Suchergebnisse

Objectives: Care.data was a 2013 UK government initiative to extract patient data from general practices in England to form a centralised whole-population database for service planning and health research. After a public outcry, the scheme was postponed and cancelled. Public views of care.data have previously been analysed; this study aimed to understand contemporary general practitioners' (GPs) views of the scheme, which may have been influential in its downfall. Design: Systematic search of media articles, followed by media content analysis. Setting: UK-based mainstream and GP-facing media in 2013 and 2014. Participants: Articles were eligible if they focused on care.data, and GPs were quoted, authored the article, or if articles were written for a majority GP audience. Interventions: N/A. Primary and secondary outcome measures: Themes which explained GPs' reactions to care.data and which could explain support for or opposition to the scheme. Results: 162 media articles met inclusion criteria and were drawn from newspapers, news websites and GP-facing websites. GPs recognised care.data's potential value for research and improving care, but had grave concerns about the scheme's implementation. These centred the lack of safeguards and purpose around the scheme which meant patients were not able to make informed decisions about opt-out. GPs perceived they were poorly resourced to meet competing demands to both share patients' data and protect confidentiality. They distrusted the government's likely uses of the data and perceived a risk of patient reidentification if the data were sold onto commercial entities. Conclusions: Findings show specific concerns which GPs had about care.data which led to the withdrawal of support. Future NHS patient data-sharing schemes should engage with GPs and other clinicians as key stakeholders from the earliest moments of planning, so that their views and needs are incorporated into the design of such schemes.

ObjectivesAdministrative data are primarily collected for operational processes and these processes can lead to sources of bias that may not be adequately considered by researchers. We provide a framework to help understand how biases might arise from using linked administrative data, and hopefully aid future study designs.
ApproachWe developed the conceptual framework based on the team's experiences with the 100 Million Brazilian Cohort (100MCohort) which contains records of more than 131 million people whose families applied for social assistance between 2001 and 2018, linked to other administrative data sources. We provide examples from the 100MCohort of where and how in the linkage process different forms of bias could arise. We make recommendations on how biases might be addressed using commonly available external data.
ResultsThe conceptual framework covers the whole data generating process from people and events occurring in the population through to deriving variables for analysis. The framework comprises three distinct stages: 1) Recording and registration of events in administrative systems such as Brazil's Mortality Information System (SIM) and the Hospital Information System (SIH); 2) Linkage of different data sources, for example using exact matching via the Social Identification Number (NIS) in Brazil's CadÚnico database or linkage algorithms; 3) Cleaning and coding data used both for analysis and linkage. The biases arising from linkage can be better understood by applying theory and making additional metadata available.
ConclusionMaximising the potential of administrative data for research requires a better understanding of how biases arise. This is best achieved by considering the entire data generating process, and better communication among all those involved in the data collection and linkage processes.

Background: We aimed to estimate the causal effect of health conditions and risk factors on social and socioeconomic outcomes in UK Biobank. Evidence on socioeconomic impacts is important to understand because it can help governments, policy makers and decision makers allocate resources efficiently and effectively. Methods: We used Mendelian randomization to estimate the causal effects of eight health conditions (asthma, breast cancer, coronary heart disease, depression, eczema, migraine, osteoarthritis, type 2 diabetes) and five health risk factors [alcohol intake, body mass index (BMI), cholesterol, systolic blood pressure, smoking] on 19 social and socioeconomic outcomes in 336 997 men and women of White British ancestry in UK Biobank, aged between 39 and 72 years. Outcomes included annual household income, employment, deprivation [measured by the Townsend deprivation index (TDI)], degree-level education, happiness, loneliness and 13 other social and socioeconomic outcomes. Results: Results suggested that BMI, smoking and alcohol intake affect many socioeconomic outcomes. For example, smoking was estimated to reduce household income [mean difference = -£22 838, 95% confidence interval (CI): -£31 354 to -£14 321] and the chance of owning accommodation [absolute percentage change (APC) = -20.8%, 95% CI: -28.2% to -13.4%], of being satisfied with health (APC = -35.4%, 95% CI: -51.2% to -19.5%) and of obtaining a university degree (APC = -65.9%, 95% CI: -81.4% to -50.4%), while also increasing deprivation (mean difference in TDI = 1.73, 95% CI: 1.02 to 2.44, approximately 216% of a decile of TDI). There was evidence that asthma decreased household income, the chance of obtaining a university degree and the chance of cohabiting, and migraine reduced the chance of having a weekly leisure or social activity, especially in men. For other associations, estimates were null. Conclusions: Higher BMI, alcohol intake and smoking were all estimated to adversely affect multiple social and socioeconomic outcomes. Effects were not detected between health conditions and socioeconomic outcomes using Mendelian randomization, with the exceptions of depression, asthma and migraines. This may reflect true null associations, selection bias given the relative health and age of participants in UK Biobank, and/or lack of power to detect effects.