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Beams A, Keegan LT, Adler FR, Samore MH, Khader K, Toth DJA. Are Staphylococcus aureus Carrier Types Evidence of Population Heterogeneity? Am J Epidemiol 2023; 192:455-466. [PMID: 36396618 PMCID: PMC10372877 DOI: 10.1093/aje/kwac201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 08/11/2022] [Accepted: 11/09/2022] [Indexed: 11/19/2022] Open
Abstract
Asymptomatic colonization by Staphylococcus aureus is a precursor for infection, so identifying the mode and source of transmission which leads to colonization could help in targeting interventions. Longitudinal studies have shown that some people are persistently colonized for years, while others seem to carry S. aureus for weeks or less, and conventional wisdom attributes this disparity to an underlying risk factor in the persistently colonized. We analyze published data with mathematical models of acquisition and carriage to compare this hypothesis with alternatives. The null model assumed a homogeneous population and still produced highly variable colonization durations (mean = 101.7 weeks; 5th percentile, 5.2 weeks; 95th percentile, 304.7 weeks). Simulations showed that this inherent variability, combined with censoring in longitudinal cohort studies, is sufficient to produce the appearance of "persistent carriers," "intermittent carriers," and "noncarriers" in data. Our estimates for colonization duration exhibited sensitivity to the assumption that false-positive test results can occur despite being rare, but our model-based approach simultaneously estimates specificity and sensitivity along with epidemiologic parameters. Our results show it is plausible that S. aureus colonizes people indiscriminately, and improved understanding of the types of exposures which result in colonization is essential.
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Khader K, Stevens VW, Thomas A, Toth D, Keegan LT, Rubin M, Samore MH. 432. Ward-level Variation in Transmission of MRSA in Veterans Affairs Hospitals. Open Forum Infect Dis 2022. [DOI: 10.1093/ofid/ofac492.507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Abstract
Background
In 2007, the Department of Veterans Affairs (VA) implemented the methicillin-resistant Staphylococcus aureus (MRSA) Prevention Initiative nationally in acute care facilities (ACFs). Since that time, numerous publications have reported declining transmission rates and infection rates due to MRSA across the VA. Recent estimates of the impact of contact precautions (CP) on transmission suggest that the infectiousness of patients while on CP was 50% lower than patients not on CP. We extend that work by estimating the variation and trend in transmission while accounting for the estimated impact of CP on transmission.
Methods
We analyzed ten years of data from 108 VA ACFs from 1/1/2008 – 12/31/2017 using a Bayesian transmission model. The data included admission, discharge, and surveillance and clinical test results for MRSA. Data were partitioned into five 2-year analysis periods to allow for estimates of transmission to change over time, including all facilities with complete data spanning the 10-year study period. We estimated person-to-person transmission rates for each ward assuming the classic mass-action transmission framework, while accounting for the impact of CP on transmission, and used random effect models with inverse variance weights to derive pooled estimates of the transmission rates and trends across ward-types and over time.
Results
In this cohort study of 108 VA hospitals during the 10-year period, more than 2 million unique individuals had over 5.6 million admissions with more than 8.4 million MRSA surveillance tests (9.3% of which were positive). We observed a decline in the overall transmission rate over time, with the reduction driven by declining rates of transmission in the ICU setting (Figure 1). We observed that the transmission rate in medical/surgical wards remained constant during the 10-year period.
Transmission trend by ward
Conclusion
We observed that in addition to the two-fold reduction in transmission of MRSA associated with CPs, that over the study period, the transmission rate declined in VA hospitals driven largely by reductions in the ICU setting. The secular trend downward in ICUs might reflect improved hand hygiene, changes in antibiotic use, or enhancements to other infection control practices. Additional work is needed to better understand and identify these factors.
Disclosures
Karim Khader, PhD, BioFire Diagnostics: Grant/Research Support.
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Affiliation(s)
| | | | | | - Damon Toth
- University of Utah , Salt Lake City, Utah
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3
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Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, Castro Rivadeneira AJ, Gerding A, Gneiting T, House KH, Huang Y, Jayawardena D, Kanji AH, Khandelwal A, Le K, Mühlemann A, Niemi J, Shah A, Stark A, Wang Y, Wattanachit N, Zorn MW, Gu Y, Jain S, Bannur N, Deva A, Kulkarni M, Merugu S, Raval A, Shingi S, Tiwari A, White J, Abernethy NF, Woody S, Dahan M, Fox S, Gaither K, Lachmann M, Meyers LA, Scott JG, Tec M, Srivastava A, George GE, Cegan JC, Dettwiller ID, England WP, Farthing MW, Hunter RH, Lafferty B, Linkov I, Mayo ML, Parno MD, Rowland MA, Trump BD, Zhang-James Y, Chen S, Faraone SV, Hess J, Morley CP, Salekin A, Wang D, Corsetti SM, Baer TM, Eisenberg MC, Falb K, Huang Y, Martin ET, McCauley E, Myers RL, Schwarz T, Sheldon D, Gibson GC, Yu R, Gao L, Ma Y, Wu D, Yan X, Jin X, Wang YX, Chen Y, Guo L, Zhao Y, Gu Q, Chen J, Wang L, Xu P, Zhang W, Zou D, Biegel H, Lega J, McConnell S, Nagraj VP, Guertin SL, Hulme-Lowe C, Turner SD, Shi Y, Ban X, Walraven R, Hong QJ, Kong S, van de Walle A, Turtle JA, Ben-Nun M, Riley S, Riley P, Koyluoglu U, DesRoches D, Forli P, Hamory B, Kyriakides C, Leis H, Milliken J, Moloney M, Morgan J, Nirgudkar N, Ozcan G, Piwonka N, Ravi M, Schrader C, Shakhnovich E, Siegel D, Spatz R, Stiefeling C, Wilkinson B, Wong A, Cavany S, España G, Moore S, Oidtman R, Perkins A, Kraus D, Kraus A, Gao Z, Bian J, Cao W, Ferres JL, Li C, Liu TY, Xie X, Zhang S, Zheng S, Vespignani A, Chinazzi M, Davis JT, Mu K, Pastore y Piontti A, Xiong X, Zheng A, Baek J, Farias V, Georgescu A, Levi R, Sinha D, Wilde J, Perakis G, Bennouna MA, Nze-Ndong D, Singhvi D, Spantidakis I, Thayaparan L, Tsiourvas A, Sarker A, Jadbabaie A, Shah D, Della Penna N, Celi LA, Sundar S, Wolfinger R, Osthus D, Castro L, Fairchild G, Michaud I, Karlen D, Kinsey M, Mullany LC, Rainwater-Lovett K, Shin L, Tallaksen K, Wilson S, Lee EC, Dent J, Grantz KH, Hill AL, Kaminsky J, Kaminsky K, Keegan LT, Lauer SA, Lemaitre JC, Lessler J, Meredith HR, Perez-Saez J, Shah S, Smith CP, Truelove SA, Wills J, Marshall M, Gardner L, Nixon K, Burant JC, Wang L, Gao L, Gu Z, Kim M, Li X, Wang G, Wang Y, Yu S, Reiner RC, Barber R, Gakidou E, Hay SI, Lim S, Murray C, Pigott D, Gurung HL, Baccam P, Stage SA, Suchoski BT, Prakash BA, Adhikari B, Cui J, Rodríguez A, Tabassum A, Xie J, Keskinocak P, Asplund J, Baxter A, Oruc BE, Serban N, Arik SO, Dusenberry M, Epshteyn A, Kanal E, Le LT, Li CL, Pfister T, Sava D, Sinha R, Tsai T, Yoder N, Yoon J, Zhang L, Abbott S, Bosse NI, Funk S, Hellewell J, Meakin SR, Sherratt K, Zhou M, Kalantari R, Yamana TK, Pei S, Shaman J, Li ML, Bertsimas D, Lami OS, Soni S, Bouardi HT, Ayer T, Adee M, Chhatwal J, Dalgic OO, Ladd MA, Linas BP, Mueller P, Xiao J, Wang Y, Wang Q, Xie S, Zeng D, Green A, Bien J, Brooks L, Hu AJ, Jahja M, McDonald D, Narasimhan B, Politsch C, Rajanala S, Rumack A, Simon N, Tibshirani RJ, Tibshirani R, Ventura V, Wasserman L, O’Dea EB, Drake JM, Pagano R, Tran QT, Ho LST, Huynh H, Walker JW, Slayton RB, Johansson MA, Biggerstaff M, Reich NG. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proc Natl Acad Sci U S A 2022; 119:e2113561119. [PMID: 35394862 PMCID: PMC9169655 DOI: 10.1073/pnas.2113561119] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 01/24/2022] [Indexed: 01/15/2023] Open
Abstract
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.
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Affiliation(s)
- Estee Y. Cramer
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Evan L. Ray
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Velma K. Lopez
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Johannes Bracher
- Chair of Econometrics and Statistics, Karlsruhe Institute of Technology, 76185 Karlsruhe, Germany
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
| | | | | | - Aaron Gerding
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Tilmann Gneiting
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
- Institute of Stochastics, Karlsruhe Institute of Technology, 69118 Karlsruhe, Germany
| | - Katie H. House
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Yuxin Huang
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Dasuni Jayawardena
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Abdul H. Kanji
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Ayush Khandelwal
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Khoa Le
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Anja Mühlemann
- Institute of Mathematical Statistics and Actuarial Science, University of Bern, CH-3012 Bern, Switzerland
| | - Jarad Niemi
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Apurv Shah
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Ariane Stark
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Yijin Wang
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Nutcha Wattanachit
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Martha W. Zorn
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | | | - Sansiddh Jain
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Nayana Bannur
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Ayush Deva
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Mihir Kulkarni
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Srujana Merugu
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Alpan Raval
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Siddhant Shingi
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Avtansh Tiwari
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Jerome White
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | | | - Spencer Woody
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - Maytal Dahan
- Texas Advanced Computing Center, Austin, TX 78758
| | - Spencer Fox
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | | | | | - Lauren Ancel Meyers
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - James G. Scott
- Department of Information, Risk, and Operations Management, University of Texas at Austin, Austin, TX 78712
| | - Mauricio Tec
- Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX 78712
| | - Ajitesh Srivastava
- Ming Hsieh Department of Computer and Electrical Engineering, University of Southern California, Los Angeles, CA 90089
| | - Glover E. George
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Jeffrey C. Cegan
- US Army Engineer Research and Development Center, Concord, MA 01742
| | - Ian D. Dettwiller
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | | | | | - Robert H. Hunter
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Brandon Lafferty
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Igor Linkov
- US Army Engineer Research and Development Center, Concord, MA 01742
| | - Michael L. Mayo
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Matthew D. Parno
- US Army Engineer Research and Development Center, Hanover, NH 03755
| | | | | | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Samuel Chen
- School of Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Stephen V. Faraone
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Jonathan Hess
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Christopher P. Morley
- Department of Public Health & Preventive Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Asif Salekin
- Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13207
| | - Dongliang Wang
- Department of Public Health & Preventive Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | | | - Thomas M. Baer
- Department of Physics, Trinity University, San Antonio, TX 78212
| | - Marisa C. Eisenberg
- Department of Complex Systems, University of Michigan, Ann Arbor, MI 48109
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109
| | - Karl Falb
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Yitao Huang
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Emily T. Martin
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109
| | - Ella McCauley
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Robert L. Myers
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Tom Schwarz
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Daniel Sheldon
- College of Information and Computer Sciences, University of Massachusetts, Amherst, MA 01003
| | - Graham Casey Gibson
- School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003
| | - Rose Yu
- Department of Computer Science and Engineering, University of California, San Diego, CA 92093
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115
| | - Liyao Gao
- Department of Statistics, University of Washington, Seattle, WA 98185
| | - Yian Ma
- Halıcıoğlu Data Science Institute, University of California, San Diego, CA 92093
| | - Dongxia Wu
- Department of Computer Science and Engineering, University of California, San Diego, CA 92093
| | - Xifeng Yan
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - Xiaoyong Jin
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - Yu-Xiang Wang
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - YangQuan Chen
- Mechatronics, Embedded Systems and Automation Lab, Department of Mechanical Engineering, University of California, Merced, CA 95301
| | - Lihong Guo
- Jilin University, Changchun City, Jilin Province, 130012, People's Republic of China
| | - Yanting Zhao
- University of Science and Technology of China, Heifei, Anhui, 230027, People's Republic of China
| | - Quanquan Gu
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Jinghui Chen
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Lingxiao Wang
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Pan Xu
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Weitong Zhang
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Difan Zou
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Hannah Biegel
- Department of Mathematics, University of Arizona, Tucson, AZ 85721
| | - Joceline Lega
- Department of Mathematics, University of Arizona, Tucson, AZ 85721
| | | | - V. P. Nagraj
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | - Stephanie L. Guertin
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | | | - Stephen D. Turner
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | - Yunfeng Shi
- Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, NY 12309
| | - Xuegang Ban
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195
| | | | - Qi-Jun Hong
- School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287
- School of Engineering, Brown University, Providence, RI 02912
| | | | | | - James A. Turtle
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | - Michal Ben-Nun
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | - Steven Riley
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, W2 1PG London, United Kingdom
| | - Pete Riley
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | | | | | - Pedro Forli
- Oliver Wyman Digital, Oliver Wyman, Sao Paolo, Brazil 04711-904
| | - Bruce Hamory
- Health & Life Sciences, Oliver Wyman, Boston, MA 02110
| | | | - Helen Leis
- Health & Life Sciences, Oliver Wyman, New York, NY 10036
| | - John Milliken
- Financial Services, Oliver Wyman, New York, NY 10036
| | | | - James Morgan
- Financial Services, Oliver Wyman, New York, NY 10036
| | | | - Gokce Ozcan
- Financial Services, Oliver Wyman, New York, NY 10036
| | - Noah Piwonka
- Health & Life Sciences, Oliver Wyman, New York, NY 10036
| | - Matt Ravi
- Core Consultant Group, Oliver Wyman, New York, NY 10036
| | - Chris Schrader
- Health & Life Sciences, Oliver Wyman, New York, NY 10036
| | | | - Daniel Siegel
- Financial Services, Oliver Wyman, New York, NY 10036
| | - Ryan Spatz
- Core Consultant Group, Oliver Wyman, New York, NY 10036
| | - Chris Stiefeling
- Financial Services, Oliver Wyman Digital, Toronto, ON, Canada M5J 0A1
| | | | | | - Sean Cavany
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Guido España
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Sean Moore
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Rachel Oidtman
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637
| | - Alex Perkins
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - David Kraus
- Department of Mathematics and Statistics, Masaryk University, 61137 Brno, Czech Republic
| | - Andrea Kraus
- Department of Mathematics and Statistics, Masaryk University, 61137 Brno, Czech Republic
| | | | | | - Wei Cao
- Microsoft, Redmond, WA 98029
| | | | | | | | | | | | | | - Alessandro Vespignani
- Institute for Scientific Interchange Foundation, Turin, 10133, Italy
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Jessica T. Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Xinyue Xiong
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Andrew Zheng
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Jackie Baek
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Vivek Farias
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Andreea Georgescu
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Retsef Levi
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Deeksha Sinha
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Joshua Wilde
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | | | | | | | - Divya Singhvi
- Technology, Operations and Statistics (TOPS) group, Stern School of Business, New York University, New York, NY 10012
| | | | | | | | - Arnab Sarker
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Ali Jadbabaie
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Devavrat Shah
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Nicolas Della Penna
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Leo A. Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | | | | | - Dave Osthus
- Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Lauren Castro
- Information Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Geoffrey Fairchild
- Information Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Isaac Michaud
- Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Dean Karlen
- Department of Physics and Astronomy, University of Victoria, Victoria, BC, V8W 2Y2, Canada
- Physical Sciences Division, TRIUMF, Vancouver, BC, V8W 2Y2, Canada
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | | | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | | | - Shelby Wilson
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | - Elizabeth C. Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Juan Dent
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Kyra H. Grantz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Alison L. Hill
- Institute for Computational Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21218
| | - Joshua Kaminsky
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | | | - Lindsay T. Keegan
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84108
| | - Stephen A. Lauer
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Joseph C. Lemaitre
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Hannah R. Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Sam Shah
- Unaffiliated, San Francisco, CA 94122
| | - Claire P. Smith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Shaun A. Truelove
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
- International Vaccine Access Center, Johns Hopkins University, Baltimore, MD 21231
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21231
| | | | - Maximilian Marshall
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Kristen Nixon
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | | | - Lily Wang
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Lei Gao
- Department of Finance, Iowa State University, Ames, IA 50011
| | - Zhiling Gu
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Myungjin Kim
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Xinyi Li
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634
| | - Guannan Wang
- Department of Mathematics, College of William & Mary, Williamsburg, VA 23187
| | - Yueying Wang
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Shan Yu
- Department of Statistics, University of Virginia, Charlottesville, VA 22904
| | - Robert C. Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Ryan Barber
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Emmanuela Gakidou
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Simon I. Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Steve Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Chris Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - David Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | | | | | | | | | - B. Aditya Prakash
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | - Bijaya Adhikari
- Department of Computer Science, University of Iowa, Iowa City, IA 52242
| | - Jiaming Cui
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | | | - Anika Tabassum
- Department of Computer Science, Virginia Tech, Falls Church, VA 22043
| | - Jiajia Xie
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | - Pinar Keskinocak
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - John Asplund
- Advanced Data Analytics, Metron, Inc., Reston, VA 20190
| | - Arden Baxter
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Buse Eylul Oruc
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Nicoleta Serban
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | | | | | | | | | | | | | | | | | | | - Thomas Tsai
- Department of Health Policy and Management, Harvard University, Cambridge, MA 02138
| | | | | | | | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Nikos I. Bosse
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Joel Hellewell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Sophie R. Meakin
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Katharine Sherratt
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Mingyuan Zhou
- McCombs School of Business, The University of Texas at Austin, Austin, TX 78712
| | - Rahi Kalantari
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712
| | - Teresa K. Yamana
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Sen Pei
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Michael L. Li
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Dimitris Bertsimas
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Omar Skali Lami
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Saksham Soni
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Hamza Tazi Bouardi
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Turgay Ayer
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
- Winship Cancer Institute, Emory University Medical School, Atlanta, GA 30322
| | - Madeline Adee
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Jagpreet Chhatwal
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Ozden O. Dalgic
- Health Economic Modeling, Value Analytics Labs, 34776 İstanbul, Turkey
| | - Mary A. Ladd
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Benjamin P. Linas
- Department of Medicine, Section of Infectious Diseases, Boston University School of Medicine, Boston, MA 02118
| | - Peter Mueller
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Jade Xiao
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Yuanjia Wang
- Department of Biostatistics, Columbia University, New York, NY 10032
- Department of Psychiatry, Columbia University, New York, NY 10032
| | - Qinxia Wang
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Shanghong Xie
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Alden Green
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Jacob Bien
- Marshall School of Business, Department of Data Sciences and Operations (DSO), University of Southern California, Los Angeles, CA 90089
| | - Logan Brooks
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Addison J. Hu
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Maria Jahja
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Daniel McDonald
- Department of Statistics, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Balasubramanian Narasimhan
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Collin Politsch
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Samyak Rajanala
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Aaron Rumack
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Noah Simon
- Department of Biostatistics, University of Washington, Seattle, WA 98195
| | - Ryan J. Tibshirani
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Rob Tibshirani
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Valerie Ventura
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Larry Wasserman
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Eamon B. O’Dea
- Odum School of Ecology, University of Georgia, Athens, GA 30602
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602
| | | | - Quoc T. Tran
- Catalog Data Science, Walmart Inc., Sunnyvale, CA 94085
| | - Lam Si Tung Ho
- Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Huong Huynh
- Virtual Power System Inc, Milpitas, CA 95035
| | - Jo W. Walker
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Rachel B. Slayton
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Michael A. Johansson
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Matthew Biggerstaff
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Nicholas G. Reich
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
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Abstract
This cross-sectional study analyzes the effectiveness of COVID-19 vaccines against Delta and other variants in Utah.
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Affiliation(s)
- Lindsay T. Keegan
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, Utah
| | - Shaun Truelove
- International Vaccine Access Center, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Justin Lessler
- Department of Epidemiology, University of North Carolina at Chapel Hill
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5
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Toth DJA, Beams AB, Keegan LT, Zhang Y, Greene T, Orleans B, Seegert N, Looney A, Alder SC, Samore MH. High variability in transmission of SARS-CoV-2 within households and implications for control. PLoS One 2021; 16:e0259097. [PMID: 34758042 PMCID: PMC8580228 DOI: 10.1371/journal.pone.0259097] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 10/12/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a high risk of transmission in close-contact indoor settings, which may include households. Prior studies have found a wide range of household secondary attack rates and may contain biases due to simplifying assumptions about transmission variability and test accuracy. METHODS We compiled serological SARS-CoV-2 antibody test data and prior SARS-CoV-2 test reporting from members of 9,224 Utah households. We paired these data with a probabilistic model of household importation and transmission. We calculated a maximum likelihood estimate of the importation probability, mean and variability of household transmission probability, and sensitivity and specificity of test data. Given our household transmission estimates, we estimated the threshold of non-household transmission required for epidemic growth in the population. RESULTS We estimated that individuals in our study households had a 0.41% (95% CI 0.32%- 0.51%) chance of acquiring SARS-CoV-2 infection outside their household. Our household secondary attack rate estimate was 36% (27%- 48%), substantially higher than the crude estimate of 16% unadjusted for imperfect serological test specificity and other factors. We found evidence for high variability in individual transmissibility, with higher probability of no transmissions or many transmissions compared to standard models. With household transmission at our estimates, the average number of non-household transmissions per case must be kept below 0.41 (0.33-0.52) to avoid continued growth of the pandemic in Utah. CONCLUSIONS Our findings suggest that crude estimates of household secondary attack rate based on serology data without accounting for false positive tests may underestimate the true average transmissibility, even when test specificity is high. Our finding of potential high variability (overdispersion) in transmissibility of infected individuals is consistent with characterizing SARS-CoV-2 transmission being largely driven by superspreading from a minority of infected individuals. Mitigation efforts targeting large households and other locations where many people congregate indoors might curb continued spread of the virus.
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Affiliation(s)
- Damon J. A. Toth
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
- Department of Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, Utah, United States of America
- Department of Mathematics, University of Utah, Salt Lake City, Utah, United States of America
| | - Alexander B. Beams
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
- Department of Mathematics, University of Utah, Salt Lake City, Utah, United States of America
| | - Lindsay T. Keegan
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
- Department of Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, Utah, United States of America
| | - Yue Zhang
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
| | - Tom Greene
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
| | - Brian Orleans
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
| | - Nathan Seegert
- Department of Finance, University of Utah David Eccles School of Business, Salt Lake City, Utah, United States of America
| | - Adam Looney
- Department of Finance, University of Utah David Eccles School of Business, Salt Lake City, Utah, United States of America
| | - Stephen C. Alder
- Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
| | - Matthew H. Samore
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
- Department of Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, Utah, United States of America
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6
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Love J, Wimmer MT, Toth DJA, Chandran A, Makhija D, Cooper CK, Samore MH, Keegan LT. Comparison of antigen- and RT-PCR-based testing strategies for detection of SARS-CoV-2 in two high-exposure settings. PLoS One 2021; 16:e0253407. [PMID: 34492025 PMCID: PMC8423454 DOI: 10.1371/journal.pone.0253407] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 08/04/2021] [Indexed: 12/14/2022] Open
Abstract
Surveillance testing for infectious disease is an important tool to combat disease transmission at the population level. During the SARS-CoV-2 pandemic, RT-PCR tests have been considered the gold standard due to their high sensitivity and specificity. However, RT-PCR tests for SARS-CoV-2 have been shown to return positive results when performed to individuals who are past the infectious stage of the disease. Meanwhile, antigen-based tests are often treated as a less accurate substitute for RT-PCR, however, new evidence suggests they may better reflect infectiousness. Consequently, the two test types may each be most optimally deployed in different settings. Here, we present an epidemiological model with surveillance testing and coordinated isolation in two congregate living settings (a nursing home and a university dormitory system) that considers test metrics with respect to viral culture, a proxy for infectiousness. Simulations show that antigen-based surveillance testing coupled with isolation greatly reduces disease burden and carries a lower economic cost than RT-PCR-based strategies. Antigen and RT-PCR tests perform different functions toward the goal of reducing infectious disease burden and should be used accordingly.
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Affiliation(s)
- Jay Love
- Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, United States of America
| | - Megan T. Wimmer
- Becton, Dickinson, and Company, Franklin Lakes, New Jersey, United States of America
| | - Damon J. A. Toth
- Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, United States of America
- Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States of America
- Department of Mathematics, University of Utah, Salt Lake City, Utah, United States of America
| | - Arthi Chandran
- Becton, Dickinson, and Company, Franklin Lakes, New Jersey, United States of America
| | - Dilip Makhija
- Becton, Dickinson, and Company, Franklin Lakes, New Jersey, United States of America
| | - Charles K. Cooper
- Becton, Dickinson, and Company, Franklin Lakes, New Jersey, United States of America
| | - Matthew H. Samore
- Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, United States of America
- Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States of America
| | - Lindsay T. Keegan
- Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, United States of America
- Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States of America
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7
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Meredith HR, Arehart E, Grantz KH, Beams A, Sheets T, Nelson R, Zhang Y, Vinik RG, Barfuss D, Pettit JC, McCaffrey K, Dunn AC, Good M, Frattaroli S, Samore MH, Lessler J, Lee EC, Keegan LT. Coordinated Strategy for a Model-Based Decision Support Tool for Coronavirus Disease, Utah, USA. Emerg Infect Dis 2021; 27:1259-1265. [PMID: 33900179 PMCID: PMC8084489 DOI: 10.3201/eid2705.203075] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
The coronavirus disease pandemic has highlighted the key role epidemiologic models play in supporting public health decision-making. In particular, these models provide estimates of outbreak potential when data are scarce and decision-making is critical and urgent. We document the integrated modeling response used in the US state of Utah early in the coronavirus disease pandemic, which brought together a diverse set of technical experts and public health and healthcare officials and led to an evidence-based response to the pandemic. We describe how we adapted a standard epidemiologic model; harmonized the outputs across modeling groups; and maintained a constant dialogue with policymakers at multiple levels of government to produce timely, evidence-based, and coordinated public health recommendations and interventions during the first wave of the pandemic. This framework continues to support the state’s response to ongoing outbreaks and can be applied in other settings to address unique public health challenges.
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8
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Borchering RK, Viboud C, Howerton E, Smith CP, Truelove S, Runge MC, Reich NG, Contamin L, Levander J, Salerno J, van Panhuis W, Kinsey M, Tallaksen K, Obrecht RF, Asher L, Costello C, Kelbaugh M, Wilson S, Shin L, Gallagher ME, Mullany LC, Rainwater-Lovett K, Lemaitre JC, Dent J, Grantz KH, Kaminsky J, Lauer SA, Lee EC, Meredith HR, Perez-Saez J, Keegan LT, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Shea K, Lessler J. Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021. MMWR Morb Mortal Wkly Rep 2021. [PMID: 33988185 DOI: 10.15585/mmwr.mm7019e3externalicon] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/27/2023]
Abstract
After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.
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9
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Borchering RK, Viboud C, Howerton E, Smith CP, Truelove S, Runge MC, Reich NG, Contamin L, Levander J, Salerno J, van Panhuis W, Kinsey M, Tallaksen K, Obrecht RF, Asher L, Costello C, Kelbaugh M, Wilson S, Shin L, Gallagher ME, Mullany LC, Rainwater-Lovett K, Lemaitre JC, Dent J, Grantz KH, Kaminsky J, Lauer SA, Lee EC, Meredith HR, Perez-Saez J, Keegan LT, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Shea K, Lessler J. Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021. MMWR Morb Mortal Wkly Rep 2021; 70:719-724. [PMID: 33988185 PMCID: PMC8118153 DOI: 10.15585/mmwr.mm7019e3] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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10
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Ahmed SM, Shah RU, Fernandez V, Grineski S, Brintz B, Samore MH, Ferrari MJ, Leung DT, Keegan LT. Robust Testing in Outpatient Settings to Explore COVID-19 Epidemiology: Disparities in Race/Ethnicity and Age, Salt Lake County, Utah, 2020. Public Health Rep 2021; 136:345-353. [PMID: 33541222 PMCID: PMC8580386 DOI: 10.1177/0033354920988612] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2020] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE US-based descriptions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have focused on patients with severe disease. Our objective was to describe characteristics of a predominantly outpatient population tested for SARS-CoV-2 in an area receiving comprehensive testing. METHODS We extracted data on demographic characteristics and clinical data for all patients (91% outpatient) tested for SARS-CoV-2 at University of Utah Health clinics in Salt Lake County, Utah, from March 10 through April 24, 2020. We manually extracted data on symptoms and exposures from a subset of patients, and we calculated the adjusted odds of receiving a positive test result by demographic characteristics and clinical risk factors. RESULTS Of 17 662 people tested, 1006 (5.7%) received a positive test result for SARS-CoV-2. Hispanic/Latinx people were twice as likely as non-Hispanic White people to receive a positive test result (adjusted odds ratio [aOR] = 2.0; 95% CI, 1.3-3.1), although the severity at presentation did not explain this discrepancy. Young people aged 0-19 years had the lowest rates of receiving a positive test result for SARS-CoV-2 (<4 cases per 10 000 population), and adults aged 70-79 and 40-49 had the highest rates of hospitalization per 100 000 population among people who received a positive test result (16 and 11, respectively). CONCLUSIONS We found disparities by race/ethnicity and age in access to testing and in receiving a positive test result among outpatients tested for SARS-CoV-2. Further research and public health outreach on addressing racial/ethnic and age disparities will be needed to effectively combat the coronavirus disease 2019 pandemic in the United States.
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Affiliation(s)
- Sharia M. Ahmed
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Rashmee U. Shah
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Valerie Fernandez
- College of Social and Behavioral Science, University of Utah, Salt Lake City, UT, USA
| | - Sara Grineski
- Center for Natural & Technological Hazards, University of Utah, Salt Lake City, UT, USA
- Department of Sociology, University of Utah, Salt Lake City, UT, USA
| | - Benjamin Brintz
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Matthew H. Samore
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Veterans Affairs, Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Matthew J. Ferrari
- Department of Biology, Pennsylvania State University, State College, PA, USA
| | - Daniel T. Leung
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Lindsay T. Keegan
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Veterans Affairs, Salt Lake City Health Care System, Salt Lake City, UT, USA
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11
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Khader K, Munoz-Price LS, Hanson R, Stevens V, Keegan LT, Thomas A, Pezzin LE, Nattinger A, Singh S, Samore MH. Transmission Dynamics of Clostridioides difficile in 2 High-Acuity Hospital Units. Clin Infect Dis 2021; 72:S1-S7. [PMID: 33512524 DOI: 10.1093/cid/ciaa1580] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 10/14/2020] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The key epidemiological drivers of Clostridioides difficile transmission are not well understood. We estimated epidemiological parameters to characterize variation in C. difficile transmission, while accounting for the imperfect nature of surveillance tests. METHODS We conducted a retrospective analysis of C. difficile surveillance tests for patients admitted to a bone marrow transplant (BMT) unit or a solid tumor unit (STU) in a 565-bed tertiary hospital. We constructed a transmission model for estimating key parameters, including admission prevalence, transmission rate, and duration of colonization to understand the potential variation in C. difficile dynamics between these 2 units. RESULTS A combined 2425 patients had 5491 admissions into 1 of the 2 units. A total of 3559 surveillance tests were collected from 1394 patients, with 11% of the surveillance tests being positive for C. difficile. We estimate that the transmission rate in the BMT unit was nearly 3-fold higher at 0.29 acquisitions per percentage colonized per 1000 days, compared to our estimate in the STU (0.10). Our model suggests that 20% of individuals admitted into either the STU or BMT unit were colonized with C. difficile at the time of admission. In contrast, the percentage of surveillance tests that were positive within 1 day of admission to either unit for C. difficile was 13.4%, with 15.4% in the STU and 11.6% in the BMT unit. CONCLUSIONS Although prevalence was similar between the units, there were important differences in the rates of transmission and clearance. Influential factors may include antimicrobial exposure or other patient-care factors.
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Affiliation(s)
- Karim Khader
- Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA.,Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | | | - Ryan Hanson
- Collaborative for Healthcare Delivery Science, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Vanessa Stevens
- Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA.,Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Lindsay T Keegan
- Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA.,Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Alun Thomas
- Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA.,Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Liliana E Pezzin
- Collaborative for Healthcare Delivery Science, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.,Institute for Health and Equity, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Ann Nattinger
- Department of Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.,Collaborative for Healthcare Delivery Science, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Siddhartha Singh
- Department of Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.,Collaborative for Healthcare Delivery Science, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Matthew H Samore
- Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA.,Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
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12
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Lemaitre JC, Grantz KH, Kaminsky J, Meredith HR, Truelove SA, Lauer SA, Keegan LT, Shah S, Wills J, Kaminsky K, Perez-Saez J, Lessler J, Lee EC. A scenario modeling pipeline for COVID-19 emergency planning. Sci Rep 2021; 11:7534. [PMID: 33824358 PMCID: PMC8024322 DOI: 10.1038/s41598-021-86811-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 03/18/2021] [Indexed: 01/05/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.
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Affiliation(s)
- Joseph C Lemaitre
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kyra H Grantz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Joshua Kaminsky
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Hannah R Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Shaun A Truelove
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Stephen A Lauer
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Lindsay T Keegan
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Sam Shah
- Unaffiliated, San Francisco, USA
| | | | | | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Elizabeth C Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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13
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Khader K, Thomas A, Stevens V, Visnovsky L, Nevers M, Toth D, Keegan LT, Jones M, Rubin M, Samore MH. Association Between Contact Precautions and Transmission of Methicillin-Resistant Staphylococcus aureus in Veterans Affairs Hospitals. JAMA Netw Open 2021; 4:e210971. [PMID: 33720369 PMCID: PMC7961311 DOI: 10.1001/jamanetworkopen.2021.0971] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
IMPORTANCE The effectiveness and importance of contact precautions for endemic pathogens has long been debated, and their use has broad implications for infection control of other pathogens. OBJECTIVE To estimate the association between contact precautions and transmission of methicillin-resistant Staphylococcus aureus (MRSA) across US Department of Veterans Affairs (VA) hospitals. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study used mathematical models applied to data from a population-based sample of adults hospitalized in 108 VA acute care hospitals for at least 24 hours from January 1, 2008, to December 31, 2017. Data were analyzed from May 2, 2019, to December 11, 2020. EXPOSURES A positive MRSA test result, presumed to indicate contact precautions use according to the VA MRSA Prevention Initiative. MAIN OUTCOMES AND MEASURES The main outcome was the association between contact precautions and MRSA transmission, defined as the relative transmissibility attributed to contact precautions. A contact precaution effect estimate (<1 indicates a reduction in transmission associated with contact precautions) was estimated for each hospital and then pooled over time and across hospitals using meta-regression. RESULTS In this cohort study of 108 VA hospitals, more than 2 million unique individuals had over 5.6 million admissions, of which 14.1% were presumed to have contact precautions with more than 8.4 million MRSA surveillance tests. Pooled estimates found associations between contact precautions and transmission to be stable from 2008 to 2017, with estimated transmission reductions ranging from 43% (95% credible interval [CrI], 38%-48%) to 51% (95% CrI, 46%-55%). Over the entire 10-year study period, contact precautions reduced transmission 47% (95% CrI, 45%-49%), and the intrafacility autocorrelation coefficient estimate was 0.99, suggesting consistent estimates over time within facilities. Larger facilities and those with higher admission screening compliance observed additional reductions in transmission associated with contact precautions (relative rate, 0.84; 95% CI, 0.74-0.96 and 0.74; 95% CI, 0.58-0.96, respectively) compared with smaller facilities and those with lower admission screening compliance. Facilities in the southern US had a smaller transmission reduction attributable to contact precautions (relative rate, 1.14; 95% CI, 1.01-1.28) compared with facilities in other regions in the US. CONCLUSIONS AND RELEVANCE In this cohort study of adults in VA hospitals, transmissibility of MRSA was found to be reduced by approximately 50% among patients with contact precautions. These results provide an explanation for decreasing acquisition rates in VA hospitals since the MRSA Prevention Initiative.
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Affiliation(s)
- Karim Khader
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
| | - Alun Thomas
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
| | - Vanessa Stevens
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
| | - Lindsay Visnovsky
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
| | - McKenna Nevers
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
| | - Damon Toth
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
| | - Lindsay T. Keegan
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
| | - Makoto Jones
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
| | - Michael Rubin
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
| | - Matthew H. Samore
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
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14
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Reimer JR, Ahmed SM, Brintz B, Shah RU, Keegan LT, Ferrari MJ, Leung DT. Using a clinical prediction rule to prioritize diagnostic testing leads to reduced transmission and hospital burden: A modeling example of early SARS-CoV-2. Clin Infect Dis 2021; 73:1822-1830. [PMID: 33621329 PMCID: PMC7929067 DOI: 10.1093/cid/ciab177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 02/19/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. METHODS Using early SARS-CoV-2 as an example, we used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive. To consider the implications of gains in daily case detection at the population level, we incorporated testing using the CPR into a compartmentalized model of SARS-CoV-2. RESULTS We found that applying this CPR (AUC: 0.69 (95% CI: 0.68 - 0.70)) to prioritize testing increased the proportion of those testing positive in settings of limited testing capacity. We found that prioritized testing led to a delayed and lowered infection peak (i.e., "flattens the curve"), with the greatest impact at lower values of the effective reproductive number (such as with concurrent community mitigation efforts), and when higher proportions of infectious persons seek testing. Additionally, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit (ICU) burden. CONCLUSION We highlight the population-level benefits of evidence-based allocation of limited diagnostic capacity.
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Affiliation(s)
- Jody R Reimer
- University of Utah, Department of Mathematics, Salt Lake City, UT, United States of America
| | - Sharia M Ahmed
- University of Utah School of Medicine, Department of Internal Medicine, Division of Infectious Diseases, Salt Lake City UT, United States of America
| | - Benjamin Brintz
- University of Utah School of Medicine, Department of Internal Medicine, Division of Infectious Diseases, Salt Lake City UT, United States of America.,University of Utah School of Medicine, Department of Internal Medicine, Division of Epidemiology, Salt Lake City UT, United States of America
| | - Rashmee U Shah
- University of Utah School of Medicine, Department of Internal Medicine, Division of Cardiovascular Medicine, Salt Lake City UT, United States of America
| | - Lindsay T Keegan
- University of Utah School of Medicine, Department of Internal Medicine, Division of Epidemiology, Salt Lake City UT, United States of America
| | - Matthew J Ferrari
- The Pennsylvania State University, Department of Biology, State College, PA, United States of America
| | - Daniel T Leung
- University of Utah School of Medicine, Department of Internal Medicine, Division of Infectious Diseases, Salt Lake City UT, United States of America
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15
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Brintz BJ, Haaland B, Howard J, Chao DL, Proctor JL, Khan AI, Ahmed SM, Keegan LT, Greene T, Keita AM, Kotloff KL, Platts-Mills JA, Nelson EJ, Levine AC, Pavia AT, Leung DT. A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea. eLife 2021; 10:63009. [PMID: 33527894 PMCID: PMC7853717 DOI: 10.7554/elife.63009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 01/17/2021] [Indexed: 11/13/2022] Open
Abstract
Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where 'pre-test’ epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.
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Affiliation(s)
- Ben J Brintz
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, United States.,Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, United States
| | - Benjamin Haaland
- Population Health Sciences, University of Utah, Salt Lake City, United States
| | - Joel Howard
- Division of Pediatric Infectious Diseases, University of Utah, Salt Lake City, United States
| | - Dennis L Chao
- Institute of Disease Modeling, Bill and Melinda Gates Foundation, Seattle, United States
| | - Joshua L Proctor
- Institute of Disease Modeling, Bill and Melinda Gates Foundation, Seattle, United States
| | - Ashraful I Khan
- International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Sharia M Ahmed
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, United States
| | - Lindsay T Keegan
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, United States
| | - Tom Greene
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, United States
| | | | - Karen L Kotloff
- Division of Infectious Disease and Tropical Pediatrics, University of Maryland, Baltimore, United States
| | - James A Platts-Mills
- Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, United States
| | - Eric J Nelson
- Departments of Pediatrics, University of Florida, Gainesville, United States.,Departments of Environmental and Global Health, University of Florida, Gainesville, United States
| | - Adam C Levine
- Department of Emergency Medicine, Brown University, Providence, United States
| | - Andrew T Pavia
- Division of Pediatric Infectious Diseases, University of Utah, Salt Lake City, United States
| | - Daniel T Leung
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, United States.,Division of Microbiology and Immunology, Department of Internal Medicine, University of Utah, Salt Lake City, United States
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16
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Khader K, Thomas A, Huskins WC, Stevens V, Keegan LT, Visnovsky L, Samore MH. Effectiveness of Contact Precautions to Prevent Transmission of Methicillin-Resistant Staphylococcus aureus and Vancomycin-Resistant Enterococci in Intensive Care Units. Clin Infect Dis 2021; 72:S42-S49. [PMID: 33512528 PMCID: PMC7844588 DOI: 10.1093/cid/ciaa1603] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Contact precautions for endemic methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE) are under increasing scrutiny, in part due to limited clinical trial evidence. METHODS We retrospectively analyzed data from the Strategies to Reduce Transmission of Antimicrobial Resistant Bacteria in Intensive Care Units (STAR*ICU) trial to model the use of contact precautions in individual intensive care units (ICUs). Data included admission and discharge times and surveillance test results. We used a transmission model to estimate key epidemiological parameters, including the effect of contact precautions on transmission. Finally, we performed multivariate meta-regression to identify ICU-level factors associated with contact precaution effects. RESULTS We found that 21% of admissions (n = 2194) were placed on contact precautions, with most for MRSA and VRE. We found little evidence that contact precautions reduced MRSA transmission. The estimated change in transmission attributed to contact precautions was -16% (95% credible interval, -38% to 15%). VRE transmission was higher than MRSA transmission due to contact precautions, but not significantly. In our meta-regression, we did not identify associations between ICU-level factors and estimated contact precaution effects. Importation and transmission were higher for VRE than for MRSA, but clearance rates were lower for VRE than for MRSA. CONCLUSIONS We found little evidence that contact precautions implemented during the STAR*ICU trial reduced transmission of MRSA or VRE. We did find important differences in the transmission dynamics between MRSA and VRE. Differences in organism and healthcare setting may impact the efficacy of contact precautions.
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Affiliation(s)
- Karim Khader
- Informatics, Decision-Enhancement, and Analytical Sciences Center of Innovation, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Alun Thomas
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - W Charles Huskins
- Division of Pediatric Infectious Diseases, Mayo Clinic, Rochester, Minnesota, USA
| | - Vanessa Stevens
- Informatics, Decision-Enhancement, and Analytical Sciences Center of Innovation, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Lindsay T Keegan
- Informatics, Decision-Enhancement, and Analytical Sciences Center of Innovation, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Lindsay Visnovsky
- Informatics, Decision-Enhancement, and Analytical Sciences Center of Innovation, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Matthew H Samore
- Informatics, Decision-Enhancement, and Analytical Sciences Center of Innovation, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah, USA
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17
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Toth DJA, Keegan LT, Samore MH, Khader K, O'Hagan JJ, Yu H, Quintana A, Swerdlow DL. Modeling the potential impact of administering vaccines against Clostridioides difficile infection to individuals in healthcare facilities. Vaccine 2020; 38:5927-5932. [PMID: 32703744 DOI: 10.1016/j.vaccine.2020.06.081] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 06/25/2020] [Accepted: 06/30/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND A vaccine against Clostridioides difficile infection (CDI) is in development. While the vaccine has potential to both directly protect those vaccinated and mitigate transmission by reducing environmental contamination, the impact of the vaccine on C. difficile colonization remains unclear. Consequently, the transmission-reduction effect of the vaccine depends on the contribution of symptomatic CDI to overall transmission of C. difficile. METHODS We designed a simulation model of CDI among patients in a network of 10 hospitals and nursing homes and calibrated the model using estimates of transmissibility from whole genome sequencing studies that estimated the fraction of CDI attributable to transmission from other CDI patients. We assumed the vaccine reduced the rate of progression to CDI among carriers by 25-95% after completion of a 3-dose vaccine course administered to randomly chosen patients at facility discharge. We simulated the administration of this vaccination campaign and tallied effects over 5 years. RESULTS We estimated 30 times higher infectivity of CDI patients compared to other carriers. Simulations of the vaccination campaign produced an average reduction of 3-16 CDI cases per 1000 vaccinated patients, with 2-11 of those cases prevented among those vaccinated and 1-5 prevented among unvaccinated patients. CONCLUSIONS Our findings demonstrate potential for a vaccine against CDI to reduce transmissions in healthcare facilities, even with no direct effect on carriage susceptibility. The vaccine's population impact will increase if received by individuals at risk for CDI onset in high-transmission settings.
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Affiliation(s)
- Damon J A Toth
- Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA; Department of Internal Medicine (Epidemiology), University of Utah, Salt Lake City, UT, USA.
| | - Lindsay T Keegan
- Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA; Department of Internal Medicine (Epidemiology), University of Utah, Salt Lake City, UT, USA
| | - Matthew H Samore
- Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA; Department of Internal Medicine (Epidemiology), University of Utah, Salt Lake City, UT, USA.
| | - Karim Khader
- Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA; Department of Internal Medicine (Epidemiology), University of Utah, Salt Lake City, UT, USA
| | - Justin J O'Hagan
- Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
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18
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Reimer JR, Ahmed SM, Brintz B, Shah RU, Keegan LT, Ferrari MJ, Leung DT. Modeling reductions in SARS-CoV-2 transmission and hospital burden achieved by prioritizing testing using a clinical prediction rule. medRxiv 2020:2020.07.07.20148510. [PMID: 32676615 PMCID: PMC7359540 DOI: 10.1101/2020.07.07.20148510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Prompt identification of cases is critical for slowing the spread of COVID-19. However, many areas have faced diagnostic testing shortages, requiring difficult decisions to be made regarding who receives a test, without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. We used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive, and found that its application to prioritize testing increases the proportion of those testing positive in settings of limited testing capacity. To consider the implications of these gains in daily case detection on the population level, we incorporated testing using the CPR into a compartmentalized disease transmission model. We found that prioritized testing led to a delayed and lowered infection peak (i.e. 'flattens the curve'), with the greatest impact at lower values of the effective reproductive number (such as with concurrent social distancing measures), and when higher proportions of infectious persons seek testing. Additionally, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit (ICU) burden. In conclusion, we present a novel approach to evidence-based allocation of limited diagnostic capacity, to achieve public health goals for COVID-19.
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Affiliation(s)
- Jody R. Reimer
- University of Utah, Department of Mathematics, Salt Lake City, UT
| | - Sharia M. Ahmed
- University of Utah School of Medicine, Department of Internal Medicine, Division of Infectious Diseases, Salt Lake City UT
| | - Benjamin Brintz
- University of Utah School of Medicine, Department of Internal Medicine, Division of Infectious Diseases, Salt Lake City UT
- University of Utah School of Medicine, Department of Internal Medicine, Division of Epidemiology, Salt Lake City UT
| | - Rashmee U. Shah
- University of Utah School of Medicine, Department of Internal Medicine, Division of Cardiovascular Medicine, Salt Lake City UT
| | - Lindsay T. Keegan
- University of Utah School of Medicine, Department of Internal Medicine, Division of Epidemiology, Salt Lake City UT
| | - Matthew J. Ferrari
- The Pennsylvania State University, Department of Biology, State College, PA
| | - Daniel T. Leung
- University of Utah School of Medicine, Department of Internal Medicine, Division of Infectious Diseases, Salt Lake City UT
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19
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Truelove SA, Keegan LT, Moss WJ, Chaisson LH, Macher E, Azman AS, Lessler J. Clinical and Epidemiological Aspects of Diphtheria: A Systematic Review and Pooled Analysis. Clin Infect Dis 2020; 71:89-97. [PMID: 31425581 PMCID: PMC7312233 DOI: 10.1093/cid/ciz808] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 08/16/2019] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Diphtheria, once a major cause of childhood morbidity and mortality, all but disappeared following introduction of diphtheria vaccine. Recent outbreaks highlight the risk diphtheria poses when civil unrest interrupts vaccination and healthcare access. Lack of interest over the last century resulted in knowledge gaps about diphtheria's epidemiology, transmission, and control. METHODS We conducted 9 distinct systematic reviews on PubMed and Scopus (March-May 2018). We pooled and analyzed extracted data to fill in these key knowledge gaps. RESULTS We identified 6934 articles, reviewed 781 full texts, and included 266. From this, we estimate that the median incubation period is 1.4 days. On average, untreated cases are colonized for 18.5 days (95% credible interval [CrI], 17.7-19.4 days), and 95% clear Corynebacterium diphtheriae within 48 days (95% CrI, 46-51 days). Asymptomatic carriers cause 76% (95% confidence interval, 59%-87%) fewer cases over the course of infection than symptomatic cases. The basic reproductive number is 1.7-4.3. Receipt of 3 doses of diphtheria toxoid vaccine is 87% (95% CrI, 68%-97%) effective against symptomatic disease and reduces transmission by 60% (95% CrI, 51%-68%). Vaccinated individuals can become colonized and transmit; consequently, vaccination alone can only interrupt transmission in 28% of outbreak settings, making isolation and antibiotics essential. While antibiotics reduce the duration of infection, they must be paired with diphtheria antitoxin to limit morbidity. CONCLUSIONS Appropriate tools to confront diphtheria exist; however, accurate understanding of the unique characteristics is crucial and lifesaving treatments must be made widely available. This comprehensive update provides clinical and public health guidance for diphtheria-specific preparedness and response.
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Affiliation(s)
- Shaun A Truelove
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Lindsay T Keegan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - William J Moss
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- International Vaccine Access Center, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Lelia H Chaisson
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | - Andrew S Azman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- Médecins Sans Frontières, Geneva, Switzerland
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
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20
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Abstract
Simulation studies are often used to predict the expected impact of control measures in infectious disease outbreaks. Typically, two independent sets of simulations are conducted, one with the intervention, and one without, and epidemic sizes (or some related metric) are compared to estimate the effect of the intervention. Since it is possible that controlled epidemics are larger than uncontrolled ones if there is substantial stochastic variation between epidemics, uncertainty intervals from this approach can include a negative effect even for an effective intervention. To more precisely estimate the number of cases an intervention will prevent within a single epidemic, here we develop a 'single-world' approach to matching simulations of controlled epidemics to their exact uncontrolled counterfactual. Our method borrows concepts from percolation approaches, prunes out possible epidemic histories and creates potential epidemic graphs (i.e. a mathematical representation of all consistent epidemics) that can be 'realized' to create perfectly matched controlled and uncontrolled epidemics. We present an implementation of this method for a common class of compartmental models (e.g. SIR models), and its application in a simple SIR model. Results illustrate how, at the cost of some computation time, this method substantially narrows confidence intervals and avoids nonsensical inferences. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.
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Affiliation(s)
- Joshua Kaminsky
- 1 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD , USA
| | - Lindsay T Keegan
- 1 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD , USA
| | - C Jessica E Metcalf
- 1 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD , USA.,2 Department of Ecology and Evolutionary Biology, Princeton University , Princeton, NJ , USA
| | - Justin Lessler
- 1 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD , USA
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21
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Khader K, Thomas A, Visnovsky LD, Toth D, Keegan LT, Jones MM, Rubin M, Samore MH. 1839. Contact Precautions’ Effects on MRSA Transmission in Department of Veterans Affairs Hospitals. Open Forum Infect Dis 2019. [PMCID: PMC6808845 DOI: 10.1093/ofid/ofz359.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
In 2007, the Department of Veterans Affairs (VA) implemented the methicillin-resistant Staphylococcus aureus (MRSA) Prevention Initiative nationally in acute care facilities (ACFs). The initiative included universal nasal surveillance for MRSA colonization and implementation of contact precautions (CP) for identified carriers for the duration of their stay. Despite subsequent declines in MRSA infection rates in the VA, debate on CP efficacy continues, due to limited and inconclusive direct evidence. This study estimated CP impact on MRSA transmission in the VA.
Methods
We analyzed 1 year of data from 36 VA ACFs in 2014 using a Bayesian transmission model. The data included admission, discharge, and surveillance and clinical test results for MRSA. Per the MRSA Prevention Initiative protocol that placed known carriers on CP, we assumed patients were on CP starting 12 hours after a positive surveillance test, 24 hours after a positive clinical culture, or at admission if the patient had a positive test within 365 days prior to admission. Our model produced estimates of ward-specific transmission rate, surveillance test sensitivity, importation probability, and the CP effect parameter (CPe). For CPe < 1, CP reduced transmission. Additionally, we combined the estimates of CPe using a random-effects model with inverse variance weights to derive pooled estimates and corresponding standard errors.
Results
Facility size varied with a median daily census of 70 patients per day (range: 44–111). During the study period, 144,386 individuals were admitted into one of 36 ACFs, for 215,207 total admissions. The median percentage of admissions requiring contact precautions was 11.0% (range: 6.4%–16.1%). The estimated CPe was less than one in each of the 36 facilities with a median of 0.43 (range: 0.25–0.68). Our pooled estimate of CPe across all facilities was 0.47 (95% CI; 0.40, 0.55).
Conclusion
We found evidence of reduced MRSA transmission from patients on CP. This result was statistically significant in 5 of the 36 facilities and our pooled estimate suggests contact precautions could reduce the transmission rate by half. Further work is needed to account for imperfect compliance with CP, and for patients on CP for other reasons.
Disclosures
All Authors: No reported Disclosures.
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Affiliation(s)
- Karim Khader
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
| | - Alun Thomas
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah
| | - Lindsay D Visnovsky
- University of Utah School of Medicine and IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
| | - Damon Toth
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
| | - Lindsay T Keegan
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah
| | - Makoto M Jones
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
| | - Michael Rubin
- IDEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
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22
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Lauer SA, Sakrejda K, Ray EL, Keegan LT, Bi Q, Suangtho P, Hinjoy S, Iamsirithaworn S, Suthachana S, Laosiritaworn Y, Cummings DAT, Lessler J, Reich NG. Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010-2014. Proc Natl Acad Sci U S A 2018; 115:E2175-E2182. [PMID: 29463757 PMCID: PMC5877997 DOI: 10.1073/pnas.1714457115] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Dengue hemorrhagic fever (DHF), a severe manifestation of dengue viral infection that can cause severe bleeding, organ impairment, and even death, affects between 15,000 and 105,000 people each year in Thailand. While all Thai provinces experience at least one DHF case most years, the distribution of cases shifts regionally from year to year. Accurately forecasting where DHF outbreaks occur before the dengue season could help public health officials prioritize public health activities. We develop statistical models that use biologically plausible covariates, observed by April each year, to forecast the cumulative DHF incidence for the remainder of the year. We perform cross-validation during the training phase (2000-2009) to select the covariates for these models. A parsimonious model based on preseason incidence outperforms the 10-y median for 65% of province-level annual forecasts, reduces the mean absolute error by 19%, and successfully forecasts outbreaks (area under the receiver operating characteristic curve = 0.84) over the testing period (2010-2014). We find that functions of past incidence contribute most strongly to model performance, whereas the importance of environmental covariates varies regionally. This work illustrates that accurate forecasts of dengue risk are possible in a policy-relevant timeframe.
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Affiliation(s)
- Stephen A Lauer
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003;
| | - Krzysztof Sakrejda
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003
| | - Evan L Ray
- Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, MA 01075
| | - Lindsay T Keegan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | - Qifang Bi
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | - Paphanij Suangtho
- Bureau of Epidemiology, Ministry of Public Health, Nonthaburi 11000, Thailand
| | - Soawapak Hinjoy
- Bureau of Epidemiology, Ministry of Public Health, Nonthaburi 11000, Thailand
| | - Sopon Iamsirithaworn
- Department of Disease Control, Bureau of Epidemiology, Ministry of Public Health, Nonthaburi 11000, Thailand
| | - Suthanun Suthachana
- Bureau of Epidemiology, Ministry of Public Health, Nonthaburi 11000, Thailand
| | | | - Derek A T Cummings
- Department of Biology and the Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | - Nicholas G Reich
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003
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23
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Abstract
When Zika virus (ZIKV) emerged in the Americas, little was known about its biology, pathogenesis, and transmission potential, and the scope of the epidemic was largely hidden, owing to generally mild infections and no established surveillance systems. Surges in congenital defects and Guillain-Barré syndrome alerted the world to the danger of ZIKV. In the context of limited data, quantitative models were critical in reducing uncertainties and guiding the global ZIKV response. Here, we review some of the models used to assess the risk of ZIKV-associated severe outcomes, the potential speed and size of ZIKV epidemics, and the geographic distribution of ZIKV risk. These models provide important insights and highlight significant unresolved questions related to ZIKV and other emerging pathogens.
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Affiliation(s)
- Lindsay T Keegan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Michael A Johansson
- Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
- Department of Epidemiology, T. H. Chan School of Public Health, Boston, Massachusetts
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