EPIC (Evaluation Platform in COPD) was originally developed for the Canadian healthcare context. However, the model’s architecture supports adaptation to different countries and healthcare systems through jurisdiction-specific configuration files. This vignette explains how to add a new country to epicR.
epicR uses JSON configuration files to store country-specific parameters. These files contain all the epidemiological, demographic, economic, and healthcare system parameters needed to run the model for a specific jurisdiction.
inst/config/config_canada.json - Fully configured
Canadian parametersinst/config/config_us.json - US template with
placeholders (requires configuration)Create a new JSON configuration file for your country in the
inst/config/ directory:
# Example: Adding Germany
file_name <- "inst/config/config_germany.json"Start by copying the structure from an existing configuration file. You can use the US template as a starting point:
# Read the US template
us_config <- jsonlite::fromJSON("inst/config/config_us.json")
# Modify for Germany
germany_config <- us_config
germany_config$jurisdiction <- "germany"
# Save the template
jsonlite::write_json(germany_config, "inst/config/config_germany.json",
pretty = TRUE, auto_unbox = TRUE)The configuration file contains several major parameter categories that need country-specific data:
{
"global_parameters": {
"age0": 40,
"time_horizon": 20,
"discount_cost": 0.03,
"discount_qaly": 0.03,
"closed_cohort": 0
}
}Required data: - Discount rates for costs and QALYs (country-specific economic guidelines)
{
"agent": {
"p_female": 0.51,
"height_0_betas": [...],
"weight_0_betas": [...],
"p_prevalence_age": [...],
"p_bgd_by_sex": {
"male": [...],
"female": [...]
}
}
}Required data sources: - Population demographics: National statistics office - Age pyramid: Current population by age group - Life tables: Age and sex-specific mortality rates - Anthropometric data: Height and weight distributions by age and sex
{
"smoking": {
"logit_p_current_smoker_0_betas": [...],
"minimum_smoking_prevalence": 0.12,
"mortality_factor_current": [...],
"mortality_factor_former": [...]
}
}Required data sources: - Smoking prevalence: National health surveys - Smoking-related mortality: Meta-analyses or national studies - Smoking cessation rates: Longitudinal studies
{
"COPD": {
"logit_p_COPD_betas_by_sex": {
"male": [...],
"female": [...]
},
"ln_h_COPD_betas_by_sex": {
"male": [...],
"female": [...]
}
}
}Required data sources: - COPD prevalence: Spirometry-based population studies - COPD incidence: Longitudinal cohort studies - Risk factors: Smoking, age, sex associations
{
"cost": {
"bg_cost_by_stage": [...],
"exac_dcost": [...],
"cost_gp_visit": 85.50,
"cost_outpatient_diagnosis": 125.50,
"cost_smoking_cessation": 485.25
}
}Required data sources: - Healthcare unit costs: National fee schedules or health economics studies - COPD treatment costs: Health administrative data or costing studies - Currency: Convert to local currency or standardize to USD/EUR
{
"outpatient": {
"ln_rate_gpvisits_COPD_by_sex": {
"male": [...],
"female": [...]
}
}
}Required data sources: - Healthcare utilization patterns: Administrative health data - GP visit rates: Primary care databases
When direct data is not available, parameters can be estimated using:
# Example: Estimating COPD prevalence coefficients
# Using logistic regression on survey data
model <- glm(copd ~ age + sex + smoking_status + pack_years,
data = survey_data, family = binomial())
coefficients(model)Once you have populated the configuration file:
# Test that the configuration loads without errors
library(epicR)
input <- get_input(jurisdiction = "germany")# Run a small simulation to check for errors
results <- simulate(
jurisdiction = "germany",
n_agents = 10000,
time_horizon = 5
)
print(results$basic)Create documentation for your new country configuration:
# Create a data sources file
data_sources <- list(
demographics = "German Federal Statistical Office, 2023",
copd_prevalence = "BOLD Study Germany, 2022",
healthcare_costs = "German Health Economics Association, 2023"
)Document any assumptions or approximations made during parameter estimation.
Here’s a simplified example of adding Germany to epicR:
# 1. Create base configuration
germany_config <- list(
jurisdiction = "germany",
global_parameters = list(
age0 = 40,
time_horizon = 20,
discount_cost = 0.03, # German health economics guidelines
discount_qaly = 0.03,
closed_cohort = 0
),
agent = list(
p_female = 0.507, # German Federal Statistical Office 2023
# ... other parameters
),
cost = list(
cost_gp_visit = 25.50, # German fee schedule 2023
cost_outpatient_diagnosis = 85.40,
# ... other costs
)
# ... other parameter categories
)
# 2. Save configuration
jsonlite::write_json(germany_config,
"inst/config/config_germany.json",
pretty = TRUE, auto_unbox = TRUE)
# 3. Test the configuration
library(epicR)
input <- get_input(jurisdiction = "germany")If you’ve successfully created a configuration for a new country:
Adding a new country to epicR requires substantial data collection and parameter estimation, but the modular configuration system makes this process systematic and reproducible. The key is to ensure high-quality, country-specific data while maintaining the model’s scientific rigor.
For questions or assistance with adding a new country, consider: - Reviewing published adaptations of EPIC - Consulting with local health economists - Engaging with the epicR development community - Collaborating with researchers who have local data access