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Data

Systems to monitor the use of antimicrobials for human medicine

See all data and research on:
Global HealthAntibiotics & Antibiotic Resistance

About this data

Source
FAO, UNEP, WHO and WOAH (2024) – processed by Our World in Data
Last updated
October 23, 2024
Next expected update
May 2026
Date range
2024–2024

Sources and processing

This data is based on the following sources

FAO, UNEP, WHO and WOAH – Global Database for Tracking Antimicrobial Resistance (AMR) Country Self- Assessment Survey (TrACSS)

TrACSS monitors the implementation of multisectoral AMR national action plans. It was jointly developed by the Quadripartite (FAO, UNEP, WHO and WOAH) and is administered annually by the WHO.

Retrieved on
October 23, 2024
Retrieved from
https://new.amrcountryprogress.org/
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.
FAO, UNEP, WHO and WOAH (2024). Global Database for Tracking AMR Country Self Assessment Survey (TrACSS)

TrACSS monitors the implementation of multisectoral AMR national action plans. It was jointly developed by the Quadripartite (FAO, UNEP, WHO and WOAH) and is administered annually by the WHO.

Retrieved on
October 23, 2024
Retrieved from
https://new.amrcountryprogress.org/
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.
FAO, UNEP, WHO and WOAH (2024). Global Database for Tracking AMR Country Self Assessment Survey (TrACSS)

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

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: Systems to monitor the use of antimicrobials for human medicine”, part of the following publication: Esteban Ortiz-Ospina and Max Roser (2016) - “Global Health”. Data adapted from FAO, UNEP, WHO and WOAH. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/national-system-to-monitor-antimicrobials-usage-for-human-medicine.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:

FAO, UNEP, WHO and WOAH (2024) – processed by Our World in Data

Full citation

FAO, UNEP, WHO and WOAH (2024) – processed by Our World in Data. “Systems to monitor the use of antimicrobials for human medicine” [dataset]. FAO, UNEP, WHO and WOAH, “Global Database for Tracking Antimicrobial Resistance (AMR) Country Self- Assessment Survey (TrACSS)” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/national-system-to-monitor-antimicrobials-usage-for-human-medicine.html (archived on March 4, 2026).

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Download the data shown in this chart as a ZIP file containing a CSV file, metadata in JSON format, and a README. The CSV file can be opened in Excel, Google Sheets, and other data analysis tools.

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Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/national-system-to-monitor-antimicrobials-usage-for-human-medicine.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/national-system-to-monitor-antimicrobials-usage-for-human-medicine.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/national-system-to-monitor-antimicrobials-usage-for-human-medicine.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/national-system-to-monitor-antimicrobials-usage-for-human-medicine.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/national-system-to-monitor-antimicrobials-usage-for-human-medicine.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/national-system-to-monitor-antimicrobials-usage-for-human-medicine.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/national-system-to-monitor-antimicrobials-usage-for-human-medicine.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/national-system-to-monitor-antimicrobials-usage-for-human-medicine.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear