Introduction
Down syndrome (DS) is the most common genetic disorder associated with intellectual disability, resulting from the presence of an additional copy of Homo sapiens chromosome 21 (Hsa21) (Antonarakis et al., 2020; Santoro et al., 2021). Approximately 3,000 to 5,000 children are estimated to be born with DS annually (Mai et al., 2019). DS affects multiple physiological systems, rendering individuals with DS more susceptible to specific health conditions, such as hypothyroidism, obstructive sleep apnea, epilepsy, hearing and vision problems, recurrent infections, and autoimmune diseases (Antonarakis et al., 2020; Roizen et al., 2014). However, beyond these well-documented medical concerns, the risk of neurodevelopmental disorders, particularly autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), remains underexplored in this population (Bradbury et al., 2021; Visootsak & Sherman, 2007).
Several studies have investigated the prevalence of ASD and ADHD among individuals with DS (Alexander et al., 2016; Baumer et al., 2023; Capone et al., 2005; Channell et al., 2019; DiGuiseppi et al., 2010; Hepburn & Maclean, 2009; Kent et al., 1999; Maatta et al., 2006; Oxelgren et al., 2017a, b, c; Richards et al., 2015a, b; Spinazzi et al., 2023a, b; Startin et al., 2020). However, findings demonstrate a wide range of comorbid rates, as summarized in Supplementary Table 1. For instance, a recent clinical study reported that out of 562 individuals with DS, 72 (13%) were diagnosed with ASD, and 54 (9.6%) were diagnosed with ADHD (Spinazzi et al., 2023a, b). In contrast, Oxelgren and colleagues found that among 41 children with DS, 17 (42%) met the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria for ASD, while 14 (34%) met the criteria for ADHD. Additionally, 9 of the 14 children diagnosed with ADHD also met the criteria for ASD, indicating a co-occurrence rate of approximately 22% among children with DS (Oxelgren et al., 2017a, b, c). Such discrepancies may stem from variations in sample sizes, inclusion criteria, diagnostic methods, and study designs. Many previous studies have relied on clinical data, often from more complex cases where patients are more likely to have co-occurring ASD or ADHD. This may contribute to a higher idenification of more severe health conditions, potentially skewing prevalence rates. Furthermore, much of the existing research has primarily focused on prevalence estimates rather than quantifying the associations (e.g., odds ratio or risk ratio) between DS and these neurodevelopmental disorders. Understanding the risk of ASD and ADHD among individuals with DS is crucial for clinical practice. Given that individuals with DS already require specialized healthcare and educational services, the presence of ASD or ADHD can further complicate their developmental progress and increase the need for tailored interventions. Early identification of these co-occurring conditions can improve diagnostic accuracy, enable timely interventions, and facilitate the development of individualized support strategies. Therefore, it is essential to use robust, population-based data to enhance our understanding of ASD and ADHD within the DS community.
Equally important, demographic and socio-economic factors may also contribute to understanding the variability in the prevalence of ASD and ADHD among individuals with DS. For instance, research has shown that ASD and ADHD diagnoses often vary across racial and socio-economic groups, with minority populations and families from lower-income backgrounds experiencing more delayed diagnoses or underdiagnosis (Mandell et al., 2009; Morgan et al., 2013). Furthermore, age-related differences in symptom expression and sex-based disparities can further complicate findings (Maenner et al., 2023). However, limited research on the prevalence of ASD and ADHD among individuals with DS has accounted for these demographic variables, limiting their ability to control for potential confounders and fully understand the complexities of co-occurring conditions in this population.
The objective of the current research was to examine the association of DS with ASD and ADHD using data from the National Health Interview Survey (NHIS) database, which provides a comprehensive representation of the U.S. national population. Additionally, we aimed to explore the potential influence of covariates through stratified analyses, providing a more comprehensive understanding of how these neurodevelopmental conditions manifest within the DS population.
Method
Study Population
The National Health Interview Survey (NHIS) is a cross-sectional household interview survey conducted in the United States by the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention. The NHIS has been continuously sampling and interviewing households since 1957. It serves as the primary source of information on the health conditions of the U.S. population due to its comprehensive data collection on a broad range of health-related topics (Adams et al., 2013). Given the relatively low prevalence of DS in the general population and the update of the NHIS by NCHS in 2019, this present analysis, focused on 214,300 children aged 3–17 years who participated in the NHIS between 1997 and 2018, allowing us to obtain a stable and comparable dataset over time. From1997 to 2018, the NHIS reported household response rates ranging from 64.2 to 91.8%, and conditional response rates for the sample child component ranging from 85.6 to 93.5%. Detailed information on the study design and methodology of the NHIS can be found in previous publications (Parsons et al., 2014; Statistics, 2000). All the NHIS datasets can be found on the U.S. CDC website: https://www.cdc.gov/nchs/nhis/data-questionnaires-documentation.htm.
We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies.
Ethics Approval Statement
The NHIS was approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS) and the U.S. Office of Management and Budget. All the respondents provided oral consent prior to participation.
Ascertainment of Variables
The NHIS is a representative health survey that gathers data for all members of a household. Participants have the option to complete the survey in either English or Spanish, and additional language support may be available for other groups, further ensuring that diverse populations are represented in the data collection process. In case where there are children aged 17 years or younger in a family being interviewed, one child is chosen at random by the field representative’s computer, without applying any differential sampling probabilities to the children (Parsons et al., 2014). Health-related data about the selected e children is usually provided by a knowledgeable adult, typically a parent or guardian.
Health status, including mental health, was obtained from the Child Conditions, Limitation of Activity, and Health Status Section (CHS). DS and ADHD were identified on a positive response to the query, “Has a doctor or health professional ever told you that [your child] had Down syndrome/Attention Deficit Hyperactivity Disorder (ADHD) or Attention Deficit Disorder (ADD)?” Due to changes in diagnostic criteria under DSM-5, the methods for ASD ascertainment varied across survey years. From 1997 to 2010, autism was identified through a 10-condition checklist, where respondents reviewed a list of conditions and indicated whether a doctor or health professional had ever diagnosed their child with any of them. However, respondents were not asked specifically about each condition. Between 2011 and 2013, autism remained part of this checklist, but the terminology was updated from “autism” to “autism/autism spectrum disorder.” In 2014, the ASD survey item became a standalone question, directly asking respondents of children aged 2–17 years: “Has a doctor or health professional ever told you that [your child] had autism, Asperger’s disorder, pervasive developmental disorder, or autism spectrum disorder?” (Zablotsky et al., 2015).
Participants’ demographic data, including age, biological sex assigned at birth, race/ethnicity, family highest education level, family income, and geographic region, were collected during the interviews using a standardized questionnaire. Race and Hispanic ethnicity, as a social construct, were self-reported, and race categories (Black and White) were defined by investigators based on the U.S. Office of Management and Budget’s Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity. Data for this study included U.S. adults who self-reported as non-Hispanic Black (hereafter, Black), Hispanic or Latino, and non-Hispanic White (hereafter, White) individuals. In the result table we used “other” as a category for individuals who self-reported being Asian or of other race and ethnicity (which included those who were American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander) because of small sample sizes.
Given the significance of the interview language in gathering crucial health metrics frequently utilized to investigate health inequalities, the NHIS is commonly conducted in various languages. This practice aims to enhance accessibility and increase participation rates among diverse racial and ethnic communities, particularly those whose primary language is not English (e.g., Spanish) (Santos-Lozada, 2023).
Family income levels were classified according to the ratio of family income to federal poverty level (< 1.0, 1.0-1.9, 2.0-3.9, and ≥ 4.0). The highest Family education level was classified as less than high school, high school, or college or higher.
Statistical Analysis
Survey weights, strata, and primary sampling units developed by the NCHS were utilized in all analyses of the NHIS data to ensure nationally representative of the U.S. population, unless stated otherwise.
Baseline characteristics of children with and without DS were compared using a t-test for continuous variables and the chi-square test for categorical variables. The odds ratio (OR) and 95% confidence interval (CI) for ASD and ADHD based on the presence of DS were calculated through multivariable logistic regression. The models were adjusted for age and sex in Model 1, additionally for race/ethnicity in Model 2, and further adjusting for family highest education level, family income-to-poverty ratio, and geographic region in Model 3. Subgroup analyses were conducted based on sex to assess effect modification. The interactions between sex and DS were examined by including multiplicative interaction terms in the multivariable models. We were not able to perform subgroup analyses on race/ethnicity due to limitation of sample size.
All the data analyses were conducted using SAS 9.4 survey procedures (SAS Institute, Inc., Cary, NC). A two-sided P value < 0.05 was considered to indicate statistical significance.
Results
A total of 214,300 children aged 3–17 years were included in the analysis, 329 were diagnosed with DS (mean age = 9.85 ± 0.29 years), 57.77% of whom were males. There were no significant sex differences between the children with DS and those without DS (57.77% vs. 51.11%). However, children with DS were more likely to be Hispanic compared to children without DS (28.97% vs. 20.36%). (Table 1).
Table 1
Descriptive statistics of the characteristics of overall participants aged 3–17 years according to Down syndrome diagnosis in the NHIS, 1997–2018 (n = 214,300)
Variables | Without Down syndrome diagnosis (n = 213,971) | With Down syndrome diagnosis (n = 329) | P value |
|---|---|---|---|
Child’s Age, Mean (SE), y | 10.01 (0.01) | 9.85 (0.29) | 0.59 |
Child’s Sex, N (%) | |||
Male | 110,222 (51.11) | 188 (57.77) | 0.06 |
Female | 103,749 (48.89) | 141 (42.23) | |
Race/ethnicity, N (%) | |||
Hispanic | 58,651 (20.36) | 108 (28.97) | 0.02 |
Non-Hispanic White | 105,181 (57.64) | 149 (47.96) | |
Non-Hispanic Black | 32,399 (14.24) | 46 (14.24) | |
Other | 17,740 (7.77) | 26 (8.83) | |
Family highest education level, N (%) | |||
Less than high school | 44,908 (18.39) | 77 (21.65) | 0.41 |
High school | 28,251 (13.13) | 44 (12.56) | |
College or higher | 139,645 (67.98) | 207 (65.62) | |
Missing | 1167 (0.49) | 1 (0.17) | |
Family income to poverty ratio, N (%) | |||
< 1.0 | 33,694 (15.98) | 58 (16.67) | 0.72 |
1.0-1.9 | 41,779 (19.50) | 69 (23.07) | |
2.0-3.9 | 56,337 (26.69) | 82 (24.76) | |
>=4.0 | 50,576 (23.89) | 73 (22.97) | |
Missing | 31,585 (13.94) | 47 (12.53) | |
Geographic region, N (%) | |||
Northeast | 35,766 (17.36) | 72 (21.66) | 0.36 |
Midwest | 43,941 (23.42) | 60 (20.64) | |
South | 77,765 (36.32) | 114 (35.48) | |
West | 56,499 (22.91) | 83 (22.21) | |
Autism Spectrum Disorder, N (%) | |||
No | 211,735 (98.93) | 308 (94.24) | 0.002 |
Yes | 2236 (1.07) | 21 (5.76) | |
Attention Deficit/Hyperactivity Disorder, N (%) | |||
No | 197,763 (92.23) | 281 (87.62) | 0.02 |
Yes | 16,208 (7.77) | 48 (12.38) | |
Concurrence of ASD and ADHD, N (%) | |||
No | 213,012 (99.52) | 323 (98.34) | 0.14 |
Yes | 959 (0.48) | 6 (1.66) | |
Among the children with DS, 21 were also diagnosed with ASD, 48 were also diagnosed with ADHD, and 6 were diagnosed with both ASD and ADHD. The weighted prevalence of ASD and ADHD were 5.76% and 12.38%, respectively, which were significantly greater than those of children without DS (P < 0.05). The weighted prevalence of the co-occurrence of ASD and ADHD was 1.66%, which did not show a significant difference when compared to the children without DS (Table 1).
Multivariable models were created to examine the association of DS with ASD and ADHD (Table 2). We found that children with DS were more than five times as likely to also have ASD as were those without DS, even after adjusting for socio-demographic factors (Model 3, OR = 5.40, 95% CI = 3.04–9.59). Similarly, individuals with DS were nearly two times as likely to also have ADHD as were those without DS (Model 3, OR = 1.72, 95% CI = 1.17–2.53). Further, individuals with DS were more than three times as likely to have both ASD and ADHD as were those without DS (Model 3, OR = 3.45, 95% CI = 1.29–9.20). The results of stepwise method for adding the demographic variables in the regression models were listed in Supplementary Table 2.
Table 2
Associations of Down syndrome with ASD and ADHD in U.S. Children aged 3–17 years in the NHIS, 1997–2018
Outcomes | OR (95% CI) | P value | |
|---|---|---|---|
Without Down syndrome | With Down syndrome | ||
ASD | |||
Cases/total | 2236/213,971 (1.07) | 21/329 (5.76) | |
Model 1 | 1.00 (reference) | 5.30 (2.99–9.39) | < 0.001 |
Model 2 | 1.00 (reference) | 5.48 (3.05–9.60) | < 0.001 |
Model 3 | 1.00 (reference) | 5.40 (3.04–9.59) | < 0.001 |
ADHD | |||
Cases/total | 16,208/213,971 (7.79) | 48/329 (12.38) | |
Model 1 | 1.00 (reference) | 1.65 (1.14–2.40) | 0.008 |
Model 2 | 1.00 (reference) | 1.73 (1.18–2.54) | 0.005 |
Model 3 | 1.00 (reference) | 1.72 (1.17–2.53) | 0.005 |
ASD and ADHD | |||
Cases/total | 959/213,971 (0.48) | 6/329 (1.66) | |
Model 1 | 1.00 (reference) | 3.31 (1.24–8.82) | < 0.001 |
Model 2 | 1.00 (reference) | 3.45 (1.29–9.24) | < 0.001 |
Model 3 | 1.00 (reference) | 3.45 (1.29–9.20) | < 0.001 |
Furthermore, we examined the associations of DS with ASD and ADHD according to sex using stratified analyses (Table 3). The results indicated a significant association between DS and ASD in both female and male (P < 0.05). Additionally, a significant interaction effect between sex and DS for ASD were observed (P for interaction = 0.001), with a more pronounced association in female compared to male (OR = 17.17 95% CI = 7.70-38.31 for female; OR = 2.93, 95% CI = 1.36–6.30 for male). However, while a significant association between DS and ADHD was found in both female and male (P < 0.05), there was no significant interaction between sex and DS for ADHD (P for interaction = 0.72). However, we did not have enough sample size for race/ethnicity subgroups, to explore the associations between DS and ASD, as well as between DS and ADHD.
Table 3
Associations of Down syndrome with ASD and ADHD stratified by sex
ASD | ADHD | |||||||
|---|---|---|---|---|---|---|---|---|
No. of ASD cases/DS participants | OR (95% CI)* | P value | P for interaction | No. of ADHD cases/DS | OR (95% CI) * | P value | P for interaction | |
Sex | ||||||||
Male | 11/188 | 2.93 (1.36–6.30) | 0.006 | 0.001 | 31/188 | 1.66 (1.05–2.63) | 0.03 | 0.72 |
Female | 10/141 | 17.17 (7.70-38.31) | < 0.001 | 17/141 | 1.88 (1.05–3.39) | 0.04 | ||
Discussion
We found significant associations of DS with ASD and ADHD in a large nationwide population-based study. Compared to children without DS, children with DS were 5.40 times and 1.72 times more likely to have ASD and ADHD, respectively. The associations persisted after adjusting for age, sex, race/ethnicity, and socioeconomic indicators. Notably, our observations indicated that odds of ASD and ADHD among children with DS may vary by sex.
Our finding of a higher odds of ASD, ADHD, as well as both ASD and ADHD among children with DS aligned with the findings of several existing studies. Meta-analysis data indicated that 16% of individuals with DS also exhibit symptoms of ASD, a proportion that is relatively lower compared to other genetic syndrome such as fragile X syndrome (30%), tuberous sclerosis complex (36%), and Cornelia de Lange syndrome (46%) (Richards et al., 2015a, b), but significantly higher compared to the ASD prevalence in the general population (Zeidan et al., 2022). Using the UK Clinical Practice Research Datalink, Alexander and the colleagues conducted a retrospective cohort study of 6430 individuals with DS and 19,176 randomly sampled up control participants, and they revealed an incidence ratio (IRR) of 4.4 (95% CI: 3.1–6.4) for ASD and 1.3 (95% CI: 0.87–2.1) for ADHD in the DS group (Alexander et al., 2016). Other studies based on clinical registry systems have reported higher comorbidity rates of ASD and ADHD in individuals with DS ranging from 12.7 to 42% and 9.6-34% (Baumer et al., 2023; Capone et al., 2005; Oxelgren et al., 2017a, b, c; Spinazzi et al., 2023a, b). For instance, two related studies based on a prospective clinical database in the U.S. indicated that 12.7%, 10.5%, and 12.5% out of DS patients had concurrent diagnosis of ASD (DS + ASD), ADHD (DS + ADHD) and both ASD and ADHD, respectively (Naerland et al., 2017; Spinazzi et al., 2023a, b).
Notably, this study represents an initial investigation utilizing a substantial nationwide general population sample, with a specific emphasis on quantifying the association of DS with ASD as well as with ADHD rather than solely on disease prevalence. By reporting the ORs for both ASD and ADHD among children with DS, we enhance the understanding of the overlap between DS and these neurodevelopmental disorders, which has been less explored in previous research. Our results highlight the increased risk of ASD and ADHD in children with DS, a group that has historically been overlooked in discussions of these comorbidities. Furthermore, by utilizing a large and representative sample, our study provides more robust conclusions compared to clinic-based samples.
Stratified analyses were also conducted to examine the associations between sex and the co-occurrence of DS and ASD or ADHD. An interesting finding from our study is that girls with DS were even more likely to be diagnosed with ASD. This finding was consistent with the finding of Startin and Ekstein (Ekstein et al., 2011; Startin et al., 2020). In their study, which investigated a cohort of 602 individuals diagnosed with DS in England and Wales, they reported that both males and females with DS exhibited elevated standardized morbidity ratios (SMRs) for ASD and ADHD, with a more pronounced increase in females diagnosed with ASD (Startin et al., 2020). However, some previous studies (Hepburn et al., 2007; Oxelgren et al., 2017a, b, c; Spinazzi et al., 2023a, b), particularly one with a larger sample size (Spinazzi et al., 2023a, b), have shown contrasting results. Specifically, the research including 562 individuals with DS indicated that those with DS + ASD were twice as likely to be male (Spinazzi et al., 2023a, b). Potential reasons for the higher odds of ASD in girls with DS in our study remain unknown. One potential reason could be that, prevalence of ASD is relatively lower in girls, and girls might under-screen for ASD in the general population. In the condition of DS, more access to medical system might increase the possibility of assessing and diagnosing ASD. Nevertheless, due to the inconsistency of our findings with previous research, additional studies are warranted to further explore this relationship.
No significant interaction was observed between sex and DS diagnosis for ADHD. This finding was consistent with the finding of Ekstein (Ekstein et al., 2011; Startin et al., 2020). In a separate study, Ekstein and colleagues examined a group of 41 children with DS to determine the prevalence of ADHD. They observed a remarkably high prevalence of ADHD (43.9%) but found no significant correlation between sex and ADHD within their DS sample (Ekstein et al., 2011).
The exact mechanisms for the higher prevalence of ASD and ADHD among individuals with DS are still unclear. However, it has been proposed that the over-expression of the Hsa21 gene may play an important role (Sturgeon et al., 2012; Vilardell et al., 2011). Over-expression of the Hsa21 gene has been shown to result in various neurologically relevant abnormalities, such as deficits in learning and memory, synaptic plasticity, and abnormalities in neuronal morphology (Rueda et al., 2012). In addition to the genetic factors, the co-occurrence of ASD and ADHD in individuals with DS may also be influenced by several shared risk factors. Research has shown that conditions such as gestational age, infantile spasms, hypothyroidism, brain injury, or a combination of these factors are more prevalent in individuals with DS and may contribute to the development of ASD and ADHD (Del Soriano et al., 2020; Esbensen et al., 2022; Moss et al., 2013; Rasmussen et al., 2001). Furthermore, there is considerable evidence linking abnormal cerebellar development to these disorders (Sathyanesan et al., 2019). Although our study does not directly investigate these mechanisms, understanding these risk factors can provide additional context for the associations observed in our research, offering a more comprehensive perspective on the relationship between DS, ASD, and ADHD.
Although the underlying mechanisms warrant further research, the findings from our study, as well as those of other previous studies, have confirmed the higher prevalence of ASD and ADHD among individuals with DS. This finding is especially important for clinical decision-making and service. The presence of these comorbidities in individuals with DS could result in a variety of challenges, not only heightened symptom complexity (Hepburn et al., 2008; Hepburn & MacLean, 2009), but also delayed diagnosis for ASD or/and ADHD. A recent survey was developed by the ASD workgroup of the Down Syndrome Medical Interest Group reported that, among parents of children with DS, who expressed their initial concerns about ASD to their primary care provider, 82% felt that their provider did not have the knowledge or experience with DS + ASD to further guide them. Also, caregivers reported a mean delay of 4.65 years between when symptoms were first noticed by parents and when the child received an ASD diagnosis (Spinazzi et al., 2024). Also, current American Academy of Pediatrics (AAP) guidelines do not recommend regular screening for attention problems or ADHD in children with DS. Therefore, it is imperative to add additional screen for ASD and ADHD in children with DS, and improve the diagnosis and evaluation of comorbidities to offer more precise and comprehensive treatment strategies for these individual patients.
This study has several strengths. First, the utilization of national population-based data provides us with a relatively larger sample of individuals with DS, as well as ADHD and ASD, and our results from these nationally representative data could be generalized to a broader population. Second, stratified analyses could be conducted to examine the impacts of potential confounders. However, several limitations should be considered. First, it is hard to diagnose ASD and ADHD in children with DS due to lack of DS-specific screeners and diagnostic tools, and there is tremendous diagnostic overshadowing. The information on physician diagnoses of DS, ASD and ADHD in current study was parent-reported, which may be subject to misreporting and recall bias. While most parents can offer accurate diagnostic information (Daniels et al., 2012), there is a high likelihood of underestimating the true prevalence of ASD and/or ADHD in DS compared to clinical assessments. Furthermore, parent-reported information may be influenced by specific expectations related to gender and cultural backgrounds, potentially impacting the levels of awareness and acceptance of ASD and how parents perceive and report their children’s behaviors and diagnosis (Øien & Eisemann, 2015). Second, over the past two decades, there have been changes in the diagnostic criteria for ASD and ADHD, which could also affect the associations. These revisions in the DSM-5 regarding the definition of ASD, could lead to either under-diagnosis or over-diagnosis of ASD in children with DS, potentially impacting the associations observed in our research. Future studies could further compare prevalence rates and associations before and after the changes in DSM-5 diagnostic criteria to better understand their impact. Third, with the existing information, we could not assess the association of DS with different subtypes of ADHD (i.e., primarily hyperactive-impulsive, primarily inattentive, or combined-type ADHD). This warrants further investigation. Fourth, we cannot rule out the possibility of residual confounding, although several potential confounders were considered.
Conclusion
In conclusion, in a nationwide population-based study of U.S. children, we found that a DS diagnosis was associated with a higher prevalence of ASD and ADHD. Our findings highlight the necessity of conducting early and routine screenings for ASD and ADHD in children with DS within clinical settings to improve the effectiveness of interventions.
Declarations
Data Sharing
Data will be made available on reasonable request from corresponding authors by email.
Conflict of Interest
The authors have indicated they have no potential conflicts of interest to disclose and have no financial relationships relevant to this article to disclose.
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