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Performs statistical analysis on antibiotic usage data, comparing distribution patterns between vaccinated and non-vaccinated groups, and generates visualization plots, summary tables, and written conclusions.

Purpose

This function serves as a complete data analysis pipeline for examining the relationship between antibiotic use and vaccination status. It loads CSV data, filters for antibiotic medications, calculates total usage metrics, creates distribution visualizations with KDE plots, generates summary statistics, and outputs interpretative conclusions. The function is designed for healthcare data analysis workflows where understanding medication patterns relative to vaccination is important.

Source Code

def main():
    print("Starting statistical analysis...")
    print(f"Query: Revisit the previous analysis and change the plot reported to become a distribution of antibiotic use versus vaccination modus.")
    
    # Load data
    try:
        df = pd.read_csv('input_data.csv')
        print(f"Data loaded successfully: {df.shape}")
    except Exception as e:
        print(f"Error loading data: {e}")
        return
    
    # Validate necessary columns
    required_columns = ['Medication_Type', 'DWTreatmentId_False', 'DWTreatmentId_True']
    for col in required_columns:
        if col not in df.columns:
            print(f"Error: Missing required column '{col}' in the data.")
            return
    
    # Filter data for antibiotics
    antibiotics_df = df[df['Medication_Type'] == 'ANTIBIOTICA']
    print(f"Filtered antibiotics data: {antibiotics_df.shape}")
    
    # Calculate total antibiotic use
    antibiotics_df['Total_Antibiotic_Use'] = antibiotics_df['DWTreatmentId_False'] + antibiotics_df['DWTreatmentId_True']
    
    # Plot distribution of antibiotic use versus vaccination modus
    plt.figure(figsize=(10, 6))
    sns.histplot(data=antibiotics_df, x='Total_Antibiotic_Use', hue='DWTreatmentId_True', bins=20, kde=True)
    plt.title('Distribution of Antibiotic Use vs. Vaccination Modus')
    plt.xlabel('Total Antibiotic Use')
    plt.ylabel('Frequency')
    plt.legend(title='Vaccination Modus', labels=['Without Vaccination', 'With Vaccination'])
    plt.tight_layout()
    plt.savefig('plot_01_antibiotic_use_vs_vaccination.png')
    print("Plot saved as 'plot_01_antibiotic_use_vs_vaccination.png'")
    
    # Create summary table
    summary_table = antibiotics_df[['Medication_Type', 'Total_Antibiotic_Use']].describe()
    summary_table.to_csv('table_01_summary_antibiotic_use.csv')
    print("Summary table saved as 'table_01_summary_antibiotic_use.csv'")
    
    # Write conclusions
    with open('conclusions.txt', 'w') as f:
        f.write("Conclusions and Interpretations:\n")
        f.write("1. The distribution plot shows the variation in antibiotic use with respect to vaccination modus.\n")
        f.write("2. The summary statistics provide insights into the central tendency and dispersion of antibiotic use.\n")
        f.write("3. Further analysis could explore the impact of different vaccination strategies on antibiotic consumption.\n")
    print("Conclusions written to 'conclusions.txt'")
    
    print("Analysis completed successfully!")

Return Value

This function returns None. It performs side effects by creating three output files: (1) 'plot_01_antibiotic_use_vs_vaccination.png' - a histogram with KDE showing antibiotic use distribution by vaccination status, (2) 'table_01_summary_antibiotic_use.csv' - descriptive statistics of antibiotic usage, and (3) 'conclusions.txt' - written interpretations of the analysis. The function prints status messages to console throughout execution and may return early (None) if errors occur during data loading or validation.

Dependencies

• pandas
• numpy
• matplotlib
• seaborn
• scipy

Required Imports

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import warnings

Usage Example

# Ensure input_data.csv exists with required columns
# Example CSV structure:
# Medication_Type,DWTreatmentId_False,DWTreatmentId_True
# ANTIBIOTICA,150,200
# ANTIBIOTICA,180,220
# OTHER,100,120

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import warnings

# Define the main function (paste the function code here)

# Execute the analysis
if __name__ == '__main__':
    main()
    # Output files will be created:
    # - plot_01_antibiotic_use_vs_vaccination.png
    # - table_01_summary_antibiotic_use.csv
    # - conclusions.txt

Best Practices

• Ensure 'input_data.csv' exists and is properly formatted before calling this function
• The function expects specific column names ('Medication_Type', 'DWTreatmentId_False', 'DWTreatmentId_True') - verify data schema compatibility
• Check that the working directory has write permissions for output files
• The function filters for 'ANTIBIOTICA' medication type - ensure this value exists in your data
• Consider wrapping the function call in a try-except block for production use to handle unexpected errors
• The function uses early returns on errors - monitor console output for error messages
• Output files are overwritten if they already exist - backup important files before running
• The histogram uses 20 bins by default - this may need adjustment for different data distributions
• The function assumes DWTreatmentId_False and DWTreatmentId_True contain numeric values suitable for addition
• For large datasets, consider memory usage as the function loads the entire CSV into memory

Tags

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