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Aggregate health impacts from multiple exposures

Source: R/multiexpose.R
multiexpose.Rd

This function aggregates health impacts from multiple exposures to environmental stressors.

Usage

multiexpose(
  output_attribute_exp_1,
  output_attribute_exp_2,
  exp_name_1,
  exp_name_2,
  approach_multiexposure = "additive"
)

Arguments

output_attribute_exp_1, output_attribute_exp_2

Output of attribute() for exposure 1 and 2, respectively. Baseline health data and population must be identical in outputs 1 and 2.

exp_name_1, exp_name_2

String referring to the name of the environmental exposures 1 and 2

approach_multiexposure

String specifying the multiple exposures approach to be used in the assessment. Options: "additive" (default), "multiplicative" or "combined".

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.

Details

Methodology

This function can add up the attributable health impacts from correlated exposures applying one of the following methods (Strak et al. 2024) :

  • Additive (Steenland and Armstrong 2006)

  • Multiplicative (Jerrett et al. 2013)

  • Combined (Steenland and Armstrong 2006)

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

References

Jerrett M, Burnett RT, Beckerman BS, Turner MC, Krewski D, Thurston G, Martin RV, van Donkelaar A, Hughes E, Shi Y, Gapstur SM, Thun MJ, Pope 3CA (2013). “Spatial analysis of air pollution and mortality in California.” American Journal of Respiratory and Critical Care Medicine, 188(5), 593–599. doi:10.1164/rccm.201303-0609OC .

Steenland K, Armstrong B (2006). “An overview of methods for calculating the burden of disease due to specific risk factors.” Epidemiology, 17(5), 512–519. doi:10.1097/01.ede.0000229155.05644.43 .

Strak M, Houthuijs D, Staatsen B (2024). “D1.2 Report on the methodology for assessing the burden of correlated exposures.” EU Project BEST-COST.

See also

Author

Alberto Castro & Axel Luyten

Examples

# Goal: determine aggregated health impacts from multiple exposures
# Step 1: create assessment with exposure 1
output_attribute_exp_1 <- attribute_health(
  erf_shape = "log_linear",
  rr_central = 1.369,
  rr_increment = 10,
  exp_central = 8.85,
  cutoff_central = 5,
  bhd_central = 30747
)
output_attribute_exp_1$health_main$impact
#> [1] 3501.962
# Step 2: create assessment with exposure 2
output_attribute_exp_2 <- attribute_mod(
  output_attribute = output_attribute_exp_1,
  exp_central = 10.9,
  rr_central = 1.031
)
output_attribute_exp_2$health_main$impact
#> [1] 548.8641
# Step 3: aggregate impacts of the two assessments
results <- multiexpose(
  output_attribute_exp_1 = output_attribute_exp_1,
  output_attribute_exp_2 = output_attribute_exp_2,
  exp_name_1 = "pm2.5",
  exp_name_2 = "no2",
  approach_multiexposure = "multiplicative"
)
results$health_main$impact
#> [1] 3988.312