Check the plausibility and acceptability of MUAC-for-age z-score (MFAZ) data

Check the overall plausibility and acceptability of MFAZ data through a structured test suite encompassing checks for sampling and measurement-related biases in the dataset. This test suite follows the recommendation made by Bilukha & Kianian (2023) on the plausibility of constructing a comprehensive plausibility check for MUAC data similar to weight-for-height z-score to evaluate its acceptability when age values are available in the dataset.

The function works on a data.frame returned from wrangling functions for age and for MUAC-for-age z-score data available from this package.

Usage

mw_plausibility_check_mfaz(df, sex, muac, age, flags, ...)

Arguments

df: A data.frame object to check.
sex: A numeric vector for child's sex with 1 = males and 2 = females.
muac: A numeric vector of child's MUAC in centimeters.
age: A vector of class double of child's age in months.
flags: A numeric vector of flagged records.
...: A vector of class character, specifying the categories for which the analysis should be summarised for. Usually geographical areas. More than one vector can be specified.

Value

A single-row summary tibble with columns containing the plausibility check results. If ungrouped analysis, the output will consist of 17 columns and one row; otherwise, the number of columns will vary according to the number vectors specified, and the number of rows to the categories within the grouping variables.

Details

Whilst the function uses the same checks and criteria as those for weight-for-height z-scores in the SMART plausibility check, the percent of flagged records is evaluated using different cut-off points, with a maximum acceptability of 2.0% as shown below:

Excellent	Good	Acceptable	Problematic
0.0 - 1.0	>1.0 - 1.5	>1.5 - 2.0	>2.0

References

Bilukha, O., & Kianian, B. (2023). Considerations for assessment of measurement quality of mid‐upper arm circumference data in anthropometric surveys and mass nutritional screenings conducted in humanitarian and refugee settings. Maternal & Child Nutrition, 19, e13478. https://doi.org/10.1111/mcn.13478

SMART Initiative (2017). Standardized Monitoring and Assessment for Relief and Transition. Manual 2.0. Available at: https://smartmethodology.org.

Examples

## First wrangle age data ----
data <- mw_wrangle_age(
  df = anthro.01,
  dos = dos,
  dob = dob,
  age = age,
  .decimals = 2
)

## Then wrangle MUAC data ----
data_muac <- mw_wrangle_muac(
  df = data,
  sex = sex,
  age = age,
  muac = muac,
  .recode_sex = TRUE,
  .recode_muac = TRUE,
  .to = "cm"
)
#> ================================================================================

## And finally run plausibility check ----
mw_plausibility_check_mfaz(
  df = data_muac,
  flags = flag_mfaz,
  sex = sex,
  muac = muac,
  age = age,
  area, team
)
#> # A tibble: 8 × 19
#> # Groups:   area, team [8]
#>   area      team     n flagged flagged_class sex_ratio sex_ratio_class age_ratio
#>   <chr>    <int> <int>   <dbl> <fct>             <dbl> <chr>               <dbl>
#> 1 Distric…     1   120 0       Excellent         0.784 Excellent          0.0762
#> 2 Distric…     2   216 0       Excellent         0.838 Excellent          0.713 
#> 3 Distric…     3   104 0       Excellent         0.281 Excellent          0.585 
#> 4 Distric…     4    65 0       Excellent         0.457 Excellent          0.619 
#> 5 Distric…     1   200 0       Excellent         0.724 Excellent          0.296 
#> 6 Distric…     6   140 0.00714 Excellent         0.447 Excellent          0.0586
#> 7 Distric…     7   188 0.0160  Acceptable        0.512 Excellent          0.561 
#> 8 Distric…    10   158 0.0127  Good              0.474 Excellent          0.375 
#> # ℹ 11 more variables: age_ratio_class <chr>, dps <dbl>, dps_class <chr>,
#> #   sd <dbl>, sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>,
#> #   kurt_class <fct>, quality_score <dbl>, quality_class <fct>