Evidence mapPaperPMID 34338404Full record

ArticleDiabetes, obesity & metabolism2021

Validation of obesity-related diagnosis codes in claims data.

Karine Suissa, Sebastian Schneeweiss, Kueiyu Joshua Lin, Gregory Brill, Seoyoung C Kim, Elisabetta Patorno

Abstract read
In one paragraph

Article in Diabetes, obesity & metabolism, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 1 pooled it
field-weighted citation impact
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

55 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Socioeconomic factors and SGLT2 inhibitor initiation in patients with heart failure-a claims data analysis.Clinical research in cardiology : official journal of the German Cardiac Society · 2026
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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

6 authors.

Karine SuissaDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0003-3922-3853
Sebastian SchneeweissDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Kueiyu Joshua LinDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Gregory BrillDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Seoyoung C KimDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Elisabetta PatornoDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-8809-9898

Funding

Novel Approaches to Monitor the Safety and Effectiveness of Newly Marketed Diabetes Medications in Older Adults Considering Frailty and MultimorbidityK08AG055670 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI Elisabetta Patorno · 2021 to 2021
$165k
NIAMS NIH HHS K24 AR078959NIA NIH HHS K08 AG055670
6 · The paper itself

Abstract

aimTo determine whether body mass index (BMI) can be accurately identified in epidemiological studies using claims databases. MATERIALS AND

methodsUsing the Mass General Brigham Research Patient Data Repository-Medicare-linked database, we identified a cohort of patients with a BMI measurement for the periods January 1 to June 31, 2014 or January 1 to June 31, 2016, to capture both the International Classification of Disease (ICD)-9 and ICD-10 eras. Patients were divided into two groups, with or without an obesity-related ICD code in the 6 months before or after the BMI measurement date. We created two binary measures, first for composite overweight, obesity, or severe obesity (BMI ≥25 kg/m

resultsThe cohort included 73 644 patients with a BMI measurement in 2014 or 2016, of whom 16 280 had an obesity-related ICD code. The specificity of obesity-related ICD codes (ICD-9 and ICD-10) was 99.7% for underweight/normal weight, 97.4% for overweight, 99.7% for obese and 98.9% for severely obese. For binary categories capturing BMI ≥25 kg/m

conclusionObesity-related ICD codes can accurately identify patients with obesity in epidemiological studies using claims databases.

Indexed as

Diabetes Mellitus, Type 2AgedBody Mass IndexDatabases, FactualHumansInternational Classification of DiseasesMedicareObesityUnited Statesdatabase researchobservational study

Identifiers

PMID34338404
PMCPMC8578343

What Socratic holds

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Registered trials

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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 Socratic graph.