The thirty-seven households zero-filling would misclassify

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What this lesson covers

• Two more instruments, two more rules
• The Household Hunger Scale
• What zero-filling actually costs
• The reduced Coping Strategy Index
• Three instruments, three analysable samples
• What comes next

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Two more instruments, two more rules

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The Household Hunger Scale — In Python

import pandas as pd

survey = pd.read_csv("food-security-survey-2024.v1.csv")
HHS = ["hhs_no_food_in_house", "hhs_sleep_hungry",
       "hhs_day_and_night_without_eating"]

complete = survey[HHS].notna().all(axis=1)
score = survey.loc[complete, HHS].sum(axis=1)

bands = pd.cut(score, [-1, 1, 3, 6],
               labels=["little or none", "moderate", "severe"])
print(f"complete: {complete.sum()} of {len(survey)}")
print((bands.value_counts(normalize=True) * 100).round(1))

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The Household Hunger Scale — In R

library(dplyr)

hhs <- c("hhs_no_food_in_house", "hhs_sleep_hungry",
         "hhs_day_and_night_without_eating")

survey |>
  filter(if_all(all_of(hhs), ~ !is.na(.x))) |>
  mutate(score = rowSums(across(all_of(hhs))),
         band = cut(score, c(-1, 1, 3, 6),
                    labels = c("little or none", "moderate", "severe"))) |>
  count(band) |> mutate(share = n / sum(n))

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The Household Hunger Scale

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The Household Hunger Scale

• 2,075 of 2,112 households answered all three — The thirty-seven that did not are the interesting ones

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What zero-filling actually costs — In Python

zero_filled = survey[HHS].fillna(0).sum(axis=1)
naive = pd.cut(zero_filled, [-1, 1, 3, 6],
               labels=["little or none", "moderate", "severe"])
print((naive.value_counts(normalize=True) * 100).round(1))

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What zero-filling actually costs — In R

survey |>
  mutate(score = rowSums(across(all_of(hhs)), na.rm = TRUE)) |>
  count(band = cut(score, c(-1, 1, 3, 6)))

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What zero-filling actually costs

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What zero-filling actually costs

• Three tenths of a percentage point — On a prevalence, zero-filling is practically harmless here, and if you stop at the…

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What zero-filling actually costs — In Python

partial = survey.loc[~complete, HHS]
print(partial.sum(axis=1).value_counts().sort_index())

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What zero-filling actually costs — In R

survey |> filter(if_any(all_of(hhs), is.na)) |>
  mutate(observed = rowSums(across(all_of(hhs)), na.rm = TRUE)) |> count(observed)

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What zero-filling actually costs

• So the cost of zero-filling depends entirely on what the analysis produces
• If the output is a prevalence, the damage is three tenths of a point and you should say you zero-filled and move on.
• If the output is a targeting list, you have just told thirty-seven households they are food secure on the strength…
• Name the output before choosing the rule — This is the same decision as the CMAM cure-rate denominator and the water…

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The reduced Coping Strategy Index — In Python

RCSI = {
    "rcsi_less_preferred_food": 1,
    "rcsi_borrowed_food": 2,
    "rcsi_limit_portion_size": 1,
    "rcsi_restrict_adult_consumption": 3,
    "rcsi_reduce_meal_numbers": 1,
}
rcsi = sum(survey[column] * weight for column, weight in RCSI.items())
print(f"median {rcsi.median():.0f}, mean {rcsi.mean():.1f}, max {rcsi.max():.0f}")
print(f"rCSI 19 or above: {(rcsi >= 19).mean():.1%}")

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The reduced Coping Strategy Index — In R

rcsi_weights <- c(rcsi_less_preferred_food = 1, rcsi_borrowed_food = 2,
                  rcsi_limit_portion_size = 1,
                  rcsi_restrict_adult_consumption = 3,
                  rcsi_reduce_meal_numbers = 1)

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The reduced Coping Strategy Index

• Median 19, mean 19.5, and 50.9% at or above 19 — The weight of 3 on restricting adult consumption so children can eat…
• It has no universal threshold — Unlike the FCS, the rCSI's cut-offs are context-specific and usually set against the…
• It goes up before consumption goes down — and it can also go down in a crisis, when a household has exhausted the…

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Three instruments, three analysable samples — In Python

denominators = pd.DataFrame({
    "instrument": ["Food Consumption Score", "Household Hunger Scale",
                   "reduced Coping Strategy Index"],
    "analysable": [1989, 2075, 2112],
    "excluded": [123, 37, 0],
    "rule": ["all eight groups answered", "all three questions answered",
             "complete in this file"],
})
print(denominators)

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Three instruments, three analysable samples — In R

# Print this before the results, every time.

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Three instruments, three analysable samples

• Three numbers on three denominators, and none of them is 2,112 — The difference is small enough to be invisible in a…

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What comes next

• Consumption and hunger say what a household is experiencing.

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Where this goes next

Read the full lesson, with runnable code Back to the lesson