,
Linda Vesel [dtc, ctb] ,
Eric O Ohuma [dtc, ctb] ,
Chitra Saraswati [rev] ,
Victor Ordu [rev] ,
Bill & Melinda Gates Foundation [fnd],
London School of Hygiene & Tropical Medicine [cph]| Title: | Assess Fetal, Newborn, and Child Growth with International Standards |
|---|---|
| Description: | Convert between anthropometric measures and z-scores/centiles in multiple growth standards, and classify fetal, newborn, and child growth accordingly. With a simple interface to growth standards from the World Health Organisation and International Fetal and Newborn Growth Consortium for the 21st Century, gigs makes growth assessment easy and reproducible for clinicians, researchers and policy-makers. |
| Authors: | Simon R Parker [aut, cre] ,
Linda Vesel [dtc, ctb] ,
Eric O Ohuma [dtc, ctb] ,
Chitra Saraswati [rev] ,
Victor Ordu [rev] ,
Bill & Melinda Gates Foundation [fnd],
London School of Hygiene & Tropical Medicine [cph] |
| Maintainer: | Simon R Parker <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.5.1.9000 |
| Built: | 2024-12-14 16:24:20 UTC |
| Source: | https://github.com/ropensci/gigs |
An environment containing six named character vectors. These define how gigs handles inputs with missing, undefined, or invalid elements:
"handle_missing_data" - How should gigs handle missing (NA) elements?
"handle_undefined_data" - How should gigs handle undefined (NaN,
Inf, -Inf) elements?
"handle_oob_xvar" - How should gigs handle elements of x
variables which are out of bounds for the standard in use?
"handle_invalid_sex" - How should gigs handle elements of sex which
are not one of "M" or "F"?
"handle_oob_centiles" - In centile2value functions, how should
gigs handle elements of p that are not between 0 and 1?
Each of these options can take one of three values:
"quiet" - Invalid elements are set to NA, silently.
"warn" - Invalid elements are set to NA, with warnings issued when
this is done.
"error" - Invalid elements will cause informative errors.
By default, each option in .gigs_options is set to "warn", so you will
be informed of any invalid data used as input. Use gigs_option_set() or
gigs_option_set() to change this behaviour.
A named environment, where each name maps onto a specific option for the GIGS package.
The gigs_option_get(), gigs_option_set() and
gigs_input_options_set() functions, which can be used to get and set
values in .gigs_options.
# Get the names of all available options names(.gigs_options)# Get the names of all available options names(.gigs_options)
Categorise head circumference-for-age z-scores into head circumference-for-age strata
categorise_headsize(hcaz)categorise_headsize(hcaz)
hcaz |
A numeric vector of length one or more containing head circumference-for-age z-scores (HCAZs). |
Cut-offs for head size categories are:
| Category | Factor level | Z-score bounds |
| Severe microcephaly | "microcephaly_severe" |
hcaz =< -3 |
| Microcephaly | "microcephaly" |
-3 < hcaz =< -2 |
| Normal head circumference | "normal_headcirc" |
abs(hcaz) < 2 |
| Macrocephaly | "macrocephaly" |
hcaz >= 2 |
| Severe macrocephaly | "macrocephaly_severe" |
hcaz >= 3
|
An object of class factor with the same length as hcaz, containing
head circumference-for-age classifications. Its levels are
c("microcephaly_severe", "microcephaly", "normal_headcirc", "macrocephaly", "macrocephaly_severe"). By default, gigs will inform you
this object contains unused factor levels. You can change this behaviour
using the GIGS package-level option
.gigs_options$handle_unused_levels.
Victora CG, Schuler-Faccini L, Matijasevich A, Ribeiro E, Pessoa A, Barros FC. Microcephaly in Brazil: how to interpret reported numbers? The Lancet 2016, 387(10019):621-624 doi:10.1016/S0140-6736(16)00273-7
Accogli A, Geraldo AF, Piccolo G, Riva A, Scala M, Balagura G, et al. Diagnostic Approach to Macrocephaly in Children. Frontiers in Paediatrics 2022, 9:794069 doi:10.3389/fped.2021.794069
hcaz <- c(-6.5, -3.5, -2.5, 0, 2.5, 3.5) categorise_headsize(hcaz)hcaz <- c(-6.5, -3.5, -2.5, 0, 2.5, 3.5) categorise_headsize(hcaz)
Categorise birthweight centiles into size-for-gestational age strata
categorise_sfga(p, severe = FALSE)categorise_sfga(p, severe = FALSE)
p |
A numeric vector with birth weight centiles from the
INTERGROWTH-21st Newborn Size standard. If elements of |
severe |
A single logical value specifying whether to categorise SGA
values below the third centile as |
Cut-offs for size-for-gestational age categorisations are:
| Category | Factor level | Centile bounds |
| Severely small-for-GA | "SGA(<3)" |
p < 0.03 |
| Small-for-GA | "SGA" |
p < 0.1 |
| Appropriate-for-GA | "AGA" |
0.1 =< p =< 0.9 |
| Large-for-GA | "LGA" |
p > 0.9
|
An object of class factor with the same length as p, containing
size-for-GA classifications. If severe = FALSE, levels are c("SGA", "AGA", "LGA"). If severe = TRUE, levels are c("SGA(<3)", "SGA", "AGA", "LGA"). By default, gigs will inform you this object contains unused
factor levels. You can change this behaviour using the
GIGS package-level option
.gigs_options$handle_unused_levels.
Input vectors are recycled by vctrs::vec_recycle_common(), and must
adhere to the vctrs recycling rules.
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.
classify_sfga(), which wraps this function for easy use in
data.frame-based analytic pipelines.
p <- c(0.01, 0.07, 0.25, 0.75, 0.93, 0.99) categorise_sfga(p = p, severe = FALSE) categorise_sfga(p = p, severe = TRUE)p <- c(0.01, 0.07, 0.25, 0.75, 0.93, 0.99) categorise_sfga(p = p, severe = FALSE) categorise_sfga(p = p, severe = TRUE)
Categorise length/height-for-age z-scores into stunting strata
categorise_stunting(lhaz, outliers = FALSE)categorise_stunting(lhaz, outliers = FALSE)
lhaz |
A numeric vector of length one or more containing length/height-for-age z-scores (LHAZs). |
outliers |
A single |
Cut-offs for stunting categories are:
| Category | Factor level | Z-score bounds |
| Severe stunting | "stunting_severe" |
lhaz =< -3 |
| Stunting | "stunting" |
-3 < lhaz =< -2 |
| No stunting | "not_stunting" |
lhaz > -2 |
| Outlier | "outlier" |
abs(lhaz) > 6
|
An object of class factor with the same length as the longest input
vector, containing stunting classifications. Its levels are
c("stunting_severe", "stunting", "not_stunting") if outliers = FALSE (the default), else c("stunting_severe", "stunting", "not_stunting", "outlier"). By default, gigs will inform you this object
contains unused factor levels. You can change this behaviour using the
GIGS package-level option
.gigs_options$handle_unused_levels.
This function assumes that your measurements were taken according to WHO guidelines, which stipulate that recumbent length should not be measured after 730 days. Instead, standing height should be used. Implausible z-score bounds are sourced from the referenced WHO report, and classification cut-offs from the DHS manual.
'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
lhaz <- c(-6.5, -5, -3, 0, 3, 5, 6.5) categorise_stunting(lhaz, outliers = FALSE) categorise_stunting(lhaz, outliers = TRUE)lhaz <- c(-6.5, -5, -3, 0, 3, 5, 6.5) categorise_stunting(lhaz, outliers = FALSE) categorise_stunting(lhaz, outliers = TRUE)
Categorise birthweight centiles and gestational ages into small vulnerable newborn strata
categorise_svn(p, gest_days)categorise_svn(p, gest_days)
p |
A numeric vector with birth weight centiles from the
INTERGROWTH-21st Newborn Size standard. If elements of |
gest_days |
A numeric vector of length one or more with gestational age(s) in days. Used to determine whether infants are term or preterm (a gestational age > 259 days means an infant is term). |
Cut-offs for small, vulnerable newborn categorisations are:
| SVN category | Factor level | Centile bounds | Gestational age range |
| Preterm-SGA | "Preterm SGA" |
p < 0.1 |
gest_days < 259 |
| Preterm-AGA | "Preterm AGA" |
0.1 =< p =< 0.9 |
gest_days < 259 |
| Preterm-LGA | "Preterm LGA" |
p > 0.9 |
gest_days < 259 |
| Term-SGA | "Term SGA" |
p < 0.1 |
gest_days >= 259 |
| Term-AGA | "Term AGA" |
0.1 =< p =< 0.9 |
gest_days >= 259 |
| Term-LGA | "Term LGA" |
p > 0.9 |
gest_days >= 259
|
Abbreviations: SGA, small-for-gestational age; AGA, appropriate-for-gestational age; LGA, large-for-gestational age.
An object of class factor with the same length as the longest input
vector, containing small vulnerable newborn classifications. Its levels
are c("Preterm SGA", "Preterm AGA", "Preterm LGA", "Term SGA", "Term AGA", "Term LGA"). By default, gigs will inform you this object contains unused
factor levels. You can change this behaviour using the
GIGS package-level option
.gigs_options$handle_unused_levels.
Input vectors are recycled by vctrs::vec_recycle_common(), and must
adhere to the vctrs recycling rules.
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
classify_svn(), which wraps this function for easy use in
analytic pipelines. Also check out categorise_sfga(), which this function
uses to stratify p into size-for-gestational age categories.
p <- c(0.01, 0.07, 0.25, 0.75, 0.93, 0.99) gest_days <- c(250, 250, 250, 280, 280, 280) # 3 preterm, 3 term categorise_svn(p = p, gest_days = gest_days)p <- c(0.01, 0.07, 0.25, 0.75, 0.93, 0.99) gest_days <- c(250, 250, 250, 280, 280, 280) # 3 preterm, 3 term categorise_svn(p = p, gest_days = gest_days)
Categorise weight-for-length/height z-scores into wasting strata
categorise_wasting(wlz, outliers = FALSE)categorise_wasting(wlz, outliers = FALSE)
wlz |
A numeric vector of length one or more containing weight-for-length/height z-scores (WLZs). |
outliers |
A single |
Cut-offs for wasting categories are:
| Category | Factor level | Z-score bounds |
| Severe wasting | "wasting_severe" |
wlz =< -3 |
| Wasting | "wasting" |
-3 < wlz =< -2 |
| No wasting | "not_wasting" |
abs(wlz) < 2 |
| Overweight | "overweight" |
wlz >= 2 |
| Outlier | "outlier" |
abs(wlz) > 5
|
An object of class factor with the same length as wlz, containing
wasting classifications. Its levels are c("wasting_severe", "wasting", "not_wasting", "overweight") if outliers = FALSE (the default), else
c("wasting_severe", "wasting", "not_wasting", "overweight", "outlier").
By default, gigs will inform you this object contains unused factor levels.
You can change this behaviour using the
GIGS package-level option
.gigs_options$handle_unused_levels.
This function assumes that your measurements were taken according to WHO guidelines, which stipulate that recumbent length should not be measured after 730 days. Instead, standing height should be used. Implausible z-score bounds are sourced from the referenced WHO report, and classification cut-offs from the DHS manual.
'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
wlz <- c(-5.5, -3, 0, 3, 5.5) categorise_wasting(wlz, outliers = FALSE) categorise_wasting(wlz, outliers = TRUE)wlz <- c(-5.5, -3, 0, 3, 5.5) categorise_wasting(wlz, outliers = FALSE) categorise_wasting(wlz, outliers = TRUE)
Categorise weight-for-age z-scores into weight-for-age strata
categorise_wfa(waz, outliers = FALSE)categorise_wfa(waz, outliers = FALSE)
waz |
A numeric vector of length one or more containing weight-for-age z-scores (WAZs). |
outliers |
A single |
Cut-offs for weight-for-age categories are:
| Category | Factor level | Z-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
|
An object of class factor with the same length as waz, containing
weight-for-age classifications. Its levels are c("underweight_severe", "underweight", "normal", "overweight") if outliers = FALSE (the
default), else c("underweight_severe", "underweight", "normal", "overweight", "outlier"). By default, gigs will inform you this object
contains unused factor levels. You can change this behaviour using the
GIGS package-level option
.gigs_options$handle_unused_levels.
'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
waz <- c(-6.5, -3.5, -2.5, 0, 2.5, 3.5) categorise_wfa(waz, outliers = FALSE) categorise_wfa(waz, outliers = TRUE)waz <- c(-6.5, -3.5, -2.5, 0, 2.5, 3.5) categorise_wfa(waz, outliers = FALSE) categorise_wfa(waz, outliers = TRUE)
This function permits classification of multiple growth indicators (stunting,
wasting, weight-for-age, and more) at once in data.frame-like objects.
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 )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 )
.data |
A |
gest_days |
< |
age_days |
< |
sex |
< |
weight_kg |
< |
lenht_cm |
< |
headcirc_cm |
< |
id |
< |
.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 |
.new |
A list with names corresponding to
|
.verbose |
A single logical value. When |
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.
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.
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
# 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) data_classified # 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") data_svn # 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"))) data_svn # 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")) data_svn_stunting# 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) data_classified # 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") data_svn # 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"))) data_svn # 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")) data_svn_stunting
data.frame-like objects with GIGS-recommended
growth standardsClassify head size in data.frame-like objects with GIGS-recommended
growth standards
classify_headsize( .data, headcirc_cm, age_days, gest_days, sex, id = NULL, .new = c("hcaz", "headsize") )classify_headsize( .data, headcirc_cm, age_days, gest_days, sex, id = NULL, .new = c("hcaz", "headsize") )
.data |
A |
headcirc_cm |
< |
age_days |
< |
gest_days |
< |
sex |
< |
id |
< |
.new |
A three-length character vector with names for the output
columns. These inputs will be repaired if necessary using
|
Cut-offs for head size categories are:
| Category | Factor level | Z-score bounds |
| Severe microcephaly | "microcephaly_severe" |
hcaz =< -3 |
| Microcephaly | "microcephaly" |
-3 < hcaz =< -2 |
| Normal head circumference | "normal_headcirc" |
abs(hcaz) < 2 |
| Macrocephaly | "macrocephaly" |
hcaz >= 2 |
| Severe macrocephaly | "macrocephaly_severe" |
hcaz >= 3
|
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):
hcaz - Numeric vector of weight-for-length/height zscores
headsize - Factor of head size categories
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.
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
See classify_growth() to run this analysis and others at the same
time.
# 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)), headcirc_cm = rep.int(c(40, 50, 60), 2) ) # Use the `id` argument to ensure that `classify_headsize()` uses the correct # standard for each observation data |> classify_headsize(headcirc_cm = headcirc_cm, age_days = agedays, gest_days = gestage, sex = sex, id = child) # If you don't specify `id`, `classify_headsize()` will assume data is from # one child only data |> classify_headsize(headcirc_cm = headcirc_cm, age_days = agedays, gest_days = gestage, sex = sex)# 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)), headcirc_cm = rep.int(c(40, 50, 60), 2) ) # Use the `id` argument to ensure that `classify_headsize()` uses the correct # standard for each observation data |> classify_headsize(headcirc_cm = headcirc_cm, age_days = agedays, gest_days = gestage, sex = sex, id = child) # If you don't specify `id`, `classify_headsize()` will assume data is from # one child only data |> classify_headsize(headcirc_cm = headcirc_cm, age_days = agedays, gest_days = gestage, sex = sex)
data.frame-like objects with the
INTERGROWTH-21st weight-for-gestational age standardClassify size-for-gestational age in data.frame-like objects with the
INTERGROWTH-21st weight-for-gestational age standard
classify_sfga( .data, weight_kg, gest_days, sex, .new = c("birthweight_centile", "sfga", "sfga_severe") )classify_sfga( .data, weight_kg, gest_days, sex, .new = c("birthweight_centile", "sfga", "sfga_severe") )
.data |
A |
weight_kg |
< |
gest_days |
< |
sex |
< |
.new |
A three-length character vector with names for the output
columns. These inputs will be repaired if necessary using
|
Cut-offs for size-for-gestational age categorisations are:
| Category | Factor level | Centile bounds |
| Severely small-for-GA | "SGA(<3)" |
p < 0.03 |
| Small-for-GA | "SGA" |
p < 0.1 |
| Appropriate-for-GA | "AGA" |
0.1 =< p =< 0.9 |
| Large-for-GA | "LGA" |
p > 0.9
|
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):
birthweight_centile - Numeric vector of birthweight centiles from the
INTERGROWTH-21st Newborn Size standard for
weight-for-GA
sfga - Factor of size-for-GA categories without severe small-for-GA
classification
sfga_severe - Factor of size-for-GA categories with severe small-for-GA
classification
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.
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.
See classify_svn() for size-for-GA classifications which are
stratified by whether a newborn is term. See classify_growth() to run
this analysis and others at the same time.
data <- data.frame( wt_kg = c(2.2, 2.5, 3.3, 4.0), gestage = 266:269, sex = c("F", "M", "F", "M") ) data |> classify_sfga(weight_kg = wt_kg, gest_days = gestage, sex = sex)data <- data.frame( wt_kg = c(2.2, 2.5, 3.3, 4.0), gestage = 266:269, sex = c("F", "M", "F", "M") ) data |> classify_sfga(weight_kg = wt_kg, gest_days = gestage, sex = sex)
data.frame-like objects with GIGS-recommended growth
standardsClassify stunting in data.frame-like objects with GIGS-recommended growth
standards
classify_stunting( .data, lenht_cm, age_days, gest_days, sex, id = NULL, .new = c("lhaz", "stunting", "stunting_outliers") )classify_stunting( .data, lenht_cm, age_days, gest_days, sex, id = NULL, .new = c("lhaz", "stunting", "stunting_outliers") )
.data |
A |
lenht_cm |
< |
age_days |
< |
gest_days |
< |
sex |
< |
id |
< |
.new |
A three-length character vector with names for the output
columns. These inputs will be repaired if necessary using
|
Cut-offs for stunting categories are:
| Category | Factor level | Z-score bounds |
| Severe stunting | "stunting_severe" |
lhaz =< -3 |
| Stunting | "stunting" |
-3 < lhaz =< -2 |
| No stunting | "not_stunting" |
lhaz > -2 |
| Outlier | "outlier" |
abs(lhaz) > 6
|
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):
lhaz - Numeric vector of length/height-for-age zscores
stunting - Factor of stunting categories without outlier flagging
stunting_outliers - Factor of stunting categories with outlier flagging
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.
'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 classify_growth() to run this analysis and others at the same
time.
# 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(40 * 7, 3)), sex = rep.int(c("M", "F"), c(3, 3)), length_cm = rep.int(c(52.2, 60.4, 75), 2) ) # Use the `id` argument to ensure that `classify_stunting` uses the correct # standard for each observation data |> classify_stunting(lenht_cm = length_cm, age_days = agedays, gest_days = gestage, sex = sex, id = child) # If you don't specify `id`, `classify_stunting` will assume data is from one # child only data |> classify_stunting(lenht_cm = length_cm, age_days = agedays, gest_days = gestage, sex = sex)# 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(40 * 7, 3)), sex = rep.int(c("M", "F"), c(3, 3)), length_cm = rep.int(c(52.2, 60.4, 75), 2) ) # Use the `id` argument to ensure that `classify_stunting` uses the correct # standard for each observation data |> classify_stunting(lenht_cm = length_cm, age_days = agedays, gest_days = gestage, sex = sex, id = child) # If you don't specify `id`, `classify_stunting` will assume data is from one # child only data |> classify_stunting(lenht_cm = length_cm, age_days = agedays, gest_days = gestage, sex = sex)
data.frame-like objects with the
INTERGROWTH-21st weight-for-gestational age standardClassify small vulnerable newborns in data.frame-like objects with the
INTERGROWTH-21st weight-for-gestational age standard
classify_svn( .data, weight_kg, gest_days, sex, .new = c("birthweight_centile", "svn") )classify_svn( .data, weight_kg, gest_days, sex, .new = c("birthweight_centile", "svn") )
.data |
A |
weight_kg |
< |
gest_days |
< |
sex |
< |
.new |
A two-length character vector with names for the output
columns. These inputs will be repaired if necessary using
|
Cut-offs for small, vulnerable newborn categorisations are:
| SVN category | Factor level | Centile bounds | Gestational age range |
| Preterm-SGA | "Preterm SGA" |
p < 0.1 |
gest_days < 259 |
| Preterm-AGA | "Preterm AGA" |
0.1 =< p =< 0.9 |
gest_days < 259 |
| Preterm-LGA | "Preterm LGA" |
p > 0.9 |
gest_days < 259 |
| Term-SGA | "Term SGA" |
p < 0.1 |
gest_days >= 259 |
| Term-AGA | "Term AGA" |
0.1 =< p =< 0.9 |
gest_days >= 259 |
| Term-LGA | "Term LGA" |
p > 0.9 |
gest_days >= 259
|
Abbreviations: SGA, small-for-gestational age; AGA, appropriate-for-gestational age; LGA, large-for-gestational age.
A tabular object of the same class that was provided as .data,
with two new columns named according to .new. These columns will be
(from left to right):
birthweight_centile - Numeric vector of birthweight centiles from the
INTERGROWTH-21st Newborn Size standard for
weight-for-GA
svn - Factor of small vulnerable newborn (SVN) categories
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.
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.
See classify_sfga() for size-for-GA classifications which are not
stratified by whether a newborn is term. See classify_growth() to run
this analysis and others at the same time.
data <- data.frame( wt_kg = c(1.5, 2.6, 2.6, 3.5), gestage = c(235, 257, 275, 295), sex = c("F", "M", "F", "M") ) data |> classify_svn(weight_kg = wt_kg, gest_days = gestage, sex = sex)data <- data.frame( wt_kg = c(1.5, 2.6, 2.6, 3.5), gestage = c(235, 257, 275, 295), sex = c("F", "M", "F", "M") ) data |> classify_svn(weight_kg = wt_kg, gest_days = gestage, sex = sex)
data.frame-like objects with GIGS-recommended growth
standardsClassify wasting in data.frame-like objects with GIGS-recommended growth
standards
classify_wasting( .data, weight_kg, lenht_cm, age_days, gest_days, sex, id = NULL, .new = c("wlz", "wasting", "wasting_outliers") )classify_wasting( .data, weight_kg, lenht_cm, age_days, gest_days, sex, id = NULL, .new = c("wlz", "wasting", "wasting_outliers") )
.data |
A |
weight_kg |
< |
lenht_cm |
< |
age_days |
< |
gest_days |
< |
sex |
< |
id |
< |
.new |
A three-length character vector with names for the output
columns. These inputs will be repaired if necessary using
|
Cut-offs for wasting categories are:
| Category | Factor level | Z-score bounds |
| Severe wasting | "wasting_severe" |
wlz =< -3 |
| Wasting | "wasting" |
-3 < wlz =< -2 |
| No wasting | "not_wasting" |
abs(wlz) < 2 |
| Overweight | "overweight" |
wlz >= 2 |
| Outlier | "outlier" |
abs(wlz) > 5
|
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
wasting - Factor of wasting categories without outlier flagging
wasting_outliers - Factor of wasting categories with outlier flagging
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.
'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 classify_growth() to run this analysis and others at the same
time.
# 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), length_cm = rep.int(c(52.2, 60.4, 75), 2) ) # Use the `id` argument to ensure that `classify_wasting` uses the correct # standard for each observation data |> classify_wasting(weight_kg = weight_kg, lenht_cm = length_cm, age_days = agedays, gest_days = gestage, sex = sex, id = child) # If you don't specify `id`, `classify_wasting` will assume data is from one # child only data |> classify_wasting(weight_kg = weight_kg, lenht_cm = length_cm, age_days = agedays, gest_days = gestage, sex = sex)# 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), length_cm = rep.int(c(52.2, 60.4, 75), 2) ) # Use the `id` argument to ensure that `classify_wasting` uses the correct # standard for each observation data |> classify_wasting(weight_kg = weight_kg, lenht_cm = length_cm, age_days = agedays, gest_days = gestage, sex = sex, id = child) # If you don't specify `id`, `classify_wasting` will assume data is from one # child only data |> classify_wasting(weight_kg = weight_kg, lenht_cm = length_cm, age_days = agedays, gest_days = gestage, sex = sex)
data.frame-like objects with GIGS-recommended
growth standardsClassify weight-for-age in data.frame-like objects with GIGS-recommended
growth standards
classify_wfa( .data, weight_kg, age_days, gest_days, sex, id = NULL, .new = c("waz", "wfa", "wfa_outliers") )classify_wfa( .data, weight_kg, age_days, gest_days, sex, id = NULL, .new = c("waz", "wfa", "wfa_outliers") )
.data |
A |
weight_kg |
< |
age_days |
< |
gest_days |
< |
sex |
< |
id |
< |
.new |
A three-length character vector with names for the output
columns. These inputs will be repaired if necessary using
|
Cut-offs for weight-for-age categories are:
| Category | Factor level | Z-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
|
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
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.
'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 classify_growth() to run this analysis and others at the same
time.
# 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) # 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)# 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) # 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)
Get head size categories using multiple vectors and GIGS-recommended growth standards
compute_headsize(headcirc_cm, age_days, gest_days, sex, id = NULL)compute_headsize(headcirc_cm, age_days, gest_days, sex, id = NULL)
headcirc_cm |
Numeric vector of length one or more with head circumference measurement(s) in cm. |
age_days |
Numeric vector of length one or more with age(s) in days
for each child. Should be between |
gest_days |
Numeric vector with gestational age(s) at birth in days. |
sex |
Character vector of length one or more with sex(es), either |
id |
A factor of length one or more with an ID for each observation,
either ordered or unordered, containing no missing ( |
Cut-offs for head size categories are:
| Category | Factor level | Z-score bounds |
| Severe microcephaly | "microcephaly_severe" |
hcaz =< -3 |
| Microcephaly | "microcephaly" |
-3 < hcaz =< -2 |
| Normal head circumference | "normal_headcirc" |
abs(hcaz) < 2 |
| Macrocephaly | "macrocephaly" |
hcaz >= 2 |
| Severe macrocephaly | "macrocephaly_severe" |
hcaz >= 3
|
An object of class factor with the same length as wlz, containing
wasting classifications. Its levels are c("wasting_severe", "wasting", "not_wasting", "overweight") if outliers = FALSE (the default), else
c("wasting_severe", "wasting", "not_wasting", "overweight", "outlier").
By default, gigs will inform you this object contains unused factor levels.
You can change this behaviour using the
GIGS package-level option
.gigs_options$handle_unused_levels.
'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
# The first observation for each infant in `id` uses the INTERGROWTH-21st # Newborn Size standards; the next two use either the INTERGROWTH-21st # Postnatal Growth standards or WHO Child Growth Standards. compute_headsize( headcirc_cm = c(31.6, 40, 50, 31.6, 40, 50), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F"), id = factor(c("A", "A", "A", "B", "B", "B")) ) # If you don't specify `id`, the function will not identify that the fourth # data point is a birth measurement, and will categorise it as microcephalic # instead of normal compute_headsize( headcirc_cm = c(31.6, 40, 50, 31.6, 40, 50), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F") )# The first observation for each infant in `id` uses the INTERGROWTH-21st # Newborn Size standards; the next two use either the INTERGROWTH-21st # Postnatal Growth standards or WHO Child Growth Standards. compute_headsize( headcirc_cm = c(31.6, 40, 50, 31.6, 40, 50), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F"), id = factor(c("A", "A", "A", "B", "B", "B")) ) # If you don't specify `id`, the function will not identify that the fourth # data point is a birth measurement, and will categorise it as microcephalic # instead of normal compute_headsize( headcirc_cm = c(31.6, 40, 50, 31.6, 40, 50), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F") )
Get size-for-gestational age categories using multiple vectors and the INTERGROWTH-21st weight-for-gestational age standard
compute_sfga(weight_kg, gest_days, sex, severe = FALSE)compute_sfga(weight_kg, gest_days, sex, severe = FALSE)
weight_kg |
Numeric vector of length one or more with weight value(s) in kg. It is assumed that weight measurements provided to this function are birth weights recorded <12 hours after an infant's birth. |
gest_days |
Numeric vector of length one or more with gestational ages
in days. Classifications can only be computed when |
sex |
Character vector of length one or more with sex(es), either |
severe |
A single logical value specifying whether to categorise SGA
values below the third centile ( |
Cut-offs for size-for-gestational age categorisations are:
| Category | Factor level | Centile bounds |
| Severely small-for-GA | "SGA(<3)" |
p < 0.03 |
| Small-for-GA | "SGA" |
p < 0.1 |
| Appropriate-for-GA | "AGA" |
0.1 =< p =< 0.9 |
| Large-for-GA | "LGA" |
p > 0.9
|
An object of class factor with the same length as the longest input
vector, containing size-for-GA classifications. If severe = FALSE, levels
are c("SGA", "AGA", "LGA"). If severe = TRUE, levels are c("SGA(<3)", "SGA", "AGA", "LGA"). By default, gigs will inform you this object
contains unused factor levels. You can change this behaviour using the
GIGS package-level option
.gigs_options$handle_unused_levels.
Input vectors are recycled by vctrs::vec_recycle_common(), and must
adhere to the vctrs recycling rules.
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.
# By default, does not differentiate between p < 0.03 and p < 0.10 compute_sfga( weight_kg = c(2.2, 2.5, 3.3, 4.0), gest_days = 266:269, sex = c("F", "M", "F", "M") ) # With severe = TRUE, highlights p < 0.03 compute_sfga( weight_kg = c(2.2, 2.5, 3.3, 4.0), gest_days = 266:269, sex = c("F", "M", "F", "M"), severe = TRUE )# By default, does not differentiate between p < 0.03 and p < 0.10 compute_sfga( weight_kg = c(2.2, 2.5, 3.3, 4.0), gest_days = 266:269, sex = c("F", "M", "F", "M") ) # With severe = TRUE, highlights p < 0.03 compute_sfga( weight_kg = c(2.2, 2.5, 3.3, 4.0), gest_days = 266:269, sex = c("F", "M", "F", "M"), severe = TRUE )
Get stunting categories using multiple vectors and GIGS-recommended growth standards
compute_stunting( lenht_cm, age_days, gest_days, sex, id = NULL, outliers = FALSE )compute_stunting( lenht_cm, age_days, gest_days, sex, id = NULL, outliers = FALSE )
lenht_cm |
Numeric vector of length one or more with length/height measurement(s) in cm. |
age_days |
Numeric vector of length one or more with age(s) in days
for each child. Should be between |
gest_days |
Numeric vector with gestational age(s) at birth in days. |
sex |
Character vector of length one or more with sex(es), either |
id |
A factor of length one or more with an ID for each observation,
either ordered or unordered, containing no missing ( |
outliers |
A single |
Cut-offs for stunting categories are:
| Category | Factor level | Z-score bounds |
| Severe stunting | "stunting_severe" |
lhaz =< -3 |
| Stunting | "stunting" |
-3 < lhaz =< -2 |
| No stunting | "not_stunting" |
lhaz > -2 |
| Outlier | "outlier" |
abs(lhaz) > 6
|
An object of class factor with the same length as the longest input
vector, containing stunting classifications. Its levels are
c("stunting_severe", "stunting", "not_stunting") if outliers = FALSE (the default), else c("stunting_severe", "stunting", "not_stunting", "outlier"). By default, gigs will inform you this object
contains unused factor levels. You can change this behaviour using the
GIGS package-level option
.gigs_options$handle_unused_levels.
This function assumes that your measurements were taken according to WHO guidelines, which stipulate that recumbent length should not be measured after 730 days. Instead, standing height should be used. Implausible z-score bounds are sourced from the referenced WHO report, and classification cut-offs from the DHS manual.
'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
# The first observation for each infant in `id` uses the INTERGROWTH-21st # Newborn Size standards; the next two use either the INTERGROWTH-21st # Postnatal Growth standards or WHO Child Growth Standards. compute_stunting( lenht_cm = c(52.2, 60.4, 75, 52.2, 60.4, 75), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F"), id = factor(c("A", "A", "A", "B", "B", "B")) ) # With outlier flagging: compute_stunting( lenht_cm = c(52.2, 75.4, 63.1), age_days = c(357, 375, 250), gest_days = c(196, 287, 266), sex = c("M", "M", "F"), id = factor(c("A", "B", "C")), outliers = TRUE ) # If you don't specify `id`, the function will not identify that the fourth # data point is a birth measurement, and will categorise it as severe # stunting instead of not stunting compute_stunting( lenht_cm = c(40.0, 60.4, 75, 40.0, 60.4, 75), age_days = c(0.45, 100, 500, 0.5, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F") )# The first observation for each infant in `id` uses the INTERGROWTH-21st # Newborn Size standards; the next two use either the INTERGROWTH-21st # Postnatal Growth standards or WHO Child Growth Standards. compute_stunting( lenht_cm = c(52.2, 60.4, 75, 52.2, 60.4, 75), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F"), id = factor(c("A", "A", "A", "B", "B", "B")) ) # With outlier flagging: compute_stunting( lenht_cm = c(52.2, 75.4, 63.1), age_days = c(357, 375, 250), gest_days = c(196, 287, 266), sex = c("M", "M", "F"), id = factor(c("A", "B", "C")), outliers = TRUE ) # If you don't specify `id`, the function will not identify that the fourth # data point is a birth measurement, and will categorise it as severe # stunting instead of not stunting compute_stunting( lenht_cm = c(40.0, 60.4, 75, 40.0, 60.4, 75), age_days = c(0.45, 100, 500, 0.5, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F") )
Get small vulnerable newborn categories using multiple vectors and the INTERGROWTH-21st weight-for-gestational age standard
compute_svn(weight_kg, gest_days, sex)compute_svn(weight_kg, gest_days, sex)
weight_kg |
Numeric vector of length one or more with weight value(s) in kg. It is assumed that weight measurements provided to this function are birth weights recorded <12 hours after an infant's birth. |
gest_days |
Numeric vector of length one or more with gestational ages
in days. Classifications can only be computed when |
sex |
Character vector of length one or more with sex(es), either |
Cut-offs for small, vulnerable newborn categorisations are:
| SVN category | Factor level | Centile bounds | Gestational age range |
| Preterm-SGA | "Preterm SGA" |
p < 0.1 |
gest_days < 259 |
| Preterm-AGA | "Preterm AGA" |
0.1 =< p =< 0.9 |
gest_days < 259 |
| Preterm-LGA | "Preterm LGA" |
p > 0.9 |
gest_days < 259 |
| Term-SGA | "Term SGA" |
p < 0.1 |
gest_days >= 259 |
| Term-AGA | "Term AGA" |
0.1 =< p =< 0.9 |
gest_days >= 259 |
| Term-LGA | "Term LGA" |
p > 0.9 |
gest_days >= 259
|
Abbreviations: SGA, small-for-gestational age; AGA, appropriate-for-gestational age; LGA, large-for-gestational age.
An object of class factor with the same length as the longest input
vector, containing small vulnerable newborn classifications. Its levels
are c("Preterm SGA", "Preterm AGA", "Preterm LGA", "Term SGA", "Term AGA", "Term LGA"). By default, gigs will inform you this object contains unused
factor levels. You can change this behaviour using the
GIGS package-level option
.gigs_options$handle_unused_levels.
Input vectors are recycled by vctrs::vec_recycle_common(), and must
adhere to the vctrs recycling rules.
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
compute_svn( weight_kg = c(1.5, 2.6, 2.6, 3.5), gest_days = c(235, 257, 275, 295), sex = c("F", "M", "F", "M") )compute_svn( weight_kg = c(1.5, 2.6, 2.6, 3.5), gest_days = c(235, 257, 275, 295), sex = c("F", "M", "F", "M") )
Get wasting categories using multiple vectors and GIGS-recommended growth standards
compute_wasting( weight_kg, lenht_cm, age_days, gest_days, sex, id = NULL, outliers = FALSE )compute_wasting( weight_kg, lenht_cm, age_days, gest_days, sex, id = NULL, outliers = FALSE )
weight_kg |
Numeric vector of length one or more with weight measurement(s) in kg. |
lenht_cm |
Numeric vector of length one or more with length/height measurement(s) in cm. |
age_days |
Numeric vector of length one or more with age(s) in days
for each child. Should be between |
gest_days |
Numeric vector with gestational age(s) at birth in days. |
sex |
Character vector of length one or more with sex(es), either |
id |
A factor of length one or more with an ID for each observation,
either ordered or unordered, containing no missing ( |
outliers |
A single |
Cut-offs for wasting categories are:
| Category | Factor level | Z-score bounds |
| Severe wasting | "wasting_severe" |
wlz =< -3 |
| Wasting | "wasting" |
-3 < wlz =< -2 |
| No wasting | "not_wasting" |
abs(wlz) < 2 |
| Overweight | "overweight" |
wlz >= 2 |
| Outlier | "outlier" |
abs(wlz) > 5
|
An object of class factor with the same length as wlz, containing
wasting classifications. Its levels are c("wasting_severe", "wasting", "not_wasting", "overweight") if outliers = FALSE (the default), else
c("wasting_severe", "wasting", "not_wasting", "overweight", "outlier").
By default, gigs will inform you this object contains unused factor levels.
You can change this behaviour using the
GIGS package-level option
.gigs_options$handle_unused_levels.
This function assumes that your measurements were taken according to WHO guidelines, which stipulate that recumbent length should not be measured after 730 days. Instead, standing height should be used. Implausible z-score bounds are sourced from the referenced WHO report, and classification cut-offs from the DHS manual.
'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
# The first observation for each infant in `id` uses the INTERGROWTH-21st # Newborn Size standards; the next two use either the INTERGROWTH-21st # Postnatal Growth standards or WHO Child Growth Standards. compute_wasting( weight_kg = c(3, 6, 11, 3, 6, 11), lenht_cm = c(52.2, 60.4, 61, 52.2, 60.4, 61), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F"), id = factor(c("A", "A", "A", "B", "B", "B")) ) # With outlier flagging: compute_wasting( weight_kg = c(3, 6, 11, 3, 6, 11), lenht_cm = c(52.2, 60.4, 61, 52.2, 60.4, 61), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F"), id = factor(c("A", "A", "A", "B", "B", "B")), outliers = TRUE ) # If you don't specify `id`, the function will not identify that the fourth # data point is a birth measurement. This means a weight-for-length z-score # (and resulting wasting classification) will be calculated for the fourth # data point, which is wrong here compute_wasting( weight_kg = c(3, 6, 11, 3, 6, 11), lenht_cm = c(52.2, 60.4, 61, 52.2, 60.4, 61), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F") )# The first observation for each infant in `id` uses the INTERGROWTH-21st # Newborn Size standards; the next two use either the INTERGROWTH-21st # Postnatal Growth standards or WHO Child Growth Standards. compute_wasting( weight_kg = c(3, 6, 11, 3, 6, 11), lenht_cm = c(52.2, 60.4, 61, 52.2, 60.4, 61), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F"), id = factor(c("A", "A", "A", "B", "B", "B")) ) # With outlier flagging: compute_wasting( weight_kg = c(3, 6, 11, 3, 6, 11), lenht_cm = c(52.2, 60.4, 61, 52.2, 60.4, 61), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F"), id = factor(c("A", "A", "A", "B", "B", "B")), outliers = TRUE ) # If you don't specify `id`, the function will not identify that the fourth # data point is a birth measurement. This means a weight-for-length z-score # (and resulting wasting classification) will be calculated for the fourth # data point, which is wrong here compute_wasting( weight_kg = c(3, 6, 11, 3, 6, 11), lenht_cm = c(52.2, 60.4, 61, 52.2, 60.4, 61), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F") )
Get weight-for-age categories using multiple vectors and GIGS-recommended growth standards
compute_wfa(weight_kg, age_days, gest_days, sex, id = NULL, outliers = FALSE)compute_wfa(weight_kg, age_days, gest_days, sex, id = NULL, outliers = FALSE)
weight_kg |
Numeric vector of length one or more with weight measurement(s) in kg. |
age_days |
Numeric vector of length one or more with age(s) in days
for each child. Should be between |
gest_days |
Numeric vector with gestational age(s) at birth in days. |
sex |
Character vector of length one or more with sex(es), either |
id |
A factor of length one or more with an ID for each observation,
either ordered or unordered, containing no missing ( |
outliers |
A single |
Cut-offs for weight-for-age categories are:
| Category | Factor level | Z-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
|
An object of class factor with the same length as wlz, containing
wasting classifications. Its levels are c("wasting_severe", "wasting", "not_wasting", "overweight") if outliers = FALSE (the default), else
c("wasting_severe", "wasting", "not_wasting", "overweight", "outlier").
By default, gigs will inform you this object contains unused factor levels.
You can change this behaviour using the
GIGS package-level option
.gigs_options$handle_unused_levels.
This function assumes that your measurements were taken according to WHO guidelines, which stipulate that recumbent length should not be measured after 730 days. Instead, standing height should be used. Implausible z-score bounds are sourced from the referenced WHO report, and classification cut-offs from the DHS manual.
'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
# The first observation for each infant in `id` uses the INTERGROWTH-21st # Newborn Size standards; the next two use either the INTERGROWTH-21st # Postnatal Growth standards or WHO Child Growth Standards. compute_wfa( weight_kg = c(2.05, 8, 18, 2.05, 8, 18), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F"), id = factor(c("A", "A", "A", "B", "B", "B")) ) # With outlier flagging: compute_wfa( weight_kg = c(2.05, 8, 18, 2.05, 8, 18), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F"), id = factor(c("A", "A", "A", "B", "B", "B")), outliers = TRUE ) # If you don't specify `id`, the function will not identify that the fourth # data point is a birth measurement, and will categorise it as underweight # instead of severely stunting compute_wfa( weight_kg = c(2.05, 8, 18, 2.05, 8, 18), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F") )# The first observation for each infant in `id` uses the INTERGROWTH-21st # Newborn Size standards; the next two use either the INTERGROWTH-21st # Postnatal Growth standards or WHO Child Growth Standards. compute_wfa( weight_kg = c(2.05, 8, 18, 2.05, 8, 18), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F"), id = factor(c("A", "A", "A", "B", "B", "B")) ) # With outlier flagging: compute_wfa( weight_kg = c(2.05, 8, 18, 2.05, 8, 18), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F"), id = factor(c("A", "A", "A", "B", "B", "B")), outliers = TRUE ) # If you don't specify `id`, the function will not identify that the fourth # data point is a birth measurement, and will categorise it as underweight # instead of severely stunting compute_wfa( weight_kg = c(2.05, 8, 18, 2.05, 8, 18), age_days = c(0, 100, 500, 2, 100, 500), gest_days = c(245, 245, 245, 280, 280, 280), sex = c("M", "M", "M", "F", "F", "F") )
By default, gigs will handle invalid input data, e.g. missing
(NA) or undefined (NaN, Inf and -Inf) values, by warning you of
their presence and replacing the invalid values with NA. It will also
warn you about unused factor levels when running growth outcome analyses.
You can change this input-checking behaviour with gigs_option_set() so
that gigs does this silently, or make gigs throw errors when confronted
with bad data. You can also make gigs drop unused factor levels in its
outputs, or silence its warnings about unused factor levels. You can also
change all options for gigs input-checking at once using
gigs_input_options_set().
gigs_option_get(option, silent = FALSE) gigs_option_set(option, new_value, silent = FALSE) gigs_input_options_set(new_value, silent = FALSE)gigs_option_get(option, silent = FALSE) gigs_option_set(option, new_value, silent = FALSE) gigs_input_options_set(new_value, silent = FALSE)
option |
A single case-sensitive character variable, one of:
|
silent |
A single logical value controlling whether a message will be printed to the console describing either:
This argument will not accept an integer in place of a logical value. |
new_value |
A single case-sensitive character variable. For all options
except
For "handle_unused_levels",
By default, GIGS options are |
A single-length character vector. For gigs_option_get(), the
current value of option; for gigs_option_get(), the new value of
option; for gigs_input_options_set(), new_value. If silent = FALSE
(default), then informative messages will be printed to the console
regarding the current/new values of option.
# Show the names of all available options names(.gigs_options) # Retrieve the value of a gigs option (if `silent = FALSE`, would print a # message) option_value <- gigs_option_get("handle_missing_data", silent = TRUE) print(option_value) # Set the value of an option gigs_option_set("handle_undefined_data", "error", silent = TRUE) # Check that change has occurred option_value <- gigs_option_get("handle_undefined_data", silent = TRUE) print(option_value) # Suppress printed output with `silent = TRUE` gigs_option_set("handle_undefined_data", "quiet", silent = TRUE) # Set all GIGS options for input checking gigs_input_options_set("warn", silent = TRUE)# Show the names of all available options names(.gigs_options) # Retrieve the value of a gigs option (if `silent = FALSE`, would print a # message) option_value <- gigs_option_get("handle_missing_data", silent = TRUE) print(option_value) # Set the value of an option gigs_option_set("handle_undefined_data", "error", silent = TRUE) # Check that change has occurred option_value <- gigs_option_get("handle_undefined_data", silent = TRUE) print(option_value) # Suppress printed output with `silent = TRUE` gigs_option_set("handle_undefined_data", "quiet", silent = TRUE) # Set all GIGS options for input checking gigs_input_options_set("warn", silent = TRUE)
These functions calculate z-scores for weight-for-age (WAZs), length/height-for-age (LHAZs), weight-for-length/height (WLZs), or head circumference-for-age (HCAZs).
The growth standard used for each observation differs based on the gestational and post-menstrual age of the infant being analysed. The different procedures are based on the advised use of each growth standard, where a measurement is considered a 'birth measurement' if it is the first measurement taken from an infant and was taken <3 days after birth:
gest_days < 24 weeks (168 days):
Birth: No standard available
Postnatal: IG-21st Postnatal Growth standards from 27 to 64 weeks' PMA
Postnatal: Uncorrected WHO standard after 64 weeks' PMA
gest_days >= 24 weeks (168 days):
Birth: IG-21st Newborn Size standards at birth (including Very Preterm Newborn Size standards if born before 33 weeks).
Postnatal: IG-21st Postnatal Growth standards from 27 to 64 weeks' PMA
Postnatal: Uncorrected WHO Child Growth standards after 64 weeks' PMA
gest_days >= 37 weeks (259 days):
Birth: IG-21st Newborn Size standards
Postnatal: Uncorrected WHO Child Growth standards
gest_days >= 43 weeks (301 days):
Birth: No standard available
Postnatal: Uncorrected WHO Child Growth standards
For gigs_wlz(), two WHO standards are available:
The weight-for-length standard, which is applied when age_days < 731.
The weight-for-height standard, which is applied when age_days >= 731.
gigs_waz(weight_kg, age_days, gest_days, sex, id = NULL) gigs_lhaz(lenht_cm, age_days, gest_days, sex, id = NULL) gigs_wlz(weight_kg, lenht_cm, age_days, gest_days, sex, id = NULL) gigs_hcaz(headcirc_cm, age_days, gest_days, sex, id = NULL)gigs_waz(weight_kg, age_days, gest_days, sex, id = NULL) gigs_lhaz(lenht_cm, age_days, gest_days, sex, id = NULL) gigs_wlz(weight_kg, lenht_cm, age_days, gest_days, sex, id = NULL) gigs_hcaz(headcirc_cm, age_days, gest_days, sex, id = NULL)
weight_kg |
A numeric vector of length one or more with weight in kg. |
age_days |
A numeric vector of length one or more with ages in days. |
gest_days |
A numeric vector of length one or more with gestational ages in days. |
sex |
Character vector of length one or more with sex(es), either |
id |
A factor of length one or more with an ID for each observation,
either ordered or unordered, containing no missing ( |
lenht_cm |
A numeric vector of length one or more with length or height
in cm. Should be recumbent length when |
headcirc_cm |
Numeric vector of length one or more with head circumference in cm. |
A numeric vector of z-scores, derived using the appropriate growth
standard for each element-wise combination of gest_days and age_days.
For gigs_wlz(), all birth WLZs will be missing (NA), as there is no
INTERGROWTH-21st Newborn standard for weight-for-length.
These functions expect vectors which are recyclable with
vctrs::vec_recycle_common().
gest_days <- c(rep(35 * 7, 3), rep(35 * 7, 3)) age_days <- c(0, 100, 500, 2, 100, 500) 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) ids <- factor(rep.int(c("A", "B"), c(3, 3))) # Weight-for-age z-score (WAZ) waz <- gigs_waz(weight_kg = wt_kg, age_days = age_days, gest_days = gest_days, sex = sex, id = ids) print(waz) # Note - if you don't specify 'id' you'll get different results! waz_no_id <- gigs_waz(weight_kg = wt_kg, age_days = age_days, gest_days = gest_days, sex = sex) print(waz == waz_no_id) # Length/height-for-age z-score (LHAZ) lhaz <- gigs_lhaz(lenht_cm = len_cm, age_days = age_days, gest_days = gest_days, sex = sex, id = ids) print(lhaz) # Weight-for-length/height z-score (WLZ) # Note: There's no at-birth standards for weight-for-length, so elements 1 # and 4 have `NA` values wlz <- gigs_wlz(weight_kg = wt_kg, lenht_cm = len_cm, age_days = age_days, gest_days = gest_days, sex = sex, id = ids) print(wlz) # Head circumference-for-age z-score (HCAZ) hcaz <- gigs_hcaz(headcirc_cm = head_cm, age_days = age_days, gest_days = gest_days, sex = sex, id = ids) print(hcaz)gest_days <- c(rep(35 * 7, 3), rep(35 * 7, 3)) age_days <- c(0, 100, 500, 2, 100, 500) 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) ids <- factor(rep.int(c("A", "B"), c(3, 3))) # Weight-for-age z-score (WAZ) waz <- gigs_waz(weight_kg = wt_kg, age_days = age_days, gest_days = gest_days, sex = sex, id = ids) print(waz) # Note - if you don't specify 'id' you'll get different results! waz_no_id <- gigs_waz(weight_kg = wt_kg, age_days = age_days, gest_days = gest_days, sex = sex) print(waz == waz_no_id) # Length/height-for-age z-score (LHAZ) lhaz <- gigs_lhaz(lenht_cm = len_cm, age_days = age_days, gest_days = gest_days, sex = sex, id = ids) print(lhaz) # Weight-for-length/height z-score (WLZ) # Note: There's no at-birth standards for weight-for-length, so elements 1 # and 4 have `NA` values wlz <- gigs_wlz(weight_kg = wt_kg, lenht_cm = len_cm, age_days = age_days, gest_days = gest_days, sex = sex, id = ids) print(wlz) # Head circumference-for-age z-score (HCAZ) hcaz <- gigs_hcaz(headcirc_cm = head_cm, age_days = age_days, gest_days = gest_days, sex = sex, id = ids) print(hcaz)
A set of nested lists containing tables with reference values at different z-scores/centiles for valid gestational ages in days. The list is ordered by acronym first, then by z-score/centile (as available - some Fetal standards only have centile tables).
Where possible, tables were taken from the online sources here. If not available on the INTERGROWTH-21st website, they were taken from the publications listed here.
INTERGROWTH-21st Pregnancy Dating (CRL)
INTERGROWTH-21st Symphysis-Fundal Height standard
INTERGROWTH-21st Fetal Growth standards
INTERGROWTH-21st Fetal Doppler standards
INTERGROWTH-21st Gestational Weight Gain standard
Papageorghiou AT, Ohuma EO, Altman DG, Todros T, Cheikh Ismail L, Lambert A et al. International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project. Lancet 2014, 384(9946):869-79. doi:10.1016/S0140-6736(14)61490-2
Stirnemann J, Villar J, Salomon LJ, Ohuma EO, Lamber A, Victoria CG et al. International Estimated Fetal Weight Standards of the INTERGROWTH-21st Project. Ultrasound Obstet Gynecol 2016, 49:478-486 doi:10.1002/uog.17347
Stirnemann J, Salomon LJ, Papageorghiou AT. INTERGROWTH-21st standards for Hadlock's estimation of fetal weight. Ultrasound Obstet Gynecol 2020, 56(6):946-948 doi:10.1002/uog.22000
Papageorghiou AT, Ohuma EO, Gravett MG, Lamber A, Noble JA, Pang R et al. International standards for symphysis-fundal height based on serial measurements from the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project: prospective cohort study in eight countries. BMJ 2016, 355:i5662 doi:10.1136/bmj.i5662
Papageorghiou AT, Kennedy SH, Salomon LJ, Ohuma EO, Cheikh Ismail L, Barros FC et al. International standards for early fetal size and pregnancy dating based on ultrasound measurement of crown-rump length in the first trimester of pregnancy. Ultrasound Obstet Gynecol 2014, 44(6):641-48 doi:10.1002/uog.13448
Cheikh Ismail L, Bishop DC, Pang R, Ohuma EO, Kac G, Abrams B et al. Gestational weight gain standards based on women enrolled in the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project: a prospective longitudinal cohort study. BMJ 2016, 352:i555 doi:10.1136/bmj.i555
Drukker L, Staines-Urias E, Villar J, Barros FC, Carvalho M, Munim S et al. International gestational age-specific centiles for umbilical artery Doppler indices: a longitudinal prospective cohort study of the INTERGROWTH-21st Project. Am J Obstet Gynecol 2021, 222(6):602.e1-602.e15 doi:10.1016/j.ajog.2020.01.012
Rodriguez-Sibaja MJ, Villar J, Ohuma EO, Napolitano R, Heyl S, Carvalho M et al. Fetal cerebellar growth and Sylvian fissure maturation: international standards from Fetal Growth Longitudinal Study of INTERGROWTH-21st Project Ultrasound Obstet Gynecol 2021, 57(4):614-623 doi:10.1002/uog.22017
Napolitano R, Molloholli M, Donadono V, Ohuma EO, Wanyonyi SZ, Kemp B et al. International standards for fetal brain structures based on serial ultrasound measurements from Fetal Growth Longitudinal Study of INTERGROWTH-21st Project Ultrasound Obstet Gynecol 2020, 56(3):359-370 doi:10.1002/uog.21990
names(gigs::ig_fet) head(gigs::ig_fet$hcfga$zscores)names(gigs::ig_fet) head(gigs::ig_fet$hcfga$zscores)
Estimate fetal weight in grams using the INTERGROWTH-21st predictive equation
ig_fet_estimate_fetal_weight(abdocirc_mm, headcirc_mm)ig_fet_estimate_fetal_weight(abdocirc_mm, headcirc_mm)
abdocirc_mm |
Numeric vector with abdominal circumference value(s) in
mm. Should have length one or same length as |
headcirc_mm |
Numeric vector with head circumference value(s) in mm.
Should have length one or same length as |
Numeric vector with estimated fetal weight(s) in g, with the same length as the longest input vector.
Inputs are recycled using vctrs::vec_recycle_common().
Stirnemann J, Villar J, Salomon LJ, Ohuma EO, Lamber A, Victoria CG et al. International Estimated Fetal Weight Standards of the INTERGROWTH-21st Project. Ultrasound Obstet Gynecol 2016, 49:478-486 doi:10.1002/uog.17347
# Estimate fetal weight in grams ig_fet_estimate_fetal_weight(abdocirc_mm = 31:33, headcirc_mm = 25:27) # Input vectors are recycled using vctrs::vec_recycle_common ig_fet_estimate_fetal_weight(abdocirc_mm = 25.0, headcirc_mm = 24:26)# Estimate fetal weight in grams ig_fet_estimate_fetal_weight(abdocirc_mm = 31:33, headcirc_mm = 25:27) # Input vectors are recycled using vctrs::vec_recycle_common ig_fet_estimate_fetal_weight(abdocirc_mm = 25.0, headcirc_mm = 24:26)
Estimate gestational age using INTERGROWTH-21st predictive equations
ig_fet_estimate_ga(crl_mm = NULL, headcirc_mm = NULL, femurlen_mm = NULL)ig_fet_estimate_ga(crl_mm = NULL, headcirc_mm = NULL, femurlen_mm = NULL)
crl_mm |
Numeric vector with crown-rump length value(s) in mm taken
from an ultrasound scan obtained 56 to 97 days since the last menstrual
period. If not |
headcirc_mm |
Numeric vector with head circumference value(s) in cm
taken from an ultrasound scan obtained 98 to 181 days since the last
menstrual period. If not |
femurlen_mm |
Numeric vector with femur length value(s) in cm. If not
|
A numeric vector with estimated gestational ages in days. This
vector will have a length equal to the outputs from
vctrs::vec_recycle_common().
Vector inputs to this function are recycled using
vctrs::vec_recycle_common(). The function will attempt to estimate the
gestational age (GA) in three steps, from highest to lowest accuracy.
First, using crown-rump length measurements obtained in early pregnancy,
then with both head circumference and femur length measurements, and
finally with head circumference alone.
Papageorghiou AT, Kennedy SH, Salomon LJ, Ohuma EO, Cheikh Ismail L, Barros FC et al. International standards for early fetal size and pregnancy dating based on ultrasound measurement of crown-rump length in the first trimester of pregnancy. Ultrasound Obstet Gynecol 2014, 44(6):641-48 doi:10.1002/uog.13448
Papageorghiou AT, Kemp B, Stones W, Ohuma EO, Kennedy SH, Purwar M et al. Ultrasound-based gestational age estimation in late pregnancy. Ultrasound Obstet Gynecol 2016. 48(6):719-26 doi:10.1002/uog.15894
# Estimate gestational age in days using crown-rump length (most accurate) ig_fet_estimate_ga(crl_mm = 40:45) # Estimate gestational age in days using head circumference and femur length # (second-most accurate) ig_fet_estimate_ga(headcirc_mm = 250:255, femurlen_mm = 55:60) # Estimate gestational age in days using head circumference only (least # accurate) ig_fet_estimate_ga(headcirc_mm = 250:255) # The function defaults to CRL if available ig_fet_estimate_ga(crl_mm = 40:45, headcirc_mm = 250:255, femurlen_mm = 55:60) # Inputs are recycled using [vctrs::vec_recycle_common] ig_fet_estimate_ga(headcirc_mm = 252, femurlen_mm = 55:60)# Estimate gestational age in days using crown-rump length (most accurate) ig_fet_estimate_ga(crl_mm = 40:45) # Estimate gestational age in days using head circumference and femur length # (second-most accurate) ig_fet_estimate_ga(headcirc_mm = 250:255, femurlen_mm = 55:60) # Estimate gestational age in days using head circumference only (least # accurate) ig_fet_estimate_ga(headcirc_mm = 250:255) # The function defaults to CRL if available ig_fet_estimate_ga(crl_mm = 40:45, headcirc_mm = 250:255, femurlen_mm = 55:60) # Inputs are recycled using [vctrs::vec_recycle_common] ig_fet_estimate_ga(headcirc_mm = 252, femurlen_mm = 55:60)
A set of nested lists containing tables with reference values at different z-scores/centiles for valid gestational ages in days. The list is ordered by acronym first, then by sex and finally by z-score/centile.
The tables in this package are combined versions of the tables published by
Villar et al. (2014) and Villar et al. (2016), so they cover 168 to
300 days' gestational age. The body composition tables (ffmfga, bfpfga,
and fmfga) cover a smaller gestational age span, ranging from only 266 to
294 days' (38 to 42 weeks') gestational age.
INTERGROWTH-21st Newborn Size in Very Preterm Infants
INTERGROWTH-21st Newborn Size Standards
INTERGROWTH-21st Newborn Size Standards - Body Composition
Villar J, Cheikh Ismail L, Victora CG, Ohuma EO, Bertino E, Altman DG, et al. International standards for newborn weight, length, and head circumference by gestational age and sex: the Newborn Cross-Sectional Study of the INTERGROWTH-21st Project. Lancet 2014, 384(9946):857-68. doi:10.1016/S0140-6736(14)60932-6
Villar J, Giuliani F, Fenton TR, Ohuma EO, Ismail LC, Kennedy SH et al. INTERGROWTH-21st very preterm size at birth reference charts. Lancet 2016, 387(10021):844-45. doi:10.1016/S0140-6736(16)00384-6
Villar J, Puglia FA, Fenton TR, Ismal LC, Staines-Urias E, Giuliani F, et al. Body composition at birth and its relationship with neonatal anthropometric ratios: the newborn body composition study of the INTERGROWTH-21st project. Pediatric Research 2017, 82:305-316. doi:10.1038/pr.2017.52
names(gigs::ig_nbs) head(gigs::ig_nbs$wfga$male$zscores)names(gigs::ig_nbs) head(gigs::ig_nbs$wfga$male$zscores)
A set of nested lists containing mu, sigma, nu and tau values across gestational ages for either sex, for the INTERGROWTH-21st weight/length/head circumference-for-gestational age standards. The lists are ordered by acronym, then sex.
Mu/sigma/nu/tau values were provided by Dr Eric Ohuma.
Villar J, Cheikh Ismail L, Victora CG, Ohuma EO, Bertino E, Altman DG, et al. International standards for newborn weight, length, and head circumference by gestational age and sex: the Newborn Cross-Sectional Study of the INTERGROWTH-21st Project. Lancet 2014, 384(9946):857-68. doi:10.1016/S0140-6736(14)60932-6
Villar J, Giuliani F, Fenton TR, Ohuma EO, Ismail LC, Kennedy SH et al. INTERGROWTH-21st very preterm size at birth reference charts. Lancet 2016, 387(10021):844-45. doi:10.1016/S0140-6736(16)00384-6
Villar J, Puglia FA, Fenton TR, Ismal LC, Staines-Urias E, Giuliani F, et al. Body composition at birth and its relationship with neonatal anthropometric ratios: the newborn body composition study of the INTERGROWTH-21st project. Pediatric Research 2017, 82:305-316. doi:10.1038/pr.2017.52
names(gigs::ig_nbs_coeffs) head(gigs::ig_nbs_coeffs$wfga$male)names(gigs::ig_nbs_coeffs) head(gigs::ig_nbs_coeffs$wfga$male)
A set of nested lists containing tables with reference values at different z-scores/centiles for valid gestational ages in days. The list is ordered by acronym first, then by sex and finally by z-score/centile.
The tables in this package are combined versions of the tables published by
Villar et al. (2014) and Villar et al. (2016), so they cover 168 to
300 days' gestational age. They have been extrapolated by Simon Parker
(with guidance from Eric Ohuma) to include a wider range of gestational ages,
and should be applied carefully by researchers. More detail on the
extrapolation process is available in an article on the package website.
INTERGROWTH-21st Newborn Size in Very Preterm Infants
INTERGROWTH-21st Newborn Size Standards
INTERGROWTH-21st Newborn Size Standards - Body Composition
Villar J, Cheikh Ismail L, Victora CG, Ohuma EO, Bertino E, Altman DG, et al. International standards for newborn weight, length, and head circumference by gestational age and sex: the Newborn Cross-Sectional Study of the INTERGROWTH-21st Project. Lancet 2014, 384(9946):857-68. doi:10.1016/S0140-6736(14)60932-6
Villar J, Giuliani F, Fenton TR, Ohuma EO, Ismail LC, Kennedy SH et al. INTERGROWTH-21st very preterm size at birth reference charts. Lancet 2016, 387(10021):844-45. doi:10.1016/S0140-6736(16)00384-6
Villar J, Puglia FA, Fenton TR, Ismal LC, Staines-Urias E, Giuliani F, et al. Body composition at birth and its relationship with neonatal anthropometric ratios: the newborn body composition study of the INTERGROWTH-21st project. Pediatric Research 2017, 82:305-316. doi:10.1038/pr.2017.52
names(gigs::ig_nbs_ext) head(gigs::ig_nbs_ext$wfga$male$zscores)names(gigs::ig_nbs_ext) head(gigs::ig_nbs_ext$wfga$male$zscores)
A set of nested lists containing mu, sigma, nu and tau values across gestational ages for either sex, for the extended INTERGROWTH-21st weight/length/head circumference-for-gestational age standards in newborns. The lists are ordered by acronym, then sex.
The mu and sigma coefficients have been extrapolated by Simon Parker (with guidance from Eric Ohuma) to include a wider range of gestational ages, and should be applied carefully by researchers. More detail on the extrapolation process is available in an article on the package website.
Mu/sigma/nu/tau values were provided by Dr Eric Ohuma.
Villar J, Cheikh Ismail L, Victora CG, Ohuma EO, Bertino E, Altman DG, et al. International standards for newborn weight, length, and head circumference by gestational age and sex: the Newborn Cross-Sectional Study of the INTERGROWTH-21st Project. Lancet 2014, 384(9946):857-68. doi:10.1016/S0140-6736(14)60932-6
Villar J, Giuliani F, Fenton TR, Ohuma EO, Ismail LC, Kennedy SH et al. INTERGROWTH-21st very preterm size at birth reference charts. Lancet 2016, 387(10021):844-45. doi:10.1016/S0140-6736(16)00384-6
Villar J, Puglia FA, Fenton TR, Ismal LC, Staines-Urias E, Giuliani F, et al. Body composition at birth and its relationship with neonatal anthropometric ratios: the newborn body composition study of the INTERGROWTH-21st project. Pediatric Research 2017, 82:305-316. doi:10.1038/pr.2017.52
names(gigs::ig_nbs_ext_coeffs) head(gigs::ig_nbs_ext_coeffs$wfga$male)names(gigs::ig_nbs_ext_coeffs) head(gigs::ig_nbs_ext_coeffs$wfga$male)
A set of nested lists containing tables with reference values at different z-scores/centiles for valid post-menstrual ages. The list is ordered by acronym first, then by sex, and finally by z-score/centile.
INTERGROWTH-21st Postnatal Growth of Preterm Infants
Villar J, Giuliani F, Bhutta ZA, Bertino E, Ohuma EO, Ismail LC et al. Postnatal growth standards for preterm infants: the Preterm Postnatal Follow-up Study of the INTERGROWTH-21st Project. Lancet Glob Health 2015, 3(11):e681-e691. doi:10.1016/S2214-109X(15)00163-1
names(gigs::ig_png) head(gigs::ig_png$wfa$male$zscores)names(gigs::ig_png) head(gigs::ig_png$wfa$male$zscores)
A subset of anthropometric data for 300 singleton infants enrolled in the Low birthweight Infant Feeding Exploration (LIFE) study. The variables are as follows:
A data frame with 2,191 rows and 10 variables:
id:Unique ID for each infant in the dataset (1–300).
visitweek:Chronological age in weeks (±1) when study visit occurred (0–26).
sex: Sex of the infant as a factor ("M" = Male; "F" =
Female).
gestage:Best obstetric estimate of gestational age in days (181–291).
age_days: Chronological age in days at each visit; equal to
pma - gestage (0–242).
pma:Post-menstrual age in days (182–528).
wt_kg:Mean weight in kg (1.24–9.40667).
len_cm:Mean length in cm (37.37–72.93).
headcirc_cm:Mean head circumference in cm (23.20–44.87).
muac_cm:Mean mid-upper arm circumference in cm (6.30–16.83).
We subsetted the full LIFE 6 month dataset for gigs. As such, this extract only includes data from 300 singleton pregnancies where the best estimate of gestational age was >168 days. We also removed rows corresponding to visit weeks where no measurement data was taken due to non-attendance of the visit.
Vesel L, Bellad RM, Manji K, Saidi F, Velasquez E, Sudfeld C, et al. Feeding practices and growth patterns of moderately low birthweight infants in resource-limited settings: results from a multisite, longitudinal observational study. BMJ Open 2023, 13(2):e067316. doi:10.1136/BMJOPEN-2022-067316
head(gigs::life6mo)head(gigs::life6mo)
Check required units for a conversion using gigs
report_units(family, acronym)report_units(family, acronym)
family |
A single string denoting the family of growth standards in use.
Must be one of |
acronym |
A single string denoting the specific growth standard in use.
The allowed values of |
Returns a message describing which units are required for conversion in the desired growth standard.
# Get units for working with the IG-21st Newborn Size Standard for # weight-for-GA report_units("ig_nbs", "wfga") # Get units for working with the IG-21st Fetal Standard for # gestational weight gain report_units("ig_fet", "gwgfga")# Get units for working with the IG-21st Newborn Size Standard for # weight-for-GA report_units("ig_nbs", "wfga") # Get units for working with the IG-21st Fetal Standard for # gestational weight gain report_units("ig_fet", "gwgfga")
Convert anthropometric measures to z-scores/centiles using international growth standards
value2zscore(y, x, sex = NULL, family, acronym) value2centile(y, x, sex = NULL, family, acronym)value2zscore(y, x, sex = NULL, family, acronym) value2centile(y, x, sex = NULL, family, acronym)
y |
Numeric vector of length one or more with anthropometric
measurement(s) to convert to centiles/z-scores. Units depend on which
combination of |
x |
Numeric vector of x values, which take on different units depending
on the growth standard in use. To know which units to use, run
By default, gigs will replace out-of-bounds elements in |
sex |
Character vector of length one or more with sex(es), either |
family |
A single string denoting the family of growth standards in use.
Must be one of |
acronym |
A single string denoting the specific growth standard in use.
The allowed values of |
Numeric vector of z-scores/centiles with length equal to the longest input vector.
Inputs other than acronym and family will be recycled by
vctrs::vec_recycle_common(), and must adhere to the vctrs recycling
rules.
For the INTERGROWTH-21st Fetal standards see the
gigs::ig_fet documentation
For the INTERGROWTH-21st Newborn Size standards see the
gigs::ig_nbs documentation
For the INTERGROWTH-21st Postnatal Growth standards see the
gigs::ig_png documentation
For the WHO Growth standards see the gigs::who_gs documentation
report_units() will tell you the units needed for y and x, for
any combination of family and acronym.
# Convert in the weight-for-age standard from the WHO Child Growth Standards value2zscore(3, 0, "M", family = "who_gs", acronym = "wfa") # Or obtain centiles... value2centile(3, 0, "M", family = "who_gs", acronym = "wfa") # Inputs will be recycled if necessary value2centile(2.5, 280, c("M", "F"), family = "ig_nbs", acronym = "wfga")# Convert in the weight-for-age standard from the WHO Child Growth Standards value2zscore(3, 0, "M", family = "who_gs", acronym = "wfa") # Or obtain centiles... value2centile(3, 0, "M", family = "who_gs", acronym = "wfa") # Inputs will be recycled if necessary value2centile(2.5, 280, c("M", "F"), family = "ig_nbs", acronym = "wfga")
A set of nested lists containing tables with reference values at different
z-scores/centiles for valid x values (usually age in days, also
length or height in cm for weight-for-length (wfl) and weight-for-height
(wfh) standards, respectively). The list is ordered by acronym first, then
by sex and finally by z-score/centile.
de Onis M, Garza C, Victora CG, Onyango AW, Frongillo EA, Martines J. The WHO Multicentre Growth Reference Study: planning, study design, and methodology Food Nutr Bull. 2004, 25(1 Suppl):S15-26. doi:10.1177/15648265040251s104
World Health Organisation. WHO child growth standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: methods and development. Technical report, WHO, Geneva, 2006.
World Health Organisation. WHO child growth standards: head circumference-for-age, arm circumference-for-age, triceps skinfold-for-age and subscapular skinfold-for-age: methods and development. Technical report, WHO, Geneva, 2007.
names(gigs::who_gs) head(gigs::who_gs$wfa$male$zscores)names(gigs::who_gs) head(gigs::who_gs$wfa$male$zscores)
A set of nested lists containing tables with LMS values for each sex in different combinations of age/length-height/BMI. The list is ordered by acronym first, then by sex.
World Health Organisation. WHO child growth standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: methods and development. Technical report, WHO, Geneva, 2006.
World Health Organisation. WHO child growth standards: head circumference-for-age, arm circumference-for-age, triceps skinfold-for-age and subscapular skinfold-for-age: methods and development. Technical report, WHO, Geneva, 2007.
names(gigs::who_gs_coeffs) head(gigs::who_gs_coeffs$lhfa$male)names(gigs::who_gs_coeffs) head(gigs::who_gs_coeffs$lhfa$male)
Convert z-scores/centiles to anthropometric measures using international growth standards
zscore2value(z, x, sex = NULL, family, acronym) centile2value(p, x, sex = NULL, family, acronym)zscore2value(z, x, sex = NULL, family, acronym) centile2value(p, x, sex = NULL, family, acronym)
z, p
|
Numeric vector of length one or more with centiles/z-scores to
convert to values. For |
x |
Numeric vector of x values, which take on different units depending
on the growth standard in use. To know which units to use, run
By default, gigs will replace out-of-bounds elements in |
sex |
Character vector of length one or more with sex(es), either |
family |
A single string denoting the family of growth standards in use.
Must be one of |
acronym |
A single string denoting the specific growth standard in use.
The allowed values of |
Numeric vector of expected measurements with length equal to the longest input vector.
For the INTERGROWTH-21st Fetal standards see the
gigs::ig_fet documentation
For the INTERGROWTH-21st Newborn Size standards see the
gigs::ig_nbs documentation
For the INTERGROWTH-21st Postnatal Growth standards see the
gigs::ig_png documentation
For the WHO Growth standards see the gigs::who_gs documentation
# Convert in the weight-for-age standard from the WHO Child Growth Standards zscore2value(-2:2, 0, "M", family = "who_gs", acronym = "wfa") # Or obtain centiles... centile2value(pnorm(-2:2), 0, "M", family = "who_gs", acronym = "wfa") # Inputs will be recycled if necessary centile2value(3, 280, c("M", "F"), family = "ig_nbs", acronym = "wfga")# Convert in the weight-for-age standard from the WHO Child Growth Standards zscore2value(-2:2, 0, "M", family = "who_gs", acronym = "wfa") # Or obtain centiles... centile2value(pnorm(-2:2), 0, "M", family = "who_gs", acronym = "wfa") # Inputs will be recycled if necessary centile2value(3, 280, c("M", "F"), family = "ig_nbs", acronym = "wfga")