pain among women: associations with socio-economic and work conditions

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European Journal of Pain 10 (2006) 435–447

Pain among women: Associations with socio-economicand work conditions

Beata Jablonska a, Joaquim J.F. Soares a,*, Orjan Sundin b

a Unit of Mental Health, Stockholm Centre of Public Health, Department of Public Health Sciences, Karolinska Institute,

P.O. Box 17533, SE-11891 Stockholm, Swedenb Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden

Received 3 January 2005; received in revised form 7 June 2005; accepted 13 June 2005Available online 27 July 2005

Abstract

We examined pain prevalence (general/body sites) and its characteristics/consequences among a randomised sample of womenfrom the general population between 18 and 64 years (n = 3616). We also scrutinised associations between pain and various factors(e.g. socio-economic) by means of multivariate logistic/linear regression analyses. The women completed a questionnaire assessingvarious areas (e.g. pain). The design was cross-sectional and data were collected during 8 consecutive weeks. Sixty-three per cent ofwomen reported pain during the last 3 months, of which 65% during more than 3 months. The multivariate analyses revealed asso-ciations between various socio-economic factors (e.g. financial strain) and pain in general/all studied body sites. In addition, psy-chosocial work conditions (i.e. work strain and social support) were significantly related to pain. Moreover, the multivariateanalyses conducted among women with pain indicated relationships between socio-economic/psychosocial work conditions, andpain characteristics (e.g. intensity) and consequences (i.e. disability). A large number of women from the general population sufferfrom pain, in particularly prolonged pain. Women in a deprived socio-economic situation not only run a higher pain risk, but alsoexperience their pain as more severe/disabling than their more privileged counterparts. Improvements of, for example, the socio-eco-nomic status among women living in deprived social and material circumstances, along with improved working environment may becrucial to reduce women�s pain problems.� 2005 European Federation of Chapters of the International Association for the Study of Pain. Published by Elsevier Ltd. All

rights reserved.

Keywords: Pain; Socio-economic factors; Psychosocial work conditions; Pain characteristics; Pain-related disability

1. Introduction

Various studies show that women are overrepre-sented with regard to certain pain conditions, e.g.neck–shoulder pain (e.g. Mullersdorf and Soderback,2000; Chrubasik et al., 1998; Andersson et al., 1993;LeResche, 1997; Punnett and Bergqvist, 1999; Picavetand Schouten, 2003; Bassols et al., 1999; Bingefors

1090-3801/$32 � 2005 European Federation of Chapters of the International

reserved.

doi:10.1016/j.ejpain.2005.06.003

* Corresponding author. Tel.: +46 08 51778147; fax: +46 0851778120.

E-mail address: [email protected] (J.J.F. Soares).

and Isacson, 2004; Bergman et al., 2001). This appearsalso to be the case in Sweden, where about 75% of pa-tients seeking treatment for prolonged musculoskeletalpain are women (Statistics Sweden, 2002). Further, inpain patient samples, women report pain of greaterintensity and frequency, and of longer duration (Unruh,1996; Brattberg et al., 1997; Celentano et al., 1990). Fi-nally, they tend to experience great pain-related disabil-ity (Celentano et al., 1990; Keefe et al., 2000), are largeconsumers of health care for pain (e.g. Mantyselkaet al., 2002; Rekola et al., 1997; Svardsudd and Korpela,1996), and are often on sick-leave/early retirement due

Association for the Study of Pain. Published by Elsevier Ltd. All rights

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436 B. Jablonska et al. / European Journal of Pain 10 (2006) 435–447

to pain (e.g. Brage et al., 1998; Hagen and Thune, 1998;RFV, 2003; Leijon et al., 2004). Although pain condi-tions among women are receiving increasing attention,there seems to be relatively little research into the rea-sons behind the higher pain prevalence amongst women.The issue of correlates of pain and its characteristics inthe female population have not attracted much interesteither.

A substantial body of research suggests that womenliving in poor socio-economic circumstances have morehealth problems than those living in better conditions(Mead et al., 2001; Arber and Lahelma, 1993). In addi-tion, studies show that a poor socio-economic situationis not only important for the occurrence of ill-health,but also for how severe it will become (e.g. Kogevinasand Porta, 1997; Blank and Diderichsen, 1996; Dalstraet al., 2002).

Despite the evidence about the association betweensocio-economic inequalities and ill-health, insufficientattention has been paid to the identification of thosesubgroups in the female population that might be athigher risk of developing pain conditions. Although var-ious studies suggest a connection between socio-eco-nomic factors and pain, gender is often considered asonly one of the explanatory variables (e.g. Bergmanet al., 2001; Picavet and Schouten, 2003). This limitationof the role of gender may lead to incorrect conclusions ifthe associations between socio-economic disadvantagesand pain are not the same, in direction or magnitude,among men and women.

Moreover, existing findings on the relationship be-tween socio-economic factors and pain among womenare to some extent incomplete. For example, a recentSwedish study revealed a connection between maritalstatus, financial difficulties, and sick-leave/early retire-ment and pain (Bingefors and Isacson, 2004). However,despite results indicating greater vulnerability to poorhealth among first- and second-generation immigrantwomen than among native Swedish women (Robertsonet al., 2003) there is a lack of investigation into the pos-sible role of foreign background on pain problems. Inaddition, there are some contradictory findings in thefield. For instance, an association between low educa-tion and pain among women has been found in somestudies (Viikari-Juntura et al., 1991; Leino-Arjas et al.,1998), but not in others (e.g. Stronks et al., 1997; Bingef-ors and Isacson, 2004).

Associations between socio-economic factors and theseverity of pain symptoms among women have beeninvestigated to even lesser extent. In a few cases wheresuch relationships were taken into account (Bergmanet al., 2001; Soares and Jablonska, 2004; Brekke et al.,2002; Eachus et al., 1999; Jensen et al., 2004; Gerdleet al., 2004), they were not addressed among men and/or women separately. Consequently, it is difficult todetermine whether the connection between socio-eco-

nomic factors and pain severity should be considereduniversal to the pain-population irrespective of genderor whether there are some associations unique to the fe-male population. Thus, one of the intentions of the cur-rent study is to investigate the significance of socio-economic factors for pain and its severity amongwomen.

Psychosocial work conditions also shape health andseverity of ill-health (Niedhammer and Chea, 2003;Cheng et al., 2000). However, a review of Niedhammeret al. (2000) suggests that women have received lessattention than men in the literature concerning thisissue. Most studies of psychosocial work factors suggestthat job strain and lack of support from co-workers arepositively associated with pain among women (Coleet al., 2001; Ahlberg-Hulten et al., 1995; Josephsonet al., 1997).

Nonetheless, this association was not confirmed in astudy of Barnekow-Bergkvist et al. (1998). Furthermore,they reported that a high level of control was associatedwith an increased risk of having neck–shoulder pain.Thus, further investigation into this issue may be war-ranted. In addition, it is plausible to assume that thejob situation may not only be associated with the occur-rence of pain, but also with its development. To the bestof our knowledge only a few studies (e.g. Rolander andBellner, 2001) have made the effort to address this issue.

In conclusion, there are relatively few studies address-ing pain in general and pain in various body sites, itscharacteristics and relation to socio-economic and psy-chosocial work factors using large, randomised samplesof women from the general population. In addition, cau-tion must be taken in viewing women as a homogenousgroup, as has often been the case. There are distinctionsaccording to socio-economic and work-related circum-stances. Therefore, women�s vulnerability to pain shouldnot to be considered as uniform, but affected by theaforementioned differences. Thus, considering womenas a heterogeneous group may shed light on factorsoperating in pain problems among females. Since formaleducation level, financial support/strain, foreign back-ground, occupational and marital status are the mostimportant factors according to which an individual levelof socio-economic status can be examined, these vari-ables were included into the umbrella concept of socio-economic status.

Finally, the extent to which socio-economic/work-re-lated variables may be confounded with variables suchas Body Mass Index (BMI) and chronic diseases, hasnot been adequately addressed in several cases. Thus,since both can be regarded as a question of socio-eco-nomic status (Rahkonen et al., 1993, 1998), the intentionis to control for the influence of these factors in the pres-ent study.

The aims of this study were to: (i) examine the asso-ciations between socio-economic and psychosocial work

B. Jablonska et al. / European Journal of Pain 10 (2006) 435–447 437

variables and the presence of pain in general/variousbody sites; (ii) describe pain characteristics (e.g. painintensity) and behavioural effects of pain (e.g. treatmentseeking) among women with pain; and (iii) examine theassociations between pain severity, pain related disabil-ity and socio-economic, psychosocial work variablesamong women with pain. Our main hypotheses werethat poor socio-economic and work conditions wouldwere associated with higher risk for pain and, amongwomen already affected by pain, with higher risk formore severe pain characteristics (e.g. greater intensity).

2. Methods

2.1. Participants

The participants consisted of 6000 women randomlyselected from the general population resident in Stock-holm�s county, albeit representative for those aged 18–64 years. Of the contacted women, 3616 participatedin the study (64.1%) and 2022 declined. We excluded362 women because they could not be reached or didnot speak/write Swedish. Participants and non-partici-pants did not differ in age. Of the 3616 participating wo-men, the majority were married/cohabited (65.1%) andworked (68%) (Table 1), and 2276 (63%) complainedof various types of pain (e.g. back-pain) (Table 3). Thus,the studied sample consisted of 2276 women with pain(WP) and 1340 women without pain (WWP).

2.2. Instruments

The participants were assessed on various areas (e.g.depression, pain). The focus of this study was on socio-economic, work- and pain-related variables.

Pain variables were assessed with The Pain Question-

naire (Arner, 1984; Carlsson, 1984), which containsnumerous items about pain (e.g. pain intensity, 0–10).Participants were asked to indicate the location of pain,where ‘‘pain in the entire body’’ constituted an alterna-tive of its own. We also rated perceived disability with adichotomised index consisting of 15 questions (yes/noanswers) covering different aspects of perceived pain-re-lated disability (e.g. mobility). High scores correspondto high levels of disability. Cronbach a for the disabilityindex among WP was 0.78. Pain was defined as pain ofat least 1 month duration experienced during the past 3months in the specific sites (e.g. back).

Job strain/social support at work was assessed withthe Karasek and colleagues� job demand-control-sup-port model (Karasek and Theorell, 1990). This scalecontains 18 items (scored from 1 to 4), of which 6 con-cern job control, 5 job demands and 7 social supportat work. Job strain is derived from the ratio betweenjob demands and control. High scores correspond to

high levels of job demands, control and social support.Cronbach a�s for job demands, control and social sup-port were in range of 0.72–0.83.

In addition, we assessed various socio-economic fac-tors such as education and current financial support.Participants who answered ‘‘no’’ to the question ‘‘Doyou have a foreign background?’’ were considered as na-tive Swedish. Financial strain (preoccupation with howto make ends meet) was measured with one questionin a ‘‘no/sometimes/often/always’’ format. An individ-ual was defined as having financial strain if she/he choseany response other than ‘‘no’’.

Finally, we assessed in a ‘‘yes/no’’ format whetherparticipants suffered from diseases (e.g. gastric ulcer).BMI was computed for each participant. Consumptionof pain-killers among women with pain was assessedusing a ‘‘never/as needed/regularly’’ format.

2.3. Design and procedure

The study was cross-sectional and data were col-lected during 8 consecutive weeks with two remindersin between. The addresses were obtained from theAdressKompaniet, which is a company with data onthe population living in Sweden. Sample was chosenusing a random selection program. Participants weresent questionnaires to their homes together with a let-ter informing them of the study. They were asked toreturn the questionnaire by post. All participants werevolunteers and gave their informed consent. Confiden-tiality was emphasised. Approval for the study wasobtained from the Ethical Committee of the Karo-linska Institute.

2.4. Statistical analyses

The data were examined with ANOVAs, t-tests,chi-square tests (v2) and Spearman�s correlation (r).Post-hoc tests were performed according to theDunn�s/Bonferroni method. For the univariate analysesand for post-hoc tests, the significance level was set atp < 0.01. For multivariate analyses, the significance levelwas set at p < 0.05.

Multivariate logistic regression analyses were com-puted among all participants to assess the contributionof socio-economic and psychosocial work-related fac-tors for pain in general (i.e. irrespective of localisation)and separately for all studied pain sites, while control-ling for age and confounders (e.g. BMI). The same can-didate explanatory factors were used in all logisticanalyses and consisted of variables that significantly dif-ferentiated participants with and without pain in theunivariate analyses (foreign background, marital status,education, occupation, present support, financial strain,job strain and social support at work). Results were ex-pressed in the form of odds ratio and R2.

Table 1Age, socio-economic, psychosocial work and health variables among women with/without pain

Characteristics WPa WWPb

n = 2276 % n = 1340 %

Age (n) (2252) (1329)Mean (SD) 41.6 12.7 39.4 12.5

Foreign background (n) (2226) (1313)No 1730 77.7 1071 81.6Yes 496 22.3 242 18.4

Marital status (n) (2258) (1329)Married/cohabiting 1493 66.1 853 64.2Single 465 20.6 326 24.5Divorced/separated 262 11.6 122 9.2Widow 38 1.7 28 2.1

Children living at home (n) (2276) (1340)No 1101 48.4 662 49.4Yes 1175 51.6 678 50.6

Education level (n) (2257) (1327)Highc 929 41.2 640 48.2Intermediated 842 37.3 499 37.6Lowe 486 21.5 188 14.2

Occupation (n) (2102) (1216)Blue-collar workers 525 25 242 19.9Low/intermediate white-collar workers 1408 67 862 70.9High white-collar workers 101 4.8 61 5Otherf 68 3.2 51 4.2

Financial support (n) (2267) (1330)Working 1462 64.5 950 71.4Studying 240 710.6 174 13.1Sick-leave/early retirement 362 16 66 5Unemployment/social security 203 8.9 140 10.5

Job strain (n) (2181) (1281)Mean (SD) 1 (0.3) 0.9 (0.3)

Social support at work (n) (2132) (1258)Mean (SD) 3.4 (0.5) 3.5 (0.5)

Financial strain (n) (2253) (1317)No 1166 51.8 849 64.5Yes 1087 48.2 468 35.5

BMI (n) (2183) (1290)<25 1559 71.4 1031 79.9>25 624 28.6 259 20.1

Diseasesg (n) (2218) (1311)No 896 40.4 801 61.1Yes 1322 59.6 510 38.9

a Women with pain.b Women without pain.c University/similar.d Upper secondary school/similar.e Primary school/similar or lower.f e.g. Students.g e.g. Gastric ulcer.


In addition, multivariate logistic and linear regressionanalyses were computed among participants with painto examine the association between socio-economic,psychosocial work-related factors and frequent/complexpain, pain intensity and pain-related disability. Eachmodel consisted of variables that were unequally distrib-

uted between participants with a more and less severepain picture in univariate analyses. Accordingly, themodel for frequent pain/disability included: foreignbackground, marital status, education, occupation,present support, financial strain, job strain and socialsupport at work. The same variables were included in


the model for complex pain/pain intensity, with theexception of foreign background and occupation con-cerning complex pain, and foreign background and mar-ital status concerning pain intensity. All models wereadjusted for age, BMI and chronic diseases. Resultswere expressed in the form of standardised bs and oddsratios.

All categorical variables with more than two catego-ries were transformed into dummy-variables. Single datawere lost for a number of variables, as indicated by the n

values and degrees of freedom.

3. Results

3.1. All women

3.1.1. Age, socio-economic and work variables

As shown in Table 1, WP compared with WWPwere more often foreigners (v2(1) = 7.4, p < 0.0064),divorcees (v2(1) = 5.1, p < 0.02), singles (v2(1) = 7.5,p < 0.006), low educated (v2(2) = 33.7, p < 0.0001)and older (t(3579) = �5.0, p < 0.0001).

WP were more often blue-collar workers thanWWP, whereas WWP tended to be low/intermediatewhite-collar workers (v2(3) = 12.3, p < 0.006). Further,WP were more often on sick-leave/early retirementthan WWP, whereas WWP tended to be on employ-ment/student allowances (v2(3) = 98.1, p < 0.0001).WP were also more often financially strained (v2(1) =54.6, p < 0.0001) than WWP. Finally, WP scored high-er on job strain (t(3460) = 8.4, p < 0.0001) and loweron social support (t(3388) = 59.6, p < 0.0001) thanWWP.

3.1.2. Health variables (BMI, chronic diseases)

As shown in Table 1, WP had higher BMI(t(3471) = 6.8, p < 0.0001) and reported suffering moreoften from chronic diseases (v2(1) = 141.5, p < 0.0001)than WWP.

3.1.3. Variables associated with pain in general and pain

sites

Pain in general. As shown in Table 2, low education,on sick-leave/early retirement, financial strain and ele-vated scores in job strain were independently associatedwith a higher pain risk. Widowhood was associated witha lower risk for pain. The model explained 8% of thevariation in pain.

Headache/facial pain. On sick-leave/early retirement,financial strain and elevated scores in job strain wereindependently associated with a higher headache/facialpain risk. Widowhood and elevated scores in social sup-port at work were related with a lower headache/facialpain risk. The model explained 13.3% of the variationin headache/facial pain.

Neck/shoulder pain. Low/intermediate education, onsick-leave/early retirement, financial strain and elevatedscores in job strain were independently related to a high-er neck/shoulder pain risk. Widowhood, on unemploy-ment benefit/social security and elevated scores insocial support at work were associated with a lowerneck/shoulder pain risk. The model explained 10.6% ofthe variation in neck/shoulder pain.

Pain in the upper limbs. Foreign background, low/intermediate education, on sick-leave/early retirement,financial strain and elevated scores in job strain wereindependently associated with a higher risk for pain inthe upper limbs. Widowhood and elevated scores in so-cial support at work were associated with a lower riskfor pain in the upper limbs. The model explained 20%of the variation in pain in the upper limbs.

Back-pain. Low education, on sick-leave/earlyretirement, financial strain and elevated scores in jobstrain were independently related to a higher back-pain risk. Widowhood and elevated scores in socialsupport at work were associated with a lower back-pain risk. The model explained 9.6% of the variationin back-pain.

Pain in the lower limbs. Foreign background, low/intermediate education, on student allowances/sick-leave/early retirement, financial strain and elevatedscores in job strain were independently associated witha higher risk for pain in the lower limbs. Widowhoodand on unemployment benefit/social security were re-lated to a lower risk for pain in the lower limbs. Themodel explained 15.3% of the variation in pain the lowerlimbs.

Pain in the entire body. Foreign background, on sick-leave/early retirement, financial strain and elevatedscores in job strain were independently associated witha higher risk for pain in the entire body. The model ex-plained 35.6% of the variation in pain in the entire body.

3.2. Women with pain

3.2.1. Pain characteristics, paramedical treatment and

pain medication

As shown in Table 3, experiences of pain during thepast 3 months were reported by 63% of women, of which65% had suffered from pain longer than 3 months (40%when contrasting with the entire sample).

The most common pain complaints were back-pain(67.4%), neck/shoulder pain (62.2%) and pain in thehips/lower limbs (47.4%). Pain in the entire body was re-ported by 10.1% of women. Mean pain intensity was 6(0–10).

About 17% of women experienced pain all the time,47% complained of complex pain (i.e. different paintypes) and 55% evaluated their pain sensation as bothdeep/light. Finally, mean pain-related disability was 4(0–15).

Table 2Multivariate Logistic (odds ratio) Regression Analyses of the associations between socio-economic, psychosocial work variables and pain in general/pain by locations among women (adjusted forage, BMI, chronic diseases)

Variables General pain Head/face Neck/shoulder Upper limbs Back Lower limbs Entire body

OR 95%CI OR 95%CI OR 95%CI OR 95%CI OR 95%CI OR 95%CI OR 95%CI

Foreign backgrounda

Nob 1.0 1.0 1.0 1.0 1.0 1.0 1.0Yes 1.1 0.9–1.4 1.3 0.9–1.8 1.2 0.9–1.5 1.4 1.0–1.9* 1.1 0.9–1.4 1.5 1.1–1.9** 2.4 1.5–4.0***

Marital statusa

Married/cohabitantb 1.0 1.0 1.0 1.0 1.0 1.0Single 0.9 0.7–1.1 0.9 0.6–1.2 0.8 0.6–1.1 0.8 0.5–1.1 0.9 0.7–1.1 1.1 0.8–1.4 0.5 0.3–1.1Divorced 0.9 0.7–1.2 1.2 0.8–1.8 0.9 0.7–1.3 1.1 0.7–1.6 1.0 0.7–1.3 0.9 0.6–1.30.3 1.0 0.5–2.0Widow 0.4 0.2–0.7** 0.3 0.1–1.0* 0.4 0.2–0.8* 0.2 0.1–0.6** 0.3 0.2–0.7** 0.3 0.1–0.7** 0.4 0.1–1.5

Education levela

Highb,c 1.0 1.0 1.0 1.0 1.0 1.0 1.0Intermediated 1.1 0.9–1.4 1.1 0.8–1.5 1.2 1.0–1.5* 1.4 1.1–1.9* 1.2 1.0–1.5 1.4 1.1–1.8** 1.0 0.6–1.8Lowe 1.6 1.3–2.1*** 1.4 0.9–2.1 1.8 1.3–2.4**** 1.6 1.1–2.3* 1.7 1.3–2.3**** 2.2 1.6–3.0**** 1.5 0.8–2.8

Occupationa

Blue-collar workersb 1.0 1.0 1.0 1.0 1.0 1.0 1.0Low/interm.f white-collar workers 1.1 0.8–1.3 1.1 0.8–1.6 1.1 0.8–1.4 0.8 0.6–1.2 1.0 0.7–1.2 1.0 0.8–1.3 0.8 0.5–1.5High white-collar workers 1.2 0.8–1.8 0.7 0.3–1.7 1.0 0.6–1.7 0.8 0.4–1.5 1.0 0.6–1.7 1.0 0.6–1.7 0.3 0.1–1.4Otherg 0.8 0.5–1.3 0.9 0.4–2.0 0.9 0.5–1.6 0.7 0.3–1.5 0.7 0.4–1.2 0.6 0.3–1.2 0.3 0.1–1.5

Financial supporta

Workingb 1.0 1.0 1.0 1.0 1.0 1.0 1.0Studying 1.2 0.8–1.6 0.9 0.5–1.5 1.0 0.7–1.4 1.0 0.6–1.8 1.1 0.8–1.6 1.7 1.1–2.5* 1.7 0.7–4.2Sick-leave/early retirement 2.6 1.8–3.7**** 3.3 2.1–5.3**** 2.7 1.9–3.9**** 4.3 2.8–6.5**** 2.8 1.9–4.0**** 3.6 2.4–5.3**** 9.8 5.6–17.2****

Unemployment/social security 0.8 0.5–1.3 0.7 0.4–1.1 0.7 0.5–1.0* 0.7 0.5–1.2 0.9 0.6–1.2 0.7 0.5–1.0 0.4 0.1–1.3

Job strainh 2.2 1.6–3.1**** 2.4 1.4–3.9*** 2.4 1.7–3.5**** 2.5 1.5–4.1*** 2.2 1.5–3.2**** 2.7 1.8–4.1**** 2.9 1.3–6.4*

Social support at workh 0.8 0.7–1.0 0.7 0.5–1.0* 0.7 0.6–0.9** 0.7 0.5–0.9* 0.8 0.6–1.0* 0.8 0.7–1.1 0.8 0.5–1.3

Financial straina

Nob 1.0 1.0 1.0 1.0 1.0 1.0 1.0Yes 1.4 1.2–1.7*** 1.6 1.2–2.2*** 1.6 1.3–2.0**** 1.7 1.3–2.3**** 1.4 1.2–1.7*** 1.3 1.1–1.6* 2.5 1.6–4.0***

a Categorical variables.b Comparison category.c University/similar.d Upper secondary school/similar.e Primary school/similar or lower.f Intermediate.g e.g. Students.h Continuos variables.* p < 0.05.

** p < 0.01.*** p < 0.001.**** p < 0.0001.

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Table 3Pain characteristics, paramedical treatments and pain medicationamong women with pain

Variables WPa

n = 2276 %

Pain localisationb

Head/face 435 19.9Neck–shoulder 1363 62.2Upper limbs 611 27.8Back 1483 67.4Hips/lower limbs 1043 47.4Entire body 220 10.1Otherc 241 11

Pain duration (2241)1 6 3 months 772 34.4>3 months 1469 65.6

Pain frequency (n) (2118)All the time 358 16.9All the timed 109 5.1Almost all the time 266 12.6Almost every day 650 30.7Almost every week 735 34.7

Pain intensity (0–10)Mean (SD) 6 (2.4)

Pain complexity (types) (n) (2172)One 1151 53Several 1021 47

Pain sensation (n) (2164)Deep 726 33.5Light 248 11.5Both 1190 55

Disability (0–15)Mean (SD) 4 (3.1)

Paramedical treatmente(n) (2208)Yes 1332 60.3No 876 39.7

Effect of paramedical treatments (n) (1325)Unchanged/worse 346 26Improved/temporarily pain free 979 74

Pain medicationf(n) (2275)No 507 22.3At need 1473 64.7Regularly 295 13

Effect of pain medication (n) (1649)None 142 8.6Poor 955 58Good 552 33.4

a Women with pain.b The number/percentage for this variable add up to more than the

number of participants/100% as one participant may have pain inseveral sites.

c e.g. Pelvic pain.d e.g. Except 1 h or so immediately after treatment.e e.g. Physiotherapy.f e.g. Analgesics.


Sixty per cent of women had received paramedicaltreatment, of which 74% reported that they improved/were temporarily pain free due to the treatments. About

80% of women used pain medication, with 66% report-ing no/poor effects.

3.2.2. Pain complexity in relation to age, socio-economic,

work and health variables (results not shown in table

format)Women with complex pain were more often divorcees

(v2(3) = 12.5, p < 0.006), low educated (v2(2) = 12.4,p < 0.002), on sick-leave/early retirement (v2(3) = 65.6,p < 0.0001), financially strained (v2(1) = 16.9, p <0.0001), suffering from diseases (v2(1) = 40, p < 0.0001)and older (t(2147) = �4.4, p < 0.0001) than women withnon-complex pain. Finally, women with complex painscored higher on job strain (t(2080) = 20.8, p < 0.0001)and lower on social support at work (t(2037) = 20.3,p < 0.0001) compared to those with non-complex pain.

3.2.3. Pain frequency in relation to age, socio-economic,


format)

Women with frequent pain were older (t(2095) =�7.8, p < 0.0001) and more often single/divorcees(v2(3) = 19.4, p < 0.0002), foreigners (v2(1) = 8.3,p < 0.004), low educated (v2(2) = 19.3, p < 0.0001),blue-collar workers (v2(3) = 11.6, p < 0.009), on sick-leave/early retirement (v2(3) = 182.3, p < 0.0001) andfinancially strained (v2(1) = 22.2, p < 0.0001) than wo-men with non-frequent pain. Women with frequent painwere more likely to have a BMI > 25 (v2(1) = 21.7,p < 0.0001) and suffer from diseases (v2(1) = 34.6,p < 0.0001) than their counterparts. Finally, they scoredhigher on job strain (t(2031) = 19.5, p < 0.0001) andlower on social support at work (t(1986) = 22.5, p <0.0001).

3.2.4. Pain intensity in relation to age, socio-economic,


format)Pain intensity was positively correlated with age

(r = 0.07, p < 0.0017). Pain intensity was related to edu-cation (F(2,2083) = 21.6, p < 0.0001) and current finan-cial support (F(3,2089) = 43.8, p < 0.0001). This wasdue to greater pain intensity among women with low/intermediate education than among women with higheducation. Women on sick-leave/early retirement dif-fered from those who were employed and on studentallowances/unemployment benefit/social security (all atp < 0.0001). Pain intensity was also associated withoccupation (F(3,1942) = 4.5, p < 0.004), due to greaterpain intensity among blue-collar workers than amonglow/intermediate white-collar workers (p < 0.001).Moreover, high pain intensity was connected with finan-cial strain (t(2079) = 36.8, p < 0.0001).

Correlation analyses revealed a positive relation be-tween pain intensity/job strain (r = 0.11, p < 0.0001)and negative between pain intensity/social support at


work (r = �0.14, p < 0.0001). Finally, women reportingdiseases (t(2050) = 72.8, p < 0.0001) and smokers(t(2066) = 18.6, p < 0.0001) complained of greater painintensity.

3.2.5. Disability in relation to age, socio-economic, workand health variables (results not shown in table format)

Disability was positively correlated with age(r = 0.18, p < 0.0001). Disability was related with mar-ital status (F(3,2252) = 18.7, p < 0.0001) and education(F(2,2252) = 41.9, p < 0.0001). This was due to greaterdisability among widows/divorcees than among mar-ried/cohabitant/single, respectively, and among womenwith low education than among women with interme-diate/high education (all at p < 0.0001). Disability wasalso connected with current financial support(F(3,2261) = 359, p < 0.0001), due to greater disabilityamong women on sick-leave/early retirement thanamong women who were employed, on student allow-ances/unemployment benefit/social security (all atp < 0.0001), and among women on unemploymentbenefit/social security than among employed women(p < 0.0014). Similarly, disability was associated withoccupation (F(3,2097) = 11.3, p < 0.0001), due togreater disability among blue-collar workers thanamong low/intermediate/high white-collar workers(both at p < 0.0001). Moreover, women with high dis-ability were more likely to be foreigners (t(2222) =58.3, p < 0.0001) and financially strained (t(2249) =95.7, p < 0.0001).

Correlation analyses showed that disability waspositively associated with job strain (r = 0.22,p < 0.0001) and negatively with social support at work(r = �0.24, p < 0.0001). Finally, women withBMI > 25 (t(2179) = 78.8, p < 0.0001) and reportingdiseases (t(2214) = 94.4, p < 0.0001) experiencedgreater disability.

3.2.6. Variables associated with complex/frequent pain,

pain intensity and disability

Complex pain. As shown in Table 4, on sick-leave/early retirement, financial strain and elevated levels injob strain were independently associated with a highercomplex pain risk. On unemployment/social securitywas related to a lower complex pain risk. The model ex-plained 4% of the variance in complex pain.

Frequent pain. On sick-leave/early retirement andfinancial strain were independently associated with ahigher frequent pain risk. Elevated scores in social sup-port at work were related to a lower frequent pain risk.The model explained 9.3% of the variance in frequentpain.

Pain intensity. Low/intermediate education, on sick-leave/early retirement and financial strain were indepen-dently associated with an increased risk for high painintensity. Elevated scores in social support at work were

inversely related to high pain intensity. The model ex-plained 11% of the variance in pain intensity.

Disability. Foreign background, low/intermediateeducation, on sick-leave/early retirement, financialstrain and elevated scores in job strain were indepen-dently associated with a higher disability risk. Elevatedscores in social support at work were inversely associ-ated with disability. The model explained 39.8% of thevariance in PD.

4. Discussion

4.1. The extent/characteristics of pain

Sixty-three per cent of women reported pain, ofwhich 65% pain lasting longer than 3 months (40% ofthe entire sample). These later figures are comparableto some findings (38–55%, Andersson, 1994; Brattberget al., 1989; Elliott et al., 1999; Gerdle et al., 2004; Pic-avet and Schouten, 2003; Smith et al., 2004), but higherthan others (24%, Rustoen et al., 2004). The discrepancymay pertain to different definitions of pain (Crombieet al., 1994), but is more likely to reflect that pain prob-lems increase in Sweden (e.g. Samhallsmedicin, 2003).

Back-pain and pain in the neck/shoulder/hips/lowerlimbs were the most common pain sites, which is inline with earlier findings (Andersson et al., 1993; Berg-man et al., 2001; Bingefors and Isacson, 2004; Picavetand Schouten, 2003). Pain in the entire body was re-ported by 10.1% of women. Mean pain intensity wassimilar to figures reported by primary care patients(Grossi et al., 2000; Soares and Jablonska, 2004).About 17% of women reported constant pain, 47%had different pain types and 55% evaluated their painsensation as both deep/light. Most women (60%) re-ceived paramedical treatment, of which 74% were sat-isfied with the results. About 70% of those using painmedication considered the effects to be none/poor.Overall, our results suggest that a large number ofwomen had relatively severe pain problems and thatparamedical treatments may be more beneficial thanpharmacological.

4.2. Socio-economic conditions and the extent/

characteristics of pain

Comparison of socio-economic characteristics be-tween women with (WP) and without pain (WWP) re-vealed that groups differed with respect to variousfactors (e.g. financial strain). Only foreign background,low education, on sick-leave/early retirement andfinancial strain were risk factors for either pain in gen-eral or pain in body sites in the regressions. Widowswere at lower risk. Socio-economic conditions (e.g.education level) differed also between WP reporting

Table 4Multivariate logistic (odds ratio) and linear (standardized betas) regression analyses of the associations between socio-economic, psychosocial workvariables and pain characteristics/disability (adjusted for age, BMI, chronic diseases)

Variables Complex paina Pain frequencya,b Pain intensityc b Disabilityc b

OR 95%CI OR 95%CI

Foreign backgrounda

No 1.0Yes 1.2 0.9–1.5 0.067***

Marital statusa

Married/cohabitantd 1.0 1.0Single 1.1 0.9–1.4 0.9 0.7–1.2 0.015Divorced 1.2 0.9–1.6 0.9 0.7–1.3 0.037Widow 1.6 0.7–3.6 0.8 0.3–2.1 0.010

Educationa

Highd,e 1.0 1.0Intermediatef 1.2 1.0–1.5 1.3 1.0–1.6 0.120**** 0.043*

Lowg 1.2 0.9–1.5 1.1 0.8–1.5 0.074** 0.086****

Occupationa

Blue-collar workersd 1.0Low/interm. white-collar workers 0.8 0.6–1.1 �0.038 �0.032High white-collar workers 1.0 0.5–1.7 �0.011 �0.022Otherh 0.7 0.4–1.5 �0.032 �0.010

Financial supporta

Workingd 1.0 1.0Studying 0.8 0.5–1.1 1.3 0.8–2.0 0.005 0.009Sick-leave/early retirement 1.6 1.2–2.2*** 3.7 2.7–5.0**** 0.172**** 0.496****

Unemployment/social security 0.7 0.5–1.0* 1.4 1.0–2.1 0.003 0.037

Job strainc 1.5 1.1–2.2* 1.1 0.7–1.6 0.004 0.081***

Social support at workc 0.9 0.7–1.0 0.8 0.6–1.0* �0.094*** �0.074***

Financial straina

Nod 1.0 1.0Yes 1.3 1.1–1.6* 1.4 1.1–1.7** 0.055* 0.072***

a Categorical variables.b By frequent pain means all the time/all the time except 1 h or so immediately after treatment.c Continuous variables.d Comparison category.e University/similar.f Upper secondary school/similar.g Primary school/similar or lower.h e.g. Students.* p < 0.05.

** p < 0.01.*** p < 0.001.**** p < 0.0001.


complex/frequent/intensive pain and those with nosuch experiences. Only sick-leave/early retirement andfinancial strain were risk factors for these pain param-eters and low education for intensive pain in the mul-tivariate analyses.

Foreign background was not an important factorfor pain in general, which is in accord with some data(Soares et al., 2004) and contrary to other (Socialstyrel-sen, 1995). However, regressions of the different bodysites revealed that foreigners were at high risk for painin the upper/lower limbs/entire body. This could per-tain to that foreign women, more than native, are em-ployed in professions characterised by high physical

and psychological demands (Leino, 1991; Socialstyrel-sen, 1998). Exposure to such work conditions has beenrecognised as a pain risk factor (Lundberg, 1999,2002). On the other hand, pain might be an indicationof mental stress accompanying the acculturation pro-cess. In any case, our results confirm previous observa-tions of an association between foreign backgroundand pain in various sites (e.g. upper limbs), althoughgender differences were not considered (Soares et al.,2004; Bergman et al., 2001).

Low education was a risk factor for pain in general/inmost sites (except headache/facial pain/entire body), butalso for pain intensity among WP. Thus, low education


might be important for both the occurrence and severityof pain. Low education could be an indirect measure ofpoor work conditions (Makela et al., 1993) or economicstatus (Marmot et al., 1997), and as such, connected topain. However, the relation between pain and low edu-cation remained after adjustment for socio-economicand work-related variables, indicating that educationper se may be associated with pain. This is in accordwith previous data (Leigh and Sheetz, 1989). An alterna-tive explanation could be that low educated individualstend to adopt poor health-related habits, increasing vul-nerability to illness (Dionne et al., 2001, for a review).Moreover, low educated pain patients seem to be less in-volved in treatment issues than more educated counter-parts (Brekke et al., 2001). Anyhow, our results confirmobservations of an association between low educationand pain risk among women (Viikari-Juntura et al.,1991; Leino-Arjas et al., 1998; Croft and Rigby, 1994)and between low education and pain intensity (Jensenet al., 2004).

Sick leave/early retirement was connected to pain ingeneral/in all sites and to all measures of pain severityamong WP. Sick-leave may have led to physical andsocial inactivity with detrimental effects on health (e.g.loss of muscular strength), which over time could haveresulted in pain (Styf, 1995). Indeed, data suggest thatphysical inactivity and pain among women are associ-ated (Kopec et al., 2004). As women are more likelythan men to be on long-term sick-leave (Brage et al.,1998; RFV, 2000) and more often prescribed activityrestrictions despite equivalent health characteristics(Safran et al., 1997), the negative effects of inactivitymay affect a substantial number of women. Anyhow,our result corroborate findings of a linkage betweensick-leave/early retirement and pain both when genderwas considered as one of the explanatory factors (Soareset al., 2004; Gerdle et al., 2004) and when females werestudied separately (Bingefors and Isacson, 2004). Inaddition, our results concerning sick-leave/early retire-ment as a correlate of pain severity are in harmony withother studies (Gerdle et al., 2004; Soares et al., 2004).However, these studies do not address the issue of gen-der separately making the interpretation of the findingsregarding women limited.

Financial strain was a risk factor for pain in general/in all sites, but also for all pain severity measures amongWP. From the late 1980s through most of the 1990s,Sweden had severe financial problems resulting inamong other things high unemployment rates. Some ofthe problems remain. The negative effects have been par-ticularly tangible on, for instance, women, not least be-cause of their difficulties in finding gainful employment(e.g. Socialstyrelsen, 2002; Statistics Sweden, 2002).These circumstances may have lead/contributed tofinancial strain or had an additive effect on alreadystrained persons. Financial strain over time may have

resulted in stress triggering muscular tension (e.g. Lund-berg et al., 1994), which could have induced pain (e.g.Johansson and Sojka, 1991). In any case, financial strainhas been associated with pain among women (e.g. Bin-gefors and Isacson, 2004) and there seems to be a rela-tion between pain intensity and low annual income(Gerdle et al., 2004), which may support the presentfindings.

Widows were at lower risk for pain in general/in mostsites than married women. This contrary to findings thatwidows are less healthy than married counterparts (Wil-cox et al., 2003). These results are difficult to reconcilewith our data. The multiple burden hypothesis suggeststhat women facing multiple role obligations could be athigher risk for poor health due to role overload andaccompanying stress (Gove, 1984). Thus, the role-strainmight be greater among married women than widows,putting the former at higher pain risk.

Finally, WP reporting adverse socio-economic condi-tions (e.g. low education level) scored higher on pain re-lated disability than their better-off counterparts. Allvariables (except marital/occupational status) were inde-pendently associated with disability in the regressions.This is in line with findings not addressing genderseparately (Badley and Ibanez, 1994; Cunningham andKelsey, 1984; Grossi et al., 1999; Hagen et al., 2000;Makela et al., 1993; Soares and Jablonska, 2004; Soareset al., 2004). An explanation could be that women indisadvantageous socio-economic conditions experienceda great deal of stress, which over time decreased theircapability to effectively manage pain. This, in turn,may have resulted in higher disability. Indeed, individu-als with a lower socio-economic status tend to adoptinadequate coping strategies more often than their moreprivileged counterparts (De Ridder, 2000).

4.3. Psychosocial working conditions and extent/

characteristics of pain

WP reported higher job strain than WWP. This fac-tor was independently associated with pain in general/in all sites in the regressions. A finding also observedby others concerning general pain and back/neck–shoulder pain (Ahlberg-Hulten et al., 1995; Amicket al., 1998; Cole et al., 2001; Josephson et al., 1997;Karlqvist et al., 2002; Krantz and Ostergren, 2000).Job strain has been shown to elicit psychophysiologicalstress responses mediated by increased activation of thelocus coeruleus-sympathetic-adreno-medullary (SAM)and the hypothalamic-pituitary-adrenocortical (HPA)axes such as heightened cardiovascular, metabolic andimmune function, and increased muscular tension(Karasek and Theorell, 1990; Lundberg et al., 1994;Melin and Lundberg, 1997). One long-term healthimplication of such reactions could be pain, which it-self is a significant stress source. Job strain was also


independently related to complex pain among WP. Aspoor decision latitude may be connected with lowerpain thresholds (Theorell et al., 1993), one could spec-ulate that job strained women may also be more proneto recognise different pain types. Further research isneeded to test this hypothesis.

WP reported lower social support at work thanWWP. This factor was independently connected to painin most sites in the regressions, indicating that womenwith high support from co-workers were a lower painrisk in respective locations. A similar finding was ob-served concerning frequent/intensive pain and disabilityamong WP. The association between high social supportand decreased pain risk among female populations hasbeen reported previously (Ahlberg-Hulten et al., 1995;Kamwendo et al., 1991). However, the exact ‘‘mecha-nisms’’ may remain largely elusive. One explanationcould be that satisfying social support has a direct ben-eficial effect on the allostatic system (Cohen and Wills,1985). Having supportive relationships with co-workersmay help an individual to cope better with stresses andthus protect against the development of allostatic loadthat, in turn, may affect onset and progression of dis-ease. It is also possible, as suggested by Hobfoll(1986), that job-related social support plays a role as a‘‘buffer’’ against the effects of job strain on pain.

The negative correlation between social support atwork, frequent/intensive pain and disability suggeststhat it plays a protective role against negative outcomesamong those already affect by pain. This may apply par-ticularly to disability. Having, for example, supportivecolleagues may help reduce workload during illness peri-ods, and thus protect against the development of disabil-ity problems. However, an indirect association couldalso exist, since individuals with a supportive job envi-ronment may be more motivated to recover and returnto work than those who lack support (Gard and Sand-berg, 1998). Finally, the negative connection betweensocial support and pain intensity is in line with previousfindings (Rolander and Bellner, 2001), but contrary tothese authors we considered the possible influence ofother factors (e.g. socio-economics), thus making our re-sults more robust.

This study has usefully confirmed previous findingsinto women�s experiences of pain and provided somenew insights. Its limitations must, nonetheless, beacknowledged. Firstly, issues of causality cannot besolved with a cross-sectional study. One would need an-other type of design, e.g. repeated measures, to firmlyestablish causal links. Secondly, the sample was recruitedin Stockholm, and may not be representative of womenin the rest of the country. Consequently, the generalis-ability of the study�s findings cannot be guaranteed.Whether respondents differed from non-respondentsconcerning the occurrence of pain could not be assessed,which may have resulted in an over- or under-estimation

of the proportion of WP. Fourthly, proportions of thevariance accounted for by the models were in some casesmodest (e.g. pain in general) indicating that other factorsmay also play role in pain problems. Finally, the accu-racy of the data was solely dependent on the participant�ssubjective assessment of their situation. No objectiveassessment strategies were incorporate to corroboratetheir responses.

In conclusion, our study confirmed that a significantnumber of women in the general population sufferfrom pain and particularly long-term pain. WP experi-enced more often socio-economic disadvantages thanWWP, which in turn appeared to be independently re-lated to pain. Thus, the current study adds to thegrowing body of evidence that women in a deprived so-cio-economic situation run a higher pain risk. In addi-tion, the results indicate that pain is a more seriouscondition in women with a less affluent socio-eco-nomic/job situation. Accordingly, our study seems toconfirm the hypothesis of ‘‘double suffering’’. That is,individuals living in worse socio-economic conditionsare not only more prone to suffer from long-term ill-health, but also more likely to experience their healthproblems as more severe in comparison with thosewho are better off (Blank and Diderichsen, 1996).

Improvement of socio-economic status among wo-men living in deprived social/material circumstances,along with improved working environment seem to beone of the goals with regard to prevention/rehabilitationof pain problems. Approaches such as stress manage-ment, promotion of self-control and empowerment areexamples of techniques that could both promote healthand supplement traditional biomedical treatments.

The majority of WP were satisfied with the results ofparamedical treatments, whereas the opposite was trueconcerning pain medication. However, it seems that, de-spite temporary pain relief, the paramedical treatmentsalone may not be effective enough to give permanent re-sults. Thus, the current findings may be valuable for thedevelopment of more satisfactory preventive and inter-vention strategies to manage pain problems.

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