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Classify weight-for-age in data.frame-like objects with GIGS-recommended growth standards

Source: R/growth_classify.R
classify_wfa.Rd

Classify weight-for-age in data.frame-like objects with GIGS-recommended growth standards

Usage

classify_wfa(
  .data,
  weight_kg,
  age_days,
  gest_days,
  sex,
  id = NULL,
  .new = c("waz", "wfa", "wfa_outliers")
)

Arguments

.data

A data.frame-like tabular object with one or more rows. Must be permissible by checkmate::assert_data_frame(), so you can also supply a tibble, data.table, or similar.

weight_kg

<data-masking> The name of a column in .data which is a numeric vector of birth weight values in kg. It is assumed that weight measurements provided to this function are birth weights recorded <12 hours after an infant's birth.

age_days

<data-masking> The name of a column in .data which is a numeric vector of age values in days.

gest_days

<data-masking> The name of a column in .data which is a numeric vector of gestational age(s) at birth in days. This column, in conjunction with the column referred to by age_days, is used to select which growth standard to use for each observation.

sex

<data-masking> The name of a column in .data which is a case-sensitive character vector of sexes, either "M" (male) or "F" (female). By default, gigs will warn you if any elements of sex are not "M" or "F", or are missing (NA). You can customise this behaviour using the GIGS package-level options.

id

<data-masking> The name of a column in .data which is a factor variable with IDs for each observation. When not NULL, this variable is used to ensure that only the first measurement taken from each infant is used as a birth measure. If all your data is from one individual, leave this parameter as NULL. Default = NULL.

.new

A three-length character vector with names for the output columns. These inputs will be repaired if necessary using vctrs::vec_as_names(), which will print any changes to the console. If any elements in .new are the same as elements in colnames(.data), the function will throw an error. Default = c("waz", "wfa", "wfa_outliers").

Value

A tabular object of the same class that was provided as .data, with three new columns named according to .new. These columns will be (from left to right):

  • wlz - Numeric vector of weight-for-length/height zscores

  • wfa - Factor of weight-for-age categories without outlier flagging

  • wfa_outliers - Factor of weight-for-age categories with outlier flagging

Details

Cut-offs for weight-for-age categories are:

CategoryFactor levelZ-score bounds
Severely underweight"underweight_severe"waz =< -3
Underweight"underweight"-3 < waz =< -2
Normal weight"normal_weight"abs(waz) < 2
Overweight"overweight"waz >= 2
Outlier"outlier"waz < -6 or waz > 5

Note

Categorical (factor) columns produced here may contain unused factor levels. By default, gigs will inform you if these columns have unused factor levels. You can change this behaviour using the GIGS package-level option .gigs_options$handle_unused_levels.

References

'Implausible z-score values' in World Health Organization (ed.) Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children's Fund UNICEF, (2019). pp. 64-65.

'Percentage of children stunted, wasted, and underweight, and mean z-scores for stunting, wasting and underweight' in Guide to DHS Statistics DHS-7 Rockville, Maryland, USA: ICF (2020). pp. 431-435. https://dhsprogram.com/data/Guide-to-DHS-Statistics/Nutritional_Status.htm

See also

See classify_growth() to run this analysis and others at the same time. See gigs_waz() to learn which growth standard will be used for different combinations of gestational/chronological age.

Examples

# This dummy dataset contains data from two people, from birth (<3 days) to
# 500 days of age.
data <- data.frame(
  child = factor(rep.int(c("A", "B"), c(3, 3))),
  agedays = c(0, 100, 500, 2, 100, 500),
  gestage  = c(rep(35 * 7, 3), rep(35 * 7, 3)),
  sex = rep.int(c("M", "F"), c(3, 3)),
  weight_kg = c(3, 6, 9, 3, 6, 9)
)

# Use the `id` argument to ensure that `classify_wfa()` uses the correct
# standard for each observation
data |>
  classify_wfa(weight_kg = weight_kg,
               age_days = agedays,
               gest_days = gestage,
               sex = sex,
               id = child)
#> Warning: There was 1 'at birth' observation where `age_days` > 0.5.
#> ℹ This occurred for ID B.
#> ! Unused factor levels kept after weight-for-age (underweight) categorisation:
#> "underweight_severe", "underweight", and "overweight".
#> ! Unused factor levels kept after weight-for-age (underweight) categorisation:
#> "underweight_severe", "underweight", "overweight", and "outlier".
#>   child agedays gestage sex weight_kg        waz           wfa  wfa_outliers
#> 1     A       0     245   M         3  1.2230003 normal_weight normal_weight
#> 2     A     100     245   M         6  0.7413417 normal_weight normal_weight
#> 3     A     500     245   M         9 -1.4866806 normal_weight normal_weight
#> 4     B       2     245   F         3  1.5420839 normal_weight normal_weight
#> 5     B     100     245   F         6  1.4395342 normal_weight normal_weight
#> 6     B     500     245   F         9 -0.7859202 normal_weight normal_weight

# If you don't specify `id`, `classify_wfa()` will assume data is from one
# child only
data |>
  classify_wfa(weight_kg = weight_kg,
               age_days = agedays,
               gest_days = gestage,
               sex = sex)
#> ! Unused factor levels kept after weight-for-age (underweight) categorisation:
#> "underweight_severe" and "underweight".
#> ! Unused factor levels kept after weight-for-age (underweight) categorisation:
#> "underweight_severe", "underweight", and "outlier".
#>   child agedays gestage sex weight_kg        waz           wfa  wfa_outliers
#> 1     A       0     245   M         3  1.2230003 normal_weight normal_weight
#> 2     A     100     245   M         6  0.7413417 normal_weight normal_weight
#> 3     A     500     245   M         9 -1.4866806 normal_weight normal_weight
#> 4     B       2     245   F         3  2.2572938    overweight    overweight
#> 5     B     100     245   F         6  1.4395342 normal_weight normal_weight
#> 6     B     500     245   F         9 -0.7859202 normal_weight normal_weight