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.

Thesis Thursday: Kevin Momanyi

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 Kevin Momanyi who has a PhD from the University of Aberdeen. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Enhancing quality in social care through economic analysis
Supervisors
Paul McNamee
Repository link
http://digitool.abdn.ac.uk/webclient/DeliveryManager?pid=240815

What are reablement and telecare services and why should economists study them?

Reablement and telecare are two types of services within homecare that enable individuals to live independently in their own homes with little or no assistance from other people. Reablement focuses on helping individuals relearn the skills needed for independent living after an illness or injury. It is a short term intervention that lasts for about 6 to 12 weeks and usually involves several health care professionals and social care workers working together to meet some set objectives. Telecare, on the other hand, entails the use of devices (e.g. community alarms and linked pill dispensers) to facilitate communication between homecare clients and their care providers in the event of an accident or negative health shock. Economists should study reablement and telecare so as to determine whether or not the services have value for money and also develop policies that would reduce social care costs without compromising the welfare of the populace.

In what ways did your study reach beyond the scope of previous research?

My study extended the previous studies in three main ways. Firstly, I estimated the treatment effects in a non-experimental setting unlike the previous studies that used either randomised controlled trials or quasi-experiments. Secondly, I used linked administrative health and social care data in Scotland for the 2010/2011 financial year. The data covered the administrative records for the entire Scottish population and was larger and more robust than the data used by the previous studies. Thirdly, the previous studies were simply concerned with quantifying the treatment effects and thus did not provide a rationale as to how the interventions affect the outcomes of interest. My thesis addressed this knowledge gap by formulating an econometric model that links the demand for reablement/telecare to several outcomes.

How did you go about trying to estimate treatment effects from observational data?

I used a theory driven approach combined with specialised econometric techniques in order to estimate the treatment effects. The theoretical model drew from the Almost Ideal Demand System (AIDS), Andersen’s Behavioural Model of Health Services Use, the Grossman Model of the demand for health capital, and Samuelson’s Revealed Preference Theory; whereas the estimation strategy simultaneously controlled for unexplained trend variations, potential endogeneity of key variables, potential sample selection bias and potential unobserved heterogeneity. For a more substantive discussion of the theoretical model and estimation strategy, see Momanyi, 2018. Although the majority of the studies in the econometric literature advocate for the use of quasi-experimental study designs in estimating treatment effects using observational data, I provided several proofs in my thesis showing that these designs do not always yield consistent results, and that estimating the econometric models in the way that I did is preferable since it nests several study designs and estimation strategies as special cases.

Are there key groups of people that could benefit from greater use of reablement and telecare services?

According to the empirical results of my thesis, there is sufficient evidence to conclude that there are certain groups within the population that could benefit from greater use of telecare. For instance, one empirical study investigating the effect of telecare use on the expected length of stay in hospital showed that the community alarm users with physical disabilities are more likely than the other community alarm users to have a shorter length of stay in hospital, holding other factors constant. Correspondingly, the results also showed that the individuals who use more advanced telecare devices than the community alarm and who are also considered to be frail elderly are expected to have a relatively shorter length of stay in hospital as compared to the other telecare users in the population, all else equal. A discussion of various econometric models that can be used to link telecare use to the length of stay in hospital can be found in Momanyi, 2017.

What would be your main recommendation for policymakers in Scotland?

The main recommendation for policymakers is that they ought to subsidise the cost of telecare services, especially in regions that currently have relatively low utilisation levels, so as to increase the uptake of telecare in Scotland. This was informed by a decomposition analysis that I conducted in the first empirical study to shed light on what could be driving the observed direct relationship between telecare use and independent living at home. The analysis showed that the treatment effect was in part due to the underlying differences (both observable and unobservable) between telecare users and non-users, and thus policymakers could stimulate telecare use in the population by addressing these differences. In addition to that, policymakers should advise the local authorities to target telecare services at the groups of people that are most likely to benefit from them as well as sensitise the population on the benefits of using community alarms. This is because the econometric analyses in my thesis showed that the treatment effects are not homogenous across the population, and that the use of a community alarm is expected to reduce the likelihood of unplanned hospitalisation, whereas the use of the other telecare devices has the opposite effect all else equal.

Can you name one thing that you wish you could have done as part of your PhD, which you weren’t able to do?

I would have liked to include in my thesis an empirical study on the effects of reablement services. My analyses focused only on telecare use as the treatment variable due to data limitations. This additional study would have been vital in validating the econometric model that I developed in the first chapter of the thesis as well as addressing the gaps in knowledge that were identified by the literature review. In particular, it would have been worthwhile to determine whether reablement services should be offered to individuals discharged from hospital or to individuals who have been selected into the intervention directly from the community.

Thesis Thursday: David Mott

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 David Mott who has a PhD from Newcastle University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
How do preferences for public health interventions differ? A case study using a weight loss maintenance intervention
Supervisors
Luke Vale, Laura Ternent
Repository link
http://hdl.handle.net/10443/4197

Why is it important to understand variation in people’s preferences?

It’s not all that surprising that people’s preferences for health care interventions vary, but we don’t have a great understanding of what might drive these differences. Increasingly, preference information is being used to support regulatory decisions and, to a lesser but increasing extent, health technology assessments. It could be the case that certain subgroups of individuals would not accept the risks associated with a particular health care intervention, whereas others would. Therefore, identifying differences in preferences is important. However, it’s also useful to try to understand why this heterogeneity might occur in the first place.

The debate on whose preferences to elicit for health state valuation has traditionally focused on those with experience (e.g. patients) and those without (e.g. the general population). Though this dichotomy is problematic; it has been shown that health state utilities systematically differ between these two groups, presumably due to the difference in relative experience. My project aimed to explore whether experience also affects people’s preferences for health care interventions.

How did you identify different groups of people, whose preferences might differ?

The initial plan for the project was to elicit preferences for a health care intervention from general population and patient samples. However, after reviewing the literature, it seemed highly unlikely that anyone would advocate for preferences for treatments to be elicited from general population samples. It has long been suggested that discrete choice experiments (DCEs) could be used to incorporate patient preferences into decision-making, and it turned out that patients were the focus of the majority of the DCE studies that I reviewed. Given this, I took a more granular approach in my empirical work.

We recruited a very experienced group of ‘service users’ from a randomised controlled trial (RCT). In this case, it was a novel weight loss maintenance intervention aimed at helping obese adults that had lost at least 5% of their overall weight to maintain their weight loss. We also recruited an additional three groups from an online panel. The first group were ‘potential service users’ – those that met the trial criteria but could not have experienced the intervention. The second group were ‘potential beneficiaries’ – those that were obese or overweight and did not meet the trial criteria. The final group were ‘non-users’ – those with a normal BMI.

What can your study tell us about preferences in the context of a weight loss maintenance intervention?

The empirical part of my study involved a DCE and an open-ended contingent valuation (CV) task. The DCE was focused on the delivery of the trial intervention, which was a technology-assisted behavioural intervention. It had a number of different components but, briefly, it involved participants weighing themselves regularly on a set of ‘smart scales’, which enabled the trial team to access and monitor the data. Participants received text messages from the trial team with feedback, reminders to weigh themselves (if necessary), and links to online tools and content to support the maintenance of their weight loss.

The DCE results suggested that preferences for the various components of the intervention varied significantly between individuals and between the different groups – and not all were important. In contrast, the efficacy and cost attributes were important across the board. The CV results suggested that a very significant proportion of individuals would be willing to pay for an effective intervention (i.e. that avoided weight regain), with very few respondents expressing a willingness to pay for an intervention that led to more than 10-20% weight regain.

Do alternative methods for preference elicitation provide a consistent picture of variation in preferences?

Existing evidence suggests that willingness to pay (WTP) estimates from CV tasks might differ from those derived from DCE data, but there aren’t a lot of empirical studies on this in health. Comparisons were planned in my study, but the approach taken in the end was suboptimal and ultimately inconclusive. The original plan was to obtain WTP estimates for an entire WLM intervention using the DCE and to compare this with the estimates from the CV task. Due to data limitations, it wasn’t possible to make this comparison. However, the CV task was a bit unusual because we asked for respondents’ WTP at various different efficacy levels. So instead the comparison made was between average WTP values for a percentage point of weight re-gain. The differences were statistically insignificant.

Are some people’s preferences ‘better defined’ than others’?

We hypothesised that those with experience of the trial intervention would have ‘better defined’ preferences. To explore this, we compared the data quality across the different user groups. From a quick glance at the DCE results, it is pretty clear that the data were much better for the most experienced group; the coefficients were larger, and a much higher proportion was statistically significant. However, more interestingly, we found that the most experienced group were 23% more likely to have passed all of the rationality tests that were embedded in the DCE. Therefore, if you accept that better quality data is an indicator of ‘better defined’ preferences, then the data do seem reasonably supportive of the hypothesis. That being said, there were no significant differences between the other three groups, begging the question: was it the difference in experience, or some other difference between RCT participants and online panel respondents?

What does your research imply for the use of preferences in resource allocation decisions?

While there are still many unanswered questions, and there is always a need for further research, the results from my PhD project suggest that preferences for health care interventions can differ significantly between respondents with differing levels of experience. Had my project been applied to a more clinical intervention that is harder for an average person to imagine experiencing, I would expect the differences to have been much larger. I’d love to see more research in this area in future, especially in the context of benefit-risk trade-offs.

The key message is that the level of experience of the participants matters. It is quite reasonable to believe that a preference study focusing on a particular subgroup of patients will not be generalisable to the broader patient population. As preference data, typically elicited from patients, is increasingly being used in decision-making – which is great – it is becoming increasingly important for researchers to make sure that their respondent samples are appropriate to support the decisions that are being made.