Thesis Thursday: Frank Sandmann

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 Frank Sandmann who has a PhD from the London School of Hygiene & Tropical Medicine. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
The true cost of epidemic and outbreak diseases in hospitals
Supervisors
Mark Jit, Sarah Deeny, Julie Robotham, John Edmunds
Repository link
http://researchonline.lshtm.ac.uk/4648208/

Do you refer to the ‘true’ cost because some costs are hidden in this context?

That’s a good observation. Economists use the term “true cost” as a synonym for “opportunity cost”, which can be defined as the net value of the forgone second-best use of a resource. The true value of a hospital bed is therefore determined by its second-best use, which may indeed be less easily observed and less obvious, or somewhat hidden.

In the context of infectious disease outbreaks in hospital, the most visible costs are the direct expenditures on treatments of infected cases and any measures of containment. However, they do not capture the full extent of the “alternative” costs and therefore cannot equal opportunity costs. Slightly less visible are the potential knock-on effects for visitors to the hospital who, unbeknown to them, may get infected and contribute to sustained transmission in the community. Least seen are the externalities borne by patients who have not been admitted so far but who are awaiting admission, and for whom there is no space in hospital yet due to the ongoing outbreak.

In my thesis, I provided a general overview of the historical development of the concept of opportunity costs of resources before I looked in detail at bed-days and the application for hospitals.

How should the opportunity cost of hospital stays be determined?

That depends on for whom you want to determine these costs.

For individual patients, it depends on the very subjective decision of how else they would spend their time instead, and how urgent it is to receive hospital care.

From the perspective of hospital administrators, it is straightforward to calculate the opportunity costs based on the revenues and expenditures of the inpatients, their length of stays, and the existing demand of care from the community. This is quite important because whether there are opportunity costs from forgone admissions will depend on whether there are other patients actually waiting to be admitted, which is somewhat reflected in occupancy rates and of course waiting lists.

Any other decision maker who is acting as an agent on behalf of a collective group or the public should look into the forgone health impact of patients who cannot be admitted when the beds are unavailable to them. In my thesis, I proposed a method for quantifying the opportunity costs of bed-days with the net benefit of the second-best patients forgone, which I illustrated with the example of norovirus-associated gastroenteritis.

How important are differences in methods for costing in the context of gastroenteritis and norovirus?

The results can differ quite substantially when using different costing methods. Norovirus is an ideal illness to illustrate this issue given that otherwise healthy people with gastrointestinal symptoms and no further comorbidities or complications shouldn’t be admitted to hospital in order to minimise the risk of an outbreak. Patients with norovirus are therefore often not the patient group that is benefitting the most from a hospital stay.

In one of the studies of my PhD, I was able to show that the annual burden of norovirus in public hospitals in England amounts to a mean £110 million using conventional costing methods, while the opportunity costs were two-to-three times higher of up to £300 million.

This means that there is the potential for a situation where an intervention is disadvantaged when using conventional methods for costing and ignoring the opportunity costs. When evaluating such an intervention against established decision rules of cost-effectiveness, this may lead to an incorrect decision.

What were some of the key challenges that you encountered in estimating the cost of norovirus to hospitals, and how did you overcome them?

There were at least four key challenges:

First was the number of admissions. Many inpatients with norovirus won’t get recorded as such if they haven’t been laboratory-confirmed. That is why I regressed national inpatient episodes of gastroenteritis against laboratory surveillance reports for ten different gastrointestinal pathogens to estimate the norovirus-attributable proportion.

Second was the number of bed-days used by inpatients that were infected with norovirus during their hospital stay. Using their total length of stay, or some form of propensity matching, suffers from time-dependent biases and overestimates the number of bed-days. Instead, I used a multi-state model and patient-level data from a local hospital.

Third was the bed-days that were left unoccupied for infection control. One of the datasets tracked them mandatorily for acute hospitals during winters, while another surveillance system was voluntary, but recorded outbreaks throughout the year. For a more accurate estimate, I compared both datasets with each other to explore their potential overlap.

Fourth was the forgone health of alternative admissions who had otherwise occupied the beds. I had to make assumptions about the disease progression with and without hospital treatment, for which I used health-state utilities that accounted for age, sex, and the primary medical condition.

If you could have wished for one additional set of data that wasn’t available, what would it have been?

I have been very fortunate to work with a number of colleagues at Public Health England and University College London who provided me with much of the epidemiological data that I needed. My research could have benefitted though from a dataset that tracked the time of infection for a larger patient population and for longer observation periods, and a dataset that included more robust estimates for the health gain from hospital care.

If I could make a wish about the existing datasets on norovirus that I have used, I would wish for a higher rate of reporting given that it became clear from our comparison of datasets that there is a highly-correlated trend, but the number of outbreaks reported and the details of reporting leave room for improvement. Another wish of mine for daily reporting of bed-days during winter became reality only recently; during my PhD, I had to impute missing values that were non-randomly missing at weekends and over the Christmas period. This was changed in winter 2016, and I have recently shown that the mean of our lowest-to-highest imputation scenarios is surprisingly close to the daily number of bed-days recorded since then.

Parts of your thesis are made up of journal articles that you published before submission. Was this always your intention and how did you find the experience?

I always wanted to publish parts of my thesis in separate journal articles as I believe this to be a great chance to reach different audiences. That is because my theoretical research on opportunity costs may be of broader interest than just to those who work on norovirus or bed-days given that my findings are generalisable to other diseases as well as other resources. At the same time, others may be more interested in my results for norovirus, and still others in my application of the various statistical, economic, and mathematical modelling techniques.

After all, I honestly suspect that some people may place a higher value on their next-best alternative use of time than reading my thesis from cover to cover.

Writing up my thoughts early on also helped me refine them, and the peer-review process was a great opportunity to get some additional feedback. It did require good time management skills though to keep coming back to previous studies to address the peer-reviewers’ comments while I was already busy working on the next studies.

All in all, I can recommend others to consider it and, looking back, I’d do it again this way.

Thesis Thursday: Wenjia Zhu

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 Wenjia Zhu who has a PhD from Boston University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Health plan innovations and health care costs in the commercial health insurance market
Supervisors
Randall P. Ellis, Thomas G. McGuire, Keith M. Ericson
Repository link
https://hdl.handle.net/2144/27355

What kinds of ‘innovations’ did you want to look at in your research, and why?

My dissertation investigated health plan “innovations” for cost containment, in which certain features are designed into health insurance contracts to influence how health care is delivered and utilized. While specifics may vary considerably across health plans, recent “innovations” feature two main strategies for constraining health spending. One is a demand-side strategy, which aims to reduce health care utilization through high cost-sharing on the consumer side. Plans using this strategy include “high-deductible” or “consumer-driven” health plans. The other is a supply-side strategy, in which insurers selectively contract with low-cost providers whom consumers have access to, thereby directing consumers to those low-cost providers. Plans employing this strategy include “narrow network” health plans.

Despite an ongoing debate about whether the demand-side or supply-side strategy is more effective at reducing costs, there is little work to guide this debate due to challenges in causal inference, estimation, and measurement. As a result, the question of cost containment through insurance benefit designs remains largely unresolved. To shed light on this debate, I investigated these two strategies using a large, multiple-employer, multiple-insurer panel dataset which allowed me to address various methodological challenges through the use of modern econometrics tools and novel estimation methods.

How easy was it to access the data that you needed to answer your research questions?

The main data for my dissertation research come from the Truven Analytic’s MarketScan® Commercial Claims and Encounters Database, which contains administrative claims of a quarter of the U.S. population insured through their employment. I was fortunate to access this database through the data supplier’s existing contract with Boston University, and the entire process of accessing the data involved low effort overall.

Occasionally I needed to refine my research questions or find alternative approaches because certain pieces of information were not available in this database and were hard to access elsewhere. For example, in Chapter 1, we did not further examine heterogeneity of plan coverage within plan types because detailed premiums or benefit features of health plans were not observed (Ellis and Zhu 2016). In Chapter 3, I sought out an alternative approach in lieu of the maximum likelihood (ML) method when estimating provider network breadth because provider identifiers were not coded consistently across health plans in my data, precluding the reliable construction of one key element in the ML method.

Your PhD research tackled several methodological challenges. Which was the most difficult to overcome?

In the course of my research, I found myself in constant need of estimating models that require controlling for multiple fixed effects, each of high dimension (something we called “high-dimensional fixed effects”). One example is health care utilization models that control for provider, patient, and county fixed effects. In these models, however, estimation often became computationally infeasible in the presence of large sample sizes and unbalanced panel datasets. Traditional approaches to absorbing fixed effects no longer worked, and the models with billions of data points could barely be handled in Stata even though it provides some convenient user-written commands (e.g. REGHDFE).

This motivated me and my coauthors to devote an entire chapter in my dissertation to looking into this issue. In Chapter 2, we developed a new algorithm that estimates models with multiple high-dimensional fixed effects while accommodating such features as unbalanced panels, instrumental variables, and cluster-robust variance estimation. The key to our approach is an iterative process of sequentially absorbing fixed effects based on the Frisch-Waugh-Lovell Theorem. By writing up our algorithm into a SAS macro that does not require all data to reside in core memory, we can handle datasets of essentially any size.

Did you identify any health plan designs that reduced health care costs?

Certainly. My dissertation shows that health plans that manage care – imposing cost-sharing, requiring gatekeepers, or restricting consumer choice of providers – spent much less (on procedures) compared to comprehensive insurance plans that do not have any of these “care management” elements, even after controlling for patient selection into plan types.

On the other hand, we did not find evidence that either of the new health plan “innovations” – high cost-sharing or narrow networks – particularly saved health care costs compared to Preferred Provider Organizations (PPOs) (Ellis and Zhu 2016). One possibility is that incentives to control one aspect of spending create compensating effects in other aspects. For example, although high-deductible/consumer-driven health plans shift cost responsibility from employers to enrollees, they did not reduce health care spending due to higher provider prices and higher coding intensity. Similarly, while narrow network plans reduced treatment utilization, they did so mostly for the less severely ill, creating the offsetting incentive of up-coding by providers on the remaining sicker patients.

Based on your findings, what would be your first recommendation to policymakers?

To improve the effectiveness of health care cost containment, my first recommendation to policymakers would be to design mechanisms to more effectively monitor and reduce service prices.

My dissertation shows that while tremendous efforts have been made by health plans to design mechanisms to manage health care utilization (e.g., through imposing a higher cost-sharing on consumers) and to direct patients to certain providers (e.g., through selective contracting), overall cost containment, if any, has been rather modest due to insufficient price reductions. For example, we found that high-deductible/consumer-driven health plans had significantly higher average procedure prices than PPOs (Ellis and Zhu 2016). Even for narrow network plans in which insurers selectively contract with providers, we did not find evidence that these plans were successful in keeping low-cost providers. Difficulties of keeping prices down may reflect unbalanced bargaining power between insurers and providers, as well as special challenges in consumers price-shopping in the presence of complex insurance contract designs (Brot-Goldberg et al. 2017).

Thesis Thursday: James Oswald

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

Title
Essays on well-being and mental health: determinants and consequences
Supervisors
Sarah Brown, Jenny Roberts, Bert Van Landeghem
Repository link
http://etheses.whiterose.ac.uk/18915/

What measures of health did you use in your research and how did these complement broader measures of well-being?

I didn’t use any measures of physical health. I used a few measures of subjective well-being (SWB) and mental health which vary across the chapters. In Chapter 2 I used life satisfaction and the Rutter Malaise Inventory. Life satisfaction is a global retrospective judgement of one’s life and is a measure of evaluative well-being (see Dolan and Metcalfe, 2012). The Rutter Malaise Inventory, a measure of affective well-being, is an index that is composed of 9 items that measure the respondent’s symptoms of psychological distress or depression. The measure of the mental health problems of adolescents that is used in Chapter 3 is the Strengths and Difficulties Questionnaire (SDQ). The SDQ is made up of four 5-item subscales: emotional problems, peer relationship problems, conduct problems, and hyperactivity/ inattention problems. Chapter 3 utilises the General Health Questionnaire (GHQ) as a measure of the mental health of parents. The GHQ is a screening instrument that was initially developed to diagnose psychiatric disorders. Chapter 4 utilises one measure of subjective well-being, which relates to the number of days of poor self-reported mental health (stress, depression, and problems with emotions) in the past 30 days.

Did your research result in any novel findings regarding the social determinants of mental well-being?

The findings of Chapter 2 suggested that bullying victimisation at age 11 has a large, adverse effect on SWB as an adult. Childhood bullying remains prevalent – recent estimates suggest that approximately 20-30% of children are bullied by other children. The evidence provided in Chapter 3 indicated that greater externalising problems of adolescents are positively associated with the likelihood that they engage in antisocial behaviour. Chapter 4 indicated two important findings. Firstly, Hurricane Katrina had a negative effect on the SWB of individuals living in the states that were directly affected by the disaster. Secondly, the analysis suggested that the Indian Ocean tsunami and the Haiti earthquake increased the SWB of Americans living closest to the affected areas.

How can natural disasters affect mental health?

My thesis presents evidence to suggest that their impact depends upon whether you live in the disaster area. I explored the role of geography by exploring the effects of three disasters – hurricane Katrina in 2005 (USA), Indian Ocean tsunami in 2004 (East Asia), and the Haiti earthquake in 2010. Firstly, Hurricane Katrina had a negative effect on the SWB of individuals living in the states that were directly affected by the disaster. As a result, the findings suggest that government intervention in the aftermath of disasters is needed to help mitigate the adverse effects of natural disasters on the SWB of people who live in the directly affected areas. For example, appropriate mental health services and counselling could be offered to people suffering unhappiness or distress. Secondly, the analysis suggested that the Indian Ocean tsunami and the Haiti earthquake increased the SWB of Americans living closest to the affected areas. This surprising finding may be explained by the interdependence of utility functions. Following the disasters, Americans were exposed to widespread coverage of the disasters via social and traditional media sources. Because of the media coverage, they may have thought about the catastrophic repercussions of the disasters for the victims. Consequently, Americans who lived closest to the affected areas may have compared themselves to the disaster victims, leading them to feel thankful that the disaster did not affect them, thus increasing their SWB.

The empirical results support the case that the utility functions of strangers may be interdependent, rather than independent, an assumption generally made in economics. Furthermore, the findings indicated no evidence that the effects of the disasters were more pronounced for individuals of the same ethnicity as the disaster victims. The results therefore suggest that geographical proximity to the affected areas, rather than sharing similar characteristics with the disaster victims, may determine the effects of natural disasters on SWB outside of the areas that were directly affected by the disasters. This issue is discussed in greater detail in Chapter 4 of my thesis.

How did you go about identifying some of the consequences of mental health problems?

My thesis uses a range of econometric methods to explore the determinants and consequences of mental health and subjective well-being. In Chapter 2 – for childhood bullying and adult subjective well-being – I used a range of methods including random effects ordered probit models, Hausman tests, and Heckman models. Chapter 3 investigates how the mental health of adolescents affects their participation in antisocial behaviour. The analysis uses random effects probit, multivariate probit, and conditional logit models. Chapter 4 investigates the effects of three natural disasters on subjective well-being in the USA. The chapter uses difference-in-differences methodology with a count data model called a zero-inflated negative binomial model.

Are there any policy recommendations that you would make in light of your research?

Chapter 2 suggests that being bullied as a child adversely affects subjective well-being as an adult. My analysis supports the case that preventing children bullying in schools may have a positive effect on the SWB of a large percentage of the adult population. Chapter 3 indicated that greater externalising problems of adolescents are positively associated with the likelihood that they engage in antisocial behaviour. Previous research has suggested that adolescents who commit antisocial behaviour have an increased probability of committing crime as adults. Consequently, the findings suggest that mental health interventions to target the externalising problems of adolescents may reduce future crime. The findings also suggest that the money spent on the “Troubled Families” programme may be spent more cost-effectively in reducing antisocial behaviour by expanding access to mental health interventions for adolescents, such as via the Improving Access to Psychological Therapies programme.

The findings of Chapter 4 suggest that government intervention in the aftermath of disasters is needed to help mitigate the adverse effects of natural disasters on the SWB of people who live in the directly affected areas. For example, appropriate mental health services and counselling could be offered to people suffering unhappiness or distress because of natural disasters.