Evidence map›Paper›PMID 32267514›Full record

ArticleJAMA network open2020

Diagnostic Category Prevalence in 3 Classification Systems Across the Transition to the International Classification of Diseases, Tenth Revision, Clinical Modification.

Randall P Ellis, Heather E Hsu, Chenlu Song, Tzu-Chun Kuo, Bruno Martins, Jeffrey J Siracuse, Ying Liu, Arlene S Ash

Abstract read
In one paragraph

Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–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

21 citing papers in PubMed.

  1. Article
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  7. Detection of emergency department patients at risk of dementia through artificial intelligence.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
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  17. Willingness to Pay for Down Syndrome Screening: A systematics Review.Medical journal of the Islamic Republic of Iran · 2022
    Review
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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

8 authors.

Randall P EllisDepartment of Economics, Boston University, Boston, Massachusetts.
Heather E HsuDepartment of Pediatrics, Boston University School of Medicine, Boston, Massachusetts.
Chenlu SongDepartment of Economics, Boston University, Boston, Massachusetts.
Tzu-Chun KuoBMC HealthNet Plan, Boston, Massachusetts.
Bruno MartinsAnalysis Group, Boston, Massachusetts.
Jeffrey J SiracuseDivision of Vascular and Endovascular Surgery, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts.
Ying LiuDepartment of Economics, Boston University, Boston, Massachusetts.
Arlene S AshDepartment of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester.

Funding

University of Massachusetts Center for Clinical and Translational ScienceUL1TR000161 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LUZURIAGA, KATHERINE F · 2012 to 2015
$11.0M
Advanced Risk Adjusters and Predictive Formulas for ICD-10 Based Risk AdjustmentR01HS026485 · AHRQ · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI ELLIS, RANDALL P. · 2018 to 2020
$1.2M
AHRQ HHS R01 HS026485NCATS NIH HHS UL1 TR000161
6 · The paper itself

Abstract

Importance: On October 1, 2015, the US transitioned to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for recording diagnoses, symptoms, and procedures. It is unknown whether this transition was associated with changes in diagnostic category prevalence based on diagnosis classification systems commonly used for payment and quality reporting. Objective: To assess changes in diagnostic category prevalence associated with the ICD-10-CM transition. Design, Setting, and Participants: This interrupted time series analysis and cross-sectional study examined level and trend changes in diagnostic category prevalence associated with the ICD-10-CM transition and clinically reviewed a subset of diagnostic categories with changes of 20% or more. Data included insurance claim diagnoses from the IBM MarketScan Commercial Database from January 1, 2010, to December 31, 2017, for more than 18 million people aged 0 to 64 years with private insurance. Diagnoses were mapped using 3 common diagnostic classification systems: World Health Organization (WHO) disease chapters, Department of Health and Human Services Hierarchical Condition Categories (HHS-HCCs), and Agency for Healthcare Research and Quality Clinical Classification System (AHRQ-CCS). Data were analyzed from December 1, 2018, to January 21, 2020. Exposures: US implementation of ICD-10-CM. Main Outcomes and Measures: Monthly rates of individuals with at least 1 diagnosis in a diagnostic classification category per 10 000 eligible members. Results: The analytic sample contained information on 2.1 billion enrollee person-months with 3.4 billion clinically assigned diagnoses; the mean (range) monthly sample size was 22.1 (18.4 to 27.1 ) million individuals. While diagnostic category prevalence changed minimally for WHO disease chapters, the ICD-10-CM transition was associated with level changes of 20% or more among 20 of 127 HHS-HCCs (15.7%) and 46 of 282 AHRQ-CCS categories (16.3%) and with trend changes of 20% or more among 12 of 127 of HHS-HCCs (9.4%) and 27 of 282 of AHRQ-CCS categories (9.6%). For HHS-HCCs, monthly rates of individuals with any acute myocardial infarction diagnosis increased 131.5% (95% CI, 124.1% to 138.8%), primarily because HHS added non-ST-segment-elevation myocardial infarction diagnoses to this category. The HHS-HCC for diabetes with chronic complications increased by 92.4% (95% CI, 84.2% to 100.5%), primarily from including new diabetes-related hypoglycemia and hyperglycemia codes, and the rate for completed pregnancy with complications decreased by 54.5% (95% CI, -58.7% to -50.2%) partly due to removing vaginal birth after cesarean delivery as a complication. Conclusions and Relevance: These findings suggest that the ICD-10-CM transition was associated with large prevalence changes for many diagnostic categories. Diagnostic classification systems developed using ICD-9-CM may need to be refined using ICD-10-CM data to avoid unintended consequences for disease surveillance, performance assessment, and risk-adjusted payments.

Indexed as

International Classification of DiseasesAdolescentAdultChildChild, PreschoolClinical CodingCross-Sectional StudiesDatabases, FactualHumansInfantInfant, NewbornInterrupted Time Series AnalysisMiddle AgedPrevalenceUnited StatesYoung Adult

Identifiers

PMID32267514
PMCPMC7142382

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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 OpenQuestion graph.