Rita Faria’s journal round-up for 28th January 2019

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.

Appraising the value of evidence generation activities: an HIV modelling study. BMJ Global Health [PubMed] Published 7th December 2018

How much should we spend on implementing our health care strategy versus getting more information to devise a better strategy? Should we devolve budgets to regions or administer the budget centrally? These are difficult questions and this new paper by Beth Woods et al has a brilliant stab at answering them.

The paper looks at the HIV prevention and treatment policies in Zambia. It starts by finding the most cost-effective strategy and the corresponding budget in each region, given what is currently known about the prevalence of the infection, the effectiveness of interventions, etc. The idea is that the regions receive a cost-effective budget to implement a cost-effective strategy. The issue is that the cost-effective strategy and budget are devised according to what we currently know. In practice, regions might face a situation on the ground which is different from what was expected. Regions might not have enough budget to implement the strategy or might have some leftover.

What if we spend some of the budget to get more information to make a better decision? This paper considers the value of perfect information given the costs of research. Depending on the size of the budget and the cost of research, it may be worthwhile to divert some funds to get more information. But what if we had more flexibility in the budgetary policy? This paper tests 2 more budgetary options: a national hard budget but with the flexibility to transfer funds from under- to overspending regions, and a regional hard budget with a contingency fund.

The results are remarkable. The best budgetary policy is to have a national budget with the flexibility to reallocate funds across regions. This is a fascinating paper, with implications not only for prioritisation and budget setting in LMICs but also for high-income countries. For example, the 2012 Health and Social Care Act broke down PCTs into smaller CCGs and gave them hard budgets. Some CCGs went into deficit, and there are reports that some interventions have been cut back as a result. There are probably many reasons for the deficit, but this paper shows that hard regional budgets clearly have negative consequences.

Health economics methods for public health resource allocation: a qualitative interview study of decision makers from an English local authority. Health Economics, Policy and Law [PubMed] Published 11th January 2019

Our first paper looked at how to use cost-effectiveness to allocate resources between regions and across health care services and research. Emma Frew and Katie Breheny look at how decisions are actually made in practice, but this time in a local authority in England. Another change of the 2012 Health and Social Care Act was to move public health responsibilities from the NHS to local authorities. Local authorities are now given a ring-fenced budget to implement cost-effective interventions that best match their needs. How do they make decisions? Thanks to this paper, we’re about to find out.

This paper is an enjoyable read and quite an eye-opener. It was startling that health economics evidence was not much used in practice. But the barriers that were cited are not insurmountable. And the suggestions by the interviewees were really useful. There were suggestions about how economic evaluations should consider the local context to get a fair picture of the impact of the intervention to services and to the population, and to move beyond the trial into the real world. Equity was mentioned too, as well as broadening the outcomes beyond health. Fortunately, the health economics community is working on many of these issues.

Lastly, there was a clear message to make economic evidence accessible to lay audiences. This is a topic really close to my heart, and something I’d like to help improve. We have to make our work easy to understand and use. Otherwise, it may stay locked away in papers rather than do what we intended it for. Which is, at least in my view, to help inform decisions and to improve people’s lives.

I found this paper reassuring in that there is clearly a need for economic evidence and a desire to use it. Yes, there are some teething issues, but we’re working in the right direction. In sum, the future for health economics is bright!

Survival extrapolation in cancer immunotherapy: a validation-based case study. Value in Health Published 13th December 2018

Often, the cost-effectiveness of cancer drugs hangs in the method to extrapolate overall survival. This is because many cancer drugs receive their marketing authorisation before most patients in the trial have died. Extrapolation is tested extensively in the sensitivity analysis, and this is the subject of many discussions in NICE appraisal committees. Ultimately, at the point of making the decision, the correct method to extrapolate is a known unknown. Only in hindsight can we know for sure what the best choice was.

Ash Bullement and colleagues take advantage of hindsight to know the best method for extrapolation of a clinical trial of an immunotherapy drug. Survival after treatment with immunotherapy drugs is more difficult to predict because some patients can survive for a very long time, while others have much poorer outcomes. They fitted survival models to the 3-year data cut, which was available at the time of the NICE technology appraisal. Then they compared their predictions to the observed survival in the 5-year data cut and to long-term survival trends from registry data. They found that the piecewise model and a mixture-cure model had the best predictions at 5 years.

This is a relevant paper for those of us who work in the technology appraisal world. I have to admit that I can be sceptical of piecewise and mixture-cure models, but they definitely have a role in our toolbox for survival extrapolation. Ideally, we’d have a study like this for all the technology appraisals hanging on the survival extrapolation so that we can take learnings across cancers and classes of drugs. With time, we would get to know more about what works best for which condition or drug. Ultimately, we may be able to get to a stage where we can look at the extrapolation with less inherent uncertainty.

Credits

Chris Sampson’s journal round-up for 28th March 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.

How did medicaid expansions affect labor supply and welfare enrollment? Evidence from the early 2000s. Health Economics Review [PubMed] Published 22nd March 2016

In the early 2000s, a number of states in the USA expanded Medicaid while others did not. These expansions covered similar populations to those that are likely to benefit from the Affordable Care Act. This study combines county-level statistics on unemployment, wages and enrollment in welfare programmes and uses a difference-in-differences regression to look at the effects of the expansion. The study is based on the assumption that some counties would be more affected than others, and that these groups can be identified based on the level of poverty before the expansion. Counties above the 75th percentile of poverty rates are considered the ‘treatment group’. The poorer counties saw a decrease in labour force participation rate, a decrease in working hours, an increase in wages and an increase in the use of food stamps. The main identification strategy isn’t very convincing, but the author argues that the findings are robust to alternatives. If we believe it, then the Affordable Care Act might have some modest negative consequences for employment and welfare enrollment.

Extrapolating survival from randomized trials using external data: a review of methods. Medical Decision Making [PubMed] Published 22nd March 2016

Modelling is built on assumptions. Many of these will be about how we extrapolate outcomes beyond what is observed in a trial. This study reviews the ways in which cost-effectiveness studies have extrapolated survival data using external information to inform the extrapolation. Such external information might come from national statistics such as life-tables, or from large cohort studies. The authors started with NIHR HTA reports and adopted a ‘pearl growing’ approach to identifying relevant studies. Based on their findings, the authors present a framework for the assumption process. This can be followed to determine which kind of approach to extrapolation should be adopted; for example, depending on whether the control group is assumed to have the same mortality as the external population or whether this differs in the short term. The authors describe the approach that should be taken in each circumstance. It’s also important to think about uncertainty. In particular, there is likely to be uncertainty regarding the effectiveness of the treatment. No studies were identified that formally used external data to quantify future changes in treatment effects. The authors discuss the potential for the use of expert elicitation to inform survival extrapolation using Bayesian inference. If you’re building a model that requires survival data from a trial to be extrapolated, you’ll find this review to be very helpful.

Harsh parenting, physical health, and the protective role of positive parent-adolescent relationships. Social Science & Medicine Published 21st March 2016

There’s plenty of evidence showing that being loved by one’s parents is crucial to development. A new study of 451 adolescents followed into adulthood supports this. 12 year olds and their families were recruited for a study in Iowa, and data were collected at multiple time points up to age 20. Harsh parenting was identified by coders who watched videotapes of parent behaviour. Harsh parenting behaviours were hostility, angry coercion, physical attacks and antisocial behaviour. Adolescents’ self-assessed health and BMI were recorded throughout the study, as was their own judgment of parental warmth for each parent. The authors use a latent change score model to investigate associations between these variables. Harsh parenting was associated with a negative impact on future self-reported health and BMI. In terms of self-reported health, a positive relationship with the father helped mitigate the health impact of having a harsh mother. But then the effect on BMI seemed to increase with warmth from the other parent. The evidence suggests that as well as preventing harsh parenting, it may be worthwhile focussing on a child’s relationship with the less harsh parent as a means of buffering against the negative effects.

Analyzing health-related quality of life in the EVOLVE trial: the joint impact of treatment and clinical events. Medical Decision Making [PubMed] Published 17th March 2016

This study reports on the EQ-5D data from a clinical trial of cinacalcet for secondary hyperparathyroidism. Using the normal approach of estimating QALYs based on the area under the curve, no difference was identified between the two arms of the trial. But then trials like this aren’t designed to identify differences in health-related quality of life. This study explores an alternative approach. EQ-5D-3L was collected at baseline and 6 follow-up points from 3547 subjects. It was additionally collected after particular clinical events. A regression model using a generalised estimating equation (GEE) approach was fitted with EQ-5D index scores as the dependent variable and clinical events as explanatory variables along with baseline utility and trial allocation. The analysis looked at acute effects (on utility within 13 weeks) and chronic effects (on utility in all subsequent months). A regression analysis with just trial allocation as an explanatory variable only found a non-significant treatment effect. However, the GEE regression that controlled for the acute and chronic effects of clinical events was able to identify a small but significant beneficial effect of the treatment. The effect could be observed independent of the effect of clinical events. Whether such results will be as convincing as traditional trial comparisons will remain to be seen, but adopting an approach of this sort could be far more informative when determining parameters for a decision model.