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ATC and DDD Classification System

This data package contains information on The Anatomical Therapeutic Chemical (ATC) Classification System which is used for the classification of active ingredients of drugs according to the organ or system on which they act and their therapeutic, pharmacological and chemical properties. It is controlled by the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC), and was first published in 1976.

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About

## Overview

The ATC (Anatomical Therapeutic Chemical) classification system divides drugs into different groups according to the organ or system on which they act, their therapeutic intent or nature, and the drug's chemical characteristics. Different brands share the same code if they have the same active substance and indications. Each bottom-level ATC code stands for a pharmaceutically used substance, or a combination of substances, in a single indication (or use). This means that one drug can have more than one code, for example, acetylsalicylic acid (aspirin) has A01AD05 (WHO) as a drug for local oral treatment, B01AC06 (WHO) as a platelet inhibitor, and N02BA01 (WHO) as an analgesic and antipyretic); as well as one code can represent more than one active ingredient, for example, C09BB04 (WHO) is the combination of perindopril with amlodipine, two active ingredients that have their own codes (C09AA04 (WHO) and C08CA01 (WHO) respectively) when prescribed alone.

The ATC classification system is a strict hierarchy, meaning that each code necessarily has one and only one parent code, except for the 14 codes at the topmost level which have no parents. The codes are semantic identifiers, meaning they depict in themselves the complete lineage of parenthood.

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## License Information

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## Metadata

Description | Value

----|-----

Data Package Complexity | Simple

Available Enrichments | N/A

Keywords | Tuberculosis, Communicable Diseases, Infectious Disease, Sexually Transmitted Disease, HIV Diagnose, Viral Hepatitis

Other Titles | Hepatitis in the United States, Hepatitis National Statistics, HIV/AIDS National Statistics, Tuberculosis Geographic Distribution

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## Included Datasets

1. Anatomical Therapeutic Chemical Alterations 2005 to 2022

This dataset contains the ATC (Anatomical Therapeutic Chemical) Alterations from 2005 to 2022.

2. Anatomical Therapeutic Chemical Codes 2018 to 2023

This dataset contains the Anatomical Therapeutic Chemical (ATC) classification system. In ATC the active substances are divided into different groups according to the organ or system on which they act and their therapeutic, pharmacological and chemical properties.

3. Defined Daily Dose Alterations 2005 to 2021

This dataset contains the DDD (Defined Daily Dose) Alterations from 2005 to 2021.

4. Defined Daily Dose Codes for 2018 to 2023

This dataset contains the Defined Daily Dose (DDD) 2018 to 2023. In order to measure drug use, it is important to have both a classification system and a unit of measurement. To deal with the objections against traditional units of measurement, a technical unit of measurement called the Defined Daily Dose (DDD) to be used in drug utilization studies was developed.

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## Data Engineering Overview

#### We deliver high-quality data

* Each dataset goes through 3 levels of quality review

* 2 Manual reviews are done by domain experts

* Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints

* Data is normalized into one unified type system

* All dates, unites, codes, currencies look the same

* All null values are normalized to the same value

* All dataset and field names are SQL and Hive compliant

* Data and Metadata

* Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters

* Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated

* Data Updates

* Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted

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#### Our data is curated and enriched by domain experts

Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:

* Field names, descriptions, and normalized values are chosen by people who actually understand their meaning

* Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset

* Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations

* The data is always kept up to date – even when the source requires manual effort to get updates

* Support for data subscribers is provided directly by the domain experts who curated the data sets

* Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.

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## Need Help?

* If you have questions about our products, contact us at <info@johnsnowlabs.com>.

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## About Us

[John Snow Labs](https://www.johnsnowlabs.com/), an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects.

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