Chapter 2Data sources and feasibility

Publication Details

Data source and extraction

The data set used was provided by the Primary Care Clinical Informatics Unit (PCCIU) at University of Aberdeen. Until the replacement of the General Practice Administration System for Scotland (GPASS) clinical information technology (IT) system in 2010, PCCIU ran the Scottish Programme for Implementing Clinical Effectiveness in Primary Care (SPICE-PC) on behalf of NHS Scotland. In SPICE-PC, a near complete copy of data held in clinical IT systems was extracted from participating practices using GPASS and used for feeding back comparative quality data. Data were anonymised at source by removing names and unique patient identifiers (IDs) and extracting other IDs with a degree of fuzziness (e.g. rather than extracting full postcode, only postcode sector was extracted to allow allocation of Carstairs deprivation score). With practices’ consent, the data were available in fully anonymised form for research purposes, and the NHS Grampian Research Ethics Committee had agreed that individual studies using the data did not require specific review, provided that data management and analysis followed PCCIU standard operating procedures, which this study has.

At its peak, approximately one-third of Scottish practices participated in SPICE-PC and agreed to research use of their data. The data available are nearly identical to those in other GP-derived data sets and include patient demography (anonymised where required), diagnoses and procedures (recorded as Read Codes), values such as blood pressures and laboratory values, and prescribing. Somewhat differently from other GP-derived data sets, information was also available about which GP or other clinician had provided care in the form of anonymised clinician codes, with some characteristics of GPs and practices (e.g. GP sex, practice list size) available for use in analysis. However, clinician IDs had not previously been used for either NHS or research purposes, so there was no experience of their use to draw on. The planned project was potentially sensitive, and data from relatively far in the past were, therefore, used (GPASS was replaced in Scotland in a phased withdrawal from 2008, which was complete by 2011; the data used for examining variation in high-risk prescribing in this analysis is from before 31 March 2007).

For this project, much initial data management was done by PCCIU, drawing on its expertise of defining conditions with comprehensive Read Code sets and comprehensively identifying drugs using British National Formulary (BNF) codes where available and free text matching where not. We used PCCIU-defined code sets where these had already been developed and used for SPICE-PC other NHS purposes and worked with PCCIU to define new code sets where necessary. Data extraction was planned to be done in stages, with the initial data extraction to explore feasibility being for practices participating in SPICE-PC that additionally were part of the NHS Scotland Practice Team Information (PTI) programme. PTI is a national morbidity-recording data set, supported by NHS Scotland Information Services Division, where practices record one or more Read Codes for the morbidities managed in all face-to-face encounters with GPs or nurses in ≈ 50 (5%) practices. These Read Codes are distinguished from other Read Codes (e.g. diagnoses based on hospital letters) by having a coded modifier associated with them, with GPs and nurses using different modifiers, making it possible to further distinguish the type of clinician the patients is having a face-to-face consultation with. PTI practices are typically more IT-literate than average and receive additional training in, and financial support for, coding, and the quality and completeness of coding is audited.

For the initial feasibility analysis, data were extracted for 39 PTI practices (although one had ceased to contribute to PTI during 2006 and was subsequently excluded). Data extracted included patient demography (age, sex, postcode-defined Carstairs Score), selected morbidities [including Read Codes ever recorded for peptic ulceration, heart failure, chronic kidney disease (CKD)] and selected prescribing (including oral anticoagulants, antiplatelet drugs, renal toxic drugs). Additional data extracted included the table recording all ‘encounters’ with the practice, anonymised clinician IDs and clinician demography and practice structural data (list size, rurality/remoteness, type of contract held with the NHS, whether or not the practice was accredited for post-graduate GP training and whether or not the practice dispensed its own medicines). The detailed contents of the encounter and clinician tables are described in later sections.

If the analysis of variation between GPs was shown to be feasible, then the intention was to extend the data extraction to all 314 practices with data available on 31 March 2007. In the event, analysis of variation between GPs was found to be feasible in only a subset of practices, and so the second round of data extraction was reconfigured to examine change in high-risk prescribing over time, using data complete up to 31 March 2009, the date chosen being a balance between the length of time over which change could be examined and the number of practices available as the switch away from GPASS gathered pace. Data for the longitudinal analysis was available for 190 practices providing care to just under 20% of the Scottish population.

Defining measures of high-risk prescribing

Our previous analysis examined cross-sectional variation between practices.5 This is relatively straightforward, as it is easy to define a population of patients who are registered with the practice and at risk from a particular pattern of prescribing on a particular date. The high-risk prescription is attributable to the practice, because someone in the practice has authorised the prescription in the past and someone in the practice has issued it now. When examining variation between GPs, attribution is more complicated because the majority of prescriptions are ‘repeats’, which are usually reauthorised once a year but issued repeatedly between authorisations with variable but typically weak clinical oversight. This is because GPs often sign repeat prescriptions for tens of patients for hundreds of separate drugs every working day with little explicit review, effectively trusting the decision of whichever colleague authorised the prescription as a repeat and giving greater thought to the quality and safety of prescribing only if the patient presents with a problem or when the IT system flags that the prescription needs a review (typically annually).110,111 In addition, although GPs remain legally liable for all the prescriptions they create and sign and are not required to follow specialist advice, a large proportion of repeat drugs for chronic conditions are initiated or recommended by specialists, and the authorising GP may, therefore, not be the sole decision-maker.

It was, therefore, decided to focus on prescriptions where the decision was strongly attributable to an individual GP and where the decision to prescribe was reasonably widely known to be significantly risky (but not absolutely contraindicated) at the time of prescription. The original intention was to examine two kinds of decision by GPs (text taken from the original application):

  • The decision to stop a high-risk drug at medication review, which is relatively strongly attributable to individual GPs and can be analysed using a simpler, strictly hierarchical multilevel model.
  • Acute prescribing of high-risk medication, which is relatively strongly attributable to individual GPs but where analysis is more complicated and will require fitting cross-classified multilevel models.

Stopping high-risk drugs at medication review

Medication review in the previous 15 months for people prescribed repeat medication was incentivised under the QOF via the Medicines 5 and Medicines 9 indicators, worth approximately £2000 to an average-sized practice.112 In principle, the stopping of a high-risk drug was a decision which would be attributable to the reviewing GP. The intention was, therefore, to examine repeat drugs inactivated on the day that a medication review was done. In GPASS, medication review was recorded by ticking a box in the repeat prescribing screen which inserted a medication review code which was visible in the patient record, and our assumption was that this was stored in the ReadCodeEvents table in the GPASS database. However, the data extracted by PCCIU from GPASS did not include this medication review code; therefore, this analysis was not feasible.

Acute prescribing of high-risk medications

We considered a number of possible indicators of high-risk prescribing, and chose to focus on high-risk prescribing of NSAIDs for a number of reasons, including being commonly prescribed, that prescriptions are usually initiated in general practice (which increases confidence in attributing the decision to an individual GP) and that the high-risk prescribing being measured was both known about in the period being examined and not absolutely contraindicated. Five indicators of high-risk NSAID prescribing and a single composite indicator were defined based on clear advice about risk in the March 2005 edition of the BNF 49.113 We chose to define indicators based on BNF statements of risk, as the BNF is the main source of day-to-day drug information in the UK and is distributed free to all UK prescribers; therefore, it has much greater dissemination than drug safety bulletins or guidelines (the bold and italic text used is as stated in the BNF). There is, therefore, a reasonable expectation that prescribers would be aware that the prescribing being measured carried risk.

  1. Prescription of a NSAID to a person with previous peptic ulcer disease [significant risk of gastrointestinal (GI) bleeding: BNF 49 states in a specifically highlighted Committee for Safety of Medicines (CSM) warning ‘All NSAIDs are associated with serious gastro-intestinal toxicity . . . all NSAIDS are contra-indicated in patients with active peptic ulceration. The CSM also contra-indicates non-selective NSAIDs in patients with a history of peptic ulceration’113 (p. 495)].
  2. Prescription of a NSAID to a person aged 75 years or over [significant risk of GI bleeding: BNF 49 states ‘should be used with caution in the elderly (risk of serious side-effects and fatalities)’ and in the blue box CSM warning ‘All NSAIDs are associated with serious gastro-intestinal toxicity; the risk is higher in the elderly’113 (p. 495)].
  3. Prescription of a NSAID to a person with heart failure [significant risk of worsening heart failure: BNF 49 states ‘In patient with renal, cardiac, or hepatic impairment caution is required’113 (p. 495)]
  4. Prescription of a NSAID to a person also prescribed an oral anticoagulant [significant risk of GI bleeding: BNF 49 identifies this as an interaction that is ‘potentially hazardous and where combined administration of the drugs involved should be avoided’113 (p. 638)].
  5. Prescription of a NSAID to a person aged 65 years or over also prescribed aspirin or clopidogrel [significant risk of GI bleeding: BNF 49 states in a specifically highlighted CSM warning ‘The combination of a NSAID and low-dose aspirin may increase the risk of gastro-intestinal side-effects; this combination should only be used if absolutely necessary’ (p. 495) and identifies as an interaction ‘increased risk of bleeding when NSAIDs given with clopidogrel’113 (p. 638)].
  6. A composite indicator was defined as prescription of a NSAID to a person with any of peptic ulcer, aged 75 years or over, heart failure, coprescribed warfarin or coprescribed aspirin or clopidogrel.

More technical definitions are provided in Table 19 in Appendix 2. Additional NSAID indicators were considered but excluded either because it was unclear how widely known the risks involved were at the time the prescriptions were issued [prescription of a NSAID to a person also prescribed a diuretic and an ACE inhibitor or angiotensin receptor blocker (ARB) (the ‘triple whammy’); prescription of a NSAID in people at high risk of cardiovascular disease] or because data were inconsistently recorded in the period studied (prescription of a NSAID to a person with CKD stage 3–5 because CKD coding improved after its inclusion in QOF from April 2006). In our previous work, we have also often accounted for whether or not patients receiving drugs with a high risk of GI bleeding are also being prescribed a gastroprotective drug. This reduces but does not abolish the excess GI bleeding risk of NSAIDs, and it can be argued such mitigation shows evidence that the prescriber has considered the risk. More recent guidance clearly recommends use of gastroprotection in various patients prescribed NSAIDs,114,115 but in the period examined this was not the case. Rather the recommendation was to avoid NSAIDs in people at high risk of peptic ulceration, and prophylaxis recommendations were focused on people who developed a NSAID-associated ulcer but who had to continue the NSAID (the example given being people with active rheumatoid arthritis).113

Examining types of non-steroidal anti-inflammatory drug prescription

Background

A feature of the previous analysis is that it does not account for variation within practices in terms of who prescribes a high-risk NSAID. Linked to this, it ignores how individual prescriptions are created and issued, in that the outcome happens when a NSAID prescription is issued to a patient vulnerable to adverse drug effects (strictly speaking, the outcome happens if such a patient receives one or more NSAID prescriptions during the final quarter of 2006, as some patients receive more than one in that time period). A crucial distinction in GP IT systems is between acute and repeat prescriptions, because this determines who is allowed to issue a prescription, and the degree of oversight exerted by the clinician who signs the prescription.

Understanding how prescriptions are created and issued in the General Practice Administration System for Scotland

As part of the feasibility testing, we used a demonstration version of GPASS to explore the relationship between the way in which a prescription was created and the name printed on the paper prescription, on the one hand, and the name recorded in GPASS electronically, on the other.

Understanding how prescriptions can be created and issued/printed

An ‘acute’ prescription could be generated in GPASS in one of two ways. First, an individual with a clinical user account can create and issue a new acute prescription. In this case, the prescription prints with the name of the clinical user creating the prescription at the bottom. Second, any user can copy and issue a prescription that has already been issued as either an acute or a repeat. In this case, the prescription can print with a variety of names on the bottom, depending on how the prescription has been created, either the name of the clinical user creating the prescription or, if a non-clinical user creates the prescription, the name of the ‘duty doctor’ or the ‘registered doctor’. Practices set a general preference for duty or registered doctor daily.

A repeat prescription could be generated in the same two ways. Clinical users can create one and, if they issue it as part of the creation process, the name printed on the prescription is theirs. Non-clinical users can also create repeat prescriptions either by copying existing acute or repeat prescription, or de novo. If a non-clinical user issues the prescription as part of the creation process, then the duty or registered doctor’s name is printed on the bottom. Subsequent issuing of repeats can be done by both clinical and non-clinical users, with the same printing rules.

Understanding how prescriptions are electronically recorded

Apart from hand-written prescriptions, issuing a prescription requires opening the patient’s electronic record. GPASS records any file opening as an encounter, distinguishing between the following types:

  • ‘encounter encounters’, which are face-to-face consultations in the practice premises (encounter encounters is what GPASS calls them, although, for clarity, in this report we refer to them as normal surgery encounters from now on)
  • out-of-hours encounters, telephone encounters, home visit encounters and clinic encounters, which are other patient consultations
  • data entry encounters, which are file openings to read or update the record without any actual patient contact.

For all encounter types except data entry, prescriptions created by clinicians will automatically have the clinician ID linked to the prescription. For data entry encounters, the clinician ID is linked electronically only if the clinician manually adds his or her name from a drop-down box on the encounter screen (although there will always be a clinician’s name printed on the paper prescription). Data entry encounters also do not have to be saved if there are no data actually entered, in which case the file opening leaves no record in GPASS (there is a separate audit trail which does record the file opening but these data are not accessible for research use).

Based on this, our expectations were that:

  • Not all prescriptions would have user IDs and that this would vary by the type of prescription.
  • It would be necessary to distinguish between clinical and non-clinical users, and to specifically identify GPs.
  • It would be necessary to distinguish different types of encounter. Although all data entry encounters in which a prescription was issued should be saved, clinicians vary in terms of whether or not they save data entry encounters where they have only looked at the record (e.g. to check that a diagnosis on a hospital letter had been coded). This is relevant for measuring a rate of high-risk prescribing if the measure denominator is the number of encounters, as variation in data entry encounter saving between GPs will affect their estimated rate of high-risk prescribing.

The aim of the analysis that follows was therefore to explore the feasibility of implementing our planned analysis of variation between GPs in high-risk prescribing given the data available. The next section, therefore, describes the data available in some detail, and the rationale for the design and data used in the analysis in the next chapter.

Data sources and methods

The data tables supplied to us had already had a substantial amount of management and merging done to them by the PCCIU. There were four types of table supplied:

  1. A demography table with one row per patient for every patient with any data extracted. This table included data about individual patients including age, sex, socioeconomic deprivation, the presence of various morbidities and the total number of active repeat drugs at the start of each year. Each row (patient) had a patient ID and a practice ID allowing linkage to other tables.
  2. A set of prescribing tables for selected drugs, defined as being drugs of interest, for example an oral NSAID table (drugs in BNF section 10.1.1), an antiplatelet table (drugs in BNF 2.9) and so on. Each table contained one row per prescription issue, identifying the drug issued by name (a mixture of coded and free text) and BNF code (only for coded drug names), dosing instructions and quantities and whether or not the drug was an acute or repeat prescription. Each row (prescription) had a patient ID (linkable to the demography table), and some rows had an encounter ID (linkable to the encounter table).
  3. An encounter table with one row per encounter with the practice. Each row (encounter) had an encounter ID (linkable to the prescribing table) and a patient ID (linkable to the demography table). Information provided for each encounter included the encounter date, the encounter type (e.g. whether a normal face-to-face surgery encounter or by telephone), the user who had created the encounter, clinician type and clinician sex (both misnamed, as they include non-clinical users), and the morbidities dealt with in face-to-face consultations (a PTI-modified Read Code which PTI practices record for every face-to-face consultation with a GP or a nurse).
  4. A practice table with one row per practice. This table had data on routinely available practice structural characteristics including list size (in quartiles to reduce identifiability), urban/rural/remote location, the type of NHS contract held by the practice [General Medical Services or one of the two locally modified alternatives called section 2c and section 17c (broadly equivalent to an English Personal Medical Services contract)], whether or not the practice was accredited for post-graduate training of GPs and whether or not the practice dispensed its own prescriptions. Each row (patient) had a practice ID (linkable to the demography table).

The feasibility analysis examined a number of different areas:

  • how common different types of NSAID prescription were in terms of whether they were acute or repeat prescriptions, and within that whether they were new or not
  • the completeness of linkage across the different tables, in terms of how often high-risk NSAID prescriptions of different types could be linked to the encounter table or had a linkable clinician ID which could be identified as a GP
  • how often high-risk NSAID prescriptions of different types could be linked to a clinician ID and how often linked clinicians could be identified as GPs
  • whether or not it was possible to define a set of encounters to provide a comparable denominator for the analysis of variation between GPs.

For this analysis we identified all oral NSAID prescriptions in calendar year 2006, and for each one calculated if it was high risk in terms of whether or not it had been issued at a time when the patient was particularly vulnerable to NSAID prescribing, as defined by the five measures listed in Acute prescribing of high-risk medications. We then linked these high-risk NSAID prescriptions to the encounter file to examine the extent to which encounter data and clinician ID were available for these prescriptions, and whether or not it was possible to identify an appropriate set of GPs and encounters to use in the modelling.

Results

Type of non-steroidal anti-inflammatory drug prescription

We defined four types of NSAID prescription according to whether they were acute or repeat, and new or subsequent. Acute and repeat prescriptions are defined by a field in the data supplied and are a key element in the way that the clinical system is set up and used. An acute prescription is a one-off issue from an encounter screen which can be reissued only in an encounter screen, and acute prescriptions are typically issued by clinicians. A repeat prescription is set up so that it can be reissued from the repeat prescribing screen, and this is typically done by administrative staff rather than clinicians. Each type of prescription is separately displayed/clearly distinguished in GPASS.

Based on the data, we then defined the following types of high-risk NSAID prescriptions:

  • ‘new acute’: an acute NSAID prescription without a NSAID prescription of any kind issued in the previous 365 days
  • ‘subsequent acute’: an acute NSAID prescription that was preceded by either an acute or a repeat NSAID prescription in the previous 365 days
  • ‘new repeat’: a repeat NSAID prescription where there had not been a repeat NSAID prescription issued in the previous 365 days
  • ‘subsequent repeat’: all other repeat NSAID prescriptions.

The distinction being made is between new decisions to create a particular type of prescription for the first time that year (new acute and new repeat NSAIDs) and a decision to reissue a recently issued drug (subsequent acute and subsequent repeat NSAIDs). Based on our knowledge of how practices organise this prescribing, subsequent decisions are subject to less scrutiny and clinical consideration than new ones. Creating new prescriptions is therefore a key decision point that partly determines future care.

Table 1 shows how common each type of prescription was in calendar year 2006, for each individual indicator and for the composite. For the composite indicator, acute prescribing accounted for 26.2% of all high-risk NSAID prescriptions issued, with new acute NSAIDs representing 10.6%. The creation of new repeat NSAIDs was relatively uncommon (1.2%) but subsequent issues of repeat NSAIDs represented nearly three-quarters (72.7%) of all NSAID prescriptions issued. There was some variation by indicator, where people with previous peptic ulceration and those with heart failure were somewhat less likely to be issued a repeat prescription, although overall the patterns were similar for all five individual indicators.

TABLE 1

TABLE 1

Types of high-risk NSAID prescription in 2006 by each individual indicator and for the composite

Linkage of high-risk non-steroidal anti-inflammatory drug prescription data to the encounter table and clinician data

The encounter data supplied to us contained a field called ‘encounter type’ with five values [C = ‘Co-op’ (i.e. out of hours), D = ‘data entry’, E = ‘encounter’ (i.e. a normal surgery encounter), P = ‘telephone’, U = ‘unknown’ (but actually a catch-all for other encounter types which includes a variety of values including ‘clinic’, ‘other practice’ and ‘home visit’)]. The encounter table also has a field for the user who recorded the encounter, which we received as an anonymised ‘clinician ID’ (although not all users are clinicians).

Table 2 examines the extent to which high-risk NSAID prescriptions for each indicator and the composite were linkable to the encounter table, and the proportion of linkable prescriptions for which encounter type and clinician ID were recorded. It shows that, of 20,759 high-risk NSAID prescriptions, 4543 (21.9%) did not have an encounter ID and so could not be linked to the encounter table. Of the 16,216 (78.1%) prescriptions that were linkable to the encounter table, only 64.9% had an encounter type recorded and only 52.4% had a clinician ID recorded, as these are not compulsory fields depending on how the record is opened. Linkage was similar for the five individual indicators, although somewhat better for peptic ulcer and somewhat worse for oral anticoagulants.

TABLE 2

TABLE 2

Percentage of high-risk NSAID prescriptions in 2006 linkable to encounter table for each individual indicator and for the composite

Table 3 examines linkage by type of high-risk NSAID prescription in terms of whether it was acute or repeat, and new or subsequent (as defined in Type of non-steroidal anti-inflammatory drug prescription). Acute high-risk NSAID prescribing was almost always linkable, and almost always had an encounter type recorded (> 99%). Of new acute prescriptions, 95.4% had a clinician ID recoded, versus 67.1% of subsequent acute prescriptions. By contrast, new repeat prescriptions were linked in only 76.3% and subsequent repeat prescriptions in 70.3% of cases overall, with over half not having a linked clinician ID. This reflects the fact that repeat prescribing can be managed either through encounter screens (which generate an encounter ID) or through the repeat prescribing screen (which does not).

TABLE 3

TABLE 3

Percentage of composite indicator defined high-risk NSAID prescriptions in 2006 linkable to encounter table the composite by prescription type

On further exploration, it was clear that the encounter ID (and therefore clinician ID) for subsequent repeat prescriptions defaulted to the encounter and clinician who had initiated the repeat prescription, with all subsequent issues having the same encounter and clinician IDs. This reflects the data structure, where the first repeat prescription is recorded in the prescription table, which has encounter ID for linkage, but subsequent issues are recorded in a separate table, which records only the date of reissue. In practice, therefore, subsequent repeat prescribing has only the initiating GP’s identity recorded, but this is missing anyway more often than not.

We concluded that it was potentially feasible to examine acute but not repeat NSAID prescribing in a GP-level analysis, and the next section examines acute prescribing further.

Defining eligible encounters and eligible general practitioners

Table 4 shows the extent to which clinician IDs were missing for new and subsequent acute high-risk NSAID prescriptions by encounter type. Clinician ID recording was high (> 95%) when issued in normal surgery encounters (i.e. face to face in a normal GP surgery) or telephone encounters as well as for new acute high-risk NSAID prescriptions that were issued in other/unknown types of encounters. However, for data entry encounters, clinician ID recording was poor (< 50%) for both types of prescriptions, and especially for ‘subsequent acute’ prescriptions (28.1%).

TABLE 4

TABLE 4

Percentage of encounter types with a clinician ID by type of acute prescription

For the planned analysis, it was also necessary to distinguish between clinical and non-clinical users, and between GPs and nurses. The data supplied to us contained a field called ‘clinician type’ with two values (G and X) which we were told was believed to distinguish GPs from other users, although there was ambiguity about whether or not ‘G’ clinicians also sometimes included nurses. We additionally extracted data about whether or not an encounter had an associated Read Code with a PTI modifier. All the practices contributed data to the NHS Scotland PTI data set, where face-to-face encounters with patients all have one or more Read Codes attached to identify the morbidity being dealt with, and these Read Codes have a specific modifier attached which identifies if they are a GP or a nurse consultation (the modifier allows data entry Read Codes, e.g. following a hospital admission, to be excluded from primary care workload analyses). Using this data, we identified clinician IDs who were GPs and nurses based on their recording of PTI-modified Read Codes.

During 2006, there were a total of 478 GPs who had a total of 181,263 encounters (normal surgery, telephone or unknown/other) with a patient at risk (i.e. vulnerable to adverse effects of NSAIDs by virtue of comorbidity or coprescription) at the time of the encounter, during which 2024 new acute NSAID prescriptions were issued. Of these, 1961 (96.9%) of new acute prescriptions had a GP ID. However, 80 GPs (who prescribed a total of eight new acute NSAIDs in 2006) had fewer than 10 encounters with at-risk patients, which is a problem when estimating variation in the prevalence of relatively rare events.

We concluded that it was potentially feasible to examine new but not subsequent acute NSAID prescribing in a GP-level analysis, and to examine new acute NSAID prescribing in normal surgery, telephone and unknown/other encounter types for GPASS users identified as GPs using analysis of PTI-modified Read Codes.

Do new acute prescriptions matter given that they are not the bulk of prescribing?

It was feasible to examine variation only in new acute NSAID prescribing in the analysis to examine variation between both practices and GPs but these prescriptions accounted for only 10.6% of high-risk prescriptions issued in 2006, raising the question of whether or not this was a important enough outcome to examine. Total high-risk prescribing in a practice will be determined by a number of processes including:

  • New acute NSAID prescribing. Our expectation is that this would be the most frequent initiation event.
  • New repeat NSAID prescribing without a preceding acute prescription. This will be rarer, but once set up is the gateway to regular issuing without much clinical oversight (although this is rare in this data set, with only one of 241 new repeat prescriptions in 2006 not being preceded by an acute prescription in the previous year, the exception having previously had a active repeat prescription issued just under 2 years earlier but subsequently cancelled).
  • For those issued a new acute prescription, transition in some patients to receiving subsequent acute prescriptions.
  • For those issued a new acute or subsequent acute prescription, transition in some patients to receiving new and then subsequent repeat prescriptions.
  • Cessation of either multiple acute or repeat prescribing, either because patients’ symptoms resolve or because of review and active withdrawal by a clinician.

To examine the relative importance of high-risk new acute NSAID prescribing, we identified 441 patients eligible for one of our five measures who received a new acute NSAID prescription in Q4 2005, and examined high-risk NSAID prescribing to these patients in 2006. Five patients received a further new acute NSAID in 2006 (i.e. an acute prescription more than 1 year after the one issued in Q4 2005), 135 patients received a total of 279 subsequent acute NSAID prescriptions in 2006, and 21 received a new repeat NSAID with 203 subsequent repeat issues. In total, 143 (31.8%) patients prescribed a new acute NSAID in Q4 2005 received one or more NSAIDs in 2006, with a total of 513 prescriptions issued, representing 2.6% of all high-risk NSAID prescribing in 2006. As well as carrying risk in its own right, new acute NSAID prescribing is, therefore, an important initiation event for chronic prescribing.

Discussion

Summary

For analysis of variation between GPs, attribution of a particular prescription to a GP is critical but is complicated for two reasons:

1.

The decision to prescribe being made by more than one clinician, notably with regard to specialist recommended prescriptions issued by GPs. In this situation, GPs clearly have the legal responsibility for the prescription because they create and sign it but attribution is not as simple as one where the GP alone decides to prescribe.

We therefore defined a set of measures relating to high-risk NSAID prescribing where the first problem was relatively unlikely to arise, because almost all decisions to prescribe NSAIDs are made by GPs.

2.

Not all prescriptions include a clinician ID, and, even if they do, the clinician ID may not be the correct ID for the clinician actually signing the prescription.

General practitioner systems distinguish between repeat and acute prescriptions. Repeat prescriptions are those for which a prescription is set up so that the patient can reorder the medication from reception staff, who print it off and pass it to a GP for signing, typically in a large pile of other repeat prescriptions. Acute prescriptions are one-offs which cannot be straightforwardly reissued by reception staff, and for which reissue is usually done by a clinician, meaning there is greater clinical oversight.

Unsurprisingly, repeat prescribing did not reliably have a clinician ID, and, where an ID was present, it related to the encounter where a new repeat prescription was originally created rather than to the signing of a repeat issue. In addition, we know that in many practices repeat prescriptions are often not signed by the GP whose name is on the prescription because the printed name is often automatically set to be the ‘duty doctor’ or the ‘registered doctor’. Hence, attribution of repeat prescriptions even in the presence of an electronically recorded clinician ID is problematic. Our planned analysis in this context was to examine cessation of high-risk prescribing after a medication review, but the data set did not have reliable recording of the person doing medication review so this was not possible. It was therefore not feasible to examine repeat prescribing at GP level.

For acute prescribing, we distinguished ‘new’ and ‘subsequent’ prescriptions (an acute prescription is a ‘one-off’ which cannot be straightforwardly reissued by reception staff and for which there is, therefore, greater clinical oversight). A new acute NSAID prescription was defined as an acute prescription where there has been no NSAID prescribed in the previous year. This is the prescription with the greatest clinical oversight, in that almost all this prescribing happens in face-to-face clinical encounters and is the key initiation event for subsequent prescribing, which is typically subject to less clinical oversight. One-third of new acute prescriptions were associated with additional high-risk NSAID prescribing in the subsequent year. Over 95% of new acute NSAIDs had a clinician ID associated with them, with most missing clinician IDs being in data entry encounters, where a clinician ID is not required. In contrast, only 67.1% of subsequent acute prescriptions had a clinician ID, reflecting the fact that they were much more commonly issued in data entry encounters, probably because they were telephone requests via reception rather than clinical encounters (reflecting the reduced clinical oversight involved, although oversight is still higher than for repeat prescribing because acute prescribing will typically be flagged as a special request to distinguish it from repeat prescribing).

Interpretation and implications for subsequent analyses

The feasibility analysis showed that we could reasonably robustly analyse new acute NSAID prescriptions in non-data entry encounters for GPs identified using PTI-modified Read Codes. The data set for analysis of variation between practices and between GPs, given the constraints described above, therefore included 181,010 (99.9%) encounters by 26,539 (99.97%) patients at risk in 2006 who consulted at least once, during which 1953 new acute NSAIDs were issued (96.5% of those with a clinician ID, 88.9% of all new acute NSAIDs).

However, problems of attribution limited the extension of this to other types of high-risk prescribing. First, the need to identify GPs using PTI modifiers means that extension to analyse new acute NSAIDs in non-PTI practices was not feasible. Second, repeat prescribing and to a lesser extent subsequent acute NSAIDs could not be reliably linked to a clinician ID. Although other clinical systems may always attach a clinician ID to a prescription issue, this in itself would not address the problem that receptionist-generated repeat prescriptions will not necessarily be signed by the GP whose name is on the prescription, which is likely to restrict analysis of prescribing at GP level to acute prescribing. In addition, many high-risk drugs were judged often to be initiated by both GPs and by specialists making recommendations to GPs. Feasible types of prescribing that could be examined are those where a large proportion of prescribing is acute rather than repeat and where the decision-maker is clearly the GP. However, in terms of high-volume drugs, this restricts the scope of analysis to drugs which are usually or often acute prescribed and initiated by GPs, such as analgesics, antibiotics and some psychotropic drugs including antidepressants and possibly hypnotics/anxiolytics (but not antipsychotics). To try to get round this problem of attributing the initiation decision to the GP who starts a repeat medication, we had intended to also examine drug cessation after medication review, which is more clearly attributable to the GP, but in GPASS medication reviews could not be reliably linked to clinician ID so this was not feasible (it might or might not be feasible in other clinical IT systems).

After discussion with the National Institute for Health Research (NIHR) Health Services and Delivery Research (HSDR) programme, it was agreed that we would complete the original study using the data on new acute NSAID prescribing from the 38 PTI practices and in addition examine change in high-risk prescribing over the period 2004–9 in 190 practices.