13 results
Identifying incarceration status in the electronic health record using large language models in emergency department settings
- Thomas Huang, Vimig Socrates, Aidan Gilson, Conrad Safranek, Ling Chi, Emily A. Wang, Lisa B. Puglisi, Cynthia Brandt, R. Andrew Taylor, Karen Wang
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- Journal:
- Journal of Clinical and Translational Science / Volume 8 / Issue 1 / 2024
- Published online by Cambridge University Press:
- 11 March 2024, e53
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Background:
Incarceration is a significant social determinant of health, contributing to high morbidity, mortality, and racialized health inequities. However, incarceration status is largely invisible to health services research due to inadequate clinical electronic health record (EHR) capture. This study aims to develop, train, and validate natural language processing (NLP) techniques to more effectively identify incarceration status in the EHR.
Methods:The study population consisted of adult patients (≥ 18 y.o.) who presented to the emergency department between June 2013 and August 2021. The EHR database was filtered for notes for specific incarceration-related terms, and then a random selection of 1,000 notes was annotated for incarceration and further stratified into specific statuses of prior history, recent, and current incarceration. For NLP model development, 80% of the notes were used to train the Longformer-based and RoBERTa algorithms. The remaining 20% of the notes underwent analysis with GPT-4.
Results:There were 849 unique patients across 989 visits in the 1000 annotated notes. Manual annotation revealed that 559 of 1000 notes (55.9%) contained evidence of incarceration history. ICD-10 code (sensitivity: 4.8%, specificity: 99.1%, F1-score: 0.09) demonstrated inferior performance to RoBERTa NLP (sensitivity: 78.6%, specificity: 73.3%, F1-score: 0.79), Longformer NLP (sensitivity: 94.6%, specificity: 87.5%, F1-score: 0.93), and GPT-4 (sensitivity: 100%, specificity: 61.1%, F1-score: 0.86).
Conclusions:Our advanced NLP models demonstrate a high degree of accuracy in identifying incarceration status from clinical notes. Further research is needed to explore their scaled implementation in population health initiatives and assess their potential to mitigate health disparities through tailored system interventions.
Veil-of-ignorance reasoning mitigates self-serving bias in resource allocation during the COVID-19 crisis
- Karen Huang, Regan M. Bernhard, Netta Barak-Corren, Max H. Bazerman, Joshua D. Greene
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- Journal:
- Judgment and Decision Making / Volume 16 / Issue 1 / January 2021
- Published online by Cambridge University Press:
- 01 January 2023, pp. 1-19
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The COVID-19 crisis has forced healthcare professionals to make tragic decisions concerning which patients to save. Furthermore, The COVID-19 crisis has foregrounded the influence of self-serving bias in debates on how to allocate scarce resources. A utilitarian principle favors allocating scarce resources such as ventilators toward younger patients, as this is expected to save more years of life. Some view this as ageist, instead favoring age-neutral principles, such as “first come, first served”. Which approach is fairer? The “veil of ignorance” is a moral reasoning device designed to promote impartial decision-making by reducing decision-makers’ use of potentially biasing information about who will benefit most or least from the available options. Veil-of-ignorance reasoning was originally applied by philosophers and economists to foundational questions concerning the overall organization of society. Here we apply veil-of-ignorance reasoning to the COVID-19 ventilator dilemma, asking participants which policy they would prefer if they did not know whether they were younger or older. Two studies (pre-registered; online samples; Study 1, N=414; Study 2 replication, N=1,276) show that veil-of-ignorance reasoning shifts preferences toward saving younger patients. The effect on older participants is dramatic, reversing their opposition toward favoring the young, thereby eliminating self-serving bias. These findings provide guidance on how to remove self-serving biases to healthcare policymakers and frontline personnel charged with allocating scarce medical resources during times of crisis.
Ten new insights in climate science 2022
- Maria A. Martin, Emmanuel A. Boakye, Emily Boyd, Wendy Broadgate, Mercedes Bustamante, Josep G. Canadell, Edward R. Carr, Eric K. Chu, Helen Cleugh, Szilvia Csevár, Marwa Daoudy, Ariane de Bremond, Meghnath Dhimal, Kristie L. Ebi, Clea Edwards, Sabine Fuss, Martin P. Girardin, Bruce Glavovic, Sophie Hebden, Marina Hirota, Huang-Hsiung Hsu, Saleemul Huq, Karin Ingold, Ola M. Johannessen, Yasuko Kameyama, Nilushi Kumarasinghe, Gaby S. Langendijk, Tabea Lissner, Shuaib Lwasa, Catherine Machalaba, Aaron Maltais, Manu V. Mathai, Cheikh Mbow, Karen E. McNamara, Aditi Mukherji, Virginia Murray, Jaroslav Mysiak, Chukwumerije Okereke, Daniel Ospina, Friederike Otto, Anjal Prakash, Juan M. Pulhin, Emmanuel Raju, Aaron Redman, Kanta K. Rigaud, Johan Rockström, Joyashree Roy, E. Lisa F. Schipper, Peter Schlosser, Karsten A. Schulz, Kim Schumacher, Luana Schwarz, Murray Scown, Barbora Šedová, Tasneem A. Siddiqui, Chandni Singh, Giles B. Sioen, Detlef Stammer, Norman J. Steinert, Sunhee Suk, Rowan Sutton, Lisa Thalheimer, Maarten van Aalst, Kees van der Geest, Zhirong Jerry Zhao
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- Journal:
- Global Sustainability / Volume 5 / 2022
- Published online by Cambridge University Press:
- 10 November 2022, e20
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Non-technical summary
We summarize what we assess as the past year's most important findings within climate change research: limits to adaptation, vulnerability hotspots, new threats coming from the climate–health nexus, climate (im)mobility and security, sustainable practices for land use and finance, losses and damages, inclusive societal climate decisions and ways to overcome structural barriers to accelerate mitigation and limit global warming to below 2°C.
Technical summaryWe synthesize 10 topics within climate research where there have been significant advances or emerging scientific consensus since January 2021. The selection of these insights was based on input from an international open call with broad disciplinary scope. Findings concern: (1) new aspects of soft and hard limits to adaptation; (2) the emergence of regional vulnerability hotspots from climate impacts and human vulnerability; (3) new threats on the climate–health horizon – some involving plants and animals; (4) climate (im)mobility and the need for anticipatory action; (5) security and climate; (6) sustainable land management as a prerequisite to land-based solutions; (7) sustainable finance practices in the private sector and the need for political guidance; (8) the urgent planetary imperative for addressing losses and damages; (9) inclusive societal choices for climate-resilient development and (10) how to overcome barriers to accelerate mitigation and limit global warming to below 2°C.
Social media summaryScience has evidence on barriers to mitigation and how to overcome them to avoid limits to adaptation across multiple fields.
Network models and sensor layers to design adaptive learning using educational mapping
- Luwen Huang, Karen E. Willcox
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- Journal:
- Design Science / Volume 7 / 2021
- Published online by Cambridge University Press:
- 19 April 2021, e9
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A network modelling approach to educational mapping leads to a scalable computational model that supports adaptive learning, intelligent tutors, intelligent teaching assistants, and data-driven continuous improvement. Current educational mapping processes are generally applied at a level of resolution that is too coarse to support adaptive learning and learning analytics systems at scale. This paper proposes a network modelling approach to structure extremely fine-grained statements of learning ability called Micro-outcomes, and a method to design sensors for inferring a learner’s knowledge state. These sensors take the form of high-resolution assessments and trackers that collect digital analytics. The sensors are linked to Micro-outcomes as part of the network model, enabling inference and pathway analysis. One example demonstrates the modelling approach applied to two community college subjects in College Algebra and Introductory Accounting. Application examples showcase how this modelling approach provides the design foundation for an intelligent tutoring system and intelligent teaching assistant system deployed at Arapahoe Community College and Quinsigamond Community College. A second example demonstrates the modelling approach deployed in an undergraduate aerospace engineering subject at the Massachusetts Institute of Technology to support course planning and teaching improvement.
Predictors of change over time in subjective daytime sleepiness among older adult recipients of long-term services and supports
- Darina V. Petrovsky, Karen B. Hirschman, Miranda Varrasse McPhillips, Justine S. Sefcik, Alexandra L. Hanlon, Liming Huang, Glenna S. Brewster, Nancy A. Hodgson, Mary D. Naylor
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- Journal:
- International Psychogeriatrics / Volume 32 / Issue 7 / July 2020
- Published online by Cambridge University Press:
- 11 June 2020, pp. 849-861
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Objectives:
Daytime sleepiness is associated with multiple negative outcomes in older adults receiving long-term services and supports (LTSS) including reduced cognitive performance, need for greater assistance with activities of daily living and decreased social engagement. The purpose of this study was to identify predictors of change in subjective daytime sleepiness among older adults during their first 2 years of receiving LTSS.
Design and Setting:Secondary analysis of data from a prospective longitudinal study of older adults who received LTSS in their homes, assisted living communities or nursing homes interviewed at baseline and every 3 months for 24 months.
Participants:470 older adults (60 years and older) newly enrolled in LTSS (mean = 81, SD = 8.7; range 60–98; 71% women).
Measurements:Subjective daytime sleepiness was assessed every 3 months through 2 years using the Epworth Sleepiness Scale. Multiple validated measures were used to capture health-related quality of life characteristics of enrollees and their environment, including symptom status (Symptom Bother Scale), cognition (Mini Mental Status Exam), physical function (Basic Activities of Daily Living), physical and mental general health, quality of life (Dementia Quality of Life, D-QoL), depressive symptoms (Geriatric Depression Scale) and social support (Medical Outcomes Survey-Social Support).
Results:Longitudinal mixed effects modeling was used to examine the relationship between independent variables and continuous measure of daytime sleepiness. Increased feelings of belonging, subscale of the D-QoL (effect size = −0.006, 95% CI: −0.013 to −0.0001, p = 0.045) and higher number of depressive symptoms (effect size = −0.002, 95% CI: −0.004 to −0.001, p = 0.001) at baseline were associated with slower rates of increase in daytime sleepiness over time.
Conclusions:Comprehensive baseline and longitudinal screening for changes in daytime sleepiness along with depression and perceived quality of life should be used to inform interventions aimed at reducing daytime sleepiness among older adults receiving LTSS.
Network models for mapping educational data
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- Karen E. Willcox, Luwen Huang
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- Journal:
- Design Science / Volume 3 / 2017
- Published online by Cambridge University Press:
- 25 October 2017, e18
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Educational mapping is the process of analyzing an educational system to identify entities, relationships and attributes. This paper proposes a network modeling approach to educational mapping. Current mapping processes in education typically represent data in forms that do not support scalable learning analytics. For example, a curriculum map is usually a table, where relationships among curricular elements are represented implicitly in the rows of the table. The proposed network modeling approach overcomes this limitation through explicit modeling of these relationships in a graph structure, which in turn unlocks the ability to perform scalable analyses on the dataset. The paper presents network models for educational use cases, with concrete examples in curriculum mapping, accreditation mapping and concept mapping. Illustrative examples demonstrate how the formal modeling approach enables visualization and learning analytics. The analysis provides insight into learning pathways, supporting design of adaptive learning systems. It also permits gap analysis of curriculum coverage, supporting student advising, student degree planning and curricular design at scales ranging from an entire institution to an individual course.
Standardized Infection Ratio for Surgical Site Infection after Colon Surgery: Discord in Models Measuring Healthcare Quality
- Raymond Chinn, Jason M. Lempp, Susan S. Huang, Rekha Murthy, Francesca J. Torriani, Jacqueline Daley, Elaine Dekker, Barbara Goss-Bottorff, Wendy Kaler, Karen Meyer, Frank Myers, Amy Nichols, Kathleen Quan, David Birnbaum
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 37 / Issue 11 / November 2016
- Published online by Cambridge University Press:
- 30 August 2016, pp. 1378-1382
- Print publication:
- November 2016
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The government publishes 3 different public report surgical site infection (SSI) metrics, all called standardized infection ratios (SIRs), that impact perceived hospital quality. We conducted a non-random cross-sectional observational pilot study of 20 California hospitals that voluntarily submitted colon surgery and SSI data. Discordant SIR values, leading to contradictory conclusions, occurred in 35% of these hospitals.
Infect Control Hosp Epidemiol 2016;1–5
Early Cognitively Based Functional Limitations Predict Loss of Independence in Instrumental Activities of Daily Living in Older Adults
- Karen M. Lau, Mili Parikh, Danielle J. Harvey, Chun-Jung Huang, Sarah Tomaszewski Farias
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- Journal:
- Journal of the International Neuropsychological Society / Volume 21 / Issue 9 / October 2015
- Published online by Cambridge University Press:
- 22 September 2015, pp. 688-698
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Older adults with early forms of neurodegenerative disease are at risk for functional disability, which is often defined by the loss of independence in instrumental activities of daily living (IADLs). The current study investigated the influence of mild changes in everyday functional abilities (referred to as functional limitations) on risk for development of incident functional disability. A total of 407 participants, who were considered cognitively normal or diagnosed with mild cognitive impairment (MCI) at baseline, were followed longitudinally over an average 4.1 years (range=0.8–9.2 years). Informant-based ratings from the Everyday Cognition (ECog; Farias et al., 2008) and the Instrumental Activities of Daily Living (Lawton & Brody, 1969) scales assessed the degree of functional limitations and incident IADL disability, respectively. Cox proportional hazards models revealed that more severe functional limitations (as measured by the Total ECog score) at baseline were associated with approximately a four-fold increased risk of developing IADL disability a few years later. Among the ECog domains, functional limitations in Everyday Planning, Everyday Memory, and Everyday Visuospatial domains were associated with the greatest risk of incident functional disability. These results remained robust even after controlling for participants’ neuropsychological functioning on tests of executive functions and episodic memory. Current findings indicate that early functional limitations have prognostic value in identifying older adults at risk for developing functional disability. Findings highlight the importance of developing interventions to support everyday abilities related to memory, executive function, and visuospatial skills in an effort to delay loss of independence in IADLs. (JINS, 2015, 21, 688–698)
vTrain: A Novel Curriculum for Patient Surge Training in a Multi-User Virtual Environment (MUVE)
- Laura S. Greci, Rameshsharma Ramloll, Samantha Hurst, Karen Garman, Jaishree Beedasy, Eric B. Pieper, Ricky Huang, Erin Higginbotham, Zia Agha
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- Journal:
- Prehospital and Disaster Medicine / Volume 28 / Issue 3 / June 2013
- Published online by Cambridge University Press:
- 07 February 2013, pp. 215-222
- Print publication:
- June 2013
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Introduction
During a pandemic influenza, emergency departments will be overwhelmed with a large influx of patients seeking care. Although all hospitals should have a written plan for dealing with this surge of health care utilization, most hospitals struggle with ways to educate the staff and practice for potentially catastrophic events.
Hypothesis/ProblemTo better prepare hospital staff for a patient surge, a novel educational curriculum was developed utilizing an emergency department for a patient surge functional drill.
MethodsA multidisciplinary team of medical educators, evaluators, emergency preparedness experts, and technology specialists developed a curriculum to: (1) train novice users to function in their job class in a multi-user virtual environment (MUVE); (2) obtain appropriate pre-drill disaster preparedness training; (3) perform functional team exercises in a MUVE; and (4) reflect on their performance after the drill.
ResultsA total of 14 students participated in one of two iterations of the pilot training program; seven nurses completed the emergency department triage course, and seven hospital administrators completed the Command Post (CP) course. All participants reported positive experiences in written course evaluations and structured verbal debriefings, and self-reported increase in disaster preparedness knowledge. Students also reported improved team communication, planning, team decision making, and the ability to visualize and reflect on their performance.
ConclusionData from this pilot program suggest that the immersive, virtual teaching method is well suited to team-based, reflective practice and learning of disaster management skills.
. ,Greci LS ,Ramloll R ,Hurst S ,Garman K ,Beedasy J ,Pieper EB ,Huang R ,Higginbotham E .Agha Z vTrain: A Novel Curriculum for Patient Surge Training in a Multi-User Virtual Environment (MUVE) . Prehosp Disaster Med.2013 ;28 (3 ):1 -8
Contributors
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- By Gregory A. Aarons, Nick Axford, Frances Wallace Bailey, Judith Bennett, Karen A. Blase, James Boyle, Tracey Bywater, Linda L. Caldwell, Jeanne Century, Anne Michelle Daniels, Thomas J. Dishion, Celene E. Domitrovich, Morgaen Donaldson, Glen Dunlap, Carl J. Dunst, Melissa Van Dyke, Dean L. Fixsen, Tamsin Ford, Lise Fox, Cassie Freeman, Robyn M. Gillies, Amy E. Green, Mark T. Greenberg, Violet H. Harada, Tim Hobbs, Cindy Huang, Robert J. Illback, Barbara Kelly, Kathryn Margolis, Elizabeth Miller, Dana T. Mitra, Jeremy J. Monsen, Julia E. Moore, Louise Morpeth, Barbara Neufeld, Colleen K. Reutebuch, Mollie Rudnick, Robert Savage, Robert E. Slavin, Elizabeth A. Stormshack, Phillip Strain, Keith J. Topping, Carol M. Trivette, Sharon Vaughn, Janet A. Welsh, Lisa Marks Woolfson, Joyce Yukawa
- Edited by Barbara Kelly, University of Strathclyde, Daniel F. Perkins, Pennsylvania State University
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- Handbook of Implementation Science for Psychology in Education
- Published online:
- 05 November 2012
- Print publication:
- 20 August 2012, pp xi-xiv
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Self-reported memory impairment and brain PET of amyloid and tau in middle-aged and older adults without dementia
- David A. Merrill, Prabha Siddarth, Nathan Y. Saito, Linda M. Ercoli, Alison C. Burggren, Vladimir Kepe, Helen Lavretsky, Karen J. Miller, Jeanne Kim, S. C. Huang, Susan Y. Bookheimer, Jorge R. Barrio, Gary W. Small
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- Journal:
- International Psychogeriatrics / Volume 24 / Issue 7 / July 2012
- Published online by Cambridge University Press:
- 16 February 2012, pp. 1076-1084
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Background: Whether perceived changes in memory parallel changes in brain pathology is uncertain. Positron emission tomography (PET) scans using 2-(1-{6-[(2-[F-18]fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene)malononitrile (FDDNP) can measure levels of amyloid plaques and tau neurofibrillary tangles in vivo. Here we investigate whether degree of self-reported memory impairment is associated with FDDNP-PET binding levels in persons without dementia.
Methods: Fifty-seven middle-aged and older adults without dementia (mean age ±standard deviation = 66.3 ± 10.6 years), including 25 with normal aging and 32 with mild cognitive impairment (MCI), were assessed. The outcome measures were the four factor scores of the Memory Functioning Questionnaire (MFQ) (frequency of forgetting, seriousness of forgetting, retrospective functioning, and mnemonics use) and FDDNP-PET binding levels in medial temporal, lateral temporal, posterior cingulate, parietal, frontal, and global (overall average) regions of interest.
Results: After controlling for age, higher reported frequency of forgetting was associated with greater medial temporal (r = −0.29, p = 0.05), parietal (r = −0.30, p = 0.03), frontal (r = −0.35, p = 0.01), and global FDDNP-PET binding levels (r = −0.33, p = 0.02). The remaining MFQ factor scores were not significantly associated with FDDNP-PET binding levels, and no significant differences were found between normal aging and MCI subjects. Item analysis of the frequency of forgetting factor revealed five questions that yielded similar results as the full 32-question scale (r = −0.52, p = 0.0002).
Conclusions: These findings suggest that some forms of memory self-awareness, in particular the reported frequency of forgetting, may reflect the extent of cerebral amyloid and tau brain pathology.
Contributors
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- By Rose Teteki Abbey, K. C. Abraham, David Tuesday Adamo, LeRoy H. Aden, Efrain Agosto, Victor Aguilan, Gillian T. W. Ahlgren, Charanjit Kaur AjitSingh, Dorothy B E A Akoto, Giuseppe Alberigo, Daniel E. Albrecht, Ruth Albrecht, Daniel O. Aleshire, Urs Altermatt, Anand Amaladass, Michael Amaladoss, James N. Amanze, Lesley G. Anderson, Thomas C. Anderson, Victor Anderson, Hope S. Antone, María Pilar Aquino, Paula Arai, Victorio Araya Guillén, S. Wesley Ariarajah, Ellen T. Armour, Brett Gregory Armstrong, Atsuhiro Asano, Naim Stifan Ateek, Mahmoud Ayoub, John Alembillah Azumah, Mercedes L. García Bachmann, Irena Backus, J. Wayne Baker, Mieke Bal, Lewis V. Baldwin, William Barbieri, António Barbosa da Silva, David Basinger, Bolaji Olukemi Bateye, Oswald Bayer, Daniel H. Bays, Rosalie Beck, Nancy Elizabeth Bedford, Guy-Thomas Bedouelle, Chorbishop Seely Beggiani, Wolfgang Behringer, Christopher M. Bellitto, Byard Bennett, Harold V. Bennett, Teresa Berger, Miguel A. Bernad, Henley Bernard, Alan E. Bernstein, Jon L. Berquist, Johannes Beutler, Ana María Bidegain, Matthew P. Binkewicz, Jennifer Bird, Joseph Blenkinsopp, Dmytro Bondarenko, Paulo Bonfatti, Riet en Pim Bons-Storm, Jessica A. Boon, Marcus J. Borg, Mark Bosco, Peter C. Bouteneff, François Bovon, William D. Bowman, Paul S. Boyer, David Brakke, Richard E. Brantley, Marcus Braybrooke, Ian Breward, Ênio José da Costa Brito, Jewel Spears Brooker, Johannes Brosseder, Nicholas Canfield Read Brown, Robert F. Brown, Pamela K. Brubaker, Walter Brueggemann, Bishop Colin O. Buchanan, Stanley M. Burgess, Amy Nelson Burnett, J. Patout Burns, David B. Burrell, David Buttrick, James P. Byrd, Lavinia Byrne, Gerado Caetano, Marcos Caldas, Alkiviadis Calivas, William J. Callahan, Salvatore Calomino, Euan K. Cameron, William S. Campbell, Marcelo Ayres Camurça, Daniel F. Caner, Paul E. Capetz, Carlos F. Cardoza-Orlandi, Patrick W. Carey, Barbara Carvill, Hal Cauthron, Subhadra Mitra Channa, Mark D. Chapman, James H. Charlesworth, Kenneth R. Chase, Chen Zemin, Luciano Chianeque, Philip Chia Phin Yin, Francisca H. Chimhanda, Daniel Chiquete, John T. Chirban, Soobin Choi, Robert Choquette, Mita Choudhury, Gerald Christianson, John Chryssavgis, Sejong Chun, Esther Chung-Kim, Charles M. A. Clark, Elizabeth A. Clark, Sathianathan Clarke, Fred Cloud, John B. Cobb, W. Owen Cole, John A Coleman, John J. Collins, Sylvia Collins-Mayo, Paul K. Conkin, Beth A. Conklin, Sean Connolly, Demetrios J. Constantelos, Michael A. Conway, Paula M. Cooey, Austin Cooper, Michael L. Cooper-White, Pamela Cooper-White, L. William Countryman, Sérgio Coutinho, Pamela Couture, Shannon Craigo-Snell, James L. Crenshaw, David Crowner, Humberto Horacio Cucchetti, Lawrence S. Cunningham, Elizabeth Mason Currier, Emmanuel Cutrone, Mary L. Daniel, David D. Daniels, Robert Darden, Rolf Darge, Isaiah Dau, Jeffry C. Davis, Jane Dawson, Valentin Dedji, John W. de Gruchy, Paul DeHart, Wendy J. Deichmann Edwards, Miguel A. De La Torre, George E. Demacopoulos, Thomas de Mayo, Leah DeVun, Beatriz de Vasconcellos Dias, Dennis C. Dickerson, John M. Dillon, Luis Miguel Donatello, Igor Dorfmann-Lazarev, Susanna Drake, Jonathan A. Draper, N. Dreher Martin, Otto Dreydoppel, Angelyn Dries, A. J. Droge, Francis X. D'Sa, Marilyn Dunn, Nicole Wilkinson Duran, Rifaat Ebied, Mark J. Edwards, William H. Edwards, Leonard H. Ehrlich, Nancy L. Eiesland, Martin Elbel, J. Harold Ellens, Stephen Ellingson, Marvin M. Ellison, Robert Ellsberg, Jean Bethke Elshtain, Eldon Jay Epp, Peter C. Erb, Tassilo Erhardt, Maria Erling, Noel Leo Erskine, Gillian R. Evans, Virginia Fabella, Michael A. Fahey, Edward Farley, Margaret A. Farley, Wendy Farley, Robert Fastiggi, Seena Fazel, Duncan S. Ferguson, Helwar Figueroa, Paul Corby Finney, Kyriaki Karidoyanes FitzGerald, Thomas E. FitzGerald, John R. Fitzmier, Marie Therese Flanagan, Sabina Flanagan, Claude Flipo, Ronald B. Flowers, Carole Fontaine, David Ford, Mary Ford, Stephanie A. Ford, Jim Forest, William Franke, Robert M. Franklin, Ruth Franzén, Edward H. Friedman, Samuel Frouisou, Lorelei F. Fuchs, Jojo M. Fung, Inger Furseth, Richard R. Gaillardetz, Brandon Gallaher, China Galland, Mark Galli, Ismael García, Tharscisse Gatwa, Jean-Marie Gaudeul, Luis María Gavilanes del Castillo, Pavel L. Gavrilyuk, Volney P. Gay, Metropolitan Athanasios Geevargis, Kondothra M. George, Mary Gerhart, Simon Gikandi, Maurice Gilbert, Michael J. Gillgannon, Verónica Giménez Beliveau, Terryl Givens, Beth Glazier-McDonald, Philip Gleason, Menghun Goh, Brian Golding, Bishop Hilario M. Gomez, Michelle A. Gonzalez, Donald K. Gorrell, Roy Gottfried, Tamara Grdzelidze, Joel B. Green, Niels Henrik Gregersen, Cristina Grenholm, Herbert Griffiths, Eric W. Gritsch, Erich S. Gruen, Christoffer H. Grundmann, Paul H. Gundani, Jon P. Gunnemann, Petre Guran, Vidar L. Haanes, Jeremiah M. Hackett, Getatchew Haile, Douglas John Hall, Nicholas Hammond, Daphne Hampson, Jehu J. Hanciles, Barry Hankins, Jennifer Haraguchi, Stanley S. Harakas, Anthony John Harding, Conrad L. Harkins, J. William Harmless, Marjory Harper, Amir Harrak, Joel F. Harrington, Mark W. Harris, Susan Ashbrook Harvey, Van A. Harvey, R. Chris Hassel, Jione Havea, Daniel Hawk, Diana L. Hayes, Leslie Hayes, Priscilla Hayner, S. Mark Heim, Simo Heininen, Richard P. Heitzenrater, Eila Helander, David Hempton, Scott H. Hendrix, Jan-Olav Henriksen, Gina Hens-Piazza, Carter Heyward, Nicholas J. Higham, David Hilliard, Norman A. Hjelm, Peter C. Hodgson, Arthur Holder, M. Jan Holton, Dwight N. Hopkins, Ronnie Po-chia Hsia, Po-Ho Huang, James Hudnut-Beumler, Jennifer S. Hughes, Leonard M. Hummel, Mary E. Hunt, Laennec Hurbon, Mark Hutchinson, Susan E. Hylen, Mary Beth Ingham, H. Larry Ingle, Dale T. Irvin, Jon Isaak, Paul John Isaak, Ada María Isasi-Díaz, Hans Raun Iversen, Margaret C. Jacob, Arthur James, Maria Jansdotter-Samuelsson, David Jasper, Werner G. Jeanrond, Renée Jeffery, David Lyle Jeffrey, Theodore W. Jennings, David H. Jensen, Robin Margaret Jensen, David Jobling, Dale A. Johnson, Elizabeth A. Johnson, Maxwell E. Johnson, Sarah Johnson, Mark D. Johnston, F. Stanley Jones, James William Jones, John R. Jones, Alissa Jones Nelson, Inge Jonsson, Jan Joosten, Elizabeth Judd, Mulambya Peggy Kabonde, Robert Kaggwa, Sylvester Kahakwa, Isaac Kalimi, Ogbu U. Kalu, Eunice Kamaara, Wayne C. Kannaday, Musimbi Kanyoro, Veli-Matti Kärkkäinen, Frank Kaufmann, Léon Nguapitshi Kayongo, Richard Kearney, Alice A. Keefe, Ralph Keen, Catherine Keller, Anthony J. Kelly, Karen Kennelly, Kathi Lynn Kern, Fergus Kerr, Edward Kessler, George Kilcourse, Heup Young Kim, Kim Sung-Hae, Kim Yong-Bock, Kim Yung Suk, Richard King, Thomas M. King, Robert M. Kingdon, Ross Kinsler, Hans G. Kippenberg, Cheryl A. Kirk-Duggan, Clifton Kirkpatrick, Leonid Kishkovsky, Nadieszda Kizenko, Jeffrey Klaiber, Hans-Josef Klauck, Sidney Knight, Samuel Kobia, Robert Kolb, Karla Ann Koll, Heikki Kotila, Donald Kraybill, Philip D. W. Krey, Yves Krumenacker, Jeffrey Kah-Jin Kuan, Simanga R. Kumalo, Peter Kuzmic, Simon Shui-Man Kwan, Kwok Pui-lan, André LaCocque, Stephen E. Lahey, John Tsz Pang Lai, Emiel Lamberts, Armando Lampe, Craig Lampe, Beverly J. Lanzetta, Eve LaPlante, Lizette Larson-Miller, Ariel Bybee Laughton, Leonard Lawlor, Bentley Layton, Robin A. Leaver, Karen Lebacqz, Archie Chi Chung Lee, Marilyn J. Legge, Hervé LeGrand, D. L. LeMahieu, Raymond Lemieux, Bill J. Leonard, Ellen M. Leonard, Outi Leppä, Jean Lesaulnier, Nantawan Boonprasat Lewis, Henrietta Leyser, Alexei Lidov, Bernard Lightman, Paul Chang-Ha Lim, Carter Lindberg, Mark R. Lindsay, James R. Linville, James C. Livingston, Ann Loades, David Loades, Jean-Claude Loba-Mkole, Lo Lung Kwong, Wati Longchar, Eleazar López, David W. Lotz, Andrew Louth, Robin W. Lovin, William Luis, Frank D. Macchia, Diarmaid N. J. MacCulloch, Kirk R. MacGregor, Marjory A. MacLean, Donald MacLeod, Tomas S. Maddela, Inge Mager, Laurenti Magesa, David G. Maillu, Fortunato Mallimaci, Philip Mamalakis, Kä Mana, Ukachukwu Chris Manus, Herbert Robinson Marbury, Reuel Norman Marigza, Jacqueline Mariña, Antti Marjanen, Luiz C. L. Marques, Madipoane Masenya (ngwan'a Mphahlele), Caleb J. D. Maskell, Steve Mason, Thomas Massaro, Fernando Matamoros Ponce, András Máté-Tóth, Odair Pedroso Mateus, Dinis Matsolo, Fumitaka Matsuoka, John D'Arcy May, Yelena Mazour-Matusevich, Theodore Mbazumutima, John S. McClure, Christian McConnell, Lee Martin McDonald, Gary B. McGee, Thomas McGowan, Alister E. McGrath, Richard J. McGregor, John A. McGuckin, Maud Burnett McInerney, Elsie Anne McKee, Mary B. McKinley, James F. McMillan, Ernan McMullin, Kathleen E. McVey, M. 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Yee, Viktor Yelensky, Yeo Khiok-Khng, Gustav K. K. Yeung, Angela Yiu, Amos Yong, Yong Ting Jin, You Bin, Youhanna Nessim Youssef, Eliana Yunes, Robert Michael Zaller, Valarie H. Ziegler, Barbara Brown Zikmund, Joyce Ann Zimmerman, Aurora Zlotnik, Zhuo Xinping
- Edited by Daniel Patte, Vanderbilt University, Tennessee
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- Book:
- The Cambridge Dictionary of Christianity
- Published online:
- 05 August 2012
- Print publication:
- 20 September 2010, pp xi-xliv
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Incidence Density and Relative Risk of Nosocomial Infection in Taiwan's Only Children's Hospital, 1999-2003
- Suh-Hwa Maa, Hsin-Lun Lee, Yhu-Chering Huang, June Hsieh Wu, Tsung-Shan Tsou, Karen MacDonald, Ivo Abraham
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 29 / Issue 8 / August 2008
- Published online by Cambridge University Press:
- 02 January 2015, pp. 767-770
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- August 2008
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We report on 2,688 nosocomial infection episodes involving 3,238 pathogens in Taiwan's only children's hospital. The mean annual incidence density was 4.06 cases per 1,000 patient-days. The relative risk was highest in intensive care units, oncology wards, and neonatal observation wards. Very young, low-birth-weight, critically ill, and oncology patients were most at risk.