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Record linkage of administrative and survey data is increasingly used to generate evidence to inform policy and services. Although a powerful and efficient way of generating new information from existing data sets, errors related to data processing before, during and after linkage can bias results. However, researchers and users of linked data rarely have access to information that can be used to assess these biases or take them into account in analyses. As linked administrative data are increasingly used to provide evidence to guide policy and services, linkage error, which disproportionately affects disadvantaged groups, can undermine evidence for public health. We convened a group of researchers and experts from government data providers to develop guidance about the information that needs to be made available about the data linkage process, by data providers, data linkers, analysts and the researchers who write reports. The guidance goes beyond recommendations for information to be included in research reports. Our aim is to raise awareness of information that may be required at each step of the linkage pathway to improve the transparency, reproducibility, and accuracy of linkage processes, and the validity of analyses and interpretation of results.

A large amount of the research undertaken in an attempt to discover the reasons underlying the late nineteenth- and early twentieth-century mortality decline in Britain has relied on the statistics published by the Registrars General. The processes by which individual causes of death are recorded and then processed in order to create the statistics are not, however, well understood. In this article, the authors build on previous work to piece together a time series of causes of death for Scotland, which removes many of the discontinuities encountered in the published statistics that result from the Registrar General deciding to update the nosology, or classification system, which was being used to compile his figures. Having regrouped individual causes of death to ‘smooth’ the time series, the authors use the new groups to examine the changing causes of death in Scotland for selected age groups, before turning to undertake a detailed examination of mortality amongst those aged 55 or more. The authors find that when deaths from ‘old age’ in the latter age group are separated from other ‘ill-defined’ causes, it becomes obvious that there was a ‘rebranding’ of cause of death. The authors then use individual-level data from two Scottish communities to further dissect the roles played by ‘informants’ and ‘doctors’ in this rebranding, in order to see how these roles may have altered over time and what the consequences might be for one's view of how mortality changed in Scotland between 1855 and 1949. Finally, the authors argue that their findings have important implications for some of historical demography's most prominent theories: the McKeown thesis and the theory of epidemiological transition. Publisher PDF

The Authors thank MacMillan Cancer Support for funding this study. Routinely collected health care data (the Scottish Cancer Registry) were record-linked to representative survey data on health and health behaviours (the Government-funded Scottish Health Survey) to compare the health and well-being of cancer survivors in Scotland with people who had never had cancer; a methodological approach which has been under-utilised. After adjusting for age, sex and occupational classification, people with a previous diagnosis of cancer (even > 6 years previously) (n = 507) were more likely to have poorer self-assessed health, reduced activity and psychological morbidity than people who had never had cancer (n = 25,631). Publisher PDF

The English indices of deprivation 2010 is the third release in a series of statistics produced to measure multiple forms of deprivation at the small spatial scale. It updates the indices of deprivation 2007 and 2004, retaining broadly the same methodology, domains and indicators.\ud \ud This report outlines the conceptualisation underpinning the model of multiple deprivation used and describes the indicators and domains that make up the indices.

Gaye, Amadou Marcon, Yannick Isaeva, Julia LaFlamme, Philippe Turner, Andrew Jones, Elinor M Minion, Joel Boyd, Andrew W Newby, Christopher J Nuotio, Marja-Liisa Wilson, Rebecca Butters, Oliver Murtagh, Barnaby Demir, Ipek Doiron, Dany Giepmans, Lisette Wallace, Susan E Budin-Ljosne, Isabelle Oliver Schmidt, Carsten Boffetta, Paolo Boniol, Mathieu Bota, Maria Carter, Kim W deKlerk, Nick Dibben, Chris Francis, Richard W Hiekkalinna, Tero Hveem, Kristian Kvaloy, Kirsti Millar, Sean Perry, Ivan J Peters, Annette Phillips, Catherine M Popham, Frank Raab, Gillian Reischl, Eva Sheehan, Nuala Waldenberger, Melanie Perola, Markus van den Heuvel, Edwin Macleod, John Knoppers, Bartha M Stolk, Ronald P Fortier, Isabel Harris, Jennifer R Woffenbuttel, Bruce H R Murtagh, Madeleine J Ferretti, Vincent Burton, Paul R eng MC_UP_A540₁021/Medical Research Council/United Kingdom MR/K006525/1/Medical Research Council/United Kingdom Medical Research Council/United Kingdom Wellcome Trust/United Kingdom Research Support, Non-U.S. Gov't England 2014/09/30 06:00 Int J Epidemiol. 2014 Dec;43(6):1929-44. doi: 10.1093/ije/dyu188. Epub 2014 Sep 26.; International audience; BACKGROUND: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK's proposed 'care.data' initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. METHODS: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC. RESULTS: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach. CONCLUSIONS: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property-the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.