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.

Meeting round-up: Health Economists’ Study Group (HESG) Winter 2019

2019 started with aplomb with the HESG Winter meeting, superbly organised by the Centre for Health Economics, University of York.

Andrew Jones kicked off proceedings with his brilliant course on data visualisation in health econometrics. The eager audience learnt about Edward Tufte’s and others’ ideas about how to create charts that help to make it much easier to understand information. The course was tremendously well received by the HESG audience. And I know that I’ll find it incredibly useful too, as there were lots of ideas that apply to my work. So I’m definitely going to be looking further into Andrew’s chapter on data visualisation to know more.

The conference proper started in the afternoon. I had the pleasure to chair the fascinating paper by Manuela Deidda et al on an economic evaluation using observational data on the Healthy Start Voucher, which was discussed by Anne Ludbrook. We had an engaging discussion, that not only delved into the technical aspects of the paper, such as the intricacies of implementing propensity score matching and regression discontinuity, but also about the policy implications of the results.

I continued with the observational data theme by enjoying the discussion led by Panos Kasteridis on the Andrew McCarthy et al paper. Then I quickly followed this by popping over to catch Attakrit Leckcivilize’s excellent discussion of Padraig Dixon’s et al paper on the effect of obesity on hospital costs. This impressive paper uses Mendelian randomisation, which is a fascinating approach using a type of instrumental variable analysis with individuals’ genetic variants as the instrument.

The meeting continued in the stunning setting of the Yorkshire Museum for the plenary session, which also proved a fitting location to pay tribute to the inspirational Alan Maynard, who sadly passed away in 2018. Unfortunately, I was unable to hear the tributes to Alan Maynard in person, but fellow attendees were able to paint a moving portrait of the event on Twitter, that kept me in touch.

The plenary was chaired by Karen Bloor and included presentations by Kalipso Chalkidou, Brian Ferguson, Becky Henderson and Danny PalnochJane Hall, Steve Birch and Maria Goddard gave personal tributes.

The health economics community was united in gratitude to Professor Alan Maynard, who did so much to advance and disseminate the discipline. It made for a wonderful way to finish day 1!

Day 2 started bright and was full of stimulating sessions to choose from.

I chose to zone in on the cost-effectiveness topic in particular. I started with the David Glynn et al paper about using “back of the envelope” calculations to inform funding and research decisions, discussed by Ed Wilson. This paper is an excellent step towards making value of information easy to use.

I then attended Matthew Quaife’s discussion of Matthew Taylor’s paper on the consequences of assuming independence of parameters to decision uncertainty. This is a relevant paper for the cost-effectiveness world, in particular for those tasked with building and appraising cost-effectiveness models.

Next up it was my turn in the hot seat, as I presented the Jose Robles-Zurita et al paper on the economic evaluation of diagnostic tests. This thought-provoking paper presents a method to account for the effect of accuracy on the uptake of the test, in the context of maximising health.

As always, we were spoilt for choice in the afternoon. The paper “Drop dead: is anchoring at ‘dead’ a theoretical requirement in health state valuation” by Chris Sampson et al, competed very strongly with “Is it really ‘Grim up North’? The causes and consequences of inequalities on health and wider outcomes” by Anna Wilding et al, for the most provocative title. “Predicting the unpredictable? Using discrete choice experiments in economic evaluation to characterise uncertainty and account for heterogeneity”, from Matthew Quaife et al, also gave them a run for their money! I’ll leave a sample here of the exciting papers in discussion, so you can make your own mind up:

Dinner was in the splendid Merchant Adventurers’ Hall. Built in 1357, it is one of the finest Medieval buildings in the UK. Another stunning setting that provided a beautiful backdrop for a wonderful evening!

Andrew Jones presented the ‘Health Economics’ PhD Poster Prize, sponsored by Health Economics Wiley. Rose Atkins took the top honours by winning the Wiley prize for best poster. With Ashleigh Kernohan’s poster being highly commended, given its brilliant use of technology. Congratulations both!

Unfortunately, the vagaries of public transport meant I had to go home straight after dinner, but I heard from many trustworthy sources, on the following day, that the party continued well into the early hours. Clearly, health economics is a most energising topic!

For me, day 3 was all about cost-effectiveness decision rules. I started with the paper by Mark Sculpher et al, discussed by Chris Sampson. This remarkable paper sums up the evidence on the marginal productivity of the NHS, discussing how to use it to inform decisions, and proposes an agenda for research. There were many questions and comments from the floor, showing how important and challenging this topic is. As are so many papers in HESG, this is clearly one to look out for when it appears in print!

The next paper was on a very different way to solve the problem of resource allocation in health care. Philip Clarke and Paul Frijters propose an interesting system of auctions to set prices. The paper was well discussed by James Lomas, which kick-started an animated discussion with the audience about practicalities and implications for investment decisions by drug companies. Great food for thought!

Last, but definitely not least, I took in the paper by Bernarda Zamora et al on the relationship between health outcomes and expenditure across geographical areas in England. David Glynn did a great job discussing the paper, and especially in explaining data envelopment analysis. As ever, the audience was highly engaged and put forward many questions and comments. Clearly, the productivity of the NHS is a central question for health economics and will keep us busy for some time to come.

As always, this was a fantastic HESG meeting that was superbly organised, providing an environment where authors, discussants and participants alike were able to excel.

I really felt a feeling of collegiality, warmth and energy permeate the event. We are part of such an amazing scientific community. Next stop, HESG Summer meeting, hosted by the University of East Anglia. I’m already looking forward to it!

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Sam Watson’s journal round-up for 30th April 2018

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.

The Millennium Villages Project: a retrospective, observational, endline evaluation. The Lancet Global Health [PubMedPublished May 2018

There are some clinical researchers who would have you believe observational studies are completely useless. The clinical trial is king, they might say, observation studies are just too biased. And while it’s true that observational studies are difficult to do well and convincingly, they can be a reliable and powerful source of evidence. Similarly, randomised trials are frequently flawed, for example there’s often missing data that hasn’t been dealt with, or a lack of allocation concealment, and many researchers forget that randomisation does not guarantee a balance of covariates, it merely increases the probability of it. I bring this up, as this study is a particularly carefully designed observational data study that I think serves as a good example to other researchers. The paper is an evaluation of the Millennium Villages Project, an integrated intervention program designed to help rural villages across sub-Saharan Africa meet the Millennium Development Goals over ten years between 2005 and 2015. Initial before-after evaluations of the project were criticised for inferring causal “impacts” from before and after data (for example, this Lancet paper had to be corrected after some criticism). To address these concerns, this new paper is incredibly careful about choosing appropriate control villages against which to evaluate the intervention. Their method is too long to summarise here, but in essence they match intervention villages to other villages on the basis of district, agroecological zone, and a range of variables from the DHS – matches were they reviewed for face validity and revised until a satisfactory matching was complete. The wide range of outcomes are all scaled to a standard normal and made to “point” in the same direction, i.e. so an increase indicated economic development. Then, to avoid multiple comparisons problems, a Bayesian hierarchical model is used to pool data across countries and outcomes. Costs data were also reported. Even better, “statistical significance” is barely mentioned at all! All in all, a neat and convincing evaluation.

Reconsidering the income‐health relationship using distributional regression. Health Economics [PubMed] [RePEcPublished 19th April 2018

The relationship between health and income has long been of interest to health economists. But it is a complex relationship. Increases in income may change consumption behaviours and a change in the use of time, promoting health, while improvements to health may lead to increases in income. Similarly, people who are more likely to make higher incomes may also be those who look after themselves, or maybe not. Disentangling these various factors has generated a pretty sizeable literature, but almost all of the empirical papers in this area (and indeed all empirical papers in general) use modelling techniques to estimate the effect of something on the expected value, i.e. mean, of some outcome. But the rest of the distribution is of interest – the mean effect of income may not be very large, but a small increase in income for poorer individuals may have a relatively large effect on the risk of very poor health. This article looks at the relationship between income and the conditional distribution of health using something called “structured additive distribution regression” (SADR). My interpretation of SADR is that, one would model the outcome y ~ g(a,b) as being distributed according to some distribution g(.) indexed by parameters a and b, for example, a normal or Gamma distribution has two parameters. One would then specify a generalised linear model for a and b, e.g. a = f(X’B). I’m not sure this is a completely novel method, as people use the approach to, for example, model heteroscedasticity. But that’s not to detract from the paper itself. The findings are very interesting – increases to income have a much greater effect on health at the lower end of the spectrum.

Ask your doctor whether this product is right for you: a Bayesian joint model for patient drug requests and physician prescriptions. Journal of the Royal Statistical Society: Series C Published April 2018.

When I used to take econometrics tutorials for undergraduates, one of the sessions involved going through coursework about the role of advertising. To set the scene, I would talk about the work of Alfred Marshall, the influential economist from the late 1800s/early 1900s. He described two roles for advertising: constructive and combative. The former is when advertising grows the market as a whole, increasing everyone’s revenues, and the latter is when ads just steal market share from rivals without changing the size of the market. Later economists would go on to thoroughly develop theories around advertising, exploring such things as the power of ads to distort preferences, the supply of ads and their complementarity with the product they’re selling, or seeing ads as a source of consumer information. Nevertheless, Marshall’s distinction is still a key consideration, although often phrased in different terms. This study examines a lot of things, but one of its key objectives is to explore the role of direct to consumer advertising on prescriptions of brands of drugs. The system is clearly complex: drug companies advertise both to consumers and physicians, consumers may request the drug from the physician, and the physician may or may not prescribe it. Further, there may be correlated unobservable differences between physicians and patients, and the choice to advertise to particular patients may not be exogenous. The paper does a pretty good job of dealing with each of these issues, but it is dense and took me a couple of reads to work out what was going on, especially with the mix of Bayesian and Frequentist terms. Examining the erectile dysfunction drug market, the authors reckon that direct to consumer advertising reduces drug requests across the category, while increasing the proportion of requests for the advertised drug – potentially suggesting a “combative” role. However, it’s more complex than that patient requests and doctor’s prescriptions seem to be influenced by a multitude of factors.

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