Chris Sampson’s journal round-up for 30th September 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.

A need for change! A coding framework for improving transparency in decision modeling. PharmacoEconomics [PubMed] Published 24th September 2019

We’ve featured a few papers in recent round-ups that (I assume) will be included in an upcoming themed issue of PharmacoEconomics on transparency in modelling. It’s shaping up to be a good one. The value of transparency in decision modelling has been recognised, but simply making the stuff visible is not enough – it needs to make sense. The purpose of this paper is to help make that achievable.

The authors highlight that the writing of analyses, including coding, involves personal style and preferences. To aid transparency, we need a systematic framework of conventions that make the inner workings of a model understandable to any (expert) user. The paper describes a framework developed by the Decision Analysis in R for Technologies in Health (DARTH) group. The DARTH framework builds on a set of core model components, generalisable to all cost-effectiveness analyses and model structures. There are five components – i) model inputs, ii) model implementation, iii) model calibration, iv) model validation, and v) analysis – and the paper describes the role of each. Importantly, the analysis component can be divided into several parts relating to, for example, sensitivity analyses and value of information analyses.

Based on this framework, the authors provide recommendations for organising and naming files and on the types of functions and data structures required. The recommendations build on conventions established in other fields and in the use of R generally. The authors recommend the implementation of functions in R, and relate general recommendations to the context of decision modelling. We’re also introduced to unit testing, which will be unfamiliar to most Excel modellers but which can be relatively easily implemented in R. The role of various tools are introduced, including R Studio, R Markdown, Shiny, and GitHub.

The real value of this work lies in the linked R packages and other online material, which you can use to test out the framework and consider its application to whatever modelling problem you might have. The authors provide an example using a basic Sick-Sicker model, which you can have a play with using the DARTH packages. In combination with the online resources, this is a valuable paper that you should have to hand if you’re developing a model in R.

Accounts from developers of generic health state utility instruments explain why they produce different QALYs: a qualitative study. Social Science & Medicine [PubMed] Published 19th September 2019

It’s well known that different preference-based measures of health will generate different health state utility values for the same person. Yet, they continue to be used almost interchangeably. For this study, the authors spoke to people involved in the development of six popular measures: QWB, 15D, HUI, EQ-5D, SF-6D, and AQoL. Their goal was to understand the bases for the development of the measures and to explain why the different measures should give different results.

At least one original developer for each instrument was recruited, along with people involved at later stages of development. Semi-structured interviews were conducted with 15 people, with questions on the background, aims, and criteria for the development of the measure, and on the descriptive system, preference weights, performance, and future development of the instrument.

Five broad topics were identified as being associated with differences in the measures: i) knowledge sources used for conceptualisation, ii) development purposes, iii) interpretations of what makes a ‘good’ instrument, iv) choice of valuation techniques, and v) the context for the development process. The online appendices provide some useful tables that summarise the differences between the measures. The authors distinguish between measures based on ‘objective’ definitions (QWB) and items that people found important (15D). Some prioritised sensitivity (AQoL, 15D), others prioritised validity (HUI, QWB), and several focused on pragmatism (SF-6D, HUI, 15D, EQ-5D). Some instruments had modest goals and opportunistic processes (EQ-5D, SF-6D, HUI), while others had grand goals and purposeful processes (QWB, 15D, AQoL). The use of some measures (EQ-5D, HUI) extended far beyond what the original developers had anticipated. In short, different measures were developed with quite different concepts and purposes in mind, so it’s no surprise that they give different results.

This paper provides some interesting accounts and views on the process of instrument development. It might prove most useful in understanding different measures’ blind spots, which can inform the selection of measures in research, as well as future development priorities.

The emerging social science literature on health technology assessment: a narrative review. Value in Health Published 16th September 2019

Health economics provides a good example of multidisciplinarity, with economists, statisticians, medics, epidemiologists, and plenty of others working together to inform health technology assessment. But I still don’t understand what sociologists are talking about half of the time. Yet, it seems that sociologists and political scientists are busy working on the big questions in HTA, as demonstrated by this paper’s 120 references. So, what are they up to?

This article reports on a narrative review, based on 41 empirical studies. Three broad research themes are identified: i) what drove the establishment and design of HTA bodies? ii) what has been the influence of HTA? and iii) what have been the social and political influences on HTA decisions? Some have argued that HTA is inevitable, while others have argued that there are alternative arrangements. Either way, no two systems are the same and it is not easy to explain differences. It’s important to understand HTA in the context of other social tendencies and trends, and that HTA influences and is influenced by these. The authors provide a substantial discussion on the role of stakeholders in HTA and the potential for some to attempt to game the system. Uncertainty abounds in HTA and this necessarily requires negotiation and acts as a limit on the extent to which HTA can rely on objectivity and rationality.

Something lacking is a critical history of HTA as a discipline and the question of what HTA is actually good for. There’s also not a lot of work out there on culture and values, which contrasts with medical sociology. The authors suggest that sociologists and political scientists could be more closely involved in HTA research projects. I suspect that such a move would be more challenging for the economists than for the sociologists.

Credits

Meeting round-up: R for Cost-Effectiveness Analysis Workshop 2019

I have switched to using R for my cost-effectiveness models, but I know that I am not using it to its full potential. As a fledgling R user, I was keen to hear about other people’s experiences. I’m in the process of updating one of my models and know that I could be coding things better. But with so many packages and ways to code, the options seem infinite and I struggle to know where to begin. In an attempt to remedy this, I attended the Workshop on R for trial and model-based cost-effectiveness analysis hosted at UCL. I was not disappointed.

The day showcased speakers with varying levels of coding expertise doing a wide range of cool things in R. We started with examples of implementing decision trees using the CEdecisiontree package and cohort Markov models. We also got to hear about a population model using the HEEMOD package, and the purrr package was suggested for probabilistic sensitivity analyses. These talks highlighted how, compared to Excel, R can be reusable, faster, transparent, iterative, and open source.

The open source nature of R, however, has its drawbacks. One of the more interesting conversations that was woven in throughout the day was around the challenges. Can we can trust open-source software? When will NICE begin accepting models coded in R? How important is it that we have models in something like Excel that people can intuitively understand? I’ve not experienced problems choosing to use R for my work; for me, it’s always been around getting the support and information I need to get things done efficiently. The steep learning curve seems to be a major hurdle for many people. I had hoped to attend the short course introduction that was held the day before the workshop, but I was not fast enough to secure my spot as the course sold out within 36 hours. Never fear, the short course will be held again next year in Bristol.

To get around some of the aforementioned barriers to using R, James O’Mahony presented work on an open-source simplified screening model that his team is developing for teaching. An Excel interface with VBA code writes the parameter values in a file that can be imported into R, which has a single file of short model code. Beautiful graphs show the impact of important parameters on the efficiency frontier. He said that they would love to have people look at the code and give suggestions as they want to keep it simple but there is a nonlinear relationship between additional features and complexity.

And then we moved on to more specific topics, such as setting up a community for R users in the NHS, packages for survival curves, and how to build packages in R. I found Gianluca Baio’s presentation on what is a package and why we should be using them really helpful. I realised that I hadn’t really thought about what a package was before (a bundle of code, data, documentation and tests that is easy to share with others) or that it was something that I could or (as he argued) should be thinking about doing for myself as a time-saving tool even if I’m not sharing with others. It’s no longer difficult to build a package when you use packages like devtools and roxygen2 and tools like rstudio and github. He pointed out that packages can be stored on github if you’re not keen to share with the wider world via CRAN.

Another talk that I found particularly helpful was on R methods to prepare routine healthcare data for disease modelling. Claire Simons from The University of Oxford outlined her experiences of using R and ended her talk with a plethora of useful tips. These included using the data.table package for big data sets as it saves time when merging, use meaningful file names to avoid confusion later, and investing in doing things properly from the start as this will save time later. She also suggested using code profiling to identify which code takes the most time. Finally, she reminded us that we should be constantly learning about R: read books on R and writing algorithms and talk to other people who are using R (programmers and other people, not just health economists).

For those who agree that the future is R, check out resources from the Decision Analysis in R for Technologies in Health, a hackathon at Imperial on 6-7th November, hosted by Nathan Green, or join the ISPOR Open Source Models Special Interest Group.

Overall, the workshop structure allowed for a lot of great discussions in a relaxed atmosphere and I look forward to attending the next one.

Thesis Thursday: Rebecca Addo

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Rebecca Addo who has a PhD from the University of Technology Sydney. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
The feasibility of health technology assessment (HTA) in the Ghanaian health system
Supervisors
Jane Hall, Stephen Goodall, Marion Haas
Repository link
http://hdl.handle.net/10453/133353

Why is now the right time to research the feasibility of HTA in Ghana?

In recent years, Ghana has been struggling to financially sustain the National Health Insurance Scheme (NHIS), through which it aims to attain universal health coverage (UHC). As a result, a number of payment methods have been explored, including capitation, but costs to the NHIS continue to escalate. The search for a more efficient NHIS funding resulted in stakeholders visiting the then NICE International, learnt of HTA, and expressed an interest in pursuing it. This interest was strengthened by the World Health Organization 2014 resolution, which encouraged its member states to adopt health interventions and technology assessments in support of UHC. In 2016, a pilot HTA study was conducted with support from international bodies that demonstrated potential cost savings with HTA. Subsequently, the Ghana National Medicines Policy, 2017, made provisions for the use of HTA in the selection of medicines. What remains uncertain is how the policy will be implemented, considering that the limited use of HTA in developing countries has been attributed to a lack of human capacity to undertake it, quality data, and limited resources to support it. With Ghana making progress towards the formal adoption of HTA for health decision-making, it is important to examine its feasibility considering the available national capacity and the health system’s particular characteristics, and to make recommendations on how Ghana can proceed, so that the anticipated positive changes can be realised.

What determines ‘feasibility’ in this context?

The usefulness of HTA to any health system is highly dependent on its availability, the quality of assessment, and the human capacity to conduct country specific appraisals. Thus ‘feasibility’ in this context is determined by the existing health resources and systems that could support the adoption and use of HTA in Ghana. Health resources include human capacity with the needed technical skills to conduct and contribute to HTA, funding for the HTA processes, and the available data, which is of good quality and easily accessible. In addition, potential users of HTA should have knowledge in HTA and be able to interpret its findings. Without these building blocks, HTA in itself cannot be successfully used in Ghana. The systems to consider are health system characteristics such as existing health decision-making processes, and political and social structures. Knowledge of this would aid with planning, design, and introduction of an HTA process that suits the Ghanaian health system’s decision-making context, which would promote its use.

How is HTA perceived by stakeholders in Ghana?

Whilst the majority of Ghanaian stakeholders who participated in my study understood HTA as a decision making tool, others saw it as using technologies such as telemedicine and mobile phone devices for healthcare delivery. Their prior understanding of HTA and its uses drove these differences. In terms of its potential use in the Ghanaian health system, most stakeholders acknowledged the benefits the health system stood to gain should HTA be adopted. They however perceived some barriers to the successful implementation of HTA and made some recommendations to address them. Perceived barriers included lack of knowledge of HTA by potential users, lack of human resource capacity to conduct it, lack of funds to support the conduct, and existing ways of making decisions. Factors perceived to promote HTA use were allocating funds for HTA activities, educating stakeholders on HTA and involving them in the planning, and introduction of HTA for health decision-making in Ghana. Also, stakeholders recommended that data be collated and managed for HTA, and for local Ghanaians to be trained to conduct HTA but rely on experts from other countries where possible.

Was it especially challenging to conduct an economic evaluation in the Ghanaian context?

Yes. Conducting a Ghanaian specific economic evaluation was very challenging, especially, in getting the appropriate data. There were no country-specific utility and clinical efficacy data, hence, I had to rely on data from elsewhere, which needed to the transformed to be context specific. The most challenging aspect was with getting appropriate clinical data due to the differences between clinical trial settings and the Ghanaian setting. Applicability issues that were addressed included differences in clinical treatment algorithm, alternative treatments, and epidemiology of disease. Cultural acceptance of available treatment for the study population also defined the appropriate comparator for the evaluation and consequently the clinical data that could be considered. This resulted in having to draw on data from two separate arms of two clinical trials for one of the models I built for my economic evaluation. To ensure applicability of data from other countries to Ghana, the data identified were transformed to be context specific with data input from Ghana either not available or not easily accessible. Therefore, clinical experts were relied upon for such inputs, adding to the limitations of the economic evaluation.

Can HTA processes from other countries be applied in Ghana?

Every health system is unique in its entirety, therefore processes used in one cannot be adopted and applied to the other. The same applies to HTA in Ghana. As part of my thesis, I reviewed a number of HTA organisations across the world to assess if one could be adopted in Ghana. The review revealed that HTA processes vary with each health system in terms of the context under which they were established, the scope or focus of HTA, outcomes, and links to funding decisions and their uses. The establishment of most of these HTA organisations was driven by country specific needs such as curbing the rising costs of healthcare and reducing variations in the availability of quality treatment and care. The available resources, such as human and data, and the health systems characteristics also influenced the HTA processes. Therefore it is not advisable for Ghana to simply adopt and use a model of HTA process from other countries. Rather, Ghana must pursue a country specific HTA process that is informed by relevant country data.

What would be your recommended ‘next step’ for HTA in Ghana?

Firstly, to ensure the acceptance, use and diffusion of HTA in Ghana, stakeholders of health should be educated on HTA and a legal framework stipulating its focus and conduct, and mandating its use, to be adopted.

Secondly, in the short-to-medium term, Ghana can leverage on ongoing collaborations with other countries and foreign organisations, such as the International Decision Support initiative (IDSi), to develop local capacity for HTA. In the long-term, it will be necessary for policy makers to explore the human resource capacity available for HTA in Ghana to guide the development of a human resource plan for HTA.

Thirdly, Ghana has to develop a country-specific methodological guideline or adapt an existing one for the conduct and reporting of economic evaluation studies in Ghana. Subsequently, guidelines for conducting HTA should be developed.

Lastly, to support HTA conduct, Ghana must create a national data repository including a manual on health resource use and their corresponding unit prices. The creation of an HTA standing panel of clinical experts and other stakeholders who could be relied upon to supply inputs for HTA when needed is also recommended. This is very important in the Ghanaian setting where availability and access to data is limited.