- Obamacare replacement?
- My drug problem.
- Do ACOs reduce post acute care use?
- Eating with 4 ingredients.
- MD Anderson benches Watson?
- Patient = partner.
That is the conclusion reacted by CMS’ Office of the Actuary. As published in a recent Health Affairs article:
Under current law, national health expenditures are projected to grow at an average annual rate of 5.6 percent for 2016–25 and represent 19.9 percent of gross domestic product by 2025. For 2016, national health expenditure growth is anticipated to have slowed 1.1 percentage points to 4.8 percent, as a result of slower Medicaid and prescription drug spending growth. For the rest of the projection period, faster projected growth in medical prices is partly offset by slower projected growth in the use and intensity of medical goods and services, relative to that observed in 2014–16 associated with the Affordable Care Act coverage expansions. The insured share of the population is projected to increase from 90.9 percent in 2015 to 91.5 percent by 2025.
Take notice, however, that these projects are based on “current law”. Current law means that these are projections assuming no changes to any laws. Clearly with a new President in office who has called for “Repeal and Replace” the likelihood of no change is unlikely. Nevertheless, have a “current law” projection is useful so that policymakers can evaluate how any changes to current law could affect long-run health spending in the U.S.
In one of my recent publications, I show that patient and physician risk preferences differ. Patients are willing to take take treatments with more both upside and downside risk, whereas physician prescribe treatments large based on which one provides the most efficacy to the average patient.
One question that remains is how do patients and physician preferences over treatments vary when both treatment efficacy and safety vary?
A paper by Ubel, Angott and Zikmund-Fisher (2011) have a novel approach to examining these tradeoffs. The authors ask physicians to select from two similar surgeries for colorectal cancer. The first surgery has better survival (i.e., lower death rate) but an increased chance of adverse events; the second surgery has worse survival prospects (higher death rate) but a reduced risk of adverse events. A similar scenario is provided for treatment of avian flu.
To examine differences in patient and physician preferences, the authors ask the physicians which treatment they would recommend for their patients, and which ones they would recommend for themselves if they were a patient. The study finds the following:
Among those asked to consider our colon cancer scenario (n=242), 37.8% chose the treatment with a higher death rate for themselves but only 24.5% recommended this treatment to a hypothetical patient (χ2=4.67, P=.03). Among those receiving our avian influenza scenario (n=698), 62.9% chose the outcome with the higher death rate for themselves but only 48.5% recommended this for patients (χ2=14.56, P<.001).
In short, when physicians prescribe treatment, they care more about efficacy and less about safety. When physicians are themselves patients, they have more concern for a treatment’s side effects.
Physicians are supposed to act as agents for patients, acting in their best interest. But a question is, how well do physician know patient preferences? How well do physician preferences reflect those of patients.
Previous research indicates that for terminal diseases, patients are risk loving. My own recently published research (with co-authors Schwartz, Okoro and Romley) confirms this finding, but adds to the literature by showing that physicians are largely risk neutral or mildly risk averse.
A recent paper by Galizzi et al. (2016), however, reach a different conclusion. The do confirm that physicians are mildly risk averse but they also conclude that patients are also mildly risk averse as well. They find the following:
First, there is a significant difference in time preferences between patients and their matched doctors, with doctors discounting future health gains and financial outcomes less heavily than patients. Second, we find no systematic difference in risk preferences in the healthcare domain between patients and doctors: in our sample both patients and their matched doctors are mildly, but significantly, risk averse. Third, doctors and patients have significantly different risk preferences in the finance domain: whilst doctors are risk averse, patients are risk neutral.
There are a number of potential reasons for the difference in findings. First, the patient populations of interest differed dramatically. Whereas Shafrin et al. examined patients with advanced state melanoma and non-small cell lung cancer, Galizi et al. examined patients and physician preference at outpatient clinics for the following specialties: pathology, cardiology, gynaecology, haematology, surgery, endocrinology, orthopaedics, urology, gastroenterology, nephrology, rheumatology, ophthalmology, and otolaryngology. Notably, oncology patients were excluded from this study. Second, Shafrin et al. (2017) was conducted in the U.S. and whereas Galizzi et al. (2016) was conducted in Greece. Differences in patient preferences could explain this difference. Third, the methodology for elicitly preferences differed. While both methods compared two treatment options, Galizzi compared two risky treatment options whereas Shafrin et al. compared a risky option against a certainty equivalent option. Further, Galizzi used the Holt and Laury (2002) multiple price list whereas Shafrin et al. used parameter estimation by sequential testing to vary the certainty equivalent value depending on the respondents previous answer.
A useful innovation of the Galizzi paper is that the patients were matched to actual physicians who treated them, whereas the patient and physician selection process was done independently in Shafrin et al.
On the other hand, Shafrin et al. calibrated the “risky” treatment option to real life therapies that were relevant to the specific patient population of interest.
Overall, both studies have useful findings. Galizzi et al. aim to measure risk preferences more generally, across diseases and not calibrated to any one specific treatment. Although Galizzi’s study is broader, it is less tied to real world choices facing patients. The results of Shafrin et al. are more robust for measuring patient and provider preferences across risky oncology treatments, but these should not be extrapolated beyond this specific clinical area.
The commentary focuses on the use of instrumental variables when conducting health care effectiveness reserach. They define instrumental variables concisely as:
An instrumental variable (IV) is a variable, generally found in administrative data, that is assumed to randomize a treatment to estimate cause and effect relationships, thus controlling for known and unknown patient characteristics affecting health outcomes. An important assumption is that the IV randomizes treatment but does not directly affect the patient outcome.
There are a number of commonly used instrumental variables that are used to predict use of health care services. These include a patient’s distance from specific types of providers (e.g., hospitals, specialty providers) and a provider’s average treatment patterns (which generally are correlated with the patient’s likelihood of getting a specific treatment but may not be correlated with the patient’s outcomes except through the treatment received).
The commentary takes aim at the first commonly used IV as applied to access to cardiac catheterization laboratories.
…the central and (we argue, dubious) assumption is that isolated people having a heart attack who live hours away from a hospital with a cath laboratory— and are therefore less likely to receive invasive procedures—are identical in their likelihood to survive as those lucky patients living very close to a cath hospital.
The authors claim that patients who live far from a cath hospital may differ from those who live closer. For instance, maybe the cath hospital is located in an urban core; or in a more affluent part of town. If this is the case, unobserved differences in patient population correlated may be correlated with both outcomes, the probability of the receive of treatment, and distance from a cath hospital. Clearly one can control for some, but not all of these factors using administrative data.
Another issue is that while IV may help identify a causal effect of treatment, it may not give policymakers a clear pathway to improving outcomes. Is the answer creating more cath hospitals? Giving incentives for people to move closer to a cath hospital?
At the same time, while Soumerai and Koppel’s critique has many valid points, their critique is a bit too broad. IV does have its limitations. However, identifying a real-world causal effect of a given treatment is a key research question of interest. How patient outcomes could be improved further is also an important question, but one should not expect a single study to answer all research questions on a single topic and other types of analyses may be preferable to get at the “how” questions rather than at the “what happened” research questions.
Soumerai and Koppel do cite some higher quality IV such as cases where public policies result in lotteries (e.g., the Oregon Medicaid expansion, birth date lotteries), and call for more use of longitudinal (e.g., interrupted time series) rather than a cross-sectional designs.
Although these methods are good to recommend, policy lotteries are not available for all research questions. More broadly, Soumerai and Koppel make a clear point that research design is key and that all analytic approaches should be tested rigorously with sensitivity analyses and falsification checks of assumed causal pathways.
My current employer, Precision Health Economics, has posted an interesting research brief describing how traditional notions of value may not be capturing the full value patients receive from oncology treatments.
A brief description is below but do check out the full report.
As health care spending continues to rise, payers and providers struggle to accurately measure value to patients, and with health systems all over the world shifting towards paying for value, not just volume, these efforts have never been more important.
Learn about PHE’s groundbreaking studies that have helped define the value of new oncology drugs, and why a variety of global stakeholders have turned to PHE to assist them in defining value in cancer care.
Value in Health has a Special Issue on Value Assessment Frameworks. The issue includes one of my papers (co-authored with Taylor T. Schwartz, Tony Okoro, John A. Romley) titled Patient Versus Physician Valuation of Durable Survival Gains: Implications for Value Framework Assessments. The abstract is below.
Background: Previous research indicates that patients value therapies that provide durable or tail-of-the-curve survival gains, but it is unclear whether physicians share these preferences.
Objective: To compare patient and physician preferences for treatments with a positive probability of durable survival gains relative to those with fixed survival gains.
Methods: Patients with advanced stage melanoma or lung cancer and the oncologists who treated these patients were surveyed. The primary end point was the share of respondents who selected a therapy with a variable survival profile, with some patients experiencing long-term durable survival and others experiencing much shorter survival, compared to a therapy with a fixed survival duration. Parameter estimation by sequential testing was applied to calculate the length of nonvarying survival that would make respondents indifferent between that survival and therapy with durable survival.
Results: The sample comprised 165 patients (lung 1⁄4 84, melanoma 1⁄4 81) and 98 physicians. For lung cancer, 65.5% of patients preferred the therapy with a variable survival profile, compared with 40.8% of physicians (Δ = 24.7%; P < 0.001). For melanoma, these figures were 63.0% for patients and 29.7% for physicians (Δ = 33.3%; P < 0.001). Patients’ indifference point implied that therapies with a variable survival profile are preferred unless the treatment with fixed survival had 13.6 months (melanoma) or 11.6 months (lung) longer mean survival; physicians would prescribe treatments with a fixed survival if the treatment had 7.5 months (melanoma) or 1.0 month (lung) shorter survival than the variable survival profile.
Conclusions: Patients place a high value on therapies that provide a chance of durable or “tail-of-the-curve” survival, whereas physicians do not. Value frameworks should incorporate measures of tail-of-the-curve survival gains into their methodologies.
For years, sexually active women and men that have wanted to avoid pregnancy have used a variety of contraceptives. From pills to condoms, there are a lot of options out there. Now add one more to the list, and its not what you might think.
Natural Cycles is an app that helps people identify the time period in which they are likely to be fertile. More about the technology is here.
The mobile application requires the input of basal body temperature recordings and the date of menstruation. Luteinising hormone (LH) test results are optional entry points. The required basal thermometer and the optional LH tests are acquired separately from the application. The users enter their fertility-related data into a device such as a smartphone, tablet or laptop computer. The underlying technology is a statistical algorithm that returns a red (unsafe) or a green (safe) day to the user depending on whether she is considered to be at risk of getting pregnant.
The question is, does it work? It seems like the answer is yes. Pharmafile reports:
Natural Cycles has been approved for use in the UK by Tüv Süd, a European regulatory body employed by the Department of Health and acting on behalf of the Medicines and Healthcare products Regulatory Agency (MHRA) following extensive clinical trial testing. Research gathered from the trials placed the app’s effectiveness at 99.5%; by contrast, the NHS places the effectiveness of the contraceptive pill at 99%.
I am, however, a bit skeptical of these results. Although they appear impressive, one of the key studies is a non-randomized study. Patients who are more motivated are more likely to sign up for Natural Cycles; but they are also the same people who would be more likely to regularly take their pill. Also, discontinuation rates of health care apps are often high and this study was no exception; about one-third of individuals discontinued use of the app within 6 months.
While Natural Cycles may be a good fit for motivated individuals or those sensitive to contraception side effects, it is unlikely to be a cure-all.