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,
  real_growth_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.
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). This value is used to adjust monetization for inflation (converting nominal into real values by appyling a deflator). If this adjustment for inflation is not needed leave this argument empty (default value = NULL).
real_growth_rate: Numeric value between 0 and 1 referring to the annual real-term appreciation in the societal value of health (e.g., income elasticity). This adjusts the valuation upward to reflect rising wealth, independent of general price inflation.
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) and considering inflation (Brealey et al. 2023) .

If the monetized values require adjustment for inflation, a deflator based on inflation_rate can be applied (HM Treasury 2022) .

If the monetized values require adjustment for base valuation upward, a factor based valuation growth can be applied (OECD 2012) .

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 .

HM Treasury (2022). The Green Book: Central Government Guidance on Appraisal and Evaluation. HM Treasury, London, UK. https://www.gov.uk/government/publications/the-green-book-appraisal-and-evaluation-in-central-government.

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.

OECD (2012). Mortality Risk Valuation in Environment, Health and Transport Policies. OECD Publishing, Paris. doi:10.1787/9789264130807-en .

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.