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ob_convertr

Convert between IOTF, WHO and CDC prevalence rates for child thinness, overweight and obesity


Description

Child thinness, overweight and obesity are defined as the child's body mass index (BMI) lying beyond a pre-specified reference cutoff. Three references are compared here: IOTF (International Obesity Task Force), WHO (World Health Organization) and CDC (US Centers for Disease Control and Prevention), each of which have their own cutoffs. ob_convertr takes age-sex-specific prevalence rates of thinness, overweight and obesity based on one cutoff, and converts them to rates based on a different cutoff, using a novel estimation algorithm.

Usage

ob_convertr(
  prev = 50,
  age,
  sex,
  from,
  to,
  prev_true = NA,
  report = c("vector", "wider", "longer"),
  plot = c("no", "density", "compare"),
  data = parent.frame()
)

Arguments

prev

vector of age-sex-specific percentage prevalence rates.

age

vector of ages between 2 and 18 years corresponding to each rate.

sex

vector of the sexes corresponding to each rate, coded as either 'boys/girls' or 'male/female' or '1/2' (upper or lower case, and only the first character considered).

from

the BMI cutoff (see Details) on which the prevalence is based.

to

the BMI cutoff (see Details) on which to base the converted prevalence.

prev_true

optional vector of known percentage prevalence rates corresponding to to, for validation purposes.

report

character controlling the format of the returned data: 'vector' for the estimated prevalence rates, 'wider' for the working tibble in wide format, i.e. the from and to data side by side, or 'longer' for the tibble in long format, i.e. two rows per rate, one for from and one for to.

plot

character controlling what if anything is plotted: 'no' for no plot, 'density' to display the BMI density distributions and cutoffs corresponding to from and to, or 'compare' to display the predicted prevalence rates plotted against the observed rates in prev_true.

data

data frame containing prev, age, sex and prev_true.

Details

The IOTF cutoffs correspond to the value of BMI (kg/m^2) at age 18: IOTF35 (morbid obesity), IOTF30 (obesity), IOTF25 (overweight), IOTF18.5 (grade 1 thinness), IOTF17 (grade 2 thinness) and IOTF16 (grade 3 thinness).

The WHO cutoffs correspond to BMI z_scores. Age 5-19 years, WHO+2 (obesity), WHO+1 (overweight) and WHO-2 (thinness). Age 0-5 years, WHO+3 (obesity), WHO+2 (overweight) and WHO-2 (thinness).

The CDC cutoffs correspond to BMI centiles: CDC95 (obesity), CDC85 (overweight) and CDC5 (thinness).

Note 1: the overweight category needs to be analysed as overweight plus obesity. To predict overweight excluding obesity, first calculate predicted overweight plus obesity then subtract predicted obesity.

Note 2: the category labels are harmonised and not necessarily as originally defined.

The conversion algorithm exploits the fact that all three references are based on the LMS method, which allows prevalence to be converted to a common BMI centile and z-score scale.

The algorithm is commutative, which means that converting a prevalence rate from cutoff A to cutoff B and then from B to A returns the original value.

Value

Either the converted prevalence rates or a plot visualizing the findings, depending on the report and plot settings.

Author(s)

Examples

## convert 10% IOTF overweight prevalence (cutoff IOTF25) in 8-year-old boys
## to the overweight prevalence based on WHO, i.e. cutoff WHO+1
ob_convertr(prev = 10, age = 8, sex = 'boys', from = 'IOTF25', to = 'WHO+1')

## compare the BMI density functions and cutoffs for IOTF25 and WHO+1
ob_convertr(prev = 10, age = 8, sex = 'boys', from = 'IOTF25', to = 'WHO+1', plot = 'density')

#' ## convert IOTF overweight prevalence to WHO overweight prevalence
## and compare with true value - boys and girls age 7-17
## note the need to first add obesity prevalence to overweight prevalence
data(deren)
deren <- within(deren, {
  IOTF25 = IOTF25 + IOTF30
  `WHO+1` = `WHO+1` + `WHO+2`})
ob_convertr(prev = IOTF25, age = Age, sex = Sex, from = 'IOTF25', to = 'WHO+1',
   prev_true = `WHO+1`, data = deren, plot = 'compare')

sitar

Super Imposition by Translation and Rotation Growth Curve Analysis

v1.2.0
GPL (>= 2)
Authors
Tim Cole [aut, cre] (<https://orcid.org/0000-0001-5711-8200>)
Initial release

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