Caregiver Incarceration Heightens the Effect of Asthma on Anxiety and Depression Among School-Aged Children: NCHS 2016

Abstract

Anxiety and depression are major challenges among school-aged children. This study investigated whether the association between asthma and anxiety and/or depression was signiﬁcantly greater among children who experienced caregiver incarceration (CGI) compared to those without CGI. Using the National Survey of Children's Health (2016–2020) data, we estimated the effects of asthma and CGI on anxiety and/or depression among school-aged children (age 6–17) (N = 125,005). Data were analyzed using multivariate techniques in R Software. Asthma and CGI were positively associated with anxiety and depression. Notably, increased anxiety and depression associated with asthma were significantly greater among children who experienced CGI versus those who did not. The joint effects of asthma and CGI on mental health are significantly greater than their individual effects. School-based interventions are needed to address the mental health challenges of children affected by asthma and CGI. Improving asthma management and supporting children with CGI may mitigate the mental health burden.

Keywords

asthma mental health school children adolescents incarceration

Caregiver incarceration (CGI) and mental health problems, including anxiety and depression, are major public health challenges affecting school-aged children in the United States (US) (Barnert & Chung, 2018; Bitsko et al., 2018; Office of Disease Prevention and Health Promotion, n.d., A). Previous research identified that anxiety and depression are heightened among children who have asthma or whose parent or caregiver has been incarcerated. However, no research has examined the combined additive effect of these risk factors on children's mental health outcomes, including anxiety and depression. Using US population-based data, this research examined the joint effects of asthma and CGI on anxiety and depression among US school-aged children 6–17 years old.

Despite prevention and treatment efforts to address these mental health challenges, the prevalence of anxiety and depression among children has increased over time (Bitsko et al., 2018, 2022). Among school-aged children, the diagnosis of either anxiety or depression increased from 5.4% in 2003 to 8% in 2007 and 8.4%.in 2011–2012 (Bitsko et al., 2018). The increased rates of anxiety and depression are concerning, given that suicide is the second leading cause of death among individuals aged 10–24 years (Curtin et al., 2024), and nearly one in five (18.8%) US adolescents seriously considered suicide during the previous year (Bitsko et al., 2022).

The prevalence of childhood depression and anxiety also increases with age, with the highest prevalence during adolescence (Bitsko et al., 2022). The National Survey of Children's Health (NSCH) estimated that 8.6% of adolescents had a diagnosis of parent-reported depression compared to 2.3% of children aged 6–11 years old in 2016–2019 (Bitsko et al., 2022). In 2021, 20.9% of adolescents aged 12–17 years reported having a major depressive episode during the previous year. Similarly, the history of anxiety was higher among adolescents (13.7%) compared to children aged 6–11 years (8.6%) (Bitsko et al., 2022).

Another national estimate from the 2019 Youth Risk Behavior Survey (YRBS) reported that greater than one-third of high school students (36.7%) self-reported persistently feeling sad or hopeless in the previous year, and nearly one-fifth (18.8%) had seriously considered suicide (Bitsko et al., 2022). Because anxiety and depression were identified as significant suicide risk factors for school-aged children, Healthy People 2030 added a goal to increase the percentage of children 4–17 years with mental health problems to receive appropriate treatment (2030 Target: 79.3%, Baseline: 70.7% in 2019) (Office of Disease Prevention and Health Promotion, n.d., A.).

Compared to the general population, anxiety and depression rates are much higher among children and adolescents with asthma (Cobham et al., 2020; Saragondlu Lakshminarasappa et al., 2021). A systematic review identified this disparity among children with asthma who showed an increased risk of anxiety disorders (Cobham et al., 2020). McGovern and colleagues (2022) reported a similar disparity among 8–12-year-olds with asthma had an increased rate of developing anxiety symptoms compared to children without asthma (McGovern et al., 2022). In addition to their risk of developing anxiety and depression, adolescents who reported asthma in their lifetime had a much higher risk of being victimized at school (Pudasainee-Kapri et al., 2023) and suicidal behaviors (Garcia-Sanchez et al., 2023; Muhammad et al., 2018) compared to those with no history of asthma.

Another risk factor for anxiety and depression is CGI, which is defined as a child's experience of ever having a parent, primary caregiver, or legal guardian who lived with them, confined in a prison or a jail during their childhood years (The Annie E. Casey Foundation, 2016, 2023). The US has among the highest incarceration rates globally (The Annie E. Casey Foundation, 2016, 2023), and these rates have increased seven-fold in the last few years (The Sentencing Project, 2024). According to the NSCH data, five million children in the United States under 18 years (7.4%; 95% CI [7.0, 7.9]) experienced CGI in 2017 to 2018 (Child and Adolescent Health Measurement Initiative, n.d.; Khazanchi et al., 2023). The highest proportion of these children was school-aged: 9.3% of children (weighted n = 4,400,000) between 2016 and 2019 (Tolliver et al., 2024).

In terms of mental health symptoms, children who experience CGI are more likely to have anxiety and depression (Bomysoad & Francis, 2022; Lee et al., 2013b), suicidal ideation and suicide attempts (Muentner et al., 2022), and lack access to primary and mental health services (Khazanchi et al., 2023). National cross-sectional estimates found that school-aged children experiencing CGI had increased odds of depression (OR 2.00; 95% CI [−1.67, 2.39]) compared to children with no experience of CGI (Tolliver et al., 2024). Similarly, adolescents experiencing CGI have two to five times the risk of developing anxiety, depression, and self-harm compared to their peers without CGI (Muentner et al., 2022). Adolescents who experienced CGI also had a significantly increased risk of depression (OR = 3.02, 95% CI [2.37, 3.84], p < .0001) and anxiety (OR = 1.77, 95% CI [1.43, 2.20], p < .0001 (Bomysoad & Francis, 2022).

CGI is considered an adverse childhood experience (ACE), which is a traumatic and potentially stressful life experience, including abuse, neglect, CGI, family instability, exposure to household member substance abuse/violence, and bullying victimization that occurs before 18 years of age (Desch et al., 2023; Felitti, 2009; Sacks & Murphey, 2018). Children experiencing CGI may feel socially isolated and excluded, and experience unique traumatic stressors such as witnessing a caregiver's arrest and living with the stigma of having a caregiver incarcerated (The Annie E. Casey Foundation, 2016). Children who experience CGI may be disadvantaged economically because employment rates of incarcerated individuals are significantly lower than non-incarcerated peers. Thus, children who experience CGI are likely to have other ACES related to this economic disadvantage (The Sentencing Project, 2024). For example, children who experience CGI are less likely to graduate from high school, and more likely to become parents at a young age, and become incarcerated themselves (Gifford et al., 2019). Additionally, children with CGI commonly have other ACES such as social isolation and illicit substance use (Gifford et al., 2019), and this cumulative effect increases their risk of mental health problems, including depression and anxiety (Turney, 2020).

The stress process perspective states that exposure to life stressors such as CGI or other ACEs have deleterious impacts on the physical health as well as mental wellbeing of children (Pearlin, 1989; Pearlin et al., 1981), and these adverse effects can impact life-long health and wellbeing (Tolliver et al., 2024). The stress process framework is particularly salient because it describes how exposure to CGI—one of the stressors arising from the social environment of children—can impair their physical and mental health well-being (Turney, 2020). To mitigate the exposure and risks of childhood CGI, Healthy People 2030 set a target to reduce the proportion of children who ever experience CGI from 7.4% to 5.2% by 2030 (Office of Disease Prevention and Health Promotion, n.d., B.).

Although increasing evidence indicates that exposure to childhood CGI has negative implications on asthma prevalence and mental health issues (Desch et al., 2023; Lee et al., 2013b; Muhammad et al., 2018), to our knowledge, no research has estimated the joint effects of asthma and CGI in predicting anxiety, depression, and/or both anxiety and depression among school children 6–17 years of age in the United States. This research focuses on CGI to identify its risks for mental health problems among children and support public policy initiatives to reduce incarceration rates for US adults. A reduction in incarceration rates would reduce the number of children who experience CGI. Therefore, the main purpose of this population-based study was to estimate the association between asthma on anxiety and depression by CGI status among US school-aged children 6–17 years old. This study also identifies the prevalence of anxiety and depression among US children with and without current asthma and/or with or without experiencing CGI.

Methods

Design, Setting, and Participants

This study used data from the nationally representative National Survey of Children's Health (NSCH), an annual cross-sectional survey of US children 0–17 years (Child and Adolescent Health Measurement Initiative, 2022). This research used pooled data from five waves of the NSCH, conducted between 2016 and 2020, and publicly available through the Child and Adolescent Health Measurement Initiative (CAHMI) (Child and Adolescent Health Measurement Initiative, 2019, 2021). The NSCH is sponsored by the Health Resources and Services Administration (HRSA) Maternal and Child Health Bureau. Data collection for NSCH is handled by the US Census Bureau on behalf of the Health Resources and Services Administration's Maternal and Child Health Bureau (Child and Adolescent Health Measurement Initiative, 2021). The US Census Bureau also oversees the NSCH Sampling plan, administers the survey via web or mail, and creates sampling weights (Child and Adolescent Health Measurement Initiative, 2022). The NSCH contains various items that measure the child's physical and mental health, access to quality health care, as well as the child's family, neighborhood, social, and school context. This secondary analysis included school-aged children 6–17 years (N = 125,005) whose adult household respondents completed either the NSCH-T2 for children 6–11 years or the NSCH-T3 for children aged 12–17 years. The nationally representative NSCH datasets contain sampling design variables and sample weights (to adjust for nonresponse and oversampling); these sampling design variables and sample weights need to be used for the estimation of population statistics. This research was approved by the university's Institutional Review Board as nonhuman subject research.

Procedure

The topical questionnaire of the NSCH included detailed questions about one randomly selected child in the household. Households received one of the three age-specific topical questionnaires based on the age of the sampled child: NSCH-T1 (or T1) for children aged 0–5 years, NSCH-T2 (or T2) for children aged 6–11 years, or NSCH-T3 (or T3) for children aged 12–17 years. The survey was completed by an adult in the household who knows the child well. NSCH randomly samples US households and administers surveys by mail, telephone, or the Web. Documentation of the NSCH sampling design, data collection methodology, and variables measured are available online (Child and Adolescent Health Measurement Initiative, 2022). The NSCH contains various items that measure the child's physical and mental health, access to quality health care, as well as the child's family, neighborhood, social, and school context.

Measures

Independent Variables

Asthma

Respondents were asked, “Has a doctor or other health care provider EVER told you that this child has asthma?” Respondents were also asked, “If yes, does this child currently have this condition.” Both questions had binary “Yes” or “No” response options. If they answered “Yes” to both questions, the child was coded as “Yes” for current asthma. If they answered “No” to either question, the child was coded as “No” for current asthma. For the rest of the article, current asthma is abbreviated as asthma.

Caregiver Incarceration

Respondents were asked, “To the best of your knowledge, has this child EVER experienced any of the following?” One of the statements was “Parent or guardian served time in jail.” If they answered “Yes” to this question, the child was coded as “Yes” for CGI. If they answered “No” to this question, the child was coded as “No” for CGI.

Control Variables

The control variables used for this research include sex, age, survey year, and race/ethnicity. Sex was recorded in the dataset as binary: male or female. The race/ethnicity variable used for analyses had four categories: non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Other (everyone not in one of the previous categories). Age was recorded in years. For analytic purposes, all variables and any two-way interactions were coded as categorical variables.

Dependent Variables

Anxiety

Respondents were asked, “Has a doctor or other health care provider EVER told you that this child has Anxiety Problems?” If they answered “Yes” to this question, the child was coded as “Yes” for Anxiety. If they answered “No” to this question, the child was coded as “No” for Anxiety.

Depression

Respondents were asked, “Has a doctor or other health care provider EVER told you that this child has Depression?” If they answered “Yes” to this question, the child was coded as “Yes” for Depression. If they answered “No” to this question, the child was coded as “No” for Depression.

Anxiety or Depression

This variable was created for data analytical purposes based upon responses to the anxiety and depression questionnaire on the original survey. Children were classified into two groups: (1) Yes = either anxiety or depression, or (2) No = neither anxiety nor depression. If responses to either the anxiety or depression questionnaire items were missing, the anxiety or depression item data were coded as missing.

Anxiety and Depression

We created an anxiety and depression variable as one based upon responses to the two items of anxiety and depression. For analytical purposes, children were classified into two groups, (1) Yes = both anxiety and depression, or (2) No = all others with non-missing responses to either anxiety or depression items. Note: if responses to either the anxiety or depression items were missing, the anxiety and depression item's data were coded as missing.

Data Analyses

For this research, multivariate analyses were performed with R and the R survey package, which incorporate the sampling design variables and sampling weights and generate nationally representative weighted estimates (Lumley, 2020; R Core Team, 2020). The R survey package function, “svypredmeans,” was used to estimate average marginal percentages, and the R survey package function, “svycontrast,” was used to estimate adjusted risk differences (RD), additive interactions (AI_RD), and their respective confidence intervals (Bieler et al., 2010; Lumley, 2018) (Note: White = Non-Hispanic White, Black = Non-Hispanic Black, and Other = Non-Hispanic others).

Rationale for Estimation of Additive Interactions and Average Marginal Percentages

As per Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, researchers who investigate interactions should report additive interactions.(Vandenbroucke et al., 2007). The absence of an additive interaction between asthma and CGI on anxiety implies that the increase in anxiety does not vary if children either experienced or did not experience CGI. Thus, if RD_CGI is the risk (percentage) difference for the effect of asthma on anxiety among children who experienced CGI and RD_NoCGI is the risk (percentage) difference for the effect of asthma on anxiety among children who never experienced CGI, then, if there is no additive interaction, the difference, AI_RD=RD_CGI–RD_NoCGI = 0 (Rothman, 2014; Rothman et al., 1980). A significant additive interaction implies that the 95% CI for the difference RD_A–RD_NoA does not include zero. Researchers also recommend that average predicted marginal percentages be reported with binary dependent variables (Bieler et al., 2010; Leeper et al., 2018; Lumley, 2018; Norton et al., 2019). Therefore, this research also reports average predicted marginal percentages (adjusted for covariates), also referred to as adjusted percentages.

Results

Prevalence of Asthma and Caregiver Incarceration by Demographic Characteristics

The prevalence of asthma and CGI is reported by children's sex, age, and race/ethnicity. The results showed that adolescents 12–17 years old had a higher prevalence of asthma (10.1%) and CGI exposure (9.6%) than children 6–11 years old (Table 1). Males were more likely to have asthma (10.3%) compared to females (8.6%) (Table 1). Results also indicate that children from minority racial/ethnic backgrounds had a higher prevalence of asthma (African American: 15.6% and Hispanics: 9.1%) and rates of CGI exposure (African American: 15.8% and Hispanics: 8.2%) compared to Whites (Asthma: 8.2% and CGI: 7.7%) (Table 1).

Table 1.

Prevalence Estimates of Asthma and Caregiver Incarceration Among Children and Adolescents Aged 6–17 Years by Sociodemographic Characteristics: NCHS 2016–2020 (N = 125,005).

	Prevalence of Current Asthma		Prevalence of Children Exposure to CGI
Characteristics	%	[95% CI]	%	[95% CI]
Age
6–11 years	8.9	[8.4, 9.4]	8.5	[7.9, 9.0]
12–17 years	10.1	[9.6, 10.6]	9.6	[9.1, 10.1]
Sex
Male	10.3	[9.8, 10.9]	9.0	[8.5, 9.5]
Female	8.6	[8.1, 9.1]	9.1	[8.5, 9.6]
Race/Ethnicity
Hispanic	9.1	[8.1, 10.1]	8.2	[7.3, 9.1]
Black, non-Hispanic	15.6	[14.4, 16.9]	15.8	[14.4, 17.1]
White, non-Hispanic	8.2	[7.8, 8.5]	7.7	[7.3, 8.1]
Others, non-Hispanic	9.0	[8.0, 9.9]	9.2	[8.9, 10.3]

% = prevalence (average marginal percentage, 95% CI = 95% confidence interval of percentage prevalence). CGI = caregiver incarceration; CI = confidence interval. The race/ethnicity variable used for analyses had four categories: non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Other (the latter included everyone not in one of the three previous categories).

Co-occurrence of Asthma and CGI Among US Children (6–17 Years)

Results show a significant positive association between the prevalence of asthma and the prevalence of CGI. The prevalence of asthma was significantly greater among US school-aged children who experienced CGI than among those who never experienced this ACE (12.8% vs. 9.2%, RD = 3.7%, t = 4.82, p < .001) (Table 2A). Similarly, the prevalence of CGI was significantly greater among US school-aged children with asthma than among those without asthma (12.2% vs. 8.7%, RD = 3.5%, t = 4.85, p < .001) (Table 2B).

Table 2A.

Prevalence of Current Asthma by Caregiver Incarceration (Children Aged 6–17 Years).

Caregiver Incarceration	%	(SE)	RD	[95% CI]	t	p
Yes	12.8	(0.73)	3.7	[2.2, 5.2]	4.82	<.001
No (Ref)	9.2	(0.20)

% = average marginal percentage of children with (current) asthma; SE = standard error of percentage; RD = risk (percentage) difference associated with caregiver incarceration; 95% CI = 95% confidence interval of RD; t = t statistic; p = probability (significance level), Ref = reference group.

Table 2B.

Prevalence of Caregiver Incarceration by Current Asthma (Children Aged 6–17 Years).

Current Asthma	%	(SE)	RD	[95% CI]	t	p
Yes	12.2	(0.69)	3.5	[2.1, 4.9]	4.85	<.001
No (Ref)	8.7	(0.20)

% = average marginal percentage of youth who experienced caregiver incarceration; SE = standard error of percentage; RD = risk (percentage) difference associated with current asthma, 95% CI = 95% confidence interval of RD; t = t statistic; p = probability (significance level); Ref = reference group.

Association Between Asthma and Anxiety Stratified by Caregiver Incarceration

Results show that asthma was significantly associated with increased anxiety among school-aged children 6–17 years in the United States; this association was significantly greater among children who experienced CGI. Among those who experienced CGI, the average marginal percentage of anxiety was significantly higher among those children with asthma (33.3%) than among those with no asthma (17.0%) (RD = 16.2%, 95% CI = [10.3, 22.2], t = 5.38, p < .001). Among children who never experienced CGI, the average marginal percentage of anxiety was significantly higher among those with asthma (18.9%) than those with no asthma (9.6%) (RD = 9.3%, 95% CI = [7.7, 10.9], t = 11.33, p < .001) (Table 3 and Figures 1 and 2).

Figure 1.

Average marginal percentage of US children 6–17 years with diagnosed anxiety, depression, stratified by asthma and caregiver incarceration.

Figure 2.

Percentage increase in diagnosed anxiety, depression associated with asthma among US children (6–17 years) stratified by experience of caregiver incarceration.

Table 3.

Association Between Current Asthma and Diagnosed Anxiety and/Depression Among US Children (6–17 Years) Stratified by Caregiver Incarceration.

	Caregiver	Current Asthma
	Incarceration	Yes		No (Ref)		Effect of Current Asthma				Asthma x Caregiver Incarceration
		%	(SE)	%	(SE)	RD	[95% CI]	t^RD	p	AI_ RD	[95% CI]	t^AI_RD	p
Anxiety	Yes	33.3	(2.93)	17.0	(0.76)	16.2	[10.3, 22.2]	5.38	<.001	7.0	[0.8, 13.1]	2.23	.026
	No	18.9	(0.80)	9.6	(0.19)	9.3	[7.7, 10.9]	11.33	<.001

Depression	Yes	24.2	(2.44)	11.8	(0.66)	12.4	[7.5, 17.4]	4.93	<.001	7.4	[2.3, 12.5]	2.85	.004
	No	9.2	(0.62)	4.2	(0.14)	5.0	[3.8, 6.3]	7.95	<.001

Either Anxiety	Yes	39.5	(3.02)	20.4	(0.84)	19.1	[13.0, 25.3]	6.11	<.001	9.4	[3.0, 15.7]	2.90	.004
or Depression	No	20.5	(0.82)	10.7	(0.21)	9.8	[8.1, 11.4]	11.49	<.001

Both Anxiety	Yes	18.4	(2.19)	8.6	(0.54)	9.8	[5.4, 14.2]	4.39	<.001	5.3	[0.8, 9.8]	2.28	.022
and Depression	No	7.6	(0.57)	3.1	(0.11)	4.5	[3.4, 5.7]	7.77	<.001

Caregiver Incarceration = child experienced caregiver incarceration; % = average marginal percentage of children (6–17 years) with diagnosed anxiety, depression; SE = standard error of estimate; RD = risk (percentage) increase in diagnosed anxiety, depression associated with current asthma (within level of caregiver incarceration); 95% CI = 95% confidence interval; t ^RD = t statistic for RD; p probability (significance level); Ref = reference group; t ^AI_RD = t statistic for additive interaction with RD as outcome variable.

AI_RD = additive interactions-risk differences.

The increase in anxiety associated with asthma was significantly greater among those who experienced CGI (16.2%) than among those who never experienced CGI (9.3%) (AI_RD = 7.0%, 95% CI = [0.8, 13.1], t = 2.23, p = .026) (Table 3 and Figure 2). This significant interaction also implies that the increase in anxiety associated with CGI was significantly greater among children who currently had asthma (14.4%) than among those who did not (9.3%) (Table 4).

Table 4.

Association Between Caregiver Incarceration and Diagnosed Anxiety, Depression Among US Youth (6–17 Years) Stratified by Current Asthma.

	Current	Effect of ACE: Caregiver Incarceration
	Asthma	RD	[95% CI]	t^RD	p
Anxiety	Yes	14.4	[8.4, 20.3]	4.75	<.001
	No	7.4	[5.9, 8.9]	9.51	<.001

Depression	Yes	15.0	[10.1, 19.9]	5.96	<.001
	No	7.6	[6.3, 8.9]	11.23	<.001

Any of Anxiety	Yes	19.0	[12.9, 25.1]	6.09	<.001
or Depression	No	9.6	[7.9, 11.3]	11.10	<.001

Both Anxiety	Yes	10.8	[6.4, 15.2]	4.78	<.001
and Depression	No	5.5	[4.4, 6.6]	10.06	<.001

RD = risk (percentage) increase in diagnosed poor anxiety/depression associated with caregiver incarceration (child experienced caregiver incarceration) within levels of current asthma; 95% CI = 95% confidence interval; t^RD = t statistic for RD; p = probability (significance level); Ref = reference group.

Association Between Asthma and Depression Stratified by CGI

Results show that asthma was significantly associated with increased depression among children; this association was significantly greater among children who experienced CGI. Among those who experienced CGI, the average marginal percentage of depression was significantly higher among those children with asthma (24.2%) than among those with no asthma (11.8%) (RD = 12.4%, 95% CI = [7.5, 17.4], t = 4.93, p < .001). Among children who never experienced CGI, the average marginal percentage of depression was significantly higher among those with asthma (9.2%) than among those with no asthma (4.2%) (RD = 5.0%, 95% CI = [3.8, 6.3], t = 7.95, p < .001) (Table 3 and Figures 1 and 2).

Association Between Asthma and Either Anxiety or Depression Stratified by CGI

Results show that asthma was significantly associated with increased anxiety or depression among school-aged children; this association was significantly greater among children who experienced CGI. Among those children who experienced the CGI, the average marginal percentage of anxiety or depression was significantly higher among those with asthma (39.5%) than among those with no asthma (20.5%) (RD = 19.1%, 95% CI = [13.0, 25.3], t = 6.11, p < .001). Among children who have never experienced CGI, the average marginal percentage of anxiety or depression was significantly higher among those with asthma (20.5%) compared to no asthma (10.7%) (RD = 9.8%, 95% CI = [8.1, 11.4], t = 11.49, p < .001) (Table 3 and Figures 1 and 2).

Association Between Asthma and Both Anxiety and Depression Stratified by CGI

Results show that asthma was significantly associated with the dual diagnosis of anxiety and depression; this association was significantly greater among children who experienced CGI. Among children who experienced CGI, the average marginal percentage of dual diagnosis of both anxiety and depression was significantly higher among those with asthma (18.4%) than among those with no asthma (8.6%) (RD = 9.8%, 95% CI = [5.4, 14.2], t = 4.39, p < .001). Among school-aged children who never experienced CGI, the average marginal percentage of both having anxiety with depression was also significantly higher among those with asthma (7.6%) than among those with no asthma (3.1%) (RD = 4.5%, 95% CI = [3.4, 5.7], t = 7.77, p < .00) (Table 3 and Figures 1 and 2).

Additive Interactions Between Asthma and CGI in Children's Mental Health

Results show that there were significant additive interactions between asthma and CGI (i.e., asthma × CGI on mental health issues, including anxiety and depression, among US school-aged children). The increase in anxiety associated with asthma was significantly greater among those children who experienced CGI (16.2%) than among those who never experienced CGI (9.3%) (AI_RD = 7.0%, 95% CI = [0.8, 13.1], t = 2.23, p = .026) (Table 3 and Figure 2). This significant interaction also implies that the increase in anxiety associated with CGI was significantly greater among children who currently had asthma (14.4%) than among those who did not (9.3%) (Table 4).

The increase in depression associated with asthma was significantly greater among children who experienced CGI (12.4%) than among children who never experienced CGI (5.0%) (AI_RD = 7.4%, 95% CI = [2.3, 12.5], t = 2.85, p = .004) (Table 3 and Figure 2). This significant interaction also implies that the increase in depression associated with CGI was significantly greater among those who currently had asthma (15.0%) than among those who did not have asthma (7.6%) (Table 4).

The increase in either anxiety or depression associated with asthma was significantly greater among school-aged children who experienced CGI (19.1%) than those who never experienced CGI (9.8%) (AI_RD = 9.4%, 95% CI = [3.0, 15.7], t = 2.90, p = .004) (Table 3 and Figure 2). This significant interaction also implies that the increase in anxiety/depression associated with CGI was significantly greater among children who currently had asthma (19.0%) than among children who did not have asthma (9.6%) (Table 4).

Finally, the increase dual diagnosis of both anxiety with depression associated with asthma was significantly greater among school-aged children who experienced CGI (9.8%) than among those who never experienced CGI (4.5%) (AI_RD = 5.3%, 95% CI = [0.8, 9.8], t = 2.28, p = .022) (Table 3 and Figure 2). This significant interaction also implies that the increase in both anxiety and depression associated with CGI was significantly greater among children who currently had asthma (10.8%) than among children who did not have asthma (5.5%) (Table 4).

Discussion

We aimed to estimate the additive interaction effects between current asthma and CGI on anxiety and depression among US school-aged children using five-year pooled national data. This study expands on previous findings by examining the joint effects of childhood asthma and CGI on anxiety and depression in a large nationally representative sample of school children, revealing that their combined effect was greater than the sum of their individual effects. The major findings of this research are that the increase in anxiety, or depression, or anxiety with depression that were associated with asthma were each between 1.7 and 2.2 times greater among children who experienced CGI versus children who never experienced CGI. Also, the increase in anxiety or depression, and/or both anxiety and depression that were associated with CGI were each between 1.7 and 2.2 times greater among children with asthma versus children without asthma. These results illustrate how an adverse childhood experience, in this case CGI, can impact on the association between chronic illness (asthma) and poor mental health outcomes among children.

Poorly controlled asthma among children and adolescents is associated with comorbid anxiety and depression, along with or in combination with low self-esteem compared to their peers with controlled asthma or without a history of asthma (Saragondlu Lakshminarasappa et al., 2021). Anxiety and depressive symptoms were found to disrupt asthma self-management strategies, thus leading to poor asthma control and asthma exacerbations among those children (Leonard et al., 2022). Conversely, a case-control study found that children with well-controlled asthma symptoms did not demonstrate higher rates of depression and anxiety compared to children with poor asthma control (Letitre et al., 2014). Therefore, symptom control and prevention of childhood asthma exacerbations should be a high priority for health care providers regardless of associated CGI. Practicing coping mechanisms and interventions targeting anxiety control is also an important approach among children with asthma, especially in school settings, as this may lead to increased self-confidence in managing asthma and result in the overall improvement of mental health among this population (McGovern et al., 2022).

Most importantly, this research extends prior research findings that addressed the deleterious effect of CGI on children's mental health (Bomysoad & Francis, 2022; Lee et al., 2013a; Turney, 2020). Our findings are consistent with the stress process perspective, which highlights that exposure to stressful adverse childhood events such as CGI have harmful impacts on children's mental health outcomes (Pearlin, 1989; Pearlin et al., 1981), and these deleterious effects can impact the long-term health and well-being (Thompson et al., 2024). This framework is specifically relevant since it explains how early exposure to stressors, such as CGI to their social environment, may negatively impact their physical and mental health (Turney, 2020). Notably, early exposure to CGI may increase the risks of social isolation and low self-esteem from traumatic experiences of witnessing a parent's incarceration and/or facing marginalization and social stigma afterward (The Annie E. Casey Foundation, 2016, 2023). Furthermore, the additive interactions of CGI on depression and anxiety among school-aged children with asthma identify the importance of identifying ways to better mitigate the effects of cumulative ACEs in this population.

Our findings are also consistent with prior research highlighting that ACEs negatively impact health, including asthma, activity limitations, and mental health issues (Turney, 2020), compared to those who did not experience them. Children who experience CGI may have additional ACEs, which further exacerbate health-harming effects of CGI such as poverty, food insecurity, exposure to violence, family substance use, and living with a caregiver who has mental health issues or suicidality (Murphey & Cooper, 2015; Tolliver et al., 2024). These cumulative ACEs may further contribute to the disparity in the prevalence of depression and anxiety among children with CGI and may serve as mediators or moderators that need to be examined in future research. Our findings extend prior research about children with asthma and CGI using population-based representative data, which enhances the generalizability of these findings.

Implications

Based on the racial and ethnic inequities and negative outcomes associated with the US incarceration system, the American Public Health Association gave a set of recommendations to radically change the penal system to a more equitable and justice-oriented system that reduces the number of incarcerated individuals (Stephan et al., 2015). This new system would address underlying social determinants of health, such as poverty, housing insecurity, unemployment, community and family violence, and focus on greater accountability through alternative sanctions, and implement research-driven interventions that foster health and wellbeing for families and communities. Policies and programs that support caregivers of children and teach positive parenting skills will have a profound impact on children with CGI (ODPHP, n.d. B). Reducing the stigma associated with CGI and the negative effects of trauma (Murphey & Cooper, 2015) and reducing the disease burden among children with asthma may decrease some mental health effects associated with CGI and chronic illness.

The US Preventative Task Force recommends universal screening for depression among adolescents 12–18 years old (United States Preventive Health Task Force, 2022). However, expanded screening for both depression and anxiety among school-aged children should be considered. This study has implications among school personnel, school nurses, and pediatric and family providers to identify at-risk children for targeted interventions since children who experience both asthma and CGI are at heightened risk for worse mental health outcomes than those facing one of those conditions. Given the high prevalence of both asthma and CGI in children, this research has implications for comprehensive school-based programs relying on school nurses as important partners in mitigating these negative outcomes. For example, school nurses should be supported in identifying these students at risk through screening for ACEs, universal depression and anxiety screening, fostering improved symptom control among children with asthma, and facilitating access to specialized resources to support children with CGI.

Upstream approaches at the policy level include advocating for routine universal screening for depression and anxiety among all school-aged children and adolescents, with a particular focus on those most at risk, including children with asthma and/or those who experience CGI and other ACEs. Given the United States’ global standing with the highest incarceration rate, further advocacy is needed from school health and education experts to advance trauma-informed approaches to care that address the harmful effects of CGI on children and to drive policies that reduce the alarming rate of imprisonment.

Limitations

Although this is the first population-based study known to examine the effects of asthma and CGI on anxiety and depression of school-aged children, the cross-sectional nature of NSCH data cannot identify causality or estimate the direction of effects of variables. Data on children's CGI were reported by adults-parents/guardians, which may introduce recall bias and or social desirability bias of children's experiences of this ACE exposure. Also, a history of asthma, anxiety, depression, or CGI were based on parental reports, which may have been influenced by a reporting bias of their children's mental health and well-being state. In this study, we solely focused on the interaction effects of asthma and CGIs on mental health outcomes of school-aged children, however, we did not account for the effects of other ACEs in the model. Children who experienced CGI may consistently experience other forms of ACEs, such as abuse, neglect, parental mental illness, domestic violence, and so on. Other ACEs may also serve as potential mediators or moderators in the relationship between asthma, CGI, and mental health outcomes among school-aged children, in which these mechanisms should be examined in future research.

Conclusions

The findings of this study indicate that having current asthma and/or exposure to CGI increases the risk of poor mental health outcomes among school-aged children in the United States. Notably, the combined effects of asthma and CGI on anxiety and/or depression were worse than the individual effects of each variable. This research extends the previous findings by using a large nationally representative heterogeneous sample of school-aged children aged 6–17 years and established that the joint effects of asthma and CGI on anxiety and/or depression are significantly higher than the individual effects. School nurses and other healthcare providers should implement upstream prevention strategies for better asthma control, which may reduce mental health disparities among this population. Similarly, school administrators, school nurses, and other health professionals should advocate for increased awareness about the impact of cumulative ACEs among children with CGI and incorporate strategies to mitigate their health-harming effects among children and adolescents. From a policy perspective, reducing mass incarceration, trauma-informed care initiatives for children who experience CGI, and school-based equity-oriented policies may reduce the burden of mental health issues. Future research should explore efforts to mitigate the negative impacts of asthma and CGI on children's mental health outcomes. Additionally, future research is needed to explore the mechanisms predicting poor mental health outcomes in school-aged children with asthma.

Human Subjects Approval Statement

This study is a secondary analysis of a publicly available dataset, and therefore, this study was approved by the University Institutional Review Board as non-human subject research (IRB: Pro2023001056).

Footnotes

ORCID iDs

Sangita Pudasainee-Kapri

Nancy M. H. Pontes

Manuel C. F. Pontes

Author Contribution(s)

Sangita Pudasainee-Kapri: Conceptualization; Investigation; Methodology; Project administration; Validation; Writing – original draft; Writing – review & editing.

Nancy M. H. Pontes: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Visualization; Writing – original draft; Writing – review & editing.

Manuel C. F. Pontes: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Visualization; Writing – original draft; Writing – review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

Barnert

E. S.

Chung

P. J.

(2018). Responding to parental incarceration as a priority pediatric health issue. Pediatrics, 142(3), e20181923. https://doi.org/10.1542/peds.2018-1923

Bieler

G. S.

Brown

G. G.

Williams

R. L.

Brogan

D. J.

(2010). Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data. American Journal of Epidemiology, 171(5), 618–623. https://doi.org/10.1093/aje/kwp440

Bitsko

R. H.

Claussen

A. H.

Lichstein

Black

L. I.

Jones

S. E.

Danielson

M. L.

Hoenig

J. M.

Jack

Brody

S. P.

Gyawali

D. J.

Maenner

Warner

M. J.

Holland

Perou

K. M.

Crosby

Blumberg

A. E.

Avenevoli

S. J.

Kaminski

Ghandour

J. W.

(2022). Mental health surveillance among children - United States, 2013-2019. Morbidity and Mortality Weekly Report. Supplement, 71(2), 1–42. https://doi.org/10.15585/mmwr.su7102a1

Bitsko

R. H.

Holbrook

J. R.

Ghandour

R. M.

Blumberg

S. J.

Visser

S. N.

Perou

Walkup

J. T.

(2018). Epidemiology and impact of health care provider-diagnosed anxiety and depression among US children. Journal of Developmental and Behavioral Pediatrics, 39(5), 395–403. https://doi.org/10.1097/DBP.0000000000000571

Bomysoad

R. N.

Francis

L. A.

(2022). Associations between parental incarceration and youth mental health conditions: The mitigating effects of adolescent resilience and positive coping strategies. Current Psychology (New Brunswick, N.J.), 41(12), 8746–8757. https://doi.org/10.1007/s12144-021-01351-0

Child and Adolescent Health Measurement Initiative. (2019). 2016-17 Child and Adolescent Health Measurement Initiative (CAHMI). Dataset Downloads. https://www.childhealthdata.org/dataset/download?rq=10048.

Child and Adolescent Health Measurement Initiative. (2021). 2018-19 Child and Adolescent Health Measurement Initiative. Dataset Downloads. https://www.childhealthdata.org/dataset/download?rq=10048.

Child and Adolescent Health Measurement Initiative. (2022). About the National Survey of Children’s Health (NSCH). https://www.childhealthdata.org/learn-about-the-nsch/NSCH.

Child and Adolescent Health Measurement Initiative. (n.d.). 2017-2018 National Survey of Children’s Health (NSCH) data query. Data Resource Center for Child and Adolescent Health supported by the U.S. Department of Health and Human Services, Health Resources and Services Administration (HRSA), Maternal and Child Health Bureau (MCHB). www.childhealthdata.org.

10.

Cobham

V. E.

Hickling

Kimball

Thomas

H. J.

Scott

J. G.

Middeldorp

C. M.

(2020). Systematic review: Anxiety in children and adolescents with chronic medical conditions. Journal of the American Academy of Child and Adolescent Psychiatry, 59(5), 595–618. https://doi.org/10.1016/j.jaac.2019.10.010

11.

Curtin

S. C.

Tejada-Vera

Bastian

B. A.

(2024). Deaths: Leading causes for 2021 (National Vital Statistics Reports, Vol. 73, No. 4). National Center for Health Statistics, Centers for Disease Control and Prevention. https://stacks.cdc.gov/view/cdc/147882

12.

Desch

Mansuri

Tran

Schwartz

S. W.

Bakour

(2023). The association between adverse childhood experiences and depression trajectories in the ADD Health study. Child Abuse & Neglect, 137, 106034. https://doi.org/10.1016/j.chiabu.2023.106034

13.

Felitti

V. J.

(2009). Adverse childhood experiences and adult health. Academic Pediatrics, 9(3), 131–132. https://doi.org/10.1016/j.acap.2009.03.001

14.

Garcia-Sanchez

Darssan

Lawler

S. P.

Warren

C. M.

De Klerk-Braasch

Osborne

N. J.

(2023). Asthma and anxiety development in Australian children and adolescents. Pediatric Allergy and Immunology, 34(3), e13941. https://doi.org/10.1111/pai.13941

15.

Gifford

E. J.

Eldred Kozecke

Golonka

Hill

S. N.

Costello

E. J.

Shanahan

Copeland

W. E.

(2019). Association of parental incarceration with psychiatric and functional outcomes of young adults. JAMA Network Open, 2(8), e1910005. https://doi.org/10.1001/jamanetworkopen.2019.10005

16.

Khazanchi

Heard-Garris

N. J.

Winkelman

T. N. A.

(2023). Health care access and use among children & adolescents exposed to parental incarceration – United States, 2019. Academic Pediatrics, 23(2), 464–472. https://doi.org/10.1016/j.acap.2022.10.001

17.

Lee

R. D.

Fang

Luo

(2013a). The impact of parental incarceration on the physical and mental health of young adults. Pediatrics (Evanston), 131(4), e1188–e1195. https://doi.org/10.1542/peds.2012-0627

18.

Lee

R. D.

Fang

Luo

(2013b). The impact of parental incarceration on the physical and mental health of young adults. Pediatrics, 131(4), e1188–e1195. https://doi.org/10.1542/peds.2012-0627

19.

Leeper

T. J.

Arnold

Arel-Bundock

(2018). Margins: Marginal effects for model objects. R Package Version 0.3, 23.

20.

Leonard

S. I.

Turi

E. R.

Powell

J. S.

Usseglio

MacDonell

K. K.

Bruzzese

J.-M.

(2022). Associations of asthma self-management and mental health in adolescents: A scoping review. Respiratory Medicine, 200, 106897. https://doi.org/10.1016/j.rmed.2022.106897

21.

Letitre

S. L.

de Groot

E. P.

Draaisma

Brand

P. L. P.

(2014). Anxiety, depression and self-esteem in children with well-controlled asthma: Case-control study. Archives of Disease in Childhood, 99(8), 744–748. https://doi.org/10.1136/archdischild-2013-305396

22.

Lumley

T. S.

(2018). Predictive margins like SUDAAN does them. GitHubGist. https://gist.github.com/tslumley/2e74cd0ac12a671d2724.

23.

Lumley

T. S.

(2020). Survey: Analysis of complex survey samples. R package (R package version 4.0) [R]. https://CRAN.R-project.org/package=survey.

24.

McGovern

Arcoleo

Melnyk

(2022). Sustained effects from a school-based intervention pilot study for children with asthma and anxiety. Journal of School Nursing, 38(4), 347–357. https://doi.org/10.1177/1059840520934178

25.

Muentner

Stone

K. J.

Davis

Shlafer

(2022). Youth at the intersection of parental incarceration and foster care: Examining prevalence, disparities, and mental health. Child Abuse & Neglect, 134, 105910. https://doi.org/10.1016/j.chiabu.2022.105910

26.

Muhammad

L. N.

Korte

J. E.

Bowman

C. M.

De Santis

M. L.

Nietert

P. J.

(2018). The effects of asthma and bullying on suicidal behaviors among US adolescents. The Journal of School Health, 88(10), 762–767. https://doi.org/10.1111/josh.12680

27.

Murphey

Cooper

P. M.

(2015). Parents behind bars: What happens to their children? Child Trends. https://cms.childtrends.org/wp-content/uploads/2015/10/2015-42ParentsBehindBars.pdf .

28.

Norton

E. C.

Dowd

B. E.

Maciejewski

M. L.

(2019). Marginal effects-quantifying the effect of changes in risk factors in logistic regression models. JAMA, 321(13), 1304–1305. https://doi.org/10.1001/jama.2019.1954

29.

Office of Disease Prevention and Health Promotion. (n.d., A.). Health People 2030 Objectives: Mental Health and Mental Disorders. https://health.gov/healthypeople/objectives-and-data/browse-objectives/mental-health-and-mental-disorders.

30.

Office of Disease Prevention and Health Promotion. (n.d., B.). Health People 2030: Social and Community Context. https://health.gov/healthypeople/objectives-and-data/browse-objectives/social-and-community-context/reduce-proportion-children-parent-or-guardian-who-has-served-time-jail-or-prison-sdoh-05.

31.

Pearlin

L. I.

(1989). The sociological study of stress. Journal of Health and Social Behavior, 30(3), 241–256. https://doi.org/10.2307/2136956

32.

Pearlin

L. I.

Menaghan

E. G.

Lieberman

M. A.

Mullan

J. T.

(1981). The stress process. Journal of Health and Social Behavior, 22(4), 337–356. https://doi.org/10.2307/2136676

33.

Pudasainee-Kapri

Pontes

N. M. H.

Pontes

M. C. F.

(2023). Sex differences in the relationship between asthma and bullying victimization among high school students in the United States. The Journal of School Health, 93(11), 982–989. https://doi.org/10.1111/josh.13353

34.

R Core Team. (2020). R: A language and environment for statistical computing (4.0) [Mac OSX]. R Foundation for Statistical Computing. https://www.R-project.org/.

35.

Rothman

K. J.

(2014). Six persistent research misconceptions. Journal of General Internal Medicine, 29(7), 1060–1064. https://doi.org/10.1007/s11606-013-2755-z

36.

Rothman

K. J.

Greenland

Walker

A. M.

(1980). Concepts of interaction. American Journal of Epidemiology, 112(4), 467–470. https://doi.org/10.1093/oxfordjournals.aje.a113015

37.

Sacks

Murphey

(2018). The prevalence of adverse childhood experiences, nationally, by state, and by race or ethnicity. Child Trends. https://www.childtrends.org/publications/prevalence-adverse-childhood-experiences-nationally-state-race-ethnicity

38.

Saragondlu Lakshminarasappa

Chandrasekaran

Kandasamy

(2021). Co-morbid anxiety and depression in childhood asthma and its effect on symptom control: A cross sectional study. Pediatric Pulmonology, 56(2), 378–383. https://doi.org/10.1002/ppul.25180

39.

Stephan

S. H.

Connors

Blizzard

A. M.

(2015). Improving quality in school mental health. Advances in School Mental Health Promotion, 8(3), 121–123. https://doi.org/10.1080/1754730X.2015.1053699

40.

The Annie E. Casey Foundation. (2016). Parental incarceration and its impact on children. https://www.aecf.org/resources/a-shared-sentence.

41.

The Annie E. Casey Foundation. (2023). Incarceration issues. Casey Family Programs. https://www.casey.org/parental-incarceration-issue/.

42.

The Sentencing Project. (2024, May 21). Mass incarceration trends. The Sentencing Project. https://www.sentencingproject.org/reports/mass-incarceration-trends/.

43.

Thompson, E. M., Corcoran, F., & Hodges, H. R. (2024). Adverse childhood experiences and resilience: Family resilience as a promotive factor in young children’s flourishing. Sage Open, 14(4), 1–12. https://doi.org/10.1177/21582440241302899

44.

Tolliver

D. G.

Hawks

L. C.

Holaday

L. W.

Wang

E. A.

(2024). Exploring parental incarceration, US government support programs, and child health and well-being: A national cross-sectional study. The Journal of Pediatrics, 264, 113764–113764. https://doi.org/10.1016/j.jpeds.2023.113764

45.

Turney

(2020). Cumulative adverse childhood experiences and children’s health. Children and Youth Services Review, 119, 105538. https://doi.org/10.1016/j.childyouth.2020.105538

46.

United States Preventive Health Task Force. (2022). Final Recommendation Statement: Depressiona dn Suicide Risk in Children and Adolescents: Screening https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/screening-depression-suicide-risk-children-adolescents .

47.

Vandenbroucke

J. P.

von Elm

Altman

D. G.

Gøtzsche

P. C.

Mulrow

C. D.

Pocock

S. J.

Poole

Schlesselman

J. J.

Egger

(2007). Strengthening the reporting of observational studies in epidemiology (STROBE): Explanation and elaboration. PLoS Medicine, 4(10), e297 1628-1654. https://doi.org/10.1371/journal.pmed.0040297

Caregiver Incarceration Heightens the Effect of Asthma on Anxiety and Depression Among School-Aged Children: NCHS 2016–2020

Abstract

Keywords

Methods

Design, Setting, and Participants

Procedure

Measures

Independent Variables

Asthma

Caregiver Incarceration

Control Variables

Dependent Variables

Anxiety

Depression

Anxiety or Depression

Anxiety and Depression

Data Analyses

Rationale for Estimation of Additive Interactions and Average Marginal Percentages

Results

Prevalence of Asthma and Caregiver Incarceration by Demographic Characteristics

Co-occurrence of Asthma and CGI Among US Children (6–17 Years)

Association Between Asthma and Anxiety Stratified by Caregiver Incarceration

Association Between Asthma and Depression Stratified by CGI

Association Between Asthma and Either Anxiety or Depression Stratified by CGI

Association Between Asthma and Both Anxiety and Depression Stratified by CGI

Additive Interactions Between Asthma and CGI in Children's Mental Health

Discussion

Implications

Limitations

Conclusions

Human Subjects Approval Statement

Footnotes

ORCID iDs

Author Contribution(s)

Funding

Declaration of Conflicting Interests

References