Evidence map›Paper›PMID 30952079›Full record

ArticleWater research2019

Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches.

Mano Sivaganesan, Tiong Gim Aw, Shannon Briggs, Erin Dreelin, Asli Aslan, Samuel Dorevitch, Abhilasha Shrestha, Natasha Isaacs, Julie Kinzelman, Greg Kleinheinz and 10 more

Open access · greenAbstract read
In one paragraph

Article in Water research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors at 12 institutions in 1 country.

Mano SivaganesanU.S. Environmental Protection Agency, National Risk Management Research Laboratory, 26 W. M.L. King Dr, Cincinnati, OH, 45268, USA.
Tiong Gim AwDepartment of Global Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 2100, New Orleans, LA, 70112, USA.
Shannon BriggsWater Resources Division, Michigan Department of Environmental Quality, P. O. Box 30458, 525 West Allegan Street, Lansing, MI, 48909, USA.
Erin DreelinCenter for Water Sciences, Michigan State University, 1405 South Harrison Road, East Lansing, MI, 48823, USA.
Asli AslanGeorgia Southern University, Department of Environmental Health Sciences, 501 Forest Drive, Statesboro, GA, 30458, USA.
Samuel DorevitchUniversity of Illinois at Chicago, School of Public Health, 2121 W. Taylor Street, Chicago, IL, 60612, USA.
Abhilasha ShresthaUniversity of Illinois at Chicago, School of Public Health, 2121 W. Taylor Street, Chicago, IL, 60612, USA.
Natasha IsaacsU.S. Geological Survey, Upper Midwest Water Science Center, 6520 Mercantile Way, Ste 5, Lansing, MI, 48911, USA.
Julie KinzelmanCity of Racine Public Health Department, 730 Washington Ave, Racine, WI, 53403, USA.
Greg KleinheinzUniversity of Wisconsin-Oshkosh, Environmental Research Laboratory, 800 Algoma Boulevard, Oshkosh, WI, 54901, USA.
Rachel NobleInstitute of Marine Sciences, University of North Carolina at Chapel Hill, 3431 Arendell Street, Morehead City, NC, 28557, USA.
Rick RediskeAnnis Water Resources Institute, Lake Michigan Center, 740 W. Shoreline Dr, Muskegon, MI, 49441, USA.
Brian ScullAnnis Water Resources Institute, Lake Michigan Center, 740 W. Shoreline Dr, Muskegon, MI, 49441, USA.
Susan RosenbergOakland County Health Division Laboratory, 1200 N. Telegraph, Pontiac, MI, 48341, USA.
Barbara WebermanOakland County Health Division Laboratory, 1200 N. Telegraph, Pontiac, MI, 48341, USA.
Tami SivySaginaw Valley State University, Department of Chemistry, 7400 Bay Road, University Center, MI, 48710, USA.
Ben SouthwellLake Superior State University, Environmental Analysis Laboratory, 650 W. Easterday Ave, Sault Ste Marie, MI, 49783, USA.
Shawn SiefringU.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 26 W. M.L. King Dr, Cincinnati, OH, 45268, USA.
Kevin OshimaU.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 26 W. M.L. King Dr, Cincinnati, OH, 45268, USA.
Richard HauglandU.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 26 W. M.L. King Dr, Cincinnati, OH, 45268, USA. Electronic address: haugland.rich@epa.gov.
Environmental Protection Agency · USNOAA Great Lakes Environmental Research Laboratory · USUniversity of Illinois Chicago · USGeorgia Southern University · USLake Superior State University · USMichigan Department of Environment, Great Lakes, and Energy · USMichigan State University · USSaginaw Valley State University · USTulane University · USUnited States Geological Survey · USUniversity of North Carolina at Chapel Hill · USUniversity of Wisconsin–Oshkosh · US

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made.

Indexed as

Escherichia coliWater QualityBathing BeachesBayes TheoremData AccuracyEnvironmental MonitoringFecesWaterWater MicrobiologyWaterData quality criteriaE. coliEPA method CqPCRWater quality criteria

Identifiers

PMID30952079
PMCPMC9943056
OpenAlexW2922366242

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.