What exactly was counted?

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What this lesson covers

• The question
• Four parts
• The provenance block
• Interrogating a figure you did not produce
• What the system cannot tell you
• Where this course leaves you
• Where module 3 leaves you

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The question

• Somebody presents a slide: penta3 coverage, 78%.

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Four parts

• What was counted? — The data element, its aggregation operator, and whether any category disaggregation was collapsed…
• Where? — Which org units, at which level, on which version of the hierarchy
• When? — Which periods, by activity date or entry date, and how complete they were at extraction
• Out of what? — The denominator, its source, its year, and how many of the expected reporting units contributed

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The provenance block — Example (cont.)

Figure            Penta3 coverage, district, 2024
Value             77.5%

What              Data element PENTA3_DOSES ("Penta 3rd dose administered"),
                  aggregation operator SUM over periods and org units.
                  No category disaggregation in the extract; the element has
                  none configured.
Where             38 facilities at org unit level 4, hierarchy as pinned in
                  extracts/2026-07-28/raw/organisationUnits.json. No facility
                  was reassigned during 2024.
When              202401 to 202412, by activity period. Analytics tables last
                  run 2026-07-27 23:14 UTC, so late entry up to that point is
                  included.
Out of what       Surviving infants, MoH catchment estimates 2024, projected
                  from the 2015 census at 2.4% annual growth. Denominator
                  pro-rated to the months each facility reported: 9,238 of a

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The provenance block — Example (cont.)

                  full-year 11,774.
Completeness      349 of 456 facility-months reported (76.5%). August 29%,
                  September 45%. The figure is coverage among reporting
                  facility-months and is a lower bound on district coverage.
Extract           extracts/2026-07-28/, manifest.json, script extract.py 1.3

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The provenance block

• Attach it to the figure, not to the report — A slide with the number and a footnote pointing at the block is what…

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Interrogating a figure you did not produce

• 1. Is it a data element or an indicator? — If an indicator, ask for the numerator and denominator expressions
• 2. What is the denominator's year? — A 2024 numerator over a 2019 projection is common and understates coverage by…
• 3. What was the reporting rate? — Below 90%, the figure is a lower bound
• 4. Which org unit level? — A figure that mixes levels double-counts; a facility-level coverage figure is unreliable for…
• 5. Was the period complete when it was pulled? — A recent month is provisional, and a figure quoted from a pull three…
• 6. What is it being compared to? — Most misuse is comparison — against a different measure, a different denominator, or…

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Interrogating a figure you did not produce — In Python

def interrogate(figure):
    return {
        "type": figure.get("element_or_indicator"),
        "denominator_year": figure.get("denominator_year"),
        "reporting_rate": figure.get("reporting_rate"),
        "org_unit_level": figure.get("level"),
        "period_complete_at_extraction": figure.get("period_closed"),
        "comparable_to": figure.get("comparable_to"),
    }

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Interrogating a figure you did not produce — In R

interrogate <- function(figure) {
  figure[c("element_or_indicator", "denominator_year", "reporting_rate",
           "level", "period_closed", "comparable_to")]
}

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Interrogating a figure you did not produce

• A blank in any of those is the finding — You are not asking somebody to justify themselves; you are establishing…

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What the system cannot tell you

• Why a facility stopped reporting — The system records that it did
• What the data entry clerk understood the field to mean — A column labelled "cases" collects whatever a person believes…
• What happened outside the catchment — Routine data sees what came to the service

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What the system cannot tell you

The system is a record of what was reported, not of what happened. Every figure it produces inherits that, and stating it once in a report is worth more than any additional decimal place.

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Where this course leaves you

• You can read a DHIS2 extract as the database it came from, aggregate up a hierarchy that moves, treat completeness as a denominator, pull the same extract every month with a manifest that says what you asked for, place a routine figure beside a survey estimate and explain the gap, and answer what was counted for any number the system produces.

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Where module 3 leaves you

• Three courses.

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Where this goes next

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