Interdependence in Women with Breast Cancer and Their Partners: An Interindividual Model of Distress.

The diagnosis and treatment of breast cancer involves substantial stress for patients and affects the well-being of their larger social networks. A multitude of psychosocial stressors occur, such as adjustments in social and family roles, worries about changes in appearance or attractiveness, anxiety about the spreading or recurrence of cancer, and fear of death (Manne et al., 2004). Psychological distress is a significant problem for cancer patients and their partners because it negatively affects other aspects of their quality of life (Ell, Sanchez, & Vourlekis, 2005; Northouse et al., 2007). Social support, especially from family members, is associated with lower psychological distress in patients with breast cancer (Baider et al., 2004). However, partners may have difficulty providing social support because of their own distress (Grunfeld et al., 2004). Approximately 30% of caregivers or partners experience significant emotional distress (Hagedoorn, Sanderman, Bolks, Tunistra, & Coyne, 2008). Emotional distress and quality of life indicators are highly correlated between women with breast cancer and their partners (Hagedoorn et al., 2008; Northouse et al., 2007). Partners often experience the same, and sometimes higher, levels of emotional distress as cancer patients themselves (e.g., Manne et al., 2006). Broadly, the systems theory concept of interdependence predicts that major events such as serious illness affect the larger family and social networks, and not just the ill individual. More specifically, family systems theory (Broderick, 1993) predicts that close network members (e.g., kin, spouses) experience increases in distress as the family member with major illness becomes more distressed, reflecting interdependence within the family system. Such findings highlight the need to focus on the distress experienced by the dyad, rather than only the individual, living with cancer. Therefore this study investigates how interdependence during cancer contributes to a dyadic, or inter-individual, model of distress. Modeling interdependence requires special data analytic approaches, and the Actor-Partner Interdependence Model (APIM; Cook & Kenny, 2005) represents a versatile approach to modeling dyadic data. Several studies using the APIM to examine interdependence among married couples living with breast cancer have documented the interdependence of psychosocial variables and adjustment among couples living with cancer, and that negative outcomes are often evident for partners of the person with cancer (Dehle & Weiss, 2002; Northouse, Templin, & Mood, 2001). Therefore the aim of this investigation is to test whether interdependence in patients and partners is expressed by crossover effects in distress. In testing an inter-individual model of distress, we expected that interdependence in close relationships might manifest as person-partner crossover effects for distress. Stress associated with a close partner’s diagnosis and treatment of cancer could be a risk factor that produces a greater level of interdependence in the adjustment of couples living with cancer. An inclusive focus on patient-partner distress would allow for better understanding of how women with breast cancer and their partners contribute to each other’s well-being throughout the experience of cancer. The potential of dyadic interdependence and crossover in distress outcomes will be tested by using the APIM (Cook & Kenny, 2005). The fundamental concept of the actor-partner interdependence model is that the dyad is treated as the unit of analysis and participants’ scores on various independent variables are used to predict both their own (actor effects) and their partners’ (partner effect) scores on the dependent variable, after taking into account the dyad’s interdependence on the independent variable. In the present investigation, participants’ depression and stress will be treated as the predictor variables, and measures of physical well-being will serve as the dependent variable.