Min Hua Jen

We have previously described a system for monitoring a number of healthcare outcomes using case-mix adjustment models. It is desirable to automate the model fitting process in such a system if monitoring covers a large number of outcome measures or subgroup analyses. Our aim was to compare the performance of three different variable selection strategies: “manual”, “automated” backward elimination and re-categorisation, and including all variables at once, irrespective of their apparent importance, with automated re-categorisation. Logistic regression models for predicting in-hospital mortality and emergency readmission within 28 days were fitted to an administrative database for 78 diagnosis groups and 126 procedures from 1996 to 2006 for National Health Services hospital trusts in England. The performance of models was assessed with Receiver Operating Characteristic (ROC) c statistics, (measuring discrimination) and Brier score (assessing the average of the predictive accuracy). Overall, discrimination was similar for diagnoses and procedures and consistently better for mortality than for emergency readmission. Brier scores were generally low overall (showing higher accuracy) and were lower for procedures than diagnoses, with a few exceptions for emergency readmission within 28 days. Among the three variable selection strategies, the automated procedure had similar performance to the manual method in almost all cases except low-risk groups with few outcome events. For the rapid generation of multiple case-mix models we suggest applying automated modelling to reduce the time required, in particular when examining different outcomes of large numbers of procedures and diseases in routinely collected administrative health data.

Background Clostridium difficile (C. difficile) infection in hospitals in developed countries continues to be a major public health hazard despite increased control measures including review of antibiotic policies and hygiene measures. Patients with colitis are thought to be particularly vulnerable to C. difficile associated diarrhoea (CDAD). Identifying the clinical burden among hospitalised patients admitted with inflammatory bowel disease is an essential first step towards identifying and treating severe C. difficile infection in such individuals. Aim To determine excess morbidity and in‐hospital mortality associated with hospital acquired CDAD in patients with inflammatory bowel disease (IBD‐CDAD‐HAI) admitted to NHS hospitals in England compared with those admitted for inflammatory bowel disease alone. Methods Time trends study of all admissions to NHS hospitals between 2002/03 and 2007/08. We developed case definitions for IBD‐CDAD‐HAI patients. The primary outcomes were in‐hospital mortality and length of stay. The secondary outcome was gastrointestinal surgery. Results Patients in the IBD‐CDAD‐HAI group were more likely to die in hospital (adjusted OR 6.32), had 27.9 days longer in‐patient stays and higher gastrointestinal surgery rates (adjusted OR 1.87) than patients admitted for inflammatory bowel disease alone. Conclusion Patients with inflammatory bowel disease admitted to NHS hospitals in England with co‐existent C. difficile infection are at risk of greater in‐hospital mortality and morbidity than patients admitted for inflammatory bowel disease alone.

Life expectancy at birth has increased substantially at the global scale over recent decades, but the improvements have not been experienced equally across all countries – in large part reflecting changes in economic and social situations. To identify the spatial variations in life expectancy at birth across a large number of countries over a 33‐year period, this paper provides an expository account of a developing modelling methodology for the analysis of spatio‐temporal trajectories. It identifies broad patterns of change and simultaneously examines between‐ and within‐country variation to assess the degree to which patterns of life expectancy are becoming more or less similar at national and sub‐national scales.

There is a commonly held assumption that early August is an unsafe period to be admitted to hospital in England, as newly qualified doctors start work in NHS hospitals on the first Wednesday of August. We investigate whether in-hospital mortality is higher in the week following the first Wednesday in August than in the previous week. A retrospective study in England using administrative hospital admissions data. Two retrospective cohorts of all emergency patients admitted on the last Wednesday in July and the first Wednesday in August for 2000 to 2008, each followed up for one week. The odds of death for patients admitted on the first Wednesday in August was 6% higher (OR 1.06, 95% CI 1.00 to 1.15, p = 0.05) after controlling for year, gender, age, socio-economic deprivation and co-morbidity. When subdivided into medical, surgical and neoplasm admissions, medical admissions admitted on the first Wednesday in August had an 8% (OR 1.08, 95% CI 1.01 to 1.16, p = 0.03) higher odds of death. In 2007 and 2008, when the system for junior doctors' job applications changed, patients admitted on Wednesday August 1^st had 8% higher adjusted odds of death than those admitted the previous Wednesday, but this was not statistically significant (OR 1.08, 95% CI 0.95 to 1.23, p = 0.24). We found evidence that patients admitted on the first Wednesday in August have a higher early death rate in English hospitals compared with patients admitted on the previous Wednesday. This was higher for patients admitted with a medical primary diagnosis.