This user’s guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or costeffectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care.

Registries for Evaluating Patient Outcomes: A User's Guide

Vast amounts of data are collected about patients and service users in the course of health and social care service delivery. Electronic data systems for patient records have the potential to revolutionise service delivery and research. But in order to achieve this, it is essential that the ability to link the data at the individual record level be retained whilst adhering to the principles of information governance. The SAIL (Secure Anonymised Information Linkage) databank has been established using disparate datasets, and over 500 million records from multiple health and social care service providers have been loaded to date, with further growth in progress. Having established the infrastructure of the databank, the aim of this work was to develop and implement an accurate matching process to enable the assignment of a unique Anonymous Linking Field (ALF) to person-based records to make the databank ready for record-linkage research studies. An SQL-based matching algorithm (MACRAL, Matching Algorithm for Consistent Results in Anonymised Linkage) was developed for this purpose. Firstly the suitability of using a valid NHS number as the basis of a unique identifier was assessed using MACRAL. Secondly, MACRAL was applied in turn to match primary care, secondary care and social services datasets to the NHS Administrative Register (NHSAR), to assess the efficacy of this process, and the optimum matching technique. The validation of using the NHS number yielded specificity values > 99.8% and sensitivity values > 94.6% using probabilistic record linkage (PRL) at the 50% threshold, and error rates were < 0.2%. A range of techniques for matching datasets to the NHSAR were applied and the optimum technique resulted in sensitivity values of: 99.9% for a GP dataset from primary care, 99.3% for a PEDW dataset from secondary care and 95.2% for the PARIS database from social care. With the infrastructure that has been put in place, the reliable matching process that has been developed enables an ALF to be consistently allocated to records in the databank. The SAIL databank represents a research-ready platform for record-linkage studies.

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The SAIL databank: linking multiple health and social care datasets

BACKGROUND: The World Health Organization's (WHO's) Health Promoting Schools (HPS) framework is an holistic, settings-based approach to promoting health and educational attainment in school. The effectiveness of this approach has not been previously rigorously reviewed. OBJECTIVES: To assess the effectiveness of the Health Promoting Schools (HPS) framework in improving the health and well-being of students and their academic achievement. SEARCH METHODS: We searched the following electronic databases in January 2011 and again in March and April 2013: Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, PsycINFO, CINAHL, Campbell Library, ASSIA, BiblioMap, CAB Abstracts, IBSS, Social Science Citation Index, Sociological Abstracts, TRoPHI, Global Health Database, SIGLE, Australian Education Index, British Education Index, Education Resources Information Centre, Database of Education Research, Dissertation Express, Index to Theses in Great Britain and Ireland, ClinicalTrials.gov, Current controlled trials, and WHO International Clinical Trials Registry Platform. We also searched relevant websites, handsearched reference lists, and used citation tracking to identify other relevant articles. SELECTION CRITERIA: We included cluster-randomised controlled trials where randomisation took place at the level of school, district or other geographical area. Participants were children and young people aged four to 18 years, attending schools or colleges. In this review, we define HPS interventions as comprising the following three elements: input to the curriculum; changes to the school's ethos or environment or both; and engagement with families or communities, or both. We compared this intervention against schools that implemented either no intervention or continued with their usual practice, or any programme that included just one or two of the above mentioned HPS elements. DATA COLLECTION AND ANALYSIS: At least two review authors identified relevant trials, extracted data, and assessed risk of bias in the trials. We grouped different types of interventions according to the health topic targeted or the approach used, or both. Where data permitted, we performed random-effects meta-analyses to provide a summary of results across studies. MAIN RESULTS: We included 67 eligible cluster trials, randomising 1443 schools or districts. This is made up of 1345 schools and 98 districts. The studies tackled a range of health issues: physical activity (4), nutrition (12), physical activity and nutrition combined (18), bullying (7), tobacco (5), alcohol (2), sexual health (2), violence (2), mental health (2), hand-washing (2), multiple risk behaviours (7), cycle-helmet use (1), eating disorders (1), sun protection (1), and oral health (1). The quality of evidence overall was low to moderate as determined by the GRADE approach. 'Risk of bias' assessments identified methodological limitations, including heavy reliance on self-reported data and high attrition rates for some studies. In addition, there was a lack of long-term follow-up data for most studies.We found positive effects for some interventions for: body mass index (BMI), physical activity, physical fitness, fruit and vegetable intake, tobacco use, and being bullied. Intervention effects were generally small but have the potential to produce public health benefits at the population level. We found little evidence of effectiveness for standardised body mass index (zBMI) and no evidence of effectiveness for fat intake, alcohol use, drug use, mental health, violence and bullying others; however, only a small number of studies focused on these latter outcomes. It was not possible to meta-analyse data on other health outcomes due to lack of data. Few studies provided details on adverse events or outcomes related to the interventions. In addition, few studies included any academic, attendance or school-related outcomes. We therefore cannot draw any clear conclusions as to the effectiveness of this approach for improving academic achievement. AUTHORS' CONCLUSIONS: The results of this review provide evidence for the effectiveness of some interventions based on the HPS framework for improving certain health outcomes but not others. More well-designed research is required to establish the effectiveness of this approach for other health topics and academic achievement.

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The WHO Health Promoting School framework for improving the health and well‐being of students and their academic achievement

Background: Vast quantities of electronic data are collected about patients and service users as they pass through health service and other public sector organisations, and these data present enormous potential for research and policy evaluation. The Health Information Research Unit (HIRU) aims to realise the potential of electronically-held, person-based, routinely-collected data to conduct and support health-related studies. However, there are cons iderable challenges that must be addressed before such data can be used for these purposes, to ensure compliance with the legislation and guidelines generally known as Information Governance. Methods: A set of objectives was identified to address the challenges and establish the Secure Anonymised Information Linkage (SAIL) system in accordance with Information Governance. These were to: 1) ensure data transportation is secure; 2) operate a reliable record matching technique to enable accurate record linkage across datasets; 3) anonymise and encrypt the data to prevent re-identification of individuals; 4) apply measures to address disclosure risk in data views created for researchers; 5) ensure data access is controlled and authorised; 6) establish methods for scrutinising proposals for data utilisation and approving output; and 7) gain external verification of compliance with Information Governance. Results: The SAIL databank has been established and it operates on a DB2 platform (Data Warehouse Edition on AIX) running on an IBM 'P' series Supercomputer: Blue-C. The findings of an independent internal audit were favourable and concluded that the systems in place provide adequate assurance of compliance with Information Governance. This expanding databank already holds over 500 million anonymised and encrypted individual-level records from a range of sources relevant to health and well-being. This includes national datasets covering the whole of Wales (approximately 3 million population) and local provider-level datasets, with further growth in progress. The utility of the databank is demonstrated by increasing engagement in high quality research studies. Conclusion: Through the pragmatic approach that has been adopted, we have been able to address the key challenges in establishing a national databank of anonymised person-based records, so that the data are available for research and evaluation whilst meeting the requirements of Information Governance.

https://link.springer.com/content/pdf/10.1186%2F1472-6963-9-157.pdf

The SAIL Databank: building a national architecture for e-health research and evaluation

Aim
To explore antidiabetic medicine prescribing to women before, during and after pregnancy in different regions of Europe.

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Ken Leake

Papers

The SAIL databank: linking multiple health and social care datasets

The SAIL Databank: building a national architecture for e-health research and evaluation