Social determinants of health in adults with whiplash associated disorders

Lisa Jasper; Ashley D. Smith

doi:10.1515/sjpain-2023-0118

Artikel Open Access

Social determinants of health in adults with whiplash associated disorders

Lisa Jasper und Ashley D. Smith

Veröffentlicht/Copyright: 29. April 2024

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Aus der Zeitschrift Scandinavian Journal of Pain Band 24 Heft 1

Abstract

Objectives

Although it is well-known that chronic diseases need to be managed within the complex biopsychosocial framework, little is known about the role of sociodemographic features in adults with whiplash-associated disorders (WAD) and their association with health outcomes. The aim of this study was to investigate the association between various sociodemographic features (age, sex, ethnicity, education, working, marriage, caring for dependents, and use of alcohol and drugs) and health outcomes (pain, disability, and physical/mental health-related quality of life) in WAD, both through their individual relationships and also via cluster analysis.

Methods

Independent t-tests and Kruskal–Wallis tests (with Mann–Whitney tests where appropriate) were used to compare data for each health outcome. Variables demonstrating a significant relationship with health outcomes were then entered into two-step cluster analysis.

Results

N = 281 participated in study (184 females, mean (±SD) age 40.9 (±10.7) years). Individually, level of education (p = 0.044), consumption of non-prescribed controlled or illegal drugs (p = 0.015), and use of alcohol (p = 0.008) influenced level of disability. Age (p = 0.014), marriage status (p = 0.008), and caring for dependents (p = 0.036) influenced mental health quality of life. Collectively, two primary clusters emerged, with one cluster defined by marriage, care of dependents, working status, and age >40 years associated with improved mental health outcomes (F _1,265 = 10.1, p = 0.002).

Discussion

Consistent with the biopsychosocial framework of health, this study demonstrated that various sociodemographic features are associated with health outcomes in WAD, both individually and collectively. Recognizing factors that are associated with poor health outcomes may facilitate positive outcomes and allow resource utilization to be tailored appropriately.

Keywords: neck pain; whiplash injuries; biopsychosocial model; social determinants of health; chronic pain; quality of life

1 Introduction

The biopsychosocial framework for management of chronic diseases highlights the complex reciprocal interaction and influence of biological, psychological, social, and contextual features on a person’s health status [1]. The complexity of these interactions is observed in people with whiplash-associated disorders (WAD), who may present with a wide variety of clinical manifestations. Various studies have outlined the physical and psychological manifestations present in WAD [2,3]. However, very few studies have investigated a broader range of sociodemographic features in WAD, particularly regarding their association with health outcomes such as pain, disability, and health-related quality of life.

Clinical practice guidelines advocate that management of WAD should be performed within a biopsychosocial framework, as per the World Health Organization’s International Classification of Functioning, Disability, and Health [4,5]. Surprisingly, recommendations within many clinical practice guidelines do not make mention of any social features that may need addressing to assist a person in their recovery [4–6]. Systematic reviews that have investigated the role of sociodemographic features in WAD report mixed findings [7–9]. One review highlights that two demographic variables, achieving less than postsecondary education and female sex, are associated with poor outcomes, while another review reported that female sex was not associated with prognosis [8,9]. A more recent meta-review reports inconsistent evidence regarding the association of sociodemographic variables and chronicity in WAD [7]. The number of social features investigated in these studies are also limited – primarily age, sex, and education status have been evaluated for their association with chronicity. Low study numbers and lack of data reporting the magnitude of association with different health outcomes are evident when reviewing the literature.

Recent studies have demonstrated the importance of evaluating social features of health in various musculoskeletal conditions, including neck pain. In a novel study using UK Biobank data (N = 72,216 cases; N = 239,125 controls), various pathologies, psychiatric traits, socioeconomic factors, and medical comorbidities contributed to an increased risk associated with a unique genetic signature of chronic pain [10]. For neck pain, diet, family (child in household), and work-related factors were protective against the development of chronic pain. In contrast, different work-related factors were risk factors for chronic pain, illustrating the complexity of these relationships. Another recent study demonstrated that social factors such as age and level of education predicted questionnaire results following whiplash injury, which themselves predicted different recovery trajectories following injury, illustrating the importance of considering sociodemographic variables when managing people with neck pain [11]. Sociodemographic factors are also associated with development of idiopathic neck pain [12]. Female gender, ex-smokers, support at work, and older age all demonstrated a moderate to strong association with poor prognosis. To the authors’ knowledge, the association of these features with different health outcomes in WAD has not been evaluated.

This observational study aimed to investigate the association between various sociodemographic features, such as age, sex, ethnicity, education, working, marriage, caring for dependents, use of alcohol and non-prescribed controlled or illegal drugs, and health outcomes (pain, disability, and physical/mental health-related quality of life) in WAD, both through their individual relationship and also via cluster analysis in an effort to determine whether a combination of these factors are associated in people presenting with WAD. Finally, we investigated the relationship between the clustered variables and health outcomes.

2 Methods

2.1 Design

This observational study was performed at a community multidisciplinary chronic musculoskeletal pain clinic in Calgary, Canada between October 2019 and December 2021. Participants were attending for further evaluation of persistent symptoms following a motor vehicle collision. Upon initial attendance, participants completed intake questionnaires, which captured sociodemographic and lifestyle factors, motor vehicle collision details, and questionnaires evaluating health outcomes. Following signed, informed consent, the de-identified data were entered into a registry database. The Conjoint Health Research Ethics Board (Ethics ID#: REB20-0355) provided ethical clearance. The study was performed in accordance with the Declaration of Helsinki.

2.2 Participants

Participants were eligible for enrolment in the study if they had greater than 3 months, but less than 10 years of WAD grade I–III (no history of fracture or dislocation) [13] and were between 18 and 65 years of age.

2.3 Outcome measures

The Pain, Enjoyment of Life and General Activity (PEG) scale was used to measure pain interference [14]. This scale consists of three separate numerical rating scales (0–10) measuring average pain intensity over the prior week and how pain interferes with their enjoyment of life and general activities. The PEG score was calculated as the average of the three scales. Moderate pain was defined as pain scores between 5 and 6, while severe pain was 7 or greater [15].

The Neck and Oswestry Disability Indices measure self-rated neck or back pain-related disability, respectively, using 10-item, Likert scored (0–5) questionnaires [16,17]. A total score of 50 is possible, with scores converted to a percentage. Scores greater than 28% were considered moderate and those above 48% severe disability [18]. The participant completed the questionnaire that most closely resembled the spinal region of their primary pain location.

Health-related quality of life was measured using the 12-item short form health survey (SF-12) [19]. Both physical and mental health domain scores were generated (mean = 50). A higher score indicates a better health state. Each 10-point reduction in score equals 1 standard deviation difference in health status for that respective domain. Thus, scores below 40 are indicative of lower health-related quality of life [19].

Sociodemographic and lifestyle measures collected included the following: sex, age, relationship status, level of education, ethnicity, working status, participation in activities or hobbies outside of work, volunteer, look after child, adult or elderly dependents, and partake in non-prescribed controlled or illegal drugs, alcohol, or smoke.

2.4 Data analysis

Data were analyzed for normality through visual inspection, box plots, and Shapiro–Wilk statistics. Descriptive and frequency statistics were obtained from the total sample. Independent t-tests were used to compare normally distributed data for outcome measures involving two-factor sociodemographic comparisons (e.g., sex, smoking status). Kruskal–Wallis tests were used to compare multiple groups for each outcome, as these data were not normally distributed across each factor (e.g., education or working status). If significance was determined, Mann–Whitney tests were then used to calculate each pairwise comparison, with Bonferroni correction used for multiple comparisons. Variables demonstrating a significant relationship with health outcomes were entered into a cluster analysis.

The categorical nature of the variables investigated in this study determined that the two-step autoclustering analysis of the SPSS was required to evaluate the optimal number of clusters. This selects the optimal number of clusters by taking the highest ratio of distance measures and the largest change information criterion measure on the Schwarz’s Bayesian Criterion (change BIC). Examination of the silhouette measure of cohesion and separation was made to determine that the cluster solution was at least “fair” or “‘good.” An iterative process of model development occurred, with individual predictors of the cluster solution examined via Chi-squared analysis. Variables were excluded from cluster analysis if they did not demonstrate a significant difference between clusters (p > 0.05), and the cluster analysis was re-run. We then evaluated if there was a significant difference in each health outcome based on the clusters via independent t-tests.

IBM SPSS for Windows Version 26 (SPSS Inc., Chicago, IL, USA) was used for all statistical analyses with significance set at 0.05.

3 Results

Two hundred and eighty-one participants (184 females) were enrolled in the study with mean (±SD) age of 40.9 (±10.7) years. The median time (interquartile range) since date of motor vehicle collision was 18 [8,27] months. Participants presented with moderate to severe pain intensity (mean ± SD: 6.5 ± 1.8), moderate to severe levels of disability (46 ± 16%), and lower levels of physical health-related quality of life (33 ± 8). Mental health-related quality of life was within population norms (42 ± 12).

Participants tended to be Caucasian (57%), married (60%), employed (74%), university educated (48%), participate in hobbies/activities (74%), not involved with volunteering (83%), non-smokers (87%), partake in alcohol beverages (61%), and not consume non-prescribed controlled or illegal drugs (91%).

3.1 Univariate analysis

There were no significant differences in any health outcome for sex, ethnicity, work status, pursuit of hobbies/activities, participating as a volunteer, or smoking status (Tables 1–3). Participants with a trade certificate demonstrated higher levels of disability than those with a university degree (Mann–Whitney U: Z = −2.67; p = 0.008; Table 2), as did those consuming non-prescribed controlled or illegal drugs (Table 3). Older (compared to younger; Table 1), married participants (Mann–Whitney U: Z = −3.20; p = 0.001; Table 2), and those participants caring for dependents (Table 2) reported higher levels of mental health quality of life. However, “other” participants reported a higher physical health quality of life when compared to married (Mann–Whitney U: Z = −2.51; p = 0.012; Table 2) participants. Participants consuming alcohol demonstrated lower levels of disability (Table 3).

Table 1

Comparison of health outcomes according to sex, age, and ethnicity

	PEG (/10)	Disability (%)	SF-12p
Sex
Female	6.7 (1.9)	46 (16)	33 (8)	42 (12)
Male	7.0 (1.8)	47 (17)	33 (7)	41 (12)
t-statistic	−1.36	−0.11	0.18	0.62
p-value	0.17	0.91	0.86	0.53
Age
>40 yo	6.8 (1.9)	47 (17)	33 (8)	44 (11)
≤40 yo	6.8 (1.8)	46 (16)	34 (8)	40 (12)
t-statistic	0.18	−0.84	1.32	−2.48
p-value	0.86	0.40	0.19	0.014*
Ethnicity
Indigenous	7.0 [5.7, 9.0]	46 [32, 70]	30 [27, 35]	39 [30, 54]
Caucasian	6.7 [5.7, 8.3]	46 [36, 58]	32 [26, 38]	41 [32, 53]
Other	7.0 [6.0, 8.4]	45 [34, 56]	35 [28, 39]	39 [32, 50]
Kruskal–Wallis	H = 0.93	H = 0.18	H = 4.30	H = 1.00
p-value	0.63	0.92	0.12	0.61

Mean values (SD) or median (IQR) for pain interference (PEG), disability, physical (SF-12p), and mental (SF-12m) health quality of life.

*Denotes significance <0.05.

Table 2

Comparison of health outcomes according to education, working status, marriage status, and caring for dependents

	PEG (/10)	Disability (%)	SF-12p	SF-12m
Level of education
Did not complete HS	6.8 [5.8, 8.3]	50 [37, 60]	27 [25, 37]	37 [33, 47]
Completed HS	7.0 [5.7, 8.5]	46 [34, 62]	33 [28, 38]	42 [32, 54]
College/trade certificate	6.7 [5.5, 8.3]	54 [39, 62]	30 [26, 37]	38 [29, 53]
University degree	7.0 [5.7, 8.0]	42 [34, 54]	33 [29, 39]	41 [33, 52]
Kruskal–Wallis	H = 0.57	H = 8.09	H = 6.82	H = 1.25
p-value	0.90	0.044*	0.08	0.74
Work status
Employed/self-employed	7.0 [5.7, 8.3]	45 [34, 56]	33 [28, 39]	42 [34, 53]
Home duties	8.3 [7.3, 8.7]	58 [46, 71]	29 [26, 37]	36 [33, 41]
Unemployed	7.0 [5.4, 8.3]	47 [40, 62]	31 [26, 36]	36 [29, 45]
Semi-/retired	6.7 [4.3, 9.3]	60 [32, 72]	31 [23, 36]	36 [27, 50]
Kruskal–Wallis	H = 3.56	H = 8.31	H = 6.21	H = 6.41
p-value	0.31	0.040*	0.10	0.09
Marriage status
Married/common law	7.0 [6.0, 8.3]	46 [36, 56]	33 [27, 37]	43 [35, 53]
Single	7.0 [5.7, 8.3]	48 [36, 60]	33 [27, 39]	37 [30, 46]
Other	6.3 [5.2, 7.6]	35 [24, 53]	38 [30, 41]	34 [29, 55]
Kruskal–Wallis	H = 2.03	H = 5.46	H = 6.19	H = 9.65
p-value	0.36	0.07	0.045*	0.008*
Dependents
No	6.8 (1.8)	47 (17)	34 (7)	40 (12)
Yes	6.9 (1.8)	47 (16)	33 (8)	43 (11)
t-test statistic	−0.54	−0.01	0.72	−2.11
p-value	0.59	0.99	0.40	0.036*

Median [IQR}) for pain interference (PEG), disability, physical (SF-12p), and mental (SF-12m) health quality of life. Although Kruskal–Wallis tests indicated the presence of a significant group difference for working status, post-hoc analysis did not demonstrate any between-group differences.

*Denotes significance <0.05.

Table 3

Comparison of health outcomes according to participation in hobbies/activities, volunteering, smoking status, consumption of non-prescribed controlled or illegal drug, and alcohol consumption

	PEG (/10)	Disability (%)	SF-12p	SF-12m
Hobbies/activities
No	7.1 (1.8)	49 (17)	33 (8)	41 (11)
Yes	6.7 (1.8)	46 (16)	33 (8)	42 (12)
t-test statistic	1.58	1.39	−0.31	−0.83
p-value	0.12	0.17	0.76	0.41
Volunteer
No	6.8 (1.9)	47 (16)	33 (7)	42 (11)
Yes	7.0 (1.5)	48 (16)	33 (8)	41 (13)
t-test statistic	−0.65	−0.36	0.24	0.61
p-value	0.52	0.72	0.81	0.54
Smoking status
Yes	6.9 (1.8)	51 (18)	32 (8)	43 (10)
No	6.8 (1.8)	46 (16)	33 (8)	41 (12)
t-test statistic	0.29	1.74	−0.56	0.46
p-value	0.77	0.08	0.58	0.64
Non-prescribed controlled or illegal drug consumption
Yes	7.5 (1.6)	56 (15)	31 (5)	37 (9)
No	6.8 (1.8)	46 (16)	33 (8)	42 (12)
t-test statistic	1.74	2.45	−1.15	−1.74
p-value	0.08	0.015*	0.25	0.08
Alcohol intake
Yes	6.7 (1.8)	45 (15)	33 (8)	42 (12)
No	7.0 (1.9)	50 (17)	33 (8)	40 (11)
t-test statistic	−1.30	−2.68	0.92	1.40
p-value	0.19	0.008*	0.36	0.16

Mean (SD) for pain interference (PEG), disability, physical (SF-12p), and mental (SF-12m) health quality of life.

*Denotes significance <0.05.

3.2 Cluster analysis

Based on their significant association with health outcomes, the following variables were entered into the TwoStep cluster analysis: age, education status, working status, marriage status, caring for dependents, drug consumption, and alcohol intake. This resulted in a 2-item cluster solution with “fair” cluster quality using the silhouette measure of cohesion and separation. As education status, alcohol, and drug consumption did not significantly differ between clusters (p > 0.05), these variables were excluded, and the TwoStep cluster analysis was performed again. This resulted in a 2-item cluster solution with “good” cluster quality (Table 4). Clusters differed on marital status, age, working status, and caring for dependents. Those in cluster 1 were all married, were more likely to care for dependents, more likely to be employed and aged greater than 40 years old. Cluster 1 demonstrated higher mental health quality of life (t ₂₆₈ = 3.30, p = 0.001; mean (±SD): 43 ± 11.3 vs 39 ± 12.0). There were no significant differences in pain, disability, or physical health quality of life measures between clusters (p > 0.14).

Table 4

Number of people in each cluster for the various sociodemographic features

	Marital status			Dependents		Age		Working
Cluster	Mar	Single	Other	No	Yes	≤40 yo	>40 yo	Emp	Un	HD	Ret
1	169	0	0	61	108	66	103	131	22	6	8
2	0	88	17	77	28	74	31	71	30	2	2

Mar = married or common law relationship; yo = years old; Emp = employed or self-employed; HD = home duties; Un = unemployed; Ret = retired or semi-retired.

4 Discussion

This study demonstrated that various sociodemographic features were associated with health outcomes in WAD, both individually and collectively. Individually, lower levels of education, consumption of non-prescribed controlled or illegal drugs, and the use of alcohol were correlated with higher levels of disability. Younger age, not being married or in a common law relationship, and not caring for dependents demonstrated correlation with lower mental health quality of life. Collectively, two primary clusters emerged, with one cluster defined by marriage, care of dependents (children or adults), working status, and age greater than 40 years old. This cluster was associated with improved mental health outcomes, when compared to the other cluster who were more likely to be younger, unmarried, not caring for dependents and having lower rates of employment.

Participants in this study demonstrated moderate-to-severe levels of pain and disability and low physical health quality of life over a median time of 18 months since their MVC injuries were sustained. Our sample’s symptom profile is consistent with 30% of participants in longitudinal studies that take a median time to recover of 6 months, and present with moderate levels of pain and disability [2,20,21]. It was surprising that our sample reported mental health quality of life consistent with that of the general population, as a previous study with a similar age distribution, but greater female participation demonstrated poorer mental health status at 6 months for people with moderate to severe levels of disability [20]. Given that our study sample was measured at a later timepoint and there were no differences in mental health scores between sexes, it may be that mental health improves over time as patients adapt to their persistent symptoms.

Individual features associated with worse health outcomes included younger age, lower level of education, not being married, not caring for dependents, consumption of non-prescribed controlled or illegal drugs, and not consuming alcohol. There is not yet clear consensus on the association of any of these factors with recovery from WAD. Although older age has been found to be associated with the onset of non-specific neck pain, a recent review did not find an association between age and pain following WAD and reported inconclusive results for age and disability [2,12]. Several studies have reported associations between lower levels of education and poorer health outcomes; however, a recent review concluded that the evidence demonstrated no association between employment factors and health outcomes [2]. Similarly, there has been mixed evidence on the association of employment factors and education with health outcomes, again leading to the conclusion that neither factor was associated with recovery from WAD [2]. The finding of alcohol consumption being associated with reduced disability was somewhat surprising and may indicate that alcohol is being used as a medication or coping strategy. However, one large (N = 33,066) prospective study has shown that moderate alcohol consumption was associated with less chronic disease burden, particularly cardiovascular disease as measured with reduced disability-adjusted life years [22]. The results were primarily observed among older participants, which may explain the finding in our study suggesting that those with lower disability may simply prefer alcohol consumption. Increased non-prescribed controlled or illegal drug intake was associated with worse disability. This finding may suggest that non-prescribed controlled or illegal drug intake was also a coping strategy for those with worse disability. Further investigation into this relationship is necessary as, consistent with our findings, factors such as being younger, having fair or poor mental health, and being unattached have also been found to increase odds of opioid misuse [23]. In contrast to our findings, being male and/or a smoker were also found to increase odds of opioid pain relief medications [23].

Other features, which included sex, ethnicity, work status, pursuit of hobbies/activities, participating as a volunteer, and smoking status were not associated with health outcomes in this study [24]. There has been inconsistency in previous literature regarding the role of sex with a recent review concluding that there was no association between sex and disability, but limited evidence that women report poorer physical health-related quality of life 1 year following WAD [2]. Consistent with our results, a recent review concluded there were no associations with either smoking or work factors and positive health outcomes following WAD [2]. The roles of ethnicity, hobbies/activities, volunteer participation have not, to the authors’ knowledge, been previously investigated [24].

Features associated with disability included a significant effect for education status and a trend for working status. Features associated with physical health quality of life included marriage status with those not married/single reporting a higher level of physical health than the married study participants. Reasons for this are unclear, with speculation that being single/not married may allow more time to pursue physical pursuits [25]. Features associated with mental health quality of life included older age, marriage, and having dependents. Somewhat surprisingly was that none of these features were associated with PEG scores, indicating that sociodemographic and lifestyle features were not directly associated with pain. It appears that other features in WAD may be more responsible for pain and disability levels. Significant research has demonstrated that nociception is prevalent in those with chronic WAD, while psychological features are predictive of pain and disability [26,27]. Associations with sleep disorders have also recently been reported [28]. All these features may be more closely associated with pain in WAD than the sociodemographic factors examined in this study.

To gain further insight into the relationship of these sociodemographic and lifestyle features, a cluster analysis was performed, indicating that a combination of being married, an older age (greater than 40 years old), being employed, and caring for dependents was associated with improved mental health quality of life. It is apparent that those in this age bracket are more likely to be married, care for dependents, and had more time to find employment. Thus, the combination of these features is not uncommon. However, the association of this cluster with improved mental health suggests that resulting increased social cohesion and support may be protective of poorer health outcomes in WAD. This supports the findings in a previous study in adults with WAD where social support was also found to be associated with long-term functioning [29]. It must be noted that our study focused on the social features of health in isolation from other clinical features such as nociception, sleep, and psychological features that are associated with poor health outcomes in chronic WAD. Additionally, factors such as adverse childhood events, which have been shown to have a long lasting effect on pain mechanisms, may also influence these results [30,31]. It is possible that sociodemographic and lifestyle features may mediate or moderate these clinical features and further influence health outcomes indirectly. Much research is required to tease out these relationships.

There are limitations to this study. The analysis is based on self-report data, which may encourage underreporting of drug and alcohol use, and be subject to external factors such as self-report bias and cultural variations [32,33]. The authors also recognize that the observational study design does not allow assumptions regarding causation, and further longitudinal research is necessary to determine the relationships between the social determinants of health outcomes. Neither economic features nor job satisfaction was captured on the intake form, both of which have been associated with worse health outcomes in people with idiopathic neck pain [12]. Our sample largely consisted of Caucasians who were university educated, married, employed, and received funding to attend the initial evaluation. This suggests that our sample demonstrated economic privilege. As such, the results may not be representative of all adults with WAD, many of whom may be unable to afford further evaluation of their persistent symptoms.

Clinically, these findings provide novel insights into adults whose sociodemographic and lifestyle features may predispose them to worse health outcomes following WAD. Many sociodemographic features are non-modifiable. However, recognizing those associated with poor outcomes allows resource utilization to be tailored appropriately. Adults that are not over 40 years old, not married, and do not care for dependents may be at risk for worse mental health quality of life following WAD, and thus strategies to support these people’s mental health would be warranted. In conclusion, our data are consistent with the biopsychosocial framework of health, in that sociodemographic and lifestyle features are associated with health outcomes, both individually and collectively. These features, however, were not directly associated with pain, suggesting that management of WAD requires a broad evaluation of all clinical features to optimally improve health status across all domains.

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Acknowledgements

The authors are grateful for Dr Masaru Teramoto’s (PhD) expertise in providing a statistical review for this manuscript. The authors would like to acknowledge the contributions of Dr Robert Burnham (Faculty of Medicine and Dentistry, University of Alberta), Dr George Deng (Department of Community Health Sciences, Cumming School of Medicine, University of Calgary), Dr Andruski (Cumming School of Medicine, University of Calgary), and all staff at Vivo Cura Health for their invaluable assistance with patient evaluation and data collection.

Research ethics: The Conjoint Health Research Ethics Board (Ethics ID#: REB20-0355) provided ethical clearance. The study was performed in accordance with the Declaration of Helsinki.
Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
Research funding: None declared.
Data availability: Not applicable.

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Received: 2023-10-06

Revised: 2024-03-14

Accepted: 2024-04-08

Published Online: 2024-04-29

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

https://doi.org/10.1515/sjpain-2023-0118

Schlagwörter für diesen Artikel

neck pain; whiplash injuries; biopsychosocial model; social determinants of health; chronic pain; quality of life

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