Evidence map›Paper›PMID 32719571›Full record

ArticleThe journal of political economy2020

Upcoding: Evidence from Medicare on Squishy Risk Adjustment.

Michael Geruso, Timothy Layton

Abstract read
In one paragraph

Article in The journal of political economy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
109citing 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

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

  1. Pooled it
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  13. A Simple Primer on Relative Value Units for Surgeons.Plastic and reconstructive surgery. Global open · 2026
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49 more citing papers are in PubMed but not listed here.

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

2 authors.

Michael GerusoUniversity of Texas at Austin and NBER. mike.geruso@gmail.com.
Timothy LaytonHarvard Medical School. layton@hcp.med.harvard.edu.

Funding

Tradeoffs in the Design of Public Long-Term Care BenefitsP01AG032952 · NIA · HARVARD MEDICAL SCHOOL · PI Bruce E. Landon · 2009 to 2026
$39.0M
Training Program in Population StudiesT32HD007081 · NICHD · UNIVERSITY OF TEXAS AUSTIN · PI Aprile Dawn Benner, Abigail Mae Weitzman · 1985 to 2026
$6.8M
Scientific & Technical CoreP2CHD042849 · NICHD · UNIVERSITY OF TEXAS AT AUSTIN · PI MARK D. HAYWARD · 2017 to 2026
$5.2M
NIA NIH HHS P01 AG032952NICHD NIH HHS P2C HD042849NICHD NIH HHS T32 HD007081
6 · The paper itself

Abstract

In most US health insurance markets, plans face strong incentives to "upcode" the patient diagnoses they report to the regulator, as these affect the risk-adjusted payments plans receive. We show that enrollees in private Medicare plans generate 6% to 16% higher diagnosis-based risk scores than they would under fee-for-service Medicare, where diagnoses do not affect most provider payments. Our estimates imply that upcoding generates billions in excess public spending and significant distortions to firm and consumer behavior. We show that coding intensity increases with vertical integration, suggesting a principal-agent problem faced by insurers, who desire more intense coding from the providers with whom they contract.

Identifiers

PMID32719571
PMCPMC7384673

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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.