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Accelerate uhc_tobacco

Source: R/accelerate_target_uhc.R
accelerate_target_uhc_tobacco.Rd

Accelerate uhc_tobacco by first dividing countries into two groups:

  • For countries without any routine (i.e., estimated) data, business as usual is returned

  • For countries with routine (i.e., estimated) data, the best of business as usual and a percent decrease of 30% between 2010 and 2025 is returned. Both scenarios are run on the crude tobacco usage values, which are then converted to their age-standardised equivalents using an approximation.

Usage

accelerate_target_uhc_tobacco(
  df,
  ind_ids = billion_ind_codes("uhc"),
  scenario_col = "scenario",
  value_col = "value",
  end_year = 2025,
  start_year = 2018,
  default_scenario = "default",
  bau_scenario = "historical",
  scenario_name = "acceleration",
  ...
)

Arguments

df

Data frame in long format, where 1 row corresponds to a specific country, year, and indicator.

ind_ids

Named vector of indicator codes for input indicators to the Billion. Although separate indicator codes can be used than the standard, they must be supplied as a named vector where the names correspond to the output of billion_ind_codes().

scenario_col

Column name of column with scenario identifiers. Useful for calculating contributions on data in long format rather than wide format.

value_col

Name of the column containing indicator value in df.

end_year

End year(s) for contribution calculation, defaults to 2019 to 2025.

start_year

Year from which the acceleration scenario begins, inclusive.

default_scenario

name of the default scenario.

bau_scenario

name of scenario to be used for business as usual. Default is historical.

scenario_name

name of scenario

...

additional parameters to be passed to scenario function

See also

Target scenario accelerate_target_anc4(), accelerate_target_art(), accelerate_target_beds(), accelerate_target_bp(), accelerate_target_doctors(), accelerate_target_dtp3(), accelerate_target_fh(), accelerate_target_fpg(), accelerate_target_fp(), accelerate_target_hwf(), accelerate_target_itn(), accelerate_target_nurses(), accelerate_target_pneumo(), accelerate_target_tb(), accelerate_target_uhc_sanitation()