Introduction
Plausibility check is a tool that evaluates the overall quality and acceptability of anthropometric data to ensure its suitability for informing decision-making process.
mwana provides a set of handy functions to facilitate this evaluation. These functions allow users to assess the acceptability of weight-for-height z-score (WFHZ) and mid upper-arm circumference (MUAC) data. The evaluation of the latter can be done on the basis of MUAC-for-age z-score (MFAZ) or raw MUAC values.
In this vignette, we will learn how to use these functions and when to consider using MFAZ plausibility check over the one based on raw MUAC values. For demonstration, we will use a mwana built-in sample dataset named anthro.01. This dataset is contains district level SMART surveys from anonymized locations. Do ?anthro.01 in R console to read more about the dataset.
We will begin the demonstration with the plausibility check that you are most familiar with and then proceed to the ones you are less familiar with.
Plausibility check of WFHZ data
We check the plausibility of WFHZ data by calling the mw_plausibility_check_wfhz() function. Before doing that, we need ensure the data is in the right “shape and format” that is accepted and understood by the function. Don’t worry, you will soon learn how to get there. But first, let’s take a moment to walk you through some key features about this function.
mw_plausibility_check_wfhz() is a replica of the plausibility check in ENA for SMART software of the SMART Methodology. Under the hood, it runs the same statistical tests you already know from SMART, and it applies the same rating and scoring criteria. Beware though that there are some small differences to have in mind:
mw_plausibility_check_wfhz()does not include MUAC in its test suite. This is simply due the fact that now you can run a more comprehensive test suite for MUAC.mw_plausibility_check_wfhz()allows user to run checks on a multiple area dataset at once, without having to repeat the same workflow over and over again for the number of areas the data holds.
That is it! Now we can begin delving into the “how to”.
It is always a good practice to start off by inspecting the dataset. Let’s check the first 6 rows of our dataset:
head(anthro.01)# A tibble: 6 × 11
area dos cluster team sex dob age weight height edema
<chr> <date> <int> <int> <chr> <date> <int> <dbl> <dbl> <chr>
1 District E 2023-12-04 1 3 m NA 59 15.6 109. n
2 District E 2023-12-04 1 3 m NA 8 7.5 68.6 n
3 District E 2023-12-04 1 3 m NA 19 9.7 79.5 n
4 District E 2023-12-04 1 3 f NA 49 14.3 100. n
5 District E 2023-12-04 1 3 f NA 32 12.4 92.1 n
6 District E 2023-12-04 1 3 f NA 17 9.3 77.8 n
# ℹ 1 more variable: muac <int>We can see that the dataset has eleven variables, and the way how their respective values are presented. This is useful to inform the data wrangling workflow.
Data wrangling
As mentioned somewhere above, before we supply a data object to mw_plausibility_check_wfhz(), we need to wrangle it first. This task is executed by mw_wrangle_age() and mw_wrangle_wfhz(). Read more about the technical documentation by doing help(mw_wrangle_age) or help(mw_wrangle_wfhz) in R console.
Age
We use mw_wrangle_age() to calculate child’s age in months based on the date of data collection and child’s date of birth. This is done as follows:
age_mo <- anthro.01 |>
mw_wrangle_age(
dos = dos,
dob = dob,
age = age,
.decimals = 2
)This will return:
# A tibble: 6 × 12
area dos cluster team sex dob age weight height edema
<chr> <date> <int> <int> <chr> <date> <int> <dbl> <dbl> <chr>
1 District E 2023-12-04 1 3 m NA 59 15.6 109. n
2 District E 2023-12-04 1 3 m NA 8 7.5 68.6 n
3 District E 2023-12-04 1 3 m NA 19 9.7 79.5 n
4 District E 2023-12-04 1 3 f NA 49 14.3 100. n
5 District E 2023-12-04 1 3 f NA 32 12.4 92.1 n
6 District E 2023-12-04 1 3 f NA 17 9.3 77.8 n
# ℹ 2 more variables: muac <int>, age_days <dbl>Wrangling all other remaining variables
For this, we call mw_wrangle_wfhz() as follows:
wrangled_df <- anthro.01 |>
mw_wrangle_wfhz(
sex = sex,
weight = weight,
height = height,
.recode_sex = TRUE
)In this example, the argument .recode_sex was set to TRUE. That is because under the hood, to compute the z-scores, a task made possible thanks to the zscorer package, it uses sex coded into 1 and 2 for male and female, respectively. This means that if our sex variable is already in 1 and 2’s, we would set it to FALSE.
Note
If by any chance your sex variable is coded in any other different way than aforementioned, then you will have to recode it outside
mwanautilities and then set.recode_sexaccordingly.
Under the hood, after recoding (or not) the sex variables, mw_wrangle_wfhz() computes the z-scores, then identifies outliers and adds them to the dataset. Two new variables (wfhz and flag_wfhz) are created and added to the dataset. We can see this below:
================================================================================# A tibble: 6 × 3
area wfhz flag_wfhz
<chr> <dbl> <dbl>
1 District E -1.83 0
2 District E -0.956 0
3 District E -0.796 0
4 District E -0.74 0
5 District E -0.679 0
6 District E -0.432 0On to de facto plausibility check of WFHZ data
We can check the plausibility of our data by calling mw_plausibility_check_wfhz() function as demonstrated below:
x <- wrangled_df |>
mw_plausibility_check_wfhz(
sex = sex,
age = age,
weight = weight,
height = height,
flags = flag_wfhz
)Or we can chain all previous functions in this way:
x <- anthro.01 |>
mw_wrangle_age(
dos = dos,
dob = dob,
age = age,
.decimals = 2
) |>
mw_wrangle_wfhz(
sex = sex,
weight = weight,
height = height,
.recode_sex = TRUE
) |>
mw_plausibility_check_wfhz(
sex = sex,
age = age,
weight = weight,
height = height,
flags = flag_wfhz
)The returned output is:
================================================================================# A tibble: 1 × 19
n flagged flagged_class sex_ratio sex_ratio_class age_ratio
<int> <dbl> <fct> <dbl> <chr> <dbl>
1 1191 0.0101 Excellent 0.297 Excellent 0.409
# ℹ 13 more variables: age_ratio_class <chr>, dps_wgt <dbl>,
# dps_wgt_class <chr>, dps_hgt <dbl>, dps_hgt_class <chr>, sd <dbl>,
# sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, kurt_class <fct>,
# quality_score <dbl>, quality_class <fct>As we can see, the returned output is a summary table of statistics and ratings. We can neat it for more clarity and readability. We can achieve this by chaining mw_neat_output_wfhz() to the previous pipeline:
anthro.01 |>
mw_wrangle_age(
dos = dos,
dob = dob,
age = age,
.decimals = 2
) |>
mw_wrangle_wfhz(
sex = sex,
weight = weight,
height = height,
.recode_sex = TRUE
) |>
mw_plausibility_check_wfhz(
sex = sex,
age = age,
weight = weight,
height = height,
flags = flag_wfhz
) |>
mw_neat_output_wfhz()This will give us:
================================================================================# A tibble: 1 × 19
`Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`
<int> <chr> <fct> <chr>
1 1191 1.0% Excellent 0.297
# ℹ 15 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>,
# `Class. of age ratio` <chr>, `DPS weight (#)` <dbl>,
# `Class. DPS weight` <chr>, `DPS height (#)` <dbl>,
# `Class. DPS height` <chr>, `Standard Dev* (#)` <dbl>,
# `Class. of standard dev` <chr>, `Skewness* (#)` <dbl>,
# `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>,
# `Class. of kurtosis` <fct>, `Overall score` <dbl>, …An already formatted table, with scientific notations converted to standard notations, etc.
When working on a multiple area dataset, for instance districts, we can check the plausibility of all districts in the dataset at once by simply chaining the previous workflow with group_by() function from the dplyr package:
## Load library ----
library(dplyr)
## The workflow ----
anthro.01 |>
mw_wrangle_age(
dos = dos,
dob = dob,
age = age,
.decimals = 2
) |>
mw_wrangle_wfhz(
sex = sex,
weight = weight,
height = height,
.recode_sex = TRUE
) |>
group_by(area) |>
mw_plausibility_check_wfhz(
sex = sex,
age = age,
weight = weight,
height = height,
flags = flag_wfhz
) |>
mw_neat_output_wfhz()This will return the following:
================================================================================# A tibble: 2 × 20
`Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`
<chr> <int> <chr> <fct>
1 District E 505 0.8% Excellent
2 District G 686 1.2% Excellent
# ℹ 16 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>,
# `Class. of age ratio` <chr>, `DPS weight (#)` <chr>,
# `Class. DPS weight` <dbl>, `DPS height (#)` <chr>,
# `Class. DPS height` <dbl>, `Standard Dev* (#)` <chr>,
# `Class. of standard dev` <dbl>, `Skewness* (#)` <chr>,
# `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>,
# `Class. of kurtosis` <dbl>, `Overall score` <fct>, …At this point, you have reached the end of your workflow 🎉 .
Plausibility check of MFAZ data
We will assess the plausibility of MUAC data through MFAZ if we have age variable available in our dataset.
Note
The plausibility check for MFAZ data was built based on the insights gotten from Bilukha, O., & Kianian, B. (2023) research presented at the 2023 High-Level Technical Assessment Workshop held in Nairobi, Kenya. Results from this research suggested a feasibility of applying the similar plausibility check as that in WFHZ for MFAZ, with a difference in the amount of flags to be considered: maximum of 2%.
We can run MFAZ plausibility check by calling mw_plausibility_check_mfaz(). As in WFHZ, we first need to ensure that the data is in the right shape and format that is accepted and understood by the function. The workflow starts with processing age; for this, we approach the same way as in Section 1.1.1.1.
Age ratio test in MFAZ
As you know, the age ratio test in WFHZ is done on children aged 6 to 29 months old over those aged 30 to 59 months old. This is different in MFAZ. The test is done on children aged 6 to 23 months over those aged 24 to 59 months old. This is as in SMART MUAC Tool. The test results is also used in the prevalence analysis to implement what the SMART MUAC tool does. This is further demonstrated in the vignette about prevalence.
Wrangling MFAZ data
This is the job of mw_wrangle_muac() function. We use it as follows:
anthro.01 |>
mw_wrangle_age(
dos = dos,
dob = dob,
age = age,
.decimals = 2
) |>
mw_wrangle_muac(
sex = sex,
muac = muac,
age = "age",
.recode_sex = TRUE,
.recode_muac = TRUE,
.to = "cm"
)Just as in WFHZ wrangler, under the hood, mw_wrangle_muac() computes the z-scores then identifies outliers and flags them. These are stored in the mfaz and flag_mfaz variables that are created and added to the dataset.
The above code returns:
================================================================================# A tibble: 1,191 × 14
area dos cluster team sex dob age weight height edema
<chr> <date> <int> <int> <dbl> <date> <int> <dbl> <dbl> <chr>
1 District… 2023-12-04 1 3 1 NA 59 15.6 109. n
2 District… 2023-12-04 1 3 1 NA 8 7.5 68.6 n
3 District… 2023-12-04 1 3 1 NA 19 9.7 79.5 n
4 District… 2023-12-04 1 3 2 NA 49 14.3 100. n
5 District… 2023-12-04 1 3 2 NA 32 12.4 92.1 n
6 District… 2023-12-04 1 3 2 NA 17 9.3 77.8 n
7 District… 2023-12-04 1 3 2 NA 20 10.1 80.4 n
8 District… 2023-12-04 1 3 2 NA 27 11.7 87.1 n
9 District… 2023-12-04 1 3 1 NA 46 13.6 98 n
10 District… 2023-12-04 1 3 1 NA 58 17.2 109. n
# ℹ 1,181 more rows
# ℹ 4 more variables: muac <dbl>, age_days <dbl>, mfaz <dbl>, flag_mfaz <dbl>Note
mw_wrangle_muac()accepts MUAC values in centimeters. This is why it takes the arguments.recode_muacand.toto control whether there is need to transform the variablemuacfunction or not. Read the function documentation to learn how to control these two arguments.
On to de facto plausibility check of MFAZ data
We achieve this by calling the mw_plausibility_check_mfaz() function:
## Load dplyr library ----
library(dplyr)
## The workflow ----
anthro.01 |>
mw_wrangle_age(
dos = dos,
dob = dob,
age = age,
.decimals = 2
) |>
mw_wrangle_muac(
sex = sex,
muac = muac,
age = "age",
.recode_sex = TRUE,
.recode_muac = TRUE,
.to = "cm"
) |>
mutate(muac = recode_muac(muac, .to = "mm")) |>
mw_plausibility_check_mfaz(
sex = sex,
muac = muac,
age = age,
flags = flag_mfaz
)And this will return:
================================================================================# 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>We can also neat this output. We just need to call mw_neat_output_mfaz() and chain it to the pipeline:
## Load dplyr library ----
library(dplyr)
## The workflow ----
anthro.01 |>
mw_wrangle_age(
dos = dos,
dob = dob,
age = age,
.decimals = 2
) |>
mw_wrangle_muac(
sex = sex,
muac = muac,
age = "age",
.recode_sex = TRUE,
.recode_muac = TRUE,
.to = "cm"
) |>
mutate(muac = recode_muac(muac, .to = "mm")) |>
mw_plausibility_check_mfaz(
sex = sex,
muac = muac,
age = age,
flags = flag_mfaz
) |>
mw_neat_output_mfaz()This will return:
================================================================================# A tibble: 1 × 17
`Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`
<int> <chr> <fct> <chr>
1 1191 0.5% Excellent 0.297
# ℹ 13 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>,
# `Class. of age ratio` <chr>, `DPS (#)` <dbl>, `Class. of DPS` <chr>,
# `Standard Dev* (#)` <dbl>, `Class. of standard dev` <chr>,
# `Skewness* (#)` <dbl>, `Class. of skewness` <fct>, `Kurtosis* (#)` <dbl>,
# `Class. of kurtosis` <fct>, `Overall score` <dbl>, `Overall quality` <fct>We can also run checks on a multiple area dataset as follows:
## Load dplyr library ----
library(dplyr)
## The workflow ----
anthro.01 |>
mw_wrangle_age(
dos = dos,
dob = dob,
age = age,
.decimals = 2
) |>
mw_wrangle_muac(
sex = sex,
muac = muac,
age = "age",
.recode_sex = TRUE,
.recode_muac = TRUE,
.to = "cm"
) |>
mutate(muac = recode_muac(muac, .to = "mm")) |>
group_by(area) |>
mw_plausibility_check_mfaz(
sex = sex,
muac = muac,
age = age,
flags = flag_mfaz
) |>
mw_neat_output_mfaz()This will return:
================================================================================# A tibble: 2 × 18
`Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`
<chr> <int> <chr> <fct>
1 District E 505 0.0% Excellent
2 District G 686 0.9% Excellent
# ℹ 14 more variables: `Class. of sex ratio` <chr>, `Age ratio (p)` <chr>,
# `Class. of age ratio` <chr>, `DPS (#)` <chr>, `Class. of DPS` <dbl>,
# `Standard Dev* (#)` <chr>, `Class. of standard dev` <dbl>,
# `Skewness* (#)` <chr>, `Class. of skewness` <dbl>, `Kurtosis* (#)` <fct>,
# `Class. of kurtosis` <dbl>, `Overall score` <fct>, `Overall quality` <dbl>,
# `` <fct>At this point, you have reached the end of your workflow ✨.
Plausibility check of raw MUAC data
We will assess the plausibility of raw MUAC data through it’s raw values when the variable age is not available in the dataset. This is a job assigned to mw_plausibility_check_muac(). The workflow for this check is the shortest one.
Data wrangling
As you can tell, z-scores cannot be computed in the absence of age. In this way, the data wrangling workflow would be quite minimal. You still set the arguments inside mw_wrangle_muac() as learned in Section 1.2.1. The only difference is that here we will set age to NULL. Fundamentally, under the hood the function detects MUAC values that are outliers and flags them and stores them in flag_muac variable that is added to the dataset.
We will continue using the same dataset:
anthro.01 |>
mw_wrangle_muac(
sex = sex,
muac = muac,
age = NULL,
.recode_sex = TRUE,
.recode_muac = FALSE,
.to = "none"
)This returns:
# A tibble: 1,191 × 12
area dos cluster team sex dob age weight height edema
<chr> <date> <int> <int> <dbl> <date> <int> <dbl> <dbl> <chr>
1 District… 2023-12-04 1 3 1 NA 59 15.6 109. n
2 District… 2023-12-04 1 3 1 NA 8 7.5 68.6 n
3 District… 2023-12-04 1 3 1 NA 19 9.7 79.5 n
4 District… 2023-12-04 1 3 2 NA 49 14.3 100. n
5 District… 2023-12-04 1 3 2 NA 32 12.4 92.1 n
6 District… 2023-12-04 1 3 2 NA 17 9.3 77.8 n
7 District… 2023-12-04 1 3 2 NA 20 10.1 80.4 n
8 District… 2023-12-04 1 3 2 NA 27 11.7 87.1 n
9 District… 2023-12-04 1 3 1 NA 46 13.6 98 n
10 District… 2023-12-04 1 3 1 NA 58 17.2 109. n
# ℹ 1,181 more rows
# ℹ 2 more variables: muac <int>, flag_muac <dbl>On to de facto plausibility check
We just have to add mw_plausibility_check_muac() to the above pipeline:
anthro.01 |>
mw_wrangle_muac(
sex = sex,
muac = muac,
age = NULL,
.recode_sex = TRUE,
.recode_muac = FALSE,
.to = "none"
) |>
mw_plausibility_check_muac(
sex = sex,
flags = flag_muac,
muac = muac
)And this will return:
# A tibble: 1 × 9
n flagged flagged_class sex_ratio sex_ratio_class dps dps_class sd
<int> <dbl> <fct> <dbl> <chr> <dbl> <chr> <dbl>
1 1191 0.00252 Excellent 0.297 Excellent 5.39 Excellent 11.1
# ℹ 1 more variable: sd_class <fct>We can also return a formatted table with mw_neat_output_muac():
anthro.01 |>
mw_wrangle_muac(
sex = sex,
muac = muac,
age = NULL,
.recode_sex = TRUE,
.recode_muac = FALSE,
.to = "none"
) |>
mw_plausibility_check_muac(
sex = sex,
flags = flag_muac,
muac = muac
) |>
mw_neat_output_muac()And we get
# A tibble: 1 × 9
`Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`
<int> <chr> <fct> <chr>
1 1191 0.3% Excellent 0.297
# ℹ 5 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <dbl>,
# `Class. of DPS` <chr>, `Standard Dev* (#)` <dbl>,
# `Class. of standard dev` <fct>When working on multiple area data, we approach the task the same way as demonstrated above:
## Load library ----
library(dplyr)
## Check plausibility ----
anthro.01 |>
mw_wrangle_muac(
sex = sex,
muac = muac,
age = NULL,
.recode_sex = TRUE,
.recode_muac = FALSE,
.to = "none"
) |>
group_by(area) |>
mw_plausibility_check_muac(
sex = sex,
flags = flag_muac,
muac = muac
) |>
mw_neat_output_muac()And we get:
# A tibble: 2 × 10
`Total children` `Flagged data (%)` `Class. of flagged data` `Sex ratio (p)`
<chr> <int> <chr> <fct>
1 District E 505 0.0% Excellent
2 District G 686 0.4% Excellent
# ℹ 6 more variables: `Class. of sex ratio` <chr>, `DPS(#)` <chr>,
# `Class. of DPS` <dbl>, `Standard Dev* (#)` <chr>,
# `Class. of standard dev` <dbl>, `` <fct>