Background

Adolescent depression is increasing nationally, yet geographic patterning and structural determinants remain underexamined at the county level.

Methods

We conducted a county-level ecological analysis of 58 California counties in 2019. Ninth-grade female depression prevalencewas the outcome. Predictors included low school connectedness, poverty rate, uninsured rate, and rural–urban classification. Multiple imputation was used to handle missing data (10–14%). Global Moran’s I assessed spatial autocorrelation, and local indicators of spatial association identified clustering. Multivariable linear regression models were estimated.  

Results

County-level female ninth-grade depression demonstrated significant positive spatial autocorrelation (Moran’s I = 0.148, p = 0.047), whereas no spatial clustering was observed for county-level ninth-grade male depression. Local indicators identified three high–high clusters in rural Northern and Sierra counties and two low–low clusters along the Central Coast. Four low–high counties were observed, representing spatial outliers with lower depression prevalence adjacent to higher-burden regions. Rural counties had a higher mean prevalence of female depression than urban counties (44.9% vs 41.6%). In adjusted models, low school connectedness (β = 0.52, p = 0.028) and rural context (urban vs rural: β = −5.12, p = 0.003) were independently associated with county-level prevalence of depression among ninth-grade females. Uninsured rate was inversely associated (β = −1.58, p < 0.001).  

Conclusions

County-level female adolescent depression exhibits modest but statistically significant geographic clustering across California. Associations were sex-specific and concentrated in rural regions. Spatial epidemiologic approaches identify disparities in adolescent mental health that may help inform county-level public health efforts.