Attributable disability-adjusted life years

This function quantifies the attributable disability-adjusted life years (DALY) by summing the two DALY components: years of life lost (YLL) and years lived with disability (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

Details

Methodology

This function sums the years of life lost (YLL) and years lived with disability (YLD) to obtain the disability-adjusted lost years (DALY) (GBD 2019 Risk Factors Collaborators 2020) .

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

• dalys

References

GBD 2019 Risk Factors Collaborators (2020). “Global burden of 87 risk factors in 204 countries and territories, 1990–2019.” The Lancet. doi:10.1016/S0140-6736(20)30752-2 .

See also

• Upstream: attribute_health, attribute_lifetable

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.