1
|
Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee CD, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Ben Toh K, Longini I, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox SJ, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, Lessler J. Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty. Nat Commun 2023; 14:7260. [PMID: 37985664 PMCID: PMC10661184 DOI: 10.1038/s41467-023-42680-x] [Citation(s) in RCA: 1] [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: 07/07/2023] [Accepted: 10/17/2023] [Indexed: 11/22/2023] Open
Abstract
Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.
Collapse
Affiliation(s)
- Emily Howerton
- The Pennsylvania State University, University Park, PA, USA.
| | | | - Luke C Mullany
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | | | | | - Samantha Bents
- National Institutes of Health Fogarty International Center, Bethesda, MD, USA
| | - Rebecca K Borchering
- The Pennsylvania State University, University Park, PA, USA
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Sung-Mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sara L Loo
- Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | - J Espino
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | | | | | - Sen Pei
- Columbia University, New York, NY, USA
| | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Lauren Shin
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | | | | | | | | | | | - Alison Hill
- Johns Hopkins University, Baltimore, MD, USA
| | - Dean Karlen
- University of Victoria, Victoria, BC, Canada
| | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | - Julie S Ivy
- North Carolina State University, Raleigh, NC, USA
| | | | | | | | - Sean Cavany
- University of Notre Dame, Notre Dame, IN, USA
| | - Sean Moore
- University of Notre Dame, Notre Dame, IN, USA
| | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | | | | | - Kaiming Bi
- University of Texas at Austin, Austin, TX, USA
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, VA, USA
| | | | - Dustin Machi
- University of Virginia, Charlottesville, VA, USA
| | - Betsy L Cadwell
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jessica M Healy
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | | | | | | | - Michael C Runge
- U.S. Geological Survey Eastern Ecological Science Center, Laurel, MD, USA
| | - Katriona Shea
- The Pennsylvania State University, University Park, PA, USA
| | - Cécile Viboud
- National Institutes of Health Fogarty International Center, Bethesda, MD, USA.
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Johns Hopkins University, Baltimore, MD, USA.
| |
Collapse
|
2
|
Jung SM, Loo SL, Howerton E, Contamin L, Smith CP, Carcelén EC, Yan K, Bents SJ, Levander J, Espino J, Lemaitre JC, Sato K, McKee CD, Hill AL, Chinazzi M, Davis JT, Mu K, Vespignani A, Rosenstrom ET, Rodriguez-Cartes SA, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore SM, Perkins A, Chen S, Paul R, Janies D, Thill JC, Srivastava A, Al Aawar M, Bi K, Bandekar SR, Bouchnita A, Fox SJ, Meyers LA, Porebski P, Venkatramanan S, Adiga A, Hurt B, Klahn B, Outten J, Chen J, Mortveit H, Wilson A, Hoops S, Bhattacharya P, Machi D, Vullikanti A, Lewis B, Marathe M, Hochheiser H, Runge MC, Shea K, Truelove S, Viboud C, Lessler J. Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub. medRxiv 2023:2023.10.26.23297581. [PMID: 37961207 PMCID: PMC10635209 DOI: 10.1101/2023.10.26.23297581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Importance COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Objective To project COVID-19 hospitalizations and deaths from April 2023-April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups). Design The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023-April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario. Setting The entire United States. Participants None. Exposure Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. Main outcomes and measures Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period. Results From April 15, 2023-April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November-January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000-4,270,000) hospitalizations and 209,000 (90% PI: 139,000-461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000-355,000) fewer hospitalizations and 33,000 (95% CI: 12,000-54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI: 29,000-69,000) fewer deaths. Conclusion and Relevance COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease.
Collapse
Affiliation(s)
- Sung-mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Sara L. Loo
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Emily Howerton
- The Pennsylvania State University, State College, Pennsylvania
| | | | - Claire P. Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Erica C. Carcelén
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Katie Yan
- The Pennsylvania State University, State College, Pennsylvania
| | - Samantha J. Bents
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
| | | | - Jessi Espino
- University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Joseph C. Lemaitre
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Koji Sato
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Clif D. McKee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alison L. Hill
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | | | - Kunpeng Mu
- University of Massachusetts Amherst, Amherst, Massachusetts
| | | | | | | | - Julie S. Ivy
- North Carolina State University, Raleigh, North Carolina
| | | | - Julie L. Swann
- North Carolina State University, Raleigh, North Carolina
| | | | - Sean Cavany
- University of Notre Dame, Notre Dame, Indiana
| | | | | | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Jean-Claude Thill
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | | | - Majd Al Aawar
- University of Southern California, Los Angeles, California
| | - Kaiming Bi
- University of Texas at Austin, Austin, Texas
| | | | | | | | | | | | | | | | | | - Brian Klahn
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, Virginia
| | | | - Dustin Machi
- University of Virginia, Charlottesville, Virginia
| | | | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia
| | | | | | | | - Katriona Shea
- The Pennsylvania State University, State College, Pennsylvania
| | - Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| |
Collapse
|
3
|
Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee C, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Toh KB, Longini I, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox S, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, Lessler J. Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub. medRxiv 2023:2023.06.28.23291998. [PMID: 37461674 PMCID: PMC10350156 DOI: 10.1101/2023.06.28.23291998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make 6-month ahead projections of the number of SARS-CoV-2 cases, hospitalizations and deaths. The SMH released nearly 1.8 million national and state-level projections between February 2021 and November 2022. SMH performance varied widely as a function of both scenario validity and model calibration. Scenario assumptions were periodically invalidated by the arrival of unanticipated SARS-CoV-2 variants, but SMH still provided projections on average 22 weeks before changes in assumptions (such as virus transmissibility) invalidated scenarios and their corresponding projections. During these periods, before emergence of a novel variant, a linear opinion pool ensemble of contributed models was consistently more reliable than any single model, and projection interval coverage was near target levels for the most plausible scenarios (e.g., 79% coverage for 95% projection interval). SMH projections were used operationally to guide planning and policy at different stages of the pandemic, illustrating the value of the hub approach for long-term scenario projections.
Collapse
Affiliation(s)
| | | | | | | | | | - Samantha Bents
- National Institutes of Health Fogarty International Center (NIH)
| | | | | | - Sara L Loo
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | - Claire P Smith
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte (UNCC)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
4
|
Borchering RK, Mullany LC, Howerton E, Chinazzi M, Smith CP, Qin M, Reich NG, Contamin L, Levander J, Kerr J, Espino J, Hochheiser H, Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Hulse JD, Kaminsky J, Lee EC, Hill AL, 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, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, España G, Cavany S, Moore S, Perkins A, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Shea K, Truelove SA, Runge MC, Viboud C, Lessler J. Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: A multi-model study. Lancet Reg Health Am 2023; 17:100398. [PMID: 36437905 PMCID: PMC9679449 DOI: 10.1016/j.lana.2022.100398] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/21/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022]
Abstract
Background The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5-11 years on COVID-19 burden and resilience against variant strains. Methods Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5-11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5-11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5-11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding Various (see acknowledgments).
Collapse
Affiliation(s)
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Emily Howerton
- The Pennsylvania State University, University Park, PA, USA
| | | | | | | | | | | | | | | | - J. Espino
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Kaitlin Lovett
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | | | | | | | | | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, VA, USA
| | | | - Dustin Machi
- University of Virginia, Charlottesville, VA, USA
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | | | | | | | - Sen Pei
- Columbia University, New York, NY, USA
| | | | | | - Sean Cavany
- University of Notre Dame, Notre Dame, IN, USA
| | - Sean Moore
- University of Notre Dame, Notre Dame, IN, USA
| | | | - Jessica M. Healy
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rachel B. Slayton
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Michael A. Johansson
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthew Biggerstaff
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Katriona Shea
- The Pennsylvania State University, University Park, PA, USA
| | | | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Borchering RK, Mullany LC, Howerton E, Chinazzi M, Smith CP, Qin M, Reich NG, Contamin L, Levander J, Kerr J, Espino J, Hochheiser H, Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Hulse JD, Kaminsky J, Lee EC, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, Espana G, Cavany S, Moore S, Perkins A, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Shea K, Truelove SA, Runge MC, Viboud C, Lessler J. Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: a multi-model study. medRxiv 2022:2022.03.08.22271905. [PMID: 35313593 PMCID: PMC8936106 DOI: 10.1101/2022.03.08.22271905] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 01/26/2023]
Abstract
Background SARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains. Methods Nine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches. Findings Absent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts. Conclusions Results from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.
Collapse
Affiliation(s)
| | | | | | | | - Claire P Smith
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte (UNCC)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Cécile Viboud
- National Institutes of Health Fogarty International Center
| | | |
Collapse
|
7
|
Shafranska O, Jones A, Perkins A, Dahlgren J, Tardiff J, Webster DC. Low‐unsaturated soybean oils in
EPDM
rubber compounds. J Appl Polym Sci 2022. [DOI: 10.1002/app.51499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Olena Shafranska
- Department of Coatings and Polymeric Materials North Dakota State University Fargo North Dakota USA
| | | | | | - Joseph Dahlgren
- Department of Coatings and Polymeric Materials North Dakota State University Fargo North Dakota USA
| | - Janice Tardiff
- Department of Coatings and Polymeric Materials North Dakota State University Fargo North Dakota USA
| | - Dean C. Webster
- Department of Coatings and Polymeric Materials North Dakota State University Fargo North Dakota USA
| |
Collapse
|
8
|
Darling S, Dawson G, Quach J, Smith R, Perkins A, Connolly A, Smith A, Moore CL, Ride J, Oberklaid F. Mental health and wellbeing coordinators in primary schools to support student mental health: protocol for a quasi-experimental cluster study. BMC Public Health 2021; 21:1467. [PMID: 34320975 PMCID: PMC8316894 DOI: 10.1186/s12889-021-11467-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 05/20/2021] [Accepted: 07/09/2021] [Indexed: 11/13/2022] Open
Abstract
Background Half of mental health disorders begin before the age of 14, highlighting the importance of prevention and early-intervention in childhood. Schools have been identified globally by policymakers as a platform to support good child mental health; however, the majority of the research is focused on secondary schools, with primary schools receiving very little attention by comparison. The limited available evidence on mental health initiatives in primary schools is hindered by a lack of rigorous evaluation. This quasi-experimental cluster study aims to examine the implementation and effectiveness of a Mental Health and Wellbeing Co-ordinator role designed to build mental health capacity within primary schools. Methods This is a primary (ages 5–12) school-based cluster quasi-experimental study in Victoria, Australia. Before baseline data collection, 16 schools selected by the state education department will be allocated to intervention, and another 16 matched schools will continue as ‘Business as Usual’. In intervention schools, a mental health and well-being coordinator will be recruited and trained, and three additional school staff will also be selected to receive components of the mental health training. Surveys will be completed by consenting staff (at 2-, 5-, 10- and 17-months post allocation) and by consenting parents/carers (at 3-, 10- and 17-months post allocation) in both intervention and business as usual schools. The primary objective is to assess the change in teacher’s confidence to support student mental health and wellbeing using the School Mental Health Self-Efficacy Teacher Survey. Secondary objectives are to assess the indirect impact on systemic factors (level of support, prioritisation of child mental health), parent and teachers’ mental health literacy (stigma, knowledge), care access (school engagement with community-based services), and student mental health outcomes. Implementation outcomes (feasibility, acceptability, and fidelity) and costs will also be evaluated. Discussion The current study will examine the implementation and effectiveness of having a trained Mental Health and Wellbeing Coordinator within primary schools. If the intervention increases teachers’ confidence to support student mental health and wellbeing and builds the capacity of primary schools it will improve student mental health provision and inform large-scale mental health service reform. Trial registration The trial was retrospectively registered in the Australian New Zealand Clinical Trials Registry (ANZCTR) on July 6, 2021. The registration number is ACTRN12621000873820.
Collapse
Affiliation(s)
- S Darling
- Centre for Community Child Health, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, USA.,Department of Paediatrics, University of Melbourne, Melbourne, VIC, 3010, USA
| | - G Dawson
- Centre for Program Evaluation, Melbourne Graduate School of Education, University of Melbourne, Carlton, VIC, 3053, USA
| | - J Quach
- Centre for Community Child Health, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, USA.,Centre for Program Evaluation, Melbourne Graduate School of Education, University of Melbourne, Carlton, VIC, 3053, USA
| | - R Smith
- Centre for Community Child Health, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, USA. .,Department of Paediatrics, University of Melbourne, Melbourne, VIC, 3010, USA.
| | - A Perkins
- Centre for Community Child Health, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, USA
| | - A Connolly
- Centre for Community Child Health, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, USA
| | - A Smith
- Centre for Community Child Health, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, USA
| | - C L Moore
- Centre for Community Child Health, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, USA
| | - J Ride
- Centre for Community Child Health, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, USA.,Health Economics Unit, Centre for Health Policy, Melbourne School of Population and Global Health, 207 Bouverie St, Parkville, VIC, 3010, USA
| | - F Oberklaid
- Centre for Community Child Health, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, USA.,Department of Paediatrics, University of Melbourne, Melbourne, VIC, 3010, USA
| |
Collapse
|
9
|
Perkins A, Patterson T, Evans R, Clayton T, Fothergill R, Redwood S. Patient consent in emergency cardiovascular medicine: lessons from the ARREST trial in out-of-hospital cardiac arrest. Eur Heart J 2020. [DOI: 10.1093/ehjci/ehaa946.3571] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Introduction
Randomised trials in emergency cardiovascular medicine are challenging but vital for improving patient care. Obtaining informed consent in such an environment is a particular issue and can be controversial. The ARREST trial is assessing whether out-of-hospital cardiac arrest patients without an obvious cause should be taken to a specialist heart centre or the closest emergency department in London, UK. This patient group presents specific difficulties: patients lack capacity to consent, presentation is unpredictable, care must not be delayed, and mortality rates can be >50%.
Purpose
Within existing consent and methodological frameworks we aimed to design a randomised clinical trial to pragmatically, safely and ethically recruit cardiac arrest patients pre-hospital.
Methods
During the set-up of ARREST we accessed the following sources of information: 1) ARREST research team; 2) cardiovascular patient groups; 3) researchers running similar trials; 4) regulatory bodies; and, 5) published literature on research in emergency contexts. The information that we collected guided the design of the trial with a focus on patient consent, documentation and follow-up.
Results
The ARREST trial uses deferred consent with remote online randomisation to enrol patients without delaying care. To minimise the risk of bias, baseline and primary endpoint data are collected on patients who die or are discharged prior to consent. Remote follow-up using electronic health records reduces the burden on the patients and researchers. Full ethical approval was received in January 2018 and the first patient was enrolled in February 2018. ARREST is recruiting to target and is on track to finish within the projected timelines.
Conclusions
Deferred consent has been key to the success of ARREST and patients have been receptive. However, further research into the experience of patients in emergency cardiovascular medicine trials using deferred consent is needed to better understand when it is an appropriate model. More broadly, there is a shortfall in high quality research in challenging environments such as emergency cardiovascular care. Innovation in consent methods and proportional research governance would facilitate higher quality research and benefit patient care.
Funding Acknowledgement
Type of funding source: Foundation. Main funding source(s): British Heart Foundation
Collapse
Affiliation(s)
- A Perkins
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - T Patterson
- Guys and St Thomas Hospital, London, United Kingdom
| | - R Evans
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - T Clayton
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - R Fothergill
- London Ambulance Service, London, United Kingdom
| | - S Redwood
- Guys and St Thomas Hospital, London, United Kingdom
| |
Collapse
|
10
|
Abstract
Electrical burns are among the most devastating of burn injuries. High voltage electrical injuries result in extensive deep tissue damage and are associ- ated with multiple complications, long term morbidity, and a high mortality rate. we describe the case of a 47 year-old electric company linesman who suffered a high voltage electrical injury (HVEI) of 14,000 volts to bilateral hands and wrists managed by the division of Plastic and Reconstructive Surgery at the Mcgill University Health Center in Montreal, Quebec, Canada. His management included multiple operative procedures, including escharotomies, fasciotomies, serial debridements, and bilat- eral pedicle groin faps, and amputation of his left hand.
Collapse
|
11
|
Abstract
RATIONALE The neuropeptide galanin has been implicated in a wide range of pathological conditions in which frontal and temporal structures are compromised. It works through three subtypes of G-protein-coupled receptors. One of these, the galanin receptor 1 (Gal-R1) subtype, is densely expressed in the ventral hippocampus (vHC) and ventral prefrontal cortex (vPFC); two brain structures that have similar actions on behavioral control. We hypothesize that Gal-R1 contributes to cognitive-control mechanisms that require hippocampal-prefrontal cortical circuitry. OBJECTIVE To examine the effect of local vHC and vPFC infusions of M617, a Gal-R1 agonist, on inhibitory mechanisms of response control. METHODS Different cohorts of rats were implanted with bilateral guide cannulae targeting the vPFC or the vHC. Following infusion of the Gal-R1 agonist, we examined the animals' behavior using a touchscreen version of the 5-choice reaction time task (5-choice task). RESULTS The Gal-R1 agonist produced opposing behaviors in the vPFC and vHC, leading to disruption of impulse control when infused in the vPFC but high impulse control when infused into the vHC. This contrast between areas was accentuated when we added variability to the timing of the stimulus, which led to long decision times and reduced accuracy in the vPFC group but a general improvement in performance accuracy in the vHC group. CONCLUSIONS These results provide the first evidence of a selective mechanism of Gal-R1-mediated modulation of impulse control in prefrontal-hippocampal circuitry.
Collapse
Affiliation(s)
- F Messanvi
- Section on Behavioral Neuroscience, National Institute of Mental Health, Bethesda, MD, USA.
| | - A Perkins
- Section on Behavioral Neuroscience, National Institute of Mental Health, Bethesda, MD, USA
| | - J du Hoffmann
- Rodent Behavioral Core, National Institute of Mental Health, Bethesda, MD, USA
| | - Y Chudasama
- Section on Behavioral Neuroscience, National Institute of Mental Health, Bethesda, MD, USA
- Rodent Behavioral Core, National Institute of Mental Health, Bethesda, MD, USA
| |
Collapse
|
12
|
Aland T, Fitzgerald R, Knesl M, Perkins A, Shannon D, Anderson L, Jones M, Bailey N, Foote M, Dally M. EP-2100 Quality in the implementation of stereotactic radiotherapy services on a national scale. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)32520-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
13
|
Abstract
Physiological and hormonal changes in pregnancy can contribute towards sleep disordered breathing in pregnant women (SDBP). When present, SDBP increases the risk of several adverse maternal and fetal outcomes independent of factors such as age, weight and pre-existing maternal comorbidities. SDBP is underdiagnosed and may be hard to recognise because the presentation can be difficult to differentiate from normal pregnancy and the severity may change over the course of gestation. Timely intervention seems likely to help reduce adverse outcomes, but the relative benefits of intervention are still unclear. The definition of what constitutes a sleep-related breathing “disorder” in pregnancy may be different to the general population and so traditional thresholds for intervention may not be relevant in pregnancy. Any modifications to the disease definition in this group, or implementation of more intensive screening, may result in overdiagnosis. Further research is needed to help clinicians evaluate the balance of benefits and harms in this process. Until this is clearer there is a strong imperative for shared decision making in screening and treatment decisions, and screening programmes should be monitored to assess whether improved outcomes can be achieved at the healthcare system level. Untreated sleep disordered breathing in pregnancy poses risks to maternal and fetal wellbeing, but thresholds for and effectiveness of intervention are unclear. Clinicians should use shared decision making for screening and treatment decisions.http://ow.ly/N0oN30noWnx
Collapse
Affiliation(s)
- Alex Perkins
- Respiratory and Sleep Physiology, College of Human and Health Sciences, Swansea University, Swansea, UK
| | - Alys Einion
- Midwifery and Reproductive Health, College of Human and Health Sciences, Swansea University, Swansea, UK
| |
Collapse
|
14
|
España G, Grefenstette J, Perkins A, Torres C, Campo Carey A, Diaz H, de la Hoz F, Burke DS, van Panhuis WG. Exploring scenarios of chikungunya mitigation with a data-driven agent-based model of the 2014-2016 outbreak in Colombia. Sci Rep 2018; 8:12201. [PMID: 30111778 PMCID: PMC6093909 DOI: 10.1038/s41598-018-30647-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [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: 11/18/2017] [Accepted: 07/25/2018] [Indexed: 11/12/2022] Open
Abstract
New epidemics of infectious diseases can emerge any time, as illustrated by the emergence of chikungunya virus (CHIKV) and Zika virus (ZIKV) in Latin America. During new epidemics, public health officials face difficult decisions regarding spatial targeting of interventions to optimally allocate limited resources. We used a large-scale, data-driven, agent-based simulation model (ABM) to explore CHIKV mitigation strategies, including strategies based on previous DENV outbreaks. Our model represents CHIKV transmission in a realistic population of Colombia with 45 million individuals in 10.6 million households, schools, and workplaces. Our model uses high-resolution probability maps for the occurrence of the Ae. aegypti mosquito vector to estimate mosquito density in Colombia. We found that vector control in all 521 municipalities with mosquito populations led to 402,940 fewer clinical cases of CHIKV compared to a baseline scenario without intervention. We also explored using data about previous dengue virus (DENV) epidemics to inform CHIKV mitigation strategies. Compared to the baseline scenario, 314,437 fewer cases occurred when we simulated vector control only in 301 municipalities that had previously reported DENV, illustrating the value of available data from previous outbreaks. When varying the implementation parameters for vector control, we found that faster implementation and scale-up of vector control led to the greatest proportionate reduction in cases. Using available data for epidemic simulations can strengthen decision making against new epidemic threats.
Collapse
Affiliation(s)
- Guido España
- University of Notre Dame, Department of Biological Sciences and Eck Institute for Global Health, Notre Dame, IN, United States.
| | - John Grefenstette
- University of Pittsburgh, Department of Health Policy and Management, Pittsburgh, PA, United States
| | - Alex Perkins
- University of Notre Dame, Department of Biological Sciences and Eck Institute for Global Health, Notre Dame, IN, United States
| | - Claudia Torres
- Universidad Nacional de Colombia, Department of Electrical Engineering, Bogotá, Colombia
| | - Alfonso Campo Carey
- Colombia Instituto Nacional de Salud, Grupo de Gestión del Riesgo y Respuesta Inmediata, Bogotá, Colombia
| | - Hernando Diaz
- Universidad Nacional de Colombia, Department of Electrical Engineering, Bogotá, Colombia
| | - Fernando de la Hoz
- Universidad Nacional de Colombia, Department of Public Health, Bogotá, Colombia
| | - Donald S Burke
- University of Pittsburgh, Department of Epidemiology, Pittsburgh, PA, United States
| | - Willem G van Panhuis
- University of Pittsburgh, Department of Epidemiology, Pittsburgh, PA, United States
- University of Pittsburgh, Department of Biomedical Informatics, Pittsburgh, PA, United States
| |
Collapse
|
15
|
Entesari-Tatafi D, Perkins A, Monogue T, Goldin J, Kee K. Delayed habitual sleep times in patients undergoing multiple sleep latency testing significantly contributes to test failure. Sleep Med 2017. [DOI: 10.1016/j.sleep.2017.11.454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
16
|
Parker HL, Tucker E, Blackshaw E, Hoad CL, Marciani L, Perkins A, Menne D, Fox M. Clinical assessment of gastric emptying and sensory function utilizing gamma scintigraphy: Establishment of reference intervals for the liquid and solid components of the Nottingham test meal in healthy subjects. Neurogastroenterol Motil 2017; 29. [PMID: 28589661 DOI: 10.1111/nmo.13122] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 05/05/2017] [Indexed: 02/08/2023]
Abstract
BACKGROUND Current investigations of stomach function are based on small test meals that do not reliably induce symptoms and analysis techniques that rarely detect clinically relevant dysfunction. This study presents the reference intervals of the modular "Nottingham test meal" (NTM) for assessment of gastric function by gamma scintigraphy (GSc) in a representative population of healthy volunteers (HVs) stratified for age and sex. METHODS The NTM comprises 400 mL liquid nutrient (0.75 kcal/mL) and an optional solid component (12 solid agar-beads (0 kcal). Filling and dyspeptic sensations were documented by 100 mm visual analogue scale (VAS). Gamma scintigraphy parameters that describe early and late phase Gastric emptying (GE) were calculated from validated models. KEY RESULTS Gastric emptying (GE) of the liquid component was measured in 73 HVs (male 34; aged 45±20). The NTM produced normal postprandial fullness (VAS ≥30 in 41/74 subjects). Dyspeptic symptoms were rare (VAS ≥30 in 2/74 subjects). Gastric emptying half-time with the Liquid- and Solid-component -NTM was median 44 (95% reference interval 28-78) minutes and 162 (144-193) minutes, respectively. Gastric accommodation was assessed by the ratio of the liquid-NTM retained in the proximal:total stomach and by Early phase emptying assessed by gastric volume after completing the meal (GCV0). No consistent effect of anthropometric measures on GE parameters was present. CONCLUSIONS AND INFERENCES Reference intervals are presented for GSc measurements of gastric motor and sensory function assessed by the NTM. Studies involving patients are required to determine whether the reference interval range offers optimal diagnostic sensitivity and specificity.
Collapse
Affiliation(s)
- H L Parker
- NIHR Biomedical Research Unit in Gastrointestinal and Liver Diseases at Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK.,Zürich Neurogastroenterology and Motility Research Group, Department of Gastroenterology and Hepatology, University Hospital Zürich, Zürich, Switzerland.,School of Medicine, Pharmacy and Health, Durham University, Queen's Campus, Stockton-On-Tees, UK.,Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK
| | - E Tucker
- NIHR Biomedical Research Unit in Gastrointestinal and Liver Diseases at Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK
| | - E Blackshaw
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - C L Hoad
- NIHR Biomedical Research Unit in Gastrointestinal and Liver Diseases at Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK.,Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - L Marciani
- NIHR Biomedical Research Unit in Gastrointestinal and Liver Diseases at Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK.,Nottingham Digestive Diseases Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - A Perkins
- Radiological Sciences, School of Medicine, University of Nottingham, Nottingham, UK.,Medical Physics and Clinical Engineering, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - D Menne
- Menne Biomed Consulting, Tübingen, Germany
| | - M Fox
- NIHR Biomedical Research Unit in Gastrointestinal and Liver Diseases at Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK.,Zürich Neurogastroenterology and Motility Research Group, Department of Gastroenterology and Hepatology, University Hospital Zürich, Zürich, Switzerland.,Abdominal Center: Gastroenterology, St. Claraspital, Basel, Switzerland
| |
Collapse
|
17
|
Affiliation(s)
- D.C. Mackey
- Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada,
- University of British Columbia, Vancouver, British Columbia, Canada
| | - A. Perkins
- University of British Columbia, Vancouver, British Columbia, Canada
| | - K. Hong Tai
- University of British Columbia, Vancouver, British Columbia, Canada
| | - J. Sims-Gould
- University of British Columbia, Vancouver, British Columbia, Canada
| | - H.A. McKay
- University of British Columbia, Vancouver, British Columbia, Canada
| |
Collapse
|
18
|
Gradney S, Manchanda S, Sigua NL, Khan SH, Perkins A, Khan B. 0629 PREOPERATIVE STOP-BANG SCORES AND THEIR ASSOCIATION WITH POSTOPERATIVE DELIRIUM AMONG THORACIC SURGERY PATIENTS. Sleep 2017. [DOI: 10.1093/sleepj/zsx050.628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
|
19
|
Dawson PA, Richard K, Perkins A, Zhang Z, Simmons DG. Review: Nutrient sulfate supply from mother to fetus: Placental adaptive responses during human and animal gestation. Placenta 2017; 54:45-51. [PMID: 28089504 DOI: 10.1016/j.placenta.2017.01.001] [Citation(s) in RCA: 7] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2016] [Revised: 12/23/2016] [Accepted: 01/04/2017] [Indexed: 01/20/2023]
Abstract
Nutrient sulfate has numerous roles in mammalian physiology and is essential for healthy fetal growth and development. The fetus has limited capacity to generate sulfate and relies on sulfate supplied from the maternal circulation via placental sulfate transporters. The placenta also has a high sulfate requirement for numerous molecular and cellular functions, including sulfate conjugation (sulfonation) to estrogen and thyroid hormone which leads to their inactivation. Accordingly, the ratio of sulfonated (inactive) to unconjugated (active) hormones modulates endocrine function in fetal, placental and maternal tissues. During pregnancy, there is a marked increase in the expression of genes involved in transport and generation of sulfate in the mouse placenta, in line with increasing fetal and placental demands for sulfate. The maternal circulation also provides a vital reservoir of sulfate for the placenta and fetus, with maternal circulating sulfate levels increasing by 2-fold from mid-gestation. However, despite evidence from animal studies showing the requirement of maternal sulfate supply for placental and fetal physiology, there are no routine clinical measurements of sulfate or consideration of dietary sulfate intake in pregnant women. This is also relevant to certain xenobiotics or pharmacological drugs which when taken by the mother use significant quantities of circulating sulfate for detoxification and clearance, and thereby have the potential to decrease sulfonation capacity in the placenta and fetus. This article will review the physiological adaptations of the placenta for maintaining sulfate homeostasis in the fetus and placenta, with a focus on pathophysiological outcomes in animal models of disturbed sulfate homeostasis.
Collapse
Affiliation(s)
- P A Dawson
- Mater Research Institute, The University of Queensland, Woolloongabba, Australia; School of Biomedical Sciences, The University of Queensland, St. Lucia, Australia.
| | - K Richard
- Conjoint Endocrine Laboratory, Chemical Pathology, Pathology Queensland, Queensland Health, Herston, Australia
| | - A Perkins
- School of Medical Science, Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Australia
| | - Z Zhang
- Mater Research Institute, The University of Queensland, Woolloongabba, Australia; School of Biomedical Sciences, The University of Queensland, St. Lucia, Australia
| | - D G Simmons
- Mater Research Institute, The University of Queensland, Woolloongabba, Australia; School of Biomedical Sciences, The University of Queensland, St. Lucia, Australia
| |
Collapse
|
20
|
Fowler H, Davis MA, Perkins A, Trufan S, Joy C, Buswell M, McElwain TF, Moore D, Worhle R, Rabinowitz PM. A survey of veterinary antimicrobial prescribing practices, Washington State 2015. Vet Rec 2016; 179:651. [PMID: 27807211 DOI: 10.1136/vr.103916] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2016] [Indexed: 01/18/2023]
Abstract
Antimicrobial resistance is a growing global health issue. It is also a recognised problem in veterinary medicine. Between September and December 2015 the authors administered a cross-sectional survey to licensed veterinarians in Washington State to assess factors affecting antimicrobial prescribing practices among veterinarians in Washington State. Two hundred and three veterinarians completed the survey. The majority of respondents (166, 82 per cent) were engaged in small animal or exotic animal practice. 24 per cent of respondents reported not ordering culture and sensitivity (C/S) testing in practice. Of the 76 per cent of veterinarians who reported ordering C/S tests, 36 per cent reported ordering such testing 'often' or 'always' when treating presumptive bacterial infections. Most respondents (65 per cent) mentioned cost as the most common barrier to ordering a C/S test. Only 16 (10 per cent) respondents reported having access to or utilising a clinic-specific antibiogram. This survey demonstrated that while antimicrobials are commonly used in veterinary practice, and veterinarians are concerned about antimicrobial resistance, cost is a barrier to obtaining C/S tests to guide antimicrobial therapy. Summaries of antimicrobial resistance patterns are rarely available to the practising veterinarian. Efforts to promote antimicrobial stewardship in a 'One Health' manner should address barriers to the judicious use of antimicrobials in the veterinary practice setting.
Collapse
Affiliation(s)
- H Fowler
- Department of Occupational and Environmental Health Sciences, Center for One Health Research (COHR), University of Washington School of Public Health, Seattle, Washington, USA
| | - M A Davis
- Washington State One Health Veterinary Workgroup
| | - A Perkins
- Washington State One Health Veterinary Workgroup
| | - S Trufan
- Department of Occupational and Environmental Health Sciences, Center for One Health Research (COHR), University of Washington School of Public Health, Seattle, Washington, USA
| | - C Joy
- Washington State One Health Veterinary Workgroup
| | - M Buswell
- Washington State One Health Veterinary Workgroup
| | - T F McElwain
- Washington State One Health Veterinary Workgroup
| | - D Moore
- Washington State One Health Veterinary Workgroup
| | - R Worhle
- Washington State One Health Veterinary Workgroup
| | - P M Rabinowitz
- Department of Occupational and Environmental Health Sciences, Center for One Health Research (COHR), University of Washington School of Public Health, Seattle, Washington, USA
| |
Collapse
|
21
|
|
22
|
Pistel B, Perkins A, Zaragoza A, Lebby P. C-78Ecological Validity of Neurocognitive Testing in Spanish-Speaking Children and Adolescents. Arch Clin Neuropsychol 2016. [DOI: 10.1093/arclin/acw043.227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|
23
|
Pistel B, Perkins A, Zaragoza A, Lebby P. C-79Clinical Utility of the LANSE-A in Forensic Settings. Arch Clin Neuropsychol 2016. [DOI: 10.1093/arclin/acw043.228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
24
|
Parker HL, Tucker E, Hoad CL, Pal A, Costigan C, Hudders N, Perkins A, Blackshaw E, Gowland P, Marciani L, Fox MR. Development and validation of a large, modular test meal with liquid and solid components for assessment of gastric motor and sensory function by non-invasive imaging. Neurogastroenterol Motil 2016; 28:554-68. [PMID: 26863609 DOI: 10.1111/nmo.12752] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 11/16/2015] [Indexed: 12/12/2022]
Abstract
BACKGROUND Current investigations of stomach function are based on small test meals that do not reliably induce symptoms and analysis techniques that rarely detect clinically relevant dysfunction. This study introduces the large 'Nottingham Test Meal' (NTM) for assessment of gastric motor and sensory function by non-invasive imaging. METHODS NTM comprises 400 mL liquid nutrient (0.75 kcal/mL) and 12 solid agar-beads (0 kcal) with known breaking strength. Gastric fullness and dyspeptic sensations were documented by 100 mm visual analogue scale (VAS). Gastric emptying (GE) were measured in 24 healthy volunteers (HVs) by gastric scintigraphy (GS) and magnetic resonance imaging (MRI). The contribution of secretion to gastric volume was assessed. Parameters that describe GE were calculated from validated models. Inter-observer agreement and reproducibility were assessed. KEY RESULTS NTM produced moderate fullness (VAS ≥30) but no more than mild dyspeptic symptoms (VAS <30) in 24 HVs. Stable binding of meal components to labels in gastric conditions was confirmed. Distinct early and late-phase GE were detected by both modalities. Liquid GE half-time was median 49 (95% CI: 36-62) min and 68 (57-71) min for GS and MRI, respectively. Differences between GS and MRI measurements were explained by the contribution of gastric secretion. Breaking strength for agar-beads was 0.8 N/m(2) such that median 25 (8-50) % intact agar-beads and 65 (47-74) % solid material remained at 120 min on MRI and GS, respectively. Good reproducibility for liquid GE parameters was present and GE was not altered by agar-beads. CONCLUSIONS & INFERENCES The NTM provided an objective assessment of gastric motor and sensory function. The results were reproducible and liquid emptying was not affected by non-nutrient agar-beads. The method is potentially suitable for clinical practice.
Collapse
Affiliation(s)
- H L Parker
- NIHR Nottingham Digestive Diseases Biomedical Research Unit and Nottingham Digestive Diseases Centre, School of Medicine, Nottingham University Hospital, University of Nottingham, Nottingham, UK.,Zürich Neurogastroenterology and Motility Research Group, Department of Gastroenterology and Hepatology, University Hospital Zürich, Zürich, Switzerland
| | - E Tucker
- NIHR Nottingham Digestive Diseases Biomedical Research Unit and Nottingham Digestive Diseases Centre, School of Medicine, Nottingham University Hospital, University of Nottingham, Nottingham, UK
| | - C L Hoad
- NIHR Nottingham Digestive Diseases Biomedical Research Unit and Nottingham Digestive Diseases Centre, School of Medicine, Nottingham University Hospital, University of Nottingham, Nottingham, UK.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
| | - A Pal
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology, Kanpur, India
| | - C Costigan
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
| | - N Hudders
- NIHR Nottingham Digestive Diseases Biomedical Research Unit and Nottingham Digestive Diseases Centre, School of Medicine, Nottingham University Hospital, University of Nottingham, Nottingham, UK
| | - A Perkins
- Radiological Sciences, School of Medicine, University of Nottingham, Nottingham, UK.,Medical Physics and Clinical Engineering, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - E Blackshaw
- Medical Physics and Clinical Engineering, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - P Gowland
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
| | - L Marciani
- NIHR Nottingham Digestive Diseases Biomedical Research Unit and Nottingham Digestive Diseases Centre, School of Medicine, Nottingham University Hospital, University of Nottingham, Nottingham, UK.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
| | - M R Fox
- NIHR Nottingham Digestive Diseases Biomedical Research Unit and Nottingham Digestive Diseases Centre, School of Medicine, Nottingham University Hospital, University of Nottingham, Nottingham, UK.,Zürich Neurogastroenterology and Motility Research Group, Department of Gastroenterology and Hepatology, University Hospital Zürich, Zürich, Switzerland.,Department of Gastroenterology, St. Claraspital, Basel, Switzerland
| |
Collapse
|
25
|
|
26
|
Cunningham B, Vercellini D, Mann S, Card A, Perkins A, Pistel B, Lebby P. DIVERSITYB-73Clinical Implications for the Assessment of Neurocognitive Performance in Rural Migrant Spanish-Speaking Children and Adolescents. Arch Clin Neuropsychol 2015. [DOI: 10.1093/arclin/acv047.168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
27
|
Card A, Vercellini D, Mann S, Cunningham B, Pistel B, Perkins A, Lebby P. B-55Cultural Influence on Neurocognitive Functioning: A Comparison of Rural Migrant Spanish-Speaking Children and Adolescents with English-Speaking Normal and Brain Injured Participants. Arch Clin Neuropsychol 2015. [DOI: 10.1093/arclin/acv047.150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
|
28
|
Abstract
Extensions of Einstein gravity with higher-order derivative terms arise in string theory and other effective theories, as well as being of interest in their own right. In this Letter we study static black-hole solutions in the example of Einstein gravity with additional quadratic curvature terms. A Lichnerowicz-type theorem simplifies the analysis by establishing that they must have vanishing Ricci scalar curvature. By numerical methods we then demonstrate the existence of further black-hole solutions over and above the Schwarzschild solution. We discuss some of their thermodynamic properties, and show that they obey the first law of thermodynamics.
Collapse
Affiliation(s)
- H Lü
- Center for Advanced Quantum Studies, Department of Physics, Beijing Normal University, Beijing 100875, China
| | - A Perkins
- The Blackett Laboratory, Imperial College London, Prince Consort Road, London SW7 2AZ, United Kingdom
| | - C N Pope
- George P. & Cynthia W. Mitchell Institute for Fundamental Physics and Astronomy, Texas A&M University, College Station, Texas 77843-4242, USA
- DAMTP, Centre for Mathematical Sciences, Cambridge University, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
| | - K S Stelle
- The Blackett Laboratory, Imperial College London, Prince Consort Road, London SW7 2AZ, United Kingdom
| |
Collapse
|
29
|
Mack S, Bright L, Hunter C, Arrick M, Kumar S, Perkins A, Eddy A, Claude A, Mujahid N, Brashier M, Nanduri B, Burgess S, Swiderski C. 1 The “pasture heaves” restricted lung transcriptome: A tool to decipher the pathophysiology of airway hyper-responsive diseases in the horse. J Equine Vet Sci 2015. [DOI: 10.1016/j.jevs.2015.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
30
|
Mui P, Perkins A, Burrows PJ, Marks SF, Turek PJ. The need for epididymovasostomy at vasectomy reversal plateaus in older vasectomies: a study of 1229 cases. Andrology 2014. [PMID: 24243789 PMCID: PMC4253133 DOI: 10.1111/andr.143] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Vasectomy reversal involves either vasovasostomy (VV) or epididymovasostomy (EV), and rates of epididymal obstruction and EV increase with time after vasectomy. However, as older vasectomies may not require EV for successful reversal, we hypothesized that sperm production falls after vasectomy and can protect the system from epididymal blowout. Our objective was to define how the need for EV at reversal changes with time after vasectomy through a retrospective review of consecutive reversals performed by three surgeons over a 10-year period. Vasovasotomy was performed with Silber score 1-3 vasal fluid. EVs were performed with Silber score 4 (sperm fragments; creamy fluid) or 5 (sperm absence) fluid. Reversal procedure type was correlated with vasectomy and patient age. Post-operative patency rates, total spermatozoa and motile sperm counts in younger (<15 years) and older (>15 years) vasectomies were assessed. Simple descriptive statistics determined outcome relevance. Among 1229 patients, 406 had either unilateral (n = 252) or bilateral EV's (n = 154) constituting 33% (406/1229) of reversals. Mean patient age was 41.4±7 years (range 22-72). Median vasectomy interval was 10 years (range 1-38). Overall sperm patency rate after reversal was 84%. The rate of unilateral (EV/VV) or bilateral EV increased linearly in vasectomy intervals of 1-22 years at 3% per year, but plateaued at 72% in vasectomy intervals of 24-38 years. Sperm counts were maintained with increasing time after vasectomy, but motile sperm counts decreased significantly (p < 0.001). Pregnancy, secondary azoospermia, varicocoele and sperm granuloma were not assessed. In conclusion, and contrary to conventional thinking, the need for EV at reversal increases with time after vasectomy, but this relationship is not linear. EV rates plateau 22 years after vasectomy, suggesting that protective mechanisms ameliorate epididymal 'blowout'. Upon reversal, sperm output is maintained with time after vasectomy, but motile sperm counts decrease linearly, suggesting epididymal dysfunction influences semen quality after reversal.
Collapse
Affiliation(s)
- P Mui
- The Turek ClinicSan Francisco, CA, USA
| | - A Perkins
- International Center for Vasectomy ReversalTucson, AZ, USA
| | - P J Burrows
- International Center for Vasectomy ReversalTucson, AZ, USA
| | - S F Marks
- International Center for Vasectomy ReversalTucson, AZ, USA
| | - P J Turek
- The Turek ClinicSan Francisco, CA, USA
| |
Collapse
|
31
|
Schmechtig A, Lees J, Perkins A, Altavilla A, Craig KJ, Dawson GR, William Deakin JF, Dourish CT, Evans LH, Koychev I, Weaver K, Smallman R, Walters J, Wilkinson LS, Morris R, Williams SCR, Ettinger U. The effects of ketamine and risperidone on eye movement control in healthy volunteers. Transl Psychiatry 2013; 3:e334. [PMID: 24326395 PMCID: PMC4030328 DOI: 10.1038/tp.2013.109] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2013] [Revised: 10/15/2013] [Accepted: 10/21/2013] [Indexed: 02/06/2023] Open
Abstract
The non-competitive N-methyl-D-aspartate receptor antagonist ketamine leads to transient psychosis-like symptoms and impairments in oculomotor performance in healthy volunteers. This study examined whether the adverse effects of ketamine on oculomotor performance can be reversed by the atypical antipsychotic risperidone. In this randomized double-blind, placebo-controlled study, 72 healthy participants performed smooth pursuit eye movements (SPEM), prosaccades (PS) and antisaccades (AS) while being randomly assigned to one of four drug groups (intravenous 100 ng ml(-1) ketamine, 2 mg oral risperidone, 100 ng ml(-1) ketamine plus 2 mg oral risperidone, placebo). Drug administration did not lead to harmful adverse events. Ketamine increased saccadic frequency and decreased velocity gain of SPEM (all P < 0.01) but had no significant effects on PS or AS (all P > or = 0.07). An effect of risperidone was observed for amplitude gain and peak velocity of PS and AS, indicating hypometric gain and slower velocities compared with placebo (both P < or = 0.04). No ketamine by risperidone interactions were found (all P > or = 0.26). The results confirm that the administration of ketamine produces oculomotor performance deficits similar in part to those seen in schizophrenia. The atypical antipsychotic risperidone did not reverse ketamine-induced deteriorations. These findings do not support the cognitive enhancing potential of risperidone on oculomotor biomarkers in this model system of schizophrenia and point towards the importance of developing alternative performance-enhancing compounds to optimise pharmacological treatment of schizophrenia.
Collapse
Affiliation(s)
- A Schmechtig
- Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK,Department of Neuroimaging, CNS Building PO89, Institute of Psychiatry, King's College London, De Crespigny Park, London SE5 8AF, UK. E-mail:
| | - J Lees
- Neuroscience and Psychiatry Unit, School of Community Based Medicine, The University of Manchester, Manchester, UK
| | - A Perkins
- Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK
| | - A Altavilla
- School of Psychology, Cardiff University, Cardiff, UK
| | - K J Craig
- P1vital Ltd, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - G R Dawson
- P1vital Ltd, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - J F William Deakin
- Neuroscience and Psychiatry Unit, School of Community Based Medicine, The University of Manchester, Manchester, UK
| | - C T Dourish
- P1vital Ltd, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - L H Evans
- School of Psychology, Cardiff University, Cardiff, UK
| | - I Koychev
- Neuroscience and Psychiatry Unit, School of Community Based Medicine, The University of Manchester, Manchester, UK
| | - K Weaver
- Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK
| | - R Smallman
- Neuroscience and Psychiatry Unit, School of Community Based Medicine, The University of Manchester, Manchester, UK
| | - J Walters
- Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
| | - L S Wilkinson
- School of Psychology, Cardiff University, Cardiff, UK,Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
| | - R Morris
- Department of Psychology, Institute of Psychiatry, King's College London, London, UK
| | - S C R Williams
- Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK
| | - U Ettinger
- Department of Psychology, University of Bonn, Bonn, Germany
| |
Collapse
|
32
|
Mui P, Perkins A, Burrows PJ, Marks SF, Turek PJ. The need for epididymovasostomy at vasectomy reversal plateaus in older vasectomies: a study of 1229 cases. Andrology 2013; 2:25-9. [DOI: 10.1111/j.2047-2927.2013.00143.x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2013] [Revised: 08/25/2013] [Accepted: 09/14/2013] [Indexed: 01/31/2023]
Affiliation(s)
- P. Mui
- The Turek Clinic San Francisco CAUSA
| | - A. Perkins
- International Center for Vasectomy Reversal Tucson AZ USA
| | - P. J. Burrows
- International Center for Vasectomy Reversal Tucson AZ USA
| | - S. F. Marks
- International Center for Vasectomy Reversal Tucson AZ USA
| | | |
Collapse
|
33
|
Montalvo J, Spencer C, Hackathorn A, Masterjohn K, Perkins A, Doty C, Arumugam A, Ongusaha PP, Lakshmanaswamy R, Liao JK, Mitchell DC, Bryan BA. ROCK1 & 2 perform overlapping and unique roles in angiogenesis and angiosarcoma tumor progression. Curr Mol Med 2013; 13:205-19. [PMID: 22934846 PMCID: PMC3580831 DOI: 10.2174/1566524011307010205] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [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] [Received: 02/28/2012] [Revised: 07/21/2012] [Accepted: 07/30/2012] [Indexed: 12/31/2022]
Abstract
The serine/threonine protein kinase paralogs ROCK1 & 2 have been implicated as essential modulators of angiogenesis; however their paralog-specific roles in endothelial function are unknown. shRNA knockdown of ROCK1 or 2 in endothelial cells resulted in a significant disruption of in vitro capillary network formation, cell polarization, and cell migration compared to cells harboring non-targeting control shRNA plasmids. Knockdowns led to alterations in cytoskeletal dynamics due to ROCK1 & 2-mediated reductions in actin isoform expression, and ROCK2-specific reduction in myosin phosphatase and cofilin phosphorylation. Knockdowns enhanced cell survival and led to ROCK1 & 2-mediated reduction in caspase 6 and 9 cleavage, and a ROCK2-specific reduction in caspase 3 cleavage. Microarray analysis of ROCK knockdown lines revealed overlapping and unique control of global transcription by the paralogs, and a reduction in the transcriptional regulation of just under 50% of VEGF responsive genes. Finally, paralog knockdown in xenograft angiosarcoma tumors resulted in a significant reduction in tumor formation. Our data reveals that ROCK1 & 2 exhibit overlapping and unique roles in normal and dysfunctional endothelial cells, that alterations in cytoskeletal dynamics are capable of overriding mitogen activated transcription, and that therapeutic targeting of ROCK signaling may have profound impacts for targeting angiogenesis.
Collapse
Affiliation(s)
- J Montalvo
- Ghosh Science and Technology Center, Department of Biology, Worcester State University, Worcester, Massachusetts, USA
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
34
|
Marks M, Perkins A, Russell H, Burrows P, Marks S. Antisperm antibodies: prevalance, patterns and impact on natural conception following vasectomy reversal. Fertil Steril 2013. [DOI: 10.1016/j.fertnstert.2013.07.755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
35
|
Abstract
BACKGROUND The use of specific behavioural strategies such as portion control or meal planning is important for weight management, although studies of determinants of strategy use are limited. The present study explored the concept of hope and its association with the use of behavioural strategies. METHODS Data were obtained from a larger cross-sectional survey conducted in 2009 among 178 patients of a city-county sponsored primary care clinic in the Midwest region of the USA. Hope was measured with subscales representing 'agency' (determination in meeting goals) and 'pathways' (perception of ways to meet goals) and a total score. Diet and physical activity-related strategies were captured with five and two scales, respectively. RESULTS Analyses showed a significant (P < 0.05) association between both the total hope score and the agency subscale and all behavioural strategy measures. The pathways subscale was significantly associated with physical activity-related strategies, and a subset of diet-related strategies. CONCLUSIONS The hope measures should be explored further in the context of a weight loss intervention to determine their predictive association with the use of specific behavioural strategies.
Collapse
Affiliation(s)
- F Nothwehr
- Department of Community and Behavioral Health, College of Public Health, University of Iowa, 105 River Street, Iowa City, IA 52242, USA.
| | | | | |
Collapse
|
36
|
Montalvo J, Spencer C, Hackathorn A, Masterjohn K, Perkins A, Doty C, Arumugam A, P. Ongusaha P, Lakshmanaswamy R, K. Liao J, C. Mitchell D, A. Bryan B. Rock1 & 2 Perform Overlapping and Unique Roles in Angiogenesis and Angiosarcoma Tumor Progression. Curr Mol Med 2013. [DOI: 10.2174/156652413804486296] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
37
|
John AE, Luckett J, Awas R, Habgood A, Ludbrook S, Blanchard A, Perkins A, Jenkins RG, Marshall JF. S66 Targeted in Vivo Imaging of the αvβ6 Integrin in Mice with Bleomycin-Induced Lung Fibrosis. Thorax 2012. [DOI: 10.1136/thoraxjnl-2012-202678.072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
|
38
|
Messina F, Weaver C, Trammel A, McDaniel M, Ervin D, Perkins A. 33 Improving Specialty Follow-up Care after an Emergency Department Visit Using a Unique Referral System. Ann Emerg Med 2012. [DOI: 10.1016/j.annemergmed.2012.06.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
39
|
Swann R, Perkins A, Velentzis L, Mulligan A, Woodside J, Cantwell M, Dutton S, Leathem A, Robertson C, Dwek M. 893 The DietCompLyf Study – a Prospective Longitudinal Study of Breast Cancer Survival. Eur J Cancer 2012. [DOI: 10.1016/s0959-8049(12)71525-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
40
|
Coentrao L, Ribeiro C, Santos-Araujo C, Neto R, Pestana M, Kleophas W, Kleophas W, Karaboyas A, LI Y, Bommer J, Pisoni R, Robinson B, Port F, Celik G, Burcak Annagur B, Yilmaz M, Demir T, Kara F, Trigka K, Dousdampanis P, Vaitsis N, Aggelakou-Vaitsi S, Turkmen K, Guney I, Turgut F, Altintepe L, Tonbul HZ, Abdel-Rahman E, Sclauzero P, Galli G, Barbati G, Carraro M, Panzetta GO, Van Diepen M, Schroijen M, Dekkers O, Dekker F, Sikole A, Severova- Andreevska G, Trajceska L, Gelev S, Amitov V, Pavleska- Kuzmanovska S, Karaboyas A, Rayner H, LI Y, Vanholder R, Pisoni R, Robinson B, Port F, Hecking M, Jung B, Leung M, Huynh F, Chung T, Marchuk S, Kiaii M, Er L, Werb R, Chan-Yan C, Beaulieu M, Malindretos P, Makri P, Zagkotsis G, Koutroumbas G, Loukas G, Nikolaou E, Pavlou M, Gourgoulianni E, Paparizou M, Markou M, Syrgani E, Syrganis C, Raimann J, Usvyat LA, Bhalani V, Levin NW, Kotanko P, Huang X, Stenvinkel P, Qureshi AR, Riserus U, Cederholm T, Barany P, Heimburger O, Lindholm B, Carrero JJ, Chang JH, Sung JY, Jung JY, Lee HH, Chung W, Kim S, Han JS, Kim S, Chang JH, Jung JY, Chung W, Na KY, Raimann J, Usvyat LA, Kotanko P, Levin NW, Fragoso A, Pinho A, Malho A, Silva AP, Morgado E, Leao Neves P, Joki N, Tanaka Y, Iwasaki M, Kubo S, Hayashi T, Takahashi Y, Hirahata K, Imamura Y, Hase H, Castledine C, Gilg J, Rogers C, Ben-Shlomo Y, Caskey F, Na KY, Kim S, Chung W, Jung JY, Chang JH, Lee HH, Sandhu JS, Bajwa GS, Kansal S, Sandhu J, Jayanti A, Nikam M, Ebah L, Summers A, Mitra S, Agar J, Perkins A, Simmonds R, Tjipto A, Amet S, Launay-Vacher V, Laville M, Tricotel A, Frances C, Stengel B, Gauvrit JY, Grenier N, Reinhardt G, Clement O, Janus N, Rouillon L, Choukroun G, Deray G, Bernasconi A, Waisman R, Montoya AP, Liste AA, Hermes R, Muguerza G, Heguilen R, Iliescu EL, Martina V, Rizzo MA, Magenta P, Lubatti L, Rombola G, Gallieni M, Loirat C, Loirat C, Mellerio H, Labeguerie M, Andriss B, Savoye E, Lassale M, Jacquelinet C, Alberti C, Aggarwal Y, Baharani J, Tabrizian S, Ossareh S, Zebarjadi M, Azevedo P, Travassos F, Frade I, Almeida M, Queiros J, Silva F, Cabrita A, Rodrigues R, Couchoud C, Kitty J, Benedicte S, Fergus C, Cecile C, Couchoud C, Sahar B, Emmanuel V, Christian J, Rene E, Barahimi H, Mahdavi-Mazdeh M, Nafar M, Petruzzi M, De Benedittis M, Sciancalepore M, Gargano L, Natale P, Vecchio MC, Saglimbene V, Pellegrini F, Gentile G, Stroumza P, Frantzen L, Leal M, Torok M, Bednarek A, Dulawa J, Celia E, Gelfman R, Hegbrant J, Wollheim C, Palmer S, Johnson DW, Ford PJ, Craig JC, Strippoli GF, Ruospo M, El Hayek B, Hayek B, Baamonde E, Bosch E, Ramirez JI, Perez G, Ramirez A, Toledo A, Lago MM, Garcia-Canton C, Checa MD, Canaud B, Canaud B, Lantz B, Pisoni R, Granger-Vallee A, Lertdumrongluk P, Molinari N, Ethier J, Jadoul M, Gillespie B, Port F, Bond C, Wang S, Alfieri T, Braunhofer P, Newsome B, Wang M, Bieber B, Guidinger M, Bieber B, Wang M, Zuo L, Pisoni R, Yu X, Yang X, Qian J, Chen N, Albert J, Yan Y, Ramirez S, Bernasconi A, Waisman R, Beresan M, Lapidus A, Canteli M, Heguilen R, Tong A, Palmer S, Manns B, Craig J, Ruospo M, Gargano L, Strippoli G, Mortazavi M, Vahdatpour B, Shahidi S, Ghasempour A, Taheri D, Dolatkhah S, Emami Naieni A, Ghassami M, Khan M, Abdulnabi K, Pai P, Ruospo M, Petruzzi M, De Benedittis M, Sciancalepore M, Gargano L, Vecchio M, Saglimbene V, Natale P, Pellegrini F, Gentile G, Stroumza P, Frantzen L, Leal M, Torok M, Bednarek A, Dulawa J, Celia E, Gelfman R, Hegbrant J, Wollheim C, Palmer S, Johnson DW, Ford PJ, Craig JC, Strippoli GF, Muqueet MA, Muqueet MA, Hasan MJ, Kashem MA, Dutta PK, Liu FX, Noe L, Quock T, Neil N, Inglese G, Qian J, Bieber B, Guidinger M, Bieber B, Chen N, Yan Y, Pisoni R, Wang M, Zuo L, Yu X, Yang X, Wang M, Albert J, Ramirez S, Ossareh S, Motamed Najjar M, Bahmani B, Shafiabadi A, Helve J, Haapio M, Groop PH, Gronhagen-Riska C, Finne P, Helve J, Haapio M, Sund R, Groop PH, Gronhagen-Riska C, Finne P, Cai M, Baweja S, Clements A, Kent A, Reilly R, Taylor N, Holt S, Mcmahon L, Usvyat LA, Carter M, Van der Sande FM, Kooman J, Raimann J, Levin NW, Kotanko P, Usvyat LA, Malhotra R, Ouellet G, Penne EL, Raimann J, Thijssen S, Levin NW, Kotanko P, Etter M, Tashman A, Guinsburg A, Grassmann A, Barth C, Marelli C, Marcelli D, Van der Sande FM, Von Gersdorff G, Bayh I, Kooman J, Scatizzi L, Lam M, Schaller M, Thijssen S, Toffelmire T, Wang Y, Sheppard P, Usvyat LA, Levin NW, Kotanko P, Neri L, Andreucci VA, Rocca-Rey LA, Bertoli SV, Brancaccio D, Tjipto A, Simmonds R, Agar J, Huang X, Stenvinkel P, Qureshi AR, Riserus U, Cederholm T, Barany P, Heimburger O, Lindholm B, Carrero JJ, Vecchio M, Palmer S, De Berardis G, Craig J, Lucisano G, Johnson D, Pellegrini F, Nicolucci A, Sciancalepore M, Saglimbene V, Gargano L, Bonifati C, Ruospo M, Navaneethan SD, Montinaro V, Stroumza P, Zsom M, Torok M, Celia E, Gelfman R, Bednarek-Skublewska A, Dulawa J, Graziano G, Gentile G, Ferrari JN, Santoro A, Zucchelli A, Triolo G, Maffei S, Hegbrant J, Wollheim C, De Cosmo S, Manfreda VM, Strippoli GF, Janus N, Janus N, Launay-Vacher V, Juillard L, Rousset A, Butel F, Girardot-Seguin S, Deray G, Hannedouche T, Isnard M, Berland Y, Vanhille P, Ortiz JP, Janin G, Nicoud P, Touam M, Bruce E, Rouillon L, Laville M, Janus N, Juillard L, Rousset A, Butel F, Girardot-Seguin S, Deray G, Hannedouche T, Isnard M, Berland Y, Vanhille P, Ortiz JP, Janin G, Nicoud P, Touam M, Bruce E, Rouillon L, Laville M, Janus N, Launay-Vacher V, Juillard L, Rousset A, Butel F, Girardot-Seguin S, Deray G, Hannedouche T, Isnard M, Berland Y, Vanhille P, Ortiz JP, Janin G, Nicoud P, Touam M, Bruce E, Rouillon L, Laville M, Grace B, Clayton P, Cass A, Mcdonald S, Baharani J, Furumatsu Y, Kitamura T, Fujii N, Ogata S, Nakamoto H, Iseki K, Tsubakihara Y, Chien CC, Wang JJ, Hwang JC, Wang HY, Kan WC, Kuster N, Kuster N, Patrier L, Bargnoux AS, Morena M, Dupuy AM, Badiou S, Canaud B, Cristol JP, Desmet JM, Fernandes V, Collart F, Spinogatti N, Pochet JM, Dratwa M, Goffin E, Nortier J, Zilisteanu DS, Voiculescu M, Rusu E, Achim C, Bobeica R, Balanica S, Atasie T, Florence S, Anne-Marie S, Michel L, Cyrille C, Emmanuel V, Strakosha A, Strakosha A, Pasko N, Kodra S, Thereska N, Lowney A, Lowney E, Grant R, Murphy M, Casserly L, O' Brien T, Plant WD, Radic J, Radic J, Ljutic D, Kovacic V, Radic M, Dodig-Curkovic K, Sain M, Jelicic I, Fujii N, Hamano T, Nakano C, Yonemoto S, Okuno A, Katayama M, Isaka Y, Nordio M, Limido A, Postorino M, Nichelatti M, Khil M, Dudar I, Khil V, Shifris I, Momtaz M, Soliman AR, El Lawindi MI, Dzekova-Vidimliski P, Pavleska-Kuzmanovska S, Trajceska L, Nikolov I, Selim G, Gelev S, Amitov V, Sikole A, Shoji T, Kakiya R, Hayashi T, Tatsumi-Shimomura N, Tsujimoto Y, Tabata T, Shima H, Mori K, Fukumoto S, Tahara H, Koyama H, Emoto M, Ishimura E, Nishizawa Y, Inaba M. Epidemiology and outcome research in CKD 5D. Nephrol Dial Transplant 2012. [DOI: 10.1093/ndt/gfs227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
41
|
Armitage J, Cuzick J, Elwood P, Longley M, Perkins A, Spencer K, Turner H, Porch S, Lyness S, Kennedy J, Henderson G. Aspirin for the older person: report of a meeting at the Royal Society of Medicine, London, 3rd November 2011. Ecancermedicalscience 2012; 6:245. [PMID: 22423252 PMCID: PMC3298410 DOI: 10.3332/ecancer.2012.245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [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] [Received: 01/18/2012] [Indexed: 12/21/2022] Open
Abstract
On November 23rd 2011, the Aspirin Foundation held a meeting at the Royal Society of Medicine in London to review current thinking on the potential role of aspirin in preventing cardiovascular disease and reducing the risk of cancer in older people. The meeting was supported by Bayer Pharma AG and Novacyl.
Collapse
Affiliation(s)
- J Armitage
- Professor of Clinical Trials and Epidemiology, Clinical Trials Surveillance Unit, Oxford
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
42
|
Arno J, Messina F, Perkins A, Allen A. P5-S7.12 STD testing in emergency department: a novel method to provide test results. Br J Vener Dis 2011. [DOI: 10.1136/sextrans-2011-050108.607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
|
43
|
Perkins A, Izadpanah A, Sinno H, Bernard C, Williams H. Giant Cell Reparative Granuloma of the Proximal Phalanx: A Case Report and Literature Review. Canadian Journal of Plastic Surgery 2011. [DOI: 10.1177/229255031101900205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The present article is a case report of a 16-year-old boy who presented with a benign bony tumour, which on histological analysis suggested giant cell reparative granuloma (GCRG), but was not corroborate by blood tests. The implications of this type of tumour and the correct diagnostic requirements were investigated. The correct identification of GCRG from other giant cell-containing tumours is important because the treatment modalities for these tumours significantly differ from one another. In most cases, histological findings are sufficient to identify the tumours. In most GCRG cases, curettage is usually a curative treatment option. However, due to high recurrence rates of GCRGs, close follow-up of these patients is warranted. Also, due to osteoclastic activity of the giant cells in GCRGs, the use of drugs such as calcitonin or bisphosphonates, which inhibit osteoclast differentiation and activation, may have an important influence on future treatments or in reducing the recurrence rate of these tumours.
Collapse
Affiliation(s)
- A Perkins
- Division of Plastic Surgery, McGill University, Montreal, Quebec
| | - A Izadpanah
- Division of Plastic Surgery, McGill University, Montreal, Quebec
| | - H Sinno
- Division of Plastic Surgery, McGill University, Montreal, Quebec
| | - C Bernard
- Division of Plastic Surgery, McGill University, Montreal, Quebec
| | - Hb Williams
- Division of Plastic Surgery, McGill University, Montreal, Quebec
| |
Collapse
|
44
|
Lee J, Sinno H, Perkins A, Tahiri Y, Luc M. 14,000 volt electrical injury to bilateral upper extremities: a case report. Mcgill J Med 2011; 13:18. [PMID: 22399869 PMCID: PMC3296183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Electrical burns are among the most devastating of burn injuries. High voltage electrical injuries result in extensive deep tissue damage and are associated with multiple complications, long term morbidity, and a high mortality rate. We describe the case of a 47 year-old electric company linesman who suffered a high voltage electrical injury (HVEI) of 14,000 volts to bilateral hands and wrists managed by the Division of Plastic and Reconstructive Surgery at the McGill University Health Center in Montreal, Quebec, Canada. His management included multiple operative procedures, including escharotomies, fasciotomies, serial debridements, and bilateral pedicle groin flaps, and amputation of his left hand.
Collapse
|
45
|
Malton CA, Hallworth GW, Padfield JM, Perkins A, Wilson C, Davis SS. Deposition and Clearance of Inhalation Aerosols in Dogs and Rabbits Using a Gamma Camera. J Pharm Pharmacol 2011. [DOI: 10.1111/j.2042-7158.1982.tb00895.x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
46
|
Perkins A, Izadpanah A, Sinno H, Bernard C, Williams HB. Giant cell reparative granuloma of the proximal phalanx: A case report and literature review. Can J Plast Surg 2011; 19:e19-e21. [PMID: 22654539 PMCID: PMC3328113] [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] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
The present article is a case report of a 16-year-old boy who presented with a benign bony tumour, which on histological analysis suggested giant cell reparative granuloma (GCRG), but was not corroborate by blood tests. The implications of this type of tumour and the correct diagnostic requirements were investigated. The correct identification of GCRG from other giant cell-containing tumours is important because the treatment modalities for these tumours significantly differ from one another. In most cases, histological findings are sufficient to identify the tumours. In most GCRG cases, curettage is usually a curative treatment option. However, due to high recurrence rates of GCRGs, close follow-up of these patients is warranted. Also, due to osteoclastic activity of the giant cells in GCRGs, the use of drugs such as calcitonin or bisphosphonates, which inhibit osteoclast differentiation and activation, may have an important influence on future treatments or in reducing the recurrence rate of these tumours.
Collapse
Affiliation(s)
- A Perkins
- Correspondence: Dr Anthony Perkins, 307-3460 Simpson Street, Montreal, Quebec H3G 2J4. Telephone 514-839-9303, fax 514-935-3238, e-mail
| | | | | | | | | |
Collapse
|
47
|
Perkins A, Izadpanah A. Giant cell reparative granuloma of the proximal phalanx: A case report and literature review. Plast Surg (Oakv) 2011. [DOI: 10.4172/plastic-surgery.1000684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
|
48
|
Bazin KA, Moosavi S, Murphy K, Perkins A, Hickson M, Howard LS. P132 Carbon dioxide sensitivity in patients with hyperventilation syndrome. Thorax 2010. [DOI: 10.1136/thx.2010.150987.33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
|
49
|
Walls RM, Samuels-Kalow M, Perkins A. A New Maneuver for Endotracheal Tube Insertion During Difficult Glidescope Intubation. J Emerg Med 2010; 39:86-8. [DOI: 10.1016/j.jemermed.2009.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2009] [Revised: 10/26/2009] [Accepted: 11/09/2009] [Indexed: 10/19/2022]
|
50
|
Madebo M, Perkins A, Fox C, Johnston P, Kron T. Study of X-ray field junction dose using an a-Si electronic portal imaging device. Australas Phys Eng Sci Med 2010; 33:45-50. [PMID: 20237889 DOI: 10.1007/s13246-010-0005-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2009] [Accepted: 02/10/2010] [Indexed: 11/28/2022]
Abstract
Field junctions between megavoltage photon beams are important in modern radiotherapy for treatments such as head and neck and breast cancer. An electronic portal imaging device (EPID) may be used to study junction dose between two megavoltage X-ray fields. In this study, the junction dose was used to determine machine characteristics such as jaw positions and their reproducibility, collimator rotation and the effect of gantry rotation. All measurements were done on Varian linear accelerators with EPID (Varian, Palo Alto, CA). The results show reproducibility in jaw positions of approximately 0.3 mm for repeated jaw placement while EPID readings were reproducible within a standard deviation of 0.4% for fixed jaw positions. Junction dose also allowed collimator rotation error of 0.1 degrees to be observed. Dependence of junction dose on gantry rotation due to gravity was observed; the gravity effect being maximum at 180 degrees gantry angle (beam pointing up). EPIDs were found to be reliable tools for checking field junctions, which in turn may be used to check jaw reproducibility and collimator rotation of linacs.
Collapse
Affiliation(s)
- Mebratu Madebo
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Locked Bag 1 A'Beckett Street, Melbourne, VIC 8006, Australia.
| | | | | | | | | |
Collapse
|