Variations in chemoprophylaxis for meningococcal disease: a retrospective case note review, analysis of routine prescribing data and questionnaire of general practitioners

BACKGROUND: Invasive meningococcal disease is a significant cause of mortality and morbidity in the UK. Administration of chemoprophylaxis to close contacts reduces the risk of a secondary case. However, unnecessary chemoprophylaxis may be associated with adverse reactions, increased antibiotic resistance and removal of organisms, such as Neisseria lactamica, which help to protect against meningococcal disease. Limited evidence exists to suggest that overuse of chemoprophylaxis may occur. This study aimed to evaluate prescribing of chemoprophylaxis for contacts of meningococcal disease by general practitioners and hospital staff. METHODS: Retrospective case note review of cases of meningococcal disease was conducted in one health district from 1st September 1997 to 31st August 1999. Routine hospital and general practitioner prescribing data was searched for chemoprophylactic prescriptions of rifampicin and ciprofloxacin. A questionnaire of general practitioners was undertaken to obtain more detailed information. RESULTS: Prescribing by hospital doctors was in line with recommendations by the Consultant for Communicable Disease Control. General practitioners prescribed 118% more chemoprophylaxis than was recommended. Size of practice and training status did not affect the level of additional prescribing, but there were significant differences by geographical area. The highest levels of prescribing occurred in areas with high disease rates and associated publicity. However, some true close contacts did not appear to receive prophylaxis. CONCLUSIONS: Receipt of chemoprophylaxis is affected by a series of patient, doctor and community interactions. High publicity appears to increase demand for prophylaxis. Some true contacts do not receive appropriate chemoprophylaxis and are left at an unnecessarily increased risk

PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm

As one of the newest members in the field of articial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by the DCA. This data preprocessing phase has received the criticism of having manually over-fitted the data to the algorithm, which is undesirable. Therefore, in this paper we have attempted to ascertain if it is possible to use principal component analysis (PCA) techniques to automatically categorise input data while still generating useful and accurate classication results. The integrated system is tested with a biometrics dataset for the stress recognition of automobile drivers. The experimental results have shown the application of PCA to the DCA for the purpose of automated data preprocessing is successful.