Social determinants of youth with mild intellectual disability in outpatient care for mental health problems: a case-control study

doi:10.21203/rs.3.rs-4945132/v1

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Social determinants of youth with mild intellectual disability in outpatient care for mental health problems: a case-control study

https://doi.org/10.21203/rs.3.rs-4945132/v1

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This study examined the unique role of diverse social determinants of mental health (SDOMH) associated with mental health problems (MHP) for children with mild intellectual disability (MID), compared to peers with and without MHP. This population-based case-control study included four groups aged 0–17 years (M_age = 10.6, 35.6% female). Two groups of children receiving outpatient mental health care for MHP with MID (n = 505) and without MID (n = 2,767), each with a matched control group from the general population (n = 2,525 and n = 13,835, respectively), were studied. Through multivariate logistic regression analyses, both MHP groups were compared to their control group and each other to examine the likelihood of a SDOMH associated with receiving mental health care for MHP in children with and without MID. Children with MID receiving mental health care showed significant differences in multiple domains compared to their control group and to children receiving mental health care without MID. They were less likely to have European-born mothers, more likely to have parents with moderate or low education levels, and tended to live in smaller, single-parent, lower-income households. Similar, though less deviant, patterns were observed for children without MID receiving mental health care compared to the general population, except for parental education. Our study highlights that diverse SDOMH are associated with the likelihood of receiving care for MHP in children. Moreover, children with MID face disproportionate disadvantages, particularly regarding low parental education and household income. Thus, interventions should not only target the child but also their family and environmental context.

intellectual disability

mental health

children in care

social environment

multivariate analysis

Children with mild intellectual disability (MID) face specific challenges threatening their development, particular their mental health. Research consistently highlights the increased risk of psychopathology among children with MID compared to peers without MID [17, 19]. In the socio-cultural, systemic paradigm, this heightened susceptibility is theorized to be shaped by a complex interplay of diverse socio-demographic factors experienced by these children [18] These factors, collectively known as social determinants of mental health (SDOMH), include ethnicity, socioeconomic status, household conditions, family dynamics, and neighborhood deprivation [2–4]. Although research has examined some SDOMH individually in relation to psychopathology in children with MID [5–8], a comprehensive understanding of the relative importance of multiple SDOMH is lacking. By identifying key SDOMH that are collectively associated with MHP in children with MID and comparing these findings with relevant groups, we aim to assess which SDOMH play a unique role for this population.

The few studies investigating the impact of SDOMH on children with MID reveal a concerning double burden. First, these children are vulnerable to experience environmental disadvantages, increasing their susceptibility to a lower social stratum [9, 10]. This, in turn, exposes them to higher levels of cumulative stress, stigmatization, and discrimination, which increases the risk of developing mental disorders [1]. Secondly, children with MID frequently face restricted access to vital resources such as wealth, social support, and problem-solving skills—key contributors to resilience in the face of adversity [6]. Consequently, it can be expected that the mental health of these children is more vulnerable to adverse SDOMH compared to their peers without MID [1, 11]. In this light, it is reasonable to hypothesize that the mental health of these children is more susceptible to decline when exposed to adverse SDOMH compared to their peers without MID. Testing this hypothesis and improving our understanding of the roles of different SDOMH will help address the unique challenges faced by children with MID and MHP.

To achieve this, we will organize SDOMH across domains using the conceptual framework of Lund et al. (2018) [28]. Each domain in this framework—demographic, social and cultural, economic, neighborhood, and environmental events—is hypothesized to be associated with mental health through various pathways. However, the last domain, encompassing factors such as war and natural disasters, is less common in the Netherlands and thus will not be addressed. This framework, initially developed for adults, will be extended to include SDOMH at parental and household levels for children.

Reviewing prior empirical studies in light of this framework, it becomes evident that demographic factors, particularly ethnicity, have been extensively studied as a variable associated with MHP among children with MID [12]. Although some studies found no link between ethnicity and MHP [7, 13], a systematic review of healthcare for minority ethnic groups with MID in the UK suggested current conclusions might be due to the potential underrepresentation of minority groups in both health services and research samples in general [14]. Second, the social and cultural domain encompasses frequently studied family and parental characteristics. For instance, studies on the impact of a single-parent household on behavior problems yielded divergent findings; some studies reported no significant association [15, 16], others showed associations between single parenthood and higher levels of behavior problems [17, 18]. Regarding economic factors, two systematic reviews came to the similar conclusion that associations between socioeconomic status (SES) and MHP were far from definite [8, 19]. Some studies found an association between lower SES and more MHP for children with MID [18, 20], whereas others reported no significant association [7, 21]. Importantly, some studies did not specify a method or criteria used for measuring SES, leaving the relative roles of different aspects of SES as contributors to MHP poorly understood [21]. Finally, few studies have explored neighborhood factors in relation to children with MID [5, 22], despite the generally well-documented impact of neighborhood deprivation on young people's mental health and well-being [24].

This study aims to deepen our understanding of the associations between SDOMH and MHP in children with MID by comparing three groups: children with MID receiving mental health care, a matched control group from the general population, and children without MID receiving mental health care. Additionally, we compare children without MID receiving mental health care to their matched control group from the general population. While some SDOMH have been individually examined in relation to MHP in both children with and without MID, a comprehensive analysis of their unique or combined effects is lacking. Comparing these associations across groups will identify key SDOMH related to MHP in children with MID. We hypothesize that children with both MID and MHP exhibit greater vulnerability to adverse SDOMH compared to the other groups. Enhancing our understanding of the diverse roles of SDOMH will help address the unique challenges faced by both children with and without MID who experience MHP, and inform preventive policy and intervention strategies to better support their mental health.

Sources of data

The study used data from the Extramural LUMC Academic Network data warehouse, a comprehensive regional population-based data infrastructure [24]. Three primary data sources were analyzed: microdata from Statistics Netherlands (SN; the central register agency), The Hague municipality data, and patient data from specialized youth mental health care institutions, including one for children with MID and MHP.

SN data provides insights into Dutch society, economy, and environment, comprising longitudinal microdata on demographics and socio-economic indicators [25]. The Hague municipality data provided neighborhood-specific information on urbanization, income, and education levels. Patient data included variables such as age, sex, and (un)registration in mental health care institutions.

Participants

Participants were categorized into four groups: two clinical case groups and two matched population-based control groups. The clinical case groups were receiving outpatient treatment for MHP at a specialized facility. One group consisted of children diagnosed with MID (IQ 55–85) and MHP (Group A: MID + MHP) and the second included those with MHP, but without MID (Group B: MPH only). Participants were identified from patient data recorded in 2017. Children were included if they: 1) were 0 to 17 years old, 2) resided in The Hague and surrounding areas, and 3) received outpatient care in 2017 at a participating mental health facility. Group A was identified from records at a center specialized for children with MID (n_A = 505). Group B was identified from registrations at other participating centers addressing a spectrum of MHP (n_B = 2,767). The year 2017 was chosen for its availability of essential demographic and patient data. Only outpatient children were included to explore family-related characteristics, as they resided at home.

Thereafter, two control groups were formed for Group A and B, comprising children from the general population matched by sex and age using the SN database: Group C1 (n_C1 = 2,525) for Group A and Group C2 (n_C2 = 13,835) for Group B. Control subjects resided in the same areas as the cases, with postal codes randomized for comparison of residential characteristics. Baseline characteristics for all groups are presented in Table 1.

Ethical Approval

and data linkage

This study was exempted from the Medical Research Involving Human Subjects Act by the Medical Ethics Committee (CEP number: N22.048). Routinely collected health data was securely stored, pseudonymized, and kept anonymous from researchers. Each participant received a unique record identification number (RIN) to ensure accurate linkage from three sources, preserving anonymity. Parents were linked to children via their RIN for family characteristics. SN staff verified output results to mitigate disclosure risks. Details on the linking procedure are available elsewhere [24].

Independent variables

The demographic domain included child’s sex, age, birth country, and parental age and birth country. For both case groups, the child’s sex was determined from mental health care data. For the control groups, sex and age information was derived from SN, along with the birth country data of children and their parents for all groups. The use of country of birth as a substitute for ethnicity is widely accepted in Dutch literature [26].

Social and cultural domain

The social and cultural domain comprised four variables sourced from SN, each offering insight into various facets of the familial context. Firstly, the number of children in a household indicates the count of individuals, regardless of age or marital status, who share a child-parent relationship with one or both parents residing within a household. This encompasses biological, adopted, and stepchildren but excludes foster children. Secondly, family type, initially comprising eight categories, was later condensed into three: dual-parent households, single-parent households, and others, which encompass for example children from institutional households. Finally, maternal and paternal educational levels were examined, categorized as low, middle, or high based on the classification provided by SN. This categorization was determined by the highest level of education attained: primary education for low, vocational training for middle, and Higher Professional Education or university for high.

Economic domain

Standardized disposable household income was included, capturing the net annual amount available for spending, expressed in percentiles. This measure accounts for household size and composition. Disposable income was determined by subtracting a household's total liabilities from its total assets. Relative disposable household income was based on population percentiles. Specifically, private households were categorized into 100 equal sized percentile groups, each defined by their standardized income. This approach ensured a fair and unbiased assessment of socioeconomic positions across the population, accounting for inflation.

Neighborhood domain

The neighborhood domain included three key factors, sourced from municipality data of The Hague. First, urbanization class was included based on average environmental address density (EAD) and categorized as densely (EAD ≥ 1500) and sparsely populated (EAD < 1500). EAD was computed as the number of addresses within a one-kilometer radius divided by the area. Second, "% low neighborhood education level" was included, representing the proportion of individuals aged fifteen to 74 with lower educational attainment in neighborhoods within The Hague, determined by district codes (e.g., primary education, lower vocational education). Third, relative economic standing of a neighborhood, was measured by low median disposable income percentiles per postal code, adjusted for household size. Derived from standardized disposable income, this variable evaluates low median disposable income percentile within neighborhoods, ranging from the first to the twentieth percentile based on a national level. This score was adjusted for household size and composition within each postal code area.

Approach to analysis

The analyses comprised several phases. First, descriptive statistics (M or %) for the SDOMH were calculated for all groups. Subsequently, univariate analyses were conducted to assess the individual association of each SDOMH with the presence of MID and MHP (Group A vs. Group C1). Full multivariate logistic regression models then assessed associations between each SDOMH and the co-occurrence of MID and MHP (Group A vs. Group C1), adjusting for all other factors in the model. Further univariate and multivariate analyses examined the association between SDOMH and MHP without MID (Groups B vs. Group C2), and compared SDOMH for children with MHP with and without MID (Group A vs. Group B).

The “mice” (version 3.16.0), “modelsummary” (version 1.4.1), “forestplot” (version 3.1.1), and “stats” (version 4.3.1) packages for the R statistical software were used (v4.2.3) [27]. Unadjusted and adjusted odds ratios (ORs), 95% confidence intervals (CIs), and p-values (p) were calculated. We applied a significance level (α) of .05 to all statistical tests. Each OR represents the likelihood of a SDOMH being associated with undergoing mental health treatment for MHP in children with or without MID, compared to the control or between clinical groups. Multicollinearity among covariates was assessed using Pearson's correlation coefficients and the Variance Inflation Factor. The logistic regression model was then fitted using the glm function, with coefficients exponentiated to yield ORs. The 'modelsummary' package derived standardized ORs from our models, allowing comparison of the relative strength and direction of the relationships. Multiple imputations handled missing values, which ranged from 0% (child’s sex, age, birth country) to 50.4% (low median neighborhood income; see Table 1). Assuming data were missing at random, the Multivariate Imputation by Chained Equations method was used, which iteratively estimates each missing value through regression models conditional upon other variables. Twenty datasets were analyzed and pooled.

Descriptive statistics

The study comprised 19,632 children (M = 10.6 years, SD = 3.2, range: 0 to 17), 35.6% of whom were female. Descriptive statistics of all the SDOMH are presented in Table 1 for each group. There were some differences in characteristics between the two case groups (Groups A and B). Children with MID receiving mental health care were significantly younger (see Fig. 3). Both case groups had a predominance of males, but children with MID receiving mental health care had the highest percentage, with 64.4% male.

Table 1

Baseline characteristics by group
	Total sample N (%) N = 19,632	Group A: MID + MHP N = 505	Group C1: Controls / GP N = 2,525	Group B: MHP-only N = 2,767	Group C2: Controls / GP N = 13,835
Demographic domain
Child sex:	19,632 (100%)	-	-	-	-
- Female	-	180 (35.6)	900 (35.6)	1,231 (44.5)	6,155 (44.5)
- Male	-	325 (64.4)	1,625 (64.4)	1,536 (55.5)	7,680 (55.5)
Age child (years)	19,632 (100%)	10.6 (4.1)	10.6 (4.1)	12.0 (3.9)	12.0 (3.9)
Child’s country of birth:	19,632 (100%)	-	-	-	-
- The Netherlands	-	462 (91.5)	2,301 (91.1)	2,443 (88.3)	12,677 (91.6)
- EU (excluding NL)	-	20 (4.0)	102 (4.1)	137 (5.0)	511 (3.7)
- Outside EU	-	23 (4.5)	122 (4.8)	187 (6.7)	647 (4.7)
Age mother (years)	19,438 (99.0%)	40.1 (7.1)	41.6 (6.4)	42.0 (6.9)	43.0 (6.4)
Mother’s country of birth:	19,632 (100%)	-	-	-	-
- The Netherlands	-	296 (58.6)	1,574 (62.3)	1,761 (63.6)	8,815 (63.7)
- EU (excluding NL)	-	43 (8.5)	203 (8.1)	216 (7.8)	1,064 (7.7)
- Outside EU	-	166 (32.9)	748 (29.6)	790 (28.6)	3,956 (28.6)
Age father (years)	18,321 (93.3%)	44.2 (7.9)	45.0 (7.2)	45.9 (7.6)	46.4 (7.0)
Father’s country of birth:	19,632 (100%)	-	-	-	-
- The Netherlands	-	281 (55.6)	1,589 (62.9)	1,730 (62.5)	8,890 (64.3)
- EU (excluding NL)	-	32 (6.4)	164 (6.5)	188 (6.8)	836 (6.0)
- Outside EU	-	192 (38.0)	772 (30.6)	849 (30.7)	4,109 (29.7)
Social and cultural domain
Number of children (in household)	19,618 (99.9%)	2.0 (1.1)	2.3 (1.0)	2.0 (1.0)	2.3 (1.0)
Family type:	19,618 (99.9%)	-	-	-	-
- Dual parent household	-	274 (54.3)	1,997 (79.1)	1,550 (56.0)	10,857 (78.5)
- Single parent household	-	187 (37.0)	500 (19.8)	1,055 (38.1)	2,825 (20.4)
- Institutional household/other	-	44 (8.7)	28 (1.1)	162 (5.9)	153 (1.1)
Education level mother:	13,566 (69.1%)	-	-	-	-
- High	-	46 (12.0)	804 (45.3)	643 (32.4)	4,199 (44.6)
- Moderate	-	165 (43.1)	523 (29.5)	681 (34.3)	2,735 (29.0)
- Low	-	172 (44.9)	446 (25.2)	781 (33.3)	2,490 (26.4)
Education level father:	11,199 (57.0%)	-	-	-	-
- High	-	38 (14.8)	759 (50.5)	539 (37.3)	4,020 (50.3)
- Moderate	-	104 (40.5)	380 (25.3)	442 (30.6)	2,069 (25.9)
- Low	-	115 (44.7)	364 (24.2)	464 (32.1)	1,905 (23.8)
Economic domain
Household income (in percentiles)	19,296 (98.3%)	33.5 (24.2)	52.0 (30.5)	42.6 (28.8)	53.7 (30.1)
Neighborhood domain
Urbanization class:	19,632 (100%)	-	-	-	-
- Densely populated (class 1–3)	-	463 (91.7)	2,108 (83.5)	2,473 (89.4)	11,374 (82.2)
- Sparsely populated (class 4,5)	-	42 (8.3)	417 (16.5)	294 (10.6)	2,461 (17.8)
% low neighborhood education level	14,456 (73.6%)	36.3 (12.8)	31.3 (14.1)	33.0 (12.9)	31.4 (14.2)
low median neighborhood income (in percentiles)	9,733 (49.6%)	15.0 (7.6)	12.9 (7.7)	13.2 (7.5)	13.1 (7.8)
Note. MID = mild intellectual disability; MHP = mental health problems, GP = general population; NL = The Netherlands; EU = Europe. N = total number of observations for each variable without missing. For continuous variables, the mean and SD are provided (SD in brackets). For categorical variables, the N and percentages are given. Percentages in parentheses are calculated based on the non-missing (valid) N.

Key findings

In our first comparison, we compared children with MID receiving mental health care for MHP (referred to as MID + MHP, Group A) against their respective controls (Group C1). Figure 1 shows a forest plot presenting both univariate and multivariate ORs. Interpreting the multivariate results, our analysis revealed statistically significant associations between SDOMH and MID + MHP. Within the demographic domain, having a mother born outside Europe, was associated with lower odds of MID + MHP (OR: 0.83, p = .02). In contrast, the univariate analysis, which considers the variable in isolation, yielded slightly higher odds (OR: 1.08, p = .12) for children whose mothers were born outside Europe. Regarding the social and cultural domain, a lower number of children in a household was associated with higher odds for MID + MHP (OR: 1.28, p < .001). Living in a single-parent household was also associated with higher odds for MID + MHP (OR: 1.18, p = .002). Additionally, having a mother with a moderate (OR: 1.38, p < .001) or low education level (OR: 1.42, p = .001) was associated with higher odds of MID + MHP. A similar result was shown for the education level of fathers (moderate, OR: 1.40, p < .001; low, OR: 1.49, p < .001). In the economic domain, low household income was associated with MID + MHP (OR: 1.33, p = .001). Finally, in the neighborhood domain, no significant associations were observed between SDOMH and MID + MHP when compared to control children.

In our second comparison, we compared children receiving mental health care for MHP, without MID (referred to MHP-only, Group B) to their respective controls (Group C2), as illustrated in Fig. 2. The following results will be based on multivariate ORs. In the demographic domain, the MHP-only group was more likely to be born outside the Netherlands within Europe (OR: 1.06, p = .02) or outside Europe (OR: 1.09, p < .001). Conversely, maternal birth outside the Netherlands, whether in Europe (OR: 0.93, p = .01) or outside Europe (OR: 0.87, p < .001), was associated with lower odds of MHP-only. Similarly, paternal birth outside Europe (OR: 0.90, p < .001) was linked to lower odds of MHP-only. In the social and cultural domain, a lower number of children in the household (OR: 1.17, p < .001) and single-parent households (OR: 1.34, p < .001) were associated with higher odds of MHP-only. In the economic domain, low household income (OR: 1.26, p < .001) was associated with MHP-only, aligning with the findings from the comparison between MID + MHP and controls. Regarding the neighborhood domain, residing in a densely populated urban area (OR: 1.19, p < .001) and areas with low neighborhood education levels (OR: 1.12, p = .001) were both linked to an increased OR for MHP-only.

In our final comparison, we specifically focused on children receiving mental health care for their MHP, comparing children with and without MID (Groups A and B, respectively), as depicted in Fig. 3. Although the multivariate results were quite similar to those in Figs. 1 and 2, some differences were observed. Within the demographic domain, as these two groups had not been matched for age and sex, significant differences emerged. Being male (OR: 1.15, p = .008), along with a younger age (OR: 0.74; p < .001) were linked to higher odds of MID + MHP. Children with MID were less likely to have mothers born outside Europe compared to children without MID (OR: 0.86, p = .03). Within the social and cultural domain, MID + MHP tended to come from households with fewer children (OR: 1.18, p < .001) and were more likely to live in single-parent households (OR: 1.13, p = .006) compared MHP-only. Moreover, MID + MHP were significantly more likely to have parents with moderate (mother: OR: 1.38, p < .001; father: OR: 1.38, p = .001) or low education levels (mother: OR: 1.39, p < .001; father: OR: 1.39, p < .001). In the economic domain, households of children with MID displayed a significantly lower income (OR: 1.36, p < .001) compared to households of children without MID, mirroring the trends observed in previous comparisons. Within the neighborhood domain, no SDOMH was significantly linked to the likelihood of MID + MHP compared to MHP-only.

In this case-control study, we examined the associations between various SDOMH and MHP in children with MID. We compared children with MID receiving mental health care to their matched control group and to children without MID receiving mental health care. Additionally, we compared the latter group to their matched controls. By analyzing a comprehensive set of SDOMH across multiple domains, we aimed to identify those uniquely connected to children with MID undergoing mental health treatment. Our findings indicate that SDOMH across various domains are associated with receiving mental health care in children, irrespective of MID status. Children with MID face greater challenges, encountering more adverse SDOMH across multiple domains, which aligns with our hypothesis. Their disproportionate vulnerabilities emphasize the need for a comprehensive approach to mental health care that addresses the diverse challenges these children face. The study highlights three primary findings: the unique contributions of various domains of SDOMH, the significant effects of low parental education and household income, and the distinct neighborhood effects on children with and without MID.

As a first key finding, this study emphasizes the unique contribution of SDOMH in multiple domains, encompassing social, cultural, economic, and neighborhood contexts [28]. To the best of our knowledge, this study is the first to offer insight into how different SDOMH collectively play a role in receiving care for MHP for children with and without MID in the Netherlands. Our analyses revealed that the independent roles of several SDOMH across these domains remained significant, even when considered together in a multivariate model. Similarly, Zhang et al. (2020) identified multiple determinants, such as lower SES and parental psychopathology, that were strongly associated with child behavioral problems in a multivariate model [41]. However, their study focused on children aged 9–11 without MID. These findings underscore the importance of recognizing the cumulative challenges faced by children with MHP and their families. Additionally, both these and our results highlight the need for a syndemic approach to address these complex healthcare needs. Such approach recognizes the interconnected nature of MHP and prioritizes integrated family care over symptom-focused interventions [29].

Our second key finding showed significant, independent links between low parental education levels and low household income with the likelihood of receiving mental health care, particularly among children with MID. Although parental education and income often correlate and are part of composite SES measures, these factors were particularly distinctive for children with MID. Regarding moderate and low parental education levels, these were only significantly more prevalent for children with MID. This association may be explained by several mechanisms. First of all, it aligns with prior research suggesting a connection between lower parental education and child disability [30]. As lower parental education levels may be indicative of lower parental cognitive abilities, this association may be driven by the genetic hereditary of (M)ID [31]. Alternatively, in the framework of Lund et al. (2018), parental education level is viewed as an integral component of an individual's social capital or network, which could be associated with the quantity and quality of social skills and support in the family [28]. Additionally, lower social support has been linked to increased parenting stress, lower child resilience, and poorer child mental health [32–34]. These factors support the double burden outlined in the introduction: children with MID face additional challenges due to a lack of resources, as a result their mental health may be more vulnerable to the adverse effects of SDOMH. Furthermore, contemporary society's shift in Western countries towards individualism may present additional challenges, particularly for individuals with lower educational levels and resources. This shift emphasizes self-reliance over collective aid, exacerbating the difficulties faced by these children [35, 36].

Regarding lower household incomes, families with children receiving care for their MHP have lower household incomes compared to matched controls, regardless of whether the children have MID. However, households with children who have MID have the lowest incomes, as indicated by an OR just below the level associated with parental education. This aligns with previous research indicating that youth with MID often come from economically disadvantaged households compared to their peers without MID [6, 9, 37], supporting the first part of the double burden discussed in the introduction. The World Health Organization stated that MHP are closely linked to poverty, creating a cycle of systemic disadvantage [38]. This link may partially be explained by family processes of insecurity and cumulative stress [2, 39], while poverty is also associated with worse physical health status or stigma, which could play an additional role for MHP [28]. On a broader policy level, the high demand for mental health care among marginalized groups raises questions regarding the inclusivity of the Dutch society and the efficacy of preventive policies, particularly given the widening gap between the affluent and the underprivileged [40].

The third main finding is the difference in findings within the neighborhood domain for the two clinical case groups. Residing in areas with low neighborhood education levels and more densely populated areas was linked to an increased OR for children receiving care for MHP without MID, compared to the general population, but not for children with MID. This finding aligns with a systematic review showing that lower neighborhood SES was associated with increased problem behaviour in children without (M)ID [23]. To our knowledge, no studies have investigated neighborhood characteristics associated with the MHP in children with (M)ID. In our study, we did not observe significant multivariate effects for neighborhood characteristics in the MID + MHP group. However, we found that these children often lived in more densely populated, lower-income areas with lower levels of educational attainment. Future research should replicate these findings to better understand the role of neighborhood characteristics in MHP development in children with MID. Two potential explanations for the lack of significant neighborhood SDOMH include the comparatively smaller size of the MID + MHP group, which might result in limited statistical power, and a relatively greater importance of individual adversities for this group.

The study has several strengths. Firstly, it represents the first large-scale investigation conducted in the Netherlands examining children with MID. Our study leveraged extensive clinical and population-based samples, enabling a detailed analysis of SDOMH. This approach facilitated comparisons between children with and without MID receiving mental health care, enhancing the representativeness of our findings. Additionally, our study benefited from utilizing all available clinical data from a specialized mental health care center, ensuring diverse representation of children across various SES and ethnic backgrounds. Finally, our interpretation of multivariate results accounted for the independent effects of a comprehensive set of factors, providing a thorough understanding of the interconnected nature of SDOMH’s impact on mental health outcomes. This nuanced approach allowed us to identify the unique contributions of SDOMH on outcomes while controlling for potential confounders.

However, the results of this study might be interpreted in light of the following limitations. First, our study predominantly relied on data from a single institution specialized in MID and MHP. Although this institution is the largest in the area, the external validity of our findings remains uncertain. Consequently, it is necessary to validate our results using samples from other institutions and in different countries, as the roles of SDOMH can vary between societies. Additionally, it is important to acknowledge that we rely on data from children already using mental health care. Some groups, such as children from certain ethnic backgrounds, may be less likely to use these services. Furthermore, our reliance on real-world data led to a substantial number of missing data for some variables. Therefore, these results should be interpreted cautiously, particularly regarding low median neighborhood income, for which 50.4% of the data were missing. Another limitation is the reliance on cross-sectional data, restricting the ability to establish temporal and potential causal relationships. Finally, our selection of SDOMH was guided by literature from the general adult population without MID [28] and the availability of measurable variables. However, it remains uncertain whether other important SDOMH were omitted from this study, potentially limiting the comprehensiveness of our findings.

Taken together, our study underscores the profound impact of SDOMH on children with MHP and MID, revealing the unique and severe challenges they face. The findings advocate for a holistic, context-oriented approach to mental health care that addresses the diverse adversities experienced by these children. Moving forward, it is crucial for policymakers and practitioners to integrate comprehensive family support and inclusive community strategies to reduce these systemic disadvantages, thereby creating a more equitable and supportive environment for all children.

Acknowledgements

We thank Randy Bax, foreign language teacher, for his critical reading and feedback on the discussion. We also acknowledge ChatGPT-4, developed by OpenAI, for improving the textual quality.

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Social determinants of youth with mild intellectual disability in outpatient care for mental health problems: a case-control study

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