The anatomy of an indicator

What it is made of

Strip the framework language away and an indicator has five parts:

• A population — who or what is being counted.
• A condition — what has to be true of them to be in the numerator.
• A denominator — what they are counted against, unless it is a plain count.
• A period — over what stretch of time.
• A unit of measure — people, doses, episodes, percent, litres per person per day.

Miss any one and the indicator is ambiguous. Miss the last and it is unusable in a sentence, which is where it will end up.

Four families, and choosing between them

Family	Shape	Example	Watch for
Count	A number of things	Children screened	No denominator; cannot be compared across places
Proportion	Part over whole, both same units	Share of children with MUAC under 125 mm	Numerator must be a subset of the denominator
Rate	Events over population-time	New admissions per 1,000 under-fives per month	The time unit must be in the label
Ratio / index	Two quantities not nested	Penta1-to-penta3 dropout; Food Consumption Score	Can exceed 1; direction is not obvious

The commonest design error is reporting a count where the audience will read a proportion. “375 children referred” sounds like a lot or a little depending entirely on how many were screened, and the reader will supply their own denominator if you do not.

screened = len(muac)
referred = muac["outcome"].str.startswith("referred").sum()

print(f"{referred} referred of {screened} screened = {referred / screened:.1%}")

screened <- nrow(muac)
referred <- sum(startsWith(muac$outcome, "referred"))

sprintf("%d referred of %d screened = %.1f%%", referred, screened, 100 * referred / screened)

375 of 4,218, or 8.9%. Report both. The count is what a logistics officer orders supplies against; the proportion is what tells you whether this commune is worse than that one.

Three numbers that look like one

Here is the ambiguity that costs the most time in practice. The screening register supports three different “number of children” indicators.

print("screening events:      ", len(muac))
print("distinct child_id:     ", muac["child_id"].nunique())

c(events = nrow(muac), ids = dplyr::n_distinct(muac$child_id))

• 4,218 screening events. What the community health workers did. The right numerator for workload, supply consumption and payment.
• 4,206 distinct identifiers. Twelve forms were submitted twice on a poor connection, so twelve events are duplicates of an event that already exists.
• About 4,200 children. Six more children were re-registered under a new identifier — findable only by the record linkage the cleaning course taught, and never exactly knowable.

Three defensible numbers, one register, and the difference between the largest and the smallest is 0.4%. The size of the gap is not the point. The point is that “children screened” does not say which one, and the moment a donor compares your figure against a partner’s, whichever of the three each of you chose determines whether you agree.

So the indicator name has to carry it: screening_events, children_screened_deduplicated. Never children_screened on its own.

The unit of measure belongs in the name

indicators = {
    "children_screened_count": screened,
    "gam_prevalence_percent": round(100 * gam_cases / assessed, 1),
    "admissions_per_1000_under5_per_month": round(1000 * admissions / under5 / 12, 2),
}

indicators <- list(
  children_screened_count              = screened,
  gam_prevalence_percent               = round(100 * gam_cases / assessed, 1),
  admissions_per_1000_under5_per_month = round(1000 * admissions / under5 / 12, 2)
)

Long names, and they are worth it. A column called rate in a spreadsheet emailed between four organisations will be multiplied by 100 by somebody, divided by 12 by somebody else, and compared against a figure with a different denominator by a third. The name is the cheapest defence available.

Direction: say which way is better

Every indicator needs a stated direction, because a surprising number are ambiguous.

• Higher is better — coverage, completion, attendance.
• Lower is better — dropout, defaulter rate, prevalence of acute malnutrition.
• Neither, on its own — referral counts. More referrals can mean better case finding or a deteriorating situation, and the indicator cannot tell you which.

That third category is larger than people expect and it is where dashboards go wrong: a red-amber-green traffic light applied to an indicator with no direction produces an alarm whose meaning nobody can state. If you cannot say which way is better, the indicator needs a partner indicator, not a colour.

Leading and lagging

A related distinction that decides whether an indicator is any use for management.

• Lagging — tells you what happened. GAM prevalence, cure rate, annual coverage. Accurate, essential for accountability, useless for steering, because by the time it moves the quarter is over.
• Leading — moves early and predicts. Stock-out days, defaulter rate in the first two weeks of treatment, consecutive-absence counts before a child drops out.

A monitoring system built entirely of lagging indicators reports faithfully on things nobody can now change. Aim for at least one leading indicator per outcome, and expect it to be noisier — that is the trade you are making.

Three questions before any code

Answer these in writing before opening an editor. They take five minutes and they prevent the rewrite.

1. What decision does this number inform? If the answer is “the donor asks for it”, say so honestly and keep it cheap. If two different decisions come out, you have two indicators wearing one name.

2. What would make it move, other than the thing I care about? Coverage moves with the denominator, with reporting completeness, with population movement, and occasionally with vaccination. Listing the alternatives is how you know what to report alongside it.

3. Who else already computes this, and how? Almost always somebody does — UNICEF, WHO, the cluster, the ministry. Lesson 6 is entirely about that, and the answer changes your definition more often than not.

An indicator you cannot compute is not an indicator

The last check, and it kills more proposed indicators than any other.

Before it goes into a LogFrame, confirm the data exists: which system holds it, at what grain, how often, and who extracts it. An indicator whose means of verification is “programme records” is a promise nobody has checked.

REQUIRED = {"child_id", "commune", "screening_date", "muac_mm", "oedema", "outcome"}
missing = REQUIRED - set(muac.columns)
assert not missing, f"indicator cannot be computed; missing {missing}"

REQUIRED <- c("child_id", "commune", "screening_date", "muac_mm", "oedema", "outcome")
stopifnot(all(REQUIRED %in% names(muac)))

The Data Quality Assessment course found timeliness unmeasurable because a field was not in the export. That is this check, discovered eighteen months late.

An indicator is a promise that a number can be produced, repeatedly, to the same definition. Everything else in this course is about keeping that promise.

What comes next

You have the anatomy. The next lesson turns it into the artefact this course is built around — the reference sheet, field by field, and the test that tells you whether yours is finished.