Frequently asked questions on how to use the GPRD/THIN databases Q. What is the main difference between EPICGPRD and THIN? Q. What is the format of the data in EPICGPRD and THIN? Q. How to link the old GPRD data and THIN data in the THIN database? Q. What is the difference between EPICGPRD and THIN with respect to availability of

lab data? And how detailed is the information Q. How are GPRD and THIN different in regards to Practice information and

Data Quality indicators? Q. Are there any external THIN data validation studies? Q. Geographic & population information? Q. How do you control for geographic distribution in THIN? Q: READ and OXMIS codes in THIN? Q. What are the three dates that appear in the Practices File? Q. What is the translation of common abbreviations in READ and OXMIS? Q. What are new developments in THIN? Q. Which codes of the variable SOURCE in the Medical Records File indicate inpatient

cases? Q. How does THIN capture information on Consultation, Therapy, and Hospital

information? Q. Which status categories to use for patient selection into a study? Q. What is the difference between Research File B and Research File C in

THIN? Q. What to do if you see an unusual pattern in the patient’s records selected for

your study? Q. How to handle the 7 practices that have gaps in their data? Q. Can you explain some strange dates in THIN? Q. How to handle medical events that have dates but no medical codes linked to

these dates?

Q. How do you use the variable EPISODE in the Medical Records File? And what's the difference between codes 1 and 2?

Q: Does a diagnosis only show up on the date it has been set?

Q. How do you use the variable PRIVATE in the Therapy File? Q. How do you use the variable OPNO in the Therapy File? Q. How do you calculate total dose, average daily dose, and duration in the Therapy File?

Q: Regarding the variable DOSGVAL, is that derived on behalf of the dosage as entered by the doctor ("dosage" the text variable) and the strength of the drug? What is dosgval= -1?

Q: Regarding the variable DOSCODE, I would need more information from EPIC to be able to use this variable right? What is a dosage index, for example?

Q: Regarding the variable PRSCDAYS (duration of prescription in days), does that refer to how many days the prescription drug purchase will last if the patient takes the drug as prescribed by the practitioner?

Q: Regarding the variable PRSCTYPE, prsctype= 0 is acute, rsctype=1 is repeat,but what is prsctype=2?

Q: Regarding the variable PRDCQTY, does quantity prescribed refer to total number of tablets prescribed/ml prescribed?

Q. Regarding the variable PRSCQTY, what does a negative value mean?

Q: Regarding the variable OPNO, what does number of original packs ordered refer to? Is it the number of packages a patient can get from the prescription? A proc freq on this variable showed 99% of the patients had "opno"=0. Is opno=0 given when the drug is a mixture?

Q: Regarding the variable PACKSIZE, does it refer to the number of tablets in the pack size selected by the practitioner? A proc freq on this variable for one of the therapy files showed 99.8% had "packsize" = 0. Is packsize=0 when the drug is a mixture?

Q: Working with the therapy records, one finds that some patients have the

same drug coded both as a repeat (PrscType = 0) and as an acute (PrscType = 1) prescription on the same day. How come? And which code is the right one?

Q. Are all lab test results ordered by the GP included in THIN?

Q. How consistently is blood pressure recorded in the THIN database? Q: Classifying smoking status in THIN?

Q: Does EPIC calculate Body Mass Index? In cases where there is a weight but not a BMI is that because there was no height for the patient?

Q. How do you identify obese patients? Q: Do GPs routinely prescribe aspirin and other OTC-available products to

patients, particularly older patients, to help ease the cost burden so that these patients do not have to purchase these medications out-of-pocket? Is there any data on the frequency of OTC aspirin use to protect against cardiovascular events? How likely is aspirin for this indication recorded in THIN?

Q: How much prescribing of antidepressants is done by GPs vs. Specialists, i.e.,

what proportion would THIN not pick up? Q: How is prescribing distributed in the UK? Q. Is it possible to get access to additional patient information? Q. How to work with the Prevention Files in EPICGPRD? Q. What is the method for creating Code lists? Q. Does EPIC provide for Training & Support? Q. Are the code lists in the Codes Library final lists that are ready for use?

Answers to questions on how to use the GPRD/THIN databases

Q. What is the main difference between EPICGPRD and THIN? The EPICGPRD has data from 755 practices, but it is now a static dataset as EPIC no longer provides updated GPRD data and only has data up to March 2002. The GPRD has over 9.8 million patients, over 38 million patient years of data and data from 1985 where practices previously used the VAMP software. During 2001 there are about 2 million active patients actively registered with a data providing practice. In contrast, the THIN data are continually updated and data are collected monthly. THIN currently (as of May 2007) has data from 332 practices, 5.5 millions patients in total. Q. What is the format of the data in EPICGPRD and THIN?

The EPICGPRD and THIN data are organized in a similar format. There are five standardized flat text ASCII files of fixed width per practice: Patient File, Medical File, Therapy File, Dosage File, and Additional Health Data File (AHD – formerly Prevention File in EPICGPRD) containing information on preventative healthcare, tests and immunizations. There is also a Practice File with information on the GP practices. These data files are provided in this format to allow researchers the greatest flexibility of use and can be loaded and imported into virtually any database or statistical application. These include Oracle, SPSS, SQL & SAS and Microsoft Access, although there are limitations with Access as the data is case sensitive. Alternatively, the data can be stored as flat files and queried using any appropriate programming language. Q. How to link the old GPRD data and THIN data in the THIN database? The old EPICGPRD data is a static dataset (i.e., there are no more updates) consisting of 755 practices with data updated through March 2002. Not all practices have continually submitted data through to that date, but those that were still part of EPICGPRD at that point will have data up to March 2002. The THIN data currently has 332 practices and is regularly being updated. Some practices which did at some point contribute to our old version of EPICGPRD now also contribute to THIN. When THIN was started, EPIC has re-collected all their historical data, as they did also with all the practices who joined the THIN scheme, and continue to collect their data regularly. In the THIN PRAC326.xls file, there is a flag which indicates the practices that once contributed data to our old EPICGPRD dataset. This flag distinguishes between previous-GPRD practices and non-GPRD practices. However, if you would like to use both the old EPICGPRD and THIN in conjunction, there is another file which lists the IDs of the practices in EPICGPRD that are now contributing data to THIN. Using this file, you will be able to exclude these practices from the EPICGPRD dataset so there is no overlap. You will obviously want to exclude the overlapped practices from the EPICGPRD dataset because THIN data has identical, but more up-to-date information. Q. What is the difference between EPICGPRD and THIN with respect to availability of

lab data? And how detailed is the information The lab data is far more complete and accurate in THIN. The electronic link was not available when EPICGPRD was created. Therefore, all the data in EPICGPRD would have been manually entered. All the test values you see entered are for one batch of tests requested. These have been auto-filed into the patient's record, removing the need for manual entry. This means that all test results are captured instead of just those that were of interest or were abnormal as happened in EPICGPRD.

Q. How are GPRD and THIN different in regards to Practice information and Data Quality indicators?

The GPRD Practice File has information on start dates and end dates for all practices that provided Up-to-Standard (UTS) data. THIN data does not use the term UTS, as initial audits showed that all practices are currently entering data to a sufficient high quality. Prior to collecting data from the first wave of THIN practices, EPIC collected aggregated statistics from those GP practices that had expressed an interest in contributing data to THIN. These statistics consisted of the consultations recorded on the practice computer and prescriptions issued from it. The statistics collected from each of the first wave of practices (approx. 115) were compared with National consultation and prescription statistics and the results showed recording of both consultations and prescriptions was comparable to National levels. The results are published in Informatics in Primary Care 12-3 (November 5th 2004). Instead of Up-to-Standard (UTS), EPIC has developed practice quality level indicators as outlined below:

• For each practice there is a computerisation date, a date when the practice converted to Vision, and the last collection date. There is also a flag to let you know whether this practice was originally contributing to EPICGPRD and in THIN we provide information on whether the practice is electronically pathology-linked to the path lab. We have recently created a data flag for each THIN practice which outlines any gaps in recording over time.

• In order to improve data recording in general practice THIN provides free

training to the practices and also provides computerised validation (feedback reports) to quantify the completeness and accuracy of recording. The results from these feedback reports are fed back to the practices with information on how to improve quality and correct omissions. The information in the feedback reports on data quality of individual practices can be shared with researchers so that, if appropriate, they can select subsets of practices with strengths in specific areas of research. The data output has been designed to be incorporated into the feedback report for the GP but can also provide information for researchers. The data output currently provides counts using active patients which are patients that are still registered in the practice as of the collection date. Events are searched for in the most recently recorded data. To date there have been 4 Feedback reports:

Death Diabetes Asthma CHD

The full file output is available on the CD sent to Steve Morant. These reports are run each time EPIC updates the THIN Data set and the summary results

have shown that the practices have increased the level of recording following receiving the feedback reports

• In addition to the feedback reports EPIC also provide researchers with a Research File. This is a report for researchers using the THIN Data and provides a summary of the extent to which certain data items are recorded. These are essentially ‘counts’ of events recorded and provide a baseline figure of the number of patients eligible for having those events recorded. The researcher’s report does not contain any interpretation of the data. It serves as a method for determining which practices could be included in a study. The research report currently includes the years 1985 to 2003. For each practice it contains counts resulting from the assessment programs on the total active patient list, various age-sex bands, death with cause, smoking, blood pressure and heights and weights. Following this, the report will be run annually for each complete calendar year or when new practices start contributing data.

A copy of the full output files are on the ancil CD. The results from these counts show the changes in data recording over time.

Q. Are there any external THIN data validation studies? In order to understand the utility of THIN data in Healthcare research and to assess the credibility and quality of THIN Data; EPIC has put together a program of academic research. The aim of this program of research is to highlight the strengths and weaknesses of the THIN Dataset in order to identify the types of studies that can be conducted in THIN and those that cannot. The program incorporates studies in a variety of topics carried out by eminent academics around the world. These are summarized below:

• Ten studies have been approved by MREC, three are under review and four awaiting submission

• Epidemiology o Consultation patterns in diabetics o Paediatric autoimmune disorders o Psoriasis o Asthma – birth cohort study

• Health Economics/Outcomes Research o Disease state modelling o Budget impact of ARBs

• Pharmacoepidemiology o Replicating known associations o Nicotine Replacement Therapy (submitted for publication)

Teratogenicity of antidepressants For full THIN bibliography please visit www.epic-uk.org Q. Geographic & population information?

The geographical spread of THIN practices and percentage of active patients has been found to be representative of the UK population spread. See graphs from the file THIN-GPRD1.ppt file. Q. How do you control for geographic distribution in THIN? If you control for practice you do control for geographic location indirectly. The information on regions will enable you to group practices into groups so your 'units' will be larger. Q: READ and OXMIS codes in THIN? Only VAMP used OXMIS codes and only in the earlier versions. The more up-to date-versions also used READ codes. Thus, only practices using an older version of VAMP software after Yr2000 would have entered OXMIS codes. All the practices in THIN use ViSion software, which only accepts READ codes. EPIC has been in contact with the software developers regarding the use of OXMIS codes after Yr2000. When INPS receive the data into their conversion department it has already gone through an extraction company that will have changed any non-READ codes to READ codes. So they cannot confirm definitely that no one used OXMIS after Yr2000 but it is unlikely. Q. What are the three dates that appear in the Practices File? Computerization Date – the date when the practice started using their system fully to issue prescriptions (calculated by EPIC). Vision Date - that date when the practice went 'live' using the Vision software. Vision was launched in 1995 so no practices will have Vision data before this point. Last Collection date - the date when the practice last collected data from their system Q. What is the translation of common abbreviations in READ and OXMIS? The following are commonly used definitions: H/O History of NOS Not otherwise specified F/H Family history O/E on examination [SO] Site of [V] Supplementary factors influencing health status or contact with health services

other than for illness, e.g. normal pregnancy [X] These Read Codes have been added to ensure mapping to all ICD codes;

necessary for hospital recording but not relevant for GPs

[M] Morphology of neoplasms [D] Working diagnosis [SP] Surgical procedure FH Family history of NEC Not elsewhere classified OS Other specified EC Elsewhere classified Q. What are new developments in THIN? SES scores EPIC can now provide anonymous postcode linked area based socioeconomic, ethnicity and environmental indices for studies using THIN Data. This information will provide important information about potential study bias and/or confounding which will enhance the usefulness of THIN Data for epidemiological, public health and health services research. Townsend scores and ethnicity variables (urban classification, ethnic classification and long term illness) were derived for each area using 2001 census data. Each output area corresponds to approximately 150 households. In addition information on local pollution levels for larger areas were derived. Free Text Comments The Vision system allows the entry of free text or scanned information. Each entry in a patient’s medical file can have comment associated with it. Since this is a free text field doctors can enter any information they want and this can include identifiers such as names of people and places which are confidential and are not provided. The top 10,000 most frequently occurring comments in the medical records have now been anonymised. This equates to 45% of all comments in the medical records. A seven character numeric identifier has been linked to each unique comment and the textid field in the medical records has been updated with this identifier. An additional ancillary file is now available with the identifiers and the free text string. This file is called THINComments. All other comments in the medical records will have a 4 character alphanumeric textid. This indicates that this comment is not one of the 10,000 anonymised comments. In order to maintain the confidentiality of THIN data, researchers will not have automatic access to these comments. However, we recognise that the information can be vital to some studies and there are mechanisms in place for it to be made available. The free text comment for these records can be provided on request; however, on EPIC’s part this is a manual exercise which requires scrutiny of each individual comment field and therefore we would appreciate it if researchers could limit the number to a maximum of 2,000 comment IDs in one request. If more comments need to be de-identified we will be happy to discuss any cost implications of contracting out this information.

Q. Which codes of the variable SOURCE in the Medical Records File indicate inpatient cases?

Codes A, D, H, S, should be used. Code D indicates an inpatient case, as it means the records has come from a discharge summary, which are only received if a patient has had a stay in hospital. Note that Code L refers to information entered into the database from letters received from specialists, usually in the hospital outpatients department. Although it is a letter from a hospital, this could be from an outpatient visit to a consultant so it does not necessarily mean the patient stayed in hospital. Thus, it depends what you are looking for: If you want diagnoses made by specialists, you should include it. If you only want to know about events for which the patient was hospitalized, you should not include this code. Note: These are the main flags required, however there are now many more flags available and this should be reviewed on a project by project basis. Q. How does THIN capture information on Consultation, Therapy, and Hospital

information? In both THIN & EPICGPRD patients will have full electronic data while they are registered within the practice, provided the practice was computerized. This will include continuous data on prescribing and consultations. Prior to computerization or registration within a GP practice, patients may have summarized information which will include significant events entered from the transferred paper notes. Secondary care under the NHS is based within hospitals. If patients need hospital treatment, a GP will normally arrange it. NHS hospital care in the UK can be accessed in two ways: referral by GP and self-referral to emergency room. The flow of information from both of these mechanisms is such that the GP will be kept fully informed of any treatment and care given to his/her patient. This is an important part of the UK health system since any follow-up problems experienced by the patient will be reported to the GP in the first instance. It is therefore imperative that the GP is aware of any care given to the patient in the hospital by specialists. Any confirmed diagnoses together with recommendations on ongoing management of the patient will be communicated to the GP and should be entered into the electronic patient record. Q. Which status categories to use for patient selection into a study? When you select patients for a study, use only those patients with a PATFLAG = A or C. This means that automatically only patients with a status of 1 = applied, 2 = permanent, 5 = transferred-out, or 99= dead are selected. The reason we advise that you restrict your patient selection to these four status categories is to ensure you only use those patients who are currently/were once permanently registered with the practice and therefore you will have their full medical record. All other status categories belong to patients who have only used the practice

on a temporary basis and their full medical record is not available so they are ineligible. The only reason you may want to change this is if your study requires patients who are still 'active' i.e., still currently registered with the practice. In which case you would only want to select patients with A/C flags and status=1 or 2. The actual status is only important for defining study end dates, i.e., if the patient has not left the practice, the end date would be the date the practice last submitted data. If the patient has left the practice, then the end date would be the transfer-out date supplied. The reason for using status category 'applied for' is that these patients are seeking all health care from the same practice and we will capture all events and prescriptions. These patients eventually become 'permanent'. The reason we sometimes exclude 'transferred out' patients is that these patients are still alive but have moved to another GP practice so they could, in theory, go elsewhere and suffer an adverse event. Q. What is the difference between Research File B and Research File C in THIN? The two files are calculated in slightly different ways. File C is not just a cumulative count of the results in File B. Research file B looks at the number of events (smoking, BP, weight, height) in a single year for patients who were registered for the entire year. Research file C does not just focus on a particular year. The count for 1985 will include any events up to 31st December 1985 for the patients permanently registered for the duration of that year. For example if patients 0001, 0002 and 0003 are all registered for the duration of 1985. Patient 0001 had a smoking record in 1983 and patients 0003 and 0002 have smoking records in 1985. The smoking count for file B would be 2 and for file C would be 3. Q. What to do if you see an unusual pattern in the patient’s records selected for

your study? For example, supposing you work on a project in cirrhotic patients with or without ascites. Since ascites is one of the major complications of cirrhosis and is often encountered in patients with cirrhosis or other severe liver disease, one would expect most of the ascites cases to have occurred after the first diagnosis of cirrhosis. Suppose, however, that you notice that among the 500 cirrhotic patients with ascites, over half of these patients have the first ascites diagnosis date actually occurring before the first date of cirrhosis diagnosis, sometimes one day prior and sometimes 20 years prior. Is there anything else you should look into? We encourage you to print out all such patient records and manually review all of the diagnoses and treatments to see if it could be clarified.

Q. How to handle the 7 practices that have gaps in their data? We recommend that you delete patient records from these practices from your study. The IDs of these practices are:

a9900 b7915 e9909 f6860 f9776 h9978 j9885

Note: Each time a new dataset is created this list may increase as all new practices are reviewed. Q. Can you explain some strange dates in THIN? For example,

Sept 10, 1901 in the Prevention file (Variable EVENTDATE) Jan 1, 1900 in the Patient file (variable OREGDATE) April 8, 1901 in the Therapy file (PRSCDATE)

This can happen when doctors enter past history. Often the patient cannot remember the date of a past event but knows that it happened. The system allows the doctor to enter a 'zero' date which is translated by the computer to 1900 Q. How to handle medical events that have dates but no medical codes linked to

these dates? These events should be ignored. Q. How do you use the variable EPISODE in the Medical Records File? And

what's the difference between codes 1 and 2? Code1 indicates the first ever occurrence of a symptom/diagnosis Code 2 can be used to indicate a new episode of an existing illness. However the information in this field is not reliable. If a researcher was looking for the first ever occurrence of a disease, it would be advisable to search back through the patients history for prior codes than to rely on this field. Many records have a 4 in this field for other.

Q: Does a diagnosis only show up on the date it has been set? For example, hypertension is a chronic condition thus even though the patient is seen for something else than hypertension on a specific date, the hypertension diagnosis will not appear for that visit?

The GP can see when he opens the patient’s medical history that the patient is hypertensive so unless there is a change in the condition or if the diagnosis of hypertension is significant in the reason for the patients visit, then it is unlikely that

the GP would then record it again. GPs only record what they require for the treatment of their patient.

Q. How do you use the variable PRIVATE in the Therapy File? This field simply indicates whether a prescription was issued privately – i.e., the patient paid the full amount for it. Some medications are not available on the NHS. They are issued on a private care basis if the patient agrees to pay the full cost of the prescription. Q. How do you use the variable OPNO in the Therapy File? It indicates how many packs were issued. However this field is rarely filled and never used for this reason. Q. How do you calculate total dose, average daily dose, and duration in the

Therapy File? CALCULATING DOSE: Link each therapy record to the dosage file using the Doscode and obtain the daily dosage value. If the daily dosage value is an unknown value, e.g., -1.00 then the daily dosage value will default to 1. (We knew from our GP adviser in the case of the drug we were studying that the standard daily dose was 1 a day.) Once a drug is linked to the dosage string it is sometimes possible to work out from the text the daily dosage for those with a -1.00 dosgval. CALCULATING THE NUMBER OF DAYS IN THE PRESCRIPTION: Divide the prescription quantity (PRSCQTY) by the daily dosage value (DOSGVAL). e.g., if the prescription quantity is 56 and the daily dosage is 2, then the prescription days is 28. CALCULATING DURATION: Calculate the prescription duration date as the prescription date plus the prescription days. Work out the true duration of the prescription i.e., if the prescription date is before the duration end date of the previous prescription then extend the current duration end date by the number of days overlap. CALCULATION EPISODE DURATION: An episode is a set of prescriptions with less than 28 days separating the calculated duration end date and the next prescription date.

Q: Regarding the variable DOSGVAL, is that derived on behalf of the dosage as entered by the doctor ("dosage" the text variable) and the strength of the drug? What is dosgval= -1? A proc freq on the variable Dosage showed for about 42% of the observations in one of the therapy files the value -1.

Dosage instructions that are recorded as free text in the THIN data have been split away from the therapy records into a file called DOSAGE. This file has an identifier linking it back to the therapy records. The top 10,000 most frequently occurring dosage instructions in the therapy records have been evaluated and anonymized.

It is not possible to evaluate every dosage instruction since doctors often enter ‘as directed’ or ‘when required’. Anything that cannot be defined as a number of units per day was not evaluated and these un-interpretable text strings are given a code -1.00 ‘dosgval’. However if you link individual drugs in these records to the dosage text, i.e., FOUR TIMES A DAY you may be able to make your own evaluation and remove the -1.

Q: Regarding the variable DOSCODE, I would need more information from EPIC to be able to use this variable right? What is a dosage index, for example?

In the data you have both the ‘doscode’ and the linked dosage instruction. You therefore have all the information from the dosage file that you need. Any ‘doscode with a 4-character alphanumeric ‘doscode’ indicates that this dosage is not one of the 10,000 anonymized dosages. In order to maintain the confidentiality of THIN data, researchers will not have automatic access to these dosages. However, EPIC recognizes that the information can be vital to some studies and can be made available on request. On EPIC’s part this is a manual exercise which requires scrutiny of each individual dosage instruction and therefore we would appreciate researchers using their discretion in their requests for this service.

Q: Regarding the variable PRSCDAYS (duration of prescription in days), does that refer to how many days the prescription drug purchase will last if the patient takes the drug as prescribed by the practitioner?

The quantity in there will be the number of days the prescription will last as per the dosage instructions from the GP. This field is not often filled. On the prescribing page on the Vision system only some variables are required.

Q: Regarding the variable PRSCTYPE, prsctype= 0 is acute, rsctype=1 is repeat,but what is prsctype=2?

This is where the doctor has indicated that it is a repeat prescription but no link could be made to the repeat master in processing to show that the repeats had been issued. Some repeat treatments may start as an acute.

Note: This is only relevant for early copies of THIN Data

Q: Regarding the variable PRDCQTY, does quantity prescribed refer to total number of tablets prescribed/ml prescribed? For example, the prescription can be used three times for purchase of 30 tablets each time then the variable "prscqty"=90.

The ‘prscqty’ variable is the number of packs/tablets prescribed by the GP. In the UK the piece of paper the prescription is written on is the means of payment for the pharmacist. They are all sent off at the end of the week so the Pharmacist gets paid. Each time a patient needs a repeat prescription they must contact the GP office to ask for one (the usual maximum is 6 repeats before the patient has to have a review with the doctor) and then can go to collect the new prescription to take it to the pharmacy.

You cannot use the same prescription and get it filled repeatedly, as is the case in the US.

Thus, the quantity refers only to the number of tablets/packs issued on that one prescription.

|j9916| 001?|19960418|94851997|Y|0000004| 14| 0|1|000| 0.00| 2.00|ONE TWICE A DAY

So the dosage duration for the patient above would be 7 days – 2 tablets per day and they’ve been prescribed 14 tablets.

Q. Regarding the variable PRSCQTY, what does a negative value mean? The negative value refers to the original pack which can't be split, i.e., inhalers or creams. So a negative value of -2 is 2 packs and a value of 30, for example, could indicate 30 tablets.

Q: Regarding the variable OPNO, what does number of original packs ordered refer to? Is it the number of packages a patient can get from the prescription? A proc freq on this variable showed 99% of the patients had "opno"=0. Is opno=0 given when the drug is a mixture?

This field is not frequently filled. It relates to the number of packs ordered by the GP.

Q: Regarding the variable PACKSIZE, does it refer to the number of tablets in the pack size selected by the practitioner? A proc freq on this variable for one of the therapy files showed 99.8% had "packsize" = 0. Is packsize=0 when the drug is a mixture?

This field is also not frequently filled.

Q: Working with the therapy records, one finds that some patients have the same drug coded both as a repeat (PrscType = 0) and as an acute (PrscType = 1) prescription on the same day. How come? And which code is the right one?

There are two reasons how this situation could occur. First, the GP entered the wrong PrscType and then re-wrote the prescription again with the correct PrscType, resulting in two records when in fact the patient actually got only one prescription. Or the GP prescribed an acute prescription and a post-dated repeat prescription for the future. However we are unable to tell from the records which of these situations has occurred and in the case of the first scenario we are unable to tell which prescription was the correct one. Q. Are all lab test results ordered by the GP included in THIN? In the UK most blood tests or other procedures ordered by the GP will be undertaken at the local hospital. Some GP surgeries may take blood samples but would still send

them to the local pathology lab in the nearby hospital for analysis. The results are sent back either by letter or electronically. All practices are now electronically linked to the pathology lab, and the majority have started to use this system. Therefore the results are sent to the surgery and auto-filed into the patients' record. Where the electronic link has not yet been established, the results are sent in a letter. You will find in these cases that not so many results are recorded, only those with an abnormal results. For the electronically-linked practices all results are recorded. Q. How consistently is blood pressure recorded in the THIN database? It depends how well or sick the patient is - and if they ever see the GP, of course. In the UK everyone is registered with a GP but that doesn't mean you ever need to see him. In every new patient screen (first visit to a GP on registration), the BP gets recorded (these checks-up are generally done by nurses so are very well entered.)! In chronic diseases like CHD and diabetes, GPs are incentivized to record BP (and we have measured this at well over 95% patients in a single 12 month period). For patients visiting for contraception or similar, the BP often gets done every 6 months. Where the gap may arise would be in a healthy patient, registered and not attending the GP for years. This commonly applies to young men who don't get sick. Actual totals for patients with a recording are to be found in Research File B by practice - so you can see the list size and the total who have the measurement done. This is counted by year. If you want the cumulative totals, then Research file C will show patients who have ever had a BP recorded electronically, by practice. This is over 70% patients overall. Q: Classifying smoking status in THIN? EPIC can share with you the algorithm they use for smoking status in their studies. The code lists may not be complete, or validated, and may not be exactly suitable for all studies, so they need to be reviewed carefully. The codes for smoking should be searched for in both the AHD and Medical files. Recent records should only be in AHD but historic records may well be in the medical files. This algorithm can be more or less complicated according to your needs, but some researchers are happy to look for the most recent code and go with that. Smoking status: i) Current - If the closest record prior to index is from the currentsmoker.med and

there is no subsequent code from exsmoker.med and non-smoker.med (Y/N) ii) Ex – If the closest record prior to index is from non-smoker but previous records are

from smoker.med or exsmoker.med, or last record is from exsmoker.med (Y/N)

iii) Non – If there are no records with a code from smoker.med or exsmoker.med at any time and a record from nonsmoker.med (Y/N)

iv) Unknown – If there are no records found with a code from smoker.med, exsmoker.med and nonsmoker.med no record of smoking status. (Y/N)

When looking at the ‘NON Smoker’ list it’s worth noting that the non smoker codes are often used where ex smoker would be more appropriate. Whenever trying to find those who have never smoked it is important to search the whole medical and AHD records for these patients and ensure they have no previous smoker codes recorded rather than rely on the correct use of the never smoked/non smoker code by the GP. GP’s are also only required to record the status of a non-smoker with the specific 1371.00 Never smoked tobacco code once in a patients record. Smokers and ex-smokers should be followed up more often. With regards to the code 137..00 (tobacco consumption) EPIC use an extra algorithm to deal with this code because it is very frequently used so cannot be overlooked but it is vague in it’s meaning. If it is found search we search for a value in AHDval1. If there is a null value the patient is deemed as non-smoker. If there is a value the patient is deemed a smoker. It is worth noting that the ‘non smoker’ codes are often used where ‘ex smoker’ would be more appropriate. Whenever trying to find those who have never smoked, rather than rely on the correct use of the never smoked/non smoker code by the GP, it is important to search the whole Medical and AHD records for these patients and ensure they have no previous smoker codes recorded. For additional information on smoking codes in GPRD, please see: Lewis JD, Brensinger C. Agreement between GPRD smoking data: a survey of general practitioners and a population-based survey. Pharmacoepidemiology & Drug Safety 2004;13:437-41. Note that this manuscript is only relevant to EPICGPRD and focused on data available as of 1997. Q: Do you have a method for teasing out smoking and alcohol use? In regards to

the values "0" and "0000000" in the AHDVALUE fields - does this refer to non-use or unknown use?

Our usual method is to search in the ahd file and then the medical file using READ codes. Recent information (since conversion to vision) will be in the AHD file but some historical or converted data historical data may well be in the medical file so it is advisable to search both. For smoking and alcohol we don’t often just use the ahdcode 1003040000/1003050000 to search for records unless we are just looking to see if a record has been entered by the GP. To find information on usage, we search using specific READ code lists. Using a code list to search for the records will help you deal with those that have no value in the ahdval fields as the READ codes generally offer more information.

For example, for codes like ‘137..00 Tobacco consumption’, we take 0000000 in the ahdval field to mean ‘non smoker’. Other codes like ‘137L.00 Current non-smoker’ you have to use your discretion because some practices use it as a non-smoking code and others use it as an ex smoker code.

Q: Does EPIC calculate Body Mass Index? In cases where there is a weight but not a BMI is that because there was no height for the patient?

The Vision system in practice automatically calculates BMI every time a new weight reading is added to the patient’s record, so EPIC extracts this measurement straight from the raw data. If there is no height record, the field is left blank. Something to watch out for is BMI’s readings which are well out of standard ranges. This may be because the only height measurement in the patient’s record is one taken during childhood. For records pre-conversion to Vision there may also be some BMI records that are not filled. BMI are calculated by the Vision System as follows: BMI = Weight in Kilograms / (Height in Meters) x (Height in Meters) It automatically calculates a BMI whenever a new weight recording is entered, using the previous height measurement. Q. How do you identify obese patients? Obesity can be captured in 2 ways: First, search the Medical file and AHD file for a READ code for obesity. This, however, may only capture a few patients. The way most researchers would find obesity would be to set a BMI limit over which you consider the patient obese (a BMI >30 is often used). You can then search for weight records in the AHD file (AHDcode - 1005010200) and those with a value >30 in pracval2 are your obese patients. All records added in Vision have a BMI automatically calculated every time a weight record is added (providing there is a height measurement recorded). It is possible that some records added before conversion to Vision may not have a BMI automatically entered so you would need to calculate these yourselves. It is also advisable that you set an upper limit for the BMI level. In a few cases it is possible to find BMI records that seem impossibly large, especially when you look at the weight measurement. This will occur because the height record used to calculate the BMI was taken during childhood. Q: Do GPs routinely prescribe aspirin and other OTC-available products to

patients, particularly older patients, to help ease the cost burden so that these

patients do not have to purchase these medications out-of-pocket? Is there any data on the frequency of OTC aspirin use to protect against cardiovascular events? How likely is aspirin for this indication recorded in THIN?

Yes, the GP does routinely prescribe aspirin, paracetamol, etc. As well as easing cost it also reinforces their advice to give a script and have a record of what patients are taking which is important for interactions, etc. If the patient requests an OTC medication or doctors suggest one, most GPs would prescribe it unless they are being exceptionally mean!

Also often for the elderly, GPs want them to take the OTC medication long term, and particularly for analgesics many OTC products are only available in small pack sizes, so GPs prescribe so the patients can get bigger packs.

Aspirin will be exceptionally well reported now, especially since 01/04/04.when the new GMS contract came in. For the new GMS contract doctors have to record aspirin use in the CHD and stroke domains. For each individual, they should code aspirin prescriptions, OTC aspirin use, and alternative anti platelet and anticoagulant medications, intolerance and sensitivity. If they do not code one of these options, they loose points. Even before then, most good practices would be aiming to record this use.

There is no data on how many patients under 65 (who don’t automatically get free prescriptions) buy OTC. But OTC use should be coded in the GP records in recent years.

Q: How much prescribing of antidepressants is done by GPs vs. Specialists, i.e.,

what proportion would THIN not pick up?

Only a small proportion of antidepressants are initiated by psychiatrists. Only a very small proportion of patients on antidepressants are seen by psychiatrists anyway. Most depressed patients will usually have had their antidepressant started by a GP before referral anyway. Psychiatrists more often change what has been started by a GP if the patient has been referred for failure to respond.

Sometimes the psychiatrist will not prescribe at all but only advise on their preferred drug and ask the GP to initiate it. Some will do a first script: usually between 7 and 28 days supply and leave the GP to take over prescribing after that.

If someone is really bad and being seen by secondary care frequently they may do all the prescribing, but that is a rarity. It is usually more convenient for the patient to take a script from the GP to the local chemist. Hospital specialists cannot prescribe on FP10S (the usual prescription which can be filled by the high street pharmacist.) Hospital scripts can only be filled in hospital pharmacy which in reality is a hassle: they close at 5pm, long waits, etc, etc: patients often prefer to have a GP script. Often the plan has to be tailored to an individual: if they are high suicide risk, they may get very small quantities at a time from either GP or psychiatrist.

Severely ill patients are more likely to have medications prescribed by the psychiatrist. The importance of this effect will be different depending on the study question and the population being studied. If you are looking at all antidepressant prescribing, the proportion of scripts initiated in secondary care will be tiny. However, if you are, say, looking at suicides/severe depression, then the importance will be greater. Q: How is prescribing distributed in the UK? For prescription dispensed in the community, 98% of prescription items are prescribed by General Medical Practitioners. Prescriptions written by dentists are 0.5%; hospital doctors and nurses account for 0.7% of prescriptions; and Community & Practice nurses account for another 0.7%. [Data Source is Statistics Bulletin for Prescriptions Dispensed in the Community 1993-2003, Crown copyright 2004.] Q. Is it possible to get access to additional patient information? An advantage of the EPICGPRD & THIN data is access to original medical records, which researchers may use to confirm diagnoses on the electronic record and to elicit further information on patients. AIS (Additional Information Services - previously Comasco Computer Services) has many years experience in the service of liaison with data providers, to obtain copies of paper patient records on behalf of researchers. AIS holds a license with the Department of Health to approach contributing practices for information on particular patients. GPs return the required information, depersonalized and labeled with the patient identification number. Such a facility is normally limited to those patients that remain registered at the practice, as the notes are not held by the practice when a patient moves elsewhere. Therefore, it is more likely that information can be obtained on patients in THIN as the information on active patients is up- to- date. The following information or documents can be requested from participating GPs: • Copies of correspondence or other documentation from a patient’s medical case

notes • Questionnaires completed by the patient or the GP • An opinion or confirmation from the doctor about a specific diagnosis in the

electronic record (usually by way of questionnaire) • A specified intervention which may be required to study an effect on a patient,

e.g. a blood test or blood pressure recording • A specified intervention which may be required to confirm a diagnosis in the

electronic record • Death certificates, or information required to confirm the cause of death As sending questionnaires can incur large cost researchers should keep in mind that all THIN practices are now ‘paperless’ and this should affect the nature of the questions asked. Previously many practices would retain some paper notes and these were generally what were consulted in a questionnaire because the electronic record was not complete. GP’S now have no paper records and will simply check for

answers within the same electronic record already available to the researcher. AIS can offer support on these issues. Q. How to work with the Prevention Files in EPICGPRD? Data recorded in the prevention file are not validated. However, these data have been used extensively in research and have been found to be accurate but incomplete (this is EPGGPRD only). However, some populations will have more complete recording e.g. patients with smoking related illnesses are likely to have a high level of smoking status entered. Each record begins with the Patid followed by the date and then may have any or all of the following recorded : Prevention code (this defines the context and content of the other two) Prevention value Event code These three elements should be incorporated into any prevention search. However, each type of search will have specific issues to be considered. Prevention searches should be discussed with EPIC staff before being undertaken. Smoking Information on smoking can be recorded in both prevention and medical records. There are OXMIS codes which relate to smoking status. The prevention code for smoking is usually accompanied by the number of cigarettes smoked in the ‘value’ field. The event code may then contain an OXMIS or Read code which may confirm or conflict with the prevention entry. There may also be an entry in the medical record which may again confirm or conflict with data recorded in the prevention record. Records in Multiple files Information on contraception and some vaccinations can also be found in more than one data file. Contraception is often recorded as a prevention entry since not all contraceptives are obtained via the GP. Patients can attend family planning clinics and obtain their contraceptive medications from there. However, where the prescription is issued by the GP, there will be a record in the therapy record. There are also OXMIS and Read codes which describe contraceptive history and may be found recorded in the medical record. Prescriptions are issued for some vaccinations in the UK. These may also be recorded as a prevention record and a therapy record. Q. What is the method for creating Code lists? Creating Medical Code lists (THIN) 1. The medical codes are made up of Read codes that are held in a dictionary called

Readcodes. There are two versions available Readcodes.mdb (access) and Readcodes.txt (text)

2. The current Readcodes dictionary is dated May 06 and has a list of 98,411 Read medical codes with descriptions.

3. To create a medical code list, firstly look up the condition in the dictionary, encyclopaedia etc and then confirm with GP advisor the key words to search on.

4. In Readcodes.mdb the easiest way to produce a comprehensive list is to set up access queries using key words and compile a large table. This is then checked to remove duplicates and any unrelated conditions.

5. The full list of medcodes sorted by the code is then checked and any missed codes are included. The Read codes are hierarchical so related codes should be together in a sorted list.

6. Once all the code lists for the study are completed they are exported as excel spread sheets.

7. The GP advisor checks these lists highlighting codes to remove or making notes of missing codes for entry.

8. After amendments and once the GP advisor has agreed the code lists can be sent as excel files or sent in the access database depending on the preference of the researcher.

9. The researcher makes a note of any codes to be added or removed and when the amendments have been completed by EPIC the researcher has to agree in writing before the codes can be used in programming.

10. Once the code lists are agreed the codes without descriptions are saved as fixed length (7 character spaces) text files in a sorted order.

11. Naming the code list files, order & format are discussed with the programmer. Creating drug code lists (THIN) 1. The drug codes are held in a dictionary called Drugcodes. There are two versions

available: drugcodes.mdb (Access file) and drugcodes.txt (text file) 2. The current (as of May 2007) drug dictionary is dated Drugcodes Feb 06.mdb and

has a list of 50,763 generic descriptions, which will either have an encrypted Multilex code, up to 3 BNF codes, the form and strength and units of the drug.

3. There are 3 methods for creating drug lists: the first includes searching for all drugs used to treat certain conditions; the second includes searching on types of drug; the third method searches for a named generic drug.

4. To create a drug code list for a medical condition or type of drug, look up the text in the index of the BNF. If, for example, a code list for diabetes is being produced, the index will give a list of all pages that diabetic drugs are listed on.

5. Looking up the page will tell you the chapter, which in turn will give you the BNF code. For example, a search for diabetic ketoacidosis on page 314 would then give you the chapter 6.1.3. A BNF code is 11 characters and is in the following format: 00.00.00.00. Therefore, the BNF code would be 06.01.03.00.

6. In drugcodes.mdb the easiest way to produce a comprehensive list is to set up queries using the BNF code to compile a large table.

7. Querying all drugs with a BNF code of 06.01.03* should give drug used for diabetic ketoacidosis. Even after all 3 BNF codes are searched for, the text in the BNF needs to be searched for mention of drugs in other sections which will need to be included.

8. The generic names of the drugs identified are then searched to ensure that no codes are missed which do not have a BNF code.

9. For lists of generic drugs such as salbutamol, just a text search is required as searching on BNF will identify other drugs in that BNF section.

10. The completed code list is then checked to remove duplicates. 11. Once all the code lists for the study are completed they are copied to a new Access

database unique to the study and exported as Excel files. 12. Once the code lists are finalized, the just Multilex codes without descriptions are

saved as fixed length (8 character spaces) text files in a sorted order. Medical code lists for use with EPICGPRD 13. There are two types of diagnosis codes: medical codes which are used to describe

medical conditions, and therapy codes which are used to describe drugs. 14. Lists of medical conditions and/or drugs to be used in studies are identified from

the protocol or directly from the researcher. 15. The medical codes are made up of OXMIS & Read codes that are held in a

dictionary called medcodes. There are two versions available medcodes.mdb (Access file) and medcodes.txt (text file)

16. For example, the Yr2000 medcodes dictionary had a list of 100,582 OXMIS/Read medical codes with descriptions.

17. To create a medical code list, first look up the condition in the dictionary, encyclopaedia, etc, and then confirm with the PI the key words to search on.

18. In medcodes.mdb the easiest way to produce a comprehensive list is to set up Access queries using key words and compile a large table. This is then checked to remove duplicates and any unrelated conditions.

19. The full list of medcodes sorted by the code is then checked and any missed codes are included. The Read codes are hierarchical so related codes should be together in a list.

20. Once all the code lists for the study are completed they are exported as excel speadsheets.

21. The PI is then able to go through these lists, highlighting codes to remove or making notes of missing codes for entry.

22. Once the code lists are agreed, the codes (without descriptions) are saved as fixed length (7 character spaces) text files in a sorted order.

Drugcode lists for use with EPICGPRD 1. The drug codes are held in a dictionary called gprddrug. There are two versions

available gprddrug.mdb (Access file) and gprddrug.txt (text file) 2. For example, the Yr2000 drug dictionary had a list of 37,703 generic descriptions,

which will either have an encrypted PPA, or encrypted Multilex code or both, up to 3 BNF codes, the form and strength and units of the drug.

3. There are 3 methods for creating drug lists: the first includes searching for all drugs used to treat certain conditions; the second includes searching on types of drug; and the third method searches for a named generic drug.

4. To create a drug code list for a medical condition or type of drug, look up the text in the index of the BNF. If, for example, a code list for diabetes is being produced, the index will give a list of all pages that diabetic drugs are listed on.

5. Looking up the page will tell you the chapter, which in turn will give you the BNF code. For example, a search for diabetic ketoacidosis on page 314 would then give you the chapter 6.1.3. A BNF code is 11 characters and is in the following format 00.00.00.00. Therefore, the BNF code would be 06.01.03.00.

6. In gprddrug.mdb the easiest way to produce a comprehensive list is to set up queries using the BNF code to compile a large table.

7. Querying all drugs with a BNF code of 06.01.03* should give drug used for diabetic ketoacidosis. Even after all 3 BNF codes are searched for, the text in the BNF needs to be searched for mention of drugs in other sections which will need to be included.

8. The generic names of the drugs identified are then searched to ensure that no codes are missed which do not have a BNF code.

9. For lists of generic drugs such as salbutamol, just a text search is required as searching on BNF will identify other drugs in that BNF section.

10. The completed code list is then checked to remove duplicates. 11. Once all the code lists for the study are completed, they are copied to a new access

database unique to the study and exported as excel files. 12. The PI is then able to go through these lists highlighting codes to remove or

making notes of missing codes for entry. 23. Once the code lists are agreed, just Multilex codes (without descriptions) are

saved as fixed length (8 character spaces) text files in a sorted order. Q. Does EPIC provide for Training & Support? EPIC offers free advice and support to researchers who are using or considering using EPICGPRD or THIN Data. EPIC provides information for proposed studies including estimating potential population size, advice on study design and assistance with preparation of code lists. EPIC also strive to resolve any technical data queries connected with ongoing studies. In order that all researchers can take advantage of this service, EPIC staff can be contacted directly via: www.epic-uk.org. Q. Are the code lists in the Codes Library final lists that are ready for use? These codes were provided voluntarily from CCEB investigators. They have been used for a variety of studies over the last decade. Some of the code lists may be incomplete as new codes are added over time. In addition, we do not have data on whether the code list underwent any validation testing. We advise you to very carefully review the codes lists you use from our Codes Library. These codes lists are not guaranteed to be 100% accurate and should be used only as a starting point in own your searches of the READ/OXMIS code dictionary (for diseases) and/or BNF/multilex dictionaries (for drugs). You will have to go through these codes lists and de-select any codes that are not pertinent to your particular study (for example, you may not want the history codes), or you may need to add new codes that were introduced in the master dictionaries since the time when these codes lists were initially used in older studies. At least you don’t have to re-invent the wheel from scratch.