Evidence map›Paper›PMID 12236385›Full record

ArticleHealth services research2002

Diagnostic cost groups (DCGs) and concurrent utilization among patients with substance abuse disorders.

Amy K Rosen, Susan A Loveland, Jennifer J Anderson, Cheryl S Hankin, James N Breckenridge, Dan R Berlowitz

Abstract read
In one paragraph

Article in Health services research, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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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

10 citing papers in PubMed.

  1. Article
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  5. Article
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  7. Article
  8. Improving risk adjustment of self-reported mental health outcomes.The journal of behavioral health services & research · 2010
    Article
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  10. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Amy K RosenCenter for Health Quality, Outcomes and Economic Research, Bedford VAMC/Edith Nourse Rogers Memorial Veterans Hospital, MA 01730, USA.
Susan A Loveland
Jennifer J Anderson
Cheryl S Hankin
James N Breckenridge
Dan R Berlowitz

Funding

HSRD VA TPM 97-009
6 · The paper itself

Abstract

objectiveTo assess the performance of Diagnostic Cost Groups (DCGs) in explaining variation in concurrent utilization for a defined subgroup, patients with substance abuse (SA) disorders, within the Department of Veterans Affairs (VA). DATA SOURCES: A 60 percent random sample of veterans who used health care services during Fiscal Year (FY) 1997 was obtained from VA administrative databases. Patients with SA disorders (13.3 percent) were identified from primary and secondary ICD-9-CM diagnosis codes. STUDY

designConcurrent risk adjustment models were fitted and tested using the DCG/HCC model. Three outcome measures were defined: (1) "service days" (the sum of a patient's inpatient and outpatient visit days), (2) mental health/substance abuse (MH/SA) service days, and (3) ambulatory provider encounters. To improve model performance, we ran three DCG/HCC models with additional indicators for patients with SA disorders. DATA COLLECTION: To create a single file of veterans who used health care services in FY 1997, we merged records from all VA inpatient and outpatient files. PRINCIPAL

findingsAdding indicators for patients with mild/moderate SA disorders did not appreciably improve the R-squares for any of the outcome measures. When indicators were added for patients with severe SA who were in the most costly category, the explanatory ability of the models was modestly improved for all three outcomes.

conclusionsModifying the DCG/HCC model with additional markers for SA modestly improved homogeneity and model prediction. Because considerable variation still remained after modeling, we conclude that health care systems should evaluate "off-the-shelf" risk adjustment systems before applying them to their own populations.

Indexed as

Concurrent ReviewAdultAgedCapitation FeeDiagnosis-Related GroupsFemaleHealth ExpendituresHospitals, VeteransHumansMaleMiddle AgedOutcome Assessment, Health CareRisk AdjustmentSubstance Abuse Treatment CentersSubstance-Related DisordersUnited States

Identifiers

PMID12236385
PMCPMC1464011

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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.