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D-score for Child Development
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README.md

dscore

Lifecycle: maturing

The D-score is a numerical score that measures generic development in children. The D-score can be used to analyze and predict development of children using tools developed for numerical measures, like height and weight.

The dscore package contains tools to

  • Calculate D-score from item level responses
  • Transform the D-scores into DAZ, age-standardised Z-scores
  • Map your item names to the GSED convention

The required input consists of item level responses on milestones from widely used instruments for measuring child development.

Installation

You can install the development version from GitHub with:

install.packages("remotes")
remotes::install_github("stefvanbuuren/dscore")

Example

Inspect the data

The milestones dataset in the dscore package contain responses of 27 preterm children measured at various age between birth and 2.5 years on the Dutch Development Instrument (DDI). The dataset looks like:

library(dscore)
head(milestones[, c(1, 3, 4, 9:15)])
#>    id   age    sex ddigmd053 ddigmd056 ddicmm030 ddifmd002 ddifmd003 ddifmm004 ddicmm031
#> 1 111 0.487   male         1         1         1         1         1         0         1
#> 2 111 0.657   male        NA        NA        NA        NA         1         1         1
#> 3 111 1.180   male        NA        NA        NA        NA        NA        NA        NA
#> 4 111 1.906   male        NA        NA        NA        NA        NA        NA        NA
#> 5 177 0.550 female         1         1         1         1         1         1         1
#> 6 177 0.767 female        NA        NA        NA        NA         1         1         1

Each row corresponds to a visit. Most children have three or four visits. Columns starting with ddi hold the responses on DDI-items. A 1 means a PASS, a 0 means a FAIL, and NA means that the item was not administered.

The get_labels() function obtains the labels of the milestones, e.g.,

items <- names(milestones)[9:15]
labels <- get_labels(items)
print(data.frame(items, labels), right = FALSE, row.names = FALSE)
#>  items     labels                                    
#>  ddigmd053 Smiles in response (M; can ask parents)   
#>  ddigmd056 vocalizes in response                     
#>  ddicmm030 Follows with eyes and head 30d  < 0 > 30d 
#>  ddifmd002 Hands open occasionally                   
#>  ddifmd003 Watches own hands                         
#>  ddifmm004 Moves legs equally well                   
#>  ddicmm031 Lifts chin off table for a moment

Calculate the D-score and DAZ

The milestones dataset has properly named columns that identifies each item. Calculating the D-score and DAZ is then done by:

ds <- dscore(milestones)
dim(ds)
#> [1] 100   6

where ds is a data.frame with the same number of rows than the input data. The first six rows are

head(ds)
#>       a  n     p    d   sem    daz
#> 1 0.487 11 0.909 31.3 1.584 -1.442
#> 2 0.657 14 0.643 34.7 0.981 -2.176
#> 3 1.180 19 0.947 48.7 1.551 -1.191
#> 4 1.906 13 0.846 60.0 1.177 -0.627
#> 5 0.550 11 0.818 29.5 1.334 -2.767
#> 6 0.767 14 0.786 36.5 0.920 -2.533

The table below provides the interpretation of each column:

Name Interpretation
a Decimal age
n Number of items used to calculate D-score
p Percentage of passed milestones
d D-score estimate, mean of posterior
sem Standard error of measurement, standard deviation of the posterior
daz D-score corrected for age

Graphs of D-score and DAZ

The milestones data and the result can be combined as

md <- cbind(milestones, ds)

The individual developmental curves of 27 children can be plotted.

library(ggplot2)
library(dplyr)
ggplot(md, aes(x = a, y = d, group = id, color = sex)) + 
  xlab("Age (in years)") + 
  ylab("D-score") +
  geom_line(lwd = 0.1) +
  geom_point(size = 1) +
  scale_colour_hue(l = 45) +
  theme_light()

The DAZ is an age-standardized D-score with a standard normal distribution with mean 0 and variance 1. The individual DAZ curves are plotted as

ggplot(md, aes(x = a, y = daz, group = id, color = sex)) + 
  theme_light() +
  geom_rect(xmin = 0, xmax = 3, ymin = -2, ymax = 2, 
            fill = "grey97", linetype = 0, show.legend = FALSE) +
  geom_hline(yintercept = -1:1, colour = "white", lwd = 0.5) +
  coord_cartesian(ylim = c(-4, 4)) +
  geom_line(lwd = 0.1) +
  geom_point(size = 1) +
  scale_colour_hue(l = 45) +
  xlab("Age (in years)") +
  ylab("DAZ")

Note that the D-scores and DAZ are a little lower than average. The explanation here is that these all children are born preterm.

Mapping items names

The dscore() function assumes that the item names in your data follow the GSED naming convention. This convention is a nine-position word that identifies the instrument, the domain, the administration mode and the item number. You may decompose an item names into these components, as follows:

decompose_itemnames(items)
#>   instrument domain mode number
#> 1        ddi     gm    d    053
#> 2        ddi     gm    d    056
#> 3        ddi     cm    m    030
#> 4        ddi     fm    d    002
#> 5        ddi     fm    d    003
#> 6        ddi     fm    m    004
#> 7        ddi     cm    m    031

which gives four components of the seven items.

In practice, you will need to spend some time in renaming your own item names according to the GSED convention. In order to ease this process, the dscore package offers several functions that can help.

First of all, the measurement instrument in your data needs to be one of the instruments supported by the dscore package. Here’s the table of instrument names and number of items that are currently defined in the package:

Code Instrument Items dutch gcdg gsed
aqi Ages & Stages Questionnaires-3 230 29 17
bar Barrera Moncada 22 15 13
bat Battelle Development Inventory and Screener-2 137
by1 Bayley Scales for Infant and Toddler Development-1 156 85 76
by2 Bayley Scales for Infant and Toddler Development-2 121 16 16
by3 Bayley Scales for Infant and Toddler Development-3 320 105 67
cro Caregiver Reported Early Development Instrument (CREDI) 149 62
ddi Dutch Development Instrument (Van Wiechenschema) 77 76 65 64
den Denver-2 111 67 50
dmc Developmental Milestones Checklist 66 43
gri Griffiths Mental Development Scales 312 104 93
iyo Infant and Young Child Development (IYCD) 90 55
kdi Kilifi Developmental Inventory 69
mac MacArthur Communicative Development Inventory 6 3 3
mds WHO Motor Development Milestones 6 1
mdt Malawi Developmental Assessment Tool (MDAT) 136 126
peg Pegboard 2 1 1
pri Project on Child Development Indicators (PRIDI) 63
sbi Stanford Binet Intelligence Scales-4/5 33 6 1
sgr Griffiths for South Africa 58 19 19
tep Test de Desarrollo Psicomotor (TEPSI) 61 33 31
vin Vineland Social Maturity Scale 50 17 17
2275 76 565 807
Extensions
rap Global Scale of Early Development - RAPID SF 139
mul Mullen Scales of Early Learning 232 85
hyp [special codes, hypothetical instrument] 5
2651 76 565 892

If your instrument is not here, you cannot calculate the D-score. But even if your instrument is in the table, there is no garantee that it can be used for the D-score. Instruments are tied to the D-score by means of a measurement model. The measurement model provides the key for converting the item responses to the D-score.

The dscore package currently support three keys: dutch, gcdg and gsed. Although there is much overlap, different keys cover different instrument. The table above displays the number of items per instrument under each of the three keys. If the entry is blank, the key does not cover the instrument.

For some instruments, e.g., for cro only one choice is possible (only key gsed). For gri, we may choose between gcdg and gsed. The actual choice will depend on the goals of your analysis. If you want to compare to other D-scores calculated under key gcdg, or reproduce an analysis made under key, then pick gcdg. If that is not the case, then gsed is the better choice since it account for a wider variety of comparisons to other instruments. The differences between the D-scores calculated under different keys are small, but these are not identical. If you don’t specify key, the dscore() function will use key = "gsed" for maximum instrument coverage.

The actual age coverage for an instrument is determined by the age design of the original cohorts used to create the key. The figure above indicates the age range currenytly supported by the GSED key. Some instruments contain many items for the first two years (e.g., by1, dmc) whereas others cover primarly upper ages (e.g., tep, mul)

The dscore package will recognize 2651 item names. Call the get_itemnames() function with any arguments to see them all. If you want to see all names of a particular instrument and a particul domain, then use

# find all item name in mdt (MDAT), domain gm (gross motor)
items <- get_itemnames(instrument = "mdt", domain = "gm")
items
#>  [1] "mdtgmd001" "mdtgmd002" "mdtgmd003" "mdtgmd004" "mdtgmd005" "mdtgmd006" "mdtgmd007" "mdtgmd008" "mdtgmd009" "mdtgmd010"
#> [11] "mdtgmd011" "mdtgmd012" "mdtgmd013" "mdtgmd014" "mdtgmd015" "mdtgmd016" "mdtgmd017" "mdtgmd018" "mdtgmd019" "mdtgmd020"
#> [21] "mdtgmd021" "mdtgmd022" "mdtgmd023" "mdtgmd024" "mdtgmd025" "mdtgmd026" "mdtgmd027" "mdtgmd028" "mdtgmd029" "mdtgmd030"
#> [31] "mdtgmd031" "mdtgmd032" "mdtgmd033" "mdtgmd034"

The labels for this set of items can be found by

head(get_labels(items))
#>                                             mdtgmd001                                             mdtgmd002 
#>                                "Lifts chin off floor"    "Prone (on tummy), can lift head up to 90 degrees" 
#>                                             mdtgmd003                                             mdtgmd004 
#>                "Holds head upright for a few seconds"                       "Pulls to sit with no head lag" 
#>                                             mdtgmd005                                             mdtgmd006 
#>          "Lifts head, shoulders and chest when prone" "Bears weight on legs when held in standing position"

Alternatively, use decompose_itemnames() to break down the item names into its components.

Using these functions, it should be relatively easy to map and rename your item names to the GSED-convention.

Resources

Books and reports

  1. Introduction into the D-score
  2. Inventory of 147 instruments for measuring early child development: Fernald et al. (2017)

Keys

  1. Project with dutch key, 0-2 years: van Buuren (2014)
  2. Project with gcdg key: Weber et al. (2019)
  3. Project with gsed key: GSED team (Maureen Black, Kieran Bromley, Vanessa Cavallera (lead author), Jorge Cuartas, Tarun Dua (corresponding author), Iris Eekhout, Günther Fink, Melissa Gladstone, Katelyn Hepworth, Magdalena Janus, Patricia Kariger, Gillian Lancaster, Dana McCoy, Gareth McCray, Abbie Raikes, Marta Rubio-Codina, Stef van Buuren, Marcus Waldman, Susan Walker and Ann Weber) (2019)

Methodology

  1. Interval scale: Jacobusse, van Buuren, and Verkerk (2006)
  2. Adaptive testing: Jacobusse and van Buuren (2007)

Literature

Fernald, L.C.H., E. Prado, P. Kariger, and A. Raikes. 2017. “A Toolkit for Measuring Early Childhood Development in Low and Middle-Income Countries.” http://documents.worldbank.org/curated/en/384681513101293811/A-toolkit-for-measuring-early-childhood-development-in-low-and-middle-income-countries.

GSED team (Maureen Black, Kieran Bromley, Vanessa Cavallera (lead author), Jorge Cuartas, Tarun Dua (corresponding author), Iris Eekhout, Günther Fink, Melissa Gladstone, Katelyn Hepworth, Magdalena Janus, Patricia Kariger, Gillian Lancaster, Dana McCoy, Gareth McCray, Abbie Raikes, Marta Rubio-Codina, Stef van Buuren, Marcus Waldman, Susan Walker and Ann Weber). 2019. “The Global Scale for Early Development (Gsed).” Early Childhood Matters. https://earlychildhoodmatters.online/2019/the-global-scale-for-early-development-gsed/.

Jacobusse, G., and S. van Buuren. 2007. “Computerized Adaptive Testing for Measuring Development of Young Children.” Statistics in Medicine 26 (13): 2629–38. https://stefvanbuuren.name/publication/2007-01-01_jacobusse2007/.

Jacobusse, G., S. van Buuren, and P.H. Verkerk. 2006. “An Interval Scale for Development of Children Aged 0-2 Years.” Statistics in Medicine 25 (13): 2272–83. https://stefvanbuuren.name/publication/2006-01-01_jacobusse2006/.

van Buuren, S. 2014. “Growth Charts of Human Development.” Statistical Methods in Medical Research 23 (4): 346–68. https://stefvanbuuren.name/publication/2014-01-01_vanbuuren2014gc/.

Weber, A.M., M. Rubio-Codina, S.P. Walker, S. van Buuren, I. Eekhout, S. Grantham-McGregor, M.C. Araujo, et al. 2019. “The D-Score: A Metric for Interpreting the Early Development of Infants and Toddlers Across Global Settings.” BMJ Global Health 4: e001724. https://gh.bmj.com/content/bmjgh/4/6/e001724.full.pdf.

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