Last data update: May 13, 2024. (Total: 46773 publications since 2009)
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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US (preprint)
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 , Lavista Ferres J , Li C , Liu TY , Xie X , Zhang S , Zheng S , Vespignani A , Chinazzi M , Davis JT , Mu K , Pastore YPiontti 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 , Skali Lami O , Soni S , Tazi Bouardi H , 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 . medRxiv 2021 2021.02.03.21250974 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. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week 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.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work.Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below. CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook. CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation. COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information& Data Science Pilot Project. Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation. DDS-NBDS: NSF III-1812699. EPIFORECASTS-ENSEMBLE1: Wellcome Trust (210758/Z/18/Z) GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowments, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines GT-DeepCOVID: CDC MInD-Healthcare U01CK000531-Supplement. NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, NRT DGE-1545362), CDC MInD program, ORNL and funds/computing resources from Georgia Tech and GTRI. IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096). IowaStateLW-STEM: Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1916204, NSF CCF-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics. JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, US Office of Foreign Disaster Assistance, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers fo Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant). LANL-GrowthRate: LANL LDRD 20200700ER. MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01. NotreDame-mobility and NotreDame-FRED: NSF RAPID DEB 2027718 UA-EpiCovDA: NSF RAPID Grant # 2028401. UCSB-ACTS: NSF RAPID IIS 2029626. UCSD-NEU: Google Faculty Award, DARPA W31P4Q-21-C-0014, COVID Supplement CDC-HHS-6U01IP001137-01. UMass-MechBayes: NIGMS R35GM119582, NSF 1749854. UMich-RidgeTfReg: The University of Michigan Physics Department and the University of Michigan Office of Research.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:UMass-Amherst IRBAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and code referred to in the manuscript are publicly available. https://github.com/reichlab/covid19-forecast-hub/ https://github.com/reichlab/covidEnsembles https://zoltardata.com/project/44 |
Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.
Ray EL , Brooks LC , Bien J , Biggerstaff M , Bosse NI , Bracher J , Cramer EY , Funk S , Gerding A , Johansson MA , Rumack A , Wang Y , Zorn M , Tibshirani RJ , Reich NG . Int J Forecast 2022 The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policy makers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful. |
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.
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 , Lavista Ferres J , Li C , Liu TY , Xie X , Zhang S , Zheng S , Vespignani A , Chinazzi M , Davis JT , Mu K , Pastore YPiontti 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 , Skali Lami O , Soni S , Tazi Bouardi H , 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 . Proc Natl Acad Sci U S A 2022 119 (15) e2113561119 SignificanceThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action. |
Possible Zika virus infection among pregnant women - United States and Territories, May 2016
Simeone RM , Shapiro-Mendoza CK , Meaney-Delman D , Petersen EE , Galang RR , Oduyebo T , Rivera-Garcia B , Valencia-Prado M , Newsome KB , Perez-Padilla J , Williams TR , Biggerstaff M , Jamieson DJ , Honein MA , Ahmed F , Anesi S , Arnold KE , Barradas D , Barter D , Bertolli J , Bingham AM , Bollock J , Bosse T , Bradley KK , Brady D , Brown CM , Bryan K , Buchanan V , Bullard PD , Carrigan A , Clouse M , Cook S , Cooper M , Davidson S , DeBarr A , Dobbs T , Dunams T , Eason J , Eckert A , Eggers P , Ellington SR , Feldpausch A , Fredette CR , Gabel J , Glover M , Gosciminski M , Gay M , Haddock R , Hand S , Hardy J , Hartel ME , Hennenfent AK , Hills SL , House J , Igbinosa I , Im L , Jeff H , Khan S , Kightlinger L , Ko JY , Koirala S , Korhonen L , Krishnasamy V , Kurkjian K , Lampe M , Larson S , Lee EH , Lind L , Lindquist S , Long J , Macdonald J , MacFarquhar J , Mackie DP , Mark-Carew M , Martin B , Martinez-Quinones A , Matthews-Greer J , McGee SA , McLaughlin J , Mock V , Muna E , Oltean H , O'Mallan J , Pagano HP , Park SY , Peterson D , Polen KN , Porse CC , Rao CY , Ropri A , Rinsky J , Robinson S , Rosinger AY , Ruberto I , Schiffman E , Scott-Waldron C , Semple S , Sharp T , Short K , Signs K , Slavinski SA , Stevens T , Sweatlock J , Talbot EA , Tonzel J , Traxler R , Tubach S , Van Houten C , VinHatton E , Viray M , Virginie D , Warren MD , Waters C , White P , Williams T , Winters AI , Wood S , Zaganjor I . MMWR Morb Mortal Wkly Rep 2016 65 (20) 514-9 Zika virus is a cause of microcephaly and brain abnormalities (1), and it is the first known mosquito-borne infection to cause congenital anomalies in humans. The establishment of a comprehensive surveillance system to monitor pregnant women with Zika virus infection will provide data to further elucidate the full range of potential outcomes for fetuses and infants of mothers with asymptomatic and symptomatic Zika virus infection during pregnancy. In February 2016, Zika virus disease and congenital Zika virus infections became nationally notifiable conditions in the United States (2). Cases in pregnant women with laboratory evidence of Zika virus infection who have either 1) symptomatic infection or 2) asymptomatic infection with diagnosed complications of pregnancy can be reported as cases of Zika virus disease to ArboNET* (2), CDC's national arboviral diseases surveillance system. Under existing interim guidelines from the Council for State and Territorial Epidemiologists (CSTE), asymptomatic Zika virus infections in pregnant women who do not have known pregnancy complications are not reportable. ArboNET does not currently include pregnancy surveillance information (e.g., gestational age or pregnancy exposures) or pregnancy outcomes. To understand the full impact of infection on the fetus and neonate, other systems are needed for reporting and active monitoring of pregnant women with laboratory evidence of possible Zika virus infection during pregnancy. Thus, in collaboration with state, local, tribal, and territorial health departments, CDC established two surveillance systems to monitor pregnancies and congenital outcomes among women with laboratory evidence of Zika virus infection(dagger) in the United States and territories: 1) the U.S. Zika Pregnancy Registry (USZPR),( section sign) which monitors pregnant women residing in U.S. states and all U.S. territories except Puerto Rico, and 2) the Zika Active Pregnancy Surveillance System (ZAPSS), which monitors pregnant women residing in Puerto Rico. As of May 12, 2016, the surveillance systems were monitoring 157 and 122 pregnant women with laboratory evidence of possible Zika virus infection from participating U.S. states and territories, respectively. Tracking and monitoring clinical presentation of Zika virus infection, all prenatal testing, and adverse consequences of Zika virus infection during pregnancy are critical to better characterize the risk for congenital infection, the performance of prenatal diagnostic testing, and the spectrum of adverse congenital outcomes. These data will improve clinical guidance, inform counseling messages for pregnant women, and facilitate planning for clinical and public health services for affected families. |
CLIA requirements for proficiency testing: the basics for laboratory professionals
Astles JR , Stang H , Alspach T , Mitchell G , Gagnon M , Bosse D . MLO Med Lab Obs 2013 45 (9) 8-10, 12, 14-5; quiz 16 Along with requirements for personnel qualifications and quality control testing, proficiency testing (PT) is one of the central safeguards of laboratory quality under the Clinical Laboratory Improvement Amendments of 1988 (CLIA)1 and its regulations.2 The CLIA regulations have often been compared to a three-legged stool, resting on requirements for personnel qualifications and two performance indicators: quality control testing and proficiency testing. Proficiency testing is the only external performance indicator required by CLIA. |
Good laboratory practices for biochemical genetic testing and newborn screening for inherited metabolic disorders
Chen B , Mei J , Kalman L , Shahangian S , Williams I , Gagnon M , Bosse D , Ragin A , Cuthbert C , Zehnbauer B . MMWR Recomm Rep 2012 61 1-44 Biochemical genetic testing and newborn screening are essential laboratory services for the screening, detection, diagnosis, and monitoring of inborn errors of metabolism or inherited metabolic disorders. Under the Clinical Laboratory Improvement Amendments of 1988 (CLIA) regulations, laboratory testing is categorized on the basis of the level of testing complexity as either waived (i.e., from routine regulatory oversight) or nonwaived testing (which includes tests of moderate and high complexity). Laboratories that perform biochemical genetic testing are required by CLIA regulations to meet the general quality systems requirements for nonwaived testing and the personnel requirements for high-complexity testing. Laboratories that perform public health newborn screening are subject to the same CLIA regulations and applicable state requirements. As the number of inherited metabolic diseases that are included in state-based newborn screening programs continues to increase, ensuring the quality of performance and delivery of testing services remains a continuous challenge not only for public health laboratories and other newborn screening facilities but also for biochemical genetic testing laboratories. To help ensure the quality of laboratory testing, CDC collaborated with the Centers for Medicare & Medicaid Services, the Food and Drug Administration, the Health Resources and Services Administration, and the National Institutes of Health to develop guidelines for laboratories to meet CLIA requirements and apply additional quality assurance measures for these areas of genetic testing. This report provides recommendations for good laboratory practices that were developed based on recommendations from the Clinical Laboratory Improvement Advisory Committee, with additional input from the Secretary's Advisory Committee on Genetics, Health, and Society; the Secretary's Advisory Committee on Heritable Disorders in Newborns and Children; and representatives of newborn screening laboratories. The recommended practices address the benefits of using a quality management system approach, factors to consider before introducing new tests, establishment and verification of test performance specifications, the total laboratory testing process (which consists of the preanalytic, analytic, and postanalytic phases), confidentiality of patient information and test results, and personnel qualifications and responsibilities for laboratory testing for inherited metabolic diseases. These recommendations are intended for laboratories that perform biochemical genetic testing to improve the quality of laboratory services and for newborn screening laboratories to ensure the quality of laboratory practices for inherited metabolic disorders. These recommendations also are intended as a resource for medical and public health professionals who evaluate laboratory practices, for users of laboratory services to facilitate their collaboration with newborn screening systems and use of biochemical genetic tests, and for standard-setting organizations and professional societies in developing future laboratory quality standards and practice recommendations. This report complements Good Laboratory Practices for Molecular Genetic Testing for Heritable Diseases and Conditions (CDC. Good laboratory practices for molecular genetic testing for heritable diseases and conditions. MMWR 2009;58 [No. RR-6]) to provide guidance for ensuring and improving the quality of genetic laboratory services and public health outcomes. Future recommendations for additional areas of genetic testing will be considered on the basis of continued monitoring and evaluation of laboratory practices, technology advancements, and the development of laboratory standards and guidelines. |
The impact of clinical laboratory improvement advisory committee recommendations on microbiology laboratories
Anderson NL , Bosse DC , Weissfeld AS . Clin Microbiol Newsl 2009 31 (17) 129-136 The Clinical Laboratory Improvement Advisory Committee (CLIAC) was mandated by the Clinical Laboratory Improvement Amendments of 1988 (CLIA) and established in 1992 to provide advice to the Department of Health and Human Services on regulation of laboratory testing and improving laboratory quality. Since then, CLIAC has met regularly and recommended regulatory changes to CLIA, provided input on good laboratory practices, and discussed critical issues related to clinical and public health testing, the laboratory workforce, and laboratory systems research. The Committee has been effective in driving changes to microbiology quality control, which have led to a decreased burden and lower laboratory costs without sacrificing quality. The issues CLIAC addresses are complex and sometimes controversial, yet members have said their time on the Committee is worthwhile and that CLIAC has a positive influence on laboratory medicine. This Committee will remain a vital resource for providing guidance as laboratory testing continues to evolve. copyright 2009 Elsevier Inc. All rights reserved. |
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