The UK quality and outcomes framework pay-for-performance scheme and spirometry: rewarding quality or just quantity? A cross-sectional study in Rotherham, UK

Background: Accurate spirometry is important in the management of COPD. The UK Quality and Outcomes Framework pay-for-performance scheme for general practitioners includes spirometry related indicators within its COPD domain. It is not known whether high achievement against QOF spirometry indicators is associated with spirometry to BTS standards. Methods: Data were obtained from the records of 3,217 patients randomly sampled from 5,649 patients with COPD in 38 general practices in Rotherham, UK. Severity of airflow obstruction was categorised by FEV1 (% predicted) according to NICE guidelines. This was compared with clinician recorded COPD severity. The proportion of patients whose spirometry met BTS standards was calculated in each practice using a random sub-sample of 761 patients. The Spearman rank correlation between practice level QOF spirometry achievement and performance against BTS spirometry standards was calculated. Results: Spirometry as assessed by clinical records was to BTS standards in 31% of cases (range at practice level 0% to 74%). The categorisation of airflow obstruction according to the most recent spirometry results did not agree well with the clinical categorisation of COPD recorded in the notes (Cohen's kappa = 0.34, 0.30 - 0.38). 12% of patients on COPD registers had FEV1 (% predicted) results recorded that did not support the diagnosis of COPD. There was no association between quality, as measured by adherence to BTS spirometry standards, and either QOF COPD9 achievement (Spearman's rho = -0.11), or QOF COPD10 achievement (rho = 0.01). Conclusion: The UK Quality and Outcomes Framework currently assesses the quantity, but not the quality of spirometry

Revising ethical guidance for the evaluation of programmes and interventions not initiated by researchers

Public health and service delivery programmes, interventions and policies (collectively, “programmes)” are typically developed and implemented for the primary purpose of effecting change rather than generating knowledge. Nonetheless, evaluations of these programmes may produce valuable learning that helps to determine effectiveness and costs as well as informing design and implementation of future programmes. Such studies might be termed “opportunistic evaluations”, since they are responsive to emergent opportunities rather than being studies of interventions that are initiated or designed by researchers. However, current ethical guidance and registration procedures make little allowance for scenarios where researchers have played no role in the development or implementation of a programme, but nevertheless plan to conduct a prospective evaluation. We explore the limitations of the guidance and procedures with respect to opportunistic evaluations, providing a number of examples. We propose that the key missing distinction in current guidance is that moral responsibility: researchers can only be held accountable for those aspects of a study over which they have control. We argue that requiring researchers to justify an intervention, programme or policy that would occur regardless of their involvement prevents or hinders research in the public interest without providing any further protections to research participants. We recommend that trial consent and ethics procedures allow for a clear separation of responsibilities for the intervention and the evaluation.SIW and RJL are funded by the NIHR Global Health Research Unit on Improving Health in Slums. CT, PJC and RJL are also supported by the National Institute for Health Research (NIHR) Collaboration for Leadership for Applied Health Research Care (CLAHRC) West Midlands initiative. EBW and ELD are employed by Partners In Health. MD-W is supported by the Health Foundation’s grant to the University of Cambridge for The Healthcare Improvement Studies (THIS) Institute. THIS Institute is supported by the Health Foundation - an independent charity committed to bringing about better health and health care for people in the UK. This work was also supported by MDW’s Wellcome Trust Investigator award WT09789. MDW is a National Institute for Health Research (NIHR) Senior Investigator. This paper presents independent research and the views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Healt

Sample size calculations for cluster randomised controlled trials with a fixed number of clusters

Background\ud Cluster randomised controlled trials (CRCTs) are frequently used in health service evaluation. Assuming an average cluster size, required sample sizes are readily computed for both binary and continuous outcomes, by estimating a design effect or inflation factor. However, where the number of clusters are fixed in advance, but where it is possible to increase the number of individuals within each cluster, as is frequently the case in health service evaluation, sample size formulae have been less well studied. \ud \ud Methods\ud We systematically outline sample size formulae (including required number of randomisation units, detectable difference and power) for CRCTs with a fixed number of clusters, to provide a concise summary for both binary and continuous outcomes. Extensions to the case of unequal cluster sizes are provided. \ud \ud Results\ud For trials with a fixed number of equal sized clusters (k), the trial will be feasible provided the number of clusters is greater than the product of the number of individuals required under individual randomisation ($n_i$) and the estimated intra-cluster correlation ($\rho$). So, a simple rule is that the number of clusters ($\kappa$) will be sufficient provided: \ud \ud $\kappa$ > $n_i$ x $\rho$\ud \ud Where this is not the case, investigators can determine the maximum available power to detect the pre-specified difference, or the minimum detectable difference under the pre-specified value for power. \ud \ud Conclusions\ud Designing a CRCT with a fixed number of clusters might mean that the study will not be feasible, leading to the notion of a minimum detectable difference (or a maximum achievable power), irrespective of how many individuals are included within each cluster. \ud \u

Improving the health and welfare of people who live in slums

Summary In the first paper in this Series we assessed theoretical and empirical evidence and concluded that the health of people living in slums is a function not only of poverty but of intimately shared physical and social environments. In this paper we extend the theory of so-called neighbourhood effects. Slums offer high returns on investment because beneficial effects are shared across many people in densely populated neighbourhoods. Neighbourhood effects also help explain how and why the benefits of interventions vary between slum and non-slum spaces and between slums. We build on this spatial concept of slums to argue that, in all low-income and-middle-income countries, census tracts should henceforth be designated slum or non-slum both to inform local policy and as the basis for research surveys that build on censuses. We argue that slum health should be promoted as a topic of enquiry alongside poverty and health

Why the fair innings argument is not persuasive

The fair innings argument (FIA) is frequently put forward as a justification for denying elderly patients treatment when they are in competition with younger patients and resources are scarce. In this paper I will examine some arguments that are used to support the FIA. My conclusion will be that they do not stand up to scrutiny and therefore, the FIA should not be used to justify the denial of treatment to elderly patients, or to support rationing of health care by age. There are six issues arising out of the FIA which are to be addressed. First, the implication that there is such a thing as a fair share of life. Second, whether it makes sense to talk of a fair share of resources in the context of health care and the FIA. Third, that 'fairness' is usually only mentioned with regard to the length of a person's life, and not to any other aspect of it. Fourth, if it is sensible to discuss the merits of the FIA without taking account of the 'all other things being equal' argument. Fifth, the difference between what is unfair and what is unfortunate. Finally, that it is tragic if a young person dies, but only unfortunate if an elderly person does

The effects of laryngeal mask airway passage simulation training on the acquisition of undergraduate clinical skills: a randomised controlled trial

Background\ud Effective use of the laryngeal mask airway (LMA) requires learning proper insertion technique in normal patients undergoing routine surgical procedures. However, there is a move towards simulation training for learning practical clinical skills, such as LMA placement. The evidence linking different amounts of mannequin simulation training to the undergraduate clinical skill of LMA placement in real patients is limited. The purpose of this study was to compare the effectiveness in vivo of two LMA placement simulation courses of different durations. \ud \ud Methods\ud Medical students (n = 126) enrolled in a randomised controlled trial. Seventy-eight of these students completed the trial. The control group (n = 38) received brief mannequin training while the intervention group (n = 40) received additional more intensive mannequin training as part of which they repeated LMA insertion until they were proficient. The anaesthetists supervising LMA placements in real patients rated the participants' performance on assessment forms. Participants completed a self-assessment questionnaire. \ud \ud Results\ud Additional mannequin training was not associated with improved performance (37% of intervention participants received an overall placement rating of > 3/5 on their first patient compared to 48% of the control group, $X^2$ = 0.81, p = 0.37). The agreement between the participants and their instructors in terms of LMA placement success rates was poor to fair. Participants reported that mannequins were poor at mimicking reality. \ud \ud Conclusions\ud The results suggest that the value of extended mannequin simulation training in the case of LMA placement is limited. Educators considering simulation for the training of practical skills should reflect on the extent to which the in vitro simulation mimics the skill required and the degree of difficulty of the procedure. \ud \u

The challenges faced in the design, conduct and analysis of surgical randomised controlled trials

Randomised evaluations of surgical interventions are rare; some interventions have been widely adopted without rigorous evaluation. Unlike other medical areas, the randomised controlled trial (RCT) design has not become the default study design for the evaluation of surgical interventions. Surgical trials are difficult to successfully undertake and pose particular practical and methodological challenges. However, RCTs have played a role in the assessment of surgical innovations and there is scope and need for greater use. This article will consider the design, conduct and analysis of an RCT of a surgical intervention. The issues will be reviewed under three headings: the timing of the evaluation, defining the research question and trial design issues. Recommendations on the conduct of future surgical RCTs are made. Collaboration between research and surgical communities is needed to address the distinct issues raised by the assessmentof surgical interventions and enable the conduct of appropriate and well-designed trials.The Health Services Research Unit is funded by the Scottish Government Health DirectoratesPeer reviewedPublisher PD

A Comparison of Administrative and Physiologic Predictive Models in Determining Risk Adjusted Mortality Rates in Critically Ill Patients

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r(2) for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting "report cards" or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models

Is general inpatient obstetrics and gynaecology evidence-based? A survey of practice with critical review of methodological issues

BACKGROUND: To examine the rates of evidence-supported care provided in an obstetrics-gynaecology unit. METHODS: The main diagnosis-intervention set was established for a sample of 325 consecutive inpatient admissions in 1998–99 in a prospective study in a UK tertiary care centre. A comprehensive literature search was conducted to obtain the evidence supporting the intervention categorised according to the following hierarchy: Grade A, care supported by evidence from randomised controlled trials; Grade B, care supported by evidence from controlled observational studies and convincing non-randomised evidence; and Grade C, care without substantial research evidence. RESULTS: Of the 325 admissions, in 135 (42%) the quality of care was based on Grade A evidence, in 157 (48%) it was based on Grade B evidence, and in 33 (10%) it was based on Grade C evidence. The patterns of care were not different amongst patients sampled in 1998 and 1999. CONCLUSION: A significant majority (90%) of obstetric and gynaecological care was found to be supported by substantial research evidence