Evidence map›Paper›PMID 27037379›Full record

ArticleClinical medicine (London, England)2016

Deprivation influences the emergency admission rate of ambulatory care sensitive conditions.

Richard Conway, Deirdre O'Riordan, Declan Byrne, Sean Cournane, Seamus Coveney, Bernard Silke

Abstract read
In one paragraph

Article in Clinical medicine (London, England), 2016. 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.

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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

6 authors.

Richard ConwayDepartment of Internal Medicine, St James's Hospital, Dublin, Ireland, and CARD Newman Research Fellow, University College Dublin, Dublin, Ireland.
Deirdre O'RiordanDepartment of Internal Medicine, St James's Hospital, Dublin, Ireland.
Declan ByrneDepartment of Internal Medicine, St James's Hospital, Dublin, Ireland.
Sean CournaneDepartment of Internal Medicine, St James's Hospital, Dublin, Ireland.
Seamus CoveneySchool of Geographical and Earth Sciences, University of Glasgow, Glasgow, UK.
Bernard SilkeDepartment of Internal Medicine, St James's Hospital, Dublin, Ireland bernardsilke@physicians.ie.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ambulatory care sensitive conditions (ACSCs) are a group of conditions judged to be suitable for healthcare efficiency initiatives to reduce the rate of hospital admissions. All emergency medical admissions to an Irish hospital between 2002 and 2013 were assessed for ACSCs. They were categorised by quintile of deprivation index and evaluated against hospital admission rate. Univariable and multivariable risk estimates were calculated, using logistic regression or zero-truncated Poisson regression. There were 66,861 admissions in 36,214 patients. ACSCs represented 66.4% of admissions. The rate of ACSC admissions increased with deprivation index, Q1 10.4 (95% confidence interval (CI) 10.2-10.5), Q2 17.3 (95% CI 17.2-17.5), Q3 34.0 (95% CI 33.7-34.2), Q4 30.2 (95% CI 30.0-30.4) and Q5 44.5 (95% CI 43.8- 45.1) (p<0.001), corresponding incidence rate ratios compared with Q1 were: Q2 1.67 (95% CI 1.64-1.70), Q3 3.28 (95% CI 3.22-3.33), Q4 2.92 (95% CI 2.87-2.97) and Q5 4.29 (95% CI 4.20-4.39) (p<0.001). ACSCs are common in acute medical admissions and are strongly influenced by the underlying social demographics of the population.

Indexed as

AdultAgedAmbulatory CareEmergency Medical ServicesFemaleHumansLength of StayMaleMiddle AgedPatient AdmissionRetrospective StudiesUnited Kingdomadmission rateambulatory care sensitive conditionDeprivation indexemergency medical admission

Identifiers

PMID27037379
PMCPMC4952963

What OpenQuestion holds

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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.