Abstract
Background
Surveillance of diagnosed mental health conditions is essential for understanding geographic disparities in healthcare access and guiding public health policy. Previous studies provide national or state-level prevalence estimates with limited geographic granularity and few comparisons between insurance types. The aim of this study was to (1) estimate geographical-varying prevalence of diagnosed mental health conditions among Medicaid-insured and commercially-insured children; (2) examine geographic variations in prevalence across urban, suburban, and rural communities; (3) identify census tracts with significantly high and low prevalence; and (4) compare prevalence patterns between public and commercial insurance, and across geographic areas.
Methods
We evaluated the prevalence of MH diagnoses among children (3–17 years) using the 2018 Transformed Medicaid Analytic Files and 2018 claims database of a large commercial insurance provider. We applied spatial projection and smoothing to estimate census tract-level prevalence for overall MH conditions, ADHD, depression, and anxiety.
Results
The overall diagnosed-MH prevalence was 11.8% in the Medicaid-insured population compared to 5.5% in the commercially insured population. Suburban communities had higher prevalence rates (Medicaid: MH-15.8%, ADHD-7.7%, Depression-3.2%, Anxiety-0.9%; Commercial: MH-5.2%, ADHD-2.4%, Depression-1.1%, Anxiety-0.2%) than rural or urban communities in both the Medicaid-insured and commercially insured populations. Seven states had significantly lower diagnosed-MH prevalence than 30 or more other states. Most states had few to no tracts with low diagnosed-MH prevalence, but some states had high percentages (e.g., 10.0% in urban Florida for Medicaid, 8.7% in urban California for commercial insurance). Conversely, few states had approximately zero tracts with high diagnosed-MH prevalence, with the highest percentages being 8.9% in urban Montana for Medicaid, 9.3% in urban Minnesota for commercial insurance, 7.0% in rural Maine for Medicaid, and 4.8% in rural Massachusetts for commercial insurance.
Conclusion
Variations in prevalence differed widely across states and by communities’ rurality-urbanity. Medicaid-insured children had consistently higher prevalence than commercially insured children throughout all states. Suburban communities demonstrated the highest prevalence rates for both insurance types, with geographic clustering of high-prevalence areas observed particularly in eastern states for Medicaid-insured children and primarily in urban areas for commercially insured children.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-25422-0.
Keywords: Prevalence, Mental health diagnosis, Medicaid, Commercial insurance
Introduction
Public health surveillance is key to informed decision-making and health policy [1]. Evaluating the prevalence of health conditions and different subpopulations enables monitoring population health and access to healthcare services, guiding public healthcare investment decisions and targeting interventions more granularly [1, 2]. Particularly, surveillance of mental health (MH) conditions has lifelong implications for children and adolescents, a crucial period for developing the social, behavioral, and emotional foundation.
Previous studies have established baseline prevalence estimates for pediatric MH conditions, using survey data from the late 1990 s survey data [3], and national-level 12-month prevalence estimates from 2001 to 2004 survey data [4, 5]. More recently, a CDC study revisited the estimation of prevalence for pediatric MH conditions using multiple nationwide datasets across 2013–2019 with some comparisons between Medicaid and commercial insurance [2], expanding on a 2013 study using 2005–2011 data systems [6]. These studies, along with reports from Medicaid and CHIP Payment and Access Commission (MACPAC) [7–9], provide insights into MH prevalence but commonly focus on symptoms or impairment rather than diagnosed conditions. Recent research using administrative claims studies have examined diagnosed-MH prevalence, including longitudinal trends in commercial populations [10], and age-gender differences [11], but focused primarily on temporal changes or demographic comparisons rather than geographic variations and insurance type (public vs. commercial) differences. Most of these studies provide national or state-level estimates, limiting the granularity needed to assess local disparities.
Importantly, access to MH diagnosis and treatment is influenced by insurance type. Medicaid and commercial insurance differ in definitions of medical necessity [12], provider network structures [13, 14], and reimbursement policies [15]. These differences can lead to substantial variation in diagnosis rates, even among children with similar symptoms. Understanding diagnosed-MH prevalence, rather than symptom-based estimates, is essential for evaluating access to care.
Stratifying prevalence rates by insurance type and determining geographically granular estimates allows for a more nuanced understanding of disparities in MH diagnosis [16]. It helps identify whether certain populations are underserved, whether policy differences between subpopulations contribute to disparities, and where targeted interventions may be needed [17, 18].
The primary objective of this study is to determine the geographically granular prevalence of diagnosed-MH conditions among children insured by Medicaid and a large commercial insurance program (including multiple plans), using nationwide administrative claims data. Secondary objectives include evaluating differences in prevalence across census tracts and states, comparing rates between insurance types, and examining variation across individual MH conditions. These stratified analyses aim to uncover how policy and access differences shape diagnosis patterns, providing critical insights for equitable MH planning and policy development.
Materials and methods
Data sources
The 2018 Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) [19] were acquired from the Centers for Medicare and Medicaid Services. The T-MSIS TAF [19][]represent comprehensive administrative claims data from all 50 states and the District of Columbia, covering virtually all Medicaid-enrolled individuals nationwide. Our study population of Medicaid-enrolled children (ages 3–17) represents the near-complete census of Medicaid-enrolled children in 2018, with minimal exclusions due to data quality issues (detailed in Online Supplement C).
In this study, the 2018 claims data for commercially insured children were nationwide, containing administrative claims from multiple commercial plans across all 50 states, covering the majority of the privately insured population in the United States, thus providing substantial representativeness for research purposes. This study used only de-identified data, exempting it from human subjects research approval, while also adhering to data use agreements with originating insurers.
Study population
The study population consisted of children (ages 3–17) enrolled in Medicaid and in the commercial insurance program during calendar year 2018. Inclusion criteria were: (1) age 3–17 years, (2) enrollment in 2018 with both medical and pharmacy coverage, (3) no mental health coverage carveout, and (4) minimum one month of enrollment. Children under age 3 were excluded because of challenges in establishing mental health (MH) diagnosis in this age group. Partial-year enrollment was defined as any enrollment duration less than 12 months; these children were included to capture the full spectrum of insured children and avoid selection bias toward those with continuous coverage.
Data quality
Medicaid data quality
An analysis of Medicaid data quality performed by Data Quality (DQ) Atlas [20] indicated no major concerns related to diagnosis data across all 50 states and the District of Columbia.
Zip code data quality
An examination of the zip code data quality of the Medicaid data revealed that Rhode Island (RI) and Vermont (VT) had extremely high percentages of the study population with invalid zip codes (RI: 100%; VT: 46.50%). Consequently, these states were deemed unusable for geospatial analysis but were retained for state-level statistics. The commercial insurance data showed no concerns regarding zip code data quality. See Online-Supplement C for details.
Diagnosed-MH prevalence measures
The diagnosed-MH prevalence measure was defined as the percentage of children with a current (2018) MH diagnosis, derived for all pediatric MH conditions, and separately for attention deficit hyperactivity disorder (ADHD), depressive disorders (DD), and anxiety disorders (AD). MH diagnosis was identified as follows: (1) at least two outpatient claims with MH primary diagnosis recorded at different dates, or (2) at least two outpatient claims with a CPT code for psychosocial services [21], or (3) at least one outpatient claim with MH primary diagnosis and one outpatient claim with a CPT code for psychosocial services recorded at different dates. Individual MH conditions (e.g., ADHD) were identified using at least one MH diagnosis claim with the specific condition among children in the study population. See details in the Online-Supplement A.
The prevalence measures were derived at the census tract level using a two-step procedure. The first step involved projecting the percentage of children in the study population within each zip code to the census tract level. The second step estimated prevalence by borrowing information across neighboring census tracts through spatial smoothing.
Projection step
The zip code is the smallest geographic unit of enrollees’ residence available in claims data; however, not an appropriate proxy for community-level outcomes. We employed a randomized geo-imputation method [22], projecting prevalence estimates from the zip code to the census tract level, using a population-based weighting method. We obtained the contribution of each zip code to a census tract using the 2018 U.S. Department of Housing and Urban Development - United States Postal Service (HUD-USPS) crosswalk file [23] then distributed the study population among relevant census tracts weighted on the zip code contribution. The weight of a census tract in a zip code was determined by the portion of the children in the zip code derived using the 2018 American Community Survey (ACS) data [24, 25].
See Online-Supplement B for details.
Smoothing step
Claims data irregularities could introduce errors in the estimation of disease prevalence [26–28], thus we applied a smoothing procedure. This approach borrows information from geographically and demographically similar neighboring census tracts to produce more stable prevalence estimates by reducing random variation while preserving meaningful spatial patterns. For each tract and its 10 closest neighbors, kernel smoothing was applied, where the weights of the neighboring tracts were specified by a similarity score between tract and its neighbors.
The similarity score was computed as a function of three factors: (1) Absolute difference in the Rural-Urban Commuting Areas (RUCA) codes [29] between the census tract and its neighbors, (2) Distance between the tract and its neighbors grouped into 10 categories based on an empirical distribution, and (3) Absolute difference between the percentage of children living under 149% of Federal Poverty Level [30] grouped into 10 categories using an empirical distribution. Communities were classified by urbanicity using RUCA codes: urban (RUCA 1–3), suburban (RUCA 4–6), and rural (RUCA 7–10). Census tracts with RUCA code 99 (no population) were excluded.
The similarity score defined the similarity measure used in the kernel smoothing. The optimal bandwidth in kernel smoothing was determined using a grid search method by maximizing the spatial correlation defined by the Moran I measure. The approach for determining the optimal bandwidth was applied separately for communities categorized as urban, suburban, and rural using the RUCA.
See Online-Supplement B for details.
Statistical analysis
Between-state analysis
Differences in the mean prevalence at census tract level were evaluated using two-way ANOVA applied to the square root transformed prevalence data, with state and rurality-urbanicity being the two categorizations. Turkey’s HSD test was used to perform pairwise mean comparisons.
High and low diagnosed-MH prevalence maps
Using existing methods [31, 32], we identified census tracts where the difference in diagnosed-MH prevalence was statistically significantly larger/lower than a high/low-prevalence threshold across all census tracts and all states. The high-prevalence threshold (IH) was defined as the 3rd quartile and the low-prevalence threshold (IL) as the 1 st quartile of census tract-level prevalence within each insurance population. Given the prevalence spatial process Z(s) where s is the centroid of a census tract, we tested whether Z(s) >I for high prevalence or whether Z(s) < I for low prevalence, (given a threshold I) for all census tracts by estimating 99% simultaneous confidence bands [U(s), L(s)] for E(Z(s)). We defined a high-prevalence community if U(s) >IH, meaning that the mean prevalence was statistically significantly higher than the threshold IH. We defined a low-prevalence community if L(s) < IL, meaning that the mean prevalence was statistically significantly lower than the threshold IL. The results were displayed as point maps, where the points corresponded to census tracts of high/low prevalence.
This study was approved by the Institutional Review Board of Georgia Institute of Technology.
Results
Study population
The study population consisted of 32,545,361 children (ages 3–17) enrolled in Medicaid and 6,383,486 children in the commercial insurance program nationwide. In the study population, the Medicaid-insured children had an average enrollment exceeding 10 months, while the commercially-insured children averaged 8.9 months across all states (eTable C8). As shown in eTable C7, the Medicaid and commercial populations had similar demographic distributions by age, sex, and residence rurality-urbanicity classification.
State-level prevalence measure summaries
In the Medicaid-insured population, the national diagnosed prevalence of overall mental health (MH) conditions, attention deficit hyperactivity disorder (ADHD), depressive disorders (DD), and anxiety disorders (AD) was 11.8% (99% CI: 11.79–11.82%), 5.5% (99% CI: 5.46–5.48%), 2.7% (99% CI: 2.66–2.67%), and 0.9% (99% CI: 0.926–0.935%) respectively (Table 1). The state-level prevalence ranged from 2.9% (99% CI: 2.8–2.9%) (FL) to 30.9% (99% CI: 30.2–31.5%) (VT) for MH conditions, 0.8% (99% CI: 0.8–0.9%) (FL) to 12.6% (99% CI: 12.5–12.7%) (LA) for ADHD, 0.2% (99% CI: 0.22–0.24%) (FL) to 6.3% (99% CI: 6.1–6.5%) (ME) for DD, and 0.1% (99% CI: 0.12–0.13%) (FL) to 2.2% (99% CI: 2.0–2.4.0.4%) (VT) for AD.
Table 1.
State-level Diagnosed-MH Prevalence (99% CI) of overall MH conditions, ADHD, Depressive Disorders (DD), and Anxiety Disorders (AD) by State, and overall US for Medicaid-insured Children and Commercially-insured Children (Aged 3 to 17 Years)
| State | Medicaid MH (99% CI) | Commercial MH (99% CI) | Medicaid ADHD (99% CI) | Commercial ADHD (99% CI) | Medicaid AD (99% CI) | Commercial AD (99% CI) | Medicaid DD (99% CI) | Commercial DD (99% CI) |
|---|---|---|---|---|---|---|---|---|
| AK | 8.6% (8.3-8.8%) | 4.4% (4.0-4.9%) | 3.7% (3.5-3.8%) | 1.5% (1.3-1.8%) | 1.0% (0.9-1.1%) | 0.9% (0.8-1.1%) | 2.6% (2.4-2.7%) | 0.2% (0.1-0.3%) |
| AL | 8.9% (8.8-9.0%) | 3.5% (3.3-3.6%) | 5.6% (5.5-5.7%) | 1.7% (1.6-1.8%) | 0.6% (0.5-0.6%) | 0.6% (0.5-0.7%) | 1.5% (1.5-1.6%) | 0.2% (0.1-0.2%) |
| AR | 18.7% (18.5-18.9%) | 6.0% (5.8-6.1%) | 9.4% (9.3-9.5%) | 2.7% (2.6-2.9%) | 1.1% (1.05-1.14%)ᵃ | 1.3% (1.2-1.3%) | 5.4% (5.3-5.5%) | 0.2% (0.1-0.2%) |
| AZ | 11.1% (11.0-11.2%) | 4.0% (3.8-4.1%) | 4.4% (4.3-4.4%) | 1.4% (1.3-1.5%) | 0.9% (0.8-0.9%) | 0.9% (0.8-0.9%) | 2.3% (2.3-2.4%) | 0.2% (0.18-0.24%)ᵃ |
| CA | 8.0% (7.96-8.03%)ᵃ | 2.1% (2.0-2.1%) | 2.0% (1.97-2.00%)ᵃ | 0.8% (0.75-0.81%)ᵃ | 0.6% (0.60-0.62%)ᵃ | 0.5% (0.5-0.6%) | 2.1% (2.11-2.15%)ᵃ | 0.1% (0.10-0.12%)ᵃ |
| CO | 6.3% (6.2-6.4%) | 4.3% (4.0-4.6%) | 2.0% (1.9-2.0%) | 1.4% (1.3-1.6%) | 0.8% (0.7-0.8%) | 1.2% (1.0-1.3%) | 1.4% (1.4-1.5%) | 0.3% (0.3-0.4%) |
| CT | 18.9% (18.7-19.1%) | 7.7% (7.3-8.2%) | 6.9% (6.8-7.1%) | 2.6% (2.3-2.9%) | 1.4% (1.4-1.5%) | 1.7% (1.5-2.0%) | 4.2% (4.1-4.3%) | 0.3% (0.3-0.5%) |
| DC | 12.0% (11.6-12.3%) | 5.5% (4.9-6.3%) | 3.8% (3.6-4.0%) | 1.8% (1.4-2.2%) | 1.2% (1.1-1.3%) | 1.0% (0.8-1.4%) | 2.3% (2.1-2.4%) | 0.1% (0.1-0.3%) |
| DE | 15.3% (15.0-15.6%) | 6.5% (6.1-6.9%) | 7.4% (7.2-7.6%) | 2.3% (2.1-2.6%) | 1.2% (1.1-1.3%) | 1.3% (1.1-1.5%) | 3.0% (2.9-3.2%) | 0.2% (0.1-0.3%) |
| FL | 2.9% (2.8-2.9%) | 4.6% (4.6-4.7%) | 0.8% (0.8-0.9%) | 2.4% (2.4-2.5%) | 0.1% (0.12-0.13%)ᵃ | 0.9% (0.8-0.9%) | 0.2% (0.22-0.24%)ᵃ | 0.1% (0.11-0.13%)ᵃ |
| GA | 11.5% (11.4-11.6%) | 5.2% (5.0-5.4%) | 7.5% (7.5-7.6%) | 3.0% (2.9-3.2%) | 0.9% (0.8-0.9%) | 1.0% (0.9-1.1%) | 2.4% (2.3-2.4%) | 0.2% (0.17-0.25%)ᵃ |
| HI | 6.0% (5.9-6.2%) | 4.6% (4.3-4.9%) | 1.9% (1.8-2.0%) | 1.7% (1.5-1.9%) | 0.4% (0.4-0.5%) | 0.6% (0.5-0.7%) | 0.8% (0.8-0.9%) | 0.1% (0.1-0.2%) |
| IA | 19.5% (19.3-19.7%) | 7.3% (7.0-7.7%) | 9.3% (9.2-9.5%) | 3.0% (2.8-3.3%) | 1.8% (1.7-1.9%) | 1.7% (1.6-1.9%) | 4.6% (4.5-4.7%) | 0.4% (0.3-0.5%) |
| ID | 11.2% (11.0-11.4%) | 5.0% (4.8-5.2%) | 4.8% (4.7-4.9%) | 1.7% (1.6-1.8%) | 1.2% (1.2-1.3%) | 1.6% (1.5-1.7%) | 3.2% (3.1-3.3%) | 0.2% (0.1-0.2%) |
| IL | 11.3% (11.3-11.4%) | 6.5% (6.4-6.6%) | 3.9% (3.8-3.9%) | 2.1% (2.0-2.2%) | 1.2% (1.19-1.24%)ᵃ | 1.4% (1.4-1.5%) | 2.6% (2.5-2.6%) | 0.3% (0.27-0.32%)ᵃ |
| IN | 13.8% (13.7-13.9%) | 5.9% (5.7-6.1%) | 6.1% (6.0-6.2%) | 2.6% (2.5-2.8%) | 0.7% (0.7-0.8%) | 1.4% (1.3-1.5%) | 3.3% (3.3-3.4%) | 0.2% (0.2-0.3%) |
| KS | 17.7% (17.5-17.9%) | 5.7% (5.5-5.9%) | 7.8% (7.6-7.9%) | 2.4% (2.3-2.5%) | 1.3% (1.2-1.4%) | 1.5% (1.4-1.6%) | 5.0% (4.9-5.1%) | 0.2% (0.2-0.3%) |
| KY | 18.6% (18.4-18.7%) | 5.5% (5.2-5.8%) | 9.3% (9.2-9.4%) | 2.7% (2.5-2.9%) | 1.2% (1.1-1.2%) | 1.2% (1.1-1.3%) | 4.5% (4.4-4.5%) | 0.2% (0.1-0.2%) |
| LA | 18.8% (18.7-19.0%) | 6.8% (6.7-7.0%) | 12.6% (12.5-12.7%) | 4.0% (3.9-4.1%) | 1.4% (1.3-1.4%) | 0.9% (0.8-0.9%) | 2.9% (2.9-3.0%) | 0.3% (0.25-0.32%)ᵃ |
| MA | 17.9% (17.8-18.1%) | 10.4% (10.2-10.5%) | 7.1% (7.1-7.2%) | 3.3% (3.2-3.4%) | 1.5% (1.5-1.6%) | 1.9% (1.9-2.0%) | 3.9% (3.8-3.9%) | 0.4% (0.38-0.45%)ᵃ |
| MD | 14.0% (13.9-14.1%) | 6.2% (6.0-6.3%) | 7.4% (7.3-7.5%) | 2.3% (2.2-2.4%) | 1.1% (1.07-1.15%)ᵃ | 1.4% (1.3-1.5%) | 3.2% (3.1-3.3%) | 0.2% (0.2-0.3%) |
| ME | 27.5% (27.1-27.8%) | 9.0% (8.3-9.8%) | 11.7% (11.5-12.0%) | 3.1% (2.7-3.6%) | 2.1% (2.0-2.2%) | 2.0% (1.6-2.4%) | 6.3% (6.1-6.5%) | 0.4% (0.2-0.6%) |
| MI | 12.9% (12.8-13.0%) | 7.5% (7.4-7.6%) | 6.7% (6.6-6.8%) | 2.9% (2.8-2.9%) | 1.1% (1.1-1.2%) | 1.7% (1.7-1.8%) | 3.2% (3.1-3.2%) | 0.3% (0.29-0.34%)ᵃ |
| MN | 21.0% (20.9-21.2%) | 8.6% (8.4-8.8%) | 7.1% (7.0-7.2%) | 3.0% (2.8-3.1%) | 1.8% (1.8-1.9%) | 2.3% (2.2-2.4%) | 4.8% (4.7-4.9%) | 0.5% (0.4-0.5%) |
| MO | 12.9% (12.7-13.0%) | 4.9% (4.7-5.1%) | 7.0% (6.9-7.1%) | 2.3% (2.2-2.4%) | 1.4% (1.3-1.4%) | 1.3% (1.2-1.4%) | 3.9% (3.9-4.0%) | 0.3% (0.2-0.3%) |
| MS | 14.9% (14.8-15.1%) | 5.1% (4.8-5.4%) | 10.2% (10.1-10.4%) | 3.4% (3.2-3.7%) | 0.7% (0.7-0.8%) | 0.6% (0.5-0.7%) | 2.5% (2.5-2.6%) | 0.1% (0.1-0.2%) |
| MT | 18.8% (18.5-19.1%) | 5.9% (5.5-6.4%) | 5.6% (5.5-5.8%) | 1.4% (1.2-1.6%) | 2.1% (2.0-2.2%) | 1.6% (1.4-1.9%) | 6.1% (5.9-6.3%) | 0.3% (0.2-0.4%) |
| NC | 13.8% (13.8-13.9%) | 6.3% (6.2-6.4%) | 8.0% (7.9-8.1%) | 2.9% (2.9-3.0%) | 1.0% (0.9-1.0%) | 1.1% (1.0-1.1%) | 2.7% (2.6-2.7%) | 0.2% (0.20-0.24%)ᵃ |
| ND | 15.8% (15.3-16.2%) | 5.2% (5.0-5.4%) | 7.2% (6.8-7.5%) | 2.1% (1.9-2.2%) | 1.7% (1.6-1.9%) | 1.5% (1.3-1.6%) | 4.8% (4.6-5.1%) | 0.2% (0.2-0.3%) |
| NE | 14.8% (14.6-15.1%) | 6.4% (6.2-6.6%) | 6.1% (5.9-6.3%) | 2.1% (2.0-2.2%) | 0.8% (0.7-0.8%) | 1.4% (1.3-1.5%) | 3.7% (3.6-3.8%) | 0.2% (0.15-0.22%)ᵃ |
| NH | 19.2% (18.8-19.5%) | 9.4% (8.9-9.9%) | 7.4% (7.2-7.7%) | 3.1% (2.9-3.5%) | 1.7% (1.6-1.8%) | 2.0% (1.8-2.3%) | 4.0% (3.8-4.2%) | 0.5% (0.4-0.6%) |
| NJ | 13.6% (13.5-13.7%) | 3.7% (3.6-3.9%) | 4.4% (4.3-4.4%) | 1.3% (1.2-1.3%) | 1.0% (0.9-1.0%) | 0.7% (0.6-0.7%) | 1.8% (1.7-1.8%) | 0.1% (0.1-0.2%) |
| NM | 14.0% (13.8-14.2%) | 4.8% (4.5-5.2%) | 4.0% (3.9-4.0%) | 1.3% (1.1-1.5%) | 1.4% (1.3-1.5%) | 1.3% (1.1-1.5%) | 3.3% (3.2-3.4%) | 0.2% (0.2-0.3%) |
| NV | 10.9% (10.7-11.0%) | 3.5% (3.1-3.8%) | 3.2% (3.1-3.3%) | 1.1% (0.9-1.4%) | 1.0% (0.9-1.0%) | 1.0% (0.8-1.2%) | 2.6% (2.5-2.7%) | 0.2% (0.1-0.3%) |
| NY | 8.3% (8.3-8.4%) | 6.0% (5.9-6.1%) | 4.0% (4.0-4.1%) | 2.1% (2.0-2.2%) | 0.7% (0.72-0.74%)ᵃ | 1.1% (1.1-1.2%) | 1.9% (1.8-1.9%) | 0.2% (0.20-0.25%)ᵃ |
| OH | 18.7% (18.6-18.8%) | 6.3% (6.1-6.5%) | 9.5% (9.4-9.6%) | 2.8% (2.7-2.9%) | 1.5% (1.4-1.5%) | 1.5% (1.4-1.6%) | 4.7% (4.7-4.8%) | 0.3% (0.3-0.4%) |
| OK | 18.1% (18.0-18.3%) | 4.8% (4.6-4.9%) | 7.3% (7.2-7.4%) | 2.0% (1.9-2.1%) | 1.2% (1.2-1.3%) | 1.0% (1.0-1.1%) | 4.0% (4.0-4.1%) | 0.2% (0.1-0.2%) |
| OR | 14.5% (14.3-14.6%) | 6.0% (5.6-6.4%) | 4.3% (4.2-4.4%) | 2.0% (1.8-2.3%) | 1.2% (1.2-1.3%) | 1.3% (1.2-1.5%) | 3.5% (3.4-3.6%) | 0.3% (0.2-0.4%) |
| PA | 16.8% (16.7-16.9%) | 5.8% (5.7-5.9%) | 8.3% (8.2-8.4%) | 2.0% (1.9-2.1%) | 1.1% (1.0-1.1%) | 1.1% (1.1-1.2%) | 3.5% (3.4-3.5%) | 0.2% (0.18-0.22%)ᵃ |
| RI | 8.9% (7.0-11.4%) | 11.9% (11.5-12.3%) | 3.8% (2.6-5.6%) | 4.5% (4.3-4.8%) | 1.4% (0.7-2.6%) | 1.9% (1.7-2.1%) | 2.5% (1.5-4.0%) | 0.4% (0.3-0.5%) |
| SC | 15.2% (15.1-15.4%) | 6.7% (6.5-6.8%) | 8.9% (8.8-9.0%) | 3.8% (3.6-3.9%) | 1.2% (1.1-1.2%) | 1.1% (1.0-1.1%) | 2.0% (1.9-2.0%) | 0.2% (0.2-0.3%) |
| SD | 14.3% (13.9-14.6%) | 5.9% (5.4-6.5%) | 6.3% (6.1-6.5%) | 2.7% (2.3-3.1%) | 1.4% (1.3-1.6%) | 1.4% (1.1-1.7%) | 4.1% (3.9-4.3%) | 0.2% (0.1-0.4%) |
| TN | 12.0% (11.9-12.1%) | 3.7% (3.5-3.8%) | 6.9% (6.8-6.9%) | 2.1% (2.0-2.2%) | 0.9% (0.9-1.0%) | 0.7% (0.7-0.8%) | 3.0% (3.0-3.1%) | 0.2% (0.1-0.2%) |
| TX | 9.4% (9.4-9.5%) | 3.7% (3.7-3.8%) | 5.7% (5.7-5.8%) | 1.9% (1.8-1.9%) | 0.7% (0.67-0.69%)ᵃ | 0.8% (0.7-0.8%) | 2.2% (2.19-2.23%)ᵃ | 0.2% (0.1-0.2%) |
| UT | 10.6% (10.4-10.8%) | 4.4% (4.1-4.7%) | 3.7% (3.6-3.9%) | 1.3% (1.1-1.5%) | 1.0% (0.9-1.1%) | 1.6% (1.4-1.8%) | 3.2% (3.1-3.3%) | 0.2% (0.1-0.2%) |
| VA | 11.4% (11.3-11.5%) | 5.2% (5.0-5.4%) | 7.0% (6.9-7.1%) | 2.3% (2.2-2.4%) | 0.9% (0.9-1.0%) | 1.1% (1.0-1.2%) | 2.6% (2.5-2.6%) | 0.2% (0.2-0.3%) |
| VT | 30.9% (30.2-31.5%) | 10.4% (9.9-10.9%) | 9.0% (8.6-9.5%) | 2.4% (2.2-2.7%) | 2.2% (2.0-2.4%) | 1.8% (1.6-2.1%) | 4.2% (3.9-4.5%) | 0.5% (0.4-0.6%) |
| WA | 11.2% (11.1-11.2%) | 6.4% (6.2-6.6%) | 4.0% (3.9-4.1%) | 2.2% (2.1-2.3%) | 1.1% (1.0-1.1%) | 1.4% (1.3-1.5%) | 3.2% (3.2-3.3%) | 0.3% (0.3-0.4%) |
| WI | 15.4% (15.2-15.5%) | 6.4% (6.1-6.7%) | 7.1% (7.0-7.2%) | 2.2% (2.1-2.4%) | 1.9% (1.9-2.0%) | 1.4% (1.3-1.6%) | 3.4% (3.4-3.5%) | 0.4% (0.3-0.4%) |
| WV | 17.7% (17.4-17.9%) | 6.1% (5.8-6.5%) | 9.0% (8.8-9.2%) | 3.0% (2.8-3.3%) | 1.1% (1.1-1.2%) | 1.3% (1.2-1.5%) | 3.6% (3.5-3.7%) | 0.3% (0.2-0.4%) |
| WY | 18.7% (18.2-19.2%) | 5.4% (5.1-5.8%) | 6.8% (6.4-7.1%) | 1.6% (1.4-1.8%) | 1.6% (1.4-1.8%) | 1.5% (1.3-1.7%) | 5.5% (5.2-5.8%) | 0.2% (0.1-0.3%) |
| U.S. | 11.8% (11.79-11.82%)ᵃ | 5.5% (5.45-5.50%)ᵃ | 5.5% (5.46-5.48%)ᵃ | 2.3% (2.2-2.3%) | 0.9% (0.926-0.935%)ᵃ | 1.1% (1.1-1.2%) | 2.7% (2.66-2.67%)ᵃ | 0.2% (0.22-0.23%)ᵃ |
a Confidence intervals are presented to one decimal place when possible; additional decimal places shown only when rounding to one decimal place would result in identical upper and lower bounds
In the commercially insured population, the overall diagnosed prevalence of MH, ADHD, DD, and AD was 5.5% (99% CI: 5.45–5.50%), 2.3% (99% CI: 2.24–2.27%), 0.2% (99% CI: 0.22–0.23%), and 1.1% (99% CI: 1.13–1.16%), respectively. The state-level prevalence ranged from 2.1% (99% CI: 2.0–2.1.0.1%) (CA) to 11.9% (99% CI: 11.5–12.3%) (RI) for MH, 0.8% (99% CI: 0.75–0.81%) (CA) to 4.5% (99% CI: 4.3–4.8%) (RI) for ADHD, 0.1% (99% CI: 0.10–0.12%) (CA) to 0.5% (99% CI: 0.4–0.6%) (VT) for DD, and 0.5% (99% CI: 0.51–0.55%) (CA) to 2.3% (99% CI: 2.2–2.4%) (MN) for AD. In 49 out of the 50 states plus the District of Columbia, the diagnosed-MH prevalence was higher in the Medicaid-insured population compared to the commercially insured population, with the difference varying from 1.4% (99% CI: 1.1–1.7%) (HI) to 20.5% (99% CI: 19.8–21.1%) (VT).
Suburban communities consistently demonstrated higher diagnosed mental health than rural communities across both insurance types. Among Medicaid-insured children, the suburban-rural prevalence differences were 1.9% for MH (99% CI: 1.7–2.1%), 1.4% for ADHD (99% CI: 1.3–1.5%), 0.8% for DD (99% CI: 0.8–0.9%), and 0.5% for AD (99% CI: 0.4–0.5%). Similar patterns emerged for commercially-insured children with smaller magnitude differences: 0.7% for MH (99% CI: 0.6–0.8%), 0.3% for ADHD (99% CI: 0.2–0.3%), 0.1% for DD (99% CI: 0.1–0.2%), and 0.0% for AD (99% CI: 0.0–0.1.0.1%) (eTable C3).
Community-level prevalence statistical analysis
Between-state analysis
From ANOVA results, 87.1% (1,024 of 1,176) and 79.1% (930 of 1,176) of the state pairwise comparisons for overall MH prevalence were statistically significant (
) in the Medicaid-insured and commercially-insured populations, respectively. For the Medicaid-insured population, AL, CO, CA, FL, AK, HI, and AZ had statistically significantly lower (p < 0.01) diagnosed-MH prevalence means than 30 + states, while AR and CT had statistically significantly higher (p < 0.01) diagnosed-MH prevalence means than 30 + states. For the commercially-insured population, CA, AL, AZ, and CO had statistically significantly lower (p < 0.01) diagnosed-MH prevalence means than 30 + states, while CT and IA had statistically significantly higher (p < 0.01) diagnosed-MH prevalence mean than 30 + states (Table 2).
Table 2.
Number of states with statistically significantly higher or lower Diagnosed-MH prevalence mean compared to each state (pairwise comparisons), by insurance program
| State | Higher means(Medicaid) | Lower Means(Medicaid) | Higher means(Commercial) | Lower Means(Commercial) |
|---|---|---|---|---|
| AK | 37 | 3 | 26 | 6 |
| AL | 42 | 3 | 42 | 1 |
| AR | 2 | 34 | 13 | 18 |
| AZ | 30 | 7 | 37 | 4 |
| CA | 40 | 3 | 44 | 0 |
| CO | 41 | 1 | 35 | 4 |
| CT | 2 | 34 | 4 | 36 |
| DC | 27 | 4 | 24 | 4 |
| DE | 14 | 22 | 5 | 19 |
| FL | 39 | 0 | 29 | 4 |
| GA | 28 | 5 | 23 | 7 |
| HI | 37 | 0 | 27 | 4 |
| IA | 2 | 27 | 4 | 32 |
| ID | 28 | 2 | 21 | 4 |
| IL | 27 | 4 | 10 | 13 |
| IN | 15 | 10 | 11 | 12 |
| KS | 7 | 20 | 13 | 10 |
| KY | 2 | 23 | 16 | 9 |
| LA | 3 | 22 | 5 | 15 |
| MA | 4 | 21 | 0 | 28 |
| MD | 12 | 9 | 14 | 10 |
| ME | 0 | 27 | 2 | 25 |
| MI | 16 | 7 | 2 | 24 |
| MN | 0 | 25 | 0 | 24 |
| MO | 15 | 7 | 16 | 4 |
| MS | 8 | 10 | 14 | 4 |
| MT | 0 | 18 | 1 | 10 |
| NC | 9 | 7 | 1 | 11 |
| ND | 10 | 6 | 8 | 5 |
| NE | 7 | 7 | 1 | 10 |
| NH | 0 | 15 | 0 | 18 |
| NJ | 8 | 7 | 14 | 0 |
| NM | 7 | 7 | 7 | 6 |
| NV | 10 | 2 | 13 | 0 |
| NY | 14 | 0 | 10 | 2 |
| OH | 0 | 12 | 0 | 6 |
| OK | 0 | 10 | 8 | 2 |
| OR | 4 | 6 | 0 | 5 |
| PA | 1 | 8 | 2 | 4 |
| RIa | ||||
| SC | 3 | 6 | 0 | 4 |
| SD | 3 | 5 | 0 | 4 |
| TN | 3 | 3 | 6 | 0 |
| TX | 6 | 0 | 6 | 0 |
| UT | 3 | 0 | 5 | 0 |
| VA | 4 | 0 | 3 | 0 |
| VTa | ||||
| WA | 3 | 0 | 0 | 0 |
| WI | 2 | 0 | 0 | 0 |
| WV | 0 | 0 | 0 | 0 |
| WY | 0 | 0 | 0 | 0 |
a Data not available
Using side-by-side box plots for Medicaid-insured children, states varied in both the median and variability of their tract-level diagnosed-MH prevalence (eFigure C1-C4). Not all states with the highest median also had the highest variability (e.g., ME: median 26.8%, IQR 7.5%). In 42 out of 49 states, the median tract-level diagnosed-MH prevalence was greater than 10%. In the commercially-insured population, 29 out of 49 states had a median diagnosed-MH prevalence greater than 5%. States with high median values tended to have higher variability. The side-by-side boxplots for the commercially-insured population showed similar patterns, though with less disparity in within-state variability and on a different scale, as the MH-diagnosed prevalence was lower than in the Medicaid-insured population. Similar results were observed for the prevalence of ADHD, depression, and anxiety.
High-prevalence and low-prevalence analysis
Figure 1 illustrates the geographic distribution of census tracts with low or high diagnosed-MH prevalence in the Medicaid-insured and commercially-insured populations. eFigures C5-C7 display the maps for individual MH conditions.
Fig. 1.
Low and High Prevalence of Diagnosed-MH Conditions among Children (Aged 3 to 17 Years) in 2018 by Urbanicity and Insurance Program. Upper Left: Medicaid-insured & Low Diagnosed-MH Prevalence; Upper Right: Medicaid-insured & High Diagnosed-MH Prevalence; Lower Left: Commercially-insured & Low Diagnosed-MH Prevalence; Lower Right: Commercially-insured & High Diagnosed-MH Prevalence
High diagnosed-MH prevalence was defined using the 3rd quartile: 16.4% for the Medicaid-insured population and 6.6% for the commercially-insured population (Table 3). Low diagnosed-MH prevalence was defined using the 1 st quartile: 8.1% for the Medicaid-insured population and 3.0% for the commercially-insured population.
Table 3.
Percentage of census tracts with low (<1st quartile) and high (>3rd quartile) Diagnosed - MH prevalence by State, urbanicity-rurality, and insurance program
| State | Medicaid urban | Medicaid suburban | Medicaid rural | Commercial urban | Commercial suburban | Commercial rural |
|---|---|---|---|---|---|---|
| AK | 0.1%; 0.0% | 0.7%; 0.0% | 2.0%; 0.0% | 0.0%; 0.0% | 0.3%; 0.0% | 5.0%; 0.0% |
| AL | 2.9%; 0.0% | 1.9%; 0.0% | 1.8%; 0.0% | 3.0%; 0.1% | 2.6%; 0.5% | 4.2%; 0.0% |
| AR | 0.0%; 6.5% | 0.0%; 4.6% | 0.1%; 4.3% | 0.0%; 2.7% | 0.0%; 0.6% | 0.3%; 0.4% |
| AZ | 0.4%; 0.5% | 0.4%; 0.4% | 2.9%; 0.2% | 2.2%; 0.1% | 8.0%; 0.0% | 5.9%; 0.8% |
| CA | 3.8%; 0.0% | 3.3%; 0.0% | 3.6%; 0.0% | 8.7%; 0.0% | 7.3%; 0.0% | 6.3%; 0.1% |
| CO | 7.2%; 0.0% | 5.1%; 0.0% | 7.4%; 0.0% | 1.4%; 0.6% | 3.4%; 0.3% | 5.9%; 0.6% |
| CT | 0.0%; 7.6% | 0.0%; 10.0% | 1.5%; 3.1% | 0.2%; 5.0% | 0.4%; 5.2% | 5.4%; 1.5% |
| DCa | 0.4%; 0.8% | 0.4%; 0.0% | ||||
| DE | 0.0%; 1.2% | 1.1%; 0.3% | 0.0%; 0.0% | 0.0%; 3.7% | 0.0%; 0.6% | 0.0%; 0.0% |
| FL | 10.0%; 0.0% | 10.0%; 0.0% | 7.7%; 0.0% | 1.8%; 0.4% | 1.0%; 2.3% | 0.5%; 0.5% |
| GA | 1.3%; 0.2% | 0.0%; 1.4% | 0.0%; 0.9% | 0.5%; 0.4% | 0.2%; 0.9% | 0.1%; 0.7% |
| HI | 9.4%; 0.0% | 5.8%; 0.0% | 4.3%; 0.0% | 0.1%; 0.6% | 1.0%; 0.6% | 2.7%; 0.0% |
| IA | 0.0%; 7.9% | 0.0%; 7.2% | 0.4%; 4.5% | 0.0%; 5.7% | 0.2%; 2.9% | 0.9%; 3.3% |
| ID | 0.3%; 0.0% | 0.6%; 0.0% | 3.0%; 0.2% | 0.1%; 1.8% | 1.0%; 0.2% | 3.3%; 0.2% |
| IL | 0.4%; 0.1% | 0.0%; 2.2% | 0.1%; 1.9% | 0.2%; 2.7% | 0.0%; 1.7% | 0.0%; 0.3% |
| IN | 0.0%; 0.8% | 0.0%; 4.2% | 0.0%; 1.5% | 0.3%; 1.8% | 0.3%; 1.6% | 0.3%; 2.3% |
| KS | 0.0%; 3.1% | 0.1%; 6.2% | 0.4%; 2.8% | 0.9%; 2.0% | 0.3%; 2.3% | 1.3%; 0.8% |
| KY | 0.0%; 5.0% | 0.0%; 6.0% | 0.0%; 3.0% | 0.0%; 1.0% | 0.0%; 0.4% | 0.4%; 0.7% |
| LA | 0.0%; 4.3% | 0.0%; 7.4% | 0.0%; 5.0% | 0.0%; 3.4% | 0.2%; 1.9% | 0.8%; 1.5% |
| MA | 0.0%; 5.5% | 0.0%; 7.7% | 0.0%; 3.0% | 0.0%; 9.3% | 0.0%; 10.0% | 0.0%; 4.8% |
| MD | 1.4%; 3.4% | 0.0%; 8.7% | 0.0%; 6.1% | 0.7%; 2.2% | 0.3%; 7.4% | 1.1%; 1.4% |
| ME | 0.0%; 10.0% | 0.0%; 9.8% | 0.0%; 7.0% | 0.0%; 6.8% | 0.0%; 6.8% | 0.0%; 0.5% |
| MI | 0.9%; 0.6% | 0.1%; 3.1% | 0.2%; 2.9% | 0.5%; 5.4% | 0.0%; 5.8% | 0.1%; 4.1% |
| MN | 0.0%; 7.4% | 0.0%; 8.1% | 0.0%; 7.0% | 0.0%; 9.3% | 0.1%; 5.2% | 0.1%; 3.2% |
| MO | 0.4%; 0.3% | 0.2%; 1.5% | 0.3%; 0.7% | 1.2%; 0.1% | 0.7%; 1.1% | 1.6%; 0.9% |
| MS | 0.0%; 0.6% | 0.1%; 1.9% | 0.1%; 0.8% | 0.8%; 1.2% | 1.0%; 0.8% | 1.7%; 0.8% |
| MT | 0.0%; 8.9% | 0.0%; 6.1% | 0.5%; 1.7% | 0.0%; 5.6% | 0.0%; 5.3% | 0.0%; 1.0% |
| NC | 0.0%; 1.0% | 0.0%; 2.0% | 0.1%; 1.3% | 0.0%; 2.8% | 0.0%; 1.9% | 0.3%; 0.7% |
| ND | 0.0%; 1.8% | 0.0%; 0.0% | 0.0%; 0.0% | 0.0%; 4.2% | 0.0%; 0.0% | 0.0%; 0.0% |
| NE | 0.1%; 1.6% | 0.0%; 2.3% | 1.8%; 0.1% | 0.0%; 7.3% | 0.1%; 1.5% | 0.1%; 0.0% |
| NH | 0.0%; 5.9% | 0.0%; 5.2% | 0.0%; 2.1% | 0.0%; 8.3% | 0.0%; 3.5% | 0.0%; 2.6% |
| NJ | 0.1%; 1.4% | 1.2%; 3.6% | 2.5%; 1.2% | 3.4%; 0.0% | 2.4%; 0.0% | 2.5%; 0.0% |
| NM | 0.1%; 3.4% | 0.5%; 0.1% | 1.8%; 0.7% | 0.0%; 2.7% | 0.6%; 0.1% | 1.8%; 1.6% |
| NV | 0.2%; 0.0% | 3.3%; 1.4% | 4.3%; 0.0% | 3.0%; 0.3% | 5.9%; 0.2% | 7.0%; 0.0% |
| NY | 5.5%; 0.0% | 0.2%; 1.3% | 1.3%; 0.3% | 2.1%; 0.6% | 0.2%; 0.3% | 0.4%; 0.7% |
| OH | 0.0%; 6.9% | 0.0%; 7.5% | 0.1%; 4.9% | 0.3%; 2.6% | 0.0%; 1.9% | 0.4%; 1.0% |
| OK | 0.0%; 5.1% | 0.2%; 5.2% | 0.4%; 3.0% | 0.3%; 0.7% | 0.5%; 0.3% | 1.9%; 0.4% |
| OR | 0.0%; 2.6% | 0.0%; 3.6% | 0.9%; 0.4% | 0.1%; 4.7% | 2.1%; 2.1% | 3.9%; 2.0% |
| PA | 0.0%; 4.1% | 0.0%; 6.8% | 0.1%; 4.6% | 0.7%; 2.4% | 0.2%; 2.8% | 0.5%; 1.2% |
| RIb | ||||||
| SC | 0.0%; 1.2% | 0.0%; 2.7% | 0.3%; 0.6% | 0.0%; 3.6% | 0.1%; 1.6% | 0.8%; 1.4% |
| SD | 0.0%; 0.0% | 0.0%; 0.0% | 0.1%; 0.0% | 0.0%; 0.0% | 0.0%; 0.8% | 0.5%; 0.7% |
| TN | 1.9%; 0.1% | 0.0%; 0.0% | 0.1%; 0.0% | 2.3%; 0.0% | 1.4%; 0.0% | 2.0%; 0.0% |
| TX | 3.0%; 0.0% | 0.5%; 0.0% | 1.1%; 0.1% | 2.3%; 0.0% | 1.9%; 0.0% | 2.4%; 0.1% |
| UT | 0.3%; 0.1% | 2.1%; 0.3% | 6.0%; 0.3% | 0.4%; 0.9% | 2.1%; 1.3% | 6.0%; 1.7% |
| VA | 3.0%; 0.1% | 0.0%; 0.1% | 0.5%; 0.8% | 0.0%; 0.7% | 0.4%; 1.1% | 1.2%; 0.5% |
| VTb | ||||||
| WA | 0.6%; 0.4% | 1.2%; 1.1% | 1.7%; 0.1% | 0.0%; 4.1% | 1.3%; 2.4% | 4.0%; 0.9% |
| WI | 0.0%; 2.9% | 0.0%; 3.2% | 0.1%; 1.2% | 0.0%; 1.7% | 0.0%; 1.5% | 0.0%; 0.5% |
| WV | 0.0%; 4.4% | 0.0%; 4.8% | 0.0%; 2.0% | 0.0%; 2.8% | 0.0%; 0.3% | 0.0%; 0.9% |
| WY | 0.0%; 10.0% | 0.2%; 3.5% | 0.8%; 3.6% | 0.0%; 0.0% | 0.0%; 0.0% | 0.6%; 0.0% |
Values represent percentage of tracts with low prevalence; percentage of tracts with high prevalence
a DC has no suburban or rural census tracts; only urban areas are present
b Data not available
In urban areas, the percentage of tracts with low diagnosed-MH prevalence ranged from 0.00% (20 states) to 10.0% (FL) in the Medicaid-insured population, and from 0.00% (19 states) to 8.7% (CA) in the commercially-insured population. The percentage of tracts with high prevalence ranged from 0.00% (8 states) to 8.9% (MT) in the Medicaid-insured population, and from 0.0% (7 states) to 9.3% (MN) in the commercially-insured population.
In suburban areas, the percentage of tracts with low prevalence ranged from 0.0% (25 states) to 5.8% (HI) in the Medicaid-insured population, and from 0.0% (13 states) to 8.1% (AZ) in the commercially-insured population. The percentage of tracts with high prevalence ranged from 0.0% (9 states) to 9.8% (ME) in the Medicaid-insured population, and from 0.0% (7 states) to 7.4% (MD) in the commercially-insured population.
In rural areas, the percentage of tracts with low prevalence ranged from 0.0% (12 states) to 7.7% (FL) in the Medicaid-insured population, and from 0.0% (8 states) to 7.0% (NV) in the commercially-insured population. The percentage of tracts with high prevalence ranged from 0.0% (11 states) to 7.0% (ME) in the Medicaid-insured population, and from 0.0% (10 states) to 4.8% (MA) in the commercially-insured population.
Discussion
Using administrative claims data, this study derived the prevalence of diagnosed mental health (MH) conditions in Medicaid-insured and commercially-insured children, for all MH conditions, and separately for attention deficit hyperactivity disorder (ADHD), depression disorders, and anxiety disorders, at census tract and state levels. The prevalence reflected current diagnosed-MH conditions, not capturing MH conditions not diagnosed within the corresponding health insurance program [15]. The study applied a two-step process to generate census tract-level rates: geographic projection from zip codes using population-weighted imputation, followed by spatial smoothing based on tract similarity. Statistical analyses included two-way ANOVA to assess state and rurality differences, and identification of high- and low-prevalence communities using quartile thresholds and confidence bands. This approach enabled fine-grained geographic comparisons across insurance types and MH conditions.
This study population consisted of over 32 million Medicaid-insured and 6 million commercially-insured children aged 3–17 across the U.S. The demographic similarity between Medicaid and commercial populations by age, sex, and geographic distribution (eTable C7) supports the prevalence comparisons between insurance types, as differences in diagnosed MH conditions are less likely to be confounded by these demographic factors. Medicaid-insured children showed significantly higher diagnosed MH prevalence than those with commercial insurance across nearly all states. ADHD, depressive disorders, and anxiety disorders followed similar patterns. Suburban areas consistently had higher diagnosed MH rates than rural areas, particularly in the Medicaid population. Statistical analysis revealed substantial variation in prevalence across states and census tracts, with notable disparities in both median rates and variability. High-prevalence and low-prevalence census tracts were identified using quartile thresholds, showing geographic clustering and differences by urbanicity. These findings highlight the importance of insurance type and location in shaping access to mental health diagnosis and care.
Overall, the national-level prevalence for diagnosed-MH conditions was 11.8% and 5.5% for Medicaid-insured and commercially-insured children, respectively, as compared to 20% population prevalence from a 2009 report [33], or 16.5% parental-reported prevalence from the 2016 National Survey of Children’s Health [34]. A review study covering children from high-income countries reported prevalence of 12.7% for children from high-income countries [35]. These studies reported higher overall prevalence. The differences in the estimates were due to the approach for identifying MH diagnosis (experienced vs. self-reported versus MH-provider diagnosed), the use of datasets (survey vs. medical claims records), the sampling approach (sampled vs. whole population) and the study population (age group, insurance status, residence). Survey data capture symptoms or impairment rather than confirmed diagnoses. In contrast, administrative claims data reflect both diagnosis and reimbursed treatment, offering a more direct measure of healthcare utilization. Moreover, estimates using administrative claims records are potentially biased due to access barriers [15]. This distinction is particularly important for commercial insurance, where families often pay out-of-pocket for MH services, leading to underrepresentation in claims data. Such out-of-pocket care is less common in Medicaid, making comparisons between insurance types critical for understanding disparities in access and service delivery.
For Medicaid-insured children, the diagnosed condition-specific prevalence was ADHD: 5.5%; depression: 2.7%; and anxiety: 0.9%, in contrast to the self-reported prevalence of ADHD: 8.7%; depression: 3.4%; and anxiety: 7.8% estimated using the 2016 − 2019 National Survey of Children’s Health (NSCH) [2]. A review study covering children from high-income countries reported prevalence for ADHD: 3.7% and anxiety: 5.2% [35]. The condition-specific prevalence for commercially-insured children in our study was lower than for the Medicaid-insured population, ADHD: 2.3%, depression: 0.2%, and anxiety: 1.1%. However, they were in line with estimates from a WHO study, reporting ADHD prevalence of 3.1% in 10–14 year-olds and 2.4% in 15–19 year-olds, and depression prevalence of 1.1% in 10–14 years, and 2.8% in 15–19 year-olds [36]. The comparison for individual conditions is more nuanced because of different diagnosis practices across countries. For example, ADHD was diagnosed at a higher rate in the Medicaid population than other populations (including those from other high-income countries).
The commercial MH-diagnosed prevalence was much lower than population or self-reported prevalence, potentially indicating the limited access to MH services available for some children reimbursed with commercial insurance, deemed as under-insured due to limited reimbursement of the full range of diagnostic codes outlined in the established clinical guidelines [37, 38].
We observed a consistent pattern of higher prevalence in suburban communities versus urban or rural communities in about 32 states for the Medicaid-insured population. This pattern, however, was found in only 14 states for the commercially-insured population. A similar pattern was also observed in a recent CDC study, which estimated overall higher prevalence in rural communities for ADHD, depression, and anxiety [2]. MH conditions were being diagnosed for Medicaid-insured children in both rural and urban communities, however commercially-insured children from rural communities had lower rates of MH diagnosis than those from urban communities, suggesting that children from rural communities with commercial insurance were less likely to be diagnosed with MH conditions, which in turn, could be due to less effective MH care access in rural settings.
The state-level estimates from our study were also widely different from the recent CDC study [2]. For example, our study estimated a lower MH prevalence in Florida (Medicaid-2.9%, commercial-4.6%), whereas the CDC reported a prevalence of 9.7%. Our study also estimated higher prevalence for the Medicaid-insured population than those in the CDC study in 41 states, with particularly large discrepancies observed in Vermont (Medicaid-30.9%, CDC-16.4%) and Maine (Medicaid-27.5%, CDC-12.9%). Noteworthy, our estimates consistently showed higher prevalence in the Medicaid-insured population than in the commercially-insured population across 49 states. These discrepancies were the largest in states like Maine and Vermont.
While there was not a consistent pattern in the prevalence estimates from our study for the Medicaid-insured and commercially-insured populations versus those provided by the CDC study, both studies identified wide variations between states. The high prevalence maps displayed a pronounced pattern, with many more high prevalence tracts in the east states, some in the northwest border for Medicaid-insured children only and almost none from the mid-country states. The low prevalence maps instead displayed a few states with low prevalence, but those states had most of the low prevalence communities. Practically, there were high prevalence states vs. low prevalence states, with very few in between. This extreme variation was not an indication of the children’s experience with MH conditions but instead state-level practices and policies, which play a role in healthcare utilization [34, 39, 40]. States administered their healthcare systems differently, some state Medicaid programs carving out MH services [41], coordinated at the county or state level, with potentially implications on the availability of MH providers [42]. While the estimates for the commercially-insured population were from the same commercial insurance program, hence practicing similar reimbursement and administration policies, the between and within-state variations were also large, impacted by the provider network available to treat children. Thus, the estimates in this study reflected healthcare delivery system barriers to mental healthcare utilization.
Our findings must be considered within the context of study limitations. This study was conducted using administrative claims data. Claims data may not fully capture MH diagnosis since these data were designed for billing purposes. While the DQ Atlas [20] indicated no major concerns with diagnosis data quality across all states, other dimensions of data quality could impact our prevalence estimates, including potential issues with eligibility and enrollment data accuracy, variations in claims submission practices, differences in encounter reporting completeness across states and providers, and inaccuracies in enrollee information that may affect diagnostic accuracy. The Medicaid claims data also included only claims that have been submitted for reimbursement and they only allow estimation of diagnosed MH conditions. Therefore, prevalence estimates may be biased where certain subgroups have difficulty in maintaining Medicaid coverage, lack access to care, or were susceptible to particularly disparate utilization. A small proportion of Medicaid-insured children might also have dual insurance, primarily those enrolled due to special healthcare needs or disability [43, 44], however most of them are using Medicaid MH services, thus not significantly impacting our results. By studying a maximum of one year of a child’s healthcare, prevalence could be underrepresented for those who sought care over prior years. The most recent years of Medicaid claims data may provide different estimates, particularly in the context of the recent pandemic and societal disruption, greatly impacting children’s mental health.
Additionally, the inclusion of children with partial-year enrollment enhances generalizability by reflecting real-world insurance coverage patterns. While enrollment duration differed between insurance types (Medicaid: 10.8 months; Commercial: 8.9 months), our prevalence estimates capture diagnosed mental health conditions among all enrolled children, providing a more comprehensive assessment than studies restricted to full-year enrollees.
Conclusions
Notwithstanding limitations, our study identified significant disparities in diagnosis of pediatric mental health (MH) conditions between and within states, and between public and commercial insurance programs. Moreover, this study also provided maps of high-prevalence and low-prevalence communities, highlighting areas to be targeted for improving access to MH services. These findings can be used by community and public health departments across the country for allocating resources towards improvement in diagnosis and guiding MH care.
Overall, the wide disparities across states identified in this study reflected the influence of systematic state-level barriers, such as policies, insurance coverage, provider networks, and economic opportunities, that shape access to MH care. Because children with similar health conditions are expected to have comparable MH care needs, the observed systematic disparities are not simply a reflection of children’s sociodemographic conditions [45, 46], but rather to whether their MH needs are being adequately met.
Thus, lower diagnosis rates in certain regions likely indicate unmet needs due to limited access and other contextual barriers, rather than true differences in underlying MH burden. Thus, these findings contribute to the multifaceted nature of access to care and underscore the importance of considering both systemic and sociocultural factors when interpreting geographic variation in mental health diagnoses.
Despite the national policy efforts to bring consistency in mental healthcare through the 2008 parity MH Act [47], the 2014 change in Free Care Rule [48] and the recent network adequacy policy [49], states and insurers had their own implementation and interpretation of these acts, resulting in differences in adherence to the parity acts, support of different care settings (e.g. in-home versus in-clinic), varying reimbursement fee schedules, and limits in treatment among others. Ultimately, such variations resulted in the wide disparities identified in this paper, calling for bringing consistency in care through more explicit national policies and enforcement of such policies.
Supplementary Information
Acknowledgements
This research was supported by the Peterson Professorship at Georgia Institute of Technology. The content is solely the responsibility of the authors. The funding agreements ensured the authors’ independence in designing and conducting the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript. Nicoleta Serban (PI) had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Authors’ contributions
Ms. Liu ’s contributions include contributions include design of the work, data acquisition and analysis, interpretation of data and the results, the development of the software implementation of the models used in this study; have drafted the work substantively; have approved the submitted manuscript; and have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. Ms. Xie ’s contributions include study conception, design of the work, data acquisition and analysis, the development of the software implementation of the models used in this study; have drafted the work partially; have approved the submitted manuscript and have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. Dr. Vinson ’s contributions include interpretation of data and the results; have drafted the work; have approved the submitted manuscript and have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. Ms. Yu ’s contributions include data acquisition and analysis, the development of the software implementation of the models used in this study; have drafted the work partially; have approved the submitted manuscript and have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. Dr. Serban ’s contributions include study conception, design of the work, data acquisition and analysis, interpretation of data and the results; have drafted the work substantively; have approved the submitted manuscript and have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.
Funding
This research was partially supported by the Peterson Professorship from the Georgia Institute of Technology.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
This study was approved by the Institutional Review Board of Georgia Tech (protocol #H11287) for using the Medicaid TAF files acquired from Centers for Medicare and Medicaid Services; this is a secondary data analysis hence the informed consent for this study has been waived by the Institutional Review Board of Georgia Tech (protocol #H11287). This study also used administrative claims from a large commercial insurance program provided by a large database system; the study used only de-identified data, exempting it from human subjects research approval, while also adhering to data use agreements with originating insurers. The exception was provided by the Institutional Review Board of the database system.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Supplementary Materials
Data Availability Statement
No datasets were generated or analysed during the current study.