Monetize health impacts

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_health(), healthiar::attribute_lifetable() 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

This function monetize health impacts valuating them and applying discounting (Frederick et al. 2002; Harvey 1986; Mazur 1987) and/or inflation (Brealey et al. 2023) .

One of the following three discount shapes can be selected:

Exponential (Frederick et al. 2002)
Hyperbolic as Harvey (1986)
Hyperbolic as Mazur (1987)

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

Monetization

References

Brealey RA, Myers SC, Allen F, Benninga S, Read J (2023). Principles of Corporate Finance, 14th edition. McGraw-Hill Education, New York, NY. ISBN 978-1264117464.

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.