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Compute Household Food Insecurity Access (HFIA) Score and Category

Source: R/compute_hfias.R
compute_hfias.Rd

This function computes the HFIA score and categorical classification (1-4) based on the standard HFIAS indicator framework developed by FANTA (Food and Nutrition Technical Assistance Project). The function assumes that the HFIAS-related variables are provided in the exact order they appear in the HFIAS questionnaire (occurrence and frequency pairs).

Usage

compute_hfias(data, hfia_cols)

Arguments

data

A data frame or tibble containing HFIAS-related variables.

hfia_cols

A character vector specifying the ordered column names corresponding to the HFIAS questions. These should include both occurrence (Yes/No) and frequency responses, alternating in sequence.

Value

A tibble containing the original data with two added columns: hfia_score and hfia_category.

Details

The function expects 18 columns for the standard HFIAS tool: nine occurrence questions and nine corresponding frequency-of-occurrence questions, ordered as:

  1. Occurrence Q1

  2. Frequency Q1

  3. Occurrence Q2

  4. Frequency Q2

  5. ... up to Q9 and its frequency

Expected frequency responses:

  • "Rarely (1-2 times)" = 1

  • "Sometimes (3 to 10 times)" = 2

  • "Often (more than 10 times)" = 3

and occurrence responses:

  • "Yes" = 1

  • "No" = 0

Numeric inputs are accepted directly (0-3). Missing values are automatically replaced with 0, as frequency questions are typically skipped when occurrence is "No." After conversion, the function checks for any unexpected text entries and raises an informative error listing them for the user to clean.

Outputs:

  • hfia_score - The total sum of the nine frequency columns.

  • hfia_category - A categorical classification:

    1

    Food secure

    2

    Mildly food insecure

    3

    Moderately food insecure

    4

    Severely food insecure

References

Coates, J., Swindale, A., & Bilinsky, P. (2007). Household Food Insecurity Access Scale (HFIAS) for Measurement of Food Access: Indicator Guide (v3). Washington, D.C.: Food and Nutrition Technical Assistance Project (FANTA).

Examples

# Example dataset with respondent ID and realistic skip patterns
df <- tibble::tibble(
  id = 1:3,
  Q1 = c("Yes", "Yes", "No"),
  F1 = c("Rarely (1-2 times)", "Often (more than 10 times)", NA),
  Q2 = c("No", "Yes", "No"),
  F2 = c(NA, "Sometimes (3 to 10 times)", NA),
  Q3 = c("No", "Yes", "No"),
  F3 = c(NA, "Rarely (1-2 times)", NA),
  Q4 = c("No", "Yes", "No"),
  F4 = c(NA, "Rarely (1-2 times)", NA),
  Q5 = c("No", "Yes", "No"),
  F5 = c(NA, "Often (more than 10 times)", NA),
  Q6 = c("No", "Yes", "No"),
  F6 = c(NA, "Rarely (1-2 times)", NA),
  Q7 = c("No", "Yes", "No"),
  F7 = c(NA, "Rarely (1-2 times)", NA),
  Q8 = c("Yes", "Yes", "No"),
  F8 = c("Sometimes (3 to 10 times)", "Rarely (1-2 times)", NA),
  Q9 = c("No", "Yes", "No"),
  F9 = c(NA, "Rarely (1-2 times)", NA)
)

# Compute HFIA score and category using the relevant columns only
compute_hfias(
  df,
  hfia_cols = c(
    "Q1", "F1", "Q2", "F2", "Q3", "F3", "Q4", "F4",
    "Q5", "F5", "Q6", "F6", "Q7", "F7", "Q8", "F8",
    "Q9", "F9"
  )
)
#> # A tibble: 3 × 21
#>      id    Q1    F1    Q2    F2    Q3    F3    Q4    F4    Q5    F5    Q6    F6
#>   <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1     1     1     1     0     0     0     0     0     0     0     0     0     0
#> 2     2     1     3     1     2     1     1     1     1     1     3     1     1
#> 3     3     0     0     0     0     0     0     0     0     0     0     0     0
#> # ℹ 8 more variables: Q7 <int>, F7 <int>, Q8 <int>, F8 <int>, Q9 <int>,
#> #   F9 <int>, hfia_score <dbl>, hfia_category <int>