Introduction
Most women with ovarian cancer have advanced stage disease at diagnosis [1]. They have to deal with radical surgery and combination chemotherapy followed by regular surveillance and a high likelihood of recurrence [2]. Although most women initially respond to primary treatment, 80% experience disease recurrence and go on to have multiple courses of chemotherapy over several years, before succumbing to their disease [2]. Not surprisingly, women diagnosed with ovarian cancer 3–56 months previously have been shown to have double the rate of clinical depression found in the general community (5.9 vs 3.0%) [3]. Understanding how women employ coping strategies to deal with the challenges associated with having ovarian cancer is of clinical importance as it may impact their distress. Yet research assessing coping among women with ovarian cancer is limited and affected by methodological shortcomings. Two small qualitative studies used selected groups [4, 5] and another two used convenience samples [6, 7] while the remaining studies measured only two [8, 9] or three individual coping strategies [10] or an overall coping score [11] and so were unable to consider the variety of coping strategies these women may use.
Coping strategies are the specific efforts, both behavioral and psychological that people employ to tolerate or minimize stressful events [12]. The effectiveness of the coping effort depends on the type of strategy used, the particular individual, and the circumstances [12]. Hundreds of different coping strategies have been identified [13]. Lazarus and Folkman conceptualized coping strategies into problem-focused coping involving efforts aimed at solving/reducing the stressor and emotion-focused coping aimed at managing negative emotions [12]. However, this taxonomy has since proved unfitting as strategies from both elements can cluster together [14]. For example, in the cancer setting, one of the largest studies of coping in men and women with mixed cancer types and severity identified five clusters of coping: seeking social support, focusing on the positive, distancing, cognitive escape-avoidance, and behavioral escape-avoidance, with some of the strategies within these clusters utilizing both problem- and emotion-focused elements [15]. Thus, clustering of coping strategies used may vary depending on context and should be assessed to determine which strategies are used together in the context of specific events or diseases.
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Coping with long-term, progressive, or fluctuating chronic illness is complex and challenging, yet the ability to cope is important to wellbeing [16]. Folkman and Greer [17] developed a theoretical model that conceptualizes wellbeing promotion in the face of serious illness, which has been adapted to coping with advanced cancer [18]. The adapted model posits that people with advanced cancer repeatedly revisit, reframe, and redevelop coping strategies in response to a series of events including not only diagnosis, adverse test results, or recurrence but also fluctuating symptoms during their daily lives [18]. They thus develop ways of “living with” and “living around” cancer which enable them to accept and reposition their adjustment enabling reattachment of positive emotions [18]. Little is known about the coping strategies used by women with ovarian cancer, whether they are used alone or together in clusters, whether they change over time, and importantly which might be effective in promoting wellbeing or reducing distress and so could be encouraged in patient care. Using Folkman and Greer’s adapted theoretical model, we aimed to determine firstly the prevalence and trajectories of use of clustered groups of coping strategies over the first 9 months after diagnosis, and, secondly, to assess whether these trajectories are associated with subsequent patient-reported outcomes including anxiety, depression, and quality of life.
Methods
Eligibility, ascertainment, and follow-up
The ovarian cancer prognosis and lifestyle (OPAL) Study is a national cohort study of women with primary invasive ovarian cancer that aims to evaluate the relation between lifestyle and outcomes. It was approved by the Human Research Ethics Committees of QIMR Berghofer Medical Research Institute and all participating hospitals/treatment centers. Data collection for long-term outcomes is still ongoing. A total of 1451 women aged 18–79 years diagnosed between 2012 and May 2015 were identified through 18 major Australian treatment centers. Of these, 219 women were excluded (too sick, unable to complete study documents) and a total of 958 women (78%) consented to participate. Participants completed a baseline questionnaire and then follow-up questionnaires about coping and patient-reported outcomes were mailed to participants at 3, 6, and 9 months after diagnosis and also at 12 months for some patient-reported outcomes. A total of 43 women withdrew or died before completing the 3-month questionnaire, 269 were identified later than 3 months after diagnosis, and 12 did not complete the coping scale at 3 months leaving 634 women who completed the 3-month questionnaire (with coping data) and contributed data to this analysis (Fig. 1). A comparison of participants included (n = 634) and excluded (n = 324) indicated minimal differences in baseline characteristics (Table 1).
Table 1
Demographic and clinical characteristics of participants included and excluded
Included (n = 634) | Excluded (n = 324) | |||
---|---|---|---|---|
n
| % |
n
| % | |
Age at diagnosis (years), median (range) | 61 (21–79) | 60 (22–79) | ||
Marital status | ||||
Married/living with partner | 445 | 70 | 199 | 64 |
Separated/divorced/widowed | 121 | 19 | 83 | 26 |
Never married or lived with partner | 68 | 11 | 31 | 10 |
Education level | ||||
High school or less | 308 | 49 | 137 | 44 |
Diploma/trade certificate | 151 | 24 | 87 | 28 |
University | 174 | 27 | 87 | 28 |
FIGO stage at diagnosis | ||||
I | 123 | 21 | 61 | 20 |
II | 56 | 9 | 22 | 7 |
III | 335 | 57 | 185 | 60 |
IV | 78 | 13 | 41 | 13 |
Not staged/unknown | 42 | – | 15 | – |
Treatment | ||||
No surgery or chemotherapy | 15 | 2 | 11 | 3 |
Surgery only | 32 | 5 | 28 | 9 |
Neoadjuvant chemotherapy and surgery | 179 | 28 | 116 | 36 |
Surgery and adjuvant chemotherapy | 407 | 64 | 168 | 52 |
Disease progressed within 12 months of diagnosis | ||||
No | 516 | 81 | 266 | 92 |
Yes | 118 | 19* | 24 | 8* |
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Measures
Demographic characteristics including age and education as well as self-reported personal medical history were collected in the baseline interview.
Clinical and histopathology data regarding FIGO stage (2009 International Federation of Gynecological Oncologists classification), histological subtype and grade, treatment, and disease recurrence were abstracted from participants’ medical records.
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Use of coping strategies was assessed via the Brief-COPE scale [19]. This instrument allows for use of all or a choice of selected subscales. We omitted two subscales (substance use and self-blame) which were deemed less relevant to this sample. The 24 items included assessed 12 pre-defined sub-scales (self-distraction, active coping, denial, use of emotional support, use of instrumental support, behavioral disengagement, venting, positive reframing, planning, humor, acceptance, religion) comprising two items each. Women responded to each item, indicating 0 = “I have not been doing this at all” to 3 = “I have been doing this a lot.”
Anxiety and depression at 12 months after diagnosis were assessed using the 14-item Hospital Anxiety and Depression Scale (HADS) which has well-described psychometric properties (reported Cronbach alpha coefficients for anxiety and depression = 0.93 and 0.90, respectively [20]; both 0.86 in this study). Higher scores indicate higher levels of anxiety and depression. Scoring cut-offs distinguish between “normal” (0–7), “subclinical” (8–10), and “clinical” (11–21) levels.
Quality of life at 12 months was assessed using the Functional Assessment of Cancer Therapy-General (FACT-G) which measures physical, social, emotional, and functional well-being [21]. The full ovarian cancer-specific scale (FACT-O) includes the FACT-G and an additional 11 ovarian cancer-specific items that were only measured at 3, 6, and 9 months after diagnosis. At 12 months, women were asked to complete only two of the additional ovarian cancer-specific items (about control of bowels and ability to get around) as five of the other items (stomach swelling, vomiting, stomach cramps, hair loss, and appetite) were duplicated on a separate instrument, the Measure of Ovarian Cancer Symptoms and Treatment Concerns (MOST) [22]. Scores for the missing 12-month FACT items were imputed from women’s responses to the corresponding MOST questions. The remaining four items (weight loss, appearance of body, feeling like a woman, and interest in sex) did not change appreciably over time and so women’s responses at 9 months were carried forward to 12 months. These data were then used to create an estimated FACT-O (eFACT-O) score. At 9 months, when full FACT-O data were available, the correlation between FACT-O and e-FACT-O scores was 0.99 (full FACT-O mean 121.0; SD 19.5; e-FACT-O mean 121.0; SD 19.4). The FACT scales have well-described psychometric properties (reported Cronbach alpha coefficients for the FACT-G subscales range from 0.69 to 0.82, FACT-G = 0.89 [21], the ovarian cancer-specific subscale = 0.74 and FACT-O = 0.92 [23]; in this study, subscales ranged from 0.72 to 0.90, FACT-G and eFACT-O were both 0.92). Higher scores indicate better outcomes.
Statistical methods
First, to determine clusters of coping strategies, an exploratory factor analysis with varimax rotation was conducted using data from the Brief-COPE at the 3-month follow-up (n = 624). A factor or cluster loading cut-off of ≥ 0.5 was used to identify items for retention [24]. Two, three, and four cluster models were examined, and the model that balanced parsimony with proportion of variance explained (the three cluster model) was selected. Identified clusters were assessed for their internal consistency using Cronbach alpha coefficients.
Second, group-based trajectory modeling [25, 26] of the three coping clusters identified in this sample across follow-ups at 3, 6, and 9 months was implemented using SAS proc traj [27] to identify patterns in coping over time. To avoid convergence problems due to missing data and to ensure that the intercepts of the trajectories were estimable, we specified that women included in these models have 3-month data and ≥ 1 observation at 6 or 9 months (n = 577 of 634) (Fig. 1). We chose censored normal models to reflect that our scales were continuous. First, models with quadratic orders were fitted, ranging from a single trajectory group to a maximum of six groups. The final number of trajectory groups was then selected based on the Bayesian information criterion (BIC). We then proceeded systematically to choose the model with maximum BIC using combinations of both linear and quadratic orders to find the best fit and a minimum sample size of ≥ 5% in each group.
Finally, associations between coping group and patient-reported outcomes including anxiety and depression, quality of life (FACT-G and eFACT-O), and FACT wellbeing subscales, at 12 months after diagnosis, were investigated using general linear models. A total of 510 women out of the 577 with trajectory group assignment were included in these analyses; of those women not included, 42 died or withdrew before 12 months and 25 did not complete the 12-month questionnaire (Fig. 1). An additional 45 women dropped out of the models of eFACT-O and ovarian cancer-specific wellbeing as they did not complete the MOST questionnaire that was used to generate these scores. All models were adjusted for baseline personal factors identified in Folkman and Greer’s theoretical model of coping [17] including age, marital status, and anxiety and depression at 3 months after diagnosis, as well as recurrence status at the 12-month follow-up (yes/no) as this was important in the model adapted for people with advanced cancer [18]. The coefficients for education and FIGO stage were not statistically significant in any models and thus these variables were not included.
To assess the value of the group-based trajectory modeling, we assessed the associations between coping scores (in tertiles) at 3 months and patient-reported outcomes at 12 months after diagnosis using general linear models.
Results
Participants
On average, the 634 women in our analysis were aged 61 years (range 21–79), most (70%) were married or living with their partner, half (51%) had completed further education after high school, and most (70%) were diagnosed with late-stage disease (FIGO stage III/IV) and were treated with surgery and adjuvant (64%) or neoadjuvant (28%) chemotherapy (Table 1). Characteristics were similar between women included in and excluded from the analysis with the exception of those excluded being less likely to have disease progression within 12 months of diagnosis (Table 1).
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Coping strategy clusters
In the factor analysis, 15 items achieved a cluster loading of ≥ 0.5 or ≤ − 0.5 (Supplementary Table 1). Cluster 1, which we identified as “taking action and positive framing,” included seven items from four constructs (planning, active coping, positive reframing, and humor). Cluster 2, which we identified as “social and emotional support,” included all four items from the instrumental and emotional support constructs. Cluster 3 included the two denial (positive loading) and two acceptance (negative loading) items; thus, this cluster is identified as “acceptance or denial.” Due to the opposite loading directions, we reversed the scores for the denial items so that lower scores indicated more denial and higher scores indicated less denial (i.e., more acceptance). The Cronbach alpha coefficients for the clusters were 0.81, 0.78, and 0.60 respectively, demonstrating adequate internal consistency.
Coping trajectories
For the taking action and positive framing cluster of coping strategies, four distinct trajectories of use were identified: low stable (44%), medium stable (32%), medium decreasing (11%), high stable (12%) (Fig. 2a). Four distinct trajectories were identified for the use of social and emotional support coping cluster: low increasing (7%), low decreasing (44%), medium decreasing (40%), high stable (8%) (Fig. 2b). Despite variation in levels of use, most women (89%) had stable trajectories for use of taking action and positive framing while use of social and emotional support mainly (84%) declined over time. Use of acceptance or denial was also largely stable over time. Two distinct groups were identified on the acceptance or denial scale: women who “accepted their reality” (26%) or “used some denial” (74%) (Fig. 2c).
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Associations with patient-reported outcomes
Women with high-stable use of taking action and positive framing or those who “accepted their reality” reported significantly less depression at 12 months, and those who accepted their reality also reported significantly less anxiety at 12 months (Table 2). Among women who accepted their reality, 12% (n = 16) and 7% (n = 9) reported clinical or subclinical levels of anxiety or depression, respectively, at 12 months compared to 26% (n = 96; p < 0.01) and 15% (n = 56; p = 0.02) among those who used “some denial” to cope. Six percent (n = 4) of women who had a high stable use of taking action and positive framing reported clinical or subclinical levels of depression compared with 14% (n = 61) who did not have a high stable use of this coping strategy.
Table 2
Association of coping trajectory groups with patient-reported outcomes at 12 months after diagnosis
Taking action and positive framing | Low stable use | Medium decreasing use | Medium stable use | High stable use | Overall | High stable vs rest |
LS mean (95% CI) | LS mean (95% CI) | LS mean (95% CI) | LS mean (95% CI) | p value | p value | |
Anxiety | 6.0 (5.4–6.6) | 7.1 (6.2–8.0) | 6.6 (6.0–7.2) | 6.0 (5.0–6.9) | 0.07 | 0.3 |
Depression | 5.6 (5.1–6.2) | 6.1 (5.2–7.0) | 5.4 (4.8–6.0) | 4.6 (3.8–5.5) | 0.07 | 0.02 |
Quality of life (FACT-G) | 77.3 (74.8–79.8) | 74.6 (70.7–78.6) | 77.5 (74.8–80.2) | 80.6 (76.7–84.6) | 0.1 | 0.06 |
Physical wellbeing | 20.9 (20.0–21.7) | 20.8 (19.6–22.1) | 20.8 (20.0–21.7) | 21.4 (20.2–22.8) | 0.8 | 0.3 |
Emotional wellbeing | 17.1 (16.5–17.8) | 16.2 (15.2–17.2) | 16.8 (16.1–17.5) | 18.1 (17.1–19.1) | 0.02 | 0.01 |
Social wellbeing | 21.6 (20.7–22.6) | 21.0 (19.6–22.4) | 21.8 (20.8–22.8) | 22.3 (20.9–23.7) | 0.5 | 0.3 |
Functional wellbeing | 17.8 (16.8–18.8) | 16.7 (15.2–18.2) | 18.2 (17.1–19.2) | 18.8 (17.3–20.4) | 0.2 | 0.1 |
Quality of life (eFACT-O) | 110.1 (106.9–113.3) | 106.7 (101.9–111.5) | 110.8 (107.4–114.2.) | 114.7 (109.8–119.6) | 0.08 | 0.04 |
Ovarian cancer-specific wellbeing | 32.0 (31.1–32.9) | 32.0 (30.7–33.4) | 32.8 (31.9–33.8) | 33.8 (32.4–35.12) | 0.046 | 0.03 |
Social and emotional support | Low decreasing use | Low increasing use | Medium decreasing use | High stable use | Overall | High stable vs rest |
LS mean (95% CI) | LS mean (95% CI) | LS mean (95% CI) | LS mean (95% CI) | p value | p value | |
Anxiety | 6.2 (5.7–6.8) | 6.0 (4.8–7.2) | 6.6 (6.0–7.2) | 5.7 (4.6–6.7) | 0.3 | 0.2 |
Depression | 5.8 (5.2–6.4) | 5.6 (4.5–6.8) | 5.3 (4.8–5.9) | 5.0 (3.9–6.0) | 0.3 | 0.2 |
Quality of life (FACT-G) | 76.7 (74.2–79.2) | 73.8 (68.7–79.0) | 77.2 (74.6–79.7) | 82.6 (78.1–87.2) | 0.04 | 0.01 |
Physical wellbeing | 21.1 (20.3–21.9) | 20.7 (19.0–22.4) | 20.7 (19.8–21.5) | 21.5 (20.0–22.9) | 0.7 | 0.4 |
Emotional wellbeing | 17.1 (16.4–17.8) | 17.9 (16.5–19.2) | 16.7 (16.0–17.4) | 17.4 (16.2–18.6) | 0.2 | 0.5 |
Social wellbeing | 21.1 (20.2–22.0) | 18.1 (16.3–19.9) | 22.2 (21.3–23.0) | 23.8 (22.3–25.4) | < 0.01 | < 0.01 |
Functional wellbeing | 17.6 (16.7–18.6) | 17.3 (15.3–19.3) | 17.7 (16.8–18.7) | 20.0 (18.3–21.8) | 0.06 | < 0.01 |
Quality of life (eFACT-O) | 109.1 (105.9–112.2) | 108.3 (101.7–114.9) | 110.1 (106.9–113.3) | 116.9 (111.3–122.5) | 0.06 | < 0.01 |
Ovarian cancer-specific wellbeing | 32.0 (31.1–32.9) | 32.9 (31.1–34.8) | 32.6 (31.8–33.5) | 33.6 (32.1–35.1) | 0.2 | 0.1 |
Acceptance or denial | Accepted reality | Some denial | ||||
LS mean (95% CI) | LS mean (95% CI) | p value | ||||
Anxiety | 5.5 (4.8–6.2) | 6.5 (6.0–7.0) | < 0.01 | |||
Depression | 4.9 (4.2–5.6) | 5.7 (5.2–6.2) | 0.02 | |||
Quality of life (FACT-G) | 81.6 (78.5–84.6) | 76.3 (74.2–78.5) | < 0.01 | |||
Physical wellbeing | 21.1 (20.1–22.1) | 20.9 (20.2–21.6) | 0.6 | |||
Emotional wellbeing | 18.2 (17.4–19.0) | 16.7 (16.1–17.2) | < 0.01 | |||
Social wellbeing | 23.1 (22.0–24.2) | 21.3 (20.6–22.1) | < 0.01 | |||
Functional wellbeing | 19.2 (18.0–20.4) | 17.6 (16.7–18.4) | < 0.01 | |||
Quality of life (eFACT-O) | 115.8 (112.0–119.6) | 109.0 (106.3–111.7) | < 0.01 | |||
Ovarian cancer-specific wellbeing | 33.4 (32.4–34.5) | 32.3 (31.5–33.0) | 0.02 | |||
Number of positive coping behaviorsa | None | One | Two or three | Overall | ||
LS mean (95% CI) | LS mean (95% CI) | LS mean (95% CI) | p value | |||
Anxiety | 6.6 (6.1–7.1) | 5.7 (5.0–6.4) | 5.7 (4.6–6.7) | 0.009 | ||
Depression | 5.7 (5.2–6.2) | 5.4 (4.7–6.0) | 4.2 (3.2–5.2) | 0.013 | ||
Quality of life (FACT-G) | 75.9 (73.7–78.1) | 79.6 (76.7–82.6) | 84.3 (79.8–88.8) | < 0.001 | ||
Physical wellbeing | 20.9 (20.2–21.6) | 20.6 (19.6–21.5) | 22.2 (20.7–23.6) | 0.125 | ||
Emotional wellbeing | 16.6 (16.0–17.2) | 17.6 (16.8–18.4) | 18.7 (17.5–19.8) | < 0.001 | ||
Social wellbeing | 21.1 (20.4–21.9) | 22.8 (21.7–23.8) | 23.6 (22.0–25.2) | < 0.001 | ||
Functional wellbeing | 17.4 (16.5–18.2) | 18.8 (17.6–19.9) | 20.0 (18.2–21.7) | 0.002 | ||
Quality of life (eFACT-O) | 108.3 (105.6–111.1) | 113.6 (109.9–117.3) | 118.9 (113.3–124.4) | < 0.001 | ||
Ovarian cancer-specific wellbeing | 32.1 (31.3–32.8) | 33.1 (32.1–34.2) | 34.2 (32.7–35.8) | 0.005 |
Women with high stable use of social and emotional support or who accepted their reality reported significantly better quality of life at 12 months (Table 2). This association was driven by different wellbeing subscales for the different types of coping, but none was associated with physical wellbeing.
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In general, better patient-reported outcomes at 12 months were reported by the 12% of women with high stable use of taking action and positive framing, the 8% of women with high stable use of social and emotional support and the 26% of women who accepted their reality. Overall, 65% (n = 375) of women were not in any of these three groups, while 26% (n = 151) were in one, 6% (35) were in two, and 3% (n = 16) of women were in all three of the positive coping behavior groups. There was a significant linear relationship between increasing number of positive coping behaviors (0/1/2+) and better overall quality of life, and having one or more positive coping behavior versus none was associated with lower levels of anxiety and depression (Table 2).
The only significant association with patient-reported outcomes found when modeling coping level in tertiles for coping clusters 1 and 2 was between higher use of social and emotional support at 3 months and better social wellbeing at 12 months. Whereas for the third coping cluster, higher acceptance of reality modeled as tertiles at 3 months was significantly associated less anxiety and depression and better quality of life and wellbeing (across all 5 subscales) at 12 months (data not shown).
Discussion
This is the first large-scale population-based study to utilize a cluster analytical approach to identify distinct groups of coping strategies used by women with ovarian cancer and to look at trajectory-based modeling of coping over time and how these trajectories relate to patient-reported outcomes. The findings provide evidence of three clusters of coping styles in this population. In line with Folkman and Greer’s theoretical model, our study confirms that women who employ a sustained coping process are able to reposition their adjustment and report reduce distress and improve quality of life. Furthermore, we show that a greater effect may be seen with the more positive coping behaviors that are adopted. Thus, assisting more women with the positive coping strategies we identified, including acceptance of their reality, action-planning, positive framing, and maintenance of psychosocial support, could improve patient-reported outcomes in this group. This information should be used to inform the design of a psychosocial intervention trial for women with ovarian cancer.
We found that women with ovarian cancer simultaneously used both problem-focused coping strategies, planning and taking action, and emotion-focused strategies, positive reframing and humor. We also found emotional support and use of instrumental or social support clustered to form a single factor. A third cluster, acceptance/denial, also emerged as an important component among women with ovarian cancer. This reflects qualitative findings that some women denied the negative risks and realities that confronted them while others said that creating new expectations for themselves was the only way to deal with their new reality [4].
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Theories about adjustment suggest patients may move from a state of denial to a constructive processing of illness- and treatment-related information [28]. Longitudinal research in the breast cancer setting has shown that virtually all forms of coping behavior peak around surgery and then decline either precipitously (e.g., active coping) or more gradually (e.g., denial) before stabilizing at 1 year post-diagnosis [29]. Similarly, it has been shown that patients with breast cancer are initially supported by social networks of family and friends and the size of these networks as well as the amount of emotional support decrease over time [30]. In contrast, with the exception of social and emotional support use where 84% of women with ovarian cancer had a decreasing trajectory, we found the use of other coping strategies was largely stable over the 3–9-month period post-diagnosis. The more stable trajectories we observed may be related to the more advanced nature of ovarian cancer at presentation and the regular surveillance for recurrence maintaining the need to use coping strategies.
A strength and innovation of our study was the use of group-based trajectory modeling as it enabled us to tease out group-based patterns rather than traditional modeling techniques such as quantiles of coping at a fixed time point or average trajectories of coping use over time. This is important because traditional modeling techniques may not have the sensitivity to detect important associations. Our results suggest that women with ovarian cancer adjust more positively, exhibiting lower levels of anxiety and depression and/or higher quality of life at 12 months after diagnosis, if they have a coping profile during the first 3–9 months post-diagnosis of continued (a) high use of action-planning, positive framing and humor (12%); (b) high use of psychosocial support (8%); or (c) acceptance of their reality (26%). This suggests that an intervention that addresses acceptance and facilitates the initial and continued use of action planning, positive framing, and social support seeking has the potential to improve patient-reported outcomes for the vast majority of women with ovarian cancer who do not have these coping profiles. Since there was a linear relationship of number of positive coping behaviors with better quality of life and depression and because only 3% of women identified as having all three of the beneficial coping profiles, we suggest that all women with ovarian cancer could be offered intervention.
Descriptive evidence on coping with ovarian cancer is accumulating. Others suggest cross-sectional associations between social support seeking and lower depression scores and higher quality of life [9] and that reductions in helplessness/hopelessness and improvements to optimism may improve both subsequent quality [10] and quantity of life [31] particularly if use of these coping behaviors is instilled prior to first progression [31]. Development of an intervention with coping strategies specifically helpful to women with ovarian cancer is the next step. A number of reviews support the efficacy of multicomponent coping skills training interventions for decreasing cancer patients’ anxiety and increasing quality of life, particularly if they are based on cognitive behavioral therapy [32, 33] or acceptance and commitment therapy [34]. However, none of the included interventions were with ovarian cancer patients.
Our study had some limitations. Firstly, while this study used Folkman and Greer’s model as a theoretical lens, it was not designed specifically to interrogate the model; we did not explore the direct connection of a disease progression event with retriggering of the use of coping strategies. Secondly, while attrition was minimal, one third of the cohort were excluded from factor analysis as they did not complete the first measure of coping at 3 months; however, this was largely random depending on how quickly women were identified. Less than 10% of women were excluded from trajectory analyses due to loss to follow-up or not completing multiple follow-up questionnaires. We compared baseline characteristics of women included and excluded from factor analysis and showed that characteristics were similar. It is also likely that the study cohort were slightly healthier than the general ovarian cancer population as the sickest women were less likely to participate. However, our progression rate of 19% within 12 months of diagnosis is not too dissimilar to 1-year survival rates of 76% [35] suggesting our estimates are largely generalizable to most patients with ovarian cancer and unbiased by missing data.
In conclusion, this research informs the development of psychosocial interventions targeting coping strategies early in the disease trajectory for all women with ovarian cancer. Guidelines for the care of women with this disease emphasize that the focus of management should be minimization of the physical and psychological impact of cancer and its treatment [36]. Future research should test the capacity of such an intervention to improve patient-reported outcomes compared with usual care. Long-term follow-up is also recommended to assess the relationship between coping strategies and survival.
Acknowledgements
We acknowledge the OPAL Study team and all the clinicians and participating institutions who helped make this study possible (see opalstudy.qimrberghofer.edu.au for a complete list). We also thank consumer representatives Karen Livingstone, Hélène O’Neill, and Merran Williams and all the women who took part.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.