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Attributable disability-adjusted life years

Source: R/daly.R
daly.Rd

This function calculates the disability-adjusted life years (DALY) attributable to the exposure to an environmental stressor by adding the two DALY components YLL and YLD.

Usage

daly(output_attribute_yll, output_attribute_yld)

Arguments

output_attribute_yll, output_attribute_yld

variable containing YLL or YLD results of a attribute_...() function call, respectively.

Value

This function returns a list containing:

1) health_main (tibble) containing the main results;

  • impact (numeric column) attributable health burden/impact in DALY

  • impact_yld (numeric column) attributable health burden/impact in YLD

  • impact_yll (numeric column) attributable health burden/impact in YLL

  • dw (numeric column) disability weight used for YLD calculation

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

Author

Alberto Castro & Axel Luyten

Examples

# Goal: obtain DALY (disability-adjusted life years) from two existing \code{attribute_...} outputs
# Step 1: Create YLL (years of life lost) assessment
results_yll <- attribute_lifetable(
  health_outcome = "yll",
  approach_exposure = "single_year",
  approach_newborns = "without_newborns",
  exp_central = 8.85,
  prop_pop_exp = 1,
  cutoff_central = 5,
  rr_central =  1.118,
  rr_increment = 10,
  erf_shape = "log_linear",
  age_group = exdat_lifetable$age_group,
  sex = exdat_lifetable$sex,
  bhd_central = exdat_lifetable$deaths,
  population = exdat_lifetable$midyear_population,
  year_of_analysis = 2019,
  min_age = 20
)
# Step 2: Create YLD (years lived with disability) assessment
results_yld  <- attribute_health(
  exp_central = 8.85,
  prop_pop_exp = 1,
  cutoff_central = 5,
  bhd_central = 1000,
  rr_central = 1.1,
  rr_increment = 10,
  erf_shape = "log_linear",
  duration_central = 100,
  dw_central = 0.5,
  info = "pm2.5_yld"
)
# Step 3: obtain DALY
results <- daly(
  output_attribute_yll = results_yll,
  output_attribute_yld = results_yld
)
# Attributable impact in DALY
results$health_main |>
  dplyr::select(impact, impact_yll, impact_yld)
#> # A tibble: 1 × 3
#>   impact impact_yll impact_yld
#>    <dbl>      <dbl>      <dbl>
#> 1 30611.     28810.      1801.