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Monetize health impacts

Source: R/monetize.R
monetize.Rd

This function monetizes health impacts

Usage

monetize(
  output_attribute = NULL,
  impact = NULL,
  valuation,
  discount_rate = NULL,
  discount_shape = "exponential",
  n_years = NULL,
  inflation_rate = NULL,
  info = NULL
)

Arguments

output_attribute

List produced by healthiar::attribute() or healthiar::compare() as results.

impact

Numberic value referring to the health impacts to be monetized (without attribute function). If a Numberic 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.

valuation

Numberic value referring to unit value of a health impact.

discount_rate

Numeric value showing the discount rate for future years. If it is a nominal discount rate, no inflation is to be entered. If it is a real discount rate, the result can be adjusted by entering inflation in this function.

discount_shape

String referring to the assumed equation for the discount factor. By default: "exponential". Otherwise: "hyperbolic_harvey_1986" or "hyperbolic_mazur_1987".

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).

inflation_rate

Numeric value between 0 and 1 referring to the annual inflation (increase of prices). Only to be entered if nominal (not real) discount rate is entered in the function. Default value = NULL (assuming no nominal discount rate).

info

String, data frame or tibble providing information about the assessment. Only attached if impact is entered by the users. If output_attribute is entered, use info in that function or add the column manually. Optional argument.

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.

Details

Methodology

Information about the methodology (including corresponding equations and literature) 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.

Author

Alberto Castro & Axel Luyten

Examples

# Goal: monetize the attributable impacts of an existing healthiar
# assessment
output_attribute <- attribute_health(
erf_shape = "log_linear",
rr_central = exdat_pm$relative_risk,
rr_increment = 10,
exp_central = exdat_pm$mean_concentration,
cutoff_central = exdat_pm$cut_off_value,
bhd_central = exdat_pm$incidence
)

results <- monetize(
  output_attribute = output_attribute,
  discount_shape = "exponential",
  discount_rate = 0.03,
  n_years = 5,
  valuation = 50000 # E.g. EURO
)

# Attributable COPD cases its monetized impact
results$monetization_main |>
  dplyr::select(impact, monetized_impact)
#> # A tibble: 1 × 2
#>   impact monetized_impact
#>    <dbl>            <dbl>
#> 1  3502.       151041149.