Evidence map›Paper›PMID 26597867›Full record

ArticleBMJ open2015

Diagnosing Potentially Preventable Hospitalisations (DaPPHne): protocol for a mixed-methods data-linkage study.

Megan E Passey, Jo M Longman, Jennifer J Johnston, Louisa Jorm, Dan Ewald, Geoff G Morgan, Margaret Rolfe, Bronwyn Chalker

Abstract read
In one paragraph

Article in BMJ open, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
  4. Healthcare Providers' Perceptions of Potentially Preventable Rural Hospitalisations: A Qualitative Study.International journal of environmental research and public health · 2021
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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

8 authors.

Megan E PasseyUniversity Centre for Rural Health-North Coast, University of Sydney, Lismore, New South Wales, Australia.ORCID http://orcid.org/0000-0001-5766-0235
Jo M LongmanUniversity Centre for Rural Health-North Coast, University of Sydney, Lismore, New South Wales, Australia.ORCID http://orcid.org/0000-0002-8257-7772
Jennifer J JohnstonUniversity Centre for Rural Health-North Coast, University of Sydney, Lismore, New South Wales, Australia.
Louisa JormCentre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia.
Dan EwaldNorth Coast Primary Health Network, Ballina, New South Wales, Australia.
Geoff G MorganUniversity Centre for Rural Health-North Coast, University of Sydney, Lismore, New South Wales, Australia.
Margaret RolfeUniversity Centre for Rural Health-North Coast, University of Sydney, Lismore, New South Wales, Australia.
Bronwyn ChalkerMid North Coast Local Health District, South West Rocks, New South Wales, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionRates of potentially preventable hospitalisations (PPH) are used as a proxy measure of effectiveness of, or access to community-based health services. The validity of PPH as an indicator in Australia has not been confirmed. Available evidence suggests that patient-related, clinician-related and systems-related factors are associated with PPH, with differences between rural and metropolitan settings. Furthermore, the proportion of PPHs which are actually preventable is unknown. The Diagnosing Potentially Preventable Hospitalisations study will determine the proportion of PPHs for chronic conditions that are deemed preventable and identify potentially modifiable factors driving these, in order to develop effective interventions to reduce admissions and improve measures of health system performance. METHODS AND ANALYSIS: This mixed methods data linkage study of approximately 1000 eligible patients with chronic PPH admissions to one metropolitan and two regional hospitals over 12 months will combine data from multiple sources to assess the: extent of preventability of chronic PPH admissions; validity of the Preventability Assessment Tool (PAT) in identifying preventable admissions; factors contributing to chronic PPH admissions. Data collected from patients (quantitative and qualitative methods), their general practitioners, hospital clinicians and hospital records, will be linked with routinely collected New South Wales (NSW) Admitted Patient Data Collection, the NSW Registry of Births, Death and Marriages death registration and Australian Bureau of Statistics mortality data. The validity of the PAT will be assessed by determining concordance between clinician assessment and that of a 'gold standard' panel. Multivariable logistic regression will identify the main predictor variables of admissions deemed preventable, using study-specific and linked data. ETHICS AND DISSEMINATION: The NSW Population and Health Services Research Ethics Committee granted ethical approval. Dissemination mechanisms include engagement of policy stakeholders through a project Steering Committee, and the production of summary reports for policy and clinical audiences in addition to peer-review papers.

Indexed as

Research DesignAgedAged, 80 and overChronic DiseaseData CollectionFemaleHealth Services ResearchHospitalizationHumansLogistic ModelsMaleMiddle AgedMultivariate AnalysisNew South WalesPrimary Health Careambulatory care sensitive conditionsavoidable hospital admissionpotentially preventable hospitalisationsPRIMARY CARE

Identifiers

PMID26597867
PMCPMC4663419

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.