>1</issue><spage>168</spage><epage>176</epage><pages>168-176</pages><issn>0090-3493</issn><eissn>1530-0293</eissn><coden>CCMDC7</coden><abstract>OBJECTIVE:To develop and disseminate a spatially explicit model of contact transmission of pathogens in the intensive care unit.
DESIGN:A model simulating the spread of a pathogen transmitted by direct contact (such as methicillin-resistant Staphylococcus aureus or vancomycin-resistant Enterococcus) was constructed. The modulation of pathogen dissemination attending changes in clinically relevant pathogen- and institution-specific factors was then systematically examined.
SETTING AND PATIENTS:The model was configured as a hypothetical 24-bed intensive care unit. The model can be parameterized with different pathogen transmissibilities, durations of caregiver and/or patient contamination, and caregiver allocation and flow patterns.
INTERVENTIONS:Pathogen- and institution-specific factors examined included pathogen transmissibility, duration of caregiver contamination, regional cohorting of contaminated or infected patients, delayed detection and isolation of newly contaminated patients, reduction of the number of caregiver visits, and alteration of caregiver allocation among patients.
MEASUREMENTS AND MAIN RESULTS:The model predicts the probability that a given fraction of the population will become contaminated or infected with the pathogen of interest under specified spatial, initial prevalence, and dynamic conditions. Per-encounter pathogen acquisition risk and the duration of caregiver pathogen carriage most strongly affect dissemination. Regional cohorting and rapid detection and isolation of contaminated patients each markedly diminish the likelihood of dissemination even absent other interventions. Strategies reducing “crossover” between caregiver domains diminish the likelihood of more widespread dissemination.
CONCLUSIONS:Spatially explicit discrete element models, such as the model presented, may prove useful for analyzing the transmission of pathogens within the intensive care unit.</abstract><cop>Hagerstown, MD</cop><pub>by the Society of Critical Care Medicine and Lippincott Williams & Wilkins</pub><pmid>15644665</pmid><doi>10.1097/01.CCM.0000150658.05831.D2</doi><tpages>9</tpages></addata></record> |
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| ispartof | Critical care medicine, 2005-01, Vol.33 (1), p.168-176 |
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| language | eng |
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| source | MEDLINE; Journals@Ovid Complete |
| subjects | Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy Biological and medical sciences Caregivers - statistics & numerical data Clinical death. Palliative care. Organ gift and preservation Cross Infection - prevention & control Cross Infection - transmission Enterococcus Gram-Positive Bacterial Infections - transmission Humans Infectious Disease Transmission, Patient-to-Professional - statistics & numerical data Infectious Disease Transmission, Professional-to-Patient - statistics & numerical data Intensive care medicine Intensive Care Units Investigative techniques, diagnostic techniques (general aspects) Likelihood Functions Medical sciences Medical Staff, Hospital Methicillin Resistance Models, Theoretical Nursing Staff, Hospital Personnel Staffing and Scheduling Probability Radiodiagnosis. Nmr imagery. Nmr spectrometry Referral and Consultation - statistics & numerical data Respiratory system Risk Staphylococcal Infections - transmission Vancomycin Resistance |
| title | An agent-based and spatially explicit model of pathogen dissemination in the intensive care unit |
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