Socioeconomic disadvantage as a driver of non-urgent emergency department presentations: A retrospective data analysis.

BACKGROUND: Globally, emergency departments (EDs) are struggling to meet the service demands of their local communities. Across Australia, EDs routinely collect data for every presentation which is used to determine the ability of EDs to meet key performance indicators. This data can also be used to provide an overall picture of service demand and has been used by healthcare planners to identify local needs and inform service provision, thus, using ED presentations as a microcosm of the communities they serve. The aim of this study was to use ED presentation data to identify who, when and why people accessed a regional Australian ED with non-urgent conditions. METHOD AND MATERIALS: A retrospective data analysis of routinely collected ED data was undertaken. This included data obtained over a seven-year period (July 2009 to June 2016) in comparison with the Australian Bureau of Statistics census data. Analysis included descriptive statistics to identify the profile of non-urgent attendees and linear regression to identify trends in ED usage.
RESULTS: This study revealed a consistently high demand for ED services by people with non-urgent conditions (54.1% of all presentations). People living in the most disadvantaged socioeconomic decile contributed to 36.8% of these non-urgent presentations while those under 25 years of age contributed to 41.1%. Diagnoses of mental health and behavioural issues and of non-specific symptoms significantly increased over the study period (p < 0.001) for both diagnostic groups.
CONCLUSION: The over-representation by those from the most socioeconomically disadvantaged areas highlights an inequity in access to services. The over-representation by those younger in age indicates behavioural patterns based on age. These key issues faced by our local community and the disparity in current service provision will be used to inform future health policy and service planning.

Introduction

Emergency departments have been described as a microcosm of the communities they serve, meaning that they encapsulate features of the wider community [1]. Challenges faced by emergency departments (EDs) can reflect deficits in community-based resources [2]. As increasing demands for ED services continue to be reported globally, it is timely and necessary to identify drivers of ED demand. In Australia, over 8.3 million people accessed ED services between July 2018 and June 2019 (335 per 1000 population), 48% of whom were triaged to the two least urgent triage categories [3]. The Australian Triage Scale (ATS) is a five-tiered triage system with ATS 4 and 5 being the least urgent categories, patients triaged to these categories are assessed as being safe to wait for one or two hours respectively [4]. For the purpose of this study, we refer to ATS 4 and 5 presentations as non-urgent. We are confident that this group of patients included some who could have had their needs met in a primary care setting.

International research investigating these least urgent presentations has identified drivers of ED demand such as: patients’ perceived need for urgent attention [5-7]; age and gender [7-9]; access to alternative services [10-12], and socioeconomic position [6, 10, 13]. Identifying drivers specific to individual EDs can inform service planning [2]. Furthermore, a mismatch between the known causes of ED demand and solutions implemented was identified in a systematic review and highlights the need to develop interventions that address specific causes [14]. These external drivers contribute to the challenge for hospitals and health services in implementing successful and sustainable solutions.

Furthermore, our understanding of the demand for ED services is complicated by contextual differences. These differences challenge the successful implementation of solutions. Variation in demographic profiles, community healthcare needs and service availability influence how and when people access services, including the decision to present to an ED with a ‘non-urgent’ condition [15]. Socioeconomic position, for example, has been identified as having both a positive and negative correlation with populations accessing EDs. This correlation is observed to vary across contexts, with one study identifying greater representation by populations from mid-high socioeconomic areas [10] while others report greater representation from lower socioeconomic areas [6, 13]. Of the studies that reported age and gender, one found a higher incidence among middle aged females [7] while another found a higher incidence among young males [9]. These studies demonstrate the unique microcosm within EDs and provide an indication of healthcare needs within their respective wider communities.

Tasmania, Australia’s smallest State, with a population of 517,000 [16], has the highest rate of non-urgent ED presentations, with 88,000 triaged as ATS 4 or 5 in 2018–19 [3]. This island State is separated into three geographic regions with governing health services in the North, Northwest and South all operating under the overarching jurisdiction of the Tasmanian Health Service [17]. The population of Northern Tasmania is older (median age 43 years compared to 38 years nationally) and more socioeconomically disadvantaged (median weekly income $537.00-AU compared to $662.00-AU nationally) than other Australian regions [18], compounded by inequitable access to primary care services in regional and rural Tasmania [19]. There are considerable regional differences in the profile of ED patients across these three regions highlighting the importance of identifying trends and types of ED presentations to inform service planning [14]. These regional variations in population healthcare trends and the mismatch between identified causes and solutions to address ED demand highlight the importance of bringing together knowledge and understanding of the drivers for ED demand before implementation of sustainable solutions.

In research conducted in Northern Tasmania, 31% of patients who present to the ED with non-urgent conditions would have preferred to be managed by their general practitioner (GP) if they had been available [8]. The limited service options [19] in this community and the distance to alternative EDs (the nearest is a smaller rural facility located 90km from the study hospital) contribute to ED demand. Moreover, there are no private EDs or urgent care facilities in Northern Tasmania. Northern Tasmanian residents also have limited access to primary care services within the community once business hours have ended. Business-hours have been defined as between 0800 to 1800 Monday to Friday and 0800 to 1200 Saturdays; public holidays and all other times are considered after-hours [20]. These limited service options indicate potential challenges around timely access to alternative services.

Emergency departments are the ‘canary in the coalmine’ for health services and the communities they serve [1]. Demands for ED services are reflective of broader population healthcare needs [2] and are influenced by the availability of services within the community [10, 21]. The aim of this paper is to establish a profile of who, when and why ED services were accessed by people with non-urgent conditions. The objectives are to:

Develop a profile and identify trends in who is presenting and when;

Identify patterns in where people come from, including the socioeconomic position, and;

Identify trends in discharge diagnoses.

This paper forms part of a larger body of work using an explanatory sequential mixed method to gain a deeper understanding of factors contributing to the decision to present to an ED with non-urgent conditions and develop relevant and sustainable strategies for health service planning.

Materials and methods

Retrospective analysis of routinely collected hospital data was undertaken for all presentations triaged as ATS 4 or 5 at a single regional ED, between 1 July 2009 and 30 June 2016. This consisted of data entered into the Emergency Department Information Systems (EDIS) by ED staff at time of the patient’s presentation, or at the time of discharge. Variables used in this analysis included: date, day of week and time of arrival to the ED; gender; mode of arrival; suburb of residence; discharge diagnosis; discharge destination, and referral on discharge. The first six variables were entered into EDIS by the triage nurse or clerical staff at the patient’s time of arrival. The latter three were added by the treating physician or nursing staff at the time of departure. Diagnoses are based on International Diagnostic Codes, revision 10, as outlined by the World Health Organisation [22]. It was beyond the scope of this project to review presentations across all triage categories.

Research ethics approval was granted by the Tasmanian Human Research Ethics Committee (H0016504). Deidentified data were provided by the Tasmanian Department of Health and Human Services (DHHS). This data is not publicly available in Australia and permission was not provided for it to be made publicly available.

Study setting & participants

This study was undertaken in a large regional hospital in Northern Tasmania with a total bed capacity of 300 and a 26 bed ED [23]. Serving as a referral centre for a population of 143,500 [24] dispersed across 20,000 square kilometres. Data used for this analysis was from July 2009 to June 2016, for ATS 4 and 5 presentations. The DHHS also provided the total count of all ED presentations by month across all triage categories so the proportion of ATS 4 and 5 could be calculated. Further explanation of the included study population is provided in Fig 1.

Fig 1

Summary of ED presentation numbers, July 2009 to June 2016.

We have included all ATS 4 and 5 presentations who resided in the regional city (Launceston) and its surrounding suburbs. Excluding those from outside this region allowed us to develop a profile of who, when and why the local community choose to access ED services, thus focusing on local drives of ED demand. This area was defined by using statistical area (SA) codes allocated by the Australian Bureau of Statistics (ABS). The greater Launceston area has an SA3 code of 60201. All suburbs with this code were included in the study area and total population was 81,029 in 2016 [25]. Population growth in this region was just 2.5% between 2011 and 2016 compared to the national growth of 8.3% [25, 26].

Data relating to socioeconomic position was derived from ABS data. Five-yearly census data is used to calculate average values of various socioeconomic indexes across geographical areas, known as Socioeconomic Indexes for Areas (SEIFA). One of these is the Index of Relative Socioeconomic Disadvantage (IRSD), which is the preferred measure to use when investigating disadvantage or lack of disadvantage [27]. This index is based on national socioeconomic classification, and takes into account income and additional variables including unemployment, disability, sole-parent status, level of education, employment classification, etc. [27]. Each suburb is given a score based on these variables, the lower the score the greater the disadvantage. The ABS also aggregate suburbs into deciles, dividing Australia’s population into ten evenly sized population groups. Ten percent of the Australian population fall into each decile with IRSD 1 being the 10% of those with greatest disadvantage and IRSD 10 being those with the greatest advantage. The histogram of IRSD scores has a long left-tail (at the end of greatest disadvantage), so the difference in disadvantage between decile 1 and decile 2 is larger than between other pairs of adjacent deciles [28]. The IRSD score and deciles were linked to ED data using the suburb of residence in order to determine socioeconomic position.

Data analysis

Initial review of the data included all presentations to the regional ED triaged as ATS 4 or 5. The patient’s suburb/town of residence was used to exclude attendees from outside this regional city. The decision to focus only on presentations from the local area was to gain greater insight and understanding of the local community and to limit outlying factors that may have influenced the decision by non-local attendees to present to the ED.

Descriptive statistics were calculated using SPSS [29] to summarise the profile of patients accessing the ED with non-urgent conditions throughout the seven-year study period. Linear regression was used to explore trends over time by mode of arrival, referral on departure, episode end status, time of arrival (in-hours versus after-hours) and International Classification of Diseases, version 10 (ICD-10) [22]. ABS national census data from 2011 and 2016 [25, 26] were used to calculate age-standardised presentation rates by suburb (age-standardised to the overall age distribution profile of the Launceston region in 2016), with linear interpolation used to estimate populations in years between 2011 and 2016. Linear regression, weighted by 2016 suburb populations, was used to fit a trend-line showing the association between age-standardized presentation rate and IRSD, with an outlier suburb excluded. RStudio [30] was used for regression analyses and plots.

Results

Between 1 July 2009 to 30 June 2016, there were 305,599 ED presentations across all triage categories (ATS 1–5). Fig 1 provides a summary of how we determined the number (n = 109,633) included as the study population. Our objectives were to: describe the profile of ED attendees and trends over time through retrospective analyses of routinely collected hospital data; identify the usual place of residence and socioeconomic position of people attending the ED with non-urgent conditions, and to summarise the most frequent discharge diagnoses of the study population and trends over time.

Profile and trends of people presenting with non-urgent conditions

The first objective was to develop a profile and identify trends in who is presenting and when. The number of non-urgent presentations to the ED revealed similar numbers between the first and last 12-month periods, July 2009 to June 2010 (n = 15,322) and July 2015 to June 2016 (n = 15,139). Over the seven-year study period the annual rate of non-urgent presentations among local residents varied between 186 to 205 per 1000 population. Fig 2A shows average daily rates by month of all non-urgent presentations. While there were short-term fluctuations in presentation numbers, regression analysis did not reveal any long-term linear trend in the number of presentations (p = 0.61). Over the seven-year study period non-urgent presentations by local residents ranged between 38 and 48 per day (Fig 2A).

Fig 2

Trends in presentation numbers and time of arrival, ATS 4 and 5, July 2009 –June 2016.

2a. Average ATS 4 and 5 presentations by month, adjusted by days in month (p = 0.6). 2b. ATS 4 and 5 presentations, July 2009 to June 2016: time of day and day of week. 2c. Average in-hours ATS 4 and 5 presentations by month, adjusted by days in month (presentations 0800 to 1800 Monday to Friday and 0800 to 1200 Saturday). P-value for downward trend: 0.006. 2d. Average after-hours ATS 4 and 5 presentations by month, adjusted by days in month (presentations at times of week not included in Fig 2C, plus all presentations on public holidays). P-value for upward trend: < 0.001.

Analysis of age identified that younger people were over-represented among non-urgent presentations. The median age of the study population was 29 years compared to a median age in this regional city of 39 years [31]. Table 1 provides a summary of presentation and population numbers aggregated by age. The age profile of the local population was recorded to remain stable between census periods, for example, those under 25 years of age continued to contribute to 31–33% of the local population between census periods.

Table 1

Profile of patients by gender, age and index for relative socioeconomic disadvantage IRSD) versus profile of local population, ATS 4 and 5, July 2009 to June 2016.

	No.	% (n = 109 633)	% of local population (n = 81,029: ABS, 2016)*
Gender
Male	56 281	51.3	48.2
Female	53 293	48.6	51.8
Age (yrs)
0–4	9 543	8.7	5.9
5–14	11 936	10.9	11.9
15–24	23 531	21.5	14.5
25–34	18 296	16.7	12.5
35–44	13 737	12.5	12.1
45–54	10 902	9.9	13.3
55–64	7 955	7.2	12.0
65–74	5 907	5.4	9.8
75–84	4 819	4.4	5.2
85+	3 037	2.8	2.5
IRSD by suburb (decile)**
1 (greatest disadvantage)	40 379	36.8	26.4
2	5 058	4.6	1.2
3	9 993	9.1	9.5
4	20 098	13.1	22.5
5	8 218	7.5	3.6
6	11 576	10.6	17.8
7	1 828	1.7	7.6
8	4 562	3.9	5.6
9	1 080	1.6	1.6
10 (lowest disadvantage)	413	0.4	0.8

*Australian Bureau of Statistics, 2016 Census Data Packs

** IRSD deciles divide 10% of nationwide population into each decile

Trends in mode of arrival revealed a consistency in the number and proportion of patients arriving by their own means (87%; Table 2). Analysis of presentation outcomes revealed a large proportion of patients either did not require any follow-up or were referred to their GP (74.7%; Table 2) and were discharged home from the ED (85.3%). For these two variables (arrival mode and presentation outcome), increases were observed in the number of patients with non-urgent conditions who: arrived by ambulance (average increase of 34 annually, p = 0.002); arrived with police (average increase of 56 annually, p<0.001), or who required admission to hospital (average increase of 56 annually, p<0.001).

Table 2

Summary and trends in ED presentations for mode of arrival and outcome of ED presentation, ATS 4 and 5, July 2009 –June 2016.

	No.	% (n = 109 663)	Trend: average annual change in presentations per year (95% confidence interval)	p-value for trend
Mode of arrival
Arrived by own means	95 412	87.0	–64 (–170, 41)	p = 0.2
Ambulance	12 350	11.3	34 (13, 55)	p = 0.002
Police	1 565	1.4	56 (44, 67)	p < 0.001
Other	336	0.3	2.2 (–0.5, 4.9)	p = 0.1
Referred to on departure
GP or no further follow-up	81 914	74.7	88 (–8, 184)	p = 0.07
Emergency department	7 370	6.7	–135 (–166, –103)	p < 0.001
Outpatient department	8 916	8.1	–12 (–32, 8)	p = 0.2
Community services	3 010	2.7	10 (1, 20)	p = 0.03
Hospital admission (same day)	7 670	7.0	108 (93, 124)	p < 0.001
Other hospital admission	465	0.4	–4.7 (–8.3, 1.1)	p = 0.01
Other	318	0.3	–84 (–119, –49)	p < 0.001
Episode end status
Discharged home	93 567	85.3	1 (–114, 115)	p = 1.0
Did not wait/Left at own risk	8 571	7.8	–43 (–76, –9)	p = 0.01
Admitted	7 336	6.7	75 (56, 94)	p < 0.001
Transferred	161	0.1	–3.1 (–5.5, –0.8)	p = 0.01
Other	28	0.0	5.3 (–0.6, 11.3)	p = 0.08

Time of day and day of week are presented in Fig 2B with most non-urgent presentations occurring between 0800hrs and 1800hrs with peaks observed on Monday and Sunday mornings. Analysis of presentations occurring in-hours or after-hours revealed that 47.0% arrived in-hours with significant trends to in-hours and after-hours presentation numbers (2c and 2d). Average annual in-hours presentations fell at a rate of 78 per year (95% confidence intervals 18 to 140, p = 0.012). This was offset by a significant increase in after-hours presentations (rate of increase 108 annually, 95% confidence intervals 31 to 184, p = 0.006).

Non-urgent ED attendees and socioeconomic levels

The second objective was to establish a profile based on the IRSD deciles according to the patient’s suburb of residence. This age-standardised analysis revealed an over-representation by residents living in suburbs categorised as having the greatest socioeconomic disadvantage (IRSD decile 1; Table 1). Ten percent of the Australian population live in suburbs rated IRSD decile 1 compared to 26.4% of the Launceston population [25]. In this study, residents of IRSD decile 1 suburbs contributed to 36.8% of non-urgent ED presentations. Further analysis using the underlying IRSD score for each suburb revealed a strong negative correlation between IRSD score and the age standardised rate of ED attendance (Fig 3). Presentation rates for people with non-urgent conditions were 4.5 times higher from the most disadvantaged suburb compared to the most advantaged. Residents from the most advantaged suburb (IRSD score 1090) presented at a rate of 96 per 1000 population while residents from the most disadvantaged suburb (IRSD score 591) presented at a rate of 434 per 1000 population.

Fig 3

Age standardised ED presentation rates for ATS 4 and 5.

Age standardised presentations per 1,000 (population), by suburb of residence and index for relative socioeconomic disadvantage (IRSD), July 2009 –June 2016.

Discharge diagnoses and trends over time

The number of presentations for the three most frequent overarching diagnostic groups are summarised in Table 3 along with the three most frequently recorded sub-diagnostic groups. Median age and results of linear regression analysis to determine trends in diagnostic groups are also reported in Table 3.

Table 3

Top three diagnostic groups and diagnostic groups with significant trends (based on international statistical classification of diseases and related health problems 10th Revision: ICD-10).

ATS 4 and 5, July 2009 to June 2016.

Diagnosis, top ten ICD-10 In order of frequency Most frequent sub-diagnoses	No. presentations (% of sub-diagnostic group)	Proportion presentations (n = 109 663) (%)	Median age (IQR, years)	Trend over time
XIX–Injury, poisoning, certain other consequences of external causes	36 567	33.3	25 (15–45)	No change
Injuries to wrist and hand; head; ankle and foot	19 988 (54.7%)			(p = 0.973)
XXI–Factors influencing health status and contact with health services	14 980	13.7	33 (21–50)	No change
Persons encountering health services for examination and investigation; in other circumstances; or for specific procedures and health care	14 443 (96.4%)			(p = 0.156)
XVIII–Symptoms, signs & abnormal clinical & laboratory findings, not elsewhere classified	8 442	7.7	34 (19–60)	Significant increase
Symptoms and signs involving the digestive system and abdomen; general symptoms and signs; or involving the circulatory and respiratory systems	6 700 (79.4%)			(p <0.001)
X–Diseases of respiratory system	7 024	6.4	22 (5–39)	Significant decrease
Acute upper respiratory infections; chronic lower respiratory diseases; or influenza and pneumonia	6 340 (90.3%)			(p = 0.002)
V–Mental & behavioural disorders Neurotic, stress-related and somatoform	2 363	2.2	34 (23–48)	Significant increase
disorders; mental and behavioural disorders due to psychoactive substance use; or Mood [affective] disorders	1 664 (70.4%)			(p <0.001)

ICD-10 –International Classification of Diseases, version 10 [22]

IQR–Interquartile range

The most notable results from this analysis were the high proportion of discharge diagnoses falling into the ICD-10 code for injury. One third of non-urgent presentations were diagnosed with an ‘injury, poisoning, certain other consequences of external causes’, the most frequent sub-diagnostic groups were injuries to distal limbs or head. These patients were younger and there was no significant trend over the study period.

Significant increases in ED attendance were observed in two diagnostic groups, the first being ‘symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified’. The proportion of patients diagnosed into this non-specific group increased from 6.6% in 2009–10 to 9.1% in 2015–16 (p < 0.001), the equivalent of 70 additional presentations per year.

Mental health conditions also increased significantly between 2009–16. These presentations increased from 1.8% of the study population to 3.1% (p < 0.001), a 73.1% increase in diagnoses relating to mental and behavioural disorders over seven years and equivalent to 31 additional presentations annually.

Discussion

This research aimed to identify who, when and why people accessed the ED with non-urgent conditions. In the analysis of seven-years’ worth of routinely collected ED data, we discovered:

No increase in total number of non-urgent presentations;

A significant over-representation by residents from socioeconomically disadvantaged areas and those younger in age;

Increasing proportion of after-hours presentations;

Significant increases in presentations for mental health and non-specific symptoms.

Consistent demand for ED services by patients with non-urgent conditions

The AIHW have consistently reported national increases in the number of annual ED presentations over the past five years [32], but an increase was not observed in the number of non-urgent presentations recorded to this ED during the study period. Monthly plots of presentation numbers demonstrate short-term fluctuations in ED usage for non-urgent conditions (Fig 2A and 2D), with presentation numbers between 186 to 205 per 1000 population per year. The simple linear regression we have performed does not adequately model fluctuations. Analysis of the fluctuations was beyond the scope of this publication but is part of an ongoing investigation by the research team.

A consistent demand for ED services by patients with non-urgent conditions has also been reported in research conducted in Northwest Tasmania where limited general practices services were identified as a driver [33]. Furthermore, international literature has identified links between the number of ED presentations and timely access to primary care services [10, 11, 21].

Presentation numbers across day of week and time of day were observed to peak between 0900 and 1100hrs and decreasing throughout the day (Fig 2B). This indicates that a significant proportion of non-urgent presentations arrive during hours when other services are open. Tuesdays to Saturdays demonstrated similar presentation times and trends, however, peaks were observed on Sunday and Monday mornings. General practice services on a Sunday are minimal in this regional community leaving residents with the ED as the primary option. The peak on a Monday morning is likely to reflect those, who have waited for regular services to open on a Monday morning but been unable to secure an appointment, thus, resulting in an ED presentation. This again highlights the availability of alternative services at the time of need as a driver of non-urgent ED presentations and may be of interest to local service providers aiming to identify peak times and plan services and staffing based on demand.

Over-representation by those from lower socioeconomic suburbs and those younger in age

The correlation between IRSD and the number of non-urgent ED presentations per 1,000 head of population demonstrates a striking over-representation by people living in the most disadvantaged areas. The ED is located close to the central business district and is surrounded by suburbs with IRSD deciles between 3 and 7[25]. Furthermore, the suburb with the highest presentation numbers per 1,000 residents is the same distance from the ED as the suburb with the lowest presentation numbers, both being 11km from the ED. This shows that socioeconomic status is a stronger contributor to ED attendance than distance in our region. A higher proportion of non-urgent ED presentations by those living in close proximity has been previously reported [11, 34]; however, this was not the case in this study and highlights the contextual nature of how local populations access health services.

The only exception to the correlation between socioeconomic position and incidence of ED presentation (Fig 3) is the city centre. This appears to have occurred when the person providing the patient’s details or staff member entering the data has listed the over-arching area of Launceston as the suburb of residence rather than the patient’s actual suburb of residence. For example, it is not uncommon for residents from Launceston’s lowest IRSD suburbs to list their suburb of residence as Launceston where it shares the same postcode as their actual suburb. These presentations were plotted in Fig 3 as they contribute to the overall number of presentations. However, the data from the city centre were excluded from the weighted regression analysis to fit a trend line due to the recording error.

Findings of over-representation among populations with greater socioeconomic disadvantage are varied across international literature. Some studies report similarly over-represented presentations by disadvantaged communities [6, 13, 34] while a Canadian study found mid-high-income communities were over-represented [10]. Additionally, a study from the UK [12] reported that disadvantaged communities had lower ratios of GPs per 1,000 head of population. While it was outside the scope of this study to measure the number of GPs per 1,000 during the study period, it was observed that none of the larger practices with ready access to additional services such as pathology and radiology are located within the most disadvantaged areas of this local community. Furthermore, northern Tasmania was reported to have fewer full-time equivalent GPs in 2014, 70.3 per 1,000 population, versus 85.4 per 1,000 in southern Tasmania [35]. These findings highlight contextual differences in the ability of populations to access health services and demonstrates a disparity in the provision of healthcare services in the most socioeconomically disadvantaged areas of this community. Further supporting this finding, are two studies, one from the US focusing on paediatric presentations [36] and the other from New South Wales looking at all presentations (adult and paediatric) [37]. Both studies found that fewer GPs per 1000 population contributed to higher rates of non-urgent ED presentations.

Being younger in age was also a significant factor with a clear over-representation by those in the 0 to 4 and 15 to 24 age groups. These two groups were 1.5 times more likely to present with a non-urgent condition than the rest of the study population. This finding is consistent with international studies from the United States [6, 34], Canada [11], Switzerland[9, 38], the United Kingdom [13], and Australia [8, 39] all observing an over-representation in non-urgent presentations by younger populations. Consideration of why this over-representation is occurring may contribute to further understanding of the decision-making processes of young people and access to alternative services for this group.

It is likely that the over-representation of residents from socioeconomically disadvantage areas and by those younger in age is reflective of challenges faced by these populations in accessing the right service at the right time and located in the right place. This information will be of interest to future service planning.

Increased non-urgent presentations after-hours

An increasing number of people arriving after-hours was also identified (Fig 2D). Most GP services in this community are available within normal business hours (0800 to 1800 weekdays and 0800–1200 Saturdays, excluding public holidays). Access to services is limited outside these times. The increase in demand for after-hours services is likely to reflect a lack in available services within the community at the time of need. Two other Tasmanian studies also found increases in after-hours presentations [17, 40] while another local study identified 31% of patients attending the ED would have preferred to be managed by their GP if they had been available at the time of need [8]. These findings further support the need for the right services to be available at the right time. As the third Tasmanian project to report a significant increase in the demand for after-hours services it is likely that further research exploring service demand and availability during these hours may assist in informing the provision of timely, patient-centred services and reduce ED demand.

Increased presentations with non-urgent mental health diagnoses and with non-specific symptoms

The final objective was to identify prominent reasons for presentations through analysis of discharge diagnosis (Table 3) based on ICD-10 codes [22]. Unsurprisingly, presentations as a result of injury were the most common discharge diagnostic group with one third of all non-urgent presentations being as a result of ‘injury, poisoning, certain other consequences of external causes’. This is consistent with non-urgent presentations across Australia, the AIHW reporting that in 2017–18 [32], 32.7% of non-urgent ED presentations were allocated into this principle diagnostic group. Other studies have also found similar proportions for this diagnostic group [8, 39].

A significant increase was observed in diagnoses into the non-specific group of ‘signs and symptoms or abnormal clinical findings not elsewhere classified’. This includes people who present to the ED for simple examination, investigation or observation, the proportion found in this study is reflective of nationwide trends for this principle diagnostic group [32]. The significant increase may be explained by international research which clearly identifies the patient’s perceived need for urgent medical attention as a major theme when investigating reasons for accessing ED services with non-urgent conditions [6, 8, 38]. The continued high proportion of patients who were discharged home and did not require specialist follow-up in this study raises questions around health literacy, health anxiety and timely access to alternative services.

Diagnoses of ‘mental and behavioural disorders’ was the only other diagnostic group observed to increase significantly with an additional 30 people per year presenting to this regional ED. To the best of our knowledge, this patient group has not been identified as an increasing proportion of non-urgent ED presentations. In 2017–18 the AIHW recorded 2.6% of ATS 4 and 5 presentations resulting in a mental health or behavioural diagnosis, for the same period this regional ED observed 3.1% [32]. While these are similar proportions to national figures, we were able to identify a concerning increase of 73.1% between 2009–10 and 2015–16 in our regional ED. Limitations in AIHW reporting meant we were not able to compare this increase with earlier national numbers. A patient triaged as an ATS 4 or 5 with a mental health presentation must demonstrate the ability to provide a clear history without signs of restlessness or aggression [4].

It is not known what has caused this dramatic increase in mental and behaviour diagnoses within the local region. However, if the ED provides an indication of people’s healthcare needs and the level of access to services within the community, this increase must be a warning to local service providers. Mental health was identified as the predominant concern for young people in a 2018 national survey of over 28,000 participants aged 15 to 19 years [41]. This report identified for the first time in 17 years that the top concern for youth was mental health. This growing concern among young people and the increasing presentation numbers within this regional community provide policy makers and service providers with a clear local need.

Limitations

This longitudinal observational study was reliant on routinely collected hospital data; efforts were made to review data for possible discrepancies. The findings are largely reliant upon the quality of data collected at the time of the patients’ presentation. Population and socioeconomic position data were based upon ABS data collected in 2011 and 2016 with changes occurring across this time period, to allow for these changes we presumed a direct linear relationship between the two data collection periods. This may not reflect true numbers but provided the closest solution to changes available between these two time periods.

Data provided by the DHHS were for ATS 4 and 5 presentations only, therefore it was not possible to compare presentation trends across all triage categories. This broader analysis was beyond the scope of this project and highlights an area for future enquiry.

Conclusion

The ED is a ‘canary in the coalmine’ for the greater health service and community. The over-representation of population groups and increases in demand provide clear indicators of the healthcare needs of members of the local community. Patients presenting to this regional ED with non-urgent conditions were younger than the local demographic profile and up to four times more likely to live in the most disadvantaged communities, raising the question of service accessibility and availability in areas of need. In addition, patients are increasingly presenting with non-specific symptoms and with mental health and behavioural issues. These findings will be of use to policy-makers in planning for enhanced primary care service for the young and for people with mental health issues from our most disadvantaged communities.

(DOCX)

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30 Jan 2020

PONE-D-19-35242

Socioeconomic disadvantage as a driver of non-urgent emergency department presentations: A retrospective data analysis

PLOS ONE

Dear Ms Unwin,

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PLOS ONE

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Additional Editor Comments:

Please pay particular attention to the technical issues raised by Reviewer 1. Also, please consider the comments of Reviewer 2 about limitations of this study.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper examines the characteristics of patients presenting in an ED for non-urgent reasons using routinely collected hospital data from a large regional hospital in northern Tasmania over the period July 1, 2009 to June 30, 2016. Data was provided by the Tasmanian Department of Health and Human Services. ED utilization and “crowding” is an important topic as it affects hospital emergency departments globally. A better understanding of the populations that are utilizing the ED for non-urgent reasons could help to target strategies to redirect patients to more appropriate health care resources. The authors find an over-representation of patients from the lower socioeconomic suburbs and younger in age. They also find growth in the volume of patients presenting with non-urgent ‘mental and behavioral disorders.’ The paper would be strengthened with a more clearly articulated methods section among other recommended improvements.

1. Materials and Methods – several questions arose in reading through the methods.

a. Page 6, lines 114-119. The authors describe bulk-billing and the fact that Tasmania has the lowest rate of bulk-billing. It seems that bulk-billing may have a large affect on an individual’s decision to seek care in an ED or a GP’s office. Yet, this is the only mention of the practice. It seems the author’s may want to include this in the discussion. It is also unclear if the authors have any information regarding bulk-billing within the various suburbs as that would be good information to include in the analysis.

b. Page 7, lines 149-153. It is unclear how the authors are using the IRSD deciles. Is it Australia’s total population that is broken into deciles, and then the suburbs within the study region are classified based on the national classification? If so, it would be helpful to have some information provided in a table related to the suburbs in the region or at least some more information on characteristics of each decile. For instance, what does it mean to be in decile 1 versus decile 2?

c. Methods – the presentation of methods is somewhat sporadic. It would be helpful to better organize the methods section. For instance, perhaps describe the variable construction first and then describe the analytic method. Right now the two seem intertwined and it makes the section unclear.

d. Methods – it might be helpful to compare the non-urgent to the urgent presentations in addition to the overall population.

2. Discussion – several clarifications are suggested in the Discussion

a. Page 11, line 233 – to the point that there is no increase in the total number of non-urgent presentations, it would be helpful to know how the population is growing (or not). For instance, if the ED volume is flat, but the population is growing, then the overall utilization is declining. Perhaps it would be helpful to report this as a utilization per 1,000 population or similar.

b. Page 11, line 243 – the authors state, “the erratic variation in presentation numbers points to external factors such as, the availability of primary care services…” However, Figure 2b shows a surge in ED volume around 0800, which, during the week, is when primary care services are available. Perhaps it correlates to bulk-billing. Please clarify.

c. Page 12, line 260. The authors state that they are certain the data from the city centre is clerical error. If so, why include in the study or why not at least run a sensitivity analysis with that suburb excluded.

3. Page 10, line 208. It is unclear where the “disadvantaged suburbs were up to four times more likely to present…” comes from. It is not clear in the data where that number is listed or if it was author calculation. This occurs in other areas of the manuscript as well. Please ensure that the results are clearly tied to the analysis.

4. Figure 2b appears to be fairly striking. The profile of presentation to the ED by time and day is consistent, regardless of access to other services being open. There seems to be a lot more here that is not really discussed in detail.

5. Figure 2a (and 2d to some extent) appears to have some sort of non-linear trend. Please clarify.

6. Table 2. For the trend, it might be helpful to have an actual table of regression results rather than just stating “increased,” “no change,” or “decrease.”

7. Table 3. There is a lot to digest in Table 3. Perhaps there is a better way to present this information. Also, are the subcategories just the top 3 within each?

Reviewer #2: The goal of this paper is to describe the population accessing the emergency department (ED) at a large regional hospital in Tasmania, including the demographics of the population and changes in utilization over time. The authors use routinely collected hospital data (on all admissions with an ATS value of 4 or 5, indicating the least urgent categories of visits, which the authors state indicate the patient could have been treated in a setting other than the ED. The authors find overrepresentation of patients from areas with a low socioeconomic status and distinct patterns by age. The paper is well written and the objectives clearly stated throughout. The introduction could be streamlined to make a better argument for the need for this analysis.

Introduction:

• The introduction sets the reader up to think that this will be a comparison of “local” environments and how that contributes to the demand for services, and in particular non-urgent services. But the paper only onlooks at one community (an important one), and so it’s so much a point about how the local environment matters so much as a depiction of what is going on in one particular local community. I think the introduction could be strengthened greatly by focusing more on why THIS community is important to study, as opposed to why local communities are important more broadly.

Methods

• The authors state that they used routinely collected hospital data, but as a reader it is not clear what this means. There are references to discharge diagnoses, but a more comprehensive description of the type of data used would be helpful.

• For context, it would be helpful to know how the changes in the presentation for a non-urgent condition compare to the overall, or to the presentations for urgent conditions. For instance, if the patterns in non-urgent conditions were simply representative of the overall patterns, then it wouldn’t necessarily suggest something specific about the need for non-urgent care and would likely change the authors interpretation.

• The authors exclude people from outside of the local community, but what about the potential for people within the community to seek care elsewhere? How much of the patterns you see are due to self selection of the population into this particular hospital?

Results

• The results are nicely written and clear. As are the figures and tables.

Discussion

• The authors state that “the erratic variation in presentation numbers points to external factors such as, the availability of primary care services, contributing to ED attendance for non-urgent conditions among the local population.” Yet, the authors do not point to any evidence in their own analysis that this is the case and in the introduction say that prior research has suggested that the literature on this topic is fairly mixed.

•

**********

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Reviewer #1: No

Reviewer #2: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

20 Mar 2020

Response to Reviewers

REVIEWERS GENERAL COMMENTS:

1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly Reviewer #2: Yes

We hope the amended article addresses reviewer #1’s concerns.

2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes

We hope that, by addressing Reviewer #1’s specific concerns as outlined below, the reviewer is convinced that the statistical analysis has been performed appropriately and rigorously.

3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data— e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: No

The data used in this analysis was provided by a third party (Tasmanian Department of Health and Human Services). In Australia this data is not publicly available, and permission has not been provided for the research team to make it publicly available.

In order to access this data, the required ethics approvals must be obtained. Once this has been done the research team would be able to access this data by special request in writing to: The Secretary, Department of Health and Human Services, GPO Box 125, Hobart, Tasmania, 7001, Australia

4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes

REVIEWER # 1: AUTHOR RESPONSE:

Reviewer #1: This paper examines the characteristics of patients presenting in an ED for non-urgent reasons using routinely collected hospital data from a large regional hospital in northern Tasmania over the period July 1, 2009 to June 30, 2016. Data was provided by the Tasmanian Department of Health and Human Services. ED utilization and “crowding” is an important topic as it affects hospital emergency departments globally. A better understanding of the populations that are utilizing the ED for non-urgent reasons could help to target strategies to redirect patients to more appropriate health care resources. The authors find an over-representation of patients from the lower socioeconomic suburbs and younger in age. They also find growth in the volume of patients presenting with nonurgent ‘mental and behavioral disorders.’ The paper would be strengthened with a more clearly articulated methods section among other recommended improvements.

The methods section has been revised as suggested by reviewer #1.

(Pg 6 – 8)

1. Materials and Methods – several questions arose in reading through the methods.

a. Page 6, lines 114-119. The authors describe bulk-billing and the fact that Tasmania has the lowest rate of bulk-billing. It seems that bulk-billing may have a large affect on an individual’s decision to seek care in an ED or a GP’s office. Yet, this is the only mention of the practice. It seems the author’s may want to include this in the discussion. It is also unclear if the authors have any information regarding bulk-billing within the various suburbs as that would be good information to include in the analysis.

Thank-you for pointing this out. Bulk billing discussion removed. This study did not investigate cost or bulk-billing practices so decision by research team to remove comments from this paper. This is part of an ongoing investigation by the research team.

b. Page 7, lines 149-153. It is unclear how the authors are using the IRSD deciles. Is it Australia’s total population that is broken into deciles, and then the suburbs within the study region are classified based on the national classification? If so, it would be helpful to have some information provided in a table related to the suburbs in the region or at least some more information on characteristics of each decile. For instance, what does it mean to be in decile 1 versus decile 2?

Discussion of IRSD deciles amended and moved to ‘study setting and participants’ (pg 7 line 141 - pg – 156)

c. Methods – the presentation of methods is somewhat sporadic. It would be helpful to better organize the methods section. For instance, perhaps describe the variable construction first and then describe the analytic method. Right now the two seem intertwined and it makes the section unclear.

Methods reformatted to separate variable construction and analytic method. (pgs 6 – 8)

d. Methods – it might be helpful to compare the non-urgent to the urgent presentations in addition to the overall population.

Addressed in limitations, data were only provided for ATS 4 and 5 presentations (pg 20, line 414-416

2. Discussion – several clarifications are suggested in the Discussion

a. Page 11line 233 – to the point that there is no increase in the total number of nonurgent presentations, it would be helpful to know how the population is growing (or not). For instance, if the ED volume is flat, but the population is growing, then the overall utilization is declining. Perhaps it would be helpful to report this as a utilization per 1,000 population or similar.

Paragraph amended to include requested information. (pg 15, line 280-285)

b. Page 11, line 243 – the authors state, “the erratic variation in presentation numbers points to external factors such as, the availability of primary care services…” However, Figure 2b shows a surge in ED volume around 0800, which, during the week, is when primary care services are available. Perhaps it correlates to bulk-billing. Please clarify.

Have clarified discussion of these figures. (pg 15, 280-285)

c. Page 12, line 260. The authors state that they are certain the data from the city centre is clerical error. If so, why include in the study or why not at least run a sensitivity analysis with that suburb excluded.

c. We think it is important to plot the data from the city centre in Figure 3, as these are actual ED presentations and contribute substantially to the overall number of presentations. The data from the city centre has been excluded in the weighted regression analysis to fit a trend line. We apologize for failing to mention this in the original manuscript. This has now been added (pg 18, line 314-322)

3. Page 10, line 208. It is unclear where the “disadvantaged suburbs were up to four times more likely to present…” comes from. It is not clear in the data where that number is listed or if it was author calculation. This occurs in other areas of the manuscript as well. Please ensure that the results are clearly tied to the analysis.

Further explanation provided (pg 13, line 228-239)

4. Figure 2b appears to be fairly striking. The profile of presentation to the ED by time and day is consistent, regardless of access to other services being open. There seems to be a lot more here that is not really discussed in detail.

Further explanation provided (pg 17, line 291-301)

5. Figure 2a (and 2d to some extent) appears to have some sort of non-linear trend. Please clarify.

Explanation provided (pg 14, line 278 - pg 15, line 285)

6. Table 2. For the trend, it might be helpful to have an actual table of regression results rather than just stating “increased,” “no change,” or “decrease.”

Table 2 amended; regression results now included.

7. Table 3. There is a lot to digest in Table 3. Perhaps there is a better way to present this information. Also, are the subcategories just the top 3 within each?

Table 3 simplified, yes the sub-categories are the top 3 within each.

REVIEWER # 2: AUTHOR RESPONSE

Reviewer #2: The goal of this paper is to describe the population accessing the emergency department (ED) at a large regional hospital in Tasmania, including the demographics of the population and changes in utilization over time. The authors use routinely collected hospital data (on all admissions with an ATS value of 4 or 5, indicating the least urgent categories of visits, which the authors state indicate the patient could have been treated in a setting other than the ED. The authors find overrepresentation of patients from areas with a low socioeconomic status and distinct patterns by age. The paper is well written and the objectives clearly stated throughout. The introduction could be streamlined to make a better argument for the need for this analysis.

Have re-structured introduction and moved Tasmanian background from methods to introduction.

Introduction:

• The introduction sets the reader up to think that this will be a comparison of “local” environments and how that contributes to the demand for services, and in particular nonurgent services. But the paper only onlooks at one community (an important one), and so it’s so much a point about how the local environment matters so much as a depiction of what is going on in one particular local community. I think the introduction could be strengthened greatly by focusing more on why THIS community is important to study, as opposed to why local communities are important more broadly.

As above

Methods

• The authors state that they used routinely collected hospital data, but as a reader it is not clear what this means. There are references to discharge diagnoses, but a more comprehensive description of the type of data used would be helpful.

Clarified (pg 6, line 110 – 120)

• For context, it would be helpful to know how the changes in the presentation for a nonurgent condition compare to the overall, or to the presentations for urgent conditions. For instance, if the patterns in non-urgent conditions were simply representative of the overall patterns, then it wouldn’t necessarily suggest something specific about the need for nonurgent care and would likely change the authors interpretation.

Sentence added to body of paper and to limitations of study (pg 6, line 119-120 & pg 22, line 414-416)

• The authors exclude people from outside of the local community, but what about the potential for people within the community to seek care elsewhere? How much of the patterns you see are due to self selection of the population into this particular hospital?

Explanation provided (pg 7, line 136-140).

Results

• The results are nicely written and clear. As are the figures and tables.

Discussion

• The authors state that “the erratic variation in presentation numbers points to external factors such as, the availability of primary care services, contributing to ED attendance for non-urgent conditions among the local population.” Yet, the authors do not point to any evidence in their own analysis that this is the case and in the introduction say that prior research has suggested that the literature on this topic is fairly mixed.

Clarified (pg 14, line 278 to pg 15, line 285)

For background re access to available services please see pg 5, line 88-91

EDITORS COMMENTS:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labelled 'Response to Reviewers'. Completed

A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labelled 'Revised Manuscript with Track Changes'. Completed

An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labelled 'Manuscript'. Completed

1. When submitting your revision, we need you to address these additional requirements. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at http://www.journals.plos.org/plosone/s/file? id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file? id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf Checked and amended where required

2. In ethics statement in the manuscript and in the online submission form, please provide additional information about the patient records used in your retrospective study. Specifically, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data from their medical records used in research, please include this information.

Ethics statement amended (Pg 6, line 121-124)

3. In your Methods section, please provide additional information about the methods used in your study. Please ensure you have provided sufficient details to replicate the analyses such as a description of any inclusion/exclusion criteria that were applied to participant inclusion (for example, please specify the definition of "regional northern Tasmania", and a definition of the variables used (for example, please describe how "discharge diagnosis" was coded).

Methods section amended to address these issues.

Variables defined (pg 6, line 113-119)

Explanation of D/C diag (pg 6, line 118-119).

Northern Tasmania defined (pg 4, line 72 – 79 and pg 7, line 126 - 128)

Regional city (Launceston) defined (pg 5, line 136 – 140)

4. Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files.

Tables included with main manuscript.

5. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-accessrestrictions. In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. We will update your Data Availability statement on your behalf to reflect the information you provide

Thank-you for querying this.

Indication that the data from this study are available upon request was incorrect. The data provided is owned by a third party (DHHS), permission to share the data was not provided.

All ABS data is readily available online, references are provided in the reference list along with the URL.

Thank-you for the opportunity to amend this paper.

24 Mar 2020

Socioeconomic disadvantage as a driver of non-urgent emergency department presentations: A retrospective data analysis

PONE-D-19-35242R1

Dear Dr. Unwin,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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With kind regards,

M Barton Laws

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

I believe you have adequately responded to the reviewers' comments.

Reviewers' comments:

30 Mar 2020

PONE-D-19-35242R1

Socioeconomic disadvantage as a driver of non-urgent emergency department presentations: A retrospective data analysis

Dear Dr. Unwin:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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