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Title: A novel approach to undertaking a pharmacoepidemiological study of Clostridium difficile infection and antimicrobial usage in the NW SHA trusts using HPA and IMS databases
Author: Pereira, Joao
ISNI:       0000 0004 2719 9726
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2012
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Abstract:
Background: The use of antimicrobials has been presented as a significant risk factor for Clostridium difficile infection (CDI). Nevertheless, it remains unclear which antimicrobials are more likely to be associated with CDI. It is mandatory for acute trusts to report the numbers of diagnosed CDI cases to the Health Protection Agency (HPA). There is no national system to collect and analyse antimicrobial usage data from the trusts. The company IMS collects antimicrobial usage data from the trusts for creating marketing research statistics. Therefore, it was hypothesised that data collected from the HPA and from IMS could be used to undertake an ecological study about the association between CDI cases and antimicrobial use in English trusts. Methods: A trust-level Antimicrobial Usage Database provided by IMS and a database, including the numbers of CDI cases for patients aged 65 years old and above, provided by the HPA, were utilised in this work. These referred to 26 out of the 29 NW SHA trusts (that managed 64 hospitals) for the quarters between 2005 and 2008 inclusive. A sample of antimicrobial usage data collected directly from trusts was used to investigate potential limitations in using the Antimicrobial Usage Database for the purpose of this work. Multilevel models were used to study antimicrobial usage and the number of CDI cases over time. These models were also used to investigate the association between the CDI cases and antimicrobial usage in the trusts. The trends of trust antimicrobial usage over time were compared with DH recommendations for the prevention of CDI through antimicrobial prescribing published in 1994, 2005 and 2008. Results: Discrepancies between the antimicrobial usage recorded in the IMS database and in a sample of antimicrobial usage data collected from trusts were found for 31 out of 155 antimicrobial usage records; only 1 of these referred to an antimicrobial with high usage. Eight out of the 23 antimicrobial groups and 10 out of 63 antimicrobials were presented as having high usage. The antimicrobial usage over time increased significantly for 7 antimicrobial groups, decreased significantly for 2 groups and remained constant for 54 groups. The number of CDI cases reported for patients aged 65 years old and above decreased significantly over the time. Trust antimicrobial usage over time changed in the opposite direction compared to the DH recommendations published in 1994, 2004 and 2008, respectively, for 2 out of 11, 3 out of 12 and 3 out of 14 antimicrobial groups/antimicrobials. The increased usage of 5 antimicrobial groups was significantly associated with an increase in the number of CDI cases and an increased usage of 4 antimicrobial groups was significantly associated with a decreased number of CDI cases. Within the antimicrobial groups that were significantly associated with an increased number of CDI cases, the usage of 8 individual antimicrobials was significantly associated with the CDI cases. Discussion/Conclusion: Collecting antimicrobial usage over time for large groups of trusts is very time consuming and requires extensive data manipulation. The similarity of the results of this study with those of previously published studies suggest that HPA and IMS data may be used to investigate the association between CDI cases and antimicrobial usage in English trusts.
Supervisor: Tully, Mary; Cooke, Jonathan Sponsor: University Hospital of South Manchester ; Pharmacy Directory ; School of Pharmacy of the University of Manchester
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.553481  DOI: Not available
Keywords: Clostridium difficile ; Antimicrobials ; Antibiotics ; Healthcare asociated infections ; HCAIs ; Multilevel modeling ; Time series analysis ; Pharmacoepidemiology
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