Estimate the prevalence of wasting based on MUAC for non-survey data

It is common to estimate prevalence of wasting from non-survey data, such as screenings or any other data derived from community-based surveillance systems. In such situations, the analysis usually consists only in estimating the point prevalence and the counts of positive cases, without necessarily estimating the uncertainty. This function serves this purpose.

The quality of the data is first evaluated by calculating and rating the standard deviation (SD) of MFAZ (in mw_estimate_prevalence_screening()) or SD of the raw MUAC values (in mw_estimate_prevalence_screening2()), and the p-value of the age ratio test in either functions. Thereafter, if the latter test is problematic, age-weighting approach is applied to the prevalence estimation, to account for the over-representation of younger children in the sample; otherwise, a non-age-weighted prevalence is estimated. This means that even if the SD in either functions is problematic, the prevalence is estimated, with no adjustments, and returned.

Usage

mw_estimate_prevalence_screening(df, muac, oedema = NULL, ...)

mw_estimate_prevalence_screening2(df, age_cat, muac, oedema = NULL, ...)

Arguments

df

A tibble object produced by mw_wrangle_muac() and mw_wrangle_age() functions. Note that MUAC values in df must be in millimetres unit after using mw_wrangle_muac(). Also, df must have a variable called cluster wherein the primary sampling unit identifiers are stored.

muac

A numeric or integer vector of raw MUAC values. The measurement unit should be millimetres.

oedema

A character vector for presence of nutritional oedema. Code values should be "y" for presence and "n" for absence. Default is NULL.

...

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.

age_cat

A character vector of child's age in categories. Code values should be "6-23" and "24-59".

Value

A summary tibble for the descriptive statistics about wasting based on MUAC, with no confidence intervals.

Details

A typical user analysis workflow is expected to begin with data quality checks, followed by a thorough review, and only thereafter proceed to prevalence estimation. This sequence places the user in the strongest position to assess whether the resulting prevalence estimates are reliable.

In mw_estimate_prevalence_screening(), outliers are identified using SMART flagging criteria applied to MFAZ, whereas mw_estimate_prevalence_screening2() are based on the raw MUAC values. In either functions, outliers are excluded from the prevalence estimation.

References

SMART Initiative (no date). Updated MUAC data collection tool. Available at: https://smartmethodology.org/survey-planning-tools/updated-muac-tool/

See also

mw_estimate_prevalence_muac(), mw_estimate_age_weighted_prev_muac(), flag_outliers() and remove_flags().

Examples

mw_estimate_prevalence_screening(
  df = anthro.02,
  muac = muac,
  oedema = oedema,
  province
)
#> # A tibble: 2 × 8
#>   province gam_n  gam_p sam_n   sam_p mam_n  mam_p     N
#>   <chr>    <dbl>  <dbl> <dbl>   <dbl> <dbl>  <dbl> <int>
#> 1 Nampula     61 0.0590    19 0.0184     42 0.0406  1034
#> 2 Zambezia    57 0.0500    10 0.00876    47 0.0412  1141

## With `oedema` set to `NULL` ----
mw_estimate_prevalence_screening(
  df = anthro.02,
  muac = muac,
  oedema = NULL,
  province
)
#> # A tibble: 2 × 8
#>   province gam_n  gam_p sam_n   sam_p mam_n  mam_p     N
#>   <chr>    <dbl>  <dbl> <dbl>   <dbl> <dbl>  <dbl> <int>
#> 1 Nampula     53 0.0513    10 0.00967    43 0.0416  1034
#> 2 Zambezia    53 0.0465     6 0.00526    47 0.0412  1141

## Specifying the grouping variables ----
mw_estimate_prevalence_screening(
  df = anthro.02,
  muac = muac,
  oedema = NULL,
  province
)
#> # A tibble: 2 × 8
#>   province gam_n  gam_p sam_n   sam_p mam_n  mam_p     N
#>   <chr>    <dbl>  <dbl> <dbl>   <dbl> <dbl>  <dbl> <int>
#> 1 Nampula     53 0.0513    10 0.00967    43 0.0416  1034
#> 2 Zambezia    53 0.0465     6 0.00526    47 0.0412  1141


anthro.01 |>
  mw_wrangle_muac(
    sex = sex,
    .recode_sex = TRUE,
    muac = muac
  ) |>
  transform(
    age_cat = ifelse(age < 24, "6-23", "24-59")
  ) |>
  mw_estimate_prevalence_screening2(
    age_cat = age_cat,
    muac = muac,
    oedema = oedema,
    area
  )
#> # A tibble: 2 × 8
#>   area       gam_n  gam_p sam_n   sam_p mam_n  mam_p     N
#>   <chr>      <dbl>  <dbl> <dbl>   <dbl> <dbl>  <dbl> <int>
#> 1 District E    13 0.0257     0 0          13 0.0257   505
#> 2 District G    23 0.0337     4 0.00586    19 0.0278   683