Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Systematic review of predictors of hospitalisation for non-specific low back pain with or without referred leg pain

  • Joseph F. Orlando ,

    Contributed equally to this work with: Joseph F. Orlando, Saravana Kumar

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Joseph.Orlando@sa.gov.au

    Affiliations UniSA Allied Health & Human Performance, University of South Australia, Adelaide, Australia, Central Adelaide Local Health Network, Adelaide, Australia

    ⨯
  • Matthew Beard ,

    Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

    ‡ MB and MG also contributed equally to this work.

    Affiliation Central Adelaide Local Health Network, Adelaide, Australia

    ⨯
  • Michelle Guerin ,

    Roles Conceptualization, Methodology, Supervision, Writing – review & editing

    ‡ MB and MG also contributed equally to this work.

    Affiliation UniSA Allied Health & Human Performance, University of South Australia, Adelaide, Australia

    ⨯
  • Saravana Kumar

    Contributed equally to this work with: Joseph F. Orlando, Saravana Kumar

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing – review & editing

    Affiliation UniSA Allied Health & Human Performance, University of South Australia, Adelaide, Australia

    ⨯

Abstract

Significant costs and utilisation of healthcare resources are associated with hospitalisations for non-specific low back pain despite clinical guidelines recommending community-based care. The aim of this systematic review was to investigate the predictors of hospitalisation for low back pain. A protocol was registered with PROSPERO international prospective register of systematic reviews (#CRD42021281827) and conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Database search of Ovid Medline, Emcare, Embase, PsycINFO, Cochrane Library, PEDro and OTSeeker was conducted. Studies were included if they examined a predictor of hospitalisation for non-specific low back pain with or without referred leg pain. Data was extracted and descriptively synthesised. Risk of bias of included studies was assessed using the Critical Appraisal Skills Programme Checklists. There were 23 studies published over 29 articles which identified 52 predictor variables of hospitalisation for low back pain. The risk of hospitalisation was grouped into themes: personal, health and lifestyle, psychology, socioeconomic, occupational, clinical, and health systems and processes. There was moderate level evidence that arrival to an emergency department via ambulance with low back pain, and older age increase the risk of hospitalisations for low back pain. There was low level evidence that high pain intensity, past history of low back pain, opioid use, and occupation type increase the risk of hospitalisation for low back pain. Further research into psychological and social factors is warranted given the paucity of available studies. Hospital avoidance strategies, improved patient screening and resource utilisation in emergency departments are considerations for practice.

Citation: Orlando JF, Beard M, Guerin M, Kumar S (2023) Systematic review of predictors of hospitalisation for non-specific low back pain with or without referred leg pain. PLoS ONE 18(10): e0292648. https://doi.org/10.1371/journal.pone.0292648

Editor: João Marcos Bernardes, UNESP: Universidade Estadual Paulista Julio de Mesquita Filho, BRAZIL

Received: July 26, 2023; Accepted: September 25, 2023; Published: October 10, 2023

Copyright: © 2023 Orlando et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by the Allied Health Research Collaborative grant awarded to the principal investigator (JO) by The Hospital Research Foundation Group (https://hospitalresearch.org.au/). This was a collaboration between the Allied & Scientific Health Office, SA Health and the University of South Australia. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Low back pain (LBP) is a highly prevalent condition and leading cause of disability [1, 2]. It represents the 9th most common condition in general practice [3] and 4.4% of all emergency department presentations worldwide [4]. Most cases of LBP represent non-serious spinal pathology [5] with clinical guidelines supporting active self-management in community-based care [6, 7].

There are challenges with implementation of evidence-based practice for LBP relating to both clinician and patient factors [8, 9]. Management of LBP that diverges from best practice contributes to higher rates of spinal imaging [10–12], opioid prescriptions [12, 13] and specialist referrals [12]. This can lead to poorer clinical outcomes and risk of harm to patients [14, 15] with increased healthcare and societal costs [16].

The burden and costs of LBP are substantial when seeking hospital-based care. A large multicentre observational study calculated a mean cost of AUD$13,137 per hospitalisation for LBP with an average length of stay of nine nights [17]. The reasons for people being hospitalised for LBP are complex and multifactorial, but emerging evidence indicates that this patent group presents with high pain severity, functional impairment and helplessness [18–20]. Little is known about the risk factors of hospitalisation for LBP and no systematic reviews have been identified on this topic. Understanding the risk factors for hospitalisation may reveal insights into better supporting people who present to hospital with LBP to manage in community-based care whilst reducing the burden and costs on hospital systems. This study aims to address the knowledge gap in understanding the predictors of hospitalisation in people with LBP with or without referred leg pain.

Materials and methods

Protocol and registration

A protocol was registered with PROSPERO international prospective register of systematic reviews (#CRD42021281827). This review was conducted and reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [21]. Refer to S1 Checklist.

Search strategy

Seven electronic databases were searched, including: Ovid Medline, Emcare, Embase, PsycINFO, Cochrane Library, PEDro and OTSeeker. The following search terms were used with MESH headings: exp back pain, exp low back pain, exp backache, exp sciatica and exp hospitalisation. The search was limited to humans, English language and published from inception to 1st November 2022. Grey literature searching was undertaken using a commonly available internet search engine (Google Scholar) and searching of reference lists of included studies was performed to identify additional records.

All search results were pooled and duplicates removed. Titles and abstracts were screened before analysing the full texts to determine their eligibility. The screening process was undertaken by two independent reviewers (JO, SK). Any disagreements were resolved and discussed with a third reviewer (MG), where required.

Study selection

All forms of primary research were considered, including prospective or retrospective cohort studies. Secondary research, such as literature reviews were excluded but their reference lists were searched to identify additional records. Studies were included if they examined a predictor of hospitalisation for non-specific LBP with or without referred leg pain. Hospitalisation was defined as a minimum overnight stay as a hospital inpatient. Predictors of hospitalisation expressed as risk ratio, odds ratio or equivalent were included in the review. There was no exclusion on the time at which data was captured, either before, during or following the LBP episode. All clinical contexts were considered, including emergency departments, inpatients, outpatients and community health clinics. Participants had to be adults 18 years of age or older at the time of hospitalisation for non-specific LBP including sprains and strains of the lumbar spine (disc, joints, muscles, ligaments) or sciatica/ radicular leg pain. Studies that included participants with specific spinal disorders (fracture, cauda equina syndrome, myelopathy, neoplasm, infection, axial spondyloarthropathy, radiculopathy or claudication with neurological loss, deformity) were included provided that data for non-specific LBP was presented separately from specific spinal disorders and that the latter represented less than 5% of participants in studies set in primary care [5] or less than 10% of participants in studies set in tertiary care [22], as is reflected in clinical practice. Exclusion criteria included participants who were children at the time of hospitalisation; primary complaint of neck or thoracic spinal pain; back pain secondary to non-spinal disorders (visceral, vascular, urogenital, pelvis/hip, widespread pain disorder); back pain secondary to pregnancy; or hospitalisation for elective spinal surgery. Studies that included data on hospitalisations for medical and surgical management of LBP were included, provided the former was presented separately and the latter could be excluded from this review. Refer to PICOTS table in S1 Appendix.

Risk of bias assessment

The Critical Appraisal Skills Programme (CASP) was used to assess the risk of bias of included articles based on the study design: CASP Cohort Study Checklist [23] or CASP Case Control Study Checklist [24]. Items included: study purpose, recruitment, exposure and/or outcome measurement, confounders, follow-up, results, statistical precision, believability, generalisability, consistency with other research, and practice implications. The individual components were rated as ‘yes’, ‘no’, ‘unclear’ or ‘not applicable’. Some items were ‘not applicable’ for rating as they comprised descriptive responses. Articles’ risk of bias was then rated as low, medium or high and used to discuss the overall findings. Articles were not excluded based on the risk of bias. The risk of bias assessment of included articles was undertaken by two independent reviewers (JO, SK). Any disagreements were resolved and discussed with a third reviewer (MG), where required.

Data extraction, synthesis and analysis

The data were extracted and collated into bespoke Microsoft Excel® spreadsheets by one reviewer (JO) and a sample cross-checked by a second reviewer (SK). The following domains were used to collate data: study design, level of evidence, setting, sample size, participant details (age, gender), data sources, data capture period and follow-up, diagnostic criteria, hospitalisation details (length of stay, percentage hospitalised), predictor variables and findings. The predictor variables from each article were expressed as its odd ratio (OR), risk ratio (RR), hazard ratio (HR), prevalence ratio (PR), or equivalent and tabulated for analysis. Meta-analysis was initially considered, but preliminary review of included studies found large heterogeneity of participants, diverse settings, and different methods of measuring variables. A descriptive synthesis was deemed more appropriate and undertaken using the FORM framework to grade the strength of recommendations based on evidence base, consistency, clinical impact and generalisability [25]. Studies’ level of evidence was based on the Australian National Health and Medical Research Council (NHMRC) hierarchy of evidence designated by the type of research question [26].

Results

Search results

The initial search identified 3784 records. After pooling searches and removing duplicates, titles and abstracts were screened leaving 37 potential articles. Grey literature search and searching through reference lists included five additional articles. The full texts were retrieved and assessed for eligibility and 23 studies published over 29 articles were identified as being eligible for review. The literature selection process is outlined in Fig 1.

thumbnail
Download:
Fig 1. PRISMA flow diagram [21].

https://doi.org/10.1371/journal.pone.0292648.g001

Study characteristics

Table 1 provides an overview of the included articles. They were published between 1987 and 2022. Studies that were published over multiple articles examined unique predictors of hospitalisation without duplication of outcomes and are therefore presented separately in this review. From the 23 studies, there were five prospective cohort studies, fifteen retrospective cohort studies, two retrospective case-control studies, and one cross-sectional study. Two types of research questions were reported: prognostic and aetiology. Studies with prognostic research question types examined the factors that predict hospitalisation in populations with LBP at baseline, and included three Level II, twelve Level III-3, and one Level IV studies on the NHMRC hierarchy of evidence. Aetiology research questions examined the causalty of hospitalisation in populations that did not have LBP at baseline, and included three Level II, two Level III-2 and two Level III-3 studies on the NHMRC hierarchy of evidence.

thumbnail
Download:
Table 1. Summary of included articles.

https://doi.org/10.1371/journal.pone.0292648.t001

The majority of studies were conducted in Finland (n = 8), followed by Australia (n = 6), the United States of America (n = 4), Canada (n = 1), Chile (n = 1), Denmark (n = 1), Ethiopia (n = 1) and Sweden (n = 1). Studies were set across a range of settings including emergency departments (n = 8), community and outpatient clinics (n = 5), hospital inpatients (n = 2), hospital inpatients and outpatients (n = 1), workplaces (n = 3) and the remainder were population-based studies (n = 4). The total number of participants across studies included 2,026,834 with LBP and 435,663 without LBP pain at baseline. Most studies excluded or unequally represented women, especially in studies set in workplaces where the majority of the workforce comprised men. Two studies included adolescents at baseline but the hospitalisations occurred almost exclusively in adulthood [46, 48].

All studies undertook audits of hospital records and databases except the cross-sectional study that used interviews and self-reported outcomes [28]. The International Classification of Diseases (ICD) or the Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT) primary discharge diagnoses for non-specific LBP conditions or sciatica were used for the inclusion criteria in all but three studies, where participants self-reported their LBP [28, 44, 51]. Data capture period or participant recruitment ranged from six months to 45 years, and follow-up of participants ranged between 180 days and 34 years from baseline. Rates of hospitalisation for LBP ranged from 1–2% in population-based studies, 1–5% in community and outpatient clinics, 1–8% in workplaces, and 3–53% in emergency departments. Details of hospital length of stay were available in three studies with averages ranging from two to seven days [28, 30, 34].

There were 52 predictor variables investigated across all studies. They have been categorised into personal factors, health and lifestyle factors, psychological factors, socioeconomic factors, occupational factors, clinical assessment findings, and health systems and processes. These categories have been based on previous reviews supporting a biopsychosocial framework for LBP [56–58]. Fig 2 illustrates the number of articles found examining each variable and its association with hospitalisation for LBP. The following sections summarise the descriptive analysis for each category.

thumbnail
Download:
Fig 2. Number of articles investigating each predictor variable and the associations with hospitalisation for LBP.

https://doi.org/10.1371/journal.pone.0292648.g002

Risk of bias assessment

The results of the risk of bias assessment are outlined in Tables 2 and 3. Of the 29 articles, six were considered low risk, thirteen of moderate risk and ten of high risk of bias. Large sample sizes, objective measures of hospitalisation outcomes, and identification and inclusion of important confounding factors into analyses were strengths across most articles. Long follow-up periods were also considered a strength, however certain predictor variables at baseline, such as smoking status or body weight are not fixed conditions and therefore accuracy in measuring such variables over time is uncertain impacting the believability of results. Various predictor variables relied on invalid, subjective measures also impacting the believability of results. Generalisability of results was often limited by unequal representation of genders amongst participants and a focus on specific populations, such as occupation types. There were some inconsistencies between study findings and previously published literature likely reflecting overall lack of methodological rigor in studies. Practice implications were also poorly addressed.

thumbnail
Download:
Table 2. Risk of bias assessment of included studies based on the Critical Appraisal Skills Programme Cohort Study Checklist [23].

https://doi.org/10.1371/journal.pone.0292648.t002

thumbnail
Download:
Table 3. Risk of bias assessment of included studies based on the Critical Appraisal Skills Programme Case Control Study Checklist [24].

https://doi.org/10.1371/journal.pone.0292648.t003

Personal factors

Gender as a predictor for hospitalisation due to LBP revealed inconsistent findings. Three studies found females were more likely to be hospitalised compared to males, including: a cross-sectional study of moderate risk of bias (PR 1.8, 95% CI 1.2–2.75) [28], a cohort study set in an emergency department with low risk of bias (OR 1.4, 95% CI 1.15–1.85) [49], and a cohort study set in a workplace of moderate risk of bias where the risk of hospitalisation for men was reduced (HR 0.46, 95% CI 0.26–0.79) [39]. One cohort study found men to have a greater risk of hospitalisation, but the population was significantly younger compared to the other studies examining gender (HR 3.2, 95% CI 2.7–3.7) [46]. There was no difference in hospitalisations between genders in four other cohort studies [27, 33, 44, 47].

Older age was a predictor of hospitalisation for LBP in people accessing a health service in five studies [27–29, 34, 47]. One cohort study with low risk of bias set in emergency departments found people 65 years of age and older had greater odds of hospitalisation compared to people of younger age (OR 3.0, 95% CI 2.59–3.59) [34]; and in a similar study also set in emergency departments, the odds were even greater in people over 85 years of age (OR 5.45 95% CI 5.34 5.57) [27]. Three other studies with moderate risk of bias found that the odds of hospitalisation increased for every year of age [29, 44] and decade of age [28, 47]. On the contrary, one cohort study with moderate risk of bias set in workplaces found workers between 30–39 years of age were at greater risk of hospitalisation due to LBP compared to older workers (RR 1.87, 95% CI 1.3–1.86) [53]; and a further two studies set in workplaces found the risk of hospitalisation for sciatica decreased with age (HR 0.59, 95% CI 0.38–0.9 women, HR 0.6, 95% CI 0.4–0.91 men) [32] and (HR 0.23, 95% CI 0.12–0.46) [33]. An experienced, work-hardened and physically fit workforce were explanations for reduced risk of hospitalisation in the older population. Two studies found no association between age and hospitalisation: one was under powered [51] and the other a retrospective cohort with high risk of bias [52]. There is moderate evidence that older age is associated with increased risk of hospitalisation for LBP in the emergency department context; while older age is associated with reduced risk of hospitalisation for LBP within workplaces.

Height, weight and body mass index were not predictors of hospitalisation due to LBP. One cohort study of moderate risk of bias conducted on a construction site found that men had greater risk of hospitalisation for LBP when body height was greater than 190 centimetres (RR 1.55, 95% CI 1.3–1.86) and body weight greater than 100 kilograms (RR 1.4, 95% CI 1.12–1.76) [53], but these results were not found by other studies [32, 33, 50, 52]. One cohort study examined childhood obesity but found no predictor of hospitalisation for LBP in adulthood [48].

Only one study examined ethnicity as a predictor of hospitalisation in LBP. The cohort study with low risk of bias set across North Carolina state health services found hospitalisations for LBP were decreased in non-white populations [37]. Whilst the reasons are unclear, racial barriers to access medical inpatient care were hypothesised by the authors.

Health and lifestyle factors

General health status self-rated as poor increased the risk of hospitalisation due to sciatica in women (HR 1.75, 95% CI 1.02–2.98) and men (HR 2.0, 95% CI 1.32–3.03) in a large cohort study with moderate risk of bias set in a community clinic [32]. Weekly childhood health complaints were also associated with increased risk of hospitalisation for LBP in adulthood (HR 1.5, 95% CI 1.2–1.9) [46]. A further two studies found no association between self-reported health status and hospitalisation for LBP [28, 33]. There was also no association between disability status and hospitalisation for LBP (p>0.05) [37].

Comorbidities as predictors of hospitalisation for LBP were examined in a large cohort study set across inpatient and outpatient settings [47]. Comorbidities including anxiety, asthma, chronic obstructive pulmonary disease, depression, diabetes, gastrointestinal disorders, heart disease, hypertension, psychosis and rheumatoid arthritis were not associated with increased risk of hospitalisation. This study did find that people using opioids (OR 1.27, 95% CI 1.02–1.59) or non-steroidal anti-inflammatory medications (OR 1.76, 95% CI 1.43–2.17) prior to the initial episode of LBP had an increased odds of hospitalisation [47]. In a retrospective cohort study of high risk of bias, opioids also increased the odds of hospitalisations for LBP in people over 65 years of age with chronic LBP who initiated treatment with opioid therapies compared to people who initiated treatment with chiropractic spinal manipulations (OR 3.64, 95% CI 2.92–4.53) [55]. Frequent use of analgesia for all causes was similarly associated with increased risk of hospitalisation in a cohort study (RR 2.1, 95% CI 1.4–3.2) [36] but the risk of bias was high. Use of sedative medications prior to an initial episode of LBP did not increase the odds of hospitalisation in a cohort with moderate risk of bias [50].

Sleep disturbance as self-reported by participants in a cohort set in a workplace found increased risk of hospitalisation for LBP (RR 2.4, 95% CI 1.2–4.6) [43]. This finding was not found in a retrospective cohort of former athletes [52] or in a cross-sectional study [28]. The studies’ risks of bias, different populations and the imprecise measurement of sleep disturbance account for the inconsistency in results.

Smoking as a predictor of hospitalisation due to LBP revealed inconsistent findings. Three cohort studies with moderate risk of bias found current smokers were more likely to be hospitalised compared to non-smokers [32, 41, 53]. Two cohort studies of moderate to high risk of bias found that childhood smoking increased the risk of hospitalisation for LBP in adulthood [46, 48], the latter in males only. The other five studies found no risk of hospitalisation [28, 33, 36, 50, 52]. The inconsistency in results can be attributed to studies’ risk of bias and the lack of detail in defining the quantity of smoking.

Alcohol consumption as a predictor of hospitalisation for LBP also revealed inconsistent findings. Two cohort studies with moderate to high risk of bias found no association of consuming three to five drinks per day with risk of hospitalisation for LBP [50, 52]; while a cross-section study with moderate risk of bias found no alcohol consumption reduced the prevalence of hospitalisation for LBP (PR 0.58, 95% CI 0.37–0.91) [28].

Physical fitness and activity during leisure time were examined by eight studies [32, 33, 36, 38, 40, 48, 50, 52]. Whilst there was a trend in reduced risk of hospitalisation for LBP, the finding was only significant in one cohort study with high risk of bias (HR 0.40, 95% CI 0.21–0.79) [40]. Participants consisted of industrial workers who participated in strenuous physical activity during leisure time estimated at greater than 500 kilocalories/hour over three days a week. On the other hand, physical fitness measured using estimated maximal oxygen consumption (VO2 max) in workers in a prospective cohort study with moderate risk of bias was not associated with risk of hospitalisation for LBP (HR 0.88, 95% CI 0.51–1.50) [38]. One study examined childhood sports participation, but this too did not have predictive value on the risk of hospitalisation for LBP in adulthood [48]. Measurements of physical activity differed across studies and was imprecise creating inconsistencies in findings.

Psychological factors

General stress and depressive symptoms were not associated with increased risk of hospitalisation for LBP [28, 42, 50]. There may be a risk for women (RR 2.1, p<0.001) as per one cohort study, but the result’s statistical precision is not reported and the risk of bias high [36]. In addition, a historical cohort study with high risk of bias examined personality traits, including extroversion, neuroticism and hostility as well as life satisfaction but found no increased odds of hospitalisation for LBP [52].

Beliefs about LBP were examined by two studies of moderate risk of bias. In a cross-sectional study of people with LBP in rural Ethiopia, hospitalisation for LBP was not linked to negative pain beliefs, such as perceived pain prognosis and life impact due to pain [28]. Odds of hospitalisation were increased in people presenting to an emergency department in Canada with LBP when they perceived the urgency of the emergency department visit as high, impact of pain on daily activities as severe, and expectation of being hospitalised [44]. Negative beliefs about LBP and treatment expectations may influence the risk of hospitalisation in emergency department settings.

Socioeconomic factors

Living at home alone was associated with increased prevalence of hospitalisation for LBP (PR 2.54, 95% CI 1.34–4.15) in a cross-sectional study conducted in rural Ethiopia [28]. This study also found a lower prevalence of hospitalisation in rural areas (PR 0.55, 95% CI 0.34–0.9) attributed to socioeconomic and cultural factors in addition to health service access rather than pain burden [28]. The finding is in contrast to a retrospective cohort study with low risk of bias examining geographical variations across North Carolina state-wide health services where hospitalisations decreased in high urban populations [37]. A third cohort study of moderate risk of bias set in emergency departments in New South Wales, Australia found mixed results with odds of hospitalisation for LBP being greater for people residing in metropolitan areas (OR 1.36, 95% CI 1.27–1.46) followed by outer regional areas (OR 1.23, 95% CI 1.12–1.35) with the odds compared to inner regional areas, which had the lowest hospitalisation rates [30]. Whilst there are inconsistent findings relating to regional and metropolitan locations on the risk of hospitalisation for LBP, contextual factors unique to a geographical location may account for the variations observed in hospitalisation for LBP.

Compensable status of patients attending an emergency department with LBP may influence the risk of hospitalisation as per a retrospective cohort study with low risk of bias [27]. Patients insured through workers’ compensation schemes (OR 0.42, 95% CI 0.40–0.43), motor vehicle accident schemes (OR 0.90, 95% CI 0.86–0.94), and or overseas visitors (OR 0.74, 95% CI 0.68–0.81) had reduced odds of hospitalisations for LBP compared to publicly funded patients, while the odds increased in defence force personnel (OR 1.29, 95% CI 1.04–1.59) [27].

There were inconsistent findings in other socioeconomic factors. Identifying with middle social class in Finland increased the risk of hospitalisation for LBP compared to upper class (RR 4.0, 95% CI 2.3–6.8) in a cohort study set in community clinics [36], however this finding was not found in a cohort of male workers [50] in Denmark. Both studies had moderate to high risk of bias, studied different populations, and definitions of social class were imprecise likely impacting the consistency of results. High socioeconomic index was associated with higher odds of hospitalisation for LBP in a state-wide cohort study of low risk of bias set across emergency departments in New South Wales (OR 1.26, 95% CI 1.24–1.29) [27]. Lower education levels were associated with increased risk of hospitalisation for LBP in two cohort studies [45, 46] while no risk was observed in four other studies [28, 33, 37, 52]. The moderate to high risk of bias across studies and the different methods for defining low education similarly impacted the consistency of results. There were inconsistent finds on hospitalisation for LBP on employment status [37, 52]. There was no predictive value in marital status [36, 52] or household income [37, 45].

Occupational factors

Occupation type as a risk of hospitalisation for LBP was examined by six studies [32, 36, 37, 39, 45, 53]. Blue-collar workers had increased risks compared to white-collar workers in five of the studies, all of which had moderate to high risk of bias. Occupations reported as having increased risks included manual labourers [39, 45, 53], metal and machine workers [36, 53], industrial workers [36, 53], and nurses [32]. There was inconsistent evidence amongst studies for workers in building trades [32, 53], agriculture and forestry [32, 36], sales [36] and transport [32, 37, 53]. Most of the studies were biased by excluding women. Only one study equally represented both genders and concluded that the risk for manual labourers was present in both men and women [45].

Physical demands of work were examined by five cohort studies with moderate to high risk of bias [33, 42, 50, 52, 54]. Sedentary workers who performed occasional heavy duties had an increased risk of hospitalisations in two studies [33, 50]. The evidence for hospitalisation in workers who perform regular heavy duties was inconsistent, where one study found performing regular heavy duties was protective and may reflect workers’ physical fitness (HR 0.48, 95% CI 0.26–0.89) [33] while another study found heavy duties increased the risk of hospitalisation (HR 2.37, 95% CI 1.36–4.13) [50]. Specific work tasks, including lifting was associated with hospitalisation [33], but awkward postures, prolonged sitting, constant and repetitive movements, or task variety and flexibility did not change the risk [33, 42, 52]. Exposure to vibrations increased the risk of hospitalisation due to LBP in a large cohort of male workers with low risk of bias (OR 1.35, 95% CI 1.12–1.63) [54] but this was not found in a much smaller cohort with sciatica with moderate risk of bias [33].

Work-absenteeism due to LBP was associated with increased risk of hospitalisation for LBP (RR 3.3, 95% CI 1.61.67) [39] as was low supervisor support (RR 2.9, 95% CI 1.3–6.3) [42] in cohort studies. There was no predictive value in mental stress at work [50] or co-worker support [42].

Clinical assessment findings

Pain intensity was associated with increased hospitalisation for LBP when presenting to an emergency department with the odds increasing for each additional point on a 11 point numerical rating scale (OR 1.95, 95% CI 1.14–3.33) in a cohort study with moderate risk of bias [51]. Hospitalisation was also more prevalent amongst community dwellers in a cross-sectional study who rated their pain intensity as moderate (PR 2.46, 95% CI 1.15–5.52) and severe (PR 8.84, CI 4.82–18.13) [28]. The risk of hospitalisation was also increased for pain distress symptoms (HR 3.69, 95% CI 1.51–9.05) [39] and past history of low back pain [39, 47] in cohort studies with moderate risk of bias.

There were inconsistent findings amongst studies on the predictive value for hospitalisations for LBP when the symptoms included leg pain [28, 39], physical examination findings including neurological deficits [39, 51] and presence of serious spinal pathology or red flags [34, 51]. There was no evidence of increased risk of hospitalisation for people with previous attendance to an emergency department with LBP [51] or with duration of current episode of symptoms [51].

Health systems and processes

When attending an emergency department with LBP, the risk of hospitalisation is increased when a person arrives via an ambulance compared to self-presentation. The odds were lower in a private hospital (OR 2.03, 95% CI 1.06–3.90) where patients’ average age was older and hospitalisation rates significantly higher compared to other studies [29], while the odds were higher in a prospective cohort of patients participating in a survey (OR 4.95, 95% CI 1.79–13.7) [44]. Attending an emergency department during working hours (OR 1.74, 95% CI 1.48–2.05), and being triaged as urgent (OR 3.37, 95% CI 1.48–9.38) or semi-urgent (OR 2.99, 95% CI 1.37–6.48) [34] increased the odds of hospitalisation; as was being referred to an emergency department by a private doctor (OR 2.18, 95% CI 2.06–2.30) [30]. There was no difference in hospitalisation rates when the treating clinician was an emergency specialist physician compared to a visiting medical officer specialist [30]. Patients treated by a medical officer or nurse practitioner in the emergency department had greater odds of hospitalisations for LBP (OR 3.38, 95% CI 2.33–4.91) compared to being treated by an advanced practice physiotherapist [49], likely attributed to physiotherapists managing cases of lower urgency. These studies were of low to moderate risk of bias and highly applicable to the Australian healthcare context.

Healthcare access and resource utilisation in emergency departments also predict hospitalisations for LBP. In a private emergency department set in a high socioeconomic area, the odds of hospitalisation for LBP increased when ordering pathology tests (OR 3.32, 95% CI 2.01–5.49), ordering computed tomography images of the lumbar spine (OR 1.86, 95% CI 1.12–3.11) and with longer treatment times in emergency departments (OR 1.16, 95% CI 1.07–1.26) [29]. In a cohort study with low risk of bias set across a state-wide health service, hospitalisation rates for LBP increased at hospitals with computed tomography or magnetic resonance imaging availability [37]. However, hospitalisations did not increase when there was high hospital unoccupied bed density (p<0.05) suggesting bed availability does not drive hospital utilisation for LBP [37].

Hospital contextual factors also accounted for hospitalisations for LBP. A retrospective study of moderate risk of bias audited 37 emergency departments in New South Wales and found that hospitalisation rates for LBP were greater amongst centres with higher specialty classifications (levels 1–3) compared to lower speciality classifications (levels 4–6) (OR 1.22, 95% CI 1.14–1.30) [30]. A state-wide audit of emergency departments across New South Wales with low risk of bias estimated that hospital factors (geographical location, hospital specialty classification) explained 10% of variations in hospitalisations for LBP (ICC = 0.10) whilst controlling for patient and clinical factors; with a median odd ratio of 2.03 at hospitals with higher hospitalisation rates [35].

There was no association between hospitalisation for LBP and access to primary care physicians, spinal surgeons or chiropractors (p>0.05) [37]. On the other hand, when receiving physical therapy interventions in primary care for any condition, the risk of hospitalisation for LBP was reduced for up to 180-days of receiving physical therapy (RR 0.68, 95% CI 0.64–0.72) [31]. This cohort study had a high risk of bias with inadequate descriptions of interventions to reproduce results and unknown effect sizes to demonstrate clinical impact.

Summary of results

There is moderate level evidence that arrival to an emergency department via ambulance with LBP, and older age increase the risk of hospitalisations for LBP. There is low level evidence that high pain intensity, past history of LBP, opioid use, and occupation type predict hospitalisation for LBP. There is emerging evidence from single study findings of increased risk of hospitalisation for LBP associated with pain distress, residing alone, ethnicity, compensable status, younger workers, residential and hospital contextual factors, emergency department triage category, hospital specialty classification, and utilisation of emergency department investigational resources. There was no evidence for gender, height and weight, self-rated health status, comorbidities, stress and depression symptoms, smoking, alcohol consumption, socioeconomic status, education level, employment status, duration of pain, leg pain, clinical examination findings, or primary care access on the risk of hospitalisation for LBP.

Grade of body of evidence

The FORM framework [25] was used in the synthesis process and the findings are described in Table 4. The body of evidence was graded as satisfactory with support for some of the research findings, but caution advised due to methodological concerns, including inconsistencies in outcome measurements, lowering the grade of the body of evidence. The clinical impact and generalisability of the evidence found in this systematic review will most likely benefit people presenting to an emergency department with LBP.

thumbnail
Download:
Table 4. FORM framework analysis of the grade of body of evidence [25].

https://doi.org/10.1371/journal.pone.0292648.t004

Discussion

While hospital-based care is sought for management by many people experiencing LBP, to date there has been limited quality research evidence to determine the predictors of hospitalisation for LBP. This systematic review aimed to address this knowledge gap and identified a body of evidence consisting of 23 studies published over 29 articles and examining 52 predictor variables. The summarised findings indicate that there is moderate level evidence that arrival to emergency departments with LBP via ambulance and older age; and low level evidence that high pain intensity, past history of LBP, opioid use, and occupation type increase the risk of hospitalisation for LBP.

Closely related to age are other variables such as functional capacity, fitness and sedentary lifestyle [59, 60]. Inactivity may be an underlying risk factor of hospitalisation for LBP or sciatica as was found by studies of sedentary workers who occasionally performed heavy work duties [33, 50]. However, studies that directly measured physical activity time or fitness did not show a reduction in hospitalisation, probably from imprecise measures. The observation of reduced risk of hospitalisation for LBP or sciatica in older workers who are physically conditioned and experienced in performing their work duties [33, 53] likely indicates that functional capacity and fitness modify the effects of ageing, which is consistent with the literature [61–63], countering the risks of hospitalisation. The higher odds of hospitalisation for LBP in the cohort with younger workers also challenged the commonly held belief that ageing and lumbar disc disease are associated with LBP [53]. The poor correlation of spinal radiological findings with LBP has been well established [64].

There remains a large knowledge gap for predictors of hospitalisation for LBP. There were no identified studies examining functional impairment and very few examining psychological factors. This review found some pain behaviours to be factors in hospitalisations for LBP, such as prior analgesic use, pain distress, and attending an emergency department for LBP via an ambulance. Low pain self-efficacy may be a characteristic of people hospitalised with LBP, but this has not been investigated. Negative beliefs about pain were associated with hospitalisations within the context of emergency departments [44], particularly around illness perception and patients’ urgency for investigation and hospital-based care. Helplessness and expectations of medical interventions to allow return to premorbid function were common themes from a qualitative study of patients hospitalised for LBP [19]. Escalating pain levels, distress and disability attributed to personal and contextual factors influenced patients’ decisions to attend an emergency department for non-specific pain conditions [65–67]. While there is support for psychological factors as components of the pain experience in people with LBP in emergency departments or hospitals, this review did not find studies of specific psychological factors, such as catastrophizing, depression or anxiety, to predict hospitalisation for LBP.

Social and environmental contributors to hospitalisation for LBP are also suspected from this review such as geographical location, but specific causations are unknown. Residing in regional or metropolitan areas was not a consistent theme of hospitalisations for LBP in this review; rather, social and cultural influences of a local community and health system accounted for some of the geographical variations in hospitalisation for LBP. Clinician biases [35, 37], patient cultural expectations of hospital-based care [28, 44] and lack of outpatient social support [35] are hypothesised as contributors to hospitalisation for LBP. The literature highlights the broad influence of social factors on outcomes of people with LBP, such as residing in regional or rural areas [68], work and financial stressors [68, 69] and inadequate social supports [69, 70].

Understanding the social and contextual factors of LBP is highlighted by this review to better recognise risk factors for poor prognosis and avoid escalating medical care such as hospitalisation in people with LBP. Clinical practice in hospitals and emergency departments has a primary focus on biomedical factors given the priorities in managing serious pathology and acute and urgent care [71]. Operating purely in this framework for non-specific pain conditions, such as LBP and sciatica may contribute to escalation of low-valued care [6, 72] including over investigation and intervention [12], and increased health costs and inefficiencies [17]. Contextual factors can not be ignored in the management of musculoskeletal conditions if it is to be person-centred and high-valued care [73, 74], and this is ultimately best delivered in community health settings [75].

The quality and access to community-based care are important in improving outcomes of people with LBP [58, 76]. Poor access and availability to community medical and allied health services, health costs, and funding models are barriers to equitable care and may contribute to escalation of management of LBP [77]. This review failed to find sufficient studies exploring whether limited access to community-based care contributes to hospitalisations for LBP.

Limitations

As with any research, there are limitations to this systematic review. While the systematic searching of the literature identified a large body of evidence to inform the review topic, there were concerns regarding the methodological quality of the included studies. An area of concern is the subjective and psychometrically untested measurements of predictor variables, such as occupation type, physical activity levels, stress levels and socioeconomic status, rendering the measures as imprecise and requiring caution with interpretation of the results. The long follow-up periods weakened the associations of certain baseline predictor variables with hospitalisation outcome at baseline, especially for variables whose conditions are not fixed and can change over time, such as smoking status or body weight. While this systematic review process was underpinned by best practice in the conduct of systematic reviews (PRISMA), likely publication and language bias should be acknowledged. While strategies were implemented to avoid publication bias, such as grey literature and secondary searching, due to the complexity and imprecise nature of searching, some publications may have been missed.

Implications for practice

With the emerging evidence of various predictors of hospitalisation for LBP, there are implications for clinical practice in different settings. After exclusion of red flags and serious pathology, clinicians in emergency departments should screen patients’ risk of hospitalisation for LBP, such as age, pain intensity, distress and mode of arrival. Artificial intelligence may have a role in predictive modelling, patient monitoring and hospital operations and there has been evidence of its use in emergency departments for a variety of health conditions [78]. Resources can be better directed based on risk profile [79, 80], such that lower risk patients are provided with reassurance, simple analgesia, education on self-management with follow-up in community-based care; while higher risk patients may benefit from input by clinicians skilled in communicative and educational approaches, optimisation of conservative management including early mobilisation and physiotherapy, a stepped approach to pharmacological management of pain, and early referral to pain management services. These approaches are emerging as evidence-based practices in emergency departments [71, 81, 82] but the effect on hospitalisations continues to be challenging. Policy makers and hospital administrators should consider innovative, alternative pathways to emergency departments for people with LBP, such as virtual hospital care [83], especially for people arriving to emergency departments via ambulance. Hospital avoidance strategies will have the greatest impact by improving access to community-based care strengthened by partnerships with hospital health services [72, 74, 84, 85].

Implications for research

Whilst this review found evidence of some predictors for hospitalisation for LBP, methodological concerns of the current evidence may guide the direction of future research. Future research should focus on psychological and social factors that may predict hospitalisation for LBP. Such factors are poorly documented in hospital medical records [86] and therefore reliance on clinical audits is limited and requires mixed-methods approaches [87], including qualitative research that explore the experiences of people with LBP and their navigation of health systems. Community healthcare access and utilisation also warrants further research on the risk of hospitalisation for LBP.

Conclusions

This systematic review, first of its kind, demonstrates the complex and multifactorial contributors to hospitalisation for people with LBP with or without leg pain. Specifically, arrival at an emergency department via an ambulance and older age appear to increase the risk of hospitalisation within this cohort. These findings could inform healthcare stakeholders’ decision-making in implementing evidence-informed strategies to better manage the condition and more efficient resource allocation.

References

  1. 1. Fatoye F, Gebrye T, Odeyemi I. Real-world incidence and prevalence of low back pain using routinely collected data. Rheumatol Int. 2019;39(4):619–26. pmid:30848349
  2. 2. Wu A, March L, Zheng X, Huang J, Wang X, Zhao J, et al. Global low back pain prevalence and years lived with disability from 1990 to 2017: estimates from the Global Burden of Disease Study 2017. Ann Transl Med. 2020;8(6):299. pmid:32355743
  3. 3. Finley CR, Chan DS, Garrison S, Korownyk C, Kolber MR, Campbell S, et al. What are the most common conditions in primary care? Systematic review. Can Fam Physician. 2018;64(11):832–40. pmid:30429181
  4. 4. Edwards J, Hayden J, Asbridge M, Gregoire B, Magee K. Prevalence of low back pain in emergency settings: a systematic review and meta-analysis. BMC Musculoskelet Disord. 2017;18(1):143. pmid:28376873
  5. 5. Bardin LD, King P, Maher CG. Diagnostic triage for low back pain: a practical approach for primary care. Med J Aust. 2017;206(6):268–73. pmid:28359011
  6. 6. Foster NE, Anema JR, Cherkin D, Chou R, Cohen SP, Gross DP, et al. Prevention and treatment of low back pain: evidence, challenges, and promising directions. Lancet. 2018;391(10137):2368–83. pmid:29573872
  7. 7. Oliveira CB, Maher CG, Pinto RZ, Traeger AC, Lin CC, Chenot JF, et al. Clinical practice guidelines for the management of non-specific low back pain in primary care: an updated overview. Eur Spine J. 2018;27(11):2791–803. pmid:29971708
  8. 8. Hall AM, Scurrey SR, Pike AE, Albury C, Richmond HL, Matthews J, et al. Physician-reported barriers to using evidence-based recommendations for low back pain in clinical practice: a systematic review and synthesis of qualitative studies using the Theoretical Domains Framework. Implement Sci. 2019;14(1):49. pmid:31064375
  9. 9. Slade SC, Kent P, Patel S, Bucknall T, Buchbinder R. Barriers to primary care clinician adherence to clinical guidelines for the management of low back pain: a systematic review and metasynthesis of qualitative studies. Clin J Pain. 2016;32(9):800–16. pmid:26710217
  10. 10. Jenkins HJ, Downie AS, Maher CG, Moloney NA, Magnussen JS, Hancock MJ. Imaging for low back pain: is clinical use consistent with guidelines? A systematic review and meta-analysis. Spine J. 2018;18(12):2266–77. pmid:29730460
  11. 11. Downie A, Hancock M, Jenkins H, Buchbinder R, Harris I, Underwood M, et al. How common is imaging for low back pain in primary and emergency care? Systematic review and meta-analysis of over 4 million imaging requests across 21 years. Br J Sports Med. 2020;54(11):642–51. pmid:30760458
  12. 12. Kamper SJ, Logan G, Copsey B, Thompson J, Machado GC, Abdel-Shaheed C, et al. What is usual care for low back pain? A systematic review of health care provided to patients with low back pain in family practice and emergency departments. Pain. 2020;161(4):694–702. pmid:31738226
  13. 13. Spears CA, Hodges SE, Kiyani M, Yang Z, Edwards RM, Musick A, et al. Health care resource utilization and management of chronic, refractory low back pain in the United States. Spine (Phila Pa 1976). 2020;45(20):E1333–E41. pmid:32453242
  14. 14. Tucker HR, Scaff K, McCloud T, Carlomagno K, Daly K, Garcia A, et al. Harms and benefits of opioids for management of non-surgical acute and chronic low back pain: a systematic review. Br J Sports Med. 2020;54(11):664. pmid:30902816
  15. 15. Stevans JM, Delitto A, Khoja SS, Patterson CG, Smith CN, Schneider MJ, et al. Risk factors associated with transition from acute to chronic low back pain in US patients seeking primary care. JAMA Netw Open. 2021;4(2):e2037371. pmid:33591367
  16. 16. Zemedikun DT, Kigozi J, Wynne-Jones G, Guariglia A, Roberts T. Methodological considerations in the assessment of direct and indirect costs of back pain: a systematic scoping review. PLoS One. 2021;16(5):e0251406. pmid:33974661
  17. 17. Coombs DM, Machado GC, Richards B, Wilson R, Chan J, Storey H, et al. Healthcare costs due to low back pain in the emergency department and inpatient setting in Sydney, Australia. Lancet Reg Health West Pac. 2021;7:100089. pmid:34327417
  18. 18. Stisen DB, Tegner H, Bendix T, Esbensen BA. The experience of patients with fear-avoidance belief hospitalised for low back pain—a qualitative study. Disabil Rehabil. 2016;38(4):307–14. pmid:25893399
  19. 19. El-Haddad C, Damodaran A, Patrick McNeil H, Hu W. The experience of patients admitted to hospital with acute low back pain: a qualitative study. Int J Rheum Dis. 2018;21(4):796–803. pmid:27125577
  20. 20. Oliveira CB, Amorim HE, Coombs DM, Richards B, Reedyk M, Maher CG, et al. Emergency department interventions for adult patients with low back pain: a systematic review of randomised controlled trials. Emerg Med J. 2021;38(1):59–68. pmid:33037020
  21. 21. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. pmid:33782057
  22. 22. Blokzijl J, Lee H, Cullen L, Coggins A, Golding M, Gillett M, et al. Diagnoses and trends in use of imaging for low back pain in four Australian emergency departments between 2012 and 2019. Emerg Med Australas. 2022;34(4):539–46. pmid:35098665
  23. 23. Critical Appraisal Skills Programme. CASP Cohort Study Checklist. 2018a [Cited 2021 September 25]. Available from: https://casp-uk.net/casp-tools-checklists/.
  24. 24. Critical Appraisal Skills Programme. CASP Case Control Study Checklist. 2018b [Cited 2023 August 30]. Available from: https://casp-uk.net/casp-tools-checklists/.
  25. 25. Hillier S, Grimmer-Somers K, Merlin T, Middleton P, Salisbury J, Tooher R, et al. FORM: an Australian method for formulating and grading recommendations in evidence-based clinical guidelines. BMC Med Res Methodol. 2011;11:23. pmid:21356039
  26. 26. National Health and Medical Research Council. NHMRC levels of evidence and grades for recommendations for developers of guidelines. Canberra: Australian Government; 2009.
    • 27. Anderson DB, Chen L, Eyles JP, Ferreira ML. Emergency department presentations and associated hospital admissions for low back pain in Australia. Emerg Med Australas. 2022;34(4):559–68. pmid:35233970
    • 28. Beyera GK O’Brien J, Campbell S. Hospital admission and associated factors among individuals presenting to healthcare facilities for low back pain in Ethiopia. Int J Rheum Dis. 2020;23(6):763–71. pmid:32239632
    • 29. Buchbinder R, Bourne A, Staples M, Lui C, Walker K, Ben-Meir M, et al. Management of patients presenting with low back pain to a private hospital emergency department in Melbourne, Australia. Emerg Med Australas. 2022;34(2):157–63. pmid:34164911
    • 30. Davidson SRE, Kamper SJ, Haskins R, O’Flynn M, Coss K, Smiles JP, et al. Low back pain presentations to rural, regional, and metropolitan emergency departments. Aust J Rural Health. 2022;30(4):458–67. pmid:35229394
    • 31. de Heer HD, Warren M. Physical therapy and hHospitalization among medicare beneficiaries with low back pain: a retrospective cohort study. Spine (Phila Pa 1976). 2016;41(19):1515–22.
    • 32. Euro U, Knekt P, Rissanen H, Aromaa A, Karppinen J, Heliövaara M. Risk factors for sciatica leading to hospitalization. Eur Spine J. 2018;27(7):1501–8. pmid:28612193
    • 33. Euro U, Heliövaara M, Shiri R, Knekt P, Rissanen H, Aromaa A, et al. Work-related risk factors for sciatica leading to hospitalization. Sci Rep. 2019;9(1):6562. pmid:31024023
    • 34. Ferreira GE, Machado GC, Abdel Shaheed C, Lin CC, Needs C, Edwards J, et al. Management of low back pain in Australian emergency departments. BMJ Qual Saf. 2019;28(10):826–34. pmid:31164487
    • 35. Ferreira G, Lobo M, Richards B, Dinh M, Maher C. Hospital variation in admissions for low back pain following an emergency department presentation: a retrospective study. BMC Health Serv Res. 2022;22(1):835. pmid:35818074
    • 36. Heliövaara M, Knekt P, Aromaa A. Incidence and risk factors of herniated lumbar intervertebral disc or sciatica leading to hospitalization. J Chronic Dis. 1987;40(3):251–8. pmid:3818881
    • 37. Joines JD, Hertz-Picciotto I, Carey TS, Gesler W, Suchindran C. A spatial analysis of county-level variation in hospitalization rates for low back problems in North Carolina. Soc Sci Med. 2003;56(12):2541–53. pmid:12742616
    • 38. Jørgensen MB, Holtermann A, Gyntelberg F, Suadicani P. Physical fitness as a predictor of herniated lumbar disc disease—a 33-year follow-up in the Copenhagen male study. BMC Musculoskelet Disord. 2013;14:86. pmid:23497269
    • 39. Kääriä S, Kaila-Kangas L, Kirjonen J, Riihimaki H, Luukkonen R, Leino-Arjas P. Low back pain, work absenteeism, chronic back disorders, and clinical findings in the low back as predictors of hospitalization due to low back disorders: a 28-year follow-up of industrial employees. Spine (Phila Pa 1976). 2005;30(10):1211–8. pmid:15897838
    • 40. Kääriä S, Kirjonen J, Telama R, Kaila-Kangas L, Leino-Arjas P. Does strenuous leisure time physical activity prevent severe back disorders leading to hospitalization? Eur Spine J. 2014;23(3):508–11. pmid:24253931
    • 41. Kaila-Kangas L, Leino-Arjas P, Riihimaki H, Luukkonen R, Kirjonen J. Smoking and overweight as predictors of hospitalization for back disorders. Spine (Phila Pa 1976). 2003;28(16):1860–8. pmid:12923477
    • 42. Kaila-Kangas L, Kivimaki M, Riihimaki H, Luukkonen R, Kirjonen J, Leino-Arjas P. Psychosocial factors at work as predictors of hospitalization for back disorders: a 28-year follow-up of industrial employees. Spine (Phila Pa 1976). 2004;29(16):1823–30. pmid:15303029
    • 43. Kaila-Kangas L, Kivimaki M, Harma M, Riihimaki H, Luukkonen R, Kirjonen J, et al. Sleep disturbances as predictors of hospitalization for back disorders-a 28-year follow-up of industrial employees. Spine (Phila Pa 1976). 2006;31(1):51–6. pmid:16395176
    • 44. Kawchuk GN, Aaskov J, Mohler M, Lowes J, Kruhlak M, Couperthwaite S, et al. A prospective study of patients with low back pain attending a Canadian emergency department: Why they came and what happened? PLoS One. 2022;17(5):e0268123. pmid:35536825
    • 45. Leino-Arjas P, Kaila-Kangas L, Keskimaki I, Notkola V, Mutanen P. Inpatient hospital care for lumbar intervertebral disc disorders in Finland in relation to education, occupational class, income, and employment. Public Health. 2002;116(5):272–8. pmid:12209402
    • 46. Mattila VM, Saarni L, Parkkari J, Koivusilta L, Rimpela A. Predictors of low back pain hospitalization—a prospective follow-up of 57,408 adolescents. Pain. 2008;139(1):209–17. pmid:18472217
    • 47. Ritzwoller DP, Crounse L, Shetterly S, Rublee D. The association of comorbidities, utilization and costs for patients identified with low back pain. BMC Musculoskelet Disord. 2006;7:72. pmid:16982001
    • 48. Rivinoja AE, Paananen MV, Taimela SP, Solovieva S, Okuloff A, Zitting P, et al. Sports, smoking, and overweight during adolescence as predictors of sciatica in adulthood: a 28-year follow-up study of a birth cohort. Am J Epidemiol. 2011;173(8):890–7. pmid:21393341
    • 49. Sayer JM, Kinsella RM, Cary BA, Burge AT, Kimmel LA, Harding P. Advanced musculoskeletal physiotherapists are effective and safe in managing patients with acute low back pain presenting to emergency departments. Aust Health Rev. 2018;42(3):321–6. pmid:28538139
    • 50. Sørensen IG, Jacobsen P, Gyntelberg F, Suadicani P. Occupational and other predictors of herniated lumbar disc disease-a 33-year follow-up in the Copenhagen male study. Spine (Phila Pa 1976). 2011;36(19):1541–6. pmid:21270695
    • 51. Urrutia J, Besa P, Meissner-Haecker A, Gonzalez R, Gonzalez J. Management of patients with low back pain in the emergency department: Is it feasible to follow evidence-based recommendations? Emerg Med Australas. 2020;32(6):1001–7. pmid:32558273
    • 52. Videman T, Sarna S, Battie MC, Koskinen S, Gill K, Paananen H, et al. The long-term effects of physical loading and exercise lifestyles on back-related symptoms, disability, and spinal pathology among men. Spine (Phila Pa 1976). 1995;20(6):699–709. pmid:7604346
    • 53. Wahlström J, Burstrom L, Nilsson T, Jarvholm B. Risk factors for hospitalization due to lumbar disc disease. Spine (Phila Pa 1976). 2012;37(15):1334–9. pmid:22281487
    • 54. Wahlström J, Burstrom L, Johnson PW, Nilsson T, Jarvholm B. Exposure to whole-body vibration and hospitalization due to lumbar disc herniation. Int Arch Occup Environ Health. 2018;91(6):689–94. pmid:29855719
    • 55. Whedon JM, Kizhakkeveettil A, Toler AW, Bezdjian S, Rossi D, Uptmor S, et al. Initial choice of spinal manipulation reduces escalation of care for chronic low back pain among older medicare beneficiaries. Spine (Phila Pa 1976). 2022;47(4):E142–E8. pmid:34474443
    • 56. Hartvigsen J, Hancock MJ, Kongsted A, Louw Q, Ferreira ML, Genevay S, et al. What low back pain is and why we need to pay attention. Lancet. 2018;391(10137):2356–67. pmid:29573870
    • 57. Mescouto K, Olson RE, Hodges PW, Setchell J. A critical review of the biopsychosocial model of low back pain care: time for a new approach? Disabil Rehabil. 2022;44(13):3270–84. pmid:33284644
    • 58. Traeger AC, Buchbinder R, Elshaug AG, Croft PR, Maher CG. Care for low back pain: can health systems deliver? Bull World Health Organ. 2019;97(6):423–33. pmid:31210680
    • 59. da Silva VD, Tribess S, Meneguci J, Sasaki JE, Garcia-Meneguci CA, Carneiro JAO, et al. Association between frailty and the combination of physical activity level and sedentary behavior in older adults. BMC Public Health. 2019;19(1):709. pmid:31174515
    • 60. Navarrete-Villanueva D, Gomez-Cabello A, Marin-Puyalto J, Moreno LA, Vicente-Rodriguez G, Casajus JA. Frailty and physical fitness in elderly people: a systematic review and meta-analysis. Sports Med. 2021;51(1):143–60. pmid:33201455
    • 61. Daskalopoulou C, Stubbs B, Kralj C, Koukounari A, Prince M, Prina AM. Physical activity and healthy ageing: a systematic review and meta-analysis of longitudinal cohort studies. Ageing Res Rev. 2017;38:6–17. pmid:28648951
    • 62. Falck RS, Davis JC, Best JR, Crockett RA, Liu-Ambrose T. Impact of exercise training on physical and cognitive function among older adults: a systematic review and meta-analysis. Neurobiol Aging. 2019;79:119–30. pmid:31051329
    • 63. Sherrington C, Fairhall N, Kwok W, Wallbank G, Tiedemann A, Michaleff ZA, et al. Evidence on physical activity and falls prevention for people aged 65+ years: systematic review to inform the WHO guidelines on physical activity and sedentary behaviour. Int J Behav Nutr Phys Act. 2020;17(1):144. pmid:33239019
    • 64. Brinjikji W, Luetmer PH, Comstock B, Bresnahan BW, Chen LE, Deyo RA, et al. Systematic literature review of imaging features of spinal degeneration in asymptomatic populations. AJNR Am J Neuroradiol. 2015;36(4):811–6. pmid:25430861
    • 65. Brady B, Andary T, Pang SM, Dennis S, Liamputtong P, Boland R, et al. A mixed-methods investigation into patients’ decisions to attend an emergency eepartment for chronic pain. Pain Med. 2021;22(10):2191–206.
    • 66. Poulin PA, Nelli J, Tremblay S, Small R, Caluyong MB, Freeman J, et al. Chronic pain in the emergency department: a pilot mixed-methods cross-sectional study examining patient characteristics and reasons for presentations. Pain Res Manag. 2016;2016:3092391. pmid:27829785
    • 67. Saggers A, Wand BM, Bulsara C, Truter P. ’I’m not in GP pain, I’m in hospital pain’: Qualitative study regarding patient decision-making factors in seeking care in the emergency department with non-specific low back pain. Emerg Med Australas. 2021;33(6):1013–20. pmid:33960124
    • 68. Karran EL, Grant AR, Moseley GL. Low back pain and the social determinants of health: a systematic review and narrative synthesis. Pain. 2020;161(11):2476–93. pmid:32910100
    • 69. Chou L, Cicuttini FM, Urquhart DM, Anthony SN, Sullivan K, Seneviwickrama M, et al. People with low back pain perceive needs for non-biomedical services in workplace, financial, social and household domains: a systematic review. J Physiother. 2018;64(2):74–83. pmid:29574167
    • 70. Philpot LM, Schumann ME, Ebbert JO. Social relationship quality among patients with chronic pain: a population-based sample. J Patient Exp. 2020;7(3):316–23. pmid:32821790
    • 71. Strudwick K, McPhee M, Bell A, Martin-Khan M, Russell T. Review article: Best practice management of low back pain in the emergency department (part 1 of the musculoskeletal injuries rapid review series). Emerg Med Australas. 2018;30(1):18–35. pmid:29232762
    • 72. Buchbinder R, van Tulder M, Oberg B, Costa LM, Woolf A, Schoene M, et al. Low back pain: a call for action. Lancet. 2018;391(10137):2384–8. pmid:29573871
    • 73. Lim YZ, Chou L, Au RT, Seneviwickrama KMD, Cicuttini FM, Briggs AM, et al. People with low back pain want clear, consistent and personalised information on prognosis, treatment options and self-management strategies: a systematic review. J Physiother. 2019;65(3):124–35. pmid:31227280
    • 74. Lin I, Wiles L, Waller R, Goucke R, Nagree Y, Gibberd M, et al. What does best practice care for musculoskeletal pain look like? Eleven consistent recommendations from high-quality clinical practice guidelines: systematic review. Br J Sports Med. 2020;54(2):79–86. pmid:30826805
    • 75. Haldeman S, Johnson CD, Chou R, Nordin M, Côté P, Hurwitz EL, et al. The Global Spine Care Initiative: care pathway for people with spine-related concerns. Eur Spine J. 2018;27(6):901–14.
    • 76. Heyward J, Jones CM, Compton WM, Lin DH, Losby JL, Murimi IB, et al. Coverage of Nonpharmacologic Treatments for Low Back Pain Among US Public and Private Insurers. JAMA Netw Open. 2018;1(6):e183044. pmid:30646222
    • 77. Lentz TA, Coronado RA, Master H. Delivering value through equitable care for low back pain: a renewed call to action. J Orthop Sports Phys Ther. 2022;52(7):414–8. pmid:35580022
    • 78. Liu N, Zhang Z, Wah Ho AF, Hock Ong ME. Artificial intelligence in emergency medicine. J Emerg Crit Care Med. 2018;2:82.
    • 79. Medeiros FC, Costa LOP, Oliveira IS, Oshima RK, Costa LCM. The use of STarT BACK Screening Tool in emergency departments for patients with acute low back pain: a prospective inception cohort study. Eur Spine J. 2018;27(11):2823–30. pmid:29671109
    • 80. Treanor C, Brogan S, Burke Y, Curley A, Galvin J, McDonagh L, et al. Prospective observational study investigating the predictive validity of the STarT Back tool and the clinical effectiveness of stratified care in an emergency department setting. Eur Spine J. 2022;31(11):2866–74.
    • 81. Pugh A, Roper K, Magel J, Fritz J, Colon N, Robinson S, et al. Dedicated emergency department physical therapy is associated with reduced imaging, opioid administration, and length of stay: a prospective observational study. PLoS One. 2020;15(4):e0231476. pmid:32324821
    • 82. Coombs DM, Machado GC, Richards B, Needs C, Buchbinder R, Harris IA, et al. Effectiveness of a multifaceted intervention to improve emergency department care of low back pain: a stepped-wedge, cluster-randomised trial. BMJ Qual Saf. 2021;30(10):825–35. pmid:33692191
    • 83. Melman A, Maher CG, Machado GC. Virtual hospitals: why we need them, how they work and what might come next. J Physiother. 2021;67(3):156–7. pmid:34148815
    • 84. van Erp RMA, Huijnen IPJ, Jakobs MLG, Kleijnen J, Smeets R. Effectiveness of primary care interventions using a biopsychosocial approach in chronic low back pain: a systematic review. Pain Pract. 2019;19(2):224–41. pmid:30290052
    • 85. Fisher M, Freeman T, Mackean T, Friel S, Baum F. Universal health coverage for non-communicable diseases and health equity: lessons from Australian primary healthcare. Int J Health Policy Manag. 2022;11(5):690–700. pmid:33300769
    • 86. Moscrop A, Ziebland S, Bloch G, Iraola JR. If social determinants of health are so important, shouldn’t we ask patients about them? BMJ. 2020;371:m4150. pmid:33234506
    • 87. Ivankova NV, Creswell JW, Stick SL. Using mixed-methods sequential explanatory design: from theory to practice. Field Methods. 2006;18(1):3–20.