Abstract
Objective
Social pain and physical pain are related bidirectionally but how these variables cluster in the population is unknown.
Methods
This study included 2,833 women from the Study of Women’s Health Across the Nation (SWAN), a community-based cohort of middle-aged women, and 3,972 women from the Pathways Study, a population-based cohort of women diagnosed with AJCC stages I-IV breast cancer between 2006 and 2013. Women provided data on measures related to social pain (social network size, social support, loneliness, social well-being) and physical pain (sensitivity to pain, bodily pain) at study baseline. Analyzing each cohort separately, we used latent class analysis to evaluate social-physical pain clusters, logistic regression to evaluate predictors of categorization into clusters, and Cox proportional hazards models to evaluate associations of clusters with all-cause mortality. We also performed a meta-analysis to combine cohort mortality associations.
Results
Each cluster analysis produced a ‘low social-physical pain’ cluster (SWAN: 48.6%; Pathways: 35.2%) characterized by low social and pain symptomatology, a ‘high social-physical pain’ cluster (SWAN: 17.9%; Pathways: 17.9%) characterized by high symptomatology, and a ‘low social/high physical pain’ cluster of women with high pain and compromised social functioning but otherwise low social symptomatology (SWAN: 33.5%; Pathways: 46.9%). In meta-analysis, categorization into the high social-physical pain cluster was associated with elevated mortality (adjHR=1.34, 95% CI:1.05–1.71, Q-statistic=0.782), compared to those in the low social-physical pain cluster.
Conclusions
In two cohorts of women, latent class analysis produced similar sets of social-physical pain clusters with the same proportion having both high social and pain symptomatology; women in this cluster had elevated mortality.
Keywords: Social support, loneliness, social networks, social function, physical pain, cluster analysis, social pain, mortality
Introduction
Previous research has shown strong associations of physical pain and social pain, the latter measured by low perceived social support, social isolation, poor social functioning, and loneliness. Neurological evidence shows strong links between social and pain receptors in the brain(1, 2). Both those who experience physical and mental pain are more likely to socially withdraw(3), and overt social rejection has been associated with physical pain(4, 5) and sensitivity to physical pain(6). Further supporting this link, social support interventions have been helpful in reducing pain in some contexts(7), greater tolerance to pain has been associated with larger social networks(8) and reducing or blocking pain through acetaminophen has also been shown to enhance sociability(9) and to reduce feelings of pain associated with social rejection(10). Furthermore, individuals with heightened biological reactivity to stress have also been described by personality traits or ‘phenotypes’ characterized by higher sensitivity to pain and greater difficulty with social relationships(11–13). In a convincing experiment, Eisenberger and colleagues showed that triggering feelings of social rejection in participants caused centers in the brain well known to be associated with physical pain show elevated activity on fMRI(4); this and other experiments(5–7)(9), taken together, provide strong biological evidence of the link between social pain and physical pain.
Given consistent, multidirectional links between social and pain variables in the literature, supported by biological evidence and clinical experiments, we specifically opted to perform a cluster analysis from the theoretical standpoint that the social and physical pain measures, currently conceptualized as separate constructs, might cluster because of their biological interconnectedness. Therefore, we sought to evaluate whether and how these variables clustered in women at the population level, given high prevalence and levels of self-reported physical pain in women in the population(14). We anticipated that cluster analysis would produce at least two clusters, one characterized by high social and physical pain symptomatology and another characterized by low symptomatology, and that the former would have worse subsequent health outcomes. Given the strong relationship between physical disease and pain, however, we also sought to examine whether clusters in the population differed in a general population-based cohort compared to a population-based cohort defined by a diagnosed disease.
Therefore, we developed and compared social-physical pain clusters in the Study of Women’s Health Across the Nation (SWAN), a community-based study of middle-aged women, and the Pathways Study, a population-based cohort of women with breast cancer. We further evaluated associations between clusters and sociodemographic, psychosocial, and lifestyle factors, and all-cause mortality.
METHODS
Study populations
SWAN is a multicenter, longitudinal, community-based study designed to characterize biological and psychosocial changes with the transition to menopause(15). From 1996 to 1997, 3,302 women aged 42–52 years were enrolled across seven recruitment sites (Boston, MA; Chicago, IL; Detroit, MI; Los Angeles, CA; Newark, NJ; Pittsburgh, PA; and Oakland, CA). SWAN eligibility criteria included an intact uterus, at least one menstrual period in the previous 3 months, at least one ovary, not being pregnant or breast-feeding, and no oral contraceptive or sex steroid hormone therapy use in the previous 3 months. From the initial cohort of 3,302 women, 469 were missing baseline psychosocial data resulting in 2,833 women with available data on the variables of interest in the cluster analysis. The protocol was approved by each site’s institutional review board, and all women provided written informed consent.
The Pathways Study includes 4,505 women from Kaiser Permanente Northern California (KPNC), an integrated healthcare system, newly diagnosed with AJCC stages I-IV invasive breast cancer recruited and enrolled in the study between January 2006 and May 2013. Details are previously reported(16). The analytic population for this study included 3,972 women from the Pathways cohort who provided data on social and pain variables employed in cluster analysis. Written informed consent was obtained from all participants. The study was approved by the KPNC Institutional Review Board.
Data collection
In each cohort, at baseline, participants provided detailed information about demographics, psychosocial factors, lifestyle factors, and medical history, through interviewer-administered or self-administered questionnaires. In Pathways, electronic health record data included data on body mass index (BMI), cancer severity, and treatment.
SWAN data
In baseline questionnaires, interviewers requested information on age, race, education, reproductive and menstrual history, health status, medication use and lifestyle factors. Height and weight were measured in light clothing without shoes using calibrated scales, and body mass index was calculated as weight (kilograms) divided by height (meters) squared. Physical activity data were collected using the Kaiser Physical Activity Survey, a self-administered reliable and valid questionnaire(17). Women were asked whether they currently smoked and were also asked a detailed medical history at baseline which provided information on comorbidity. In SWAN, participant deaths were identified when attempting to schedule study visits and were typically reported by a family member. Some sites also reviewed obituaries to identify deaths. Death certificates were requested from participants’ family members.
Pathways data
Clinical and mortality data
Data on number of positive lymph nodes, American Joint Committee on Cancer (AJCC) stage, breast surgery (lumpectomy, mastectomy), chemotherapy and radiation therapy were obtained from the KPNC Cancer Registry (KPNCCR)(18). Breast surgery and radiation therapy data were also supplemented by other KPNC electronic data sources. Information on adjuvant hormonal therapy was abstracted from outpatient pharmacy records. Mortality data came from several sources, including family members, medical records, and linkage with the KPNC mortality file, which incorporates data from KPNC sources, the State of California, and the Social Security Administration. Underlying cause of death was determined from the death certificate, hospital discharge summary, autopsy or coroner’s report, or physician notes.
Sociodemographic and lifestyle data
Data on socio-demographics (race/ethnicity, education, marital status, household income), weight, height, and lifestyle (smoking, physical activity), were collected by trained staff who conducted in-home interviews.
Body mass index (BMI) was computed from self-reported height and weight and missing values were supplemented by concurrent data from KPNC electronic data sources. Physical activity in MET (metabolic equivalent)-hours/week was assessed from the Arizona Activity Frequency Questionnaire (AAFQ)(19). For pre-cancer comorbidity, we abstracted common conditions by ICD-9 codes from the electronic data sources and used these data to calculate the Deyo-Charlson comorbidity index(20). Based on the distribution of comorbidity scores, we dichotomized scores as 0–1 versus two or more. Smoking was assessed as current, past, or never. Menopausal status was assessed as pre- or postmenopausal.
Cluster analysis variables (Social and physical pain variables)
In both cohorts, social support was measured by the Medical Outcomes-Social Support survey(21); all 19 items were included in Pathways and four items on instrumental and emotional support were measured in SWAN. In each cohort, women completed a single item measure about feelings of loneliness in the past week as a part of the Center for Epidemiological Studies-Depression (CES-D) survey(22). Social integration was assessed based on a previously published social network index(23) as the sum of a spouse/intimate partner, engagement in community participation, engagement in religious participation, and quartile of number of close friends and relatives. Social well-being/functioning and bodily pain were assessed as subscales of the RAND Short Form (SF)-36(24) in SWAN and subscales of the Functional Assessment of Cancer Therapy (FACT) in Pathways(25). In SWAN, sensitivity to symptoms was assessed as a single-item measure of self-reported sensitivity to symptoms (‘I can’t stand pain as well as most people can’)(26); Pathways did not include a measure of sensitivity to physical pain symptoms and so this item was not included in the cohort-specific analysis. A summary of terms, measures, and definitions can be found in Table 1. To prepare variables for cluster analysis, we divided each variable into four categories for consistency, using either quartiles or original response options.
Table 1.
Social and physical pain variables used in cluster analysis and assessment
| Measure | Availability by cohort | Definition | |
|---|---|---|---|
| Social pain variables | |||
| Social support | MOS-SS(21) | X*, 4 items in SWAN | The perception and reality of the exchange of assistance through social relationships |
| Social network index | SNI(23) | X | Indicator of the size of the web of social relationships that surround an individual |
| Loneliness | CES-D single item measure(22) | X | Subjective feeling of distress due to lack of connection and intimacy |
| Social functioning | SF-36 (SWAN) (24), FACT (Pathways) (25) | X | Ability to perform everyday social tasks and maintain social roles |
| Physical pain variables | |||
| Bodily pain | SF-36 (SWAN) (24), FACT (Pathways) (25) | X | Self-reported physical pain |
| Sensitivity to pain | Single item measure(26) | SWAN only | Self-reported intolerance to physical pain |
| Other psychosocial variables | |||
| Energy | FACT (Pathways) | X | Self-reported feelings of vitality, absence of fatigue and weariness |
| Depressive symptoms | CES-D | X | Signs and symptoms of depression |
X denotes availability in both cohorts
Statistical analyses
All analyses were conducted separately by cohort. We evaluated distributions of sociodemographic, disease severity, and other baseline characteristics by cohort (Table 2).
Table 2:
Baseline characteristics by cohort
| Pathways Study | SWAN | |
|---|---|---|
| N (%) | 3,972 (100) | 2,833 (100) |
| Postmenopausal (%) | 2,758 (69.4) | 0 (0.0) |
| Comorbidity/any morbidity (%) | 381 (10.4) | 229 (8.1) |
| Demographic variables | ||
| Age (mean years) | 58.9 ± 12.1 | 46.4 ± 2.7 |
| Race/ethnicity (%) | ||
| Non-Hispanic White (NHW) | 2578 (64.9) | 1384 (48.9) |
| Black | 303 (7.6) | 735 (25.9) |
| Hispanic | 451 (11.4) | 212 (7.5) |
| Asian/Pacific Islander (API) | 539 (12.6) | 502 (17.7) |
| Other | 101 (2.5) | 0 (0.0) |
| Low Income* (%) | 368 (9.3) | 370 (13.4) |
| College graduate or higher (%) | 1,993 (50.2) | 1265 (44.7) |
| Disease severity/treatment | ||
| AJCC Stage I (%) | 2,742 (69.0) | -- |
| Chemotherapy (%) | 1,980 (49.9) | -- |
| Her2 positive | 520 (13.1) | -- |
| Lymph node involvement | 1,190 (30.0) | -- |
| ER positive | 3,309 (83.3) | -- |
| Mastectomy | 1,556 (39.2) | -- |
Low income defined as income <$25,000 in Pathways or <$35,000 in SWAN
Cluster analysis
We verified that candidate variables for each cluster analysis were not highly correlated (r<.5). We used latent class analysis (SAS PROC LCA) to develop and evaluate social-physical pain clusters. We fit models with 2 to 8 clusters using the BIC criterion to determine optimal numbers of clusters and entropy as a secondary criterion to evaluate separation of clusters. We evaluated means and standard deviations of variables included in cluster analysis, by cluster.
Analysis of risk factors and clusters
We used logistic regression (SAS PROC LOGISTIC) to evaluate associations of sociodemographic, psychosocial, lifestyle and other descriptive factors with likelihood of categorization in the ‘high social-physical pain’ (high social and pain symptomatology) or ‘low social/high physical pain’ (high pain, low social symptomatology) clusters vs. the ‘low social-physical pain’ cluster (low social and pain symptomatology) that resulted from cluster analysis. Models were simultaneously adjusted for sociodemographic variables (age/age at diagnosis, race/ethnicity, education, income), lifestyle factors (physical activity, body mass index, smoking), menopausal status, and comorbidity. In SWAN, analyses were adjusted additionally for SWAN study site and cycle day. In Pathways, analyses were adjusted additionally for time between social assessment and diagnosis, disease severity (AJCC stage, hormone receptor status, nodal status, Her2 status), and treatment (chemotherapy, radiation, type of surgery).
In survival analyses, we used Cox proportional hazards regression (SAS PROC PHREG) models to evaluate associations of clusters with all-cause mortality, adjusted minimally for age, race/ethnicity and cohort-specific variables (site, menopausal status, cycle day, hormone use in SWAN; time between diagnosis and survey date, stage in Pathways), and then fully adjusted for covariates described above. We further generated Kaplan-Meier curves. We subsequently conducted a meta-analysis of fully adjusted associations with mortality evaluating study heterogeneity using the Q-statistic. In a final model, we adjusted additionally for the CES-D measure (excluding the loneliness item which was included in cluster analysis). In SWAN, person-time was computed as time from study entry to death or to January 31, 2017, whichever came first (range 2.3–21.2, mean=20.7, median=21.1 years). In Pathways, person-time was computed as time from study entry to death or to October 20, 2015, whichever came first (range 0.2–9.9, mean=6.3, median=6.6) years. All tests of significance were two-sided. Statistical analyses were conducted use SAS 9.4.
RESULTS
At study baselines, women in the Pathways cohort were on average 12.7 years older (Pathways mean 59.1 years; SWAN mean 46.4 years) than women in SWAN and differed by postmenopausal status (69.4% vs. 0%). Women in the Pathways cohort were also more likely to be non-Hispanic white or Hispanic and less likely to be Black or Asian/Pacific Islanders, in part because of the explicit study design of SWAN to enroll minority participants at each clinical site. SWAN participants were less likely to be college graduates and slightly more likely to be low income (Table 2).
Cluster analysis
In latent class analysis, a model with three clusters was determined to be optimal for both cohorts using the BIC criterion. Specifically, BIC was lowest (3679.03 in SWAN and 1531.61 in Pathways) with the selection of 3 clusters. In each cohort, the three clusters included a ‘low social-physical pain’ cluster characterized by low social and pain symptomatology (48.6% of study participants in SWAN; 35.2% in Pathways), a ‘low social/high physical pain’ cluster characterized by high pain and compromised social function but otherwise high support (33.5% in SWAN; 46.9% of participants in Pathways), and a ‘high social-physical pain’ cluster characterized by both high social and pain symptomatology (17.9% in SWAN; 17.9% in Pathways). Means and standard deviations of all social-physical pain variables by cohort and social-physical pain clusters are show in Figures 1 and 2. Group differences were highly statistically significant (p<0.001 for all variables). Entropy was 0.62 in SWAN and 0.58 in Pathways thus suggesting a modest separation of clusters. No cluster emerged at baseline in which women were characterized as having high social symptomatology but low levels of physical pain. Mean values for each social-physical pain variable within clusters were strikingly similar across the two cohorts and the ordering of variables within the high social-physical pain cluster was the same for the two studies (Figures 1 and 2).
Figure 1*:
Standardized** social-physical pain variables by social-physical pain cluster from lowest to highest level of social-physical pain, SWAN
*Energy and CES-D measures were evaluative and not included in the development of clusters
**To avoid negative values, means were standardized to 0 and the absolute value of the largest negative score plus 0.1 were added to scores.
Figure 2*:
Standardized** social-physical pain variables by social-physical pain cluster from lowest to highest level of social-physical pain, Pathways Study
*Energy and CES-D measures were evaluative and not included in the development of clusters
**To avoid negative values, means were standardized to 0 and the absolute value of the largest negative score plus 0.1 were added to scores
Analysis of risk factors and clusters
In SWAN, early perimenopausal (vs. premenopausal) women (OR=1.58, 95% CI: 1.24–2.01), Hispanic (OR=2.04, 95% CI: 1.00–4.17) and Chinese (OR=2.00, 95% CI: 1.08–3.69) (vs. NLW) women, women with lower incomes (<$20K (OR=7.77, 95% CI: 4.95–12.2), $20-<$75K (OR=2.40, 95% CI: 1.71–3.36) (vs. $75K or more), those with comorbidity (OR=1.95, 95% CI: 1.29–2.96) (vs. no comorbidity), and current smokers (OR=1.51, 95% CI: 1.10–2.07) (vs. never smokers) were more likely to be categorized in the high vs. low social-physical pain cluster. By contrast, women in the upper two age tertiles (vs. lowest tertile) were less likely to be categorized in the high social-physical pain or the low social/high physical pain clusters. Women with in the two lower (vs. the highest) income categories were also more likely to be categorized in the low social/high physical pain (vs. low social-physical pain) cluster (Table 3).
Table 3:
Relative odds* of categorization into clusters, SWAN
| N | Low social/high physical (v. low social-physical pain) | OR | 95% CI | High (v. low) social-physical pain | OR | 95% CI | |
|---|---|---|---|---|---|---|---|
| Age tertile (years) | |||||||
| T1 (42–44.7) | 931 | 317 | Ref | 205 | Ref | ||
| T2 (44.8–47.5) | 953 | 315 | 0.81 | (0.66–1.00) | 156 | 0.63 | (0.47–0.83) |
| T3 (47.6–53) | 949 | 317 | 0.82 | (0.66–1.01) | 146 | 0.52 | (0.39–0.70) |
| Menopausal status | |||||||
| Pre | 1498 | 495 | Ref | 230 | Ref | ||
| Peri | 1270 | 431 | 1.18 | (0.99–1.41) | 263 | 1.58 | (1.24–2.01) |
| Race/ethnicity | |||||||
| White | 1384 | 466 | Ref | 193 | Ref | ||
| Hispanic | 212 | 60 | 0.94 | (0.52–1.72) | 96 | 2.04 | (1.00–4.17) |
| Chinese | 234 | 81 | 1.54 | (0.99–2.41) | 50 | 2.00 | (1.08–3.69) |
| Japanese | 268 | 88 | 0.99 | (0.66–1.49) | 21 | 0.54 | (0.28–1.05) |
| Black | 735 | 254 | 1.09 | (0.86–1.39) | 147 | 1.08 | (0.78–1.50) |
| Income | |||||||
| <$20K | 370 | 119 | 2.14 | (1.48–3.07) | 161 | 7.77 | (4.95–12.2) |
| $20–<$75K | 1578 | 539 | 1.24 | (1.01–1.52) | 272 | 2.40 | (1.71–3.36) |
| $75K or more | 815 | 273 | Ref | 58 | Ref | ||
| Education | |||||||
| HS or less | 634 | 197 | 0.99 | (0.77–1.28) | 189 | 1.28 | (0.94–1.74) |
| Some college | 910 | 309 | Ref | 170 | Ref | ||
| College graduate | 585 | 190 | 0.93 | (0.73–1.19) | 79 | 0.93 | (0.67–1.31) |
| Post graduate | 680 | 248 | 1.11 | (0.87–1.40) | 63 | 0.77 | (0.54–1.10) |
| Site | |||||||
| Detroit, MI | 439 | 135 | Ref | 115 | Ref | ||
| Boston, MA | 377 | 126 | 0.98 | (0.70–1.37) | 54 | 0.76 | (0.49–1.18) |
| Chicago, IL | 392 | 139 | 1.10 | (0.79–1.52) | 44 | 0.63 | (0.41–0.99) |
| Oakland, CA | 431 | 146 | 1.07 | (0.71–1.63) | 74 | 0.98 | (0.55–1.76) |
| Los Angeles, CA | 476 | 159 | 1.22 | (0.80–1.85) | 49 | 1.07 | (0.60–1.89) |
| Newark, NJ | 315 | 99 | 1.60 | (0.95–2.70) | 115 | 1.28 | (0.65–2.51) |
| Pittsburgh, PA | 403 | 145 | 1.10 | (0.80–1.52) | 56 | 0.66 | (0.43–1.01) |
| Comorbidity | 478 | 173 | 1.65 | (1.18–2.29) | 125 | 1.95 | (1.29–2.96) |
| Parity | |||||||
| 0 | 496 | 176 | Ref | 77 | Ref | ||
| 1–2 | 1434 | 484 | 0.95 | (0.75–1.21) | 246 | 0.99 | (0.71–1.38) |
| 3+ | 889 | 287 | 0.89 | (0.68–1.16) | 180 | 0.94 | (0.65–1.35) |
| BMI (kg/m2) | |||||||
| <25 | 1173 | 365 | 0.81 | (0.65–1.01) | 170 | 0.80 | (0.59–1.09) |
| 25–<30 | 752 | 251 | Ref | 136 | Ref | ||
| 30+ | 877 | 325 | 1.23 | (0.97–1.55) | 192 | 1.12 | (0.83–1.53) |
| Smoking | |||||||
| Never | 1651 | 556 | Ref | 279 | Ref | ||
| Past | 729 | 249 | 0.98 | (0.80–1.21) | 104 | 0.86 | (0.64–1.15) |
| Current | 433 | 138 | 1.14 | (0.87–1.47) | 119 | 1.51 | (1.10–2.07) |
| Physical activity | |||||||
| Quartile 1 | 145 | 52 | Ref | 48 | Ref | ||
| Quartile 2 | 689 | 237 | 1.02 | (0.82–1.25) | 113 | 0.86 | (0.64–1.15) |
| Quartile 3 | 431 | 141 | 1.04 | (0.80–1.34) | 83 | 1.19 | (0.85–1.66) |
| Quartile 4 | 396 | 132 | 1.05 | (0.77–1.30) | 59 | 0.74 | (0.51–1.09) |
Models adjusted for age, race/ethnicity, site, cycle day, menopausal status, hormone status, education, income, comorbidity, parity, smoking, BMI, and physical activity
In Pathways, income <$25K (OR=11.9, 95% CI: 7.90–18.0), $25-<$70K (OR=3.72, 95% CI: 2.91–4.75) (vs. $70K or more), API race/ethnicity (OR=1.79, 95% CI: 1.30–2.45) (vs. non-Latina white), graduate level education (OR=1.52, 95% CI: 1.14–2.03) (vs. some college), current smoking (OR=2.62, 95% CI: 1.65–4.16) (vs. never smoking), and presence of a comorbidity (OR=1.67, 95% CI: 1.16–2.39) (vs. no comorbidity) were associated with higher odds of categorization in the high vs. low social-physical pain cluster. Low income, API race/ethnicity, and comorbidity were related to higher odds of categorization in the low social/high physical pain (vs. the low social-physical pain) cluster. By contrast, those with a HS education or less, those who received chemotherapy, those with children, and those with higher levels of physical activity were less likely to be categorized in the high social-physical pain cluster. Women who were premenopausal, those receiving chemotherapy, past smokers, and those with high levels of physical activity were less likely to be categorized in the low social/high physical vs. low social-physical pain cluster (Table 4).
Table 4:
Relative odds* of categorization into clusters, Pathways Study
| N | Low social/high physical (v. low social-physical pain) | OR | 95% CI | High (v. low) social-physical pain | OR | 95% CI | |
|---|---|---|---|---|---|---|---|
| Age tertile (years) | |||||||
| T1 (23–53) | 1341 | 573 | Ref | 218 | Ref | ||
| T2 (54–64) | 1332 | 618 | 1.03 | (0.79–1.33) | 246 | 1.08 | (0.74–1.56) |
| T3 (65 or more) | 1299 | 672 | 1.04 | (0.77–1.39) | 247 | 0.77 | (0.51–1.17) |
| Menopausal status | |||||||
| Pre | 1214 | 504 | 0.76 | (0.59–0.98) | 197 | 0.88 | (0.62–1.27) |
| Post | 2758 | 1359 | Ref | 514 | Ref | ||
| Race/ethnicity | |||||||
| White | 2894 | 1369 | Ref | 554 | Ref | ||
| Hispanic | 496 | 243 | 1.08 | (0.86–1.35) | 83 | 1.00 | (0.72–1.38) |
| Asian | 539 | 263 | 1.34 | (1.06–1.70) | 118 | 1.79 | (1.30–2.45) |
| Black | 303 | 136 | 0.88 | (0.66–1.17) | 60 | 0.80 | (0.54–1.19) |
| Other | 101 | 39 | 0.71 | (0.45–1.12) | 19 | 0.87 | (0.46–1.63) |
| Income | |||||||
| <$25K | 368 | 190 | 3.20 | (2.27–4.53) | 126 | 11.9 | (7.90–18.0) |
| $25–<$70K | 1354 | 680 | 1.81 | (1.52–2.16) | 302 | 3.72 | (2.91–4.75) |
| $70K or more | 1852 | 803 | Ref | 206 | Ref | ||
| Unknown | 398 | 190 | 1.33 | (1.03–1.73) | 77 | 2.65 | (1.85–3.78) |
| Education | |||||||
| HS or less | 614 | 324 | 1.02 | (0.81–1.29) | 104 | 0.65 | (0.46–0.90) |
| Some college | 1365 | 649 | Ref | 254 | Ref | ||
| College graduate | 1109 | 506 | 0.95 | (0.79–1.15) | 187 | 1.03 | (0.79–1.35) |
| Post graduate | 884 | 384 | 1.03 | (0.84–1.26) | 166 | 1.52 | (1.14–2.03) |
| AJCC Stage | |||||||
| I | Ref | Ref | |||||
| II | 1080 | 507 | 0.88 | (0.46–1.71) | 171 | 0.89 | (0.34–2.30) |
| III | 84 | 35 | 0.71 | (0.31–1.64) | 17 | 0.89 | (0.27–2.89) |
| IV | 62 | 23 | 0.63 | (0.28–1.43) | 14 | 1.19 | (0.42–3.40) |
| Her2+ | 520 | 245 | 1.07 | (0.86–1.34) | 87 | 1.03 | (0.75–1.41) |
| ER+ | 3309 | 1553 | 0.94 | (0.76–1.15) | 594 | 0.88 | (0.66–1.18) |
| Node positive | 1190 | 552 | 1.22 | (0.63–2.35) | 194 | 1.18 | (0.46–3.05) |
| Chemotherapy | 1980 | 887 | 0.76 | (0.63–0.92) | 309 | 0.55 | (0.42–0.71) |
| Radiotherapy | 1743 | 808 | 0.84 | (0.70–1.02) | 309 | 0.77 | (0.59–1.00) |
| Mastectomy | 1556 | 735 | 0.95 | (0.78–1.14) | 269 | 0.83 | (0.64–1.08) |
| Comorbidity | 381 | 206 | 1.51 | (1.14–1.99) | 88 | 1.67 | (1.16–2.39) |
| BMI (kg/m2) | |||||||
| <25 | 1448 | 672 | Ref | 237 | Ref | ||
| 25–<30 | 1185 | 573 | 1.02 | (0.85–1.22) | 200 | 1.03 | (0.80–1.34) |
| 30+ | 1314 | 611 | 0.96 | (0.80–1.16) | 261 | 1.17 | (0.91–1.52) |
| Smoking | |||||||
| Never | 2265 | 1087 | Ref | 388 | Ref | ||
| Past | 1514 | 689 | 0.82 | (0.70–0.95) | 263 | 0.83 | (0.66–1.03) |
| Current | 187 | 84 | 1.22 | (0.82–1.81) | 59 | 2.62 | (1.65–4.16) |
| Parity | |||||||
| 0 | 749 | 332 | Ref | 169 | Ref | ||
| 1 | 2021 | 944 | 0.92 | (0.75–1.13) | 348 | 0.73 | (0.56–0.96) |
| 2 or more | 1202 | 587 | 0.80 | (0.64–1.01) | 194 | 0.59 | (0.43–0.80) |
| Physical activity | |||||||
| Quartile 1 | 943 | 467 | Ref | 214 | Ref | ||
| Quartile 2 | 1001 | 473 | 0.92 | (0.74–1.14) | 204 | 0.87 | (0.66–1.16) |
| Quartile 3 | 1009 | 495 | 0.91 | (0.74–1.13) | 162 | 0.73 | (0.54–0.97) |
| Quartile 4 | 1018 | 427 | 0.63 | (0.50–0.78) | 131 | 0.44 | (0.32–0.60) |
Models adjusted for age, race/ethnicity, days between diagnosis and baseline survey, menopausal status, education, income, stage, ER-status, nodal status, Her2 status, chemotherapy, radiation, type of surgery, comorbidity, parity, smoking, BMI, and physical activity
In both cohorts, low income, comorbidity, and current smoking were each associated with higher odds of being in the high social-physical pain cluster. Hispanic and Chinese ethnicities in SWAN and Asian race in Pathways, were also associated with the high social-physical pain cluster. BMI was unrelated to clusters in both cohorts. We noted differences in associations of age and menopausal status with clusters across cohorts though comparison was complicated by life course differences in the two populations which may underlie additional differences. In SWAN, education, parity, and physical activity were unrelated to clusters though lower education, greater parity and higher physical activity were related to lower social-physical pain in Pathways. Breast cancer severity markers were unrelated to clusters in Pathways though women receiving chemotherapy were less likely to be categorized in the high social-physical pain cluster (Tables 3 and 4).
Survival analysis
By cohort, 134 SWAN participants and 418 Pathways participants died during follow-up. Categorization into the high social-physical pain cluster was associated with higher mortality in both the Pathways and SWAN cohorts in minimally- and fully-adjusted models (Table 5; Figures 3 and 4). In meta-analysis, associations in the two cohorts did not differ (Q-statistic=0.782). We identified a trend of elevated mortality with increasing social-physical pain across clusters (p-trend=0.022).
Table 5:
Relative hazards of mortality by cluster among women in the Pathways (N=3,972) and SWAN (N=2,833) studies.
| Low social-physical pain | Low social/high physical pain | High social-physical pain | p-value† | |
|---|---|---|---|---|
| N, Pathways | 1398 | 1863 | 711 | |
| Overall mortality | 132 | 195 | 91 | |
| HR, Model 1 | 1.00 | 1.18 | 1.38 | 0.017 |
| 95% CI | (0.95, 1.48) | (1.06, 1.81) | ||
| HR, Model 2 | 1.00 | 1.14 | 1.47 | 0.010 |
| 95% CI | (0.91, 1.43) | (1.11, 1.95) | ||
| HR, Model 3 (2+lifestyle) | 1.00 | 1.10 | 1.31 | 0.070 |
| 95% CI | (0.88, 1.39) | (0.99, 1.75) | ||
| N, SWAN | 1377 | 949 | 507 | |
| Overall mortality | 50 | 47 | 37 | |
| HR, Model 1 | 1.00 | 1.38 | 2.14 | <0.001 |
| 95% CI | (0.93, 2.06) | (1.38, 3.32) | ||
| HR, Model 2 | 1.00 | 1.23 | 1.49 | 0.089 |
| 95% CI | (0.82, 1.85) | (0.94, 2.38) | ||
| HR, Model 3 (2+lifestyle) | 1.00 | 1.19 | 1.41 | 0.150 |
| 95% CI | (0.79, 1.79) | (0.88, 2.26) | ||
| N, Meta-analysis | 2775 | 2812 | 1218 | |
| Overall mortality | 182 | 242 | 128 | |
| HR, Model 3 (2+lifestyle) | 1.00 | 1.12 | 1.34 | 0.022 |
| 95% CI | (0.92, 1.37) | (1.05, 1.71) | ||
| Q-statistic | 0.782 | |||
| HR, Model 3 + CES-D | 1.00 | 1.12 | 1.32 | 0.048 |
| 95% CI | (0.91, 1.37) | (1.01, 1.73) | ||
| Q-statistic | 0.867 | |||
p-trend
Model 1: Minimally-adjusted model adjusted for age, race, site (SWAN), time in cycle and hormone use (SWAN), lag time between diagnosis and survey (Pathways), and AJCC stage (Pathways).
Model 2 adjusted additionally for comorbidity, menopausal status, education, income, and parity. Pathways adjusted additionally for ER status, Her2 status, nodal status, chemotherapy, radiotherapy, and type of surgery
Model 3 adjusted additionally for smoking, BMI, and physical activity. The CES-D measure included in the final model did not include the loneliness item.
Figure 3:
Social-physical pain cluster and mortality in the SWAN Study, N=2,833
Figure 4:
Social-physical pain cluster and mortality in the Pathways Study, N=3,972
Categorization into the high social-physical pain cluster was associated with elevated mortality in the meta-analysis combining both cohorts (adjHR=1.34, 95% CI: 1.05–1.71), compared to those in the low social-physical pain cluster. Adjusting further for depressive symptoms did not markedly influence the association (adjHR=1.32, 95% CI: 1.01–1.73).
DISCUSSION
Variables describing social and pain characteristics clustered similarly in each cohort; 18% of each population, one a healthy community-based population and another a cohort of women diagnosed with breast cancer, were described by a ‘high social-physical pain’ cluster, with high levels of both social and pain symptomatology. A second cluster of women with ‘low social-physical pain’ had very low levels of social or pain symptomatology. In each cohort, cluster analysis also produced a third cluster of women characterized by generally low levels of social symptomatology, but high levels of pain that compromised social functioning. About half of women from the Pathways cohort were categorized into this ‘low social/high physical pain’ cluster vs. a third in the SWAN cohort. Interestingly, no cluster emerged in which patients had high social symptomatology and low pain. Clusters were characterized by modest separation and were associated similarly in the two cohorts with multiple sociodemographic, psychosocial, and lifestyle characteristics, as well as with overall mortality. To our knowledge, this is the first study to consider population-level relevance of the clustering of social and pain symptoms.
We noted a strikingly similar prevalence of the high social-physical pain cluster –17.9%– across the two cohorts despite different age and chronic disease status in the two populations. Most women from Pathways were diagnosed with early stage breast cancer, suggesting possibly smaller differences between populations than would be expected; however, women in SWAN were also substantially younger. Comorbidity was higher in the low social/high physical pain and high social-physical pain clusters suggesting, unsurprisingly, that disease processes influence levels and development of pain and distress (27–29).
Though we lacked the data to fully examine antecedents and correlates of clusters, we speculate that clusters may be influenced by the aforementioned factors in addition a multiplicity of other individual and environmental factors over the life course including heritable components such as temperament(30–33) and personality(34, 35) and environmental factors including early and later life trauma, racism, socioeconomic and relationship stress, and varied material and psychosocial resources and stressors(36).
The much higher likelihood of high social-physical pain in women of low socioeconomic status (SES) in our findings certainly supports the notion that social-physical pain(1–13) is strongly influenced by the social environment in adulthood(37). The prevalence in the population may differ across populations depending on other contextual and cultural factors exemplified by the higher rates of the high social-physical pain cluster in API women in Pathways and Latina and Chinese women in SWAN. This is consistent with literature showing rates of psychological distress in Chinese immigrants double that of the expected population rate(38) and suggests that acculturative stresses may contribute to higher levels of social-physical pain in these women. Thus, the occurrence of high, and conversely, low social-physical pain in the population may be a result of the interaction of personal characteristics such as emotional self-regulation and the ability to cope with stress, as well as the social environment. The higher odds of categorization in the high social-physical pain cluster among those with a graduate-level education in Pathways was intriguing though the association might have differed if all the women hadn’t been uniformly insured within KPNC. Nevertheless, women with high levels of education in the Pathways cohort may have unique concerns that make it difficult to manage their diagnosis and treatment. This result suggests that this cluster, while related, is not synonymous with SES. Future research should examine the cumulative impact of individual and environmental factors on development of clusters.
The largest differences between the two populations were the proportions described by the low social/high physical and low social-physical pain clusters suggesting that diagnosis and treatment-related pain may have decreased social function but did not otherwise influence women’s perceptions of social support among those in the low social/high physical pain cluster. This is consistent with literature showing that chronic disease symptoms can alter sociability in patients(39). However, effects on social symptomatology may differ depending both on type and severity of illness and the quality of relationships.
Notably, categorization in the high social-physical pain cluster was associated with higher mortality. Consistent with results in Pathways, women in the high social-physical pain (OR=1.66, 95% CI: 1.12–2.47) cluster were more likely to be diagnosed with late stage cancer than those in the low social/high physical pain cluster. In addition, they had the highest mortality of the clusters; high levels of social-physical pain symptomatology could lead to delays in seeking care initially if high background levels of social-physical pain lead to greater avoidance coping(40). However, women from SWAN in the high social-physical pain cluster also had higher mortality suggesting that delays in cancer diagnosis did not explain this finding. Additionally, women from Pathways in the low social-physical pain (OR=1.29, 95% CI: 0.89–1.89) cluster were also more likely than those in the low social/high physical pain cluster to be diagnosed with late stage cancer, counter to expectation; women in the low social/high physical pain cluster were most likely to be diagnosed early suggesting that new symptomatology in this group of women may have caused them to obtain care earlier. Overall, the significant associations between the high social-physical pain cluster and mortality is consistent with the literature on personality (e.g., neuroticism)(41), psychosocial distress(42), depression(43), and mortality. Furthermore, psychological distress in highly symptomatic patients (GHQ-12 scores 6–12) was associated with a 41% higher risk of cancer death(42).
Intriguingly, in neither study was a cluster of women with high social, but low pain, symptomatology identified. Instead, we noted that clusters were oriented along a social-physical pain axis that were linearly related to depressive symptomatology. Despite this, adjustment for depressive symptoms did not diminish the association with mortality. One implication is that clinicians, rather than addressing social, pain, depressive or anxious symptoms in isolation, may need to consider multipronged approaches given symptomatology in one area may signify a cluster of symptoms.
A limitation of the current study was the inability to compare social-physical pain variables with numerous psychosocial constructs which may overlap conceptually with social-physical pain clusters. However, the approach in this study afforded nuance over other psychosocial measures including depressive symptoms, anxiety, and neuroticism in linking pain specifically to social distress rather than to stress more generally. Strong evidence based on clinical experimentation has shown that social and pain variables are biologically linked providing support for the approach here and reducing concerns that these clusters are synonymous with previously developed constructs. Our findings need replication and expansion.
In summary, social and pain symptomatology clustered to form similar sets of clusters in two population-based cohorts of women, one a cohort of women newly diagnosed with breast cancer and the other a community-based cohort. Women in the high social-physical pain cluster, comprising 18% of each cohort, had the highest mortality rates. Social symptomatology co-occurred with physical pain in US-living women in two disparate cohorts. Similar representation of a high social-physical pain cluster in two cohorts suggests a vulnerable group shaped both by personal characteristics and the social environment.
Acknowledgements
Clinical Centers: University of Michigan, Ann Arbor – Siobán Harlow, PI 2011 – present, MaryFran Sowers, PI 1994–2011; Massachusetts General Hospital, Boston, MA – Joel Finkelstein, PI 1999 – present; Robert Neer, PI 1994 – 1999; Rush University, Rush University Medical Center, Chicago, IL – Howard Kravitz, PI 2009 – present; Lynda Powell, PI 1994 – 2009; University of California, Davis/Kaiser – Ellen Gold, PI; University of California, Los Angeles – Gail Greendale, PI; Albert Einstein College of Medicine, Bronx, NY – Carol Derby, PI 2011 – present, Rachel Wildman, PI 2010 – 2011; Nanette Santoro, PI 2004 – 2010; University of Medicine and Dentistry – New Jersey Medical School, Newark – Gerson Weiss, PI 1994 – 2004; and the University of Pittsburgh, Pittsburgh, PA – Karen Matthews, PI.
NIH Program Office: National Institute on Aging, Bethesda, MD – Chhanda Dutta 2016- present; Winifred Rossi 2012–2016; Sherry Sherman 1994 – 2012; Marcia Ory 1994 – 2001; National Institute of Nursing Research, Bethesda, MD – Program Officers.
Central Laboratory: University of Michigan, Ann Arbor – Daniel McConnell (Central Ligand Assay Satellite Services).
Coordinating Center: University of Pittsburgh, Pittsburgh, PA – Maria Mori Brooks, PI 2012 - present; Kim Sutton-Tyrrell, PI 2001 – 2012; New England Research Institutes, Watertown, MA - Sonja McKinlay, PI 1995 – 2001.
Steering Committee: Susan Johnson, Current Chair
Chris Gallagher, Former Chair
We thank the study staff at each site and all the women who participated in SWAN. We further thank study staff and the women who participated in the Pathways Study.
The Study of Women’s Health Across the Nation (SWAN) has grant support from the National Institutes of Health (NIH), DHHS, through the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR) and the NIH Office of Research on Women’s Health (ORWH) (Grants U01NR004061; U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NINR, ORWH or the NIH.
Supplemental funding from the National Cancer Institute and American Cancer Society is also gratefully acknowledged. This study was supported by the National Institutes of Health, National Cancer Institute Grants K07 CA187403 (PI: C. Kroenke), R01 CA230440 (PI: C. Kroenke), R01 CA105274 (PI: L Kushi) and U01 CA195565 (mPI: L Kushi, C. Ambrosone), as well as the American Cancer Society grant RSG-16-167-01-CPPB (PI: C. Kroenke).
Glossary
- adjHR
adjusted hazard ratio
- AJCC
American Joint Committee on Cancer
- CI
confidence interval
- SWAN
Study of Women’s Health Across the Nation
Footnotes
Conflicts of Interest and Source of Funding
No conflicts of interests declared.
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