eduKateSG · Why Science?
Compare medicine use through a governed classification and technical unit—without confusing a DDD with a prescribed dose, recommended dose, treatment quality or patient-level exposure
Choose the closest route, then return to the full index whenever you need the wider evidence chain.
Science learning becomes powerful when students can tell a measured pattern from a model, a statistical decision from a mechanism, and a useful lead from a finished conclusion. The Anatomical Therapeutic Chemical classification and Defined Daily Dose methodology is maintained through the World Health Organization system for drug-utilisation monitoring and research. ATC classifies active substances through five hierarchical levels according to organ or system, therapeutic, pharmacological and chemical characteristics. A DDD is a technical unit based on the assumed average maintenance dose per day for a medicine’s main indication in adults. The official ATC/DDD Index checked for this article is the 2026 edition. A DDD is not a recommended or prescribed dose and may not match an individual patient’s treatment. Comparable statistics require the ATC/DDD version, product-to-code link, route, quantity conversion, population denominator, period and known coverage. This guide supports Primary Science, PSLE Science, Secondary Science, O-Level Science and STEM education as a longform evidence-reading route; it is not medical advice, a laboratory protocol, an admission promise or a career guarantee.
Related eduKate reading: preserving NDC, RxNorm RxCUI and ATC identity across languages; OMOP CDM and reproducible observational-health research; ICD-11 and comparable health classification; careers by subject capability; Science Learning Hub; Education Hub; STEM Education; Careers by Subject Capability.
Primary and official sources checked on 12 October 2026: WHO official ATC/DDD classification overview, checked 12 October 2026; WHO official ATC/DDD Toolkit methodology, checked 12 October 2026; WHO official ATC classification guidance, checked 12 October 2026; WHO Collaborating Centre official ATC/DDD Index 2026, checked 12 October 2026; WHO Collaborating Centre Guidelines for ATC Classification and DDD Assignment 2026, checked 12 October 2026; 2026 Singapore–Cambridge O-Level Biology syllabus; MOE G2/G3 Lower Secondary Science teaching and learning syllabus; 2026 MOE G2 Computing syllabus. Standards, terminologies, classifications and recommendations can change, so preserve dated documentation, exact source data and software versions.
A deeper reading route
Medicine-use datasets start with packages, strengths, forms, quantities and dates. Those records are difficult to compare across markets and time. The ATC/DDD methodology provides a shared classification and technical measurement unit so researchers can aggregate use more consistently.
ATC has five levels. The first is an anatomical main group; the second is a therapeutic or pharmacological subgroup; the third and fourth refine therapeutic, pharmacological or chemical groupings; the fifth identifies the chemical substance. The hierarchy allows analysis at different grains, but roll-ups answer different questions.
A medicinal substance is generally classified by its main therapeutic use under governed principles. Route and, in some cases, strength can affect classification. A substance can have more than one ATC code when it has clearly different therapeutic uses or routes under the rules. Product mapping therefore needs formulation context.
The Defined Daily Dose is an assumed average maintenance dose per day for the main indication in adults. It is a technical unit for utilisation research, not a treatment recommendation. The prescribed daily dose can differ because of indication, age, kidney function, severity, local practice or other clinical factors.
Researchers convert product quantities into numbers of DDDs using the official assigned DDD, unit and administration route. Every arithmetic step should be visible: packages multiplied by units per package, strength per unit and an appropriate conversion divided by the DDD.
Common denominators include DDDs per 1,000 inhabitants per day or DDDs per 100 bed-days. Those measures are not interchangeable. Population coverage, hospital occupancy, observation period and data source determine the denominator and the interpretation.
ATC/DDD values are versioned. Codes, assignments and DDDs can be added or changed. The 2026 guidelines stress citing the version used when presenting consumption figures. A trend can shift because practice changed, because data coverage changed or because the standard changed.
Combination products follow specific classification and DDD principles. Counting each active ingredient independently can overstate utilisation, while treating every combination as one undifferentiated item can hide structure. Use the official combination code and unit rules for the edition.
The methodology supports comparison, not automatic judgement. Higher DDDs can reflect disease burden, access, duration, substitution or inappropriate use. Lower figures can reflect prevention, under-treatment or poor capture. Interpretation needs clinical, epidemiological and policy context.
For students, ATC/DDD is a strong lesson in denominators. Raw package counts look concrete but are not comparable when strength, pack size and population differ. Normalisation makes patterns visible, while careful scientists keep the assumptions next to every graph.
Product registries often contain national identifiers rather than ATC codes. Link each package to the correct official code and DDD using ingredient, strength, dose form and route. Preserve the national product ID and mapping source so errors can be traced.
DDD assignments are not generally made for every product or all special populations. Paediatric use, topical products and some categories may need other measures. Report exclusions and do not invent a DDD to complete a table.
Prescribed Daily Dose is an empirical measure of actual prescribing and can differ from DDD. A study comparing the two should state population, indication, dose calculation and data limitations rather than treating disagreement as an error in either measure.
Days of therapy and DDD measure different things. Days of therapy counts days a patient receives an agent; DDD standardises total quantity. Antimicrobial stewardship often considers both because dose intensity and exposure duration answer different questions.
A rate needs an uncertainty and coverage story. Missing private-sector sales, incomplete hospitals, stockpiling, returns or cross-border use can bias totals. Publish numerator provenance and coverage changes beside the denominator.
ATC levels can support drill-down, but category membership is not a causal explanation. A change in one fifth-level substance can drive an apparent second-level trend. Inspect both aggregated and detailed views before drawing conclusions.
Small-area or rare-medicine statistics can identify patients or institutions. Suppress, aggregate or restrict access where necessary, and never assume a technical unit makes sensitive data anonymous.
Cross-country comparison requires aligned years, versions, sectors, routes, denominators and capture. Even then, results describe recorded utilisation rather than appropriateness. Use cautious language and involve domain experts in interpretation.
| Stage | Question | Evidence | Responsible output |
|---|---|---|---|
| Map | Which product and substance are represented? | Product ID, ingredient, strength, form, route and ATC 2026 code | A traceable classification |
| Convert | How much standardised quantity is present? | Package quantity, assigned DDD, unit and arithmetic | A number of DDDs |
| Normalise | What population and period are compared? | Inhabitants, bed-days, sector, coverage and dates | A comparable utilisation rate |
| Interpret | What can the statistic support? | Version, uncertainty, clinical and policy context | A bounded medicine-use claim |
Inside this guide
1–12 · Foundations and design
- 1. Start with the utilisation question
- 2. Pin the 2026 ATC/DDD edition
- 3. Read the first ATC level
- 4. Read the second level
- 5. Read the third and fourth levels
- 6. Read the fifth level
- 7. Preserve route and use context
- 8. Handle multiple ATC codes
- 9. Handle combination products
- 10. Understand the DDD definition
- 11. Separate DDD from recommended dose
- 12. Separate DDD from prescribed daily dose
13–24 · Models and estimation
- 13. Convert packages transparently
- 14. Apply the correct administration route
- 15. Choose a population denominator
- 16. Choose a hospital denominator
- 17. Distinguish DDD from days of therapy
- 18. Preserve the national product link
- 19. Version historical series
- 20. Validate mapping coverage
- 21. Validate arithmetic
- 22. Report sector coverage
- 23. Interpret comparisons cautiously
- 24. Connect ATC/DDD to RxNorm deliberately
25–36 · Diagnostics and interpretation
- 25. Protect privacy
- 26. Separate statistics from clinical judgement
- 27. Preserve provenance
- 28. Connect to Primary Science
- 29. Connect to Secondary Science
- 30. Connect to mathematics
- 31. Connect to computing
- 32. Connect to pathways
- 33. Did you know?
- 34. Write a reader-ready methods note
- 35. Build a validation ladder
- 36. Finish with a reproducible bundle
Section 1 of 36
1. Start with the utilisation question
Define whether the study concerns sales, dispensing, administration, prescribing or patient exposure.
Working checkpoint — ATC classification: Write the event, population, sector and time window. Keep product ID, ATC code, DDD, edition, route, quantity conversion, denominator, coverage and access decision together.
For a classroom model, calculate rates from fictional packages and populations.
Section 2 of 36
2. Pin the 2026 ATC/DDD edition
The official searchable index checked is ATC/DDD Index 2026.
Working checkpoint — Defined Daily Dose: Store index and guidelines year with every output. Read every utilisation rate beside population, sector, period, version, missing data and clinical context.
For future study, connect pharmacology to epidemiology and policy while checking pathways separately.
Section 3 of 36
3. Read the first ATC level
The first level groups medicines by anatomical main group.
Working checkpoint — drug utilisation research: Use it for broad orientation, not product identity. Preserve product records and prior editions; reclassification is not invisible historical rewriting.
For writing, cite ATC/DDD edition, product mapping, conversion, denominator and coverage.
Section 4 of 36
4. Read the second level
The second level gives a therapeutic or pharmacological subgroup.
Working checkpoint — medicine-use statistics: State whether analysis intentionally aggregates this broadly. Separate statistical comparability from patient exposure, prescribing quality and causal explanation.
For students, ask why raw package counts are not comparable across strengths and populations.
Section 5 of 36
5. Read the third and fourth levels
Intermediate levels refine therapeutic, pharmacological or chemical groupings.
Working checkpoint — ATC code: Inspect the actual hierarchy in the pinned edition. Treat route, pack strength, DDD assignment and denominator as scientific inputs: one mismatch can move the rate.
For parents, ask whether a medicine-use graph names its denominator, sector and standard year.
Section 6 of 36
6. Read the fifth level
The fifth level identifies a chemical substance within its classified use.
Working checkpoint — DDD per 1,000 inhabitants per day: Do not substitute an ingredient string for an official code. Keep product ID, ATC code, DDD, edition, route, quantity conversion, denominator, coverage and access decision together.
For a classroom model, calculate rates from fictional packages and populations.
Section 7 of 36
7. Preserve route and use context
Route and main therapeutic use can affect an ATC assignment.
Working checkpoint — ATC classification: Map from documented product attributes. Read every utilisation rate beside population, sector, period, version, missing data and clinical context.
For future study, connect pharmacology to epidemiology and policy while checking pathways separately.
Section 8 of 36
8. Handle multiple ATC codes
A substance can have more than one code under governed circumstances.
Working checkpoint — Defined Daily Dose: Choose by product route and intended classification rule. Preserve product records and prior editions; reclassification is not invisible historical rewriting.
For writing, cite ATC/DDD edition, product mapping, conversion, denominator and coverage.
Section 9 of 36
9. Handle combination products
Combination products use specific ATC codes and DDD principles.
Working checkpoint — drug utilisation research: Follow the edition’s combination guidance. Separate statistical comparability from patient exposure, prescribing quality and causal explanation.
For students, ask why raw package counts are not comparable across strengths and populations.
Section 10 of 36
10. Understand the DDD definition
A DDD is the assumed average maintenance dose per day for the main indication in adults.
Working checkpoint — medicine-use statistics: Quote it as a technical unit, not a prescription. Treat route, pack strength, DDD assignment and denominator as scientific inputs: one mismatch can move the rate.
For parents, ask whether a medicine-use graph names its denominator, sector and standard year.
Section 11 of 36
11. Separate DDD from recommended dose
Clinical doses vary by person, indication and guidance.
Working checkpoint — ATC code: Never turn a DDD into patient advice. Keep product ID, ATC code, DDD, edition, route, quantity conversion, denominator, coverage and access decision together.
For a classroom model, calculate rates from fictional packages and populations.
Section 12 of 36
12. Separate DDD from prescribed daily dose
PDD describes actual prescribed doses in a defined population and can differ from DDD.
Working checkpoint — DDD per 1,000 inhabitants per day: Name which measure the study uses. Read every utilisation rate beside population, sector, period, version, missing data and clinical context.
For future study, connect pharmacology to epidemiology and policy while checking pathways separately.
Section 13 of 36
13. Convert packages transparently
Package count, units per package and strength determine the total active quantity.
Working checkpoint — ATC classification: Publish the arithmetic and unit conversions. Preserve product records and prior editions; reclassification is not invisible historical rewriting.
For writing, cite ATC/DDD edition, product mapping, conversion, denominator and coverage.
Section 14 of 36
14. Apply the correct administration route
Assigned DDDs can differ by route.
Working checkpoint — Defined Daily Dose: Match product route before dividing by a DDD. Separate statistical comparability from patient exposure, prescribing quality and causal explanation.
For students, ask why raw package counts are not comparable across strengths and populations.
Section 15 of 36
15. Choose a population denominator
DDDs per 1,000 inhabitants per day requires a defined population and period.
Working checkpoint — drug utilisation research: Record population source and coverage. Treat route, pack strength, DDD assignment and denominator as scientific inputs: one mismatch can move the rate.
For parents, ask whether a medicine-use graph names its denominator, sector and standard year.
Section 16 of 36
16. Choose a hospital denominator
DDDs per 100 bed-days needs bed-day definitions and institutional coverage.
Working checkpoint — medicine-use statistics: Do not compare directly with population rates. Keep product ID, ATC code, DDD, edition, route, quantity conversion, denominator, coverage and access decision together.
For a classroom model, calculate rates from fictional packages and populations.
Section 17 of 36
17. Distinguish DDD from days of therapy
Total standardised quantity and exposure days answer different questions.
Working checkpoint — ATC code: Use both when the research question requires. Read every utilisation rate beside population, sector, period, version, missing data and clinical context.
For future study, connect pharmacology to epidemiology and policy while checking pathways separately.
Section 18 of 36
18. Preserve the national product link
A package-level identifier should remain connected to its ATC mapping and quantity fields.
Working checkpoint — DDD per 1,000 inhabitants per day: Keep mapping source, reviewer and date. Preserve product records and prior editions; reclassification is not invisible historical rewriting.
For writing, cite ATC/DDD edition, product mapping, conversion, denominator and coverage.
Section 19 of 36
19. Version historical series
ATC codes and DDD assignments can change between editions.
Working checkpoint — ATC classification: Recalculate consistently or mark breaks in series. Separate statistical comparability from patient exposure, prescribing quality and causal explanation.
For students, ask why raw package counts are not comparable across strengths and populations.
Section 20 of 36
20. Validate mapping coverage
Unmapped products and wrong routes can bias category totals.
Working checkpoint — Defined Daily Dose: Review high-volume and high-impact exceptions. Treat route, pack strength, DDD assignment and denominator as scientific inputs: one mismatch can move the rate.
For parents, ask whether a medicine-use graph names its denominator, sector and standard year.
Section 21 of 36
21. Validate arithmetic
Use unit-aware tests for strength, pack size, combinations and decimal precision.
Working checkpoint — drug utilisation research: Reconcile totals to source quantities. Keep product ID, ATC code, DDD, edition, route, quantity conversion, denominator, coverage and access decision together.
For a classroom model, calculate rates from fictional packages and populations.
Section 22 of 36
22. Report sector coverage
Retail, hospital, public, private and reimbursement data can cover different use.
Working checkpoint — medicine-use statistics: Name included and missing sectors. Read every utilisation rate beside population, sector, period, version, missing data and clinical context.
For future study, connect pharmacology to epidemiology and policy while checking pathways separately.
Section 23 of 36
23. Interpret comparisons cautiously
Recorded utilisation reflects disease, access, practice, policy and capture as well as quality.
Working checkpoint — ATC code: Offer plausible alternatives before causal claims. Preserve product records and prior editions; reclassification is not invisible historical rewriting.
For writing, cite ATC/DDD edition, product mapping, conversion, denominator and coverage.
Section 24 of 36
24. Connect ATC/DDD to RxNorm deliberately
RxNorm normalises U.S. clinical-drug meaning; ATC classifies substances for utilisation analysis.
Working checkpoint — DDD per 1,000 inhabitants per day: Publish the mapping and preserved product grain. Separate statistical comparability from patient exposure, prescribing quality and causal explanation.
For students, ask why raw package counts are not comparable across strengths and populations.
Section 25 of 36
25. Protect privacy
Rare medicines, small areas and dates can identify people or institutions.
Working checkpoint — ATC classification: Apply aggregation, access controls and disclosure review. Treat route, pack strength, DDD assignment and denominator as scientific inputs: one mismatch can move the rate.
For parents, ask whether a medicine-use graph names its denominator, sector and standard year.
Section 26 of 36
26. Separate statistics from clinical judgement
A rate cannot determine whether an individual prescription was appropriate.
Working checkpoint — Defined Daily Dose: Keep patient-level decisions outside aggregate inference. Keep product ID, ATC code, DDD, edition, route, quantity conversion, denominator, coverage and access decision together.
For a classroom model, calculate rates from fictional packages and populations.
Section 27 of 36
27. Preserve provenance
A reusable statistic links products, codes, DDDs, edition, conversions, denominator, coverage and analysis code.
Working checkpoint — drug utilisation research: Make every plotted point reconstructable. Read every utilisation rate beside population, sector, period, version, missing data and clinical context.
For future study, connect pharmacology to epidemiology and policy while checking pathways separately.
Section 28 of 36
28. Connect to Primary Science
A fictional fictional medicine-use table helps younger learners distinguish the thing being observed from the code, label or category used to organise it.
Working checkpoint — medicine-use statistics: Use invented examples, define every label and never use personal health records. Preserve product records and prior editions; reclassification is not invisible historical rewriting.
For writing, cite ATC/DDD edition, product mapping, conversion, denominator and coverage.
Section 29 of 36
29. Connect to Secondary Science
ATC/DDD supports lessons about classification, measurement, models, evidence, systems and uncertainty.
Working checkpoint — ATC code: Trace one coded statement back to its observable evidence. Separate statistical comparability from patient exposure, prescribing quality and causal explanation.
For students, ask why raw package counts are not comparable across strengths and populations.
Section 30 of 36
30. Connect to mathematics
Sets, identifiers, hierarchies, ratios, units and denominators compare safely only when definitions and versions agree.
Working checkpoint — DDD per 1,000 inhabitants per day: Work through one small boundary case by hand before scaling. Treat route, pack strength, DDD assignment and denominator as scientific inputs: one mismatch can move the rate.
For parents, ask whether a medicine-use graph names its denominator, sector and standard year.
Section 31 of 36
31. Connect to computing
Hierarchical codes, unit-aware arithmetic, denominators, versioned registries and reproducible scripts show how software supports population comparisons.
Working checkpoint — ATC classification: Break one identifier, version, unit or relationship, then validate and repair it transparently. Keep product ID, ATC code, DDD, edition, route, quantity conversion, denominator, coverage and access decision together.
For a classroom model, calculate rates from fictional packages and populations.
Section 32 of 36
32. Connect to pathways
ATC/DDD connects pharmacology, epidemiology, public health, statistics, data engineering and health policy and can illuminate STEM education and career pathways.
Working checkpoint — Defined Daily Dose: Check current official education and qualification routes separately from this conceptual guide. Read every utilisation rate beside population, sector, period, version, missing data and clinical context.
For future study, connect pharmacology to epidemiology and policy while checking pathways separately.
Section 33 of 36
33. Did you know?
A Defined Daily Dose is a technical research unit and is not necessarily the recommended or prescribed dose for any individual patient.
Working checkpoint — drug utilisation research: Explain the fact in plain language, then name what it does not prove. Preserve product records and prior editions; reclassification is not invisible historical rewriting.
For writing, cite ATC/DDD edition, product mapping, conversion, denominator and coverage.
Section 34 of 36
34. Write a reader-ready methods note
State the standard and release, source system, mapping or selection rule, validation, privacy controls and supported claim.
Working checkpoint — medicine-use statistics: Keep the note beside the machine-readable record. Separate statistical comparability from patient exposure, prescribing quality and causal explanation.
For students, ask why raw package counts are not comparable across strengths and populations.
Section 35 of 36
35. Build a validation ladder
Check bytes, identifiers, version, structure, terminology, source fidelity, scientific plausibility, privacy and a known fixture in sequence.
Working checkpoint — ATC code: Stop at the earliest unresolved layer. Treat route, pack strength, DDD assignment and denominator as scientific inputs: one mismatch can move the rate.
For parents, ask whether a medicine-use graph names its denominator, sector and standard year.
Section 36 of 36
36. Finish with a reproducible bundle
Deliver source receipts, releases, mappings, software versions, tests, hashes and limitations without exposing governed health records.
Working checkpoint — DDD per 1,000 inhabitants per day: Make every published claim traceable. Keep product ID, ATC code, DDD, edition, route, quantity conversion, denominator, coverage and access decision together.
For a classroom model, calculate rates from fictional packages and populations.
Contents · Previous section · Continue to the Science Learning Hub