Evidence map›Paper›PMID 36396038›Full record

ArticleAmerican journal of obstetrics & gynecology MFM2023

Assessment of the Validity of Administrative Data for Gestational Diabetes Ascertainment.

Sarah Hsu, Daryl J Selen, Kaitlyn James, Sijia Li, Carlos A Camargo, Anjali Kaimal, Camille E Powe

Open access · greenAbstract read
In one paragraph

Article in American journal of obstetrics & gynecology MFM, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
3.3field-weighted citation impact, top 7% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
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  7. Gestational Glucose Intolerance and Risk of Obesity in Childhood and Adolescence.The Journal of clinical endocrinology and metabolism · 2025
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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

7 authors at 3 institutions in 1 country.

Sarah HsuDiabetes Unit, Massachusetts General Hospital, Boston, MA (Ms Hsu and Drs Selen and Powe); Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA (Ms Hsu and Dr Powe).
Daryl J SelenDiabetes Unit, Massachusetts General Hospital, Boston, MA (Ms Hsu and Drs Selen and Powe); Harvard Medical School, Boston, MA (Drs Selen, James, Camargo, Kaimal, and Powe).
Kaitlyn JamesHarvard Medical School, Boston, MA (Drs Selen, James, Camargo, Kaimal, and Powe); Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA (Drs James, Kaimal, and Powe).
Sijia LiDepartment of Medicine, Division of Endocrinology, Warren Alpert Medical School of Brown University, Providence, RI (Dr. Selen), Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA (Ms Li and Dr Camargo).
Carlos A CamargoHarvard Medical School, Boston, MA (Drs Selen, James, Camargo, Kaimal, and Powe); Department of Medicine, Division of Endocrinology, Warren Alpert Medical School of Brown University, Providence, RI (Dr. Selen), Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA (Ms Li and Dr Camargo).
Anjali KaimalHarvard Medical School, Boston, MA (Drs Selen, James, Camargo, Kaimal, and Powe); Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA (Drs James, Kaimal, and Powe).
Camille E PoweDiabetes Unit, Massachusetts General Hospital, Boston, MA (Ms Hsu and Drs Selen and Powe); Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA (Ms Hsu and Dr Powe); Harvard Medical School, Boston, MA (Drs Selen, James, Camargo, Kaimal, and Powe); Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA (Drs James, Kaimal, and Powe). Electronic address: camille.powe@mgh.harvard.edu.
Harvard University · USBroad Institute · USMassachusetts General Hospital · US

Funding

TRAINING PROGRAM IN ENDOCRINOLOGY AND DIABETEST32DK007028 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI Karen K Miller · 1986 to 2026
$18.5M
NIDDK NIH HHS T32 DK007028
6 · The paper itself

Abstract

backgroundAdministrative data, including International Classification of Diseases codes and birth certificate records, are often used for retrospective gestational diabetes research investigations to describe associations of gestational diabetes with perinatal complications and long-term outcomes, and to determine gestational diabetes prevalence. Research investigating the validity of using International Classification of Diseases codes and birth certificates for gestational diabetes ascertainment shows varying degrees of reliability.

objectiveThis study aimed to evaluate the accuracy of both International Classification of Diseases codes and birth certificate diagnosis for gestational diabetes ascertainment in a large hospital-based cohort of pregnant individuals, using laboratory criteria for gestational diabetes mellitus as the reference. STUDY

designWe studied individuals who received prenatal care at an academic hospital and affiliated community health centers between 1998 and 2016. In the setting of universal 2-step screening for gestational diabetes, pregnant individuals were classified as having gestational diabetes if ≥2 oral glucose tolerance test values met or exceeded National Diabetes Data Group thresholds. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value for International Classification of Diseases code and birth certificate ascertainment of gestational diabetes, and their exact binomial 95% confidence intervals.

resultsIn a cohort of 51,059 pregnancies with complete glucose screening, 1303 (2.6%) met National Diabetes Data Group laboratory criteria for gestational diabetes. Gestational diabetes International Classification of Diseases codes had moderate sensitivity of 70.5% (95% confidence interval, 67.9-72.9), high specificity of 99.3% (95% confidence interval, 99.3-99.4), a positive predictive value of 73.3% (95% confidence interval, 70.8-75.8), and a negative predictive value of 99.2% (95% confidence interval, 99.1-99.3). In the 46,512 pregnancies linked to birth certificate data, birth certificate diagnosis had moderate sensitivity (66.3% [95% confidence interval, 63.6-69.0]), high specificity (98.9% [95% confidence interval, 98.8-99.0]), moderate positive predictive value (62.1% [95% confidence interval, 59.8-64.4]), and high negative predictive value (99.1% [95% confidence interval, 99.0-99.2]).

conclusionAscertainment of gestational diabetes using administrative data, including International Classification of Diseases codes or birth certificates, has moderate sensitivity, moderate positive predictive value, high specificity, and high negative predictive value. Our findings provide context for interpreting the validity of studies that depend on administrative data for ascertainment of gestational diabetes and comparing them with prospective studies that use laboratory-based gestational diabetes criteria.

Indexed as

Diabetes, GestationalFemaleGlucose Tolerance TestHumansPregnancyProspective StudiesReproducibility of ResultsRetrospective Studiesdiabetes in pregnancyelectronic health recordsepidemiologygestational diabetes mellitusglucosehealth services researchInternational Classification of Diseases codesobstetricspregnancywomen's health

Identifiers

PMID36396038
PMCPMC10071626
OpenAlexW4308986757

What OpenQuestion holds

Textmetadata
LicenceTDM
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.