Introduction
On March 11, 2020, the novel coronavirus disease (COVID-19) was declared a pandemic by the World Health Organization[31]. To mitigate against the spread of the virus, communities across the United States instituted abrupt closures of schools, childcare, community services, workplaces, and face-to-face health care services[30; 40]. These social disruptions pose a potential threat to the health and well-being of children and families due to a myriad of ripple effects including financial insecurity, reduced access to health care, changes in daily routines and structures, and caregiving burden[6; 28; 45; 46]. Youth with chronic pain and their families may be vulnerable to these secondary effects because of preexisting sleep disturbances, elevated anxiety/depressive symptoms, parenting stress, and/or economic strain[2; 9; 11; 17; 24; 26; 35; 36; 42]. However, the extent to which COVID-19 is impacting youth and parent health and well-being is largely unknown.
Recently, Prime and colleagues[39] proposed a conceptual framework outlining processes of risk and resilience within families during the COVID-19 pandemic. The framework is based on systemic models of human development and family functioning, and existing literature on family adaptation to historical adversities. Prime and colleagues[39] propose that there will be considerable individual variability in family adaptation to the pandemic; many families are expected to successfully adjust to these challenges, while others will experience significant declines in their health and well-being over time. The combination of preexisting risk factors in addition to experiencing pandemic-related social disruptions (i.e., greater cumulative risk) is expected to distinguish between these sub-groups. Preexisting sociodemographic factors include experiencing lower socioeconomic status, and/or identifying as Black, Indigenous and/or People of Color (BIPOC)[39]. Social disruptions that may have distinct and variable impacts on families include: 1) secondary economic stress (i.e., job loss, financial insecurity), and 2) direct exposures to COVID-19 (i.e., suspected/confirmed COVID-19 diagnosis, being a front-line worker) that increase the burden of social distancing and confinement practices[39]. To our knowledge, there are not any published studies that have examined change over time in health and well-being among youth with chronic pain and their parents in the context of this pandemic.
To address this gap, we conducted a mixed-methods study to: 1) describe direct exposures to COVID-19 (i.e., suspected or confirmed COVID-19 diagnosis, being a front-line worker) and secondary economic stress (i.e., job loss, financial insecurity) experienced by parents and families, 2) identify youth and parent symptom trajectories (pain interference, anxiety, depression and sleep disturbance) in the 4 months following the onset of the pandemic (April – July 2020), 3) determine associations between direct exposures to COVID-19 and economic stress with youth and parent symptom trajectories, and 4) summarize youth and parent perspectives on the impact of the COVID-19 pandemic on the youth’s pain problem using qualitative data. We expected that most youth and parents would experience adaptive adjustment as demonstrated by stable or improving symptom trajectories, but that symptom burden would be high. We also hypothesized that greater direct exposure to COVID-19 and secondary economic stress would modify these symptom trajectories.
Methods
Procedures
All study procedures were approved by the Institutional Review Board at the academic medical center where the study was conducted. Potential participants were recruited from two existing study cohorts: 1) Youth with chronic pain who were enrolled from one of 8 clinics (5 pain clinics and 3 gastroenterology clinics) at 5 children’s hospitals across the United States, and 2) Youth with chronic headache who were enrolled from a neurology clinic and the community. Further details on the participants and these prior studies can be found in our previous publications[27; 34].
In April 2020, youth and parents from the two existing study cohorts received an email invitation from study staff to complete surveys at four waves about the impact of the COVID-19 pandemic on their daily lives. All participants provided electronic informed consent before initiating study procedures. Youth and parents received separate links to online questionnaires administered via REDCap[12; 13]. They completed questionnaires at 4 waves: enrollment (April 2020), 4-weeks (May 2020), 8-weeks (June 2020), and 12-weeks follow-up (July 2020). Youth and parents were instructed to complete their assessments privately and independently. Upon completion of assessments at each wave, we provided parents and youth with gift cards ($10 per wave). In total, parents could receive up to $40 in gift cards and youth could receive up to $40 in gift cards if they completed their assessments at all four waves.
Study Flow
In order to maximize participation rates across the study period, youth and parents received an invitation to participate at each study wave, meaning that those who did not respond to the invitation for the first wave continued to receive an invitation to participate at the subsequent waves. Youth and/or parents could also opt-out of the study at each wave; in that case, they did not receive an invitation to participate in subsequent waves. Youth and parents who agreed to participate completed informed consent prior to any study procedures.
We invited youth and parents from 382 families to participate in this research study. Across the 4 waves, 250 families enrolled in the study and completed at least one wave of data collection (enrollment/recruitment rate = 65.4%). Families who enrolled in the study did not differ on youth age, sex, race, ethnicity, parent education, or annual household income compared to families who did not enroll (ps > .05). Median completion was 4/4 waves for parents, and 3/4 waves for youth. Most parent-youth dyads completed assessments at the same waves (median completion for dyads = 3/4 waves). See Figure 1 for response rates at each wave for parents and youth. Participants who completed only one data wave did not differ from those who completed all four data waves on any sociodemographic characteristics including youth age, sex, race, ethnicity, parent education, and annual household income (ps > .05).
Figure 1.
Participant Flow
Measures
Sociodemographic characteristics.
Parents reported on race (Response options: White, Asian, Black or African-American, American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, Other/Write In) and ethnic background (Response options: Hispanic or Latino, Not Hispanic or Latino, Unknown) for themselves and their child. Parents also reported their child’s date of birth (to calculate age), and their child’s sex at birth.
Pain interference.
Youth completed the Brief Pain Inventory (BPI), a measure of pain-related interference over the past 7 days that has demonstrated reliability and validity in youth and adults with chronic pain[10; 16]. Youth reported on the degree of pain-related interference with 7 daily activities (general activity, walking, work/school, mood, enjoyment of life, relations with others, sleep) over the past week using an 11-point numerical rating scale (0 = ‘does not interfere’, 10 = ‘interferes completely’). A total score was calculated as the mean of the 7 pain-interference items.
Anxiety.
Youth and parents completed the GAD-7 to report on their own anxiety symptoms. The GAD-7 is a widely used 7-item measure of generalized anxiety symptoms validated for use by youth and adults[43]. Participants rated how often they were bothered by anxiety symptoms over the past two weeks with responses ranging from 0 (‘not at all’) to 3 (‘nearly every day’). Items were summed to create a total score, with higher scores indicating greater anxiety symptoms (range = 0 – 21). Scores of 8 or higher indicate clinically significant anxiety symptoms [43].
Depression.
Youth and parents reported on their own depressive symptoms using the Patient Health Questionnaire-9 (PHQ-9), which has demonstrated validity for pediatric and adult populations[21]. The PHQ-9 includes 9 items where participants rate how often they have been bothered by depressive symptoms over the past two weeks. Responses range from 0 (‘not at all’) to 3 (‘nearly every day’) with higher scores indicating more severe depressive symptoms. Scores of 10 or higher are indicative of clinically significant depressive symptoms (range = 0–27). Scores of 10 or higher indicate moderate to severe depression[21].
Insomnia.
Youth and parents completed the Insomnia Severity Index (ISI) to report on their own insomnia symptoms[1]. The ISI includes 7 items that are rated on a 5-point Likert scale (0–4). Items are summed to create a total score (range = 0–28) with higher scores indicating more severe insomnia symptoms. Total scores of 8 or higher are considered clinically elevated in[1; 7]. The ISI has demonstrated good reliability and validity for identifying clinically significant insomnia in pediatric and adult populations[1; 7; 33].
Direct exposure to COVID-19.
At each assessment time point, parents reported on their family’s direct exposures to COVID-19 by answering the following 4 questions (yes/no): (1) “Has anyone in your family been tested for COVID-19?”, (2) “Has anyone in your family tested positive for COVID-19?”, (3) “Has anyone in your family had to quarantine at home or stay overnight in the hospital because of known or suspected COVID-19?”, and (4) “Does anyone in your family identify as a frontline responder to the COVID-19 pandemic?”. Responses were coded as ‘No’ = 0 and ‘Yes’ = 1. For this report, the four items were summed and then participants were classified into one of three groups based on their response patterns across the 4 waves: (1) ‘No exposure’ if responses were ‘0’ at all four waves, (2) ‘Low exposure’ if responses were non-zero for one or two waves, or (3) ‘High exposure’ if responses were non-zero at three or four waves.
Secondary economic stress due to the COVID-19 pandemic.
At each assessment wave, parents reported on whether or not their family was experiencing secondary economic stress due to the COVID-19 pandemic by answering the following question (yes/no): “Family members have experienced financial problems or unemployment because of the COVID-19 pandemic.” Responses were coded as ‘No’ = 0 and ‘Yes’ = 1. For this report, participants were classified into one of three groups indicating the severity of secondary economic stress due to the pandemic: (1) ‘No impact’ if responses were ‘0’ at all four waves, (2) ‘Low impact’ if responses were ‘Yes’ for one or two waves, and ‘High impact’ if responses were ‘Yes’ for three or four waves.
Open-ended responses on the impact of the COVID-19 pandemic on pain.
Youth and their parents responded to the following open-ended question at each wave: “Is there anything you would like to share with us about how the COVID-19 pandemic has impacted your [child’s] pain problem?”
Data Analysis Plan
Sample characteristics.
We summarized sample characteristics by examining means, variances and frequencies. For analyses, race was recoded as ‘White’ vs. ‘Minority Racial Group’ (Black/African American, Asian, American Indian/Alaska Native, Native Hawaiian or Other Pacific Islander, More than one race). We evaluated associations between demographic characteristics (youth age, sex, ethnicity, race, parental education, annual household income) with direct exposures to COVID-19 (none vs. low vs. high) and secondary economic stress (none vs. low vs. high) using one-way analysis of variance (ANOVA) and chi-square tests.
All analyses were conducted using R 4.0.3 statistical software[44]. Primary hypothesis testing was conducted using the full sample with missing data handled through linear mixed-effects (LME) regression modeling procedures. LME regression modeling retains all observations, accommodates missing data, and accounts for repeated measures between subjects.
Predicting trajectories of youth symptoms across 4 data waves.
To evaluate change in youth pain interference, anxiety, depression, and insomnia symptoms over time, we used linear mixed-effects (LME) regression models in which time was modeled as a discrete variable and covariates were adjusted for including youth age, sex, race, parental education, and study cohort (chronic pain vs. headache). A subject-specific random intercept was included in each model to account for within-subject correlations. The time-specific coefficients captured the average changes in symptoms from initial levels to each later time point. We ran separate LME regression models using the total score for each of the youth-reported symptom measures. We hypothesized that overall trajectories of youth pain interference, anxiety, depression, and insomnia would either remain stable (no change) or decrease (improve) over time.
We also evaluated whether direct exposure to COVID-19 and secondary economic impact modified the change over time in youth pain interference anxiety, depression, and insomnia symptoms. We used a similar LME model as above (controlling for youth age, sex, parent education, and study group), and included direct exposure to COVID-19 and secondary economic stress as predictors in the model. Interactions between direct exposure to COVID-19 and time, and between secondary economic impact and time, were entered into the models separately. The interaction terms between the direct exposure to COVID-19 coefficients (none vs. low, none vs. high) and the time-specific coefficients were examined to test our hypothesis that direct exposure to COVID-19 would be associated with greater deteriorations in youth-reported symptoms from wave 1 to each subsequent time point. Similarly, the interaction terms between the secondary economic stress coefficients (none vs. low, none vs. high) and the time-specific coefficients were examined to test our hypothesis that experiencing secondary economic stress would be associated with greater deteriorations in youth-reported symptoms from wave 1 to each subsequent time point. We ran separate LME models using the total scores for each youth-reported symptom measure. Coefficient estimates from the LMEs were tested based on Type II Analysis of Variance Table with Kenward-Roger’s method[22].
Predicting trajectories of parent symptoms across 4 data waves.
To evaluate change in parent anxiety, depression, and insomnia symptoms over time, we used the same linear mixed-effects (LME) regression models as above in which time was modeled as a discrete variable and covariates were adjusted for including youth age, parent sex, parent race, parental education, and study cohort (chronic pain vs. headache). A subject-specific random intercept was included in each model to account for within-subject correlations. The time-specific coefficients captured the average changes in symptoms from initial levels to each later time point. We ran separate LME regression models using the total score for each of the parent-reported symptom measures. We hypothesized that overall trajectories of parent anxiety, depression, and insomnia would either remain stable (no change) or decrease (improve) over time.
We also evaluated whether direct exposure to COVID-19 and secondary economic impact modified the change over time in parent anxiety, depression, and insomnia symptoms. We used a similar LME model as above (controlling for youth age, parent sex, parent race, parent education, and study group), and included direct exposure to COVID-19 and secondary economic stress as predictors in the model. Interactions between direct exposure to COVID-19 and time, and between secondary economic impact and time, were entered into the models separately. The interaction terms between the direct exposure to COVID-19 coefficients (none vs. low, none vs. high) and the time-specific coefficients were examined to test our hypothesis that direct exposure to COVID-19 would be associated with greater deteriorations in parent-reported symptoms from wave 1 to each subsequent time point. Similarly, the interaction terms between the secondary economic stress coefficients (none vs. low, none vs. high) and the time-specific coefficients were examined to test our hypothesis that experiencing secondary economic stress would be associated with greater deteriorations in parent-reported symptoms from wave 1 to each subsequent time point. We ran separate LME models using the total scores for each parent-reported symptom measure. Coefficient estimates from the LMEs were tested based on Type II Analysis of Variance Table with Kenward-Roger’s method[22].
Qualitative coding.
Following the guidelines of Braun and Clarke (2006)[3; 4], we applied inductive semantic thematic analysis such that themes were generated from the data (i.e., not pre-determined by investigators). In other words, we did not have an a priori focus for the themes generated from the qualitative data. We followed the recommendations of Wu and colleagues[47] to ensure rigor in our qualitative coding methodology. First, two primary coders familiarized themselves with the data by reviewing all responses to the open-ended survey question. Based on this initial review, we analyzed parent and youth responses across all 4 assessment time points together and we retained the source of each response (parent vs. youth) but not the time point. Codes were assigned by isolating meaningful units of text within each response. Then, the two coders worked together to group codes into categories and then the categories were grouped into over-arching themes. At each stage of coding, operational definitions and examples/not-example quotes for each code, category, and theme were recorded in a codebook. Using this codebook, the two coders worked together to achieve consensus and disagreements were arbitrated by a third team member. Using this process, we achieved 100% agreement at each stage. To increase credibility of the final set of themes, a fourth team member reviewed the original responses from participants to ensure that the quotes were representative of the final set of codes, categories, and themes.
Results
Sample Characteristics
Sociodemographic and clinical characteristics.
As shown in Table 1, we enrolled 250 families of youth between the ages of 12 and 21 years of age (M = 17.21 years, SD = 1.97). Youth were primarily female (71.2%), 82.0% were White (Black/African American = 2.0%, Native Hawaiian/Pacific Islander = 0.8%, Asian = 2.8%, Native American/Alaska Native = 2.4%, More than one race = 6.8%), and 89.2% were non-Hispanic. Among parents, 92.1% were White (Black/African American = 1.2%, Native Hawaiian/Pacific Islander = 0.4%, Asian = 2.8%, Native American/Alaskan Native = 1.6%, More than one race = 2.4%) and 95.0% were non-Hispanic. Most parents had a college degree or higher (74.8%) and 69.8% had pre-pandemic annual household incomes above $70,000/year.
Table 1.
Participant Characteristics for the Full Sample, by Direct COVID-19 Exposure Level, and by Secondary Economic Stress Level.
| Direct COVID-19 Exposure Level | Secondary Economic Stress Level | ||||||
|---|---|---|---|---|---|---|---|
| Full Sample (n=250) |
None (n=126) |
Low (n=77) |
High (n=47) |
None (n=139) |
Low (n=45) |
High (n=66) |
|
| Youth Age (years), M(SD) | 17.21 (1.97) | 17.34 (1.95) | 17.39 (1.89) | 16.67 (2.04) | 17.31 (1.95) | 17.22 (2.05) | 17.02 (1.97) |
| Recruitment Group, n% | |||||||
| Headache Group | 157 (62.8) | 81 (64.3) | 51 (66.2) | 25 (53.2) | 89 (64.0) | 28 (62.2) | 40 (60.6) |
| Chronic Pain Group | 93 (37.2) | 45 (35.7) | 26 (33.8) | 22 (46.8) | 50 (36.0) | 17 (37.8) | 26 (39.4) |
| Youth Sex, n(%) | |||||||
| Female | 178 (71.2) | 88 (69.8) | 56 (72.7) | 34 (72.3) | 103 (74.1) | 32 (71.1) | 43 (65.2) |
| Male | 72 (28.8) | 38 (30.2) | 21 (27.3) | 13 (27.7) | 36 (25.9) | 13 (28.9) | 23 (34.8) |
| Youth Race, n(%) | |||||||
| White | 205 (82.0) | 107 (84.9) | 64 (83.1) | 34 (72.3) | 115 (82.7) | 40 (88.9) | 50 (75.8) |
| Minority Racial Group | 37 (14.8) | 14 (11.1) | 12 (15.6) | 11 (23.4) | 18 (12.9) | 5 (11.1) | 14 (21.2) |
| Missing | 8 (3.2) | 5 (4.0) | 1 (1.3) | 2 (4.3) | 6 (4.3) | 0 (0.0) | 2 (3.0) |
| Youth Ethnicity, n(%) | |||||||
| Hispanic or Latino | 20 (8.0) | 6 (4.8) | 10 (13.0) | 4 (8.5) | 9 (6.5) | 4 (8.9) | 7 (10.6) |
| Not Hispanic or Latino | 223 (89.2) | 116 (92.1) | 66 (85.7) | 41 (87.2) | 127 (91.4) | 40 (88.9) | 56 (84.8) |
| Missing | 7 (2.8) | 4 (3.2) | 1 (1.3) | 2 (4.2) | 3 (2.1) | 1 (2.2) | 3 (4.5) |
| Parent Race, n(%) | |||||||
| White | 226 (90.4) | 117 (92.9) | 69 (89.6) | 40 (85.1) | 128 (92.1) | 38 (84.4) | 60 (90.9) |
| Minority Racial Group | 21 (8.4) | 7 (5.6) | 8 (10.4) | 6 (12.8) | 9 (6.5) | 7 (15.6) | 5 (7.6) |
| Missing | 3 (1.2) | 2 (1.6) | 0 (0.0) | 1 (2.1) | 2 (1.4) | 0 (0.0) | 1 (1.5) |
| Parent Ethnicity, n(%)* | |||||||
| Hispanic or Latino | 9 (3.6) | 0 (0.0) | 7 (9.1) | 2 (4.3) | 4 (2.9) | 2 (4.4) | 3 (4.5) |
| Not Hispanic or Latino | 235 (94.0) | 123 (97.6) | 69 (89.6) | 43 (91.5) | 132 (95.0) | 42 (93.3) | 61 (92.4) |
| Missing | 6 (2.4) | 3 (2.4) | 1 (1.3) | 2 (4.2) | 3 (2.1) | 1 (2.2) | 2 (3.0) |
| Parent Marital Status | |||||||
| Married | 193 (77.2) | 99 (78.6) | 60 (77.9) | 34 (72.3) | 110 (79.1) | 36 (80.0) | 47 (71.2) |
| Not Married | 55 (22.0) | 26 (20.6) | 17 (22.1) | 12 (25.5) | 28 (20.1) | 9 (20.0) | 18 (27.3) |
| Missing | 2 (0.8) | 1 (0.8) | 0 (0.0) | 1 (2.1) | 1 (0.7) | 0 (0.0) | 1 (1.5) |
| Parent Education | |||||||
| Some College or Less | 68 (27.2) | 29 (23.0) | 23 (29.9) | 16 (34.0) | 34 (24.5) | 11 (24.4) | 23 (34.8) |
| College and above | 180 (72.0) | 96 (76.2) | 54 (70.1) | 30 (63.8) | 104 (74.8) | 34 (75.6) | 42 (63.6) |
| Missing | 2 (0.8) | 1 (0.8) | 0 (0.0) | 1 (2.1) | 1 (0.7) | 0 (0.0) | 1 (1.5) |
| Annual Household Income** | |||||||
| <$10,000–$29,999 | 23 (9.2) | 11 (8.7) | 6 (7.8) | 6 (12.3) | 8 (5.7) | 6 (13.3) | 9 (13.6) |
| $30,000–$49,999 | 29 (11.6) | 12 (9.5) | 7 (9.1) | 10 (21.3) | 11 (7.9) | 5 (11.1) | 13 (19.7) |
| $50,000–$69,999 | 44 (17.6) | 20 (15.9) | 14 (18.2) | 10 (21.3) | 18 (12.9) | 9 (20.0) | 17 (25.8) |
| >=$70,000 | 147 (58.8) | 81 (64.3) | 46 (59.7) | 20 (42.6) | 97 (69.8) | 24 (53.3) | 26 (39.4) |
| Missing | 7 (2.8) | 2 (1.6) | 4 (5.2) | 1 (2.1) | 5 (3.6) | 1 (2.2) | 1 (1.5) |
Notes.
p = .038 for group differences by Direct COVID-19 Exposure Level;
p = .013 for group differences by Secondary Economic Stress
Families were recruited from our existing study cohorts of youth with chronic headache (64.0%) or chronic pain (36.0%). At enrollment, youth reported on average mild to moderate pain intensity (M = 3.63, SD = 2.13). As expected, primary pain locations included head (82.5%), abdomen (20.6%), and musculoskeletal (29.4%) and 9.0% of youth had a co-morbid chronic medical condition such as arthritis, chronic pancreatitis, or sickle cell disease.
Persistent and clinically elevated symptoms of depression, anxiety and insomnia were common among youth and parents across the 4 data waves. Nearly half of youth reported persistent and clinically elevated depression (range = 37.4–45.1%) or anxiety (range = 41.6–46.2%). For parents, about a quarter reported elevated depression across the 4 data waves (range = 16.4–21.2%) and nearly one-third had elevated anxiety (range = 26.9–29.9%). Problematic insomnia symptoms were most common, with two-thirds of youth and two-thirds of parents reporting persistent and clinically elevated insomnia across the study period (youth range = 62.3–66.7%; parent range = 58.0–64.9%).
Direct exposures to COVID-19.
Across all 4 study waves, about one-third of parents (35.6%) reported an immediate family member had been tested for COVID-19 and, of these, 4.8% were positive for COVID-19. About a quarter of parents (22.8%) reported that an immediate family member had self-quarantined or stayed overnight in the hospital due to known or suspected COVID-19, and about a quarter (23.6%) reported that an immediate family member worked as a front-line responder. Using our classification for no exposure, low exposure, and high exposure, 50.4% of participants were classed as ‘No exposure’, 30.8% were classed as ‘Low exposure’ and 18.8% were classed as ‘High exposure’. See Figures 2 and 3 for unadjusted summaries (Mean, SD) of youth pain interference and youth and parent insomnia, depression, and anxiety symptoms across the 4 study waves by COVID-19 exposure sub-group.
Figure 2.
Youth Outcomes by Direct COVID-19 Exposure Level
Figure 3.
Parent Outcomes by Direct COVID-19 Exposure Level and Economic Stress Level
Secondary economic stress.
Nearly half of parents (44.4%) reported that their family had experienced financial problems or unemployment due to the pandemic at one or more waves during the study period. Using our classification for no economic stress, low economic stress, and high economic stress, 55.6% were classed as ‘No economic stress’, 18.0% were classed as ‘Low economic stress’, and 26.4% were classed as ‘High economic stress’. See Figures 3 and 4 for unadjusted summaries (Mean, SD) of youth pain interference and youth and parent insomnia, depression, and anxiety symptoms across the 4 study waves for each economic stress sub-group.
Figure 4.
Youth Outcomes by COVID-19 Related Economic Stress Level
Sociodemographic Predictors of Direct Exposure to COVID-19 and Secondary Economic Stress
Direct exposure to COVID-19 (none vs. low vs. high) was not significantly associated with youth age, youth sex (male vs. female), youth race/ethnicity, parent race, parent marital status, parent education, or annual household income (see Table 1). While the sample size was very small, Hispanic/Latino caregivers were more likely to report direct exposure to COVID-19 (n = 9/9) than Non-Hispanic/Latino caregivers (n = 112/235) (p = .038, see Table 1). Secondary economic stress (none vs. low vs. high) was significantly associated with pre-pandemic annual household income (p = .013, see Table 1). Compared to families with pre-pandemic annual household incomes above $70,000, families with lower household incomes were more likely to experience secondary economic stress due to the pandemic. Secondary economic stress was not associated with any other demographic characteristics.
Predicting Trajectories of Youth Symptoms across 4 Data Waves
Youth pain interference, anxiety, depression, and insomnia symptoms generally remained stable from April 2020 to July 2020 (wave 1 to wave 4) (see Tables 2 and 3). In contrast to our hypothesis, direct exposure to COVID-19 and secondary economic stress due to the pandemic did not significantly affect trajectories of youth depression, anxiety, or insomnia symptoms (all ps > .05, see Tables 2 and 3. Direct exposure to COVID-19 also did not significantly affect trajectories of youth pain interference (p > .05, see Table 2).
Table 2.
Impact of COVID-19 direct exposure on trajectories of outcomes based on linear mixed effects regression models
| Outcomes | COVID-19 Direct Exposure1 | P-value for COVID-19 exposure X time interaction2 | ||
|---|---|---|---|---|
| None β (95% CI) |
Low β (95% CI) |
High β (95% CI) |
||
| Pain Interference-Youth | 0.43 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | 0.16 (−0.17, 0.49) | 0.20 (−0.38, 0.77) | 0.27 (−0.34, 0.88) | |
| Wave 3 | 0.13 (−0.21, 0.46) | 0.11 (−0.46, 0.67) | 0.66 (0.04, 1.28)* | |
| Wave 4 | −0.10 (−0.44, 0.24) | −0.56 (−1.14, 0.03) | 0.10 (−0.53, 0.73) | |
| Insomnia-Youth | 0.13 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | 1.13 (0.16, 2.09)* | 0.24 (−1.39, 1.87) | −0.14 (−1.74, 1.45) | |
| Wave 3 | 1.19 (0.22, 2.16)* | 1.71 (0.12, 3.29) | 0.34 (−1.29, 1.97) | |
| Wave 4 | 0.76 (−0.24, 1.76) | −1.24 (−2.89, 0.40) | −0.44 (−2.08, 1.21) | |
| Depression-Youth | 0.39 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | 0.95 (0.19, 1.70)* | 1.00 (−0.47, 2.47) | 0.18 (−1.25, 1.60) | |
| Wave 3 | 0.53 (−0.22, 1.29) | −0.09 (−1.53, 1.35) | 0.68 (−0.77, 2.14) | |
| Wave 4 | 0.26 (−0.52, 1.04) | −1.05 (−2.53, 0.43) | 0.10 (−1.37, 1.57) | |
| Anxiety-Youth | 0.88 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | 0.46 (−0.26, 1.18) | −0.07 (−1.43, 1.30) | 0.55 (−0.79, 1.89) | |
| Wave 3 | 0.34 (−0.39, 1.06) | 0.24 (−1.10, 1.58) | 0.49 (−0.88, 1.87) | |
| Wave 4 | −0.09 (−0.84, 0.65) | −1.09 (−2.47, 0.29) | −0.07 (−1.45, 1.32) | |
| Insomnia-Parent | 0.15 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | 0.89 (−0.06, 1.84) | −0.86 (−2.04, 0.32) | 1.49 (0.15, 2.84)* | |
| Wave 3 | −0.05 (−1.00, 0.90) | −0.84 (−2.01, 0.33) | 0.59 (−0.76, 1.94) | |
| Wave 4 | −0.61 (−1.57, 0.35) | −0.92 (−2.10, 0.26) | 0.53 (−0.81, 1.87) | |
| Depression-Parent | 0.25 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | 0.51 (−0.21, 1.22) | −0.69 (−1.79, 0.40) | 0.47 (−0.77, 1.70) | |
| Wave 3 | −0.28 (−1.00, 0.44) | −0.92 (−2.01, 0.17) | 0.68 (−0.55, 1.92) | |
| Wave 4 | −0.21 (−0.94, 0.51) | −0.80 (−1.89, 0.30) | 0.65 (−0.58, 1.87) | |
| Anxiety-Parent | 0.99 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | −0.10 (−0.91, 0.71) | −0.66 (−1.85, 0.53) | −0.14 (−1.46, 1.18) | |
| Wave 3 | −0.06 (−0.87, 0.75) | −0.25 (−1.45, 0.95) | −0.02 (−1.34, 1.30) | |
| Wave 4 | 0.08 (−0.74, 0.89) | −0.26 (−1.46, 0.93) | 0.002 (−1.31, 1.31) | |
All models controlled for child age at baseline, child sex, child race, caregiver education, recruitment clinic, and COVID-19 related economic stress level.
P-values are based on Type II Analysis of Variance Table with Kenward-Roger’s method and linear mixed effects regression models
Table 3.
Impact of COVID-19 related economic stress on trajectories of outcomes based on linear mixed effects regression models
| Outcomes | COVID-19 Related Economic Stress1 | P-value for secondary economic stress X time interaction2 | ||
|---|---|---|---|---|
| None β (95% CI) |
Low β (95% CI) |
High β (95% CI) |
||
| Pain Interference-Youth | 0.03* | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | −0.14 (−0.49, 0.20) | 0.56 (−0.13, 1.24) | 0.48 (−0.02, 0.97) | |
| Wave 3 | −0.16 (−0.51, 0.19) | 0.86 (0.18, 1.54)* | 0.50 (0.01, 1.00)* | |
| Wave 4 | −0.40 (−0.75, −0.04)* | −0.23 (−0.91, 0.46) | 0.20 (−0.31, 0.71) | |
| Insomnia-Youth | 0.22 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | 0.22 (−0.77, 1.21) | 1.78 (−0.09, 3.64) | 0.45 (−0.96, 1.85) | |
| Wave 3 | 0.20 (−0.79, 1.19) | 2.81 (0.95, 4.68)* | 1.64 (0.23, 3.04)* | |
| Wave 4 | −0.75 (−1.78, 0.28) | 1.29 (−0.58, 3.17) | 0.16 (−1.29, 1.61) | |
| Depression-Youth | 0.75 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | 0.44 (−0.45, 1.33) | 0.72 (−0.75, 2.19) | 1.24 (0.08, 2.41)* | |
| Wave 3 | 0.36 (−0.54, 1.26) | 0.27 (−1.20, 1.75) | 0.49 (−0.67, 1.66) | |
| Wave 4 | −0.47 (−1.39, 0.46) | −0.59 (−2.07, 0.89) | 0.63 (−0.57, 1.83) | |
| Anxiety-Youth | 0.86 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | 0.30 (−0.54, 1.13) | −0.24 (−1.73, 1.25) | 0.67 (−0.40, 1.73) | |
| Wave 3 | 0.32 (−0.52, 1.16) | 0.07 (−1.42, 1.56) | 0.55 (−0.52, 1.61) | |
| Wave 4 | −0.67 (−1.54, 0.19) | −0.06 (−1.56, 1.43) | −0.11 (−1.21, 0.98) | |
| Insomnia-Parent | 0.003 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | 0.29 (−0.68, 1.26) | −0.65 (−2.21, 0.91) | 1.54 (0.52, 2.56)* | |
| Wave 3 | −1.32 (−2.30, −0.34)* | −0.39 (−1.95, 1.18) | 1.39 (0.38, 2.40)* | |
| Wave 4 | −1.40 (−2.38, −0.43)* | −0.26 (−1.82, 1.30) | 0.65 (−0.37, 1.67) | |
| Depression-Parent | 0.53 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | −0.38 (−1.22, 0.45) | 0.31 (−0.82, 1.44) | 0.69 (−0.30, 1.68) | |
| Wave 3 | −0.67 (−1.51, 0.18) | −0.49 (−1.62, 0.64) | 0.50 (−0.47, 1.48) | |
| Wave 4 | −0.51 (−1.35, 0.34) | −0.33 (−1.46, 0.80) | 0.37 (−0.62, 1.35) | |
| Anxiety-Parent | 0.83 | |||
| Wave 1 | Ref | Ref | Ref | |
| Wave 2 | −0.30 (−1.16, 0.57) | −0.78 (−2.21, 0.65) | 0.05 (−0.99, 1.09) | |
| Wave 3 | −0.51 (−1.39, 0.36) | −0.05 (−1.49, 1.40) | 0.33 (−0.70, 1.36) | |
| Wave 4 | −0.25 (−1.13, 0.62) | 0.11 (−1.32, 1.53) | 0.09 (−0.95, 1.13) | |
All models controlled for child age at baseline, child sex, child race, caregiver education, recruitment clinic, and COVID-19 direct exposure level.
P-values are based on Type II Analysis of Variance Table with Kenward-Roger’s method and linear mixed effects regression models
However, in support of our hypothesis, there was significant variability in trajectories of youth pain interference over time depending on whether families experienced secondary economic stress due to the pandemic (p = .03). Specifically, youth in families who did not experience secondary economic stress due to the pandemic reported improvements in pain interference at all later waves and a significant decrease in pain interference from wave 1 to wave 4. In contrast, for youth in the low and high economic stress sub-groups, there was a trend for pain interference to increase at wave 2 with a significant increase at wave 3. At wave 4, pain interference returned to initial levels for both the low and high economic stress sub-groups.
Predicting Trajectories of Parent Symptoms across 4 Data Waves
Parents demonstrated a significant decrease (improvement) in insomnia symptoms from wave 1 (April 2020) to wave 4 (July 2020), whereas symptoms of parental depression and anxiety were stable across the study period (see Tables 2 and 3). Contrary to our hypothesis, direct exposures to COVID-19 and secondary economic stress due to the pandemic did not significantly affect trajectories of parent depression or anxiety (see Tables 2 and 3). Direct exposures to COVID-19 also did not significantly affect trajectories of parent insomnia over time (see Table 2).
Consistent with our hypothesis, however, there was significant variability in trajectories of parent insomnia symptoms depending on whether the family experienced secondary economic stress due to the pandemic (p = .003, see Table 3). Specifically, parents in the high economic stress sub-group reported increased insomnia symptoms at wave 2 and wave 3 compared to wave 1. For parents in the high economic stress sub-group, insomnia symptoms returned to initial levels at wave 4. In contrast, parents who did not experience secondary economic stress due to the pandemic reported a significant reduction (improvement) in insomnia symptoms from wave 1 to wave 3 and this improvement was maintained at wave 4. For parents in the low economic stress sub-group, insomnia symptoms remained stable from initial levels to wave 4.
Qualitative Survey Responses: Youth and Parent Perspectives on the Impact of the COVID-19 Pandemic on the Youth’s Pain Problem
Semantic analysis of adolescent and parent responses to the question “Is there anything you would like to share with us about how the COVID-19 pandemic has impacted [your child’s] pain problem?” revealed one overarching theme describing the impact of school closures on adolescent pain and stress. Improvements in pain and stress were attributed to the benefits of school closures, whereas exacerbations in pain and stress were attributed to detrimental effects of school closures.
Pandemic-related school closures helped to reduce pain and stress.
Youth who described improvements in pain since the onset of the pandemic attributed this to lower stress due to school closures.
“It has actually made my pain better, I think that is because there is less stress from school.”
(Adolescent, Headache Group)
“I have had less pain during the pandemic because there is less stress. I don’t have to worry so much like I did in school.”
(Adolescent, Chronic Pain Group)
“I’m helping my family run our small business from home, so I am more stressed with that, but less stressed because I do not have to be healthy enough to physically go to school.”
(Adolescent, Headache Group)
“Since I’m at home all the time I have time to actually relax as compared to before when I was busy with school and work.”
(Adolescent, Chronic Pain Group)
Parents also attributed improvements in their adolescent’s pain to the benefits of school closures. According to these parents, providing youth with greater control over their daily schedules led to reductions in pain and stress by facilitating nighttime sleep, breaks for rest during the school day, and flexibility to work on schooling when they felt their best.
“With physical school cancelled she can sleep in if need or rest if need and then do the homework when she is feeling good so it has actually lessened a little of the stress of daily school and schedule.”
(Parent, Headache Group)
“His pain is less with at home school - he sleeps as much as he wants and completes school work much later in the day, we love having him home, he misses friends but likes not having to ‘put on the mask’ (his words) for school.”
(Parent, Chronic Pain Group).
“With schools closed she is actually truly thriving at home while using an e-learning model. She sleeps better, nutrition is better, and she is just happier overall. I wish I never had to send her back to full time school.”
(Parent, Chronic Pain Group).
“For the better as its allowed her to regulate herself as needed instead of against an artificial schedule.”
(Parent, Headache Group)
Pandemic-related school closures and social isolation exacerbated pain and stress.
Youth who reported that their pain increased since the onset of the pandemic attributed this to the lack of a daily routine caused by school closures and social isolation.
“Stress, combined with the fact that I don’t have to wake up for school in the morning, makes me stay up later and leads to headaches.”
(Adolescent, Headache Group)
“I have lost my only social interactions with people and activity’s that helped me maintain a healthy routine and mindset about my pain.”
(Adolescent, Chronic Pain Group)
“My knee problems are worsened by lack of daily movement - usually I walk up and down the school several times a day. Now that there’s no school, mental health issues are more prevalent and motivation for physical activity is lessened.”
(Adolescent, Headache Group)
“I feel as though Covid has taken away everything that kept me going and not thinking about my pain (school, church, seeing friends, youth group, etc.).”
(Adolescent, Chronic Pain Group)
Parents who reported that their children experienced increased pain since the onset of the pandemic attributed this to the lack of daily routine and increased distress caused by school closures.
“As much as we have tried to keep a schedule, it has not worked. My son sleeps days, and is up communicating with friends, at night. He has severe nausea and his headaches that had nearly disappeared are back.”
(Parent, Headache Group).
“Online school has impacted motivation, schedule and anxiety which I believe have impacted pain levels.”
(Parent, Headache Group).
“With the lack of normal routine and so much extra time, and extra family time together (which was great at first-but too much of a good thing can be still be too much), my daughter’s pain has increased. She seems much more uncomfortable, overwhelmed and stressed.”
(Parent, Chronic Pain Group).
Discussion
We conducted a mixed methods study to describe early adaptation to the COVID-19 pandemic among 250 families of youth with chronic pain and their parents across 4 waves from April to July 2020. To our knowledge, this study is the first to examine early adaptation to the COVID-19 pandemic in a cohort of youth with chronic pain. Our findings confirmed that in the early months of the pandemic, about half of families (49.6%) had direct exposure to COVID-19 (suspected or confirmed COVID-19 diagnosis, being a front line worker) and nearly half (44.4%) dealt with secondary economic stress (financial problems, unemployment). Youth with chronic pain who had lower pre-pandemic annual household incomes were more likely to experience secondary economic stress, and Hispanic/Latino caregivers were more likely to experience direct exposures to COVID-19 than their non-Hispanic/Latino peers. Consistent with our hypothesis and the conceptual model proposed by Prime et al. (2020)[39], most youth and parents experienced adaptive adjustment demonstrated by stable or improving trajectories of pain interference, insomnia, and depression/anxiety during the study period.
It is reassuring that symptom trajectories did not worsen for many of the youth and parents in our study, and that direct exposure to COVID-19 did not appear to modify these trajectories. However, overall symptom burden was high with two-thirds of youth and parents reporting persistent and clinically elevated insomnia symptoms and one-quarter to one-half reporting elevated depressive and anxiety symptoms. Similar rates of persistent, elevated symptoms have been reported in prior longitudinal studies of sleep disturbance and emotional functioning in youth with chronic pain and their parents[24; 27; 36; 37]. Our findings support recommendations for pediatric pain clinicians to provide distance assessment and treatment via technology (telemedicine, e-health interventions) to avoid pandemic-related interruptions in pain care[8].
Importantly, we found support for our hypothesis that secondary economic stress modified trajectories of youth pain interference and parent insomnia symptoms. Specifically, youth pain interference and parent insomnia worsened in families who experienced secondary economic stress. In contrast, these symptom trajectories remained stable in families who did not experience secondary economic stress. However, secondary economic stress did not significantly affect trajectories of the other symptoms evaluated in our study for youth or parents. Further, direct exposure to COVID-19 did not significantly affect trajectories of any of the symptom domains examined in our study. These findings are generally consistent with the conceptual model proposed by Prime and colleagues[39], which suggests that many families will successfully adapt to the challenges of the pandemic while others will experience exacerbations in symptoms due to the social disruptions caused by the virus. Our findings indicate that economic stress caused by the pandemic may increase disease burden for a subset of youth with chronic pain and their parents. The role of parent and family factors has been well studied in pediatric chronic pain, with growing evidence demonstrating bi-directional associations between parental distress and adolescent adjustment over time [5; 23–25; 38]. Our findings suggest that it is important to consider the broader family context (including socioeconomic factors and parental effects) in developing treatment recommendations for youth with significant pain interference.
To our knowledge, there are only two published reports describing psychosocial adaptation to COVID-19 among adults with chronic pain[15; 20], and none among pediatric chronic pain populations. In one cross-sectional study in the U.S., 150 adults with chronic pain completed questionnaire measures at a single time point 4–8 weeks following the onset of social distancing mandates in the state of Massachusetts[15]. Results indicated that female gender and higher pain catastrophizing were associated with greater pain interference[15]. A second study examined prospective changes in symptoms in 43 adults with chronic pain from prior to the pandemic to 2 weeks after the onset of social distancing mandates in Germany[20]. From baseline to follow-up, pain interference, sleep, and emotional functioning remained stable or improved from baseline to follow-up for the majority of participants[20]. While limited, findings from our study with youth and other data from adults with chronic pain indicate that many individuals experienced adaptive adjustment in the initial weeks and months of the pandemic. However, as the virus and its associated social changes persist, long-term studies using well-characterized samples and validated assessments will be needed to fully understand the impact of COVID-19 on pediatric and adult chronic pain populations. Research is also needed across the lifespan in individuals with chronic pain who belong to communities disproportionately impacted by the pandemic (i.e., identifying as BIPOC, experiencing lower socioeconomic status).
We analyzed qualitative data to gain additional insights into the experience of youth and parents during the early months of the pandemic. The major theme in their responses related to the impact of school closures on the youth’s symptoms, revealing an interesting narrative around both perceived benefits and harms from school closures on the youth’s pain problem. Some youth and parents reported that distance learning helped to reduce pain by lowering stress and increasing opportunity to engage in strategies that support pain management throughout the day. Other families reported that distance learning worsened the youth’s pain problem because they no longer had a structured daily routine and their social interactions had decreased. These data complement and extend our questionnaire data on symptom trajectories and provide more nuanced consideration of individual differences in youth response to COVID-19 restrictions. It is possible that families chose to focus on school closures because this was not addressed in the symptom measures that were used, leaving a gap in understanding the impact of the pandemic on youth pain experiences. As schools begin to re-open, there is a high degree of uncertainty and little data to help clinicians and families navigate school re-entry. Our qualitative data suggest that some youth may benefit from returning to in-person instruction, while others may have more success by continuing with remote learning. In either case, pain psychologists can provide valuable support including working with families and school staff to develop appropriate plans for accommodations to support the student’s academic success, and delivering targeted intervention (when indicated) to improve school functioning [18].
Our study has several limitations that should be considered. Our sample is predominantly Caucasian and middle- to upper-class, which precludes our ability to definitively evaluate the impact of race/ethnicity and socioeconomic status on adaptation to the virus. Our findings may not generalize to youth who identify with racial/ethnic minority groups or those experiencing lower socioeconomic standing. We did not have pre-pandemic survey data, and so we are not able to evaluate changes in symptom burden from pre- to post-pandemic. In addition, we did not have a comparator control group. We do not know whether rates of direct exposure to COVID-19, secondary economic stress, and symptom burden among youth with chronic pain are greater than that experienced by families of healthy youth or youth with other chronic medical conditions. Our assessment of pain was also limited and thus we are unable to evaluate differential impact of the pandemic by pain frequency or duration. However, our study has several strengths, including the relatively large sample size, repeat symptom measurement at 4 waves over 4 months using well-established standardized assessment tools, evaluation of psychosocial adaptation for both youth and their parents, and the use of complementary qualitative data.
There are several additional future directions. Research is needed to develop and evaluate standardized approaches to identify families of youth with chronic pain who are at greatest risk for poor adjustment. Kazak (2020)[19] recently developed the COVID-19 Exposure and Family Impact Survey (CEFIS), a brief caregiver-report screening tool that evaluates potentially traumatic aspects of COVID-19 and assesses the impact of the pandemic on family functioning. Studies are needed to evaluate the utility of the CEFIS as a screening tool for youth with chronic pain.
From a public health perspective, our findings support recommendations for COVID-19 to be addressed as a syndemic in the United States[14; 29; 41]. A syndemic occurs when there are two or more concurrent epidemics or disease clusters that interact with social conditions (e.g., poverty, systemic racism) to exacerbate the prognosis and the burden of disease. COVID-19 can be considered a syndemic because infection with the virus interacts with pre-existing medical conditions as well as sociopolitical factors that underly health disparities (i.e., poverty, systemic racism, public distrust in science, weakened government-funded ‘safety nets’ for vulnerable youth and families)[14; 29; 41]. One important implication of a syndemic approach to COVID-19 is that a traditional pandemic or biomedical approach (i.e., focusing on vaccination and slowing transmission) will be insufficient [14; 41]. A syndemic perspective is highly consistent with the biopsychosocial conceptual models that are the bedrock of multidisciplinary pediatric chronic pain assessment and treatment[32].
Conclusions
To our knowledge this is the first published report describing the experience of youth with chronic pain and their parents in the early months of the COVID-19 pandemic. Our findings highlight potential psychosocial treatment needs during this public health crisis.
Acknowledgements
We thank the youth and parents who enrolled in this study for their steadfast dedication to pediatric pain research. We would like to acknowledge Dr. Rocio de la Vega for her assistance developing our COVID-19 questionnaire, Kristen Daniels and Yeon Joo Ko for their assistance with database preparation, and Olivia Ohls for coordinating this study. Financial support for this project included NIH/NINDS K23NS089966 (PI: Law) and Pfizer Improving Chronic Pain Care Grant, Web-based self-management of adolescent chronic pain: National implementation project, Grant ID #27971161 (PI: Palermo).
Footnotes
Conflict of Interest Statement. We have no conflicts of interest to report.
References
- [1].Bastien CH, Vallieres A, Morin CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med 2001;2(4):297–307. [DOI] [PubMed] [Google Scholar]
- [2].Blaauw BA, Dyb G, Hagen K, Holmen TL, Linde M, Wentzel-Larsen T, Zwart JA. Anxiety, depression and behavioral problems among adolescents with recurrent headache: the Young-HUNT study. J Headache Pain 2014;15:38. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology 2006;3(2):77–101. [Google Scholar]
- [4].Braun V, Clarke V. What can “thematic analysis” offer health and wellbeing researchers? Int J Qual Stud Health Well-being 2014;9:26152. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [5].Chow ET, Otis JD, Simons LE. The Longitudinal Impact of Parent Distress and Behavior on Functional Outcomes Among Youth With Chronic Pain. J Pain 2016;17(6):729–738. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [6].Christakis DA. Pediatrics and COVID-19. JAMA 2020;324(12):1147–1148. [DOI] [PubMed] [Google Scholar]
- [7].Clarke G, McGlinchey EL, Hein K, Gullion CM, Dickerson JF, Leo MC, Harvey AG. Cognitive-behavioral treatment of insomnia and depression in adolescents: A pilot randomized trial. Behav Res Ther 2015;69:111–118. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Eccleston C, Blyth FM, Dear BF, Fisher EA, Keefe FJ, Lynch ME, Palermo TM, Reid MC, Williams ACC. Managing patients with chronic pain during the COVID-19 outbreak: considerations for the rapid introduction of remotely supported (eHealth) pain management services. Pain 2020;161(5):889–893. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Eccleston C, Crombez G, Scotford A, Clinch J, Connell H. Adolescent chronic pain: Patterns and predictors of emotional distress in adolescents with chronic pain and their parents. Pain 2004;108(3):221–229. [DOI] [PubMed] [Google Scholar]
- [10].Engel JM, Kartin D, Carter GT, Jensen MP, Jaffe KM. Pain in youths with neuromuscular disease. Am J Hosp Palliat Care 2009;26(5):405–412. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Groenewald CB, Essner BS, Wright D, Fesinmeyer MD, Palermo TM. The economic costs of chronic pain among a cohort of treatment-seeking adolescents in the United States. The journal of pain : official journal of the American Pain Society 2014;15(9):925–933. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, McLeod L, Delacqua G, Delacqua F, Kirby J, Duda SN, Consortium RE. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform 2019;95:103208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009;42(2):377–381. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Horton R Offline: COVID-19 is not a pandemic. Lancet 2020;396(10255):874–874. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Hruschak V, Flowers KM, Azizoddin DR, Jamison RN, Edwards RR, Schreiber KL. Cross-sectional study of psychosocial and pain-related variables among chronic pain patients during a time of social distancing imposed by the coronavirus disease 2019 (COVID-19) pandemic. Pain 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Jensen MP, Turner JA, Romano JM, Fisher LD. Comparative reliability and validity of chronic pain intensity measures. Pain 1999;83(2):157–162. [DOI] [PubMed] [Google Scholar]
- [17].Jordan AL, Eccleston C, Osborn M. Being a parent of the adolescent with complex chronic pain: an interpretative phenomenological analysis. Eur J Pain 2007;11(1):49–56. [DOI] [PubMed] [Google Scholar]
- [18].Karvounides D, Marzouk M, Ross AC, VanderPluym JH, Pettet C, Ladak A, Ziplow J, Patterson Gentile C, Turner S, Anto M, Barmherzig R, Chadehumbe M, Kalkbrenner J, Malavolta CP, Clementi MA, Gerson T, Szperka CL. The intersection of COVID-19, school, and headaches: Problems and solutions. Headache 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Kazak A COVID-19 Exposure and Family Impact Survey (CEFIS), 2020.
- [20].Kersebaum D, Fabig SC, Sendel M, Sachau J, Lassen J, Rehm S, Hullemann P, Baron R, Gierthmuhlen J. The early influence of COVID-19 pandemic-associated restrictions on pain, mood, and everyday life of patients with painful polyneuropathy. Pain Rep 2020;5(6):e858. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [21].Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001;16(9):606–613. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: Tests in Linear Mixed Effects Models. J Stat Softw 2017;82(13):1–26. [Google Scholar]
- [23].Law E, Fisher E, Eccleston C, Palermo TM. Psychological interventions for parents of children and adolescents with chronic illness. Cochrane Database Syst Rev 2019;3:Cd009660. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [24].Law EF, Blume H, Palermo TM. Longitudinal Impact of Parent Factors in Adolescents With Migraine and Tension-Type Headache. Headache 2020;60(8):1722–1733. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25].Law EF, Fisher E, Howard WJ, Levy R, Ritterband L, Palermo TM. Longitudinal change in parent and child functioning after internet-delivered cognitive-behavioral therapy for chronic pain. Pain 2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Law EF, Palermo TM, Zhou C, Groenewald CB. Economic Impact of Headache and Psychiatric Comorbidities on Healthcare Expenditures Among Children in the United States: A Retrospective Cross-Sectional Study. Headache 2019;59(9):1504–1515. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [27].Law EF, Powers SW, Blume H, Palermo TM. Screening Family and Psychosocial Risk in Pediatric Migraine and Tension-Type Headache: Validation of the Psychosocial Assessment Tool (PAT). Headache 2019;59(9):1516–1529. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [28].Masonbrink AR, Hurley E. Advocating for Children During the COVID-19 School Closures. Pediatrics 2020;146(3). [DOI] [PubMed] [Google Scholar]
- [29].Mendenhall E The COVID-19 syndemic is not global: context matters. Lancet 2020;396(10264):1731–1731. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30].Moreland A, Herlihy C, Tynan MA, Sunshine G, McCord RF, Hilton C, Poovey J, Werner AK, Jones CD, Fulmer EB, Gundlapalli AV, Strosnider H, Potvien A, Garcia MC, Honeycutt S, Baldwin G, Program CDCPHL, Cdc Covid-19 Response Team MPAU. Timing of State and Territorial COVID-19 Stay-at-Home Orders and Changes in Population Movement - United States, March 1-May 31, 2020. MMWR Morb Mortal Wkly Rep 2020;69(35):1198–1203. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [31].Organization WH. WHO Director-General’s opening remarks at the media briefing on COVID-19. 2020, 2020. [Google Scholar]
- [32].Palermo TM. Cognitive-behavioral therapy for chronic pain in children and adolescents. New York, NY: Oxford University Press, 2012. [Google Scholar]
- [33].Palermo TM, Beals-Erickson S, Bromberg M, Law E, Chen M. A Single Arm Pilot Trial of Brief Cognitive Behavioral Therapy for Insomnia in Adolescents with Physical and Psychiatric Comorbidities. J Clin Sleep Med 2017;13(3):401–410. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [34].Palermo TM, de la Vega R, Murray C, Law E, Zhou C. A digital health psychological intervention (WebMAP Mobile) for children and adolescents with chronic pain: results of a hybrid effectiveness-implementation stepped-wedge cluster randomized trial. Pain 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [35].Palermo TM, Eccleston C. Parents of children and adolescents with chronic pain. Pain 2009;146(1–2):15–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [36].Palermo TM, Law E, Churchill SS, Walker A. Longitudinal course and impact of insomnia symptoms in adolescents with and without chronic pain. Journal of Pain 2012;13(11):1099–1106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [37].Palermo TM, Slack M, Zhou C, Aaron R, Fisher E, Rodriguez S. Waiting for a Pediatric Chronic Pain Clinic Evaluation: A Prospective Study Characterizing Waiting Times and Symptom Trajectories. J Pain 2019;20(3):339–347. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [38].Palermo TM, Valrie CR, Karlson CW. Family and parent influences on pediatric chronic pain: a developmental perspective. Am Psychol 2014;69(2):142–152. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [39].Prime H, Wade M, Browne DT. Risk and resilience in family well-being during the COVID-19 pandemic. Am Psychol 2020;75(5):631–643. [DOI] [PubMed] [Google Scholar]
- [40].Schuchat A, Team CC-R. Public Health Response to the Initiation and Spread of Pandemic COVID-19 in the United States, February 24-April 21, 2020. MMWR Morb Mortal Wkly Rep 2020;69(18):551–556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [41].Singer M, Bulled N, Ostrach B, Mendenhall E. Syndemics and the biosocial conception of health. Lancet 2017;389(10072):941–950. [DOI] [PubMed] [Google Scholar]
- [42].Soltani S, Kopala-Sibley DC, Noel M. The Co-occurrence of Pediatric Chronic Pain and Depression: A Narrative Review and Conceptualization of Mutual Maintenance. Clin J Pain 2019;35(7):633–643. [DOI] [PubMed] [Google Scholar]
- [43].Spitzer RL, Kroenke K, Williams JB, Lowe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 2006;166(10):1092–1097. [DOI] [PubMed] [Google Scholar]
- [44].Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria, 2020. [Google Scholar]
- [45].Thompson LA, Rasmussen SA. What Does the Coronavirus Disease 2019 (COVID-19) Mean for Families? JAMA Pediatr 2020;174(6):628. [DOI] [PubMed] [Google Scholar]
- [46].Valenzuela J, Crosby LE, Harrison RR. Commentary: Reflections on the COVID-19 Pandemic and Health Disparities in Pediatric Psychology. J Pediatr Psychol 2020;45(8):839–841. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [47].Wu YP, Thompson D, Aroian KJ, McQuaid EL, Deatrick JA. Commentary: Writing and Evaluating Qualitative Research Reports. J Pediatr Psychol 2016;41(5):493–505. [DOI] [PMC free article] [PubMed] [Google Scholar]