There are multiple advantages to using the CPRD GOLD for epidemiological research, including the breadth of coverage, the size and long-term follow-up, its representativeness and data quality.(208) The database has data on morbidity and Figure 2: Strategy for identifying codes for asthma in the CPRD GOLD
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lifestyle variables, linkage to secondary care via HES and mortality via ONS. It has a median follow-up of 5.1 years, which facilitates long-term epidemiological studies,(129,233) and is broadly representative of the UK population.(208,234,235) Furthermore, validation studies of some diagnoses have shown high positive predictive values (PPVs),(229) and studies on incidence rates have shown similar results to other UK data sources.(236,237) Another advantage of the CPRD GOLD is that the data quality is promoted by the Quality Outcomes Framework (QOF).
Quality Outcomes Framework
The QOF encourages recording of key data by GPs in England through an incentive payment, and therefore influences data quality in the CPRD GOLD.(238) It is an annual reward and incentive programme which gives more information on GP practice achievement results. This programme aims to reward practices for the administration of quality care and helps standardise advancement in the delivery of primary medical services (65). It is a voluntary process for all surgeries in England and was introduced in 2004, so this year was chosen as the index for most work included in this document. The indicators for the QOF are set annually. The QOF awards practices achievement points for the management of common chronic diseases (including asthma since 2006), the management of public health concerns and the implementation of preventative measures. This programme has enhanced aspects of the data in English General Practice.(208) The QOF indicators for asthma include sleeping difficulties, symptoms during the day and interference with daily activities.
Limitations
The primary purpose of the data in CPRD GOLD is to facilitate clinical care rather than research, so the data quality can be variable. The weaknesses of the CPRD GOLD include variability in completeness of data (for example, full blood counts are not conducted for every patient), sparse standard definitions (the need for code lists), missing information from secondary care (these are only available in CPRD GOLD if
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the clinician manually records discharge or referral letters) and non-captured data (such as household information or age of disease onset).(208) In detail, if a Read code for a disease is absent in a patient, the disease must be considered as absent in the patient which might not necessarily be the case. There are no standardised definitions of diseases, so Read code lists and algorithms are needed. If secondary care information is not entered manually, this information is not recorded in CPRD GOLD, which might be the case if the information is not directly relevant for patient care. Free text data was not available for the purposes of this PhD project. Finally, some data may be missing, including some lifestyle data, family composition and over-the- counter medication. In addition, the data only provide information on medication prescriptions, not on medication dispensing or adherence to medication.
The CPRD GOLD records prescriptions of medication, which does not guarantee that patients also used their medication. The studies included in this PhD thesis did not directly study at the effects of medication, so the implications of this are limited. The validation study (Chapter 4) defined some of the algorithms using medication use, but the most practical algorithm only used a specific asthma code.
The outcome of the cohort study described in Chapter 6 are asthma exacerbations, and an asthma exacerbation can be defined by the prescription of oral corticosteroids. The medications a patient takes by BTS step were used as measurement of asthma severity. The healthcare practitioner who prescribed the medications assessed the exacerbation or severity of asthma, so the prescription records would be a good proxy for both regardless of whether the patient used the medication.
While the direct adherence cannot be measured in EHR, it is possible to estimate it by studying the percentage of time for which patients at least had medication to cover. For example, if a patient has a prescription every 45 days but the amount prescribed
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only covers 30 days, the patient would be covered for 30/45 of the time (the medication possession rate).
Prescription records and patient’s self-reported drug exposure were compared in the French PGRx database (Pharmacoepidemiologic General Research eXtension). Self- reported drug exposure itself is not a perfect measure, as it can be affected by memory errors and other biases. The agreement between the two data sources was kappa = 0.83, (95% CI: 0.81-0.85).(239)
Another way to study adherence is estimating the percentage of issued prescriptions that are obtained from the pharmacy, in which case pharmacy-level data are needed. Treatment adherence can also be defined as missing one or more scheduled appointment if recorded, or coding indicating medication non-compliance.(240)
A cohort of asthma patients containing primary and secondary care information can be obtained by using a linked cohort from patients attending practices in England and linking their CPRD GOLD and HES records. Linkage from CPRD GOLD to patient- level datasets, including HES and ONS is only available for consenting English practices. These linkages are present in about 70% of English practices and 55% of all UK practices contributing to the CPRD GOLD.(241,242)
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