Clare Marshall

Frequency, severity, and duration of rhinovirus infections in asthmatic and non-asthmatic individuals: a longitudinal cohort study.

BACKGROUND Rhinovirus infections cause exacerbations of asthma. We postulated that people with asthma are more susceptible to rhinovirus infection than people without the disease and compared the susceptibility of these groups. METHODS We recruited 76 cohabiting couples. One person in every couple had atopic asthma and one was healthy. Participants completed daily diary cards of upper-respiratory-tract (URT) and lower-respiratory-tract (LRT) symptoms and measured peak expiratory flow twice daily. Every 2 weeks nasal aspirates were taken and examined for rhinovirus. Mixed models were used to compare risks of infection between groups. We also compared the severity and duration of infections. FINDINGS We analysed 753 samples. Rhinovirus was detected in 10.1% (38/378) of samples from participants with asthma and 8.5% (32/375) of samples from healthy participants. After adjustment for confounding factors, asthma did not significantly increase risk of infection (odds ratio 1.15, 95% CI 0.71-1.87). Groups did not differ in frequency, severity, or duration of URT infections or symptoms associated with rhinovirus infection. First rhinovirus infection was associated more frequently with LRT infection in participants with asthma than in healthy individuals (12 of 28 infections vs four of 23, respectively, p=0.051). Symptoms of LRT associated with rhinovirus infection were significantly more severe (p=0.001) and longer-lasting in participants with asthma than in healthy participants (p=0.005). INTERPRETATION People with atopic asthma are not at greater risk of rhinovirus infection than healthy individuals but suffer from more frequent LRT infections and have more severe and longer-lasting LRT symptoms.

Following Shipman: a pilot system for monitoring mortality rates in primary care

As part of the investigations into the crimes of Harold Shipman, it has become clear that there is little monitoring of deaths in general practice. By use of data on annual deaths at family physician and practice level for five English health authorities for 1993-99, we investigate whether cumulative sum charts (a type of statistical process control chart) could be used to create a workable monitoring system. On such charts, thresholds for deaths can be set, which, if crossed, may indicate a potential problem. We chose thresholds based on empirical calculations of the probabilities of false and successful detection after allowing for multiple testing over physicians or practices. We also statistically adjusted the charts for extra-Poisson variation due to unmeasured case mix. Of 1009 family physicians, 33 (including Shipman) crossed the alarm threshold designed to detect a 2 SD increase in standardised mortality, with 97% successful detection and a 5% false-alarm rate. Poor data quality, plus factors such as the proportion of patients treated by these physicians in nursing homes or hospices are likely explanations for most of these additional alarms. If used appropriately, such charts represent a useful tool for monitoring deaths in primary care. However, improvement in data quality is essential.

Discussion on the meeting on 'Statistical modelling and analysis of genetic data'

David J. Balding .Imperial College School of Medicine, London/ I extendmy apologies to the authors that an unavoidable commitment arising unexpectedly in the 18 hours before the meeting robbed me of my final preparation, so that my comments at the meeting were poorly presented. I shall try to do a better job in this written version, and to leave enough space I shall omit my introductory comments about the role of statisticians in bioinformatics.