When considering how the innumerable social and cultural aspects of well-being have been well-researched from many perspectives (Deferio et al., 2019; Laliberte & Varcoe, 2021; Prokosch et al., 2022; Robins, 2021; Robins et al., 2024), it is important to note that few studies have comprehensively investigated the determinants that compound adverse mental health in adolescents. Though there is a significant gap in the literature surrounding this topic, some researchers have acknowledged the importance of uncovering and analyzing indicators that collectively cause disparities in mental health access and utilization, inequitable healthcare, and mental health disorders (Laliberte & Varcoe, 2021; Prokosch et al., 2022; Robins et al., 2024). While previous studies have addressed single aspects of social and cultural determinants concerning mental health in general, there is a limited acknowledgment of how multiple determinants and intersectionality impact overall mental health and well-being in adolescents (Artiga & Hinton, 2019). Intersectionality, which examines how various social identities (e.g., race, gender, socioeconomic status) intersect and create unique experiences of oppression and privilege, is crucial to understanding how different sociocultural determinants, such as economic stability, education, health care access and quality, neighborhood and built environment, and social and community context, intersect and intensify mental health issues (López & Gadsden, 2017). For instance, an individual experiencing economic instability might also face inadequate access to quality education and health care, live in a deprived neighborhood, and lack supportive social networks. These overlapping disadvantages do not simply add up but interact in ways that compound their impact on mental health, creating a complex web of challenges that a one-dimensional approach might overlook (Zambrana et al., 2022). In fact, previous literature has highlighted that research acknowledging the complexity of intersectionality often focuses on the interplay of oppression and privilege within systemic, societal, and economic contexts, thereby challenging entrenched systems of inequality (López & Gadsden, 2017).
Considering the importance of intersectionality and the acknowledgment of how intersecting domains tend to increase the impact on mental health, it is important to conceptualize these intersecting components through the overarching term of social determinants of health (SDOH). SDOH have been defined as the biopsychosocial, environmental, and cultural conditions in which people live and develop, as well as a broad set of social, structural, and political systems and forces that shape the needs of daily life (World Health Organization [WHO], 2014). To assist in properly addressing SDOH and achieving optimal mental health during adolescence, the federal government developed a prevention agenda entitled Healthy People, 2020, which suggested five categories of SDOH: economic stability (e.g., employment, housing instability, food insecurity by the means to purchase food, and poverty), health and healthcare (e.g., access to health care, access to primary care, health insurance, and health literacy), social and community context (e.g., social support/cohesion, discrimination, and incarceration), education (e.g., early childhood education, high school enrollment, graduation rates, and literacy), and neighborhood and built environment (e.g., quality of housing, neighborhood crime, environmental conditions, and access to full-service grocery stores and healthy food options; Office of Disease Prevention and Health Promotion [ODPHP], 2020). While these conditions can positively or adversely influence the mental health and well-being of individuals ((2020) [CDC], 2020), most of the research in all fields focuses solely on adverse and inequitable outcomes (Artiga & Hinton, 2019). Altogether, these conditions and determinants largely shape individuals’ physical and mental health. While past and current literature has focused on the physical aspects of health regarding SDOH, mental health has been equally acknowledged as an integral part of overall health (Artiga & Hinton, 2019).
Social Determinants of Health During Adolescence
When considering overall mental health across the lifespan, it is important to note that adolescence is a vital period for developing and upholding healthy emotional and social practices for mental well-being and working toward optimal mental health (WHO, 2020). Optimal mental health is a consistent state of well-being when the individual can realize their abilities, properly cope with everyday life stressors, work productively, and adequately contribute to their family (WHO, 2014). By being aware and knowledgeable about how the prevalence of certain mental health disorders occurs within specific age ranges, many symptoms can be detected early (Petito et al., 2020), which is necessary for proper treatment planning and interventions (Lenz & Litam, 2023). This study specifically explored the adolescent age range, with the prevalence of depression being a main contributor to adolescent’s overall mental health. Researchers have found that most mental health conditions begin by 14, with 10-20% of adolescents globally experiencing adverse mental health conditions; however, most cases are undetected and untreated (Kessler et al., 2007). One mental health issue in particular that presents during adolescence is depression. Depression has been defined by the World Health Organization as a common mental disorder that involves a depressed mood or loss of interest in activities for a prolonged period (WHO, 2020). Further, the World Health Organization (2020) has deemed depression to be one of the leading causes of disability and illness among adolescents (WHO, 2020). More specifically, depression is the fifteenth leading cause of disability and illness among adolescents aged 10 to 14 years old and fourth for adolescents who are 15 to 19 years old (WHO, 2020). Researchers have found that many SDOH regularly impact the ability of adolescents to achieve optimal mental health that is sustainable throughout the lifespan (Prokosch et al., 2022; Robins, 2021). In alignment with these alarming statistics, researchers have also posed a need for increased attention to depression during adolescence, specifically looking at the various elements that lead to this diagnosis (Monroe et al., 2023; Petito et al., 2020; Robins, 2021).
Depression During Adolescence
Depression during adolescence is intricately linked to various social determinants of health (SDOH) domains, profoundly impacting individuals’ mental well-being. Socioeconomic status (SES) is a critical determinant influencing adolescent mental health outcomes. Research indicates that adolescents from low-income families are at a higher risk of experiencing depression due to limited access to resources and support systems (Nurius et al., 2020). Additionally, family dynamics play a pivotal role in shaping adolescents’ mental health. Family conflicts, parental separation, or inadequate familial support can contribute to depressive symptoms (Hammen, 2018). Furthermore, neighborhood and community environments can significantly influence adolescent depression rates. Adolescents residing in disadvantaged neighborhoods with high crime rates and limited access to recreational facilities may face increased stressors, exacerbating their vulnerability to depression (J. Liu et al., 2017). Moreover, systemic factors such as discrimination and social exclusion based on race and ethnicity can adversely affect adolescents’ mental health (Williams et al., 2019). Addressing depression during adolescence requires a holistic approach that considers the multifaceted interplay of social determinants, emphasizing the need for policies and interventions that promote equity and address systemic inequalities rooted in systemic racism to support adolescent mental health and well-being.
The Impact of Racism in Adolescence
In addition to considering how adolescents are impacted in general, it is important to mention how racism plays a huge part, especially in historically oppressed communities. The impacts of racism on SDOH and its outcomes in adolescents are profound and multifaceted. Racism can manifest in various forms, including systemic discrimination, microaggressions, and implicit biases, all of which contribute to disparities in health outcomes among adolescents from marginalized racial and ethnic groups (Coyne-Beasley et al., 2023). These experiences of racism can directly impact SDOH and yield adverse outcomes by exacerbating socioeconomic inequalities, limiting access to quality education, healthcare, and housing, and perpetuating cycles of poverty and marginalization. More specifically, a recent study found that these adverse childhood experiences which intersect with racism increase the likelihood of poor mental health during adolescence, especially for those from historically oppressed communities (Berry et al., 2021). These negative impacts on SDOH outcomes not only affect individual adolescents but also have broader implications for their families, communities, and society as a whole, perpetuating inequities and hindering efforts to achieve health equity and social justice (Shim et al., 2021). It is integral to acknowledge racism as the root cause of adverse social determinants of health and adverse mental health (Johnson, 2021; Johnson et al., in press), as an initial effort to begin mitigating its harmful effects on SDOH outcomes and promoting the well-being of adolescents from all racial and ethnic backgrounds.
Social Determinants of Health Framework
Though social determinants have been explored in isolation, it is important to investigate them individually and collectively, to be able to navigate the influences of intersectional stigma and discrimination on adverse mental health outcomes during adolescence. This is also important to understand how interprofessional collaboration can be a strategy for minimizing the impact that negative determinants have on mental health in adolescence. We used the SDOH framework provided by Healthy People, 2020 (Office of Disease Prevention and Health Promotion [ODPHP], 2020) to explore the five domains SDOH domains: Economic stability, health and health care, education, social and community context, and neighborhood and built environment. These domains will be thoroughly explored below.
Economic Stability
Economic stability includes poverty, employment, food insecurity, and housing instability (ODPHP, 2020). In the United States, over 28 million (39%) of children live in low-income families (National Center for Children in Poverty [NCCP], 2021), which can in turn lead to housing instability and food insecurity. Studies show that poverty is a risk factor for adolescents’ emotional, mental, and behavioral disorders (National Research Council & Institute of Medicine, 2009) and that income levels, food insecurity, and housing instability are predictors of adverse mental health (Braveman et al., 2017). Researchers have extensively addressed employment (Evans & Booth, 2019) and poverty (Clark et al., 2017); however, research on the collective impact of SDOH on adverse mental health outcomes and overall well-being is inadequate (Harper et al., 2015). Therefore, acknowledging and challenging the cycle of poverty and mental health is particularly urgent.
Health and Healthcare
The health and healthcare domain includes access to health care (e.g., primary, emergency, specialist) as well as health literacy (ODPHP, 2020). Access to care, as well as utilization of care, is often complicated by sociocultural factors, such as mistrust of providers, policies that cause high rates of uninsured clients, and external factors (e.g., economic instability, transportation, location, and healthcare professional shortages; Planey et al., 2019). While medical mistrust may have origins in unethical medical research like the Tuskegee syphilis studies, it continues to be a pervasive concern among communities of color who continue to experience discrimination from healthcare providers (Benkert et al., 2019). One study found that marginalized racial and ethnic groups are not only less likely to have adequate access to mental health but are also less likely to use mental health services when available (J. Chen et al., 2016). Although access to comprehensive and effective mental health care can promote good mental health (Cook et al., 2017), nearly 30 million adults and children who currently have a mental illness go without treatment (National Alliance on Mental Health, 2016). Additionally, people who have less access to care are at a higher risk for their mental health conditions to progressively worsen (Dowrick et al., 2009).
Social and Community Context
The social and community context domain encompasses discrimination, civic participation, incarceration, and social cohesion (ODPHP, 2020). Researchers have acknowledged the importance of community, social cohesion, and resilience in addressing adverse childhood experiences (Ellis & Dietz, 2017). Perceived social cohesion was reported to act as a moderator between structural and socioeconomic disadvantages and adolescents’ depressive symptoms (Dawson et al., 2019), influence emotion regulation, and lower occurrences of depression (d’Arbeloff et al., 2018). When looking specifically at adolescents, studies show that a lack of emotional support and poor emotion regulation can lead to increased depression (Young et al., 2019). Participation in recreational activities (e.g., sports, clubs, organized activities) can act as a protective factor against depression and increase resiliency (McPhie & Rawana, 2015), yields positive mental health outcomes overall, and lowers the possibility of depression in adolescents (Murphy et al., 2020; Tajik et al., 2017).
Education
The education domain includes education, enrollment in higher education, language and literacy, and high school graduation (ODPHP, 2020). It is important to acknowledge the parents’ education level (i.e., high school graduation, higher education) to understand how these determinants impact the mental health of the adolescents in the household. Although research in counseling has addressed how poor mental health impacts education (e.g., high rates of depression; Winzer et al., 2018), little is available regarding how education impacts mental health and health equity. Several studies have proposed a positive relationship between successful school completion and favorable mental health outcomes (Dahmann & Schnitzlein, 2019; World Health Organization, 2014). Contrarily, failure to complete high school and enroll in higher education correlated to poor mental health outcomes (e.g., depression), marginal social and familial support, and fewer resources (Ramsdal et al., 2018). While poverty and job insecurities have been known to directly impact adolescents’ mental health, research on the impacts of such educational factors is lacking.
Neighborhood and Built Environment
The neighborhood and built environment domains include access to food, crime and violence, environmental conditions, and quality housing (ODPHP, 2020). Researchers have explored neighborhood greenspace (e.g., parks, open spaces) and found that greenspace reduces stress, increases social cohesion, lowers adverse mental health outcomes in adolescents (Templeton, 2019), and is pivotal in increasing favorable mental health outcomes and overall wellness (Y. Liu et al., 2020). Additively, recreation centers have been found to positively impact mental health, providing an opportunity for adolescent development and increased cohesion (Pryor & Outley, 2017). Rundown housing, which is typically present within poverty-stricken areas and underserved communities, has been found to increase the occurrence and longevity of disparities and cause poor mental health (Cutrona et al., 2006). It is important to note that children who live in lower socioeconomic status neighborhoods are less likely to have access to green spaces and recreation centers than those living in affluent neighborhoods and that many parks in lower-income neighborhoods are associated with higher crime rates (Rigolon et al., 2021; US Department of Health and Human Services, 2021). Therefore, it is critical to not only consider accessibility to community spaces but to also consider the quality.
Purpose of the Study
As evidenced above, it is well documented that various demographics and SDOH factors impact mental health (Alegría et al., 2019). The association between psychosocial and physiological processes becomes increasingly problematic when adverse aspects of SDOH are present (Torche & Villarreal, 2014). Further, unfavorable factors associated with SDOH are known to cause a variety of mental health issues (e.g., depression; Alegría et al., 2019), which also intensifies present adverse determinants. Although these negative determinants adversely impact individuals’ mental health, it has yet to be deemed a priority in the counseling field. Properly and adequately addressing SDOH is imperative to improve adolescents’ mental health and reduce growing health disparities (Artiga & Hinton, 2019; Office of Disease Prevention and Health Promotion, 2020). Using the SDOH framework as a guide, the research question was: “Do social determinants of health significantly predict depression within adolescents, as diagnosed by a doctor or another health care professional?” When considering social determinants of health, the predictor variables were adult employment, food instability and housing insecurity, the highest level of parent’s education, emotional support, recreational activities, current health insurance coverage, needed healthcare not received, green space, and recreation center. These predictor variables will be expounded upon in the section below.
Methods
We utilized a quantitative, multivariate, cross-sectional research design. A cross-sectional study is a descriptive study that analyzes data from a specific population during a particular time, allowing a clear picture of prevalent characteristics (i.e., determinants). A quantitative design provided a thorough statistical understanding of how SDOH contributes to adverse mental health outcomes (i.e., depression) within adolescents.
Study Population
Archival data from the National Survey of Children’s Health (NSCH) 2018 was utilized in this study (Child and Adolescent Health Measurement Initiative, 2019). The NSCH is a national survey of households with children, ranging from 1 to 17 years old. The United States Census Bureau conducted the NSCH on behalf of the United States Department of Health and Human Services (USDHHS). The U.S. Census Bureau determined the population and monitored the sampling strategy through the mail and the Internet. Households were randomly sampled and contacted by mail to identify those with one or more children under eighteen. Within each of these households, one child was randomly chosen to be the subject of the NSCH. A total of 30,530 surveys were completed nationally for households with at least one child. We used participants between the ages of 10 to 17 (n = 16,013), due to the World Health Organization (2020) defining adolescents as those who are 10 to 19 years old. While this was a national survey, the race/ethnicity was not particularly diverse, with 77% White (n = 12,407), 0.9% American Indian (n = 143), 4.9% Asian (n = 790), 7.1% African American (n = 1,134), 0.3% Native Hawaiian (n = 48), 3.0% Some Other Race (n = 473), and 6.4% Two or More Races (n = 1,018). Interestingly, this demographic distribution closely aligns with the United States, with White individuals accounting for approximately 60% and the remaining ethnicities accounting for 40% (United States Census Bureau, 2019). Additionally, a total of 52.5% (n = 8,406) identified as male and 47.5% (n = 7,607) as female. The mean age for this study was 13.81.
Measures
The measures for this study came from the NSCH 2018. The independent variables were inclusive of demographics and SDOH domains. The demographic variables were race/ethnicity, gender, and age. The variables that aligned with the five SDOH domains were adult employment (“Were you employed at least 50 out of the past 52 weeks?”), food instability and housing insecurity (“Since this child was born, how often has it been very hard to cover the basics, like food or housing, on your family’s income?”), the highest level of parent’s education (“Highest level of parents education”), emotional support (“Do you have someone to turn to for emotional support?”), recreational activities (“During the past 12 months, did this child participate in a sports team, clubs or organizations, or organized activities or lessons?”), current health insurance coverage (“Is this child currently covered by any kind of health insurance or health coverage plan?”), needed healthcare not received (“During the past 12 months, was there any time when this child needed healthcare, but it was not received?”), greenspace (“In your neighborhood, is/are there sidewalks, walking paths, parks, or playgrounds?”), recreation center (“In your neighborhood, is/are there: a recreation center, community center, or boys’ and girls’ club?”), and rundown housing (“In your neighborhood, is/are there: poorly kept or rundown housing?”). The dependent variable in this study was depression. Depression was assessed through one nominal, dichotomous item on the NSCH, which read, “Has a doctor or other health care provider ever told you that this child has depression?” This variable posits that the child was diagnosed by a medical doctor upon the completion of the necessary diagnostic testing procedures, to further solidify the diagnosis.
Data Analysis
A stepwise logistic regression provided the multivariate statistical analysis approach for this study. Logistic regression analysis utilizes criterion measures on a binary outcome (Meyers et al., 2016) and displays the probability of a particular outcome when reviewing each case (Tabachnick & Fidell, 2013). The stepwise method allowed the independent variables to be analyzed within one block to assess their predictive ability while controlling for the potential effects of other variables in the model (Tabachnick & Fidell, 2013). All assumptions were satisfied before performing the analysis. The first assumption was for the dependent variable to be binary or dichotomous (Tabachnick & Fidell, 2013). As the dependent variable was binary, there was no multicollinearity between the predictor variables (VIF values = 1.02 to 1.21; tolerance values = 0.83-0.98). The second assumption was to have an appropriate sample size. G*Power 3 (Faul et al., 2007) was utilized to determine the necessary sample size for the desired power in the logistic regression. An apriori sample size for the logistic regression was confirmed with a two-tailed test, desired power of 0.8, a significance level of .05, and an odds ratio of 2.23; the recommended sample size of 87 was reached, as the sample size was 16, 013.
Results
The complete model containing all SDOH predictors was statistically significant, χ 2 (10) = 514.628, p < 0.001. The model explained between 5.50% (Cox and Snell R2) and 12.0% (Nagelkerke R2) of the variance in the likelihood of being depressed, and correctly classified 90.87% of cases. The Hosmer and Lemeshow test showed that the model with the predictors is significantly better than the baseline model with just the predicted values, χ2 (8) = 4.219, p = 0.837. As shown in Table 1, nine of the independent variables made a unique statistically significant contribution to the model (i.e., age, sex, race, adult employment, food instability and housing insecurity, recreational activities, current health insurance, needed healthcare not received, and rundown housing).
Age, sex, adult employment, food instability, housing insecurity, and current health insurance coverage were negative and significant predictors of depression (p < .001), while race, recreational activities, needed healthcare not received, and rundown housing were positive and significant predictors of depression (p = <.01). Parent’s education, emotional support, green space, and recreation were negative and insignificant predictors of depression (p > .05). The strongest predictor of depression was needed healthcare not received, which indicated that needed healthcare not received can predict the likelihood of adolescents being diagnosed with depression when controlling for all other factors in the model. For a full representation of the logistic regression results, see Table 1 below.
Discussion
Systemic, structural, and health inequities surrounding SDOH continuously introduce an abundance of challenges known to adversely impact adolescents (Andermann, 2016; S. Y. Chen et al., 2021). While the CDC (2020) has prioritized mental health equity, the lingering health inequities related to adverse social determinants will continue to be a substantial social justice issue that can lessen the likeliness of favorable health among adolescents (Wilkinson & Pickett, 2020). Properly and sufficiently addressing adverse SDOH is essential to improve adolescents’ mental health and reduce current and projected growing health disparities (Artiga & Hinton, 2019; Office of Disease Prevention and Health Promotion, 2020). However, although earlier studies have focused on specific components of social determinants of mental health in general, there has been little recognition of how numerous variables affect adolescents’ overall mental health and well-being (Artiga & Hinton, 2019). Also, there are not many studies on the overall influence of SDOH on negative mental health outcomes and general well-being (Harper et al., 2015). Uniquely, we addressed this limitation by accounting for the social determinants of health conjointly in explaining depression among adolescents.
The Impact of Race, Age, and Gender on Depression
While the primary purpose of this study was to uncover how SDOH can predict depression in adolescents, demographic variables (i.e., race, age, gender) were included. Race was a positive and significant predictor of depression, meaning that race significantly predicted depression in adolescents; however, the logistic regression did not provide insight into how each race can predict depression. This finding is consistent with previous literature that has found that race can impact mental health, making it essential to explore further as the annual prevalence rate of mental illness varies by race (Mezuk et al., 2010). Age was a negative and significant predictor of the absence of depression; meaning that it significantly predicted the absence of depression in adolescents, corroborating current research on rates of depression in adolescents (WHO, 2020). The current findings that gender was a negative and significant predictor of depression align with research that shows females were not only more likely to experience depression but were twice as likely compared to males (Albert, 2015).
The Impact of SDOH Domains on Depression
The results of this study exemplified how a lack of adverse determinants predicts the unlikeliness of adolescents experiencing adverse mental health outcomes (i.e., depression), while also partially showing evidence that adverse determinants predict depression. Aligned with previous literature on economic stability and mental health outcomes (Braveman et al., 2017; Evans & Booth, 2019), results suggested that consistent parent employment, food stability, and housing security significantly predicted the absence of depression in adolescents. Although researchers have found that minimal parental education contributes to poor mental health (Ramsdal et al., 2018), this was not evidenced in this study’s results. Regarding the social and community context domain, emotional support was not found to be a significant predictor of depression. However, a lack of involvement in recreational activities significantly predicted depression among adolescents, which is consistent with previous research (Tajik et al., 2017). When considering the health and healthcare domain, results suggested that insurance coverage significantly predicted the absence of depression in adolescents, aligning with previous literature that reported consistent insurance coverage yields favorable mental health outcomes (Bernstein et al., 2010). Additionally, needed healthcare not received significantly predicted depression in adolescents, which is consistent with previous studies (J. Chen et al., 2016). Finally, regarding the neighborhood and built environment domain, the presence of rundown housing in neighborhoods significantly predicted depression in adolescents, which aligns with previous research that found that such factors correlate to depression (Ford & Rechel, 2012).
Limitations
First, there are limitations to using secondary analysis of existing data, specifically due to the inability to select your variables (Cheng & Phillips, 2014); however, the use of the NSCH dataset allowed for an extensive number of variables that aligned seamlessly with the SDOH domains. Additionally, researcher bias can be seen as a limitation (Tabachnick & Fidell, 2013). To avoid researcher bias, we thoroughly examined the dataset, codebooks, and variables and selected variables that best aligned with the SDOH domains. This was an important step in minimizing researcher bias, as it ensured that the chosen variables were in alignment with the SDOH domains, rather than handpicked and chosen at random, due to preference. While the NSCH was comparable to the U.S. demographics, it was not diverse regarding race/ethnicity, which can be seen as a limitation when investigating SDOH, as minority populations typically endure the most adverse determinants (Weeks et al., 2023). Another limitation of this study was solely using logistic regression for data analysis because it does not imply causation (Tabachnick & Fidell, 2013); however, it provided tremendous insight into the predictability of the SDOH domains on depression. Finally, we ensured that the discussion of the results was aligned with the statistics that were produced, ensuring that they were free of biases and opinions, related to this topic. Regardless of these limitations, this study provided distinctive contributions to the counseling literature regarding SDOH and adolescents’ mental health.
Implications for Mental Health Professionals
This study narrowed the gap in counseling research and practice regarding SDOH and mental health among adolescents, supporting that SDOH domains can predict mental health outcomes (i.e., depression) of adolescents. In relation to practice, SDOH not only falls under the multicultural competencies (Ratts et al., 2016; Ratts & Pedersen, 2014) but also best practices for counseling in the American Counseling Association’s (ACA) Code of Ethics (ACA, 2014). The results of this study provide further evidence of the importance of recognizing various determinants as they relate to adolescents’ overall mental health and well-being. Counselors have expressed feelings of frustration and helplessness when facing the complexity of SDOH constructs (Johnson et al., in press; Johnson & Mahan, 2019), necessitating the use of foundational frameworks to assist with acknowledgment, conceptualization, organization, and implementation (Andermann, 2016; Johnson et al., in press). Thus, a well-researched SDOH framework, such as The Commission on Social Determinants of Health (CSDH), should be used to strengthen health equity through a framework for action on SDOH (Solar & Irwin, 2010). Furthermore, the findings exemplify a need for counselors to become knowledgeable about SDOH screening tools. This study found that several determinants can predict depression in adolescents, demanding the need for the use of screening tools in all populations, especially underserved populations, to understand the individual and tackle the underlying issues before they create irreversible mental health issues. For example, The AHC and The Well RX contain constructs that align with the various SDOH domains and could be essential in minimizing the impact of SDOH on adolescents. Lastly, the results of this study increase the need for SDOH to be included in counselors’ training and overall counseling and counselor education curriculums. Counselor education minimally addresses important constructs related to SDOH. When addressed, it is often within social and diversity courses due to the emphasis on privilege and power, but it is not discussed through an SDOH framework that could provide more intentional guidance on course goals, objectives, and expected outcomes. (Johnson & Robins, 2021; Sharma et al., 2018).
Implications for Human Services Professionals
In addition to mental health professionals, human services professionals play a crucial role in addressing the multifaceted challenges facing adolescents’ mental health, particularly concerning social and cultural determinants. Recognizing the complexity of these issues, there’s a growing emphasis on interprofessional collaboration (IPC) across helping fields, which facilitates a comprehensive approach to intervention by leveraging diverse expertise and resources for better outcomes. Interprofessional collaboration refers to an intentional and effective process that is interpersonal in nature and facilitates the achievement of goals that are often unattainable when individual professionals act on their own (Callahan & Higgins, 2023). While IPC is becoming a leading strategy for working with individuals with poor mental health, specifically adults (Johnson & Mahan, 2020), this strategy has not been well-researched in the realm of adverse social and cultural determinants occurring within adolescents, especially when considering racial/ethnic differences (Monroe et al., 2023). Many researchers have posited this lack of collaboration is due to the dearth of research on how various determinants occur during adolescence simultaneously (Artiga & Hinton, 2019; Robins, 2021). To effectively address these challenges, human services professionals must have a comprehensive understanding of SDOH domains, including economic stability, access to healthcare, education, social and community context, and neighborhood environment. Frameworks like Healthy People, 2020 provide structured approaches for assessing and addressing SDOH, guiding interventions, and program development, which human services professionals can follow. Additionally, early detection and intervention are critical, as adolescence is a pivotal period for mental health development. Advocating for mental health equity, continuous training, and prevalent education will be essential for ensuring the prominent role that human services professionals can play in combatting the impact of adverse SDOH during adolescence. Further, proficiency in efficiently using screening tools aligned with SDOH domains and multicultural competence is fundamental for effective and sustainable practice. Going beyond the scope of the office, collaboration with community resources further enhances support for adolescents and their families, expanding access to resources and improving overall outcomes. Furthermore, actionable steps for human services professionals include establishing IPC networks, cultivating cultural competence, implementing comprehensive assessment protocols, advocating for policy change, promoting community engagement and empowerment, developing culturally tailored interventions, providing accessible and culturally competent services, collaborating with schools and educational systems, fostering resilience and protective factors, and continuously evaluating and adapting interventions to meet the needs of the populations served. By taking these steps, human services professionals can contribute to meaningful and sustainable improvements in adolescent mental health outcomes.
Implications for Successful Interventions or Policies
Successful interventions or policies that promote resilience and mitigate the negative effects of Social Determinants of Health (SDOH) on adolescent mental health often involve a multifaceted approach targeting various aspects of adolescents’ lives. One effective intervention is implementing trauma-informed care practices within schools and healthcare settings, providing adolescents with supportive environments and resources to cope with adversity. Additionally, investing in community-based programs that offer mentorship, counseling, and skill-building activities can empower adolescents to develop resilience and navigate challenges related to SDOH. Strengthening social support networks through peer support groups, family therapy, and community engagement initiatives also plays a crucial role in buffering the impact of SDOH on adolescent mental health, specifically depression. Furthermore, addressing structural determinants of health inequities, such as poverty, housing instability, and access to healthcare, through policy changes and advocacy efforts can create more equitable conditions for adolescents to thrive. Incorporating an intersectional approach into interventions and policies is crucial for effectively addressing the multifaceted challenges impacting adolescent mental health. Interventions should recognize and respond to the unique experiences of adolescents who face overlapping social determinants such as race, gender, socioeconomic status, and access to healthcare. By doing so, policies can better target and mitigate the compounded disadvantages these adolescents experience, leading to more equitable and comprehensive mental health support. For instance, trauma-informed care practices and community-based programs should be tailored to consider the specific needs of adolescents with intersecting identities, thereby promoting resilience and improving mental health outcomes. Implementing comprehensive mental health services in schools, including early screening, intervention, and access to mental health professionals, can further support adolescents in managing stressors related to SDOH and building resilience. Overall, a combination of targeted interventions, supportive environments, and systemic changes is essential for promoting adolescent mental health and mitigating the negative effects of SDOH.
Suggestions for Future Research
Future research could benefit from conducting longitudinal studies to examine the long-term impact of social determinants of health (SDOH) on adolescent mental health outcomes. Tracking adolescents over time would allow researchers to explore how changes in social and environmental factors influence their mental well-being. Additionally, investigating the intersectionality of social determinants with race/ethnicity and gender might be particularly insightful in understanding how multiple factors intersect to shape mental health outcomes among adolescents. Evaluating the effectiveness of interventions aimed at addressing social determinants of adolescent mental health would be crucial in identifying which strategies, such as community-based programs, policy initiatives, and school-based interventions, are most impactful in reducing mental health disparities. Moreover, exploring the role of cultural competence in interprofessional collaboration among helping professionals working with adolescents could shed light on how cultural differences impact collaboration efforts and the effectiveness of interventions. In alignment, it would be beneficial to conduct a study that aims to address how racism impacts social determinants of health, further disrupting the opportunity for optimal mental health in adolescence. Research into the role of technology in addressing social determinants of adolescent mental health, along with examining the influence of family dynamics, community resources, and social support networks, could provide valuable insights into factors that shape adolescents’ mental well-being. Investigating the policy implications of addressing social determinants of adolescent mental health and advocating for policy changes at various levels could help promote equity, access to resources, and social justice for adolescents experiencing mental health disparities. Finally, exploring trauma-informed approaches to addressing social determinants of adolescent mental health, particularly among vulnerable populations, could inform intervention strategies and improve outcomes for adolescents with complex needs. Through these research avenues, scholars can enhance our understanding of the complex relationship between social determinants and adolescent mental health, identify effective intervention strategies, and inform policy and practice efforts aimed at promoting adolescent well-being.
Conclusion
This study provided meaningful results regarding the impact of SDOH on adolescents’ mental health. While this study sought to examine how adverse SDOH can adversely impact mental health, the opposite finding surfaced. The results signified that a lack of adverse determinants decreases the likelihood of adolescents experiencing adverse mental health outcomes (i.e., depression). More specifically, this study found that adult employment, food instability and housing insecurity, and health insurance coverage can significantly predict the absence of depression in adolescents. However, the results displayed a minor indication of the presence of poor mental health outcomes when adolescents experienced adverse determinants such as minimal recreational activity, needed healthcare not received, and rundown housing. Further, the research findings highlighted the importance of intersectionality in understanding adolescent mental health outcomes. Intersectionality was displayed in the results by showing how multiple social determinants of health, such as race, socioeconomic status, and access to healthcare, interact and compound the risk of depression in adolescents. For instance, adolescents from marginalized racial backgrounds who also experience economic instability and lack of healthcare access are at a heightened risk of depression due to the overlapping and intersecting disadvantages they face. While bearing in mind the lack of racial diversity of the NSCH, the findings of this study illuminated that positive determinants decrease the likelihood of depression. Though this study was unable to fully uncover how adverse determinants work individually and collectively to yield poor mental health, the findings from this study lay a foundation for future research to build upon. The results also necessitate the need for interprofessional collaboration amongst helping professionals to minimize the impact the adverse determinants have on adolescents. Conclusively, the results of this study deliver a sense of urgency to intentionally investigate adverse determinants and SDOH domains collectively in underserved and racially diverse communities and their impact on adolescents.