Evidence map›Paper›PMID 29945622›Full record

ArticleBMC health services research2018

Hospital length of stay variation and comorbidity of mental illness: a retrospective study of five common chronic medical conditions.

Nazlee Siddiqui, Mitchell Dwyer, Jim Stankovich, Gregory Peterson, David Greenfield, Lei Si, Leigh Kinsman

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 16% 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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.

  1. Pooled it
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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

7 authors at 2 institutions in 1 country.

Nazlee SiddiquiAustralian Institute of Health Services Management (AIHSM), Tasmanian School of Business and Economics, University of Tasmania, Rozelle campus, Cnr Glover and Church Streets, Sydney, NSW, 2039, Australia. Nazlee.siddiqui@utas.edu.au.ORCID 0000-0002-1841-3095
Mitchell DwyerSchool of Health Sciences, University of Tasmania, Tasmania, Australia.
Jim StankovichHealth Services Innovation Tasmania, School of Medicine, University of Tasmania, Tasmania, Australia.
Gregory PetersonHealth Services Innovation Tasmania, School of Medicine, University of Tasmania, Tasmania, Australia.
David GreenfieldAustralian Institute of Health Services Management (AIHSM), Tasmanian School of Business and Economics, University of Tasmania, Rozelle campus, Cnr Glover and Church Streets, Sydney, NSW, 2039, Australia.
Lei SiCentre for the Health Economy, Macquarie University, Sydney, Australia.
Leigh KinsmanConjoint appointment, University of Tasmania and Tasmanian Health Service (North), Tasmania, Australia.
University of Tasmania · AUMacquarie University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the increasing burden of mental illness globally, it is becoming common for hospitalised patients with chronic medical conditions to have a comorbidity of mental illness. This combination could prolong length of stay (LOS) of this patient cohort. We conducted an investigation in Tasmania, Australian hospitals to characterise this cohort and assess if co-morbidity of mental illness is a distinguishing factor that generates LOS variation across different chronic medical conditions.

methodsThe retrospective study analysed 16,898 admissions of patients with a primary diagnosis of one of five chronic medical conditions: lung or colorectal cancer, chronic obstructive pulmonary disease (COPD), type II diabetes, ischaemic heart disease (IHD) and stroke. Data were from July 2010 to June 2015, across four hospitals that collectively cover 95% of public hospital admissions in Tasmania, Australia. Descriptive statistics were used to compare characteristics of patients between the scenarios of with and without co-morbidity of mental illness. We used negative binomial regression models to assess whether co-morbidity of mental illness, along with its sub-types, after adjustment for potential confounding variables, associated with LOS variation in patients of each medical condition. Based on the adjusted LOS variation, we estimated differences in bed days' use between patients with and without comorbidity of mental illness.

resultsPatients with co-morbidity of mental illness were significantly younger in comparison to patients without mental illness. With each medical condition, patients with comorbidity of mental illness had incurred higher bed days' use than for those without mental illness. In cancer and stroke cohorts, co-morbidity of mental illness unfavourably affected the LOS variation by as high as 97% (CI: 49.9%-159%) and 109% (78%-146%), respectively. Though mental and behavioural disorders due to psychoactive substances was a dominant sub-type of mental illness across the medical conditions, it contributed significant unfavourable LOS variation only in the stroke patients i.e. 36.3% (CI: 16.2%-59.9%).

conclusionsMental illness consistently produced unfavourable LOS variation. Upskilling of healthcare teams and greater reporting and analysis of LOS variation for this patient cohort, and the sub-cohorts within it, are necessary to provide improved medical care and achieve system efficiencies.

Indexed as

Noncommunicable DiseasesAgedDiabetes Mellitus, Type 2FemaleHumansLength of StayMaleMental DisordersMiddle AgedNeoplasmsPulmonary Disease, Chronic ObstructiveRetrospective StudiesStrokeTasmaniaComorbidities, LOS variation, hospital

Identifiers

PMID29945622
PMCPMC6020383
OpenAlexW2810783309

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.