compare: Compare the attributable health impacts between two scenarios

In healthiar: Quantifying and Monetizing Health Impacts Attributable to Exposure

Compare the attributable health impacts between two scenarios

Description

This function calculates the health impacts between two scenarios (e.g. before and after a intervention in a health impact assessments) using either the delta or pif approach.

Usage

compare(
  output_attribute_scen_1,
  output_attribute_scen_2,
  approach_comparison = "delta"
)

Arguments

output_attribute_scen_1	Scenario 1 as in the output of attribute()
output_attribute_scen_2	Scenario 2 as in the output of attribute()
approach_comparison	String showing the method of comparison. Options: "delta" or "pif".

Details

Methodology This function compares the attributable health impacts in scenario 1 with scenario 2. It can use two approaches:

• Delta: Subtraction of health impacts in the two scenarios (two PAF) \insertCiteWHO2014_bookhealthiar

• Potential impact fraction (PIF): Single PIF for both scenarios \insertCiteWHO2003_reporthealthiar

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

• comparison of two health scenarios

Specifications of the comparison approach

Please, note that the PIF comparison approach assumes same baseline health data for scenario 1 and 2 (e.g. comparison of two scenarios at the same time point). With the delta comparison approach, the difference between two scenarios is obtained by subtraction. The delta approach is suited for all comparison cases, allowing a comparison of a situation now with a situation in the future.

IMPORTANT: If your aim is to quantify health impacts from a policy intervention, be aware that you should use the same year of analysis and therefore same health baseline data in both scenarios. The only variable that should change is the exposure (as a result of the intervention).

Comparing DALY

If you want to use compare() DALY with daly(), do not enter the output of daly() in compare(). Instead, follow these steps:

1) use compare() for YLL and YLD separately

2) use daly() inserting the output of both compare()

Alternatively, you can use attribute_health to quantify DALY entering DALY in the argument bhd_central and then use compare()

Value

This function returns a list containing:

1) health_main (tibble) containing the main results from the comparison;

• impact (numeric column) difference in attributable health burden/impact between scenario 1 and 2

• impact_scen_1 (numeric column) attributable health impact of scenario 1

• impact_scen_2 (numeric column) attributable health impact of scenario 2

• And many more

2) health_detailed (list) containing detailed (and interim) results from the comparison.

• results_raw (tibble) containing comparison results for each combination of input uncertainty for both scenario 1 and 2

• results_by_geo_id_micro (tibble) containing comparison results for each geographic unit under analysis (specified in geo_id_micro argument)

• results_by_geo_id_macro (tibble) containing comparison results for each aggregated geographic unit under analysis (specified in geo_id_macro argument))

• input_table (list) containing the inputs to each relevant argument for both scenario 1 and 2

• input_args (list) containing all the argument inputs for both scenario 1 and 2 used in the background

• scen_1 (tibble) containing results for scenario 1

• scen_2 (tibble) containing results for scenario 2

Author(s)

Alberto Castro & Axel Luyten

References

\insertAllCited

See Also

• Upstream: attribute_health, attribute_mod, standardize,

• Downstream: daly

Examples

# Goal: comparison of two scenarios with delta approach
scenario_A <- attribute_health(
  exp_central = 8.85,   # EXPOSURE 1
  cutoff_central = 5,
  bhd_central = 25000,
  approach_risk = "relative_risk",
  erf_shape = "log_linear",
  rr_central = 1.118,
  rr_increment = 10
)
scenario_B <- attribute_health(
  exp_central = 6,     # EXPOSURE 2
  cutoff_central = 5,
  bhd_central = 25000,
  approach_risk = "relative_risk",
  erf_shape = "log_linear",
  rr_central = 1.118,
  rr_increment = 10
)
results <- compare(
approach_comparison = "delta",
output_attribute_scen_1 = scenario_A,
output_attribute_scen_2 = scenario_B
)
# Inspect the difference, stored in the \code{impact} column
results$health_main |>
  dplyr::select(impact, impact_scen_1, impact_scen_2) |>
  print()

# Goal: comparison of two scenarios with potential impact fraction (pif) approach
output_attribute_scen_1 <- attribute_health(
  exp_central = 8.85,   # EXPOSURE 1
  cutoff_central = 5,
  bhd_central = 25000,
  approach_risk = "relative_risk",
  erf_shape = "log_linear",
  rr_central = 1.118, rr_lower = 1.060, rr_upper = 1.179,
  rr_increment = 10
)
output_attribute_scen_2 <- attribute_health(
  exp_central = 6,      # EXPOSURE 2
  cutoff_central = 5,
  bhd_central = 25000,
  approach_risk = "relative_risk",
  erf_shape = "log_linear",
  rr_central = 1.118, rr_lower = 1.060, rr_upper = 1.179,
  rr_increment = 10
)
results <- compare(
  output_attribute_scen_1 = output_attribute_scen_1,
  output_attribute_scen_2 = output_attribute_scen_2,
  approach_comparison = "pif"
)
# Inspect the difference, stored in the impact column
results$health_main$impact

healthiar documentation built on March 12, 2026, 5:07 p.m.