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Data

Antibiotic consumption rate

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Global Health

What you should know about this indicator

  • For Kenya: data is incomplete since it's not collected from all sources.
  • For Portugal: data are incomplete for antituberculosis medicines.
  • For Austria, Germany and Iceland: only antimicrobial consumption in the community is reported.
  • For Nepal: data is incomplete, not all antibiotics reported systematically.
  • For Kuwait, Oman, Papua New Guinea, Peru, Qatar, Rwanda, Saudi Arabia, South Africa and United Kingdom: only consumption in the public sector reported and this is estimated to represent less than 90% of total antimicrobial usage.
  • For Bhutan, Cote d'Ivoire, Ethiopia, France, Gabon, Georgia, Laos, Malaysia, Maldives, Mali, Switzerland, Tunisia, Tanzania and Palestine: for antibiotics, only antibiotics for systemic use (ATC code J01) and nitroimidazole derivatives (ATC code P01AB) are reported.
  • For Bhutan, Burkina Faso and Sudan: for antibiotics, only antibiotics for systemic use (ATC code J01) are reported
Antibiotic consumption rate
Total of antibiotics and antituberculosis drugs used in a given year per 1,000 inhabitants per day.
Source
WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) (2024) – processed by Our World in Data
Last updated
November 12, 2024
Next expected update
May 2026
Date range
2016–2022
Unit
defined daily doses per 1,000 inhabitants per day

Related research and writing

Antibiotics and Antibiotic Resistance

Saloni Dattani, Fiona Spooner, Hannah Ritchie, and Max Roser

Sources and processing

This data is based on the following sources

WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) – WHO GLASS

Inappropriate use of antimicrobials in humans is a primary driver of antimicrobial resistance (AMR) emergence and spread. In 2020, WHO launched GLASS antimicrobial use (GLASS AMU), previously called GLASS AMC, to monitor the quantity and types of antimicrobial s us ed at the national and global levels. WHO invites Countries, Areas, and Territories (CTAs) to enrol in GLASS AMU and commit to building or strengthening their national AMU surveillance system and, when ready, to reporting their national AMU data. Data calls are opened every year.

Retrieved on
November 12, 2024
Retrieved from
https://worldhealthorg.shinyapps.io/glass-dashboard/_w_053a572c/#!/amu
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Antimicrobial Resistance and Use Surveillance System (GLASS) 2024: Antimicrobial Use data contextual information and antimicrobial use estimates by ATC4 subgroup and AWaRe, 2016-2022. Geneva, World Health Organization; 2024.

Inappropriate use of antimicrobials in humans is a primary driver of antimicrobial resistance (AMR) emergence and spread. In 2020, WHO launched GLASS antimicrobial use (GLASS AMU), previously called GLASS AMC, to monitor the quantity and types of antimicrobial s us ed at the national and global levels. WHO invites Countries, Areas, and Territories (CTAs) to enrol in GLASS AMU and commit to building or strengthening their national AMU surveillance system and, when ready, to reporting their national AMU data. Data calls are opened every year.

Retrieved on
November 12, 2024
Retrieved from
https://worldhealthorg.shinyapps.io/glass-dashboard/_w_053a572c/#!/amu
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Antimicrobial Resistance and Use Surveillance System (GLASS) 2024: Antimicrobial Use data contextual information and antimicrobial use estimates by ATC4 subgroup and AWaRe, 2016-2022. Geneva, World Health Organization; 2024.

How we process data at Our World in Data

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At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Antibiotic consumption rate”, part of the following publication: Esteban Ortiz-Ospina and Max Roser (2016) - “Global Health”. Data adapted from WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS). Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/antibiotic-consumption-rate.html [online resource] (archived on March 4, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) (2024) – processed by Our World in Data

Full citation

WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) (2024) – processed by Our World in Data. “Antibiotic consumption rate” [dataset]. WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS), “WHO GLASS” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/antibiotic-consumption-rate.html (archived on March 4, 2026).

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Data API

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Data URL (CSV format)
https://ourworldindata.org/grapher/antibiotic-consumption-rate.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/antibiotic-consumption-rate.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/antibiotic-consumption-rate.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/antibiotic-consumption-rate.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/antibiotic-consumption-rate.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/antibiotic-consumption-rate.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/antibiotic-consumption-rate.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/antibiotic-consumption-rate.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear