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.

Thesis Thursday: Matthew Quaife

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

Title
Using stated preferences to estimate the impact and cost-effectiveness of new HIV prevention products in South Africa
Supervisors
Fern Terris-Prestholt, Peter Vickerman
Repository link
http://researchonline.lshtm.ac.uk/4646708

Stated preferences for what?

Our main study looked at preferences for new HIV prevention products in South Africa – estimating the uptake and cost-effectiveness of multi-purpose prevention products, which protect against HIV, pregnancy and STIs. You’ll notice that condoms do this, so why even bother? Condom use needs both partners to agree (for the duration of a given activity) and, whilst female partners tend to prefer condom-protected sex, there is lots of evidence that male partners – who also have greater bargaining power in many contexts – do not.

Oral pre-exposure prophylaxis (PrEP), microbicide gels, and vaginal rings are new products which prevent HIV infection. More importantly, they are female-initiated and can generally be used without a male partner’s knowledge. But trials and demonstration projects among women at high risk of HIV in sub-Saharan Africa have shown low levels of uptake and adherence. We used a DCE to inform the development of attractive and usable profiles for these products, and also estimate how much additional demand – and therefore protection – would be gained from adding contraceptive or STI-protective attributes.

We also elicited the stated preferences of female sex workers for client risk, condom use, and payments for sex. Sex workers can earn more for risky unprotected sex, and we used a repeated DCE to predict risk compensation (i.e. how much condom use would change) if they were to use HIV prevention products.

What did you find most influenced people’s preferences in your research?

Unsurprisingly for products, HIV protection was most important to people, followed by STI and then pregnancy protection. But digging below these averages with a latent class analysis, we found some interesting variation within female respondents: over a third were not concerned with HIV protection at all, instead strongly caring about pregnancy and STI protection. Worryingly, these were more likely to be respondents from high-incidence adolescent and sex worker groups. The remainder of the sample overwhelmingly chose based on HIV protection.

In the second sex worker DCE, we found that using a new HIV prevention product made condoms become less important and price more important. We predict that the price premium for unprotected sex would reduce by two thirds, and the amount of condomless sex would double. This is an interesting labour market/economic finding, but – if true – also has real public health implications. Since economic changes mean sex workers move from multi-purpose condoms to single-purpose products which need high levels of adherence, we thought this would be interesting to model.

How did you use information about people’s preferences to inform estimates of cost-effectiveness?

In two ways. First, we used simple uptake predictions from DCEs to parameterise an HIV transmission model, allowing for condom substitution uptake to vary by condom users and non-users (it was double in the latter). We were also able to model the potential uptake of multipurpose products which don’t exist yet – e.g. a pill protecting from HIV and pregnancy. We predict that this combination, in particular, would double uptake among high-risk young women.

Second, we predicted risk compensation among sex workers who chose new products instead of condoms. We were also able to calculate the price elasticity of supply of unprotected sex, which we built into a dynamic transmission model as a determinant of behaviour.

Can discrete choice experiments accurately predict the kinds of behaviours that you were looking at?

To be honest, when I started the PhD I was really sceptical – and I still am to an extent. But two things make me think DCEs can be useful in predicting behaviours.

First is the data. We published a meta-analysis of how well DCEs predict real-world health choices at an individual level. We only found six studies with individual-level data, but these showed DCEs predict with an 88% sensitivity but just a 34% specificity. If a DCE says you’ll do something, you more than likely will – which is important for modelling heterogeneity in uptake. We desperately need more studies following up DCE participants making real-world choices.

Second is the lack of alternative inputs. Where products are new and potential users are inexperienced, modellers pick an uptake number/range and hope for the best. Where we don’t know efficacy, we may assume that uptake and efficacy are linearly related – but they may not be (e.g. if proportionately more people use a 95% effective product than a 45% effective one). Instead, we might assume uptake and efficacy are independent, but that might sound even less realistic. I think that DCEs can tell us something about these behaviours that are useful for the parameters and structures of models, even if they are not perfect predictors.

Your tread the waters of infectious disease modelling in your research – was the incorporation of economic factors a challenge?

It was pretty tricky, though not as challenging as building the simple dynamic transmission model as a first exposure to R. In general, behaviours are pretty crudely modelled in transmission models, largely due to assumptions like random mixing and other population-level dynamics. We made a simple mechanistic model of sex work based on the supply elasticities estimated in the DCE, and ran a few scenarios, each time estimating the impact of prevention products. We simulated the price of unprotected sex falling and quantity rising as above, but also overlaid a few behavioural rules (e.g. Camerer’s constant income hypothesis) to simulate behavioural responses to a fall in overall income. Finally, we thought about competition between product users and non-users, and how much the latter may be affected by the market behaviours of the former. Look out for the paper at Bristol HESG!

How would you like to see research build on your work to improve HIV prevention?

I did a public engagement event last year based on one statistic: if you are a 16-year old girl living in Durban, you have an 80% lifetime risk of acquiring HIV. I find it unbelievable that, in 2018, when millions have been spent on HIV prevention and we have a range of interventions that can prevent HIV, incidence among some groups is still so dramatically and persistently high.

I think research has a really important role in understanding how people want to protect themselves from HIV, STIs, and pregnancy. In addition to highlighting the populations where interventions will be most cost-effective, we show that variation in preferences drives impact. I hope we can keep banging the drum to make attractive and effective options available to those at high risk.