Evidence map›Paper›PMID 32695611›Full record

ArticleJournal of clinical & translational endocrinology2020

Identifying optimal survey-based algorithms to distinguish diabetes type among adults with diabetes.

Jennifer G Nooney, M Sue Kirkman, Kai McKeever Bullard, Zachary White, Kristi Meadows, Joanne R Campione, Russ Mardon, Gonzalo Rivero, Stephen R Benoit, Emily Pfaff and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical & translational endocrinology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

8 citing papers in PubMed, 12 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

12 authors at 3 institutions in 1 country.

Jennifer G NooneyWestat, Rockville, MD, USA.
M Sue KirkmanDivision of Endocrinology and Metabolism, Department of Medicine, University of North Carolina, Chapel Hill, NC, USA.
Kai McKeever BullardDivision of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Zachary WhiteWestat, Rockville, MD, USA.
Kristi MeadowsWestat, Rockville, MD, USA.
Joanne R CampioneWestat, Rockville, MD, USA.
Russ MardonWestat, Rockville, MD, USA.
Gonzalo RiveroWestat, Rockville, MD, USA.
Stephen R BenoitDivision of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Emily PfaffNC TraCS Institute, University of North Carolina, Chapel Hill, NC, USA.
Deborah RolkaDivision of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Sharon SaydahDivision of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Westat (United States) · USCenters for Disease Control and Prevention · USUniversity of North Carolina at Chapel Hill · US

Funding

Re-Entry Supplement: Investigation of Oral Microbial Enzymes for the Detection and Treatment of Periodontal DiseaseUL1TR002489 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BUSE, JOHN BERNARD, SHAHEEN, NICHOLAS J · 2018 to 2022
$48.6M
Validation of Survey Questions to Distinguish Type 1 and Type 2 Diabetes Among Adults With DiabetesU01DP006327 · DP · WESTAT, INC. · PI KIRKMAN, MARIAN SUE, NOONEY, JENNIFER · 2017 to 2018
$999k
ACL HHS U01DP006327NCATS NIH HHS UL1 TR002489NCCDPHP CDC HHS U01 DP006327
6 · The paper itself

Abstract

objectivesSurveys for U.S. diabetes surveillance do not reliably distinguish between type 1 and type 2 diabetes, potentially obscuring trends in type 1 among adults. To validate survey-based algorithms for distinguishing diabetes type, we linked survey data collected from adult patients with diabetes to a gold standard diabetes type. RESEARCH DESIGN AND

methodsWe collected data through a telephone survey of 771 adults with diabetes receiving care in a large healthcare system in North Carolina. We tested 34 survey classification algorithms utilizing information on respondents' report of physician-diagnosed diabetes type, age at onset, diabetes drug use, and body mass index. Algorithms were evaluated by calculating type 1 and type 2 sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to a gold standard diagnosis of diabetes type determined through analysis of EHR data and endocrinologist review of selected cases.

resultsAlgorithms based on self-reported type outperformed those based solely on other data elements. The top-performing algorithm classified as type 1 all respondents who reported type 1 and were prescribed insulin, as "other diabetes type" all respondents who reported "other," and as type 2 the remaining respondents (type 1 sensitivity 91.6%, type 1 specificity 98.9%, type 1 PPV 82.5%, type 1 NPV 99.5%). This algorithm performed well in most demographic subpopulations.

conclusionsThe major federal health surveys should consider including self-reported diabetes type if they do not already, as the gains in the accuracy of typing are substantial compared to classifications based on other data elements. This study provides much-needed guidance on the accuracy of survey-based diabetes typing algorithms.

Indexed as

AlgorithmsDiabetes surveillanceSurveillance methodologyType 1 diabetes

Identifiers

PMID32695611
PMCPMC7365930
OpenAlexW3040667245

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.