Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.
Elevated mortality among weekend hospital admissions is not associated with adoption of seven day clinical standards. Emergency Medicine Journal [PubMed] Published 8th November 2017
Our esteemed colleagues in Manchester brought more evidence to the seven-day NHS debate (debacle?). Patients who are admitted to hospital in an emergency at weekends have higher mortality rates than those during the week. Despite what our Secretary of State will have you believe, there is an increasing body of evidence suggesting that once case-mix is adequately adjusted for, the ‘weekend effect’ becomes negligible. This paper takes a slightly different angle for examining the same phenomenon. It harnesses the introduction of four priority clinical standards in England, which aim to reduce the number of deaths associated with the weekend effect. These are time to first consultant review; access to diagnostics; access to consultant-directed interventions; and on-going consultant review. The study uses publicly available data on the performance of NHS Trusts in relation to these four priority clinical standards. For the latest financial year (2015/16), Trusts’ weekend effect odds ratios were compared to their achievement against the four clinical standards. Data were available for 123 Trusts. The authors found that adoption of the four clinical standards was not associated with the extent to which mortality was elevated for patients admitted at the weekend. Furthermore, they found no association between the Trusts’ performance against any of the four standards and the magnitude of the weekend effect. The authors offer three reasons as to why this may be the case. First, data quality could be poor, second, it could be that the standards themselves are inadequate for reducing mortality, finally, it could be that the weekend effect in terms of mortality may be the wrong metric by which to judge the benefits of a seven-day service. They note that their previous research demonstrated that the weekend effect is driven by admission volumes at the weekend rather than the number of deaths, so it will not be impacted by care provision, and this is consistent with the findings in this study. The spectre of opportunity cost looms over the implementation of these standards; although no direct harm may arise from the introduction of these standards, resources will be diverted away from potentially more beneficial alternatives, this is a serious concern. The seven-day debate continues.
The effect of level overlap and color coding on attribute non-attendance in discrete choice experiments. Value in Health Published 16th November 2017
I think discrete choice experiments (DCE) are difficult to complete. That may be due to me not being the sharpest knife in the drawer, or it could be due to the nature of DCEs, or a bit of both. For this reason, I like best-worst scaling (BWS). BWS aside, DCEs are a common tool used in health economics research to assess and understand preferences. Given the difficulty of DCEs, people often resort to heuristics, that is, respondents often simplify choice tasks by taking shortcuts, e.g. ignoring one or more attribute (attribute non-attendance) or always selecting the option with the highest level of a certain attribute. This has downstream consequences leading to bias within preference estimates. Furthermore, difficulty with comprehension leads to high attrition rates. This RCT sought to examine whether participant dropout and attribute non-attendance could be reduced through two methods: level overlap, and colour coding. Level overlap refers to the DCE design whereby in each choice task a certain number of attributes are presented with the same level; in different choice tasks different attributes are overlapped. The idea of this is to prevent dominant attribute strategies whereby participants always choose the option with the highest level of one specific attribute and forces them to evaluate all attributes. The second method involves colour coding and the provision of other visual cues to reduce task complexity, e.g. colour coding levels to make it easy to see which levels are equal. There were five trial arms. The control arm featured no colour coding and no attribute overlap. The other four arms featured either colour coding (two different types were tested), attribute overlap, or a combination of them both. A nationally (Dutch) representative sample in relation to age, gender, education and geographic region were recruited online. In total 3394 respondents were recruited and each arm contained over 500 respondents. Familiarisation and warm-up questions were followed by 21 pairwise choice tasks in a randomised order. For the control arm (no overlap, no colour coding) 13.9% dropped out whilst only attending to on average 2.1 out of the five attributes. Colour coding reduced this to 9.6% with 2.8 attributes being attended. Combining level overlap with intensity colour coding reduced drop out further to 7.2% whilst increasing attribute attendance to four out of five. Thus, the combination of level overlap and colour coding nearly halved the dropout and doubled the attribute attendance within the DCE task. An additional, and perhaps most important benefit of the improvement in attribute attendance is that it reduces the need to model for potential attribute non-attendance post-hoc. Given the difficult of DCE completion, it seems colour coding in combination with level overlap should be implored for future DCE tasks.
Evidence on the longitudinal construct validity of major generic and utility measures of health-related quality of life in teens with depression. Quality of Life Research [PubMed] Published 17th November 2017
There appears to be increasing recognition of the prevalence and seriousness of youth mental health problems. Nearly 20% of young people will suffer depression during their adolescent years. To facilitate cost-utility analysis it is necessary to have a measure of preference based health-related quality of life (HRQL). However, there are few measures designed for use in adolescents. This study sought to examine various existing HRQL measures in relation to their responsiveness for the evaluation of interventions targeting depression in young people. This builds on previous work conducted by Brazier et al that found the EQ-5D and SF-6D performed adequately for depression in adults. In total 392 adolescents aged between 13 and 17 years joined the study, 376 of these completed follow up assessments. Assessments were taken at baseline and 12 weeks. The justification for 12 weeks is that it represented the modal time to clinical change. The following utility instruments were included: the HUI suite, the EQ-5D-3L, Quality of Well-Being Scale (QWB), and the SF-6D (derived from SF-36). Other non-preference based HRQL measures were also included: disease-specific ratings and scales, and the PedsQL 4.0. All (yes, you read that correctly) measures were found to be responsive to change in depression symptomology over the 12-week follow up period and each of the multi-attribute utility instruments was able to detect clinically meaningful change. In terms of comparing the utility instruments, the HUI-3, the QWB and the SF-6D were the most responsive whilst the EQ-5D-3L was the least responsive. In summary, any of the utility instruments could be used. One area of disappointment for me was that the CHU-9D was not included within this study – it’s one of the few instruments that has been developed by and for children and would have very much been a worthy addition. Regardless, this is an informative study for those of us working within the youth mental health sphere.
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