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

This function assesses the attributable health impacts in a new scenario 2 which is obtained by modifying an existing scenario 1. Supply an existing attribute output and specify how scenario 1 should be modified to create 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.

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.

results_raw (tibble) containing results for each combination of input uncertainty
results_by_geo_id_micro (tibble) containing results for each geographic unit under analysis (specified in geo_id_micro argument)
input_table (tibble) containing the inputs to each relevant argument
input_args (list) containing all the argument inputs used in the background

Details

Please see the function documentation of attribute_health for the methods used.

Author

Alberto Castro & Axel Luyten

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
#> [1] 1050.86

## 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
#> [1] 277.304