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(), 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.
valuation: Numeric value referring to unit value of a health impact. If output_attribute is entered, a numeric vector parallel to the arguments of the attribute function can be entered instead (e.g. one valuation per exposure-outcome pair) to monetize all of them in one single call. The values must be the same within each geographic unit and dimension entered in main_results_by.
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).
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).
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). Alternatively, a numeric vector of year-specific rates can be entered (at least as many values as years to be considered, the first value referring to the first year after the present), assuming then that inflation varies over time instead of being constant. 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. If output_attribute contains more than one assessment (several geographic units or dimensions entered in main_results_by), a numeric vector parallel to the arguments of the attribute function can be entered instead (e.g. one rate per country), constant over the years. Otherwise, as in inflation_rate, a numeric vector of year-specific rates can be entered instead of one single value.
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. 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 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)

Single values and vectors

Arguments describing the value of the health impact (valuation, discount_rate, discount_shape) accept one value or, if output_attribute is entered, a vector parallel to the arguments of the attribute function (e.g. one value per exposure-outcome pair). inflation_rate accepts one value or a vector with one value per year, so that all valuations share one price base. real_growth_rate reflects income growth, which differs by country and region. Therefore, if output_attribute contains more than one assessment, it accepts one value or a vector parallel to the arguments of the attribute function (one rate per row, constant over the years). For one single assessment, it accepts one value or a vector with one value per year.

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.