Evidence map›Paper›PMID 23703646›Full record

ArticleMedical care2013

Risk adjustment for health care financing in chronic disease: what are we missing by failing to account for disease severity?

Theodore A Omachi, Steven E Gregorich, Mark D Eisner, Renee A Penaloza, Irina V Tolstykh, Edward H Yelin, Carlos Iribarren, R Adams Dudley, Paul D Blanc

Abstract read
In one paragraph

Article in Medical care, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Unlocking the Potential of Electronic Health Records for Health Research.International journal of population data science · 2020
    Article
  3. Review
  4. Article
  5. Article
  6. 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

9 authors.

Theodore A OmachiDepartment of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of California, San Francisco, CA 94118-0920, USA. omachi@ucsf.edu
Steven E Gregorich
Mark D Eisner
Renee A Penaloza
Irina V Tolstykh
Edward H Yelin
Carlos Iribarren
R Adams Dudley
Paul D Blanc

Funding

UCSF CLINICAL AND TRANSLATIONAL SCIENCE INSTITUTEUL1RR024131 · NCRR · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI JOHNSTON, S. CLAIBORNE · 2006 to 2011
$111.2M
Disability and Health Outcomes in COPDR01HL077618 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BLANC, PAUL D · 2004 to 2008
$3.3M
The Overlap Syndrome of COPD and OSA: Determinants of Health Status and OutcomesK23HL102159 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI OMACHI, THEODORE A · 2010 to 2012
$399k
NCRR NIH HHS UL1 RR024131NHLBI NIH HHS K23 HL102159NHLBI NIH HHS R01 HL077618
6 · The paper itself

Abstract

backgroundAdjustment for differing risks among patients is usually incorporated into newer payment approaches, and current risk models rely on age, sex, and diagnosis codes. It is unknown the extent to which controlling additionally for disease severity improves cost prediction. Failure to adjust for within-disease variation may create incentives to avoid sicker patients. We address this issue among patients with chronic obstructive pulmonary disease (COPD).

methodsCost and clinical data were collected prospectively from 1202 COPD patients at Kaiser Permanente. Baseline analysis included age, sex, and diagnosis codes (using the Diagnostic Cost Group Relative Risk Score) in a general linear model predicting total medical costs in the following year. We determined whether adding COPD severity measures-forced expiratory volume in 1 second, 6-Minute Walk Test, dyspnea score, body mass index, and BODE Index (composite of the other 4 measures)-improved predictions. Separately, we examined household income as a cost predictor.

resultsMean costs were $12,334/y. Controlling for Relative Risk Score, each ½ SD worsening in COPD severity factor was associated with $629 to $1135 in increased annual costs (all P<0.01). The lowest stratum of forced expiratory volume in 1 second (<30% normal) predicted $4098 (95% confidence interval, $576-$8773) additional costs. Household income predicted excess costs when added to the baseline model (P=0.038), but this became nonsignificant when also incorporating the BODE Index.

conclusionsDisease severity measures explain significant cost variations beyond current risk models, and adding them to such models appears important to fairly compensate organizations that accept responsibility for sicker COPD patients. Appropriately controlling for disease severity also accounts for costs otherwise associated with lower socioeconomic status.

Indexed as

Severity of Illness IndexAdultAgedAge FactorsBody Mass IndexChronic DiseaseExercise TestFemaleForced Expiratory VolumeHealth ExpendituresHumansIncomeMaleMiddle AgedModels, EconomicProspective Studies

Identifiers

PMID23703646
PMCPMC3714354

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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.