GS:SFHS is a family-based genetic epidemiology study with DNA and socio-demographic and clinical data from about 24 000 volunteers across Scotland aged 18-98 years, from February 2006 to March 2011. Biological samples and anonymized data form a resource for research on the genetics of health, disease and quantitative traits of current and projected public health importance. Specific and important features of GS:SFHS include the family-based recruitment, with the intent of obtaining family groups; the breadth and depth of phenotype information, including detailed data on cognitive function, personality traits and mental health; consent and mechanisms for linkage of all data to comprehensive routine health-care records; and 'broad' consent from participants to use their data and samples for a wide range of medical research, including commercial research, and for re-contact for the potential collection of other data or samples, or for participation in related studies and the design and review of the protocol in parallel with in-depth sociological research on (potential) participants and users of the research outcomes. These features were designed to maximize the power of the resource to identify, replicate or control for genetic factors associated with a wide spectrum of illnesses and risk factors, both now and in the future.

Background: Generation Scotland: the Scottish Family Health Study aims to identify genetic variants accounting for variation in levels of quantitative traits underlying the major common complex diseases (such as cardiovascular disease, cognitive decline, mental illness) in Scotland.

Objective-To determine the rate of patients not redeeming their prescriptions (primary noncompliance) and assess the factors influencing this.Design Main outcome measures-The rate of nonredemption ofprescriptions.Results-Seven hundred and two patients (14X5%) did not redeem 1072 (5X20/) prescriptions during the study period, amounting to 11X5% of men and 16X3% of women. Non-redemption was highest in women aged [16][17][18][19][20][21][22][23][24][25][26][27][28][29] (27.6% of women) and men aged [40][41][42][43][44][45][46][47][48][49] (18.3% of men). Of prescriptions issued to women for oral contraceptives 24-8% were not redeemed during the study period. In those who redeemed prescriptions 17% were not exempt from prescription charges compared with 33% of patients who failed to redeem them. The non-redemption rate was highest for prescriptions issued at the weekends, although this was a small proportion of all prescribing. Prescriptions issued by trainee general practitioners were also less likely to be redeemed.Conclusions-Non-redemption varies with age, sex, general practitioner, exemption status, and with day ofthe week the prescription was written. Observational studies of drug exposure can be more accurately estimated from dispensing rather than prescribing data.

BackgroundEnormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems.ObjectiveIn this paper, we discuss whether data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of data.MethodsThis paper is based on the authors’ experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records.ResultsWe first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature.ConclusionsThere are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven health care will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

This study provides evidence that non-steroidal anti-inflammatory toxicity persists with continuous exposure. There seems to be carryover toxicity after the end of prescribing. These findings have implications for the management of patients requiring non-steroidal anti-inflammatory drugs.