In gpw13/billionaiRe: Calculate the WHO Triple Billions

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billionaiRe

The goal of billionaiRe is to provide an easy interface for using long format data to calculate the World Health Organization's Triple Billions.

Installation

You can install billionaiRe from GitHub with:

remotes::install_github("gpw13/billionaiRe", build_vignettes = TRUE)

You will need to have already installed the wppdistro and whdh packages, which is stored in a private repo and only made public upon request from valid WHO users. Please contact [e-mail] to request access.

Calculations

The package is built around a set of functions that calculate the Billions for the three Billions separately:

• Healthier Populations (HPOP)
• Health Emergencies Protection (HEP)
• Universal Health Coverage (UHC)

HPOP Billion calculation

UHC Billion calculation

HEP Billion calculation

Scenarios

In the Triple Billions and the billionaiRe package context, scenarios are understood as alternative, plausible, description of how the future may develop based on a set of defined assumptions.

Scenarios must:

1. Be tidy: each row is a unique combination of iso3 country-code, year, indicator, and scenario (if relevant).
2. Contain all and only the data that is strictly needed for calculations with billionaiRe

Four main sets of scenarios can be identified, from the most basic to the more complex:

1. Basic scenarios: Those scenarios are the building blocks of the other scenarios, but they can also be called on their own. They can reach a fixed target at a specified year (scenario_fixed_target()), follow a specific rate of change (scenario_aroc()), etc. See Basic scenario for more details.
2. Target scenarios: Target-based scenarios apply indicator-specific targets or trajectories to be reached by a specific date (e.g. Sustainable Development Goal (SDG) targets (called sdg scenarios)).
3. Benchmarking scenarios: Benchmarking scenarios compare performance of countries given various grouping and aim at a specified sub-set of best performing countries.
4. Mixed scenarios: Developped with indicator-level subject matter knowledge to pick realistic improvement scenarios (e.g. acceleration scenarios)

If billionaiRe require data that is missing in the scenario, they will be recycled from other scenarios (see Data recycling).

See Scenarios vignette for more details.

Quick start on billionaiRe scenarios

add_scenario() is the entry point function to all other scenario functions. It essentially allow to pass a typical billionaiRe data frame (df) and apply the selected scenario function.

library(billionaiRe)

df <- tibble::tibble(
    value = 60:80,
    year = 2010:2030,
    ind = "pm25",
    type = "reported",
    iso3 = "AFG",
    scenario = "default",
    source = NA_character_
  ) %>%
    dplyr::mutate(scenario = dplyr::case_when(
      year > 2021 ~ "historical",
      TRUE ~ scenario
    ),
    type = dplyr::case_when(
      year > 2021 ~ "projected",
      TRUE ~ type
    ))

The choice of scenario function to apply to the df is done through the scenario_function parameter. Additional parameters can be passed through the ellipsis (...).

For instance, to halt the rise to the 2010 value by end_year (2025 by default), we can apply a simple function to df. This will apply the halt_rise function to all unique combination of country and indicator. In this case, there is just one combination:

df %>%
  add_scenario(
    scenario_function = "halt_rise",
    baseline_year = 2010
  )

To apply the SDG targets, we use the sdg:

df %>%
  add_scenario(
    scenario_function = "sdg"
  )

By default, the scenarios start from the last reported or estimated value in the default scenario. This can be bypassed by setting start_scenario_last_default to FALSE. The scenario will then start at start_year (2018 by default):

df %>%
  add_scenario(
    scenario_function = "sdg",
    start_scenario_last_default = FALSE
  )

gpw13/billionaiRe documentation built on Sept. 27, 2024, 10:05 p.m.