Menopause: The Journal of The North American Menopause Society Vol. 22, No. 11, pp. 1153-1154 DOI: 10.1097/GME.0000000000000555 ß 2015 by The North American Menopause Society

EDITORIAL Menopause, symptom clusters, and the complexity of women’s lives


ver the past decade, investigators studying symptoms experienced by midlife women have increasingly focused on understanding clusters of symptoms as well as single symptoms such as the hot flash. A symptom cluster is a set of co-occurring symptoms, related to one another in time, but not necessarily sharing the same etiology.1 A syndrome is a pattern of symptoms that is presumably disease-specific and results from a common underlying mechanisms. Avis et al2 investigated whether there was a ‘‘menopausal syndrome,’’ concluding that evidence did not support a single syndrome. Instead of pursuing study of a nonexistent syndrome, investigators studying menopause have confirmed the existence of symptom clusters by identifying groups of similar symptoms, such as vasomotor symptoms, sleep symptoms, or mood symptoms, using strategies such as factor analysis.3,4 Empiricist strategy has been useful for obtaining a profile of symptoms that help clinicians visualize different patterns of prevalence or severity for a single woman in a clinical practice or researchers to identify patterns exhibited by populations of women study participants. Others have employed latent class analysis to identify how different types of symptoms, such as vasomotor, sleep, mood, cognitive, and pain symptoms, might co-occur with different levels of prevalence or severity, allowing identification of groups of women with different symptom clusters that varied in type and severity. For example, Cray et al5 identified three clusters of symptoms in a community-based population of women: cluster 1—low-severity symptoms (hot flashes, mood, sleep, pain, and cognitive); cluster 2—high-severity hot flashes with low to moderate levels of the other symptoms; and cluster 3—low-severity hot flashes and moderate-severity sleep, mood, cognitive, and pain symptoms. The value of this study is beginning to emerge as investigators have identified correlates that point to potential mechanisms that may, in turn, help identify treatment options for women. For example, we found that the symptom cluster in which hot flash was the most severe and dominant symptom was related to having lower levels of urinary estrone, higher levels of follicle stimulating hormone, lower epinephrine, and higher norepinephrine levels, suggesting that this cluster of symptoms may be mediated by both ovarian steroids and the autonomic nervous system.6 This same cluster was more likely to occur during the late menopausal transition and early postmenopause, and was associated with a variant in

the 17 hydroxysteroid dehydrogenase (HSD) gene polymorphism, as well as higher levels of stress, than observed in the low-symptom–severity cluster (unpublished data). In a recent study guided by interpretive methodology, we found that women could group their symptoms into clusters, rate them for severity, prioritize them, share their heuristics about what they represent, and identify what makes them better or worse.7 Thus, both empiricist and interpretive contributions have informed our understanding of symptom clusters and assured us that the notion is not just relevant to scientists and clinicians, but also to women. Women have unique ways of clustering their symptoms guided by their individual experiences. When one considers the many ways in which midlife women are unique, this area of research becomes even more complex. To date, few investigators have studied ethnic or cultural differences in the experience of symptom clusters. Sievert and Obermeyer’s8 study provided an important foundation for understanding the diversity of symptom clusters women experience by contrasting the experiences of women from various countries/cultures. Using both quantitative and qualitative data about somatic symptoms to determine how women from different countries grouped or clustered their symptoms revealed differences among women from different countries in how women grouped somatic and emotional symptoms. Women (n > 300) from Lebanon, Morocco, Spain, and Massachusetts used a 25-item symptom checklist to rate their symptoms and also responded to openended interview questions about health changes they had experienced. Sievert and Obermeyer found that factor analysis revealed clusters of somatic and emotional symptoms that were unique to each country. For example, palpitations were reported along with emotional symptoms by 15/37 women reporting palpitations in Lebanon, but only 2/11 in Spain, 2/10 in Morocco, and 1/5 in Massachusetts. Fatigue was commonly reported with anxiety, depression, and other emotional symptoms: 29/40 in Lebanon, 23/51 in Morocco, 7/19 in Massachusetts, but 0/5 in Spain. Women frequently attributed differences in qualitative reports, relating symptoms to the social context of their lives, including effects of war, poverty, or family worries. Im et al, in this issue of Menopause, emphasize the importance of understanding ethnic and racial differences in symptom experiences and simultaneously caution us to consider that within ethnic groups of women, there is heterogeneity.9 It

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Copyright @ 2015 The North American Menopause Society. Unauthorized reproduction of this article is prohibited.


is not sufficient to think about a woman as African American or Asian American, but it is also necessary to consider effects of other factors such as those which marginalize her and limit her opportunities for good health. Im et al initiated this investigation by identifying sleep-related symptoms from published research about sleep quality in a variety of health conditions that included a broad view of possible symptoms, for example, physical symptoms including poor appetite, painful swollen joints, back pain, night sweats, and nighttime urination. These symptoms were classified by the investigator as physical, psychological, or psychosomatic, and women were queried about their experiences using an internet data-collection protocol. Through recruitment of over 1,000 women from ethnic/racial groups including Hispanic, African American, and Asian and whites, Im et al were able to cluster women according to the symptoms they reported. Identifying four groups (low-severity total symptoms, moderate-severity physical and psychosomatic symptoms, moderate-severity psychological symptoms, and high-severity total symptoms), Im et al compared women in the four clusters with respect to ethnicity/race, as well as several other characteristics. Of interest was that ethnicity/race was related to the total number and total severity scores only among women who reported the low-severity total symptom cluster. When women reporting other symptom clusters were considered, there were no consistent ethnic/racial differences. Differences that were evident among women in the low-severity symptom cluster indicated that Asians experienced significantly lower total numbers and lower total severity sleep-related symptoms. Factors that differentiated women in other clusters of sleep-related symptoms included education, employment status, family income, social support, country of birth, body mass index, perceived general health, diagnosed disease/s, and access to healthcare and menopausal status. The multiplicity of factors differentiating the clusters of women with varying symptom types, numbers, and severity reminds us of the complexity of the population of women and the heterogeneity within ethnic/racial groups, as well as among these groups. Note that the majority of factors differentiating women with more from less severe sleep-related symptom clusters were indicators of their life contexts. The concept of intersectionality is a useful lens through which to view health. Intersectionality is grounded in assumptions about heterogeneity within groups of women and men: individuals are defined by multiple intersecting dimensions. Women simultaneously have a sex, class, race, ethnicity, ability/disability, sexual orientation, and age. Consideration of these dimensions, in addition to sex, supports efforts to promote equality and equity, denoting populations at risk for poor health more precisely.10 Im et al’s results point to the necessity of considering intersectionality as we attempt to understand women’s experiences of symptom clusters, as well as other dimensions of their health. Although symptom science has been enriched by


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the consideration of symptom clusters as demonstrated above, Im et al remind us that we have much to accomplish if we are to understand the diversity of symptom experiences women report. The recently published Lancet Commission report, ‘‘Women and health: the key for sustainable development,’’ emphasizes the importance of considering women’s health in the context of women’s everyday lives.11 Addressing women’s roles as both users and providers of health care, the Commission proposed that women who are ‘‘healthy through their lives experience gender equality and are enabled, empowered, and valued in their societies’’ . . . go on to achieve their potential and make substantial contributions to their own health and well being, as well as to their families, communities, and their nations. Im et al’s study has reminded us that understanding the context of women’s lives is a key to understanding their health and health disparities, and that the context of a woman’s life is shaped by the intersection of her multiple identities. Personalizing health care warrants consideration of women in all our complexity! Financial disclosure/conflicts of interest: None reported. Nancy F. Woods, PhD, RN, FAAN University of Washington School of Nursing Seattle, WA

REFERENCES 1. Dodd MJ, Miaskowski C, Paul SM. Symptom clusters and their effect on the functional status of patients with cancer. Oncol Nurs Forum 2001;28:465-470. 2. Avis NE, Brockwell S, Colvin A. A universal menopausal syndrome? Am J Med 2005;118(suppl 12B):S37-S46. 3. Greenblum CA, Rowe MA, Neff DF, Greenblum JS. Midlife women symptoms associated with menopausal transition and early postmenopause and quality of life. Menopasue 2013;20:22-27. 4. Cray LA, Woods NF, Mitchell ES. Identifying symptom clusters during the menopausal transition: observations from the Seattle Midlife Women’s Health Study. Climacteric 2013;16:539-549. 5. Cray LA, Woods NF, Herting JR, Mitchell ES. Symptom clusters during the late reproductive stage through the early postmenopause: observations form the Seattle Midlife Women’s Health Study. Menopause 2012; 19:864-869. 6. Woods NF, Cray L, Mitchell ES, Herting JR. Endocrine biomarkers and symptom clusters during the menopausal transition and early postmenopause: observations from the Seattle Midlife Women’s Health Study. Menopause 2014;21:646-652. 7. Woods NF, Ismail R, Linder L, Macpherson CF. Midlife women’s symptom cluster heuristics: evaluation of an iPad application for data collection. Menopause 2015;22:1058-1066. 8. Sievert LL, Obermeyer CM. Symptom clusters at midlife: a four-country comparison of checklist and qualitative responses. Menopause 2012; 19:133-144. 9. Im E-O, Ko Y, Chee E, Chee W. Cluster analysis of midlife women’s sleep-related symptoms: racial/ethnic differences. Menopause 2015; 22:1182-1189. 10. Havkinsky O. Women’s Health, men’s health, and gender and health: Implications of intersectionality. Social Sci Med 2012;74:1712-1720. 11. Langer A, Meleis FA, Knaul FM, et al. Women and health: the key for sustainable development. Lancet 2015;386:1165-1210.

ß 2015 The North American Menopause Society

Copyright @ 2015 The North American Menopause Society. Unauthorized reproduction of this article is prohibited.

Menopause, symptom clusters, and the complexity of women's lives.

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