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.

The economics of health inequality in the English National Health Service
Richard Cookson, Tim Doran
Repository link

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.


Chris Sampson’s journal round-up for 8th May 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.

Verification of decision-analytic models for health economic evaluations: an overview. PharmacoEconomics [PubMed] Published 29th April 2017

Increasingly, it’s expected that model-based economic evaluations can be validated and shown to be fit-for-purpose. However, up to now, discussions have focussed on scientific questions about conceptualisation and external validity, rather than technical questions, such as whether the model is programmed correctly and behaves as expected. This paper looks at how things are done in the software industry with a view to creating guidance for health economists. Given that Microsoft Excel remains one of the most popular software packages for modelling, there is a discussion of spreadsheet errors. These might be errors in logic, simple copy-paste type mistakes and errors of omission. A variety of tactics is discussed. In particular, the authors describe unit testing, whereby individual parts of the code are demonstrated to be correct. Unit testing frameworks do not exist for application to spreadsheets, so the authors recommend the creation of a ‘Tests’ spreadsheet with tests for parameter assignments, functions, equations and exploratory items. Independent review by another modeller is also recommended. Six recommendations are given for taking model verification forward: i) the use of open source models, ii) standardisation in model storage and communication (anyone for a registry?), iii) style guides for script, iv) agency and journal mandates, v) training and vi) creation of an ISPOR/SMDM task force. This is a worthwhile read for any modeller, with some neat tactics that you can build into your workflow.

How robust are value judgments of health inequality aversion? Testing for framing and cognitive effects. Medical Decision Making [PubMed] Published 25th April 2017

Evidence shows that people are often extremely averse to health inequality. Sometimes these super-egalitarian responses imply such extreme preferences that monotonicity is violated. The starting point for this study is the idea that these findings are probably influenced by framing effects and cognitive biases, and that they may therefore not constitute a reliable basis for policy making. The authors investigate 4 hypotheses that might indicate the presence of bias: i) realistic small health inequality reductions vs larger one, ii) population- vs individual-level descriptions, iii) concrete vs abstract intervention scenarios and iv) online vs face-to-face administration. Two samples were recruited: one with a face-to-face discussion (n=52) and the other online (n=83). The questionnaire introduced respondents to health inequality in England before asking 4 questions in the form of a choice experiment, with 20 paired choices. Responses are grouped according to non-egalitarianism, prioritarianism and strict egalitarianism. The main research question is whether or not the alternative strategies resulted in fewer strict egalitarian responses. Not much of an effect was found with regard to large gains or to population-level descriptions. There was evidence that the abstract scenarios resulted in a greater proportion of people giving strong egalitarian responses. And the face-to-face sample did seem to exhibit some social desirability bias, with more egalitarian responses. But the main take-home message from this study for me is that it is not easy to explain-away people’s extreme aversion to health inequality, which is heartening. Yet, as with all choice experiments, we see that the mode of administration – and cognitive effects induced by the question – can be very important.

Adaptation to health states: sick yet better off? Health Economics [PubMed] Published 20th April 2017

Should patients or the public value health states for the purpose of resource allocation? It’s a question that’s cropped up plenty of times on this blog. One of the trickier challenges is understanding and dealing with adaptation. This paper has a pretty straightforward purpose – to look for signs of adaptation in a longitudinal dataset. The authors’ approach is to see whether there is a positive relationship between the length of time a person has an illness and the likelihood of them reporting better health. I did pretty much the same thing (for SF-6D and satisfaction with life) in my MSc dissertation, and found little evidence of adaptation, so I’m keen to see where this goes! The study uses 4 waves of data from the British Cohort Study, looking at self-assessed health (on a 4-point scale) and self-reported chronic illness and health shocks. Latent self-assessed health is modelled using a dynamic ordered probit model. In short, there is evidence of adaptation. People who have had a long-standing illness for a greater duration are more likely to report a higher level of self-assessed health. An additional 10 years of illness is associated with an 8 percentage point increase in the likelihood of reporting ‘excellent’ health. The study is opaque about sample sizes, but I’d guess that finding is based on not-that-many people. Further analyses are conducted to show that adaptation seems to become important only after a relatively long duration (~20 years) and that better health before diagnosis may not influence adaptation. The authors also look at specific conditions, finding that some (e.g. diabetes, anxiety, back problems) are associated with adaptation, while others (e.g. depression, cancer, Crohn’s disease) are not. I have a bit of a problem with this study though, in that it’s framed as being relevant to health care resource allocation and health technology assessment. But I don’t think it is. Self-assessed health in the ‘how healthy are you’ sense is very far removed from the process by which health state utilities are obtained using the EQ-5D. And they probably don’t reflect adaptation in the same way.


Brent Gibbons’s journal round-up for 12th December 2016

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.

As the U.S. moves into a new era with the recent election results, Republicans will have a chance to modify or repeal the Affordable Care Act. The Affordable Care Act (ACA), also called Obamacare, is a comprehensive health reform that was enacted on the 23rd of March, 2010, that helped millions of uninsured individuals and families gain coverage through new private insurance coverage and through expanded Medicaid coverage for those with very low income. The ACA has been nothing short of controversial and has often been at the forefront of partisan divides. The ACA was an attempt to fill the insurance coverage gaps of the patchwork American health insurance system that was built on employer-sponsored insurance (ESI) and a mix of publicly funded programs for various vulnerable subpopulations. The new administration and republican legislators are promising to repeal the law, at least in part, and have suggested plans that will re-emphasize the private insurance model based on ESI. For this reason, the following articles selected for this week’s round-up highlight different aspects of ESI.

The Mental Health Parity and Addiction Equity Act evaluation study: Impact on specialty-behavioral health utilization and expenditures among “carve-out” enrollees. Journal of Health Economics [PubMed] Published December 2016

Behavioral health services have historically been covered at lower levels and with more restrictions by ESI than physical health services. Advocates for behavioral health system reform have pushed for equal coverage of behavioral health services for decades. In 2008, the Mental Health Parity and Addiction Equity Act (MHPAEA) was passed with a fairly comprehensive set of rules for how behavioral health coverage would need to be comparable to medical/surgical coverage, including for ESI. This first article in our round-up examines the impact of this law on utilization and expenditures of behavioral health services in ESI plans. The authors use an individual-level interrupted time series design using panel data with monthly measures of outcomes. Administrative claims and enrollment data are used from a large private insurance company that provides health insurance for a number of large employers in the years 2008 – 2013. A segmented regression analysis is used in order to measure the impact of the law at two different time points, first in 2010 for what is considered a transition year, and then in the 2011 – 2013 period, both compared to the pre-MHPAEA time period, 2008 – 2009. Indicator variables are used for the different periods as well as spline variables to measure the change in level and slope of the time trends, controlling for other explanatory variables. Results suggest that MHPAEA had little effect on utilization and total expenditures, but that out-of-pocket expenditures were shifted from the patient to the health plan. For patients who had positive expenditures, there was a post-MHPAEA level increase in health plan expenditures of $58.03 and a post-MHPAEA level decrease in out-of-pocket expenditure of $21.58, both per-member-per-month. To address worries of confounding time trends, the authors performed several sensitivity analyses, including a difference-in-difference (DID) analysis that used states that already had strict parity legislation as a comparison population. The authors also examined those with a bipolar or schizophrenia disorder to test the hypothesis that impacts may be stronger for individuals with more severe conditions. Sensitivity analyses tended to result in larger p-values. These results, which were examined at the mean, are consistent with reports that the primary change in behavioral health coverage in ESI was the elimination of treatment limits. In addition to using a sensitivity analysis with individuals with bipolar and schizophrenia, it would have been interesting to see impacts for individuals defined as “high-utilizers”. It would also have been nice to see a longer pre-MHPAEA time period since insurers could have adjusted plans prior to the 2010 effective date.

Health plan type variations in spells of health-care treatment. American Journal of Health Economics [RePEcPublished 12th October 2016

Health care costs in the U.S. were roughly 17.8 percent of the GDP in 2015 and attempts to rein in health insurance costs have largely proved elusive. Different private insurance health plans have tried to rein in costs through different plan types that have a mix of supply-side mechanisms and demand-side mechanisms. Two recent plan types that have emerged are exclusive provider organizations (EPOs) and consumer-driven/high-deductible health plans (CDHPs). EPOs use a more narrowly restricted network of providers that agree to lower payments and presumably also deliver quality care while CDHPs give patients broader networks but shift cost-sharing to patients. EPOs therefore are more focused on supply-side mechanisms of cost reduction, while CDHPs emphasize demand-side incentives to reduce costs. Ellis and Zhu use a large ESI claims-based dataset to examine the impact of these two health plan types and to try to answer whether supply-side or demand-side mechanisms of cost reduction are more effective. The authors present an extremely extensive analysis that is really worth reading. They use a technique for modeling periods of care, called treatment “spells” that is a mix of monthly treatment periods and episode-based models of care. Utilization and expenditures are examined in the context of these treatment “spells” for the different health plan types. A 2SLS regression model is used that controls for endogenous plan choice in the first-stage. The predicted probabilities from plan choice are used as an instrument in the second stage along with a number of controls, including risk-adjustment techniques and individual fixed effects. The one drawback in using the predicted probabilities as the sole instrument is it is not possible to perform an exclusion test. The results, however, suggest that neither of the new plan types performs better than a standardly used health plan. EPOs have the lowest overall spending, but are not significantly different than the standard plan type, and CDHPs have 16 percent higher spending than the standard plan type. The CDHPs in particular have not been studied carefully and these results suggest that previous research on CDHPs found cost-savings due to younger and healthier patients and not because of plan type effects. There are also worries with high deductible plans that patients may elect to forgo necessary healthcare services.

The financial burdens of high-deductible plans. Health Affairs [PubMed] Published December 2016

Having discussed the consumer-directed/high deductible health plans, this third journal article looks at the Medical Expenditure Panel Survey (MEPS) data to examine the burden high deductible health plans place on individuals and families with low incomes. High deductible health plans like the CDHPs are increasingly offered. High deductible plans are sometimes paired with the option to use a flexible spending account (FSA). An FSA gives the patient the option to set aside money from her salary or paycheck that can only be used for healthcare costs, with the benefit that the money set aside will not be subject to various income taxes. The benefit of the high deductible plan is supposed to be lower premiums and the possibility of saving money through the FSA, if that option is available. Yet descriptive analyses using MEPS data from 2011 – 2013 from ESI plans show that high deductible plans impose a particularly high burden on individuals with family incomes below 250 percent of the poverty line. Specifically, the authors found that 29.1 percent of individuals with high deductible plans had financial costs exceeding 20 percent of family income, compared to 20.6 percent of individuals with low deductible plans. For individuals with family income greater than 400 percent of the poverty line, financial burden was not different for high deductible plans compared to other plan types. Yet worryingly, individuals with low incomes were just as likely to have high deductible plans as individuals with high incomes.