Socioeconomic Factors in Inflicted Traumatic Brain Injury: Examining the Area Deprivation Index

Abstract

Purpose/Objective:

Inflicted traumatic brain injury (iTBI), or abusive head injury, is a common cause of mortality and disability among infants and toddlers. Social determinants of health (SDoH) have a critical and multifaceted impact on iTBI, influencing both prevalence and outcomes. The Area Deprivation Index (ADI) is a comprehensive metric of SdoH developed to assist in understanding how community-level socioeconomic factors influence patient outcomes. The current study sought to describe the sociodemographic characteristics, including ADI, of a cohort of 373 infants and young children who sustained an iTBI.

Research Method/Design:

This study was a retrospective analysis utilizing a cohort of pediatric patients treated for iTBI at a large, tertiary care children’s hospital serving seven states in the Rocky Mountain region.

Results:

Mortality prevalence was higher among older children, and older children were more likely to have a longer stay in the Pediatric Intensive Care Unit. Children who were identified as Hispanic/Latino lived in areas with greater socioeconomic disadvantage than children identified as non-Hispanic/Latino. Specifically, participants who were identified as White Hispanic/Latino lived in areas with greater disadvantage than children who were identified as White non-Hispanic/Latino. There were no other significant differences by race. Contrary to hypotheses, ADI was not significantly related to mortality, injury severity, or follow-up visits.

Conclusions/Implications:

While SDoH are known to influence outcomes in iTBI, it may be necessary to incorporate individual or family-level SDoH variables within this clinical sample and examine the interaction between individual and community-level factors.

Inflicted traumatic brain injury (iTBI), or abusive head injury, is the second most common cause of death in infants and toddlers (Barlow et al., 2005; Eismann et al., 2020; Keenan et al., 2003; Nuño et al., 2015; Shah et al., 2022). Several studies have shown that children who have sustained iTBIs fare worse than those who have sustained accidental TBIs even when controlling for injury severity, suggesting that there are distinct etiological and sociodemographic factors to consider in the context of intentional harm (Ewing-Cobbs et al., 1998, 2006; Keenan et al., 2006, 2007). Although there are a variety of factors that increase the risk for poor outcomes after iTBI, such as injury mechanisms and delays in seeking care, social determinants of health (SDoH) may also be contributory (Paul & Adamo, 2014).

The World Health Organization and Centers for Disease Control and Prevention have underscored the role of SDoH, defined as “the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life,” in impacting the health status of individuals and communities (Centers for Disease Control, 2014; Rodriguez, 2017). Specifically, higher mortality risk, rates of hospital readmission, and more severe disease have been linked to socioeconomic disparities (Jencks et al., 2019; Kind et al., 2014). Across several studies examining iTBI as well as child development more broadly, SDoH factors are a powerful predictor of outcome, often independent of injury severity or disease alone (Burchinal et al., 2008; Chapman et al., 2010; Jencks et al., 2019; Lind et al., 2016; Yeates et al., 1997).

At the household level, data suggests that family income may be related to the severity of injury in iTBI, with the children of families living below the poverty line more likely to die of their injuries when compared to those from higher-income strata (Nuño et al., 2015; Shah et al., 2022). Proxies for household socioeconomic disadvantage, such as public versus private insurance, maternal age, and education level, have been utilized in studies when more detailed SDoH indicators are not available and have been implicated in iTBI outcomes (Eismann et al., 2020; Keenan et al., 2007; Lind et al., 2016; Parrish et al., 2013; Ricci et al., 2003). While there is limited research on sociodemographic factors and follow-up care specifically in iTBI (Eismann et al., 2020), there is extensive data in the general medical literature demonstrating a higher likelihood of follow-up care in patients with fewer SDoH risk factors (Fleegler et al., 2007; Jencks et al., 2019; Kind et al., 2014). As noted, many extant studies have utilized single-factor proxies for familial-level socioeconomic disadvantage (e.g., family income), which could lead to the prioritization of data that are readily available, potentially overlooking other important factors and nuances or interactions between indicators. Further, operationalizing socioeconomic disadvantage in this manner often confines examination of SDoH to individual or family-level factors, precluding the consideration of the impact of SDoH at a broader environmental (i.e., community) level.

Health equity researchers have increasingly underscored the importance of moving beyond the extant focus on individual and family-level risk factors by incorporating community-level variables (Levine & Breshears, 2019). Neighborhood or community-level factors include broader environmental and social contexts that shape health outcomes within communities, such as environmental exposures and access to resources (Cottrell et al., 2020). Exclusively focusing on individual-level risk factors (e.g., parental education, family income) may inadvertently attribute blame for adverse outcomes to the family, overlooking the systemic disparities that disproportionately affect marginalized populations at the neighborhood level. Examining community-level characteristics of SDoH may be particularly integral in the context of populations that are especially vulnerable or affected by structural inequities, such as with iTBI. For example, child physical abuse has been associated with community or neighborhood-level factors, such as income disparities within a community and food accessibility, even after taking patient-level characteristics into account (Hong et al., 2023).

The Area Deprivation Index (ADI) was developed in response to the awareness that community-level SDoH are critical to better understand the impact of health disparities on patient care and outcomes (Kind & Buckingham, 2018). The ADI is constructed with information collected from census blocks, the smallest geographical area for which census data is collected from the American Community Survey through the United States Census Bureau (Kind & Buckingham, 2018; Singh, 2003). The ADI is represented at both the state and national levels and is a validated composite variable of 17 factors, including education, income/employment, housing quality, and household characteristics (Kind et al., 2014; Kind & Buckingham, 2018). It is represented as a percentile value from 1 to 100 at the national level and a decile value from 1 to 10 at the state level, with the least resourced neighborhoods (i.e., census block groups) characterized by higher scores. Recent research has shown that high ADI is associated with injury characteristics and adverse outcomes among various medical populations, including broadly increased rates of hospital readmission across conditions (Hu et al., 2018), increased rates of cancer prevalence and mortality (Fairfield et al. 2020; Hufnagel et al., 2021), higher injury severity in pediatric burn patients (Zhang et al., 2023), and poorer rates of follow-up care following vascular injuries (Boutrous et al., 2021).

In this study, we examined the ADI in a patient population of infants and young children who sustained an iTBI, with the primary aims of 1) retrospectively describing the SDoH characteristics of the cohort, including distribution of national and state-level ADI metrics and the associations between ADI and patient demographic data (race, ethnicity, age at injury), and 2) determining the associations between ADI decile, injury characteristics (injury severity, mortality), and the extent of follow-up clinical care. We hypothesized that ADI would explain a significant proportion of variance in injury severity, mortality, and follow-up care, such that patients living in communities with a higher ADI decile (i.e., more socioeconomic disadvantage) would be at higher risk for severe injury and mortality, and less likely to receive follow-up care than children in lower deciles.

Methods

Patients

This study was a retrospective analysis utilizing a cohort of pediatric patients treated for iTBI at a large, tertiary care children’s hospital serving seven states in the Rocky Mountain region. Patients are diagnosed with iTBI by the hospital child protection team (i.e., a multidisciplinary team led by a medical provider with specialized training in child abuse) through a multi-disciplinary assessment (Narang et al., 2020). Children with TBI are referred for consultation when there is concern that the clinical history or mechanism of injury is not consistent with the findings. Patients who survive their injuries are referred to an outpatient Non-Accidental Brain Injury Care Clinic (NABICC), an interdisciplinary clinic where children are followed by rehabilitation medicine, child protection, nursing, neurology, neurosurgery, ophthalmology, nutrition, and neuropsychology, as well as speech/language, occupational and physical therapies. The complete dataset included a total of 408 children who were hospitalized due to diagnosed iTBI from 2012 to 2019; demographic data were available to calculate ADI for 373 patients. There was a significant difference in ADI between the seven states in the hospital catchment area, such that Colorado’s national ADI percentile was significantly lower than its surrounding states, indicating higher socioeconomic advantage. This difference may potentially be driven by the inclusion of variables such as median home value within the calculation of the ADI, which varies greatly between Colorado and its neighboring states. This discrepancy between states may introduce confounding effects, particularly for disadvantaged communities living in areas characterized by high housing prices, such as the Denver-Aurora metropolitan area (Hannan et al., 2023), and including participants from surrounding states may thus obfuscate the impact of ADI on Colorado patients. Given that the significant majority of the study population was from Colorado, analyses beyond initial descriptive characteristics were conducted with the 299 children who were residing in Colorado at the time of injury to facilitate the examination of more nuanced associations. Analyses predicting injury severity and clinic follow-up care were conducted with patients who survived only (n=262).

Procedure

The study protocol was reviewed and approved by the Colorado Multiple Institutional Review Board. Data extracted from retrospective medical chart reviews were primarily collected utilizing the initial hospitalization, Suspected Child Abuse/Neglect (SCAN) reports, or as part of routine clinical care during NABICC visits. Once referred to NABICC, patients are typically seen clinically by the multidisciplinary team in standardized intervals for the first two years post-injury before being transitioned to other multidisciplinary clinics.

Measures

Demographic Information

Charts of all iTBI patients were reviewed, and data extracted by NABICC research assistants into a database hosted in the Research Electronic Data Capture (REDCap) virtual platform (Harris et al., 2009). Data were documented retrospectively for encounters before 12/1/2019 and prospectively thereafter. Patients’ sex, race, ethnicity, and age at injury were extracted from REDCap. Patient address at injury was also extracted for geocoding purposes, explained further below.

Area Deprivation Index

The Area Deprivation Index (ADI) was used as a proxy to assess community-level SDoH. The 2020 ADI rankings, compiled using data from the American Community Surveys between 2016 and 2020, were downloaded from the Centers for Health Disparities Research at the University of Wisconsin Madison (Kind & Buckingham, 2018). Residential addresses at the time of injury were geocoded to the block group level geographic identifiers using the R package, tigris (Walker, 2022), and then converted to ADI rankings. As noted above, the ADI allows for rankings of neighborhoods by socioeconomic disadvantage at a national level via percentile and at a state level via decile, such that higher numbers indicate greater disadvantage. State-level data was utilized in the current study to determine the associations between ADI decile, injury characteristics (injury severity, mortality), and the extent of follow-up clinical care, as described above.

Injury Characteristics

Injury characteristics included mortality during initial hospital admission, as well as the length of stay in the Pediatric Intensive Care Unit (PICU) among patients who survived their injuries. As there is currently no single universally accepted way to measure injury severity in TBI in infancy, the length of PICU stay was utilized as the primary proxy for injury severity among surviving children. Length of PICU stay has been shown to correlate with various injury characteristics, including Glasgow Coma Scale score and neurocognitive outcomes following iTBI (Eismann et al., 2020).

Follow-up Care

The total number of NABICC follow-up visits during the first two years following patient injury was summed retrospectively. Patients are typically seen for follow-up visits at 1-, 3-, 6-, 12, 18-, and 24-months post-injury.

Analytic Plan

Given that the majority of patients were from the state of Colorado (80.16%), statistical analyses beyond descriptive data only included Colorado patients and were calculated utilizing state-level ADI. We identified outliers in Colorado participant data using Mahalanobi’s distance and the standard critical alpha value of .001. Though three outliers were identified, their exclusion did not affect the findings; therefore, the final analyses are presented with the complete data. In order to address the first study aim of retrospectively describing the SDoH characteristics of the cohort, including ADI metrics and the associations between ADI and patient demographic data, we calculated descriptive statistics and conducted Kendall’s rank-order correlations and Welch’s t-tests between ADI rankings and individual-level demographic characteristics (i.e., race/ethnicity, age at injury). In addition to analyzing race and ethnicity separately, we conducted a one-way analysis of variance using a combined ethnoracial variable which was operationalized as follows: children identified as 1) White non-Hispanic/Latino, 2) White Hispanic/Latino, 3) non-White (i.e., Minoritized) non-Hispanic/Latino, and 4) non-White Hispanic/Latino. Acknowledging the immense diversity and unique experiences amongst minoritized individuals, ethnoracial identity was operationalized in this manner to facilitate the examination of power and obtain a more granular understanding of the intersecting identities of families in association with ADI. To address the second aim of determining the extent to which state-level ADI predicts injury severity and follow-up visits, and test our hypotheses that ADI would explain a significant proportion of variance in injury severity, mortality, and follow-up care, a series of parallel multiple regression models predicting injury severity from ADI were conducted, including a logistic regression to predict patient mortality. Deceased patients were excluded from analytic models predicting the length of PICU stay and follow-up visits. Prior to conducting regression analyses, associations between outcome variables (i.e., PICU length of stay, mortality, and follow-up visits) and demographic variables (sex, age of injury, race, and ethnicity) were examined to assess whether they should be accounted for as covariates. Post-hoc power analyses were conducted using G*Power specifying an alpha level of .05, showing that the sample was well-powered to detect medium and large effect sizes (≥ .99), but underpowered to detect small effect sizes (< .80).

Transparency and Openness

All analyses were conducted using R software 4.2.0 (R Core Team, 2022). This study’s design and analytic plan were not pre-registered. The JARS-Quant guidelines were followed to enhance readability and replicability of the research. We report how we determined our sample and all measures in the study. Data and materials for this study are not available. Analytic code from study may be available from the senior author upon reasonable request.

Results

Descriptive Analyses

Demographic characteristics and descriptive statistics are shown in Table 1. The majority of the cohort was male (64%) and White (69%). Around one-third of participants were identified as Hispanic/Latino (31%). National ADI percentile was slightly positively skewed (M = 43.10, SD = 22.49), with 34 families (9.12%) residing in the most disadvantaged quintile (i.e., the highest 20%) of neighborhoods (see Figure 1). For the subset of Colorado participants, the ADI decile was negatively skewed (M = 6.56, SD = 2.47), with 80 families (26.78%) from the most disadvantaged quintile (see Figure 2). Patient mortality, PICU length of stay, and number of follow-up visits were not associated with patient ethnicity or race (ps > .05). There was a significant association between patient age at injury and both mortality, t(35.83)= 2.38, p = .02, length of PICU stay, r = .20, p = .001, and number of follow-up visits, r = −.13, p = .047, with older children more likely to die from their injuries, require a longer PICU stay, and have a fewer number of follow-up visits. Therefore, patient age at injury was included as a covariate in the regression models predicting outcome variables. The number of clinic follow-up visits was associated with child sex, such that the mean number of follow-up visits was slightly higher for males (M= 4.52) than females (M= 4.05), t(158.28)= −2.01, p = .046 . Child sex was therefore included as a covariate in the regression model predicting follow-up visits.

Table 1.

Demographic Characteristics

Variable	Mean (Standard Deviation) or Frequency (%)
ADI Percentile (National)	43 (±22)
ADI Decile (State)	6.6 (±2.5)
Child Sex
Female	134 (36%)
Male	234 (64%)
Ethnicity
Hispanic or Latino	114 (31%)
Not Hispanic or Latino	227 (62%)
Not Reported	10 (3%)
Unknown	17 (5%)
Race
American Indian	11(3%)
Black/African American	19 (5%)
Multiracial	35 (10%)
Other	46 (13%)
White	242 (69%)
Age of Injury (months)	11 (±14)
PICU length of stay (days)	3.7 (±5.6)

Note. ADI = Area Deprivation Index; PICU = Pediatric Intensive Care Unit; N = 373.

Figure 1. Histogram of Area Deprivation Index (ADI) national percentiles.

Note. N = 373

Figure 2. Histogram Of Area Deprivation Index (ADI) Deciles For Colorado Participants.

Note. N = 299.

Associations between State ADI Decile and Demographic Characteristics

ADI decile was not significantly associated with child age of injury (τ = .060, p = .15) or race, t (252.57) = 1.29, p = .20. There were no significant differences between ADI with regard to race. There was a significant difference in ADI between children who were identified as Hispanic/Latino and children identified as non-Hispanic/Latino, such that Hispanic/Latino children resided in neighborhoods with higher disadvantage (M = 7.01, SD = 2.38) compared to non-Hispanic children (M = 6.28, SD = 2.50), t(215.57) = −2.38, p = .018. An analysis of variance examining both race and ethnicity simultaneously significant, F(3,266) = 2.85, p = .038, with post hoc analyses via Dunnett’s test demonstrating that children who were identified as White Hispanic/Latino (M = 7.12, SD = 2.35) resided in geographical areas with significantly higher disadvantage than children who were identified as White non-Hispanic/Latino (M = 6.13, SD = 2.61), p = .046. There were no other significant differences in ADI decile when examining race and ethnicity in conjunction.

Associations between State ADI Decile and Injury Characteristics and Follow-up Visits.

Three parallel regression models were conducted predicting child mortality, PICU length of stay, and number of NABICC follow-up visits in the first two years post-injury from ADI decile, controlling for child age at injury (see Table 2). ADI decile did not significantly predict mortality status, PICU length of stay, or number of follow-up visits.

Table 2.

Regression Models Predicting Mortality Status, PICU Length Of Stay, And Number Of NABICC Visits From ADI State Decile, Controlling For Child Age At Injury For Colorado Patients

	Dependent variable:
	Mortality Status	PICU Length of Stay (Days)	# of NABICC Visits
	logistic (1)	OLS (2)	OLS (3)
ADI Decile	−0.02	−0.03	−0.02
	(0.08)	(0.13)	(0.05)
Age of Injury (Months)	−0.03***	0.09***	−0.02**
	(0.01)	(0.03)	(0.01)
Male Sex			0.35
			(0.24)
Constant	2.67***	2.74***	4.47***
	(0.57)	(0.94)	(0.38)
Observations	294	261	211
R2		0.04	0.03
Adjusted R2		0.03	0.02
Log Likelihood	−97.87
Akaike Inf. Crit.	201.75
Residual Std. Error		5.18 (df = 258)	1.68 (df = 207)
F Statistic		5.66*** (df = 2; 258)	2.33* (df = 3; 207)

Note. The values presented are beta coefficients, standard errors are presented in parentheses.

^*p<0.1;

^**p<0.05;

^***p<0.01;

PICU = Pediatric Intensive Care Unit; NABICC = Nonaccidental Brain Injury Care Clinic; ADI = Area Deprivation Index

Discussion

The present study described the sociodemographic characteristics of a large cohort of pediatric patients who had experienced iTBI, including ADI and its associations with individual-level patient variables. Contrary to our hypotheses, controlling for child age at injury, ADI was not significantly associated with mortality, PICU length of stay, or number of follow-up visits. Mortality prevalence was higher among older children, and older children were also more likely to have a longer PICU stay, our proxy for injury severity. There were no differences in ADI by race. Children who were identified as Hispanic/Latino tended to reside in areas with higher ADI than children identified as non-Hispanic/Latino. When looking at race and ethnicity together, participants who were identified as White Hispanic/Latino resided in areas with greater disadvantage than children who were identified as White non-Hispanic/Latino.

State-level ADI was not related to mortality, PICU length of stay, or the number of follow-up visits attended. This was an unexpected finding as SDoH has been repeatedly shown to be associated with outcomes in iTBI, accidental TBI, and in the general health literature (Eismann et al., 2020; Jencks et al., 2019; Keenan et al., 2006; Yeates et al., 1997). In iTBI literature specifically, insurance type, public or private, is frequently used and has been linked to injury severity and outcomes (Eismann et al., 2020; Nuño et al., 2015). While insurance type is a very general proxy for socioeconomic status and much less granular than ADI, it may be tapping into something specific to the family that is more predictive of outcome than ADI, which is defined more by community-level risks. Infants and young children may be more influenced by these family-level factors, as suggested by the association between maternal age and education with iTBI outcomes in previous studies (Keenan et al., 2006; Parrish et al., 2013). Thus, it may be that infants’ lives are more insular and centered in the home than older children and adults, whose health outcomes may be more influenced by the communities in which they live.

Children who were identified as Hispanic/Latino tended to live in areas with higher ADI than those identified as non-Hispanic, with post-hoc analyses further showing that children who were identified as White Hispanic/Latino resided in geographical areas with significantly higher disadvantage than children who were identified as White non-Hispanic/Latino. This was not an unexpected finding as families identifying as Hispanic/Latino in Colorado have lower mean household incomes, lower rates of high school completion, and a higher rate of households living below the poverty line than state averages (Https://Www.Census.Gov/Quickfacts/CO, n.d.; Latino.Ucla.Edu, n.d.). Of course, the reasons for this are complex and multifaceted and include immigration status and systemic and historical inequities, which lead to barriers in access and opportunity(Brener et al., 2023; Escobedo et al., 2023). However, despite these barriers, children living in areas with high ADI did not have higher rates of mortality, more severe injuries, or lower rates of follow-up care in the state of Colorado.

Older children were more likely to die from their injuries, and older children who survived were more likely to have a longer stay in the PICU. This was also an unexpected finding, as most studies of iTBI suggest that younger children are more vulnerable to mortality and severe injury(Anderson et al., 2005; Ewing-Cobbs et al., 1999; Prasad et al., 2002). Our large sample size included children somewhat older than are included in many studies of iTBI, including a few outliers, which may have skewed results in this part of the analysis. Despite its utilization in prior TBI literature, it is also possible that PICU length of stay was not an adequate proxy in the context of this unique cohort and was not specific enough to discriminate well between differing levels of injury severity. Future studies should examine the various indicators utilized within TBI and iTBI research to determine the proxy or set of proxies that accounts for the greatest variability in injury outcome variables. Lastly, there may have been other interactions with age that influenced results, such as delays in seeking care or prior abuse (Ewing-Cobbs et al., 1999; Keenan et al., 2007), that were beyond the scope of this study and were not explored.

There were several strengths to this study. This is a large sample and may be one of the largest clinical samples of iTBI in the literature to date. Many studies of the SDoH in iTBI have used general proxies, like insurance type (Eismann et al., 2020; Nuño et al., 2015), and this is the first study that we are aware of to examine SDoH in a more detailed manner within this population. Future studies with this population will continue to examine all of the factors, modifiable or not, that may confer additional risk or protection in iTBI and its outcomes. Given the null findings, future studies will also attempt to explore if there are family-specific factors related to SDoH, such as parental age and educational level, as well as interactions between family and community-level factors, that help better predict outcomes in this population. Ecological systems models underscore the need to examine the dynamic interactions between spheres of influence (e.g., between the individual and family and the communities within which they reside), and there have been recent calls to update these models (Levine & Breshears, 2019) to call attention to the role of racism and systemic inequities at each sphere of influence. Adequate consideration of systemic factors and their associated interactions will be integral not only at a research level, but also to inform clinical, operational, and policy-related decisions. Finally, although these preliminary findings showed that ADI was not predictive of initial injury characteristics (i.e., days of PICU stay or mortality) or number of follow-up visits, it will be important to examine the extent to which ADI and other community factors impact recovery trajectories and various post-injury outcomes.

Constraints on Generality

There were also limitations to this study that limit the generalizability of findings. While adequately powered to detect large and medium effect sizes, the study was underpowered to detect small effect sizes. As stated above, because of the significant economic disparities between states in the hospital’s seven-state catchment area, it was impossible to include all states and children who were not living in Colorado at the time of injury were excluded. Usage of the ADI, which utilizes geocodes US Census data, also inherently has disadvantages. For example, certain addresses were not associated with a geocode, with a primary reason for lacking a code, as cited by the US Census Bureau, being that the address is non-residential or commercial (US Census Bureau, 2021). Thus, individuals in transitory locations (e.g., in an RV park, hotel, or very remote locations) would be less likely to be represented, further reinforcing the impacts of systemic inequity. Patients were predominantly identified as White, and our sample lacked the diversity representation that may be seen in other parts of the country. As noted above, PICU length of stay may not have adequately captured nuances of injury severity, which may have been better described by another metric.

Finally, an important point of consideration is that all participants in the current study were cared for by the same hospital with the same systems of care and support for follow-up care, which may have obscured any potential effects of ADI. For example, all children received the same care during inpatient hospitalizations, and NABICC has a designated nurse and scheduling team who help families make follow-up appointments, ensure transportation and accommodation have been arranged if needed, and make personal appointment reminder calls. Given that this type of clinic model is unique, most children in the US with iTBI are likely not receiving these level of follow-up care. Given that a primary aim of health equity research is to identify leverage points and strategies to mitigate health disparities, multi-site studies or studies that include various medical care infrastructures will be integral to identifying specific mechanisms within healthcare systems that facilitate or impede access to care in the context of community-level socioeconomic deprivation.

Conclusion

This study was among the first to examine social determinants of health using a community-level, multifactor index in the context of key outcomes in pediatric patients with inflicted traumatic brain injury (iTBI). Our findings emphasize the need for continued nuanced exploration of individual and community-level determinants, as well as the interaction between both systems. Future research should focus on uncovering modifiable mechanisms at this intersection to inform targeted interventions and decrease barriers to care.

Impact.

• Although the importance of considering social determinants of health (SDoH) in the context of inflicted traumatic brain injury (iTBI) has been well-established, few studies have incorporated community-level indices when examining the impact of SDoH.

• This study is among the first to examine community-level factors in a large cohort of pediatric patients being treated following iTBI, using the Area Deprivation Index (ADI), a 17-factor index of economic deprivation, alongside individual sociodemographic factors.

• Future research and policy decisions must consider the dynamic interactions between both individual and community-level factors in order to identify modifiable mechanisms, ultimately facilitating access to services and mitigating health inequities.