Thesis Thursday: Logan Trenaman

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 Logan Trenaman who has a PhD from the University of British Columbia. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Economic evaluation of interventions to support shared decision-making: an extension of the valuation framework
Supervisors
Nick Bansback, Stirling Bryan
Repository link
http://hdl.handle.net/2429/66769

What is shared decision-making?

Shared decision-making is a process whereby patients and health care providers work together to make decisions. For most health care decisions, where there is no ‘best’ option, the most appropriate course of action depends on the clinical evidence and the patient’s informed preferences. In effect, shared decision-making is about reducing information asymmetry, by allowing providers to inform patients about the potential benefits and harms of alternative tests or treatments, and patients to express their preferences to their provider. The goal is to reach agreement on the most appropriate decision for that patient.

My thesis focused on individuals with advanced osteoarthritis who were considering whether to undergo total hip or knee replacement, or use non-surgical treatments such as pain medication, exercise, or mobility aids. Joint replacement alleviates pain and improves mobility for most patients, however, as many as 20-30% of recipients have reported insignificant improvement in symptoms and/or dissatisfaction with results. Shared decision-making can help ensure that those considering joint replacement are aware of alternative treatments and have realistic expectations about the potential benefits and harms of each option.

There are different types of interventions available to help support shared decision-making, some of which target the patient (e.g. patient decision aids) and some of which target providers (e.g. skills training). My thesis focused on a randomized controlled trial that evaluated a pre-consultation patient decision aid, which generated a summary report for the surgeon that outlined the patient’s knowledge, values, and preferences.

How can the use of decision aids influence health care costs?

The use of patient decision aids can impact health care costs in several ways. Some patient decision aids, such as those evaluated in my thesis, are designed for use by patients in preparation for a consultation where a treatment decision is made. Others are designed to be used during the consultation with the provider. There is some evidence that decision aids may increase up-front costs, by increasing the length of consultations, requiring investments to integrate decision aids into routine care, or train clinicians. These interventions may impact downstream costs by influencing treatment decision-making. For example, the Cochrane review of patient decision aids found that, across 18 studies in major elective surgery, those exposed to decision aids were less likely to choose surgery compared to those in usual care (RR: 0.86, 95% CI: 0.75 to 1.00).

This was observed in the trial-based economic evaluation which constituted the first chapter of my thesis. This analysis found that decision aids were highly cost-effective, largely due to a smaller proportion of patients undergoing joint replacement. Of course, this conclusion could change over time. One of the challenges of previous cost-effectiveness analysis (CEA) of patient decision aids has been a lack of long-term follow-up. Patients who choose not to have surgery over the short-term may go on to have surgery later. To look at the longer-term impact of decision aids, the third chapter of my thesis linked trial participants to administrative data with an average of 7-years follow-up. I found that, from a resource use perspective, the conclusion was the same as observed during the trial: fewer patients exposed to decision aids had undergone surgery, resulting in lower costs.

What is it about shared decision-making that patients value?

On the whole, the evidence suggests that patients value being informed, listened to, and offered the opportunity to participate in decision-making (should they wish!). To better understand how much shared decision-making is valued, I performed a systematic review of discrete choice experiments (DCEs) that had valued elements of shared decision-making. This review found that survey respondents (primarily patients) were willing to wait longer, pay, and in some cases willing to accept poorer health outcomes for greater shared decision-making.

It is important to consider preference heterogeneity in this context. The last chapter of my PhD performed a DCE to value shared decision-making in the context of advanced knee osteoarthritis. The DCE included three attributes: waiting time, health outcomes, and shared decision-making. The latent class analysis found four distinct subgroups of patients. Two groups were balanced, and traded between all attributes, while one group had a strong preference for shared decision-making, and another had a strong preference for better health outcomes. One important finding from this analysis was that having a strong preference for shared decision-making was not associated with demographic or clinical characteristics. This highlights the importance of each clinical encounter in determining the appropriate level of shared decision-making for each patient.

Is it meaningful to estimate the cost-per-QALY of shared decision-making interventions?

One of the challenges of my thesis was grappling with the potential conflict between the objectives of CEA using QALYs (maximizing health) and shared decision-making interventions (improved decision-making). Importantly, encouraging shared decision-making may result in patients choosing alternatives that do not maximize QALYs. For example, informed patients may choose to delay or forego elective surgery due to potential risks, despite it providing more QALYs (on average).

In cases where a CEA finds that shared decision-making interventions result in poorer health outcomes at lower cost, I think this is perfectly acceptable (provided patients are making informed choices). However, it becomes more complicated when shared decision-making interventions increase costs, result in poorer health outcomes, but provide other, non-health benefits such as informing patients or involving them in treatment decisions. In such cases, decision-makers need to consider whether it is justified to allocate scarce health care resources to encourage shared decision-making when it requires sacrificing health outcomes elsewhere. The latter part of my thesis tried to inform this trade-off, by valuing the non-health benefits of shared decision-making which would not otherwise be captured in a CEA that uses QALYs.

How should the valuation framework be extended, and is this likely to indicate different decisions?

I extended the valuation framework by attempting to value non-health benefits of shared decision-making. I followed guidelines from the Canadian Agency for Drugs and Technologies in Health, which state that “the value of non-health effects should be based on being traded off against health” and that societal preferences be used for this valuation. Requiring non-health benefits to be valued relative to health reflects the opportunity cost of allocating resources toward these outcomes. While these guidelines do not specifically state how to do so, I chose to value shared decision-making relative to life-years using a chained (or two-stage) valuation approach so that they could be incorporated within the QALY.

Ultimately, I found that the value of the process of shared decision-making was small, however, this may have an impact on cost-effectiveness. The reasons for this are twofold. First, there are few cases where shared decision-making interventions improve health outcomes. A 2018 sub-analysis of the Cochrane review of patient decision aids found little evidence that they impact health-related quality of life. Secondly, the up-front cost of implementing shared decision-making interventions may be small. Thus, in cases where shared decision-making interventions require a small investment but provide no health benefit, the non-health value of shared decision-making may impact cost-effectiveness. One recent example from Dr Victoria Brennan found that incorporating process utility associated with improved consultation quality, resulting from a new online assessment tool, increased the probability that the intervention was cost-effective from 35% to 60%.

Thesis Thursday: Firdaus Hafidz

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

Title
Assessing the efficiency of health facilities in Indonesia
Supervisors
Tim Ensor, Sand Tubeuf
Repository link
http://etheses.whiterose.ac.uk/id/eprint/21575

What are some of the key features of health and health care in Indonesia?

Indonesia is a diverse country, with more than 17 thousand islands and 500 districts. Thus, there is a wide discrepancy of health outcomes across Indonesia, which also reflects the country’s double burden of both communicable and emerging non-communicable diseases. Communicable diseases such as tuberculosis, diarrhoea and lower respiratory tract infections remain as significant issues in Indonesia, especially in remote areas. At the same time, non-communicable diseases are becoming a major public health problem, especially in urban areas.

Total healthcare expenditure per capita grew rapidly, but in certain outcomes, such as maternal mortality rate, Indonesia performs less well than other low- and middle-income countries. Health facilities represent the largest share of healthcare expenditures, but utilisation is still considered low in both hospitals and primary healthcare facilities. Given the scarcity of public healthcare resources, out-of-pocket expenditure remains considerably higher than the global average.

To reduce financial barriers, the Government of Indonesia introduced health insurance in 1968. Between 2011 and 2014, there were three major insurance schemes: 1) Jamkesmas – poor scheme; 2) Jamsostek – formal sector workers scheme; and 3) Askes – civil servant scheme. In 2014, the three schemes were combined into a single-entity National Health Insurance scheme.

What methods can be used to measure the efficiency of health care in low and middle-income countries?

We reviewed measurements of efficiency in empirical analyses conducted in low- and middle-income countries. Methods, including techniques, variables, and efficiency indicators were summarised. There was no consensus on the most appropriate technique to measure efficiency, though most existing studies have relied on ratio analysis and data envelopment analysis because it is simple, easy to compute, low-cost and can be performed on small samples. The physical inputs included the type of capital (e.g. the number of beds and size of health facilities) and the type of labour (e.g. the number of medical and non-medical staff). Most of the published literature used health services as outputs (e.g. the number of outpatient visits, admission and inpatient days). However, because of poor data availability, fewer studies used case-mix and quality indicators to adjust outputs. So most of the studies in the literature review assumed that there was no difference in the severity and effectiveness of healthcare services. Despite the complexity of the techniques, researchers are responsible to provide interpretable results to the policymakers to guide their decisions for a better health policy on efficiency. Adopting appropriate methods that have been used globally would be beneficial to benchmark empirical studies.

Were you able to identify important sources of inefficiency in Indonesia?

We used several measurement techniques including frontier analysis and ratio analysis. We explored contextual variables to assess factors determining efficiency. The range of potential models produced help policymakers in the decision-making process according to their priority and allow some control over the contextual variables. The results revealed that the efficiency of primary care facilities can be explained by population health insurance coverage, especially through the insurance scheme for the poor. Geographical factors, such as the main islands (Java or Bali), better access to health facility, and location in an urban area also have a strong impact on efficiency. At the hospitals, the results highlighted higher efficiency levels in larger hospitals; they were more likely to present in deprived areas with low levels of education; and they were located on Java or Bali. Greater health insurance coverage also had a positive and significant influence on efficiency.

How could policymakers improve the efficiency of health care in Indonesia or other similar settings?

I think there are several ideas. First, we need to have a careful tariff adjustment as we found an association between low unit costs and high efficiency scores. Case base group tariffs need to account for efficiency scores to prevent unnecessary incentives for the providers, exacerbating inefficiency in the health system.

Secondly, we need flexibility in employment contracts, particularly for the less productive civil servant worker so the less productive worker could be reallocated. We also need a better remuneration policy to attract skilled labour and improve health facilities efficiency.

From the demand side, reducing physical barriers by improving infrastructure could increase efficiency in the rural health care facilities through higher utilisation of care. Facilities with very low utilisation rates still incur a fixed cost and thus create inefficiency. Through the same argument we also need to reduce financial barriers using incentives programmes and health insurance, thus patients who are economically disadvantaged can access healthcare services.

How would you like to see other researchers build on your work?

Data quality is crucial in secondary data analysis research, and it was quite a challenge in an Indonesian setting. Meticulous data management is needed to mitigate data errors such as inconsistency, outliers and missing values.

As this study used a 2011 cross-sectional dataset, replicating this study using a more recent and even longitudinal data would highlight changes in efficiency due to policy changes or interventions. Particularly interesting is the effect of the 2014 implementation of Indonesian national health insurance.

My study has some limitations and thus warrants further investigation. The stochastic frontier analysis failed to identify any inefficiency at hospitals when outpatient visits were included. The statistical errors of the frontier function cannot be distinguished from the inefficiency effect of the model. It might be related to the volume and heterogeneity of outpatient services which swamps the total volume of services and masks any inefficiency.

Thesis Thursday: Ernest Law

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 Ernest Law who has a PhD from the University of Illinois at Chicago. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Examining sources of variation in developing a societal health state value set
Supervisors
Simon Pickard, Todd Lee, Surrey Walton, Alan Schwartz, Feng Xie
Repository link
http://hdl.handle.net/10027/23037

How did you come to study EQ-5D valuation methods, and why are they important?

I came across health preferences research after beginning my studies at UIC with my thesis supervisor, Prof. Simon Pickard. Before this, I was a clinical pharmacist who spent a lot of time helping patients and their families navigate the trade-offs between the benefits and harms of pharmacotherapy. So, when I was introduced to a set of methods that seeks to quantify such trade-offs, I was quickly captivated and set on a path to understanding more. I continued on to expand my interests in valuation methods pertinent to health-system decision-making. Naturally, I collided with societal health state value sets – important tools developed from generic preference-based measures, such as the EQ-5D.

During my studies at UIC, our group received a grant (PI: Simon Pickard) from the EuroQol Research Foundation to develop the United States EQ-5D-5L value set. While developing the study protocol, we built in additional data elements (e.g., EQ-5D-3L valuation tasks, advance directive status) that would help answer important questions in explaining variation in value sets. By understanding these sources of variation, we could inform researchers and policymakers alike on the development and application of EQ-5D value sets.

What does your thesis add to the debate about EQ-5D-3L and -5L value sets?

As a self-reported measure, the literature appears reasonably clear regarding the 5L’s advantages over the 3L: reduced ceiling effects, more unique self-reported health states, and improved discriminatory power. However, less was known on how differences in descriptive systems impact direct valuations.

Previous comparisons focused on differences in index scores and QALYs generated from existing value sets. But these value sets differed in substantive ways: preferences from different respondents, in different time periods, from different geographic locations, using different study protocols. This makes it difficult to isolate the differences due to the descriptive system.

In our study, we asked respondents in the US EQ-5D valuation study to complete time trade-off tasks for 3L and 5L health states. By doing so, we were able to hold many of the aforementioned factors constant except the valued health state. From a research perspective, we provide strong evidence on how even small changes in the descriptive system can have a profound impact on the valuations. From a policy perspective, and an HTA agency deciding specifically between the 3L and 5L, we’ve provided critical insight into the kind of value set one might expect to obtain using either descriptive system.

Why are health state valuations by people with advance directives particularly interesting?

The interminable debate over “whose preferences” should be captured when obtaining preferences for the purposes of generating QALYs is well-known among health outcomes researchers and policy-makers. Two camps typically emerge, those that argue for capturing preferences from the general population and those that argue for patients to be the primary source. The supporting arguments for both sides have been well-documented. One additional approach has recently emerged which may reconcile some of the differences by using informed preferences. Guidance from influential groups in the US, such as the First and Second Panels of Cost-Effectiveness in Health and Medicine have also maintained that “the best articulation of a society’s preferences… would be gathered from a representative sample of fully informed members”.

We posited that individuals with advance directives may represent a group that had reflected substantially on their current health state, as well as the experience and consequences of a range of (future) health states. Individuals who complete an advance directive undergo a process that includes discussion and documentation of an individual’s preferences concerning their goals of care in the event they are unable to do so themselves. So we set out to examine this relationship between advance directives and stated preferences, and whether the completion of an advance directive was associated with differences in health state preferences (spoiler: it was).

Is there evidence that value sets should be updated over time?

We sought to address this literature gap by using respondent-level data from the US EQ-5D-3L study that collected TTO values in 2002 and from our EQ-5D-5L study, which also collected 3L TTO values in 2017. However there were inherent challenges with using these data collected so many years apart: demographics shift, new methods and modes of administration are implemented, etc.

So, we attempted to account for what was possible by controlling for respondent characteristics and restricting health state values to those obtained using the same preference elicitation technique (i.e., conventional TTO). We found that values in 2017 were modestly higher, implying that the average adult in the US in 2017 was less willing to trade time for quality of life than in 2002, i.e. 6 months over a 10-year time-horizon. Our research suggests that time-specific differences in societal preferences exist and that the time period in which values were elicited may be an important factor to consider when selecting or applying a value set.

Based on your research, do you have any recommendations for future valuation studies?

I would encourage researchers conducting future valuation studies, particularly societal value sets, to consider some of the following:

1) Consider building in small but powerful methodological sub-aims into your study. Of course, you must balance resource constraints, data quality, and respondent burden against such add-ons, but a balance can be struck!

2) Pay attention to important developments in the population being sampled; for example, we incorporated advance directives because it is becoming an important topic in the US healthcare debate, in addition to contributing to the discussion surrounding informed preferences.

3) Take a close look at the most commonly utilized health state values sets representing your health-system/target population. Is it possible that existing value sets are “outdated”? If so, a proposal to update this value set might fill a very important need. While you’re at it, consider an analysis to compare current and previous values. The evidence is scarce (and difficult to study!) so it’s important to continue building evidence that can inform the broader scientific and HTA community as to the role that time plays in changes to societal preferences.