Prescribing of opioids (total oral morphine equivalence)

Below are the database queries which are used to create this measure. These are run against a copy of the BSA prescribing data which we store in Google BigQuery. We're working on making our BigQuery tables publicly available at which point it will be possible to run and modify these queries yourself. But even where code and database queries are not directly useable by others we believe it is always preferable to make them public.

Description Total opioid prescribing (as oral morphine equivalence) per 1000 patients
Why it matters The Opioids Aware project seeks to improve prescribing of opioid analgesia. There is little evidence that opioids are helpful in long term pain, and the risk of harm increases significantly above 120mg morphine (or equivalent) per day, without much increase in benefit.

The NHS England National Medicines Optimisation Opportunities for 2023/24 identify reducing opioid use in chronic non-cancer pain as an area for improvement.

This measure describes the total Oral Morphine Equivalence (OME) in ALL opioid prescribing (excluding prescribing for addiction) including low-dose opioids in drugs such as co-codamol and co-dydramol. This measure is experimental and should be used with caution, as OME conversions vary in different reference sources. We have amended the measure to take into account changes in equivalency in the BNF.

We have written a paper about the increase in opioid prescribing in England since 1999, which can be found in The Lancet Psychiatry.
Tags Standard, Opioids, National medicines optimisation opportunities, Pain, Safety
Implies cost savings No
Authored by richard.croker
Checked by andrew.brown
Last reviewed 2023-09-12
Next review due 2024-09-12
History View change history on GitHub →

Numerator SQL

SELECT
     CAST(month AS DATE) AS month,
     practice AS practice_id,
     SUM(ome_dose) AS numerator
 FROM measures.vw__opioids_total_dmd
 GROUP BY month, practice_id

Denominator SQL

SELECT
     CAST(month AS DATE) AS month,
     practice AS practice_id,
     SUM(total_list_size / 1000.0) AS denominator
 FROM hscic.practice_statistics
 GROUP BY month, practice_id
Feedback