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The Relationship Between Parent Distress and Child Quality of Life in Pediatric Cancer: A Meta-Analysis
Journal of Pediatric Nursing 50 (2020) 14–19
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Journal of Pediatric Nursing journal homepage: www.pediatricnursing.org
The relationship between parent distress and child quality of life in pediatric cancer: A meta-analysis Dana M. Bakula ⁎, Christina M. Sharkey, Megan N. Perez, Hannah C. Espeleta, Kaitlyn L. Gamwell, Marissa Baudino, Alexandria M. Delozier, John M. Chaney, R. Matt Alderson, Larry L. Mullins Department of Psychology, Oklahoma State University, United States of America
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Article history: Received 10 July 2019 Revised 12 September 2019 Accepted 12 September 2019 Available online xxxx Keywords: Pediatric cancer Quality of life Distress Adjustment Parents
a b s t r a c t Problem: Pediatric cancer places both parents and children at risk for psychosocial difficulties, including a specific risk for diminished quality of life. Previous research has identified relationships between parent and child psychosocial adjustment outcomes (e.g., depression, anxiety), yet the relationships between parent adjustment and child quality of life have yet to be comprehensively evaluated via meta-analysis. Eligibility criteria: A systematic review and meta-analysis were conducted using EBSCO, with PsychINFO, MEDLINE, Academic Search Premiere, and Health Source: Nursing/Academic Edition. Sample: Fourteen studies met inclusion criteria. Results: Fourteen correlations from 1646 parents of children with cancer were evaluated, resulting in a mediummagnitude correlation between parent psychosocial adjustment and child quality of life (r = 0.23, p b .001). Additional analyses evaluating the relationship between parent psychosocial adjustment and child social/emotional quality of life resulted in a medium-magnitude correlation (r = 0.24, p b .001). Conclusions: A significant relationship exists between parent psychosocial adjustment and child quality of life. However, this relationship appears slightly less strong than those found in meta-analyses evaluating other child psychosocial adjustment outcomes. Implications: Parent distress is an important factor to evaluate in the context of pediatric cancer, as it appears to have implications for child quality of life, in addition to other child psychosocial adjustment outcomes. © 2019 Elsevier Inc. All rights reserved.
Pediatric cancer affects approximately 15,000 families in the Unites States per year (Alderfer & Kazak, 2006; Ward, DeSantis, Robbins, Kohler, & Jemal, 2014). A diagnosis of pediatric cancer and its treatment can cause significant disruptions in a child and family's life, including the experience of painful procedures and treatments, unpredictable and impairing physical symptoms, financial stressors, and social isolation (e.g., Rodriguez et al., 2011). Thus, it is not surprising that both parents and children are at risk for negative psychosocial outcomes following a pediatric cancer diagnosis, including depressive symptoms, anxious symptoms, and posttraumatic stress symptoms (Dolgin et al., 2007; Pai et al., 2007; Pinquart & Shen, 2011a, 2011b, 2011c; Vrijmoet-Wiersma et al., 2008). In this context, the past decade has witnessed increased research on child quality of life outcomes, with findings demonstrating that children are also at risk for reduced quality of life following a pediatric cancer diagnosis (e.g., Zebrack & Chesler, 2002). The World Health Organization defines quality of life as an “individuals' perception of their position in life in the context of the culture ⁎ Corresponding author at: Department of Psychology, Oklahoma State University, Stillwater, OK 74078, United States of America. E-mail address: [email protected] (D.M. Bakula).
https://doi.org/10.1016/j.pedn.2019.09.024 0882-5963/© 2019 Elsevier Inc. All rights reserved.
and value systems in which they live and in relation to their goals, expectations, standards and concerns” (Whoqol Group, 1995). This conceptualization of quality of life led to the development of a multitude of measures that aim to assess an individuals' well-being across multiple core dimensions of functioning. Within the context of pediatric cancer, evaluation across quality of life domains such as social and emotional well-being, in addition to the domain of physical health, supports the ability of providers to identify and address concerns related to a child's functioning. To conceptualize child functioning following pediatric cancer diagnosis, many theoretical models have been proposed. These include the pediatric medical traumatic stress model (Kazak et al., 2005), the stress and coping model (Thompson & Gustafson, 1996), and the socialecological model (Bronfenbrenner, 1979). Among these, the socialecological model is one of the most widely used models in pediatric cancer research (Bronfenbrenner, 1979; Kazak, 1989). According to this model, children exist within multiple systems, including microsystems, such as parents and family, as well as larger ecosystems, which may include hospital systems and other institutions with which a child interacts. Most notably, this model underscores the importance of a child's family system in understanding child functioning, as do each of the
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other aforementioned models that conceptualize child adjustment in the context of a pediatric medical condition. Thus, it is not surprising that considerable research has found that factors such as family functioning and parent psychosocial functioning are of the utmost importance in understanding child functioning (e.g., Robinson, Gerhardt, Vannatta, & Noll, 2006). Indeed, multiple meta-analyses evaluating the relationship between parent and child psychosocial functioning have identified moderate associations in the general population (e.g., Lambert, Holzer, & Hasbun, 2014; Morris, Gabert-Quillen, & Delahanty, 2012). Further, it has long been hypothesized that parent psychosocial functioning is a contributor to child psychosocial functioning in pediatric cancer populations (Drotar, 1997). However, substantive support for this transactional relationship between parent and child outcomes following a pediatric cancer diagnosis was not available until a recent meta-analysis confirmed that parent and child distress are indeed linked (Bakula et al., 2019). However, this review was limited to specific child psychological outcomes and did not evaluate the relationship between dimensions of parent functioning and child quality of life. Given the theorized importance of parent functioning in understanding child functioning, as well as the observed relationships between parent and child psychological outcomes, parent psychosocial functioning could similarly be an important variable in understanding child quality of life following a pediatric cancer diagnosis. Notably, the evaluation of this relationship holds great significance, as the overarching focus of psychosocial care is specifically targeted at improving child quality of life (Wiener, Kazak, Noll, Patenaude, & Kupst, 2015). Thus, there is a clear need to identify modifiable factors, such as parent adjustment, that could impact child quality of life in the context of pediatric cancer. Thus, the present study sought to expand on the findings of Bakula et al. (2019) by assessing the relationship between parent psychosocial distress (e.g., depressive, anxious, and posttraumatic stress symtpoms) and child quality of life. Specifically, we systematically reviewed the literature in this area, and meta-analytically evaluated the relationship between parent distress and child quality of life following a pediatric cancer diagnosis. The current study did not evaluate potential, studylevel moderators (e.g., child age, time since diagnosis) of the relationship due to homogeneity of effect sizes. Methods Literature search and inclusion criteria The present study includes articles that were extracted using a literature search conducted with EBSCO, using PsychINFO, MEDLINE, Academic Search Premiere, and Health Source: Nursing/Academic Edition databases. The search was limited to articles from peer-reviewed journals written in English. The search was also conducted using PubMed, limited to articles written in English and human subjects research. Search terms included parent (parent*, caregiver*, mother*, father*, maternal, paternal), distress (PTSS, posttraumatic, posttraumatic, post traumatic, PTSD, depress*, anxiety, anxious, withdraw*, externalizing, internalizing, psychosocial, distress, quality of life, QOL, HRQOL, psychiatric, stress, social, well-being, mental health, psychological, adjustment, emotion*), child (pediatric, paediatric, child, youth, teen*, adolescen*, juvenil*, children, childhood), and cancer (maligna*, sarcom*, tumor, tumour, neoplasm, aml, b-cell, carcinom*, ewing*, gliom*, hematolo*, hepatoblastom*, hepatom*, hodgkin*, leukaemi*, leukemi*, lymphom*, medulloblastom*, meningiom*, nephroblastom*, neuroblastom*, non-hodgkin, oncolog*, osteosarcom*, pnet*, retinoblastom*, rhabdomyosarcom*, t-cell*, teratom*, wilms*, cancer, cancers, tumors, tumours). It should be noted that asterisks (*) used in the search term were used to indicate that all iterations of the word stem are included in the search. Given the breadth of this systematic search, no additional methods were utilized to identify potential studies.
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Articles that met inclusion for this study consisted of examinations of both parent psychosocial outcomes and child quality of life, as well as the relationship between these variables. Additionally, both parent psychosocial outcomes and quality of life had to be measured using validated measures. For quality of life, specifically, the study had to evaluate at least one domain of quality of life, in line with the WHO definition of quality of life (Whoqol Group, 1995). Included studies were written in English, published in peer-reviewed academic journals, and included children diagnosed with cancer and their parents. Study selection The initial search was completed on May 30th, 2017, yielding 27,399 articles, and was updated on July 7th, 2018, yielding 1719 additional articles. EndNote (Reuters, 2013) was used to screen out duplicate articles, resulting in 18,576 articles for review. Abstracts were screened independently by two authors, and discrepencies were resolved by the first author. A resulting 14 studies were included in the present meta-analysis (Fig. 1). Data extraction Study characteristics and effect size data were extracted from all studies by authors one and two. Basic assumptions of meta-analyses require that only one effect size per study can be included, and thus when multiple relationships were reported between parent outcomes and child quality of life, the effect sizes were averaged. For studies which included both parent proxy- and child self-report of child quality of life, one effect size was randomly selected via a random number generator. Two tiers of analyses were conducted. Tier I analyses included all studies and examined the relationship between parent psychosocial outcomes and child overall quality of life. Tier II analyses examined a subset of studies that included more specific child emotional and social quality of life outcomes in relation to parent psychosocial outcomes. Tier II analyses probe potential differences that might exist between emotional and social quality of life. Study quality Nine independent criteria were evaluated to determine study quality for each included article, using a rating system established by Alderfer et al. (2010). The criteria included evaluation of the scientific basis for the study, use of appropriate methods, reliability, statistical power, internal validity, measurement validity, external validity, appropriate discussion, and significant contribution to scientific knowledge. Criterion were rated on a scale of 1 (little or no evidence) to 3 (high quality of good evidence). An average score was calculated for each study. One third of study quality ratings were rated by the second author and demonstrated good inter-rater reliability (ICC = 0.87). Data analytic strategy Estimation of effect sizes A single correlation coefficient for each study was entered into Comprehensive Meta-analysis Version 2.2 (CMA-3; Borenstein, Hedges, Higgins, & Rothstein, 2014). A mixed-effects model was used given lack of heterogeneity in the present sample of studies. Cohen's conventions were used to classify correlation coefficient magnitudes as small (0.10), medium (0.30), or large (0.50; Cohen, 1992). Publication bias In order to assess for potential upward bias associated with greater likelihood of significant results being published and non-significant results being discarded (i.e., the file-drawer phenomenon), multiple methods were used to assess for publication bias. Egger's regression
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Fig. 1. PRISMA Flow Diagram.
intercept was used to examine the overall effect sizes, with greater intercept values indicating a higher potential for publication bias (Egger, Smith, Schneider, & Minder, 1997). Contour-enhanced funnel plots, with Zr coefficients on the x-axis and standard error on the y-axis, were evaluated for symmetry, with asymmetrical distributions being indicative of publication bias. Finally, the Fail-Safe N was used to determine the number of unpublished studies that would need to exist with non-significant findings in order to determine that the overall effect is not significant.
Homogeneity analysis and moderation analysis The distribution of correlation coefficients across all studies in both Tier I and Tier II were evaluated by the Q-test. The Q-test is used to evaluate heterogeneity within the effect size distribution. A statistically significant Q value indicates that there is significant heterogeneity and examination of potential moderator variables is warranted (Lipsey & Wilson, 2001).
Results Two studies were longitudinal, and the remainder were crosssectional in nature. Seven of the studies included only families that were in the new diagnosis phase, with the remainder including either off-treatment groups or a full spectrum, ranging from a new diagnosis to multiple years off-treatment. The participants in the included studies were predominantly Caucasian. The majority of participants were mothers (82.87%). The mean child age reported in each study ranged from 6.6 years to 14.3 years of age, and most studies relied on parent proxy-report of child quality of life (78.5%). Study quality scores ranged from 2.00 to 2.78, with an average of 2.37 (SD = 0.29). See Table 1 for an overview of study characteristics. Tier I: overall correlation Fourteen correlations from 1646 parents of children with cancer were used as part of the present meta-analysis, and yielded a small-
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Table 1 Summary of Study Characteristics and Findings. Citation
Parent N
Time since diagnosis
Child age (mean)
Child age (SD)
% Mothers
Eiser, Eiser, and Stride (2005) Harper et al. (2014) Huang et al. (2018) Kazak and Barakat (1997) Link and Fortier (2016) Malpert et al. (2015) Molzon et al. (2018) Mullins et al. (2016) Olagunju, Sarimiye, Olagunju, Habeebu, and Aina (2016) Pierce et al. (2017) Racine, Khu, Reynolds, Guilcher, and Schulte (2018) Roddenberry and Renk (2008) Vance, Jenney, Eiser, and Morse (2001) Wahi, Phelan, Sherman-Bien, Sender, and Fortier (2016)
87 103 213 29 353 127 150 138 72 67 52 63 36 156
New diagnosis/on treatment New diagnosis/on treatment Survivorship/off treatment Survivorship/off treatment New diagnosis/on treatment Survivorship/off treatment New diagnosis/on treatment New diagnosis/on treatment New diagnosis/on treatment New diagnosis/on treatment Survivorship/off treatment Survivorship/off treatment Survivorship/off treatment Survivorship/off treatment
7 years 6.6 years 14.3 years 9.8 years 10.6 years 12.3 years 8.57 years 8.01 years 10 years 9.5 years 11.92 years 13.7 years 8.9 years 12 years
Not reported 3.2 4.8 3.2 4.9 2.84 5.18 5.22 2 5.4 3.1 2.7 2 3
100 0.46⁎⁎⁎ 85 NS 81.2 0.28⁎⁎⁎ Not reported NS 83 0.33⁎⁎⁎ 79 0.29⁎ 82.7 0.40⁎⁎⁎ 83.5 NS 84 0.58⁎ 74.6 0.35⁎ 90.4 0.48⁎⁎⁎ 75 0.69⁎ 81 0.55⁎ 78 0.73⁎⁎
Aggregated correlation
Note. Correlation Coefficients reflect an aggregate of all correlations included in a given study, when multiple were reported from a single study. NS = Non-significant. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.
magnitude correlation (r = 0.23, p b .001). The Egger's regression intercept was significant, suggesting some publication bias (intercept = 1.41, 95% CI [0.34, 2.48], p b .05). However, visual inspection of the contour-enhanced funnel plot did not indicate publication bias, as the plot did not have noticeable asymmetry. Additionally, the Fail-Safe N revealed that 309 unpublished studies with null effects would need to be published to result in the overall correlation being non-significant. The distribution of effects was not heterogeneous, ranging from 0.18 to 0.28 (Q (13) = 7.38, p = .88), suggesting that examination of potential moderator variables was not warranted. Thus, no moderators were evaluated in this model. Finally, no correlations included in these analyses were determined to be outliers. Tier II: correlation including only child emotional and social quality of life Seven correlations from 771 parents of children with cancer were included as part of the meta-analysis evaluating the relationship between child emotional and social quality of life in relation to parent distress, and resulted in a small-magnitude correlation (r = 0.24, p b .001). The Egger's regression intercept was non-significant, which is not indicative of publication bias (intercept = 1.24, 95% CI [−0.31, 2.81], p = .09). Further, the Fail-Safe N revealed that 73 unpublished studies with null effects would need to be published to result in the overall correlation being non-significant. Visual inspection of the funnel plot did indicate some asymmetry, however, and thus suggested that some publication bias may be possible. The distribution of effects was not heterogeneous, ranging from 0.17 to 0.31 (Q (6) = 4.00, p = .68), suggesting that examination of potential moderator variables was not warranted. Thus, no moderators could be evaluated in this model. Finally, no correlations included in these analyses were determined to be outliers. Discussion Findings from the present meta-analysis, which evaluated 1646 families from fourteen studies, confirm a significant relationship between parent psychosocial distress and child quality of life outcomes in families of children with cancer. Thus, these findings add additional rationale for the importance of considering parent psychosocial adjustment when providing psychosocial care to children with cancer. Although this is the first study to evaluate the relationship between parent distress and child quality of life in pediatric cancer, these findings align with other related studies. Specifically, these findings are consistent with the results of Bakula et al. (2019), who found mediummagnitude effects when evaluating the relationship between parent and child psychosocial distress in pediatric cancer. In addition, these findings are consistent with other meta-analyses indicating a
relationship between parent and child psychosocial adjustment in the general population (e.g., Lambert et al., 2014; Morris et al., 2012). Examination of potential publication bias yielded mixed results, suggesting some caution is warranted in interpreting the overall aggregate correlation. Nevertheless, the Fail safe N of 309 suggests that there is sufficient evidence to confirm a relationship between these variables. Although the effects sizes derived from the present study were significant, the effects (0.23–0.24) are smaller than those found for the relationship between parental psychosocial distress and child distress (0.31–0.51) by Bakula et al. (2019), as well as the medium effects found by others such as Lambert et al. (2014) and Morris et al. (2012). Thus, although the present findings indicate a significant relationship, it calls into question why these effects are smaller than those found in studies assessing other adjustment outcomes. A number of hypotheses should be considered. First, it may be that the effect sizes could be different between physical and emotional/social outcomes, such that relationships would be stronger among emotional and social outcomes, as compared to physical quality of life. However, Tier II analyses revealed comparable effect sizes as evidenced by the overall correlation evaluated in Tier I. Further, it was considered that study-level variables may moderate the strength of the effect. However, in evaluating this hypothesis, it was surprising that a lack of significant heterogeneity was present in either tiers of analyses. Notably, this homogeneity in the effect size distributions may be viewed positively, as it suggests that the effects, albeit small, are robust and reliable. Conversely, lack of significant heterogeneity in effect sizes implies minimal variability in methodology across studies, which in turn precludes inferences about potential moderator variables that may strengthen or weaken the effects, should alternative methodologies be used. Although speculative, other factors could help explain lower effect sizes observed in these studies. In the Bakula et al. (2019) metaanalysis, there was greater similarity between the same type of parent psychosocial outcomes and child psychosocial outcomes (e.g., parent depressive symptoms and child depressive symptoms). In contrast, quality of life is quite broad in definition, encompassing multiple domains of one's life, which may be less related to the parent distress outcomes captured by Bakula et al. (2019). Further, the brief measures used to capture this broad construct are also limited in the number of items assessing each domain; therefore, these measures may not be as sensitive to difficulties as other, more specific measures, such as those targeting depression, pain, fatigue, anxiety, or posttraumatic stress. Indeed, the five to six items devoted to measuring emotional quality of life may not detect nuances in emotional functioning, such as frequent intrusive thoughts about an event, that can be highly impairing to one's emotional wellbeing. Previous research supports this explanation, as a study that evaluated relationships between child quality of life and
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specific domains of child psychosocial adjustment (e.g., depression, anxiety) in pediatric epilepsy found only moderate effect sizes between the two constructs (Stevanovic, Jancic, & Lakic, 2011). Therefore, although impairment in quality of life is clinically meaningful, measures of this construct may not be as sensitive to specific psychosocial difficulties.
Strengths and limitations The present meta-analysis has a number of strengths. First, this is the first systematic evaluation of this body of literature, and includes data from a large number of families. Further, the present metaanalysis includes a heterogenous range of ages and time since diagnosis. The search terms utilized by the systematic review were broad, and thus were able to adequately capture the literature in this area. In addition, the use of multiple raters to screen for articles, extract data, and code study quality allows for more accuracy and consistency in methodology. Although a research librarian was not utilized in the search process, all search procedures were conducted consistent with established guidelines for systematic reviews (Moher, Liberati, Tetzlaff, Altman, & the PRISMA group, 2009). However, this meta-analysis and the included studies are not without limitations. Specifically, few studies included large, ethnically diverse samples, and thus the present findings may be limited in generalizability. Additionally, few fathers were included in these studies, which limit inferences about how this relationship holds between children and their fathers. The lack of heterogeneity in the findings prevented the evaluation of moderator variables, which could indicate that the relationship is truly consistent across factors, or could be related to the largely homogenous sample characteristics across the studies. Regardless, the present study provides strong evidence of a relationship between parent psychosocial adjustment and child quality of life, and there is little evidence that publication bias accounts for these findings. Implications Given the limited diversity among the included studies, future research should seek to evaluate this relationship in more ethnically diverse samples, with mothers, fathers, and other caregivers (e.g., grandparents and other kinship care arrangements). Differential relationships may occur between mothers' and fathers' reports of distress and child quality of life (Davis, Davies, Waters, & Priest, 2008). Further, parental distress may influence parent proxyreports (Driscoll, Buscemi, & Holmbeck, 2018; Sato et al., 2013). Therefore, it has been recommended that both parent and child self-report be utilized for both clinical and research purposes (Sato et al., 2013; Varni, Seid, & Kurtin, 2001). In addition, further research is needed to understand the differences between child quality of life outcomes and child psychological distress, in relation to parent psychosocial adjustment given the demonstrated differences across these relationships. Conclusion In conclusion, child quality of life is significantly related to parent psychosocial adjustment. These findings underscore the importance of evaluating physical and psychosocial health of the whole family when providing care for children with cancer. Additionally, these findings confirm that, for at least a subset of families affected by pediatric cancer, parents are experiencing psychological distress concurrent with reduced child quality of life. In sum, there appears to be a significant link between how parents are functioning and child outcomes, necessitating a family-focused approach in pediatric cancer care.
CRediT authorship contribution statement Dana M. Bakula:Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Projectplease check that the role "project management" was changed to "project administration" to match roles if appropriate and amend if necessary. administration. Christina M. Sharkey:Conceptualization, Investigation, Writing - original draft, Writing - review & editing, Visualization.Megan N. Perez:Investigation, Writing - original draft, Writing - review & editing. Hannah C. Espeleta:Investigation, Writing - review & editing, Methodology.Kaitlyn L. Gamwell:Investigation, Writing - review & editing. Marissa Baudino:Investigation, Writing - review & editing.Alexandria M. Delozier:Investigation, Writing - review & editing.John M. Chaney: Conceptualization, Writing - review & editing, Supervision.R. Matt Alderson:Conceptualization, Methodology, Formal analysis, Writing review & editing, Supervision.Larry L. Mullins:Conceptualization, Methodology, Writing - review & editing, Supervision.
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Robinson, K. E., Gerhardt, C. A., Vannatta, K., & Noll, R. B. (2006). Parent and family factors associated with child adjustment to pediatric cancer. Journal of Pediatric Psychology, 32(4), 400–410. Roddenberry, A., & Renk, K. (2008). Quality of life in pediatric cancer patients: The relationships among parents’ characteristics, children’s characteristics, and informant concordance. Journal of Child and Family Studies, 17(3), 402–426. Rodriguez, E. M., Dunn, M. J., Zuckerman, T., Vannatta, K., Gerhardt, C. A., & Compas, B. E. (2011). Cancer-related sources of stress for children with cancer and their parents. Journal of Pediatric Psychology, 37(2), 185–197. Sato, I., Higuchi, A., Yanagisawa, T., Mukasa, A., Ida, K., Sawamura, Y., & Kamibeppu, K. (2013). Factors influencing self- and parent-reporting health-related quality of life in children with brain tumors. Quality of Life Research, 22(1), 185–201. Stevanovic, D., Jancic, J., & Lakic, A. (2011). The impact of depression and anxiety disorder symptoms on the health-related quality of life of children and adolescents with epilepsy. Epilepsia, 52(8), e75–e78. Thompson, R. J., & Gustafson, K. E. (1996). Adaptation to chronic childhood illness. (A. P. Association, Ed.). Vance, Y. H., Jenney, M. E., Eiser, C., & Morse, R. C. (2001). Issues in measuring quality of life in childhood cancer: measures, proxies, and parental mental health. The Journal of Child Psychology and Psychiatry and Allied Disciplines, 42(5), 661–667. Varni, J. W., Seid, M., & Kurtin, P. S. (2001). PedsQL 4.0: Reliability and validity of the Pediatric Quality of Life Inventory Version 4.0 generic core scales in healthy and patient populations. Medical Care, 39(8), 800–812. Vrijmoet-Wiersma, C. M. J., van Klink, J. M. M., Kolk, A. M., Koopman, H. M., Ball, L. M., & Maarten Egeler, R. (2008). Assessment of parental psychological stress in pediatric cancer: A review. Journal of Pediatric Psychology, 33(7), 694–706. Wahi, A., Phelan, M., Sherman-Bien, S., Sender, L. S., & Fortier, M. A. (2016). The Impact of Ethnicity, Language, and Anxiety on Quality of Life in Children with Cancer. Applied Research in Quality of Life, 11(3), 817–836. Ward, E., DeSantis, C., Robbins, A., Kohler, B., & Jemal, A. (2014). Childhood and adolescent cancer statistics, 2014. CA: a Cancer Journal for Clinicians, 64(2), 83–103. Whoqol Group (1995). The World Health Organization quality of life assessment (WHOQOL): Position paper from the World Health Organization. Social Science & Medicine, 41(10), 1403–1409. Wiener, L., Kazak, A. E., Noll, R. B., Patenaude, A. F., & Kupst, M. J. (2015). Standards for the psychosocial care of children with cancer and their families: An introduction to the special issue. Pediatric Blood & Cancer, 62(S5), S419–S424. Zebrack, B. J., & Chesler, M. A. (2002). Quality of life in childhood cancer survivors. PsychoOncology: Journal of the Psychological, Social and Behavioral Dimensions of Cancer, 11 (2), 132–141.
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Journal of Pediatric Nursing journal homepage: www.pediatricnursing.org
The relationship between parent distress and child quality of life in pediatric cancer: A meta-analysis Dana M. Bakula ⁎, Christina M. Sharkey, Megan N. Perez, Hannah C. Espeleta, Kaitlyn L. Gamwell, Marissa Baudino, Alexandria M. Delozier, John M. Chaney, R. Matt Alderson, Larry L. Mullins Department of Psychology, Oklahoma State University, United States of America
a r t i c l e
i n f o
Article history: Received 10 July 2019 Revised 12 September 2019 Accepted 12 September 2019 Available online xxxx Keywords: Pediatric cancer Quality of life Distress Adjustment Parents
a b s t r a c t Problem: Pediatric cancer places both parents and children at risk for psychosocial difficulties, including a specific risk for diminished quality of life. Previous research has identified relationships between parent and child psychosocial adjustment outcomes (e.g., depression, anxiety), yet the relationships between parent adjustment and child quality of life have yet to be comprehensively evaluated via meta-analysis. Eligibility criteria: A systematic review and meta-analysis were conducted using EBSCO, with PsychINFO, MEDLINE, Academic Search Premiere, and Health Source: Nursing/Academic Edition. Sample: Fourteen studies met inclusion criteria. Results: Fourteen correlations from 1646 parents of children with cancer were evaluated, resulting in a mediummagnitude correlation between parent psychosocial adjustment and child quality of life (r = 0.23, p b .001). Additional analyses evaluating the relationship between parent psychosocial adjustment and child social/emotional quality of life resulted in a medium-magnitude correlation (r = 0.24, p b .001). Conclusions: A significant relationship exists between parent psychosocial adjustment and child quality of life. However, this relationship appears slightly less strong than those found in meta-analyses evaluating other child psychosocial adjustment outcomes. Implications: Parent distress is an important factor to evaluate in the context of pediatric cancer, as it appears to have implications for child quality of life, in addition to other child psychosocial adjustment outcomes. © 2019 Elsevier Inc. All rights reserved.
Pediatric cancer affects approximately 15,000 families in the Unites States per year (Alderfer & Kazak, 2006; Ward, DeSantis, Robbins, Kohler, & Jemal, 2014). A diagnosis of pediatric cancer and its treatment can cause significant disruptions in a child and family's life, including the experience of painful procedures and treatments, unpredictable and impairing physical symptoms, financial stressors, and social isolation (e.g., Rodriguez et al., 2011). Thus, it is not surprising that both parents and children are at risk for negative psychosocial outcomes following a pediatric cancer diagnosis, including depressive symptoms, anxious symptoms, and posttraumatic stress symptoms (Dolgin et al., 2007; Pai et al., 2007; Pinquart & Shen, 2011a, 2011b, 2011c; Vrijmoet-Wiersma et al., 2008). In this context, the past decade has witnessed increased research on child quality of life outcomes, with findings demonstrating that children are also at risk for reduced quality of life following a pediatric cancer diagnosis (e.g., Zebrack & Chesler, 2002). The World Health Organization defines quality of life as an “individuals' perception of their position in life in the context of the culture ⁎ Corresponding author at: Department of Psychology, Oklahoma State University, Stillwater, OK 74078, United States of America. E-mail address: [email protected] (D.M. Bakula).
https://doi.org/10.1016/j.pedn.2019.09.024 0882-5963/© 2019 Elsevier Inc. All rights reserved.
and value systems in which they live and in relation to their goals, expectations, standards and concerns” (Whoqol Group, 1995). This conceptualization of quality of life led to the development of a multitude of measures that aim to assess an individuals' well-being across multiple core dimensions of functioning. Within the context of pediatric cancer, evaluation across quality of life domains such as social and emotional well-being, in addition to the domain of physical health, supports the ability of providers to identify and address concerns related to a child's functioning. To conceptualize child functioning following pediatric cancer diagnosis, many theoretical models have been proposed. These include the pediatric medical traumatic stress model (Kazak et al., 2005), the stress and coping model (Thompson & Gustafson, 1996), and the socialecological model (Bronfenbrenner, 1979). Among these, the socialecological model is one of the most widely used models in pediatric cancer research (Bronfenbrenner, 1979; Kazak, 1989). According to this model, children exist within multiple systems, including microsystems, such as parents and family, as well as larger ecosystems, which may include hospital systems and other institutions with which a child interacts. Most notably, this model underscores the importance of a child's family system in understanding child functioning, as do each of the
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other aforementioned models that conceptualize child adjustment in the context of a pediatric medical condition. Thus, it is not surprising that considerable research has found that factors such as family functioning and parent psychosocial functioning are of the utmost importance in understanding child functioning (e.g., Robinson, Gerhardt, Vannatta, & Noll, 2006). Indeed, multiple meta-analyses evaluating the relationship between parent and child psychosocial functioning have identified moderate associations in the general population (e.g., Lambert, Holzer, & Hasbun, 2014; Morris, Gabert-Quillen, & Delahanty, 2012). Further, it has long been hypothesized that parent psychosocial functioning is a contributor to child psychosocial functioning in pediatric cancer populations (Drotar, 1997). However, substantive support for this transactional relationship between parent and child outcomes following a pediatric cancer diagnosis was not available until a recent meta-analysis confirmed that parent and child distress are indeed linked (Bakula et al., 2019). However, this review was limited to specific child psychological outcomes and did not evaluate the relationship between dimensions of parent functioning and child quality of life. Given the theorized importance of parent functioning in understanding child functioning, as well as the observed relationships between parent and child psychological outcomes, parent psychosocial functioning could similarly be an important variable in understanding child quality of life following a pediatric cancer diagnosis. Notably, the evaluation of this relationship holds great significance, as the overarching focus of psychosocial care is specifically targeted at improving child quality of life (Wiener, Kazak, Noll, Patenaude, & Kupst, 2015). Thus, there is a clear need to identify modifiable factors, such as parent adjustment, that could impact child quality of life in the context of pediatric cancer. Thus, the present study sought to expand on the findings of Bakula et al. (2019) by assessing the relationship between parent psychosocial distress (e.g., depressive, anxious, and posttraumatic stress symtpoms) and child quality of life. Specifically, we systematically reviewed the literature in this area, and meta-analytically evaluated the relationship between parent distress and child quality of life following a pediatric cancer diagnosis. The current study did not evaluate potential, studylevel moderators (e.g., child age, time since diagnosis) of the relationship due to homogeneity of effect sizes. Methods Literature search and inclusion criteria The present study includes articles that were extracted using a literature search conducted with EBSCO, using PsychINFO, MEDLINE, Academic Search Premiere, and Health Source: Nursing/Academic Edition databases. The search was limited to articles from peer-reviewed journals written in English. The search was also conducted using PubMed, limited to articles written in English and human subjects research. Search terms included parent (parent*, caregiver*, mother*, father*, maternal, paternal), distress (PTSS, posttraumatic, posttraumatic, post traumatic, PTSD, depress*, anxiety, anxious, withdraw*, externalizing, internalizing, psychosocial, distress, quality of life, QOL, HRQOL, psychiatric, stress, social, well-being, mental health, psychological, adjustment, emotion*), child (pediatric, paediatric, child, youth, teen*, adolescen*, juvenil*, children, childhood), and cancer (maligna*, sarcom*, tumor, tumour, neoplasm, aml, b-cell, carcinom*, ewing*, gliom*, hematolo*, hepatoblastom*, hepatom*, hodgkin*, leukaemi*, leukemi*, lymphom*, medulloblastom*, meningiom*, nephroblastom*, neuroblastom*, non-hodgkin, oncolog*, osteosarcom*, pnet*, retinoblastom*, rhabdomyosarcom*, t-cell*, teratom*, wilms*, cancer, cancers, tumors, tumours). It should be noted that asterisks (*) used in the search term were used to indicate that all iterations of the word stem are included in the search. Given the breadth of this systematic search, no additional methods were utilized to identify potential studies.
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Articles that met inclusion for this study consisted of examinations of both parent psychosocial outcomes and child quality of life, as well as the relationship between these variables. Additionally, both parent psychosocial outcomes and quality of life had to be measured using validated measures. For quality of life, specifically, the study had to evaluate at least one domain of quality of life, in line with the WHO definition of quality of life (Whoqol Group, 1995). Included studies were written in English, published in peer-reviewed academic journals, and included children diagnosed with cancer and their parents. Study selection The initial search was completed on May 30th, 2017, yielding 27,399 articles, and was updated on July 7th, 2018, yielding 1719 additional articles. EndNote (Reuters, 2013) was used to screen out duplicate articles, resulting in 18,576 articles for review. Abstracts were screened independently by two authors, and discrepencies were resolved by the first author. A resulting 14 studies were included in the present meta-analysis (Fig. 1). Data extraction Study characteristics and effect size data were extracted from all studies by authors one and two. Basic assumptions of meta-analyses require that only one effect size per study can be included, and thus when multiple relationships were reported between parent outcomes and child quality of life, the effect sizes were averaged. For studies which included both parent proxy- and child self-report of child quality of life, one effect size was randomly selected via a random number generator. Two tiers of analyses were conducted. Tier I analyses included all studies and examined the relationship between parent psychosocial outcomes and child overall quality of life. Tier II analyses examined a subset of studies that included more specific child emotional and social quality of life outcomes in relation to parent psychosocial outcomes. Tier II analyses probe potential differences that might exist between emotional and social quality of life. Study quality Nine independent criteria were evaluated to determine study quality for each included article, using a rating system established by Alderfer et al. (2010). The criteria included evaluation of the scientific basis for the study, use of appropriate methods, reliability, statistical power, internal validity, measurement validity, external validity, appropriate discussion, and significant contribution to scientific knowledge. Criterion were rated on a scale of 1 (little or no evidence) to 3 (high quality of good evidence). An average score was calculated for each study. One third of study quality ratings were rated by the second author and demonstrated good inter-rater reliability (ICC = 0.87). Data analytic strategy Estimation of effect sizes A single correlation coefficient for each study was entered into Comprehensive Meta-analysis Version 2.2 (CMA-3; Borenstein, Hedges, Higgins, & Rothstein, 2014). A mixed-effects model was used given lack of heterogeneity in the present sample of studies. Cohen's conventions were used to classify correlation coefficient magnitudes as small (0.10), medium (0.30), or large (0.50; Cohen, 1992). Publication bias In order to assess for potential upward bias associated with greater likelihood of significant results being published and non-significant results being discarded (i.e., the file-drawer phenomenon), multiple methods were used to assess for publication bias. Egger's regression
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Fig. 1. PRISMA Flow Diagram.
intercept was used to examine the overall effect sizes, with greater intercept values indicating a higher potential for publication bias (Egger, Smith, Schneider, & Minder, 1997). Contour-enhanced funnel plots, with Zr coefficients on the x-axis and standard error on the y-axis, were evaluated for symmetry, with asymmetrical distributions being indicative of publication bias. Finally, the Fail-Safe N was used to determine the number of unpublished studies that would need to exist with non-significant findings in order to determine that the overall effect is not significant.
Homogeneity analysis and moderation analysis The distribution of correlation coefficients across all studies in both Tier I and Tier II were evaluated by the Q-test. The Q-test is used to evaluate heterogeneity within the effect size distribution. A statistically significant Q value indicates that there is significant heterogeneity and examination of potential moderator variables is warranted (Lipsey & Wilson, 2001).
Results Two studies were longitudinal, and the remainder were crosssectional in nature. Seven of the studies included only families that were in the new diagnosis phase, with the remainder including either off-treatment groups or a full spectrum, ranging from a new diagnosis to multiple years off-treatment. The participants in the included studies were predominantly Caucasian. The majority of participants were mothers (82.87%). The mean child age reported in each study ranged from 6.6 years to 14.3 years of age, and most studies relied on parent proxy-report of child quality of life (78.5%). Study quality scores ranged from 2.00 to 2.78, with an average of 2.37 (SD = 0.29). See Table 1 for an overview of study characteristics. Tier I: overall correlation Fourteen correlations from 1646 parents of children with cancer were used as part of the present meta-analysis, and yielded a small-
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Table 1 Summary of Study Characteristics and Findings. Citation
Parent N
Time since diagnosis
Child age (mean)
Child age (SD)
% Mothers
Eiser, Eiser, and Stride (2005) Harper et al. (2014) Huang et al. (2018) Kazak and Barakat (1997) Link and Fortier (2016) Malpert et al. (2015) Molzon et al. (2018) Mullins et al. (2016) Olagunju, Sarimiye, Olagunju, Habeebu, and Aina (2016) Pierce et al. (2017) Racine, Khu, Reynolds, Guilcher, and Schulte (2018) Roddenberry and Renk (2008) Vance, Jenney, Eiser, and Morse (2001) Wahi, Phelan, Sherman-Bien, Sender, and Fortier (2016)
87 103 213 29 353 127 150 138 72 67 52 63 36 156
New diagnosis/on treatment New diagnosis/on treatment Survivorship/off treatment Survivorship/off treatment New diagnosis/on treatment Survivorship/off treatment New diagnosis/on treatment New diagnosis/on treatment New diagnosis/on treatment New diagnosis/on treatment Survivorship/off treatment Survivorship/off treatment Survivorship/off treatment Survivorship/off treatment
7 years 6.6 years 14.3 years 9.8 years 10.6 years 12.3 years 8.57 years 8.01 years 10 years 9.5 years 11.92 years 13.7 years 8.9 years 12 years
Not reported 3.2 4.8 3.2 4.9 2.84 5.18 5.22 2 5.4 3.1 2.7 2 3
100 0.46⁎⁎⁎ 85 NS 81.2 0.28⁎⁎⁎ Not reported NS 83 0.33⁎⁎⁎ 79 0.29⁎ 82.7 0.40⁎⁎⁎ 83.5 NS 84 0.58⁎ 74.6 0.35⁎ 90.4 0.48⁎⁎⁎ 75 0.69⁎ 81 0.55⁎ 78 0.73⁎⁎
Aggregated correlation
Note. Correlation Coefficients reflect an aggregate of all correlations included in a given study, when multiple were reported from a single study. NS = Non-significant. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.
magnitude correlation (r = 0.23, p b .001). The Egger's regression intercept was significant, suggesting some publication bias (intercept = 1.41, 95% CI [0.34, 2.48], p b .05). However, visual inspection of the contour-enhanced funnel plot did not indicate publication bias, as the plot did not have noticeable asymmetry. Additionally, the Fail-Safe N revealed that 309 unpublished studies with null effects would need to be published to result in the overall correlation being non-significant. The distribution of effects was not heterogeneous, ranging from 0.18 to 0.28 (Q (13) = 7.38, p = .88), suggesting that examination of potential moderator variables was not warranted. Thus, no moderators were evaluated in this model. Finally, no correlations included in these analyses were determined to be outliers. Tier II: correlation including only child emotional and social quality of life Seven correlations from 771 parents of children with cancer were included as part of the meta-analysis evaluating the relationship between child emotional and social quality of life in relation to parent distress, and resulted in a small-magnitude correlation (r = 0.24, p b .001). The Egger's regression intercept was non-significant, which is not indicative of publication bias (intercept = 1.24, 95% CI [−0.31, 2.81], p = .09). Further, the Fail-Safe N revealed that 73 unpublished studies with null effects would need to be published to result in the overall correlation being non-significant. Visual inspection of the funnel plot did indicate some asymmetry, however, and thus suggested that some publication bias may be possible. The distribution of effects was not heterogeneous, ranging from 0.17 to 0.31 (Q (6) = 4.00, p = .68), suggesting that examination of potential moderator variables was not warranted. Thus, no moderators could be evaluated in this model. Finally, no correlations included in these analyses were determined to be outliers. Discussion Findings from the present meta-analysis, which evaluated 1646 families from fourteen studies, confirm a significant relationship between parent psychosocial distress and child quality of life outcomes in families of children with cancer. Thus, these findings add additional rationale for the importance of considering parent psychosocial adjustment when providing psychosocial care to children with cancer. Although this is the first study to evaluate the relationship between parent distress and child quality of life in pediatric cancer, these findings align with other related studies. Specifically, these findings are consistent with the results of Bakula et al. (2019), who found mediummagnitude effects when evaluating the relationship between parent and child psychosocial distress in pediatric cancer. In addition, these findings are consistent with other meta-analyses indicating a
relationship between parent and child psychosocial adjustment in the general population (e.g., Lambert et al., 2014; Morris et al., 2012). Examination of potential publication bias yielded mixed results, suggesting some caution is warranted in interpreting the overall aggregate correlation. Nevertheless, the Fail safe N of 309 suggests that there is sufficient evidence to confirm a relationship between these variables. Although the effects sizes derived from the present study were significant, the effects (0.23–0.24) are smaller than those found for the relationship between parental psychosocial distress and child distress (0.31–0.51) by Bakula et al. (2019), as well as the medium effects found by others such as Lambert et al. (2014) and Morris et al. (2012). Thus, although the present findings indicate a significant relationship, it calls into question why these effects are smaller than those found in studies assessing other adjustment outcomes. A number of hypotheses should be considered. First, it may be that the effect sizes could be different between physical and emotional/social outcomes, such that relationships would be stronger among emotional and social outcomes, as compared to physical quality of life. However, Tier II analyses revealed comparable effect sizes as evidenced by the overall correlation evaluated in Tier I. Further, it was considered that study-level variables may moderate the strength of the effect. However, in evaluating this hypothesis, it was surprising that a lack of significant heterogeneity was present in either tiers of analyses. Notably, this homogeneity in the effect size distributions may be viewed positively, as it suggests that the effects, albeit small, are robust and reliable. Conversely, lack of significant heterogeneity in effect sizes implies minimal variability in methodology across studies, which in turn precludes inferences about potential moderator variables that may strengthen or weaken the effects, should alternative methodologies be used. Although speculative, other factors could help explain lower effect sizes observed in these studies. In the Bakula et al. (2019) metaanalysis, there was greater similarity between the same type of parent psychosocial outcomes and child psychosocial outcomes (e.g., parent depressive symptoms and child depressive symptoms). In contrast, quality of life is quite broad in definition, encompassing multiple domains of one's life, which may be less related to the parent distress outcomes captured by Bakula et al. (2019). Further, the brief measures used to capture this broad construct are also limited in the number of items assessing each domain; therefore, these measures may not be as sensitive to difficulties as other, more specific measures, such as those targeting depression, pain, fatigue, anxiety, or posttraumatic stress. Indeed, the five to six items devoted to measuring emotional quality of life may not detect nuances in emotional functioning, such as frequent intrusive thoughts about an event, that can be highly impairing to one's emotional wellbeing. Previous research supports this explanation, as a study that evaluated relationships between child quality of life and
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specific domains of child psychosocial adjustment (e.g., depression, anxiety) in pediatric epilepsy found only moderate effect sizes between the two constructs (Stevanovic, Jancic, & Lakic, 2011). Therefore, although impairment in quality of life is clinically meaningful, measures of this construct may not be as sensitive to specific psychosocial difficulties.
Strengths and limitations The present meta-analysis has a number of strengths. First, this is the first systematic evaluation of this body of literature, and includes data from a large number of families. Further, the present metaanalysis includes a heterogenous range of ages and time since diagnosis. The search terms utilized by the systematic review were broad, and thus were able to adequately capture the literature in this area. In addition, the use of multiple raters to screen for articles, extract data, and code study quality allows for more accuracy and consistency in methodology. Although a research librarian was not utilized in the search process, all search procedures were conducted consistent with established guidelines for systematic reviews (Moher, Liberati, Tetzlaff, Altman, & the PRISMA group, 2009). However, this meta-analysis and the included studies are not without limitations. Specifically, few studies included large, ethnically diverse samples, and thus the present findings may be limited in generalizability. Additionally, few fathers were included in these studies, which limit inferences about how this relationship holds between children and their fathers. The lack of heterogeneity in the findings prevented the evaluation of moderator variables, which could indicate that the relationship is truly consistent across factors, or could be related to the largely homogenous sample characteristics across the studies. Regardless, the present study provides strong evidence of a relationship between parent psychosocial adjustment and child quality of life, and there is little evidence that publication bias accounts for these findings. Implications Given the limited diversity among the included studies, future research should seek to evaluate this relationship in more ethnically diverse samples, with mothers, fathers, and other caregivers (e.g., grandparents and other kinship care arrangements). Differential relationships may occur between mothers' and fathers' reports of distress and child quality of life (Davis, Davies, Waters, & Priest, 2008). Further, parental distress may influence parent proxyreports (Driscoll, Buscemi, & Holmbeck, 2018; Sato et al., 2013). Therefore, it has been recommended that both parent and child self-report be utilized for both clinical and research purposes (Sato et al., 2013; Varni, Seid, & Kurtin, 2001). In addition, further research is needed to understand the differences between child quality of life outcomes and child psychological distress, in relation to parent psychosocial adjustment given the demonstrated differences across these relationships. Conclusion In conclusion, child quality of life is significantly related to parent psychosocial adjustment. These findings underscore the importance of evaluating physical and psychosocial health of the whole family when providing care for children with cancer. Additionally, these findings confirm that, for at least a subset of families affected by pediatric cancer, parents are experiencing psychological distress concurrent with reduced child quality of life. In sum, there appears to be a significant link between how parents are functioning and child outcomes, necessitating a family-focused approach in pediatric cancer care.
CRediT authorship contribution statement Dana M. Bakula:Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Projectplease check that the role "project management" was changed to "project administration" to match roles if appropriate and amend if necessary. administration. Christina M. Sharkey:Conceptualization, Investigation, Writing - original draft, Writing - review & editing, Visualization.Megan N. Perez:Investigation, Writing - original draft, Writing - review & editing. Hannah C. Espeleta:Investigation, Writing - review & editing, Methodology.Kaitlyn L. Gamwell:Investigation, Writing - review & editing. Marissa Baudino:Investigation, Writing - review & editing.Alexandria M. Delozier:Investigation, Writing - review & editing.John M. Chaney: Conceptualization, Writing - review & editing, Supervision.R. Matt Alderson:Conceptualization, Methodology, Formal analysis, Writing review & editing, Supervision.Larry L. Mullins:Conceptualization, Methodology, Writing - review & editing, Supervision.
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