Adverse neighborhood conditions show larger and more consistent associations with diagnosed mental health outcomes among U.S. children and adolescents: a cross-sectional analysis of the National Survey of Children's Health, 2018 to 2019

Adverse neighborhood conditions show larger and more consistent associations with diagnosed mental health outcomes among U.S. children and adolescents: a cross-sectional analysis of the National Survey of Children's Health, 2018 to 2019 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adverse neighborhood conditions show larger and more consistent associations with diagnosed mental health outcomes among U.S. children and adolescents: a cross-sectional analysis of the National Survey of Children's Health, 2018 to 2019 Robert Lewis Burries, Paris N. Johnson, Napoleon Ayibo Wokoma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9259996/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Mental health conditions among children and adolescents represent a major U.S. public health concern. Neighborhood environments are recognized as structural determinants of youth mental health, yet a critical empirical question remains underexamined: do adverse neighborhood features, disorder and perceived safety produce stronger associations with diagnosed outcomes than protective amenities? This study compared associations between neighborhood amenities, detracting elements, and perceived safety with parent-reported diagnoses of anxiety, depression, and ADHD among U.S. children and adolescents. Methods Cross-sectional analysis of the 2018–2019 National Survey of Children’s Health (NSCH) included 43,213 children and adolescents aged 6 to 17 years. Neighborhood conditions were operationalized as distinct composite measures modeled separately across each outcome. Survey-weighted logistic regression incorporated NSCH sampling weights, stratum indicators, and primary sampling unit identifiers, adjusting for age, gender, race/ethnicity, income-to-poverty ratio, and parental education. Results Survey-weighted prevalence of diagnosed anxiety, ADHD, and depression was 11.9%, 11.5%, and 5.9%, respectively. In design-corrected bivariate analyses, amenities were not significantly associated with any outcome. Detracting elements were associated with anxiety (p=.018) and depression (p<.001) but not ADHD (p=.231). Perceived safety was significantly associated with all three outcomes (all p≤.004). In multivariable models, amenities showed no significant association with any outcome. Detracting elements were associated with higher odds of anxiety (OR = 1.194, 95% CI: 1.113–1.281) and depression (OR = 1.374, 95% CI: 1.223–1.542), but not ADHD (OR = 1.060, 95% CI: 0.979–1.149). Perceived safety demonstrated the largest associations: anxiety (OR = 1.470, 95% CI: 1.346–1.606), depression (OR = 1.649, 95% CI: 1.431–1.900), and ADHD (OR = 1.192, 95% CI: 1.088–1.307), all p<.001. In joint models, perceived safety remained independently associated with all outcomes; detracting elements attenuated for anxiety and ADHD but remained significant for depression. Conclusions Adverse neighborhood conditions showed larger and more consistent associations with diagnosed youth mental health outcomes; amenities were not independently associated in any model. Perceived safety was the most consistent and robust domain across all outcomes. These findings suggest neighborhood disorder and perceived safety may represent stronger and more consistent population-level associations with youth mental health than supportive infrastructure. Longitudinal and intervention research is needed to clarify directionality and determine whether reducing adverse conditions yields measurable mental health benefits. Mental health Child and adolescent health Social determinants of health Neighborhood environment Depression Anxiety Attention deficit hyperactivity disorder National Survey of Children's Health Place-based intervention Neighborhood disorder Figures Figure 1 Figure 2 Figure 3 Background Mental health conditions among children and adolescents are highly prevalent in the United States and have increased markedly over the past decade, creating urgent challenges for health care systems, schools, and population-level prevention efforts ( 1 , 2 , 3 ). Between 2016 and 2023, diagnosed mental or behavioral health conditions among U.S. adolescents increased 35 percent, with diagnosed anxiety rising 61 percent over that period ( 4 ). Diagnosed depression, anxiety, and attention deficit hyperactivity disorder (ADHD) affect millions of youths and are associated with impaired academic performance, disrupted social and emotional development, and elevated risk of persistent mental health problems across the life course ( 1 , 2 , 5 ). At the population level, these conditions contribute to widening disparities by socioeconomic status and race and ethnicity, while placing increasing strain on educational, clinical, and social service infrastructures ( 3 , 6 , 7 ). Despite expanded access to clinical treatment and school-based supports, substantial variation in childhood mental health outcomes persists across communities, indicating that upstream, place-based conditions shape risk in ways not fully addressed by individual-level interventions alone ( 8 , 9 ). Neighborhood environments represent a core domain of the social determinants of health for children and adolescents, structuring daily exposure to stress, safety, and opportunity during critical developmental periods ( 8 , 10 ). Neighborhoods simultaneously encompass protective features, such as amenities that support physical activity and social interaction, and adverse exposures, including physical disorder and perceived lack of safety ( 9 , 11 ). These conditions may influence youth mental health through multiple pathways, including chronic activation of stress response systems, constraints on outdoor play and social engagement, and sustained vigilance in environments perceived as unsafe ( 10 , 12 ). Consistent with ecological systems theory, such exposures are expected to interact with developmental processes, shaping emotional regulation, attention, and coping in ways that may differ across internalizing and externalizing conditions ( 8 ). Importantly, theoretical frameworks offer specific reasons to expect adverse neighborhood features to exert stronger effects than protective amenities. Stress sensitization models propose that chronic exposure to threat and disorder during development lowers the threshold for subsequent stress responses, amplifying the psychological impact of adverse conditions beyond what protective resources can counterbalance ( 8 , 12 ). Negativity bias, the well-established tendency for negative stimuli to exert disproportionate influence on affect, cognition, and behavior relative to positive stimuli of equivalent magnitude, further suggests that physical disorder and perceived unsafety may carry greater developmental salience than the presence of parks or libraries ( 10 , 12 ). Together these frameworks generate a testable asymmetry hypothesis: adverse neighborhood features should show larger and more consistent associations with diagnosed youth mental health outcomes than protective amenities, a prediction this study is designed to evaluate. However, a fundamental empirical question has received insufficient direct attention in the literature: are adverse neighborhood features, disorder and perceived safety more strongly and consistently associated with diagnosed youth mental health outcomes than positive neighborhood amenities? Answering this question has important implications for hypothesis generation and, ultimately, for intervention prioritization. If cross-sectional associations are consistently larger for adverse features than for protective ones, and if this pattern holds across diagnostically distinct outcomes, that will provide a meaningful signal about which neighborhood domains may warrant priority attention in future longitudinal and intervention research. Population-level prevention strategies require this kind of domain-specific, comparative evidence to move beyond broad statements that neighborhoods matter and toward more precise identification of which neighborhood features carry the greatest risk signal across which diagnostic outcomes. Prior research has documented associations between neighborhood disadvantage, disorder, safety, and adverse mental health outcomes among children and adolescents ( 9 , 12 , 13 ). However, three limitations constrain the utility of existing evidence for intervention prioritization. First, most studies examine neighborhood features in isolation or combine them into composite disadvantage indices, preventing direct comparison of distinct domains; domain-level separation remains rare even in recent literature, with most studies relying on composite indices that preclude cross-domain comparison ( 14 , 15 ). Second, many studies rely on localized or regional samples or symptom-based measures rather than nationally representative data with parent-reported clinician diagnoses. Third, mental health outcomes are frequently combined into composite measures, obscuring whether neighborhood features relate differently to internalizing conditions such as depression and anxiety versus externalizing and neurodevelopmental conditions such as ADHD, a limitation evident even in recent work examining neighborhood moderators of adolescent mental health ( 13 ). Without domain-specific, outcome-specific comparative evidence, the field cannot determine which neighborhood features should be prioritized for population-level intervention ( 10 , 11 , 15 ). This study addresses that gap directly. Using nationally representative data from the 2018 to 2019 National Survey of Children's Health, we simultaneously examined associations between three distinct neighborhood domains, amenities, detracting elements, and perceived safety, and three separately analyzed parent-reported clinician diagnoses, depression, anxiety, and ADHD, in U.S. children and adolescents aged 6 to 17 years. By modeling each neighborhood domain and each outcome independently within the same sample and analytic framework, the study enables direct cross-domain comparison of association magnitude and consistency. The contribution is best understood as comparative refinement: clarifying which neighborhood features show stronger and more uniform associations with diagnosed youth mental health conditions, across multiple outcomes simultaneously, using nationally representative data. The findings are intended to generate hypotheses about which neighborhood-level intervention domains may warrant prioritization and to identify where longitudinal and experimental research efforts could be most informative. Methods Study Population This cross-sectional study used data from the 2018 to 2019 National Survey of Children's Health (NSCH), a nationally representative survey of U.S. households with children conducted by the U.S. Census Bureau in partnership with the Health Resources and Services Administration. The NSCH employs a complex, multistage, stratified probability sampling design with oversampling of selected demographic subgroups to support population-representative inference at both national and state levels. The 2018–2019 cycle was selected to reflect pre-pandemic neighborhood and mental health patterns and to avoid potential structural disruptions introduced by COVID-19, which may have altered both environmental exposures and diagnostic patterns. The 2018 and 2019 survey years were treated as a single pooled cross-sectional sample consistent with NSCH analytic guidance for multi-year files; year-specific estimates were not examined separately, as the primary aim was population-level inference rather than temporal trend analysis. Survey design variables, including sampling weights, stratum indicators, and primary sampling unit identifiers, were incorporated in all analyses using SPSS Complex Samples procedures in accordance with NSCH analytic guidance to account for unequal probabilities of selection, differential nonresponse, and post-stratification calibration to U.S. Census population controls. The analytic population included children and adolescents aged 6 to 17 years, corresponding to the age range for which parent-reported mental health diagnosis items are administered in the NSCH and for which diagnosed mental health conditions are more prevalent and diagnostic reporting is considered more reliable in population-based surveys. The overall proportion of missing data across neighborhood exposures, mental health outcomes, and covariates was low. Item nonresponse was generally below three percent for all variables, and no single variable demonstrated substantial missingness. To assess potential bias, weighted comparisons were conducted between respondents with complete and incomplete data. These analyses showed no meaningful differences in mental health outcome prevalence or neighborhood exposure distributions, indicating no evidence of differential missingness with respect to primary study variables. Because missingness was limited in magnitude and not associated with key exposure or outcome variables, multiple imputations were not expected to materially alter effect estimates; complete-case analysis produces unbiased estimates when data are missing completely at random or missing at random with respect to observed covariates, conditions consistent with the pattern of missingness observed here (9, 22). Of the 59,963 total respondents in the 2018–2019 NSCH, 16,750 were excluded because they fell outside the 6 to 17 year age range for which the relevant questionnaire modules are administered, yielding 43,213 age-eligible respondents. A complete-case approach was applied, excluding respondents with missing data on neighborhood exposures, mental health outcomes, or covariates; this resulted in the exclusion of approximately 4.9% of age-eligible respondents in the most restrictive model, with outcome-specific analytic samples ranging from 42,795 to 43,080. Outcome-specific analytic sample sizes reflect this approach and vary modestly due to item-level missingness: anxiety n=43,080; depression n=43,060; ADHD n=42,795. Assessment of Neighborhood Conditions Neighborhood conditions were assessed using parent-reported items from the NSCH and operationalized across three theoretically distinct domains: neighborhood amenities, neighborhood detracting elements, and perceived neighborhood safety. Modeling these as separate domains, rather than combining them into a composite disadvantage index, was central to the study objective of directly comparing the relative contributions of adverse and protective neighborhood features to each mental health outcome. Neighborhood amenities captured the presence of positive environmental and social infrastructure within the child’s neighborhood, including sidewalks or walking paths, parks or playgrounds, recreation centers, and libraries (NSCH composite variable: NbhdAmenities_1819, derived from items K10Q11–K10Q14). Neighborhood detracting elements reflected indicators of physical disorder and environmental stressors, including litter or garbage, vandalism, and poorly maintained housing (NSCH composite variable: NbhdDetract_1819, derived from items K10Q20, K10Q22, K10Q23). Perceived neighborhood safety was assessed using a parent-reported item indicating how safe the child was perceived to be in the neighborhood (NSCH variable: K10Q40_R), an approach commonly used in epidemiologic studies of neighborhood context and child and adolescent mental health. Composite indices for neighborhood amenities and neighborhood detracting elements were constructed by summing affirmative responses across component items, yielding domain-specific scores ranging from 0 to 4 for neighborhood amenities and 0 to 3 for detracting elements, with higher values indicating a greater number of amenities or detracting elements present. These indices were treated as continuous measures representing cumulative neighborhood exposure, consistent with prior NSCH-based analytic approaches. Perceived neighborhood safety was modeled as an ordinal variable with four response levels - definitely agree, somewhat agree, somewhat disagree, and definitely disagree that the neighborhood is safe coded so that higher values reflected lower perceived safety; that is, each unit increase represents a step toward greater perceived unsafety. Each neighborhood domain was modeled separately rather than jointly to estimate domain-specific associations with mental health outcomes, reduce potential multicollinearity among correlated neighborhood constructs, and preserve interpretability of effect estimates on the natural scale of each exposure. Collinearity diagnostics, including variance inflation factors and condition indices, were examined and did not indicate problematic multicollinearity among modeled predictors. Modeling domains independently aligns directly with the study objective of comparing adverse versus protective neighborhood features as distinct explanatory domains, and the pattern of associations across separately estimated models provides a basis for descriptive comparison of effect magnitude. As a complementary and pre-specified robustness analysis, all three neighborhood domains were entered simultaneously in joint models to assess independent associations net of shared variance across domains; results are presented in Table 7. Assessment of Mental Health Outcomes Mental health outcomes included parent-reported clinician-diagnosed depression, anxiety, and ADHD. For each condition, parents were asked whether a doctor or other health care provider had ever informed them that the child had the condition. Responses were coded as binary indicators reflecting diagnosis status. Each mental health outcome was analyzed independently to allow condition-specific associations with neighborhood exposures and to avoid conflation of distinct diagnostic, developmental, and etiologic pathways. These measures capture diagnosed prevalence rather than current symptom severity and therefore reflect both underlying disorder risk and access to clinical evaluation and diagnosis. Diagnostic access and service utilization may vary by sociodemographic characteristics, a consideration of particular relevance for ADHD, where diagnosis rates are known to vary by race, income, and healthcare access, and this limitation is addressed in the discussion. To the extent that parent-reported diagnosis may misclassify true mental health status, such misclassification is expected to be largely non-differential with respect to neighborhood exposure measures, which would bias associations toward the null rather than inflate effect estimates. Despite these limitations, parent-reported diagnosis measures remain standard for population-level surveillance and are appropriate for estimating population-level associations using NSCH data. Covariates All models adjusted a priori for child age in years, gender, race and ethnicity, household income-to-poverty ratio, and parental education, consistent with prior NSCH-based studies examining neighborhood context and child mental health. Age was modeled as a continuous variable. Gender was included as reported by the parent or caregiver. Race and ethnicity were categorized as White non-Hispanic, Black non-Hispanic, Hispanic, and Other or multiracial, consistent with NSCH reporting conventions. Household income-to-poverty ratio was modeled as a continuous indicator of socioeconomic position, with higher values reflecting greater household income relative to the federal poverty threshold. Parental education was categorized based on the highest level of educational attainment among parents or caregivers in the household. Covariates were selected based on established literature and conceptual relevance to neighborhood selection processes, exposure patterns, and the likelihood of receiving a mental health diagnosis. Statistical Analysis All analyses incorporated NSCH survey weights, strata, and primary sampling units using the IBM SPSS Statistics (IBM SPSS) Complex Samples logistic regression procedure, in accordance with NSCH analytic guidance. A sampling plan file was constructed specifying the stratification variable (STRATUM), primary sampling unit (HHID), and final child weight (FWC) with replacement estimation, consistent with NSCH documentation for two-stratum designs. This approach produces design-consistent variance estimates that account for the clustered, stratified structure of the NSCH sample, yielding standard errors and confidence intervals that appropriately reflect the complex survey design rather than treating the sample as a simple random sample. All analyses were conducted using SPSS Complex Samples procedures (IBM SPSS v29), which implement Taylor series linearization to generate design-corrected standard errors accounting for stratification, clustering, and unequal probabilities of selection. Weighted descriptive statistics summarized baseline characteristics of the analytic sample. Survey-weighted bivariate associations between each neighborhood domain and each mental health outcome were tested using the Rao-Scott second-order adjusted F statistic, the design-corrected test of independence implemented in SPSS Complex Samples. This statistic accounts for survey weighting, clustering, and stratification when assessing categorical associations and is the appropriate test for complex sample crosstabulations. Descriptive weighted bivariate associations between sociodemographic characteristics and each mental health outcome were also examined to assess subgroup variation in outcome prevalence and are presented in Additional file 3: Table S1. Survey-weighted multivariable logistic regression models were estimated separately for each mental health outcome. For each outcome, three adjusted primary models were fitted corresponding to neighborhood amenities, neighborhood detracting elements, and perceived neighborhood safety, with each neighborhood predictor entered individually. This modeling strategy was selected to estimate domain-specific associations while minimizing collinearity, and critically, to enable direct comparison of effect magnitude and consistency across adverse versus protective neighborhood domains and across diagnostically distinct mental health outcomes. Although SPSS Complex Samples procedures do not generate variance inflation factors, collinearity diagnostics from equivalent unweighted regression models indicated low intercorrelation among neighborhood domains, with variance inflation factors ranging from 1.006 to 1.146 and tolerance values exceeding 0.87. Additionally, three joint models were estimated, one per outcome, entering all three neighborhood domains simultaneously to assess the independent contribution of each domain net of shared variance with the others. These joint models were pre-specified to evaluate the independence of domain-specific associations and to assess potential confounding across correlated neighborhood features, rather than serving as post hoc exploratory analyses. Neighborhood amenity and detracting element indices were modeled on their natural unit scale, representing the incremental association per additional neighborhood feature. Perceived neighborhood safety was modeled as an ordinal predictor consistent with its response structure. Adjusted odds ratios and 95 percent confidence intervals were reported for all neighborhood predictors. Statistical significance was assessed using two-sided tests with an alpha level of 0.05. Model fit was evaluated using pseudo-R-squared measures generated by the Complex Samples logistic regression procedure. All statistical analyses were conducted using IBM SPSS version 29. A formal power calculation was not conducted, as the analytic sample was determined by NSCH survey design and eligibility criteria rather than investigator-controlled enrollment. Additional sensitivity analyses were conducted to assess the robustness of findings to alternative exposure specifications. Neighborhood amenity and detracting element indices were re-estimated using categorical indicator variables representing each exposure level. Results from these categorical models were substantively consistent with the primary analyses, with no meaningful changes in the direction or statistical significance of associations, supporting the robustness of the continuous modeling approach. Marginal predicted probabilities across all neighborhood domains and outcomes are presented in Additional file 1: Figure S1. Several potential sources of bias were addressed by design and analysis. Selection bias was minimized through the use of NSCH complex survey weights, which correct for unequal probabilities of selection, differential nonresponse, and oversampling of demographic subgroups, ensuring population-representative estimates. To assess potential bias from missing data, weighted comparisons between respondents with complete and incomplete data confirmed no meaningful differences in outcome prevalence or exposure distributions, supporting the validity of the complete-case approach. Measurement bias arising from parent-reported diagnosis was expected to produce non-differential misclassification with respect to neighborhood exposures, which would bias associations toward the null rather than produce false-positive findings. Confounding was addressed through a priori covariate adjustment for established sociodemographic determinants of both neighborhood conditions and mental health diagnosis, age, gender, race and ethnicity, household income-to-poverty ratio, and parental education, selected based on their conceptual relevance to neighborhood residential selection processes. Residual confounding from unmeasured factors cannot be excluded and is addressed in the Limitations. Ethical Considerations This study involved secondary analysis of publicly available, de-identified data from the National Survey of Children's Health and was exempt from institutional review board review. All analyses were conducted in accordance with applicable ethical standards for research involving human participants. This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional analyses. Artificial intelligence language model tools were used during the preparation of this manuscript to assist with editing and formatting. All scientific content, analysis, and conclusions are the sole work of the authors, who reviewed and verified all content and take full responsibility for the accuracy and integrity of the work. Results Baseline Characteristics and Neighborhood Condition Distributions The analytic sample comprised 43,213 U.S. children and adolescents aged 6 to 17 years from the 2018–2019 NSCH. Survey-weighted prevalence of parent-reported clinician diagnoses was 11.9% for anxiety, 11.5% for ADHD, and 5.9% for depression. The weighted sample was approximately evenly distributed by gender. Approximately half of participants were White non-Hispanic (49.3%), with Hispanic children representing 26.3% of the sample. The majority resided in households at or above 200% of the federal poverty level, and nearly half had parents with a college degree or higher (47.7%). Children with any diagnosed mental health condition differed significantly from those without diagnoses across multiple demographic and socioeconomic characteristics (all p<.001) (Table 1). In weighted bivariate analyses, the association between poverty level and anxiety status was not statistically significant (p = .645), indicating that the apparent unweighted association was attributable to the NSCH oversampling of lower-income households rather than a true population-level relationship (Additional file 3: Table S1). Weighted bivariate associations between all sociodemographic characteristics and each mental health outcome, providing a descriptive examination of subgroup variation in outcome prevalence across gender, age group, race/ethnicity, poverty level, and parental education, are also presented in Additional file 3: Table S1. Neighborhood conditions varied across the sample. Most children lived in neighborhoods with multiple amenities; approximately 38.3% resided in neighborhoods with all four amenities and 10.7% in neighborhoods with none. The majority (73.3%) lived in neighborhoods without any detracting elements, though a meaningful subset experienced one or more. Most parents definitely agreed their neighborhood was safe (64.8%), with progressively smaller proportions reporting lower perceived safety (Table 1). Bivariate Associations Between Neighborhood Conditions and Anxiety In survey-weighted bivariate analyses, neighborhood amenities were not significantly associated with anxiety status (p=.085). Weighted anxiety prevalence varied narrowly across amenity levels, from 11.8% among children in neighborhoods with no amenities to 13.2% among those with two amenities, with no consistent protective gradient. Detracting elements were significantly associated with anxiety (p=.018), with weighted prevalence increasing from 11.3% with no detractors to 14.8% with two. Perceived neighborhood safety demonstrated the strongest and most consistent gradient (p<.001), with anxiety prevalence rising from 10.7% among children in definitively safe neighborhoods to 21.0% among those in the least safe (Table 2). Bivariate Associations Between Neighborhood Conditions and Depression In survey-weighted bivariate analyses, neighborhood amenities were not significantly associated with depression status (p=.108), with prevalence ranging from 5.2% to 6.9% across amenity levels and no consistent dose-response pattern. Detracting elements showed a strong, significant, and monotonically increasing association (p<.001), with weighted prevalence rising from 5.2% among children with no detractors to 11.9% among those exposed to all three. Perceived safety demonstrated the steepest gradient across all three outcomes and domains (p<.001), with depression prevalence increasing from 4.9% in definitively safe neighborhoods to 16.7% in the least safe (Table 3). Bivariate Associations Between Neighborhood Conditions and ADHD In survey-weighted bivariate analyses, neighborhood amenities were not significantly associated with ADHD status (p=.056), with prevalence ranging from 10.6% with all four amenities to 13.4% with one and no consistent gradient. Detracting elements were also not significantly associated with ADHD (p=.231), with no dose-response relationship across exposure levels. Perceived neighborhood safety was the only domain significantly associated with ADHD at the bivariate level (p=.004), with prevalence increasing from 11.0% in definitively safe neighborhoods to 16.3% among those in less safe neighborhoods (Table 4). Weighted prevalence gradients across all neighborhood condition levels for each outcome are illustrated in Figure 2. Multivariable Logistic Regression: Primary Domain-Specific Models Table 6 and Figure 1 present adjusted odds ratios and 95% confidence intervals from survey-weighted domain-specific logistic regression models for all three neighborhood exposures and all three outcomes. In adjusted models incorporating NSCH survey weights, strata, and primary sampling units, neighborhood amenities showed no significant association with anxiety (OR=1.004, 95% CI: 0.968–1.042, p=.820), depression (OR=0.983, 95% CI: 0.925–1.044, p=.576), or ADHD (OR=0.981, 95% CI: 0.941–1.022, p=.352). Associations for amenities corresponded to predicted probability differences of less than one percentage point across the full range of the index, underscoring the negligible population-level magnitude of these effects. It is important to note that no formal statistical test of differences in association magnitude across neighborhood domains was conducted; all cross-domain comparisons in this study are descriptive, based on inspection of odds ratio magnitude and confidence interval overlap, and should not be interpreted as evidence of statistically superior associations for any one domain. Detracting elements showed significant positive associations with anxiety (OR=1.194, 95% CI: 1.113–1.281, p<.001) and depression (OR=1.374, 95% CI: 1.223–1.542, p<.001), but not ADHD (OR=1.060, 95% CI: 0.979–1.149, p=.152). The association with depression was notably larger in magnitude, with each additional detracting element associated with 37.4% higher odds of depression compared with 19.4% higher odds of anxiety. The non-significant ADHD finding is consistent with the absence of a bivariate dose-response relationship for this outcome and distinguishes detracting elements from perceived safety in terms of cross-outcome consistency. Perceived neighborhood safety demonstrated the largest associations across all outcomes: anxiety (OR=1.470, 95% CI: 1.346–1.606), depression (OR=1.649, 95% CI: 1.431–1.900), and ADHD (OR=1.192, 95% CI: 1.088–1.307), all p<.001. Marginal predicted probabilities of each outcome across perceived safety levels are presented in Figure 3. Multivariable Logistic Regression: Joint Models with All Domains Entered Simultaneously To assess the independent contribution of each neighborhood domain net of shared variance with the others, joint models entered amenities, detracting elements, and perceived safety simultaneously for each outcome. Table 7 presents adjusted odds ratios from these joint models, which allow direct comparison of each domain's independent association after controlling for the others. Perceived neighborhood safety remained independently and significantly associated with all three outcomes: anxiety (OR=1.422, 95% CI: 1.291–1.566, p<.001), depression (OR=1.500, 95% CI: 1.287–1.747, p<.001), and ADHD (OR=1.182, 95% CI: 1.062–1.315, p=.002). Detracting elements attenuated to non-significance for anxiety (OR=1.069, p=.086) and ADHD (OR=1.007, p=.885) when all three domains were simultaneously controlled, but retained significance for depression (OR=1.208, 95% CI: 1.064–1.370, p=.003). Amenities showed no independent association with any outcome in joint models. These findings confirm that perceived safety is the most robustly independent neighborhood domain, and that the detracting elements associations for anxiety and ADHD in primary models partially reflect shared variance with the safety dimension. A direct comparison of domain-specific and joint model estimates across all neighborhood domains and outcomes is presented in Additional file 2: Figure S2. Model Fit All survey-weighted joint multivariable logistic regression models were statistically significant based on design-adjusted Wald F tests, indicating that the set of predictors collectively contributed to model fit. The anxiety model demonstrated significant overall fit (F(10, 41321)=45.539, p<.001), with a Nagelkerke pseudo R² of 0.062. The depression model likewise demonstrated significant overall fit (F(10, 41306)=45.806, p<.001) and exhibited the largest relative explanatory power among the three outcomes (Nagelkerke pseudo R²=0.119). The ADHD model was also statistically significant (F(10, 41072)=31.693, p<.001), with a Nagelkerke pseudo R² of 0.060. Pseudo R² values in logistic regression are not directly interpretable as proportion of variance explained and are expected to be modest in population-level behavioral health models; their primary utility is as a relative index of model fit for comparison across outcomes. The observed Nagelkerke pseudo R² values (0.060–0.119) are consistent with values reported in comparable NSCH-based and neighborhood-mental health studies, where pseudo R² values in the range of 0.04–0.15 are typical for population-level logistic regression models predicting diagnosed mental health conditions (9, 13). Consistent with the pattern of regression coefficients, the depression model demonstrated the greatest relative model fit, while anxiety and ADHD models exhibited comparable but smaller explanatory magnitude. Covariate Patterns Covariate associations were consistent across all primary and joint models and are summarized by sociodemographic subgroup in Table 5. Female gender was associated with higher odds of anxiety and depression and lower odds of ADHD. Older age was associated with higher odds of all three outcomes. White non-Hispanic children had substantially higher odds of all three diagnoses relative to Other or multiracial children, a pattern likely reflecting differential diagnostic access and provider practices rather than differential underlying disorder prevalence. Higher household income-to-poverty ratio was inversely associated with all three outcomes. Parental education showed a positive association with anxiety but was non-significant for depression and ADHD in survey-weighted models, a divergence consistent with detection bias in anxiety screening among more highly educated households (Table 5). Discussion In this nationally representative cross-sectional analysis of U.S. children and adolescents aged 6 to 17 years, adverse neighborhood features, disorder and perceived safety, showed consistently larger and more uniform associations with diagnosed mental health outcomes than positive neighborhood amenities. This pattern held across diagnostically distinct outcomes, including internalizing conditions (depression and anxiety) and a neurodevelopmental condition (ADHD), and persisted after adjustment for key demographic and socioeconomic covariates and after incorporation of complex survey design features. Rather than establishing causal effects, these findings clarify which neighborhood domains show the strongest and most consistent associations across outcomes, a necessary prior step to informing where longitudinal and intervention research efforts may be most fruitfully directed. This comparative evidence extends the neighborhood-health literature in a way that studies examining single domains or composite indices cannot. Prior work has documented associations between individual neighborhood features and child mental health, but has rarely compared amenities, disorder, and safety within the same nationally representative sample and across the same outcomes simultaneously. The present findings suggest that this comparison is consequential: amenities showed no significant association with any outcome in survey-weighted bivariate or adjusted models, while detracting elements showed significant positive associations with anxiety and depression in primary models, and perceived safety showed the largest associations of any domain across all three outcomes. It is important to emphasize that all comparisons of association magnitude across neighborhood domains in this study are descriptive; no formal statistical test of domain differences was conducted, and the observed patterns should be interpreted accordingly rather than as evidence of statistically established superiority of any one domain. Studies examining only amenities may therefore characterize a narrower slice of the neighborhood risk picture than studies that also capture disorder and perceived safety. This study contributes comparative evidence clarifying how distinct neighborhood domains relate to multiple youth mental health outcomes within a single nationally representative analytic framework, allowing direct cross-domain comparison that composite neighborhood indices cannot provide. Although the observed odds ratios are modest in magnitude, associations of this size may translate into meaningful population-level differences when exposures such as neighborhood safety are widely distributed across communities, as the weighted population estimates in Additional file 3: Table S1 illustrate. The bivariate results reflect design-consistent survey weighting: amenities lost significance across all three outcomes and detracting elements lost significance for ADHD after applying the Rao-Scott adjusted F statistic, patterns that differed from unweighted analyses. This attenuation represents appropriate population-representative inference rather than a methodological limitation. The association between poverty level and anxiety, significant in unweighted analyses, was no longer significant after weighting (p = .645), underscoring the importance of design-consistent variance estimation in complex survey data. Weighted bivariate associations for all sociodemographic variables are presented in Additional file 3: Table S1 . The joint model results add important inferential nuance. When all three neighborhood domains were entered simultaneously, perceived safety remained independently and significantly associated with all three outcomes, while detracting elements attenuated to non-significance for anxiety and ADHD. This pattern suggests that the associations between detracting elements and anxiety and ADHD in primary models partially reflect shared variance with perceived safety, and that safety perception captures a distinct and more independently robust dimension of the neighborhood experience. For depression, detracting elements retained significance in the joint model, suggesting a partially independent contribution beyond the safety dimension. This outcome-specific differential persistence warrants attention in future research examining the mechanisms by which physical neighborhood disorder relates to internalizing outcomes. Perceived neighborhood safety was particularly strongly linked to depression, where each unit decrease in perceived safety was associated with 64.9% higher odds of diagnosis, the largest single neighborhood association observed. Although effect sizes are modest in absolute magnitude, approximately 35% of U.S. children in this sample lived in neighborhoods where parents reported less than definite agreement that their neighborhood was safe (derived from survey-weighted frequency distributions in Table 1 ); even modest per-unit associations translate into a substantial population-level burden when applied across this exposure distribution. This pattern is consistent with developmental theory emphasizing the sensitivity of emotional regulation and stress response systems to chronic perceptions of threat ( 10 , 12 ) and aligns with recent systematic review evidence that adolescents who perceived their neighborhoods as unsafe had substantially greater risk of psychological distress than those who perceived them as safe ( 16 ). Notably, associations between neighborhood safety perceptions and youth mental health have also been observed in studies incorporating objective neighborhood characteristics alongside subjective measures, suggesting the relationship is not solely attributable to shared reporter bias ( 13 ). Conceptually, the attenuation of detracting elements associations when perceived safety was simultaneously controlled in joint models suggests that perceived safety may partially mediate the relationship between physical disorder and mental health outcomes or may serve as a proxy for unmeasured contextual stressors; this interpretation is conceptual rather than the product of a formal mediation analysis. The comparatively smaller association for ADHD most plausibly reflects two non-mutually exclusive explanations: genuine etiologic insensitivity of this neurodevelopmental condition to neighborhood stress pathways, and differential diagnostic access that systematically undercounts true ADHD prevalence in disadvantaged neighborhoods in ways that anxiety and depression diagnoses do not. Neighborhood detracting elements emerged as a significant correlate of anxiety and depression in primary models and retained significance for depression in joint models. Physical disorder, including litter, vandalism, and deteriorated housing, may signal broader patterns of social disinvestment and weakened informal social control. Prior theoretical work suggests that chronic exposure may affect cognitive, emotional, and behavioral development through sustained background stress, constrained opportunities for outdoor play and peer interaction, and altered caregiver supervision practices ( 8 , 12 ). The stronger association with depression relative to anxiety and ADHD, and the persistence of the depression association in joint models, is consistent with a model in which cumulative physical disorder has particular relevance for mood-related outcomes. The non-significant detracting elements association for ADHD at both bivariate and regression levels further distinguishes ADHD from the internalizing outcomes in terms of neighborhood sensitivity. Neighborhood amenities demonstrated no significant associations across any of the three outcomes in survey-weighted bivariate or adjusted models. The most plausible explanation is a measurement limitation inherent in presence-based indices: parks and recreation centers may matter less for mental health than whether children can safely access and actively use them, a distinction the current data cannot resolve. Residential selection processes may also play a role, as discussed in the Limitations section. Future work incorporating measures of amenity quality, accessibility, and utilization will be necessary to determine whether protective associations emerge under more refined operationalizations. These findings have implications for how neighborhood features are prioritized in population mental health research and surveillance, while stopping short of prescribing specific intervention strategies on the basis of cross-sectional associations alone. Disorder and perceived safety may warrant greater prominence in neighborhood-level public health monitoring frameworks, including population-level surveillance dashboards that track community risk indicators alongside mental health outcomes. As one concrete example, community health needs assessments conducted by local health departments and hospital systems could be strengthened by systematically incorporating perceived neighborhood safety measures alongside existing socioeconomic indicators, enabling more targeted identification of communities where adverse neighborhood conditions co-occur with elevated youth mental health burden. Similarly, Medicaid managed care organizations and state children’s health programs that use geographic risk stratification for population health targeting could incorporate neighborhood safety and disorder indicators to better identify high-risk communities for preventive outreach and mental health service expansion. Place-based interventions targeting physical disorder and safety have shown promise for producing population-level health effects in related domains ( 17 ), and experimental evidence suggests that disorder reduction may improve mental health partly through improvements in perceived safety, the same domain showing the strongest associations in the present study ( 18 ). These implications are framed as directions for future research and surveillance rather than prescriptions for action; all cross-domain comparisons in this study are descriptive and should not be interpreted as causally established differences across neighborhood domains. Causal claims require longitudinal and intervention evidence beyond what this study provides. Several limitations warrant consideration. Regarding design limitations, the cross-sectional design precludes conclusions about temporality or directionality. Reverse causation is a genuine concern: families with children experiencing mental health conditions may disproportionately reside in more disordered neighborhoods through residential selection processes, and children with mental health diagnoses may be more likely to remain in high-disorder environments due to the economic constraints that often accompany caregiving burden, meaning the observed associations may partly reflect selection into rather than effects of adverse neighborhood conditions. Regarding measurement limitations, parental mental health, itself associated with neighborhood conditions, may independently drive both neighborhood perception and the likelihood of obtaining a child mental health diagnosis, inflating associations through a shared parental pathway rather than a true environmental one. Shared respondent reporting for both neighborhood conditions and mental health outcomes introduces potential common method bias that cannot be separated from true environmental associations in these data, and future work using administrative or observational neighborhood data linked to independent clinical records would substantially strengthen causal inference. This concern is particularly salient for perceived neighborhood safety, which, as a subjective parental perception rather than an observable environmental feature, may share greater conceptual and measurement overlaps with parent-reported mental health diagnoses than amenity or detracting element indices, potentially inflating its associations relative to the other domains. Future research incorporating objective safety indicators or multi-informant mental health assessments would be necessary to disentangle perceptual bias from true environmental effects. Although perceived neighborhood safety is subjectively reported by the same parent who reports the child’s mental health diagnosis, the presence of consistent associations across three diagnostically distinct outcomes and the persistence of associations in joint models after controlling for other neighborhood domains suggest that shared reporter bias alone is unlikely to fully explain the observed patterns. Regarding unmeasured confounders, residual confounding remains possible, including unmeasured factors such as parental mental health, school environments, neighborhood social cohesion, and direct exposure to violence. For ADHD specifically, diagnostic rates are known to vary by healthcare access and provider practices in ways not fully captured by the sociodemographic covariates included here a pattern that Pearce et al. ( 19 ) have documented across multiple stages of the ADHD diagnostic pathway, and neighborhood associations for ADHD should therefore be interpreted with particular caution regarding diagnostic versus etiologic pathways. Similarly, Calub et al. ( 20 ) demonstrated that neighborhood conditions relate to ADHD symptom severity in clinical samples including autistic youth, suggesting that the present findings in a general population sample may reflect a combination of true environmental risk and differential diagnostic access that future work should disentangle. Regarding analytic limitations, the separate modeling approach does not allow formal tests of whether associations differ statistically across neighborhood domains; descriptive comparisons of odds ratio magnitude should not be interpreted as evidence of statistically superior associations for any domain. Findings are generalizable to noninstitutionalized U.S. children and adolescents aged 6 to 17 years. Weighted and unweighted bivariate associations did not always converge, most notably the poverty-anxiety association was significant in unweighted analyses but not after survey weighting, underscoring the importance of design-consistent variance estimation and the potential for unweighted descriptive analyses in complex survey data to mischaracterize population-level patterns. Sensitivity analyses using categorical exposure specifications produced substantively similar results, supporting the robustness of the primary continuous modeling approach. Formal interaction analyses testing whether neighborhood-mental health associations were modified by age, gender, or race/ethnicity were not conducted; subgroup variation in outcome prevalence is described in Additional file 3: Table S1 but effect modification remains an open question for future research. Conclusion In this nationally representative cross-sectional analysis of U.S. children and adolescents, adverse neighborhood conditions, disorder and perceived safety, showed consistently larger and more uniform associations with diagnosed depression, anxiety, and ADHD than positive neighborhood amenities. Perceived neighborhood safety was the most consistent and independently robust domain, remaining significantly associated with all three outcomes in both primary and joint models. This domain asymmetry held across diagnostically distinct outcomes and after adjustment for sociodemographic covariates and complex survey design features, suggesting that adverse neighborhood conditions may carry stronger population-level associations with youth mental health than supportive infrastructure. These findings are descriptive and hypothesis-generating; they indicate that adverse neighborhood features may warrant greater priority in population mental health surveillance and research. They do not, on their own, establish that reducing adverse conditions will improve mental health outcomes; that determination requires longitudinal and experimental evidence. By directly comparing distinct neighborhood domains within a single nationally representative analytic framework incorporating design-consistent survey weighting, this study contributes comparative, domain-specific evidence to the neighborhood-health literature that composite index approaches cannot provide. Future research should employ longitudinal designs to clarify temporal sequencing and potential bidirectional relationships between neighborhood environments and mental health outcomes. Experimental and quasi-experimental studies evaluating whether targeted reductions in neighborhood disorder and improvements in perceived safety produce measurable mental health benefits, and whether such effects differ from those produced by amenity-focused investments, would provide the causal evidence needed to translate the present descriptive findings into actionable prevention guidance for U.S. children and adolescents. Abbreviations ADHD Attention deficit hyperactivity disorder CI Confidence interval FPL Federal poverty level FWC Final child weight GED General educational development IBM SPSS IBM SPSS Statistics NSCH National Survey of Children’s Health OR Odds ratio PSU Primary sampling unit STROBE Strengthening the Reporting of Observational Studies in Epidemiology U.S. United States Declarations Ethics approval and consent to participate The National Survey of Children's Health study protocols were approved by the US Census Bureau Institutional Review Board. The survey was conducted in accordance with relevant federal regulations and institutional requirements. Written informed consent was obtained from all participating parents or legal guardians prior to participation. The present study involved secondary analysis of publicly available, de-identified data and did not require additional institutional review board approval. Consent for publication Not applicable. Availability of data and materials The datasets analyzed during the current study are available in the Data Resource Center for Child and Adolescent Health repository, supported by the U.S. Department of Health and Human Services Health Resources and Services Administration, https://www.childhealthdata.org (21). Competing interests The author declares that there are no competing interests. Funding The author declares that no financial support was received for the research, authorship, or publication of this article. Authors’ contributions R.L.B. conceptualized the study, designed the methodology, conducted formal data analysis, and led the writing and overall development of the manuscript. R.L.B. also served as the principal investigator and is responsible for the integrity of the data and accuracy of the analysis. P.N.J. played a central role in the analytical presentation of the work, preparing all tables and figures and contributing to methodological refinement throughout the research process. P.N.J. provided critical review of the study design, supported the interpretation of findings, and contributed substantively to manuscript revisions, ensuring analytical rigor and clarity of reporting. N.A.W. contributed to the theoretical framing, assisted with literature synthesis, and provided substantive revisions to enhance intellectual content. All authors reviewed, edited, and approved the final manuscript and agreed to be accountable for all aspects of the work. Acknowledgements Not applicable. 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Examining the association of neighborhood conditions on attention-deficit or hyperactivity disorder symptoms in autistic youth using the Child Opportunity Index 2.0. JCPP Adv. 2024;5(1):e12267. https://doi.org/10.1002/jcv2.12267 Child and Adolescent Health Measurement Initiative (CAHMI). 2018–2019 National Survey of Children's Health (NSCH), SPSS indicator dataset. Data Resource Center for Child and Adolescent Health, supported by Cooperative Agreement U59MC27866 from the U.S. Department of Health and Human Services, Health Resources and Services Administration, Maternal and Child Health Bureau. 2020. https://www.childhealthdata.org. Accessed 1 Jul 2023. Sterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. https://doi.org/10.1136/bmj.b2393 Tables Tables 1 to 7 are available in the supplementary files section Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1FigureS1TitleFull3by3panelofpredictedprobabilitiesbyneighborhoodconditionandmentalhealthoutcomeNSCH20182019..docx Additional file 1: Figure S1. Format: PNG. Title: Full 3×3 panel of predicted probabilities by neighborhood condition and mental health outcome, NSCH 2018–2019. Description: Full 3×3 panel of marginal predicted probabilities of diagnosed anxiety, depression, and ADHD by neighborhood condition level among U.S. children and adolescents aged 6 to 17 years, National Survey of Children’s Health 2018–2019. Rows represent outcomes (anxiety, depression, ADHD); columns represent neighborhood domains (amenities, detracting elements, perceived safety). Probabilities are derived from survey-weighted domain-specific multivariable logistic regression models adjusted for age, gender, race and ethnicity, household income-to-poverty ratio, and parental education. Shaded bands represent 95% confidence intervals. Dashed horizontal lines indicate overall weighted prevalence for each outcome. AdditionalFile2FigureS2TitleCoefficientconfidenceintervalcomparisongriddomainspecificandjointmodelsNSCH20182019..docx Additional file 2: Figure S2. Format: PNG. Title: Coefficient confidence interval comparison grid, domain-specific and joint models, NSCH 2018–2019. Description: Coefficient confidence interval comparison grid showing adjusted odds ratios from domain-specific models (filled circles, solid lines) and joint models (open squares, dashed lines) for each neighborhood domain and outcome among U.S. children and adolescents aged 6 to 17 years, National Survey of Children’s Health 2018–2019. Rows represent outcomes (anxiety, depression, ADHD); columns represent neighborhood domains (amenities, detracting elements, perceived safety). All models are survey-weighted and adjusted for age, gender, race and ethnicity, household income-to-poverty ratio, and parental education. Vertical dashed line at OR = 1.0 represents the null. Attenuation from domain-specific to joint models indicates shared variance across neighborhood domains. AdditionalFile3TableS1TitleWeightedBivariateAssociationsBetweenSociodemographicCharacteristicsandDiagnosedMentalHealthOutcomes.docx Additional file 3: Table S1. Format: DOCX. Title: Weighted bivariate associations between sociodemographic characteristics and diagnosed mental health outcomes, NSCH 2018–2019. Description: Survey-weighted bivariate associations between sociodemographic characteristics (gender, age group, race/ethnicity, household poverty level, parental education) and parent-reported diagnoses of anxiety, depression, and ADHD among U.S. children and adolescents aged 6 to 17 years, National Survey of Children’s Health 2018–2019. Weighted population size estimates, column percentages, and Rao-Scott design-adjusted chi-square statistics are reported. Significance levels: *p < .05; **p < .01; ***p < .001. Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers invited by journal 31 Mar, 2026 Editor invited by journal 31 Mar, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 29 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9259996","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617819766,"identity":"71352d3a-6cd2-4fde-8da4-02697f6a3f3c","order_by":0,"name":"Robert Lewis Burries","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACewYGxsMgBhuQ8YCB4QBhLYYNDAwwLcwGRGkxOADVAtIlQZyW22cfHC5su5fPx97+rJqn5o4cPwPzw0c38Gk5l25weGZbsWUbzxmz2zzHnhlLNrAZG+fg03KGjeEwb1uCAZtEDtttHrbDiRsO8LBJE6dF/vmzYp5/JGmRYDBj5m0jQothD1DLjHNALTw5xpJz+w4bSzYT8Is9Dxvj44KyBAP59uMPP7z5dliOn7354WN8WlAAEw+IZCZWOQgw/iBF9SgYBaNgFIwYAADGp0nxQCzJNQAAAABJRU5ErkJggg==","orcid":"","institution":"Walden University","correspondingAuthor":true,"prefix":"","firstName":"Robert","middleName":"Lewis","lastName":"Burries","suffix":""},{"id":617819767,"identity":"7fb7471b-f02c-4cfc-bfc2-b804b19dd1e6","order_by":1,"name":"Paris N. 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Odds ratios and 95% confidence intervals are derived from survey-weighted domain-specific multivariable logistic regression models adjusted for age, gender, race and ethnicity, household income-to-poverty ratio, and parental education.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9259996/v1/457e30b52da89038c1595d18.png"},{"id":106300348,"identity":"10377112-cb52-4176-9965-421cb65b62d4","added_by":"auto","created_at":"2026-04-07 09:13:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":168781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eWeighted prevalence of diagnosed mental health outcomes by neighborhood condition level, NSCH 2018–2019. Prevalence estimates reflect survey-weighted proportions incorporating NSCH sampling weights, strata, and primary sampling units. Error bars represent 95% confidence intervals. Neighborhood amenity levels range from 0 (no amenities) to 4 (all four amenities); detracting element levels range from 0 (none) to 3 (all three); perceived safety is coded so that higher values indicate lower perceived safety.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9259996/v1/ee859d5842acca7ae2dbf303.png"},{"id":106300335,"identity":"53b5564e-e459-4cfc-bd90-9a951bf44bc3","added_by":"auto","created_at":"2026-04-07 09:13:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":201903,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMarginal predicted probability of mental health outcomes by perceived neighborhood safety, NSCH 2018–2019. Probabilities are derived from survey-weighted logistic regression models adjusted for age, gender, race and ethnicity, household income-to-poverty ratio, and parental education. 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Title: Full 3×3 panel of predicted probabilities by neighborhood condition and mental health outcome, NSCH 2018–2019. Description: Full 3×3 panel of marginal predicted probabilities of diagnosed anxiety, depression, and ADHD by neighborhood condition level among U.S. children and adolescents aged 6 to 17 years, National Survey of Children’s Health 2018–2019. Rows represent outcomes (anxiety, depression, ADHD); columns represent neighborhood domains (amenities, detracting elements, perceived safety). Probabilities are derived from survey-weighted domain-specific multivariable logistic regression models adjusted for age, gender, race and ethnicity, household income-to-poverty ratio, and parental education. Shaded bands represent 95% confidence intervals. 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Description: Coefficient confidence interval comparison grid showing adjusted odds ratios from domain-specific models (filled circles, solid lines) and joint models (open squares, dashed lines) for each neighborhood domain and outcome among U.S. children and adolescents aged 6 to 17 years, National Survey of Children’s Health 2018–2019. Rows represent outcomes (anxiety, depression, ADHD); columns represent neighborhood domains (amenities, detracting elements, perceived safety). All models are survey-weighted and adjusted for age, gender, race and ethnicity, household income-to-poverty ratio, and parental education. Vertical dashed line at OR = 1.0 represents the null. 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Description: Survey-weighted bivariate associations between sociodemographic characteristics (gender, age group, race/ethnicity, household poverty level, parental education) and parent-reported diagnoses of anxiety, depression, and ADHD among U.S. children and adolescents aged 6 to 17 years, National Survey of Children’s Health 2018–2019. Weighted population size estimates, column percentages, and Rao-Scott design-adjusted chi-square statistics are reported. Significance levels: *p \u0026lt; .05; **p \u0026lt; .01; ***p \u0026lt; .001.\u003c/p\u003e","description":"","filename":"AdditionalFile3TableS1TitleWeightedBivariateAssociationsBetweenSociodemographicCharacteristicsandDiagnosedMentalHealthOutcomes.docx","url":"https://assets-eu.researchsquare.com/files/rs-9259996/v1/7e6ee26d2436f9b66ee68788.docx"},{"id":106300323,"identity":"c2cc98d0-6a43-47e3-9c8e-9544d9d54758","added_by":"auto","created_at":"2026-04-07 09:13:07","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":33504,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9259996/v1/bca6a07938624327df199741.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adverse neighborhood conditions show larger and more consistent associations with diagnosed mental health outcomes among U.S. children and adolescents: a cross-sectional analysis of the National Survey of Children's Health, 2018 to 2019","fulltext":[{"header":"Background","content":"\u003cp\u003eMental health conditions among children and adolescents are highly prevalent in the United States and have increased markedly over the past decade, creating urgent challenges for health care systems, schools, and population-level prevention efforts (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Between 2016 and 2023, diagnosed mental or behavioral health conditions among U.S. adolescents increased 35 percent, with diagnosed anxiety rising 61 percent over that period (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Diagnosed depression, anxiety, and attention deficit hyperactivity disorder (ADHD) affect millions of youths and are associated with impaired academic performance, disrupted social and emotional development, and elevated risk of persistent mental health problems across the life course (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). At the population level, these conditions contribute to widening disparities by socioeconomic status and race and ethnicity, while placing increasing strain on educational, clinical, and social service infrastructures (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Despite expanded access to clinical treatment and school-based supports, substantial variation in childhood mental health outcomes persists across communities, indicating that upstream, place-based conditions shape risk in ways not fully addressed by individual-level interventions alone (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNeighborhood environments represent a core domain of the social determinants of health for children and adolescents, structuring daily exposure to stress, safety, and opportunity during critical developmental periods (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Neighborhoods simultaneously encompass protective features, such as amenities that support physical activity and social interaction, and adverse exposures, including physical disorder and perceived lack of safety (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These conditions may influence youth mental health through multiple pathways, including chronic activation of stress response systems, constraints on outdoor play and social engagement, and sustained vigilance in environments perceived as unsafe (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Consistent with ecological systems theory, such exposures are expected to interact with developmental processes, shaping emotional regulation, attention, and coping in ways that may differ across internalizing and externalizing conditions (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Importantly, theoretical frameworks offer specific reasons to expect adverse neighborhood features to exert stronger effects than protective amenities. Stress sensitization models propose that chronic exposure to threat and disorder during development lowers the threshold for subsequent stress responses, amplifying the psychological impact of adverse conditions beyond what protective resources can counterbalance (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Negativity bias, the well-established tendency for negative stimuli to exert disproportionate influence on affect, cognition, and behavior relative to positive stimuli of equivalent magnitude, further suggests that physical disorder and perceived unsafety may carry greater developmental salience than the presence of parks or libraries (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Together these frameworks generate a testable asymmetry hypothesis: adverse neighborhood features should show larger and more consistent associations with diagnosed youth mental health outcomes than protective amenities, a prediction this study is designed to evaluate.\u003c/p\u003e \u003cp\u003eHowever, a fundamental empirical question has received insufficient direct attention in the literature: are adverse neighborhood features, disorder and perceived safety more strongly and consistently associated with diagnosed youth mental health outcomes than positive neighborhood amenities? Answering this question has important implications for hypothesis generation and, ultimately, for intervention prioritization. If cross-sectional associations are consistently larger for adverse features than for protective ones, and if this pattern holds across diagnostically distinct outcomes, that will provide a meaningful signal about which neighborhood domains may warrant priority attention in future longitudinal and intervention research. Population-level prevention strategies require this kind of domain-specific, comparative evidence to move beyond broad statements that neighborhoods matter and toward more precise identification of which neighborhood features carry the greatest risk signal across which diagnostic outcomes.\u003c/p\u003e \u003cp\u003ePrior research has documented associations between neighborhood disadvantage, disorder, safety, and adverse mental health outcomes among children and adolescents (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, three limitations constrain the utility of existing evidence for intervention prioritization. First, most studies examine neighborhood features in isolation or combine them into composite disadvantage indices, preventing direct comparison of distinct domains; domain-level separation remains rare even in recent literature, with most studies relying on composite indices that preclude cross-domain comparison (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Second, many studies rely on localized or regional samples or symptom-based measures rather than nationally representative data with parent-reported clinician diagnoses. Third, mental health outcomes are frequently combined into composite measures, obscuring whether neighborhood features relate differently to internalizing conditions such as depression and anxiety versus externalizing and neurodevelopmental conditions such as ADHD, a limitation evident even in recent work examining neighborhood moderators of adolescent mental health (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Without domain-specific, outcome-specific comparative evidence, the field cannot determine which neighborhood features should be prioritized for population-level intervention (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study addresses that gap directly. Using nationally representative data from the 2018 to 2019 National Survey of Children's Health, we simultaneously examined associations between three distinct neighborhood domains, amenities, detracting elements, and perceived safety, and three separately analyzed parent-reported clinician diagnoses, depression, anxiety, and ADHD, in U.S. children and adolescents aged 6 to 17 years. By modeling each neighborhood domain and each outcome independently within the same sample and analytic framework, the study enables direct cross-domain comparison of association magnitude and consistency. The contribution is best understood as comparative refinement: clarifying which neighborhood features show stronger and more uniform associations with diagnosed youth mental health conditions, across multiple outcomes simultaneously, using nationally representative data. The findings are intended to generate hypotheses about which neighborhood-level intervention domains may warrant prioritization and to identify where longitudinal and experimental research efforts could be most informative.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Population\u003c/h2\u003e\n\u003cp\u003eThis cross-sectional study used data from the 2018 to 2019 National Survey of Children\u0026apos;s Health (NSCH), a nationally representative survey of U.S. households with children conducted by the U.S. Census Bureau in partnership with the Health Resources and Services Administration. The NSCH employs a complex, multistage, stratified probability sampling design with oversampling of selected demographic subgroups to support population-representative inference at both national and state levels. The 2018\u0026ndash;2019 cycle was selected to reflect pre-pandemic neighborhood and mental health patterns and to avoid potential structural disruptions introduced by COVID-19, which may have altered both environmental exposures and diagnostic patterns. The 2018 and 2019 survey years were treated as a single pooled cross-sectional sample consistent with NSCH analytic guidance for multi-year files; year-specific estimates were not examined separately, as the primary aim was population-level inference rather than temporal trend analysis. Survey design variables, including sampling weights, stratum indicators, and primary sampling unit identifiers, were incorporated in all analyses using SPSS Complex Samples procedures in accordance with NSCH analytic guidance to account for unequal probabilities of selection, differential nonresponse, and post-stratification calibration to U.S. Census population controls.\u003c/p\u003e\n\u003cp\u003eThe analytic population included children and adolescents aged 6 to 17 years, corresponding to the age range for which parent-reported mental health diagnosis items are administered in the NSCH and for which diagnosed mental health conditions are more prevalent and diagnostic reporting is considered more reliable in population-based surveys.\u003c/p\u003e\n\u003cp\u003eThe overall proportion of missing data across neighborhood exposures, mental health outcomes, and covariates was low. Item nonresponse was generally below three percent for all variables, and no single variable demonstrated substantial missingness. To assess potential bias, weighted comparisons were conducted between respondents with complete and incomplete data. These analyses showed no meaningful differences in mental health outcome prevalence or neighborhood exposure distributions, indicating no evidence of differential missingness with respect to primary study variables. Because missingness was limited in magnitude and not associated with key exposure or outcome variables, multiple imputations were not expected to materially alter effect estimates; complete-case analysis produces unbiased estimates when data are missing completely at random or missing at random with respect to observed covariates, conditions consistent with the pattern of missingness observed here (9, 22). Of the 59,963 total respondents in the 2018\u0026ndash;2019 NSCH, 16,750 were excluded because they fell outside the 6 to 17 year age range for which the relevant questionnaire modules are administered, yielding 43,213 age-eligible respondents. A complete-case approach was applied, excluding respondents with missing data on neighborhood exposures, mental health outcomes, or covariates; this resulted in the exclusion of approximately 4.9% of age-eligible respondents in the most restrictive model, with outcome-specific analytic samples ranging from 42,795 to 43,080. Outcome-specific analytic sample sizes reflect this approach and vary modestly due to item-level missingness: anxiety n=43,080; depression n=43,060; ADHD n=42,795.\u003c/p\u003e\n\u003ch2\u003eAssessment of Neighborhood Conditions\u003c/h2\u003e\n\u003cp\u003eNeighborhood conditions were assessed using parent-reported items from the NSCH and operationalized across three theoretically distinct domains: neighborhood amenities, neighborhood detracting elements, and perceived neighborhood safety. Modeling these as separate domains, rather than combining them into a composite disadvantage index, was central to the study objective of directly comparing the relative contributions of adverse and protective neighborhood features to each mental health outcome.\u003c/p\u003e\n\u003cp\u003eNeighborhood amenities captured the presence of positive environmental and social infrastructure within the child\u0026rsquo;s neighborhood, including sidewalks or walking paths, parks or playgrounds, recreation centers, and libraries (NSCH composite variable: NbhdAmenities_1819, derived from items K10Q11\u0026ndash;K10Q14). Neighborhood detracting elements reflected indicators of physical disorder and environmental stressors, including litter or garbage, vandalism, and poorly maintained housing (NSCH composite variable: NbhdDetract_1819, derived from items K10Q20, K10Q22, K10Q23). Perceived neighborhood safety was assessed using a parent-reported item indicating how safe the child was perceived to be in the neighborhood (NSCH variable: K10Q40_R), an approach commonly used in epidemiologic studies of neighborhood context and child and adolescent mental health.\u003c/p\u003e\n\u003cp\u003eComposite indices for neighborhood amenities and neighborhood detracting elements were constructed by summing affirmative responses across component items, yielding domain-specific scores ranging from 0 to 4 for neighborhood amenities and 0 to 3 for detracting elements, with higher values indicating a greater number of amenities or detracting elements present. These indices were treated as continuous measures representing cumulative neighborhood exposure, consistent with prior NSCH-based analytic approaches. Perceived neighborhood safety was modeled as an ordinal variable with four response levels - definitely agree, somewhat agree, somewhat disagree, and definitely disagree that the neighborhood is safe coded so that higher values reflected lower perceived safety; that is, each unit increase represents a step toward greater perceived unsafety.\u003c/p\u003e\n\u003cp\u003eEach neighborhood domain was modeled separately rather than jointly to estimate domain-specific associations with mental health outcomes, reduce potential multicollinearity among correlated neighborhood constructs, and preserve interpretability of effect estimates on the natural scale of each exposure. Collinearity diagnostics, including variance inflation factors and condition indices, were examined and did not indicate problematic multicollinearity among modeled predictors. Modeling domains independently aligns directly with the study objective of comparing adverse versus protective neighborhood features as distinct explanatory domains, and the pattern of associations across separately estimated models provides a basis for descriptive comparison of effect magnitude. As a complementary and pre-specified robustness analysis, all three neighborhood domains were entered simultaneously in joint models to assess independent associations net of shared variance across domains; results are presented in Table 7.\u003c/p\u003e\n\u003ch2\u003eAssessment of Mental Health Outcomes\u003c/h2\u003e\n\u003cp\u003eMental health outcomes included parent-reported clinician-diagnosed depression, anxiety, and ADHD. For each condition, parents were asked whether a doctor or other health care provider had ever informed them that the child had the condition. Responses were coded as binary indicators reflecting diagnosis status. Each mental health outcome was analyzed independently to allow condition-specific associations with neighborhood exposures and to avoid conflation of distinct diagnostic, developmental, and etiologic pathways.\u003c/p\u003e\n\u003cp\u003eThese measures capture diagnosed prevalence rather than current symptom severity and therefore reflect both underlying disorder risk and access to clinical evaluation and diagnosis. Diagnostic access and service utilization may vary by sociodemographic characteristics, a consideration of particular relevance for ADHD, where diagnosis rates are known to vary by race, income, and healthcare access, and this limitation is addressed in the discussion. To the extent that parent-reported diagnosis may misclassify true mental health status, such misclassification is expected to be largely non-differential with respect to neighborhood exposure measures, which would bias associations toward the null rather than inflate effect estimates. Despite these limitations, parent-reported diagnosis measures remain standard for population-level surveillance and are appropriate for estimating population-level associations using NSCH data.\u003c/p\u003e\n\u003ch2\u003eCovariates\u003c/h2\u003e\n\u003cp\u003eAll models adjusted a priori for child age in years, gender, race and ethnicity, household income-to-poverty ratio, and parental education, consistent with prior NSCH-based studies examining neighborhood context and child mental health. Age was modeled as a continuous variable. Gender was included as reported by the parent or caregiver. Race and ethnicity were categorized as White non-Hispanic, Black non-Hispanic, Hispanic, and Other or multiracial, consistent with NSCH reporting conventions. Household income-to-poverty ratio was modeled as a continuous indicator of socioeconomic position, with higher values reflecting greater household income relative to the federal poverty threshold. Parental education was categorized based on the highest level of educational attainment among parents or caregivers in the household. Covariates were selected based on established literature and conceptual relevance to neighborhood selection processes, exposure patterns, and the likelihood of receiving a mental health diagnosis.\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eAll analyses incorporated NSCH survey weights, strata, and primary sampling units using the IBM SPSS Statistics (IBM SPSS) Complex Samples logistic regression procedure, in accordance with NSCH analytic guidance. A sampling plan file was constructed specifying the stratification variable (STRATUM), primary sampling unit (HHID), and final child weight (FWC) with replacement estimation, consistent with NSCH documentation for two-stratum designs. This approach produces design-consistent variance estimates that account for the clustered, stratified structure of the NSCH sample, yielding standard errors and confidence intervals that appropriately reflect the complex survey design rather than treating the sample as a simple random sample. All analyses were conducted using SPSS Complex Samples procedures (IBM SPSS v29), which implement Taylor series linearization to generate design-corrected standard errors accounting for stratification, clustering, and unequal probabilities of selection.\u003c/p\u003e\n\u003cp\u003eWeighted descriptive statistics summarized baseline characteristics of the analytic sample. Survey-weighted bivariate associations between each neighborhood domain and each mental health outcome were tested using the Rao-Scott second-order adjusted F statistic, the design-corrected test of independence implemented in SPSS Complex Samples. This statistic accounts for survey weighting, clustering, and stratification when assessing categorical associations and is the appropriate test for complex sample crosstabulations. Descriptive weighted bivariate associations between sociodemographic characteristics and each mental health outcome were also examined to assess subgroup variation in outcome prevalence and are presented in Additional file 3: Table S1.\u003c/p\u003e\n\u003cp\u003eSurvey-weighted multivariable logistic regression models were estimated separately for each mental health outcome. For each outcome, three adjusted primary models were fitted corresponding to neighborhood amenities, neighborhood detracting elements, and perceived neighborhood safety, with each neighborhood predictor entered individually. This modeling strategy was selected to estimate domain-specific associations while minimizing collinearity, and critically, to enable direct comparison of effect magnitude and consistency across adverse versus protective neighborhood domains and across diagnostically distinct mental health outcomes. Although SPSS Complex Samples procedures do not generate variance inflation factors, collinearity diagnostics from equivalent unweighted regression models indicated low intercorrelation among neighborhood domains, with variance inflation factors ranging from 1.006 to 1.146 and tolerance values exceeding 0.87. Additionally, three joint models were estimated, one per outcome, entering all three neighborhood domains simultaneously to assess the independent contribution of each domain net of shared variance with the others. These joint models were pre-specified to evaluate the independence of domain-specific associations and to assess potential confounding across correlated neighborhood features, rather than serving as post hoc exploratory analyses.\u003c/p\u003e\n\u003cp\u003eNeighborhood amenity and detracting element indices were modeled on their natural unit scale, representing the incremental association per additional neighborhood feature. Perceived neighborhood safety was modeled as an ordinal predictor consistent with its response structure. Adjusted odds ratios and 95 percent confidence intervals were reported for all neighborhood predictors. Statistical significance was assessed using two-sided tests with an alpha level of 0.05. Model fit was evaluated using pseudo-R-squared measures generated by the Complex Samples logistic regression procedure. All statistical analyses were conducted using IBM SPSS version 29. A formal power calculation was not conducted, as the analytic sample was determined by NSCH survey design and eligibility criteria rather than investigator-controlled enrollment. Additional sensitivity analyses were conducted to assess the robustness of findings to alternative exposure specifications. Neighborhood amenity and detracting element indices were re-estimated using categorical indicator variables representing each exposure level. Results from these categorical models were substantively consistent with the primary analyses, with no meaningful changes in the direction or statistical significance of associations, supporting the robustness of the continuous modeling approach. Marginal predicted probabilities across all neighborhood domains and outcomes are presented in Additional file 1: Figure S1.\u003c/p\u003e\n\u003cp\u003eSeveral potential sources of bias were addressed by design and analysis. Selection bias was minimized through the use of NSCH complex survey weights, which correct for unequal probabilities of selection, differential nonresponse, and oversampling of demographic subgroups, ensuring population-representative estimates. To assess potential bias from missing data, weighted comparisons between respondents with complete and incomplete data confirmed no meaningful differences in outcome prevalence or exposure distributions, supporting the validity of the complete-case approach. Measurement bias arising from parent-reported diagnosis was expected to produce non-differential misclassification with respect to neighborhood exposures, which would bias associations toward the null rather than produce false-positive findings. Confounding was addressed through a priori covariate adjustment for established sociodemographic determinants of both neighborhood conditions and mental health diagnosis, age, gender, race and ethnicity, household income-to-poverty ratio, and parental education, selected based on their conceptual relevance to neighborhood residential selection processes. Residual confounding from unmeasured factors cannot be excluded and is addressed in the Limitations.\u003c/p\u003e\n\u003ch2\u003eEthical Considerations\u003c/h2\u003e\n\u003cp\u003eThis study involved secondary analysis of publicly available, de-identified data from the National Survey of Children\u0026apos;s Health and was exempt from institutional review board review. All analyses were conducted in accordance with applicable ethical standards for research involving human participants. This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional analyses. Artificial intelligence language model tools were used during the preparation of this manuscript to assist with editing and formatting. All scientific content, analysis, and conclusions are the sole work of the authors, who reviewed and verified all content and take full responsibility for the accuracy and integrity of the work.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eBaseline Characteristics and Neighborhood Condition Distributions\u003c/h2\u003e\n\u003cp\u003eThe analytic sample comprised 43,213 U.S. children and adolescents aged 6 to 17 years from the 2018–2019 NSCH. Survey-weighted prevalence of parent-reported clinician diagnoses was 11.9% for anxiety, 11.5% for ADHD, and 5.9% for depression. The weighted sample was approximately evenly distributed by gender. Approximately half of participants were White non-Hispanic (49.3%), with Hispanic children representing 26.3% of the sample. The majority resided in households at or above 200% of the federal poverty level, and nearly half had parents with a college degree or higher (47.7%). Children with any diagnosed mental health condition differed significantly from those without diagnoses across multiple demographic and socioeconomic characteristics (all p\u0026lt;.001) (Table 1). In weighted bivariate analyses, the association between poverty level and anxiety status was not statistically significant (p = .645), indicating that the apparent unweighted association was attributable to the NSCH oversampling of lower-income households rather than a true population-level relationship (Additional file 3: Table S1). Weighted bivariate associations between all sociodemographic characteristics and each mental health outcome, providing a descriptive examination of subgroup variation in outcome prevalence across gender, age group, race/ethnicity, poverty level, and parental education, are also presented in Additional file 3: Table S1.\u003c/p\u003e\n\u003cp\u003eNeighborhood conditions varied across the sample. Most children lived in neighborhoods with multiple amenities; approximately 38.3% resided in neighborhoods with all four amenities and 10.7% in neighborhoods with none. The majority (73.3%) lived in neighborhoods without any detracting elements, though a meaningful subset experienced one or more. Most parents definitely agreed their neighborhood was safe (64.8%), with progressively smaller proportions reporting lower perceived safety (Table 1).\u003c/p\u003e\n\u003ch2\u003eBivariate Associations Between Neighborhood Conditions and Anxiety\u003c/h2\u003e\n\u003cp\u003eIn survey-weighted bivariate analyses, neighborhood amenities were not significantly associated with anxiety status (p=.085). Weighted anxiety prevalence varied narrowly across amenity levels, from 11.8% among children in neighborhoods with no amenities to 13.2% among those with two amenities, with no consistent protective gradient. Detracting elements were significantly associated with anxiety (p=.018), with weighted prevalence increasing from 11.3% with no detractors to 14.8% with two. Perceived neighborhood safety demonstrated the strongest and most consistent gradient (p\u0026lt;.001), with anxiety prevalence rising from 10.7% among children in definitively safe neighborhoods to 21.0% among those in the least safe (Table 2).\u003c/p\u003e\n\u003ch2\u003eBivariate Associations Between Neighborhood Conditions and Depression\u003c/h2\u003e\n\u003cp\u003eIn survey-weighted bivariate analyses, neighborhood amenities were not significantly associated with depression status (p=.108), with prevalence ranging from 5.2% to 6.9% across amenity levels and no consistent dose-response pattern. Detracting elements showed a strong, significant, and monotonically increasing association (p\u0026lt;.001), with weighted prevalence rising from 5.2% among children with no detractors to 11.9% among those exposed to all three. Perceived safety demonstrated the steepest gradient across all three outcomes and domains (p\u0026lt;.001), with depression prevalence increasing from 4.9% in definitively safe neighborhoods to 16.7% in the least safe (Table 3).\u003c/p\u003e\n\u003ch2\u003eBivariate Associations Between Neighborhood Conditions and ADHD\u003c/h2\u003e\n\u003cp\u003eIn survey-weighted bivariate analyses, neighborhood amenities were not significantly associated with ADHD status (p=.056), with prevalence ranging from 10.6% with all four amenities to 13.4% with one and no consistent gradient. Detracting elements were also not significantly associated with ADHD (p=.231), with no dose-response relationship across exposure levels. Perceived neighborhood safety was the only domain significantly associated with ADHD at the bivariate level (p=.004), with prevalence increasing from 11.0% in definitively safe neighborhoods to 16.3% among those in less safe neighborhoods (Table 4). Weighted prevalence gradients across all neighborhood condition levels for each outcome are illustrated in Figure 2.\u003c/p\u003e\n\u003ch2\u003eMultivariable Logistic Regression: Primary Domain-Specific Models\u003c/h2\u003e\n\u003cp\u003eTable 6 and Figure 1 present adjusted odds ratios and 95% confidence intervals from survey-weighted domain-specific logistic regression models for all three neighborhood exposures and all three outcomes. In adjusted models incorporating NSCH survey weights, strata, and primary sampling units, neighborhood amenities showed no significant association with anxiety (OR=1.004, 95% CI: 0.968–1.042, p=.820), depression (OR=0.983, 95% CI: 0.925–1.044, p=.576), or ADHD (OR=0.981, 95% CI: 0.941–1.022, p=.352). Associations for amenities corresponded to predicted probability differences of less than one percentage point across the full range of the index, underscoring the negligible population-level magnitude of these effects. It is important to note that no formal statistical test of differences in association magnitude across neighborhood domains was conducted; all cross-domain comparisons in this study are descriptive, based on inspection of odds ratio magnitude and confidence interval overlap, and should not be interpreted as evidence of statistically superior associations for any one domain.\u003c/p\u003e\n\u003cp\u003eDetracting elements showed significant positive associations with anxiety (OR=1.194, 95% CI: 1.113–1.281, p\u0026lt;.001) and depression (OR=1.374, 95% CI: 1.223–1.542, p\u0026lt;.001), but not ADHD (OR=1.060, 95% CI: 0.979–1.149, p=.152). The association with depression was notably larger in magnitude, with each additional detracting element associated with 37.4% higher odds of depression compared with 19.4% higher odds of anxiety. The non-significant ADHD finding is consistent with the absence of a bivariate dose-response relationship for this outcome and distinguishes detracting elements from perceived safety in terms of cross-outcome consistency. Perceived neighborhood safety demonstrated the largest associations across all outcomes: anxiety (OR=1.470, 95% CI: 1.346–1.606), depression (OR=1.649, 95% CI: 1.431–1.900), and ADHD (OR=1.192, 95% CI: 1.088–1.307), all p\u0026lt;.001. Marginal predicted probabilities of each outcome across perceived safety levels are presented in Figure 3.\u003c/p\u003e\n\u003ch2\u003eMultivariable Logistic Regression: Joint Models with All Domains Entered Simultaneously\u003c/h2\u003e\n\u003cp\u003eTo assess the independent contribution of each neighborhood domain net of shared variance with the others, joint models entered amenities, detracting elements, and perceived safety simultaneously for each outcome. Table 7 presents adjusted odds ratios from these joint models, which allow direct comparison of each domain's independent association after controlling for the others. Perceived neighborhood safety remained independently and significantly associated with all three outcomes: anxiety (OR=1.422, 95% CI: 1.291–1.566, p\u0026lt;.001), depression (OR=1.500, 95% CI: 1.287–1.747, p\u0026lt;.001), and ADHD (OR=1.182, 95% CI: 1.062–1.315, p=.002). Detracting elements attenuated to non-significance for anxiety (OR=1.069, p=.086) and ADHD (OR=1.007, p=.885) when all three domains were simultaneously controlled, but retained significance for depression (OR=1.208, 95% CI: 1.064–1.370, p=.003). Amenities showed no independent association with any outcome in joint models. These findings confirm that perceived safety is the most robustly independent neighborhood domain, and that the detracting elements associations for anxiety and ADHD in primary models partially reflect shared variance with the safety dimension. A direct comparison of domain-specific and joint model estimates across all neighborhood domains and outcomes is presented in Additional file 2: Figure S2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Fit\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll survey-weighted joint multivariable logistic regression models were statistically significant based on design-adjusted Wald F tests, indicating that the set of predictors collectively contributed to model fit. The anxiety model demonstrated significant overall fit (F(10, 41321)=45.539, p\u0026lt;.001), with a Nagelkerke pseudo R² of 0.062. The depression model likewise demonstrated significant overall fit (F(10, 41306)=45.806, p\u0026lt;.001) and exhibited the largest relative explanatory power among the three outcomes (Nagelkerke pseudo R²=0.119). The ADHD model was also statistically significant (F(10, 41072)=31.693, p\u0026lt;.001), with a Nagelkerke pseudo R² of 0.060. Pseudo R² values in logistic regression are not directly interpretable as proportion of variance explained and are expected to be modest in population-level behavioral health models; their primary utility is as a relative index of model fit for comparison across outcomes. The observed Nagelkerke pseudo R² values (0.060–0.119) are consistent with values reported in comparable NSCH-based and neighborhood-mental health studies, where pseudo R² values in the range of 0.04–0.15 are typical for population-level logistic regression models predicting diagnosed mental health conditions (9, 13). Consistent with the pattern of regression coefficients, the depression model demonstrated the greatest relative model fit, while anxiety and ADHD models exhibited comparable but smaller explanatory magnitude.\u003c/p\u003e\n\u003ch2\u003eCovariate Patterns\u003c/h2\u003e\n\u003cp\u003eCovariate associations were consistent across all primary and joint models and are summarized by sociodemographic subgroup in Table 5. Female gender was associated with higher odds of anxiety and depression and lower odds of ADHD. Older age was associated with higher odds of all three outcomes. White non-Hispanic children had substantially higher odds of all three diagnoses relative to Other or multiracial children, a pattern likely reflecting differential diagnostic access and provider practices rather than differential underlying disorder prevalence. Higher household income-to-poverty ratio was inversely associated with all three outcomes. Parental education showed a positive association with anxiety but was non-significant for depression and ADHD in survey-weighted models, a divergence consistent with detection bias in anxiety screening among more highly educated households (Table 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationally representative cross-sectional analysis of U.S. children and adolescents aged 6 to 17 years, adverse neighborhood features, disorder and perceived safety, showed consistently larger and more uniform associations with diagnosed mental health outcomes than positive neighborhood amenities. This pattern held across diagnostically distinct outcomes, including internalizing conditions (depression and anxiety) and a neurodevelopmental condition (ADHD), and persisted after adjustment for key demographic and socioeconomic covariates and after incorporation of complex survey design features. Rather than establishing causal effects, these findings clarify which neighborhood domains show the strongest and most consistent associations across outcomes, a necessary prior step to informing where longitudinal and intervention research efforts may be most fruitfully directed.\u003c/p\u003e \u003cp\u003eThis comparative evidence extends the neighborhood-health literature in a way that studies examining single domains or composite indices cannot. Prior work has documented associations between individual neighborhood features and child mental health, but has rarely compared amenities, disorder, and safety within the same nationally representative sample and across the same outcomes simultaneously. The present findings suggest that this comparison is consequential: amenities showed no significant association with any outcome in survey-weighted bivariate or adjusted models, while detracting elements showed significant positive associations with anxiety and depression in primary models, and perceived safety showed the largest associations of any domain across all three outcomes. It is important to emphasize that all comparisons of association magnitude across neighborhood domains in this study are descriptive; no formal statistical test of domain differences was conducted, and the observed patterns should be interpreted accordingly rather than as evidence of statistically established superiority of any one domain. Studies examining only amenities may therefore characterize a narrower slice of the neighborhood risk picture than studies that also capture disorder and perceived safety. This study contributes comparative evidence clarifying how distinct neighborhood domains relate to multiple youth mental health outcomes within a single nationally representative analytic framework, allowing direct cross-domain comparison that composite neighborhood indices cannot provide. Although the observed odds ratios are modest in magnitude, associations of this size may translate into meaningful population-level differences when exposures such as neighborhood safety are widely distributed across communities, as the weighted population estimates in Additional file 3: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e illustrate.\u003c/p\u003e \u003cp\u003eThe bivariate results reflect design-consistent survey weighting: amenities lost significance across all three outcomes and detracting elements lost significance for ADHD after applying the Rao-Scott adjusted F statistic, patterns that differed from unweighted analyses. This attenuation represents appropriate population-representative inference rather than a methodological limitation. The association between poverty level and anxiety, significant in unweighted analyses, was no longer significant after weighting (p = .645), underscoring the importance of design-consistent variance estimation in complex survey data. Weighted bivariate associations for all sociodemographic variables are presented in Additional file 3: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe joint model results add important inferential nuance. When all three neighborhood domains were entered simultaneously, perceived safety remained independently and significantly associated with all three outcomes, while detracting elements attenuated to non-significance for anxiety and ADHD. This pattern suggests that the associations between detracting elements and anxiety and ADHD in primary models partially reflect shared variance with perceived safety, and that safety perception captures a distinct and more independently robust dimension of the neighborhood experience. For depression, detracting elements retained significance in the joint model, suggesting a partially independent contribution beyond the safety dimension. This outcome-specific differential persistence warrants attention in future research examining the mechanisms by which physical neighborhood disorder relates to internalizing outcomes.\u003c/p\u003e \u003cp\u003ePerceived neighborhood safety was particularly strongly linked to depression, where each unit decrease in perceived safety was associated with 64.9% higher odds of diagnosis, the largest single neighborhood association observed. Although effect sizes are modest in absolute magnitude, approximately 35% of U.S. children in this sample lived in neighborhoods where parents reported less than definite agreement that their neighborhood was safe (derived from survey-weighted frequency distributions in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e); even modest per-unit associations translate into a substantial population-level burden when applied across this exposure distribution. This pattern is consistent with developmental theory emphasizing the sensitivity of emotional regulation and stress response systems to chronic perceptions of threat (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and aligns with recent systematic review evidence that adolescents who perceived their neighborhoods as unsafe had substantially greater risk of psychological distress than those who perceived them as safe (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Notably, associations between neighborhood safety perceptions and youth mental health have also been observed in studies incorporating objective neighborhood characteristics alongside subjective measures, suggesting the relationship is not solely attributable to shared reporter bias (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Conceptually, the attenuation of detracting elements associations when perceived safety was simultaneously controlled in joint models suggests that perceived safety may partially mediate the relationship between physical disorder and mental health outcomes or may serve as a proxy for unmeasured contextual stressors; this interpretation is conceptual rather than the product of a formal mediation analysis. The comparatively smaller association for ADHD most plausibly reflects two non-mutually exclusive explanations: genuine etiologic insensitivity of this neurodevelopmental condition to neighborhood stress pathways, and differential diagnostic access that systematically undercounts true ADHD prevalence in disadvantaged neighborhoods in ways that anxiety and depression diagnoses do not.\u003c/p\u003e \u003cp\u003eNeighborhood detracting elements emerged as a significant correlate of anxiety and depression in primary models and retained significance for depression in joint models. Physical disorder, including litter, vandalism, and deteriorated housing, may signal broader patterns of social disinvestment and weakened informal social control. Prior theoretical work suggests that chronic exposure may affect cognitive, emotional, and behavioral development through sustained background stress, constrained opportunities for outdoor play and peer interaction, and altered caregiver supervision practices (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The stronger association with depression relative to anxiety and ADHD, and the persistence of the depression association in joint models, is consistent with a model in which cumulative physical disorder has particular relevance for mood-related outcomes. The non-significant detracting elements association for ADHD at both bivariate and regression levels further distinguishes ADHD from the internalizing outcomes in terms of neighborhood sensitivity.\u003c/p\u003e \u003cp\u003eNeighborhood amenities demonstrated no significant associations across any of the three outcomes in survey-weighted bivariate or adjusted models. The most plausible explanation is a measurement limitation inherent in presence-based indices: parks and recreation centers may matter less for mental health than whether children can safely access and actively use them, a distinction the current data cannot resolve. Residential selection processes may also play a role, as discussed in the Limitations section. Future work incorporating measures of amenity quality, accessibility, and utilization will be necessary to determine whether protective associations emerge under more refined operationalizations.\u003c/p\u003e \u003cp\u003eThese findings have implications for how neighborhood features are prioritized in population mental health research and surveillance, while stopping short of prescribing specific intervention strategies on the basis of cross-sectional associations alone. Disorder and perceived safety may warrant greater prominence in neighborhood-level public health monitoring frameworks, including population-level surveillance dashboards that track community risk indicators alongside mental health outcomes. As one concrete example, community health needs assessments conducted by local health departments and hospital systems could be strengthened by systematically incorporating perceived neighborhood safety measures alongside existing socioeconomic indicators, enabling more targeted identification of communities where adverse neighborhood conditions co-occur with elevated youth mental health burden. Similarly, Medicaid managed care organizations and state children\u0026rsquo;s health programs that use geographic risk stratification for population health targeting could incorporate neighborhood safety and disorder indicators to better identify high-risk communities for preventive outreach and mental health service expansion. Place-based interventions targeting physical disorder and safety have shown promise for producing population-level health effects in related domains (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and experimental evidence suggests that disorder reduction may improve mental health partly through improvements in perceived safety, the same domain showing the strongest associations in the present study (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These implications are framed as directions for future research and surveillance rather than prescriptions for action; all cross-domain comparisons in this study are descriptive and should not be interpreted as causally established differences across neighborhood domains. Causal claims require longitudinal and intervention evidence beyond what this study provides.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant consideration. Regarding design limitations, the cross-sectional design precludes conclusions about temporality or directionality. Reverse causation is a genuine concern: families with children experiencing mental health conditions may disproportionately reside in more disordered neighborhoods through residential selection processes, and children with mental health diagnoses may be more likely to remain in high-disorder environments due to the economic constraints that often accompany caregiving burden, meaning the observed associations may partly reflect selection into rather than effects of adverse neighborhood conditions. Regarding measurement limitations, parental mental health, itself associated with neighborhood conditions, may independently drive both neighborhood perception and the likelihood of obtaining a child mental health diagnosis, inflating associations through a shared parental pathway rather than a true environmental one. Shared respondent reporting for both neighborhood conditions and mental health outcomes introduces potential common method bias that cannot be separated from true environmental associations in these data, and future work using administrative or observational neighborhood data linked to independent clinical records would substantially strengthen causal inference. This concern is particularly salient for perceived neighborhood safety, which, as a subjective parental perception rather than an observable environmental feature, may share greater conceptual and measurement overlaps with parent-reported mental health diagnoses than amenity or detracting element indices, potentially inflating its associations relative to the other domains. Future research incorporating objective safety indicators or multi-informant mental health assessments would be necessary to disentangle perceptual bias from true environmental effects. Although perceived neighborhood safety is subjectively reported by the same parent who reports the child\u0026rsquo;s mental health diagnosis, the presence of consistent associations across three diagnostically distinct outcomes and the persistence of associations in joint models after controlling for other neighborhood domains suggest that shared reporter bias alone is unlikely to fully explain the observed patterns.\u003c/p\u003e \u003cp\u003eRegarding unmeasured confounders, residual confounding remains possible, including unmeasured factors such as parental mental health, school environments, neighborhood social cohesion, and direct exposure to violence. For ADHD specifically, diagnostic rates are known to vary by healthcare access and provider practices in ways not fully captured by the sociodemographic covariates included here a pattern that Pearce et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) have documented across multiple stages of the ADHD diagnostic pathway, and neighborhood associations for ADHD should therefore be interpreted with particular caution regarding diagnostic versus etiologic pathways. Similarly, Calub et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) demonstrated that neighborhood conditions relate to ADHD symptom severity in clinical samples including autistic youth, suggesting that the present findings in a general population sample may reflect a combination of true environmental risk and differential diagnostic access that future work should disentangle. Regarding analytic limitations, the separate modeling approach does not allow formal tests of whether associations differ statistically across neighborhood domains; descriptive comparisons of odds ratio magnitude should not be interpreted as evidence of statistically superior associations for any domain. Findings are generalizable to noninstitutionalized U.S. children and adolescents aged 6 to 17 years. Weighted and unweighted bivariate associations did not always converge, most notably the poverty-anxiety association was significant in unweighted analyses but not after survey weighting, underscoring the importance of design-consistent variance estimation and the potential for unweighted descriptive analyses in complex survey data to mischaracterize population-level patterns. Sensitivity analyses using categorical exposure specifications produced substantively similar results, supporting the robustness of the primary continuous modeling approach. Formal interaction analyses testing whether neighborhood-mental health associations were modified by age, gender, or race/ethnicity were not conducted; subgroup variation in outcome prevalence is described in Additional file 3: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e but effect modification remains an open question for future research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this nationally representative cross-sectional analysis of U.S. children and adolescents, adverse neighborhood conditions, disorder and perceived safety, showed consistently larger and more uniform associations with diagnosed depression, anxiety, and ADHD than positive neighborhood amenities. Perceived neighborhood safety was the most consistent and independently robust domain, remaining significantly associated with all three outcomes in both primary and joint models. This domain asymmetry held across diagnostically distinct outcomes and after adjustment for sociodemographic covariates and complex survey design features, suggesting that adverse neighborhood conditions may carry stronger population-level associations with youth mental health than supportive infrastructure. These findings are descriptive and hypothesis-generating; they indicate that adverse neighborhood features may warrant greater priority in population mental health surveillance and research. They do not, on their own, establish that reducing adverse conditions will improve mental health outcomes; that determination requires longitudinal and experimental evidence.\u003c/p\u003e \u003cp\u003eBy directly comparing distinct neighborhood domains within a single nationally representative analytic framework incorporating design-consistent survey weighting, this study contributes comparative, domain-specific evidence to the neighborhood-health literature that composite index approaches cannot provide. Future research should employ longitudinal designs to clarify temporal sequencing and potential bidirectional relationships between neighborhood environments and mental health outcomes. Experimental and quasi-experimental studies evaluating whether targeted reductions in neighborhood disorder and improvements in perceived safety produce measurable mental health benefits, and whether such effects differ from those produced by amenity-focused investments, would provide the causal evidence needed to translate the present descriptive findings into actionable prevention guidance for U.S. children and adolescents.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAttention deficit hyperactivity disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFPL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFederal poverty level\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFWC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFinal child weight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral educational development\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIBM SPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIBM SPSS Statistics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSCH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Survey of Children\u0026rsquo;s Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrimary sampling unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTROBE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eU.S.\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Survey of Children\u0026apos;s Health study protocols were approved by the US Census Bureau Institutional Review Board. The survey was conducted in accordance with relevant federal regulations and institutional requirements. Written informed consent was obtained from all participating parents or legal guardians prior to participation. The present study involved secondary analysis of publicly available, de-identified data and did not require additional institutional review board approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the Data Resource Center for Child and Adolescent Health repository, supported by the U.S. Department of Health and Human Services Health Resources and Services Administration, https://www.childhealthdata.org (21).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that no financial support was received for the research, authorship, or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.L.B. conceptualized the study, designed the methodology, conducted formal data analysis, and led the writing and overall development of the manuscript. R.L.B. also served as the principal investigator and is responsible for the integrity of the data and accuracy of the analysis. P.N.J. played a central role in the analytical presentation of the work, preparing all tables and figures and contributing to methodological refinement throughout the research process. P.N.J. provided critical review of the study design, supported the interpretation of findings, and contributed substantively to manuscript revisions, ensuring analytical rigor and clarity of reporting. N.A.W. contributed to the theoretical framing, assisted with literature synthesis, and provided substantive revisions to enhance intellectual content. All authors reviewed, edited, and approved the final manuscript and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBitsko RH, Holbrook JR, Ghandour RM, Blumberg SJ, Visser SN, Perou R, et al. Epidemiology and impact of health care provider diagnosed anxiety and depression among US children. J Dev Behav Pediatr. 2018;39(5):395\u0026ndash;403. https://doi.org/10.1097/DBP.0000000000000571\u003c/li\u003e\n \u003cli\u003eWhitney DG, Peterson MD. US national and state level prevalence of mental health disorders and disparities of mental health care use in children. JAMA Pediatr. 2019;173(4):389\u0026ndash;391. https://doi.org/10.1001/jamapediatrics.2018.5399\u003c/li\u003e\n \u003cli\u003eOlfson M, Wall MM, Wang S, Blanco C. 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Child Adolesc Ment Health. 2024;29(2):126\u0026ndash;135. https://doi.org/10.1111/camh.12707\u003c/li\u003e\n \u003cli\u003eCalub CA, Hertz-Picciotto I, Bennett D, Schweitzer JB. Examining the association of neighborhood conditions on attention-deficit or hyperactivity disorder symptoms in autistic youth using the Child Opportunity Index 2.0. JCPP Adv. 2024;5(1):e12267. https://doi.org/10.1002/jcv2.12267\u003c/li\u003e\n \u003cli\u003eChild and Adolescent Health Measurement Initiative (CAHMI). 2018\u0026ndash;2019 National Survey of Children\u0026apos;s Health (NSCH), SPSS indicator dataset. Data Resource Center for Child and Adolescent Health, supported by Cooperative Agreement U59MC27866 from the U.S. Department of Health and Human Services, Health Resources and Services Administration, Maternal and Child Health Bureau. 2020. https://www.childhealthdata.org. Accessed 1 Jul 2023.\u003c/li\u003e\n \u003cli\u003eSterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. https://doi.org/10.1136/bmj.b2393\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 7 are available in the supplementary files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mental health, Child and adolescent health, Social determinants of health, Neighborhood environment, Depression, Anxiety, Attention deficit hyperactivity disorder, National Survey of Children's Health, Place-based intervention, Neighborhood disorder","lastPublishedDoi":"10.21203/rs.3.rs-9259996/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9259996/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMental health conditions among children and adolescents represent a major U.S. public health concern. Neighborhood environments are recognized as structural determinants of youth mental health, yet a critical empirical question remains underexamined: do adverse neighborhood features, disorder and perceived safety produce stronger associations with diagnosed outcomes than protective amenities? This study compared associations between neighborhood amenities, detracting elements, and perceived safety with parent-reported diagnoses of anxiety, depression, and ADHD among U.S. children and adolescents.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCross-sectional analysis of the 2018\u0026ndash;2019 National Survey of Children\u0026rsquo;s Health (NSCH) included 43,213 children and adolescents aged 6 to 17 years. Neighborhood conditions were operationalized as distinct composite measures modeled separately across each outcome. Survey-weighted logistic regression incorporated NSCH sampling weights, stratum indicators, and primary sampling unit identifiers, adjusting for age, gender, race/ethnicity, income-to-poverty ratio, and parental education.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSurvey-weighted prevalence of diagnosed anxiety, ADHD, and depression was 11.9%, 11.5%, and 5.9%, respectively. In design-corrected bivariate analyses, amenities were not significantly associated with any outcome. Detracting elements were associated with anxiety (p=.018) and depression (p\u0026lt;.001) but not ADHD (p=.231). Perceived safety was significantly associated with all three outcomes (all p\u0026le;.004). In multivariable models, amenities showed no significant association with any outcome. Detracting elements were associated with higher odds of anxiety (OR\u0026thinsp;=\u0026thinsp;1.194, 95% CI: 1.113\u0026ndash;1.281) and depression (OR\u0026thinsp;=\u0026thinsp;1.374, 95% CI: 1.223\u0026ndash;1.542), but not ADHD (OR\u0026thinsp;=\u0026thinsp;1.060, 95% CI: 0.979\u0026ndash;1.149). Perceived safety demonstrated the largest associations: anxiety (OR\u0026thinsp;=\u0026thinsp;1.470, 95% CI: 1.346\u0026ndash;1.606), depression (OR\u0026thinsp;=\u0026thinsp;1.649, 95% CI: 1.431\u0026ndash;1.900), and ADHD (OR\u0026thinsp;=\u0026thinsp;1.192, 95% CI: 1.088\u0026ndash;1.307), all p\u0026lt;.001. In joint models, perceived safety remained independently associated with all outcomes; detracting elements attenuated for anxiety and ADHD but remained significant for depression.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAdverse neighborhood conditions showed larger and more consistent associations with diagnosed youth mental health outcomes; amenities were not independently associated in any model. Perceived safety was the most consistent and robust domain across all outcomes. These findings suggest neighborhood disorder and perceived safety may represent stronger and more consistent population-level associations with youth mental health than supportive infrastructure. Longitudinal and intervention research is needed to clarify directionality and determine whether reducing adverse conditions yields measurable mental health benefits.\u003c/p\u003e","manuscriptTitle":"Adverse neighborhood conditions show larger and more consistent associations with diagnosed mental health outcomes among U.S. children and adolescents: a cross-sectional analysis of the National Survey of Children's Health, 2018 to 2019","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 09:10:14","doi":"10.21203/rs.3.rs-9259996/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"55470491839797224568937597752018097180","date":"2026-04-30T02:04:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127456612217004020651923644491119136231","date":"2026-04-27T14:22:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280033599404056436176531063930037790922","date":"2026-04-19T15:44:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T14:56:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-31T10:18:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T10:17:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T10:17:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-03-29T16:05:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"495c9ace-d106-4f10-9501-c2f36bf32f96","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"55470491839797224568937597752018097180","date":"2026-04-30T02:04:18+00:00","index":61,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T09:10:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 09:10:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9259996","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9259996","identity":"rs-9259996","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}