Mental health–related hospitalizations among adolescents and emerging adults with autism in the United States: A retrospective,cross-sectional analysis of national hospital discharge data

Abstract

Autistic adolescents and emerging adults experience high rates of mental health conditions—a risk factor for hospitalization. Using nationally representative data from the 2016 HCUP KID, we estimated the prevalence and costs of mental health–related hospitalizations for autistic adolescents and emerging adults in the United States. Hospital discharges for ages 10–20 years (n = 1,346,849) were analyzed using survey-adjusted logistic regression and general linear modeling. Mental health–related hospitalizations among autistic young people (n = 14,368) were mostly for neurodevelopmental, disruptive, depressive, and bipolar disorders. Mental health–related hospitalizations were almost 11 times more likely (OR = 10.98, 95% CI = 10.00–12.00) for autistic youth compared youth with complex and chronic conditions, and two times (OR = 2.03, 95% CI = 1.88–2.19) more likely compared to population controls. Predicted mean service delivery costs of mental health–related hospitalizations for autistic adolescents and emerging adults were US$7,401.23 per stay, resulting in US$106 million in estimated total service delivery costs in 2016. Mental health–related hospitalizations can indicate poor quality care, be traumatic, and increase suicide risk. Community mental health care for autistic young people is needed.

Lay abstract

Autistic young people are more likely to have mental health conditions, like depression and bipolar disorder, than people without autism. These mental health issues sometimes lead to hospitalizations, which can be expensive and traumatic. Because of this, we wanted to understand mental health–related hospitalizations among autistic young people aged 10–20. We found that the main mental health reasons for the hospitalization of autistic young people were neurodevelopmental, disruptive, depressive, and bipolar disorders. These hospitalizations cost an average of US$7401.23 per stay, for a total of US$106 million in service delivery costs in 2016. Mental health–related hospitalizations were compared between young people with autism, young people with complex and chronic conditions, and young people with no chronic conditions. Autistic young people were almost 11 times more likely to be hospitalized for mental health reasons than young people with complex and chronic conditions, and two times more likely than young people with no complex and chronic conditions. We believe the United States needs better community-based mental health care for young people with autism.

Keywords

autism costs HCUP hospitalization mental health survey analysis

Mental health issues are increasingly recognized as a serious threat to the health and well-being of adolescents and emerging adults—that is, young people or youth—in the United States. Adolescence and emerging adulthood are critical transition periods characterized by instability, exploring identities, and assuming the responsibilities of adulthood (Jensen-Arnett, 2004; Lenz, 2001). Mental health issues can increase during these developmental stages and shape long-term health trajectories. Over the past decade, the rates of major depressive episodes increased dramatically among adolescence (52% increase) and emerging adults (63% increase), with emerging adults experiencing skyrocketing rates of suicide-related outcomes (71% increase) (Twenge et al., 2019). Among college students the rates of mental health–related diagnoses and treatment increased by almost 80% and 60%, respectively (Lipson et al., 2019). In addition, most pediatric hospitalizations for mental health conditions (also called mental health–related hospitalizations or psychiatric hospitalizations) are concentrated in the overlapping ages of adolescence and emerging adulthood. In a recent study of hospitalizations of rural young people with mental health conditions, 93% of hospital discharges were for young people ages 10–18 (Bettenhausen et al., 2021).

Adolescents and young adults with disabilities may experience intersecting and compounding stressors that exacerbate the already poor mental health status of their age cohort. Recent studies highlight the increased vulnerability of young people with disabilities to adverse psychosocial and mental health outcomes compared to young people without disabilities (McMaughan, Rhoads, et al., 2021; Platt et al., 2019; Soria et al., 2020). For example, college students with disabilities were more likely to report increased depression and anxiety during the COVID-19 pandemic compared to their peers without disabilities (McMaughan, Rhoads, et al., 2021; Soria et al., 2020). In an analysis of the National Comorbidity Survey Adolescent Supplement (NCS-A), a national population sample of adolescents in the United States, 65% of adolescents with an intellectual disability had a co-occurring mental health issue (Platt et al., 2019). Following this trend, autistic people¹ are diagnosed with mental health conditions at higher rates compared to their allistic (non-autistic) peers, with recent findings suggesting that the vast majority of autistic youth in the United States experience at least one mental health condition (Lai et al., 2019; Rosen et al., 2018). These rates may be even higher as diagnostic overshadowing, where the symptoms of mental health conditions are attributed to autism, contributes to underdiagnosing (Rosen et al., 2018).

The drivers of higher rates of mental health issues among autistic people are complex and multifactorial. Internal factors such as genetic predispositions and neurological processes most likely play a role. However, systemic factors external to the autistic person, experienced as compounding stressors, may also shape overall well-being and at least partially explain the increased rates of mental health conditions, particularly the rates of anxiety and depression (Cage et al., 2018; Cage & Troxell-Whitman, 2019; Cook et al., 2021; Mandy, 2019; Rodriguez et al., 2021). Autistic people are at a greater risk of being bullied, and autistic children and adolescents who experience bullying may be at a greater risk of certain mental health issues, such as anxiety (Rodriguez et al., 2021). Camouflaging, or masking, are strategies used by autistic people to hide autistic traits to appear less autistic and are associated with worse mental well-being (Cage & Troxell-Whitman, 2019; Cook et al., 2021; Mandy, 2019). Autism acceptance by non-autistic people may also influence the mental health of autistic people, with higher levels of acceptance associated with lower levels of mental distress (Cage et al., 2018).

While previous studies have examined hospitalizations among autistic youth, there is minimal research that provides a systematic picture of nationwide, all-payer, mental health–related hospitalizations in community-based hospitals. The existing literature suggests that autistic youth are more commonly hospitalized for psychiatric conditions than allistic youth or youth with chronic conditions (Carbone et al., 2015; Croen et al., 2006; McMaughan, Imanpour et al., 2022; Nayfack et al., 2014). Moreover, having a mental health condition is a risk factor for hospitalization for autistic youth (Righi et al., 2018) and the risk of psychiatric hospitalization increases with age (Mandell, 2008). In addition, this research suggests that autistic youth are more likely to experience mental health–related emergency department (ED) visits than youth without autism, and these ED visits more often lead to autistic youth being hospitalized (Iannuzzi et al., 2015, 2022). Several studies also provide evidence that autistic youth accrue greater medical expenditures than other youth (Croen et al., 2006; Mandell et al., 2006; Peacock et al., 2012). This body of research, while formative, forms a piecemeal glimpse into psychiatric hospitalizations and autism using regional or single-payer healthcare administrative data, nationwide hospitalizations for specific ambulatory care sensitive conditions, charges rather than costs, and single-diagnosis comparison groups or adult-focused chronic condition indicators. In addition, since these studies, many states mandated that health insurance providers cover autism-specific therapies (National Conference of State Legislatures, 2021) which may affect the use of inpatient mental health care. Thus, an updated understanding of psychiatric hospitalizations among young people with autism is needed to direct future policy, research, and service efforts. Our study builds upon previous research on nationwide, all-payer data (Carbone et al., 2015) by focusing on psychiatric conditions as the primary discharge diagnosis, using updated hospitalization data with ICD-10-CM codes, creating comparison groups using pediatric-specific complex and chronic conditions, and analyzing service delivery cost rather than charge data.

Using the nationally representative 2016 Kids’ Inpatient Database (KID) from the Healthcare Cost and Utilization Project (HCUP), we estimated and compared the 1-year rate of mental health–related hospitalizations and average service delivery costs among autistic young people, young people with complex and chronic conditions, and population controls aged 10–20 years (HCUP KID, 2016). We target hospitalizations in autistic adolescents and young adults for multiple reasons. First, adolescence and young adulthood are key developmental periods when mental health issues emerge among autistic youth (Gadke et al., 2016; Simonoff et al., 2008). Second, inpatient care is disruptive, emotionally difficult, can negatively impact the development of disabled youth (Carlton et al., 2021; Lapillonne et al., 2012; Preyde et al., 2017; Turcotte et al., 2018; Woodruff & Choong, 2021). This in particularly germane considering the high readmission rates of mental health–related hospitalizations among youth (Feng et al., 2017). Finally, addressing the mental health needs of autistic people is a recognized research priority (Benevides et al., 2020).

Positionality and community involvement

To describe the context of this study and the researchers who conducted it, we provide our positionality as dimensions of identity, experience, and epistemology. Historically an integral part of qualitative research, positionality is also valuable in quantitative research for understanding the lens through which the research was conducted (Jafar, 2018). Several co-authors are autistic and neurodivergent, and several are caregivers for autistic or neurodivergent youth. There was no other community involvement in this work. Understanding, as Bottema-Beutel and colleagues (2021) asserted, that “language is a powerful means for shaping how people view autism,” we intentionally interrogate our language use in our research and how it dismantles or buttresses ableism and anti-autism sentiments. We acknowledge that language is an expressive form that evolves relative to time, history, culture, and preference, and take responsibility for our own language choices. We also acknowledge that the only way to perceive social reality is from the people enmeshed in that reality and encourage readers to take this into consideration.

Research methods

Data source

Our study design is retrospective and cross-sectional. The HCUP KID is maintained by the Agency for Healthcare Research and Quality (AHRQ) and contains hospital discharge abstracts with sociodemographic data on the patient, ICD-10-CM codes for up to 30 diagnostic levels, the number of days the patient was hospitalized (length of stay), and the amount billed for the hospitalization (total hospital charges). The data are aggregated from over 4000 community hospitals in 46 states and the District of Columbia. This constitutes a 20% stratified sample of all U.S. community hospitals. It is the only nationwide, all-payer pediatric hospitalizations data available for public use in the United States. Hospital characteristics from the 2016 AHRQ HCUP KID Hospital File, the HCUP Clinical Classification Software Refined (CCSR) file, and HCUP Cost-to-Charge Ratio (CCR) file were merged with the 2016 KID hospital discharge file to create the final analytic file. The Oklahoma State University institutional review board (IRB) exempted this study.

Population

The 2016 HCUP-KID contains 3,116,445 discharge abstracts of hospitalized people ages 0–20 in the United States in 2016. The weighted population size is 6,262,821. The HCUP does not contain unique identifiers for each person hospitalized. This means the unit of analysis is the hospitalization rather than the individual person, and people can be represented more than once in the data limiting the independence of the observations. While many definitions exist in the literature, adolescence is often defined as the ages between 10 and 18 years, and young or emerging adulthood from about 18 to 25 (Arnett, 2018; Tanner & Arnett, 2016). Hospitalized adolescents and young adults between the ages of 10 and 20 years were identified using age at admission for a weighted subpopulation size of 1,348,488. We further categorized the discharges as associated with autism, complex and chronic conditions, or population controls to create comparison groups.

Adolescents and emerging adults with autism (autism group)

Autistic adolescents and emerging adults were identified in the data using discharge abstracts containing the ICD-10-CM codes for autism (ICD-10 F84.0), Asperger’s (ICD-10 F84.5), or pervasive developmental disorders (ICD-10 F84.9) in any of the 30 diagnosis levels.

Adolescents and emerging adults with complex and chronic conditions (CCC group)

Young people with complex and chronic conditions were identified based on whether the hospital discharge abstract contained at least one of the complex and chronic conditions from the Pediatric Complex Chronic Conditions Classification System (Feudtner et al., 2014). This system uses ICD-10 codes to determine whether a hospital discharge contains an ICD-10 code associated with a complex and chronic condition, which was defined as

any medical condition that can be reasonably expected to last at least 12 months (unless death intervenes) and to involve either several different organ systems or one organ system severely enough to require specialty pediatric care and probably some period of hospitalization in a tertiary care center. (Feudtner et al., 2014)

The codes are aggregated into over 60 subcategories and 12 categories that include neuromuscular, cardiovascular, respiratory, renal, gastrointestinal, hematologic, immunologic, metabolic, other congenital or genetic defect, malignancy, and prematurity and neonatal conditions.

Population controls (PC group)

Young people with no chronic conditions served as population controls and were identified based on whether the discharge abstract contained no diagnosis of autism and none of the complex and chronic condition diagnoses.

Measures

Outcome variables

Mental health–related hospitalization

Mental health–related hospitalizations were identified using the Clinical Classification Software Refined (CCSR). The CCSR aggregated ICD-10 codes into 34 clinically meaningful categories describing mental health–related diagnoses. These include mental, behavioral, and neurodevelopmental disorders such as mood disorders, substance-related disorders, and suicide-related behaviors associated with ICD-10 codes ranging from F01–F99, and the CCSR body system abbreviation “MBD.” For this analysis, we used the MBD categories MBD001-MBD014, excluding substance-related disorders and categories inappropriate for the primary discharge diagnosis (see Supplemental Appendix 1 for the MBD categories and associated ICD-10 CM codes used to identify mental health–related discharges).

A discharge was considered a mental health–related discharge if any of these codes were listed as the primary discharge diagnosis. We reported the default principal CCSR categories rather than other CCSR categories or ICD-10-CM codes. The default principal CCSR categories represent the condition responsible for hospitalization. Using the CCSR instead of ICD-10-CM codes is recommended when reporting utilization statistics like length of stay and costs (Agency for Healthcare Research and Quality, 2021).

Several mental health–related hospitalization variables were created. The first was an overall variable indicating whether the individual discharge abstract had any of the CCSR categories describing mental health–related diagnosis as the primary discharge diagnosis. Since the primary discharge diagnosis is considered the reason for hospitalization, this binary variable was coded as “mental health related hospitalization” (yes/no). The remaining mental health–related hospitalization variables were a series of binary variables indicating whether the primary discharge diagnosis on the discharge abstract was for each one of the 14 CCSR categories used to identify mental health–related discharges (e.g. “anxiety” yes/no; “disruptive, impulse control, and conduct disorder” yes/no).

Service delivery costs

Service delivery costs were included as a continuous outcome variable estimating the total costs associated with treating the patient during a mental health–related hospitalization. The HCUP KID contains the total hospital charges, which is the amount the hospital billed for the hospitalization. Service delivery costs include this amount plus costs not typically captured by hospital charges, for example, labor, supplies, and overhead. Charges were converted to service delivery costs with the HCUP Cost-to-Charge Ratio (CCR) file. The CCR file multiplies total hospital charges by the CCR to estimated service delivery costs using a cost-to-charge ratio for each hospital (Pickens et al., 2021). Service delivery costs for mental health–related hospitalizations were estimated for each group.

Length of stay

To determine average length of stay, we used the HCUP variable that calculated length of stay for each hospitalization that included an overnight stay by subtracting the date of admission from the date the person was discharged from the hospital. This variable was averaged over all mental health–related hospitalizations for each group.

Control variables

Sociodemographic characteristics

Age (10–20), gender (male or female), race and ethnicity (Asian or Pacific Islander, Black, Hispanic, Native American, White, and Other), rural (yes/no), median household income (US$1–US$42,999; US$$43,000–US$53,999; US$54,000–US$70,999; and US$71,000 and up), primary payer (Medicaid, Medicare, private, self-pay, no charge, and other), and emergency department use (yes/no) were included as sociodemographic characteristics. Median household income and rurality were determined by AHRQ HCUP using the patient’s residential zip code listed on the hospital discharge abstract. Evidence of emergency department use was also determined by AHRQ HCUP and based on whether the hospital discharge abstract (1) contained an emergency department revenue code or charge, or (2) the point of origin or admission source was the emergency department.

Hospital characteristics

Hospital characteristics included region (Northeast, Midwest, South, and West), ownership (governmental but non-federal, not-for-profit, and for profit), and hospital size (small, medium, or large). Hospital region was based on census regions, and size was determined by AHRQ HCUP using the number of short-term acute care beds staffed by the hospital plus the hospital’s location and teaching status.

Statistical analyses

HCUP hospital discharge weights were applied to create national estimates, minimize bias in the standard errors, and account for the lack of independence of observations. These weights were based on stratifying hospitals on rurality, census region, size, teaching status, ownership, and whether the hospital was a children’s hospital. Rates and 95% confidence intervals were calculated for sociodemographic characteristics, hospital characteristics, and each CCSR code associated with mental health–related diagnoses. We tested for significant differences in sociodemographic and hospital characteristics and mental health–related diagnoses between the autism group and the comparison groups (autism vs CCC, and autism vs PC) using one of the two approaches depending on the type of variable (categorical or continuous). For the categorical variables, differences in rates of sociodemographic and hospital characteristic and mental health–related diagnosis between the comparison groups (autism vs CCC, and autism vs PC) were computed using the difference between weighted proportions. For the continuous variables we calculated the two-sample t-test for difference in means with sampling weights between the autism group and the comparison groups (autism vs CCC, and autism vs PC). Logistic regression models and gamma general linearized cost analyses were used to estimate the adjusted odds of a mental health–related hospitalization and the predicted mean service delivery costs associated with mental health–related hospitalizations across the autism group and the comparison groups (autism vs CCC, and autism vs PC). Hospital cost data tend to be positively skewed, with a few hospitalizations having very high costs. The gamma model accounts for this skewness by down-weighting “outlier” hospital discharges with unusually high costs (Barber & Thompson, 2004). Predicted mean service delivery costs for each comparison group were calculated using the mean of the predicted values of service delivery cost for each observation.

Results

Table 1 presents the weighted sample statistics across discharges of autistic adolescents and emerging adults, the complex and chronic conditions group, and population controls. Most of the discharges (73%) were for adolescents and young adults without chronic conditions (the population controls), followed by those with complex and chronic conditions (24%). Autistic young people represented about 2% of the weighted discharge sample. Autistic adolescents and young adults were more likely to be male, White, and live in an area with a higher median household income compared to those with complex and chronic conditions and population controls.

Table 1.

Description of the weighted sample of hospital discharges for adolescents and emerging adults ages 10–20, United States, 2016, N = 1,348,488.

Characteristic	Autism	CCC	PC	p (Autism vs CCC)	p (Autism vs PC)
Discharges per group	2.36%(31,814)	24.18%(326,089)	73.46%(990,585)	<0.001	<0.001
Patient characteristics % (n) and mean (SD)
Age	14.9(3.5)	15.9 (3.7)	16.9 (3.4)	<0.001	<0.001
Gender: Female	24.8% (7,899)	52.1% (169,992)	70.8% (702,141)	<0.001	<0.001
Race and ethnicity
White	62.8% (20,007)	46.4% (151,523)	47.0%465,566)	<0.001	<0.001
Black	11.6%(3,693)	20.8% (67,886)	17.5%173,959)	<0.001	<0.001
Hispanic	11.2%(3,563)	17.7%57,767)	21.6%214,568)	<0.001	<0.001
Asian or Pacific Islander	1.90% (606)	2.59% (8446)	2.01%19,951)	<0.001
Native American	0.39%125)	0.66%(2175)	0.97%(9648)	<0.001	<0.001
Other	3.69%1176)	4.01%13,076)	4.21%41,752)		<0.05
Median household income
US$1–US$42,999	24.0%7641)	30.3%98,918)	34.8%344,854)	<0.001	<0.001
US$43,000–US$53,999	24.4%7791)	23.3%76,066)	25.2%250,413)	<0.05
US$54,000–US$70,999	25.0%7978)	23.3%76,085)	21.9%217,824)	<0.01	<0.001
US$71,000+	25.0%7966)	21.1%69,072)	16.5%163,459)	<0.01	<0.01
Primary payer
Medicare	1.16%372)	1.31%(4297)	0.41%(4105)		<0.001
Medicaid	48.8%15,539)	46.6%152,002)	54.1%535,942)	<0.05	<0.001
Private	44.1%14,046)	43.8%143,134)	37.3%370,333)		<0.001
Self-pay	1.23%394)	3.15%10,277)	3.95%39,177)	<0.001	<0.001
No charge	0.02%7)	0.16%(516)	0.20%1980)	<0.001	<0.001
Other	4.46%(1419)	4.74%(15,457)	3.75%37,178)		<0.05
Rural (patient)	20.0%6386)	21.0%(68,787)	25.0%248,268)		<0.001
ED service use	49.4%15,730)	48.5%158,461)	43.2%(428,283)		<0.001
Hospital characteristics,
Region of hospital
Northeast	22.3%(1199)	17.2%56,112)	16.0%159,220)	<0.001	<0.001
Midwest	26.6%1257)	21.9%71,474)	22.8%226,621)	<0.05	<0.05
South	33.7%1660)	39.2%127,813)	40.7%403,247)	<0.01	<0.001
West	17.2%747)	21.6%70,690)	20.3%201,497)	<0.001	<0.05
Hospital bed size
Small	8.65%2754)	11.0%360,616)	13.3%132,255)	<0.05	<0.001
Medium	24.1%7697)	23.2%75,666)	26.3%260,730)
Large	67.1%21,363)	65.7%214,407)	60.3%507,600)		<0.001
Hospital ownership
Government^a	12.6%4015)	12.9%42,322)	14.0%139,400)
Private, not for profit	79.2%25,220)	78.7%256,893)	70.5%699,042)		<0.001
Private, for profit	8.10%2580)	8.24%26,875)	15.3%152,143)		<0.001

Source: Healthcare Cost and Utilization Project (HCUP) Kids’ Inpatient Database (KID), 2016.

CCC: complex and chronic conditions; PC: population controls; ED: emergency department.

Non-federal.

Mental health–related discharges

Table 2 presents mental health–related hospitalizations for adolescents and emerging adults in 2016. Almost half of the total discharges (45%) among autistic young people were mental health–related discharges, compared to 6% for young people with complex and chronic conditions and 23% for population controls. The mean service delivery costs for mental health–related hospitalizations were US$7401.23 per event among autistic young people, which was lower than the mean service delivery costs for the complex and chronic condition group (US$9506.88) but higher than costs for population controls (US$5388.08). The average length of stay for mental health related hospitalizations was highest among autistic young people (8.42 compared to 7.24 for the CCC group and 6.02 for the PC group).

Table 2.

Weighted prevalence of mental health related hospitalizations for adolescents and emerging adults by group ages 10–20, United States, 2016, N = 1,348,488.

Primary reason for hospitalization	Autism (n = 31,814)	CCC (n = 326,089)	PC (n = 990,585)	p (autism vs CCC)	p (autism vs PC)
Any mental health condition	46.4%(14,787)	6.33%(20,672)	22.9%(227,025)	<0.001	<0.001
Adjusted mean costs per event^a	US$9515.17	US$12,784.98	US$7315.97	<0.001	<0.001
Average length of stay	8.42	7.24	6.02	<0.01	<0.001
Specific mental health conditions
Anxiety	0.81%(257)	0.09%(316)	0.39%(3918)	<0.001	<0.001
Bipolar disorder	5.72%(1819)	0.61%(2012)	2.70%(26,765)	<0.001	<0.001
Depressive disorders	10.0%(3192)	1.8%(6165)	10.7(106,516)	<0.001	Not significant
Other specified and unspecified mood disorders	6.60%(2101)	0.36%(1197)	1.99%(19,790)	<0.001	<0.001
Disruptive, impulse control, and conduct disorder	5.77%(1835)	0.20%(649)	0.77%(7726)	<0.001	<0.001
Schizophrenia	4.03%(1283)	0.45%(1480)	1.89%(17,231)	<0.001	<0.001
Obsessive compulsive disorder	0.20%(62)	0.01%(40.1)	0.03%(381)	<0.01	<0.01
Trauma	1.62%(515)	0.31%(1011)	1.64%(16,326)	<0.001	Not significant
Personality disorders	0.13%(42)	0.03%(115)	0.12%(1248)	<0.01	Not significant
Feeding and eating disorders	0.28%(88)	0.6%(2184)	0.12%(1507)	<0.001	<0.05
Somatic disorders	0.03%(11)	0.02%(76.1)	0.02%(261)	not significant	Not significant
Suicide	1.57%(502)	1.58%(5144)	2.20%(21,840)	not significant	<0.001
Miscellaneous^b	0.06%(21)	0.02%(79)	0.04%(467)	not significant	Not significant
Neurodevelopmental	9.62%(3061)	0.06%(207)	0.30%(3048)	<0.001	<0.001

Source: Healthcare Cost and Utilization Project (HCUP) Kids’ Inpatient Database (KID), 2016.

CCC: complex and chronic conditions; PC: population controls.

Adjusted for race, gender, age, payer, location, income, ED use, hospital ownership, hospital size, and hospital region using general linear modeling to account for skewness. ^bMiscellaneous includes ICD-10 codes related to gender identity, gambling, sexual disorders, other and unspecified childhood emotional disorders, and mental disorder not otherwise specified.

Using total discharges as the denominator, depressive disorders (10%) and neurodevelopmental disorders (9.6%) were the most common reasons for mental health–related hospitalizations among adolescents and young adults with autism, followed by other mood disorders (6.6%); disruptive, impulse control, and conduct disorders (5.7%); bipolar disorder (5.7%); and schizophrenia (4%). Depressive disorders were also the most common reason for mental health–related discharges among population controls (10%) and youth with complex and chronic conditions (1.8%). Autistic adolescents and emerging adults had significantly higher rates of hospitalizations due to anxiety, bipolar disorder, other mood disorders, disruptive, impulse control, and conduct disorder, schizophrenia, obsessive compulsive disorder, eating disorders, and neurodevelopmental disorders compared to young people with complex and chronic conditions and young people in the population controls group.

Table 3 presents the adjusted odds of a discharge with a primary diagnosis of any one of the 14 CCSR mental health diagnoses for autistic young people compared with discharges of young people with complex and chronic conditions and population controls. After adjusting for sociodemographic and hospital characteristics the adjusted odds of mental health–related hospitalizations were higher for adolescents and emerging adults with autism compared to any other group (autism vs CCC: OR = 10.98, 95% CI: 10.0–12.0 and autism vs PC: OR = 2.03, 95% CI: 1.88–2.19).

Table 3.

Adjusted odds of a primary discharge diagnosis of any mental health condition for adolescents and emerging adults ages 10–20, United States, 2016, N = 1,348,489.

	Adjusted odds ratio (95% CI) any mental health condition
Autism vs CCC	10.98 (10.0–12.0)**
Autism vs PC	2.03 (1.88–2.19)**
Patient characteristics
Age	0.92 (0.90–0.94)**
Gender: female	0.82 (0.77–0.86)**
Race and ethnicity
White	Reference
Black	0.79 (0.73–0.85)**
Hispanic	0.52 (0.44–0.61)**
Asian or Pacific Islander	0.81 (0.70–0.94)*
Native American	1.24 (0.92–1.67)
Other	0.73 (0.63–0.84)**
Missing	1.17 (0.85–1.61)
Median household income
US$1–US$42,999	Reference
US$43,000–US$53,999	1.16 (1.07–1.25)***
US$54,000–US$70,999	1.23 (1.13–1.34)***
US$71,000+	1.34 (1.17–1.52)***
Primary payer
Medicare	Reference
Medicaid	0.67 (0.54–0.83)**
Private	0.79 (0.64–0.98)*
Self-pay	0.81 (0.64–1.02)
No charge	0.93 (0.67–1.27)
Other	0.97 (0.76 –1.23)
Patient location: rural	0.86 (0.75–0.98)*
ED service use	1.17 (1.01–1.34)*
Hospital characteristics
Region of hospital
Northeast	Reference
Midwest	1.33 (1.01–1.74)
South	0.74 (0.57–0.95)*
West	0.52 (0.40–0.68)***
Hospital bed size
Small	Reference
Medium	1.81 (1.32–2.48)***
Large	1.72 (1.29–2.29)***
Hospital ownership
Government^a	Reference
Private, not for profit	0.81 (0.63–1.05)
Private, investor-owned	1.02 (0.70–1.50)

Source: Healthcare Cost and Utilization Project (HCUP) Kids’ Inpatient Database (KID), 2016.

CI: confidence interval; CCC: complex and chronic conditions; PC: population controls; ED: emergency department.

Non-federal.

p < 0.05. **p < 0.01. ***p < 0.001.

Table 4 presents the influence of sociodemographic and hospital characteristics on the odds of a mental health–related hospitalization among adolescents and emerging adults with autism. Age and number of chronic conditions were associated with mental health–related discharges among autistic young people. Each 1-year increase in age resulted in a 7% increase in the odds of a mental health–related discharge (OR = 1.07, 95% CI: 1.05–1.10) while the presence of any chronic condition resulted in a 15% decrease (95% CI: 0.13–0.17).

Table 4.

Adjusted odds of a mental health–related hospitalization for adolescents and emerging adults ages 10–20 with autism, United States, 2016, N = 31,814.

Characteristic	Adjusted odds ratio (95% CI)
Patient characteristics
Age	1.07 (1.05–1.10)***
Presence of any other chronic condition	0.15 (0.13–0.17)***
Gender: Female	1.06 (0.97–1.15)
Race and ethnicity
White	Reference
Black	0.94 (0.81–1.10)
Hispanic	0.59 (0.46–0.76)***
Asian or Pacific Islander	0.79 (0.57–1.09)
Native American	0.92 (0.45–1.88)
Other	0.73 (0.55–0.95)*
Missing	1.19 (0.70–2.00)
Median household income
US$1–US$42,999	Reference
US$43,000–US$53,999	1.07 (0.93–1.22)
US$54,000–US$70,999	1.02 (0.88–1.22)
US$71,000+	0.94 (0.76–1.17)
Missing	1.34 (0.95–1.89)
Primary payer
Medicare	Reference
Medicaid	1.11 (0.81–1.53)
Private	1.08 (0.77–1.51)
Self–pay	1.22 (0.75–1.97)
No charge	2.92 (0.11–75.5)
Other	1.17 (0.79–1.74)
Missing	2.20 (0.78–6.21)
Patient location: rural	0.84 (0.69–1.02)
ED service use	0.97 (0.79–1.19)
Hospital characteristics
Region of hospital
Northeast	Reference
Midwest	1.60 (1.00–2.54)
South	0.88 (0.58–1.33)
West	0.64 (0.40–1.00)
Hospital bed size
Small	Reference
Medium	2.09 (1.26–3.46)**
Large	1.64 (1.05–2.55)*
Hospital ownership
Government^a	Reference
Private, not for profit	0.61 (0.42–0.88)*
Private, investor-owned	0.91 (0.51–1.62)

Source: Healthcare Cost and Utilization Project (HCUP) Kids’ Inpatient Database (KID), 2016.

CI: confidence interval; ED: emergency department.

Non-federal.

p < 0.05. **p < 0.01. ***p < 0.001.

Discussion

In our study, we characterized differences in the rates and likelihood of mental health–related hospitalizations in community hospitals between autistic and non-autistic youth using the largest data set of pediatric hospital discharge abstracts in the United States. To our knowledge, this study is the first all-payer, nationally representative analysis of mental health–related hospitalizations of autistic young people that uses ICD-10 codes, the pediatric complex and chronic conditions algorithm to create comparison groups, and service delivery costs rather than charges.

Rates and likelihood of mental health–related hospitalizations

Our study suggests that, among all hospitalizations of young people in the United States, hospitalizations for mental health conditions in adolescents and emerging adults with autism are more frequent than for young people without autism. Just under half (45%) of hospital discharges with a diagnosis of autism were mental health discharges. After controlling for sociodemographic and hospital characteristics, we found that autistic youth were almost 11 times more likely than youth with complex and chronic conditions, and two times more likely than population controls to have a mental health–related hospitalization. Previous studies in a variety of contexts confirm our results and suggest that mental health care for autistic people has not improved in the decades since these formative studies (Carbone et al., 2015; Croen et al., 2006; Nayfack et al., 2014).

Study results in the context of psychiatric hospitalizations in the United States

Hospital admissions for mental health issues are on the rise in the United States. Even though all-cause hospitalizations dropped 6.6% from 2005 to 2014, mental health–related hospitalizations rose by 12.2% during that same period (McDermott et al., 2017). According to recent estimates, inpatient mental health care accounts for just over 10% of all types of mental health services utilized by adolescents in the United States (Mojtabai & Olfson, 2020). It is the most costly and restrictive form of care and typically reserved for rapid crisis stabilization in the event of immediate danger of self-harm or harm to others. Psychiatric hospitalization can be necessary to stabilize and protect young people in crisis, however, the use of community general hospitals still raises questions. Hospitalization in community general hospitals can provide a safe and stable holding environment while medications are revisited, and post-discharge supports and services are lined up. However, according to best practices, hospitalization should only be used to address mental health issues when less restrictive options are unavailable (American Academy of Child and Adolescent Psychiatry, 1989).

It is concerning that even with the decades-long push for community-based mental health care in the United States, there is still a nationwide shortage of mental health care providers, particularly for young people (McBain et al., 2019). This may leave community hospitalizations as the only option for mental health care for youth in crisis. As an example of mental health care provider shortages, in a longitudinal study of the distribution of child psychiatrists in the United States, McBain et al. (2019) reported that 70% of U.S. counties had no child psychiatrist in 2007 and 2016. The impact of these shortages is compounded among young autistic people, as health care providers with an understanding of the unique support needs of people with autism are limited (Morris et al., 2019). Outpatient and community-based services may be unable to provide adequate care to prevent or manage mental health-related crises, particularly when experienced by autistic people (Maddox et al., 2020). In a study of about 8000 mental health treatment facilities in the United States, Cantor and colleagues (2020) found that less than half (43%) provided care for autistic youth, and an abysmal 12% had a clinician with training in autism care. Focusing on community-based care, they found only half of outpatient mental health care facilities offered services for autistic youth (Cantor et al., 2021). In addition, while evidence suggests that specialized inpatient care conveys benefits for autistic people, general psychiatric inpatient units may not have the resources or skills to provide appropriate services for people with autism (McGuire & Siegel, 2018). Indeed, we found that the rate of mental health discharges for autistic patients was over 600% higher than the rate of mental health discharges for patients with other chronic conditions and just over 100% higher than patients without chronic conditions. This disparity between young autistic people and young people without autism may suggest a gap in accessible and appropriate community-based care for people with autism.

Costs

In addition to signaling a potential failure of community-based supports and services, the use of community general hospitals for autistic young people with mental health conditions is expensive. For example, using claims data from the Health Care Cost Institute, which includes data from the commercial health insurers Aetna, Humana, and UnitedHealth, Mandell and colleagues (2019) estimated the spending (the amount patients paid out-of-pocket plus the amount insurers paid to the hospitals) associated with psychiatric hospitalizations for youth with autism at an average of US$4000 per week. While lower than our findings because of the different formulas used to calculate service delivery costs versus spending, data from all-payers versus three commercial health insurers, and different timeframes, the conclusions are similar: psychiatric hospitalizations are an expensive form of mental health care for autistic youth (Mandell et al., 2019). Similarly, in our study, we found that the service delivery costs associated with mental health related hospitalizations of autistic youth was US$7401.23 per episode for a total of US$106,337,568 in 2016 dollars, which was less than the service delivery costs per episode for mental health discharges with complex chronic conditions (US$9506.88, a 28% decrease), but several thousand dollars more than population controls (US$5388.08, a 27% increase). Our results line up with previous studies, in which medical expenditures and psychiatric inpatient care of young people with autism were greater than those of the general population (Croen et al., 2006; Mandell et al., 2006; Peacock et al., 2012).

Limitations

This study has several limitations associated with the constraints of the data. First, the analysis only contains 2016 data. Second, due to the absence of unique identifiers, we are unable to discern primary admissions from readmission and to generate readmission rates. While we may interpret our results as more autistic adolescents and emerging adults being hospitalized for mental health related conditions compared to others, an alternative scenario is higher readmission rates among fewer autistic patients. This can appear as more autistic patients being hospitalized compared to other patients, when in fact a smaller number of autistic patients are hospitalized multiple times during the year the data were collected. Finally, another limitation is the dependence on ICD-10 codes on hospital discharge abstracts to determine if a discharge was associated with an autistic adolescent or emerging adult. It is possible that a young person with autism was admitted to the hospital, but autism was not mentioned or noted in the hospital abstract. In their study of health administrative data in Canada, Bickford and colleagues (2020) found that single encounter ICD-9-based algorithms did not distinguish well between children with autism and children with similar developmental diagnoses. Thus, it is also possible that our subgroup of autistic adolescents and young adults contains discharges of patients without autism. However, Dodds and colleagues (2009) concluded that using diagnostic codes and healthcare administrative data are moderately successful at identifying autistic people, with the use of just one encounter (as in our current study) representing a cost-efficient and accessible way to maximize sensitivity and conduct large-scale autism research. Without an all-payer, nationally representative database with patient identifiers and multiple encounters, we follow the lead of other researchers conducting autism research using the HCUP series (Akobirshoev et al., 2020; Carbone et al., 2015) and make our limitations known.

Implications

Our results demonstrate that mental health–related hospitalizations are still a leading cause of expensive inpatient utilization among autistic youth, even after substantial policy changes intended to improve health care for people with autism. This is problematic, as mental health–related hospitalizations in community-based hospitals suggest a deficit in community-based care (Chen et al., 2018), are costly, and disruptive. Young people hospitalized for mental health issues may feel lonely, inadequate, fearful, criminalized, and stigmatized (Geanellos, 2002; Rice et al., 2021). There is also an increased risk of suicide post-discharge (Doupnik et al., 2018). This is especially problematic as rates of suicidal behavior are already higher in autistic adolescents and young adults compared to others (Conner et al., 2020; Culpin et al., 2018; Kirby et al., 2019).

While mental health conditions are more common among autistic people and may explain the higher rates of psychiatric hospitalizations compared to allistic people, the higher rates of mental health–related hospitalizations should still be considered an indicator of health disparities. Furthermore, the higher rates of hospitalizations should not be justified because there is a lack of appropriate, effective, less restrictive, and less disruptive community-based care. The question is what systemic changes to supports and services need to occur to better support and promote the mental well-being of young autistic people. With adolescence and early adulthood being transitional periods for long-term trajectories of health, leaving these issues unrectified has ramifications for the future adults lives of autistic adolescents and young adults.

Future research

Alternatives to inpatient care, such as intensive home and community-based services and autism-specific outpatient services, are showing promise (Boege et al., 2021; Cidav et al., 2014; Mandell et al., 2019; Ougrin et al., 2021) and should be explored in future research. In line with this, the American Psychological Association issued a call to action for mental health care providers to develop competencies in working with people with autism (Huff, 2021). As such, it would be beneficial to understand the role of social inclusion in those pathways as social inclusion can affect symptom severity, treatment, and prognosis of mental health conditions. Social inclusion, which includes social support, community participation, and access to competent mental health services (Huxley et al., 2012), can protect against poor mental well-being (Cage et al., 2018; Kapp, 2018), and is a priority among autistic people (Benevides et al., 2020). Research to understand the role of social inclusion in reducing the need for psychiatric hospitalizations among young people with autism is key to improving the mental health and well-being of autistic people. This research must be conducted with the autistic community (Poulsen et al., 2022). Co-creating this knowledge with transparency, an understanding of contexts in the autistic community, and through acknowledging the authority of autistic people in their lived experiences is important as conceptual models incorporating community experiences are more relevant and beneficial to the community and more effectively translated into practice (Jagosh et al., 2012; McMaughan, Grieb, et al., 2021).

Conclusion

Autistic adolescents and emerging adults are more likely to be hospitalized for mental health–related hospitalizations compared to others. These hospitalizations are expensive, can indicate poor quality care, be traumatic, and increase suicide risk. Community mental health care for autistic young people is needed.

Supplemental Material

sj-docx-1-aut-10.1177_13623613221143592 – Supplemental material for Mental health–related hospitalizations among adolescents and emerging adults with autism in the United States: A retrospective, cross-sectional analysis of national hospital discharge data

Supplemental material, sj-docx-1-aut-10.1177_13623613221143592 for Mental health–related hospitalizations among adolescents and emerging adults with autism in the United States: A retrospective, cross-sectional analysis of national hospital discharge data by Darcy Jones (DJ) McMaughan, Sara Imanpour, Abigail Mulcahy, Jennifer Jones and Michael M Criss in Autism

Footnotes

Declaration of conflicting interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Darcy Jones (DJ) McMaughan

Supplemental material

Supplemental material for this article is available online.

Notes

References

American Psychological Association. (2022). Disability. APA Style. https://apastyle.apa.org/style-grammar-guidelines/bias-free-language/disability

Agency for Healthcare Research and Quality. (2021). HCUP clinical classification software refined (CCSR) ICD-10-CM diagnoses (v2022.1). https://hcup-us.ahrq.gov/toolssoftware/ccsr/DXCCSR-User-Guide-v2022-1.pdf

Akobirshoev

Mitra

Dembo

Lauer

(2020). In-hospital mortality among adults with autism spectrum disorder in the United States: A retrospective analysis of U.S. hospital discharge data. Autism, 24(1), 177–189. https://doi.org/10.1177/1362361319855795

American Academy of Child and Adolescent Psychiatry. (1989). Inpatient hospital treatment of children and adolescents. https://www.aacap.org/AACAP/Policy_Statements/1989/Inpatient_Hospital_Treatment_of_Children_and_Adolescents.aspx

Arnett

J. J.

(2018). Conceptual foundations of emerging adulthood. In Murray

J. M.

Arnett

J. J.

(Eds.), Emerging adulthood and higher education (pp. 11–24). Routledge.

Barber

Thompson

(2004). Multiple regression of cost data: Use of generalised linear models. Journal of Health Services Research & Policy, 9(4), 197–204. https://doi.org/10.1258/1355819042250249

Benevides

T. W.

Shore

S. M.

Palmer

Duncan

Plank

Andresen

M.-L.

Caplan

Cook

Gassner

Hector

B. L.

Morgan

Nebeker

Purkis

Rankowski

Wittig

Coughlin

S. S.

(2020). Listening to the autistic voice: Mental health priorities to guide research and practice in autism from a stakeholder-driven project. Autism, 24(4), 822–833. https://doi.org/10.1177/1362361320908410

Bettenhausen

J. L.

Hall

Doupnik

S. K.

Markham

J. L.

Feinstein

J. A.

Berry

J. G.

Gay

J. C.

(2021). Hospitalization outcomes for rural children with mental health conditions. The Journal of Pediatrics, 229, 240.e–246.e1. https://doi.org/10.1016/j.jpeds.2020.09.067

Bickford

C. D.

Oberlander

T. F.

Lanphear

N. E.

Weikum

W. M.

Janssen

P. A.

Ouellette-Kuntz

Hanley

G. E.

(2020). Identification of pediatric autism spectrum disorder cases using health administrative data. Autism Research, 13(3), 456–463. https://doi.org/10.1002/aur.2252

10.

Boege

Corpus

Weichard

Schepker

Young

Fegert

J. M.

(2021). Long-term outcome of intensive home treatment for children and adolescents with mental health problems: 4 years after a randomized controlled clinical trial. Child and Adolescent Mental Health, 26(4), 310–319. https://doi.org/10.1111/camh.12500

11.

Bottema-Beutel

Kapp

S. K.

Lester

J. N.

Sasson

N. J.

Hand

B. N.

(2021). Avoiding ableist language: Suggestions for autism researchers. Autism in Adulthood, 3(1), 18–29. https://doi.org/10.1089/aut.2020.0014

12.

Cage

Di Monaco

Newell

(2018). Experiences of autism acceptance and mental health in autistic adults. Journal of Autism and Developmental Disorders, 48(2), 473–484. https://doi.org/10.1007/s10803-017-3342-7

13.

Cage

Troxell-Whitman

(2019). Understanding the reasons, contexts and costs of camouflaging for autistic adults. Journal of Autism and Developmental Disorders, 49(5), 1899–1911. https://doi.org/10.1007/s10803-018-03878-x

14.

Cantor

McBain

R. K.

Kofner

Stein

B. D.

(2020). Fewer than half of US mental health treatment facilities provide services for children with autism spectrum disorder. Health Affairs, 39(6), 968–974. https://doi.org/10.1377/hlthaff.2019.01557

15.

Cantor

McBain

R. K.

Kofner

Stein

B. D.

(2021). Where are U.S. outpatient mental health facilities that serve children with autism spectrum disorder? A national snapshot of geographic disparities. Autism, 26, 169–177. https://doi.org/10.1177/13623613211024046

16.

Carbone

P. S.

Young

P. C.

Stoddard

G. J.

Wilkes

Trasande

(2015). A comparison of ambulatory care sensitive hospitalizations among children with and without autism spectrum disorder. Academic Pediatrics, 15(6), 626–635. https://doi.org/10.1016/j.acap.2015.07.006

17.

Carlton

E. F.

Donnelly

J. P.

Prescott

H. C.

Asaro

L. A.

Barbaro

R. P.

Watson

R. S.

Curley

M. A. Q.

& RESTORE Study Investigators. (2021). School and work absences after critical care hospitalization for pediatric acute respiratory failure: A secondary analysis of a cluster randomized trial. Journal of the American Medical Association Network Open, 4(12), Article e2140732. https://doi.org/10.1001/jamanetworkopen.2021.40732

18.

Chen

Bloodworth

Novak

Cook

B. L.

Goldman

H. H.

Rendall

M. S.

Thomas

S. B.

Reynolds

C. F.

(2018). Reducing preventable hospitalization and disparity: Association with local health department mental health promotion activities. American Journal of Preventive Medicine, 54(1), 103–112. https://doi.org/10.1016/j.amepre.2017.10.011

19.

Cidav

Marcus

S. C.

Mandell

D. S.

(2014). Home- and community-based waivers for children with autism: Effects on service use and costs. Intellectual and Developmental Disabilities, 52(4), 239–248. https://doi.org/10.1352/1934-9556-52.4.239

20.

Conner

C. M.

Golt

Righi

Shaffer

Siegel

Mazefsky

C. A.

(2020). A comparative study of suicidality and its association with emotion regulation impairment in large ASD and U.S. census-matched samples. Journal of Autism and Developmental Disorders, 50(10), 3545–3560. https://doi.org/10.1007/s10803-020-04370-1

21.

Cook

Hull

Crane

Mandy

(2021). Camouflaging in autism: A systematic review. Clinical Psychology Review, 89, Article 102080. https://doi.org/10.1016/j.cpr.2021.102080

22.

Croen

L. A.

Najjar

D. V.

Ray

G. T.

Lotspeich

Bernal

(2006). A comparison of health care utilization and costs of children with and without autism spectrum disorders in a large group-model health plan. Pediatrics, 118(4), e1203–e1211. https://doi.org/10.1542/peds.2006-0127

23.

Culpin

Mars

Pearson

R. M.

Golding

Heron

Bubak

Carpenter

Magnusson

Gunnell

Rai

(2018). Autistic traits and suicidal thoughts, plans, and self-harm in late adolescence: Population-based cohort study. Journal of the American Academy of Child & Adolescent Psychiatry, 57(5), 313–320e6. https://doi.org/10.1016/j.jaac.2018.01.023

24.

Dodds

Spencer

Shea

Fell

Armson

B. A.

Allen

A. C.

Bryson

(2009). Validity of autism diagnoses using administrative health data. Chronic Diseases in Canada, 29(3), 102–107.

25.

Doupnik

Rodean

Zima

B. T.

Coker

T. R.

Worsley

Rehm

K. P.

Gay

J. C.

Hall

Marcus

(2018). Readmissions after pediatric hospitalization for suicide ideation and suicide attempt. Journal of Hospital Medicine, 13(11), 743–751. https://doi.org/10.12788/jhm.3070

26.

Feng

J. Y.

Toomey

S. L.

Zaslavsky

A. M.

Nakamura

M. M.

Schuster

M. A.

(2017). Readmission after pediatric mental health admissions. Pediatrics, 140(6), Article e20171571. https://doi.org/10.1542/peds.2017-1571

27.

Feudtner

Feinstein

J. A.

Zhong

Hall

Dai

(2014). Pediatric complex chronic conditions classification system version 2: Updated for ICD-10 and complex medical technology dependence and transplantation. BMC Pediatrics, 14, Article 199. https://doi.org/10.1186/1471-2431-14-199

28.

Gadke

D. L.

McKinney

Oliveros

(2016). Autism spectrum disorder symptoms and comorbidity in emerging adults. Child Psychiatry & Human Development, 47(2), 194–201. https://doi.org/10.1007/s10578-015-0556-9

29.

Geanellos

(2002). Transformative change of self: The unique focus of (adolescent) mental health nursing? International Journal of Mental Health Nursing, 11(3), 174–185. https://doi.org/10.1046/j.1440-0979.2002.00245.x

30.

HCUP Kids’ Inpatient Database (KID). Healthcare Cost and Utilization Project (HCUP). 2016. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/kidoverview.jsp

31.

Huff

(2021). Working with adults with developmental disabilities, 52(8). https://www-apa-org.argo.library.okstate.edu; http://www.apa.org/monitor/2021/11/feature-developmental-disabilities

32.

Huxley

Evans

Madge

Webber

Burchardt

McDaid

Knapp

(2012). Development of a social inclusion index to capture subjective and objective life domains (phase ii): Psychometric development study. Health Technology Assessment, 16(1), iii–viii, x–xii, 1–241. https://doi.org/10.3310/hta16010

33.

Iannuzzi

Cheng

E. R.

Broder-Fingert

Bauman

M. L.

(2015). Brief report: Emergency department utilization by individuals with autism. Journal of Autism and Developmental Disorders, 45(4), 1096–1102. https://doi.org/10.1007/s10803-014-2251-2

34.

Iannuzzi

Hall

Oreskovic

N. M.

Aryee

Broder-Fingert

Perrin

J. M.

Kuhlthau

K. A.

(2022). Emergency department utilization of adolescents and young adults with autism spectrum disorder. Journal of Autism and Developmental Disorders, 52(2), 617–622. https://doi.org/10.1007/s10803-021-04969-y

35.

Jafar

A. J. N.

(2018). What is positionality and should it be expressed in quantitative studies? Emergency Medicine Journal, 35(5), 323–324. https://doi.org/10.1136/emermed-2017-207158

36.

Jagosh

Macaulay

A. C.

Pluye

Salsberg

Bush

P. L.

Henderson

Sirett

Wong

Cargo

Herbert

C. P.

Seifer

S. D.

Green

L. W.

Greenhalgh

(2012). Uncovering the benefits of participatory research: Implications of a realist review for health research and practice. The Milbank Quarterly, 90(2), 311–346. https://doi.org/10.1111/j.1468-0009.2012.00665.x

37.

Jensen-Arnett

(2004). Adolescence and emerging adulthood: A cultural approach (2nd ed.). Prentice Hall.

38.

Kapp

S. K.

(2018). Social support, well-being, and quality of life among individuals on the autism spectrum. Pediatrics, 141(Suppl. 4), S362–S368. https://doi.org/10.1542/peds.2016-4300N

39.

Kirby

A. V.

Bakian

A. V.

Zhang

Bilder

D. A.

Keeshin

B. R.

Coon

(2019). A 20-year study of suicide death in a statewide autism population. Autism Research, 12(4), 658–666. https://doi.org/10.1002/aur.2076

40.

Lai

M.-C.

Kassee

Besney

Bonato

Hull

Mandy

Szatmari

Ameis

S. H.

(2019). Prevalence of co-occurring mental health diagnoses in the autism population: A systematic review and meta-analysis. The Lancet Psychiatry, 6(10), 819–829. https://doi.org/10.1016/S2215-0366(19)30289-5

41.

Lapillonne

Regnault

Gournay

Gouyon

J.-B.

Gilet

Anghelescu

Miloradovich

Arnould

Moriette

(2012). Impact on parents of bronchiolitis hospitalization of full-term, preterm and congenital heart disease infants. BMC Pediatrics, 12(1), 171. https://doi.org/10.1186/1471-2431-12-171

42.

Lenz

(2001). The transition from adolescence to young adulthood: A theoretical perspective. The Journal of School Nursing, 17(6), 300–306. https://doi.org/10.1177/10598405010170060401

43.

Lipson

S. K.

Lattie

E. G.

Eisenberg

(2019). Increased rates of mental health service utilization by U.S. college students: 10-year population-level trends (2007–2017). Psychiatric Services, 70(1), 60–63. https://doi.org/10.1176/appi.ps.201800332

44.

Maddox

B. B.

Crabbe

Beidas

R. S.

Brookman-Frazee

Cannuscio

C. C.

Miller

J. S.

Nicolaidis

Mandell

D. S.

(2020). “I wouldn’t know where to start”: Perspectives from clinicians, agency leaders, and autistic adults on improving community mental health services for autistic adults. Autism, 24(4), 919–930. https://doi.org/10.1177/1362361319882227

45.

Mandell

D. S.

(2008). Psychiatric hospitalization among children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 38(6), 1059–1065. https://doi.org/10.1007/s10803-007-0481-2

46.

Mandell

D. S.

Candon

M. K.

Xie

Marcus

S. C.

Kennedy-Hendricks

Epstein

A. J.

Barry

C. L.

(2019). Effect of outpatient service utilization on hospitalizations and emergency visits among youths with autism spectrum disorder. Psychiatric Services, 70(10), 888–893. https://doi.org/10.1176/appi.ps.201800290

47.

Mandell

D. S.

Cao

Ittenbach

Pinto-Martin

(2006). Medicaid expenditures for children with autistic spectrum disorders: 1994 to 1999. Journal of Autism and Developmental Disorders, 36(4), 475–485. https://doi.org/10.1007/s10803-006-0088-z

48.

Mandy

(2019). Social camouflaging in autism: Is it time to lose the mask? Autism, 23(8), 1879–1881. https://doi.org/10.1177/1362361319878559

49.

McBain

R. K.

Kofner

Stein

B. D.

Cantor

J. H.

Vogt

W. B.

(2019). Growth and distribution of child psychiatrists in the United States: 2007–2016. Pediatrics, 144(6), Article e20191576. https://doi.org/10.1542/peds.2019-1576

50.

McDermott

K. W.

Elixhauser

Sun

(2017). Trends in hospital inpatient stays in the United States, 2005-2014 (Statistical brief #225, p. 18). Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality.

51.

McGuire

Siegel

(2018). Psychiatric hospital treatment of youth with autism spectrum disorder in the United States: Needs, outcomes, and policy. International Review of Psychiatry, 30(1), 110–115. https://doi.org/10.1080/09540261.2018.1433134

52.

McMaughan

Jones

Mulcahy

Tucker

Beverly

Perez-Patron

(2022) Hospitalizations among children and youth with autism in the United States: Frequency, characteristics, and costs. Intellectual and Developmental Disabilities, 60(6): 484–503. https://doi-org.argo.library.okstate.edu/10.1352/1934-9556-60.6.484

53.

McMaughan

D. J.

Grieb

S. M. D.

Kteily-Hawa

Key

K. D.

(2021). Promoting and advocating for ethical community engagement: Transparency in the community-engaged research spectrum. Progress in Community Health Partnerships: Research, Education, and Action, 15(4), 419–424. https://doi.org/10.1353/cpr.2021.0054

54.

McMaughan

D. J.

Rhoads

K. E.

Davis

Chen

Han

Jones

R. A.

Mahaffey

C. C.

Miller

B. M.

(2021). COVID-19 related experiences among college students with and without disabilities: Psychosocial impacts, supports, and virtual learning environments. Frontiers in Public Health, 9, Article 782793. https://doi.org/10.3389/fpubh.2021.782793

55.

Mojtabai

Olfson

(2020). National trends in mental health care for US adolescents. Journal of the American Medical Association Psychiatry, 77(7), 703–714. https://doi.org/10.1001/jamapsychiatry.2020.0279

56.

Morris

Greenblatt

Saini

(2019). Healthcare providers’ experiences with autism: A scoping review. Journal of Autism and Developmental Disorders, 49(6), 2374–2388. https://doi.org/10.1007/s10803-019-03912-6

57.

National Conference of State Legislatures. (2021). Autism and insurance coverage state laws. https://www.ncsl.org/research/health/autism-and-insurance-coverage-state-laws.aspx

58.

Nayfack

A. M.

Huffman

L. C.

Feldman

H. M.

Chan

Saynina

Wise

P. H.

(2014). Hospitalizations of children with autism increased from 1999 to 2009. Journal of Autism and Developmental Disorders, 44(5), 1087–1094. https://doi.org/10.1007/s10803-013-1965-x

59.

Ougrin

Corrigall

Stahl

Poole

Zundel

Wait

Slater

Reavey

Byford

Ivens

Crommelin

Hayes

Middleton

Young

Taylor

(2021). Supported discharge service versus inpatient care evaluation (SITE): A randomised controlled trial comparing effectiveness of an intensive community care service versus inpatient treatment as usual for adolescents with severe psychiatric disorders—Self-harm, functional impairment, and educational and clinical outcomes. European Child & Adolescent Psychiatry, 30(9), 1427–1436. https://doi.org/10.1007/s00787-020-01617-1

60.

Peacock

Amendah

Ouyang

Grosse

S. D.

(2012). Autism spectrum disorders and health care expenditures: The effects of co-occurring conditions. Journal of Developmental and Behavioral Pediatrics, 33(1), 2–8. https://doi.org/10.1097/DBP.0b013e31823969de

61.

Pickens

G. T.

Liang

Roemer

(2021). HCUP cost-to-charge ratio methodologies (No. 2021–05; HCUP methods series, p. 14). U.S. Agency for Healthcare Research and Quality. https://www.hcup-us.ahrq.gov/reports/methods/MS2021-05-CCR-Methodologies.pdf

62.

Platt

J. M.

Keyes

K. M.

McLaughlin

K. A.

Kaufman

A. S.

(2019). Intellectual disability and mental disorders in a U.S. population representative sample of adolescents. Psychological Medicine, 49(6), 952–961. https://doi.org/10.1017/S0033291718001605

63.

Poulsen

Brownlow

Lawson

Pellicano

(2022). Meaningful research for autistic people? Ask autistics!. Autism, 26(1), 3–5. https://doi.org/10.1177/13623613211064421

64.

Preyde

Parekh

Warne

Heintzman

(2017). School reintegration and perceived needs: The perspectives of child and adolescent patients during psychiatric hospitalization. Child and Adolescent Social Work Journal, 34(6), 517–526. https://doi.org/10.1007/s10560-017-0490-8

65.

Rice

J. L.

Tan

T. X.

(2021). In their voices: Experiences of adolescents during involuntary psychiatric hospitalization. Children and Youth Services Review, 126, Article 106045. https://doi.org/10.1016/j.childyouth.2021.106045

66.

Righi

Benevides

Mazefsky

Siegel

Sheinkopf

S. J.

Morrow

E. M.

Siegel

Erickson

Gabriels

R. L.

Kaplan

Mazefsky

Morrow

E. M.

Righi

Santangelo

S. L.

Wink

Benevides

Beresford

Best

Bowen

, & The Autism Developmental Disabilities Inpatient Research Collaborative. (2018). Predictors of inpatient psychiatric hospitalization for children and adolescents with autism spectrum disorder. Journal of Autism and Developmental Disorders, 48(11), 3647–3657. https://doi.org/10.1007/s10803-017-3154-9

67.

Rodriguez

Drastal

Hartley

S. L.

(2021). Cross-lagged model of bullying victimization and mental health problems in children with autism in middle to older childhood. Autism, 25(1), 90–101. https://doi.org/10.1177/1362361320947513

68.

Rosen

T. E.

Mazefsky

C. A.

Vasa

R. A.

Lerner

M. D.

(2018). Co-occurring psychiatric conditions in autism spectrum disorder. International Review of Psychiatry, 30(1), 40–61. https://doi.org/10.1080/09540261.2018.1450229

69.

Simonoff

Pickles

Charman

Chandler

Loucas

Baird

(2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a population-derived sample. Journal of the American Academy of Child & Adolescent Psychiatry, 47(8), 921–929. https://doi.org/10.1097/CHI.0b013e318179964f

70.

Soria

K. M.

Horgos

Chirikov

Jones

(2020). The experiences of undergraduate students with physical, learning, neurodevelopmental, and cognitive disabilities during the COVID-19 pandemic [SERU consortium]. University of California-Berkeley and University of Minnesota. https://docs.google.com/document/d/JrPktBoLNcJHxwGwLorgCKFnlRklkMBeyPbey/edit?usp=embed_facebook1286150

71.

Tanner

J. L.

Arnett

J. J.

(2016). The emergence of emerging adulthood: The new life stage between adolescence and young adulthood. In Furlang

(Ed.), Routledge handbook of youth and young adulthood (pp. 50–56). Routledge.

72.

Turcotte

Shea

L. L.

Mandell

(2018). School discipline, hospitalization, and police contact overlap among individuals with autism spectrum disorder. Journal of Autism and Developmental Disorders, 48(3), 883–891. https://doi.org/10.1007/s10803-017-3359-y

73.

Twenge

J. M.

Cooper

A. B.

Joiner

T. E.

Duffy

M. E.

Binau

S. G.

(2019). Age, period, and cohort trends in mood disorder indicators and suicide-related outcomes in a nationally representative dataset, 2005–2017. Journal of Abnormal Psychology, 128(3), 185–199. https://doi.org/10.1037/abn0000410

74.

Woodruff

A. G.

Choong

(2021). Long-term outcomes and the post-intensive care syndrome in critically ill children: A North American perspective. Children, 8(4), Article 4. https://doi.org/10.3390/children8040254

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB