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---
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# Background Information and Methodology {.toc-ignore}
## September 2024 {.toc-ignore}
### 1. Background
The Dependency-Forming Medicines publication has been developed in response to the Public Health England (PHE) dependence and withdrawal associated with some prescribed medicines review also known as the [prescribed medicines review (PMR)](https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/940255/PHE_PMR_report_Dec2020.pdf), particularly its recommendation that the availability and use of data on the prescribing of medicines that can cause dependence be increased.
The PMR report was published by PHE in September 2019. Analysis showed that 11.5 million adults in England (26% of the adult population) in 2017/18 received and had dispensed one or more prescriptions for the following five identified medicine classes:
* Antidepressants
* Opioid pain medicine
* Gabapentinoids
* Benzodiazepines
* Z-drugs
The Dependency-Forming Medicines annual publication shows the volume and cost of prescription items and number unique patients for a subset of drugs selected in the PMR report:
* Opioid pain medicine
* Gabapentinoids
* Benzodiazepines
* Z-drugs
These statistics also include breakdowns of patients receiving this prescribing by age band, gender, and the Index of Multiple Deprivation (IMD).
Details of the drugs in this publication are available at chemical substance level in appendix A and at presentation level in a separate excel appendix.
Antidepressants are not included in the volume and cost or demographics measures. The current [National Institute for Health and Care Excellence (NICE) guidance](https://www.nice.org.uk/guidance/ng215) makes the distinction that antidepressants are historically not dependency-forming but can cause withdrawal symptoms. Additionally the statistics for antidepressants can be found in the [Medicines Used in Mental Health publication.](https://www.nhsbsa.nhs.uk/statistical-collections/medicines-used-mental-health-england) Antidepressants are included in the co-prescribing measures in this publication.
This publication can have a wide range of uses including informing government or local NHS policy, and allowing public scrutiny of national and regional prescribing habits.
Prescription data is a long-standing administrative source of data that has been used by commissioners, providers, government, academia, industry, and media to inform local and national policy, in academic research, to monitor medicine uptake, and allow public scrutiny of prescribing habits. It is collected by the NHS Business Services Authority (NHSBSA) for the operational purpose of reimbursing and remunerating dispensing contractors for the costs of supplying drugs and devices, along with essential and advanced services, to NHS patients. The data that forms the basis of these statistics is collected as a by-product of this process.
#### 1.1. How prescription data is collected
Data is collected from the submission of prescriptions by dispensing contractors to the NHSBSA. These prescriptions can be issued by GPs and other authorised prescribers such as nurses, dentists, and allied health professionals. Prescriptions that are issued by hospitals can also be dispensed in the community and submitted for reimbursement. Prescriptions that are issued in hospitals and fulfilled by the hospital pharmacy or dispensary are not included in this data.
Prescriptions can be issued as either a paper form or as an electronic message using the Electronic Prescription Service (EPS). EPS prescriptions make up most of prescribing and dispensing activity carried out in England, accounting for 89% of all prescriptions dispensed in England during 2022. EPS messages are submitted by the dispensing contractor once the prescription has been fulfilled and issued to the patient. The message is initially sent to the [NHS Spine](https://digital.nhs.uk/services/spine), maintained by NHS Digital, and then sent to the NHSBSA for processing. Paper prescriptions are compiled by the dispensing contractor and sent to the NHSBSA at the end of each month by secure courier. These paper prescriptions are then scanned and transformed into digital images, which are passed through intelligent character recognition (ICR) to extract the relevant data from them. Most paper forms go through ICR without any manual intervention. However, there are cases where operator intervention is required to accurately capture information from the prescription form. This manual intervention can be required for many reasons, such as if a form is handwritten or information is obscured by a pharmacy stamp.
After this processing for the reimbursement and remuneration of dispensing contractors, data is extracted from the NHSBSA transactional systems alongside data from the NHSBSA drug and organisational databases and loaded in to the NHSBSA Enterprise Data Warehouse (EDW). During this extract, load and transform (ELT) process a series of business logic is applied to the data to make it easier to use and more useful than if it were to be kept in its raw form. The EDW is the source used by many of our reporting systems and data extracts, including ePACT2, eDEN, eOPS, the English Prescribing Dataset (EPD), and Official Statistics publications.
#### 1.2. Drug data held by the NHSBSA
The NHSBSA has a single drug database that is used for both the reimbursement and reporting of drugs, appliances, and medical devices. This database is called Common Drug Reference (CDR) and holds all drug related information, including BNF classification, SNOMED CT, price and much more. This database is also the basis for the [NHS Dictionary of Medicines and Devices (DM+D)](https://www.nhsbsa.nhs.uk/pharmacies-gp-practices-and-appliance-contractors/dictionary-medicines-and-devices-dmd), which the NHSBSA maintain with support from NHS Digital.
Drugs are held on CDR at an individual pack level. For example, Paracetamol 500mg tablets 16 pack and Paracetamol 500mg tablets 32 pack have separate entries in the database, along with separate entries for each supplier of the pack and any proprietary versions.
The NHSBSA holds drug and prescription data at a pack level and uses this information for the correct reimbursement of dispensing contractors. However, the most granular data that we release in our reporting systems and other data outputs, including these statistics, is at a product level.
##### 1.2.1. British National Formulary (BNF) hierarchy
These statistics use the therapeutic classifications defined in the BNF to group medicines together based on their primary therapeutic indication. The NHSBSA uses and maintains the classification system of the BNF implemented prior to the release of edition 70, including the six pseudo BNF chapters (18 to 23) created by NHS Prescription Services used to classify products that fall outside of chapters 1 to 15. Most of the presentations held in these pseudo chapters are dressings, appliances, and medical devices. Each January the NHSBSA updates the classification of drugs within the BNF hierarchy which may involve some drugs changing BNF codes and moving within the hierarchy.
The BNF has multiple levels, in descending order from the largest grouping to smallest they are chapter, section, paragraph, sub-paragraph, chemical substance, product, and individual presentation. Presentations in chapters 20 to 23 do not have assigned BNF paragraphs, sub-paragraphs, chemical substances, or products.
#### 1.3. Other data held by the NHSBSA
##### 1.3.1 Personal Demographic Service data
The Personal Demographic Service (PDS) is a part of NHS Digital that holds information that allows healthcare professionals to identify patients and match them to their health records. This includes information such as NHS number, date of birth, gender, registered address, and registered GP practice.
Each month when data is loaded into the NHSBSA Data & Insight Data Warehouse, NHS numbers that have been captured are sent to PDS to verify them. That list includes all NHS numbers that were scanned in that month and previously verified NHS numbers that have a birthday in that month. Details held by PDS are returned to the NHSBSA, including updates to previously verified NHS numbers.
Because this process takes time, new and updated verified data from PDS is loaded into the NHSBSA Data & Insight Data Warehouse the month after the NHS numbers were first scanned. For example, a new NHS number received in January and subsequently verified would be classed as ‘not verified’ in January and ‘verified’ in February. In February the additional information about that patient such as gender and age would become available.
### 2. Methodology
#### 2.1 Drug details
##### 2.1.1. Drug classification
In these statistics the 5 categories the drugs were classified into were taken from the PHE PMR and the assigning of a drug to a category has mirrored that of the comparators in the [Opioid Comparator Dashboard](https://www.nhsbsa.nhs.uk/access-our-data-products/epact2/dashboards-and-specifications/opioid-prescribing-comparators-dashboard) to support Primary Care Networks (PCN) and GP practices also published by the NHS BSA
##### 2.1.2. Co-prescribing
In these statistics, co-prescribing refers to the reporting of 2 or more drug categories of medicine for the same identified patient in the same month. Co-prescribing is reported based on the number of categories (up to 5) that were reported in the same month, with greater than one deemed to be co-prescribing.
The main limitation is that it is not possible to distinguish whether multiple prescriptions have been reported for the same month are given consecutively or concurrently. As such, some activity will be flagged in this analysis as co-prescribing (implying concurrent receipt) when in fact the individual was prescribed one medicine and then separately the other, where both were reported in the same month.
#### 2.2 Patient details
##### 2.2.1. Patient age
In this publication we take the age of a patient to be their age on the 30 September of the given financial year, using the patient’s date of birth taken from the PDS. This is done so that a patient’s age can be reported consistently across the financial year. Records with NHS numbers that have not been verified by PDS are displayed in the ‘Unknown’ age band group in the tables in this publication.
There are some inconsistencies within the PDS data that is held within the NHSBSA Data & Insight Data Warehouse. These occur when a patient has had their information updated and can hold more than one date of birth. In these instances, multiple counting can occur for patients, although this is estimated to only affect a very small number of patients. We will investigate methodologies to reduce the impact of these inconsistencies.
##### 2.2.2. Patient gender
The NHSBSA does not capture information relating to a patient’s sex or gender from a prescription during processing activities. Gender is instead obtained from the PDS. Therefore, gender information is only available for patients that we have been able to obtain a matched NHS number for.
A patient is classified in one of four ways for gender by PDS:
0 – Unknown
1 – Male
2 – Female
9 – Indeterminate (unable to be classified as either male or female)
In these statistics patients that hold a gender of 0 or 9 have been grouped together into a single ‘Unknown’ category.
The NHSBSA also codifies gender for patients where we have been unable to match their NHS number to PDS as ‘Unknown’.
Where we have matched a patient’s NHS number to PDS data and the patient’s gender has a value of 0 - Unknown, or 9 - Indeterminate, prescribing for that patient will appear in these statistics with a gender value of ‘Unknown’ and an identified patient flag value of ‘Y’. Where we have not been able to match an NHS number to PDS data, we will not hold a value for patient gender. Prescribing for that NHS number will appear in these statistics with a gender value of ‘Unknown’ and an identified patient flag value of ‘N’.
In these statistics we use the latest gender information we have on record for that patient. If we receive new information on gender for a patient, this will be applied to all previous prescribing for that patient. This may impact historical data.
For example, this can affect data involving patients whose gender was previously not known in PDS, but subsequently becomes known. When gender becomes known for that patient, the patient can move out of the ‘Unknown’ gender category into ‘Male’ or ‘Female’. You should always use the latest version of the data available.
##### 2.2.3. Patient deprivation
The [English Indices of Deprivation 2019](https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019) have been used in these statistics as a measure of the level of deprivation of the areas in which a patient lived when they were prescribed an item. In particular, the headline Index of Multiple Deprivation (IMD) is included. IMD data has been joined to the National Statistics Postcode Lookup (NSPL) UK May 2024 release using lower-layer super output area (LSOA) 2011. This combined dataset has then been joined to prescription data using the postcode of the patient the prescription was for.
The IMD calculates deciles by ranking LSOAs from most deprived to least deprived and dividing them into 10 equal groups. These range from the most deprived 10% (decile 1) of small areas nationally to the least deprived 10% (decile 10) of small areas nationally. We have aggregated these deciles into quintiles in this publication.
The measure of deprivation reported in these statistics is therefore the IMD quintile of the area in which the patient is located. For some patients where a patient postcode is not available, the postcode of the prescribing practice has been used as a secondary way of assigning to an IMD quintile if present in the data. Where both the patient’s postcode or practice postcode has not been able to be matched to the NSPL or the patient has not been identified, the records are reported as ‘unknown’ IMD quintile.
Due to the methodology used to assign IMD quintile, some historical data in the IMD level summary tables may change between releases due to the following situations:
if a patient moves postcode between releases to an LSOA in a different IMD quintile
if the postcode of the prescribing practice is being used and the practice moves postcode between releases to an LSOA in a different IMD quintile
if the NSPL is updated and a postcode is recorded against a different area in the new NSPL edition, then the IMD quintile for the new area is used
#### 2.3. Changes to BNF classifications
These statistics use the BNF therapeutic classifications defined in the British National Formulary (BNF) using the classification system prior to BNF edition 70. Each January the NHSBSA updates the classification of drugs within the BNF hierarchy which may involve some drugs changing classification between years of prescribing data. The NHSBSA publishes the latest BNF information each year via its information systems. This is currently done via the [Information Services Portal (ISP)](https://www.nhsbsa.nhs.uk/information-services-portal-isp) but may in the near future be transitioned to the [NHSBSA Open Data Portal (ODP).
](https://opendata.nhsbsa.net/)
### 3. Changes to this publication
This is an experimental official statistic release. Experimental statistics are newly developed or innovative statistics. These are published so that users and stakeholders can be involved in the assessment of their suitability and quality at an early stage. We will regularly be reviewing the methodology used within the statistics.
For this release we have changed some of the underlying methodology used to join patient demographic data to our prescriptions data. This has been done to align with best practices in reporting patient gender, and to be consistent with other statistical outputs from across the NHS. When we can match an identified patient’s NHS number with Patient Demographic Service (PDS) data, we now use the latest recorded gender for that patient. If we receive new information on gender for a patient, this will be applied to all previous prescribing for the patient. For example, this will affect data involving patients whose gender was previously not known in PDS, but subsequently becomes known. This may impact historical figures as patients can move between gender categories.
### 4. Strengths and limitations
#### 4.1. Strengths
The main strength of these statistics is the completeness of the dataset and accuracy of information captured during processing activities carried out by the NHSBSA. This dataset covers all prescribing in England that has subsequently been dispensed in the community in England, Scotland, Wales, and the Channel Islands, with consistency in the way data has been captured across the whole dataset. All the data has come from the same administrative source. This administrative data is required to be as accurate as possible as it is used for paying dispensing contractors for services provided to NHS patients.
#### 4.2. Limitations
These statistics exclude prescriptions that were issued but not presented for dispensing and prescriptions that were not submitted to the NHSBSA for processing and reimbursement. Prescriptions issued and dispensed in prisons, hospitals and private prescriptions are also excluded.
Additional patient information received from PDS for matched NHS numbers is not returned until after the monthly ETL process for prescription data into the NHSBSA Data & Insight Data Warehouse is complete, and so the year in progress monthly or quarterly patient counts may include some unverified data and subsequently be revised in a later publication.
The NHSBSA do not capture the clinical indication of a prescription and therefore do not know the reason why a prescription was issued, or the condition it is intended to treat. Many drugs have multiple uses, and although classified in the BNF by their primary therapeutic use may be issued to treat a condition outside of this. Due to this, these statistics may not give accurate estimations of prescribing to treat specific conditions. This also means that unlike the PMR which excluded opioids prescribed for cancer. NHSBSA data cannot be filtered to exclude patients diagnosed with cancer and who are using an opioid to manage the pain that can be associated with malignant diseases, especially as part of end of life care. This means that some measures and the data for opioid pain medicines include more patients than the PMR analysis.
### 5. Revisions
Any revisions that we make to these statistics will be made in line with our [Revisions and Corrections policy.](https://www.nhsbsa.nhs.uk/policies-and-procedures) Any significant errors that are identified within these statistics after their publication that would result in the contradiction of conclusions previously drawn from the data will be notified of prominently on our website and any other platforms that host these statistics, corrected as soon as possible, and communicated clearly to users and stakeholders.
### 6. Related statistics, comparability and useful resources
The NHSBSA releases the Official Statistics publication on Dependency-Forming Medicines in England.
#### 6.1 ONS opioids statistics
The ONS has published several [Freedom of Information requests](https://www.ons.gov.uk/search?q=opioids) relating to opioids.
#### 6.2 Faculty of Pain Medicine Opioids Aware
A resource for patients and healthcare professionals to support prescribing of [opioid medicines for pain.](https://fpm.ac.uk/opioids-aware)
#### 6.3 NHSBSA Open Data Portal
The [NHSBSA Open Data Portal](https://opendata.nhsbsa.net/) is the platform where we host our open data products, including the presentation level data tables released as part of these statistics.
Items prescribed for dependency-forming medicines are also available to view at presentation level through our annual [Prescription Cost Analysis publication](https://www.nhsbsa.nhs.uk/statistical-collections/prescription-cost-analysis-england), which takes a dispensing view and covers all items prescribed throughout the UK but dispensed in the community in England only.
#### 6.4 Code of Practice for Statistics
These statistics have been produced in compliance of the Code of Practice for Statistics. You can find more on the code of practice and its pillars, principles and practices from the [UK Statistics Authority website.](https://www.statisticsauthority.gov.uk/code-of-practice/)
### 7. Quality of the statistics
We aim to provide users of this publication with an evidence-based assessment of its quality and the quality of the data from which it is produced. We do so to demonstrate our commitment to comply with the UK Statistics Authority’s (UKSA) Code of Practice for Statistics, particularly the pillar of Quality and its principles.
**Q1 Suitable data sources** – Statistics should be based on the most appropriate data to meet intended uses. The impact of any data limitations for use should be assessed, minimised, and explained.
**Q2 Sound methods** – Producers of statistics and data should use the best available methods and recognised standards and be open about their decisions.
**Q3 Assured quality** – Producers of statistics and data should explain clearly how they assure themselves that statistics and data are accurate, reliable, coherent, and timely.
This is an assessment of the quality of these statistics against the European standard for quality reporting and its dimensions specific to statistical outputs, particularly:
* relevance
* accuracy and reliability
* timeliness and punctuality
* accessibility
* coherence and comparability
These principles guide us and are complimented by the UKSA’s regulatory standard for the Quality Assurance of Administrative Data (QAAD). You can view our QAAD assessment of prescription data on our website.
#### 7.1. Relevance
This dimension covers the degree to which the product meets user need in both coverage and content.
The Dependency-Forming Medicines publication, released annually, summarises the number of items prescribed for drugs considered to be dependency-forming. The statistics also give patient breakdowns including by 5-year age band, gender, and IMD decile. These statistics cover from financial year 2015/16 onwards, allowing the analysis of long-term trends in prescribing. We believe that they can used to inform policy decisions at a national and local level, by the public to scrutinise prescribing habits, and by academia and applied health researchers for matters relating to public health. The NHSBSA also routinely receives Freedom of Information requests and parliamentary questions about this subject matter.
We will be gathering feedback from users of these statistics on an on-going basis to help shape them and ensure that they remain relevant and of use.
#### 7.2 Accuracy and Reliability
This dimension covers the statistics proximity between an estimate and the unknown true value.
##### 7.2.1. Accuracy
These statistics are derived from data collected during processing activities carried out by the NHSBSA to reimburse dispensing contractors for providing services to NHS patients. Prescriptions are scanned and subject to rigorous automatic and manual validation processes to ensure accurate payments are made to dispensing contractors. Where electronic prescriptions are used the scope for manual intervention and input into data is reduced dramatically.
The figures used are collected as an essential part of the process of reimbursing dispensing contractors (mainly pharmacists and dispensing doctors) for medicines supplied. All prescriptions which are dispensed in England need to be submitted to the NHSBSA if the dispenser is to be reimbursed, and so coverage should be complete. Due to the manual processes involved in the processing of prescriptions there may be random inaccuracies in capturing prescription information which are then reflected in the data. NHS Prescription Services, a division of NHSBSA, internally quality assures the data that is captured from prescriptions to a 99.70% level via a statistically valid random sample of 50,000 items that are reprocessed monthly. The latest reported [Prescription Processing Information Accuracy](https://www.nhsbsa.nhs.uk/pharmacies-gp-practices-and-appliance-contractors/payments-and-pricing/how-we-process-prescriptions) from NHS Prescriptions services, which covers the 12 month period March 2021 to February 2022 is 99.92%.
##### 7.2.2. Reliability
As there is a manual data entry element to this system then inevitably some small errors may occur in the data. The NHSBSA and NHS Prescription Services take measures to minimise these errors. This includes the presence of a permanent dedicated accuracy team within NHS Prescription services which provides feedback to operators around any errors identified to help prevent regular occurrence.
#### 7.3. Timeliness and punctuality
Timeliness refers to the time gap between publication and the reference period. Punctuality refers to the gap between planned and actual publication dates.
The Dependency-Forming Medicine publication is published annually. The publication date is determined by the availability of the data, dependent on the completion of processing by NHS Prescription Services, allowing adequate time for the compilation, and quality assurance, of the publication. The data is usually available six weeks after the end of the month that the data relates to. We aim to release the annual publication, along with the quarterly updates as close to availability of data as possible for the time period in question. The date of release for the annual publication will be announced in advance in line with our statistical release calendar.
##### 7.4. Accessibility and clarity
Accessibility is the ease with which users can access the data, also reflecting the format in which the data are available and the availability of supporting information. Clarity refers to the quality and sufficiency of the metadata, illustrations, and accompanying advice.
The statistical summary narrative for this publication is presented as an HTML webpage, with supporting documentation released in PDF format. Summary data and additional analysis is presented in tables in Excel files, with the most granular tables available in CSV format.
The R code used to produce the publication will also be made available from the [NHSBSA GitHub](https://github.com/nhsbsa-data-analytics) in due course
**Clarity**
A glossary of terms is included in this document.
#### 7.5. Coherence and comparability
Coherence is the degree to which data have been derived from different sources or methods but refer to the same topic or similar. Comparability is the degree to which data can be compared over time and domain.
The Dependency-Forming Medicine publication is the only statistics available on the prescribing of drugs identified as dependency-forming that have been dispensed in the community. The statistics are all derived from the same administrative data source with the same methodology applied to all data points.
The figures released in these statistics relating to item counts and total costs can be recreated from the English Prescribing Dataset (EPD) administrative data feed, available from the NHSBSA Open Data Portal (ODP). NHSBSA Information Services provide this data feed, and this feed is not an official statistic.
Comparability with other publications produced by the NHSBSA can be determined using the [Official Statistics guidance table](https://www.nhsbsa.nhs.uk/statistical-collections), which is maintained with the release of each new publication. This table shows how all of the NHSBSA’s publications compare across a range of measures to help users identify the best publication for their needs or understand where differences in figures may occur.
##### 7.5.1. Comparisons over time
In order to allow for comparisons to be made over time these statistics cover the whole period for which data is available, from financial year 2015/16 onwards.
Changes to the figures over time should be interpreted in the wider context of the prescribing system as a whole, including in the availability of medicines, release of new medicines, their costs and changing prescribing guidelines. All medicines are shown by their latest BNF classification, as described in section 2 – methodology.
**Trade-offs between output quality components**
This dimension describes the extent to which different aspects of quality are balanced against each other.
The main trade-off in this publication is the balance between timeliness and data quality. Sufficient time is allowed from the data being made available to allow for the information to be produced and quality assured.
We are releasing these experimental Official Statistics to allow users to begin analysing them, however we intend to introduce further data cleansing in the future that will improve the quality and accuracy of these statistics. This will be introduced once the data cleansing can be done in a timely manner and will not impact the release of the publication. The impact expected to be a small amount of reclassification – that will not impact most of the main conclusions or user’s analysis.
**Assessment of user needs and perceptions**
This dimension covers the processes for finding out about users and uses and their views on the statistical products.
Alongside the release of these statistics the NHSBSA will also be releasing a continuous feedback survey, allowing users to quickly tell us their thoughts on the content and utility of these statistics. This feedback, along with feedback gathered from other routes such as direct contact, will be used to shape the content and style of future Dependency-Forming Medicine publications and other statistical products from the NHSBSA. This publication also has a detailed user engagement plan specific to Dependency-Forming Medicine.
**Performance, cost and respondent burden**
This dimension describes the effectiveness, efficiency and economy of the statistical output.
There is no respondent burden for Dependency-Forming Medicine data, as all data are extracted from existing NHSBSA information and transactional systems.
This release has been developed with a reproducible analytical pipeline (RAP) in mind and RAP principles applied where possible. This development has been done in R and the code used will be made publicly available at the [NHSBSA GitHub.](https://github.com/nhsbsa-data-analytics) Further development is planned to the RAP used for this publication to automate as many tasks as possible.
**Confidentiality, transparency and security**
The procedures and policy used to ensure sound confidentiality, security and transparent practices.
Trustworthy statistics and the data behind them are an important part of well informed decision making, and are vital to support improvement across the wider health and social care system. It is accepted, however, that where statistics provide information on small numbers of individuals, the NHS Business Services Authority have a duty under data protection law to avoid directly or indirectly revealing any personal details. In addition, NHSBSA staff members are required to adhere to relevant NHS data confidentiality guidelines.
The NHSBSA has robust confidentiality and security policies that were adhered to during the production of these statistics. More information on these policies and how we follow them can be found in our [Confidentiality and Access Statement.](https://www.nhsbsa.nhs.uk/sites/default/files/2021-05/Confidentiality_and_Access_Statement_v002.pdf)
A risk assessment around potential disclosure of personal identifiable information through these statistics was carried out during their production. In line with the NHSBSA’s Statistical Disclosure Control Policy, patient counts less than five, or item and cost information where a patient count of less than five can be inferred, have been redacted. Patient counts have also been omitted from geographical breakdowns below national level due to the level of redaction that would be applied.
**Quality assurance of administrative data**
In addition to the assessment, we have followed the Quality Assurance of Administrative Data (QAAD) toolkit, as described by the Office for Statistics Regulation (OSR). Using the toolkit, we established the level of assurance we are seeking (or “benchmark”) for each source. The assurance levels are set as basic, enhanced, or comprehensive depending on the risk of quality concerns for that source, based on various factors.
We have made a judgement about the suitability of the administrative data for use in producing this publication, this is designed to be pragmatic and proportionate. [The QAAD assessment for prescription data can be found on the NHSBSA website.](https://www.nhsbsa.nhs.uk/statistical-collections)
### 8. Glossary of terms used in these statistics
#### Age
A patient’s age, and therefore 5-year age band, has been calculated at 30 September for the given financial year. This age has been calculated using the patient date of birth shared with the NHSBSA from PDS.
#### British National Formulary (BNF)
Dependency-Forming Medicine data uses the therapeutic classifications defined in the British National Formulary (BNF) using the classification system prior to edition 70. NHS Prescription Services have created pseudo BNF chapters for items not included in BNF chapters 1 to 15. The majority of such items are dressings and appliances, which have been classified into six pseudo BNF chapters (18 to 23).
Information on why a drug is prescribed is not available in this dataset. Since drugs can be prescribed to treat more than one condition, it may not be possible to separate the different conditions for which a drug may have been prescribed.
The BNF has multiple levels, in descending order from largest grouping to smallest:
* chapter
* section
* paragraph
* sub-paragraph
* chemical substance#
* product
* presentation
Presentations in chapters 20-23 do not have an assigned BNF paragraph, sub-paragraph, chemical substance or product.
#### Chemical substance
A chemical substance is the name of the main active ingredient in a drug. Appliances do not hold a true chemical substance. It is determined by the British National Formulary (BNF) for drugs, or the NHSBSA for appliances. For example, Amoxicillin.
#### Integrated Care Board (ICB)
Integrated care boards (ICBs) are a statutory NHS organisation responsible for developing a plan in collaboration with NHS trusts/foundation trusts and other system partners for meeting the health needs of the population, managing the NHS budget and arranging for the provision of health services in the defined area. They took over the functions of Clinical Commissioning Groups (CCG) in July 2022.
#### Cost
In British pound sterling (GBP). The amount that would be paid using the basic price of the prescribed drug or appliance and the quantity prescribed, sometimes called 'Net Ingredient Cost' (NIC). The basic price is given either in the Drug Tariff or is determined from prices published by manufacturers, wholesalers, or suppliers. Basic price is set out in Parts VIII and IX of the Drug Tariff. For any drugs or appliances not in Part VIII, the price is usually taken from the manufacturer, wholesaler, or supplier of the product.
#### Dispensed in the community
When a prescription item is dispensed in the community this means that it has been dispensed by a community pharmacy or other dispensing contractor. This does not include medicines dispensed within hospitals and prisons.
#### Dispensing contractor / dispenser
A dispensing contractor or dispenser can be a community pharmacy or appliance contractor (a dispenser that specialises in dispensing dressing, appliances and medical devices).
Prescriptions can also be dispensed by the dispensary of a dispensing practice or personally administered at a practice. Dispensing practices usually exist in more rural areas where the need for a dispenser is deemed necessary but it is not deemed financially viable to establish a community pharmacy.
#### Gender
Information relating to a patient’s gender is not captured by the NHSBSA. This is instead derived by PDS data that is shared with the NHSBSA for NHS numbers
that have been matched. This term is not consistent with national data standards. For more information on this please see section 2 – methodology.
#### Identified patients
An identified patient is where an NHS number captured by the NHSBSA during prescription processing activities has been successfully matched to an NHS number held by the Personal Demographic Service (PDS), and PDS data, such as date of birth and gender, returned to the NHSBSA.
#### Items
The term Items refers to the number of times a product appears on a prescription form. Prescription forms include both paper prescriptions and electronic messages.
#### Prescription/prescription form
A prescription (also referenced as a prescription form) has two incarnations: a paper form, and an electronic prescription available via EPS. A paper prescription can hold up to a maximum of ten items. A single electronic prescription can hold a maximum of four items.
#### Presentation
A presentation is the name given to the specific type, strength, and pharmaceutical formulation of a drug or the specific type of an appliance. For example, Paracetamol 500mg tablets.
### 9. Feedback and contact us
Feedback is important to us. We welcome any questions and comments about this document and its contents. Please quote ‘Dependency-Forming Medicine – Background and Methodology Note’ in the subject title of any correspondence.
A [continuous feedback survey](https://online1.snapsurveys.com/Official_Statistics_Feedback) is available on the Dependency-Forming Medicine web page that can be completed by users.
#### 9.1 Contact us
You can contact us by:
Email: [email protected]
You can also write us at:
NHSBSA - Statistics <br>
NHS Business Services Authority <br>
Stella House <br>
Goldcrest Way <br>
Newburn Riverside <br>
Newcastle upon Tyne <br>
NE15 8NY
END
### 10 Appendix A – Medicines included in the statistics.
```{r echo = FALSE}
library("knitr")
library("kableExtra")
# Create a vector with the category names
categories <- c("Antidepressants", "Benzodiazepines", "Gabapentinoids", "Opioids", "Z-Drugs")
# Create a list to store the substances for each category
substances <- list(
Antidepressants = c(
"Amitriptyline hydrochloride", "Amoxapine", "Clomipramine hydrochloride", "Dosulepin hydrochloride",
"Doxepin", "Imipramine hydrochloride", "Lofepramine hydrochloride", "Maprotiline hydrochloride",
"Mianserin hydrochloride", "Nortriptyline", "Trazodone hydrochloride", "Trimipramine maleate",
"Isocarboxazid", "Moclobemide", "Phenelzine sulfate", "Tranylcypromine sulfate",
"Citalopram hydrobromide", "Citalopram hydrochloride", "Escitalopram", "Fluoxetine hydrochloride",
"Fluvoxamine maleate", "Paroxetine hydrochloride", "Sertraline hydrochloride", "Agomelatine",
"Duloxetine hydrochloride", "Flupentixol hydrochloride", "Mirtazapine",
"Nefazodone hydrochloride", "Oxitriptan", "Reboxetine",
"Tryptophan", "Venlafaxine", "Vortioxetine"
),
Benzodiazepines = c(
"Flurazepam hydrochloride", "Loprazolam mesilate", "Lormetazepam", "Nitrazepam", "Temazepam",
"Alprazolam", "Chlordiazepoxide hydrochloride", "Diazepam", "Lorazepam", "Oxazepam"
),
Gabapentinoids = c("Gabapentin", "Pregabalin"),
Opioids = c(
"Co-codamol (Codeine phosphate/ paracetamol)", "Co-dydramol (Dihydrocodeine/ paracetamol)",
"Buprenorphine", "Codeine phosphate", "Dextromoramide tartrate", "Diamorphine hydrochloride (Systemic)",
"Dihydrocodeine tartrate", "Dipipanone hydrochloride", "Fentanyl", "Hydromorphone hydrochloride",
"Meptazinol hydrochloride", "Methadone hydrochloride", "Morphine hydrochloride", "Morphine sulfate",
"Morphine tartrate and cyclizine tartrate", "Oxycodone", "Oxycodone hydrochloride",
"Oxycodone hydrochloride/naloxone hydrochloride", "Papaveretum", "Pentazocine hydrochloride",
"Pentazocine lactate", "Pethidine hydrochloride", "Tapentadol hydrochloride", "Tapentadol phosphate", "Tramadol hydrochloride"
),
`Z-Drugs` = c("Zaleplon", "Zolpidem tartrate", "Zopiclone"))
# Create an empty list to store the Markdown content for each category
category_content <- list()
# Loop through the categories and generate the Markdown content
for (category in categories) {
substances_list <- substances[[category]]
substances_md <- paste0("- ", substances_list, collapse = "\n")
category_content[[category]] <- paste0(category, "\n\n", substances_md)
}
# Combine the Markdown content for all categories
markdown_content <- paste(category_content, collapse = "\n\n")
kable(markdown_content, col.names = "")
```