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# Sure, let's consider that we have a CSV file with the following columns: Country, Health_Group, and Population. We will use PySpark to load the data, and then we will perform some basic analysis such as calculating the total population per country and per health group.

# Please replace 'path_to_your_dataset.csv' with the actual path to your dataset.

from pyspark.sql import SparkSession
from pyspark.sql.functions import *

# Create a SparkSession
spark = SparkSession.builder.appName('health_group_analysis').getOrCreate()

# Load the dataset
df = spark.read.csv('path_to_your_dataset.csv', inferSchema=True, header=True)

# Check the schema of the dataset
df.printSchema()

# Compute the total population per country
total_population_per_country = df.groupBy('Country').agg(sum('Population').alias('Total_Population')).sort('Country')
total_population_per_country.show()

# Compute the total population per health group
total_population_per_health_group = df.groupBy('Health_Group').agg(sum('Population').alias('Total_Population')).sort('Health_Group')
total_population_per_health_group.show()

# Stop the SparkSession
spark.stop()

# This is a simple analysis. Depending on the specific requirements of your task, you might need more complex queries or data preprocessing.

# Running this code requires a Spark environment. If you're running this locally, you'll need to have Apache Spark installed and configured properly. If you're using a cloud-based service like Databricks or AWS EMR, these environments are already set up to run PySpark code.