Evidence map›Paper›PMID 37165320›Full record

ArticleBMC psychiatry2023

Using health administrative data to model associations and predict hospital admissions and length of stay for people with eating disorders.

Marcellinus Kim, Matthew Holton, Arianne Sweeting, Eyza Koreshe, Kevin McGeechan, Jane Miskovic-Wheatley

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 7% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Observational
  4. Article
  5. 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.

Marcellinus KimThe University of Sydney, Sydney, Australia. marcellinus.kim@sydney.edu.au.
Matthew HoltonSydney Local Health District, New South Wales Health, Sydney, Australia.
Arianne SweetingThe University of Sydney, Sydney, Australia.
Eyza KoresheThe University of Sydney, Sydney, Australia.
Kevin McGeechanThe University of Sydney, Sydney, Australia.
Jane Miskovic-WheatleyThe University of Sydney, Sydney, Australia.
University of Sydney · AUSydney Local Health District · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEating disorders are serious mental illnesses requiring a whole of health approach. Routinely collected health administrative data has clinical utility in describing associations and predicting health outcome measures. This study aims to develop models to assess the clinical utility of health administrative data in adult eating disorder emergency presentations and length of stay.

methodsRetrospective cohort study on health administrative data in adults with eating disorders from 2014 to 2020 in Sydney Local Health District. Emergency and admitted patient data were collected with all clinically important variables available. Multivariable regression models were analysed to explore associations and to predict admissions and length of stay.

resultsEmergency department modelling describes some clinically important associations such as decreased odds of admission for patients with Bulimia Nervosa compared to Anorexia Nervosa (Odds Ratio [OR] 0.31, 95% Confidence Interval [95%CI] 0.10 to 0.95; p = 0.04). Admitted data included more predictors and therefore further significant associations including an average of 0.96 days increase in length of stay for each additional count of diagnosis/comorbidities (95% Confidence Interval [95% CI] 0.37 to 1.55; p = 0.001) with a valid prediction model (R

conclusionsHealth administrative data has clinical utility in adult eating disorders with valid exploratory and predictive models describing associations and predicting admissions and length of stay. Utilising health administrative data this way is an efficient process for assessing impacts of multiple factors on patient care and predicting health care outcomes.

Indexed as

Feeding and Eating DisordersRoutinely Collected Health DataAdultEmergency Service, HospitalHospitalsHumansLength of StayRetrospective StudiesEating disordersEmergency serviceLength of StayModelsPredict

Identifiers

PMID37165320
PMCPMC10170048
OpenAlexW4376130984

What OpenQuestion holds

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LicenceCC BY
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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.