Accuracy
The figure agrees with the service that happened. A child was vaccinated, and the register says so. Accuracy fails when 28 is copied as 82, or when a tally stroke is added for a child who left before the injection.
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MODULE 1 · LESSON 1.6
Introduction to Health Data Quality
Estimated time: 20 minutes
WRITTEN ON THE BOARD
Lesson 1.6 is data quality. We
Slide 1 of 3
Read complete notesREAD COMPLETE NOTES
Data quality asks whether a figure is fit for the decision in front of you. A complete number can still be late. A timely number can still be wrong. These five dimensions are the ones we will use in this module.
Completeness
The required fields, rows, and reports are present. A register line without a date, or a ward that sent no monthly summary, is a completeness problem. A blank is not a zero.
Timeliness
The report covers the right period and arrives while it can still be used. A March summary that arrives after the local government has already redistributed vaccines cannot guide that redistribution.
Consistency
Related records tell the same story. The immunisation register, the tally sheet, the monthly summary, and the doses opened in the store should be reconcilable. They will not always match exactly, but the difference should be explainable.
Validity
The value is a possible one for that field. Age 150, a delivery date before the pregnancy was recorded, or a vaccine count above the doses supplied are validity problems. A valid-looking number can still be inaccurate, so validity is not the same as accuracy.
Three facility scenes
The immunisation register is missing for the last Friday in the month. The monthly summary still has a number, written from memory. Completeness of the source has failed, and the accuracy of the summary is now in doubt.
The tally sheet says 46 pentavalent-1 doses. The register has 40 complete lines. The officer enters 46 into DHIS2 because it is the larger number. Consistency has failed. The primary record does not support the value that will be seen at the local government.
The summary is correct, but it is submitted nine days after the deadline. The local government meeting has used last month's figures. Timeliness has failed even though the arithmetic is right.
Why the dimensions matter
Vaccine supply, malaria medicines, and supervision visits are planned from these figures. If a clinic over-reports, it may receive stock another clinic needed. If it under-reports, it may run out. A partner reading a late or inconsistent figure may also misjudge whether a funded activity reached the people it was meant to reach.
Poor quality is not only a records-room problem. It can change who receives a commodity and how a programme is described.
Lesson 1.6 knowledge check
Choose an answer for each question, then check. The suggested answers stay hidden until you do.
1. The register and the tally sheet disagree, and nobody checks before the summary is sent. Which dimension is failing first?
2. A correct March report arrives after the vaccine has already been redistributed. Which dimension failed?
3. A blank age is filled with zero so the cell is not empty. What is wrong with that?