Evidence map›Paper›PMID 41557539›Full record

ArticleMedical care2026

Differences in the Measurement of Comorbidities Based on ICD-10-CM Coding Definitions Using Medicare Advantage Encounter Data and Fee-For-Service Claims.

Emilie D Duchesneau, Allison Musty, Elyse Miller, Anna Kuzma, Bailey Reutinger, Til Stürmer, Amresh Hanchate, Dae Hyun Kim, Michael Webster-Clark, Meng-Yun Lin and 1 more

Abstract read
In one paragraph

Article in Medical care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

11 authors.

Emilie D DuchesneauDepartment of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC.ORCID 0000-0002-0974-8539
Allison MustyDepartment of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC.
Elyse MillerDepartment of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC.
Anna KuzmaDepartment of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC.
Bailey ReutingerDepartment of Surgery, Section on Hypertension, Wake Forest University School of Medicine, Winston-Salem, NC.
Til StürmerDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Amresh HanchateDepartment of Social Sciences and Health Policy, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC.
Dae Hyun KimMarcus Institute for Aging Research, Hebrew SeniorLife, Harvard Medical School, Boston, MA.
Michael Webster-ClarkDepartment of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC.
Meng-Yun LinDepartment of Social Sciences and Health Policy, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC.
Jennifer L LundDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.

Funding

North Carolina Translational and Clinical Sciences Institute (NC TraCS)UM1TR004406 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI NICHOLAS J SHAHEEN · 2023 to 2026
$37.5M
Biostatstics for Research in Environmental HealthT32ES007018 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Stephanie Engel, Rebecca Fry · 1985 to 2026
$31.3M
Pilot & Feasibility ProgramP30DK124723 · NIDDK · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI P Darrell Neufer · 2020 to 2026
$11.0M
Propensity scores and preventive drug use in the elderlyR01AG056479 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Til Sturmer · 2017 to 2026
$5.0M
Enhancing UNderGraduate Education and Research in AGing to Eliminate Health Disparities (ENGAGED)R25AG060912 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI BRINKLEY, TINA E, GWATHMEY, TANYA M. · 2019 to 2024
$2.1M
Applying causal inference methods to improve estimation of the real-world benefits and harms of lung cancer screening - NCI Diversity SupplementR01CA277756 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Louise Henderson, Jennifer Lund · 2023 to 2026
$1.5M
Mid-Career Mentoring Award For Patient-Oriented Research in Frailty and Health OutcomesK24AG073527 · NIA · HEBREW REHABILITATION CENTER FOR AGED · PI Dae Hyun Kim · 2022 to 2026
$862k
NCATS NIH HHS UM1 TR004406NCI NIH HHS R01 CA277756NIA NIH HHS K24 AG073527NIA NIH HHS R01 AG056479NIA NIH HHS R25 AG060912NIDDK NIH HHS P30 DK124723NIEHS NIH HHS T32 ES007018
6 · The paper itself

Abstract

backgroundMedicare Advantage (MA) encounter data became available for research in 2019; data quality concerns remain.

objectivesWe evaluated the consistency of ICD-10-CM comorbidity coding between MA and Fee-For-Service (FFS) data.

methodsWe used round 7 (2017) of the National Health and Aging Trends Study (NHATS) linked to Medicare enrollment, MA encounter, and FFS claims (2016-2017). We included participants continuously enrolled in MA or FFS for 1 year before round 7. Comorbidities were identified using ICD-10-CM codes from the Gagne combined comorbidity index. Demographic, socioeconomic, and clinical covariates from NHATS for FFS beneficiaries were standardized to resemble those for MA beneficiaries. We estimated crude and standardized comorbidity prevalence differences (PDs) between MA and FFS beneficiaries.

resultsAmong 5158 beneficiaries (MA: 40%, FFS: 60%), MA beneficiaries were more likely to be Black, Hispanic, and socioeconomically disadvantaged. After standardization, comorbidity prevalence was similar between groups. Peripheral vascular disorder (PD=7.2%, 95% CI: 3.8%-10.6%) and renal failure (PD=3.7%, 95% CI: 0.9%-6.5%) were more common in MA beneficiaries; fluid/electrolyte disorders (PD=-3.2%, 95% CI: -5.5 to -1.0%) and deficiency anemias (PD=-5.0%, 95% CI: -7.6 to -2.3%) were more common in FFS beneficiaries. Other PDs were less than 3 percentage points.

conclusionsDiscrepancies in comorbidity prevalence may reflect true differences or coding variations influenced by provider incentives, documentation standards, or diagnostic priorities. Comorbidity prevalence was largely consistent between MA encounters and FFS claims, supporting the reliability of MA encounter data for aging research. Additional validation studies should address remaining discrepancies.

Indexed as

ComorbidityFee-for-Service PlansInternational Classification of DiseasesMedicare Part CAgedAged, 80 and overFemaleHumansMaleUnited Statescomorbiditiesencounter dataICD-10-CMinsurance claimsMedicare Advantage

Identifiers

PMID41557539
PMCPMC12893144

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.