micronutrients

micronutrients is currently an early prototype to compute micronutrient indicators from survey data.

It is not meant for serious use yet and still work in progress.

API

The following functions compute results:

• individual_classification to compute row level indicators
• mn_stats to compute less-detailed prevalence estimates and other summary statistics

Indicators:

• indicator_anaemia()
• indicator_ferritin(adjustment)
• indicator_ida()
• indicator_iodine()

Adjustments:

• adjustment_ferritin_arithmetic_correction()
• adjustment_ferritin_cutoff()
• adjustment_ferritin_regression_correction()
• adjustment_ferritin_rm_agp_crp()

Depending on the indicators and adjustment methods you use you have to input specific values, like CRP or AGP.

Example

To compute row-level classification you can do the following:

dataset <- read.csv("some_data_set")
result <- individual_classification(
  indicators = list(
    indicator_iodine(),
    indicator_ferritin(adjustment_ferritin_cutoff()),
    indicator_anaemia(),
    indicator_ida()
  ),
  age = dataset$age_years,
  sex = dataset$sex,
  pregnancy_status = dataset$pregnancy_status,
  lactating_status = dataset$lactating_status,
  ferritin = dataset$ferritin_measurement,
  iodine = dataset$iodine,
  CRP = dataset$crp_measurement,
  AGP = dataset$agp_measurement,
  haemoglobin = dataset$haemoglobin_measurement,
  is_smoker = dataset$is_smoker,
  altitude = dataset$altitude,
  smokes_cigarettes_per_day = dataset$smokes_cigarettes_per_day,
  pregnancyweeks = dataset$pregnancyweeks,
  pregnancymonths = dataset$pregnancymonths
)

Prevalence functions accept the same arguments and compute a summary table.

result <- mn_stats(
  indicators = list(
    indicator_iodine(),
    indicator_ferritin(adjustment_ferritin_cutoff()),
    indicator_anaemia(),
    indicator_ida()
  ),
  age = dataset$age_years,
  sex = dataset$sex,
  pregnancy_status = dataset$pregnancy_status,
  lactating_status = dataset$lactating_status,
  ferritin = dataset$ferritin_measurement,
  iodine = dataset$iodine,
  CRP = dataset$crp_measurement,
  AGP = dataset$agp_measurement,
  haemoglobin = dataset$haemoglobin_measurement,
  is_smoker = dataset$is_smoker,
  altitude = dataset$altitude,
  smokes_cigarettes_per_day = dataset$smokes_cigarettes_per_day,
  pregnancyweeks = dataset$pregnancyweeks,
  pregnancymonths = dataset$pregnancymonths
)

Test coverage

covr::package_coverage()
#> micronutrients Coverage: 78.10%
#> R/concept-fasting-status.R: 0.00%
#> R/concept-helpers.R: 0.00%
#> R/concept-iron-deficiency.R: 0.00%
#> R/concept-lactating-status.R: 0.00%
#> R/concept-mothers-education.R: 0.00%
#> R/concept-wealth-quintile.R: 0.00%
#> R/indicators-iron-deficiency-anaemia.R: 0.00%
#> R/indicators-anaemia.R: 6.45%
#> R/measurements.R: 20.00%
#> R/concept-sex.R: 22.22%
#> R/indicators-iodine.R: 33.33%
#> R/utils.R: 39.58%
#> R/indicators-composite.R: 56.67%
#> R/indicators-ferritin.R: 63.45%
#> R/concepts.R: 85.37%
#> R/age.R: 85.71%
#> R/concept-pregnancy-status.R: 87.50%
#> R/indicators.R: 89.13%
#> R/age-groups.R: 89.61%
#> R/prevalence.R: 96.80%
#> R/adjustments-export.R: 100.00%
#> R/classifications.R: 100.00%
#> R/concept-area.R: 100.00%
#> R/indicators-export.R: 100.00%