Effects of increased distance to urgent and emergency care facilities resulting from health services reconfiguration: a systematic review

Article history

The research reported here is the product of an HS&DR Evidence Synthesis Centre, contracted to provide rapid evidence syntheses on issues of relevance to the health service, and to inform future HS&DR calls for new research around identified gaps in evidence. Other reviews by the Evidence Synthesis Centres are also available in the HS&DR journal.

The research reported in this issue of the journal was funded by the HS&DR programme or one of its preceding programmes as project number 16/47/17. The contractual start date was in December 2018. The final report began editorial review in July 2019 and was accepted for publication in September 2019. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HS&DR editors and production house have tried to ensure the accuracy of the authors’ report and would like to thank the reviewers for their constructive comments on the final report document. However, they do not accept liability for damages or losses arising from material published in this report.

Declared competing interests of authors

Andrew Booth is a member of the National Institute for Health Research (NIHR) Health Services and Delivery Research Funding Board and the NIHR Systematic Reviews Advisory Group, and is Convener of the Cochrane Qualitative and Implementation Methods Group.

Copyright statement

© Queen’s Printer and Controller of HMSO 2020. This work was produced by Chambers et al. under the terms of a commissioning contract issued by the Secretary of State for Health and Social Care. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.

2020 Queen’s Printer and Controller of HMSO

Research questions and aims

The aim of this systematic review was to identify, appraise and synthesise existing research evidence regarding the outcomes and impacts of service reconfigurations that have the effect of increasing the time and/or distance for patients to reach an UEC facility. A list of potentially time-sensitive conditions requiring treatment at a UEC facility was developed in advance (see Inclusion and exclusion criteria). The list prioritised conditions more likely to be affected by service reconfiguration or requiring a decision whether or not to travel further to reach a more specialist facility. However, this list was not intended to be exhaustive.

We also aimed to examine the available evidence regarding associations between distance to an UEC facility and outcomes for patients and services, together with factors that may influence (moderate or mediate) these associations. A moderating factor was defined as one that may directly alter the relationship (e.g. weather/climate). A mediating factor was defined as one that acts indirectly and may help to explain the relationship (e.g. patient age, acting indirectly by influencing the patient’s risk status). Mitigating factors were those that could potentially reduce the influence of other factors on outcomes (e.g. starting treatment in the ambulance rather than at the hospital).

In our synthesis of the identified factors, we also used the terminology of ‘influencing factors’ (which influenced outcomes via travel time) and ‘explanatory factors’ (which influenced outcomes directly). These factors were broadly classified as patient related, context related or service related.

The research questions were as follows:

• What is the evidence regarding effects on patients of service reconfigurations that increase the time/distance to an UEC facility?

• What is the evidence regarding associations between time/distance from an UEC facility and outcomes for patients requiring UEC?

• What is the evidence regarding effects on the health system of service reconfigurations that have the effect of increasing the time/distance to an UEC facility?

• What factors might mediate, moderate or mitigate the effects of increased distance to an UEC facility on patient outcomes and/or the health-care system?

Literature search and screening

A comprehensive literature search was conducted in February 2019. The search was developed on MEDLINE and utilises diverse medical subject heading (MeSH) and free-text terms. The search comprised four broad facets: (1) emergency care, (2) rural or island services, (3) service reconfiguration and (4) potentially relevant emergency conditions. The search was limited to papers from 2000 to February 2019 and in English. The MEDLINE search was translated to the other databases. The following databases were searched:

• MEDLINE via OvidSP (1946–present).

• EMBASE via Ovid (1974–present).

• Cochrane Database of Systematic Reviews via Wiley Online Library (2003–present).

• Cochrane Central Register of Controlled Trials via Wiley Online Library

• CINAHL (Cumulative Index to Nursing and Allied Health Literature) via EBSCOhost (1981–present).

• HMIC (Health Management Information Consortium) via OpenAthens (1983–present).

• Web of Science (Science Citation Index and Social Sciences Citation Index; Clarivate Analytics, Philadelphia, PA, USA).

All of the references were imported into EndNote [Clarivate Analytics (formerly Thomson Reuters), Philadelphia, PA, USA] and then automatic and manual deduplication was conducted.

The MEDLINE search is provided in Appendix 1 with details of how the different facets of the search were combined.

Additionally, a search was conducted for current reviews of telehealth. The search was conducted on the databases listed above and was limited to 2009–19 and research published in English. After deduplication, there were 550 references. The MEDLINE search strategy is provided in Appendix 1.

Citation-tracking of the included reconfiguration studies was performed on Web of Science and Google Scholar (Google Inc., Mountain View, CA, USA) in April 2019. Web of Science identified 52 new references and Google Scholar identified 63.

Given the diffuse nature of the topic and the associated terminology, the reference lists of all included articles were manually screened to identify additional studies.

Search results were stored in a reference manager system (EndNote X8.2) and imported into EPPI-Reviewer software (Evidence for Policy and Practice Information and Co-ordinating Centre, University of London, London, UK) for screening, data extraction and quality assessment. The search results were screening against the inclusion criteria by one reviewer, with a 10% sample screened by a second reviewer. Uncertainties were resolved by discussion among the review team.

Inclusion and exclusion criteria

Data extraction and quality/strength of evidence assessment

We extracted and tabulated key data from the included studies, including study design, population/setting, results and key limitations. The full data extraction template is provided in Appendix 2. Data extraction was performed by one reviewer, with a 10% sample checked for accuracy and consistency.

Quality (risk-of-bias) assessment was undertaken using The Joanna Briggs Institute Checklist for Quasi-Experimental Studies. This nine-question checklist was chosen because of its relative simplicity and because the questions are applicable to a wide range of non-randomised study designs. Quality assessment was performed by one reviewer with a 10% sample checked for accuracy and consistency. Details of the quality assessment tool can be found at http://joannabriggs.org/research/critical-appraisal-tools.html (accessed 1 November 2019).

Evidence synthesis

We performed a narrative synthesis structured around the prespecified research questions and outcomes. The included primary studies were classified into three groups (i.e. reconfiguration, association and mitigation studies) corresponding to the prespecified research questions. We first described the characteristics of the groups as a whole. We then summarised the results in terms of the types of patients included (e.g. general UEC population, AMI, trauma, etc.), again considering reconfiguration, association and mitigation studies separately. Further analyses examined the results in terms of setting to assess the relevance of the evidence as a whole to the UK health-care system and to rural as against urban and suburban settings. Given the current background of changes to services in the UK NHS, we also summarised information from the studies about how health systems implemented service reconfigurations and subsequent adaptations to increased travel distances/times affecting emergency medical services (EMS) vehicles as well as patients’ own transport.

Mediating, moderating and mitigating factors identified by study authors were extracted from included studies and used to populate a logic model incorporating effects on patient outcomes via time to access UEC (‘influencing factors’) and directly (‘explanatory factors’).

Summary tables were generated from extracted data using the reporting function of the EPPI-Reviewer program. The overall strength of evidence was assessed using a previously described method. 3 Evidence was rated as ‘stronger’, ‘weaker’, ‘inconsistent’ or ‘very limited’ based on study numbers and design. Specifically, ‘stronger evidence’ represented generally consistent findings in multiple studies with a comparator group design or comparative diagnostic accuracy studies; ‘weaker evidence’ represented generally consistent findings in one study with a comparator group design and several non-comparator studies or multiple non-comparator studies; ‘very limited evidence’ represented an outcome reported by a single study; and finally, ‘inconsistent evidence’ represented an outcome where < 75% of studies agreed on the direction of effect. All studies included in the review were included in the analysis of overall strength of evidence.

Public and patient involvement

We had input from our Evidence Synthesis Centre Public Advisory Group during all stages of the review. In the early stages, a meeting was convened to outline the topic of the review and to seek input regarding the questions and focus from a patient and public perspective. There was considerable interest in this topic among group members, and discussion regarding the high profile nature of this area and importance to members of the public. In particular, the experiences of local reconfigurations were described and discussed, and challenges for individual patients when reconfigurations occurred, particularly where travelling was not straightforward, and there were limitations in available transport options. This input provided the team with valuable insights into the concerns and worries of local communities, and highlighted the need to consider potential impacts on differing local communities. Our group members emphasised that distance and travelling time were very different concepts, with traffic conditions and road networks of key importance in time to access UEC, which focused the team on carefully examining the nature of the measurements used within the identified literature. The group also highlighted the potentially positive impacts on the health-care system of reducing ease of access to care, in terms of reducing unnecessary use of accident and EDs, which provided the team with an important area to consider when examining the literature.

Towards the final stages of the review, a further meeting was convened to examine the emerging results, and to consider the findings in regard to key messages for members of the public, and implications for the health-care system. At this meeting the key results were presented by a member of the team. There was considerable discussion regarding the applicability of the findings to the UK system, with members of the group wishing for greater information regarding the country of origin of the evidence underpinning the findings. There was consensus that it was important that in our reporting, we highlighted where evidence came from countries with very different health-care systems, or very different geography to the UK as this could be crucial to interpreting the findings. This input was therefore very helpful to the team in consideration of interpretation of the findings. Members also emphasised that it should be made clear where there was insufficient evidence available and uncertainty, as this was important for members of the public to be aware of. This input informed our reporting, and will be considered during future dissemination activities. At this meeting members also drafted the Plain English summary to be included in the final report.

Registration and outputs

The protocol was registered as PROSPERO CRD42019123061 and is also available via the HSDR programme website (www.journalslibrary.nihr.ac.uk/programmes/hsdr/164717/; accessed 16 May 2019).

Results of the literature search

The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flow diagram (Figure 1) details the study selection process.

FIGURE 1.

The PRISMA flow diagram. Adapted from Chambers et al. 1 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/. This figure includes minor additions and formatting changes to the original text.

Adapted from Chambers et al. 1 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/. This figure includes minor additions and formatting changes to the original text.

After deduplication of the searches from the different databases, there were 8870 references from the main database search and 550 from the telemedicine/telehealth search. Citation searching of Web of Science identified 52 new references and Google Scholar identified 63 new references.

All titles and abstracts were screened by one of the review team with a subset (about 10%) of the titles and abstracts being screened by two reviewers. Any queries were resolved by discussion. A similar process was followed for final decisions on inclusion/exclusion based on full-text documents. Calculation of the kappa coefficient demonstrated good agreement between reviewers [K = 0.729, 95% confidence internal (CI) 0.542 to 0.916]. Studies were excluded at the full-text stage for a variety of reasons (e.g. they covered access to services generally and not emergency care in particular; the intervention was not relevant, e.g. public access defibrillators; or the study discussed changes to services without relating outcomes to travel time or distance).

Characteristics of included studies

We included 44 publications in the review, reporting 43 unique studies. Of these, 12 evaluated the effects of an intervention or change to the health-care system (reconfiguration studies); 30 examined associations between travel distance or time and outcomes in the absence of a specific intervention (association studies); and two evaluated interventions to mitigate the effects of being at distance from an emergency care facility (mitigation studies).

Reconfiguration studies

Table 1 summarises the basic characteristics of the included reconfiguration studies. Six of the included studies were conducted in the USA, with just two2,4 being from the UK. The remaining studies were conducted in other European countries; there were no studies in this group from Canada, Australia or New Zealand.

Six of the included studies focused on ED reconfiguration, providing data on patients with many different types of emergency conditions. Three looked specifically at patients with AMI requiring access to percutaneous coronary interventions (PCI). Two studies examined the effects of service changes involving specialist trauma centres and one looked at the effects of maternity unit closures in France (see Table 1).

The studies used a variety of observational designs, with before–after and cohort designs being most common. Knowles et al. 2 and Mustonen et al. 11 were the only studies with independent control sites where no reconfiguration had taken place.

Risk-of-bias assessment

Results of the risk-of-bias assessment are presented in Tables 21–23 (see Appendix 3). Many of the studies were inherently at high risk of bias because of lack of an independent control group. In the reconfiguration group, the most common design was before–after and only four studies compared outcomes between settings with and without changes in distance/time. 2,5,9,11 Association studies generally used a cohort or cross-sectional design (see Table 2).

With regard to each question on the evaluation tool, the following comments can be made about the overall quality of the included literature. Most of the included studies were clear about the temporal relationship of the variables of interest (i.e. which was the ‘cause’ and which the ‘effect’; Q1), although the issue was sometimes confused by the use of linked data sets. Similarity between populations being compared (Q2) varied across the studies, with some being clinically heterogeneous. Similarly, it was often unclear whether comparison groups were being treated similarly other than the intervention or exposure of interest (Q3). This was related to differences over time as well as to studies recruiting clinically diverse populations. Absence of a separate independent control group (Q4) was noted in most of the studies and similarly few studies carried out measurements at multiple time points before and after an intervention or exposure (interrupted time series design; Q5). Completeness of follow-up (Q6) did not show a clear pattern across studies; this question was not applicable for studies with measurement at a single time point. Most studies measured outcomes in a standard (Q7) and reliable (Q8) way, although again some exceptions were identified. Statistical analysis (Q9) was judged to be appropriate with the exception of one study. 42 However, as with all observational studies, the possibility of unmeasured confounders affecting the results could not be ruled out.

Results by condition

For each condition, the reconfiguration studies are considered first, followed by the association studies, concluding with an overall summary for each group.

Summary of reconfiguration studies

Most of the included reconfiguration studies reported on changes in mortality rates following reconfiguration (Table 15). For studies of general UEC populations, there was no evidence that reconfiguration resulting in increased travel time/distance affected mortality rates. This was classed as stronger evidence being derived from studies with control groups. By contrast, there was evidence of increased risk from studies restricted to patients with AMI. Evidence for other conditions was inconsistent or very limited and none of the included studies looked at stroke patients specifically (although people with stroke were an identifiable subgroup in the study by Hsia et al. 9).

Evidence on health system outcomes was inconsistent (Table 16), reflecting the diverse outcomes and settings included. El-Sayed et al. 7 identified some short-term negative effects on EMS systems following closure of an ED in Boston, MA, USA. More significantly, the closED study in the UK (England) identified some evidence of increased pressure on ambulance services in areas where an ED had been closed or downgraded, including an increase in emergency ambulance incidents. 2 Across England as a whole, distances travelled to access emergency care increased only slightly between 2001/2 and 2011/12. 4 Case studies analysed by Roberts et al. 4 demonstrate how reconfigurations affects travel distances and hospital admissions in specific local contexts.

Summary of association studies

The association studies found evidence that increased travel time or distance is associated with increased mortality risk for the AMI and trauma populations, whereas for maternity the evidence was inconsistent (Table 17). Where evidence was classified as ‘weaker’, this reflects the absence of true control groups but the large size of some of the studies and plausibility of the association are additional supporting factors. There was also weaker evidence of an association from two studies of patients with a range of conditions typically requiring emergency care. 18,42

Other outcomes (Table 18) were reported in smaller numbers of studies. Although evidence for these outcomes was inconsistent for patients with AMI, weaker but consistent evidence was found for adverse maternity outcomes and access to thrombolysis for stroke patients being influenced by distance from specialist services.

Quantitative estimates of the relationship between travel distance or time and mortality risk are summarised in Table 19. Studies varied widely in their methodology. In particular, authors calculated either an effect measure per unit of distance (e.g. 10 miles or 10 km) or between different distance categories (e.g. highest vs. lowest quintile). This, together with the variety of different outcomes measured (e.g. in-hospital mortality, 30-day mortality or mortality at various follow-up points) makes it difficult to pool outcomes across studies.

As an example, two studies from the UK18,19 used different distance measures (km vs. distance categories in miles) and measured mortality at different time points, as well as studying different populations (general life-threatening conditions vs. AMI). Qualitatively, the increase in risk with distance appears greater for the AMI population, but interpretation is complicated by the fact that differences between distance categories were statistically significant for pre-hospital and follow-up mortality but not for deaths in hospital. 19 There was more uniformity in UK-based maternity studies, but these were conducted by the same research team in broadly the same setting16,17

Summary of mitigation studies

The two included mitigation studies provide very limited evidence for the effectiveness of improved service co-ordination for STEMI in a US rural setting46 and of a specialist stroke ambulance in an urban setting. 45

Results by setting

Health system context and implementation of service changes

All of the studies included in the reconfiguration group mentioned the context in which changes to services are implemented and the need for planning and co-operation to minimise the impact on patients. However, only six of them provided any substantial information. Broader implications for health services are considered in Chapter 5 (see Implications for service delivery).

The closED study authors sought information about relevant service changes through a documentary analysis of material from NHS trusts, ambulance trusts and commissioning organisations (clinical commissioning groups and primary care trusts). 2 No information on how the health service planned for the impact of ED closures or downgrading was obtained from either NHS annual reports or ambulance service sources. Data analysed in the study suggested that ambulance services made changes in anticipation of ED closures as increases in average journey times were lower than expected. The authors identified understanding the processes used by health-care providers and commissioners to minimise adverse effects of implementing service reorganisation as a priority for future research. The study also called for improved consistency of recording and coding of routine data.

In the context of the UK NHS, the case studies provided by Roberts et al. 4 provided examples of UEC reconfigurations carried out in England and their effect on travel times. However, there was no background information on the rationale for and implementation of the changes.

Some additional evidence came from non-UK studies. The most detailed information was reported by Yaghoubian et al. 14 in their before–after study of a trauma centre closure. Changes made in anticipation of increased patient numbers included additional staffing and changes to team organisation. Increased experience and background improvements in care were identified as factors that supported an improvement in adjusted mortality rates in the post-closure period. A ‘mature’ regional trauma system ensured that remaining trauma centres were able to handle additional patients.

In another study from the USA, El-Sayed et al. 7 commented on the importance of early notification of a planned ED closure and ‘continuous and dynamic co-operation’ between hospital and EMS administrations in minimising the impact on EMS services.

In addition to early planning, there was evidence of health systems adapting during the first few years after a reconfiguration. Avdic5 noted that the effect of ED closures on AMI mortality was only statistically significant for the first year, attributing this to attempts to compensate for increased travel distance by, for example, increased investment in emergency services or greater use of preventative care. Shen and Hsia12 identified a 4-year transition period after which outcomes were similar to the pre-reconfiguration period.

Moderating and mediating factors

Key moderating and mediating factors extracted from included studies are summarised in Figure a in relation to patient and health system outcomes. Not all relevant studies are included in the figure, but fuller details of influencing and explanatory factors extracted from included studies are presented in Appendix 4, Table 24.

A point not stressed elsewhere in this report is the influence of measurement factors on the reported travel time/distance to hospital. The included studies differed in their methodological approach according to the availability of data. For example, data on the exact location of incidents were often not available and the patient’s residential postcode or similar was used instead. Studies also differed in whether they measured direct distance to a UEC facility or attempted to assess distance or time using the actual road network.

Patient-related and context-related influences on pre-hospital time identified in Figure a were often found in similar forms in multiple studies. In particular, optimal deployment of EMS services and the choice between use of private or ambulance transport can substantially influence the time taken to reach hospital. Service-related factors that influence outcomes for patients range from pre-hospital initiation of treatment to the range and location of specialist facilities available. These can potentially mitigate the effects of travelling further for emergency care.

Included studies have also evaluated impacts of service changes on broader health system activity and performance, including primary care11 and alternative UEC facilities8 as well as indicators of ambulance service quality. 2

Quantity and scope of included reviews

We included 12 systematic reviews published between 2010 and 2019. Seven of the reviews were published in 2017 or later. Four reviews dealt with ‘telestroke’, two dealt with trauma care and the remainder with a variety of other telehealth applications. Table 20 summarises the basic characteristics of the included reviews.

Summary of review findings

Main findings

This systematic review addressed the effect of increasing distance to UEC on patient and health system outcomes by looking at two main groups of studies. In relation to the first research question (What is the evidence regarding effects on patients of service reconfigurations which increase the time/distance to an UEC facility?), the reconfiguration studies found no evidence that increasing travel time or distance increased mortality risk for general populations of patients attending UEC facilities. There was some evidence from studies that were restricted to patients with AMI of an increased risk of mortality, whereas evidence for other conditions was inconsistent or very limited.

The association studies addressed the second research question (What is the evidence regarding associations between time/distance from an UEC facility and outcomes for patients requiring UEC?). These studies found evidence of an association between distance and mortality for general, AMI and trauma populations, whereas evidence for maternity was inconsistent. Pregnant women differ from other populations requiring emergency care in that their period of highest risk is finite and predictable. In some health systems, primarily those with potentially very long travel distances or times, this risk can be mitigated by the option of temporary relocation closer to a specialist unit. 6,39

Telehealth care is clearly the most important intervention for mitigating the effects of distance from specialist emergency care. Because of the large volume of available research, we performed a narrative overview of recent systematic reviews (see Chapter 4). Telehealth technology mitigates distance effects by facilitating access to clinical expertise and/or by speeding up treatment, as demonstrated in two meta-analyses. 48,54 Although we did not formally assess the quality of the included reviews, review quality was clearly variable and most of the reviews used a mapping or narrative synthesis approach. Telehealth is most strongly established as an intervention for timely treatment of stroke patients. 51 Telehealth technology in pre-hospital settings is relatively new and included reviews have identified a range of barriers to adoption and questions about how best to implement the technology. 58,59 None of the systematic reviews focused on cost-effectiveness, which has been a barrier to the adoption of telehealth in some UK settings.

Much of the research included in our review comes from non-UK settings and we have endeavoured to keep applicability in mind throughout. Absolute distances and times of travel vary within countries, including the UK, but large countries such as the USA, Canada and Australia are likely to have longer travel times/distances on average outside urban areas. This is also true for some of the Scandinavian countries where travel times can be long as the population is centred in fewer areas, meaning that remote communities have less access to UEC.

Several included studies suggest that the effects of service reconfiguration on outcomes, particularly patient outcomes, may be short-lived, with health systems adapting to the new situation over a few years. In the study by Avdic,5 effects of ED closures on AMI mortality were statistically significant only for the first year after closure, while Shen and Hsia12 identified a 4-year transition period. Some studies reported on efforts by health-care commissioners and providers to mitigate the effects of reconfiguration. Avdic5 mentioned increased investment in both emergency service provision and prevention although the study did not evaluate whether or not these actually occurred. A hospital in Boston, USA, provided early notice of its intention to close an ED and worked closely with EMS providers to minimise the effect on the EMS system in the city. 7 Yaghoubian et al. 14 also in the USA, reported on changes in trauma centre staffing and organisation to prepare for the closure of a nearby centre. The limited insights provided by these studies emphasise the need for greater understanding of how health service stakeholders prepare for the system-wide impacts of changes that require patients to travel further for treatment, as also mentioned by Knowles et al. 2

We identified a large number of potential mediating, moderating and mitigating factors (see Figure a and Appendix 4). The extent to which these factors occur and their amenability to modification by health system interventions is highly variable. Most of the included studies adjusted for the influence of common moderators and mediators in their statistical analysis, which provides reasonable confidence in the observed associations between travel distance/time and patient outcomes. However, an influence of confounding factors not allowed for in authors’ analyses and statistical models cannot be ruled out.

Service reconfigurations are often supported by decision-makers on the grounds that increased patient volume and/or specialisation in a smaller number of UEC facilities will increase the quality of patient care overall. This review did not directly address the relationship between volume and outcomes, which has been the subject of a large volume of research. However, one included study14 attributed successful adaptation to a trauma centre closure partly to staff gaining experience from treating more patients during the study period. We also note that there is substantial evidence for the benefits of transporting patients with stroke or severe trauma to specialist centres, often bypassing nearer non-specialist facilities. Studies included in this review identified benefits of transport to a PCI-capable hospital for patients with AMI, particularly STEMI. 22–24

Interpretation of the findings of included studies should be guided by the quality and strength of the included evidence. We have assessed risk of bias at the level of the individual study and overall strength of evidence for key findings using a scheme successfully employed in previous reviews. Many of the included studies were judged to be at relatively high risk of bias because of their observational design and the absence of an independent control group in many cases. On the positive side, most studies acknowledged and attempted to adjust for the influence of confounding factors and some were large and/or long term. Furthermore, studies using routinely collected data may reflect real clinical practice more accurately than those involving carefully selected and knowingly recruited research participants. In view of this uncertainty, we have been relatively conservative in assessing the overall strength of evidence for effects and associations (see Tables 15–18).

Strengths and limitations

This systematic review was undertaken by an experienced team including both methodological and topic experts. We followed a protocol developed in collaboration with the National Institute for Health Research (NIHR) HSDR programme team as the review was designed in part to help clarify research priorities. The protocol was registered prospectively with the PROSPERO database of systematic review protocols (registration number CRD42019123061).

We performed a thorough search for published literature dating back to 2000 supplemented by citation-tracking of key studies and reference list checking of all included studies. This was clearly worthwhile as six included studies were identified by reference list checking and three by citation-tracking.

Resource constraints meant that we abbreviated the review process by using a single reviewer to perform study selection, quality assessment and data extraction, with checking of a 10% sample by a second member of the review team. This may be considered a limitation when measured against the gold standard of double independent performance of these stages but analysis of study selection revealed a high level of agreement among three reviewers. Although there is a risk of some errors or subjective assessments, we do not believe that these would have influenced the review’s main findings and conclusions.

The coverage of telehealth by an overview of relevant systematic reviews was partly determined by the limited available resources but we believe the decision was justified by the 12 largely recent systematic reviews included. The included reviews provide a thorough overview of this heavily researched field, using a mixture of quantitative (including meta-analysis) and narrative synthesis. It was encouraging that the reviews covered aspects of the implementation of telehealth58 as well as reviews of intervention effectiveness.

A strength of the review is that we approached the research question from two different angles, represented by the reconfiguration and association groups of studies. The reconfiguration studies address a dynamic situation where change to services has actually happened, while the association studies take a more static approach, examining how distance to UEC influences outcomes in a particular setting. Both groups of studies represent substantial bodies of research using different study designs and with largely complementary results. In the narrative synthesis, we considered these groups of studies (together with the mitigation and telehealth studies) separately but also assessed the body of evidence as a whole in relation to relevance to the UK and to rural settings. We have summarised the overall strength of evidence from the two main groups of studies using a published framework that we hope will be helpful for decision-makers needing a quick overview (see Tables 15–18).

We extracted details of moderating and mediating factors from the included studies, as summarised in Figure a. Figure a includes both factors that influence travel time and those that influence patient outcome (either alone or through interaction with time/distance). The figure is not comprehensive and the relative importance of different factors will vary between settings. In spite of this, the information in the figure and related appendix (see Appendix 4) may be of value to decision-makers by helping them to identify service factors that can be modified to enable faster access to treatment and potentially better outcomes.

The relationship between distance/time to emergency care and patient outcomes is clearly a complex one. The diverse measurement methods and outcomes in the included studies meant that we were unable to perform any meta-analyses to estimate pooled outcome measures across studies. Although this may be considered a limitation of the review, we did not have meta-analysis as one of our predefined objectives and any estimate would only be meaningful in the absence of major clinical and/or statistical heterogeneity. The summary table of association studies (see Table 19) illustrates the wide range of measures used in included studies and allows readers to identify studies relevant to their population and setting of interest.

Implications for service delivery

Timely and equitable access to UEC is important to all population groups in both urban and rural settings. Ensuring such access requires commissioners and providers of health services to work effectively together, informed by their understanding of the evidence and data relevant to their local context. As many of the studies included in this review demonstrate, the relationship between time or distance and outcomes is influenced by many factors, including patient characteristics, clinical care, geography and infrastructure.

Studies included in this review provide insight into issues that decision-makers may wish to consider and act on when reconfiguration that may affect access to UEC is under consideration. For England, Roberts et al. 4 identified the following considerations for planners: size of population served, availability of specialist centres (e.g. for trauma, stroke), availability of workforce, organisation of training and whether or not there are sufficient ‘support facilities’. The authors also pointed out the need for political decisions at the national and local level about which services are affordable.

Empirical studies of the effects of ED closures and reconfigurations have provided insights into how change can be managed to minimise any adverse effects on patients or the stability of the wider health and care system. El-Sayed et al. 7 noted the importance of early notification of planned closure and ‘continuous and dynamic co-operation’ between hospital and EMS administrations. Yaghoubian et al. 14 prepared for closure of a nearby trauma centre by putting in place additional staffing and changes to team organisation. A strong regional trauma system also helped to ensure that remaining trauma centres were able to accommodate additional patients.

Several included studies suggest that the effects of increased travel distance/time on outcomes may be temporary, lasting 1 or a few years. The research suggests that health services may be able to minimise the transition period by measures such as investment in EMS5 and by providing capacity elsewhere before any closures take place. 12 Studies also suggest that the scale of any changes in distance or time is important and small changes may have a minimal impact on outcomes at the population level. 4,9 Service design needs to take into account the effects of distance on outcomes but also allow for the fact that rural and remote areas are less densely populated than towns and cities, resulting in lower demand/need for services. The benefits to individuals of having services located close by have to be balanced against overall population benefits that may accrue from centralisation of services and concentration of expertise at selected sites.

Another approach to handling increased distance to UEC facilities is through new service delivery models. This review has identified a number of different models that decision-makers may wish to consider. In the maternity setting, some health-care systems provide the option for women from more remote regions to travel to a more central facility for a short period before the expected date of delivery. 6 Service models based on telehealth have been extensively researched, as covered in Chapter 4. For example, telehealth technologies may enable EDs in rural and remote areas to be run on a ‘hub-and-spoke’ model, with less critical emergencies being handled by nurses with remote clinical support. 55 The use of telehealth in ambulances allows service models based on early initiation of diagnosis and/or treatment by paramedics as well as triage to divert less seriously ill patients away from EDs when clinically appropriate. 52,58,59 However, barriers remain to the widespread adoption of telehealth technology in some settings. Other mitigation interventions, including improved co-ordination between services,46 are discussed in the relevant sections of Chapter 3 (see Mitigation studies).

Some service models that may be of interest to decision-makers fell outside the scope of this review (e.g. air ambulance services) or were excluded after full consideration (e.g. public access defibrillators). We did not identify any evaluations of community first responders, although this service is available in many parts of the UK. Descriptions of the service suggest that first responders may be of most value for conditions such as cardiac arrest for which distance to the nearest UEC facility is unlikely to influence survival.

Although increased distance to UEC is generally discussed in terms of possible risks, included studies also suggested some potential benefits to patients and the health system. EDs may close or be downgraded for reasons to do with quality of care,2,9 potentially encouraging patients to use superior services. Reconfiguration of services may encourage EDs and other centres to organise their work more efficiently and a greater volume of patients may enable staff to improve the quality of the care they deliver through increased experience. 14 This aspect is reflected most clearly in the increasing trend to transport patients to specialist centres, especially for stroke and trauma, rather than to closer but less specialised facilities.

Two studies from Scandinavia provide further insights into the effects of UEC service reconfigurations on the wider health and care system. In Denmark, Hansen et al. 8 reported that EDs up to 40 km away were able to compensate, at least in part, for closure of a local ED. However, the authors noted that this was dependent on free and rapid access to ambulance services and high levels of car ownership. A controlled before–after study in Finland reported that closure of a local primary care ED decreased use of ED services in nearby areas without increasing use of other office-hour services. 11 The authors concluded that distance is an important influence on ED attendance and ED closure can be used to encourage patients to access care via other routes. Differences between the primary care-based ED system in Finland and that of the UK should be taken into account when considering this evidence.

Despite these caveats, the review provides some evidence that closing EDs may reduce self-presentation at an ED by patients who could be treated elsewhere. The relationship between distance and ED attendance in the UK was studied by Rudge et al. ,21 who found that the availability of a MIU nearby reduced ED use by those living at a distance from an ED. This suggests that if alternatives can be put in place, there will not necessarily be a commensurate increase in workload for an ED if a neighbouring ED closes. Effects on patient flow could be taken into account at an early stage of planning for possible service reconfigurations.

Studies included in this review illustrate the importance of up-to-date research and evidence to inform decision-making. An example is the care of patients with AMI, where PCI is now the therapy of choice in most regions. This implies the need for health systems to monitor the long-term effects of service reconfigurations as they become mature and to account for other innovations which may ultimately offset any disadvantages from having to travel further.

There is a consistent message from both UK and international research about the importance of considering the EMS implications of planned service changes. 2,7,34 EMS staff cover the whole catchment area of a specialist service, meaning that increased travel distances result in increased job cycle times and more resources needed to maintain the same response to demand. The challenge for decision-makers is how to fund and staff the service while also using EMS staff in a wider variety of roles (e.g. treating more patients at the scene rather than transporting them to hospital).

Health services need to ensure that increases in time or distance to UEC are not associated with increased health inequity. A large UK study suggested that there may be a risk of this. 21 Rudge et al. 21 found that ED attendance decreased as distance to the nearest ED increased but the effect was more notable in deprived areas than less deprived ones. Mustonen et al. 11 also emphasised the importance of ensuring that UEC services are not located far away from socially deprived areas.

Finally, although the focus of this review is on UEC and other health services, actions taken elsewhere can influence distance/time to UEC and hence outcomes for patients. Examples include improvements to road networks and associated factors, such as junctions and lighting, as well as regulations surrounding alcohol and drug use by drivers (which, in turn, affect actual consumption).

Implications for research

We have identified the following implications for further research.

There is a need for further time series analyses along the lines of the closED study2 to examine the longer-term effects of service reconfigurations on the whole UEC system and to take into account the impact of other service and technological changes over time. Although such studies should ideally be controlled, uncontrolled time series also have some value and are easier to organise.

Research is needed to better understand how local and regional health systems plan for and adapt to increases in travel distance/time. As suggested by other researchers,2 this could take the form of qualitative research and/or documentary analysis. The current programme of service reconfiguration provides opportunities for prospective studies in a range of different settings. Research should aim to capture the perspectives of a range of different stakeholders including health professionals, managers in both commissioner and provider organisations and the general public.

Data analysis is needed to address uncertainty about how risk increases with distance/time within the range relevant to UK urban and rural populations. This could involve analysis of existing data or new research to ascertain whether or not the findings of earlier studies18,19 remain valid with current road conditions and technology.

Analysis of routine data to examine whether UEC reconfigurations reduce overall demand for ED care or merely displace demand to other parts of the health-care system. Data can also be used to examine the nature and extent of variation between different localities with a view to reducing unnecessary variation and improving overall quality of care.

Research is needed to assess patient outcomes other than mortality and hospital admission/length of stay. This could include effects of service reconfiguration on families who may incur additional social and financial costs because of increased travel distance/time to visit patients.

Public attitudes to reconfiguration and involvement in decision-making were outside the scope of this review. Nevertheless, it is clear that proposals to reconfigure UEC services are often opposed by local communities based on concerns that increased travel distance/time may increase the risk of adverse outcomes. Further research would be valuable to understand public attitudes to risk and preferences for different alternatives. Research could involve a variety of methods including consultation via citizens’ assemblies or similar.

Conclusions

This systematic review found no evidence that service changes that increased average travel time or distance increased mortality risk for general populations of patients attending UEC facilities. There was some evidence of an increased risk from studies restricted to patients with AMI, whereas evidence for other conditions was inconsistent or very limited. Studies that examined the association between distance and outcomes in the absence of reconfiguration found evidence of an association between distance and mortality for general, AMI and trauma populations, whereas evidence for maternity was inconsistent. The relatively low quality of much of the research suggests that findings should be interpreted cautiously. In particular ‘no evidence of increased risk’ does not necessarily mean ‘evidence of no increase in risk’, as the finding could be overturned by further research in the future.

Empirical studies of the effects of ED closures and reconfigurations have provided insights into how change can be managed to minimise any adverse effects on patients or the stability of the wider health and care system. Important factors include early notification and discussion of planned changes, co-operation between different stakeholders, and appropriate changes to staffing and organisation of the workforce. Several included studies suggest that the effects of increased travel distance/time on outcomes may be temporary, lasting one or a few years. Health-care services may be able to minimise the transition period by measures such as investment in EMS and by providing capacity elsewhere before any closures take place.

There is a consistent message from both UK and international research about the importance of considering the EMS implications of planned service changes. We also found evidence that people in more deprived areas were less willing/able to travel to attend an ED. This suggests that consideration should be given to ensuring that UEC services are not located far away from socially deprived areas.

Research priorities include work to examine the longer-term effects of service reconfigurations on the whole UEC system and to better understand how local and regional health-care systems plan for and adapt to increases in travel distance/time.

Contributions of authors

Duncan Chambers (https://orcid.org/0000-0002-0154-0469) (Research Fellow in Public Health) was involved in the project co-ordination, study selection, data extraction, quality assessment and report writing.

Anna Cantrell (https://orcid.org/0000-0003-0040-9853) (Research Associate in Health Economics and Decision Science) was involved in information retrieval, study selection, data extraction, quality assessment and report writing.

Susan K Baxter (https://orcid.org/0000-0002-6034-5495) (Senior Research Fellow in Public Health) was involved in the protocol development, study selection and report writing, and she co-ordinated the patient and public involvement meeting.

Janette Turner (https://orcid.org/0000-0003-3884-7875) (Reader in Emergency and Urgent Care Research) provided topic expert advice and was involved in the report writing.

Andrew Booth (https://orcid.org/0000-0003-4808-3880) (Reader in Evidence-Based Information Practice) was involved in information retrieval, study selection and report writing.

All authors commented on drafts of the protocol and report.

Chambers D, Cantrell A, Baxter S, Turner J, Booth A. Effects of service changes affecting distance/time to access urgent and emergency care facilities on patient outcomes: a systematic review. BMC Medicine 2020;18:117.

Publication

Chambers D, Cantrell A, Baxter S, Turner J, Booth A. Effects of service changes affecting distance/time to access urgent and emergency care facilities on patient outcomes: a systematic review. BMC Medicine 2020;18:117.

Data-sharing statement

Any additional data not included in this report and its appendices are available on request. All queries should be submitted to the corresponding author.

Disclaimers

This report presents independent research funded by the National Institute for Health Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, NETSCC, the HS&DR programme or the Department of Health and Social Care. If there are verbatim quotations included in this publication the views and opinions expressed by the interviewees are those of the interviewees and do not necessarily reflect those of the authors, those of the NHS, the NIHR, NETSCC, the HS&DR programme or the Department of Health and Social Care.

Main database search example search strategy from MEDLINE

Database: Ovid MEDLINE(R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily <1946 to February 05, 2019>.

Medline search strategy for telehealth systematic reviews

Database: Ovid MEDLINE(R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily <1946 to February 26, 2019>.