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Space-Time Relations of Clostridium difficile cases within a health economy: A Social Network Analysis Presented by Mark Reacher network analysis by Iain.

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Presentation on theme: "Space-Time Relations of Clostridium difficile cases within a health economy: A Social Network Analysis Presented by Mark Reacher network analysis by Iain."— Presentation transcript:

1 Space-Time Relations of Clostridium difficile cases within a health economy: A Social Network Analysis Presented by Mark Reacher network analysis by Iain Roddick Wendy Rice, Rowan Slowther, Judy Ames, Giri Shankar

2 Clostridium difficile natural history A common antibiotic associated infectious diarrhoea Transmission by ingestion of toxin producing spores in faeces of an infected case. Spores may remain “dormant” in large gut as part of microbiota until ….. Antibiotic treatment perturbs large gut microflora, spores no longer held in check and proliferate causing Clostridium difficile Associated Disease 2 Space-Time Relations of Clostridium difficile cases within a health economy: A Social Network Analysis

3 Clostridium difficile Associated Disease (CTAD) Comprises a wide range of illness from self limiting diarrhoea to severe protracted diarrhoea, fluid loss, toxic shock, toxic megacolon (requiring life saving colectomy) and acute death. 3Presentation title - edit in Header and Footer

4 Clostridium difficile control Universal infection control essential including hand hygiene in care staff : cleaning lavatories and all sanitation surfaces wards and fomites - oxidising disinfectants as well as physical cleaning with detergent required to destroy spores 4Presentation title - edit in Header and Footer

5 BBC Friday, 5 November, 2004 Hospital superbug must be halved. Bloodstream infections with the hospital superbug MRSA must be halved in three years, the government has said. Health Secretary John Reid tasked NHS hospitals with achieving a year on year reduction up to and beyond March Presentation title - edit in Header and Footer

6 Development of Health care associated infections Mandatory surveillance 2004 April CDI > 65 years; MRSA bacteraemia 2005 April Enhanced MRSA bacteraemia 2007 April CDI > 2 years 2011 January MSSA and E.coli bacteraemia added 6Presentation title - edit in Header and Footer

7 Attributing site of acquisition of infection Cases of Clostridium difficile infection occurring within 72 hours of admission have been regarded as acquiring infection prior to admission to that hospital – at another hospital or in the community Hospitals have only been held accountable for infections occurring greater than 72 hours following admission 7Presentation title - edit in Header and Footer

8 Clostridium difficile reports mandatory surveillance 2007 to 2013 in England 8Presentation title - edit in Header and Footer

9 Examination of space time relations of Community attributed Clostridium difficile cases A substantial proportion of Clostridium difficile cases arising in the North Norfolk health economy were attributed to acquisition in the community Community health services in Norfolk recorded the dates of arrival and discharge in care settings and to home for all new cases of Clostridium difficile infection diagnosed by GP testing in the community over a two year period 9Presentation title - edit in Header and Footer

10 The Norfolk Community attributed Clostridium difficile cases 199 county residents who had samples submitted by a GP and tested positive for C.difficile between had been admitted at least once to Hospital A 40 had been admitted at least once to Hospital B No cases had been admitted to both hospitals 26 cases had been to Care Homes with at least one other resident case 10 Space-Time Relations of Clostridium difficile cases within a health economy: A Social Network Analysis

11 Social Network Analysis (SNA) 11 Space-Time Relations of Clostridium difficile cases within a health economy: A Social Network Analysis The methods have their roots in the work of 18 th Century mathematicians on Graph Theory Focuses on relationships between individuals rather than their attributes Applied in a wide variety of scientific research – e.g. genetics, linguistics, electrical engineering, sociology Advances in computing power and availability of software have made it easier to apply SNA to a wide range of problems outside of formal research

12 Data & Software 12 Space-Time Relations of Clostridium difficile cases within a health economy: A Social Network Analysis PatientIDLocationStart DateEnd Date1 st Positive Specimen Date Very simple dataset – Just 5 data fields, captured in Excel: Procedures written in SQL Server to a) detect space-time coincidences between people in the cohort b) Measure time spent at risk of infection

13 Co-location of people C.diffiicile status not considered 85 of 199 people did spend some time together …in 2 hospitals, and in 5 care homes 13 Space-Time Relations of Clostridium difficile cases within a health economy: A Social Network Analysis Hospital A Hospital B CARE HOME CARE HOMES: A CH CH + B B

14 14 Space-Time Relations of Clostridium difficile cases within a health economy: A Social Network Analysis Hospital A – Taking C.difficile status into consideration – Who was at risk? Arrow direction represents possible transmission Arrow size is indicative of Number of Days at Risk Hospital A – Who spent time with who? Positive specimens not taken into account Joining lines represent co-location for 1 or more days CARE HOME

15 15 Space-Time Relations of Clostridium difficile cases within a health economy: A Social Network Analysis Conclusions Most community diagnosed Clostridium difficile infections had space time overlaps with earlier onset cases at their local hospital The “72 hour rule” of attributing cases to community acquisition denies the centrality of hospital transmission Higher levels of infection control are still urgently needed in hospitals to contain the continuing risk of Clostridium difficile

16 16 Space-Time Relations of Clostridium difficile cases within a health economy: A Social Network Analysis Acknowledgements Alongside my co-authors I’d like to acknowledge the contributions of The Norfolk Health System HCAI Group, and Mr. Steven Martin of Cambridge Institute of Public Health


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