Estimating the prevalence of wasting

Introduction

This vignette demonstrates how to use the mwana package’s functions to estimate the prevalence of wasting. The package allow users to estimate prevalence based on:

Weight-for-height z-score (WFHZ) and/or edema;
Raw MUAC values and/or edema;
MUAC-for-age z-score (MFAZ) and/or edema, and
Combined prevalence.

The prevalence functions in mwana were carefully conceived and designed to simplify the workflow of a nutrition data analyst, especially when dealing with data sets containing imperfections that require additional layers of analysis. Let’s try to clarify this with two scenarios that I believe will remind you of the complexity involved:

When analysing a multi-area data set, users will likely need to estimate the prevalence for each area individually. Afterward, they must extract the results and collate in a summary table to share.
When working with MUAC data, if age ratio test is rated as problematic, an additional tool is required to weight the prevalence and correct for age bias, thus the associated likely overestimation of the prevalence. In unfortunate cases where multiple areas face this issue, the workflow must be repeated several times, making the process cumbersome and highly error-prone 😬.

With mwana you no longer have to worry about this 🥳 as the functions are designed to deal with that. To demonstrate their use, we will use different data sets containing some imperfections alluded above:

anthro.02 : a survey data with survey weights. Read more about this data with ?anthro.02.
anthro.03 : district-level SMART surveys with two districts whose WFHZ standard deviations are rated as problematic while the rest are within range. Do ?anthro.03 for more details.
anthro.04 : a community-based sentinel site data. The data has different characteristics that require different analysis approaches.

Now we can begin delving into each function.

Estimation of the prevalence of wasting based on WFHZ

To estimate the prevalence of wasting based on WFHZ we use the mw_estimate_prevalence_wfhz() function. The data set to supply must have been wrangled by mw_wrangle_wfhz().

As usual, we start off by inspecting our data set:

tail(anthro.02)

## # A tibble: 6 × 14
##   province strata cluster   sex   age weight height edema  muac wtfactor   wfhz
##   <chr>    <chr>    <int> <dbl> <dbl>  <dbl>  <dbl> <chr> <dbl>    <dbl>  <dbl>
## 1 Nampula  Urban      285     1  59.5   13.8   90.7 n       149     487.  0.689
## 2 Nampula  Rural      234     1  59.5   17.2  105.  n       193    1045.  0.178
## 3 Nampula  Rural      263     1  59.6   18.4  100   n       156     952.  2.13 
## 4 Nampula  Rural      257     1  59.7   15.9  100.  n       149     987.  0.353
## 5 Nampula  Rural      239     1  59.8   12.5   91.5 n       135     663. -0.722
## 6 Nampula  Rural      263     1  60.0   14.3   93.8 n       142     952.  0.463
## # ℹ 3 more variables: flag_wfhz <dbl>, mfaz <dbl>, flag_mfaz <dbl>

We can see that the data set contains the required variables for a WFHZ prevalence analysis, including for a weighted analysis. This data set has already been wrangled, so we do not need to call the WFHZ wrangler in this case. We will begin the demonstration with an unweigthed analysis - typical of SMART surveys - and then we proceed to a weighted analysis.

Estimation of unweighted prevalence

To achieve this we do:

anthro.02 |>
  mw_estimate_prevalence_wfhz(
    wt = NULL,
    edema = edema
  )

This will return:

## # A tibble: 1 × 16
##   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low sam_p_upp
##   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>     <dbl>
## 1    86 0.0408    0.0322    0.0494        Inf    14 0.00664   0.00273    0.0106
## # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>,
## #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl>

If for some reason the variable edema is not available in the data set, or it’s there but not plausible, we can exclude it from the analysis by setting the argument edema to NULL:

anthro.02 |>
  mw_estimate_prevalence_wfhz(
    wt = NULL,
    edema = NULL # Setting edema to NULL
  )

And we get:

## # A tibble: 1 × 16
##   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n sam_p sam_p_low sam_p_upp
##   <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl> <dbl>     <dbl>     <dbl>
## 1    72 0.0342    0.0263    0.0420        Inf     0     0         0         0
## # ℹ 7 more variables: sam_p_deff <dbl>, mam_n <dbl>, mam_p <dbl>,
## #   mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>, wt_pop <dbl>

If we inspect the gam_n and gam_p columns of this output table and the previous, we notice differences in the numbers. This occurs because edema cases were excluded in the second implementation. Note that you will observed a change if there are positive cases of edema in the data set; otherwise, setting edema = NULL will have no effect whatsoever.

The above output summary does not show results by province. We can control this by supplying the variable or set of variables containing the locations where the data was collected, or any other category (such as teams, sex, etc.) after edema. In our case, we will use the column province:

anthro.02 |>
  mw_estimate_prevalence_wfhz(
    wt = NULL,
    edema = edema,
    province # province is the variable's name holding data on where the survey was conducted.
  )

And voila :

## # A tibble: 2 × 17
##   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low
##   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>
## 1 Nampula     53 0.0546    0.0397    0.0695        Inf    10 0.0103  0.00282  
## 2 Zambezia    33 0.0290    0.0195    0.0384        Inf     4 0.00351 0.0000639
## # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>,
## #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>,
## #   wt_pop <dbl>

A table with two rows is returned with each province’s statistics.

Estimation of weighted prevalence

To get the weighted prevalence, we make the use of the wt argument. We pass to it the column name containing the final survey weights. In our case, the column name is wtfactor:

anthro.02 |>
  mw_estimate_prevalence_wfhz(
    wt = wtfactor, # Passing the wtfactor to wt
    edema = edema,
    province
  )

And you get:

## # A tibble: 2 × 17
##   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low
##   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>
## 1 Nampula     53 0.0595    0.0410    0.0779       1.52    10 0.0129   0.00272 
## 2 Zambezia    33 0.0261    0.0161    0.0361       1.16     4 0.00236 -0.000255
## # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>,
## #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>,
## #   wt_pop <dbl>

The work under the hood of `mw_estimate_prevalence_wfhz`

Under the hood, before starting the prevalence estimation, the function first checks the quality of the WFHZ standard deviation. If it is not rated as problematic, it proceeds with a complex sample-based analysis; otherwise, prevalence is estimated applying the PROBIT method. This is as you see in the body of the plausibility report generated by ENA. The anthro.02 data set has no such issues, so you don’t see mw_estimate_prevalence_wfhz in action on this regard. To see that, let’s use the anthro.03 data set.

anthro.03 contains problematic standard deviation in Metuge and Maravia districts, while the remaining districts are within range.

Let’s inspect our data set:

## # A tibble: 6 × 9
##   district cluster  team sex     age weight height edema  muac
##   <chr>      <int> <int> <chr> <dbl>  <dbl>  <dbl> <chr> <int>
## 1 Metuge         2     2 m      9.99   10.1   69.3 n       172
## 2 Metuge         2     2 f     43.6    10.9   91.5 n       130
## 3 Metuge         2     2 f     32.8    11.4   91.4 n       153
## 4 Metuge         2     2 f      7.62    8.3   69.5 n       133
## 5 Metuge         2     2 m     28.4    10.7   82.3 n       143
## 6 Metuge         2     2 f     12.3     6.6   69.4 n       121

Now let’s apply the prevalence function. This is data is not wrangled, so we will have to wrangle it before passing to the prevalence function:

anthro.03 |>
  mw_wrangle_wfhz(
    sex = sex,
    .recode_sex = TRUE,
    height = height,
    weight = weight
  ) |>
  mw_estimate_prevalence_wfhz(
    wt = NULL,
    edema = edema,
    district
  )

The returned output will be:

## ================================================================================

## # A tibble: 4 × 17
##   district   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low
##   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>
## 1 Cahora-Ba…    22 0.0738    0.0348    0.113         Inf     1 0.00336  -0.00348
## 2 Chiuta        10 0.0444    0.0129    0.0759        Inf     1 0.00444  -0.00466
## 3 Maravia       NA 0.0450   NA        NA              NA    NA 0.00351  NA      
## 4 Metuge        NA 0.0251   NA        NA              NA    NA 0.00155  NA      
## # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>,
## #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>,
## #   wt_pop <dbl>

Can you spot the differences? 😎 Yes, you’re absolutely correct! While in Cahora-Bassa and Chiúta districts all columns are populated with numbers, in Metuge and Maravia, only the gam_p, sam_p and mam_p columns are filled with numbers, and everything else with NA. These are district where the PROBIT method was applied, while in Cahora-Bassa and Chiúta ditricts the standard complex sample analysis was done.

Estimation of the prevalence of wasting based on MFAZ

The prevalence of wasting based on MFAZ can be estimated using the mw_estimate_prevalence_mfaz() function. This function works and is implemented the same way as demonstrated in @sec-prevalence-wfhz, with the exception of the data wrangling that is based on MUAC. This was demonstrated in the plausibility checks. In this way, to avoid redundancy, we will not demonstrate the workflow.

Estimation of the prevalence of wasting based on raw MUAC values

This job is assigned to mw_estimate_prevalence_muac(). Once you call the function, before starting the prevalence estimation, it first evaluates the acceptability of the MFAZ standard deviation and the age ratio test. Yes, you read well, MFAZ’s standard deviation, not on the raw values MUAC.

Although the acceptability is evaluated on the basis of MFAZ, the actual prevalence is estimated on the basis of the raw MUAC values. MFAZ is also used to detect outliers and flag them to be excluded from the prevalence analysis.

The MFAZ standard deviation and the age ratio test results are used to control the prevalence analysis flow in this way:

If the MFAZ standard deviation and the age ratio test are both not problematic, a standard complex sample-based prevalence is estimated.
If the MFAZ standard deviation is not problematic but the age ratio test is problematic, the SMART MUAC tool age-weighting approach is applied.
If the MFAZ standard deviation is problematic, even if age ratio is not problematic, no prevalence analysis is estimated, instead NA are thrown.

When working with a multiple-area data set, these conditionals will still be applied according to each area’s situation.

How does it work on a multi-area data set

Fundamentally, the function performs the standard deviation and age ratio tests, evaluates their acceptability, and returns a summarized table by area. It then iterates over that summary table row by row checking the above conditionals. Based on the conditionals of each row (area), the function accesses the original data set, computes the prevalence accordingly, and returns the results.

To demonstrate this we will use the anthro.04 data set.

As usual, let’s first inspect it:

## # A tibble: 6 × 8
##   province   cluster   sex   age  muac edema   mfaz flag_mfaz
##   <chr>        <int> <dbl> <int> <dbl> <chr>  <dbl>     <dbl>
## 1 Province 3     743     2    21   130 n     -1.50          0
## 2 Province 3     743     2     9   126 n     -1.33          0
## 3 Province 3     743     2    12   128 n     -1.27          0
## 4 Province 3     743     2    34   145 n     -0.839         0
## 5 Province 3     743     2    11   130 n     -1.04          0
## 6 Province 3     743     2    33   140 n     -1.23          0

You see that this data has already been wrangled, so we will go straight to the prevalence estimation.

As in ENA Software, make sure you run the plausibility check before you call the prevalence function. This is good to know about the acceptability of your data. If we do that with anthro.04 we will see which province has issues, hence what we should be expecting to see in below demonstrations based on the conditionals stated above.

anthro.04 |>
  mw_estimate_prevalence_muac(
    wt = NULL,
    edema = edema,
    province
  )

This will return:

## # A tibble: 3 × 17
##   province   gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n   sam_p sam_p_low
##   <chr>      <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>   <dbl>     <dbl>
## 1 Province 1   133  0.104    0.0778     0.130        Inf    17  0.0133   0.00682
## 2 Province 2    NA  0.112   NA         NA             NA    NA  0.0201  NA      
## 3 Province 3    NA NA       NA         NA             NA    NA NA       NA      
## # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>,
## #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>,
## #   wt_pop <dbl>

We see that in Province 1, all columns are filled with numbers; in Province 2, some columns are filled with numbers, while other columns are filled with NAs: this is where the age-weighting approach was applied. Lastly, in Province 3 a bunch of NA are filled everywhere - you know why 😉 .

Alternatively, we can choose to apply the function that calculates the age-weighted prevalence estimates inside mw_estimate_prevalence_muac() directly on to our data set. This can be done by calling the mw_estimate_smart_age_wt() function. It worth noting that although possible, we recommend to use the main function. This is simply due the fact that if we decide to use the function independently, then we must, before calling it, check the acceptability of the standard deviation of MFAZ and of the age ratio test, and then evaluate if the conditions that fits the use mw_estimate_smart_age_wt() are there. We would have to do that ourselves. This introduces some kind of cumbersomeness in the workflow, and along with that, a risk of picking a wrong analysis workflow.

Nonetheless, if for any reason we decide to go for it anyway, then we would apply the function as demonstrated below. We will continue using the anthro.04 data set. For this demonstration, we will just pull out the data set from Province 2 where we already know that the conditions to apply mw_estimate_smart_age_wt() are met, and then we will pipe it in to the function:

anthro.04 |>
  subset(province == "Province 2") |>
  mw_estimate_smart_age_wt(
    edema = edema
  )

This returns the following:

## # A tibble: 1 × 3
##    sam_p  mam_p gam_p
##    <dbl>  <dbl> <dbl>
## 1 0.0201 0.0922 0.112

Estimation of weighted prevalence

For this we go back anthro.02 data set.

We approach this task as follows:

## Load library ----
library(dplyr)

## Compute prevalence ----
anthro.02 |>
  mw_wrangle_age(
    age = age,
    .decimals = 2
  ) |>
  mw_wrangle_muac(
    sex = sex,
    .recode_sex = FALSE,
    muac = muac,
    .recode_muac = TRUE,
    .to = "cm",
    age = age
  ) |>
  mutate(
    muac = recode_muac(muac, .to = "mm")
  ) |>
  mw_estimate_prevalence_muac(
    wt = wtfactor,
    edema = edema,
    province
  )

This will return:

## ================================================================================

## # A tibble: 2 × 17
##   province gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n  sam_p sam_p_low
##   <chr>    <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>  <dbl>     <dbl>
## 1 Nampula     61 0.0571    0.0369    0.0773       2.00    19 0.0196   0.00706
## 2 Zambezia    57 0.0552    0.0380    0.0725       1.67    10 0.0133   0.00412
## # ℹ 8 more variables: sam_p_upp <dbl>, sam_p_deff <dbl>, mam_n <dbl>,
## #   mam_p <dbl>, mam_p_low <dbl>, mam_p_upp <dbl>, mam_p_deff <dbl>,
## #   wt_pop <dbl>

You may have noticed that in the above code block, we called the recode_muac() function inside mutate(). This is because after you use mw_wrangle_muac(), it puts the MUAC variable in centimeters. The mw_estimate_prevalence_muac() function was defined to accept MUAC in millimeters. Therefore, it must be converted to millimeters.

Estimation for non-survey data

Thus far, the demonstration has been around survey data. However, it is also common in day-to-day practice to come across non-survey data to analyse. Non-survey data can be screenings or any other kind of community-based surveillance data. With this kind of data, the analysis workflow usually consists in a simple estimation of the point prevalence and the counts of the positive cases, without necessarily estimating the uncertainty. mwana provides two handy functions for this task: mw_estimate_prevalence_screening() and mw_estimate_prevalence_screening2().

Under the hood, the former function works the same way as mw_estimate_prevalence_muac(). However, it was specifically designed to handle non-survey data. The latter function is intended for cases where a child’s age is provided in categorical form in its turn, is used for cases in which the child’s age is given in age categories (“6-23” and “24-59” months) rather than in a continuous form (age in months). In such cases, outliers are identified and excluded from the prevalence analysis using raw MUAC values, as MFAZ cannot be computed when age in months is missing.

The anthro.04 dataset will be used to illustrate the application of these functions.

anthro.04 |>
  mw_estimate_prevalence_screening(
    muac = muac,
    edema = edema,
    province
  )

The returned output is:

## # A tibble: 3 × 7
##   province   gam_n  gam_p sam_n   sam_p mam_n   mam_p
##   <chr>      <dbl>  <dbl> <dbl>   <dbl> <dbl>   <dbl>
## 1 Province 1   133  0.104    17  0.0133   116  0.0908
## 2 Province 2    NA  0.112    NA  0.0201    NA  0.0922
## 3 Province 3    NA NA        NA NA         NA NA

The mw_estimate_prevalence_screening2() function is applied as demonstrated below. In this example, the input data contains age in months rather than in categories. To meet the function’s requirements, we convert the age variable into two categories and store the result in a new age_cat variable.

anthro.04 |>
  mutate(
    age_cat = ifelse(age < 24, "6-23", "24-59") 
  ) |>
  mw_wrangle_muac(
    sex = sex,
    .recode_sex = FALSE,
    muac = muac
  ) |>
  mw_estimate_prevalence_screening2(
    age_cat = age_cat,
    muac = muac,
    edema = edema,
    province
  )

This will return:

anthro.04 |>
  mutate(
    age_cat = ifelse(age < 24, "6-23", "24-59")
  ) |>
  mw_wrangle_muac(
    sex = sex,
    .recode_sex = FALSE,
    muac = muac
  ) |>
  mw_estimate_prevalence_screening2(
    age_cat = age_cat,
    muac = muac,
    edema = edema,
    province
  )

Estimation of the combined prevalence of wasting

The estimation of the combined prevalence of wasting is a task attributed to the mw_estimate_prevalence_combined() function. The case-definition is based on the WFHZ, the raw MUAC values and edema. From the workflow standpoint, it combines the workflow demonstrated in @sec-prevalence-wfhz and in @sec-prevalence-muac.

To demonstrate it’s implementation we will use the anthro.01 data set.

Let’s inspect the data:

## # 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>

Data wrangling

Fundamentally, it combines the data wrangling workflow of WFHZ and MUAC:

## Load library ----
library(dplyr)

## Apply the wrangling workflow ----
anthro.01 |>
  mw_wrangle_age(
    dos = dos,
    dob = dob,
    age = age,
    .decimals = 2
  ) |>
  mw_wrangle_muac(
    sex = sex,
    .recode_sex = TRUE,
    muac = muac,
    .recode_muac = TRUE,
    .to = "cm",
    age = age
  ) |>
  mutate(
    muac = recode_muac(muac, .to = "mm")
  ) |>
  mw_wrangle_wfhz(
    sex = sex,
    weight = weight,
    height = height,
    .recode_sex = FALSE
  )

This is to get the wfhz and flag_wfhz the mfaz and flag_mfaz added to the data set. In the output below, we have just selected these columns:

## ================================================================================
## ================================================================================

## # A tibble: 1,191 × 5
##    area         wfhz flag_wfhz   mfaz flag_mfaz
##    <chr>       <dbl>     <dbl>  <dbl>     <dbl>
##  1 District E -1.83          0 -1.45          0
##  2 District E -0.956         0 -1.67          0
##  3 District E -0.796         0 -0.617         0
##  4 District E -0.74          0 -1.02          0
##  5 District E -0.679         0 -0.93          0
##  6 District E -0.432         0 -1.10          0
##  7 District E -0.078         0 -0.255         0
##  8 District E -0.212         0 -0.677         0
##  9 District E -1.07          0 -2.18          0
## 10 District E -0.543         0 -0.403         0
## # ℹ 1,181 more rows

Under the hood, mw_estimate_prevalence_combined() applies the same analysis approach as in mw_estimate_prevalence_wfhz and in mw_estimate_prevalence_muac(). It checks the acceptability of the standard deviation of WFHZ and MFAZ and of the age ratio test. The following conditionals are checked and applied:

If the standard deviation of WFHZ and of MFAZ, and the age ratio test are all concurrently not problematic, the standard complex sample-based estimation is applied.
If any of the above is rated problematic, the prevalence is not computed and NAs are thrown.

In this function, a concept of “combined flags” is used.

What is combined flag?

Combined flags consists of defining as flag any observation that is flagged in either flag_wfhz or flag_mfaz vectors. A new column cflags for combined flags is created and added to the data set. This ensures that all flagged observations from both WFHZ and MFAZ data are excluded from the prevalence analysis.

A glimpse of case-definition of combined flag {#tbl-Table 1}
flag_wfhz	flag_mfaz	cflags
1	0	1
0	1	1
0	0	0

Now that we understand what happens under the hood, we can now proceed to implement it:

## Load library ----
library(dplyr)

## Apply the workflow ----
anthro.01 |>
  mw_wrangle_age(
    dos = dos,
    dob = dob,
    age = age,
    .decimals = 2
  ) |>
  mw_wrangle_muac(
    sex = sex,
    .recode_sex = TRUE,
    muac = muac,
    .recode_muac = TRUE,
    .to = "cm",
    age = age
  ) |>
  mutate(
    muac = recode_muac(muac, .to = "mm")
  ) |>
  mw_wrangle_wfhz(
    sex = sex,
    weight = weight,
    height = height,
    .recode_sex = FALSE
  ) |>
  mw_estimate_prevalence_combined(
    wt = NULL,
    edema = edema,
    area
  )

We get this:

## ================================================================================
## ================================================================================

## # A tibble: 2 × 17
##   area   cgam_p   csam_p  cmam_p cgam_n cgam_p_low cgam_p_upp cgam_p_deff csam_n
##   <chr>   <dbl>    <dbl>   <dbl>  <dbl>      <dbl>      <dbl>       <dbl>  <dbl>
## 1 Dist… NA      NA       NA          NA    NA         NA               NA     NA
## 2 Dist…  0.0703  0.00747  0.0643     47     0.0447     0.0958         Inf      5
## # ℹ 8 more variables: csam_p_low <dbl>, csam_p_upp <dbl>, csam_p_deff <dbl>,
## #   cmam_n <dbl>, cmam_p_low <dbl>, cmam_p_upp <dbl>, cmam_p_deff <dbl>,
## #   wt_pop <dbl>

In district E NAs were returned because there were issues with the data. I leave it to you to figure out what was/were the issue/issues.

Consider running the plausibility checkers.

Tomás Zaba

Introduction

Estimation of the prevalence of wasting based on WFHZ

Estimation of unweighted prevalence

Estimation of weighted prevalence

The work under the hood of mw_estimate_prevalence_wfhz

Estimation of the prevalence of wasting based on MFAZ

Estimation of the prevalence of wasting based on raw MUAC values

How does it work on a multi-area data set

Estimation of weighted prevalence

Estimation for non-survey data

Estimation of the combined prevalence of wasting

Data wrangling

What is combined flag?

The work under the hood of `mw_estimate_prevalence_wfhz`