pygrowup2 1.0.0

Calculate z-scores of anthropometric measurements based on WHO child growth standards

pygrowup

pygrowup calculates z-scores for the growth measurements of children. The indicators it supports are:

• weight-for-age
• length/height-for-age
• weight-for-length/height
• head circumference-for-age
• arm circumference-for-age (MUAC)
• BMI-for-age
• subscapular skinfold-for-age
• triceps skinfold-for-age

These are all based on the WHO Child Growth Standards (with the exception of MUAC for 5-19 years; see notes below about an additional data source).

It requires Python 3.10 or later.

Installation

pip install pygrowup2

Or install from source:

pip install git+https://github.com/jbaldivieso/pygrowup2.git

Usage

pygrowup is modelled based on an "observation" of a child. The age of the child at the time of the observation is captured either explicitly, or inferred based on the child's date of birth and the date of the observation. Once an Observation object is instantiated, multiple methods are available to compute the z-scores for the various indicators.

Examples:

from datetime import date, timedelta
from decimal import Decimal

from pygrowup import Observation

# Three ways of instantiating roughly comparable Observations for a
# 13-month-old boy.
today = date.today()
thirteen_months_ago = today - timedelta(days=365 + 30)
obs = Observation(sex=Observation.MALE, age_in_days=13*30)
obs = Observation(sex=Observation.MALE, age_in_months=13)
obs = Observation(sex=Observation.MALE, dob=thirteen_months_ago,
                  date_of_observation=today)

# Now calculate some z-scores for this child
print(obs.head_circumference_for_age(Decimal('46.5')))

# Or, if you're into the whole brevity thing, each method has an alias. And
# measurements can be expressed as ints, floats, or Decimals.
print(obs.hcfa(46.5))  # Same result as the previous computation.

print(obs.weight_for_age(Decimal('9.6')))
print(obs.length_or_height_for_age(Decimal('77')))
print(obs.weight_for_length(Decimal('9.6'), Decimal('77')))

Except for weight-for-length and weight-for-height, the z-score methods expect a single argument for the measurement. (That means BMI must be calculated separately and passed in as the measurement.)

Each method for calculating z-scores has a verbose name and a more succinct alias. The full list of them (aliases in parentheses) is:

• arm_circumference_for_age (acfa)
• bmi_for_age (bmifa)
• head_circumference_for_age (hcfa)
• length_or_height_for_age (lhfa)
• subscapular_skinfold_for_age (ssfa)
• triceps_skinfold_for_age (tsfa)
• weight_for_age (wfa)
• weight_for_height (wfh)
• weight_for_length (wfl)

Differences from earlier versions of pygrowup

Some differences from the current version and version 0.8.1 and before are:

• This package employs precision based off of a child's age to the day-level where possible (up to 5 years of age); the original rounds to nearest completed month
• There is no CDC-data-based option for the growth metrics
• This package includes the more esoteric metrics (subscapular skinfold-for-age, triceps skinfold-for-age, arm circumference-for-age)

About tests

Run the tests like this:

python -m pygrowup.tests

The tests run against two distinct datasets: one originating from the R implementation of WHO's igrowup software, and the other based on data SPOON (see credits) collected and processed using the Stata version of igrowup. Using the WHO data, this package's z-scores are generally within 0.1 of the test datasets with 7 (with earlier versions of Python, this was only 2; unclear why) exceptions. However, a number of results differ by at least 0.05. It's not clear if that's due to the other implementations' use of floating point arithmetic (in contrast with this package's use of python's Decimal library for more precise computation) or if there are small inaccuracies in this package. So, caveat emptor!

About MUAC data extension

The WHO dataset for mid-upper arm circumference only goes to 5 years. However, it can be a useful proxy when weight is difficult to measure. Therefore, an option is available to extend the range to 19 years by employing a dataset compiled by Lazarus Mramba, et al. The data themselves were derived from a PDF from that publication.

Credits

The initial implementation of this work was by SPOON, and was heavily inspired by Evan Wheeler's original pygrowup.