Mapping Mental Health

Mental health disorders are prevalent across the United States, significantly affecting individuals' well-being and society as a whole. This project aims to conduct a spatial analysis of mental health conditions within the US, with a special focus on the state of Alabama, and explore its association with socioeconomic factors such as poverty as well as access to mental healthcare providers. Using GIS spatial analysis techniques, this study will map the prevalence of mental health conditions across areas to look for any patterns that may emerge, to answer the question: What are the spatial patterns of poor mental health conditions? How are they affected by socioeconomic factors and access to mental healthcare?

GIS is the appropriate method for studying this question because it allows for spatial analysis and visualization of complex data. It integrates geographic information with various datasets (socioeconomic, environmental, health) to explore relationships and patterns across geographic areas. GIS provides tools for mapping, spatial statistics, and spatial analysis techniques (such as hot-spot analysis and density estimation), essential for understanding spatial disparities in mental health disorders and informing targeted interventions.

The preliminary objective of this study is to map the spatial distribution of mental health disorder prevalence rates across the Southeast region of the United States, analyze their spatial clustering using GIS spatial statistics, and identify factors or commonalities associated with high prevalence areas. Specifically, I aimed to achieve a detailed understanding of where mental health disorders were most concentrated, why these patterns existed, and how this information could guide public health strategies and resource allocation.

Previous studies have highlighted the importance of studying the spatial distribution of mental health disorders to understand disparities and inform public health interventions. One study in particular, looked at data for communities in Seoul and showed that socio-demographic factors (sex, age, education) as well as environmental factors (urbanization, green spaces) can influence mental health outcomes (Kim & Kim, 2017). Additional mapping studies have extensively explored the spatial distribution of mental health across the United States, revealing significant regional disparities and socioeconomic correlates (Cortina & Hardin, 2023). In the Southeast region, including states like Alabama, Mississippi, and Georgia, we see higher prevalence rates of poor mental health incidents being observed compared to national averages. (As shown in the map provided below.)

In mapping and analyzing mental health data, queries were used to look at both the entire United States as well as focusing in on the counties within Alabama. This approach allowed for a broader picture of the major regions of concern related to poor mental health rates within the United States, specifically the Southeastern portion. As well as, providing a detailed examination of mental health indicators and their correlates within Alabama. Variables such as poor mental health incidents, poverty rates, and mental healthcare access were mapped to identify spatial patterns and disparities, providing insights into areas where mental health challenges may be more pronounced. By focusing on Alabama, this study aims to contribute valuable insights into the factors influencing mental health outcomes within our community, facilitating targeted interventions and policies to improve mental health services and support systems for residents. The area was narrowed down to counties within Alabama where data granularity and availability were highest, ensuring robust analysis and meaningful insights. I also chose to focus on Alabama, since it is one of the states with the highest rate of poor mental health incidents and because it is where I call home.

Our initial analysis shows that the Social Vulnerability Index (SVI), which assesses communities' resilience and susceptibility to environmental and socioeconomic stressors, also demonstrates spatial patterns that mirror those observed in mental health. Regions with higher SVI scores often exhibit a greater prevalence of poor mental health outcomes. The map below shows the average number of poor mental health days compared to the percentage of the population 150% below the poverty estimate.

In looking at the United States, we see that spatial analyses reveal intriguing parallels between the SVI and mental health outcomes, especially in the Southeastern region. Regions characterized by high social vulnerability frequently coincide with those experiencing elevated rates of poor mental health incidents. To look closer at these parallels seen in the southeast region, I chose to focus more narrowly on spatial patterns in Alabama. Specifically, I chose to look at the mental health distress crude rates in Alabama by county (as shown in the map below).

Next, I used kernel interpolation to show the spatial analysis of these findings throughout Alabama.

Lastly, I chose to look at the spatial clustering for the number of mental health providers across the United States.

The study found clear spatial patterns in mental health disorder prevalence across counties in Alabama, with notable concentrations in rural areas characterized by higher poverty rates. This is to be expected given there is a notable correlation between higher incidences of poor mental health and elevated rates of poverty across the United States. Counties and regions within these areas often exhibit a higher average number of poor mental health days reported by residents annually, alongside a substantial portion of the population living below 150% of the poverty line. This overlap highlights a complex interplay between socioeconomic conditions and mental health outcomes. Areas facing economic hardship frequently experience heightened stressors related to financial instability, limited access to healthcare resources, and higher levels of social deprivation, all of which could contribute to poorer mental health outcomes.

The findings generally aligned with expectations based on existing literature, confirming the influence of socioeconomic factors on mental health outcomes. Specifically, Alabama shows higher rates of poverty in mostly rural portions of the state along with a higher rate of poor mental health incidents in those areas. However, the precise geographic distribution of poor mental health incidents and access to mental healthcare provided new insights into local variations within the state that may not have been previously highlighted. For instance, some urban centers across the country may experience pockets of high prevalence due to concentrated poverty and limited access to mental health services, but this doesn't seem to be the case in Alabama. Instead, our study shows that while there are higher rates of poverty and mental health incidents in rural parts of Alabama, mental health providers tend to be clustered more in urban areas closer to cities with larger populations. (This is shown in the map to the left.)

From this study, we learned that effective public health strategies for addressing mental health disparities must consider both socioeconomic inequalities and whether those in need have appropriate access to care. The similarity in spatial patterns between poor mental health and poverty underscores systemic challenges that affect community well-being. Factors such as limited educational opportunities, inadequate healthcare infrastructure, and insufficient social support networks can exacerbate both mental health issues and economic hardship within these regions. Addressing these challenges requires comprehensive interventions that address economic disparities while enhancing mental health services and community support systems. By understanding and mapping these spatial relationships using GIS, policymakers and public health professionals can better target resources and interventions to alleviate the burden of poor mental health and poverty in these vulnerable communities.

While the study of spatial distribution of mental health across the United States provides valuable insights, it is not without limitations. One major limitation is the availability and consistency of data across different regions and demographic groups. Data collection methods, reporting practices, and definitions of mental health disorders can vary, affecting the comparability of findings between states and over time. Additionally, while GIS and spatial analysis techniques offer powerful tools for mapping and visualizing data, they may not fully capture the complex and nuanced factors that contribute to mental health disparities, such as cultural differences, community resilience, and individual-level determinants.

Future research could expand upon this study by incorporating longitudinal data to assess trends over time and exploring the impact of specific interventions on mental health outcomes. Additionally, integrating qualitative data through community surveys or focus groups could provide deeper insights into the lived experiences and perceptions of mental health within different neighborhoods.

Understanding these spatial patterns is crucial for designing targeted interventions and policies that address the interconnected challenges of mental health, socioeconomic factors, and access to mental healthcare. By leveraging GIS technology to map and analyze these relationships, stakeholders can identify priority areas for intervention, allocate resources effectively, and implement holistic strategies that promote health equity and improve overall community well-being. This integrated approach is essential for developing sustainable solutions that address the complex health disparities faced by vulnerable populations across the United States.

In conclusion, this study provides valuable insights into the spatial patterns and determinants of mental health disorders across the United States and within the counties of Alabama. By identifying geographic disparities and understanding the underlying factors, this research hopes to contribute to evidence-based strategies for promoting mental well-being and reducing inequalities in mental health outcomes.

Banner Photo. Pixabay.  https://pixabay.com/vectors/mental-health-anxiety-depressed-7323725/ 

Photo of Depressed Woman. Pexels.  https://www.pexels.com/photo/depressed-woman-having-headache-and-stress-5699864/ 

County Health Rankings. (2022). Esri, USGS | Esri, TomTom, Garmin, FAO, NOAA, USGS, EPA, USFWS | University of Wisconsin Population Health Institute, Robert Wood Johnson Foundation.  https://uabsoph.maps.arcgis.com/apps/mapviewer/index.html?layers=3a684a0851e74ff1b55225dbdfde78b4  

Cortina, J., & Hardin, S. (2023). The geography of mental health, urbanicity, and affluence. International journal of environmental research and public health, 20(8), 5440. 

Kim, J., & Kim, H. (2017). Demographic and environmental factors associated with mental health: a cross-sectional study. International journal of environmental research and public health, 14(4), 431.

PLACES: Mental Health. CONANP, Esri, TomTom, Garmin, FAO, NOAA, USGS, EPA, USFWS | Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA.  https://uabsoph.maps.arcgis.com/apps/mapviewer/index.html?webmap=fd13e839f72d42a6ab0000c794c71d78  

CDC/ATSDR Social Vulnerability Index (2022). Esri, USGS | Esri, TomTom, Garmin, FAO, NOAA, USGS, EPA, USFWS | Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry.  https://uabsoph.maps.arcgis.com/apps/mapviewer/index.html?webmap=30aafddd0a684dbfb01887aa49077a3e