Lazaros Andronis’s journal round-up for 4th September 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.

The effect of spending cuts on teen pregnancy. Journal of Health Economics [PubMed] Published July 2017

High teenage pregnancy rates are an important concern that features high in many countries’ social policy agendas. In the UK, a country which has one of the highest teen pregnancy rates in the world, efforts to tackle the issue have been spearheaded by the Teenage Pregnancy Strategy, an initiative aiming to halve under-18 pregnancy rates by offering access to sex education and contraception. However, the recent spending cuts have led to reductions in grants to local authorities, many of which have, in turn, limited or cut a number of teenage pregnancy-related programmes. This has led to vocal opposition by politicians and organisations, who argue that cuts jeopardise the reductions in teenage pregnancy rates seen in previous years. In this paper, Paton and Wright set out to examine whether this is the case; that is, whether cuts to Teenage Pregnancy Strategy-related services have had an impact on teenage pregnancy rates. To do so, the authors used panel data from 149 local authorities in England collected between 2009 and 2014. To capture changes in teenage pregnancy rates across local authorities over the specified period, the authors used a fixed effects model which assumed that under-18 conception rates are a function of annual expenditure on teenage pregnancy services per 13-17 year female in the local authority, and a set of other socioeconomic variables acting as controls. Area and year dummies were also included in the model to account for unobservable effects that relate to particular years and localities and a number of additional analysis were run to get around spurious correlations between expenditure and pregnancy rates. Overall, findings showed that areas which implemented bigger cuts to teenage pregnancy-targeting programmes have, on average, seen larger drops in teenage pregnancy rates. However, these drops are, in absolute terms, small (e.g. a 10% reduction in expenditure is associated with a 0.25% decrease in teenage conception rates). Various explanations can be put forward to interpret these findings, one of which is that cuts might have trimmed off superfluous or underperforming elements of the programme. If this is the case, Paton and Wright’s findings offer some support to arguments that spending cuts may not always be bad for the public.

Young adults’ experiences of neighbourhood smoking-related norms and practices: a qualitative study exploring place-based social inequalities in smoking. Social Science & Medicine [PubMed] Published September 2017

Smoking is a universal problem affecting millions of people around the world and Canada’s young adults are no exception. As in most countries, smoking prevalence and initiation is highest amongst young groups, which is bad news, as many people who start smoking at a young age continue to smoke throughout adulthood. Evidence suggests that there is a strong socioeconomic gradient in smoking, which can be seen in the fact that smoking prevalence is unequally distributed according to education and neighbourhood-level deprivation, being a greater problem in more deprived areas. This offers an opportunity for local-level interventions that may be more effective than national strategies. Though, to come up with such interventions, policy makers need to understand how neighbourhoods might shape, encourage or tolerate certain attitudes towards smoking. To understand this, Glenn and colleagues saw smoking as a practice that is closely related to local smoking norms and social structures, and sought to get young adult smokers’ views on how their neighbourhood affects their attitudes towards smoking. Within this context, the authors carried out a number of focus groups with young adult smokers who lived in four different neighbourhoods, during which they asked questions such as “do you think your neighbourhood might be encouraging or discouraging people to smoke?” Findings showed that some social norms, attitudes and practices were common among neighbourhoods of the same SES. Participants from low-SES neighbourhoods reported more tolerant and permissive local smoking norms, whereas in more affluent neighbourhoods, participants felt that smoking was more contained and regulated. While young smokers from high SES neighbourhoods expressed some degree of alignment and agency with local smoking norms and practices, smokers in low SES described smoking as inevitable in their neighbourhood. Of interest is how individuals living in different SES areas saw anti-smoking regulations: while young smokers in affluent areas advocate social responsibility (and downplay the role of regulations), their counterparts in poorer areas called for more protection and spoke in favour of greater government intervention and smoking restrictions. Glenn and colleagues’ findings serve to highlight the importance of context in designing public health measures, especially when such measures affect different groups in entirely different ways.

Cigarette taxes, smoking—and exercise? Health Economics [PubMed] Published August 2017

Evidence suggests that rises in cigarette taxes have a positive effect on smoking reduction and/or cessation. However, it is also plausible that the effect of tax hikes extends beyond smoking, to decisions about exercise. To explore whether this proposition is supported by empirical evidence, Conway and Niles put together a simple conceptual framework, which assumes that individuals aim to maximise the utility they get from exercise, smoking, health (or weight management) and other goods subject to market inputs (e.g. medical care, diet aids) and time and budget constraints. Much of the data for this analysis came from the Behavioral Risk Factor Surveillance System (BRFSS) in the US, which includes survey participants’ demographic characteristics (age, gender), as well as answers to questions about physical activities and exercise (e.g. intensity and time per week spent on activities) and smoking behaviour (e.g. current smoking status, number of cigarettes smoked per day). Survey data were subsequently combined with changes in cigarette taxes and other state-level variables. Conway and Niles’s results suggest that increased cigarette costs reduce both smoking and exercise, with the decline in exercise being more pronounced among heavy and regular smokers. However, the direction of the effect varied according to one’s age and smoking experience (e.g. higher cigarette cost increased physical activity among recent quitters), which highlights the need for caution in drawing conclusions about the exact mechanism that underpins this relationship. Encouraging smoking cessation and promoting physical exercise are important and desirable public health objectives, but, as Conway and Niles’s findings suggest, pursuing both of them at the same time may not always be plausible.

Credits

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.

Title
Overall unfair inequality in health care: an application to Brazil
Supervisor
Richard Cookson
Repository link
http://etheses.whiterose.ac.uk/16649/

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 www.ibge.gov.br). 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.

Thesis Thursday: Miqdad Asaria

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 Miqdad Asaria 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.

Title
The economics of health inequality in the English National Health Service
Supervisors
Richard Cookson, Tim Doran
Repository link
http://etheses.whiterose.ac.uk/16189

What types of inequality are relevant in the context of the NHS?

For me the inequalities that really matter are the inequalities in health outcomes, in the English context it is particularly the socioeconomic patterning of these inequalities that is of concern. The focus of health policy in England over the last 200 years has been on improving the average health of the population as well as on providing financial risk protection against catastrophic health expenditure. Whilst great strides have been made in improving average population health through various pioneering interventions including the establishment of the NHS, health inequality has in fact consistently widened over this period. Recent research suggests that in terms of quality-adjusted life expectancy the gap between people living in the most deprived fifth of neighbourhoods in the country as compared to those living in the most affluent fifth is now approximately 11 quality-adjusted life years.

However, these socio-economic inequalities in health typically accumulate across the life course and there is a limited amount that health care on its own can do to prevent these gaps from widening or indeed to close these gaps once they emerge. This is why health systems including the NHS typically focus on measuring and tackling the inequalities that they can influence even though eliminating such inequalities can have at best only modest impacts on reducing health inequality overall. These comprise of inequalities in access to and quality of healthcare as well as inequality of those health outcomes specifically amenable to healthcare.

What were the key methods and data that you used to identify levels of health inequality?

I am currently working on a project with the Ministry of Health and Family Welfare in India and it is really making me appreciate the amazingly detailed and comprehensive administrative datasets available to researchers in England. For the work underpinning my thesis I linked 10 years of data looking at every hospital admission and outpatient visit in the country with the quality and outcomes achieved for patients registered at each primary care practice, the number of doctors working at each primary care practice, general population census data, cause-specific mortality data, hospital cost data and deprivation data all at neighbourhood level. I spent a lot of time assembling, cleaning and linking these data sets and then used this data platform to build a range of health inequality indicators – some of which can be seen in an interactive tool I built to present the data to clinical commissioning groups.

As well as measuring inequality retrospectively in order to provide evidence to evaluate past NHS policies, and building tools to enable the NHS to monitor inequality going forward, another key focus of my thesis was to develop methods to model and incorporate health inequality impacts into cost-effectiveness analysis. These methods allow analysts to evaluate proposed health interventions in terms of their impact on the distribution of health rather than just their impact on the mythical average citizen. The distributional cost-effectiveness analysis framework I developed is based on the idea of using social welfare functions to evaluate the estimated health distributions arising from the rollout of different health care interventions and compute the equity-efficiency trade-offs that would need to be made in order to prefer one intervention over another. A key parameter in this analysis required in order to make equity-efficiency trade-offs is the level of health inequality aversion. This parameter was quite tricky to estimate with methods used to elicit it from the general public being prone to various framing effects. The preliminary estimates that I used in my analysis for this parameter suggested that at the margin the general public thought people living in the most deprived fifth of neighbourhoods in the country deserve approximately 7 times the priority in terms of health care spending as those who live in the most affluent fifth of neighbourhoods.

Does your PhD work enable us to attach a ‘cost’ to inequality, and ‘value’ to policies that reduce it?

As budding economists, we are ever cautious to distinguish association and causation. My thesis starts by estimating the cost associated with inequality to the NHS. That is the additional cost to the NHS spent on treating the excess morbidity in those living in relatively deprived neighbourhoods. I estimated the difference between the actual NHS hospital budget and what the cost would have been if everybody in the country had the morbidity profile of those who live in just the most affluent fifth of neighbourhoods. For inpatient hospital costs this difference came to £4.8 billion per year and widening this to all NHS costs this came to £12.5 billion per year approximately a fifth of the total NHS budget. I looked both cross-sectionally and also modelled lifetime estimated health care use and found that even over their entire lifetimes people living in more deprived neighbourhoods consumed more health care despite their substantially shorter life expectancies.

This cost is of course very different to the value of policies to reduce inequality. This difference arises for two main reasons. First, my estimates were not causal but rather associations so we are unable to conclude that reducing socioeconomic inequality would actually result in everybody in the country gaining the morbidity profile of those living in the most affluent fifth of neighbourhoods. Second and perhaps more significantly, my estimates do not value any of the health benefits that would result from reducing health inequality they just count the costs that could be saved by the NHS due to the excess morbidity avoided. The value of these health benefits forgone in terms of quality adjusted life years gained would have to be converted into monetary terms using an estimate of willingness to pay for health and added to these cost savings (which themselves would need to be converted to consumption values) to get a total value of reducing inequality from a health perspective. There would also, of course, be a range of non-health impacts of reducing inequality that would need to be accounted for if this exercise were to be comprehensively conducted.

In simple terms, if the causal link between socioeconomic inequality and health could be determined then the value to the health sector of policies that could substantially reduce this inequality would likely be far greater than the costs quoted here.

How did you find the PhD-by-publication route? Would you recommend it?

I came to academia relatively late having previously worked in both the government and the private sector for a number of years. The PhD by publication route suited me well as it allowed me to get stuck into a number of projects, work with a wide range of academics and build an academic career whilst simultaneously curating a set of papers to submit as a thesis. However, it is certainly not the fastest way to achieve PhD status, my thesis took 6 years to compile. The publication route is also still relatively uncommon in England and I found both my supervisors and examiners somewhat perplexed about how to approach it. Additionally, my wife who did her PhD by the traditional route assures me that it is not a ‘proper’ PhD!

For those fresh out of an MSc programme the traditional route probably works well, giving you the opportunity to develop research skills and focus on one area in depth with lots of guidance from a dedicated supervisor. However, for people like me who probably would never have got around to doing a traditional PhD, it is nice that there is an alternative way to acquire the ‘Dr’ title which I am finding confers many unanticipated benefits.

What advice would you give to a researcher looking to study health inequality?

The most important thing that I have learnt from my research is that health inequality, particularly in England, has very little to do with health care and everything to do with socioeconomic inequality. I would encourage researchers interested in this area to look at broader interventions tackling the social determinants of health. There is lots of exciting work going on at the moment around basic income and social housing as well as around the intersection between the environment and health which I would love to get stuck into given the chance.