Evidence map›Paper›PMID 29896771›Full record

ArticleHealth services research2018

Empirical-Based Typology of Health Care Utilization by Medicare Eligible Veterans.

Mary Vaughan Sarrazin, Gary E Rosenthal, Carolyn L Turvey

Abstract read
In one paragraph

Article in Health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Mary Vaughan SarrazinIowa City VA Health Care System, Iowa City, IA.ORCID 0000-0001-8717-1061
Gary E RosenthalDepartment of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC.
Carolyn L TurveyComprehensive Access and Delivery Research and Evaluation (CADRE), Iowa City VA Health Care System, Iowa City, IA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveUp to 70 percent of patients who receive care through Veterans Health Administration (VHA) facilities also receive care from non-VA providers. Using applied classification techniques, this study sought to improve understanding of how elderly VA patients use VA services and complementary use of non-VA care.

methodsThe study included 1,721,900 veterans age 65 and older who were enrolled in VA and Medicare during 2013 with at least one VA encounter during 2013. Outpatient and inpatient encounters and medications received in VA were classified, and mutually exclusive patient subsets distinguished by patterns of VA service use were derived empirically using latent class analysis (LCA). Patient characteristics and complementary use of non-VA care were compared by patient subset.

resultsFive patterns of VA service use were identified that were distinguished by quantity of VA medical and specialty services, medication complexity, and mental health services. Low VA Medical users tend to be healthier and rely on non-VA services, while High VA users have multiple high cost illnesses and concentrate their care in the VA.

conclusionsVA patients distinguished by patterns of VA service use differ in illness burden and the use of non-VA services. This information may be useful for framing efforts to optimize access to care and care coordination for elderly VA patients.

Indexed as

AgedAged, 80 and overAmbulatory CareFemaleHealth StatusHospitalizationHumansInsurance Claim ReviewMaleMedicare Part AMedicineMental Health ServicesPolypharmacySocioeconomic FactorsTransportationUnited Statesaccess/demand/utilization of servicesAdministrative data useschronic diseaseMedicareVA Health Care System

Identifiers

PMID29896771
PMCPMC6235816

What OpenQuestion holds

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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.