Title: Data
Funder: Department of Health and Social Care

Abstract: The ultimate objective is to produce automated, generalizable technology for information integration and surveillance, using semantic technologies and model-driven techniques to develop a re-usable infrastructure for automatic, secure data linkage, management, and analysis, providing a generic platform for surveillance of HCAI and AMR, and for the investigation of other key PHE priorities, including respiratory and gastrointestinal infection. This rich data will be used to optimise models to inform public policy. Short (1-2 years) a) Assess the performance of an automated process for ascertaining surgical site infection (SSI) from electronic medical record information with conventionally ascertained SSI, as reported nationally. Specific Research Theme HPRU 2013 Application Ref No: HPRU-2012-10041 2 This will allow validation of the electronic approach vs. a gold standard. b) Provide a process for automatically generating estimates, based on (a) above, of SSI incidence in patients across all surgical strata, suitable for use as an outcome measure in the intervention studies described in the ‘Interventions’ theme. c) Enhance the current national reporting framework for C. difficile, S. aureus bacteraemia and E. coli bacteraemia by linking existing reports with rich linked patient data and information from whole genomic microbial sequencing (from the MMM HICF project), producing integrated surveillance solution with unprecedented discriminatory ability. d) Establish validated systems for automated extraction of electronic prescribing data for inpatients within the study sites, which will be used (as in (b)) as secondary outcome measures in intervention studies. e) Compare the antimicrobial usage data obtained above in the study sites with that acquired by the newly initiated English Surveillance Programme for Antibiotic Usage and Resistance (ESPAUR).This will allow validation, along with other collaborating centres, of the nationally collected data. f) Modelling has proved a powerful technology to compare different HCAI control strategies and inform policy. We will investigate the impact of recent inferences we have made about in-hospital transmission, from hospital and whole-genome sequencing data, and our estimates of the dynamics of long term carriage, on mathematical models assessing MRSA control strategies. We will also investigate the potential role of antimicrobial restriction on S. aureus transmission. Medium (2 – 3 years) a) Extend the data linkage model and abstractions of data being developed for Oxford primary care and hospitals to Thames Valley (via the NIHR-funded Oxford Academic Health Sciences Network clinical informatics programme) and with our collaborating HICF-hospitals (Brighton, Birmingham and Leeds) as a proof of principle for scalability. Based on this data evaluate: i. In collaboration with PHE, identify areas of health protection (in addition to surgical site surveillance) where linked primary and secondary healthcare data could provide substantial increases in surveillance capacity and/or economies of scale, and conduct comparative analyses validating the data sources. Tracking spread of multi-resistant organisms across community and hospitals is an example. ii. Develop new methodologies for fast and efficient analysis of large scale linked genomic and/or rich clinical data to provide automated routine surveillance and early threat detection. These approaches will benefit from the establishment of the Oxford Big Data Institute. b) Modelling: Unintended consequences of interventions arise from creative responses to pressure to achieve specific outcomes under severe resource constraints, but have not typically been incorporated in mathematical models of different infection control strategies to date. We will construct a model for C. difficile testing and infection to explicitly quantify the impact of unintended consequences of reducing testing rates in reaction to financial penalties for infection cases. Long term (4 – 5 years) a) The long-term objective is to establish a robust model for linking NHS patient record and genomics data across primary and secondary care which can then be implemented nationally. b) A programme of research will be established including method development for statistically analysing, mining and modelling combined genomics and epidemiological data to meet PHE surveillance and threat detection priorities on a national scale, as well as widespread application to patientrelevant infections. c) Using these linked resources as a basis, a mathematical model will be developed for ESBL E. coli infections, to assess the effectiveness of interventions as well as explicitly consider their unintended consequences.


Research activity codes: 2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY)
Health categories: INFECTION;GENERIC HEALTH RELEVANCE