Evidence map›Paper›PMID 28004385›Full record

ArticleHealth services research2018

Space-Time Cluster Analysis to Detect Innovative Clinical Practices: A Case Study of Aripiprazole in the Department of Veterans Affairs.

Robert B Penfold, James F Burgess, Austin F Lee, Mingfei Li, Christopher J Miller, Marjorie Nealon Seibert, Todd P Semla, David C Mohr, Lewis E Kazis, Mark S Bauer

Open access · greenAbstract 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. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–field-weighted citation impact, top 87% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

10 authors at 7 institutions in 2 countries.

Robert B PenfoldGroup Health Research Institute, Seattle, WA.
James F BurgessDepartment of Veterans Affairs Center for Healthcare Organization & Implementation Research (CHOIR), VA Boston Healthcare System-152M, Boston, MA.
Austin F LeeDepartment of Surgeries, Massachusetts General Hospital, Boston, MA.
Mingfei LiDepartment of Veterans Affairs Center for Healthcare Organization & Implementation Research (CHOIR), VA Boston Healthcare System-152M, Boston, MA.
Christopher J MillerDepartment of Veterans Affairs Center for Healthcare Organization & Implementation Research (CHOIR), VA Boston Healthcare System-152M, Boston, MA.
Marjorie Nealon SeibertDepartment of Veterans Affairs Center for Healthcare Organization & Implementation Research (CHOIR), VA Boston Healthcare System-152M, Boston, MA.
Todd P SemlaU. S. Department of Veterans Affairs, Pharmacy Benefits Management Services (10P4P), Hines, IL.
David C MohrDepartment of Veterans Affairs Center for Healthcare Organization & Implementation Research (CHOIR), VA Boston Healthcare System-152M, Boston, MA.
Lewis E KazisBoston University School of Public Health, Health Law, Policy & Management, Boston, MA.
Mark S BauerDepartment of Veterans Affairs Center for Healthcare Organization & Implementation Research (CHOIR), VA Boston Healthcare System-152M, Boston, MA.
Boston University · USHarvard University · USVA Boston Healthcare System · USBentley University · USDepartment of Veterans Affairs · AUMassachusetts General Hospital · USUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo identify space-time clusters of changes in prescribing aripiprazole for bipolar disorder among providers in the VA. DATA SOURCES: VA administrative data from 2002 to 2010 were used to identify prescriptions of aripiprazole for bipolar disorder. Prescriber characteristics were obtained using the Personnel and Accounting Integrated Database. STUDY

designWe conducted a retrospective space-time cluster analysis using the space-time permutation statistic. DATA EXTRACTION

methodsAll VA service users with a diagnosis of bipolar disorder were included in the patient population. Individuals with any schizophrenia spectrum diagnoses were excluded. We also identified all clinicians who wrote a prescription for any bipolar disorder medication. PRINCIPAL

findingsThe study population included 32,630 prescribers. Of these, 8,643 wrote qualifying prescriptions. We identified three clusters of aripiprazole prescribing centered in Massachusetts, Ohio, and the Pacific Northwest. Clusters were associated with prescribing by VA-employed (vs. contracted) prescribers. Nurses with prescribing privileges were more likely to make a prescription for aripiprazole in cluster locations compared with psychiatrists. Primary care physicians were less likely.

conclusionsEarly prescribing of aripiprazole for bipolar disorder clustered geographically and was associated with prescriber subgroups. These methods support prospective surveillance of practice changes and identification of associated health system characteristics.

Indexed as

AdultAgedAntipsychotic AgentsAripiprazoleBipolar DisorderFemaleHumansMaleMiddle AgedNursesPhysicians, Primary CarePractice Patterns, Physicians'PsychiatryResidence CharacteristicsRetrospective StudiesSpace-Time ClusteringAntipsychotic AgentsAripiprazoleantipsychoticdiffusioninnovationspace-time clusterVA

Identifiers

PMID28004385
PMCPMC5785329
OpenAlexW2564591001

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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.