Estimate the prevalence of wasting based on MUAC for survey data

Estimate the prevalence of wasting based on MUAC and/or nutritional oedema. The function allows users to estimate prevalence in accordance with complex sample design properties, such as accounting for survey sample weights when needed or applicable.

It first evaluates the quality of the data to determine the appropriate prevalence-analysis flow to be employed. Quality is evaluated by estimating the observed proportion of children aged 24-59 months of the total children in the dataset, then it estimates the p-value for the difference between the above-mentioned category against the expected (0.66) and rates it.

If age ratio test is "problematic" and the proportion of children aged 24-59 months is < 0.66, age-weighting approach is applied to prevalence estimation, to account for the over-representation of younger children in the sample; otherwise, a non-age-weighted prevalence is estimated.

Usage

mw_estimate_prevalence_muac(df, age, muac, wt = NULL, 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 after using mw_wrangle_muac(). Also, df must have a variable called cluster wherein the primary sampling unit identifiers are stored.

age

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

muac

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

wt

A vector of class double of the survey sampling weights. Default is NULL, which assumes a self-weighted survey, the case of SMART surveys. Otherwise, a weighted analysis is implemented.

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.

Value

A summary tibble for the descriptive statistics about wasting based on MUAC, with 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.

Outliers are identified using SMART flagging criteria applied to MFAZ, and 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_age_weighted_prev_muac() mw_estimate_prevalence_mfaz() mw_estimate_prevalence_screening()

Examples

## Ungrouped analysis ----
anthro.04 |>
  mw_wrangle_age(age = age) |>
  mw_wrangle_muac(
    muac = muac,
    .recode_muac = TRUE,
    .to = "cm",
    age = age,
    sex = sex,
    .recode_sex = FALSE
  ) |>
  transform(muac = recode_muac(muac, "mm")) |>
  mw_estimate_prevalence_muac(
    muac = muac,
    age = age,
    wt = NULL,
    oedema = oedema,
    analysis_unit
  )
#> ================================================================================
#> # A tibble: 3 × 17
#>   analysis_unit gam_n  gam_p gam_p_low gam_p_upp gam_p_deff sam_n  sam_p
#>   <chr>         <dbl>  <dbl>     <dbl>     <dbl>      <dbl> <dbl>  <dbl>
#> 1 Unit A           39 0.0644    0.0389    0.0898        Inf     7 0.0116
#> 2 Unit B           NA 0.121    NA        NA              NA    NA 0.0293
#> 3 Unit C           19 0.0909    0.0492    0.133         Inf     5 0.0239
#> # ℹ 9 more variables: sam_p_low <dbl>, 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>, N <dbl>