Marginal costs of hospital-acquired conditions : information for priority-setting for patient safety programmes and research

Objective: To estimate the relative inpatient costs of hospital-acquired conditions. Methods: Patient level costs were estimated using computerized costing systems that log individual utilization of inpatient services and apply sophisticated cost estimates from the hospital's general ledger. Occurrence of hospital-acquired conditions was identified using an Australian ‘condition-onset' flag for diagnoses not present on admission. These were grouped to yield a comprehensive set of 144 categories of hospital-acquired conditions to summarize data coded with ICD-10. Standard linear regression techniques were used to identify the independent contribution of hospital-acquired conditions to costs, taking into account the case-mix of a sample of acute inpatients (n = 1,699,997) treated in Australian public hospitals in Victoria (2005/06) and Queensland (2006/07). Results: The most costly types of complications were post-procedure endocrine/metabolic disorders, adding AU$21,827 to the cost of an episode, followed by MRSA (AU$19,881) and enterocolitis due to Clostridium difficile (AU$19,743). Aggregate costs to the system, however, were highest for septicaemia (AU$41.4 million), complications of cardiac and vascular implants other than septicaemia (AU$28.7 million), acute lower respiratory infections, including influenza and pneumonia (AU$27.8 million) and UTI (AU$24.7 million). Hospital-acquired complications are estimated to add 17.3% to treatment costs in this sample. Conclusions: Patient safety efforts frequently focus on dramatic but rare complications with very serious patient harm. Previous studies of the costs of adverse events have provided information on ‘indicators’ of safety problems rather than the full range of hospital-acquired conditions. Adding a cost dimension to priority-setting could result in changes to the focus of patient safety programmes and research. Financial information should be combined with information on patient outcomes to allow for cost-utility evaluation of future interventions.

The 'gender gap' in authorship in nursing literature

OBJECTIVES: Gender bias has been found in medical literature, with more men than women as first or senior authors of papers, despite about half of doctors being women. Nursing is about 90% female, so we aimed to determine if similar biases exist in nursing literature. DESIGN: Taking the eight non-specialist nursing journals with the highest impact factors for that profession, we counted the numbers of men and women first authors over 30 years. SETTING: We used nursing journals from around the world which attract the highest impact factors for nursing publication. PARTICIPANTS: Eight journals qualified for entry, three from the United Kingdom, four from the United States of America, and one from Australia. MAIN OUTCOME MEASURES Using Chi-square and Fisher exact tests, we determined differences between the numbers of men and women across all the journals, between countries (USA, UK and Australia), changes over the 30 years, and changes within journals over time. RESULTS Despite the small proportion of men in the nursing workforce, up to 30% of first authors were men. UK journals were more likely to have male authors than USA journals, and this increased over time. USA journals had proportions of male first authors consistent with the male proportion of its nursing workforce. CONCLUSIONS In the UK (though not in the USA) gender bias in nursing publishing exists, even though the nursing workforce is strongly feminized. This warrants further research, but is likely to be due to the same reasons for the gender gap in medical publishing; that is, female nurses take time out to have families, and social and family responsibilities prevent them taking opportunities for career progression, whereas men's careers often are not affected in such ways.

A quasi-likelihood approach for ordered categorical data with overdispersion

Quasi-likelihood (QL) methods are often used to account for overdispersion in categorical data. This paper proposes a new way of constructing a QL function that stems from the conditional mean-variance relationship. Unlike traditional QL approaches to categorical data, this QL function is, in general, not a scaled version of the ordinary log-likelihood function. A simulation study is carried out to examine the performance of the proposed QL method. Fish mortality data from quantal response experiments are used for illustration.