How to Study the Impact of Social Determinants of Health on Health Outcomes Using EDA

Understanding the Impact of Social Determinants of Health on Health Outcomes Using Exploratory Data Analysis (EDA)

Social determinants of health (SDH) are non-medical factors that influence health outcomes. These include social, economic, environmental, and cultural conditions in which people are born, grow, live, work, and age. The study of these determinants is crucial to understanding health disparities and guiding public health policies.

Exploratory Data Analysis (EDA) is an essential technique for identifying patterns, trends, and relationships in data, particularly when studying the impact of SDH on health outcomes. EDA helps uncover hidden insights and prepares data for more sophisticated analyses like statistical modeling or machine learning.

This article explores how EDA can be applied to study the effects of social determinants of health on health outcomes, offering a step-by-step approach for public health researchers and data analysts.

1. Identifying Key Social Determinants of Health

To study the relationship between SDH and health outcomes, the first step is identifying the key social determinants. According to the World Health Organization (WHO), the most prominent SDH include:

• Economic Stability: Income, employment status, and financial security.

• Education: Level of education attained and access to quality education.

• Social and Community Context: Social support, community engagement, and social networks.

• Health Care Access and Quality: Availability of healthcare services, insurance coverage, and healthcare quality.

• Neighborhood and Built Environment: Living conditions, including housing quality, access to nutritious food, air quality, and overall safety.

• Psychosocial Factors: Stress, social isolation, and mental health.

2. Collecting Data for EDA

The next step is to collect data on both SDH and health outcomes. Researchers often combine datasets from various sources:

• Census Data: Provides demographic and economic data at the population level.

• Health Surveys: Health-related data from national health surveys, such as the CDC’s National Health Interview Survey (NHIS).

• Environmental Data: Information about air quality, water quality, housing conditions, and access to green spaces.

• Insurance and Healthcare Data: Data from healthcare providers or insurance companies can provide insights into access to and quality of healthcare services.

Some key health outcome variables to consider include:

• Chronic diseases (diabetes, hypertension, cardiovascular diseases)

• Mental health conditions (depression, anxiety)

• Life expectancy

• Infant mortality rates

• Disability-adjusted life years (DALY)

3. Conducting Exploratory Data Analysis (EDA)

Once you have gathered your data, EDA can be employed to examine the relationships between SDH and health outcomes. Here’s how to conduct an effective EDA:

Step 1: Data Cleaning and Preprocessing

Data cleaning is the foundation of any effective EDA. This involves:

• Handling Missing Data: If certain variables have missing values, you can either fill them with a default value (like the mean or median) or remove rows/columns with too many missing values.

• Normalization: Variables like income or education level may have different scales, so normalizing these variables ensures that they are comparable.

• Outlier Detection: Detecting and handling outliers helps avoid skewed analyses. For instance, extremely high or low income levels may need to be corrected or excluded.

Step 2: Univariate Analysis

Univariate analysis examines individual variables. Key techniques include:

• Summary Statistics: Measure central tendencies (mean, median) and dispersion (variance, standard deviation).

• Histograms and Density Plots: Visualizing the distribution of variables like income, education, or health outcomes can help identify patterns.

• Box Plots: Useful for identifying the spread and outliers in data such as life expectancy or health scores.

For instance, a histogram of income levels can reveal skewness in the data, or a box plot of health outcomes by income group can highlight disparities.

Step 3: Bivariate Analysis

Bivariate analysis helps understand the relationship between two variables. Key techniques include:

• Correlation Coefficients: The Pearson correlation can help assess the strength and direction of the relationship between social determinants and health outcomes. For example, a positive correlation between income and life expectancy might be observed.

• Scatter Plots: These can visually depict relationships between variables, like plotting income against rates of chronic diseases.

• Cross-tabulations: Used to analyze the relationship between categorical variables, such as education level and access to healthcare.

For example, cross-tabulating education level with the rate of hypertension can reveal potential health disparities tied to education.

Step 4: Multivariate Analysis

For a deeper understanding, multivariate analysis can be used to examine how multiple SDH simultaneously affect health outcomes. Techniques include:

• Pairwise Plots: Visualize the relationships between multiple variables at once.

• Heatmaps: Correlation heatmaps can show the strength of relationships between various social determinants and health outcomes.

• Principal Component Analysis (PCA): PCA can reduce the dimensionality of your data while maintaining the key patterns, which is useful if you are dealing with a large number of variables.

4. Identifying Patterns and Insights

Through EDA, you can identify meaningful patterns that might indicate how SDH influence health outcomes. For example:

• Geographic Variation: Health outcomes may vary across neighborhoods or regions due to differences in environmental quality or access to healthcare. A spatial analysis can reveal these disparities.

• Socioeconomic Gradient: Lower income or educational levels may correlate with higher rates of chronic illness or poorer mental health outcomes. Identifying these patterns can suggest areas where intervention might be necessary.

• Causal Inferences: While EDA can’t prove causation, it can suggest potential causal relationships. For example, if lower levels of education are strongly correlated with poorer health outcomes, further statistical analysis or research may be needed to explore causality.

5. Visualization of Findings

Data visualization plays an essential role in EDA by providing an intuitive understanding of complex relationships. Some of the most powerful visualization tools for this type of analysis include:

• Heatmaps: To show correlations or the distribution of key variables.

• Bar Charts: For comparing health outcomes across different social determinants.

• Geospatial Maps: To visualize how health outcomes and social determinants vary by geography.

• Line Graphs: To show trends over time, such as the relationship between income growth and life expectancy.

Effective visualization allows policymakers, health professionals, and researchers to quickly identify key areas for intervention.

6. Moving Forward with Advanced Analysis

While EDA is crucial for uncovering insights, further statistical modeling or machine learning techniques can help establish more definitive relationships between SDH and health outcomes. Regression models (e.g., linear or logistic regression) or machine learning algorithms (e.g., decision trees, random forests) can test hypotheses developed during the EDA phase and predict the impact of social determinants on health.

However, EDA should remain an integral part of the analysis process, as it provides valuable insights for interpreting and validating results.

Conclusion

Using EDA to study the impact of social determinants of health on health outcomes allows researchers to identify and understand key factors influencing public health. Through careful data collection, cleaning, and analysis, you can uncover patterns that shed light on the disparities caused by various social determinants. By leveraging advanced visualization and statistical techniques, EDA not only helps in describing the data but also guides decision-making and policy formulation aimed at improving health equity.

Ultimately, EDA is an essential first step in addressing health disparities, as it provides the necessary insights to inform more detailed and targeted interventions.