attribute_mod: Create a scenario 2 by modifying an existing scenario 1 and...

In healthiar: Quantifying and Monetizing Health Impacts Attributable to Exposure

Create a scenario 2 by modifying an existing scenario 1 and determine attributable health impacts in it

Description

This function quantifies the attributable health impacts in a new scenario 2 as follows:

• taking the input data of an existing scenario 1 (obtained using attribute_health()),

• modifying some of these input data of scenario 1 for the scenario 2 and

• calling in the background attribute_health() with the new data for scenario 2

Usage

attribute_mod(
  output_attribute,
  erf_shape = NULL,
  rr_central = NULL,
  rr_lower = NULL,
  rr_upper = NULL,
  rr_increment = NULL,
  erf_eq_central = NULL,
  erf_eq_lower = NULL,
  erf_eq_upper = NULL,
  exp_central = NULL,
  exp_lower = NULL,
  exp_upper = NULL,
  prop_pop_exp = NULL,
  pop_exp = NULL,
  cutoff_central = NULL,
  cutoff_lower = NULL,
  cutoff_upper = NULL,
  bhd_central = NULL,
  bhd_lower = NULL,
  bhd_upper = NULL,
  geo_id_micro = NULL,
  geo_id_macro = NULL,
  age_group = NULL,
  sex = NULL,
  population = NULL,
  info = NULL,
  min_age = NULL,
  max_age = NULL,
  approach_exposure = NULL,
  approach_newborns = NULL,
  year_of_analysis = NULL
)

Arguments

output_attribute	List containing the output of the function attribute() for scenario 1.
erf_shape	String value specifying the exposure-response function shape to be assumed. Options (no default): "linear", log_linear", "linear_log", "log_log". Only applicable in RR pathways; not required if erf_eq_... argument(s) already specified.
rr_central, rr_lower, rr_upper	Numeric value specifying the central relative risk estimate and (optionally) the corresponding lower and upper 95% confidence interval bounds. Only applicable in RR pathways; not required if erf_eq_... argument(s) already specified.
rr_increment	Numeric value specifying the exposure increment for which the provided relative risk is valid. See Details for more info. Only applicable in RR pathways; not required if erf_eq_... argument(s) already specified.
erf_eq_central, erf_eq_lower, erf_eq_upper	String or function specifying the exposure-response function and (optionally) the corresponding lower and upper 95% confidence interval functions. See Details for more info. Required in AR pathways; in RR pathways required only if rr_... argument(s) not specified.
exp_central, exp_lower, exp_upper	Numeric value or numeric vector specifying the exposure level(s) to the environmental stressor and (optionally) the corresponding lower and upper bound of the 95% confidence interval. See Details for more info.
prop_pop_exp	Numeric value or numeric vector specifying the population fraction(s) exposed for each exposure (category). Default: 1. See Details for more info. Only applicable in RR pathways.
pop_exp	Numeric vector specifying the absolute size of the population(s) exposed to each exposure category. See Details for more info. Only applicable in AR pathways; always required.
cutoff_central, cutoff_lower, cutoff_upper	Numeric value specifying the exposure cut-off value and (optionally) the corresponding lower and upper 95% confidence interval bounds. Default: 0. See Details for more info.
bhd_central, bhd_lower, bhd_upper	Numeric value or numeric vector providing the baseline health data of the health outcome of interest in the study population and (optionally) the corresponding lower bound and the upper 95% confidence interval bounds. See Details for more info. Only applicable in RR pathways; always required.
geo_id_micro, geo_id_macro	Numeric vector or string vector providing unique IDs of the geographic area considered in the assessment (geo_id_micro) and (optionally) providing higher-level IDs (geo_id_macro) to aggregate the geographic areas at. See Details for more info. Only applicable in assessments with multiple geographic units.
age_group	Numeric vector or string vector providing the age groups considered in the assessment. In case of use in attribute_lifetable)(), it must be a numeric and contain single year age groups. See Details for more info. Optional argument for attribute_health(); needed for attribute_lifetable().
sex	Numeric vector or string vector specifying the sex of the groups considered in the assessment.Optional argument.
population	Numeric vector For attribute_lifetable(), it is an obligatory argument specifying the mid-year populations per age (i.e. age group size = 1 year) for the (first) year of analysis. For attribute_health() it is an optional argument which specifies the population used to calculate attributable impacts rate per 100 000 population. See Details for more info.
info	String, data frame or tibble providing information about the assessment. See Details for more info. Optional argument.
min_age, max_age	Numberic value specifying the minimum and maximum age for which the exposure will affect the exposed population, respectively. Default min_age: 30. Default max_age: none. See Details for more info.
approach_exposure	String specifying whether exposure is constant or only in one year. Options: "single_year" (default), "constant".
approach_newborns	String specifying whether newborns are to be considered in the years after the year of analysis or not. Options: "without_newborns" (default), "with_newborns". See Details for more info.
year_of_analysis	Numeric value providing the first with exposure to the environmental stressor.

Details

Methodology

This function calls in the background attribute_health(), which can use the relative risk \insertCiteMurray2003_ehealthiar and absolute risk \insertCiteWHO2011_reporthealthiar.

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

• Modification of scenarios

• Relative risk

• Absolute risk

Value

This function returns a list containing:

1) health_main (tibble) containing the main results;

• impact (numeric column) attributable health burden/impact

• pop_fraction (numeric column) population attributable fraction; only applicable in relative risk assessments

• And many more

2) health_detailed (list) containing detailed (and interim) results.

• input_args (list) containing all the argument inputs used in the background

• input_table (tibble) containing the inputs after preparation

• results_raw (tibble) containing results for all combinations of input (geo units, uncertainty, age and sex specific data...)

• results_by_... (tibble) containing results stratified by each geographic unit, age or sex.

Author(s)

Alberto Castro & Axel Luyten

References

\insertAllCited

See Also

• Upstream: attribute_health, attribute_lifetable

• Downstream: compare

Examples

# Goal: adjust an existing healthiar scenario and determine the health
# impacts in the modified scenario

## First create a scenario to be modified
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_A$health_main$impact # Attributable impact in scenario A

## Modify scenario (adjust exposure value)
scenario_B <- attribute_mod(
  output_attribute = scenario_A,
  exp_central = 6       # EXPOSURE 2
)

scenario_B$health_main$impact # Attributable impact in scenario B

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