Package {mwana}

Type:	Package
Title:	An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R
Version:	0.2.5
Description:	A simple and streamlined workflow for plausibility checks and prevalence analysis of wasting based on the Standardized Monitoring and Assessment of Relief and Transition (SMART) Methodology https://smartmethodology.org/, with application in R.
License:	GPL (≥ 3)
Depends:	R (≥ 4.1)
Imports:	dplyr, lubridate, nipnTK, rlang, scales, srvyr (≥ 1.3.0), stats, zscorer, tibble, methods, purrr
Suggests:	knitr, rmarkdown, quarto, spelling, testthat (≥ 3.0.0),
Encoding:	UTF-8
Language:	en-GB
LazyData:	true
URL:	https://github.com/mphimo/mwana, https://mphimo.github.io/mwana/
BugReports:	https://github.com/mphimo/mwana/issues
VignetteBuilder:	quarto
Config/testthat/edition:	3
Config/roxygen2/version:	8.1.0
NeedsCompilation:	no
Packaged:	2026-08-22 16:39:54 UTC; tomaszaba
Author:	Tomás Zaba [aut, cre, cph], Ernest Guevarra [aut, cph], Mark Myatt [aut]
Maintainer:	Tomás Zaba <tomas.zaba@outlook.com>
Repository:	CRAN
Date/Publication:	2026-09-02 12:20:02 UTC

mwana: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R

Description

A simple and streamlined workflow for plausibility checks and prevalence analysis of wasting based on the Standardized Monitoring and Assessment of Relief and Transition (SMART) Methodology https://smartmethodology.org/, with application in R.

Author(s)

Maintainer: Tomás Zaba tomas.zaba@outlook.com (ORCID) [copyright holder]

Authors:

• Tomás Zaba tomas.zaba@outlook.com (ORCID) [copyright holder]

• Ernest Guevarra (ORCID) [copyright holder]

• Mark Myatt

See Also

Useful links:

• https://github.com/mphimo/mwana

• https://mphimo.github.io/mwana/

• Report bugs at https://github.com/mphimo/mwana/issues

A sample data of district level SMART surveys with location anonymised

Description

anthro.01 is a two-stage cluster-based survey with probability of selection of clusters proportional to the size of the population. The survey employed the SMART methodology.

Usage

anthro.01

Format

A tibble of 1,191 rows and 11 columns.

Source

Anonymous

Examples

anthro.01

A sample of an already wrangled survey data

Description

A household budget survey data conducted in Mozambique in 2019/2020, known as IOF (Inquérito ao Orçamento Familiar in Portuguese). IOF is a two-stage cluster-based survey, representative at province level (second administrative level), with probability of the selection of the clusters proportional to the size of the population. Its data collection spans for a period of 12 months.

Usage

anthro.02

Format

A tibble of 2,267 rows and 14 columns.

Source

Mozambique National Institute of Statistics. The data is publicly available at https://mozdata.ine.gov.mz/index.php/catalog/88#metadata-data_access. Data was wrangled using this package's wranglers. Details about survey design can be read from: https://mozdata.ine.gov.mz/index.php/catalog/88#metadata-sampling

Examples

anthro.02

A sample data of district level SMART surveys conducted in Mozambique

Description

anthro.03 contains survey data of four districts. Each district dataset presents distinct data quality scenarios that require a specific prevalence analysis approach. Data from two districts have a problematic WFHZ standard deviation. The data from the remaining two districts are all within range.

This sample data is useful to demonstrate the use of the prevalence functions on a multiple-domain survey data where there can be variations in the rating of acceptability of the standard deviation, hence requiring different analytical approach for each survey domain to ensure accurate estimation.

Usage

anthro.03

Format

A tibble of 943 x 9.

Source

Anonymous

Examples

anthro.03

A sample data from a community-based sentinel site with location anonymised

Description

Data herein was derived from population-based assessments in three locations (analysis unit). Each unit presents distinct scenarios that requires also-distinct handling during the prevalence analysis:

• Unit A has flawless data.

• Unit B has age-ratio-test results rated as problematic, with an observed proportion of children aged 24-59 months < 0.66.

• Unit C has age-ratio-test results rated as problematic, with an observed proportion of children aged 24-59 months >= 0.66.

This sample data is useful to demonstrate designed behaviours of MUAC-prevalence functions to dealing with certain-and-expected flaws in the data.

Usage

anthro.04

Format

A tibble of 2,192 × 6.

Source

Anonymous

Examples

anthro.04

Define wasting

Description

Determine if a given observation in the dataset is wasted or not, and its respective form of wasting (global, severe or moderate) on the basis of z-scores of weight-for-height (WFHZ), muac-for-age (MFAZ), raw MUAC values and combined case-definition.

Usage

define_wasting(
  df,
  zscores = NULL,
  muac = NULL,
  oedema = NULL,
  .by = c("zscores", "muac", "combined")
)

Arguments

Value

The tibble object df with additional columns named named gam, sam and mam, each of class numeric containing coded values of either 1 (case) and 0 (not a case). If .by = "combined", additional columns are named cgam, csam and cmam.

Examples

## Case-definition by z-scores ----
z <- anthro.02 |>
  define_wasting(
    zscores = wfhz,
    muac = NULL,
    oedema = oedema,
    .by = "zscores"
  )
head(z)

## Case-definition by MUAC ----
m <- anthro.02 |>
  define_wasting(
    zscores = NULL,
    muac = muac,
    oedema = oedema,
    .by = "muac"
  )
head(m)

## Case-definition by combined ----
c <- anthro.02 |>
  define_wasting(
    zscores = wfhz,
    muac = muac,
    oedema = oedema,
    .by = "combined"
  )
head(c)

Identify, flag, and remove outliers

Description

Identify outlier z-scores for weight-for-height (WFHZ) and MUAC-for-age (MFAZ) following the SMART methodology. The function can also be used to detect outliers for height-for-age (HFAZ) and weight-for-age (WFAZ) z-scores following the same approach.

For flagging z-scores, z-scores that deviate substantially from the sample's z-score mean are considered outliers and are unlikely to reflect accurate measurements. For raw MUAC, values that are less than 100 millimetres or greater than 200 millimetres are considered outliers as recommended by Bilukha & Kianian (2023). Including these values in the analysis could compromise the accuracy of the resulting estimates.

To remove outliers, their values are set to NA rather than removing the record from the dataset. This process is also called censoring. By assigning NA values to these outliers, they can be effectively removed during statistical operations with functions that allow for removal of NA values such as mean() for getting the mean value or sd() for getting the standard deviation.

Usage

flag_outliers(x, .from = c("zscores", "raw_muac"))

remove_flags(x, .from = c("zscores", "raw_muac"))

Arguments

Value

An vector of the same length as x of flagged records coded as 1 for a flagged record and 0 for a non-flagged record.

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. Available at https://onlinelibrary.wiley.com/doi/10.1111/mcn.13478

SMART Initiative (2017). Standardized Monitoring and Assessment for Relief and Transition. Manual 2.0. Available at: https://smartmethodology.org.

Examples

## Sample data of raw MUAC values ----
x <- anthro.01$muac

## Apply the function with `.from` set to "raw_muac" ----
m <- flag_outliers(x, .from = "raw_muac")
head(m)

## Sample data of z-scores (be it WFHZ, MFAZ, HFAZ or WFAZ) ----
x <- anthro.02$mfaz

# Apply the function with `.from` set to "zscores" ----
z <- flag_outliers(x, .from = "zscores")
tail(z)

## With `.from` set to "zscores" ----
z <- remove_flags(
  x = wfhz.01$wfhz,
  .from = "zscores"
)

head(z)

## With `.from` set to "raw_muac" ----
m <- remove_flags(
  x = mfaz.01$muac,
  .from = "raw_muac"
)

tail(m)

Calculate child's age in months

Description

Calculate child's age in months based on the date of birth and the date of data collection.

Usage

get_age_months(dos, dob)

Arguments

Value

A numeric vector of child's age in months. Any value less than 6.0 and greater than or equal to 60.0 months are set to NA.

Examples

## Take two vectors of class "Date" ----
surv_date <- as.Date(
  c(
    "2024-01-05", "2024-01-05", "2024-01-05", "2024-01-08", "2024-01-08",
    "2024-01-08", "2024-01-10", "2024-01-10", "2024-01-10", "2024-01-11"
  )
)
bir_date <- as.Date(
  c(
    "2022-04-04", "2021-05-01", "2023-05-24", "2017-12-12", NA,
    "2020-12-12", "2022-04-04", "2021-05-01", "2023-05-24", "2020-12-12"
  )
)

## Apply the function ----
get_age_months(
  dos = surv_date,
  dob = bir_date
)

A sample mid-upper arm circumference (MUAC) screening data

Description

A sample mid-upper arm circumference (MUAC) screening data

Usage

mfaz.01

Format

A tibble with 661 rows and 4 columns.

Source

Anonymous

Examples

mfaz.01

A sample SMART survey data with mid-upper arm circumference measurements

Description

A sample SMART survey data with mid-upper arm circumference measurements

Usage

mfaz.02

Format

A tibble with 303 rows and 7 columns.

Source

Anonymous

Examples

mfaz.02

Check whether sample size requirements for IPC Acute Malnutrition (IPC AMN) analysis are met

Description

Data for estimating the prevalence of acute malnutrition used in the IPC AMN can come from different sources: surveys, screenings or community-based surveillance systems. The IPC has set minimum sample size requirements for each source. This function verifies whether these requirements are met.

Usage

mw_check_ipcamn_ssreq(
  df,
  cluster,
  .source = c("survey", "screening", "ssite"),
  ...
)

Arguments

Value

A summary tibble containing check results for:

• n_clusters - the total number of unique clusters or screening or site identifiers;

• n_obs - the corresponding total number of children in the dataset; and,

• meet_ipc - whether the IPC AMN requirements were met.

References

IPC Global Partners. 2021. Integrated Food Security Phase Classification Technical Manual Version 3.1.Evidence and Standards for Better Food Security and Nutrition Decisions. Rome. Available at: https://www.ipcinfo.org/ipcinfo-website/resources/ipc-manual/en/.

Examples

mw_check_ipcamn_ssreq(
  df = anthro.01,
  cluster = cluster,
  .source = "survey",
  area
)

Estimate age-weighted prevalence of wasting by MUAC

Description

Estimates age‑weighted prevalence of wasting using MUAC. Accepts age in months or in categories ('6–23', '24–59'). The default is age in months.

The prevalence is weighted as:

( prevalence_{6-23} + (2 \times prevalence_{24-59} )) / 3

Whilst the function is exported to users as a standalone, it is embedded into the following major MUAC prevalence functions of this package: mw_estimate_prevalence_muac(), mw_estimate_prevalence_screening(), and mw_estimate_prevalence_screening2().

Usage

mw_estimate_age_weighted_prev_muac(
  df,
  muac,
  has_age = TRUE,
  age = NULL,
  age_cat = NULL,
  oedema = NULL,
  raw_muac = FALSE,
  ...
)

Arguments

Details

As a standalone function, the user must check data quality before calling the function.

Value

A summary tibble with wasting prevalence estimates, as given by the SMART updated MUAC tool (see references below).

References

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

Examples

## Example application when age is given in months ----
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_age_weighted_prev_muac(
    muac = muac,
    has_age = TRUE,
    age = age,
    age_cat = FALSE,
    oedema = oedema,
    raw_muac = FALSE,
    analysis_unit
  )

## Example application when age is given in categories ----
anthro.04 |>
  transform(age_cat = ifelse(age < 24, "6-23", "24-59")) |>
  mw_wrangle_muac(
    muac = muac,
    .recode_muac = FALSE,
    .to = "none",
    sex = sex,
    .recode_sex = FALSE
  ) |>
  mw_estimate_age_weighted_prev_muac(
    has_age = FALSE,
    age = NULL,
    age_cat = age_cat,
    oedema = oedema,
    raw_muac = TRUE
  )

Estimate the prevalence of combined wasting

Description

Estimate the prevalence of wasting based on the combined case-definition of weight-for-height z-scores (WFHZ), MUAC and/or 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.

The data quality is first assessed by calculating and rating the standard deviation (SD) of WFHZ. Then it calculates the observed proportion of children aged 24–59 months out of all children in the dataset. Next, it estimates the p-value for the difference between this observed proportion and the expected (0.66), and rates the result.

Prevalence is estimated only when the WFHZ SD is not problematic and the age ratio test is not problematic, or — if the age ratio test is problematic — the proportion of children aged 24–59 months is greater than or equal to 0.66.

Usage

mw_estimate_prevalence_combined(df, wt = NULL, oedema = NULL, ...)

Arguments

Details

A concept of combined flags is introduced in this function. Any observation that is flagged for either flag_wfhz or flag_mfaz is flagged under a new variable named cflags added to df. This ensures that all flagged observations from both WFHZ and MFAZ data are excluded from the prevalence analysis.

Value

A summary tibble for the descriptive statistics about combined wasting.

Examples

## When wt are set to NULL ----
mw_estimate_prevalence_combined(
  df = anthro.02,
  wt = NULL,
  oedema = oedema
)

## When `wt` is not set to NULL ----
mw_estimate_prevalence_combined(
  df = anthro.02,
  wt = wtfactor,
  oedema = oedema
)

Estimate the prevalence of wasting based on z-scores of muac-for-age (MFAZ)

Description

Calculate the prevalence estimates of wasting based on z-scores of MUAC-for-age and/or bilateral 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. The quality of the data is first evaluated by calculating and rating the standard deviation of MFAZ. Standard approach to prevalence estimation is calculated only when the standard deviation of MFAZ is rated as not problematic. If the standard deviation is problematic, prevalence is estimated using the PROBIT estimator. Outliers are detected based on SMART flagging criteria. Identified outliers are then excluded before prevalence estimation is performed.

Usage

mw_estimate_prevalence_mfaz(df, wt = NULL, oedema = NULL, ...)

Arguments

Value

A summary tibble for the descriptive statistics about wasting.

Examples

## Without grouping variables ----
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_mfaz(
    wt = NULL,
    oedema = oedema,
    analysis_unit
  )

Estimate the prevalence of wasting based on MUAC for survey data

Description

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

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.

Value

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

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
  )

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

Description

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.

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_screening(df, muac, age, oedema = NULL, ...)

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

Arguments

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 in mw_estimate_prevalence_screening2() are based on the raw MUAC values. In either functions, outliers are excluded from the prevalence estimation.

Value

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

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,
  age = age,
  oedema = oedema,
  province
)

## With `oedema` set to `NULL` ----
mw_estimate_prevalence_screening(
  df = anthro.02,
  muac = muac,
  age = age,
  oedema = NULL,
  province
)

## Specifying the grouping variables ----
mw_estimate_prevalence_screening(
  df = anthro.02,
  muac = muac,
  age = age,
  oedema = NULL,
  province
)


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
  )

Estimate the prevalence of wasting based on weight-for-height z-scores (WFHZ)

Description

Calculate the prevalence estimates of wasting based on z-scores of weight-for-height 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. The quality of the data is first evaluated by calculating and rating the standard deviation of WFHZ. Standard approach to prevalence estimation is calculated only when the standard deviation of MFAZ is rated as not problematic. If the standard deviation is problematic, prevalence is estimated using the PROBIT estimator. Outliers are detected based on SMART flagging criteria. Identified outliers are then excluded before prevalence estimation is performed.

Usage

mw_estimate_prevalence_wfhz(df, wt = NULL, oedema = NULL, ...)

Arguments

Value

A summary tibble for the descriptive statistics about wasting.

Examples

## When .by = NULL ----
### Start off by wrangling the data ----
data <- mw_wrangle_wfhz(
  df = anthro.03,
  sex = sex,
  weight = weight,
  height = height,
  .recode_sex = TRUE
)

### Now run the prevalence function ----
mw_estimate_prevalence_wfhz(
  df = data,
  wt = NULL,
  oedema = oedema
)

## Now when .by is not set to NULL ----
mw_estimate_prevalence_wfhz(
  df = data,
  wt = NULL,
  oedema = oedema,
  district
)

## When a weighted analysis is needed ----
mw_estimate_prevalence_wfhz(
  df = anthro.02,
  wt = wtfactor,
  oedema = oedema,
  province
)

Clean and format the output tibble returned from the MUAC-for-age z-score plausibility check

Description

Converts scientific notations to standard notations, rounds off values, and renames columns to meaningful names.

Usage

mw_neat_output_mfaz(df)

Arguments

Value

A data.frame object of the same length and width as df, with column names and values formatted as appropriate.

Examples

## First wrangle age data ----
data <- mw_wrangle_age(
  df = anthro.01,
  dos = dos,
  dob = dob,
  age = age,
  .decimals = 2
)

## Then wrangle MUAC data ----
data_mfaz <- mw_wrangle_muac(
  df = data,
  sex = sex,
  age = age,
  muac = muac,
  .recode_sex = TRUE,
  .recode_muac = TRUE,
  .to = "cm"
)

## Then run plausibility check ----
pl <- mw_plausibility_check_mfaz(
  df = data_mfaz,
  flags = flag_mfaz,
  sex = sex,
  muac = muac,
  age = age,
  area
)

## Now neat the output table ----
mw_neat_output_mfaz(df = pl)

Clean and format the output tibble returned from the MUAC plausibility check

Description

Converts scientific notations to standard notations, rounds off values, and renames columns to meaningful names.

Usage

mw_neat_output_muac(df)

Arguments

Value

A data.frame object of the same length and width as df, with column names and values formatted for clarity and readability.

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 ----
pl_muac <- mw_plausibility_check_muac(
  df = df_muac,
  flags = flag_muac,
  sex = sex,
  muac = muac
)

## Neat the output table ----

mw_neat_output_muac(df = pl_muac)

Clean and format the output tibble returned from the WFHZ plausibility check

Description

Converts scientific notations to standard notations, rounds off values, and renames columns to meaningful names.

Usage

mw_neat_output_wfhz(df)

Arguments

Value

A tibble object of the same length and width as df, with column names and values formatted for clarity and readability.

Examples

## First wrangle age data ----
data <- mw_wrangle_age(
  df = anthro.01,
  dos = dos,
  dob = dob,
  age = age,
  .decimals = 2
)

## Then wrangle WFHZ data ----
data_wfhz <- mw_wrangle_wfhz(
  df = data,
  sex = sex,
  weight = weight,
  height = height,
  .recode_sex = TRUE
)

## Now run the plausibility check ----
pl <- mw_plausibility_check_wfhz(
  df = data_wfhz,
  sex = sex,
  age = age,
  weight = weight,
  height = height,
  flags = flag_wfhz,
  area
)

## Now neat the output table ----
mw_neat_output_wfhz(df = pl)

Check the plausibility and acceptability of MUAC-for-age z-score (MFAZ) data

Description

Check the overall plausibility and acceptability of MFAZ data through a structured test suite encompassing checks for sampling and measurement-related biases in the dataset. This test suite follows the recommendation made by Bilukha & Kianian (2023) on the plausibility of constructing a comprehensive plausibility check for MUAC data similar to weight-for-height z-score to evaluate its acceptability when age values are available in the dataset.

The function works on a data.frame returned from wrangling functions for age and for MUAC-for-age z-score data available from this package.

Usage

mw_plausibility_check_mfaz(df, sex, muac, age, flags, ...)

Arguments

Details

Whilst the function uses the same checks and criteria as those for weight-for-height z-scores in the SMART plausibility check, the percent of flagged records is evaluated using different cut-off points, with a maximum acceptability of 2.0% as shown below:

Value

A single-row summary tibble with columns containing the plausibility check results. If ungrouped analysis, the output will consist of 17 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.

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://onlinelibrary.wiley.com/doi/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_age() mw_wrangle_muac() mw_stattest_ageratio() flag_outliers()

Examples

## First wrangle age data ----
data <- mw_wrangle_age(
  df = anthro.01,
  dos = dos,
  dob = dob,
  age = age,
  .decimals = 2
)

## Then wrangle MUAC data ----
data_muac <- mw_wrangle_muac(
  df = data,
  sex = sex,
  age = age,
  muac = muac,
  .recode_sex = TRUE,
  .recode_muac = TRUE,
  .to = "cm"
)

## And finally run plausibility check ----
mw_plausibility_check_mfaz(
  df = data_muac,
  flags = flag_mfaz,
  sex = sex,
  muac = muac,
  age = age,
  area, team
)

Check the plausibility and acceptability of raw MUAC data

Description

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

Details

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

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.

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://onlinelibrary.wiley.com/doi/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
)

Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data

Description

Check the overall plausibility and acceptability of WFHZ data through a structured test suite encompassing checks for sampling and measurement-related biases in the dataset. The test suite, including the criteria and corresponding rating of acceptability, follows the standards in the SMART plausibility check.

The function works on a data frame returned by this package's wrangling functions for age and for WFHZ data.

Usage

mw_plausibility_check_wfhz(df, sex, age, weight, height, flags, ...)

Arguments

Value

A single-row summary tibble with columns containing the plausibility check results. If ungrouped analysis, the output will consist of 19 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.

References

SMART Initiative (2017). Standardized Monitoring and Assessment for Relief and Transition. Manual 2.0. Available at: https://smartmethodology.org.

See Also

mw_plausibility_check_mfaz() mw_plausibility_check_muac() mw_wrangle_age()

Examples

## First wrangle age data ----
data <- mw_wrangle_age(
  df = anthro.01,
  dos = dos,
  dob = dob,
  age = age,
  .decimals = 2
)

## Then wrangle WFHZ data ----
data_wfhz <- mw_wrangle_wfhz(
  df = data,
  sex = sex,
  weight = weight,
  height = height,
  .recode_sex = TRUE
)

## Now run the plausibility check ----
mw_plausibility_check_wfhz(
  df = data_wfhz,
  sex = sex,
  age = age,
  weight = weight,
  height = height,
  flags = flag_wfhz,
  area, team
)

Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old

Description

Calculate the observed age ratio of children aged 24 to 59 months old over those aged 6 to 23 months old and test if there is a statistically significant difference between the observed and the expected.

Usage

mw_stattest_ageratio(age, .expectedP = 0.66)

mw_stattest_ageratio2(age_cat, .expectedP = 0.66)

Arguments

Details

This function should be used specifically when assessing the quality of MUAC data. For age ratio test of children aged 6 to 29 months old over 30 to 59 months old, as performed in the SMART plausibility check, use nipnTK::ageRatioTest() instead.

Value

A list object with three elements: p for p-value of the difference between the observed and the expected proportion of children aged 24 to 59 months old over those aged 6 to 23 months old, observedR for the observed ratio, and observedP for the observed proportion.

References

SMART Initiative. Updated MUAC data collection tool. Available at: https://smartmethodology.org/survey-planning-tools/updated-muac-tool/

Examples

mw_stattest_ageratio(
  age = anthro.02$age,
  .expectedP = 0.66
)


age <- ifelse(anthro.02$age < 24, "6-23", "24-59")

mw_stattest_ageratio2(
  age = age,
  .expectedP = 0.66
)

Wrangle child's age

Description

Wrangle child's age for downstream analysis. This includes calculating age in months based on the date of data collection and the child's date of birth, and setting to NA the age values that are less than 6.0 and greater than or equal to 60.0 months old.

Usage

mw_wrangle_age(df, dos = NULL, dob = NULL, age, .decimals = 2)

Arguments

Value

A tibble based on df. The variable age will be automatically filled in each row where age value was missing and both the child's date of birth and the date of data collection are available. Rows where age is less than 6.0 and greater than or equal to 60.0 months old will be set to NA. Additionally, a new variable named age_days of class double for calculated age of child in days is added to df.

Examples

## A sample data ----
df <- data.frame(
  surv_date = as.Date(c(
    "2023-01-01", "2023-01-01", "2023-01-01", "2023-01-01", "2023-01-01"
  )),
  birth_date = as.Date(c(
    "2019-01-01", NA, "2018-03-20", "2019-11-05", "2021-04-25"
  )),
  age = c(NA, 36, NA, NA, NA)
)

## Apply the function ----
mw_wrangle_age(
  df = df,
  dos = surv_date,
  dob = birth_date,
  age = age,
  .decimals = 3
)

Wrangle MUAC data

Description

Calculate z-scores for MUAC-for-age (MFAZ) and identify outliers based on the SMART methodology. When age is not supplied, only outliers are detected from the raw MUAC values. The function only works after age has gone through mw_wrangle_age().

Usage

mw_wrangle_muac(
  df,
  sex,
  muac,
  age = NULL,
  .recode_sex = TRUE,
  .recode_muac = TRUE,
  .to = c("cm", "mm", "none"),
  .decimals = 3
)

Arguments

Value

A tibble based on df. If age = NULL, flag_muac variable for detected MUAC outliers based on raw MUAC is added to df. Otherwise, variables named mfaz for child's MFAZ and flag_mfaz for detected outliers based on SMART guidelines are added to df.

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://onlinelibrary.wiley.com/doi/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

flag_outliers() remove_flags() mw_wrangle_age()

Examples

## When age is available, wrangle it first before calling the function ----
w <- mw_wrangle_age(
  df = anthro.02,
  dos = NULL,
  dob = NULL,
  age = age,
  .decimals = 2
)

### Then apply the function to wrangle MUAC data ----
mw_wrangle_muac(
  df = w,
  sex = sex,
  age = age,
  muac = muac,
  .recode_sex = TRUE,
  .recode_muac = TRUE,
  .to = "cm",
  .decimals = 3
)

## When age is not available ----
mw_wrangle_muac(
  df = anthro.02,
  sex = sex,
  age = NULL,
  muac = muac,
  .recode_sex = TRUE,
  .recode_muac = TRUE,
  .to = "cm",
  .decimals = 3
)

Wrangle weight-for-height data

Description

Calculate z-scores for weight-for-height (WFHZ) and identify outliers based on the SMART methodology.

Usage

mw_wrangle_wfhz(df, sex, weight, height, .recode_sex = TRUE, .decimals = 3)

Arguments

Value

A data frame based on df with new variables named wfhz for child's WFHZ and flag_wfhz for detected outliers added.

References

SMART Initiative (2017). Standardized Monitoring and Assessment for Relief and Transition. Manual 2.0. Available at: https://smartmethodology.org.

See Also

flag_outliers() remove_flags()

Examples

mw_wrangle_wfhz(
  df = anthro.01,
  sex = sex,
  weight = weight,
  height = height,
  .recode_sex = TRUE,
  .decimals = 2
)

Rate the acceptability of the age and sex ratio test p-values

Description

Rate the acceptability of the age and sex ratio test p-values

Usage

rate_agesex_ratio(p)

Arguments

Value

A character vector with the same length as p for the acceptability rate.

Rate the overall acceptability of the data

Description

Rate the overall data acceptability score into "Excellent", "Good", "Acceptable" or "Problematic".

Usage

rate_overall_quality(q)

Arguments

Value

A vector of class factor with the same length as q of overall rate of acceptability of the data.

Rate the acceptability of the proportion of flagged records

Description

Rate the acceptability of the proportion of flagged records in WFHZ, MFAZ, and raw MUAC data following the SMART methodology criteria.

Usage

rate_propof_flagged(p, .in = c("mfaz", "wfhz", "raw_muac"))

Arguments

Value

A vector of class factor with the same length as p for the acceptability rate.

Rate the acceptability of the skewness and kurtosis test results

Description

Rate the acceptability of the skewness and kurtosis test results

Usage

rate_skewkurt(sk)

Arguments

Value

A vector of class factor with the same length as sk for the acceptability rate.

Rate the acceptability of the standard deviation

Description

Rate the acceptability of the standard deviation of WFHZ, MFAZ, and raw MUAC data. Rating follows the SMART methodology criteria.

Usage

rate_std(sd, .of = c("zscores", "raw_muac"))

Arguments

Value

A vector of class factor of the same length as sd for the acceptability rate.

Convert MUAC values to either centimetres or millimetres

Description

Convert MUAC values to either centimetres or millimetres

Usage

recode_muac(x, .to = c("cm", "mm"))

Arguments

Value

A numeric vector of the same length as x with values set to specified unit of measurement.

Examples

## Recode from millimetres to centimetres ----
muac_cm <- recode_muac(
  x = anthro.01$muac,
  .to = "cm"
)
head(muac_cm)

## Using the `muac_cm` object to recode it back to "mm" ----
muac_mm <- recode_muac(
  x = muac_cm,
  .to = "mm"
)
tail(muac_mm)

Get the overall acceptability score from the acceptability rate scores

Description

Get the overall acceptability score from the acceptability rate scores

Usage

score_overall_quality(
  cl_flags,
  cl_sex,
  cl_age,
  cl_dps_m = NULL,
  cl_dps_w = NULL,
  cl_dps_h = NULL,
  cl_std,
  cl_skw,
  cl_kurt,
  .for = c("wfhz", "mfaz")
)

Arguments

Value

A numeric value for the overall data quality (acceptability) score.

Score the acceptability rating of the check results that constitutes the plausibility check suite

Description

Attribute a score, also known as penalty point, for a given rate of acceptability of the standard deviation, proportion of flagged records, age and sex ratio, skewness, kurtosis and digit preference score check results. The scoring criteria and thresholds follows the standards in the SMART plausibility check.

Usage

score_std_flags(x)

score_agesexr_dps(x)

score_skewkurt(x)

Arguments

Value

An integer vector with the same length as x of the acceptability score.

References

SMART Initiative (2017). Standardized Monitoring and Assessment for Relief and Transition. Manual 2.0. Available at: https://smartmethodology.org.

A sample SMART survey data with weight-for-height z-score standard deviation rated as problematic

Description

A sample SMART survey data with weight-for-height z-score standard deviation rated as problematic

Usage

wfhz.01

Format

A tibble with 303 rows and 6 columns.

Source

Anonymous

Examples

wfhz.01