Too little, too late, too flawed

No abstract

ObjectivesTo quantify mortality associated with sepsis in the whole population of England.DesignDescriptive statistics of multiple cause of death data.SettingEngland between 2001 and 2010.ParticipantsAll people whose death was registered in England between 2001 and 2010 and whose certificate contained a sepsis-associated International Classification of Diseases, 10th Revision (ICD-10) code.Data sourcesMultiple cause of death data extracted from Office for National Statistics mortality database.Statistical methodsAge-specific and sex-specific death rates and direct age-standardised death rates.ResultsIn 2010, 5.1% of deaths in England were definitely associated with sepsis. Adding those that may be associated with sepsis increases this figure to 7.7% of all deaths. Only 8.6% of deaths definitely associated with sepsis in 2010 had a sepsis-related condition as the underlying cause of death. 99% of deaths definitely associated with sepsis have one of the three ICD-10 codes—A40, A41 and P36—in at least one position on the death certificate. 7% of deaths definitely associated with sepsis in 2001–2010 did not occur in hospital.ConclusionsSepsis is a major public health problem in England. In attempting to tackle the problem of sepsis, it is not sufficient to rely on hospital-based statistics, or methods of intervention, alone. A robust estimate of the burden of sepsis-associated mortality in England can be made by identifying deaths with one of the three ICD-10 codes in multiple cause of death data. These three codes could be used for future monitoring of the burden of sepsis-associated mortality.

Background: Data of quality are needed to identify ethnic disparities in health and healthcare and to meet the challenges in governance of race relations. Yet concerns over completeness, accuracy and timeliness have been long-standing and inhibitive with respect to the analytical use of the data. Aims: To identify incompleteness of ethnicity data across routine health and healthcare datasets and to investigate the utility of analytical strategies for using data that is of suboptimal quality. Methods: An analysis by government office regions of ethnicity data incompleteness in routine datasets and a comprehensive review and evaluation of the literature on appropriate analytical strategies to address the use of such data. Results: There is only limited availability of ethnically coded routine datasets on health and healthcare, with substantial variability in valid ethnic coding: although a few have high levels of completeness, the majority are poor (notably hospital episode statistics, drug treatment data and non-medical workforce). In addition, there is also a more than twofold regional difference in quality. Organisational factors seem to be the main contributor to the differentials, and these are amenable-yet, in practice, difficult-to change. This article discusses the strengths and limitations of a variety of analytical strategies for using data of suboptimal quality and explores how they may answer important unresolved questions in relation to ethnic inequalities. Conclusions: Only by using the data, even when of suboptimal quality, and remaining close to it can healthcare organisations drive up quality.