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 trubetskoyv@who.int 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

To calculate the HPOP Billion, there are a series of functions made available through the billionaiRe package:

• transform_hpop_data() to transform raw values into normalized values used within the calculations.
• add_hpop_populations() to get relevant population groups for each country and indicator.
• calculate_hpop_contributions() to calculate indicator level changes and contributions to the Billion.
• calculate_hpop_billion() to calculate indicator level changes, country-level Billion, adjusting for double counting, and all contributions.

Run in sequence, these can calculate the entire HPOP Billion, or they can be run separately to produce different outputs as required. Details on the inputs of each function are available in their individual documentation, but below you can see the quick and easy Billions calculation done using the sample fake HPOP data provided in the package, hpop_df.

library(billionaiRe)

hpop_df %>%
  transform_hpop_data() %>%
  add_hpop_populations() %>%
  calculate_hpop_billion() %>%
  dplyr::filter(stringr::str_detect(ind, "hpop_healthier"))
#> # A tibble: 6 × 10
#>   iso3   year ind            value type  trans…¹ popul…² contr…³ contr…⁴ contr…⁵
#>   <chr> <dbl> <chr>          <dbl> <chr>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1 AFG    2023 hpop_healthie…    NA <NA>       NA  4.45e7  2.72e7    61.2      NA
#> 2 AFG    2023 hpop_healthie…    NA <NA>       NA  4.45e7 -3.84e7   -86.3      NA
#> 3 AFG    2023 hpop_healthier    NA <NA>       NA  4.45e7 -1.12e7   -25.1      NA
#> 4 AFG    2023 hpop_healthie…    NA <NA>       NA  4.45e7  3.20e7    71.9      NA
#> 5 AFG    2023 hpop_healthie…    NA <NA>       NA  4.45e7 -7.46e7  -168.       NA
#> 6 AFG    2023 hpop_healthie…    NA <NA>       NA  4.45e7 -4.26e7   -95.7      NA
#> # … with abbreviated variable names ¹​transform_value, ²​population,
#> #   ³​contribution, ⁴​contribution_percent, ⁵​contribution_percent_total_pop

UHC Billion calculation

To calculate the UHC Billion, there are a series of functions made available through the billionaiRe package:

• transform_uhc_data() to transform raw values into normalized values used within the calculations.
• calculate_uhc_billion() to calculate average service coverage, financial hardship, and the UHC single measure for each country and year in the data frame..
• calculate_uhc_contribution() to calculate country-level Billion for specified beginning and end year.

Run in sequence, these can calculate the entire UHC Billion, or they can be run separately to produce different outputs as required. Details on the inputs of each function are available in their individual documentation, but below you can see the quick and easy Billions calculation done using the the sample fake UHC data provided in the package, uhc_df.

library(billionaiRe)

uhc_df %>%
  transform_uhc_data(end_year = 2023) %>%
  calculate_uhc_billion() %>%
  calculate_uhc_contribution(end_year = 2023, pop_year = 2023) %>% 
  dplyr::filter(ind %in% c("uhc_sm", "asc", "fh"),
                year == 2023)
#> # A tibble: 3 × 9
#>   iso3   year ind    value type      transform_value source      contr…¹ contr…²
#>   <chr> <dbl> <chr>  <dbl> <chr>               <dbl> <chr>         <dbl>   <dbl>
#> 1 AFG    2023 fh      25.4 Projected            74.6 <NA>        -3.00e6  -7.11 
#> 2 AFG    2023 asc     45.3 projected            45.3 WHO DDI ca…  1.72e6   4.06 
#> 3 AFG    2023 uhc_sm  33.8 projected            33.8 WHO DDI ca…  4.41e4   0.104
#> # … with abbreviated variable names ¹​contribution, ²​contribution_percent

HEP Billion calculation

To calculate the HEP Billion, there are a series of functions made available through the billionaiRe package:

• transform_hep_data() to transform raw values into normalized values used within the calculations. For now, this is primarily calculating the total prevent numerators and denominators for campaign and routine data.
• calculate_hep_components() to calculate component indicators (Prevent coverages), the HEP index, and levels for all components.
• calculate_hep_billion() to calculate the change for the three HEP components (DNR, Prepare, and Prevent), their contribution to the Billion, and overall HEPI change and contribution.

Run in sequence, these can calculate the entire HEP Billion, or they can be run separately to produce different outputs as required. Details on the inputs of each function are available in their individual documentation, but below you can see the quick and easy Billions calculation done using the sample fake HEP data provided in the package, hep_df.

library(billionaiRe)

hep_df %>%
  transform_hep_data() %>%
  calculate_hep_components() %>%
  calculate_hep_billion(end_year = 2023) %>%
  dplyr::filter(ind %in% c("prevent",
                           "espar",
                           "detect_respond",
                           "hep_idx"),
                year == 2023)
#> # A tibble: 4 × 12
#>   iso3   year ind       value type  source trans…¹ use_d…² use_c…³ level contr…⁴
#>   <chr> <dbl> <chr>     <dbl> <chr> <chr>    <dbl> <lgl>   <lgl>   <dbl>   <dbl>
#> 1 AFG    2023 espar      51.2 Proj… <NA>      51.2 NA      NA          3  5.00e6
#> 2 AFG    2023 detect_r…  91   Proj… <NA>      91   NA      NA          5  2.23e6
#> 3 AFG    2023 prevent    NA   proj… Unite…   100   NA      NA          5  0     
#> 4 AFG    2023 hep_idx    NA   proj… WHO D…    80.7 NA      NA          4  7.22e6
#> # … with 1 more variable: contribution_percent <dbl>, and abbreviated variable
#> #   names ¹​transform_value, ²​use_dash, ³​use_calc, ⁴​contribution

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
  )