37 results
Variability in antimicrobial use in pediatric ventilator-associated events
- Manjiree V. Karandikar, Susan E. Coffin, Gregory P. Priebe, Thomas J. Sandora, Latania K. Logan, Gitte Y. Larsen, Philip Toltzis, James E. Gray, Michael Klompas, Julia S. Sammons, Marvin B. Harper, Kelly Horan, Matthew Lakoma, Noelle M. Cocoros, Grace M. Lee
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 40 / Issue 1 / January 2019
- Published online by Cambridge University Press:
- 09 November 2018, pp. 32-39
- Print publication:
- January 2019
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Objective
To assess variability in antimicrobial use and associations with infection testing in pediatric ventilator-associated events (VAEs).
DesignDescriptive retrospective cohort with nested case-control study.
SettingPediatric intensive care units (PICUs), cardiac intensive care units (CICUs), and neonatal intensive care units (NICUs) in 6 US hospitals.
PatientsChildren≤18 years ventilated for≥1 calendar day.
MethodsWe identified patients with pediatric ventilator-associated conditions (VACs), pediatric VACs with antimicrobial use for≥4 days (AVACs), and possible ventilator-associated pneumonia (PVAP, defined as pediatric AVAC with a positive respiratory diagnostic test) according to previously proposed criteria.
ResultsAmong 9,025 ventilated children, we identified 192 VAC cases, 43 in CICUs, 70 in PICUs, and 79 in NICUs. AVAC criteria were met in 79 VAC cases (41%) (58% CICU; 51% PICU; and 23% NICU), and varied by hospital (CICU, 20–67%; PICU, 0–70%; and NICU, 0–43%). Type and duration of AVAC antimicrobials varied by ICU type. AVAC cases in CICUs and PICUs received broad-spectrum antimicrobials more often than those in NICUs. Among AVAC cases, 39% had respiratory infection diagnostic testing performed; PVAP was identified in 15 VAC cases. Also, among AVAC cases, 73% had no associated positive respiratory or nonrespiratory diagnostic test.
ConclusionsAntimicrobial use is common in pediatric VAC, with variability in spectrum and duration of antimicrobials within hospitals and across ICU types, while PVAP is uncommon. Prolonged antimicrobial use despite low rates of PVAP or positive laboratory testing for infection suggests that AVAC may provide a lever for antimicrobial stewardship programs to improve utilization.
Contributors
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- By Magdalena Anitescu, Charles E. Argoff, Arash Asher, Nyla Azam, Nomen Azeem, Sachin K. Bansal, Jose E. Barreto, Rodrigo A Benavides, Niteesh Bharara, Justin B. Boge, Robert B. Bolash, Thomas K. Bond, Christopher Centeno, Zachariah W. Chambers, Jonathan Chang, Grace Chen, Hamilton Chen, Jeffry Chen, Jianguo Cheng, Natalia Covarrubias, Claire J. Creutzfeldt, Gulshan Doulatram, Amirpasha Ehsan, Ike Eriator, Jeff Ericksen, Mark Etscheidt, Frank J. E. Falco, Jack Fu, Timothy Furnish, Annemarie E. Gallagher, Kingsuk Ganguly, Eugene Garvin, Cliff Gevirtz, Scott E. Glaser, Brandon J. Goff, Harry J. Gould, Christine Greco, Jay S. Grider, Maged Guirguis, Qiao Guo, Justin Hata, John Hau, Garett J. Helber, Eric R. Helm, Lori Hill Marshall, Dean Hommer, Jeffrey Hopcian, Eric S. Hsu, Jakun Ing, Tracy P. Jackson, Gaurav Jain, Chrystina Jeter, Alan David Kaye, James Kelly, Soorena Khojasteh, Ankur Khosla, Daniel Krashin, Monika A. Krzyzek, Prasad Lakshminarasimhiah, Steven Michael Lampert, Garrett LaSalle, Quan D. Le, Ankit Maheshwari, Edward R. Mariano, Joaquin Maury, John P. McCallin, John Michels, Natalia Murinova, Narendren Narayanasamy, Rebekah L. Nilson, Elliot Palmer, Vikram B. Patel, Devin Peck, Donald B. Penzien, Danielle Perret Karimi, Tilak Raj, Michael R. Rasmussen, Mohit Rastogi, Rahul Rastogi, Nashaat N. Rizk, Rinoo V. Shah, Paul A. Sloan, Julian Sosner, A. Raj Swain, Minyi Tan, Natacha Telusca, Santhosh A. Thomas, Andrea Trescot, Michael Truong, Jason Tucker, Richard D. Urman, Brandon A. Van Noord, Nihir Waghela, Irene Wu, Jiang Wu, Jijun Xu, Jinghui Xie, William Yancey
- Edited by Alan David Kaye, Louisiana State University, Rinoo V. Shah
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- Book:
- Case Studies in Pain Management
- Published online:
- 05 October 2014
- Print publication:
- 16 October 2014, pp xi-xv
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- By Lenard A. Adler, Pinky Agarwal, Rehan Ahmed, Jagga Rao Alluri, Fawaz Al-Mufti, Samuel Alperin, Michael Amoashiy, Michael Andary, David J. Anschel, Padmaja Aradhya, Vandana Aspen, Esther Baldinger, Jee Bang, George D. Baquis, John J. Barry, Jason J. S. Barton, Julius Bazan, Amanda R. Bedford, Marlene Behrmann, Lourdes Bello-Espinosa, Ajay Berdia, Alan R. Berger, Mark Beyer, Don C. Bienfang, Kevin M. Biglan, Thomas M. Boes, Paul W. Brazis, Jonathan L. Brisman, Jeffrey A. Brown, Scott E. Brown, Ryan R. Byrne, Rina Caprarella, Casey A. Chamberlain, Wan-Tsu W. Chang, Grace M. Charles, Jasvinder Chawla, David Clark, Todd J. Cohen, Joe Colombo, Howard Crystal, Vladimir Dadashev, Sarita B. Dave, Jean Robert Desrouleaux, Richard L. Doty, Robert Duarte, Jeffrey S. Durmer, Christyn M. Edmundson, Eric R. Eggenberger, Steven Ender, Noam Epstein, Alberto J. Espay, Alan B. Ettinger, Niloofar (Nelly) Faghani, Amtul Farheen, Edward Firouztale, Rod Foroozan, Anne L. Foundas, David Elliot Friedman, Deborah I. Friedman, Steven J. Frucht, Oded Gerber, Tal Gilboa, Martin Gizzi, Teneille G. Gofton, Louis J. Goodrich, Malcolm H. Gottesman, Varda Gross-Tsur, Deepak Grover, David A. Gudis, John J. Halperin, Maxim D. Hammer, Andrew R. Harrison, L. Anne Hayman, Galen V. Henderson, Steven Herskovitz, Caitlin Hoffman, Laryssa A. Huryn, Andres M. Kanner, Gary P. Kaplan, Bashar Katirji, Kenneth R. Kaufman, Annie Killoran, Nina Kirz, Gad E. Klein, Danielle G. Koby, Christopher P. Kogut, W. Curt LaFrance, Patrick J.M. Lavin, Susan W. Law, James L. Levenson, Richard B. Lipton, Glenn Lopate, Daniel J. Luciano, Reema Maindiratta, Robert M. Mallery, Georgios Manousakis, Alan Mazurek, Luis J. Mejico, Dragana Micic, Ali Mokhtarzadeh, Walter J. Molofsky, Heather E. Moss, Mark L. Moster, Manpreet Multani, Siddhartha Nadkarni, George C. Newman, Rolla Nuoman, Paul A. Nyquist, Gaia Donata Oggioni, Odi Oguh, Denis Ostrovskiy, Kristina Y. Pao, Juwen Park, Anastas F. Pass, Victoria S. Pelak, Jeffrey Peterson, John Pile-Spellman, Misha L. Pless, Gregory M. Pontone, Aparna M. Prabhu, Michael T. Pulley, Philip Ragone, Prajwal Rajappa, Venkat Ramani, Sindhu Ramchandren, Ritesh A. Ramdhani, Ramses Ribot, Heidi D. Riney, Diana Rojas-Soto, Michael Ronthal, Daniel M. Rosenbaum, David B. Rosenfield, Durga Roy, Michael J. Ruckenstein, Max C. Rudansky, Eva Sahay, Friedhelm Sandbrink, Jade S. Schiffman, Angela Scicutella, Maroun T. Semaan, Robert C. Sergott, Aashit K. Shah, David M. Shaw, Amit M. Shelat, Claire A. Sheldon, Anant M. Shenoy, Yelizaveta Sher, Jessica A. Shields, Tanya Simuni, Rajpaul Singh, Eric E. Smouha, David Solomon, Mehri Songhorian, Steven A. Sparr, Egilius L. H. Spierings, Eve G. Spratt, Beth Stein, S.H. Subramony, Rosa Ana Tang, Cara Tannenbaum, Hakan Tekeli, Amanda J. Thompson, Michael J. Thorpy, Matthew J. Thurtell, Pedro J. Torrico, Ira M. Turner, Scott Uretsky, Ruth H. Walker, Deborah M. Weisbrot, Michael A. Williams, Jacques Winter, Randall J. Wright, Jay Elliot Yasen, Shicong Ye, G. Bryan Young, Huiying Yu, Ryan J. Zehnder
- Edited by Alan B. Ettinger, Albert Einstein College of Medicine, New York, Deborah M. Weisbrot, State University of New York, Stony Brook
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- Book:
- Neurologic Differential Diagnosis
- Published online:
- 05 June 2014
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- 17 April 2014, pp xi-xx
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- By James Ahn, Eric L. Anderson, Annette L. Beautrais, Dennis Beedle, Jon S. Berlin, Benjamin L. Bregman, Peter Brown, Suzie Bruch, Jonathan Busko, Stuart Buttlaire, Laurie Byrne, Gerald Carroll, Valerie A. Carroll, Margaret Cashman, Joseph R. Check, Lara G. Chepenik, Robert N. Cuyler, Preeti Dalawari, Suzanne Dooley-Hash, William R. Dubin, Mila L. Felder, Avrim B. Fishkind, Reginald I. Gaylord, Rachel Lipson Glick, Travis Grace, Clare Gray, Anita Hart, Ross A. Heller, Amanda E. Horn, David S. Howes, David C. Hsu, Andy Jagoda, Margaret Judd, John Kahler, Daryl Knox, Gregory Luke Larkin, Patricia Lee, Jerrold B. Leikin, Eddie Markul, Marc L. Martel, J. D. McCourt, MaryLynn McGuire Clarke, Mark Newman, Anthony T. Ng, Barbara Nightengale, Kimberly Nordstrom, Jagoda Pasic, Jennifer Peltzer-Jones, Marcia A. Perry, Larry Phillips, Paul Porter, Seth Powsner, Michael S. Pulia, Erin Rapp, Divy Ravindranath, Janet S. Richmond, Silvana Riggio, Harvey L. Ruben, Derek J. Robinson, Douglas A. Rund, Omeed Saghafi, Alicia N. Sanders, Jeffrey Sankoff, Lorin M. Scher, Louis Scrattish, Richard D. Shih, Maureen Slade, Susan Stefan, Victor G. Stiebel, Deborah Taber, Vaishal Tolia, Gary M. Vilke, Alvin Wang, Michael A. Ward, Joseph Weber, Michael P. Wilson, James L. Young, Scott L. Zeller
- Edited by Leslie S. Zun
- Edited in association with Lara G. Chepenik, Mary Nan S. Mallory
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- Book:
- Behavioral Emergencies for the Emergency Physician
- Published online:
- 05 April 2013
- Print publication:
- 21 March 2013, pp viii-xii
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15 - Structural equation modeling and the analysis of long-term monitoring data
- Edited by Robert A. Gitzen , University of Missouri, Columbia, Joshua J. Millspaugh, University of Missouri, Columbia, Andrew B. Cooper, Simon Fraser University, British Columbia, Daniel S. Licht
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- Book:
- Design and Analysis of Long-term Ecological Monitoring Studies
- Published online:
- 05 July 2012
- Print publication:
- 07 June 2012, pp 325-358
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Summary
Introduction
The analysis of long-term monitoring data is increasingly important; not only for the discovery and documentation of changes in environmental systems, but also as an enterprise whose fruits validate the allocation of effort and scarce funds to monitoring. In simple terms, we may distinguish between the detection of change in some ecosystem attribute versus the investigation of causes and consequences associated with that change. The statistical framework known as structural equation modeling (SEM) can contribute to both detection of changes and the search for causes. This chapter summarizes some of the capabilities of SEM and shows a few ways it can be used to model temporal change. Because of its ability to test hypotheses about whether rates of change are zero or nonzero, it can be used for change detection with repeated-measures data. As more of the capabilities of SEM are presented, its capacity for evaluating causal networks is highlighted. Here is where its potential for making a unique contribution to the analysis of long-term monitoring data is revealed. Thus, one's primary motivation for using SEM with monitoring data will be to investigate hypotheses about what factors may be driving change (Box 15.1).
Contributors
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- By Aakash Agarwala, Linda S. Aglio, Rae M. Allain, Paul D. Allen, Houman Amirfarzan, Yasodananda Kumar Areti, Amit Asopa, Edwin G. Avery, Patricia R. Bachiller, Angela M. Bader, Rana Badr, Sibinka Bajic, David J. Baker, Sheila R. Barnett, Rena Beckerly, Lorenzo Berra, Walter Bethune, Sascha S. Beutler, Tarun Bhalla, Edward A. Bittner, Jonathan D. Bloom, Alina V. Bodas, Lina M. Bolanos-Diaz, Ruma R. Bose, Jan Boublik, John P. Broadnax, Jason C. Brookman, Meredith R. Brooks, Roland Brusseau, Ethan O. Bryson, Linda A. Bulich, Kenji Butterfield, William R. Camann, Denise M. Chan, Theresa S. Chang, Jonathan E. Charnin, Mark Chrostowski, Fred Cobey, Adam B. Collins, Mercedes A. Concepcion, Christopher W. Connor, Bronwyn Cooper, Jeffrey B. Cooper, Martha Cordoba-Amorocho, Stephen B. Corn, Darin J. Correll, Gregory J. Crosby, Lisa J. Crossley, Deborah J. Culley, Tomas Cvrk, Michael N. D'Ambra, Michael Decker, Daniel F. Dedrick, Mark Dershwitz, Francis X. Dillon, Pradeep Dinakar, Alimorad G. Djalali, D. John Doyle, Lambertus Drop, Ian F. Dunn, Theodore E. Dushane, Sunil Eappen, Thomas Edrich, Jesse M. Ehrenfeld, Jason M. Erlich, Lucinda L. Everett, Elliott S. Farber, Khaldoun Faris, Eddy M. Feliz, Massimo Ferrigno, Richard S. Field, Michael G. Fitzsimons, Hugh L. Flanagan Jr., Vladimir Formanek, Amanda A. Fox, John A. Fox, Gyorgy Frendl, Tanja S. Frey, Samuel M. Galvagno Jr., Edward R. Garcia, Jonathan D. Gates, Cosmin Gauran, Brian J. Gelfand, Simon Gelman, Alexander C. Gerhart, Peter Gerner, Omid Ghalambor, Christopher J. Gilligan, Christian D. Gonzalez, Noah E. Gordon, William B. Gormley, Thomas J. Graetz, Wendy L. Gross, Amit Gupta, James P. Hardy, Seetharaman Hariharan, Miriam Harnett, Philip M. Hartigan, Joaquim M. Havens, Bishr Haydar, Stephen O. Heard, James L. Helstrom, David L. Hepner, McCallum R. Hoyt, Robert N. Jamison, Karinne Jervis, Stephanie B. Jones, Swaminathan Karthik, Richard M. Kaufman, Shubjeet Kaur, Lee A. Kearse Jr., John C. Keel, Scott D. Kelley, Albert H. Kim, Amy L. Kim, Grace Y. Kim, Robert J. Klickovich, Robert M. Knapp, Bhavani S. Kodali, Rahul Koka, Alina Lazar, Laura H. Leduc, Stanley Leeson, Lisa R. Leffert, Scott A. LeGrand, Patricio Leyton, J. Lance Lichtor, John Lin, Alvaro A. Macias, Karan Madan, Sohail K. Mahboobi, Devi Mahendran, Christine Mai, Sayeed Malek, S. Rao Mallampati, Thomas J. Mancuso, Ramon Martin, Matthew C. Martinez, J. A. Jeevendra Martyn, Kai Matthes, Tommaso Mauri, Mary Ellen McCann, Shannon S. McKenna, Dennis J. McNicholl, Abdel-Kader Mehio, Thor C. Milland, Tonya L. K. Miller, John D. Mitchell, K. Annette Mizuguchi, Naila Moghul, David R. Moss, Ross J. Musumeci, Naveen Nathan, Ju-Mei Ng, Liem C. Nguyen, Ervant Nishanian, Martina Nowak, Ala Nozari, Michael Nurok, Arti Ori, Rafael A. Ortega, Amy J. Ortman, David Oxman, Arvind Palanisamy, Carlo Pancaro, Lisbeth Lopez Pappas, Benjamin Parish, Samuel Park, Deborah S. Pederson, Beverly K. Philip, James H. Philip, Silvia Pivi, Stephen D. Pratt, Douglas E. Raines, Stephen L. Ratcliff, James P. Rathmell, J. Taylor Reed, Elizabeth M. Rickerson, Selwyn O. Rogers Jr., Thomas M. Romanelli, William H. Rosenblatt, Carl E. Rosow, Edgar L. Ross, J. Victor Ryckman, Mônica M. Sá Rêgo, Nicholas Sadovnikoff, Warren S. Sandberg, Annette Y. Schure, B. Scott Segal, Navil F. Sethna, Swapneel K. Shah, Shaheen F. Shaikh, Fred E. Shapiro, Torin D. Shear, Prem S. Shekar, Stanton K. Shernan, Naomi Shimizu, Douglas C. Shook, Kamal K. Sikka, Pankaj K. Sikka, David A. Silver, Jeffrey H. Silverstein, Emily A. Singer, Ken Solt, Spiro G. Spanakis, Wolfgang Steudel, Matthias Stopfkuchen-Evans, Michael P. Storey, Gary R. Strichartz, Balachundhar Subramaniam, Wariya Sukhupragarn, John Summers, Shine Sun, Eswar Sundar, Sugantha Sundar, Neelakantan Sunder, Faraz Syed, Usha B. Tedrow, Nelson L. Thaemert, George P. Topulos, Lawrence C. Tsen, Richard D. Urman, Charles A. Vacanti, Francis X. Vacanti, Joshua C. Vacanti, Assia Valovska, Ivan T. Valovski, Mary Ann Vann, Susan Vassallo, Anasuya Vasudevan, Kamen V. Vlassakov, Gian Paolo Volpato, Essi M. Vulli, J. Matthias Walz, Jingping Wang, James F. Watkins, Maxwell Weinmann, Sharon L. Wetherall, Mallory Williams, Sarah H. Wiser, Zhiling Xiong, Warren M. Zapol, Jie Zhou
- Edited by Charles Vacanti, Scott Segal, Pankaj Sikka, Richard Urman
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- Book:
- Essential Clinical Anesthesia
- Published online:
- 05 January 2012
- Print publication:
- 11 July 2011, pp xv-xxviii
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- By Ashok Agarwal, Joseph P Alukal, Deborah J Anderson, Linda D Applegarth, Saleh Binsaleh, Elizabeth M Bloom, Karen E Boyle, Nancy L Brackett, Robert E Brannigan, James V Bruckner, Victor M Brugh, Ettore Caroppo, Grace M Centola, Aleksander Chudnovsky, Susan L Crockin, Fnu Deepinder, David M. Fenig, Aaron B Grotas, Matthew P. Hardy, Wayne J. G. Hellstrom, Stanton C Honig, Stuart S Howards, Keith Jarvi, Rajasingam S Jeyendran, William E Kaplan, Edward Karpman, Sanjay S Kasturi, Mohit Khera, Nancy A Klein, Dolores J Lamb, Jane M Lewis, Larry I Lipshultz, Kirk C Lo, Charles M Lynne, R. Dale McClure, Antoine A Makhlouf, Myles Margolis, Clara I. Marín-Briggiler, Randall B Meacham, Jesse N Mills, John P Mulhall, Alexander Müller, Christine Mullin, Harris M Nagler, Craig S Niederberger, Robert D Oates, Dana A Ohl, E. Charles Osterberg, Rodrigo L Pagani, Vassilios Papadopoulos, Joseph A Politch, Gail S Prins, Angela A Reese, Susan A Rothmann, Edmund S Sabanegh, Denny Sakkas, Jay I Sandlow, Richard A Schoor, Paulo C Serafini, Mark Sigman, Suresh C Sikka, Rebecca Z Sokol, Jens Sønksen, Miguel Srougi, James Stelling, Justin Tannir, Anthony J Thomas, Paul J Turek, Terry T Turner, Mónica H. Vazquez-Levin, Moshe Wald, Thomas J Walsh, Thomas M Wheeler, Daniel H Williams, Armand Zini, Barry R Zirkin
- Edited by Larry I. Lipshultz, Stuart S. Howards, University of Virginia, Craig S. Niederberger, University of Illinois, Chicago
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- Book:
- Infertility in the Male
- Published online:
- 19 May 2010
- Print publication:
- 24 September 2009, pp vii-x
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Analysis of regional congenital cardiac surgical outcomes in Florida using The Society of Thoracic Surgeons Congenital Heart Surgery Database
- Part of
- Jeffrey P. Jacobs, James A. Quintessenza, Redmond P. Burke, Mark S. Bleiweis, Barry J. Byrne, Eric L. Ceithaml, William M. DeCampli, Jorge M. Giroud, Richard A. Perryman, Eliot R. Rosenkranz, Grace Wolff, Vicki Posner, Sue Steverson, William B. Blanchard, Gerry L. Schiebler
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- Journal:
- Cardiology in the Young / Volume 19 / Issue 4 / August 2009
- Published online by Cambridge University Press:
- 01 August 2009, pp. 360-369
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Background
Florida is the fourth largest state in the United States of America. In 2004, 218,045 live babies were born in Florida, accounting for approximately 1744 new cases of congenital heart disease. We review the initial experience of The Society of Thoracic Surgeons Congenital Heart Surgery Database with a regional outcomes report, namely the Society of Thoracic Surgeons Florida Regional Report.
MethodsEight centres in Florida provide services for congenital cardiac surgery. The Children’s Medical Services of Florida provide a framework for quality improvement collaboration between centres. All congenital cardiac surgical centres in Florida have voluntarily agreed to submit data to the Society of Thoracic Surgeons Database. The Society of Thoracic Surgeons and Duke Clinical Research Institute prepared a Florida Regional Report to allow detailed regional analysis of outcomes for congenital cardiac surgery.
ResultsThe report of 2007 from the Society of Thoracic Surgeons Congenital Heart Surgery Database includes details of 61,014 operations performed during the 4 year data harvest window, which extended from 2003 through 2006. Of these operations, 6,385 (10.5%) were performed in Florida. Discharge mortality in the data from Florida overall, and from each Florida site, with 95% confidence intervals, is not different from cumulative data from the entire Society of Thoracic Surgeons Database, both for all patients and for patients stratified by complexity.
ConclusionsA regional consortium of congenital heart surgery centres in Florida under the framework of the Children’s Medical Services has allowed for inter-institutional collaboration with the goal of quality improvement. This experience demonstrates, first, that the database maintained by the Society of Thoracic Surgeons can provide the framework for regional analysis of outcomes, and second, that voluntary regional collaborative efforts permit the pooling of data for such analysis.
10 - The systematic use of SEM: an example
- James B. Grace
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- Book:
- Structural Equation Modeling and Natural Systems
- Published online:
- 04 December 2009
- Print publication:
- 17 August 2006, pp 259-274
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Summary
This chapter illustrates the systematic application of a multivariate perspective using SEM to explore a topic. In this presentation, the statistical details of the analyses will be ignored; these have been presented in earlier chapters, or can be found in the various publications referenced throughout. Here, the emphasis is on illustrating the broad enterprise of developing, evaluating, refining, and expanding multivariate models in order to understand system behavior and regulation. Throughout, the focus will be on the research enterprise rather than the analytical details. Thus, the philosophy and practice of SEM will be in the forefront, while the analysis of covariances, maximum likelihood, and mathematical details will be de-emphasized.
Background studies and findings
In 1992, Laura Gough and I conducted a study designed to examine the relationship between plant community biomass and species richness. This work was conducted in coastal marsh communities. The purpose of this study was to first characterize the relationship between biomass and richness. Then we planned to determine the role of competition in controlling the relationship. We expected that we would find a unimodal relationship between biomass and richness, primarily because of several key papers that had been published previously (Al-Mufti et al. 1977, Huston 1980, Wheeler and Giller 1982, Moore and Keddy 1989, Wisheu and Keddy 1989, Shipley et al. 1991, Wheeler and Shaw 1991). We also expected this relationship because there were several competing theories attempting to explain this phenomenon (Grime 1979, Huston 1979, Tilman 1982, Taylor et al. 1990, Keddy 1990).
7 - Additional techniques for complex situations
- James B. Grace
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- Book:
- Structural Equation Modeling and Natural Systems
- Published online:
- 04 December 2009
- Print publication:
- 17 August 2006, pp 181-204
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Summary
As mentioned earlier, the array of techniques associated with structural equation modeling has grown continuously since the LISREL synthesis. Some of this growth has been associated with refinements and some from new inventions. It is important for the reader to realize that the development of SEM methodology is still a work in progress. As described in Chapter 6, the ability to formulate and solve models containing composite variables, though long discussed, is only now being achieved. Refinements continue to be made in our ability to create models that are appropriate for the questions of interest and the data at hand. Structural equation modeling attempts to do something quite ambitious, to develop and evaluate multivariate models appropriate to almost any situation. Not surprisingly, the development of methods to accomplish this goal takes time and certain statistical limitations must be overcome.
For the most part, the material covered in Chapters 3, 4, and 50 represents basic principles relating to SEM. Many additional capabilities exist beyond those covered thus far, and many new developments are emerging. Some of these additional techniques and new developments are described in this chapter, although because this is such a vast subject, the treatments presented are only brief introductions to a select set of topics.
While the material in this chapter is in a section called Advanced topics, in my experience, the models that are most appropriate to understanding natural systems often require advanced elements or procedures.
5 - Principles of estimation and model assessment
- James B. Grace
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- Book:
- Structural Equation Modeling and Natural Systems
- Published online:
- 04 December 2009
- Print publication:
- 17 August 2006, pp 115-140
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Summary
Introduction
In previous chapters, I have presented structural equation model parameters, such as path coefficients, and we have considered their interpretation. Regression equations have been presented to help the reader understand the meaning of parameters and how they are commonly expressed. However, we have not yet considered the important question of how parameter values are estimated. Since we are dealing with the solution of complex multi equational systems, this is not a minor matter.
Historically, path models were solved primarily by the use of the familiar technique of least squares regression (also referred to as ordinary least squares, OLS). Today, most applications of SEM rely on model fitting programs that offer a variety of options for estimation methods, as well as many other supporting features. In this chapter our emphasis will be on maximum likelihood estimation, both because of its central role in the synthetic development of modern SEM, and because it provides a means of solving nonrecursive and latent variable models.
Another important issue that is related to the topic of estimation has to do with the assessment of model fit. One of the most powerful features of SEM is that techniques exist for comparing the observed relations in data to those expected based on the structure of the model and the estimated parameters. The degree to which the data match the model-derived expectations provides us with the capability of evaluating the overall suitability of a model.
Structural Equation Modeling and Natural Systems
- James B. Grace
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- Published online:
- 04 December 2009
- Print publication:
- 17 August 2006
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This book, first published in 2006, presents an introduction to the methodology of structural equation modeling, illustrates its use, and goes on to argue that it has revolutionary implications for the study of natural systems. A major theme of this book is that we have, up to this point, attempted to study systems primarily using methods (such as the univariate model) that were designed only for considering individual processes. Understanding systems requires the capacity to examine simultaneous influences and responses. Structural equation modeling (SEM) has such capabilities. It also possesses many other traits that add strength to its utility as a means of making scientific progress. In light of the capabilities of SEM, it can be argued that much of ecological theory is currently locked in an immature state that impairs its relevance. It is further argued that the principles of SEM are capable of leading to the development and evaluation of multivariate theories of the sort vitally needed for the conservation of natural systems.
1 - Introduction
- James B. Grace
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- Book:
- Structural Equation Modeling and Natural Systems
- Published online:
- 04 December 2009
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- 17 August 2006, pp 3-21
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Summary
The purpose and organization of this book
Structural equation modeling (SEM) represents both a different way of analyzing data, and a different way of doing science. A major theme of this book is that one of the factors that has limited the advance of ecological science has been the absence of methods for developing and evaluating multivariate theories. Understanding systems requires the capacity to examine simultaneous influences and responses. Conventional univariate analyses are typically limited to the examination of a single or at most a few processes at a time. Further, as will be illustrated in this book, characterizing interacting systems using univariate methods is commonly misleading and often inadequate. As I will argue in the final section of the book, conventional univariate hypothesis testing propagates a reliance on “theories of pieces” where one or two interacting processes are presumed to explain major characteristics of natural systems. Single-factor hypotheses seldom provide an adequate representation of system behavior. Worse still, such hypotheses are unable to be placed into a broader context or to evolve into more complex theories, regardless of the empirical evidence. Many of the simplistic theories that have occupied ecologists for so long seem irrelevant when we are faced with the task of predicting the responses of natural systems to environmental change. I believe ecologists have remained focused on univariate questions because we have lacked the scientific tools to ask and answer more complex questions.
6 - Composite variables and their uses
- James B. Grace
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- Book:
- Structural Equation Modeling and Natural Systems
- Published online:
- 04 December 2009
- Print publication:
- 17 August 2006, pp 143-180
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Summary
Introduction
It would seem that structural equation modeling holds the promise of providing scientists the capacity to evaluate a wide range of complex questions about systems. The incorporation of both conceptual and observed variables can be particularly advantageous by allowing data to interface directly with theory. Up to this present time, the emphasis in SEM has been on latent variables as the means of conveying theoretical concepts. It is my view that this is quite limiting. As we saw in the final section of Chapter 4, causal relationships in a model may deviate quite a lot from the stereotypic “hybrid” model. In the current chapter, I discuss the use of an additional variable type, the composite, in structural equation models. In simple terms, composite variables represent the influences of collections of other variables. As such, they can be helpful for (1) representing complex, multifaceted concepts, (2) managing model complexity, and (3) facilitating our ability to generalize. In my experience, these are all highly desirable capabilities when representing ecological systems and, as a result, I frequently find myself including composites in models.
While long recognized as a potentially important element of SEM, composite variables have received very limited use, in part because of a lack of theoretical consideration, but also because of difficulties that arise in parameter estimation when using conventional solution procedures. In this chapter I tackle both the theoretical and practical issues associated with composites.
9 - Multivariate experiments
- James B. Grace
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- Book:
- Structural Equation Modeling and Natural Systems
- Published online:
- 04 December 2009
- Print publication:
- 17 August 2006, pp 233-258
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Summary
Basic issues
The gold standard for studying causal relations is experimentation. As Fisher (1956) labored so hard to demonstrate, experimental manipulations have the ability to disentangle factors in a way that is usually not possible with nonexperimental data. By creating independence among causes, experimentation can lead to a great reduction in ambiguity about effects. There is little doubt for most scientists that well designed and properly analyzed experiments provide the most powerful way of assessing the importance of processes, when appropriate and relevant experiments are possible.
In this chapter I address a topic that generally receives little attention in discussions of SEM, its applicability to experimental studies. I hope to deal with two common misconceptions in this chapter, (1) that multivariate analysis is only for use on nonexperimental data, and (2) that when experiments are possible, there is no need for SEM. In fact, I would go one step further and say that the value of studying systems using SEM applies equally well to experimental and nonexperimental investigations.
There are several reasons why one might want to combine the techniques of SEM with experimentation. First, using experiments to evaluate multivariate relationships provides inherently more information about the responses of a system to manipulation. It is often difficult and sometimes impossible to exert independent control over all the variables of interest in a system. Examination of how the various pathways among variables respond to experimental treatment can yield important insights into system function and regulation.
PART I - A BEGINNING
- James B. Grace
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- Structural Equation Modeling and Natural Systems
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- 04 December 2009
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Preface
- James B. Grace
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- Book:
- Structural Equation Modeling and Natural Systems
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- 04 December 2009
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- 17 August 2006, pp ix-x
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Summary
This book is about an approach to scientific research that seeks to look at the system instead of the individual processes. In this book I share with the reader my perspective on the study of complex relationships. The methodological framework I use in this enterprise is structural equation modeling. For many readers, this will be new and unfamiliar. Some of the new ideas relate to statistical methodology and some relate to research philosophy. For others already familiar with the topic, they will find contained in this volume some new examples and even some new approaches they might find useful. In my own personal experience, the approaches and methods described in this book have been very valuable to me as a scientist. It is my assessment that they have allowed me to develop deeper insights into the relationships between ecological pattern and process. Most importantly, they have given me a framework for studying ecological systems that helps me to avoid getting lost in the detail, without requiring me to ignore the very real complexities. It is my opinion, after some years of careful consideration, that potentially they represent the means to a revolutionary change in scientific inquiry; one that allows us to ask questions of interacting systems that we have not been able to ask before. These methods provide many new opportunities for science, I believe, and it is my hope that others will see their value as well.
3 - The anatomy of models I: observed variable models
- James B. Grace
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- Book:
- Structural Equation Modeling and Natural Systems
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- 04 December 2009
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- 17 August 2006, pp 37-76
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Summary
Overview of more complex models
The versatility of structural equation modeling is reflected in the wide variety of models that can be developed using this methodology. To make the material easier to understand, we will start with the simplest types of model, those that only involve the use of observed variables. In later chapters we will introduce abstract variables into our models. The inclusion of these other types of variable greatly expands the variety of problems that can be addressed. As a preview of things to come later, here I present an example of a more complex type of model (Figure 3.1).
The model in Figure 3.1 includes four types of variables. Observed variables (represented by boxes) represent things that have been directly measured. Examples of observed variables include the recorded sex of animals in a sample, or the estimated plant biomass in a plot. Observed variables can also be used to represent experimental treatments (e.g., predators excluded, yes or no?), spatial locations (e.g., different sample sites), or interactions among other variables. Latent variables (represented by circles) represent unmeasured variables, which are often used to represent underlying causes. In the earliest example of a latent variable path model, Wright (1918) hypothesized that the relationships amongst bone dimensions in rabbits could be explained by a number of latent growth factors. While these latent factors could not be directly measured, their effects on bone dimensions could be inferred from the pattern of correlations amongst observed variables.
PART V - THE IMPLICATIONS OF STRUCTURAL EQUATION MODELING FOR THE STUDY OF NATURAL SYSTEMS
- James B. Grace
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- Structural Equation Modeling and Natural Systems
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- 04 December 2009
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- 17 August 2006, pp 289-290
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Acknowledgments
- James B. Grace
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- Structural Equation Modeling and Natural Systems
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- 04 December 2009
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- 17 August 2006, pp xi-xii
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