Calculate percent stool adequacy

Creates a summary table of stool adequacy. The missing parameter defines how missing data is treated. "good" classifies missing data as good quality (POLIS method). "bad" classifies all missing as bad quality. "missing" excludes missing from the calculations.

Usage

f.stool.ad.01(
  afp.data,
  pop.data,
  start.date,
  end.date,
  spatial.scale,
  missing = "good",
  bad.data = "inadequate",
  rolling = F,
  sp_continuity_validation = T,
  admin.data = lifecycle::deprecated()
)

Arguments

afp.data

tibble AFP data which includes GUID at a given spatial scale formatted as adm(0,1,2)guid, onset date as date and cdc.classification.all2 which includes "NOT-AFP".

pop.data

tibble Full list of country administrative units by a given spatial scale including year, adm(0,1,2)guid, and ctry/prov/dist (as appropriate).

start.date

str Starting date for analysis formatted as "YYYY-MM-DD".

end.date

str Ending date for analysis as "YYYY-MM-DD".

spatial.scale

str Geographic level to group analysis on.

• "prov" Province level.

• "dist" District level.

• "ctry" Country level.

missing

str How to treat missing data. Valid values are: "good", "bad", "remove". Defaults to "good". When calculating the adequacy.final column:

• "good" uses adequacy.03

• "bad" uses adequacy.01

• "exclude" uses adequacy.02

bad.data

str How to treat bad data. Valid values are:"remove", "inadequate". Defaults to "inadequate". "inadequate" treats samples with bad data as inadequate.

rolling

logical Should data be analyzed on a rolling bases? Defaults to FALSE.

sp_continuity_validation

logical Should GUIDs not present in all years of the dataset be excluded? Default TRUE.

admin.data

tibble Population data. Renamed in favor of pop.data.

Value

tibble Long format stool adequacy evaluations.

Examples

if (FALSE) { # \dontrun{
raw.data <- get_all_polio_data()
stool.ads <- f.stool.ad.01(raw.data$afp, raw.data$ctry.pop,
  "2021-01-01", "2023-12-31",
  "ctry",
  sp_continuity_validation = FALSE
)
} # }