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 sampling and measurement-related biases checks in the data set. The test suite in this function follows the recommendation made by Bilukha, O., & Kianian, B. (2023) on the plausibility of constructing a comprehensive plausibility check for MUAC data similar to WFHZ to evaluate its acceptability when the variable age exists in the data set.

The function works on a data frame returned from this package's wrangling function for age and for MFAZ data.

Usage

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

Arguments

df

A data set object of class data.frame to check.

sex

A vector of class numeric of child's sex.

muac

A vector of class numeric of child's MUAC in centimeters.

age

A vector of class double of child's age in months.

flags

A vector of class numeric of flagged records.

Value

A summarized table of class data.frame, of length 17 and width 1, for the plausibility test results and their respective acceptability ratings.

Details

Whilst the function uses the same test checks and criteria as that of WFHZ in the SMART plausibility check, the percent of flagged data is evaluated using a 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.

See also

mw_wrangle_age() mw_wrangle_muac() mw_stattest_ageratio() flag_outliers()

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
)
#> # A tibble: 1 × 17
#>       n flagged flagged_class sex_ratio sex_ratio_class age_ratio
#>   <int>   <dbl> <fct>             <dbl> <chr>               <dbl>
#> 1  1191 0.00504 Excellent         0.297 Excellent           0.636
#> # 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>