How to Visualize the Relationship Between Mental Health Services and Public Health Outcomes Using EDA

Exploratory Data Analysis (EDA) is a fundamental step in understanding the underlying patterns, relationships, and distributions in data. When it comes to studying the relationship between mental health services and public health outcomes, EDA can offer deep insights that guide further statistical modeling or policy formulation. This article outlines a structured approach to visualize and interpret this relationship using EDA techniques, leveraging both univariate and multivariate analyses to uncover valuable patterns.

Understanding the Variables

Before diving into visualization, it’s essential to define the types of data typically involved in studying mental health services and public health outcomes.

Mental Health Services Indicators:

• Number of mental health facilities per capita

• Access to mental health professionals

• Insurance coverage for mental health

• Government or private mental health expenditure

• Waiting times for appointments

• Usage rates of services (inpatient, outpatient)

Public Health Outcomes Indicators:

• Suicide rates

• Substance abuse rates

• Depression and anxiety prevalence

• Crime rates (correlated with untreated mental illness)

• Productivity loss due to mental illness

• Quality-adjusted life years (QALYs)

Data Preparation

Start by collecting data from trusted sources such as:

• World Health Organization (WHO)

• Centers for Disease Control and Prevention (CDC)

• National Institute of Mental Health (NIMH)

• Local or regional health departments

Merge datasets using common keys such as geographic region (state, country) and year to create a unified dataframe for analysis.

Clean the data to handle:

• Missing values (imputation or removal)

• Outliers (detection via Z-score or IQR)

• Standardization/Normalization (if metrics are on different scales)

Univariate Analysis

Begin with simple visualizations to understand the distribution of individual variables.

Histograms and Density Plots

Use these to explore variables like suicide rates, access to mental health care, and depression prevalence. They help identify skewness, modality, and presence of outliers.

Example:

python
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sns.histplot(data=df, x='suicide_rate', kde=True)

Box Plots

Box plots provide a quick snapshot of central tendency and dispersion and are useful for spotting outliers.

Example:

python
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sns.boxplot(x='access_to_services', data=df)

Bivariate Analysis

To visualize relationships between two variables, use:

Scatter Plots

Ideal for examining the correlation between two continuous variables.

Example Use Case:

• Mental health service access vs. suicide rate

python
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sns.scatterplot(x='mental_health_facilities_per_100k', y='suicide_rate', data=df)
sns.regplot(x='mental_health_facilities_per_100k', y='suicide_rate', data=df, ci=None)

This can visually show if more facilities correlate with reduced suicide rates.

Correlation Matrix

A heatmap of Pearson or Spearman correlations helps identify the strength and direction of relationships between multiple variables.

python
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corr = df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')

You can quickly pinpoint which mental health indicators most strongly associate with public health outcomes.

Bar Plots and Violin Plots

These are useful when comparing categorical variables like region or policy types with outcomes.

Example:

• Suicide rate across different states with and without mental health legislation

python
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sns.barplot(x='state_policy_type', y='suicide_rate', data=df)

Multivariate Analysis

To get a comprehensive view of interactions among multiple variables:

Pair Plots

Great for a holistic look at relationships between multiple continuous variables.

python
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sns.pairplot(df[['suicide_rate', 'depression_prevalence', 'access_to_services']])

Bubble Charts

Allow encoding three variables — e.g., x-axis as service access, y-axis as suicide rate, bubble size as healthcare spending.

python
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plt.scatter(df['access_to_services'], df['suicide_rate'], s=df['mental_health_spending']*10, alpha=0.5)

Facet Grids

Facet grids enable segmented analysis by category (like region or year) and help reveal conditional relationships.

python
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g = sns.FacetGrid(df, col="region")
g.map_dataframe(sns.scatterplot, x="access_to_services", y="suicide_rate")

Temporal Trends

Time series plots are valuable when your data includes multiple years. This shows whether increasing investment in mental health is followed by improved outcomes over time.

Line Plots

python
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sns.lineplot(x='year', y='suicide_rate', hue='region', data=df)

Use different lines to represent varying levels of access or policy intervention.

Geospatial Visualization

Mapping mental health and public health indicators can highlight regional disparities.

Choropleth Maps

If data is available by geographic region, use libraries like Plotly or Geopandas to map mental health service access against outcomes.

python
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import geopandas as gpd
map_df = gpd.read_file('your_shapefile.shp')
merged = map_df.merge(df, on='region')
merged.plot(column='suicide_rate', cmap='OrRd', legend=True)

This visual can make disparities in mental health services and their impact highly intuitive.

Dimensionality Reduction

When dealing with high-dimensional data, dimensionality reduction techniques like PCA (Principal Component Analysis) can simplify visualization without significant information loss.

python
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from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

features = ['suicide_rate', 'access_to_services', 'depression_prevalence', 'mental_health_spending']
x = StandardScaler().fit_transform(df[features])
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(x)

You can then plot the first two principal components to explore clusters and patterns.

Clustering Analysis

Apply clustering (e.g., KMeans) to group regions or populations based on similarities in mental health access and outcomes.

python
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from sklearn.cluster import KMeans

kmeans = KMeans(n_clusters=3)
df['cluster'] = kmeans.fit_predict(x)
sns.scatterplot(x=principalComponents[:,0], y=principalComponents[:,1], hue=df['cluster'])

This method can highlight areas needing policy attention or showcase successful interventions.

Final Thoughts on Visualization Strategy

When visualizing the relationship between mental health services and public health outcomes:

• Always start with simple plots and progress to more complex ones.

• Use multiple visualizations to validate and complement findings.

• Interpret visualizations in the context of domain knowledge and local conditions.

• Ensure that charts are clearly labeled and accessible to non-technical stakeholders for broader impact.

EDA is a powerful toolkit not only for statistical understanding but also for driving actionable insights in public health policy, especially when evaluating the efficacy of mental health interventions.