Estimates of opiate and crack use in England 2022 to 2023: main points and methodology
Published 11 December 2025
Applies to England
© Crown copyright 2025
This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: psi@nationalarchives.gov.uk.
Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concerned.
This publication is available at https://www.gov.uk/government/publications/opiate-and-crack-cocaine-use-prevalence-estimates-2022-to-2023/estimates-of-opiate-and-crack-use-in-england-2022-to-2023-main-points-and-methodology
Main points
Estimation methodology update
These estimates of opiate and/or crack cocaine users (OCU) were produced by the Department of Health and Social Care and the UK Health Security Agency. We have updated the methodology we used to produce the previous report, Opiate and crack cocaine use: prevalence estimates 2016 to 2020. As they use different methodologies, you should not compare the estimates for 2016 to 2020 with the estimates for 2022 to 2023.
We are continuing to revise the methodology, particularly to:
- mitigate the impact of the COVID-19 pandemic on services and surveillance data
- better account for regional patterns, especially in London
- provide more detail on people aged 35 to 64
You can find more information about this in the methodology section below.
National prevalence estimates and rate
The estimated total number of OCU for 2022 to 2023 in England is 310,718, with a 95% confidence interval between 298,138 and 326,648. This means that there is a 95% probability that the true value lies between these upper and lower limits.
The prevalence rate of OCU is 8.5 per 1,000 general population in 2022 to 2023, with a 95% confidence interval between 8.1 and 8.9.
Regional prevalence estimates and rates
Of the 310,718 estimated OCU in England:
- 17.4% were in the North West (54,206)
- 13.9% were in London (43,318)
- 13.1% were in Yorkshire and the Humber (40,747)
- 7% were in the North East (21,676)
Taking into account the size of the general population in each region, the North East, Yorkshire and the Humber and the North West had the highest OCU rates in 2022 to 2023 (12.8, 11.6 and 11.3, respectively). The rates in these regions are significantly higher than the England national average of 8.5. The lowest rates were in the East of England and the South East (6.6 and 6.3, respectively).
Table 1: regional and national OCU prevalence estimates and rates with 95% confidence intervals, 2022 to 2023
| Region | OCU estimates | OCU estimates - lower limit | OCU estimates - upper limit | Rate per 1,000 general population | Rate per 1,000 general population - lower limit | Rate per 1,000 general population - upper limit |
|---|---|---|---|---|---|---|
| East Midlands | 23,974 | 22,829 | 25,360 | 7.7 | 7.3 | 8.1 |
| East of England | 26,251 | 24,988 | 27,764 | 6.6 | 6.2 | 6.9 |
| London | 43,318 | 38,473 | 51,578 | 7.0 | 6.2 | 8.3 |
| North East | 21,676 | 20,765 | 22,868 | 12.8 | 12.3 | 13.5 |
| North West | 54,206 | 52,200 | 57,486 | 11.3 | 10.9 | 12.0 |
| South East | 37,223 | 35,073 | 39,184 | 6.3 | 5.9 | 6.6 |
| South West | 27,855 | 26,464 | 29,146 | 7.8 | 7.4 | 8.2 |
| West Midlands | 35,469 | 34,017 | 37,371 | 9.3 | 9.0 | 9.8 |
| Yorkshire and the Humber | 40,747 | 38,923 | 42,708 | 11.6 | 11.1 | 12.1 |
| England | 310,718 | 298,138 | 326,648 | 8.5 | 8.1 | 8.9 |
Prevalence by substance group and region
The estimates show that opiates were the most misused substance in all regions of England in 2022 to 2023, either on their own or with crack. For people using opiates only, or opiates and crack together, there was a combined rate of more than 7 per 1,000 general population. For people who misused crack without opiates, the rate was 1.4 per 1,000 general population.
In 2022 to 2023, the highest rate of opiate-only users was in the North East with 8.8 per 1,000 general population. This was more than double the national average rate of 3.8 opiate-only users per 1,000 general population. Yorkshire and the Humber and the North West followed with 5.7 and 5.4 respective opiate-only users per 1,000 general population. The North West, Yorkshire and the Humber and the West Midlands had the highest rates of crack use (crack only and opiates and crack).
Table 2: rates per 1,000 general population by substance group and region, 2022 to 2023
| Region | OCU | Opiates and crack | Opiates only | Crack only |
|---|---|---|---|---|
| East Midlands | 7.7 | 2.9 | 3.7 | 1.1 |
| East of England | 6.6 | 2.9 | 2.5 | 1.2 |
| London | 7.0 | 3.3 | 2.3 | 1.4 |
| North East | 12.8 | 2.0 | 8.8 | 2.0 |
| North West | 11.3 | 4.2 | 5.4 | 1.8 |
| South East | 6.3 | 2.6 | 2.6 | 1.1 |
| South West | 7.8 | 2.8 | 3.7 | 1.3 |
| West Midlands | 9.3 | 4.1 | 4.1 | 1.2 |
| Yorkshire and the Humber | 11.6 | 4.1 | 5.7 | 1.8 |
| England | 8.5 | 3.3 | 3.8 | 1.4 |
Prevalence by age group
In 2022 to 2023, 39.5% of England’s general population was aged 15 to 34 and 39.8% was aged 45 to 64. In contrast, 24% of the total estimated OCU population was aged 15 to 34 and 42% was aged 45 to 64. This age pattern does not differ much between the regions, with the North West having an older OCU population (20% compared to 53%) and the North East a slightly younger OCU population (28.9% compared to 29.5%).
London had more of its OCU population in the youngest and oldest age groups than other regions. It had 6.2% in the 15 to 24 age group and 17.5% OCU in the 55 to 64 age group.
The North East had the highest concentration of OCU in the 35 to 44 age group (41.7%). The North West had the lowest proportion in this group (27%).
Per 1,000 general population, the North East had the highest OCU rates for the:
- 15 to 24 age group (4.5 per 1,000 general population)
- 25 to 34 age group (14.3 per 1,000 general population)
- 35 to 44 age group (27.4 per 1,000 general population)
All 3 rates are around twice the national average in those age groups.
The second highest rates in the age groups 15 to 24, 25 to 34 and 35 to 44 years were in Yorkshire and the Humber at 3.1, 10.7 and 20.6 per 1,000 general population respectively. The rates were still notably lower than for the same age groups in the North East.
In the remaining 45 to 54 and 55 to 64 age groups, the highest OCU rates were in Yorkshire and the Humber, followed by the North West.
Table 3: OCU prevalence estimates and rates per 1,000 general population by age group and region, 2022 to 2023
| Region | Estimate (rate) 15 to 24 years | Estimate (rate) 25 to 34 years | Estimate (rate) 35 to 44 years | Estimate (rate) 45 to 54 years | Estimate (rate) 55 to 64 years |
|---|---|---|---|---|---|
| East Midlands | 1,375 (2.3) | 4,829 (7.7) | 9,219 (14.9) | 6,349 (10.0) | 2,203 (3.4) |
| East of England | 1,404 (2.0) | 5,021 (6.2) | 8,961 (10.6) | 7,293 (8.7) | 3,572 (4.3) |
| London | 2,683 (2.4) | 7,545 (4.7) | 13,545 (9.7) | 11,946 (10.3) | 7,600 (8.1) |
| North East | 1,435 (4.5) | 4,822 (14.3) | 9,036 (27.4) | 4,866 (14.7) | 1,517 (4.1) |
| North West | 2,413 (2.7) | 8,416 (8.4) | 14,662 (15.2) | 19,930 (21.1) | 8,785 (9.0) |
| South East | 2,075 (2.0) | 6,885 (6.0) | 12,838 (10.3) | 10,009 (8.0) | 5,416 (4.5) |
| South West | 1,508 (2.3) | 5,202 (7.5) | 9,784 (14.1) | 7,875 (10.9) | 3,486 (4.4) |
| West Midlands | 1,997 (2.7) | 7,185 (9.2) | 13,222 (17.2) | 9,475 (12.4) | 3,590 (4.8) |
| Yorkshire and the Humber | 2,115 (3.1) | 7,741 (10.7) | 14,380 (20.6) | 12,322 (17.7) | 4,188 (5.9) |
| England (total) | 17,004 (2.5) | 57,644 (7.5) | 105,647 (14.0) | 90,065 (12.3) | 40,358 (5.6) |
Prevalence by sex
In 2022 to 2023, the OCU prevalence estimates show that 78% were male and 22% female. This proportion is similar across all regions.
Table 4: OCU prevalence (and rates per 1,000 general population) by sex and region, 2022 to 2023
| Region | Male | Female |
|---|---|---|
| East Midlands | 18,577 (12.0) | 5,398 (3.4) |
| East of England | 20,440 (10.3) | 5,811 (2.9) |
| London | 34,668 (11.5) | 8,650 (2.7) |
| North East | 16,726 (20.1) | 4,950 (5.8) |
| North West | 42,601 (18.0) | 11,605 (4.8) |
| South East | 28,995 (10.0) | 8,228 (2.7) |
| South West | 21,652 (12.3) | 6,203 (3.4) |
| West Midlands | 27,266 (14.5) | 8,203 (4.3) |
| Yorkshire and the Humber | 31,703 (18.2) | 9,043 (5.1) |
| England | 242,627 (13.5) | 68,090 (3.7) |
Methodology
Data sources for the OCU estimate modelling
The modelling uses 3 data sources, which were:
- National Drug Treatment Monitoring System (NDTMS) information on people in community drug treatment
- criminal justice system information, combining data from the Home Office’s Police National Computer, the Ministry of Justice’s Offender Assessment System and NDTMS data on people in drug treatment in prisons
- drug-related mortality information from the Office for National Statistics’ data on deaths registered in England
Estimation method
Capture-recapture technique
To produce these estimates, we used a technique known as ‘capture-recapture’. The capture-recapture technique partly evolved from estimating the levels of hidden wildlife populations and is one of the most widely used in estimating hidden populations.
The basic premise of the capture-recapture technique is that individuals may be observed in multiple linked data sources. The technique uses information about how individuals are observed in different data sources, particularly the extent of overlap between the different sources, to estimate the number of people who are not observed in any of them.
To produce OCU prevalence estimates, we identified people across the 3 data sources using probabilistic matching (a statistical approach to measure the probability that 2 records represent the same person). We applied this approach across the data sources by matching a person’s:
- first initial of their first name and surname
- date of birth
- sex
- local authority or region where they live
In general, if there is a large amount of overlap between data sources, this implies that most of the total population has been captured, and the hidden population is relatively small. However, if only a small proportion overlap, this implies there is a larger hidden population that is only rarely observed in each source.
The basic model structure
The possible 8 different combinations (A to H) of being observed or not observed in the 3 data sources are listed in table 5. The combinations A to G represent the observed number of people in each local authority and combination H represents the number of OCU not observed in any of the 3 data sources.
Table 5: combinations of observed and unobserved records across used data sources
| Combination | Observed in NDTMS community data | Observed in criminal justice system data | Observed in drug-related mortality data |
|---|---|---|---|
| A | Yes | No | No |
| B | Yes | Yes | No |
| C | Yes | Yes | Yes |
| D | No | Yes | No |
| E | No | Yes | Yes |
| F | No | No | Yes |
| G | Yes | No | Yes |
| H | No | No | No |
To model the dependence structure in the observed cells (combinations A to G), we fitted a single statistical model (a Poisson model) to all local authorities using:
- the observed counts in combinations A to G
- information on a person’s age, sex, substance use and injecting status
- the size of the general population in each local authority by age and sex as a denominator
We used the resulting model to estimate the number of people not observed (combination H). The total of all calculated combination counts (A to H) is the estimated prevalence for each local authority.
Our model also includes interaction terms between data sources. This allows for dependencies between sources, which helps us to estimate whether a person appearing in one source is more or less likely to appear in another source (for example, people in contact with the criminal justice system may be more likely to enter treatment).
Additions to the basic model structure
To include the likely effect of the COVID-19 pandemic on individual and institutional behaviour, we developed a trend model that is fitted to multiple years of data to produce stable estimates over time. Given the potential effects of the COVID-19 pandemic, we omitted data for 2020 to 2021 and 2021 to 2022 and fitted the model using data for the years 2018 to 2019, 2019 to 2020 and 2022 to 2023.
We needed to estimate the OCU prevalence in a ‘London-only’ model and an ‘England-without-London’ model due to the:
- different pattern of overlap between NDTMS community and criminal justice data sources
- differences in age structure and drug use in London compared to the rest of England
So, we fitted the same model to London and the rest of England separately.
We included random effects to allow for:
- local differences in the chance of being in treatment
- local variations in the composition of the age groups, substance use and injecting status
We also included variation in the proportions observed in community drug treatment, the criminal justice system and drug-related mortality sources in the modelling. The random effect terms allow for the effect of shrinkage. This is where data shows a difference to the national average. So, estimates can pull away from the estimated baseline prevalence, but where data is sparse (and potentially subject to random variation) estimates shrink towards the estimated baseline prevalence.
Since the model estimates all OCU sub-populations (by age group, sex, substance use and injecting behaviour) jointly for each local authority, the OCU numbers can be aggregated to higher level OCU numbers (for example, at a regional or national level).