Featured Article: Trajectories of Posttraumatic Stress Symptoms in Parents of Children With a Serious Childhood Illness or Injury

Featured Article: Trajectories of Posttraumatic Stress Symptoms in Parents of Children With a... Abstract Objective Serious childhood illness is associated with significant parent psychological distress. This study aimed to (a) document acute and posttraumatic stress symptoms (PTSS) in parents of children with various life-threatening illnesses; (b) identify trajectory patterns of parental PTSS and recovery over 18 months; (c) determine psychosocial, demographic, and illness factors associated with trajectory group membership. Methods In total, 159 parents (115 mothers, 44 fathers) from 122 families participated in a prospective, longitudinal study that assessed parent psychological responses across four time points—at diagnosis, and 3, 6, and 18 months later. Children were admitted to the Cardiology, Oncology, and Pediatric Intensive Care Departments in a tertiary pediatric hospital. The primary outcome was parent PTSS. Results Three distinct parent recovery profiles were identified—“Resilient,” “Recovery,” and “Chronic.” The “Resilient” class (33%) showed low distress responses across the trajectory period, whereas the “Recovery” class (52%) showed significantly higher levels of distress at the time of diagnosis that gradually declined over the first months following their child’s illness. Both of these classes nevertheless remained within the normative range throughout. In contrast, the “Chronic” class (13%) was consistently high in severity, remaining within the clinical range across the entire period. Psychosocial factors such as mood, anxiety, and emotional responses predicted group membership, whereas demographic and illness factors did not. Conclusions Parents show considerable resilience in the face of children’s life-threatening illnesses. Early assessment of parent psychosocial factors may aid identification of those who would benefit from early intervention. critically ill children, longitudinal research, mental health, parent stress, posttraumatic stress, stress Introduction Having a child hospitalized because of a life-threatening illness or injury can lead to significant psychological distress in parents (Bronner et al., 2010; Landolt, Ystrom, Sennhauser, Gnehm, & Vollrath, 2012; Le Brocque, Hendrikz, & Kenardy, 2010; Muscara, McCarthy, et al., 2015; Woolf, Muscara, Anderson, & McCarthy, 2016). While high levels of distress in the acute period are predictive of later difficulties (Bryant, Creamer, O’donnell, Silove, & McFarlane, 2012; McCarthy, Ashley, Lee, & Anderson, 2012), our understanding of which parents are at risk of long-term traumatic distress is limited. The Integrative Model of Pediatric Medical Traumatic Stress (Kazak et al., 2006; Price, Kassam-Adams, Alderfer, Christofferson, & Kazak, 2016) conceptualizes parent- and family-level adaptation to a child diagnosis or hospital admission as progressing through three phases: acute reaction to the medical event; evolving traumatic stress response; and longer-term traumatic stress response. In this model, psychosocial, demographic, and medical factors are proposed as potential factors contributing to the emergence and persistence of posttraumatic stress symptoms (PTSS). A stress-coping model proposed by Bonanno (2004) posits four potential distress–recovery trajectories following the death of a loved one: a resilient trajectory characterized by low initial levels of distress, which stay low over time; a delayed trajectory where distress increases after starting low; a chronic trajectory where distress is initially high and remains high; and a recovery trajectory where initially high levels of distress decline over time. This model has been extrapolated to other potentially traumatic events, including adolescents and young adults diagnosed with cancer (Zebrack et al., 2014), parents of children diagnosed with cancer (Dolgin et al., 2007; Steele, Dreyer, & Phipps, 2004), and parents of injured children (Le Brocque et al., 2010). Distinct distress–recovery trajectories were identified for patient/parents in these studies, with the majority reporting improvement in levels of distress over the first months post diagnosis, and a smaller subset displaying more chronic distress patterns. Longitudinal studies of parents’ distress–recovery trajectories are rare and mostly confined to single illness samples, precluding examination of whether the nature of the child’s illness confers different risks on distress–recovery trajectories. We address this significant gap in a prospective study of parents of children with various life-threatening illnesses. Specifically, the study aimed to (a) document acute and long-term PTSS in parents at four time points: acutely following diagnosis/hospitalization, and then 3, 6, and 18 months later; (b) identify specific trajectory patterns of parental PTSS and recovery across this 18 month period; and c) determine psychosocial, demographic, and illness factors associated with specific recovery trajectories. We hypothesized that (i) distinct differences in PTSS severity and recovery trajectories over time would be identified and (ii) psychosocial variables (such as parent mental health and subjective appraisals of the illness) would be more strongly associated with parent distress trajectories compared with demographic and objective illness factors. Methods Design The Take a Breath cohort study is a prospective, longitudinal study of parents conducted at the Royal Children’s Hospital (RCH), a large tertiary pediatric hospital in Melbourne, Australia. Data were collected from participating parents within 4 weeks of child diagnosis or hospital admission (T1), and then 3 (T2), 6 (T3), and 18 months (T4) later. Participants Participants were parents of children diagnosed and/or admitted to the RCH, where the illness or injury involved the threat of life or physical integrity to the child (such as a severe traumatic injury). Parents were recruited from consecutive first admissions to the RCH Cardiology, Oncology, and pediatric intensive care unit (PICU) Departments between November 2010 and August 2012. Inclusion criteria were parents with a current parenting role with the hospitalized child; child of age 0–18 years; child diagnosed with or admitted because of a life-threatening illness within the previous 4 weeks; and child’s first presentation for the illness. Definitions of a life-threatening illness within each department were Cardiology—infant underwent surgery within the first month of life; Oncology—new diagnoses of all cancer types; and PICU—child’s length of stay ≥48 hr. Exclusion criteria included the experience of another major trauma (e.g., death of child, partner, or other loved one) in the 2 months before or concurrent with the child’s diagnosis; limited spoken English and/or literacy; previous diagnosis of a chronic health condition in the ill child; or the child was palliative. Of the 311 eligible families that were approached, 29 declined to participate, 69 did not return their baseline questionnaires, 20 withdrew, and 1 was removed from the study, as their child passed away. A further 70 families did not complete questionnaires at more than one time point (89% of these families completed their single data point at baseline) and were therefore excluded from the study analyses. The final sample consisted of 159 parents from 122 eligible families, with a total of 115 mothers and 44 fathers participating and returning at least two data points (Table I). This final sample represents 39.2% of the eligible families approached over a 21-month period. The main reasons given by parents for withdrawing or declining participation included time constraints mainly associated with the illness or were currently too overwhelmed with managing their child’s condition to participate. With regard to the final sample, response rates at each time point were 89.3% for T1, 86.2% for T2, 75.5% for T3, and 81.1% for T4. In addition, 32 (20.1%) provided data at two time points, 44 (27.7%) provided data at three time points, and 83 (52.2%) provided data at all four time points. For further detail regarding recruitment and retention, see (Muscara, Burke, et al., 2015). Table I. Parent and Child Demographics, Compared Between-Illness Groups Oncology Cardiology PICU p Parent, N 58 53 48  Sex [female], n (%) 39 (67.2) 38 (71.7) 38 (79.2) .390  Parent age, M (SD) 38.0 (7.7) 34.9 (8.9) 36.3 (6.1) .114  Education [<=high school], n (%) 13 (22.4) 5 (9.4) 8 (16.7) .181  Country of birth [Australia], n (%) 55 (94.8) 42 (79.3) 41 (85.4) .050  Relationship status [married/partnered], n (%) 54 (93.1) 51 (96.2) 45 (93.8) .759 Child, N 45 38 39  Sex [female], % (n) 21 (46.7) 14 (36.8) 17 (43.6) .659  Child age [years], M (SD) 6.0 (4.4) 0.1 (0.1) 2.8 (4.1) <.001  Length of stay (days), M (SD) 16.3 (9.6) 20.9 (7.9) 19.4 (9.7) .064 Oncology Cardiology PICU p Parent, N 58 53 48  Sex [female], n (%) 39 (67.2) 38 (71.7) 38 (79.2) .390  Parent age, M (SD) 38.0 (7.7) 34.9 (8.9) 36.3 (6.1) .114  Education [<=high school], n (%) 13 (22.4) 5 (9.4) 8 (16.7) .181  Country of birth [Australia], n (%) 55 (94.8) 42 (79.3) 41 (85.4) .050  Relationship status [married/partnered], n (%) 54 (93.1) 51 (96.2) 45 (93.8) .759 Child, N 45 38 39  Sex [female], % (n) 21 (46.7) 14 (36.8) 17 (43.6) .659  Child age [years], M (SD) 6.0 (4.4) 0.1 (0.1) 2.8 (4.1) <.001  Length of stay (days), M (SD) 16.3 (9.6) 20.9 (7.9) 19.4 (9.7) .064 Table I. Parent and Child Demographics, Compared Between-Illness Groups Oncology Cardiology PICU p Parent, N 58 53 48  Sex [female], n (%) 39 (67.2) 38 (71.7) 38 (79.2) .390  Parent age, M (SD) 38.0 (7.7) 34.9 (8.9) 36.3 (6.1) .114  Education [<=high school], n (%) 13 (22.4) 5 (9.4) 8 (16.7) .181  Country of birth [Australia], n (%) 55 (94.8) 42 (79.3) 41 (85.4) .050  Relationship status [married/partnered], n (%) 54 (93.1) 51 (96.2) 45 (93.8) .759 Child, N 45 38 39  Sex [female], % (n) 21 (46.7) 14 (36.8) 17 (43.6) .659  Child age [years], M (SD) 6.0 (4.4) 0.1 (0.1) 2.8 (4.1) <.001  Length of stay (days), M (SD) 16.3 (9.6) 20.9 (7.9) 19.4 (9.7) .064 Oncology Cardiology PICU p Parent, N 58 53 48  Sex [female], n (%) 39 (67.2) 38 (71.7) 38 (79.2) .390  Parent age, M (SD) 38.0 (7.7) 34.9 (8.9) 36.3 (6.1) .114  Education [<=high school], n (%) 13 (22.4) 5 (9.4) 8 (16.7) .181  Country of birth [Australia], n (%) 55 (94.8) 42 (79.3) 41 (85.4) .050  Relationship status [married/partnered], n (%) 54 (93.1) 51 (96.2) 45 (93.8) .759 Child, N 45 38 39  Sex [female], % (n) 21 (46.7) 14 (36.8) 17 (43.6) .659  Child age [years], M (SD) 6.0 (4.4) 0.1 (0.1) 2.8 (4.1) <.001  Length of stay (days), M (SD) 16.3 (9.6) 20.9 (7.9) 19.4 (9.7) .064 Procedure The study was approved by the RCH Human Research Ethics Committee (HREC 30044). Daily admission lists of the target inpatient wards were monitored by the research team, who liaised with clinical staff to identify eligible families. Parents were approached during their child’s hospital admission, and consenting parents were asked to return study questionnaires within 4 weeks of their child’s admission or surgery. All parents provided informed, written consent before participation. Follow-up questionnaires were mailed out for completion at subsequent time points. Measures Primary Outcomes Parent traumatic stress reactions were measured using two validated questionnaires: Acute Stress Reactions: Acute Stress Disorder Scale (ASDS) is a 19-item self-report measure designed to assess acute stress disorder up to 4 weeks after a traumatic event, in this instance their child’s diagnosis/hospitalization (Bryant, Moulds, & Guthrie, 2000). Completed at T1, respondents rated the extent to which they have experienced symptoms of dissociation, reexperiencing, avoidance, and arousal on a five-point scale (1 = not at all, 5 = very much). Higher scores indicate greater symptom severity, and a clinical cutoff score of 56 or above represents a risk for developing posttraumatic stress disorder (R. A. Bryant et al., 2000). The ASDS has good psychometric properties (27); the Cronbach’s alpha coefficient for the current study was .88. Posttraumatic Stress Reactions: The Posttraumatic Stress Checklist-Specific Version (PCL-S) is a 17-item self-report instrument designed to assess PTSS based on Diagnostic and Statistical Manual criteria (Weathers, Litz, Herman, Huska, & Keane, 1993). Completed at T2, T3, and T4, respondents rated the extent to which they have been bothered by symptoms of reexperiencing, avoidance, and arousal over the past month on a five-point scale (1 = not at all, 5 = extremely). This was completed in the context of their child’s illness onset being the traumatic event, with the wording within the questionnaire modified slightly within each illness group to reflect this. A clinical cutoff of 30 was used based on the recommended cutoff for screening in primary care (Walker, Newman, Dobie, Ciechanowski, & Katon, 2002). The PCL-S has been shown to have good internal consistency and reliability (Weathers et al., 1993); the Cronbach’s alpha coefficient for the current study was .91. Predictor Variables Illness and Demographic Factors Patient medical and demographic information was obtained from medical records and the T1 questionnaire, which captured self-reported parent age and sex, country of birth, level of education, and marital status. Psychosocial Factors Psychosocial factors were assessed at baseline (T1) unless indicated otherwise. Parent Mental Health: Parent depression, anxiety, and general stress symptoms were measured by the Depression Anxiety Stress Scales (DASS) (Lovibond & Lovibond, 1995); a 21 item self-report instrument where respondents rate their symptoms of depression, anxiety, and stress over the past week on a four-point scale. Scores above 9, 7, and 14, respectively, on the Depression, Anxiety and Stress subscales fall within the clinical range. The DASS has good internal consistency; Cronbach’s alpha coefficients in the current study were .85, .88, and .80 for the Depression, Anxiety and Stress subscales, respectively. Parent Experience of Child Illness: The 25-item Parent Experience of Child Illness (PECI) (Bonner et al., 2006; Bonner, Hardy, Willard, Hutchinson, & Guill, 2008) measures parent adjustment to their child’s serious or chronic illness. It contains three “distress” subscales—Guilt and Worry, Unresolved Sorrow and Long-term Uncertainty, and one positively orientated subscale, Emotional Resources, rated on a five-point scale. The PECI has been validated with parents of children with cancer, with test–retest reliability correlation coefficients of .83–.86 and Cronbach’s alphas between .74 and .85 (Bonner et al., 2006). A modified version was used in this study, based on our analysis of the PECI psychometrics across illness groups. Two PECI items were removed (Items 11 and 25) (manuscript in preparation). The Cronbach’s alpha of the four scales for this study was between .78 and .93. As the PECI assesses how parents subjectively experience their child’s illness and care needs, it was not appropriate to collect acutely. It was administered at T2, 4 months after diagnosis/admission. Parent Trait Anxiety: The State Trait Anxiety Inventory Y2 (STAI Y-2) is a 20-item scale measuring trait anxiety, a relatively stable construct involving anxiety proneness (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983). Items on the STAI Y-2 are rated on a four-point scale with higher levels of trait anxiety indicated by a higher Total Score. Psychometric properties are well established, and internal consistency is excellent (α = .91) (Spielberger et al., 1983). For this study, α =.92. Family Psychosocial Risk: Psychosocial risk factors were assessed using the Psychosocial Assessment Tool (PAT) (Pai et al., 2008), which was adapted for Australian families and includes a modified version for families with children of age <2 years (Hearps et al., 2014; McCarthy, Hearps, et al., 2016). The PAT comprises seven subscales: Family Structure and Resources; Family Social Support; Family Problems; Parent Stress Reactions; Family Beliefs; Child Problems; and Sibling Problems. Adjusted subscale scores range from 0 to 1 and are summed to form a total PAT score (range = 0–7). Higher scores indicate higher levels of psychosocial risk. The PAT Total Score has good internal consistency; for this study, α =.72 for children of age <2 years, and α = 0.81 for children of age ≥2 years. Statistical Analyses Analyses were conducted using Stata 14.2, and a significance level of p < .05 was applied to all models. Description of the sample included presentation of parent and child demographic characteristics, split by illness group. Between-illness group comparisons were undertaken to establish relative homogeneity of the total sample. Analysis of variance models were applied to continuous variables and chi-squared tests to categorical variables. Similar models were carried out between the final analytic sample and those who dropped out or were lost to follow-up, and again for those that provided only one data point (and were consequently excluded). Latent class growth (LCG) modeling was used to determine underlying trajectory groups over the four time points (Nagin, 2009), using the user-written “traj” Stata command (Jones & Nagin, 2013). Allowing for curvilinear trajectories of growth, LCG modeling explores putative latent groups of trajectories within the data set, based on user-defined parameters: number of groups and expected group trajectory polynomials. Similar to other multilevel modeling techniques, LCG makes use of all available data. Although it is possible that participants need only have responded to one of the four time points to be included in analyses, we restricted the sample to those who have completed two or more data points to provide the best trajectory estimates. The long-term stress outcome was collected via different instruments over time, and so the ASDS and PCL-S needed to be harmonized. The two measures follow different metrics (i.e., are not standardized), and so standardization to a z-score (M = 0, SD = 1) based on a similar population was undertaken. Given that the ASDS and PCL-S were not administered at the same time point, it was not possible to determine standardization metrics from published data from one sample, and so Ms and SDs were obtained from two similar samples: (1) a cohort of 219 Australian parents of children with cancer who completed the ASDS at the time of their child’s diagnosis (McCarthy et al., 2012); and (2) a national sample of 204 Australian parents of adolescents and young adults with cancer who completed the PCL-S within 2 years of their child’s diagnosis (McCarthy, McNeil, et al., 2016). The ASDS was standardized against M = 48.98 and SD = 15.46, and the PCL-S to M = 30.84 and SD = 13.19. The raw mean z-score values for the total sample were −0.40, −0.25, −0.36, and −0.34 for T0 though T3, respectively, suggesting that the current sample was experiencing less acute and PTSS than found in these previous studies. Spearman nonparametric correlations (r) found “moderate-to-strong” and acceptable relationships between standardized ASDS at T1and PCL-S measures at T2, T3, and T4 (rT2 = .60, rT3 = .59, rT4 = .60; all p < .001). With the outcome defined, a censored normal LCG model explored the change of distress over the study period with nonlinear trajectories tested, with time modeled in terms of average months at each time point (0, 4, 7, and 19). The most parsimonious model was determined by exploring models with up to four classes as predicted by Bonanno’s model (2004), with each class having up to cubic polynomial trajectories applied. A top-down approach was used, where the highest significant polynomial term was retained (at p < .05) (Andruff, Carraro, Thompson, Gaudreau, & Louvet, 2009). Model parsimony was based on a combination of the lowest Bayesian information criterion (BIC), BIC difference >2, as well as clinical appropriateness of the model. Summary statistics of the selected model are presented, and latent class mean estimates (and 95% confidence intervals [95% CIs]) are plotted for ease of interpretation. Finally, trajectory group allocation was determined by the highest posterior membership probability. Between-trajectory group comparisons were carried out on baseline psychosocial, demographics, and illness data collected throughout the study. For these comparisons, within-couple variance was accounted for using mixed-effects logistic regression for binary outcomes, clustered chi-squared test for multinomial outcomes, and mixed-effects regression (and model effect size [ES] R2) for continuous outcomes. Post hoc Bonferroni comparisons explored pairwise comparisons within significant omnibus regression models. Results Between-illness group analyses are shown in Table I. No significant differences were found, with the exception of child age, as expected, with the Cardiology group being significantly younger than Oncology and PICU. Overall, the majority of the parents were born in Australia (87%), had post-high school education (84%), were married/partnered (94%), and just under three-quarters were mothers. Approximately 43% of children were female, and they had an average length of stay of 18.7 days (SD = 9.7). A comparison of the analytic sample to those who were lost to follow-up or dropped out, and to those who were excluded because of only providing one data point, showed no statistically significant group differences (see Supplementary Table S1). Of the 159 participating parents, 105 (66.0%) remained in the “normative” range for posttraumatic stress reactions across the entirety of the study, and 34 (21.4%) indicated recovery from the clinical range by the final time point. A further 13 (8.2%) remained in the clinical range (without recovery) at the final time point, with the remaining 7 (4.4%) fluctuating between the normal and clinical ranges over the study period. Analyses of latent class trajectories of standardized long-term distress are presented in Supplementary Table S2. Model comparison resulted in a three-class model being the most parsimonious (the lowest BIC and a change in BIC > 2 compared with the next best fitting model) (Kass & Raftery, 1995). A fourth small class was excluded. This comprised three parents, including two from the same family with highly variable trajectories that were not consistent with other parents. The final three-class model is summarized in Table II and displayed in Figure 1. The trajectories for each class are described as follows: Table II. Model Estimates From the Selected LCG Model Class Time polynomial B 95% CI p 1 (n = 52) Intercept −1.161 (−1.350, −0.971) <.001 Linear 0.084 (0.035, 0.132) .001 Quadratic −0.003 (−0.006, −0.001) .003 2 (n = 84) Intercept −0.068 (−0.210, 0.074) .347 Linear −0.058 (−0.097, −0.018) .005 Quadratic 0.002 (0.000, 0.004) .019 3 (n = 20) Intercept 0.824 (0.675, 0.972) <.001 a4 (n = 3) Intercept −0.970 (−1.568, −0.373) .002 Linear 0.113 (0.053, 0.172) <.001 BIC −511.62 AIC −491.67 Class Time polynomial B 95% CI p 1 (n = 52) Intercept −1.161 (−1.350, −0.971) <.001 Linear 0.084 (0.035, 0.132) .001 Quadratic −0.003 (−0.006, −0.001) .003 2 (n = 84) Intercept −0.068 (−0.210, 0.074) .347 Linear −0.058 (−0.097, −0.018) .005 Quadratic 0.002 (0.000, 0.004) .019 3 (n = 20) Intercept 0.824 (0.675, 0.972) <.001 a4 (n = 3) Intercept −0.970 (−1.568, −0.373) .002 Linear 0.113 (0.053, 0.172) <.001 BIC −511.62 AIC −491.67 AIC = Akaike information criterion; B = polynomial time term estimate; BIC = Bayesian information criterion; LCG = latent class growth; p = term p-value; 95% CI = 95% confidence interval. a Excluded because of small size and large variability in outcome. Table II. Model Estimates From the Selected LCG Model Class Time polynomial B 95% CI p 1 (n = 52) Intercept −1.161 (−1.350, −0.971) <.001 Linear 0.084 (0.035, 0.132) .001 Quadratic −0.003 (−0.006, −0.001) .003 2 (n = 84) Intercept −0.068 (−0.210, 0.074) .347 Linear −0.058 (−0.097, −0.018) .005 Quadratic 0.002 (0.000, 0.004) .019 3 (n = 20) Intercept 0.824 (0.675, 0.972) <.001 a4 (n = 3) Intercept −0.970 (−1.568, −0.373) .002 Linear 0.113 (0.053, 0.172) <.001 BIC −511.62 AIC −491.67 Class Time polynomial B 95% CI p 1 (n = 52) Intercept −1.161 (−1.350, −0.971) <.001 Linear 0.084 (0.035, 0.132) .001 Quadratic −0.003 (−0.006, −0.001) .003 2 (n = 84) Intercept −0.068 (−0.210, 0.074) .347 Linear −0.058 (−0.097, −0.018) .005 Quadratic 0.002 (0.000, 0.004) .019 3 (n = 20) Intercept 0.824 (0.675, 0.972) <.001 a4 (n = 3) Intercept −0.970 (−1.568, −0.373) .002 Linear 0.113 (0.053, 0.172) <.001 BIC −511.62 AIC −491.67 AIC = Akaike information criterion; B = polynomial time term estimate; BIC = Bayesian information criterion; LCG = latent class growth; p = term p-value; 95% CI = 95% confidence interval. a Excluded because of small size and large variability in outcome. Figure 1. View largeDownload slide Extracted group trajectories from the selected LCG model. Note: The clinical cutoff for the PTSS measures is indicated by the split dashed line. The grey-shaded areas represent the 95% confidence intervals from consideration. LCG = latent class growth; PTSS = posttraumatic stress symptoms. Figure 1. View largeDownload slide Extracted group trajectories from the selected LCG model. Note: The clinical cutoff for the PTSS measures is indicated by the split dashed line. The grey-shaded areas represent the 95% confidence intervals from consideration. LCG = latent class growth; PTSS = posttraumatic stress symptoms. Class 1—Resilient: Quadratic trajectory, slight increase, consistently low (n = 52, 33% of the sample), Class 2—Recovery: Quadratic trajectory, slight decrease, consistently low (n = 82, 52% of the sample), Class 3—Chronic: Constant (intercept) trajectory, consistently high (n = 20, 13% of the sample). Though statistically significantly different in severity overall, both the “Resilient” and “Recovery” trajectories fell approximately between 0 and −1 SD on average. However, they followed differing quadratic inflections between baseline and 7 months. The “Recovery” class showed significantly higher levels of distress at the time of diagnosis that gradually declined over the first months following their child’s illness, whereas the “Resilient” class showed low distress responses across the trajectory. Both quadratic curves may be because of regression to the M, and the changes over time within both curves do not represent clinically meaningful change. In contrast, the “Chronic” trajectory was consistently high in severity, just under 1 SD above the M. As indicated by the clinical cutoffs (dashed red line, Figure 1), the “Chronic” trajectory is estimated wholly in the “clinical” category across the entire period, whereas the “Resilient” and “Recovery” trajectories both remained within the normative range throughout. In addition, the 95% CIs (shaded areas, Figure 1) do not overlap, indicating they are three fully distinct trajectories. Comparisons between extracted latent classes showed no significant differences in parent demographics: sex; country of birth; education; relationship status; and age (Table III). Nor were any significant differences found in objective illness factors; illness group; and length of stay. However, groups differed on all psychosocial variables. Post hoc Bonferroni-adjusted pairwise comparisons found statistically significant differences between “Resilient” and “Chronic” groups on all measures, with the “Chronic” group having consistently poorer mental health functioning. Moreover, the “Chronic” group was significantly more distressed compared to the “Recovery” group on all outcomes except the DASS. Table III. Between-Class Comparisons on PTSS Outcomes, and Psychosocial, Demographic, and Illness Characteristics N Resilient Recovery Chronic p ES ICC n = 52 n = 84 n = 20 PTSS outcomes across trajectories  ASDS @ T1, M (SD) 139 29.8 6.0 47.8 7.9 62.6 10.0 <.001a 0.67 0.25  PCL-S, M (SD)   Total @ T2 134 19.9 3.4 27.7 6.9 43.2 10.6 <.001a 0.55 0.69   Total @ T3 117 19.3 2.8 25.7 5.8 40.9 10.1 <.001a 0.57 0.65   Total @ T4 126 19.6 2.6 26.0 5.9 41.9 10.2 <.001a 0.59 0.52 Characteristics of trajectory members  Demographic characteristics @ T1   Parent sex [male], n % 156 32 61.5 65 77.4 17 85.0 .062 – 0.00   Country of birth [Australia], n % 156 47 90.4 72 85.7 17 85.0 .722 – 0.09   Education [<high school], n % 156 7 13.5 16 19.1 3 15.0 .722 – 0.55   Relationship status [married], n % 156 51 98.1 78 92.9 18 90.0 .530 – 0.82   Parent age [years], M (SD) 156 35.6 6.5 37.4 8.6 34.5 6.6 .183 0.02 0.09   Child age [years]+, M (SD) 156 2.1 2.9 3.7 4.9 2.7 3.9 .364 – 0.91  Illness characteristics @ T1   Department, n %    Oncology 156 19 36.5 28 33.3 9 45.0 .488 – 0.29    Cardiology 156 14 26.9 30 35.7 8 40.0    PICU 156 19 36.5 26 31.0 3 15.0   Length of stay (days)+, M (SD) 156 16.2 7.7 20.7 9.5 16.8 9.6 .330 – 0.92  Psychosocial characteristics   DASS @ T1, M (SD)    Depression 130 1.6 3.6 3.5 4.3 10.6 6.2 <.001b 0.30 0.38    Anxiety 130 0.7 1.5 2.0 3.1 9.5 7.9 <.001b 0.37 0.53    Stress 130 5.2 5.8 7.7 5.6 17.9 8.0 <.001b 0.31 0.24  STAI @ T1, M (SD) 140 30.5 6.7 37.4 9.1 49.3 7.6 <.001a 0.33 0.62  PECI @ T2, M (SD)   Guilt and worry 129 0.8 0.5 1.5 0.6 2.5 0.8 <.001a 0.48 0.48   Emotional resources 134 3.6 0.4 2.9 0.6 2.6 0.8 <.001a 0.28 0.54   Uncertainty 132 0.6 0.4 1.2 0.7 2.0 0.9 <.001a 0.31 0.71   Unresolved sorrow and anger 132 0.4 0.5 1.1 0.6 2.0 0.9 <.001a 0.39 0.44  PAT @ T1, M (SD) 114 0.4 0.3 0.8 0.5 1.2 0.6 <.001a 0.29 0.59 N Resilient Recovery Chronic p ES ICC n = 52 n = 84 n = 20 PTSS outcomes across trajectories  ASDS @ T1, M (SD) 139 29.8 6.0 47.8 7.9 62.6 10.0 <.001a 0.67 0.25  PCL-S, M (SD)   Total @ T2 134 19.9 3.4 27.7 6.9 43.2 10.6 <.001a 0.55 0.69   Total @ T3 117 19.3 2.8 25.7 5.8 40.9 10.1 <.001a 0.57 0.65   Total @ T4 126 19.6 2.6 26.0 5.9 41.9 10.2 <.001a 0.59 0.52 Characteristics of trajectory members  Demographic characteristics @ T1   Parent sex [male], n % 156 32 61.5 65 77.4 17 85.0 .062 – 0.00   Country of birth [Australia], n % 156 47 90.4 72 85.7 17 85.0 .722 – 0.09   Education [<high school], n % 156 7 13.5 16 19.1 3 15.0 .722 – 0.55   Relationship status [married], n % 156 51 98.1 78 92.9 18 90.0 .530 – 0.82   Parent age [years], M (SD) 156 35.6 6.5 37.4 8.6 34.5 6.6 .183 0.02 0.09   Child age [years]+, M (SD) 156 2.1 2.9 3.7 4.9 2.7 3.9 .364 – 0.91  Illness characteristics @ T1   Department, n %    Oncology 156 19 36.5 28 33.3 9 45.0 .488 – 0.29    Cardiology 156 14 26.9 30 35.7 8 40.0    PICU 156 19 36.5 26 31.0 3 15.0   Length of stay (days)+, M (SD) 156 16.2 7.7 20.7 9.5 16.8 9.6 .330 – 0.92  Psychosocial characteristics   DASS @ T1, M (SD)    Depression 130 1.6 3.6 3.5 4.3 10.6 6.2 <.001b 0.30 0.38    Anxiety 130 0.7 1.5 2.0 3.1 9.5 7.9 <.001b 0.37 0.53    Stress 130 5.2 5.8 7.7 5.6 17.9 8.0 <.001b 0.31 0.24  STAI @ T1, M (SD) 140 30.5 6.7 37.4 9.1 49.3 7.6 <.001a 0.33 0.62  PECI @ T2, M (SD)   Guilt and worry 129 0.8 0.5 1.5 0.6 2.5 0.8 <.001a 0.48 0.48   Emotional resources 134 3.6 0.4 2.9 0.6 2.6 0.8 <.001a 0.28 0.54   Uncertainty 132 0.6 0.4 1.2 0.7 2.0 0.9 <.001a 0.31 0.71   Unresolved sorrow and anger 132 0.4 0.5 1.1 0.6 2.0 0.9 <.001a 0.39 0.44  PAT @ T1, M (SD) 114 0.4 0.3 0.8 0.5 1.2 0.6 <.001a 0.29 0.59 + Note. p = Model p-value. Mixed-effects logistic regression for binary outcomes, clustered chi-squared test for multinomial outcome (department), and mixed-effects regression for continuous outcomes. Generalized estimating equation model; negative binomial distribution for child age. ASDS = Acute Stress Disorder Scale; DASS = Depression Anxiety Stress Scales; ES = effect size (model R2); ICC = intraclass correlation coefficient; PAT = Psychosocial Assessment Tool; PECI = Parent Experience of Child Illness; STAI = State Trait Anxiety Inventory. Significant post hoc Bonferroni test between: aRecovery versus Resilient, Chronic versus Resilient, and Chronic versus Recovery. bRecovery versus Resilient and Chronic versus Resilient. Table III. Between-Class Comparisons on PTSS Outcomes, and Psychosocial, Demographic, and Illness Characteristics N Resilient Recovery Chronic p ES ICC n = 52 n = 84 n = 20 PTSS outcomes across trajectories  ASDS @ T1, M (SD) 139 29.8 6.0 47.8 7.9 62.6 10.0 <.001a 0.67 0.25  PCL-S, M (SD)   Total @ T2 134 19.9 3.4 27.7 6.9 43.2 10.6 <.001a 0.55 0.69   Total @ T3 117 19.3 2.8 25.7 5.8 40.9 10.1 <.001a 0.57 0.65   Total @ T4 126 19.6 2.6 26.0 5.9 41.9 10.2 <.001a 0.59 0.52 Characteristics of trajectory members  Demographic characteristics @ T1   Parent sex [male], n % 156 32 61.5 65 77.4 17 85.0 .062 – 0.00   Country of birth [Australia], n % 156 47 90.4 72 85.7 17 85.0 .722 – 0.09   Education [<high school], n % 156 7 13.5 16 19.1 3 15.0 .722 – 0.55   Relationship status [married], n % 156 51 98.1 78 92.9 18 90.0 .530 – 0.82   Parent age [years], M (SD) 156 35.6 6.5 37.4 8.6 34.5 6.6 .183 0.02 0.09   Child age [years]+, M (SD) 156 2.1 2.9 3.7 4.9 2.7 3.9 .364 – 0.91  Illness characteristics @ T1   Department, n %    Oncology 156 19 36.5 28 33.3 9 45.0 .488 – 0.29    Cardiology 156 14 26.9 30 35.7 8 40.0    PICU 156 19 36.5 26 31.0 3 15.0   Length of stay (days)+, M (SD) 156 16.2 7.7 20.7 9.5 16.8 9.6 .330 – 0.92  Psychosocial characteristics   DASS @ T1, M (SD)    Depression 130 1.6 3.6 3.5 4.3 10.6 6.2 <.001b 0.30 0.38    Anxiety 130 0.7 1.5 2.0 3.1 9.5 7.9 <.001b 0.37 0.53    Stress 130 5.2 5.8 7.7 5.6 17.9 8.0 <.001b 0.31 0.24  STAI @ T1, M (SD) 140 30.5 6.7 37.4 9.1 49.3 7.6 <.001a 0.33 0.62  PECI @ T2, M (SD)   Guilt and worry 129 0.8 0.5 1.5 0.6 2.5 0.8 <.001a 0.48 0.48   Emotional resources 134 3.6 0.4 2.9 0.6 2.6 0.8 <.001a 0.28 0.54   Uncertainty 132 0.6 0.4 1.2 0.7 2.0 0.9 <.001a 0.31 0.71   Unresolved sorrow and anger 132 0.4 0.5 1.1 0.6 2.0 0.9 <.001a 0.39 0.44  PAT @ T1, M (SD) 114 0.4 0.3 0.8 0.5 1.2 0.6 <.001a 0.29 0.59 N Resilient Recovery Chronic p ES ICC n = 52 n = 84 n = 20 PTSS outcomes across trajectories  ASDS @ T1, M (SD) 139 29.8 6.0 47.8 7.9 62.6 10.0 <.001a 0.67 0.25  PCL-S, M (SD)   Total @ T2 134 19.9 3.4 27.7 6.9 43.2 10.6 <.001a 0.55 0.69   Total @ T3 117 19.3 2.8 25.7 5.8 40.9 10.1 <.001a 0.57 0.65   Total @ T4 126 19.6 2.6 26.0 5.9 41.9 10.2 <.001a 0.59 0.52 Characteristics of trajectory members  Demographic characteristics @ T1   Parent sex [male], n % 156 32 61.5 65 77.4 17 85.0 .062 – 0.00   Country of birth [Australia], n % 156 47 90.4 72 85.7 17 85.0 .722 – 0.09   Education [<high school], n % 156 7 13.5 16 19.1 3 15.0 .722 – 0.55   Relationship status [married], n % 156 51 98.1 78 92.9 18 90.0 .530 – 0.82   Parent age [years], M (SD) 156 35.6 6.5 37.4 8.6 34.5 6.6 .183 0.02 0.09   Child age [years]+, M (SD) 156 2.1 2.9 3.7 4.9 2.7 3.9 .364 – 0.91  Illness characteristics @ T1   Department, n %    Oncology 156 19 36.5 28 33.3 9 45.0 .488 – 0.29    Cardiology 156 14 26.9 30 35.7 8 40.0    PICU 156 19 36.5 26 31.0 3 15.0   Length of stay (days)+, M (SD) 156 16.2 7.7 20.7 9.5 16.8 9.6 .330 – 0.92  Psychosocial characteristics   DASS @ T1, M (SD)    Depression 130 1.6 3.6 3.5 4.3 10.6 6.2 <.001b 0.30 0.38    Anxiety 130 0.7 1.5 2.0 3.1 9.5 7.9 <.001b 0.37 0.53    Stress 130 5.2 5.8 7.7 5.6 17.9 8.0 <.001b 0.31 0.24  STAI @ T1, M (SD) 140 30.5 6.7 37.4 9.1 49.3 7.6 <.001a 0.33 0.62  PECI @ T2, M (SD)   Guilt and worry 129 0.8 0.5 1.5 0.6 2.5 0.8 <.001a 0.48 0.48   Emotional resources 134 3.6 0.4 2.9 0.6 2.6 0.8 <.001a 0.28 0.54   Uncertainty 132 0.6 0.4 1.2 0.7 2.0 0.9 <.001a 0.31 0.71   Unresolved sorrow and anger 132 0.4 0.5 1.1 0.6 2.0 0.9 <.001a 0.39 0.44  PAT @ T1, M (SD) 114 0.4 0.3 0.8 0.5 1.2 0.6 <.001a 0.29 0.59 + Note. p = Model p-value. Mixed-effects logistic regression for binary outcomes, clustered chi-squared test for multinomial outcome (department), and mixed-effects regression for continuous outcomes. Generalized estimating equation model; negative binomial distribution for child age. ASDS = Acute Stress Disorder Scale; DASS = Depression Anxiety Stress Scales; ES = effect size (model R2); ICC = intraclass correlation coefficient; PAT = Psychosocial Assessment Tool; PECI = Parent Experience of Child Illness; STAI = State Trait Anxiety Inventory. Significant post hoc Bonferroni test between: aRecovery versus Resilient, Chronic versus Resilient, and Chronic versus Recovery. bRecovery versus Resilient and Chronic versus Resilient. Discussion This study examined PTSS in parents of children with life-threatening illnesses/injuries, across four time points, from the acute diagnostic period up to 18 months post their child’s initial hospital admission. It also examined whether psychosocial, demographic, and medical factors were associated with patterns of parent distress and recovery. Consistent with the study hypotheses, distinct trajectories were found with psychosocial variables the strongest predictors of group membership. The current study makes a significant contribution to enhancing our understanding of parent psychological adaptation beyond the acute phase and early months of their child’s illness. Importantly, the inclusion of parents of children with a range of medical conditions extends the generalizability of the study results. The study findings also add empirical support to Bonanno’s theoretical model (Bonanno, 2004), which proposes variable trajectories of parent psychological recovery following a traumatic event, as well as the Pediatric Medical Traumatic Stress model (Kazak et al., 2006; Price et al., 2016), which posits the importance of psychosocial factors, ahead of demographic and illness variables in predicting outcomes. The finding of three distinct parent distress trajectories confirms theoretical models postulating differing distress–recovery trajectories for individuals who experience a traumatic event and extend previous findings in oncology and bereaved populations. The “Recovery” trajectory was characterized by a decrease in PTSS symptoms, with time suggestive of a gradual improvement in parent distress levels. Across other studies with populations, including mothers of children with cancer (Dolgin et al., 2007; Steele et al., 2004), adolescents and young adults with cancer (Zebrack et al., 2014), and parents of children who have suffered an injury (Le Brocque et al., 2010), a “Recovery” trajectory has commonly been found. However, the nature and definition of the recovery has varied, from those that have shown moderate recovery over time, to those who have shown significant recovery from a clinical level of distress, to nonclinical levels. In the current study, while parents in the “Recovery” group showed a reduction in PTSS, the symptoms experienced by these parents did not reach clinical cutoff scores. This finding supports the notion that many parents have the intra- and interpersonal resources to adjust over time to a traumatic and distressing illness in their child. However, previous studies have indicated that considerable distress and functional impacts may still be seen in parents with subclinical levels of PTSS (Kazak et al., 1997) and have emphasized the importance of psychosocial services in preventing chronic difficulties (Kazak, 2006). The parents in this study had access to psychosocial services within a pediatric hospital setting as part of universal care (e.g., social work, mental health, child life therapists), and thus, it is possible that these services mitigated the PTSS of parents and facilitated a reduction in their overall distress symptomatology. The finding of a “Resilient” trajectory, comprising 33% of the study sample, supports previous findings that a sizeable proportion of parents is able to cope with the distressing impact of having a seriously ill child and the treatment associated with that illness. For example, Dolgin and colleagues (2007) reported that 51% of mothers of children with cancer reported “low stable” PTSS across 6 months following their child’s diagnosis of cancer. In another study, a higher proportion of parents (78%) was classified as “resilient” up to 2 years post their child being admitted to hospital for an injury; however, the majority of this sample experienced injuries that had a lower risk of death and serious long-term impairment, including fractures, dislocations, and lacerations (Le Brocque et al., 2010). These two previous studies found that considerably higher proportions of families were classified as resilient. There may be a number of reasons for these differences. The differences in illness severity, as well as the periods that the trajectories were determined in these previous studies may have contributed to these differences observed. This supports findings of recent studies that suggest there is considerable variability in symptomatology across the phases of the medical traumatic stress trajectory (Price et al., 2016) and highlights the need for repeated screening and assessment to ensure timely psychosocial intervention (Kazak et al., 2012). Notwithstanding differences across populations, these results, taken together with the small number of other longitudinal studies that have examined childhood illness/injury, support the contention that there is a range of normative responses to medical traumatic events (Price et al., 2016) and Bonanno’s theory that resilience is a common feature of adults’ response to adverse events (Bonanno, 2004). Consistent with other research (Bonanno, 2004; Zebrack et al., 2014), the “Chronic” group comprised 10–15% of parents and tended to exhibit more chronic distress reactions following their child’s illness. This group has been detected across various populations: adolescents and young adults with cancer (Zebrack et al., 2014), parents of children that have died as a result of cancer (McCarthy et al., 2010), mothers of preterm infants (Holditch-Davis et al., 2009), parents of injured children (Landolt et al., 2012), and parents of children with meningococcal disease (Garralda et al., 2009). This chronic PTSS trajectory reaction appears to be consistent across medical conditions and age groups and suggests that there is a subgroup of parents who require mental health support after experiencing their child’s diagnosis/hospitalization. Importantly, however, early identification of parents who are likely to fall within this group will assist with the allocation of psychosocial resources to these families, to implement preventative strategies, and reduce the likelihood of these parents from developing chronic and clinically significant levels of PTSS. This group may therefore benefit from early interventions to prevent acute distress reactions from developing into chronic mental health and adjustment problems. In this study, evidence of a “Delayed” distress group, as proposed by Bonanno’s theoretical model (Bonanno, 2004), was not found. Other studies have also not found evidence of delayed distress (Dolgin et al., 2007; Le Brocque et al., 2010), and Bonanno (2004) also maintains that there is little empirical evidence to support the presence of such a distress trajectory. This may be because of the presence of a potential misconception in the grief literature, that an absent early distress reaction is generally considered pathological and will eventually resurface in the form of delayed grief or trauma reactions (Bonanno, 2004). Empirical data within the literature are suggesting, however, that a lack of a significant early distress reaction is not because of psychopathology but because of resilience and is a healthy adjustment that does not lead to delayed grief reactions (Bonanno, 2004). With respect to factors associated with parental distress, we found that acute distress reactions (acute traumatic stress, as well as depression, anxiety, and stress) were all predictive of trajectory group membership. In addition, psychosocial risk factors, parent trait anxiety, and parent subjective appraisals of the illness, such as guilt and worry and unresolved sorrow and anger, were all significantly able to predict trajectory group membership. The “Resilient” group had lower scores on all acute measures of psychological and distress symptomology, with the “Chronic” group scoring highest in all measures. This pattern was consistent across all psychosocial measures, at all time points of measurement, except for the parent mental health measure (DASS), which distinguished the “Recovery” and “Chronic” groups from the “Resilient” group, but not from each other. As predicted, the same pattern was not found in the demographic and illness-related variables. Demographic factors, such as parent age, sex, education, ethnicity and relationship status, were neither predictive of group membership nor the objective medical factors of illness group and length of hospital stay. These findings are consistent with previous studies, which have found that psychosocial factors most strongly predict parent mental health outcomes or trajectory group membership, and demographic and illness-related variables are either not predictive or weak predictors (Kassam-Adams, Fleisher, & Winston, 2009; Rayner et al., 2016; Steele et al., 2004). Limitations A limitation of this study is that we cannot claim that the sample is representative of all parents with seriously ill children, as a high number of parents withdrew or declined to participate. Given the main reasons for withdrawing or nonparticipation were time constraints associated with the illness, or being too overwhelmed with managing their child’s condition to participate, it is possible that this group may have been experiencing higher levels of distress, potentially impacting on the trajectories found, or the size of the samples in each trajectory. Despite this, no differences in demographic and illness factors were found between the analytic sample and those who dropped out or who were lost to follow-up. Nevertheless, these findings need to be interpreted with caution. Another study limitation is that data were mostly collected from a single source, being parent ratings. While it is possible that the associations reported are inflated by shared method variance, recent work has shown that the extent to which this occurs has been overstated in the past (Lance, Dawson, Birkelbach, & Hoffman, 2010). In addition, children with a very wide age range were involved in this study. Despite this limitation, it was not found to predict group membership. This is consistent with findings in the literature, which has found that child age is not associated with parent trauma reactions and the development of PTSS following a significant illness (Woolf et al., 2016). Further, the majority of parents in the current sample had a spouse or partner; hence, the findings and trajectories may not generalize to single parents. Any changes in treatment or improvements in the child’s condition were also not tracked throughout the study period, with these factors potentially influencing the current findings. Data from parents whose child passed away during the study were, however, removed; hence, the death of a child was not a factor that influenced these results. It is also acknowledged that the main latent class trajectory analysis did not adjust for within-couple correlation of data. However, our exploration of the data suggested that treating parents as separate units for trajectory modeling was appropriate. Importantly, the majority of the samples were mothers; hence, it may not be possible to generalize these findings to fathers specifically, given the small number in the current sample. Finally, the universal multidisciplinary supportive services that are potentially offered to families in a pediatric hospital setting were not able to be measured or controlled for. It is possible these services may have ameliorated parent distress and contributed to the levels of resilience demonstrated in this study. Future studies evaluating the usual care families receive, which may include social work and psychology visits, are needed. Similarly, further research is also needed across a wider array of medical conditions, in particular parents who have children with chronic illnesses or developmental disabilities and those with less severe illnesses or diagnoses. Clinical Implications From a clinical perspective, the findings of the current study confirm that, overall, a significant proportion of parents will adapt to the diagnosis of their child’s serious or life-threatening medical condition. It is possible that more targeted psychological resources (including mental health interventions) directed to those parents showing higher initial and/or chronic distress may reduce potential long-term impacts on parents and families. Although the findings suggest that “Resilient” and “Recovery” groups do not experience long-term PTSS, it is critical that health-care providers ensure trauma-informed practice in medical care to mitigate psychosocial distress (Kazak, 2006). Trauma-informed care includes ensuring that psychosocial services are well integrated into medical care, that procedural and medical distress is minimized, that appropriate and timely medical information is provided to families, and that there is empathic communication and consistency of care wherever possible (Marsac et al., 2016). This care could also could provide basic mental health support for these families in the form of psychoeducation, to inform parents of the common psychological reactions to having a sick child, along with information regarding referral pathways in the event that psychological symptoms do develop over time. Such trauma-informed care will not only help to reduce the likelihood of chronic distress from occurring as a result of the illness but will also minimize the potential for the medical care itself to trigger trauma reactions (Kazak, 2006; Marsac et al., 2016). A critical finding is that membership of parent distress trajectories is well predicted by early psychosocial factors and acute distress responses. This suggests that early screening practices within a pediatric hospital setting may help to identify those families that require more support in the acute stages, to direct psychosocial and mental health resources to where they are most needed. Screening by medical teams may involve brief assessment of psychosocial variables such as acute stress reactions, early signs of depression or anxiety symptoms, prior trauma exposure, the degree of psychosocial and family support, and family conflict or problems. The use of a short evidence-based screening tool, such as the Psychosocial Assessment Tool (Pai et al., 2008), may be useful in measuring these psychosocial risk factors in the acute stages of the child’s admission. This may help medical teams to identify which parents and families are likely to fall within the Chronic group, and therefore be at most risk of developing longer-term, clinically significant PTSS. Importantly, the support and management of parents of very ill children is critical, given that parent mental health and well-being are also established predictors of child outcomes. This link between parent mental health and child outcomes has been found across various illnesses (Kolaitis et al., 2011; Landolt et al., 2012), suggesting that, in a clinical setting, the consideration of parents’ adjustment should be considered as an aspect of a holistic approach to the management of the child’s illness and their recovery. Furthermore, the finding that illness-related factors poorly predict long-term mental health outcomes in parents, and their group membership, indicates that these trajectories may be applicable across the wider pediatric hospital setting. These findings may challenge the traditional psychosocial hospital service delivery models, as they suggest that services or interventions may be delivered across the hospital setting, irrespective of the child’s serious medical condition. Importantly, the finding that psychosocial risk factors were the strongest predictors suggests that potential interventions for parents could target these modifiable psychosocial risk factors, and that these interventions may be relevant across illness groups. Further research is needed to explore the “Chronic” population, to identify the best time for intervening to maximize the benefit of the intervention. Conclusions Notwithstanding the limitations described, the current study is the first to systematically explore the longitudinal response trajectories of parent PTSS reactions across different child illnesses and hospital specialist departments, and has further investigated the assumptions of existing theoretical models within the literature. As evidence-based interventions for parents of ill children continue to emerge, early identification of parent subgroups and targeting of interventions to those parents with higher and/or persistent distress will potentially lead to stronger intervention effects, and provide a more robust rationale for allocation of psychosocial resources. Acknowledgments The authors acknowledge the generosity of the participating families along with the contributions to the research made by Amy Coe and Nathan Dowling. K.B. and J.M.N. were employed at the Parenting Research Centre when this work was undertaken. Funding This work was supported by the Pratt Foundation and the Victorian Government’s Operational Infrastructure Support Program. Conflicts of interest: None declared. References Andruff H. , Carraro N. , Thompson A. , Gaudreau P. , Louvet B. ( 2009 ). Latent class growth modelling: A tutorial . Tutorials in Quantitative Methods for Psychology , 5 , 11 – 24 . Google Scholar Crossref Search ADS Bonanno G. ( 2004 ). Loss, trauma, and human resilience: Have we underestimated the human capacity to thrive after extremely aversive events? American Psychologist , 59 , 20 – 28 . Google Scholar Crossref Search ADS PubMed Bonner M. J. , Hardy K. K. , Guill A. B. , McLaughlin C. , Schweitzer H. , Carter K. ( 2006 ). Development and validation of the parent experience of child illness . Journal of Pediatric Psychology , 31 , 310 – 321 . Google Scholar Crossref Search ADS PubMed Bonner M. J. , Hardy K. K. , Willard V. W. , Hutchinson K. C. , Guill A. B. ( 2008 ). Further validation of the Parent Experience of Child Illness Scale . Children's Health Care , 37 , 145 – 157 . Google Scholar Crossref Search ADS Bronner M. B. , Peek N. , Knoester H. , Bos A. P. , Last B. F. , Grootenhuis M. A. ( 2010 ). Course and predictors of posttraumatic stress disorder in parents after pediatric intensive care treatment of their child . Journal of Pediatric Psychology , 35 , 966 – 974 . Google Scholar Crossref Search ADS PubMed Bryant R. A. , Creamer M. , O’donnell M. , Silove D. , McFarlane A. C. ( 2012 ). The capacity of acute stress disorder to predict posttraumatic psychiatric disorders . Journal of Psychiatric Research , 46 , 168 – 173 . Google Scholar Crossref Search ADS PubMed Bryant R. A. , Moulds M. L. , Guthrie R. M. ( 2000 ). Acute Stress Disorder Scale: A self-report measure of acute stress disorder . Psychological Assessment , 12 , 61 – 68 . Google Scholar Crossref Search ADS PubMed Dolgin M. J. , Phipps S. , Fairclough D. L. , Sahler O. J. , Askins M. , Noll R. B. , Butler R. W. , Varni J. W. , Katz E. R. ( 2007 ). Trajectories of adjustment in mothers of children with newly diagnosed cancer: A natural history investigation . Journal of Pediatric Psychology , 32 , 771 – 782 . Google Scholar Crossref Search ADS PubMed Garralda M. E. , Gledhill J. , Nadel S. , Neasham D. , O'connor M. , Shears D. ( 2009 ). Longer-term psychiatric adjustment of children and parents after meningococcal disease . Pediatric Critical Care Medicine , 10 , 675 – 680 . Google Scholar Crossref Search ADS PubMed Hearps S. J. , McCarthy M. C. , Muscara F. , Hearps S. J. , Burke K. , Jones B. , Anderson V. A. ( 2014 ). Psychosocial risk in families of infants undergoing surgery for a serious congenital heart disease . Cardiology Young , 24 , 632 – 639 . Google Scholar Crossref Search ADS Holditch-Davis D. , Miles M. S. , Weaver M. A. , Black B. , Beeber L. , Thoyre S. , Engelke S. ( 2009 ). Patterns of distress in African-American mothers of pre-term infants . Journal of Developmental and Behavioral Pediatrics , 30 , 193 – 205 . Google Scholar Crossref Search ADS PubMed Jones B. L. , Nagin D. S. ( 2013 ). A note on a Stata plugin for estimating group-based trajectory models . Sociological Methods and Research , 42 , 608 – 613 . Google Scholar Crossref Search ADS Kass R. E. , Raftery A. E. ( 1995 ). Bayes factors . Journal of the American Statistical Association , 90 , 773 – 795 . Google Scholar Crossref Search ADS Kassam-Adams N. , Fleisher C. L. , Winston F. K. ( 2009 ). Acute stress disorder and posttraumatic stress disorder in parents of injured children . Journal of Traumatic Stress , 22 , 294 – 302 . Google Scholar Crossref Search ADS PubMed Kazak A. E. ( 2006 ). Pediatric Psychosocial Preventative Health Model (PPPHM): Research, practice, and collaboration in pediatric family systems medicine . Families, Systems and Health , 24 , 381 – 395 . Google Scholar Crossref Search ADS Kazak A. E. , Barakat L. P. , Meeske K. , Christakis D. , Meadows A. T. , Casey R. , Penati B. , Stuber M. L. ( 1997 ). Posttraumatic stress, family functioning, and social support in survivors of childhood leukemia and their mothers and fathers . Journal of Consulting and Clinical Psychology , 65 , 120 – 129 . Google Scholar Crossref Search ADS PubMed Kazak A. E. , Brier M. , Alderfer M. A. , Reilly A. , Fooks Parker S. , Rogerwick S. , Ditaranto S. , Barakat L. P. ( 2012 ). Screening for psychosocial risk in pediatric cancer . Pediatric Blood and Cancer , 59 , 822 – 827 . Google Scholar Crossref Search ADS PubMed Kazak A. E. , Kassam-Adams N. , Schneider S. , Zelikovsky N. , Alderfer M. A. , Rourke M. ( 2006 ). An integrative model of pediatric medical traumatic stress . Journal of Pediatric Psychology , 31 , 343 – 355 . Google Scholar Crossref Search ADS PubMed Kolaitis G. , Giannakopoulos G. , Liakopoulou M. , Pervanidou P. , Charitaki S. , Mihas C. , Ferentinos S. , Papassotiriou I. , Chrousos G. P. , Tsiantis J. ( 2011 ). Predicting pediatric posttraumatic stress disorder after road traffic accidents: The role of parental psychopathology . Journal of Traumatic Stress , 24 , 414 – 421 . Google Scholar Crossref Search ADS PubMed Lance C. E. , Dawson B. , Birkelbach B. , Hoffman B. J. ( 2010 ). Method effects, measurement error, and substantive conclusions . Organizational Research Methods , 13 , 435 – 455 . Google Scholar Crossref Search ADS Landolt M. A. , Ystrom E. , Sennhauser F. , Gnehm H. , Vollrath M. ( 2012 ). The mutual prospective influence of child and parental post-traumatic stress symptoms in pediatric patients . Journal of Child Psychology and Psychiatry , 53 , 767 – 774 . Google Scholar Crossref Search ADS PubMed Le Brocque R. M. , Hendrikz J. , Kenardy J. A. ( 2010 ). Parental response to child injury: Examination of parental posttraumatic stress symptom trajectories following child accidental injury . Journal of Pediatric Psychology , 35 , 646 – 655 . Google Scholar Crossref Search ADS PubMed Lovibond P. , Lovibond S. ( 1995 ). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventory . Behavior Research and Therapy , 33 , 335 – 343 . Google Scholar Crossref Search ADS Marsac M. L. , Kassam-Adams N. , Hildenbrand A. K. , Nicholls E. , Winston F. K. , Leff S. S. , Fein J. ( 2016 ). Implementing a trauma-informed approach in pediatric health care networks . JAMA Pediatrics , 170 , 70 – 77 . Google Scholar Crossref Search ADS PubMed McCarthy M. C. , Ashley D. M. , Lee K. J. , Anderson V. A. ( 2012 ). Predictors of acute and posttraumatic stress symptoms in parents following their child's cancer diagnosis . Journal of Traumatic Stress , 25 , 558 – 566 . Google Scholar Crossref Search ADS PubMed McCarthy M. C. , Clarke N. E. , Ting C. L. , Conroy R. , Anderson V. A. , Heath J. A. ( 2010 ). Prevalence and predictors of parental grief and depression following the death of a child from cancer . Journal of Palliative Medicine , 13 , 1321 – 1326 . Google Scholar Crossref Search ADS PubMed McCarthy M. C. , Hearps S. J. , Muscara F. , Anderson V. A. , Burke K. , Hearps S. J. , Kazak A. E. ( 2016 ). Family psychosocial screening in infants and older children in the acute pediatric hospital setting: The psychosocial assessment tool . Journal of Pediatric Psychology , 41 , 820 – 829 . Google Scholar Crossref Search ADS PubMed McCarthy M. C. , McNeil R. , Drew S. , Dunt D. , Kosola S. , Orme L. , Sawyer S. M. ( 2016 ). Psychological distress and posttraumatic stress symptoms in adolescents and young adults with cancer and their parents . Journal of Adolescent and Young Adult Oncology , 5 , 322 – 329 . Google Scholar Crossref Search ADS PubMed Muscara F. , Burke K. , McCarthy M. C. , Anderson V. A. , Hearps S. J. C. , Hearps S. J. , Dimovski A. , Nicholson J. M. ( 2015 ). Parent distress reactions following a serious illness or injury in their child: A protocol paper for The Take a Breath Cohort Study . BMC Psychiatry , 15 , 153. Google Scholar Crossref Search ADS PubMed Muscara F. , McCarthy M. C. , Woolf C. , Hearps S. J. C. , Burke K. , Anderson V. A. ( 2015 ). Early psychological reactions in parents of children with a life threatening illness within a pediatric hospital setting . European Psychiatry , 30 , 555 – 561 . Google Scholar Crossref Search ADS PubMed Nagin D. S. ( 2009 ). Group-based modeling of development . Cambridge, MA : Harvard University Press . Pai A. L. H. , Patiño-Fernández A. M. , McSherry M. , Beele D. , Alderfer M. A. , Reilly A. T. , Hwang W. T. , Kazak A. E. ( 2008 ). The Psychosocial Assessment Tool (PAT2.0): Psychometric properties of a screener for psychosocial distress in families of children newly diagnosed with cancer . Journal of Pediatric Psychology , 33 , 50 – 62 . Google Scholar Crossref Search ADS PubMed Price J. , Kassam-Adams N. , Alderfer M. , Christofferson J. , Kazak A. E. ( 2016 ). Systematic review: A reevaluation and update of the Integrative (Trajectory) Model of Pediatric Medical Traumatic Stress . Journal of Pediatric Psychology , 41 , 86 – 97 . Google Scholar Crossref Search ADS PubMed Rayner M. , Muscara F. , Dimovski A. , McCarthy M. C. , Yamada J. , Anderson V. A. , Burke K. , Walser R. , Nicholson J. M. ( 2016 ). Take a Breath: Study protocol for a randomized controlled trial of an online group intervention to reduce traumatic stress in parents of children with a life threatening illness or injury . BMC Psychiatry , 16 , 169 . Google Scholar Crossref Search ADS PubMed Spielberger C. , Gorsuch R. , Lushene R. , Vagg P. , Jacobs G. ( 1983 ). Manual of the state-trait anxiety inventory . Palo Alto, CA : Consulting Psychologists Press . Steele R. , Dreyer M. , Phipps S. ( 2004 ). Patterns of maternal distress among children with cancer and their association with child emotional and somatic distress . Journal of Pediatric Psychology , 29 , 507 – 514 . Google Scholar Crossref Search ADS PubMed Walker E. , Newman E. , Dobie D. , Ciechanowski P. , Katon W. ( 2002 ). Validation of the PTSD checklist in an HMO sample of women . General Hospital Psychiatry , 24 , 375 – 380 . Google Scholar Crossref Search ADS PubMed Weathers F. W. , Litz B. T. , Herman D. S. , Huska J. A. , Keane T. M. ( 1993 ). The PTSD checklist: Reliability, validity, & diagnostic utility. Paper presented at the Annual Meeting of the International Society for Traumatic Stress Studies, San Antonio, TX. Woolf C. , Muscara F. , Anderson V. A. , McCarthy M. C. ( 2016 ). Early traumatic stress responses in parents following a serious illness in their child: A systematic review . Journal of Clinical Psychology in Medical Settings , 23 , 53 – 66 . Google Scholar Crossref Search ADS PubMed Zebrack B. J. , Corbett V. , Embry L. , Aguilar C. , Meeske K. A. , Hayes-Lattin B. , Block R. , Zeman D. T. , Cole S. ( 2014 ). Psychological distress and unsatisfied need for psychosocial support in adolescent and young adult cancer patients during the first year following diagnosis . Psycho-Oncology , 23 , 1267 – 1275 . Google Scholar Crossref Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. 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Featured Article: Trajectories of Posttraumatic Stress Symptoms in Parents of Children With a Serious Childhood Illness or Injury

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© The Author(s) 2018. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
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Abstract

Abstract Objective Serious childhood illness is associated with significant parent psychological distress. This study aimed to (a) document acute and posttraumatic stress symptoms (PTSS) in parents of children with various life-threatening illnesses; (b) identify trajectory patterns of parental PTSS and recovery over 18 months; (c) determine psychosocial, demographic, and illness factors associated with trajectory group membership. Methods In total, 159 parents (115 mothers, 44 fathers) from 122 families participated in a prospective, longitudinal study that assessed parent psychological responses across four time points—at diagnosis, and 3, 6, and 18 months later. Children were admitted to the Cardiology, Oncology, and Pediatric Intensive Care Departments in a tertiary pediatric hospital. The primary outcome was parent PTSS. Results Three distinct parent recovery profiles were identified—“Resilient,” “Recovery,” and “Chronic.” The “Resilient” class (33%) showed low distress responses across the trajectory period, whereas the “Recovery” class (52%) showed significantly higher levels of distress at the time of diagnosis that gradually declined over the first months following their child’s illness. Both of these classes nevertheless remained within the normative range throughout. In contrast, the “Chronic” class (13%) was consistently high in severity, remaining within the clinical range across the entire period. Psychosocial factors such as mood, anxiety, and emotional responses predicted group membership, whereas demographic and illness factors did not. Conclusions Parents show considerable resilience in the face of children’s life-threatening illnesses. Early assessment of parent psychosocial factors may aid identification of those who would benefit from early intervention. critically ill children, longitudinal research, mental health, parent stress, posttraumatic stress, stress Introduction Having a child hospitalized because of a life-threatening illness or injury can lead to significant psychological distress in parents (Bronner et al., 2010; Landolt, Ystrom, Sennhauser, Gnehm, & Vollrath, 2012; Le Brocque, Hendrikz, & Kenardy, 2010; Muscara, McCarthy, et al., 2015; Woolf, Muscara, Anderson, & McCarthy, 2016). While high levels of distress in the acute period are predictive of later difficulties (Bryant, Creamer, O’donnell, Silove, & McFarlane, 2012; McCarthy, Ashley, Lee, & Anderson, 2012), our understanding of which parents are at risk of long-term traumatic distress is limited. The Integrative Model of Pediatric Medical Traumatic Stress (Kazak et al., 2006; Price, Kassam-Adams, Alderfer, Christofferson, & Kazak, 2016) conceptualizes parent- and family-level adaptation to a child diagnosis or hospital admission as progressing through three phases: acute reaction to the medical event; evolving traumatic stress response; and longer-term traumatic stress response. In this model, psychosocial, demographic, and medical factors are proposed as potential factors contributing to the emergence and persistence of posttraumatic stress symptoms (PTSS). A stress-coping model proposed by Bonanno (2004) posits four potential distress–recovery trajectories following the death of a loved one: a resilient trajectory characterized by low initial levels of distress, which stay low over time; a delayed trajectory where distress increases after starting low; a chronic trajectory where distress is initially high and remains high; and a recovery trajectory where initially high levels of distress decline over time. This model has been extrapolated to other potentially traumatic events, including adolescents and young adults diagnosed with cancer (Zebrack et al., 2014), parents of children diagnosed with cancer (Dolgin et al., 2007; Steele, Dreyer, & Phipps, 2004), and parents of injured children (Le Brocque et al., 2010). Distinct distress–recovery trajectories were identified for patient/parents in these studies, with the majority reporting improvement in levels of distress over the first months post diagnosis, and a smaller subset displaying more chronic distress patterns. Longitudinal studies of parents’ distress–recovery trajectories are rare and mostly confined to single illness samples, precluding examination of whether the nature of the child’s illness confers different risks on distress–recovery trajectories. We address this significant gap in a prospective study of parents of children with various life-threatening illnesses. Specifically, the study aimed to (a) document acute and long-term PTSS in parents at four time points: acutely following diagnosis/hospitalization, and then 3, 6, and 18 months later; (b) identify specific trajectory patterns of parental PTSS and recovery across this 18 month period; and c) determine psychosocial, demographic, and illness factors associated with specific recovery trajectories. We hypothesized that (i) distinct differences in PTSS severity and recovery trajectories over time would be identified and (ii) psychosocial variables (such as parent mental health and subjective appraisals of the illness) would be more strongly associated with parent distress trajectories compared with demographic and objective illness factors. Methods Design The Take a Breath cohort study is a prospective, longitudinal study of parents conducted at the Royal Children’s Hospital (RCH), a large tertiary pediatric hospital in Melbourne, Australia. Data were collected from participating parents within 4 weeks of child diagnosis or hospital admission (T1), and then 3 (T2), 6 (T3), and 18 months (T4) later. Participants Participants were parents of children diagnosed and/or admitted to the RCH, where the illness or injury involved the threat of life or physical integrity to the child (such as a severe traumatic injury). Parents were recruited from consecutive first admissions to the RCH Cardiology, Oncology, and pediatric intensive care unit (PICU) Departments between November 2010 and August 2012. Inclusion criteria were parents with a current parenting role with the hospitalized child; child of age 0–18 years; child diagnosed with or admitted because of a life-threatening illness within the previous 4 weeks; and child’s first presentation for the illness. Definitions of a life-threatening illness within each department were Cardiology—infant underwent surgery within the first month of life; Oncology—new diagnoses of all cancer types; and PICU—child’s length of stay ≥48 hr. Exclusion criteria included the experience of another major trauma (e.g., death of child, partner, or other loved one) in the 2 months before or concurrent with the child’s diagnosis; limited spoken English and/or literacy; previous diagnosis of a chronic health condition in the ill child; or the child was palliative. Of the 311 eligible families that were approached, 29 declined to participate, 69 did not return their baseline questionnaires, 20 withdrew, and 1 was removed from the study, as their child passed away. A further 70 families did not complete questionnaires at more than one time point (89% of these families completed their single data point at baseline) and were therefore excluded from the study analyses. The final sample consisted of 159 parents from 122 eligible families, with a total of 115 mothers and 44 fathers participating and returning at least two data points (Table I). This final sample represents 39.2% of the eligible families approached over a 21-month period. The main reasons given by parents for withdrawing or declining participation included time constraints mainly associated with the illness or were currently too overwhelmed with managing their child’s condition to participate. With regard to the final sample, response rates at each time point were 89.3% for T1, 86.2% for T2, 75.5% for T3, and 81.1% for T4. In addition, 32 (20.1%) provided data at two time points, 44 (27.7%) provided data at three time points, and 83 (52.2%) provided data at all four time points. For further detail regarding recruitment and retention, see (Muscara, Burke, et al., 2015). Table I. Parent and Child Demographics, Compared Between-Illness Groups Oncology Cardiology PICU p Parent, N 58 53 48  Sex [female], n (%) 39 (67.2) 38 (71.7) 38 (79.2) .390  Parent age, M (SD) 38.0 (7.7) 34.9 (8.9) 36.3 (6.1) .114  Education [<=high school], n (%) 13 (22.4) 5 (9.4) 8 (16.7) .181  Country of birth [Australia], n (%) 55 (94.8) 42 (79.3) 41 (85.4) .050  Relationship status [married/partnered], n (%) 54 (93.1) 51 (96.2) 45 (93.8) .759 Child, N 45 38 39  Sex [female], % (n) 21 (46.7) 14 (36.8) 17 (43.6) .659  Child age [years], M (SD) 6.0 (4.4) 0.1 (0.1) 2.8 (4.1) <.001  Length of stay (days), M (SD) 16.3 (9.6) 20.9 (7.9) 19.4 (9.7) .064 Oncology Cardiology PICU p Parent, N 58 53 48  Sex [female], n (%) 39 (67.2) 38 (71.7) 38 (79.2) .390  Parent age, M (SD) 38.0 (7.7) 34.9 (8.9) 36.3 (6.1) .114  Education [<=high school], n (%) 13 (22.4) 5 (9.4) 8 (16.7) .181  Country of birth [Australia], n (%) 55 (94.8) 42 (79.3) 41 (85.4) .050  Relationship status [married/partnered], n (%) 54 (93.1) 51 (96.2) 45 (93.8) .759 Child, N 45 38 39  Sex [female], % (n) 21 (46.7) 14 (36.8) 17 (43.6) .659  Child age [years], M (SD) 6.0 (4.4) 0.1 (0.1) 2.8 (4.1) <.001  Length of stay (days), M (SD) 16.3 (9.6) 20.9 (7.9) 19.4 (9.7) .064 Table I. Parent and Child Demographics, Compared Between-Illness Groups Oncology Cardiology PICU p Parent, N 58 53 48  Sex [female], n (%) 39 (67.2) 38 (71.7) 38 (79.2) .390  Parent age, M (SD) 38.0 (7.7) 34.9 (8.9) 36.3 (6.1) .114  Education [<=high school], n (%) 13 (22.4) 5 (9.4) 8 (16.7) .181  Country of birth [Australia], n (%) 55 (94.8) 42 (79.3) 41 (85.4) .050  Relationship status [married/partnered], n (%) 54 (93.1) 51 (96.2) 45 (93.8) .759 Child, N 45 38 39  Sex [female], % (n) 21 (46.7) 14 (36.8) 17 (43.6) .659  Child age [years], M (SD) 6.0 (4.4) 0.1 (0.1) 2.8 (4.1) <.001  Length of stay (days), M (SD) 16.3 (9.6) 20.9 (7.9) 19.4 (9.7) .064 Oncology Cardiology PICU p Parent, N 58 53 48  Sex [female], n (%) 39 (67.2) 38 (71.7) 38 (79.2) .390  Parent age, M (SD) 38.0 (7.7) 34.9 (8.9) 36.3 (6.1) .114  Education [<=high school], n (%) 13 (22.4) 5 (9.4) 8 (16.7) .181  Country of birth [Australia], n (%) 55 (94.8) 42 (79.3) 41 (85.4) .050  Relationship status [married/partnered], n (%) 54 (93.1) 51 (96.2) 45 (93.8) .759 Child, N 45 38 39  Sex [female], % (n) 21 (46.7) 14 (36.8) 17 (43.6) .659  Child age [years], M (SD) 6.0 (4.4) 0.1 (0.1) 2.8 (4.1) <.001  Length of stay (days), M (SD) 16.3 (9.6) 20.9 (7.9) 19.4 (9.7) .064 Procedure The study was approved by the RCH Human Research Ethics Committee (HREC 30044). Daily admission lists of the target inpatient wards were monitored by the research team, who liaised with clinical staff to identify eligible families. Parents were approached during their child’s hospital admission, and consenting parents were asked to return study questionnaires within 4 weeks of their child’s admission or surgery. All parents provided informed, written consent before participation. Follow-up questionnaires were mailed out for completion at subsequent time points. Measures Primary Outcomes Parent traumatic stress reactions were measured using two validated questionnaires: Acute Stress Reactions: Acute Stress Disorder Scale (ASDS) is a 19-item self-report measure designed to assess acute stress disorder up to 4 weeks after a traumatic event, in this instance their child’s diagnosis/hospitalization (Bryant, Moulds, & Guthrie, 2000). Completed at T1, respondents rated the extent to which they have experienced symptoms of dissociation, reexperiencing, avoidance, and arousal on a five-point scale (1 = not at all, 5 = very much). Higher scores indicate greater symptom severity, and a clinical cutoff score of 56 or above represents a risk for developing posttraumatic stress disorder (R. A. Bryant et al., 2000). The ASDS has good psychometric properties (27); the Cronbach’s alpha coefficient for the current study was .88. Posttraumatic Stress Reactions: The Posttraumatic Stress Checklist-Specific Version (PCL-S) is a 17-item self-report instrument designed to assess PTSS based on Diagnostic and Statistical Manual criteria (Weathers, Litz, Herman, Huska, & Keane, 1993). Completed at T2, T3, and T4, respondents rated the extent to which they have been bothered by symptoms of reexperiencing, avoidance, and arousal over the past month on a five-point scale (1 = not at all, 5 = extremely). This was completed in the context of their child’s illness onset being the traumatic event, with the wording within the questionnaire modified slightly within each illness group to reflect this. A clinical cutoff of 30 was used based on the recommended cutoff for screening in primary care (Walker, Newman, Dobie, Ciechanowski, & Katon, 2002). The PCL-S has been shown to have good internal consistency and reliability (Weathers et al., 1993); the Cronbach’s alpha coefficient for the current study was .91. Predictor Variables Illness and Demographic Factors Patient medical and demographic information was obtained from medical records and the T1 questionnaire, which captured self-reported parent age and sex, country of birth, level of education, and marital status. Psychosocial Factors Psychosocial factors were assessed at baseline (T1) unless indicated otherwise. Parent Mental Health: Parent depression, anxiety, and general stress symptoms were measured by the Depression Anxiety Stress Scales (DASS) (Lovibond & Lovibond, 1995); a 21 item self-report instrument where respondents rate their symptoms of depression, anxiety, and stress over the past week on a four-point scale. Scores above 9, 7, and 14, respectively, on the Depression, Anxiety and Stress subscales fall within the clinical range. The DASS has good internal consistency; Cronbach’s alpha coefficients in the current study were .85, .88, and .80 for the Depression, Anxiety and Stress subscales, respectively. Parent Experience of Child Illness: The 25-item Parent Experience of Child Illness (PECI) (Bonner et al., 2006; Bonner, Hardy, Willard, Hutchinson, & Guill, 2008) measures parent adjustment to their child’s serious or chronic illness. It contains three “distress” subscales—Guilt and Worry, Unresolved Sorrow and Long-term Uncertainty, and one positively orientated subscale, Emotional Resources, rated on a five-point scale. The PECI has been validated with parents of children with cancer, with test–retest reliability correlation coefficients of .83–.86 and Cronbach’s alphas between .74 and .85 (Bonner et al., 2006). A modified version was used in this study, based on our analysis of the PECI psychometrics across illness groups. Two PECI items were removed (Items 11 and 25) (manuscript in preparation). The Cronbach’s alpha of the four scales for this study was between .78 and .93. As the PECI assesses how parents subjectively experience their child’s illness and care needs, it was not appropriate to collect acutely. It was administered at T2, 4 months after diagnosis/admission. Parent Trait Anxiety: The State Trait Anxiety Inventory Y2 (STAI Y-2) is a 20-item scale measuring trait anxiety, a relatively stable construct involving anxiety proneness (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983). Items on the STAI Y-2 are rated on a four-point scale with higher levels of trait anxiety indicated by a higher Total Score. Psychometric properties are well established, and internal consistency is excellent (α = .91) (Spielberger et al., 1983). For this study, α =.92. Family Psychosocial Risk: Psychosocial risk factors were assessed using the Psychosocial Assessment Tool (PAT) (Pai et al., 2008), which was adapted for Australian families and includes a modified version for families with children of age <2 years (Hearps et al., 2014; McCarthy, Hearps, et al., 2016). The PAT comprises seven subscales: Family Structure and Resources; Family Social Support; Family Problems; Parent Stress Reactions; Family Beliefs; Child Problems; and Sibling Problems. Adjusted subscale scores range from 0 to 1 and are summed to form a total PAT score (range = 0–7). Higher scores indicate higher levels of psychosocial risk. The PAT Total Score has good internal consistency; for this study, α =.72 for children of age <2 years, and α = 0.81 for children of age ≥2 years. Statistical Analyses Analyses were conducted using Stata 14.2, and a significance level of p < .05 was applied to all models. Description of the sample included presentation of parent and child demographic characteristics, split by illness group. Between-illness group comparisons were undertaken to establish relative homogeneity of the total sample. Analysis of variance models were applied to continuous variables and chi-squared tests to categorical variables. Similar models were carried out between the final analytic sample and those who dropped out or were lost to follow-up, and again for those that provided only one data point (and were consequently excluded). Latent class growth (LCG) modeling was used to determine underlying trajectory groups over the four time points (Nagin, 2009), using the user-written “traj” Stata command (Jones & Nagin, 2013). Allowing for curvilinear trajectories of growth, LCG modeling explores putative latent groups of trajectories within the data set, based on user-defined parameters: number of groups and expected group trajectory polynomials. Similar to other multilevel modeling techniques, LCG makes use of all available data. Although it is possible that participants need only have responded to one of the four time points to be included in analyses, we restricted the sample to those who have completed two or more data points to provide the best trajectory estimates. The long-term stress outcome was collected via different instruments over time, and so the ASDS and PCL-S needed to be harmonized. The two measures follow different metrics (i.e., are not standardized), and so standardization to a z-score (M = 0, SD = 1) based on a similar population was undertaken. Given that the ASDS and PCL-S were not administered at the same time point, it was not possible to determine standardization metrics from published data from one sample, and so Ms and SDs were obtained from two similar samples: (1) a cohort of 219 Australian parents of children with cancer who completed the ASDS at the time of their child’s diagnosis (McCarthy et al., 2012); and (2) a national sample of 204 Australian parents of adolescents and young adults with cancer who completed the PCL-S within 2 years of their child’s diagnosis (McCarthy, McNeil, et al., 2016). The ASDS was standardized against M = 48.98 and SD = 15.46, and the PCL-S to M = 30.84 and SD = 13.19. The raw mean z-score values for the total sample were −0.40, −0.25, −0.36, and −0.34 for T0 though T3, respectively, suggesting that the current sample was experiencing less acute and PTSS than found in these previous studies. Spearman nonparametric correlations (r) found “moderate-to-strong” and acceptable relationships between standardized ASDS at T1and PCL-S measures at T2, T3, and T4 (rT2 = .60, rT3 = .59, rT4 = .60; all p < .001). With the outcome defined, a censored normal LCG model explored the change of distress over the study period with nonlinear trajectories tested, with time modeled in terms of average months at each time point (0, 4, 7, and 19). The most parsimonious model was determined by exploring models with up to four classes as predicted by Bonanno’s model (2004), with each class having up to cubic polynomial trajectories applied. A top-down approach was used, where the highest significant polynomial term was retained (at p < .05) (Andruff, Carraro, Thompson, Gaudreau, & Louvet, 2009). Model parsimony was based on a combination of the lowest Bayesian information criterion (BIC), BIC difference >2, as well as clinical appropriateness of the model. Summary statistics of the selected model are presented, and latent class mean estimates (and 95% confidence intervals [95% CIs]) are plotted for ease of interpretation. Finally, trajectory group allocation was determined by the highest posterior membership probability. Between-trajectory group comparisons were carried out on baseline psychosocial, demographics, and illness data collected throughout the study. For these comparisons, within-couple variance was accounted for using mixed-effects logistic regression for binary outcomes, clustered chi-squared test for multinomial outcomes, and mixed-effects regression (and model effect size [ES] R2) for continuous outcomes. Post hoc Bonferroni comparisons explored pairwise comparisons within significant omnibus regression models. Results Between-illness group analyses are shown in Table I. No significant differences were found, with the exception of child age, as expected, with the Cardiology group being significantly younger than Oncology and PICU. Overall, the majority of the parents were born in Australia (87%), had post-high school education (84%), were married/partnered (94%), and just under three-quarters were mothers. Approximately 43% of children were female, and they had an average length of stay of 18.7 days (SD = 9.7). A comparison of the analytic sample to those who were lost to follow-up or dropped out, and to those who were excluded because of only providing one data point, showed no statistically significant group differences (see Supplementary Table S1). Of the 159 participating parents, 105 (66.0%) remained in the “normative” range for posttraumatic stress reactions across the entirety of the study, and 34 (21.4%) indicated recovery from the clinical range by the final time point. A further 13 (8.2%) remained in the clinical range (without recovery) at the final time point, with the remaining 7 (4.4%) fluctuating between the normal and clinical ranges over the study period. Analyses of latent class trajectories of standardized long-term distress are presented in Supplementary Table S2. Model comparison resulted in a three-class model being the most parsimonious (the lowest BIC and a change in BIC > 2 compared with the next best fitting model) (Kass & Raftery, 1995). A fourth small class was excluded. This comprised three parents, including two from the same family with highly variable trajectories that were not consistent with other parents. The final three-class model is summarized in Table II and displayed in Figure 1. The trajectories for each class are described as follows: Table II. Model Estimates From the Selected LCG Model Class Time polynomial B 95% CI p 1 (n = 52) Intercept −1.161 (−1.350, −0.971) <.001 Linear 0.084 (0.035, 0.132) .001 Quadratic −0.003 (−0.006, −0.001) .003 2 (n = 84) Intercept −0.068 (−0.210, 0.074) .347 Linear −0.058 (−0.097, −0.018) .005 Quadratic 0.002 (0.000, 0.004) .019 3 (n = 20) Intercept 0.824 (0.675, 0.972) <.001 a4 (n = 3) Intercept −0.970 (−1.568, −0.373) .002 Linear 0.113 (0.053, 0.172) <.001 BIC −511.62 AIC −491.67 Class Time polynomial B 95% CI p 1 (n = 52) Intercept −1.161 (−1.350, −0.971) <.001 Linear 0.084 (0.035, 0.132) .001 Quadratic −0.003 (−0.006, −0.001) .003 2 (n = 84) Intercept −0.068 (−0.210, 0.074) .347 Linear −0.058 (−0.097, −0.018) .005 Quadratic 0.002 (0.000, 0.004) .019 3 (n = 20) Intercept 0.824 (0.675, 0.972) <.001 a4 (n = 3) Intercept −0.970 (−1.568, −0.373) .002 Linear 0.113 (0.053, 0.172) <.001 BIC −511.62 AIC −491.67 AIC = Akaike information criterion; B = polynomial time term estimate; BIC = Bayesian information criterion; LCG = latent class growth; p = term p-value; 95% CI = 95% confidence interval. a Excluded because of small size and large variability in outcome. Table II. Model Estimates From the Selected LCG Model Class Time polynomial B 95% CI p 1 (n = 52) Intercept −1.161 (−1.350, −0.971) <.001 Linear 0.084 (0.035, 0.132) .001 Quadratic −0.003 (−0.006, −0.001) .003 2 (n = 84) Intercept −0.068 (−0.210, 0.074) .347 Linear −0.058 (−0.097, −0.018) .005 Quadratic 0.002 (0.000, 0.004) .019 3 (n = 20) Intercept 0.824 (0.675, 0.972) <.001 a4 (n = 3) Intercept −0.970 (−1.568, −0.373) .002 Linear 0.113 (0.053, 0.172) <.001 BIC −511.62 AIC −491.67 Class Time polynomial B 95% CI p 1 (n = 52) Intercept −1.161 (−1.350, −0.971) <.001 Linear 0.084 (0.035, 0.132) .001 Quadratic −0.003 (−0.006, −0.001) .003 2 (n = 84) Intercept −0.068 (−0.210, 0.074) .347 Linear −0.058 (−0.097, −0.018) .005 Quadratic 0.002 (0.000, 0.004) .019 3 (n = 20) Intercept 0.824 (0.675, 0.972) <.001 a4 (n = 3) Intercept −0.970 (−1.568, −0.373) .002 Linear 0.113 (0.053, 0.172) <.001 BIC −511.62 AIC −491.67 AIC = Akaike information criterion; B = polynomial time term estimate; BIC = Bayesian information criterion; LCG = latent class growth; p = term p-value; 95% CI = 95% confidence interval. a Excluded because of small size and large variability in outcome. Figure 1. View largeDownload slide Extracted group trajectories from the selected LCG model. Note: The clinical cutoff for the PTSS measures is indicated by the split dashed line. The grey-shaded areas represent the 95% confidence intervals from consideration. LCG = latent class growth; PTSS = posttraumatic stress symptoms. Figure 1. View largeDownload slide Extracted group trajectories from the selected LCG model. Note: The clinical cutoff for the PTSS measures is indicated by the split dashed line. The grey-shaded areas represent the 95% confidence intervals from consideration. LCG = latent class growth; PTSS = posttraumatic stress symptoms. Class 1—Resilient: Quadratic trajectory, slight increase, consistently low (n = 52, 33% of the sample), Class 2—Recovery: Quadratic trajectory, slight decrease, consistently low (n = 82, 52% of the sample), Class 3—Chronic: Constant (intercept) trajectory, consistently high (n = 20, 13% of the sample). Though statistically significantly different in severity overall, both the “Resilient” and “Recovery” trajectories fell approximately between 0 and −1 SD on average. However, they followed differing quadratic inflections between baseline and 7 months. The “Recovery” class showed significantly higher levels of distress at the time of diagnosis that gradually declined over the first months following their child’s illness, whereas the “Resilient” class showed low distress responses across the trajectory. Both quadratic curves may be because of regression to the M, and the changes over time within both curves do not represent clinically meaningful change. In contrast, the “Chronic” trajectory was consistently high in severity, just under 1 SD above the M. As indicated by the clinical cutoffs (dashed red line, Figure 1), the “Chronic” trajectory is estimated wholly in the “clinical” category across the entire period, whereas the “Resilient” and “Recovery” trajectories both remained within the normative range throughout. In addition, the 95% CIs (shaded areas, Figure 1) do not overlap, indicating they are three fully distinct trajectories. Comparisons between extracted latent classes showed no significant differences in parent demographics: sex; country of birth; education; relationship status; and age (Table III). Nor were any significant differences found in objective illness factors; illness group; and length of stay. However, groups differed on all psychosocial variables. Post hoc Bonferroni-adjusted pairwise comparisons found statistically significant differences between “Resilient” and “Chronic” groups on all measures, with the “Chronic” group having consistently poorer mental health functioning. Moreover, the “Chronic” group was significantly more distressed compared to the “Recovery” group on all outcomes except the DASS. Table III. Between-Class Comparisons on PTSS Outcomes, and Psychosocial, Demographic, and Illness Characteristics N Resilient Recovery Chronic p ES ICC n = 52 n = 84 n = 20 PTSS outcomes across trajectories  ASDS @ T1, M (SD) 139 29.8 6.0 47.8 7.9 62.6 10.0 <.001a 0.67 0.25  PCL-S, M (SD)   Total @ T2 134 19.9 3.4 27.7 6.9 43.2 10.6 <.001a 0.55 0.69   Total @ T3 117 19.3 2.8 25.7 5.8 40.9 10.1 <.001a 0.57 0.65   Total @ T4 126 19.6 2.6 26.0 5.9 41.9 10.2 <.001a 0.59 0.52 Characteristics of trajectory members  Demographic characteristics @ T1   Parent sex [male], n % 156 32 61.5 65 77.4 17 85.0 .062 – 0.00   Country of birth [Australia], n % 156 47 90.4 72 85.7 17 85.0 .722 – 0.09   Education [<high school], n % 156 7 13.5 16 19.1 3 15.0 .722 – 0.55   Relationship status [married], n % 156 51 98.1 78 92.9 18 90.0 .530 – 0.82   Parent age [years], M (SD) 156 35.6 6.5 37.4 8.6 34.5 6.6 .183 0.02 0.09   Child age [years]+, M (SD) 156 2.1 2.9 3.7 4.9 2.7 3.9 .364 – 0.91  Illness characteristics @ T1   Department, n %    Oncology 156 19 36.5 28 33.3 9 45.0 .488 – 0.29    Cardiology 156 14 26.9 30 35.7 8 40.0    PICU 156 19 36.5 26 31.0 3 15.0   Length of stay (days)+, M (SD) 156 16.2 7.7 20.7 9.5 16.8 9.6 .330 – 0.92  Psychosocial characteristics   DASS @ T1, M (SD)    Depression 130 1.6 3.6 3.5 4.3 10.6 6.2 <.001b 0.30 0.38    Anxiety 130 0.7 1.5 2.0 3.1 9.5 7.9 <.001b 0.37 0.53    Stress 130 5.2 5.8 7.7 5.6 17.9 8.0 <.001b 0.31 0.24  STAI @ T1, M (SD) 140 30.5 6.7 37.4 9.1 49.3 7.6 <.001a 0.33 0.62  PECI @ T2, M (SD)   Guilt and worry 129 0.8 0.5 1.5 0.6 2.5 0.8 <.001a 0.48 0.48   Emotional resources 134 3.6 0.4 2.9 0.6 2.6 0.8 <.001a 0.28 0.54   Uncertainty 132 0.6 0.4 1.2 0.7 2.0 0.9 <.001a 0.31 0.71   Unresolved sorrow and anger 132 0.4 0.5 1.1 0.6 2.0 0.9 <.001a 0.39 0.44  PAT @ T1, M (SD) 114 0.4 0.3 0.8 0.5 1.2 0.6 <.001a 0.29 0.59 N Resilient Recovery Chronic p ES ICC n = 52 n = 84 n = 20 PTSS outcomes across trajectories  ASDS @ T1, M (SD) 139 29.8 6.0 47.8 7.9 62.6 10.0 <.001a 0.67 0.25  PCL-S, M (SD)   Total @ T2 134 19.9 3.4 27.7 6.9 43.2 10.6 <.001a 0.55 0.69   Total @ T3 117 19.3 2.8 25.7 5.8 40.9 10.1 <.001a 0.57 0.65   Total @ T4 126 19.6 2.6 26.0 5.9 41.9 10.2 <.001a 0.59 0.52 Characteristics of trajectory members  Demographic characteristics @ T1   Parent sex [male], n % 156 32 61.5 65 77.4 17 85.0 .062 – 0.00   Country of birth [Australia], n % 156 47 90.4 72 85.7 17 85.0 .722 – 0.09   Education [<high school], n % 156 7 13.5 16 19.1 3 15.0 .722 – 0.55   Relationship status [married], n % 156 51 98.1 78 92.9 18 90.0 .530 – 0.82   Parent age [years], M (SD) 156 35.6 6.5 37.4 8.6 34.5 6.6 .183 0.02 0.09   Child age [years]+, M (SD) 156 2.1 2.9 3.7 4.9 2.7 3.9 .364 – 0.91  Illness characteristics @ T1   Department, n %    Oncology 156 19 36.5 28 33.3 9 45.0 .488 – 0.29    Cardiology 156 14 26.9 30 35.7 8 40.0    PICU 156 19 36.5 26 31.0 3 15.0   Length of stay (days)+, M (SD) 156 16.2 7.7 20.7 9.5 16.8 9.6 .330 – 0.92  Psychosocial characteristics   DASS @ T1, M (SD)    Depression 130 1.6 3.6 3.5 4.3 10.6 6.2 <.001b 0.30 0.38    Anxiety 130 0.7 1.5 2.0 3.1 9.5 7.9 <.001b 0.37 0.53    Stress 130 5.2 5.8 7.7 5.6 17.9 8.0 <.001b 0.31 0.24  STAI @ T1, M (SD) 140 30.5 6.7 37.4 9.1 49.3 7.6 <.001a 0.33 0.62  PECI @ T2, M (SD)   Guilt and worry 129 0.8 0.5 1.5 0.6 2.5 0.8 <.001a 0.48 0.48   Emotional resources 134 3.6 0.4 2.9 0.6 2.6 0.8 <.001a 0.28 0.54   Uncertainty 132 0.6 0.4 1.2 0.7 2.0 0.9 <.001a 0.31 0.71   Unresolved sorrow and anger 132 0.4 0.5 1.1 0.6 2.0 0.9 <.001a 0.39 0.44  PAT @ T1, M (SD) 114 0.4 0.3 0.8 0.5 1.2 0.6 <.001a 0.29 0.59 + Note. p = Model p-value. Mixed-effects logistic regression for binary outcomes, clustered chi-squared test for multinomial outcome (department), and mixed-effects regression for continuous outcomes. Generalized estimating equation model; negative binomial distribution for child age. ASDS = Acute Stress Disorder Scale; DASS = Depression Anxiety Stress Scales; ES = effect size (model R2); ICC = intraclass correlation coefficient; PAT = Psychosocial Assessment Tool; PECI = Parent Experience of Child Illness; STAI = State Trait Anxiety Inventory. Significant post hoc Bonferroni test between: aRecovery versus Resilient, Chronic versus Resilient, and Chronic versus Recovery. bRecovery versus Resilient and Chronic versus Resilient. Table III. Between-Class Comparisons on PTSS Outcomes, and Psychosocial, Demographic, and Illness Characteristics N Resilient Recovery Chronic p ES ICC n = 52 n = 84 n = 20 PTSS outcomes across trajectories  ASDS @ T1, M (SD) 139 29.8 6.0 47.8 7.9 62.6 10.0 <.001a 0.67 0.25  PCL-S, M (SD)   Total @ T2 134 19.9 3.4 27.7 6.9 43.2 10.6 <.001a 0.55 0.69   Total @ T3 117 19.3 2.8 25.7 5.8 40.9 10.1 <.001a 0.57 0.65   Total @ T4 126 19.6 2.6 26.0 5.9 41.9 10.2 <.001a 0.59 0.52 Characteristics of trajectory members  Demographic characteristics @ T1   Parent sex [male], n % 156 32 61.5 65 77.4 17 85.0 .062 – 0.00   Country of birth [Australia], n % 156 47 90.4 72 85.7 17 85.0 .722 – 0.09   Education [<high school], n % 156 7 13.5 16 19.1 3 15.0 .722 – 0.55   Relationship status [married], n % 156 51 98.1 78 92.9 18 90.0 .530 – 0.82   Parent age [years], M (SD) 156 35.6 6.5 37.4 8.6 34.5 6.6 .183 0.02 0.09   Child age [years]+, M (SD) 156 2.1 2.9 3.7 4.9 2.7 3.9 .364 – 0.91  Illness characteristics @ T1   Department, n %    Oncology 156 19 36.5 28 33.3 9 45.0 .488 – 0.29    Cardiology 156 14 26.9 30 35.7 8 40.0    PICU 156 19 36.5 26 31.0 3 15.0   Length of stay (days)+, M (SD) 156 16.2 7.7 20.7 9.5 16.8 9.6 .330 – 0.92  Psychosocial characteristics   DASS @ T1, M (SD)    Depression 130 1.6 3.6 3.5 4.3 10.6 6.2 <.001b 0.30 0.38    Anxiety 130 0.7 1.5 2.0 3.1 9.5 7.9 <.001b 0.37 0.53    Stress 130 5.2 5.8 7.7 5.6 17.9 8.0 <.001b 0.31 0.24  STAI @ T1, M (SD) 140 30.5 6.7 37.4 9.1 49.3 7.6 <.001a 0.33 0.62  PECI @ T2, M (SD)   Guilt and worry 129 0.8 0.5 1.5 0.6 2.5 0.8 <.001a 0.48 0.48   Emotional resources 134 3.6 0.4 2.9 0.6 2.6 0.8 <.001a 0.28 0.54   Uncertainty 132 0.6 0.4 1.2 0.7 2.0 0.9 <.001a 0.31 0.71   Unresolved sorrow and anger 132 0.4 0.5 1.1 0.6 2.0 0.9 <.001a 0.39 0.44  PAT @ T1, M (SD) 114 0.4 0.3 0.8 0.5 1.2 0.6 <.001a 0.29 0.59 N Resilient Recovery Chronic p ES ICC n = 52 n = 84 n = 20 PTSS outcomes across trajectories  ASDS @ T1, M (SD) 139 29.8 6.0 47.8 7.9 62.6 10.0 <.001a 0.67 0.25  PCL-S, M (SD)   Total @ T2 134 19.9 3.4 27.7 6.9 43.2 10.6 <.001a 0.55 0.69   Total @ T3 117 19.3 2.8 25.7 5.8 40.9 10.1 <.001a 0.57 0.65   Total @ T4 126 19.6 2.6 26.0 5.9 41.9 10.2 <.001a 0.59 0.52 Characteristics of trajectory members  Demographic characteristics @ T1   Parent sex [male], n % 156 32 61.5 65 77.4 17 85.0 .062 – 0.00   Country of birth [Australia], n % 156 47 90.4 72 85.7 17 85.0 .722 – 0.09   Education [<high school], n % 156 7 13.5 16 19.1 3 15.0 .722 – 0.55   Relationship status [married], n % 156 51 98.1 78 92.9 18 90.0 .530 – 0.82   Parent age [years], M (SD) 156 35.6 6.5 37.4 8.6 34.5 6.6 .183 0.02 0.09   Child age [years]+, M (SD) 156 2.1 2.9 3.7 4.9 2.7 3.9 .364 – 0.91  Illness characteristics @ T1   Department, n %    Oncology 156 19 36.5 28 33.3 9 45.0 .488 – 0.29    Cardiology 156 14 26.9 30 35.7 8 40.0    PICU 156 19 36.5 26 31.0 3 15.0   Length of stay (days)+, M (SD) 156 16.2 7.7 20.7 9.5 16.8 9.6 .330 – 0.92  Psychosocial characteristics   DASS @ T1, M (SD)    Depression 130 1.6 3.6 3.5 4.3 10.6 6.2 <.001b 0.30 0.38    Anxiety 130 0.7 1.5 2.0 3.1 9.5 7.9 <.001b 0.37 0.53    Stress 130 5.2 5.8 7.7 5.6 17.9 8.0 <.001b 0.31 0.24  STAI @ T1, M (SD) 140 30.5 6.7 37.4 9.1 49.3 7.6 <.001a 0.33 0.62  PECI @ T2, M (SD)   Guilt and worry 129 0.8 0.5 1.5 0.6 2.5 0.8 <.001a 0.48 0.48   Emotional resources 134 3.6 0.4 2.9 0.6 2.6 0.8 <.001a 0.28 0.54   Uncertainty 132 0.6 0.4 1.2 0.7 2.0 0.9 <.001a 0.31 0.71   Unresolved sorrow and anger 132 0.4 0.5 1.1 0.6 2.0 0.9 <.001a 0.39 0.44  PAT @ T1, M (SD) 114 0.4 0.3 0.8 0.5 1.2 0.6 <.001a 0.29 0.59 + Note. p = Model p-value. Mixed-effects logistic regression for binary outcomes, clustered chi-squared test for multinomial outcome (department), and mixed-effects regression for continuous outcomes. Generalized estimating equation model; negative binomial distribution for child age. ASDS = Acute Stress Disorder Scale; DASS = Depression Anxiety Stress Scales; ES = effect size (model R2); ICC = intraclass correlation coefficient; PAT = Psychosocial Assessment Tool; PECI = Parent Experience of Child Illness; STAI = State Trait Anxiety Inventory. Significant post hoc Bonferroni test between: aRecovery versus Resilient, Chronic versus Resilient, and Chronic versus Recovery. bRecovery versus Resilient and Chronic versus Resilient. Discussion This study examined PTSS in parents of children with life-threatening illnesses/injuries, across four time points, from the acute diagnostic period up to 18 months post their child’s initial hospital admission. It also examined whether psychosocial, demographic, and medical factors were associated with patterns of parent distress and recovery. Consistent with the study hypotheses, distinct trajectories were found with psychosocial variables the strongest predictors of group membership. The current study makes a significant contribution to enhancing our understanding of parent psychological adaptation beyond the acute phase and early months of their child’s illness. Importantly, the inclusion of parents of children with a range of medical conditions extends the generalizability of the study results. The study findings also add empirical support to Bonanno’s theoretical model (Bonanno, 2004), which proposes variable trajectories of parent psychological recovery following a traumatic event, as well as the Pediatric Medical Traumatic Stress model (Kazak et al., 2006; Price et al., 2016), which posits the importance of psychosocial factors, ahead of demographic and illness variables in predicting outcomes. The finding of three distinct parent distress trajectories confirms theoretical models postulating differing distress–recovery trajectories for individuals who experience a traumatic event and extend previous findings in oncology and bereaved populations. The “Recovery” trajectory was characterized by a decrease in PTSS symptoms, with time suggestive of a gradual improvement in parent distress levels. Across other studies with populations, including mothers of children with cancer (Dolgin et al., 2007; Steele et al., 2004), adolescents and young adults with cancer (Zebrack et al., 2014), and parents of children who have suffered an injury (Le Brocque et al., 2010), a “Recovery” trajectory has commonly been found. However, the nature and definition of the recovery has varied, from those that have shown moderate recovery over time, to those who have shown significant recovery from a clinical level of distress, to nonclinical levels. In the current study, while parents in the “Recovery” group showed a reduction in PTSS, the symptoms experienced by these parents did not reach clinical cutoff scores. This finding supports the notion that many parents have the intra- and interpersonal resources to adjust over time to a traumatic and distressing illness in their child. However, previous studies have indicated that considerable distress and functional impacts may still be seen in parents with subclinical levels of PTSS (Kazak et al., 1997) and have emphasized the importance of psychosocial services in preventing chronic difficulties (Kazak, 2006). The parents in this study had access to psychosocial services within a pediatric hospital setting as part of universal care (e.g., social work, mental health, child life therapists), and thus, it is possible that these services mitigated the PTSS of parents and facilitated a reduction in their overall distress symptomatology. The finding of a “Resilient” trajectory, comprising 33% of the study sample, supports previous findings that a sizeable proportion of parents is able to cope with the distressing impact of having a seriously ill child and the treatment associated with that illness. For example, Dolgin and colleagues (2007) reported that 51% of mothers of children with cancer reported “low stable” PTSS across 6 months following their child’s diagnosis of cancer. In another study, a higher proportion of parents (78%) was classified as “resilient” up to 2 years post their child being admitted to hospital for an injury; however, the majority of this sample experienced injuries that had a lower risk of death and serious long-term impairment, including fractures, dislocations, and lacerations (Le Brocque et al., 2010). These two previous studies found that considerably higher proportions of families were classified as resilient. There may be a number of reasons for these differences. The differences in illness severity, as well as the periods that the trajectories were determined in these previous studies may have contributed to these differences observed. This supports findings of recent studies that suggest there is considerable variability in symptomatology across the phases of the medical traumatic stress trajectory (Price et al., 2016) and highlights the need for repeated screening and assessment to ensure timely psychosocial intervention (Kazak et al., 2012). Notwithstanding differences across populations, these results, taken together with the small number of other longitudinal studies that have examined childhood illness/injury, support the contention that there is a range of normative responses to medical traumatic events (Price et al., 2016) and Bonanno’s theory that resilience is a common feature of adults’ response to adverse events (Bonanno, 2004). Consistent with other research (Bonanno, 2004; Zebrack et al., 2014), the “Chronic” group comprised 10–15% of parents and tended to exhibit more chronic distress reactions following their child’s illness. This group has been detected across various populations: adolescents and young adults with cancer (Zebrack et al., 2014), parents of children that have died as a result of cancer (McCarthy et al., 2010), mothers of preterm infants (Holditch-Davis et al., 2009), parents of injured children (Landolt et al., 2012), and parents of children with meningococcal disease (Garralda et al., 2009). This chronic PTSS trajectory reaction appears to be consistent across medical conditions and age groups and suggests that there is a subgroup of parents who require mental health support after experiencing their child’s diagnosis/hospitalization. Importantly, however, early identification of parents who are likely to fall within this group will assist with the allocation of psychosocial resources to these families, to implement preventative strategies, and reduce the likelihood of these parents from developing chronic and clinically significant levels of PTSS. This group may therefore benefit from early interventions to prevent acute distress reactions from developing into chronic mental health and adjustment problems. In this study, evidence of a “Delayed” distress group, as proposed by Bonanno’s theoretical model (Bonanno, 2004), was not found. Other studies have also not found evidence of delayed distress (Dolgin et al., 2007; Le Brocque et al., 2010), and Bonanno (2004) also maintains that there is little empirical evidence to support the presence of such a distress trajectory. This may be because of the presence of a potential misconception in the grief literature, that an absent early distress reaction is generally considered pathological and will eventually resurface in the form of delayed grief or trauma reactions (Bonanno, 2004). Empirical data within the literature are suggesting, however, that a lack of a significant early distress reaction is not because of psychopathology but because of resilience and is a healthy adjustment that does not lead to delayed grief reactions (Bonanno, 2004). With respect to factors associated with parental distress, we found that acute distress reactions (acute traumatic stress, as well as depression, anxiety, and stress) were all predictive of trajectory group membership. In addition, psychosocial risk factors, parent trait anxiety, and parent subjective appraisals of the illness, such as guilt and worry and unresolved sorrow and anger, were all significantly able to predict trajectory group membership. The “Resilient” group had lower scores on all acute measures of psychological and distress symptomology, with the “Chronic” group scoring highest in all measures. This pattern was consistent across all psychosocial measures, at all time points of measurement, except for the parent mental health measure (DASS), which distinguished the “Recovery” and “Chronic” groups from the “Resilient” group, but not from each other. As predicted, the same pattern was not found in the demographic and illness-related variables. Demographic factors, such as parent age, sex, education, ethnicity and relationship status, were neither predictive of group membership nor the objective medical factors of illness group and length of hospital stay. These findings are consistent with previous studies, which have found that psychosocial factors most strongly predict parent mental health outcomes or trajectory group membership, and demographic and illness-related variables are either not predictive or weak predictors (Kassam-Adams, Fleisher, & Winston, 2009; Rayner et al., 2016; Steele et al., 2004). Limitations A limitation of this study is that we cannot claim that the sample is representative of all parents with seriously ill children, as a high number of parents withdrew or declined to participate. Given the main reasons for withdrawing or nonparticipation were time constraints associated with the illness, or being too overwhelmed with managing their child’s condition to participate, it is possible that this group may have been experiencing higher levels of distress, potentially impacting on the trajectories found, or the size of the samples in each trajectory. Despite this, no differences in demographic and illness factors were found between the analytic sample and those who dropped out or who were lost to follow-up. Nevertheless, these findings need to be interpreted with caution. Another study limitation is that data were mostly collected from a single source, being parent ratings. While it is possible that the associations reported are inflated by shared method variance, recent work has shown that the extent to which this occurs has been overstated in the past (Lance, Dawson, Birkelbach, & Hoffman, 2010). In addition, children with a very wide age range were involved in this study. Despite this limitation, it was not found to predict group membership. This is consistent with findings in the literature, which has found that child age is not associated with parent trauma reactions and the development of PTSS following a significant illness (Woolf et al., 2016). Further, the majority of parents in the current sample had a spouse or partner; hence, the findings and trajectories may not generalize to single parents. Any changes in treatment or improvements in the child’s condition were also not tracked throughout the study period, with these factors potentially influencing the current findings. Data from parents whose child passed away during the study were, however, removed; hence, the death of a child was not a factor that influenced these results. It is also acknowledged that the main latent class trajectory analysis did not adjust for within-couple correlation of data. However, our exploration of the data suggested that treating parents as separate units for trajectory modeling was appropriate. Importantly, the majority of the samples were mothers; hence, it may not be possible to generalize these findings to fathers specifically, given the small number in the current sample. Finally, the universal multidisciplinary supportive services that are potentially offered to families in a pediatric hospital setting were not able to be measured or controlled for. It is possible these services may have ameliorated parent distress and contributed to the levels of resilience demonstrated in this study. Future studies evaluating the usual care families receive, which may include social work and psychology visits, are needed. Similarly, further research is also needed across a wider array of medical conditions, in particular parents who have children with chronic illnesses or developmental disabilities and those with less severe illnesses or diagnoses. Clinical Implications From a clinical perspective, the findings of the current study confirm that, overall, a significant proportion of parents will adapt to the diagnosis of their child’s serious or life-threatening medical condition. It is possible that more targeted psychological resources (including mental health interventions) directed to those parents showing higher initial and/or chronic distress may reduce potential long-term impacts on parents and families. Although the findings suggest that “Resilient” and “Recovery” groups do not experience long-term PTSS, it is critical that health-care providers ensure trauma-informed practice in medical care to mitigate psychosocial distress (Kazak, 2006). Trauma-informed care includes ensuring that psychosocial services are well integrated into medical care, that procedural and medical distress is minimized, that appropriate and timely medical information is provided to families, and that there is empathic communication and consistency of care wherever possible (Marsac et al., 2016). This care could also could provide basic mental health support for these families in the form of psychoeducation, to inform parents of the common psychological reactions to having a sick child, along with information regarding referral pathways in the event that psychological symptoms do develop over time. Such trauma-informed care will not only help to reduce the likelihood of chronic distress from occurring as a result of the illness but will also minimize the potential for the medical care itself to trigger trauma reactions (Kazak, 2006; Marsac et al., 2016). A critical finding is that membership of parent distress trajectories is well predicted by early psychosocial factors and acute distress responses. This suggests that early screening practices within a pediatric hospital setting may help to identify those families that require more support in the acute stages, to direct psychosocial and mental health resources to where they are most needed. Screening by medical teams may involve brief assessment of psychosocial variables such as acute stress reactions, early signs of depression or anxiety symptoms, prior trauma exposure, the degree of psychosocial and family support, and family conflict or problems. The use of a short evidence-based screening tool, such as the Psychosocial Assessment Tool (Pai et al., 2008), may be useful in measuring these psychosocial risk factors in the acute stages of the child’s admission. This may help medical teams to identify which parents and families are likely to fall within the Chronic group, and therefore be at most risk of developing longer-term, clinically significant PTSS. Importantly, the support and management of parents of very ill children is critical, given that parent mental health and well-being are also established predictors of child outcomes. This link between parent mental health and child outcomes has been found across various illnesses (Kolaitis et al., 2011; Landolt et al., 2012), suggesting that, in a clinical setting, the consideration of parents’ adjustment should be considered as an aspect of a holistic approach to the management of the child’s illness and their recovery. Furthermore, the finding that illness-related factors poorly predict long-term mental health outcomes in parents, and their group membership, indicates that these trajectories may be applicable across the wider pediatric hospital setting. These findings may challenge the traditional psychosocial hospital service delivery models, as they suggest that services or interventions may be delivered across the hospital setting, irrespective of the child’s serious medical condition. Importantly, the finding that psychosocial risk factors were the strongest predictors suggests that potential interventions for parents could target these modifiable psychosocial risk factors, and that these interventions may be relevant across illness groups. Further research is needed to explore the “Chronic” population, to identify the best time for intervening to maximize the benefit of the intervention. Conclusions Notwithstanding the limitations described, the current study is the first to systematically explore the longitudinal response trajectories of parent PTSS reactions across different child illnesses and hospital specialist departments, and has further investigated the assumptions of existing theoretical models within the literature. As evidence-based interventions for parents of ill children continue to emerge, early identification of parent subgroups and targeting of interventions to those parents with higher and/or persistent distress will potentially lead to stronger intervention effects, and provide a more robust rationale for allocation of psychosocial resources. Acknowledgments The authors acknowledge the generosity of the participating families along with the contributions to the research made by Amy Coe and Nathan Dowling. K.B. and J.M.N. were employed at the Parenting Research Centre when this work was undertaken. Funding This work was supported by the Pratt Foundation and the Victorian Government’s Operational Infrastructure Support Program. Conflicts of interest: None declared. References Andruff H. , Carraro N. , Thompson A. , Gaudreau P. , Louvet B. ( 2009 ). Latent class growth modelling: A tutorial . Tutorials in Quantitative Methods for Psychology , 5 , 11 – 24 . Google Scholar Crossref Search ADS Bonanno G. ( 2004 ). Loss, trauma, and human resilience: Have we underestimated the human capacity to thrive after extremely aversive events? American Psychologist , 59 , 20 – 28 . Google Scholar Crossref Search ADS PubMed Bonner M. J. , Hardy K. K. , Guill A. B. , McLaughlin C. , Schweitzer H. , Carter K. ( 2006 ). Development and validation of the parent experience of child illness . Journal of Pediatric Psychology , 31 , 310 – 321 . Google Scholar Crossref Search ADS PubMed Bonner M. J. , Hardy K. K. , Willard V. W. , Hutchinson K. C. , Guill A. B. ( 2008 ). Further validation of the Parent Experience of Child Illness Scale . Children's Health Care , 37 , 145 – 157 . Google Scholar Crossref Search ADS Bronner M. B. , Peek N. , Knoester H. , Bos A. P. , Last B. F. , Grootenhuis M. A. ( 2010 ). Course and predictors of posttraumatic stress disorder in parents after pediatric intensive care treatment of their child . Journal of Pediatric Psychology , 35 , 966 – 974 . Google Scholar Crossref Search ADS PubMed Bryant R. A. , Creamer M. , O’donnell M. , Silove D. , McFarlane A. C. ( 2012 ). The capacity of acute stress disorder to predict posttraumatic psychiatric disorders . Journal of Psychiatric Research , 46 , 168 – 173 . Google Scholar Crossref Search ADS PubMed Bryant R. A. , Moulds M. L. , Guthrie R. M. ( 2000 ). Acute Stress Disorder Scale: A self-report measure of acute stress disorder . Psychological Assessment , 12 , 61 – 68 . Google Scholar Crossref Search ADS PubMed Dolgin M. J. , Phipps S. , Fairclough D. L. , Sahler O. J. , Askins M. , Noll R. B. , Butler R. W. , Varni J. W. , Katz E. R. ( 2007 ). Trajectories of adjustment in mothers of children with newly diagnosed cancer: A natural history investigation . Journal of Pediatric Psychology , 32 , 771 – 782 . Google Scholar Crossref Search ADS PubMed Garralda M. E. , Gledhill J. , Nadel S. , Neasham D. , O'connor M. , Shears D. ( 2009 ). Longer-term psychiatric adjustment of children and parents after meningococcal disease . Pediatric Critical Care Medicine , 10 , 675 – 680 . Google Scholar Crossref Search ADS PubMed Hearps S. J. , McCarthy M. C. , Muscara F. , Hearps S. J. , Burke K. , Jones B. , Anderson V. A. ( 2014 ). Psychosocial risk in families of infants undergoing surgery for a serious congenital heart disease . Cardiology Young , 24 , 632 – 639 . Google Scholar Crossref Search ADS Holditch-Davis D. , Miles M. S. , Weaver M. A. , Black B. , Beeber L. , Thoyre S. , Engelke S. ( 2009 ). Patterns of distress in African-American mothers of pre-term infants . Journal of Developmental and Behavioral Pediatrics , 30 , 193 – 205 . Google Scholar Crossref Search ADS PubMed Jones B. L. , Nagin D. S. ( 2013 ). A note on a Stata plugin for estimating group-based trajectory models . Sociological Methods and Research , 42 , 608 – 613 . Google Scholar Crossref Search ADS Kass R. E. , Raftery A. E. ( 1995 ). Bayes factors . Journal of the American Statistical Association , 90 , 773 – 795 . Google Scholar Crossref Search ADS Kassam-Adams N. , Fleisher C. L. , Winston F. K. ( 2009 ). Acute stress disorder and posttraumatic stress disorder in parents of injured children . Journal of Traumatic Stress , 22 , 294 – 302 . Google Scholar Crossref Search ADS PubMed Kazak A. E. ( 2006 ). Pediatric Psychosocial Preventative Health Model (PPPHM): Research, practice, and collaboration in pediatric family systems medicine . Families, Systems and Health , 24 , 381 – 395 . Google Scholar Crossref Search ADS Kazak A. E. , Barakat L. P. , Meeske K. , Christakis D. , Meadows A. T. , Casey R. , Penati B. , Stuber M. L. ( 1997 ). Posttraumatic stress, family functioning, and social support in survivors of childhood leukemia and their mothers and fathers . Journal of Consulting and Clinical Psychology , 65 , 120 – 129 . Google Scholar Crossref Search ADS PubMed Kazak A. E. , Brier M. , Alderfer M. A. , Reilly A. , Fooks Parker S. , Rogerwick S. , Ditaranto S. , Barakat L. P. ( 2012 ). Screening for psychosocial risk in pediatric cancer . Pediatric Blood and Cancer , 59 , 822 – 827 . Google Scholar Crossref Search ADS PubMed Kazak A. E. , Kassam-Adams N. , Schneider S. , Zelikovsky N. , Alderfer M. A. , Rourke M. ( 2006 ). An integrative model of pediatric medical traumatic stress . Journal of Pediatric Psychology , 31 , 343 – 355 . Google Scholar Crossref Search ADS PubMed Kolaitis G. , Giannakopoulos G. , Liakopoulou M. , Pervanidou P. , Charitaki S. , Mihas C. , Ferentinos S. , Papassotiriou I. , Chrousos G. P. , Tsiantis J. ( 2011 ). Predicting pediatric posttraumatic stress disorder after road traffic accidents: The role of parental psychopathology . Journal of Traumatic Stress , 24 , 414 – 421 . Google Scholar Crossref Search ADS PubMed Lance C. E. , Dawson B. , Birkelbach B. , Hoffman B. J. ( 2010 ). Method effects, measurement error, and substantive conclusions . Organizational Research Methods , 13 , 435 – 455 . Google Scholar Crossref Search ADS Landolt M. A. , Ystrom E. , Sennhauser F. , Gnehm H. , Vollrath M. ( 2012 ). The mutual prospective influence of child and parental post-traumatic stress symptoms in pediatric patients . Journal of Child Psychology and Psychiatry , 53 , 767 – 774 . Google Scholar Crossref Search ADS PubMed Le Brocque R. M. , Hendrikz J. , Kenardy J. A. ( 2010 ). Parental response to child injury: Examination of parental posttraumatic stress symptom trajectories following child accidental injury . Journal of Pediatric Psychology , 35 , 646 – 655 . Google Scholar Crossref Search ADS PubMed Lovibond P. , Lovibond S. ( 1995 ). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventory . Behavior Research and Therapy , 33 , 335 – 343 . Google Scholar Crossref Search ADS Marsac M. L. , Kassam-Adams N. , Hildenbrand A. K. , Nicholls E. , Winston F. K. , Leff S. S. , Fein J. ( 2016 ). Implementing a trauma-informed approach in pediatric health care networks . JAMA Pediatrics , 170 , 70 – 77 . Google Scholar Crossref Search ADS PubMed McCarthy M. C. , Ashley D. M. , Lee K. J. , Anderson V. A. ( 2012 ). Predictors of acute and posttraumatic stress symptoms in parents following their child's cancer diagnosis . Journal of Traumatic Stress , 25 , 558 – 566 . Google Scholar Crossref Search ADS PubMed McCarthy M. C. , Clarke N. E. , Ting C. L. , Conroy R. , Anderson V. A. , Heath J. A. ( 2010 ). Prevalence and predictors of parental grief and depression following the death of a child from cancer . Journal of Palliative Medicine , 13 , 1321 – 1326 . Google Scholar Crossref Search ADS PubMed McCarthy M. C. , Hearps S. J. , Muscara F. , Anderson V. A. , Burke K. , Hearps S. J. , Kazak A. E. ( 2016 ). Family psychosocial screening in infants and older children in the acute pediatric hospital setting: The psychosocial assessment tool . Journal of Pediatric Psychology , 41 , 820 – 829 . Google Scholar Crossref Search ADS PubMed McCarthy M. C. , McNeil R. , Drew S. , Dunt D. , Kosola S. , Orme L. , Sawyer S. M. ( 2016 ). Psychological distress and posttraumatic stress symptoms in adolescents and young adults with cancer and their parents . Journal of Adolescent and Young Adult Oncology , 5 , 322 – 329 . Google Scholar Crossref Search ADS PubMed Muscara F. , Burke K. , McCarthy M. C. , Anderson V. A. , Hearps S. J. C. , Hearps S. J. , Dimovski A. , Nicholson J. M. ( 2015 ). Parent distress reactions following a serious illness or injury in their child: A protocol paper for The Take a Breath Cohort Study . BMC Psychiatry , 15 , 153. Google Scholar Crossref Search ADS PubMed Muscara F. , McCarthy M. C. , Woolf C. , Hearps S. J. C. , Burke K. , Anderson V. A. ( 2015 ). Early psychological reactions in parents of children with a life threatening illness within a pediatric hospital setting . European Psychiatry , 30 , 555 – 561 . Google Scholar Crossref Search ADS PubMed Nagin D. S. ( 2009 ). Group-based modeling of development . Cambridge, MA : Harvard University Press . Pai A. L. H. , Patiño-Fernández A. M. , McSherry M. , Beele D. , Alderfer M. A. , Reilly A. T. , Hwang W. T. , Kazak A. E. ( 2008 ). The Psychosocial Assessment Tool (PAT2.0): Psychometric properties of a screener for psychosocial distress in families of children newly diagnosed with cancer . Journal of Pediatric Psychology , 33 , 50 – 62 . Google Scholar Crossref Search ADS PubMed Price J. , Kassam-Adams N. , Alderfer M. , Christofferson J. , Kazak A. E. ( 2016 ). Systematic review: A reevaluation and update of the Integrative (Trajectory) Model of Pediatric Medical Traumatic Stress . Journal of Pediatric Psychology , 41 , 86 – 97 . Google Scholar Crossref Search ADS PubMed Rayner M. , Muscara F. , Dimovski A. , McCarthy M. C. , Yamada J. , Anderson V. A. , Burke K. , Walser R. , Nicholson J. M. ( 2016 ). Take a Breath: Study protocol for a randomized controlled trial of an online group intervention to reduce traumatic stress in parents of children with a life threatening illness or injury . BMC Psychiatry , 16 , 169 . Google Scholar Crossref Search ADS PubMed Spielberger C. , Gorsuch R. , Lushene R. , Vagg P. , Jacobs G. ( 1983 ). Manual of the state-trait anxiety inventory . Palo Alto, CA : Consulting Psychologists Press . Steele R. , Dreyer M. , Phipps S. ( 2004 ). Patterns of maternal distress among children with cancer and their association with child emotional and somatic distress . Journal of Pediatric Psychology , 29 , 507 – 514 . Google Scholar Crossref Search ADS PubMed Walker E. , Newman E. , Dobie D. , Ciechanowski P. , Katon W. ( 2002 ). Validation of the PTSD checklist in an HMO sample of women . General Hospital Psychiatry , 24 , 375 – 380 . Google Scholar Crossref Search ADS PubMed Weathers F. W. , Litz B. T. , Herman D. S. , Huska J. A. , Keane T. M. ( 1993 ). The PTSD checklist: Reliability, validity, & diagnostic utility. Paper presented at the Annual Meeting of the International Society for Traumatic Stress Studies, San Antonio, TX. Woolf C. , Muscara F. , Anderson V. A. , McCarthy M. C. ( 2016 ). Early traumatic stress responses in parents following a serious illness in their child: A systematic review . Journal of Clinical Psychology in Medical Settings , 23 , 53 – 66 . Google Scholar Crossref Search ADS PubMed Zebrack B. J. , Corbett V. , Embry L. , Aguilar C. , Meeske K. A. , Hayes-Lattin B. , Block R. , Zeman D. T. , Cole S. ( 2014 ). Psychological distress and unsatisfied need for psychosocial support in adolescent and young adult cancer patients during the first year following diagnosis . Psycho-Oncology , 23 , 1267 – 1275 . Google Scholar Crossref Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

Journal of Pediatric PsychologyOxford University Press

Published: Nov 1, 2018

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