Thesis Thursday: Estela Capelas Barbosa

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 Estela Capelas Barbosa who graduated with a PhD from the University of York. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Overall unfair inequality in health care: an application to Brazil
Richard Cookson
Repository link

What’s the difference between fair and unfair inequality, and why is it important to distinguish the two?

Not all inequality is the same. Whilst most inequality in health and health care is unwanted, one could argue that some inequality is even desirable. For example, we all agree that women should receive more care than men because they have a higher need for health care. The same argument could be used for children. Therefore, when looking into inequality, from a philosophical point of view, it is important to distinguish between inequality that is deemed fair (as in my women’s example) and that considered unfair. But there is a catch! Because ‘fair’ and ‘unfair’ are normative value judgements, different people may have different views as to what is fair or unfair. That’s why, in the thesis, I worked hard to come up with a framework that was flexible enough to allow for different views of fair and unfair.

Your thesis describes a novel way of thinking about inequality. What led you to believe that other conceptualisations were inadequate?

Previously, inequality in health care was either dealt with in overall terms, using a Gini coefficient type of analysis, or focused on income and socioeconomic inequality (see Wagstaff and Van Doorslaer, 2004). As a field researcher in Brazil, I had first-hand experience that there was more to unfair inequality than income. I remember personally meeting a very wealthy man that had many difficulties in accessing the healthcare system simply because he lived in a very remote rural area of the country. I wanted to better understand this and look beyond income to explain inequality in Brazil. Thus, neither of the well-established methods seemed really appropriate for my analysis. I knew I could adjust my Gini for need, but this type of analysis did not explicitly allow for a distinction between unfair and fair inequality. At the other extreme, income-related inequality was just a very narrow definition of unfairness. Although the established methods were my starting point, I agreed with Fleurbaey and Schokkaert that there could be yet another way of looking at inequality in health care, and I drew inspiration from their proposed method for health and made adjustments and modifications for the application to health care.

What were some of your key findings about the sources of inequality, and how were they measured in your data?

I guess my most important finding is that the sources of unfair inequality have changed between 1998 and 2013. For example, the contribution of income to unfair inequality decreased in this time for physician visits and mammography screening, yet for cervical screening it nearly doubled between 2003 and 2013. I have also found that there are other sources of inequality which are important (sometimes even more than income), as for example having private health insurance, education, living in urban areas and region.

As to my data, it came from Health Supplement of the Brazilian National Household Sample Survey for the years 1998, 2003 and 2008 and the first National Health Survey, conducted in 2013 (see The surveys use standardised questionnaires and rely on self-report for most questions, particularly those related to health care coverage and health status.

Your analysis looks at a relatively long period of time. What can you tell us about long-term trends in Brazil?

It is difficult to talk about long-term trends in Brazil at the moment. Our (universal) healthcare system has only been in place since 1988 and, since the last wave of data (in 2013), there has been a strong political movement to dismantle the national system and sell it to the private sector. I guess the movement to reduce and/or privatise the NHS also exists here, but, unlike in the UK, our national system has always been massively under-resourced, so it is not as highly-regarded by the population.

Having said that, it is fair to say that in its first 25 years of existence, Brazil has accomplished a lot in terms of healthcare (I have described – in Portuguese – some of the achievements and challenges). The Brazilian National Health System covers over 200 million people and accounts for nearly 500 thousand hospital beds. In terms of inequality, over time, it has decreased for physician visits and cervical screening, though for mammography there is no clear trend.

What would you like to see policymakers in Brazil prioritise in respect to reducing inequality?

First and foremost, I would like policymakers to understand that over three-quarters of the Brazilian population relies on the national system as their one and only health care provider. Second, I would like to reinforce the idea that social inequality in health care in Brazil is not only and indeed not primarily related to income. In fact, other social variables such as education, region, urban or rural residency and health insurance status are as important or even more important than income. This implies that there are supply side actions that can be taken, which should be much easier to implement. For example, more health care equipment, such as MRIs and CT scanners could be purchased for the North and Northeast regions. This could potentially reduce unfair inequality. Policies can also be directed at improving access to care in rural regions, although this factor is not as important a contributor to inequality as it used to be. I guess the overall message is: there are several things that can be done to reduce unfair inequality in Brazil, but all depend on political will and understanding the importance of the healthcare system for the health of the population.


Chris Sampson’s journal round-up for 14th August 2017

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.

Does paying service providers by results improve recovery outcomes for drug misusers in treatment in England? Addiction [PubMedPublished 10th August 2017

‘Getting what you pay for’ is a fundamentally attractive funding model, which is why we see lots of pay for performance (P4P) initiatives cropping up in the NHS. But P4P plans can go awry. This study considers an experimental setting in which 8 areas participated in P4P pilots for drug misuse treatment, from 2012-2014. Payments were aligned with 3 national priorities: i) abstinence, ii) reduced offending and iii) improved health and well-being. The participating areas allocated differing proportions of payments to the P4P model, between 10% and 100%. Data were drawn from the National Drug Treatment Monitoring System, which includes information on drug use, assessment and interventions received. Other national sources were used to identify criminal activity and mortality rates. Drug misusers attending treatment services during the 2 years before and after the introduction of the P4P scheme were included in the study. Using a difference-in-differences analysis, the researchers compared outcomes in the 8 participating areas with those in 143 non-participating areas. Separate multilevel regression models were used for a set of outcomes, each controlling for a variety of individual-level characteristics. The authors analysed ‘treatment journeys’, of which there were around 20,000 for those in participating areas and 280,000 for those in non-participating areas; roughly half before the introduction and half after. The results don’t look good for P4P. Use of opiates, crack cocaine and injecting increased. Treatment initiation increased in non-participating areas but decreased in participating areas. Moreover, longer waiting times were observed in participating areas as well as more unplanned discharges. P4P was associated with people being less likely to successfully complete treatment within 12 months. In P4P’s favour, there was evidence that abstinence increased. I’d’ve liked to have seen some attempt at matching between the areas, given that there was an element of self-selection into the scheme. Or at least, better control for the characteristics of the areas before P4P was introduced. This paper isn’t quite the final nail in the coffin. I don’t see P4P disappearing anytime soon. There’s a lot to be learnt from the paper’s discussion, which outlines some of the likely reasons and mechanisms underlying the findings. Commissioners should take note.

The short- and long-run effects of smoking cessation on alcohol consumption. International Journal of Health Economics and Management [PubMedPublished 7th August 2017

Anecdotally, it seems as if smoking and drinking are complementary behaviours. Generally, the evidence suggests that this is true. Smoking cessation programmes may, therefore, have value in their ability to reduce alcohol consumption (and vice versa). But only if the relationship is causal. This study seeks to add to that causal evidence. Using data from 5887 individuals in the Lung Health Study, the author runs a two-stage least squares estimation, with randomisation to smoking cessation treatment as an instrumental variable for smoking status. In the short term, there is some evidence that smokers tend to drink more (especially men). But findings in the longer term, up to 5 years, are more persuasive. It’s unfortunate that the (largely incoherent) rational addiction theory makes an appearance and that the findings are presented as supportive of it. A stopped clock is right twice a day. In line with rational addiction theory, the long-term relationship is measured in terms of a ‘smoking stock’, which is an aggregate measure of smoking behaviour over the 5 year period. Smoking and drinking are found to be complementary in the long term. Crucially, the extent of their complementarity is associated with particular factors. For example, people who smoke more cigarettes or who abstain for longer exhibit larger reductions in alcohol consumption when they stop smoking. People who smoke relatively few cigarettes per day do not drink more alcohol. Those smoking 6-10 per day consume around 1 extra drink per week compared with non-smokers. Quitting for 5 years can reduce alcohol consumption by more than 50%. In the long run, the effect is more pronounced for women and for people who are married. This highlights important opportunities for targeted public policy, which could achieve a win-win in terms of reducing both cigarette and alcohol consumption.

Time for a change in how new antibiotics are reimbursed: development of an insurance framework for funding new antibiotics based on a policy of risk mitigation. Health Policy Published 5th August 2017

Antibiotics have become a key component of health care, but antimicrobial resistance threatens their usefulness and we don’t see new antibiotics in the pipeline to help overcome this. It’s a fundamentally difficult problem; we want new antibiotics but we want to use them as sparingly as possible. Antibiotic development is relatively unattractive (financially) to pharmaceutical companies. Provision of research funding and regulatory changes haven’t solved the problem to date. This paper considers why this might be the case, and explores 2 alternative approaches: a premium price model and an insurance-type model. Essentially, the authors conduct a spreadsheet analysis to compare the alternative models with a base case of no incentives. The expected net present value of the base case was negative (to the tune of about $1.5 billion), demonstrating why much-needed new antibiotics aren’t being developed. Current incentives – including public-private funding partnerships and market exclusivity – are also shown to fail to reach a positive net present value. The premium price model, whereby there is an enhanced price per unit, is not particularly attractive. The daily cost of the resulting antibiotics would likely be too high, and manufacturers’ pursuit of profit would be at odds with conservative prescribing. Furthermore, it exposes areas experiencing outbreaks to serious financial risk. The insurance model, which involved an annual fee paid by each healthcare system (to manufacturers), is more promising. Pharmaceutical companies would be insured against low prices and variable use and health systems would be insured against a lack of antibiotics and the risk of an infection outbreak. The key feature here is that manufacturers’ revenues are de-linked from sales volume. This is important when we consider the need for conservative prescribing. The authors estimate that the necessary fee (for the global market) would be around $262 million per year, or $114 million if combined with current funding and regulatory incentives. Of course, these findings are based on major assumptions about infection rates, research costs and plenty besides. A number of sensitivity analyses are conducted that highlight uncertainty about what the insurance fee might need to be in the future. I think this uncertainty is somewhat understated – there are far more sensitivity and scenario analyses that would be warranted if such a policy were being seriously considered. Nevertheless, pooling risk in an insurance model looks like a promising strategy that’s worthy of further investigation and piloting.


Sam Watson’s journal round-up for 7th August 2017

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.

Financing transformative health systems towards achievement of the health Sustainable Development Goals: a model for projected resource needs in 67 low-income and middle-income countries. Lancet: Global Health [PubMedPublished 17th July 2017

Achieving universal health coverage is a key aspect of the UN’s sustainable development goals. However, what this means in practice is complicated. People need to be able to access health services free at the point of use, but once those services are accessed there needs to be sufficient labour, capital, skill, and quality to correctly diagnose and treat them. For many health systems worldwide, this will require large investments in infrastructure and staffing, but the potential cost of achieving these goals is unclear. This article sets out to estimate these costs. Clearly, this is a complicated task – health care systems are incredibly complex. From a basic microeconomic standpoint, one might need some understanding of the production function of different health care systems, and the marginal productivity of labour and capital inputs to these systems. There is generally good evidence of what is effective and cost-effective for the treatment of different diseases, and so given the amenable disease burden for a particular country, we could begin to understand what would be required to combat it. This is how this article tackles this question, more or less. They take a bottom-up costing approach to a wide range of interventions, governance requirements, and, where required, other interventions such as water and sanitation. However, there are other mechanisms at play. At national levels, economies of scale and scope play a role. Integration of care programs can reduce the costs, improve the quality, or both, of the individual programs. Similarly, the levels of investment considered are likely to have relevant macroeconomic effects, boosting employment, income, and subsequent socioeconomic indicators. Credit is due to the authors, they do consider financing and health impacts of investment, and their paper is the most comprehensive to date on the topic. However, their projections (~$300 billion annually) are perhaps more uncertain than they let on, a criticism I made of similar papers recently. While I should remind myself not to let the perfect be the enemy of the good, detailed case studies of particular countries may help me to see how the spreadsheet model may actually translate into real-world changes.

Precommitment, cash transfers, and timely arrival for birth: evidence from a randomized controlled trial in Nairobi Kenya. American Economic Review [RePEcPublished May 2017

A great proportion of the gains in life expectancy in recent years has been through the reduction of childhood mortality. The early years of life are some of the most precarious. A newborn child, if she survives past five years of age, will not face the same risk of dying until late adulthood. Many of the same risk factors that contribute to childhood mortality also contribute to maternal death rates and many low-income countries still face unacceptably high rates of dying for both mother and child. One way of tackling this is to ensure mothers have access to adequate antenatal and postnatal care. In Kenya, for example, the government legislated to provide free delivery services in government health facilities in 2013. However, Kenya still has some of the highest death rates for mother and child in the world. It is speculated that one reason for this is the delay in receiving services in the case of complications with a pregnancy. A potential cause of this delay in Nairobi is a lack of adequate planning from women who face a large number of heterogeneous treatment options for birth. This study presents an RCT in which pregnant women were offered a “precommitment transfer package”, which consisted of a cash transfer of 1000 KSh (~£7) during pregnancy and a further 1000 KSh if women stuck to a delivery plan they had earlier committed to. The transfer was found to increase the proportion of women arriving early to delivery facilities. The study was a fairly small pilot study and the results somewhat uncertain, but the intervention appears promising. Cost-effectiveness comparisons are warranted with other interventions aiming to achieve the same ends.

Bans on electronic cigarette sales to minors and smoking among high school students. Journal of Health Economics [PubMedPublished July 2017

E-cigarettes have provoked quite a debate among public health researchers and campaigners as we’ve previously discussed. E-cigarettes are a substitute for tobacco smoking and are likely to be significantly less harmful. They may have contributed to large declines in the use of tobacco in the UK in the last few years. However, some have taken a “think of the children!” position. While e-cigarette use per se among adolescents may not be a significant public health issue, it could lead to increased use of tobacco. Others have countered that those young people using e-cigarettes would have been those that used tobacco anyway, so banning e-cigarettes among minors may lead them to go back to the tobacco. This paper takes data from repeated surveys of high school students in the US to estimate the effects of banning the sale of e-cigarettes to minors on the prevalence of tobacco smoking. Interestingly, bans appear to reduce tobacco smoking prevalence; the results appear fairly robust and the modelling is sensible. This conflicts with other recent similar studies. The authors argue that this shows that e-cigarettes and tobacco smoking are complements, so reducing one reduces the other. But I am not sure this explains the decline since no increase in youth smoking was observed as e-cigarettes became more popular. Certainly, such a ban would not have reduced smoking prevalence years ago. At the very least e-cigarettes have clearly had a significant effect on attitudes towards smoking. Perhaps smoking was on the decline anyway – but the authors estimate a model with state-specific time trends, and no declines were seen in control states. Whatever our prior beliefs about the efficacy of regulating or banning e-cigarettes, the evidence is complex, reflecting the complex behaviour of people towards drugs, alcohol, and tobacco.