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Discount health impacts

Source: R/discount.R
discount.Rd

This function calculates discounted health impacts (without valuation).

Usage

discount(
  output_attribute = NULL,
  impact = NULL,
  discount_rate = NULL,
  n_years = NULL,
  discount_shape = "exponential"
)

Arguments

output_attribute

List produced by healthiar::attribute_health(), healthiar::attribute_lifetable(), healthiar::compare() or healthiar::daly() as results. The YLL and YLD in the output of healthiar::daly() are monetized separately and then summed. Thus, YLL from the life table are discounted year by year, while n_years is used for the YLD.

impact

Numeric value referring to the health impacts to be monetized (without attribute function). If a Numeric vector is entered multiple assessments (by year) will be carried out. Be aware that the value for year 0 (current) must be entered, while n_years does not include the year 0. Thus, length of impact = n_years + 1.

discount_rate

Numeric value showing the discount rate for future years. If output_attribute is entered, a numeric vector parallel to the arguments of the attribute function can be entered instead (e.g. one discount rate per exposure-outcome pair).

n_years

Numeric value referring to number of years in the future to be considered in the discounting and/or inflation. Be aware that the year 0 (without discounting/inflation, i.e. the present) is not be counted here. If a vector is entered in the argument impact, n_years does not need to be entered (length of impact = n_years + 1).

discount_shape

String referring to the assumed equation for the discount factor. By default: "exponential". Otherwise: "hyperbolic_harvey_1986" or "hyperbolic_mazur_1987". If output_attribute is entered, a character vector parallel to the arguments of the attribute function can be entered instead (e.g. one discount shape per exposure-outcome pair).

Value

This function returns a list containing:

1) monetization_main (tibble) containing the main monetized results;

  • monetized_impact (numeric column)

  • discount_factor (numeric column) calculated based on the entered discount_rate

  • And many more

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

  • results_by_year (tibble)

  • health_raw (tibble) containing the monetized results for each for each combination of input uncertainty that were provided to the initial attribute_health() call

If the argument output_attribute was specified, then the two results elements are added to the existing output. If output_attribute comes from healthiar::daly(), monetization_main contains the monetized YLL and YLD (suffixes _yll and _yld) and their sum, while monetization_detailed contains the monetization of YLL and YLD (monetization_yll and monetization_yld).

Details

Methodology

This function applies a discount to attributable health impacts into the future.

One of the following three discount shapes can be selected:

  • Exponential (Frederick et al. 2002)

  • Hyperbolic as Harvey (1986)

  • Hyperbolic as Mazur (1987)

Burden of disease studies may be interested in calculating + discounted health impacts over time, and these may also be used in economic evaluation models, where benefits are not monetized. For this specific purpose, this function is offered.

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

References

Frederick S, Loewenstein G, O'Donoghue T (2002). “Time Discounting and Time Preference: A Critical Review.” Journal of Economic Literature, 40(2), 351–401. doi:10.1257/002205102320161311 .

Harvey CM (1986). “Value Functions for Infinite-Period Planning.” Management Science, 32(9), 1123–1139. doi:10.1287/mnsc.32.9.1123 .

Mazur JE (1987). “An adjusting procedure for studying delayed reinforcement.” In Commons ML, Mazur JE, Nevin JA, Rachlin H (eds.), Quantitative Analyses of Behavior: Volume V. The Effect of Delay and of Intervening Events on Reinforcement Value, 55–73. Lawrence Erlbaum Associates, Hillsdale, NJ. ISBN 0-89859-800-1.

See also

Author

Alberto Castro & Axel Luyten

Examples

# Goal: discount attributable health impacts
results <- discount(
  impact = 20000,
  discount_shape = "exponential",
  discount_rate = 0.03,
  n_years = 20
)
results$monetization_main$monetized_impact
#> [1] 11073.52