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_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
UHC acceleration scenarios
accelerate_anc4(),
accelerate_art(),
accelerate_beds(),
accelerate_bp(),
accelerate_doctors(),
accelerate_dtp3(),
accelerate_fh(),
accelerate_fpg(),
accelerate_fp(),
accelerate_hwf(),
accelerate_itn(),
accelerate_nurses(),
accelerate_pneumo(),
accelerate_tb(),
accelerate_uhc_sanitation()