Tag health care costs

Harold Hastings’s journal round-up for 24th December 2018

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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.

Mandatory Medicare bundled payment program for lower extremity joint replacement and discharge to institutional postacute care: interim analysis of the first year of a 5-year randomized trial. JAMA [PubMed] Published 4th September 2018

I will focus on two themes: one local to the United States – bundled payments for Medicare, and one global – the economic burden of sepsis. Finkelstein, Ji, Mahoney, and Skinner described the results of a study aimed at assessing the effects of bundled Medicare payments (as opposed to payments for each component of treatment) upon care and costs of lower extremity joint replacement. Finkelstein et al. found only one significant difference between the bundled carte group and a control group: the percentage discharged to institutional care decreased from 33.7% in the control group to 30.8% in the bundled care group, that is, one fewer patient per 33 treated. There was no significant difference in costs or quality of care. In this sense I must differ from the optimism of an associated editorial; to me, a true success would include a significant reduction in cost together with an improvement in outcome. Thus, in terms of bundled Medicare payments, we are not at the end, not even the beginning of the end, but perhaps near the end of the beginning (my apologies to Winston Churchill).

Epidemiology and costs of sepsis in the United States—an analysis based on timing of diagnosis and severity level. Critical Care Medicine [PubMed] Published 1st December 2018

Epidemiology of sepsis in Brazil: incidence, lethality, costs, and other indicators for Brazilian Unified Health System hospitalizations from 2006 to 2015. PLoS One [PubMed] Published 13th April 2018

Sepsis care continues to pose among the most significant health challenges world-wide, both in terms of economics and mortality, with mortality ranging from 10% to almost 80% depending upon severity. In terms of cost, sepsis treatment in the US averages over $18,000 per hospitalization with almost 1 million cases admitted annually, while Brazil spends 1/30 of this amount (~$600 per hospitalization), and 1/10 of this amount for sepsis treatment in the ICU ($1,700 per hospitalization). Mortality in Brazil is higher than that in the US and higher in public hospitals than in private hospitals. The studies offer complementary suggestions for improvement: in the US study, Paoli et al. call for early detection of sepsis as a way to reduce its severity and thus its cost. In the Brazilian study, Neira et al. conclude that limited economic resources may contribute significantly to high mortality, an observation that should concern all of us interested in world-wide health. Clearly both improved detection and more effective, lower cost treatments are essential to address the health and economic burdens of sepsis. The following paper reviews a potential answer to the latter question – that of more effective, lower cost treatments.

Ascorbic acid, corticosteroids, and thiamine in sepsis: a review of the biologic rationale and the present state of clinical evaluation. Critical Care [PubMed] Published 29th October 2018

In terms of the cost of sepsis treatment, it is interesting to note that an intervention successful in a single-site, retrospective review involved a combination of three “cheap and readily available agents with a long safety record in clinical use since 1949.” Mortality decreased from 40% to 8.5%. The 2018 review describes mixed reaction based on informal cost/benefit/risk analysis while nine trials are underway. If these trials prove successful, it might be hoped that the low cost would spur world-wide incorporation of ascorbate-corticosteroid-thiamine therapy for sepsis – addressing world-wide incidence of 15 million cases annually and mortality approaching 60% in less developed countries. An optimist might even hope for reduced mortality at significantly reduced costs, reminiscent of oral rehydration therapy for diarrhoea developed in Bangladesh 50 years ago and responsible for a 90% relative reduction in mortality.

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James Altunkaya’s journal round-up for 3rd September 2018

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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.

Sensitivity analysis for not-at-random missing data in trial-based cost-effectiveness analysis: a tutorial. PharmacoEconomics [PubMed] [RePEc] Published 20th April 2018

Last month, we highlighted a Bayesian framework for imputing missing data in economic evaluation. The paper dealt with the issue of departure from the ‘Missing at Random’ (MAR) assumption by using a Bayesian approach to specify a plausible missingness model from the results of expert elicitation. This was used to estimate a prior distribution for the unobserved terms in the outcomes model.

For those less comfortable with Bayesian estimation, this month we highlight a tutorial paper from the same authors, outlining an approach to recognise the impact of plausible departures from ‘Missingness at Random’ assumptions on cost-effectiveness results. Given poor adherence to current recommendations for the best practice in handling and reporting missing data, an incremental approach to improving missing data methods in health research may be more realistic. The authors supply accompanying Stata code.

The paper investigates the importance of assuming a degree of ‘informative’ missingness (i.e. ‘Missingness not at Random’) in sensitivity analyses. In a case study, the authors present a range of scenarios which assume a decrement of 5-10% in the quality of life of patients with missing health outcomes, compared to multiple imputation estimates based on observed characteristics under standard ‘Missing at Random’ assumptions. This represents an assumption that, controlling for all observed characteristics used in multiple imputation, those with complete quality of life profiles may have higher quality of life than those with incomplete surveys.

Quality of life decrements were implemented in the control and treatment arm separately, and then jointly, in six scenarios. This aimed to demonstrate the sensitivity of cost-effectiveness judgements to the possibility of a different missingness mechanism in each arm. The authors similarly investigate sensitivity to higher health costs in those with missing data than predicted based on observed characteristics in imputation under ‘Missingness at Random’. Finally, sensitivity to a simultaneous departure from ‘Missingness at Random’ in both health outcomes and health costs is investigated.

The proposed sensitivity analyses provide a useful heuristic to assess what degree of difference between missing and non-missing subjects on unobserved characteristics would be necessary to change cost-effectiveness decisions. The authors admit this framework could appear relatively crude to those comfortable with more advanced missing data approaches such as those outlined in last month’s round-up. However, this approach should appeal to those interested in presenting the magnitude of uncertainty introduced by missing data assumptions, in a way that is easily interpretable to decision makers.

The impact of waiting for intervention on costs and effectiveness: the case of transcatheter aortic valve replacement. The European Journal of Health Economics [PubMed] [RePEc] Published September 2018

This paper appears in print this month and sparked interest as one of comparatively few studies on the cost-effectiveness of waiting lists. Given interest in using constrained optimisation methods in health outcomes research, highlighted in this month’s editorial in Value in Health, there is rightly interest in extending the traditional sphere of economic evaluation from drugs and devices to understanding the trade-offs of investing in a wider range of policy interventions, using a common metric of costs and QALYs. Rachel Meacock’s paper earlier this year did a great job at outlining some of the challenges involved broadening the scope of economic evaluation to more general decisions in health service delivery.

The authors set out to understand the cost-effectiveness of delaying a cardiac treatment (TVAR) using a waiting list of up to 12 months compared to a policy of immediate treatment. The effectiveness of treatment at 3, 6, 9 & 12 months after initial diagnosis, health decrements during waiting, and corresponding health costs during wait time and post-treatment were derived from a small observational study. As treatment is studied in an elderly population, a non-ignorable proportion of patients die whilst waiting for surgery. This translates to lower modelled costs, but also lower quality life years in modelled cohorts where there was any delay from a policy of immediate treatment. The authors conclude that eliminating all waiting time for TVAR would produce population health at a rate of ~€12,500 per QALY gained.

However, based on the modelling presented, the authors lack the ability to make cost-effectiveness judgements of this sort. Waiting lists exist for a reason, chiefly a lack of clinical capacity to treat patients immediately. In taking a decision to treat patients immediately in one disease area, we therefore need some judgement as to whether the health displaced in now untreated patients in another disease area is of greater, less or equal magnitude to that gained by treating TVAR patients immediately. Alternately, modelling should include the cost of acquiring additional clinical capacity (such as theatre space) to treat TVAR patients immediately, so as not to displace other treatments. In such a case, the ICER is likely to be much higher, due to the large cost of new resources needed to reduce waiting times to zero.

Given the data available, a simple improvement to the paper would be to reflect current waiting times (already gathered from observational study) as the ‘standard of care’ arm. As such, the estimated change in quality of life and healthcare resource cost from reducing waiting times to zero from levels observed in current practice could be calculated. This could then be used to calculate the maximum acceptable cost of acquiring additional treatment resources needed to treat patients with no waiting time, given current national willingness-to-pay thresholds.

Admittedly, there remain problems in using the authors’ chosen observational dataset to calculate quality of life and cost outcomes for patients treated at different time periods. Waiting times were prioritised in this ‘real world’ observational study, based on clinical assessment of patients’ treatment need. Thus it is expected that the quality of life lost during a waiting period would be lower for patients treated in the observational study at 12 months, compared to the expected quality of life loss of waiting for the group of patients judged to need immediate treatment. A previous study in cardiac care took on the more manageable task of investigating the cost-effectiveness of different prioritisation strategies for the waiting list, investigating the sensitivity of conclusions to varying a fixed maximum wait-time for the last patient treated.

This study therefore demonstrates some of the difficulties in attempting to make cost-effectiveness judgements about waiting time policy. Given that the cost-effectiveness of reducing waiting times in different disease areas is expected to vary, based on relative importance of waiting for treatment on short and long-term health outcomes and costs, this remains an interesting area for economic evaluation to explore. In the context of the current focus on constrained optimisation techniques across different areas in healthcare (see ISPOR task force), it is likely that extending economic evaluation to evaluate a broader range of decision problems on a common scale will become increasingly important in future.

Understanding and identifying key issues with the involvement of clinicians in the development of decision-analytic model structures: a qualitative study. PharmacoEconomics [PubMed] Published 17th August 2018

This paper gathers evidence from interviews with clinicians and modellers, with the aim to improve the nature of the working relationship between the two fields during model development.

Researchers gathered opinion from a variety of settings, including industry. The main report focusses on evidence from two case studies – one tracking the working relationship between modellers and a single clinical advisor at a UK university, with the second gathering evidence from a UK policy institute – where modellers worked with up to 11 clinical experts per meeting.

Some of the authors’ conclusions are not particularly surprising. Modellers reported difficulty in recruiting clinicians to advise on model structures, and further difficulty in then engaging recruited clinicians to provide relevant advice for the model building process. Specific comments suggested difficulty for some clinical advisors in identifying representative patient experiences, instead diverting modellers’ attention towards rare outlier events.

Study responses suggested currently only 1 or 2 clinicians were typically consulted during model development. The authors recommend involving a larger group of clinicians at this stage of the modelling process, with a more varied range of clinical experience (junior as well as senior clinicians, with some geographical variation). This is intended to help ensure clinical pathways modelled are generalizable. The experience of one clinical collaborator involved in the case study based at a UK university, compared to 11 clinicians at the policy institute studied, perhaps may also illustrate a general problem of inadequate compensation for clinical time within the university system. The authors also advocate the availability of some relevant training for clinicians in decision modelling to help enhance the efficiency of participants’ time during model building. Clinicians sampled were supportive of this view – citing the need for further guidance from modellers on the nature of their expected contribution.

This study ties into the general literature regarding structural uncertainty in decision analytic models. In advocating the early contribution of a larger, more diverse group of clinicians in model development, the authors advocate a degree of alignment between clinical involvement during model structuring, and guidelines for eliciting parameter estimates from clinical experts. Similar problems, however, remain for both fields, in recruiting clinical experts from sufficiently diverse backgrounds to provide a valid sample.

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Thesis Thursday: Wenjia Zhu

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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).