Categorise weight-for-age z-scores into weight-for-age strata

Usage

categorise_wfa(waz, outliers = FALSE)

Arguments

waz: A numeric vector of length one or more containing weight-for-age z-scores (WAZs).
outliers: A single TRUE or FALSE value specifying whether implausible z-score thresholds should be applied. Default = FALSE.

Value

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.

Details

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

References

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

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

Examples

waz <- c(-6.5, -3.5, -2.5, 0, 2.5, 3.5)
categorise_wfa(waz, outliers = FALSE)
#> [1] underweight_severe underweight_severe underweight        normal_weight     
#> [5] overweight         overweight        
#> Levels: underweight_severe underweight normal_weight overweight
categorise_wfa(waz, outliers = TRUE)
#> [1] outlier            underweight_severe underweight        normal_weight     
#> [5] overweight         overweight        
#> Levels: underweight_severe underweight normal_weight overweight outlier