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Classify multiple growth indicators at the same time using GIGS-recommended growth standards

Source: R/growth_classify.R
classify_growth.Rd

This function permits classification of multiple growth indicators (stunting, wasting, weight-for-age, and more) at once in data.frame-like objects.

Usage

classify_growth(
  .data,
  gest_days,
  age_days,
  sex,
  weight_kg = NULL,
  lenht_cm = NULL,
  headcirc_cm = NULL,
  id = NULL,
  .outcomes = c("sfga", "svn", "stunting", "wasting", "wfa", "headsize"),
  .new = list(sfga = c("birthweight_centile", "sfga", "sfga_severe"), svn =
    c("birthweight_centile", "svn"), stunting = c("lhaz", "stunting",
    "stunting_outliers"), wasting = c("wlz", "wasting", "wasting_outliers"), wfa =
    c("waz", "wfa", "wfa_outliers"), headsize = c("hcaz", "headsize")),
  .verbose = TRUE
)

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.

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.

age_days

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

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.

weight_kg

<data-masking> The name of a column in .data which is a numeric vector of weight values in kg. When performing size-for-GA and small vulnerable newborn classifications, centiles and classifications will only be provided where measurements were taken <12hrs after birth, i.e. age_days < 0.5.

lenht_cm

<data-masking> The name of a column in .data which is a numeric vector of length/height values in cm.

headcirc_cm

<data-masking> The name of a column in .data which is a numeric vector of head circumference values in cm.

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.

.outcomes

A character vector of up to six elements in length describing which growth outcomes you want to assess. Use this when you supply data that can be used to generate multiple growth indicators, but only want to run a specific set. This argument is case-sensitive, and an error will be thrown if you supply a string in .analyses which is either NA or is not a member of this argument's default. Default = c("sfga", "svn", "stunting", "wasting", "wfa", "headsize").

.new

A list with names corresponding to .analyses, which describes the names of new columns to be added to .data. If any elements of the vectors in .new are equal to existing elements of colnames(.data), the function will throw an error. Excluding the birthweight centiles produced when getting size-for-gestational age ("sfga") and small vulnerable newborn ("svn") classifications, all strings in .new must be unique. Each character vector in .new is repaired with vctrs::as_names(), which will issue messages if any elements in .new are changed.

"sfga":

c("birthweight_centile", "sfga", "sfga_severe")

"svn":

c("birthweight_centile", "svn")

"stunting":

c("lhaz", "stunting", "stunting_outliers")

"wasting":

c("wlz", "wasting", "wasting_outliers")

"wfa":

c("waz", "wfa", "wfa_outliers")

"headsize":

c("hcaz", "headsize")

.verbose

A single logical value. When TRUE (the default), messages about which analyses were requested versus which were performed will be printed to the console. Warnings from classify_growth() will still be printed even if .verbose is FALSE.

Value

A tabular object of the same class that was provided as .data, with new columns named according to .new. The new columns will depend on the values supplied for .outcomes.

Note

For size-for-GA and small vulnerable newborn analyses, centiles and categorisations will only be applied for birth measurements. These are considered to be the observation per level of id where age_days is smallest, provided that age_days is between <3.

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

WHO. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organisation Technical Report Series 1995, 854: 1–452

Royal College of Obstetricians and Gynaecologists. The Investigation and Management of the Small-for-Gestational-Age Fetus: Green-top Guideline No. 31. Technical report, Royal College of Obstetricians and Gynaecologists, London, 2013.

Lawn JE, Ohuma EO, Bradley E, Idueta LS, Hazel E, Okwaraji YB et al. Small babies, big risks: global estimates of prevalence and mortality for vulnerable newborns to accelerate change and improve counting. The Lancet 2023, 401(10389):1707-1719. doi:10.1016/S0140-6736(23)00522-6

'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

Victora CG, Schuler-Faccini L, Matijasevich A, Ribeiro E, Pessoa A, Barros FC. Microcephaly in Brazil: how to interpret reported numbers? The Lancet 2016, 387(10019):621-624 doi:10.1016/S0140-6736(16)00273-7

Accogli A, Geraldo AF, Piccolo G, Riva A, Scala M, Balagura G, et al. Diagnostic Approach to Macrocephaly in Children. Frontiers in Paediatrics 2022, 9:794069 doi:10.3389/fped.2021.794069

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)),
  wt_kg = c(3, 6, 9, 3, 6, 9),
  len_cm = rep.int(c(52.2, 60.4, 75), 2),
  head_cm = rep.int(c(30, 40, 49), 2)
)

data_classified <- classify_growth(data,
                                   age_days = agedays,
                                   gest_days = gestage,
                                   weight_kg = wt_kg,
                                   lenht_cm = len_cm,
                                   headcirc_cm = head_cm,
                                   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 size-for-GA categorisation: "SGA".
#> ! Unused factor levels kept after size-for-GA categorisation: "SGA(<3)" and
#> "SGA".
#> ! Unused factor levels kept after small vulnerable newborn categorisation:
#> "Preterm SGA", "Term SGA", "Term AGA", and "Term LGA".
#> ! Unused factor levels kept after stunting categorisation: "stunting_severe".
#> ! Unused factor levels kept after stunting categorisation: "stunting_severe"
#> and "outlier".
#> ! Unused factor levels kept after wasting categorisation: "wasting_severe",
#> "wasting", and "overweight".
#> ! Unused factor levels kept after wasting categorisation: "wasting_severe",
#> "wasting", "overweight", and "outlier".
#> ! 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".
#> ! Unused factor levels kept after head size categorisation:
#> "microcephaly_severe", "microcephaly", and "macrocephaly_severe".
#> 
#> ── `gigs::classify_growth()` ───────────────────────────────────────────────────
#> ✔ Size-for-gestational age: Success
#> ✔ Small vulnerable newborns: Success
#> ✔ Stunting: Success
#> ✔ Wasting: Success
#> ✔ Weight-for-age: Success
#> ✔ Head size: Success

data_classified
#>   child agedays gestage sex wt_kg len_cm head_cm birthweight_centile sfga
#> 1     A       0     245   M     3   52.2      30           0.8893352  AGA
#> 2     A     100     245   M     6   60.4      40                  NA <NA>
#> 3     A     500     245   M     9   75.0      49                  NA <NA>
#> 4     B       2     245   F     3   52.2      30           0.9384734  LGA
#> 5     B     100     245   F     6   60.4      40                  NA <NA>
#> 6     B     500     245   F     9   75.0      49                  NA <NA>
#>   sfga_severe         svn       lhaz     stunting stunting_outliers        wlz
#> 1         AGA Preterm AGA  2.8236856 not_stunting      not_stunting         NA
#> 2        <NA>        <NA>  0.8876444 not_stunting      not_stunting  1.6408675
#> 3        <NA>        <NA> -2.1690082     stunting          stunting -0.6621871
#> 4         LGA Preterm LGA  3.4154524 not_stunting      not_stunting         NA
#> 5        <NA>        <NA>  1.8205012 not_stunting      not_stunting  1.3314150
#> 6        <NA>        <NA> -1.4423063 not_stunting      not_stunting -0.1829182
#>       wasting wasting_outliers        waz           wfa  wfa_outliers
#> 1        <NA>             <NA>  1.2230003 normal_weight normal_weight
#> 2 not_wasting      not_wasting  0.7413417 normal_weight normal_weight
#> 3 not_wasting      not_wasting -1.4866806 normal_weight normal_weight
#> 4        <NA>             <NA>  1.5420839 normal_weight normal_weight
#> 5 not_wasting      not_wasting  1.4395342 normal_weight normal_weight
#> 6 not_wasting      not_wasting -0.7859202 normal_weight normal_weight
#>         hcaz        headsize
#> 1 -1.5588136 normal_headcirc
#> 2  0.9510032 normal_headcirc
#> 3  1.4502711 normal_headcirc
#> 4 -1.3289704 normal_headcirc
#> 5  1.7433668 normal_headcirc
#> 6  2.2148421    macrocephaly

# Use `.outcomes` to set which growth indicators will be computed
data_svn <- classify_growth(data,
                            age_days = agedays,
                            gest_days = gestage,
                            sex = sex,
                            weight_kg = wt_kg,
                            id = child,
                            .outcomes = "svn")
#> Warning: There was 1 'at birth' observation where `age_days` > 0.5.
#> ℹ This occurred for ID B.
#> ! Unused factor levels kept after small vulnerable newborn categorisation:
#> "Preterm SGA", "Term SGA", "Term AGA", and "Term LGA".
#> 
#> ── `gigs::classify_growth()` ───────────────────────────────────────────────────
#> ✔ Small vulnerable newborns: Success

data_svn
#>   child agedays gestage sex wt_kg len_cm head_cm birthweight_centile
#> 1     A       0     245   M     3   52.2      30           0.8893352
#> 2     A     100     245   M     6   60.4      40                  NA
#> 3     A     500     245   M     9   75.0      49                  NA
#> 4     B       2     245   F     3   52.2      30           0.9384734
#> 5     B     100     245   F     6   60.4      40                  NA
#> 6     B     500     245   F     9   75.0      49                  NA
#>           svn
#> 1 Preterm AGA
#> 2        <NA>
#> 3        <NA>
#> 4 Preterm LGA
#> 5        <NA>
#> 6        <NA>

# Use `.new` to specify new column names
data_svn <- classify_growth(data,
                            age_days = agedays,
                            gest_days = gestage,
                            sex = sex,
                            weight_kg = wt_kg,
                            id = child,
                            .outcomes = "svn",
                            .new = list("svn" = c("ig_nbs_centile",
                                                  "SVN_Category")))
#> Warning: There was 1 'at birth' observation where `age_days` > 0.5.
#> ℹ This occurred for ID B.
#> ! Unused factor levels kept after small vulnerable newborn categorisation:
#> "Preterm SGA", "Term SGA", "Term AGA", and "Term LGA".
#> 
#> ── `gigs::classify_growth()` ───────────────────────────────────────────────────
#> ✔ Small vulnerable newborns: Success
data_svn
#>   child agedays gestage sex wt_kg len_cm head_cm ig_nbs_centile SVN_Category
#> 1     A       0     245   M     3   52.2      30      0.8893352  Preterm AGA
#> 2     A     100     245   M     6   60.4      40             NA         <NA>
#> 3     A     500     245   M     9   75.0      49             NA         <NA>
#> 4     B       2     245   F     3   52.2      30      0.9384734  Preterm LGA
#> 5     B     100     245   F     6   60.4      40             NA         <NA>
#> 6     B     500     245   F     9   75.0      49             NA         <NA>

# The function will warn you if you don't provide data for your outcomes
data_svn_stunting <- classify_growth(data,
                                     age_days = agedays,
                                     gest_days = gestage,
                                     sex = sex,
                                     weight_kg = wt_kg,
                                     id = child,
                                     .outcomes = c("svn", "stunting"))
#> Warning: There was 1 'at birth' observation where `age_days` > 0.5.
#> ℹ This occurred for ID B.
#> ! Unused factor levels kept after small vulnerable newborn categorisation:
#> "Preterm SGA", "Term SGA", "Term AGA", and "Term LGA".
#> 
#> ── `gigs::classify_growth()` ───────────────────────────────────────────────────
#> ✔ Small vulnerable newborns: Success
#> ! Stunting: Not computed (`lenht_cm` not supplied)
data_svn_stunting
#>   child agedays gestage sex wt_kg len_cm head_cm birthweight_centile
#> 1     A       0     245   M     3   52.2      30           0.8893352
#> 2     A     100     245   M     6   60.4      40                  NA
#> 3     A     500     245   M     9   75.0      49                  NA
#> 4     B       2     245   F     3   52.2      30           0.9384734
#> 5     B     100     245   F     6   60.4      40                  NA
#> 6     B     500     245   F     9   75.0      49                  NA
#>           svn
#> 1 Preterm AGA
#> 2        <NA>
#> 3        <NA>
#> 4 Preterm LGA
#> 5        <NA>
#> 6        <NA>