Tag HIV

Rita Faria’s journal round-up for 15th April 2019

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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.

Emulating a trial of joint dynamic strategies: an application to monitoring and treatment of HIV‐positive individuals. Statistics in Medicine [PubMed] Published 18th March 2019

Have you heard about the target trial approach? This is a causal inference method for using observational evidence to compare strategies. This outstanding paper by Ellen Caniglia and colleagues is a great way to get introduced to it!

The question is: what is the best test-and-treat strategy for HIV-positive individuals? Given that patients weren’t randomised to each of the 4 alternative strategies, chances are that their treatment was informed by their prognostic factors. And these also influence their outcome. It’s a typical situation of bias due to confounding. The target trial approach consists of designing the RCT which would estimate the causal effect of interest, and to think through how its design can be emulated by the observational data. Here, it would be a trial in which patients would be randomly assigned to one of the 4 joint monitoring and treatment strategies. The goal is to estimate the difference in outcomes if all patients had followed their assigned strategies.

The method is fascinating albeit a bit complicated. It involves censoring individuals, fitting survival models, estimating probability weights, and replicating data. It is worthy of a detailed read! I’m very excited about the target trial methodology for cost-effectiveness analysis with observational data. But I haven’t come across any application yet. Please do get in touch via comments or Twitter if you know of a cost-effectiveness application.

Achieving integrated care through commissioning of primary care services in the English NHS: a qualitative analysis. BMJ Open [PubMed] Published 1st April 2019

Are you confused about the set-up of primary health care services in England? Look no further than Imelda McDermott and colleagues’ paper.

The paper starts by telling the story of how primary care has been organised in England over time, from its creation in 1948 to current times. For example, I didn’t know that there are new plans to allow clinical commissioning groups (CCGs) to design local incentive schemes as an alternative to the Quality and Outcomes Framework pay-for-performance scheme. The research proper is a qualitative study using interviews, telephone surveys and analysis of policy documents to understand how the CCGs commission primary care services. CCG Commissioning is intended to make better and more efficient use of resources to address increasing demand for health care services, staff shortage and financial pressure. The issue is that it is not easy to implement in practice. Furthermore, there seems to be some “reinvention of the wheel”. For example, from one of the interviewees: “…it’s no great surprise to me that the three STPs that we’ve got are the same as the three PCT clusters that we broke up to create CCGs…” Hum, shall we just go back to pre-2012 then?

Even if CCG commissioning does achieve all it sets out to do, I wonder about its value for money given the costs of setting it up. This paper is an exceptional read about the practicalities of implementing this policy in practice.

The dark side of coproduction: do the costs outweight the benefits for health research? Health Research Policy and Systems [PubMed] Published 28th March 2019

Last month, I covered the excellent paper by Kathryn Oliver and Paul Cairney about how to get our research to influence policy. This week I’d like to suggest another remarkable paper by Kathryn, this time with Anita Kothari and Nicholas Mays, on the costs and benefits of coproduction.

If you are in the UK, you have certainly heard about public and patient involvement or PPI. In this paper, coproduction refers to any collaborative working between academics and non-academics, of which PPI is one type, but it includes working with professionals, policy makers and any other people affected by the research. The authors discuss a wide range of costs to coproduction. From the direct costs of doing collaborative research, such as organising meetings, travel arrangements, etc., to the personal costs on an individual researcher to manage conflicting views and disagreements between collaborators, of having research products seen to be of lower quality, of being seen as partisan, etc., and costs to the stakeholders themselves

As a detail, I loved the term “hit-and-run research” to describe the current climate: get funding, do research, achieve impact, leave. Indeed, the way that research is funded, with budgets only available for the period that the research is being developed, does not help academics to foster relationships.

This paper reinforced my view that there may well be benefits to coproduction, but that there are also quite a lot of costs. And there tends to be not much attention to the magnitude of those costs, in whom they fall, and what’s displaced. I found the authors’ advice about the questions to ask oneself when thinking about coproduction to be really useful. I’ll keep it to hand when writing my next funding application, and I recommend you do too!

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

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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.

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Sam Watson’s journal round-up for 12th November 2018

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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 health opportunity costs in low-income and middle-income countries: a novel approach and evidence from cross-country data. BMJ Global Health. Published November 2017.

The relationship between health care expenditure and population health outcomes is a topic that comes up often on this blog. Understanding how population health changes in response to increases or decreases in the health system budget is a reasonable way to set a cost-effectiveness threshold. Purchasing things above this threshold will, on average, displace activity with greater benefits. But identifying this effect is hard. Commonly papers use some kind of instrumental variable method to try to get at the causal effect with aggregate, say country-level, data. These instruments, though, can be controversial. Years ago I tried to articulate why I thought using socio-economic variables as instruments was inappropriate. I also wrote a short paper a few years ago, which remains unpublished, that used international commodity price indexes as an instrument for health spending in Sub-Saharan Africa, where commodity exports are a big driver of national income. This was rejected from a journal because of the choice of instruments. Commodity prices may well influence other things in the country that can influence population health. And a similar critique could be made of this article here, which uses consumption:investment ratios and military expenditure in neighbouring countries as instruments for national health expenditure in low and middle income countries.

I remain unconvinced by these instruments. The paper doesn’t present validity checks on them, which is forgiveable given medical journal word limitations, but does mean it is hard to assess. In any case, consumption:investment ratios change in line with the general macroeconomy – in an economic downturn this should change (assuming savings = investment) as people switch from consumption to investment. There are a multitude of pathways through which this will affect health. Similarly, neighbouring military expenditure would act by displacing own-country health expenditure towards military expenditure. But for many regions of the world, there has been little conflict between neighbours in recent years. And at the very least there would be a lag on this effect. Indeed, in all the models of health expenditure and population health outcomes I’ve seen, barely a handful take into account dynamic effects.

Now, I don’t mean to let the perfect be the enemy of the good. I would never have suggested this paper should not be published as it is, at the very least, important for the discussion of health care expenditure and cost-effectiveness. But I don’t feel there is strong enough evidence to accept these as causal estimates. I would even be willing to go as far to say that any mechanism that affects health care expenditure is likely to affect population health by some other means, since health expenditure is typically decided in the context of the broader public sector budget. That’s without considering what happens with private expenditure on health.

Strategic Patient Discharge: The Case of Long-Term Care Hospitals. American Economic Review. [RePEc] Published November 2018.

An important contribution of health economics has been to undermine people’s trust that doctors act in their best interest. Perhaps that’s a little facetious, nevertheless there has been ample demonstration that health care providers will often act in their own self-interest. Often this is due to trying to maximise revenue by gaming reimbursement schemes, but also includes things like doctors acting differently near the end of their shift so they can go home on time. So when I describe a particular reimbursement scheme that Medicare in the US uses, I don’t think there’ll be any doubt about the results of this study of it.

In the US, long-term acute care hospitals (LTCHs) specialise in treating patients with chronic care needs who require extended inpatient stays. Medicare reimbursement typically works on a fixed rate for each of many diagnostic related groups (DRGs), but given the longer and more complex care needs in LTCHs, they get a higher tariff. To discourage admitting patients purely to get higher levels of reimbursement, the bulk of the payment only kicks in after a certain length of stay. Like I said – you can guess what happened.

This article shows 26% of patients are discharged in the three days after the length of stay threshold compared to just 7% in the three days prior. This pattern is most strongly observed in discharges to home, and is not present in patients who die. But this may still be just by chance that the threshold and these discharges coincide. Fortunately for the authors the thresholds differ between DRGs and even move around within a DRG over time in a way that appears unrelated to actual patient health. They therefore estimate a set of decision models for patient discharge to try to estimate the effect of different reimbursement policies.

Estimating misreporting in condom use and its determinants among sex workers: Evidence from the list randomisation method. Health Economics. Published November 2018.

Working on health and health care research, especially if you conduct surveys, means you often want to ask people about sensitive topics. These could include sex and sexuality, bodily function, mood, or other ailments. For example, I work a fair bit on sanitation, where frequently self-reported diarrhoea in under fives (reported by the mother that is) is the primary outcome. This could be poorly reported particularly if an intervention includes any kind of educational component that suggests it could be the mother’s fault for, say, not washing her hands, if the child gets diarrhoea. This article looks at condom use among female sex workers in Senegal, another potentially sensitive topic, since unprotected sex is seen as risky. To try and get at the true prevalence of condom use, the authors use a ‘list randomisation’ method. This randomises survey participants to two sets of questions: a set of non-sensitive statements, or the same set of statements with the sensitive question thrown in. All respondents have to do is report the number of the statements they agree with. This means it is generally not possible to distinguish the response to the sensitive question, but the difference in average number of statements reported between the two groups gives an unbiased estimator for the population proportion. Neat, huh? Ultimately the authors report an estimate of 80% of sex workers using condoms, which compares to the 97% who said they used a condom when asked directly.

 

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