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Factors predictive of hospital admission for children via emergency departments in Australia and Sweden: an observational cross-sectional study



Identifying factors predictive of hospital admission can be useful to prospectively inform bed management and patient flow strategies and decrease emergency department (ED) crowding. It is largely unknown if admission rate or factors predictive of admission vary based on the population to which the ED served (i.e., children only, or both adults and children). This study aimed to describe the profile and identify factors predictive of hospital admission for children who presented to four EDs in Australia and one ED in Sweden.


A multi-site observational cross-sectional study using routinely collected data pertaining to ED presentations made by children < 18 years of age between July 1, 2011 and October 31, 2012. Univariate and multivariate analysis were undertaken to determine factors predictive of hospital admission.


Of the 151,647 ED presentations made during the study period, 22% resulted in hospital admission. Admission rate varied by site; the children’s EDs in Australia had higher admission rates (South Australia: 26%, Queensland: 23%) than the mixed (adult and children’s) EDs (South Australia: 13%, Queensland: 17%, Sweden: 18%). Factors most predictive of hospital admission for children, after controlling for triage category, included hospital type (children’s only) adjusted odds ratio (aOR):2.3 (95%CI: 2.2–2.4), arrival by ambulance aOR:2.8 (95%CI: 2.7–2.9), referral from primary health aOR:1.5 (95%CI: 1.4–1.6) and presentation with a respiratory or gastrointestinal condition (aOR:2.6, 95%CI: 2.5–2.8 and aOR:1.5, 95%CI: 1.4–1.6, respectively). Predictors were similar when each site was considered separately.


Although the characteristics of children varied by site, factors predictive of hospital admission were mostly similar. The awareness of these factors predicting the need for hospital admission can support the development of clinical pathways.

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The overall number of patient presentations made to emergency departments (EDs) have increased over the years in many developed countries, including Sweden [1, 2], Australia [3], the United States (US) [4], and England [5]. This growth includes children, where the number of ED presentations made to public hospitals have increased from 1.4 M in 2011–12 to 1.7 M in 2021–22 (for those aged ≤ 14 years) in Australia [3, 6], from 3.5 M in 2011–12 to 4.7 M in 2021–22 (for those aged ≤ 14 years) in England [5, 7] and from 32.1 M in 2016 to 34.7 M in 2019 (for those aged < 18 years) in the US [8]. In Australia, children ≤ 14 years of age comprised 19.5% of the 8.8 million presentations made to public hospital EDs in 2021–22 [3]; in England, children ≤ 14 years of age comprised 20.5% of the 22.8 M ED presentations in 2021–22 [5], and in the US, children < 18 years of age comprised 23.1% of the 150.6 M ED presentations in 2019 [8].

A commonly reported measure in emergency health services research is admission rate. Wide variation in the reporting of childhood admission rate is however evident. For example, in Australia and New Zealand, the admission rate from EDs for children aged ≤ 18 years was 24% [9], in Sweden, the admission rate for children aged ≤ 17 years was 19% [10], in the Netherlands, the admission rate from children aged < 16 years was 20–23%, and in England, the admission rate from children aged < 16 years was 10% [11]. Being able to predict if a child is likely to require admission early in the ED journey may be useful to prospectively inform bed management and patient flow strategies, speed up the admission process, and decrease ED crowding.

Models predicting admission from ED have been developed in several countries [12]. From studies undertaken in Australia, factors associated with hospital admission for adults have been reported to include older age, arrival by ambulance, higher triage urgency classification [13, 14], referral from local doctor, need for blood test, arrival during evening hours, weekday presentation [13], previous admission (within 30 days), and presenting problem (gastrointestinal, febrile illness, social) [14]. From studies undertaken in the US and Ireland, factors associated with hospital admission for children have been reported and include: various presenting problems [15,16,17,18,19,20], weekday presentation [18], triage classification [15,16,17,18], arrival by ambulance [16, 19], distance travelled [17, 18], and abnormal vital signs [15,16,17, 19, 20]. It remains largely unknown though, if admission rate or other factors are predictive of admission for children based on the population to which the ED served (i.e., children only, or both adults and children) and in other countries. Examining variation in these aspects between hospitals domestically and internationally allows opportunities for shared learning and practice and process development as well as enhanced research understanding. The use of routinely collected data to inform admission likelihood is one way to assist this process.

The aim of this study was to describe the profile of and identify factors predictive of hospital admission for children who presented to four EDs in Australia and one ED in Sweden as some similar, but also different demographic profiles and health system structures exist between these two countries. Life expectancy and common causes of death (heart disease, Alzheimer’s disease, chronic obstructive pulmonary disease) are similar in both countries [21]. Issues with ED crowding [22, 23] and pressures to meet performance targets are also common across both countries [24, 25]. Despite these similarities, differences exist in terms of the percentage of population with private health insurance (less frequently utilised in children): 5% in Sweden and 50% in Australia [26], and the specialisation of emergency medicine which is much newer in Sweden, compared to Australia.


Study design, setting and sample

This was a multi-site observational cross-sectional study conducted using routinely collected data from five hospital EDs: two in Queensland (QLD), two in South Australia (SA), and one in Sweden to offer some international comparability. The sites were specifically chosen for their variation in terms of setting and servicing population. Table 1 provides an overview of each study site. We included data from all patient presentations made to the EDs by children < 18 years of age between July 1, 2011 and October 31, 2012.

Table 1 Overview of study sites

Data collection

Data used for this study was based on routinely collected demographic and clinical data obtainable from each site’s ED information system. Variables requested were informed by those reported in previous research (e.g., triage category, mode of arrival, presenting problem) [15, 16, 20], and expert clinical advice. In preparation for analysis data provided required standardisation and were categorised and coded (Supplementary Material, Table S1). For four variables (triage category, referral source, discharge disposition, and arrival mode) consultation with investigators at each site was required to support standardisation. Hospital admission in our study was defined as an admission to the study site hospital (either short stay unit, hospital-in-the-home, or non-emergency department hospital ward), aligning with national reporting [3].

Data analysis

Descriptive statistics were used to describe demographic characteristics, ED clinical characteristics and outcomes for all presentations. Median and inter-quartile range (IQR) were used for continuous variables that were not normally distributed. Frequency distributions were used for categorical variables. Inferential statistics were used to test for differences between groups, including the chi-square test for categorical variables and the independent samples median test for comparisons of medians.

Variables with more than 10% missing data were excluded from models predicting admission. The amount of missing data by site, and for all sites combined, is presented in Supplementary Material, Table S2. Custom models were built for each site individually (due to varied data availability). Crude odds ratios (OR) and 95% confidence intervals (CI) were produced for each variable that had 95% or more of data complete at each site, and multivariate regression was performed for each site, producing Adjusted OR (aOR).

One multi-site multivariate model was developed including all sites’ data, and testing the variables age category, sex, hospital type (children’s or mixed), season, time of arrival, day type of arrival (weekday or weekend), arrival mode, referral source, triage category and the top five major diagnostic categories. Analysis of the variables: Aboriginal and Torres Strait Islander status, insurance status, and language spoken, was limited to crude ORs and 95% CIs at the multi-site level because these were collected at Australian sites only. Multi-variate models were also developed for each site.

Multivariate regression was performed using a forward conditional approach. A two-sided p-value of < 0.05 was considered statistically significant. Data were analysed using SPSS (IBM Corp. SPSS Version 22.0. Armonk, NY).

The study was approved by the Health Services (QLD: HREC/13/QPAH/347; SA: HREC/14/WCHN/66) and Griffith University (NRS/05/15/HREC) Human Research Ethics Committees in Australia as well as the Swedish Regional Ethical Board in Sweden (Dnr: 2013/11636–31-/1; 2014/566–32).


Demographic characteristics

A total of 151,647 ED presentations were made to the five EDs over the 15-month period by children aged under 18 years. The demographic characteristics of children are presented in Table 2. Whilst the median age was 4 years (IQR 1–11) for all sites combined, children presenting to the three mixed-EDs tended to be slightly older. The proportion of children in the oldest age group (12–17 years) varied by site, from 11.6% of presentations at the children’s hospital in QLD to 31.2% at the mixed hospital in QLD. A higher proportion of males presented to all EDs (55%, n = 83,227). This was consistent across sites. At the Australian sites, the proportion of presentations from children of Aboriginal or Torres Strait Islander ethnicity ranged from 2.7% in the QLD mixed ED to 7.0% in the SA mixed ED.

Table 2 Characteristics of children presenting at five hospital EDs in Australia and Sweden

ED characteristics

The ED characteristics of children are presented in Table 3. Across all sites, most children (87%, n = 131,698) arrived to the ED through privately arranged transport, with 13% (n = 19,285) arriving by ambulance, although this ranged from 7% in the Swedish mixed ED to 22% in the QLD mixed ED. Considerable variation in assigned triage categories between sites was noted, with 7.4% of presentations to the SA mixed ED considered emergency (requiring attention immediately/within 10 min) and 20.6% assigned this urgency at the Swedish ED. The proportion of children presenting on the weekend (29.7%) or after-hours (43.8%) was relatively consistent across sites. Across all sites, the top five most common ED assigned diagnostic categories were trauma (26%, n = 38,629), infectious disease (14%, n = 20,971), ear, nose and throat (ENT) condition (9%, n = 13,162), gastrointestinal condition (8%, n = 12,636), and respiratory condition (8%, n = 11,639). The proportion of trauma presentations was higher at the mixed EDs when compared to the children’s EDs.

Table 3 Emergency department characteristics of children presenting to five hospitals in Australia and Sweden

About 22% of children were admitted to hospital following ED presentation; this varied between sites, from 13.2% to 26.1% at the SA mixed and SA children’s hospitals, respectively. The admission rate for children at both children’s EDs was higher (23% and 26%) than the three mixed EDs (13%-18%), with an increased likelihood of admission at children’s hospitals (crudeOR 1.5, 95% CI: 1.5–1.5). For the Australian sites combined, 6,270 children (4.6%) did not wait to see a doctor in the time period, although this ranged considerably between sites (0.9%-12.3% at the QLD children’s and mixed EDs, respectively).

Predictors of hospital admission

In univariate regression analysis, except for sex, all variables tested were statistically significantly associated with hospital admission. Compared to the Swedish model, children presenting to any of the Australian sites except for the SA mixed hospital, had a higher likelihood of admission. For the Australian sites, children of Aboriginal or Torres Strait Islander ethnicity were more likely to be admitted than their non-Indigenous counterparts (OR 1.3, 95% CI: 1.2–1.4), whilst not having insurance and not speaking English were significantly associated with a lower likelihood of admission (Table 4).

Table 4 Predictors of hospital admission for all sites combined for children under 18 years of age

In multivariate modelling, predictors of a more than two-fold higher risk of admission were high urgency triage categories, arrival by ambulance, presentation at children’s hospital, and respiratory diagnosis (Table 4).

At each site individually, common factors predictive of admission included: triage category, referral source, arrival by ambulance, presentation in summer vs. winter, and for the Australian sites, presentation with a respiratory or gastrointestinal condition (Table 5). The magnitude of effect of some of these predictors varied by site. For example, the OR of admission at the Swedish mixed ED when the child arrived by ambulance was 3.9 (95% CI 3.4–4.5) whereas at the mixed ED in QLD, Australia, it was 1.8 (95% CI 1.6–2.0). Triage urgency was a major predictor of hospital admission in all EDs, however this also varied by site. For example, the OR of admission at the Swedish mixed ED when the child was assigned an emergency triage category was 6.4 (95% CI 5.6–7.5) and at the children’s ED in QLD, it was 26.5 (95% CI 21.7–32.3).

Table 5 Predictors of admission for children: All sites and site-specific multivariate models, showing adjusted odds ratios and 95% confidence intervals


In this multi-site study of children presenting to ED certain characteristics were predictive of admission to hospital, regardless of hospital type or location. To our knowledge, this study is the first to describe higher admission rates for children presenting to a children’s only hospital ED compared to a mixed hospital ED. Whilst our study controlled for different age distributions, diagnostic categories, arrival modes, and urgency, it could be that the children presenting to the children’s only EDs were ‘sicker’ or more complex than those presenting to the mixed EDs. This finding is reflective of a US report revealing that children with complex medical conditions represented 33% of admissions to children’s hospitals but only 20% of admissions for children seen in mixed hospitals [27]. In Australia, children’s only EDs are located within tertiary level hospitals that are resourced to provide specialist care. Thus, our findings may reflect practices where attendance and or referral of children with more complex medical problems and severe trauma/illness to these EDs are more common. Differences in treatment regimens between paediatric emergency medicine clinicians and general clinicians [28] may also explain the varying admission rates seen. Thus, whether or not the threshold for admission, case complexity, or clinician specialisation is different in children’s hospitals compared to mixed hospitals merits further study.

Some predictors of admission varied by location. Comparing the Swedish mixed ED to the two Australian mixed EDs, the same variables were significantly associated with admission (i.e., referral from primary care, arrival by ambulance, and triage category). However, in Sweden, the magnitude of the effect was greater for the first two variables, suggesting that these types of presentations represented more acutely unwell children than in the Australian sites, or access to and utilisation of community based services is different in some way, despite health care (including use of ambulance services) for children being free in both countries. In the Australian sites, Aboriginal or Torres Strait Islander status was a significant predictor of admission at two of the four EDs. This could be because of the higher proportion of Aboriginal or Torres Strait Islander children living in the hospital catchment areas, potential varying accuracy of data capture between sites or varying practices and policies underpinning care delivery of Aboriginal or Torres Strait Islander children. Presentation after-hours resulted in a lower admission rate for children presenting to the two hospitals where this analysis was possible (SA children’s and QLD mixed). Whilst differences in admission rates by time of presentation may be associated with the availability of paediatric emergency specialists [29], access to primary care centres and general practitioners, this particular factor was not able to be comprehensively reported across all sites in our study.

Some factors predictive of hospital admission for children in our study have been noted elsewhere [15,16,17,18,19,20]. A summary of findings from one Irish study [18], four US studies [15, 16, 19, 20] and this study (all of which used paediatric populations and reported multivariate factors predictive of admission) is presented in Table 6. Ambulance arrival carried an increased likelihood of admission in all of our study sites and two US studies [16, 19]. Referral from primary care (i.e., general practitioner) was predictive of admission for children in this study, and in other US paediatric populations [20]. Triage category was amongst the most powerful of predictors of admission in our study and also predictive in other US and Irish studies [15, 16, 18]. The lower odds of admission during weekends and after-hours noted in the Australian sites of our study may suggest challenges with the admission process during these times. This is not necessarily unique to our study, with higher odds of admission on weekdays for children reported elsewhere [18]. Of possible concern here is the subsequent outcomes with children admitted on the weekend found in one US study to have significantly higher odds of unplanned readmission within 30 days of discharge compared to children admitted on weekdays [30]. Further research in this area is required in other countries. Some factors not collected in our study but reported elsewhere as predictive of hospital admission for children (such as distance travelled [17, 18] and vital signs [15, 16, 19, 20]) can be easily integrated into existing health data systems and further used to support the development of models for early hospital admission decision making.

Table 6 Predictors of hospital admission in children from multivariate modelling studies from Ireland, USA, Australia and Sweden


Although the data sets in this study are from 2011–2012, some characteristics of the sample in the four Australian hospitals were broadly similar to national data available on children from 2021–22 [3]. We recognise that there have been some significant changes in paediatric emergency care delivery over the last decade. This includes tremendous growth [3, 6], and a pandemic which has altered presentation patterns [31,32,33] since the time of this study potentially impacting on the relevance of our findings. This research provides baseline evidence to inform if and how practice changes may have impacted hospital admission. This study used retrospective data where there is the potential for inaccuracies and a cause-and-effect relationship cannot be established. Considerable missing data was evident in some fields (e.g., language spoken at home, Indigenous Status) impacting on our ability to comprehensively predict hospital admission with these variables. Efforts to better understand and competently cater for a culturally and linguistically diverse patient population are warranted [34, 35]. Improving the collection of culturally and linguistically diverse related information would be one way to contribute towards such efforts. Our analysis considered all hospital admissions. In Australia and Sweden, ED short stay unit admissions and ward admissions tend to reflect different patient groups. Thus, care is required in interpreting our findings. We used forward stepwise regression to identify factors predictive of hospital admission. We acknowledge other approaches (e.g., penalised maximum likelihood estimation) [36] may be also used, however with large datasets and relatively frequent outcome of interest (as is the case in our study), standard and penalised models have been noted to perform similarly [37]. Although this was a multi-site study with data from four sites in Australia (from two different states) and one site from Sweden, we cannot generalise our findings to other sites with different profiles. Our findings do however provide a much more informed understanding of predictors of hospital admission for children that may be used to assist clinicians and hospital managers, supported by the use of artificial intelligence and machine learning algorithms. Such application has great potential to improve information use, especially in resource poor settings [38].


Hospital admission rates for children varied based on hospital type (children’s only or mixed). Most factors predictive of hospital admission (triage category, referral from primary care, arrival by ambulance and older age) were consistent between sites. Children with certain diagnoses (especially pertaining to respiratory and gastrointestinal illnesses) were admitted in much higher proportions, regardless of age, triage category, location or type of hospital. Together, this information may be used to inform the development/enhancement of clinical pathways to potentially expedite admission processes for children.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due to ethics and data privacy requirements.



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Inter-quartile range


Confidence intervals


Odds ratios


Adjusted odds ratios


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We wish to acknowledge and thank the Health Informatics Directorate staff from each hospital for providing data for this study. Queensland Health, South Australia Health, Griffith University, Uppsala University, the KI Research Foundations and the Emergency Department, Karolinska University Hospital, Solna, Dalarna County provided in-kind contributions of project personnel to enable this work to be undertaken. This work was supported by the Gold Coast Health, Study, Education and Research Trust Account (SERTA).


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JC, KG, AMu made a substantial contribution to the study conception. JC, KG, AMu made a substantial contribution to design of the work. JC, KG, AMu, LM, LC, MH, AK, SD, AMy, TD made a substantial contribution to the acquisition of data. AS made a substantial contribution to the analysis of data. All authors made a substantial contribution to the interpretation of data and drafting the work. All authors read and approved the final manuscript.

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Correspondence to Julia Crilly.

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The study was approved by the Metro South Human Research Ethics Committee (HREC) (HREC/13/QPAH/347); Women’s and Children’s Health Network HREC (HREC/14/WCHN/66) and Griffith University HREC (NRS/05/15/HREC) in Australia as well as the Swedish Regional Ethical Board in Sweden (Dnr: 2013/11636–31-/1; 2014/566–32). The aforementioned HREC approvals, Queensland Government Department of Health for Public Health Act 2005 approval, and Site Specific Assessment approvals accounted for the use of de-identified confidential information, negating the requirement to obtain informed consent to perform the work described herein. All methods were performed in accordance with relevant guidelines and regulations.

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Supplementary Information

Additional file 1: Table S1.

Variables and categorisations applied. Table S2. Variables available for modelling and the percentage of data missing by hospital.

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Crilly, J., Sweeny, A., Muntlin, Å. et al. Factors predictive of hospital admission for children via emergency departments in Australia and Sweden: an observational cross-sectional study. BMC Health Serv Res 24, 235 (2024).

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