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Check the plausibility and acceptability of raw MUAC data

Source: R/plausibility_check_muac.R
mw_plausibility_check_muac.Rd

Check the overall plausibility and acceptability of raw MUAC data through a structured test suite encompassing checks for sampling and measurement-related biases in the dataset. The test suite in this function follows the recommendation made by Bilukha & Kianian (2023).

Usage

mw_plausibility_check_muac(df, sex, muac, flags, ...)

Arguments

df

A data.frame object to check. It must have been wrangled using the mw_wrangle_muac() function.

sex

A numeric vector for child's sex with 1 = males and 2 = females.

muac

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

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 nine 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

Cut-off points used for the percent of flagged records:

ExcellentGoodAcceptableProblematic
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_muac() flag_outliers()

Examples

## First wrangle MUAC data ----
df_muac <- mw_wrangle_muac(
  df = anthro.01,
  sex = sex,
  muac = muac,
  age = NULL,
  .recode_sex = TRUE,
  .recode_muac = FALSE,
  .to = "none"
)

## Then run the plausibility check ----
mw_plausibility_check_muac(
  df = df_muac,
  flags = flag_muac,
  sex = sex,
  muac = muac,
  area, team # group analysis by survey area and by survey team
)
#> # A tibble: 8 × 11
#> # Groups:   area, team [8]
#>   area        team     n flagged flagged_class sex_ratio sex_ratio_class   dps
#>   <chr>      <int> <int>   <dbl> <fct>             <dbl> <chr>           <dbl>
#> 1 District E     1   120  0      Excellent         0.784 Excellent        9.94
#> 2 District E     2   216  0      Excellent         0.838 Excellent        5.17
#> 3 District E     3   104  0      Excellent         0.281 Excellent        7.75
#> 4 District E     4    65  0      Excellent         0.457 Excellent       22.3 
#> 5 District G     1   200  0      Excellent         0.724 Excellent       24.4 
#> 6 District G     6   140  0      Excellent         0.447 Excellent        5.11
#> 7 District G     7   188  0.0160 Acceptable        0.512 Excellent       12.7 
#> 8 District G    10   158  0      Excellent         0.474 Excellent       12.3 
#> # ℹ 3 more variables: dps_class <chr>, sd <dbl>, sd_class <fct>