TY - JOUR
AU1 - Lai, Betty, S
AU2 - La Greca, Annette, M
AU3 - Colgan, Courtney, A
AU4 - Herge,, Whitney
AU5 - Chan,, Sherilynn
AU6 - Medzhitova,, Julia
AU7 - Short,, Mary
AU8 - Auslander,, Beth
AB - Abstract Objective Sleep plays a critical role in children’s growth and development. This study examined the frequency and persistence of children’s sleep problems following a natural disaster, risk factors for children’s sleep problems, and the bidirectional relationship between children’s sleep problems and posttraumatic stress symptoms (PTSS) over time. Methods This study assessed 269 children (53% female, M = 8.70 years, SD = 0.95) exposed to Hurricane Ike at 8 months (Time 1) and 15 months (Time 2) post-disaster. Children completed measures of hurricane exposure and related stressors, stressful life events, sleep problems, and PTSS. Results Children’s sleep problems were significantly correlated from Time 1 to Time 2 (r = .28, p < .001). Risk factors for sleep problems at Time 2 were younger age, sleep problems at Time 1, and PTSS, not including sleep items, at Time 1. Examinations of the bidirectional relationship between sleep problems and PTSS indicated that PTSS significantly predicted later sleep problems, but sleep problems did not significantly predict later PTSS. Conclusions Findings demonstrate that PTSS may contribute to the development and course of children’s sleep problems post-disaster. disaster, life stress, posttraumatic stress, sleep Introduction Natural disasters are projected to impact an estimated 175 million children per year worldwide by the year 2022 (United Nations Children’s Fund (UNICEF), 2013). Children exposed to disasters are vulnerable to developing psychological distress (Kar, 2009), primarily posttraumatic stress symptoms (PTSS) (Lai et al., 2017; McDermott et al., 2014). A growing body of literature indicates that natural disasters are also associated with sleep problems in children (e.g., hard time falling asleep, hard time staying asleep, sleeping more than usual) (Fricke-Oerkermann et al., 2007; Kovachy et al., 2013). This is concerning, as sleep is important in helping children cope with stress (Wamser-Nanney & Chesher, 2018), and sleep plays a critical role in children’s growth and development (Dahl, 2007; Turnbull et al., 2013). This study evaluated sleep problems and PTSS among children exposed to a large-scale natural disaster, Hurricane Ike. Hurricane Ike was a category 2 hurricane that made landfall near Galveston, Texas on September 12, 2008. Ike’s hurricane-force winds extended 120 miles from its center and caused an estimated $29.5 billion worth of property damage (Berg, 2009; Blake et al., 2011). Frequency and Persistence of Children’s Post-Disaster Sleep Problems The first study aim was to examine the frequency and persistence of children’s sleep problems the first year after a disaster and into the following year post-disaster. To date, limited research has examined children’s sleep problems within the first-year post-disaster. Instead, studies examining children’s sleep problems after disasters have primarily been cross-sectional or conducted 1 year (e.g., Geng et al., 2013) or several years after the disaster (e.g., 2 years after Hurricane Katrina in Brown et al., 2011). Nevertheless, studies indicate that post-disaster sleep problems may be significant for children. Tang et al. (2018b) reported that 3 years after the 2013 Ya’an earthquake, more than a quarter of 6,132 adolescents surveyed reported sleep problems during the past month, including difficulty falling asleep, difficulty staying asleep, poor sleep quality, nightmares, and sleeping fewer than seven hours per night. Another study by Usami et al. (2013) examined sleep duration in children (M = 10.9 years, SD = 2.7 years) 8 months after the 2011 earthquake, tsunami, and subsequent nuclear disaster in Japan. Sleep duration was significantly shorter for children who experienced home damage or evacuation, compared to children who did not. Risk Factors for Children’s Post-Disaster Sleep Problems The second study aim was to examine risk factors for children’s post-disaster sleep problems. This information is needed in order to better identify, early on, which children may develop sleep problems. Based on conceptual models of disasters (e.g., La Greca et al., 1996, 2010) we focused on disaster-related stressors and child characteristics as potential risk factors for sleep problems. Disaster-related stressors, such as witnessing life-threatening experiences or being displaced, have been linked to behavioral and psychological distress symptoms in children (La Greca et al., 2010). For example, post-disaster displacement is associated with increased high-risk behaviors and poor academic outcomes in children (Peek et al., 2017). Child demographic characteristics are also associated with child difficulties after disasters. Specifically, girls, younger children, and children belonging to minoritized ethnic/racial groups have been identified as having more severe disaster reactions (see Tang et al., 2018b; Yelland et al., 2010). These groups, in particular, are more susceptible because of social vulnerability factors (e.g., young age, sexism, systemic racism)—that have been found to amplify risk during and after disasters (Peek et al., 2017). Based on this literature, we expected that higher levels of sleep problems would be reported by girls, younger children, children belonging to minoritized ethnic/racial groups, and children who experienced more disaster-related stressors. Bidirectional Relationship between Sleep Problems and PTSS over Time The third study aim was to evaluate the bidirectional relationship between sleep problems and PTSS among children exposed to Hurricane Ike. Sleep problems are associated with PTSS among children after disasters (Brown et al., 2011; Tang et al., 2018a,b; Usami et al., 2013), and children with PTSS report higher levels of sleep problems (Kovachy et al., 2013). Yet, the directionality of the relationship between sleep problems and PTSS remains unclear. More specifically, it is not clear if sleep problems are a result of PTSS, or if sleep problems influence the development and maintenance of PTSS in children post-disaster. This information is needed to understand whether sleep is a secondary feature of PTSS, or whether sleep may be a core or mediating symptom of post-disaster PTSS among children. Several studies outside of the child disaster field address this question of sleep as a secondary feature, core symptom, or mediator of PTSS (Atkinson, 2003; Spilsbury, 2009; Spoormaker & Montgomery, 2008). A study of police officers found that sleep quality mediated the relationship between posttraumatic stress disorder and physical health (i.e., physical functioning, somatic symptoms) (Atkinson, 2003). A review of the literature on adult sleep disturbance concluded that sleep disturbance seems to be a core feature of posttraumatic stress disorder, rather than just a secondary symptom (Spoormaker & Montgomery, 2008). Finally, a review of the literature examining the role of sleep as a mediator between traumatic stress and health/behavioral outcomes in violence-exposed children concluded that sleep disturbance was a key mediator between traumatic stress and health/behavioral outcomes (Spilsbury, 2009). In the child disaster field, we identified only five longitudinal studies of children’s sleep and PTSS (Brown et al., 2011; Fan et al., 2017; Iwadare et al., 2014; Zhou et al., 2014, 2017). These studies were limited by the fact that they did not examine causal relationships between sleep problems and PTSS over time (Iwadare et al., 2014); only examined how sleep problems influence PTSS (Brown et al., 2011; Fan et al., 2017); or only examined sleep and PTSS after the first year post-disaster (Brown et al., 2011; Fan et al., 2017; Zhou et al., 2014, 2017). Of these five studies, only one group of researchers investigated the potential bidirectional relationship between sleep problems and PTSS. Zhou et al. (2014, 2017) examined sleep problems and PTSS in a sample of 350 adolescents (ages 12–19 years) at 1 year, 1.5 years, and 2 years after the Wenchuan earthquake in China. They found that PTSS was predictive of sleep problems between 1 and 1.5 years, but these relationships weakened over time. Conversely, sleep problems were associated only with intrusion symptoms of PTSS. This relationship was evident between 1 and 1.5 years post-disaster, but not between 1.5 and 2 years. However, these studies did not control for factors known to influence post-disaster PTSS, including ongoing loss/disruption and life events (see La Greca et al., 2010). Another limitation of these studies was that they did not focus on younger, elementary school-aged children. This is surprising, given evidence that younger children may be at risk for greater post-disaster distress (Kronenberg et al., 2010), and given the changes that occur in children’s sleep patterns during adolescence (Fan et al., 2017; Iwadare et al., 2014). Four of the five longitudinal studies sampled adolescents exclusively (Fan et al., 2017, Iwadare et al., 2014, Zhou et al., 2014, 2017). Only one study included elementary-aged children (Brown et al., 2011). Brown et al. (2011) studied children ages 8–15 years and found that younger children reported more severe PTSS at 24 and 30 months post-disaster. However, they did not find differences in sleep problems by age at either timepoint. The current study addresses these gaps in the literature by studying the frequency, persistence, and risk factors for children’s sleep problems among a sample of children in grades two through four who were exposed to Hurricane Ike. We also assessed the bidirectional relationship between post-disaster sleep problems and PTSS. We expected children who experienced more disaster-related stressors, girls, younger children, and children belonging to minoritized ethnic/racial groups to report higher levels of sleep problems. We also expected to find a bidirectional relationship between sleep problems and PTSS such that PTSS would predict sleep problems and, conversely, sleep problems would predict PTSS, even when controlling for disaster-related stressors and child characteristics. Methods Participants This study assessed children in the Galveston Independent School District (GISD), which was severely impacted by Hurricane Ike. Prior to Hurricane Ike, the GISD served approximately 8,000 students (KHOU 11 News Houston, 2009), 77% of whom qualified for free or reduced lunch (U.S. Department of Education, 2010). The GISD serves students residing in Galveston and Bolivar, Texas. Prior to Hurricane Ike, six schools served elementary school students. After Hurricane Ike, two schools closed. Students from the two closed schools were relocated to schools that remained open. Thousands of children were displaced by the hurricane, such that student enrollment in the GISD declined by 25% the following year (KHOU 11 News Houston, 2009). Students in the GISD missed approximately one month of instruction post-hurricane (KHOU 11 News Houston, 2009). This study consisted of a subsample (n = 269) of participants from a larger study (n = 328) of children’s reactions following Hurricane Ike (PI: A. M. La Greca; La Greca et al., 2013; Lai et al., 2013). La Greca et al. (2013) focused on PTSS and genetic markers. Lai et al. (2013) focused exclusively on PTSS and depression symptoms. Neither paper included analyses of sleep problems. Inclusion criteria for the study consisted of children enrolled in grades two through four in the GISD during the spring of 2009. No explicit exclusion criteria existed for this study. Consent forms were distributed to general education classrooms. All children were assumed to speak English, but Spanish language consent forms were sent to households where parents/guardians were Spanish speaking. The children in this sub-sample ranged in age from 7 to 11 years (M = 8.70 years, SD = 0.95). Among participants who provided information about ethnicity, 37% identified as Hispanic, 29% as White, 18% as Black, 12% as Mixed or Other ethnicity, and 4% as Asian. Procedures The study protocol was reviewed and approved by the Institutional Review Boards for the University of Miami and the University of Texas Medical Branch as well as the participating school district. Active, informed parental consent was obtained for all child participants. Letters describing the overall study and parental consent forms were distributed to all second, third, and fourth graders attending elementary schools in the GISD in April of 2009 (N = 1594). Thirty-one percent (n = 494) of the children returned consent forms to their homeroom teachers. Of the consent forms returned, 69% (n = 340) indicated parental permission to participate. Written child assent was also obtained from all children prior to their participation in the study. At Time 1 (May 2009; 8 months post-disaster), of the 340 children who had parental permission to participate, nine children were absent and three refused to participate (n = 328). Prior to Time 2 (December 2009; 15 months post-disaster), parents were re-contacted; attrition at Time 2 was 16% (n = 51). Time 1(8 months post-disaster) was the earliest timepoint by which the team was able to obtain IRB approvals and coordinate study assessments with school staff. Time 2 (15 months post-disaster) was chosen as the second assessment timepoint in order to assess children in the following school year, after the active hurricane season. Of the 277 children who participated at both timepoints, 8 children were excluded from our subsample because of incomplete sleep data at Time 1 or Time 2. Our final subsample (n = 269, 53%) did not differ from the children lost to attrition at Time 2 (n = 51) on any demographic variables (i.e., age, gender, ethnicity) or any of the key study variables at Time 1 (see Table I for a full list). Children included in the subsample (n = 269) did not differ from the children excluded due to incomplete sleep data (n = 8) on any demographic variables or key study variables, with the exception of major life events occurring during the recovery period. The children excluded due to incomplete sleep data reported significantly more major life events at Time 1 compared to the children included in the subsample (t (273) = −2.43, p < .05). Table I. Descriptive Statistics for Key Study Variables (n = 269) . Time 1 (8 months post-disaster) . Time 2 (15 months post-disaster) . Study variable . M (SD) . n (%) . M (SD) . n (%) . Perceived life threat 36 (0.48) — Actual life threat 81 (0.97) — Immediate loss/disruption 3.46 (2.07) — Ongoing loss/disruption 1.50 (1.23) — Life events 1.62 (1.69) 2.13 (2.09) PTSS (Non-Sleep) 20.72 (13.00) 16.19 (12.51) PTSS (full scale) 24.04 (14.87) 19.07 (14.38)  Doubtful (0–11) 61 (23%) 92 (34%)  Mild (12–24) 88 (33%) 103 (38%)  Moderate (25–39) 71 (26%) 43 (16%)  Severe (40–59) 47 (17%) 30 (11%)  Very severe (60–68) 2 (1%) 1 (1%) Sleep problems 1.96 (1.64) 1.91 (1.72)  Hard time falling asleep 132 (49%) 132 (49%)  Trouble staying asleep 114 (42%) 104 (39%)  Sleep more than usual 121 (45%) 115 (43%) . Time 1 (8 months post-disaster) . Time 2 (15 months post-disaster) . Study variable . M (SD) . n (%) . M (SD) . n (%) . Perceived life threat 36 (0.48) — Actual life threat 81 (0.97) — Immediate loss/disruption 3.46 (2.07) — Ongoing loss/disruption 1.50 (1.23) — Life events 1.62 (1.69) 2.13 (2.09) PTSS (Non-Sleep) 20.72 (13.00) 16.19 (12.51) PTSS (full scale) 24.04 (14.87) 19.07 (14.38)  Doubtful (0–11) 61 (23%) 92 (34%)  Mild (12–24) 88 (33%) 103 (38%)  Moderate (25–39) 71 (26%) 43 (16%)  Severe (40–59) 47 (17%) 30 (11%)  Very severe (60–68) 2 (1%) 1 (1%) Sleep problems 1.96 (1.64) 1.91 (1.72)  Hard time falling asleep 132 (49%) 132 (49%)  Trouble staying asleep 114 (42%) 104 (39%)  Sleep more than usual 121 (45%) 115 (43%) Note: Dashes signify that these variables were not assessed at Time 2. For PTSS (full scale), the numbers and percentages indicate how many participants were in each PTSS clinical category. The score range for each PTSS clinical category is listed next to its corresponding category. For sleep problems, the numbers and percentages for each item indicate how many participants responded “Somewhat/Sometimes True” or “Very True or Often True”. Open in new tab Table I. Descriptive Statistics for Key Study Variables (n = 269) . Time 1 (8 months post-disaster) . Time 2 (15 months post-disaster) . Study variable . M (SD) . n (%) . M (SD) . n (%) . Perceived life threat 36 (0.48) — Actual life threat 81 (0.97) — Immediate loss/disruption 3.46 (2.07) — Ongoing loss/disruption 1.50 (1.23) — Life events 1.62 (1.69) 2.13 (2.09) PTSS (Non-Sleep) 20.72 (13.00) 16.19 (12.51) PTSS (full scale) 24.04 (14.87) 19.07 (14.38)  Doubtful (0–11) 61 (23%) 92 (34%)  Mild (12–24) 88 (33%) 103 (38%)  Moderate (25–39) 71 (26%) 43 (16%)  Severe (40–59) 47 (17%) 30 (11%)  Very severe (60–68) 2 (1%) 1 (1%) Sleep problems 1.96 (1.64) 1.91 (1.72)  Hard time falling asleep 132 (49%) 132 (49%)  Trouble staying asleep 114 (42%) 104 (39%)  Sleep more than usual 121 (45%) 115 (43%) . Time 1 (8 months post-disaster) . Time 2 (15 months post-disaster) . Study variable . M (SD) . n (%) . M (SD) . n (%) . Perceived life threat 36 (0.48) — Actual life threat 81 (0.97) — Immediate loss/disruption 3.46 (2.07) — Ongoing loss/disruption 1.50 (1.23) — Life events 1.62 (1.69) 2.13 (2.09) PTSS (Non-Sleep) 20.72 (13.00) 16.19 (12.51) PTSS (full scale) 24.04 (14.87) 19.07 (14.38)  Doubtful (0–11) 61 (23%) 92 (34%)  Mild (12–24) 88 (33%) 103 (38%)  Moderate (25–39) 71 (26%) 43 (16%)  Severe (40–59) 47 (17%) 30 (11%)  Very severe (60–68) 2 (1%) 1 (1%) Sleep problems 1.96 (1.64) 1.91 (1.72)  Hard time falling asleep 132 (49%) 132 (49%)  Trouble staying asleep 114 (42%) 104 (39%)  Sleep more than usual 121 (45%) 115 (43%) Note: Dashes signify that these variables were not assessed at Time 2. For PTSS (full scale), the numbers and percentages indicate how many participants were in each PTSS clinical category. The score range for each PTSS clinical category is listed next to its corresponding category. For sleep problems, the numbers and percentages for each item indicate how many participants responded “Somewhat/Sometimes True” or “Very True or Often True”. Open in new tab Child report measures were administered at Time 1 and Time 2. Children were assembled in groups of approximately 25–40 students in the school cafeteria or auditorium. Six to eight research assistants and at least two of the study investigators were present at all times to oversee the testing sessions and assist children who needed individual help. Questions were read aloud to children as they followed along and marked their answers. Individual assistance was provided to any child who had difficulty reading or following the group instructions. Measures At Time 1, children completed measures of hurricane exposure, hurricane-related stressors, major life events occurring during the recovery period, PTSS, and reports of sleep-related problems. All measures were repeated at Time 2, with the exception of hurricane exposure (i.e., perceived and actual life threat) and hurricane-related stressors (i.e., immediate and ongoing loss/disruption). Child-report measures are preferred in disaster contexts because this minimizes assessment burden on single individuals (e.g., teachers); school-aged children are able to verbalize and report internalizing symptoms (Lai et al., 2015); and parent- and child-report measures demonstrate low concordance, with discrepancies in reports predicting greater levels of child distress (Lai et al., 2015). For measures in this study, internal consistency was not reported for indices with formative indicators, as it is inappropriate to do so (Bollen & Bauldry, 2011; Bollen & Lennox, 1991; Fayers & Hand, 1997; Hardin et al., 2011). Hurricane Exposure and Hurricane-Related Stressors The Hurricane-Related Traumatic Experiences—Revised (HURTE-R; La Greca et al., 2010; Vernberg et al., 1996) was used to assess children’s hurricane exposure (i.e., perceived and actual life threat during the hurricane) and hurricane-related stressors (i.e., loss/disruption immediately following the hurricane and ongoing loss/disruption). The first section of the HURTE-R contains one item that measures children’s perceptions of life threat (i.e., “Did you think you were going to die during the hurricane?”), which has been widely used in child disaster research and is a predictor of children’s post-disaster PTSS (e.g., La Greca et al., 2010; Yelland et al., 2010). The first section of the HURTE-R also contains six Yes/No items that assess children’s exposure to actual life-threatening events that occurred during the hurricane (e.g., “Did a door or window break in the place you stayed during the hurricane?”). These items are summed to obtain a score of actual life-threatening events, which can range from 0 to 6. The HURTE-R also contains 10 Yes/No items that assess hurricane-related stressors that occur during the recovery period. Specifically, it measures immediate loss and disruption that ensues following the hurricane (e.g., “Did you move to a new place because of the hurricane?”) with a potential score range of 0–10. The third section of the HURTE-R contains six Yes/No items that assess children’s ongoing loss and disruption (e.g., “Are you still living in a house that has a roof that leaks because of the hurricane?”). As in prior research (e.g., La Greca et al., 2010), the six Yes/No items are summed to calculate a total score, which can range from 0 to 6. Major Life Events Occurring During the Recovery Period The short form of the Life Events Checklist (LEC) was used to assess major life events that occurred during the recovery period (i.e., since the hurricane occurred). The short form of the LEC has been used in previous disaster research (e.g., La Greca et al., 1996, 2010), and contains 14 items that represent major life events, such as the death or illness of a family member, parental separation or divorce, and the birth of a new sibling. These events may or may not be directly related to the hurricane. Children indicated (Yes/No) the occurrence of each of the life events since the hurricane (Time 1) or in the prior 6 months (Time 2). For each time point, a sum of stressful life events was computed, with a potential score range of 0–14. The test–retest reliability of the LEC over a 2-week interval has been shown to be .72, and several studies have provided support for the validity of the LEC as a measure of the amount of life events experienced by adolescents (e.g., Greenberg et al., 1983) and elementary school-aged children (e.g., La Greca et al., 2010). PTSS Children’s PTSS over the past month were assessed using the UCLA Posttraumatic Stress Disorder Reaction Index for DSM IV—Revised (PTSD-RI-R; Pynoos et al., 1998; Steinberg et al., 2004). The PTSD-RI-R evaluates PTSS based on the criteria in the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR;American Psychiatric Association, 2000). A simplified 3-point response scale was used in this study, which matches the scoring used in prior disaster studies (e.g., La Greca et al., 2010). This simplified 3-point response scale (i.e., 0 = none of the time, 2 = some of the time, 4 = most of the time) was designed to match the original 5-point response scale for the measure. Eighteen items are used to compute a Total PTSS Severity Score. For two of these eighteen items (i.e., “I have trouble feeling happiness or love” and “I have trouble feeling sadness or anger”) only the item with the highest score was used to compute the Total PTSS Severity Score. The Total PTSS Severity Score ranges from 0–68 and indicates whether PTSS is doubtful (0—11), mild (12—24), moderate (25–39), severe (40–59), or very severe (60+) (Steinberg et al., 2004). The PTSD-RI-R has demonstrated strong convergent validity, test–retest reliability, and internal consistency (Steinberg et al., 2004). Internal consistency was .86 in Yelland et al. (2010) and La Greca et al. (2010). Internal consistency in this study was .88 at Time 1 and .89 at Time 2. PTSS Non-Sleep Score For analyses examining relationships between PTSS and sleep problems, two items that reflected sleep difficulties (i.e., “I have dreams about the hurricane or other bad dreams” and “I have trouble going to sleep or I wake up often during the night”) were removed from the Total PTSS Severity Score. This approach is consistent with how existing longitudinal studies on children’s post-disaster reactions addressed item overlap. Four studies excluded sleep-related PTSS items from their analyses (Brown et al., 2011; Fan et al., 2017; Zhou et al., 2014, 2017). Zhou et al. (2014, 2017) reported that the exclusion of these sleep-related PTSS items did not alter model fit. Iwadare et al. (2014) reported no overlap between PTSS items and the assessed sleep items. The variable created for this study, PTSS Non-Sleep, had a potential score range of 0–60. Cronbach’s alpha was .86 at Time 1 and .87 at Time 2. Sleep Problems Children’s sleep problems in the past 30 days were assessed using three items (i.e., “Have a hard time falling asleep,” “Have trouble staying asleep,” and “Sleep more than usual”). Nightmares were not assessed. Items were rated using the scale in the Youth Self Report (Achenbach & Rescorla, 2001). Children reported how often they experienced sleep problems over the past 30 days (0 = Not True, 1 = Somewhat/Sometimes True, and 2 = Very True or Often True). Scores on two items reflected insufficient sleep (i.e., “Have a hard time falling asleep” and “Have trouble staying asleep”), and one item reflected excessive sleep (i.e., “Sleep more than usual”). A sleep summary score was computed by summing the scores on these three items. This sleep summary score had a potential range of 0–6. The sleep items used in this study align with existing longitudinal studies that assessed difficulties falling or staying asleep (Brown et al., 2011; Fan et al., 2017; Zhou et al., 2014, 2017) and sleep duration (Fan et al., 2017; Iwadare et al., 2014) in children post-disaster. Analytic Plan All analyses were conducted with SPSS Statistics (Version 24). To address the first aim, we evaluated sleep problem frequency by calculating the percentage of children who responded “Somewhat/Sometimes True” or “Very True or Often True” to each type of sleep problem (i.e., hard time falling asleep, trouble staying asleep, sleep more than usual). To examine the persistence of sleep problems, we calculated correlation coefficients for each type of sleep problem and overall sleep problems at Time 1 and Time 2. To address the second and third aims, we used hierarchical regression analyses to evaluate risk factors for children’s sleep problems (Aim 2) and to evaluate bidirectional relationships between children’s sleep problems and PTSS over time (Aim 3), controlling for other risk factors (i.e., child characteristics, hurricane-related life threat, hurricane-related loss/disruption, life events, and Time 1 PTSS Non-Sleep or sleep problem scores). First, we examined whether Time 1 PTSS Non-Sleep scores predicted Time 2 sleep problems (see Table II). With children’s Time 2 sleep problems as the outcome, we evaluated the following risk factors: demographic characteristics (i.e., female gender, age, minoritized ethnic/racial group status) (Step 1), reports of hurricane-related life threat (Step 2), hurricane-related loss/disruption (Step 3), life events (Step 4), Time 1 PTSS Non-Sleep (Step 5), and Time 1 sleep problems (Step 6).The final step (i.e., Step 6) controls for Time 1 sleep problems, since Time 1 sleep problems are a potential risk factor for Time 2 sleep problems. Table II. Hierarchical Regression Predicting Children’s Time 2 Sleep Problems (15 Months Post-Disaster) Study variable . Step β . Final β . ΔF . ΔR2 . Step 1 4.81** .05  Female .12* .08  Age −.19** −.15*  Minoritized ethnic/racial group .06 .02 Step 2 .11 .001  Perceived life threat −.02 −.10  Actual life threat −.02 −.12 Step 3 1.87 .01  Immediate loss/disruption .14 .03  Ongoing loss/disruption −.03 −.02 Step 4 3.30 .01  Life events .13 .05 Step 5 11.70*** .04  Time 1 PTSS Non-Sleep score (8 months post-disaster) .26*** .17* Step 6 9.07** .03  Time 1 sleep problems (8 months post-disaster) .20** .20** Study variable . Step β . Final β . ΔF . ΔR2 . Step 1 4.81** .05  Female .12* .08  Age −.19** −.15*  Minoritized ethnic/racial group .06 .02 Step 2 .11 .001  Perceived life threat −.02 −.10  Actual life threat −.02 −.12 Step 3 1.87 .01  Immediate loss/disruption .14 .03  Ongoing loss/disruption −.03 −.02 Step 4 3.30 .01  Life events .13 .05 Step 5 11.70*** .04  Time 1 PTSS Non-Sleep score (8 months post-disaster) .26*** .17* Step 6 9.07** .03  Time 1 sleep problems (8 months post-disaster) .20** .20** Note. F = 4.46***; total R2 = .15 for the full model with all six steps entered. Step β refers to the standardized coefficients for the variables in each step when that step was entered into the regression. Final β refers to the standardized coefficients for the variables when all six steps were entered into the regression. * p  ≤ .05; ** p  ≤ .01; *** p  ≤ .001. Open in new tab Table II. Hierarchical Regression Predicting Children’s Time 2 Sleep Problems (15 Months Post-Disaster) Study variable . Step β . Final β . ΔF . ΔR2 . Step 1 4.81** .05  Female .12* .08  Age −.19** −.15*  Minoritized ethnic/racial group .06 .02 Step 2 .11 .001  Perceived life threat −.02 −.10  Actual life threat −.02 −.12 Step 3 1.87 .01  Immediate loss/disruption .14 .03  Ongoing loss/disruption −.03 −.02 Step 4 3.30 .01  Life events .13 .05 Step 5 11.70*** .04  Time 1 PTSS Non-Sleep score (8 months post-disaster) .26*** .17* Step 6 9.07** .03  Time 1 sleep problems (8 months post-disaster) .20** .20** Study variable . Step β . Final β . ΔF . ΔR2 . Step 1 4.81** .05  Female .12* .08  Age −.19** −.15*  Minoritized ethnic/racial group .06 .02 Step 2 .11 .001  Perceived life threat −.02 −.10  Actual life threat −.02 −.12 Step 3 1.87 .01  Immediate loss/disruption .14 .03  Ongoing loss/disruption −.03 −.02 Step 4 3.30 .01  Life events .13 .05 Step 5 11.70*** .04  Time 1 PTSS Non-Sleep score (8 months post-disaster) .26*** .17* Step 6 9.07** .03  Time 1 sleep problems (8 months post-disaster) .20** .20** Note. F = 4.46***; total R2 = .15 for the full model with all six steps entered. Step β refers to the standardized coefficients for the variables in each step when that step was entered into the regression. Final β refers to the standardized coefficients for the variables when all six steps were entered into the regression. * p  ≤ .05; ** p  ≤ .01; *** p  ≤ .001. Open in new tab Next, we evaluated whether Time 1 sleep problems predicted Time 2 PTSS Non-Sleep (see Table III). With children’s Time 2 PTSS Non-Sleep as the outcome, we evaluated the following risk factors: demographic characteristics (Step 1), reports of hurricane-related life threat (Step 2), hurricane-related loss/disruption (Step 3), life events (Step 4), Time 1 sleep problems (Step 5), and Time 1 PTSS Non-Sleep (Step 6). The final step (i.e., Step 6) controls for Time 1 PTSS Non-Sleep, since Time 1 PTSS are a potential risk factor for Time 2 PTSS. Table III. Hierarchical Regression Predicting Children’s Time 2 PTSS (15 Months Post-Disaster) Study variable . Step β . Final β . ΔF . ΔR2 . Step 1 8.90*** .09  Female .06 −.03  Age −.23*** −.14**  Minoritized ethnic/racial group .20*** .09 Step 2 14.50*** .09  Perceived life threat .22*** .07  Actual life threat .17** −.01 Step 3 6.16** .04  Immediate loss/disruption .21** .01  Ongoing loss/disruption .02 .02 Step 4 10.01** .03  Life events .20** .07 Step 5 16.20*** .05  Time 1 sleep problems (8 months post-disaster) .23*** .08 Step 6 58.00*** .13  Time 1 PTSS Non-Sleep score (8 months post-disaster) .49*** .49*** Study variable . Step β . Final β . ΔF . ΔR2 . Step 1 8.90*** .09  Female .06 −.03  Age −.23*** −.14**  Minoritized ethnic/racial group .20*** .09 Step 2 14.50*** .09  Perceived life threat .22*** .07  Actual life threat .17** −.01 Step 3 6.16** .04  Immediate loss/disruption .21** .01  Ongoing loss/disruption .02 .02 Step 4 10.01** .03  Life events .20** .07 Step 5 16.20*** .05  Time 1 sleep problems (8 months post-disaster) .23*** .08 Step 6 58.00*** .13  Time 1 PTSS Non-Sleep score (8 months post-disaster) .49*** .49*** Note. F = 18.95***; total R2 = .43 for the full model with all six steps entered. Step β refers to the standardized coefficients for the variables in each step when that step was entered into the regression. Final β refers to the standardized coefficients for the variables when all six steps were entered into the regression. * p  ≤ .05; ** p  ≤ .01; *** p  ≤ .001. Open in new tab Table III. Hierarchical Regression Predicting Children’s Time 2 PTSS (15 Months Post-Disaster) Study variable . Step β . Final β . ΔF . ΔR2 . Step 1 8.90*** .09  Female .06 −.03  Age −.23*** −.14**  Minoritized ethnic/racial group .20*** .09 Step 2 14.50*** .09  Perceived life threat .22*** .07  Actual life threat .17** −.01 Step 3 6.16** .04  Immediate loss/disruption .21** .01  Ongoing loss/disruption .02 .02 Step 4 10.01** .03  Life events .20** .07 Step 5 16.20*** .05  Time 1 sleep problems (8 months post-disaster) .23*** .08 Step 6 58.00*** .13  Time 1 PTSS Non-Sleep score (8 months post-disaster) .49*** .49*** Study variable . Step β . Final β . ΔF . ΔR2 . Step 1 8.90*** .09  Female .06 −.03  Age −.23*** −.14**  Minoritized ethnic/racial group .20*** .09 Step 2 14.50*** .09  Perceived life threat .22*** .07  Actual life threat .17** −.01 Step 3 6.16** .04  Immediate loss/disruption .21** .01  Ongoing loss/disruption .02 .02 Step 4 10.01** .03  Life events .20** .07 Step 5 16.20*** .05  Time 1 sleep problems (8 months post-disaster) .23*** .08 Step 6 58.00*** .13  Time 1 PTSS Non-Sleep score (8 months post-disaster) .49*** .49*** Note. F = 18.95***; total R2 = .43 for the full model with all six steps entered. Step β refers to the standardized coefficients for the variables in each step when that step was entered into the regression. Final β refers to the standardized coefficients for the variables when all six steps were entered into the regression. * p  ≤ .05; ** p  ≤ .01; *** p  ≤ .001. Open in new tab Results Descriptive statistics for all study variables at Time 1 and Time 2 are presented in Table I. On average, children’s PTSS fell in the “mild” range at Time 1 (M = 24.04, SD = 14.87) and Time 2 (M = 19.07, SD = 14.38). Study Aim 1: Frequency and Persistence of Children’s Post-Disaster Sleep Problems Percentages of children who responded “Somewhat/Sometimes True” or “Very True or Often True” to having a hard time falling asleep, trouble staying asleep, or sleeping more than usual at Time 1 and Time 2 are reported in Table I. Approximately half of the sample endorsed having a hard time falling asleep at Time 1 and Time 2. A sizeable percentage of the sample also reported trouble staying asleep or sleeping more than usual at Time 1, although these percentages both decreased from Time 1 to Time 2. “Hard time falling asleep” was significantly correlated from Time 1 to Time 2 (r = .31, p < .001), as were “trouble staying asleep” (r = .28, p < .001) and “sleeping more than usual” (r = .16, p = .01). To examine the persistence of children’s overall sleep problems over time, we also evaluated whether the sleep summary scores were correlated. Children’s sleep-problem summary scores were significantly correlated from Time 1 to Time 2 (r = .28, p < .001). Study Aim 2: Risk Factors for Children’s Post-Disaster Sleep Problems Female gender (β = .12), younger age (β = −.19), and higher Time 1 PTSS Non-Sleep scores (β = .26) predicted higher levels of Time 2 sleep problems (see Table II). The addition of Time 1 PTSS Non-Sleep (Step 5) explained an additional 4% of the variance in Time 2 sleep problems, after controlling for the other predictors. When Time 1 sleep problems were entered in the final step of the regression (Step 6) (β = .20), younger age and Time 1 PTSS Non-Sleep scores remained significant predictors of Time 2 sleep problems. The addition of Time 1 sleep problems in the final step explained an additional 3% of the variance in Time 2 sleep problems, after controlling for the other predictors. Overall, younger age, higher levels of Time 1 PTSS Non-Sleep, and higher levels of Time 1 sleep problems were associated with higher levels of Time 2 sleep problems. Study Aim 3: Bidirectional Relationship between Sleep Problems and PTSS over Time Younger age (β = −.23), minoritized ethnic/racial group status (β = .20), higher perceived life threat (β = .22) and actual life threat (β = .17), higher loss/disruption immediately following the hurricane (β =.21), higher numbers of stressful life events (β = .20), and higher Time 1 sleep problems (β = .23), predicted higher levels of Time 2 PTSS Non-Sleep scores (see Table III). The addition of Time 1 sleep problems (Step 5) explained an additional 5% of the variance in Time 2 PTSS Non-Sleep, after controlling for the other predictors. The addition of Time 1 PTSS Non-Sleep in the final step explained an additional 13% of the variance in Time 2 PTSS Non-Sleep, after controlling for the other predictors. However, when Time 1 PTSS Non-Sleep was entered in the final step of the regression (β = .49), only younger age remained a significant predictor. Therefore, Time 1 sleep problems did not significantly predict Time 2 PTSS Non-Sleep scores in the final model. Discussion This study addresses gaps in our understanding of the relationship between sleep problems and PTSS among children exposed to disasters. Overall, a sizable minority of children reported sleep problems, and these sleep problems persisted over time into the second-year post-disaster (i.e., 15 months after Hurricane Ike). Risk factors for Time 2 sleep problems (15 months post-disaster) included younger age, as well as Time 1 sleep problems and Time 1 PTSS (excluding sleep items) at 8 months post-disaster. Regarding the bidirectional relationship between sleep and PTSS, PTSS significantly predicted later sleep problems, but sleep problems did not significantly predict later PTSS. Below, we discuss each of these findings in detail. In our study, a sizeable minority of children reported sleep problems at 8 months post-disaster, and these sleep problems persisted over a year post-disaster. Our findings add to a growing body of literature indicating that disasters may impact sleep (Rifkin et al., 2018). Rifkin et al. (2018) systematically reviewed epidemiological studies on sleep and disasters. Across studies, diminished total sleep times and sleep disruptions were reported following disasters. Similarly, children in our study reported difficulties with falling asleep, staying asleep, and sleeping more than usual. Proportions of children reporting sleep problems in our study were similar to those observed in some longitudinal studies of children post-disaster (e.g., Fan et al., 2017) and different from others (e.g., Geng et al., 2013). For example, in a study of children after the Wenchuan earthquake, Fan et al. (2017) found that 27% of their sample reported difficulty falling asleep at least three times per week, when assessed 12 months after the Wenchuan earthquake. In contrast, in our study, 24% of children responded “Very True or Often True” to the question about difficulty falling asleep at 15 months post-Hurricane Ike. Geng et al. (2013) observed lower rates of difficulty staying asleep at 12, 18, 24, and 30 months post-disaster in their study of adolescents after the Wenchuan earthquake. However, both Fan et al. (2017) and Geng et al. (2013) assessed older-aged samples (mean age 15.6 and 15 years, respectively), while our study focused on elementary school-aged youth. Further, their sleep assessments differed from those used in the current study. Developing consensus guidelines for assessing sleep after acute traumatic events will be important for the trauma field. Findings indicated that younger age was a risk factor for sleep problems. This may be due, in part, to the fact that younger children experience sleep problems more often, regardless of trauma exposure. For example, sleep problems are common among children, generally (Fricke-Oerkermann et al., 2007; Kovachy et al., 2013). In a study of 832 children ages 8–11, approximately 30–40% had difficulty falling asleep (Fricke-Oerkermann et al., 2007). One year later, 60% of these children continued to report difficulties falling asleep. A recent systematic review similarly found that approximately 25% of preschool and school-aged children experience sleep problems, including difficulties falling and staying asleep (Newton et al., 2020). Similar to our study, Tang et al. (2018b) and Zhou et al. (2014, 2017) found PTSS to be a risk factor for sleep problems. In this study, children were assessed closer in time to the disaster event than in past research (e.g., Brown et al., 2011; Fan et al., 2017; Geng et al., 2013; Zhou et al., 2014, 2017). This is desirable, as the longer the lag time between the disaster and the assessment point, the more difficult it is to rule out the influence of other variables on children’s sleep. Results indicated that non-remitting PTSS may serve to maintain or exacerbate a child’s sleep problems, thereby negatively impacting their clinical profile. This is important given the impact sleep has on children’s functioning—including their physical growth and development, school performance, and psychological adjustment. Inadequate sleep and poor sleep quality are associated with childhood obesity (Fatima et al., 2016), learning impairment (Curcio et al., 2006), and socio-emotional problems (Spruyt, 2019). In our key analyses, although PTSS predicted sleep problems, sleep problems did not significantly predict later PTSS. Time 1 sleep problems initially predicted Time 2 PTSS but were no longer significant after Time 1 PTSS scores were added to the model. This was contrary to our hypothesis; however, results suggest that sleep problems may be a potential pathway through which early-onset PTSS affects later PTSS. This question of directionality has not been adequately evaluated in previous studies of child disaster victims. Longitudinal studies that found sleep problems to be a predictor of PTSS have mainly been conducted in adolescent or adult samples (Fan et al., 2017; Geng et al., 2013; Zhou et al., 2014). Our data suggest that the association between PTSS and subsequent sleep problems may be complex. The high stability of PTSS over time, coupled with the strong association between PTSS and sleep problems at Time 1 may have made it difficult to observe an underlying association. These results point to the need for further and more comprehensive evaluation of the role of sleep problems in the development and maintenance of PTSS in children post-disaster. Sleep problems may be a clinical indication of the need for a more thorough trauma or PTSS assessment. Regarding study limitations, our measures featured a limited number of items assessing sleep problems. More evidence-based, pediatric sleep measures are recommended for future research (Lewandowski et al., 2011). For example, it would be helpful to understand sleep onset, sleep-wake activity, sleep efficiency, and number of awakenings (Kovachy et al., 2013). Furthermore, sleep problems were measured via self-report. Future studies that use parent report, actigraphy, and/or polysomnography may illuminate the nature and prevalence of sleep problems after disasters with greater accuracy (Kovachy et al., 2013; Meltzer et al., 2012). Also, this study did not include pre-disaster measures of functioning. Additionally, the response rate was low for this study, which is common in the disaster literature due to the difficulties of disaster-based research (see Pfefferbaum et al., 2013 for a review). Student displacement following Hurricane Ike (KHOU 11 News Houston, 2009) may have contributed to the low response rate. Given this, our findings may underestimate child difficulties, as displaced families and those coping with high levels of stressors may have been less likely to participate in this study. It is not possible to determine whether some of the children in our sample were already experiencing difficulties with sleep, PTSS, or other mental health symptoms prior to the disaster. It is also unclear whether children’s post-disaster sleep problems were attributable to disaster exposure or some other cause (e.g., a medical condition, anxiety unrelated to the disaster). Further, this study did not include a measure of socioeconomic status; thus, we were not able to control for socioeconomic status in analyses. Overall, our findings demonstrate that sleep problems and PTSS are reported by children exposed to disasters. This adds to the growing body of literature highlighting the various downstream sequelae of disasters (Self-Brown et al., 2017). Unfortunately, the humanitarian response literature has largely ignored sleep health (Rifkin et al., 2018). Findings underscore the need for sleep health to be included in post-disaster recovery planning. Providers treating youth post-disaster should assess for and address both sleep problems and PTSS following disasters. Supplementary Data Supplementary data can be found at: https://academic.oup.com/jpepsy. Acknowledgments The authors wish to thank Barbora Hoskova for her feedback on the manuscript. Funding This work was supported by the National Institutes of Mental Health [1R03MH113849-01 to B. S. L. and A. M. L. G.]; a fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine; and funds from the Buehler Sesquicentennial Assistant Professor Chair Position at Boston College. Conflicts of interest: None declared. References Achenbach T. M. , Rescorla L. A. ( 2001 ). Manual for the ASEBA school-age forms & profiles: an integrated system of multi-informant assessment . University of Vermont , Research Center for Children, Youth, & Families . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC American Psychiatric Association. ( 2000 ). Diagnostic and statistical manual of mental disorders . (4th edn, Text Revision). American Psychiatric Association . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Atkinson M. J. ( 2003 ). Does sleep disturbance mediate the health impacts of post-traumatic stress disorder? Sleep Medicine , 4 , 591 – 592 . https://doi.org/10.1016/j.sleep.2003.09.003 Google Scholar Crossref Search ADS PubMed WorldCat Berg R. ( 2009 ). Tropical cyclone report: Hurricane Ike (AL092008) . National Oceanic and Atmospheric Administration, National Hurricane Center . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Blake E. S. , Landsea C. W. , Gibney E. J. ( 2011 ). The deadliest, costliest, and most intense United States tropical cyclones from 1851 to 2010 (and other frequently requested hurricane facts) (NOAA Technical Memorandum NWS NHC-6) . National Oceanic and Atmospheric Association . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Bollen K. A. , Bauldry S. ( 2011 ). Three Cs in measurement models: Causal indicators, composite indicators, and covariates . Psychological Methods , 16 , 265 – 284 . Google Scholar Crossref Search ADS PubMed WorldCat Bollen K. , Lennox R. ( 1991 ). Conventional wisdom on measurement: A structural equation perspective . Psychological Bulletin , 110 , 305 – 314 . Google Scholar Crossref Search ADS WorldCat Brown T. H. , Mellman T. A. , Alfano C. A. , Weems C. F. ( 2011 ). Sleep fears, sleep disturbance, and PTSD symptoms in minority youth exposed to Hurricane Katrina . Journal of Traumatic Stress , 24 , 575 – 580 . https://doi.org/10.1002/jts.20680 Google Scholar Crossref Search ADS PubMed WorldCat Curcio G. , Ferrara M. , De Gennaro L. ( 2006 ). Sleep loss, learning capacity and academic performance . Sleep Medicine Reviews , 10 , 323 – 337 . Google Scholar Crossref Search ADS PubMed WorldCat Dahl R. E. ( 2007 ). Sleep and the developing brain . Sleep , 30 , 1079 – 1080 . https://doi.org/10.1093/sleep/30.9.1079 Google Scholar Crossref Search ADS PubMed WorldCat Fan F. , Zhou Y. , Liu X. ( 2017 ). Sleep disturbance predicts posttraumatic stress disorder and depressive symptoms: a cohort study of Chinese adolescents . Journal of Clinical Psychiatry , 78 , 882 – 888 . https://doi.org/10.4088/JCP.15m10206 Google Scholar Crossref Search ADS PubMed WorldCat Fatima Y. , Doi S. A. R. , Mamun A. A. ( 2016 ). Sleep quality and obesity in young subjects: a meta-analysis . Obesity Reviews , 17 , 1154 – 1166 . https://doi.org/10.1111/obr.12444 Google Scholar Crossref Search ADS PubMed WorldCat Fayers P. M. , Hand D. J. ( 1997 ). Factor analysis, causal indicators and quality of life . Quality of Life Research , 6 , 139 – 150 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Fricke-Oerkermann L. , Plück J. , Schredl M. , Heinz K. , Mitschke A. , Wiater A. , Lehmkuhl G. ( 2007 ). Prevalence and course of sleep problems in childhood . Sleep , 30 , 1371 – 1377 . https://doi.org/10.1093/sleep/30.10.1371 Google Scholar Crossref Search ADS PubMed WorldCat Geng F. , Fan F. , Mo L. , Simandl I. , Liu X. ( 2013 ). Sleep problems among adolescent survivors following the 2008 Wenchuan earthquake in China: A cohort study . Journal of Clinical Psychiatry , 74 , 67 – 74 . https://doi.org/10.4088/JCP.12m07872 Google Scholar Crossref Search ADS PubMed WorldCat Greenberg M. T. , Siegel J. M. , Leitch C. J. ( 1983 ). The nature and importance of attachment relationships to parents and peers during adolescence . Journal of Youth and Adolescence , 12 , 373 – 386 . https://doi.org/10.1007/BF02088721 Google Scholar Crossref Search ADS PubMed WorldCat Hardin A. M. , Chang J. C.-J. , Fuller M. A. , Torkzadeh G. ( 2011 ). Formative measurement and academic research: in search of measurement theory . Educational and Psychological Measurement , 71 , 281 – 305 . Google Scholar Crossref Search ADS WorldCat Iwadare Y. , Usami M. , Ushijima H. , Tanaka T. , Watanabe K. , Kodaira M. , Harada M. , Tanaka H. , Sasaki Y. , Saito K. ( 2014 ). Changes in traumatic symptoms and sleep habits among junior high school students after the Great East Japan earthquake and tsunami . Sleep and Biological Rhythms , 12 , 53 – 61 . https://doi.org/10.1111/sbr.12047 Google Scholar Crossref Search ADS WorldCat Kar N. ( 2009 ). Psychological impact of disasters on children: review of assessment and interventions . World Journal of Pediatrics , 5 , 5 – 11 . Google Scholar Crossref Search ADS PubMed WorldCat KHOU 11 News Houston. ( 2009 , October 23). School Zone: Galveston ISD revving up for school after Ike. https://www.khou.com/article/news/local/school-zone-galveston-isd-revving-up-for-school-after-ike/285-413277563 Kovachy B. , O'Hara R. , Hawkins N. , Gershon A. , Primeau M. M. , Madej J. , Carrion V. ( 2013 ). Sleep disturbance in pediatric PTSD: Current findings and future directions . Journal of Clinical Sleep Medicine , 09 , 501 – 510 . https://doi.org/10.5664/jcsm.2678 Google Scholar Crossref Search ADS WorldCat Kronenberg M. E. , Hansel T. C. , Brennan A. M. , Osofsky H. J. , Osofsky J. D. , Lawrason B. ( 2010 ). Children of Katrina: Lessons learned about postdisaster symptoms and recovery patterns . Child Development , 81 , 1241 – 1259 . Google Scholar Crossref Search ADS PubMed WorldCat La Greca A. M. , Lai B. S. , Joormann J. , Auslander B. B. , Short M. A. ( 2013 ). Children's risk and resilience following a natural disaster: Genetic vulnerability, posttraumatic stress, and depression . Journal of Affective Disorders , 151 , 860 – 867 . Google Scholar Crossref Search ADS PubMed WorldCat La Greca A. M. , Silverman W. K. , Lai B. , Jaccard J. ( 2010 ). Hurricane-related exposure experiences and stressors, other life events, and social support: Concurrent and prospective impact on children’s persistent posttraumatic stress symptoms . Journal of Consulting and Clinical Psychology , 78 , 794 – 805 . https://doi.org/10.1037/a0020775 Google Scholar Crossref Search ADS PubMed WorldCat La Greca A. M. , Silverman W. K. , Vernberg E. M. , Prinstein M. J. ( 1996 ). Symptoms of posttraumatic stress in children after Hurricane Andrew: A prospective study . Journal of Consulting and Clinical Psychology , 64 , 712 – 723 . Google Scholar Crossref Search ADS PubMed WorldCat Lai B. S. , Beaulieu B. , Ogokeh C. E. , Self-Brown S. , Kelley M. L. ( 2015 ). Mother and child reports of hurricane-related stressors: Data from a sample of families exposed to Hurricane Katrina . Child and Youth Care Forum , 44 , 549 – 565 . Google Scholar Crossref Search ADS PubMed WorldCat Lai B. S. , La Greca A. M. , Auslander B. A. , Short M. B. ( 2013 ). Children’s symptoms of posttraumatic stress and depression after a natural disaster: Comorbidity and risk factors . Journal of Affective Disorders , 146 , 71 – 78 . https://doi.org/10.1016/j.jad.2012.08.041 Google Scholar Crossref Search ADS PubMed WorldCat Lai B. S. , Lewis R. , Livings M. S. , La Greca A. M. , Esnard A.-M. ( 2017 ). Posttraumatic stress symptom trajectories among children after disaster exposure: A review . Journal of Traumatic Stress , 30 , 571 – 582 . https://doi.org/10.1002/jts.22242 Google Scholar Crossref Search ADS PubMed WorldCat Lewandowski A. S. , Toliver-Sokol M. , Palermo T. M. ( 2011 ). Evidence-based review of subjective pediatric sleep measures . Journal of Pediatric Psychology , 36 , 780 – 793 . Google Scholar Crossref Search ADS PubMed WorldCat McDermott B. , Cobham V. , Berry H. , Kim B. ( 2014 ). Correlates of persisting posttraumatic symptoms in children and adolescents 18 months after a cyclone disaster . Australian & New Zealand Journal of Psychiatry , 48 , 80 – 86 . https://doi.org/10.1177/0004867413500349 Google Scholar Crossref Search ADS WorldCat Meltzer L. J. , Montgomery-Downs H. E. , Insana S. P. , Walsh C. M. ( 2012 ). Use of actigraphy for assessment in pediatric sleep research . Sleep Medicine Reviews , 16 , 463 – 475 . https://doi.org/10.1016/j.smrv.2011.10.002 Google Scholar Crossref Search ADS PubMed WorldCat Newton A. T. , Honaker S. M. , Reid G. J. ( 2020 ). Risk and protective factors and processes for behavioral sleep problems among preschool and early school-aged children: A systematic review . Sleep Medicine Reviews , 52 , 101303 . Google Scholar Crossref Search ADS PubMed WorldCat Peek L. , Abramson D.M. , Cox R.S. , Fothergill A. , Tobin J. ( 2017 ). Children and disasters. In Rodríguez H. , Donner W. , Trainor J. E. (Eds.), Handbook of Disaster Research (2nd edn, pp. 243 – 257 ). Springer International Publishing . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Pfefferbaum B. , Weems C. F. , Scott B. G. , Nitiéma P. , Noffsinger M. A. , Pfefferbaum R. L. , Varma V. , Chakraburtty A. ( 2013 ). Research methods in child disaster studies: A review of studies generated by the September 11, 2001, terrorist attacks; the 2004 Indian Ocean tsunami; and Hurricane Katrina . Child & Youth Care Forum , 42 , 285 – 337 . Google Scholar Crossref Search ADS PubMed WorldCat Pynoos R. S. , Rodriguez N. , Steinberg A. , Stuber M. , Frederick C. ( 1998 ). The University of California at Los Angeles posttraumatic stress disorder reaction index for DSM-IV (UCLA PTSD-RI-R (Revision 1) . University of California . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Rifkin D. I. , Long M. W. , Perry M. J. ( 2018 ). Climate change and sleep: A systematic review of the literature and conceptual framework . Sleep Medicine Reviews , 42 , 3 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat Self-Brown S. , Lai B. , Patterson A. , Glasheen T. ( 2017 ). The impact of natural disasters on youth: A focus on emerging research beyond internalizing disorders . Current Psychiatry Reports , 19 , 53 . Google Scholar Crossref Search ADS PubMed WorldCat Spilsbury J. C. ( 2009 ). Sleep as a mediator in the pathway from violence-induced traumatic stress to poorer health and functioning: A review of the literature and proposed conceptual model . Behavioral Sleep Medicine , 7 , 223 – 244 . Google Scholar Crossref Search ADS PubMed WorldCat Spoormaker V. I. , Montgomery P. ( 2008 ). Disturbed sleep in post-traumatic stress disorder: Secondary symptom or core feature? Sleep Medicine Reviews , 12 , 169 – 184 . Google Scholar Crossref Search ADS PubMed WorldCat Spruyt K. ( 2019 ). A review of developmental consequences of poor sleep in childhood . Sleep Medicine , 60 , 3 – 12 . https://doi.org/10.1016/j.sleep.2018.11.021 Google Scholar Crossref Search ADS PubMed WorldCat Steinberg A. M. , Brymer M. J. , Decker K. B. , Pynoos R. S. ( 2004 ). The University of California at Los Angeles post-traumatic stress disorder reaction index . Current Psychiatry Reports , 6 , 96 – 100 . https://doi.org/10.1007/s11920-004-0048-2 Google Scholar Crossref Search ADS PubMed WorldCat Tang W. , Lu Y. , Xu J. ( 2018 a). Post-traumatic stress disorder, anxiety and depression symptoms among adolescent earthquake victims: Comorbidity and associated sleep-disturbing factors . Social Psychiatry and Psychiatric Epidemiology , 53 , 1241 – 1251 . Google Scholar Crossref Search ADS PubMed WorldCat Tang W. , Lu Y. , Yang Y. , Xu J. ( 2018 b). An epidemiologic study of self-reported sleep problems in a large sample of adolescent earthquake survivors: The effects of age, gender, exposure, and psychopathology . Journal of Psychosomatic Research , 113 , 22 – 29 . Google Scholar Crossref Search ADS PubMed WorldCat Turnbull K. , Reid G. J. , Morton J. B. ( 2013 ). Behavioral sleep problems and their potential impact on developing executive function in children . Sleep , 36 , 1077 – 1084 . Google Scholar Crossref Search ADS PubMed WorldCat U.S. Department of Education. ( 2010 ). Galveston independent school district. https://www2.ed.gov/programs/magnet/2010/galveston.pdf United Nations Children’s Fund (UNICEF). ( 2013 ). Climate change, food systems and children: A case for greater action. https://www.unicef.org.uk/publications/climate-change-food-systems-and-children-a-case-for-greater-action/ Usami M. , Iwadare Y. , Kodaira M. , Watanabe K. , Aoki M. , Katsumi C. , Matsuda K. , Makino K. , Iijima S. , Harada M. , Tanaka H. , Sasaki Y. , Tanaka T. , Ushijima H. , Saito K. , Saito K. ( 2013 ). Sleep duration among children 8 months after the 2011 Japan earthquake and tsunami . PLoS One , 8 , e65398 .https://doi.org/10.1371/journal.pone.0065398 Google Scholar Crossref Search ADS PubMed WorldCat Vernberg E. M. , La Greca A. M. , Silverman W. K. , Prinstein M. J. ( 1996 ). Prediction of posttraumatic stress symptoms in children after Hurricane Andrew . Journal of Abnormal Psychology , 105 , 237 – 248 . https://doi.org/10.1037/0021-843X.105.2.237 Google Scholar Crossref Search ADS PubMed WorldCat Wamser-Nanney R. , Chesher R. E. ( 2018 ). Trauma characteristics and sleep impairment among trauma-exposed children . Child Abuse and Neglect , 76 , 469 – 479 . Google Scholar Crossref Search ADS PubMed WorldCat Yelland C. , Robinson P. , Lock C. , La Greca A. M. , Kokegei B. , Ridgway V. , Lai B. ( 2010 ). Bushfire impact on youth . Journal of Traumatic Stress , 23 , 274 – 277 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Zhou X. , Wu X. , An Y. , Fu F. ( 2014 ). Longitudinal relationships between posttraumatic stress symptoms and sleep problems in adolescent survivors following the Wenchuan earthquake in China . PLoS One , 9 , e104470 .https://doi.org/10.1371/journal.pone.0104470 Google Scholar Crossref Search ADS PubMed WorldCat Zhou X. , Wu X. , Chen Q. , Zhen R. ( 2017 ). Why did adolescents have sleep problems after earthquakes? Understanding the role of traumatic exposure, fear, and PTSD . Scandinavian Journal of Psychology , 58 , 221 – 227 . https://doi.org/10.1111/sjop.12366 Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2020. 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/open_access/funder_policies/chorus/standard_publication_model)
TI - Sleep Problems and Posttraumatic Stress: Children Exposed to a Natural Disaster
JF - Journal of Pediatric Psychology
DO - 10.1093/jpepsy/jsaa061
DA - 2020-10-01
UR - https://www.deepdyve.com/lp/oxford-university-press/sleep-problems-and-posttraumatic-stress-children-exposed-to-a-natural-wY1xSDSWIc
SP - 1016
EP - 1026
VL - 45
IS - 9
DP - DeepDyve
ER -