Intimate Partner Violence and the Risk of Developing Fibromyalgia and Chronic Fatigue Syndrome

Abstract

Intimate partner violence (IPV) is a global public health issue with a variety of ill health consequences associated with exposure. Due to the stimulation of chronic stress and inflammatory pathways, childhood abuse has been associated with the subsequent development of functional syndromes such as fibromyalgia and chronic fatigue syndrome (CFS). Although IPV in women appears to elicit similar biochemical responses, this association has not been tested thoroughly in IPV survivors. These functional syndromes are complex in etiology and any indication of their risk factors would benefit health care professionals managing this population. Therefore, we aimed to investigate the association between exposure to IPV with functional syndromes: fibromyalgia and CFS. We conducted a retrospective open cohort study using “The Heath Improvement Network” database between January 1, 1995 and December 1, 2017. A total of 18,547 women who were exposed to IPV were each matched by age to four controls who were not exposed (n = 74,188). The main outcome measures were the risk of developing fibromyalgia and CFS. These were presented as adjusted incidence rate ratios (aIRR) with 95% confidence intervals (CIs). We found that 97 women in the exposed group developed fibromyalgia (incidence rate [IR] = 1.63 per 1,000 person-years) compared to 239 women in the unexposed group (IR = 0.83 per 1,000 person-years). Following adjustment, this translated to an IRR of 1.73 (95% CI = [1.36, 2.22]). Similarly, 19 women developed CFS in the exposed group (IR = 0.32 per 1,000 person-years), compared to 53 in the unexposed group (0.18 per 1,000 person-years), which translates to an aIRR of 1.92 (95% CI = [1.11, 3.33]). Therefore, we have identified an association between a history of IPV in women and the development of these functional syndromes, which may provide more information to inform the biopsychosocial pathway precipitating the development of fibromyalgia and CFS.

Keywords

intimate partner violence domestic abuse domestic violence fibromyalgia chronic fatigue syndrome

Introduction

Domestic abuse, defined as “any incident or patterns of incidents of controlling, coercive, threatening behavior, violence or abuse between those aged 16 or over who are, or have been, intimate partners or family members regardless of gender or sexuality,” is a global public health issue, with as many as one in three women affected worldwide (UK Government, 2016; World Health Organization [WHO], 2018). Intimate partner violence (IPV) is a form of domestic abuse that occurs between intimate partners. Recent U.K. estimates suggest that 27.1% of women have experienced some form of domestic abuse, with a large proportion of these cases expected to be IPV (Office for National Statistics, 2016). Recent systematic reviews have identified that being a survivor of IPV is associated with a variety of short-, medium-, and long-term ill health consequences ranging from physical (e.g., cardiovascular disease and diabetes or sexually transmitted infections and fractures following the abuse), mental (e.g., depression and suicide), familial (e.g., intergenerational abuse), as well as hazardous lifestyle choices such as smoking, drug use, or alcohol use (Bacchus et al., 2018; Crane et al., 2013; Sprague et al., 2013; Suglia et al., 2015). Using U.K. primary care data, it has been shown that even in a short time period, exposure to IPV can lead to an increased risk of developing depression, anxiety, serious mental illness, and pain syndromes such as temporomandibular joint disorder (Chandan, Thomas, Bradbury-Jones, Russell, et al., 2019; Chandan, Thomas, Bradbury-Jones, Taylor, et al., 2019).

Although there may be overlap in presenting symptoms, fibromyalgia and chronic fatigue syndrome (CFS) are two separate conditions. Fibromyalgia is characterized by widespread pain, whereas CFS is characterized by pathological malaise which gets worse after exertion (Bested & Marshall, 2015; Clauw, 2014). Derived from routinely collected U.K. primary care data, the overall annual incidence of fibromyalgia and CFS was 33.3 and 14.8 per 100,000 people, in line with the estimated prevalence of fibromyalgia of between 1% and 5% compared to a prevalence of 0.8% for CFS (Collin et al., 2017b). The etiologies of fibromyalgia and CFS are not clear; however, they may develop as a result of genetic influences, viral triggers, immune dysfunction, endocrine imbalances with inadequate cortisol responses, or neurochemical abnormalities, which may affect pain processing or exacerbate malaise (Reid et al., 2000; Sumpton & Moulin, 2014). Diagnosis with fibromyalgia or CFS can have debilitating effects on health and has been associated with significant comorbidity and also increased requirements for health care utilization (Collin et al., 2017a; Sumpton & Moulin, 2014).

Globally, there have been few studies designed to assess the relationship between IPV and functional syndromes such as fibromyalgia and CFS (Bacchus et al., 2018). This is particularly surprising, as there are multiple hypotheses which have considered the relationship between the pathophysiological effects of stress exacerbated by IPV and how these responses share a pathway potentially responsible for the development of functional syndromes that can cause pain and fatigue. One such hypothesis is that IPV exposure can lead to abnormal inflammatory modulation, as well as inappropriate activation of neuroendocrine and immune pathways, which are likely contributing factors for the development of fibromyalgia and CFS (Bacchus et al., 2018; Miller, 1998; WHO, 2018). Other immune system response changes that have been noted in studies examining the stressful effects of childhood abuse (such as increased circulating interleukins, tumor necrosis factor, and blunted cortisol responses) have also been associated with the subsequent development of fibromyalgia and CFS (Borsini et al., 2014). However, these biochemical responses have not yet been explored in individuals who have experienced abuse during adulthood.

Other hypotheses exploring possible reasons for the association between IPV and pain syndrome focus on the psychological changes that can happen as a result of IPV. One such theory is the “fear avoidance model,” which suggests that when pain is perceived following an injury, the individual will determine the severity of how the pain is interpreted (Turk & Wilson, 2010). Individuals who are likely to “catastrophize” the pain (more common in those who have experienced child and adulthood abuse (Nicol et al., 2016)) precipitate a cycle of pain-related fear, which leads to avoidance and ultimately to disuse, depression, and disability, all of which are factors associated with fibromyalgia and CFS (Turk & Wilson, 2010).

Of existing studies, a Spanish case-control study identified partner abuse as a strong risk factor for CFS (Gimeno Pi et al., 2016). In contrast, an American case-control study did not find any type of abuse during adulthood as a risk factor for CFS (Taylor & Jason, 2002). A separate case-control study identified no association between fibromyalgia and intimate partner abuse (Ruiz-Pérez et al., 2009). However, this study did identify frequent non-partner abuse as a risk factor for fibromyalgia.

To date, there are no cohort studies or sufficiently powered studies that have investigated the relationship between IPV and the development of fibromyalgia or CFS. As patients experiencing fibromyalgia and CFS often face delays in diagnosis due to limited understanding of etiology, there is a clinical need for studies to shed light on potential risk factors (Bidari et al., 2018). Therefore, our aim was to conduct a retrospective cohort study using “The Health Improvement Network” (THIN) database to identify whether IPV is associated with a risk of developing fibromyalgia or CFS, within the United Kingdom. This is the first cohort study the authors know of to assess this risk and it provides further insight into factors responsible for the development of fibromyalgia and CFS.

Methods

Study Design and Data Source

We conducted a population-based, retrospective open cohort study using THIN database. THIN database consists of U.K. primary care records capturing data from more than 750 general practices (family practices) spread throughout the country and comprises approximately 3.6 million active patients at the time of this study (IQVIA, 2018). The database is deemed to be representative of the general U.K. population in terms of demographic structure and prevalence of key comorbidities (Blak et al., 2011; IQVIA, 2018). Symptoms, examinations, and diagnoses in THIN are recorded using a hierarchical clinical coding system called Read codes (Booth, 1994; NHS Digital, 2017).

Study Population

Between January 1, 1995 and December 1, 2017, we highlighted all women who were recorded via an appropriate Read code (Appendix A), by their General Practitioner (GP) as being subject to IPV, forming our exposed group. Each survivor’s index date was taken as either the earliest date an exposure code was documented while the patient was eligible to be in the study or taken as the study start date where the exposure code preceded the start date (prevalent patients). The same index date was assigned to corresponding control patients to mitigate immortality time bias (Lévesque et al., 2010).

Each woman in the exposed group was matched by age to four controls from the database, who did not have a code relating to IPV (unexposed group). A total of 18,547 women who had experienced IPV were matched to 74,188 controls.

Outcomes

The primary outcome measures were the development of fibromyalgia and CFS identified using Read codes specific to their diagnosis (Appendix A). Both fibromyalgia and CFS have been investigated in U.K. primary care databases (Collin et al., 2017a, 2017b). If an individual had the outcome of interest at baseline, they were excluded from the analysis. At baseline, 251 (1.4%) women had fibromyalgia and 121 (0.7%) women had CFS in the exposed group as compared to 525 (0.7%) and 480 (0.7%) in the unexposed group, respectively. These women were excluded from the analysis.

Covariates

The following baseline data were collected on all women, age, Townsend deprivation score (Townsend et al., 1988), drinking status, smoking status, and body mass index (BMI). These covariates were also used as control variables in the estimation of the final risk model. Control selection was influenced by analysis of published clinical reviews describing risk factors for the development of fibromyalgia and CFS, as well as examining other risk factors included as control variables in similar analyses (Chen et al., 2017; Gimeno Pi et al., 2016; Häuser et al., 2015; Hempel et al., 2008; Kim et al., 2013; Moukaddem et al., 2017; Varinen et al., 2017). Mental ill health has been used previously as a control variable in other similar studies. However, our recently published study (Chandan, Thomas, Bradbury-Jones, Russell, et al., 2019) examining mental ill health outcomes in the same cohort of women identified that mental ill health is present in almost half of the IPV population at cohort entry. Therefore, the relationship between mental ill health, IPV, and the development of functional disorders may not be easy to delineate by including it as a control variable. To explore this further, we have included a sensitivity analysis which includes depression and anxiety as control variables.

Statistical Analysis

Baseline data are described using means with standard deviations (continuous variables) and proportions (categorical variables). Differences in continuous variables and categorical variables between the groups were compared using T-tests and chi-squared tests, respectively. Poisson regression was used to determine incidence rate ratio (IRR) and adjusted IRR for each outcome of interest. Adjusted IRRs were calculated controlling for covariates discussed above; 95% confidence intervals (CIs) are given, and statistical significance is set at p < .05.

STATA version 14.2 software (StataCorp) was used for the analysis.

The strobe reporting guideline can be found in Appendix B.

Results

Baseline Characteristics

Table 1 describes the baseline characteristics in both the exposed and unexposed groups. The mean age was similar between groups at index date, but follow-up was significantly longer in the unexposed group. There were significantly greater levels of deprivation, smoking, obesity, and excessive drinking in the exposed group compared to the unexposed.

Table 1.

Baseline Characteristics (Standard Deviation or Percentage).

	Exposed Group	Unexposed Group
Number of patients	18,547	74,188
Follow-up period (person-years)*	2.2 (SD 2.3)	3.2 (SD 2.8)
Age (years)	36.9 (SD 12.5)	36.9 (SD 12.5)
Body mass index*
<25 kg/m²	7,916 (42.7%)	32,330 (43.6%)
25–30 kg/m²	3,999 (21.6%)	16,346 (22.0%)
>30 kg/m²	3,568 (19.2%)	13,934 (18.8%)
Not available	3,064 (16.5%)	11,578 (15.6%)
Current smoking status*	8,096 (44.7%)	16,039 (21.6%)
Drinking status*
Non-drinker	5,149 (27.8%)	13,771 (18.6%)
Drinker not excess	8,353 (45.0%)	44,112 (59.5%)
Excessive drinker	1,870 (10.1%)	1,580 (2.1%)
Not available	3,175 (17.1%)	14,725 (19.9%)
Townsend index*
(least deprived) 1	1,773 (9.6%)	14,160 (19.1%)
2	2,104 (11.3%)	12,881 (17.3%)
3	3,149 (17.0%)	13,548 (18.3%)
4	4,215 (22.7%)	12,601 (17.0%)
5	4,266 (23.0%)	9,330 (12.6%)
Not available	3,040 (16.4%)	11,668 (15.7%)
Fibromyalgia/chronic fatigue syndrome at baseline
Fibromyalgia*	251 (1.4%)	525 (0.7%)
Chronic fatigue syndrome	93 (0.5%)	327 (0.4%)

Statistically significantly different at baseline (p < .05).

Risk of Developing Fibromyalgia and CFS

During the defined study period, 97 exposed women developed fibromyalgia (incidence rate [IR] = 1.63 per 1,000 person-years) compared to the 239 unexposed women (IR = 0.83 per 1,000 person-years). Without adjustment, this translated to an IRR of 1.95 (95% CI = [1.54, 2.47], p < .001) and following adjustment an aIRR of 1.75 (95% CI = [1.36, 2.26], p < .001). In the same study period, 19 women developed CFS in the exposed group (IR = 0.32 per 1,000 person-years), compared to 53 in the unexposed group (IR = 0.18 per 1,000 person-years). Unadjusted IRR = 1.71 (95% CI = [1.01, 2.89], p = .044) and adjusted IRR = 1.92 (95% CI = [1.11, 3.33], p = .020). Results are shown in Table 2. The sensitivity analysis (Supplemental 1) which included adjustment for depression and anxiety demonstrated a reduced effect size for both fibromyalgia (IRR = 1.39; 95% CI = [1.08, 1.78], p = .010) and CFS (1.46; 95% CI = [0.83, 2.55], p = .189).

Table 2.

Risk of Developing Fibromyalgia and CFS.

	Fibromyalgia		Chronic Fatigue Syndrome
	Exposed	Unexposed	Exposed	Unexposed
Number of patients	18,296	73,663	18,454	73,861
Numbers of outcomes	97	239	19	53
Person-years	59,657	286,786	60,315	288,024
Incidence rate (per 1,000 person year)	1.63	0.83	0.32	0.18
Incidence rate ratio (95% confidence intervals)^a	1.95 [1.54, 2.47]		1.71 [1.01, 2.89]
p value	<.001		.044
Adjusted incidence rate ratio (95% confidence intervals)^b	1.73 [1.36, 2.22]		1.92 [1.11, 3.33]
p value	<.001		.020

Note. CFS = chronic fatigue syndrome; BMI = body mass index.

Unadjusted incidence rate ratio. ^b Adjusted incidence rate ratio: adjusted for BMI, age, smoking status, drinking status, and Townsend deprivation index at baseline.

Discussion

Summary of Key Results

This brief study found that female survivors of IPV during adulthood were almost twice as likely to develop fibromyalgia and CFS. Following adjustment for important covariates, this relationship persists suggesting an underlying association between a history of IPV and development of fibromyalgia or CFS.

Relationship to Current Literature

As there have been no cohort studies assessing the relationship between IPV with fibromyalgia and CFS, our comparisons are limited to previous case-control and cross-sectional studies.

A Spanish single-center case-control study (Ruiz-Pérez et al., 2009), where women with fibromyalgia (n = 287) were compared to controls (n = 287), disputed a relationship between fibromyalgia and IPV (odds ratio [OR] = 1.14; 95% CI = [0.77, 1.69]), although this study had limitations. This study was small in size as it compared 79 women who had recalled IPV in the case group to 70 women in the control group, and at baseline, these groups were significantly different. Our results contradict these findings, as we demonstrate a strongly positive association, which could be due to increased study power in our cohort and matching/adjusting for important covariates.

Previously, there was contrasting evidence regarding the association between CFS and IPV, with a case-control study in Spain (Gimeno Pi et al., 2016), adjusted for covariates, suggesting an association (adjusted odds ratio [aOR] = 10.2; 95% CI = [2.2, 456.7]), compared to an American study (Taylor & Jason, 2002) which suggested a nonsignificant relationship between abuse in adulthood (sexual and physical) and CFS. Although our results concur with the Spanish study, both the Spanish study (77 cases vs. 77 controls) and American study (225 cases vs. 72 controls) were small in size and therefore unlikely to be generalizable. Second, the American study used a broad definition of adulthood abuse, not necessarily translating to IPV. Therefore, these two studies are unlikely to be generalizable to our context. This further warranted the need for our study and its findings.

Although there is limited literature examining the relationship between IPV and the development of fibromyalgia/CFS, our findings are in agreement with the well described relationship where longitudinal cohort studies and meta-analyses demonstrated a similarly high twofold to threefold increased risk in the development of fibromyalgia/CFS in those who had experienced childhood maltreatment (Borsini et al., 2014; Clark et al., 2011; Häuser et al., 2011). Our research adds to the known literature by demonstrating a twofold increase in the risk of developing fibromyalgia/CFS following adulthood IPV exposure in women. It is possible that the biological rationale linking functional disorders to childhood maltreatment is similar to the etiology in IPV survivors including factors such as abnormal inflammatory modulation or changes in psychological processing following abuse through mechanisms such as catastrophizing of pain.

Our findings remain clinically important as we have demonstrated IPV is a risk for disease development, independent of age and deprivation. Therefore, a history of abuse should be explored in patients suspected of fibromyalgia or CFS. Equally, a diagnosis of fibromyalgia/CFS should be considered if women present with fatigue, chronic pain, cognitive impairment, or sleep disorder, particularly if there is a known background of IPV. Nonetheless, it is equally important to highlight that not all women who have experienced IPV go onto develop fibromyalgia/CFS.

In addition, of note to clinicians, addressing resilience levels in abuse survivors may be another important factor in preventing the subsequent development of functional syndromes (Casale et al., 2019). Low levels of resilience have been associated with chronic pain perception and interoception (an internal understanding of bodily changes) which are likely to precipitate symptom development in functional syndromes such as fibromyalgia and CFS (Haase et al., 2016; Yeung et al., 2012). Whereas, the introduction of interventions aiming to build resilience in abuse survivors have been shown to be protective in the development of subsequent ill health (Callaghan et al., 2019; Meng et al., 2018). Although we have not been able to capture resilience levels in this study, it may be useful for clinicians who manage IPV survivors suffering from functional syndromes to consider available interventions aimed at improving resilience in those following IPV including strengths-based, compassionate, spiritual, and person-centered approaches (Anderson, 2010).

Study Limitations

The key limitation of this study relates to the limited recording of women with IPV in routinely collected primary care data, as the expected prevalence would be higher (UK Government, 2016; WHO, 2018). In addition, for outcomes (fibromyalgia/CFS), we have equally relied on the validity of coding by the GP taking care of these patients. This means that the validity of the diagnosis of fibromyalgia or CFS was decided by their GP prior to input. It is not possible to say whether the GP made the diagnosis using specific guidelines or were inputting data sent via secondary care reports of each patient. Although we were unable to assess severity of IPV, it is possible that we only have accounted for cases severe enough to warrant discussion with the GP; therefore, our results may be an overestimate of the effect size presented. However, given a proportion of patients in the control group may be victim of IPV, the observed effect size could also be an underestimate. Interestingly, the follow-up period of the exposed group also appeared shorter than the unexposed. This is due to the exposed group moving from general practices more frequently than the unexposed group. Finally, there is significant baseline variability between the lifestyle factors between the groups, most apparent with smoking and deprivation. Although in this study we have been able to control for age, deprivation, and lifestyle factors, other similar research studies exploring functional disorders as outcomes also take into consideration the impact of educational and marital status. These factors are not recorded reliably in THIN; however, we do advise that future research takes them into consideration during analysis. We attempted to consider accounting for depression and anxiety at baseline which may be related to the subsequent development of fibromyalgia/CFS. It appears that mental ill health plays an important role in confounding the relationship between IPV and fibromyalgia/CFS. However, the reduction of effect size is not easily interpretable in light of temporality of the relationship, as the IPV population experience a high prevalence of mental ill health at the time of exposure recording (Chandan, Thomas, Bradbury-Jones, Russell, et al., 2019).

Another interesting challenge in this study relates to diversity in this cohort. We purposefully identified female survivors rather than male and female survivors, as a potential limitation of primary care coding could identify male perpetrators as victims. However, we are conscious that men are also survivors of IPV; therefore, future work must be done to validate the coding in this cohort to identify survivors and assess their health outcomes. Unfortunately, ethnicity coding is limited in this cohort, otherwise subgroup analysis by ethnicity and cultural influences would be of interest in the results. Again, future cohort studies can take this into consideration to assess the impact of diversity on these results.

Conclusion

This is the first cohort study the authors know of to assess the relationship between being a survivor of IPV and the development of fibromyalgia and CFS. We have identified a positive association between the development of these conditions with exposure to IPV. Considering the prevalence of IPV, it is important for clinicians to bear in mind this as a risk factor, thereby reducing diagnostic delay for fibromyalgia and CFS. We recommend the need for further work to establish the biopsychosocial pathway which may precipitate this relationship.

Supplemental Material

Supplementary_1_depression_and_anxiety_v2 – Supplemental material for Intimate Partner Violence and the Risk of Developing Fibromyalgia and Chronic Fatigue Syndrome

Supplemental material, Supplementary_1_depression_and_anxiety_v2 for Intimate Partner Violence and the Risk of Developing Fibromyalgia and Chronic Fatigue Syndrome by Joht Singh Chandan, Tom Thomas, Karim Raza, Caroline Bradbury-Jones, Julie Taylor, Siddhartha Bandyopadhyay and Krishnarajah Nirantharakumar in Journal of Interpersonal Violence

Footnotes

Appendix A

Fibromyalgia

Clinical Code	Description
N239.00	Fibromyalgia
N248.00	Fibromyalgia

Appendix B

Authors’ Note

Joht Singh Chandan is also affiliated with Warwick Medical School, University of Warwick, Coventry, UK.

Ethical Approval

Anonymized data were used throughout the study provided by the data provider IQVIA to University of Birmingham. Studies using The Health Improvement Network (THIN) database have had initial ethical approval from the NHS South-East Multicentre Research Ethics Committee, subject to prior independent scientific review. The Scientific Review Committee (IMS Health) approved the study protocol (SRC Reference Number: SRC18THIN034) prior to its undertaking.

Author Contributions

This study contributed to the PhD thesis for the main author J.S.C. All authors supported conceptualization of the study. Ethical approval, data collection, extraction, analysis, and first draft of the manuscript were conducted by J.S.C. All authors reviewed the final manuscript and supported publication.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Joht Singh Chandan

Julie Taylor

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Joht Singh Chandan is an academic clinical fellow in public health based at University of Warwick. Dr Chandan is also a PhD student at the University of Birmingham and his work focuses on the health outcomes following childhood and adulthood abuse. He also works for the West Midlands Police in the public protection unit investigating child abuse and domestic violence inquiries.

Tom Thomas is a clinical fellow in gastroenterology. He has extensive experience in the use of big data to answer health care questions. Alongside his work in gastroenterology, he has worked on several projects exploring the identification of abuse.

Karim Raza is the Arthritis Research UK Professor of Rheumatology at the University of Birmingham and Honorary Consultant Rheumatologist and Director of Research and Development at Sandwell and West Birmingham Hospitals NHS Trust.

Caroline Bradbury-Jones is a registered nurse, midwife, and health visitor. Her research interests lie broadly within the scope of addressing inequalities and more specifically are focused on issues of family violence and child abuse and neglect. Caroline leads the Risk, Abuse, and Violence research program at the University of Birmingham.

Julie Taylor is a nurse scientist specializing in child maltreatment. She is the professor of child protection at the University of Birmingham, in partnership with Birmingham Women’s and Children’s Hospital NHS Foundation Trust. Current work is focused on witchcraft-related violence and abuse against children in East Africa. Taylor also has a strong focus on developing strategic research leadership.

Siddhartha Bandyopadhyay is an internationally recognized economist in the field of political economy and public policy. His research interests in this area includes game theoretic modeling of criminal behavior, econometric (statistical) analysis of factors determining crime, cost benefit analysis of alternate interventions, and the impact of initiatives such as citizen reporting.

Krishnarajah Nirantharakumar is a senior clinical academic with substantial experience in health informatics research. He is the theme lead for health informatics at the Institute of Applied Health Research, University of Birmingham. He has particular interests in developing novel digital tools that enable effective/efficient health care systems and expedite health care research.

References

Anderson

K. M.

(2010). Enhancing resilience in survivors of family violence. www.copyright.com

Bacchus

L. J.

Ranganathan

Watts

Devries

(2018). Recent intimate partner violence against women and health: A systematic review and meta-analysis of cohort studies. BMJ Open, 8(7), e019995. https://doi.org/10.1136/bmjopen-2017-019995

Bested

A. C.

Marshall

L. M.

(2015). Review of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: An evidence-based approach to diagnosis and management by clinicians. Reviews on Environmental Health, 30(4), 223–249. https://doi.org/10.1515/reveh-2015-0026

Bidari

Ghavidel Parsa

Ghalehbaghi

(2018). Challenges in fibromyalgia diagnosis: From meaning of symptoms to fibromyalgia labeling. The Korean Journal of Pain, 31(3), 147–154. https://doi.org/10.3344/kjp.2018.31.3.147

Blak

B. T.

Thompson

Dattani

Bourke

(2011). Generalisability of The Health Improvement Network (THIN) database: Demographics, chronic disease prevalence and mortality rates. Informatics in Primary Care, 19(4), 251–255. http://www.ncbi.nlm.nih.gov/pubmed/22828580

Booth

(1994). What are the Read Codes? Health Libraries Review, 11(3), 177–182. https://doi.org/10.1046/J.1365-2532.1994.1130177.X

Borsini

Hepgul

Mondelli

Chalder

Pariante

C. M.

(2014, July 7). Childhood stressors in the development of fatigue syndromes: A review of the past 20 years of research. Psychological Medicine. https://doi.org/10.1017/S0033291713002468

Callaghan

J. E. M.

Fellin

L. C.

Alexander

J. H.

(2019). Promoting resilience and agency in children and young people who have experienced domestic violence and abuse: The “MPOWER” intervention. Journal of Family Violence, 34(6), 521–537. https://doi.org/10.1007/s10896-018-0025-x

Casale

Sarzi-Puttini

Botto

Alciati

Batticciotto

Marotto

Torta

(2019). Fibromyalgia and the concept of resilience. Clinical and Experimental Rheumatology, 37(1), 105–113. http://www.ncbi.nlm.nih.gov/pubmed/30747098

10.

Chandan

J. S.

Thomas

Bradbury-Jones

Russell

Bandyopadhyay

Nirantharakumar

Taylor

(2019). Female survivors of intimate partner violence and risk of depression, anxiety and serious mental illness. The British Journal of Psychiatry, 1-6. https://doi.org/10.1192/bjp.2019.124

11.

Chandan

J. S.

Thomas

Bradbury-Jones

Taylor

Bandyopadhyay

Nirantharakumar

(2019). Intimate partner violence and temporomandibular joint disorder. Journal of Dentistry, 82, 98–100. https://doi.org/10.1016/J.JDENT.2019.01.008

12.

Chen

J.-H.

Muo

C.-H.

Kao

C.-H.

Tsai

C.-H.

Tseng

C.-H.

(2017). Increased risk of new-onset fibromyalgia among chronic osteomyelitis patients: Evidence from a Taiwan cohort study. The Journal of Pain, 18(2), 222–227. https://doi.org/10.1016/j.jpain.2016.11.002

13.

Clark

Goodwin

Stansfeld

S. A.

Hotopf

White

P. D.

(2011). Premorbid risk markers for chronic fatigue syndrome in the 1958 British birth cohort. British Journal of Psychiatry, 199(4), 323–329. https://doi.org/10.1192/bjp.bp.110.083956

14.

Clauw

D. J.

(2014). Fibromyalgia. Journal of the American Medical Association, 311(15), 1547–1555. https://doi.org/10.1001/jama.2014.3266

15.

Collin

S. M.

Bakken

I. J.

Nazareth

Crawley

White

P. D.

(2017a). Health care resource use by patients before and after a diagnosis of chronic fatigue syndrome (CFS/ME): A Clinical Practice Research Datalink study. BMC Family Practice, 18(1), Article 60. https://doi.org/10.1186/s12875-017-0635-z

16.

Collin

S. M.

Bakken

I. J.

Nazareth

Crawley

White

P. D.

(2017b). Trends in the incidence of chronic fatigue syndrome and fibromyalgia in the UK, 2001–2013: A Clinical Practice Research Datalink study. Journal of the Royal Society of Medicine, 110(6), 231–244. https://doi.org/10.1177/0141076817702530

17.

Crane

C. A.

Hawes

S. W.

Weinberger

A. H.

(2013). Intimate partner violence victimization and cigarette smoking. Trauma, Violence, & Abuse, 14(4), 305–315. https://doi.org/10.1177/1524838013495962

18.

Gimeno Pi

Guitard Sein-Echaluce

M. L.

Rosselló Aubach

Torres Puig-Gros

Fernández Solà

(2016). Stressful events in the onset of chronic fatigue syndrome. Revista Espanola de Salud Publica, 90, e1–e7. http://www.ncbi.nlm.nih.gov/pubmed/27535808

19.

Haase

Stewart

J. L.

Youssef

May

A. C.

Isakovic

Simmons

A. N.

Paulus

M. P

. (2016). When the brain does not adequately feel the body: Links between low resilience and interoception. Biological Psychology, 113, 37–45. https://doi.org/10.1016/j.biopsycho.2015.11.004

20.

Häuser

Ablin

Fitzcharles

M.-A.

Littlejohn

Luciano

J. V.

Usui

Walitt

(2015). Fibromyalgia. Nature Reviews Disease Primers, 1(1), Article 15022. https://doi.org/10.1038/nrdp.2015.22

21.

Häuser

Kosseva

Üceyler

Klose

Sommer

(2011). Emotional, physical, and sexual abuse in fibromyalgia syndrome: A systematic review with meta-analysis. Arthritis Care & Research, 63(6), 808–820. https://doi.org/10.1002/acr.20328

22.

Hempel

Chambers

Bagnall

A.-M.

Forbes

(2008). Risk factors for chronic fatigue syndrome/myalgic encephalomyelitis: A systematic scoping review of multiple predictor studies. Psychological Medicine, 38(7), 915–926. https://doi.org/10.1017/S0033291707001602

23.

IQVIA. (2018). IQVIA medical research data. Health Research Authority. https://www.hra.nhs.uk/planning-and-improving-research/application-summaries/research-summaries/the-health-improvement-network-thin-database/

24.

Kim

C. H.

Vincent

Clauw

D. J.

Luedtke

C. A.

Thompson

J. M.

Schneekloth

T. D.

T. H.

(2013). Association between alcohol consumption and symptom severity and quality of life in patients with fibromyalgia. Arthritis Research & Therapy, 15(2), R42. https://doi.org/10.1186/ar4200

25.

Lévesque

L. E.

Hanley

J. A.

Kezouh

Suissa

(2010). Problem of immortal time bias in cohort studies: Example using statins for preventing progression of diabetes. British Medical Journal, 340, b5087. https://doi.org/10.1136/BMJ.B5087

26.

Meng

Fleury

M.-J.

Xiang

Y.-T.

D’Arcy

(2018). Resilience and protective factors among people with a history of child maltreatment: A systematic review. Social Psychiatry and Psychiatric Epidemiology, 53(5), 453–475. https://doi.org/10.1007/s00127-018-1485-2

27.

Miller

A. H.

(1998). Neuroendocrine and immune system interactions in stress and depression. The Psychiatric Clinics of North America, 21(2), 443–463. http://www.ncbi.nlm.nih.gov/pubmed/9670236

28.

Moukaddem

Chaaya

Slim

Z. F. N.

Jaffa

Sibai

A. M.

Uthman

(2017). Fibromyalgia: Epidemiology and risk factors, a population-based case-control study in Lebanon. International Journal of Rheumatic Diseases, 20(2), 169–176. https://doi.org/10.1111/1756-185X.12701

29.

NHS Digital. (2017). Read codes. https://digital.nhs.uk/article/1104/Read-Codes

30.

Nicol

A. L.

Sieberg

C. B.

Clauw

D. J.

Hassett

A. L.

Moser

S. E.

Brummett

C. M.

(2016). The association between a history of lifetime traumatic events and pain severity, physical function, and affective distress in patients with chronic pain. The Journal of Pain, 17(12), 1334–1348. https://doi.org/10.1016/j.jpain.2016.09.003

31.

Office for National Statistics. (2016). Intimate personal violence and partner abuse. https://www.ons.gov.uk/peoplepopulationandcommunity/crimeandjustice/compendium/focusonviolentcrimeandsexualoffences/yearendingmarch2015/chapter4intimatepersonalviolenceandpartnerabuse

32.

Reid

Chalder

Cleare

Hotopf

Wessely

(2000). Chronic fatigue syndrome. British Medical Journal, 320(7230), 292–296. https://doi.org/10.1136/bmj.320.7230.292

33.

Ruiz-Pérez

Plazaola-Castaño

Cáliz-Cáliz

Rodríguez-Calvo

García-Sánchez

Ferrer-González

M. Á.

López-Chicheri García

(2009). Risk factors for fibromyalgia: The role of violence against women. Clinical Rheumatology, 28(7), 777–786. https://doi.org/10.1007/s10067-009-1147-6

34.

Sprague

Madden

Dosanjh

Godin

Goslings

J. C.

Schemitsch

E. H.

Bhandari

(2013). Intimate partner violence and musculoskeletal injury: Bridging the knowledge gap in orthopaedic fracture clinics. BMC Musculoskeletal Disorders, 14, Article 23. https://doi.org/10.1186/1471-2474-14-23

35.

Suglia

S. F.

Sapra

K. J.

Koenen

K. C.

(2015). Violence and cardiovascular health: A systematic review. American Journal of Preventive Medicine, 48(2), 205–212. https://doi.org/10.1016/j.amepre.2014.09.013

36.

Sumpton

J. E.

Moulin

D. E.

(2014). Fibromyalgia. Handbook of Clinical Neurology, 119, 513–527. https://doi.org/10.1016/B978-0-7020-4086–3.00033-3

37.

Taylor

R. R.

Jason

L. A.

(2002). Chronic fatigue, abuse-related traumatization, and psychiatric disorders in a community-based sample. Social Science & Medicine, 55(2), 247–256. https://doi.org/10.1016/S0277-9536(01)00168-X

38.

Townsend

Phillimore

Beattie

(1988). Health and deprivation: Inequality and the North. 1988. Croom Helm Google Scholar.

39.

Turk

D. C.

Wilson

H. D.

(2010). Fear of pain as a prognostic factor in chronic pain: Conceptual models, assessment, and treatment implications. Current Pain & Headache Reports, 14(2), 88–95. https://doi.org/10.1007/s11916-010-0094-x

40.

UK Government. (2016). Guidance: Domestic violence and abuse. https://www.gov.uk/guidance/domestic-violence-and-abuse#domestic-violence-and-abuse-new-definition

41.

Varinen

Kosunen

Mattila

Koskela

Sumanen

(2017). The relationship between childhood adversities and fibromyalgia in the general population. Journal of Psychosomatic Research, 99, 137–142. https://doi.org/10.1016/j.jpsychores.2017.06.011

42.

World Health Organization. (2018). Violence against women. http://www.who.int/mediacentre/factsheets/fs239/en/

43.

Yeung

E. W.

Arewasikporn

Zautra

A. J.

(2012). Resilience and Chronic Pain. Journal of Social & Clinical Psychology, 31(6), 593–617. https://doi.org/10.1521/jscp.2012.31.6.593

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.01 MB