Evidence map›Paper›PMID 28548005›Full record

ArticleInquiry : a journal of medical care organization, provision and financing2017

Geographic Variation in Diagnostic Ability and Quality of Care Metrics: A Case Study of Ankylosing Spondylitis and Low Back Pain.

Jason Shafrin, Jenny Griffith, Jin Joo Shim, Caroline Huber, Arijit Ganguli, Wade Aubry

Open access · goldAbstract readCase Reports
In one paragraph

Article in Inquiry : a journal of medical care organization, provision and financing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 42% of its field
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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

6 authors at 2 institutions in 1 country.

Jason Shafrin1 Precision Health Economics, Los Angeles, CA, USA.
Jenny Griffith2 AbbVie Inc, North Chicago, IL, USA.
Jin Joo Shim1 Precision Health Economics, Los Angeles, CA, USA.
Caroline Huber1 Precision Health Economics, Los Angeles, CA, USA.
Arijit Ganguli2 AbbVie Inc, North Chicago, IL, USA.
Wade Aubry3 University of California, San Francisco, USA.
AbbVie (United States) · USUniversity of California, San Francisco · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studies examining geographic variation in care for low back pain often focus on process and outcome measures conditional on patient diagnosis but generally do not take into account a physician's ability to diagnose the root cause of low back pain. In our case study, we used increased detection of ankylosing spondylitis-a relatively rare inflammatory back disease-as a proxy for diagnostic ability and measured the relationship between ankylosing spondylitis detection, potentially inappropriate low back pain care, and cost. Using 5 years of health insurance claims data, we found significant variation in ankylosing spondylitis detection across metropolitan statistical areas (MSAs), with 8.1% of the variation in detection explained by a region's racial composition. Furthermore, low back pain patients in MSAs with higher ankylosing spondylitis detection had 7.9% lower use of corticosteroids, 9.0% lower use of opioids, and 8.2% lower pharmacy cost, compared with patients living in low-detection MSAs.

Indexed as

Geography, MedicalCost of IllnessFemaleHumansInsurance Claim ReviewLow Back PainMaleMedicareMiddle AgedOrganizational Case StudiesQuality of Health CareSpondylitis, AnkylosingUnited Statesback paindiagnosishealth care costs

Identifiers

PMID28548005
PMCPMC5798677
OpenAlexW2617640382

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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 OpenQuestion graph.