DHRUVI MEHTA

Hi! I'm Dhruvi, a data analyst and visualisation specialist with a background in graphic design based in New York City.














2026 by Dhruvi Mehta

The Mental Health Divide: Understanding Mental Health Disparities
Data Analysis

According to the National Institute of Mental Health, more than one in five adults in the United States experience mental illness each year. Yet access to mental health treatment varies significantly depending on where you live. Data from the National Alliance on Mental Illness (NAMI) shows that fewer than half of adults with a mental illness received care in 2021, a figure that drops even lower among marginalized groups and rural populations.

These gaps are particularly pronounced among members of the LGBTQ+ community, communities of color, and people living in rural areas, many of whom face barriers such as provider shortages, affordability concerns, and limited access to facilities. The consequences are far-reaching: 33.5% of adults with mental illness also experience substance use disorders, and 70% of youth in juvenile detention have been diagnosed with a mental health condition. Untreated mental illness is also associated with higher rates of school dropout, unemployment, and incarceration.

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These inequalities motivated this project, which centers on a critical question:
Does the number of mental health treatment facilities in a state improve mental health outcomes for its residents?
As mental health concerns continue to grow nationally, it is easy to assume that building more facilities is the answer. By examining national datasets from 2023, this project investigates whether availability alone is sufficient to improve outcomes, or whether deeper structural issues must also be addressed to ensure meaningful mental health support for all.


Research Question and Hypothesis:

Research Question: How does the geographic distribution of mental health treatment facilities impact community mental health outcomes in the United States? 

Hypotheses:
H0: There is no statistically significant relationship between facility access and mental health outcomes.
H1: There is a statistically significant relationship between the availability of mental health treatment facilities and mental health related outcomes. 


Data Source and Variables:

National Substance Use and Mental Health Services Survey (NSUMHSS)
  • Source: Substance Abuse and Mental Health Services Administration (SAMHSA)
  • Unit of aggregation: Facility-level data, aggregated to the state level
  • Time: 2023
  • Key variables:
    • MHFLAG: Whether the facility offers mental health services
    • SERVMHOSP, SERVMOUT, SERVMRES, SERVMPHP: Types of mental health care provided (inpatient, outpatient, residential, and partial hospitalization)
    • INSURMCAID, INSURMCARE, INSURPRIV: Accepted insurance types
    • OWNERSHIP: Ownership model (public, nonprofit, or for-profit)

     Behavioral Risk Factor Surveillance System (BRFSS), 2023
    • Source: Centers for Disease Control and Prevention (CDC)
    • Unit of aggregation: State level
    • Time: 2023
    • Key variables:
      • _MENTI14D: Number of days of poor mental health reported in the past 30 days (continuous outcome)
      • ADDEPEV3: Depression diagnosis status (binary outcome)
      • EDUCA: Education level
      • INCOME2: Income bracket
      • HLTHPLN1: Insurance status
      • _STATE: FIPS code for state-level merging


    How I Approached the Analysis:

    This project involved merging and cleaning state-level data from two sources: the 2023 Behavioral Risk Factor Surveillance System (BRFSS) and the 2023 National Substance Use and Mental Health Services Survey (NSUMHSS).

    Data Preparation:

    I began by filtering the NSUMHSS data to include only mental health facilities, then counted the number of facilities per state. Using state population data, I calculated facility density per 100,000 residents, allowing for meaningful comparisons across states of varying sizes.

    For the BRFSS dataset, I selected variables related to mental health and socioeconomic status:
    • Average number of poor mental health days
    • Prevalence of depression
    • Income level, education, and health insurance coverage

    After removing missing or invalid responses, I aggregated the dataset at the state level.  

    Creating a Socioeconomic Index:

    I applied Principal Component Analysis (PCA) to combine income, education, and insurance coverage into a single socioeconomic status (SES) index, capturing broader structural conditions while reducing multicollinearity and simplifying the regression model.

    Statistical Analysis:

    I conducted a correlation analysis to explore the relationships between facility access, mental health outcomes, and socioeconomic status, then built a multiple linear regression model to examine the impact of facility density and SES on poor mental health days and depression rates. Logistic regression was considered as a future extension but was not implemented in this version of the analysis.


    Results and Interpretation:
    Going into the analysis, it seemed reasonable to expect that a greater number of mental health treatment facilities would lead to better outcomes. The findings, however, paint a more nuanced picture. States like Maine and Vermont have higher facility density, yet the correlation between facility density and reported poor mental health days was weak and not statistically significant (r = -0.19, p > 0.05). A comparable pattern emerged for depression diagnosis rates, which showed a slight positive correlation with facility density (r = 0.19) that was similarly non-significant.

    These findings suggest that simply increasing the number of facilities does not directly translate to improved mental health outcomes at the population level.


    This choropleth map visualizes the density of mental health facilities per 100,000 residents across the United States, drawn from the National Substance Use and Mental Health Services Survey (NSUMHSS). A color gradient ranging from bright yellow (low density) to deep purple (high density) conveys the variation in mental health infrastructure across states. Maine and Vermont stand out with the highest facility density, while Texas, Florida, and Georgia, shown in yellow, indicate limited access relative to their populations.

    The map highlights critical regional disparities that may reflect broader systemic issues, including public health investment, mental health policy priorities, and social stigma around treatment. Areas across the Southeast and parts of the Midwest show particularly low facility density, which may affect both treatment access and outcomes.

    To explore what factors more meaningfully predict mental health outcomes, I constructed a Socioeconomic Status (SES) index using Principal Component Analysis, combining education, income, and insurance coverage into a single measure. The SES index proved to be a considerably stronger predictor than facility density. States with higher SES scores reported fewer poor mental health days, reflecting greater access to economic and educational resources. In a multiple linear regression, the SES index was the only statistically significant predictor of mental health outcomes (p < 0.001), while facility density remained insignificant.


    The findings point to an important distinction: while the number of facilities affects access, it is the broader socioeconomic conditions that more meaningfully shape mental health outcomes. States with adequate treatment infrastructure can still struggle with poverty, limited education, and insufficient insurance coverage, and continue to report higher levels of mental distress.

    This suggests that the barrier to care is not simply a matter of physical proximity, but rather whether people have the resources, knowledge, and support to access treatment in the first place.


    This study makes clear that access alone is not enough. Expanding the mental health safety net in the United States must go beyond increasing facility counts. Meaningful progress will require policies that address the underlying structural inequalities driving poor outcomes, including improvements to education, income support, and insurance coverage.


    Conclusion:

    This project set out to explore whether the number of mental health treatment facilities in a state significantly improves mental health outcomes. By integrating national datasets from the NSUMHSS and BRFSS and constructing a Socioeconomic Status (SES) index using Principal Component Analysis, the analysis found that while facility counts varied considerably across states, they were not a significant predictor of outcomes such as poor mental health days or depression diagnoses. Socioeconomic factors including income, education, and insurance coverage proved far more influential, suggesting that meaningful progress requires looking beyond surface-level access toward the structural conditions that shape mental health.

    The study was not without limitations. A large portion of the analysis relied on self-reported data, which is susceptible to bias and underreporting, particularly around mental health. As a cross-sectional study drawing on a single year of data (2023), it was not possible to assess changes over time or establish causality. Aggregating data at the state level also meant that important local nuances, such as urban and rural differences, could not be fully explored.

    Future extensions of this project could incorporate multi-year data to track trends over time, and apply logistic regression or multilevel modeling to better understand the interaction between regional and individual factors. Integrating qualitative data or community-based surveys would also help capture barriers that numbers alone cannot reflect, such as cultural attitudes, stigma, and perceptions of care quality. The expanding role of telehealth is another dimension worth examining, particularly given its rapid growth following the COVID-19 pandemic. Incorporating data on virtual care access and usage could shed light on whether telehealth offers a viable pathway to support for underserved communities.

    Ultimately, this project lays the groundwork for reconsidering how mental health infrastructure is measured and improved, shifting the focus from quantity toward equity and impact.


    References:

    1. Centers for Disease Control and Prevention (CDC). 2023. Behavioural Risk Factor Surveillance System (BRFSS). U.S. Department of Health and Human Services. https://www.cdc.gov/brfss/index.html.
    2. National Alliance on Mental Illness (NAMI). 2023. Mental Health by the Numbers. https://www.nami.org/about-mental-illness/mental-health-by-the-numbers/
    3. National Institute of Mental Health (NIMH). 2023. Mental Illness. U.S. Department of Health and Human Services. https://www.nimh.nih.gov/health/statistics/mental-illness.
    4. Substance Abuse and Mental Health Services Administration (SAMHSA). 2023. National Substance Use and Mental Health Services Survey (NSUMHSS). U.S. Department of Health and Human Services. 
      https://www.samhsa.gov/
    5. U.S. Census Bureau. 2023. Population Estimates Program. https://www.census.gov/programs-surveys/popest.html