Alastair Canaway’s journal round-up for 27th November 2017

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 [PubMedPublished 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.

Credits

Chris Sampson’s journal round-up for 11th April 2016

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.

Estimating the medical care costs of obesity in the United States: systematic review, meta-analysis, and empirical analysis. Value in Health Published 6th April 2016

I’m always a little wary of the “[insert disease] costs the economy $[insert big number] per year” studies. There is just too much up for debate: whether a cost can be attributed to the disease; who bears the cost; whether in fact it should be considered a cost at all. A second look through the lense of a critical review is just what these studies need. Obesity is a big deal, but there is wide variation in estimates of its cost to the US economy. This study includes a systematic review and meta-analysis looking at the medical costs of obesity estimated by studies between 2008 and 2012. Twelve studies were included in the review. The annual cost of obesity per person that was reported in the studies ranged from $227 to $7269. Wow! The pooled estimate from the meta-analysis was $1910; around $150 billion for the US as a whole. The authors looked at the methods used in the studies, but due to the variation in methods chosen and data used they weren’t able to learn that much about how this might affect estimates. The studies aren’t entirely comparable to one another. So the authors also carry out an original analysis using data from the Medical Expenditure Panel Survey to explore the impact on estimates of alternative modelling strategies. The analysis was varied by 4 age groups, 5 statistical models and 4 sets of confounders to give 80 estimates in total. The alternative statistical models didn’t make much difference, but the authors found that their extended estimating equation had the best goodness of fit. This analysis found an average cost of $1343 per person. Age groups and confounders were important. Costs were especially high in the over 65s. Older obese people have a lot of obesity-related diseases, while obese children have very few and have relatively low costs. Controlling for obesity-related disease explained away most of the incremental cost. This brings us back to the question of what should and what shouldn’t be considered a cost of the disease. What we really want to know is the counterfactual cost of the presence of obesity; what if these people weren’t obese? It remains unclear how studies might even go about defining this, let alone actually estimating it.

Introduction of a national minimum wage reduced depressive symptoms in low-wage workers: a quasi-natural experiment in the UK. Health Economics [PubMedPublished 4th April 2016

The introduction to this paper states that “no study has investigated the health effects of the UK National Minimum Wage”. That took me by surprise. So – apparently – here is the first, and it’s particularly relevant given the recent introduction of the so-called ‘National Living Wage’. The authors use data from the BHPS to test whether the increase in wages for low earners associated with the introduction of the minimum wage resulted in a positive health effect. A difference-in-differences analysis was performed using data from just before and just after the introduction of the minimum wage. Health effect is measured using the General Health Questionnaire (GHQ), which asks about current mental health problems relative to what the respondent normally feels. The intervention group was those earning less than £3.60 per hour in 1998 and between £3.60 and £4.00 per hour in 1999. There are 2 alternative control groups; one consisting those earning just above the minimum wage in 1998 and another for people whose employer did not comply. Plenty of effort is made to try and isolate the effect by incorporating physical health changes into the model and exploring the role of financial strain as a mediating effect. The results show a (statistically significant) positive impact on the GHQ. But the results aren’t quite as compelling as they might at first seem. There are a lot of exclusions that might not stand up to scrutiny, and the intervention group was made up of just 63 people. It would be good to see the analysis adapted into an economic evaluation of the policy.

An econometric model of healthcare demand with nonlinear pricing. Health Economics [PubMedPublished 4th April 2016

In Germany, health insurance is mandatory and most people receive their coverage through a public system. Between 2004 and 2013 it operated an interesting policy: the first visit to a doctor in each calendar quarter was subject to a co-payment of €10, with no copayment for subsequent visits. That’s not a lot of money for most people, but instinct would tell me that at least some people would probably avoid a single visit within a quarter and perhaps bunch-up visits if possible. This study tests that instinct. The authors develop a model of health care demand based on health shocks arriving as a Poisson process. It assumes that the co-payment increases the probability of no visit taking place and that if one does take place then this is more likely to be later in the quarter. A joint analysis of two difference-in-differences experiments is used, based on both the introduction and the repeal of the policy. The control group consists the people with private health insurance who were not affected by the policy changes. Data come from the German Socio-Economic Panel and the main analysis included over 30,000 observations. This was in part thanks to the development and successful implementation of a method to address mismatching between observation date and calendar quarters. None of the various model specifications identified a statistically significant effect of the policy on the number of doctor visits, so I suspect it won’t be reintroduced any time soon.

Diagnosing the causes of rising health-care expenditure in Canada: does Baumol’s cost disease loom large? American Journal of Health Economics Published 31st March 2016

Baumol’s cost disease is a neat idea: health care costs will rise faster than most others because health care is labour intensive and – while wages will grow in line with other industries – productivity growth cannot keep up. There’s some evidence that Baumol’s cost disease does exist, but there is less evidence about how big a deal it is compared to other non-observable drivers of rising health care expenditure. As for many other countries, Canada’s health care spending has grown at a much faster rate than the consumer price index. This new study looks at national and provincial data from Canada for 1982-2011 and decomposes the growth rate into that driven by the cost disease, technological progress and observable factors. Observable variables include population ageing, per capita income growth, economic recession and social determinants of health. The analysis uses a recently developed method, referred to as the Hartwig-Colombier test, to evaluate the impact of Baumol’s cost disease. In line with previous research, growth in per capita income is shown to be the most important driver of health care spending growth. For all provinces, the analysis finds that the cost disease is relatively unimportant. Technological progress appears to have a far greater influence, accounting for at least 31% of spending increases. Furthermore, the authors find that population ageing is not such a big concern and that the spending increases resulting from it are manageable. The implication is that if Canada wants to control spending growth then it should focus on managing the adoption of new technologies.

The economics of bereavement

I recently signed up for a clinical trial (www.intervalstudy.org.uk). The baseline questionnaire included the SF-36. Under normal circumstances I would be at full health for all questions but, on this occasion, I was not. If the researchers go on to estimate my SF-6D score, my ‘utility’ will be suboptimal. The reason for this is that I recently lost a loved one.

There are many problems with the way we value death; some have been discussed on this blog. One which has become clear to me recently is that we don’t seem to fully take into account the effect on others of a person’s death. Whether or not we should is another question, but it’s worth considering what we might be missing.

Utility losses

Bereavement has been shown to cause substantial mental distress. This should come as no surprise. Whether or not this distress is sufficiently ‘health-related’ for our current method of valuing health is debatable. However, as demonstrated above, our current tools are likely to reflect this distress. More concretely, bereavement has been identified as a trigger for depression in older people. Conjugal bereavement has also been shown to increase mortality (even when controlling for changes in health care utilisation).

Clearly, bereavement due to an individual’s death can affect a person’s health-related quality of life.

Productivity losses

Sometimes, in an economic evaluation, we might collect data regarding time taken off work due to a partner’s or a child’s illness. Usually this will be in relation to the sick person needing some extra care. It is less common to collect information about time off work due to bereavement. In the UK there is no law granting people a right to compassionate leave, and its provision is at an employer’s discretion. Nevertheless, people do take compassionate leave. Within a family, productivity losses from absenteeism could extend to weeks or months.

Losses from presenteeism are also likely to be high. A link between well-being and productivity has been identified; bereavement is likely to lead to a drop in productivity.There is some evidence on the labour market effects of the loss of a child. Not only are people more likely to lose income and lose their jobs, but they are also more likely to leave the labour market altogether.

Remaining questions

It seems important to collect data, at least from immediate family members, regarding the effects of bereavement. I know that work is currently underway to properly capture carers’ utility, and this is likely to raise similar ethical questions. Given the evidence highlighted above, it seems that services to address bereavement could be cost-effective. Their current provision in the English NHS is limited. NICE don’t have much to say on the matter.

Fully taking the above into account raises some equity issues that need considering. If the death is unexpected there is likely to be a greater loss in productivity and utility; should interventions to prevent these deaths be prioritised? Should we prioritise interventions for people with more friends? I don’t know, but it seems likely that we should be doing things that we currently are not.