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Regional Surveillance Indicators by Year (Multi-country aggregate)

generate_surv_reg_tab.Rd

Computes region-level time series of surveillance indicators by aggregating across all countries present in your inputs (or implicitly after the joins). The function:

  1. builds district-level indicators, restricts to districts with U15 ≥ 100,000,

  2. derives the % population in adequate districts (NPAFP rate ≥ 2 and stool adequacy ≥ 80%), and counts of adequate vs. total ≥100k districts,

  3. aggregates country-level indicators to the region using simple means for rates and sums for AFP cases, and

  4. returns either a tidy tibble or a formatted flextable.

Usage

generate_surv_reg_tab(
  ctry_data,
  ctry.extract,
  dist.extract,
  cstool,
  dstool,
  afp.cases,
  start_year = NULL,
  end_year = NULL,
  output = c("flextable", "tibble")
)

Arguments

ctry_data

A list-like object that contains at least dist.pop, a data frame with district metadata. Must include columns:

  • ctry (country code/name),

  • adm2guid (district id), and preferably year and a U15 population column (e.g., u15pop).

ctry.extract

Country-level dataset (one row per country-year). Should contain year and either adm0guid (to join with cstool) or ctry. It should include npafp_rate and per.stool.ad, or columns that can be unambiguously renamed to these.

dist.extract

District-level dataset (one row per district-year) with at minimum adm2guid and year. If it already contains u15pop, npafp_rate, per.stool.ad, those are used; otherwise they may be pulled in via joins.

cstool

Country-level surveillance stool adequacy dataset with year and either adm0guid or ctry, containing npafp_rate and per.stool.ad (or columns that can be renamed to these).

dstool

District-level surveillance stool adequacy dataset with adm2guid, year, and indicator columns such as npafp_rate, per.stool.ad.

afp.cases

Either:

  • country-year AFP case data with columns ctry, year, and a cases column (e.g., afp.cases, afp.case, cases) or

  • region/overall year totals with year and a cases column. The function will rename the first matching cases column to afp.cases.

start_year, end_year

Optional numeric bounds to filter the analysis years, inclusive.

output

One of "flextable" (default) or "tibble". When "tibble", returns a data frame with columns: year, afp.cases, npafp_rate, per.stool.ad, prop.dist.adeq, prop.

Value

A flextable (default) for presentation, or a tibble when output = "tibble".

Details

U15 population column detection: the function tries to ensure a column named u15pop exists in the joined district data by:

  1. coalescing u15pop.x/u15pop.y after joins,

  2. renaming common aliases (e.g., u15_pop, U15Pop, under15_pop), or

  3. importing from ctry_data$dist.pop by adm2guid and year.

Adequacy rule (district level):

  • Adequate if npafp_rate >= 2 and per.stool.ad >= 80.

  • The % population in adequate ≥100k districts is: \(100 * (sum(U15 in adequate ≥100k districts) / sum(U15 in all ≥100k districts))\).

    *Aggregation*:

  • AFP cases: summed across countries per year.

  • npafp_rate and per.stool.ad: simple means across countries per year (switch to a weighted mean if needed).

The output flextable renders year as plain text (no thousands separator).

Examples

if (FALSE) { # \dontrun{
ft <- generate_surv_reg_tab(
  ctry_data    = ctry_data,
  ctry.extract = ctry.extract,
  dist.extract = dist.extract,
  cstool       = cstool,
  dstool       = dstool,
  afp.cases    = afp.cases,
  start_year   = 2021,
  end_year     = 2024,
  output       = "flextable"
)
} # }