This is the key question we are left with after some startling news from a startling article from Oren et al. in JAMA. Using data from the CDC Underlying Cause of Death database between 2000 and 2017, they find that:
The suicide rate at ages 15 to 19 years and 20 to 24 years increased in 2017 to its highest point since 2000, with a recent increase especially in males and in ages 15 to 19 years.
The key figures from the article are below:
So what are the reasons for this rise? The study does not get into the causes of the rise in suicide, but PBS Newshour offers ideas for speculation. One possibility is the growing opioid epidemic. Another potential culprit is additional mental health stress due to social media. The data could also be a statistical anomaly. If suicide is less stigmatized, families may feel less pressure to try to hide that a death was due to suicide.
But 10 years after President Barack Obama signed a law to accelerate the digitization of medical records — with the federal government, so far, sinking $36 billion into the effort — America has little to show for its investment…Today, 96 percent of hospitals have adopted EHRs, up from just 9 percent in 2008. But on most other counts, the newly installed technology has fallen well short. Physicians complain about clumsy, unintuitive systems and the number of hours spent clicking, typing and trying to navigate them — which is more than the hours they spend with patients. Unlike, say, with the global network of ATMs, the proprietary EHR systems made by more than 700 vendors routinely don’t talk to one another, meaning that doctors still resort to transferring medical data via fax and CD-ROM. Patients, meanwhile, still struggle to access their own records — and, sometimes, just plain can’t.
The article claims that EHRs are often optimized for billing (i.e., to extract as much money from insurance companies as possible) rather than for patient care. Further, because of the lack of EHR interoperability, lab orders get lost, prescriptions mixed up and patient outcomes can be worse. Lawsuits have resulted.
While the article focuses on the downsides of EHR, clearly they have the potential to improve efficiencies and communication. How this can work in practice, however, is an area where more research is needed.
Why is the cost of cancer treatments growing so much in recent years? A new paper in the American Journal of Managed Care (AJMC) by my at Precision Health Economics colleague Jesse Sussell and co-authors (2019) has an explanation using data between 1997 and 2015 on cancer prices and the size of each treatment’s indicated population using both the IQVIA National Sales Perspective (NSP) data and the Medicare Current Beneficiary Survey (MCBS). They find that:
…prices have roughly tripled, whereas average patient counts per therapy have fallen by 85% to 90% over this period. However, the entire distribution of annual revenues has fallen: For instance, median revenues for drugs launched in the early 2010s are about half of what they were for drugs launched in the late 1990s.
The authors also argue that market power doesn’t explain these rising prices (and falling revenues):
As a result, revenues have fallen at every point in the distribution, after accounting for life cycle growth in revenues over years since launch. This suggests that price growth is unlikely to have resulted from greater pricing power, at least within this market segment. Profit-maximizing firms with more pricing power would never willingly make decisions that lead to lower revenues for each drug launched.
In short, therapies are becoming more targeted. Thus, rising prices may be a good thing. Whereas before cancer treatments were given to a large number of people for whom only a small share of people would benefit. Now that cancer therapies are much more targeted, only the patients who will benefit from the therapy get the treatment. To offset the reduced indicated population, drug companies have raised prices but not in a way that was sufficient to offset the smaller populations targeted.
The study makes a major contribution for understanding how patients and payers can complain about the rising prices, while manufacturers can rightfully argue that the prices need to be this high to maintain revenues.
Entities such as the Panel on Cost Effectiveness in Health and Medicine have argued that we should include the societal perspective when measuring the value of a certain treatment through cost-effectiveness analysis (CEA). However, societal perspective is not always the one used. Further, even when the societal perspective is taken, this is often limited to incorporating productivity losses and caregiver burden.
One group who is not traditionally mentioned in CEA is patients. Yet, patients are the most important stakeholders in the health care system. Nevertheless, CEA models may measure patient quality of life using generic utility metrics–e.g., those derived from EQ-5D–which may have poor sensitivity for how different treatments affect the quality of life for patients with a specific disease.
A paper by Slejko et al. (2019), argues that a patient-informed societal perpsective is needed. What does that mean?
Well, one way to better capture patient preferences is to use more disease specific survey metrics to quantify patient tradeoffs across treatment attributes.
In fact, there are data showing that stated preference methods, like a discrete choice experiment, render important information about treatment value to which traditionally derived QALYs are not sensitive. For example, the value sets for preference instruments can be estimated with discrete choice experiments.24 Furthermore, it is important to examine whether the domains measured by instruments used for QALY estimation reflect attributes of interest to patients.
The authors also not while patient input is already included as part of standard CEA, more could be done. For instance, incorporating novel elements of value such as ability to plan, convenience, and effects on family. Even if all value components are included in a model, having patient input into the CEA model is helpful. In particular, information about the patient journey can be useful for defining model health states as well as relevant time horizons.
In short, as patients are the ones who are the end users of our health care system, it is important that they have a same in terms of how treatments are valued.
Value measurement using cost-effectiveness analysis (CEA) is
one of the core stables of health economic research. CEA often uses quality adjusted life years
(QALYs) to capture how a treatment affects a patients mortality and
morbidity. CEA makes explicit
assumptions about the tradeoffs between mortality and morbidity by assuming
these are additive. Further, this
approach, however, ignores much that may be important to patients including
mode of administration, caregiver burden, risk preferences, and other
A 2019 paper by Charles Phelps and Guruprasad Madhavan argues that a better approach may be to incorporate the use of multi-criteria decision analysis (MCDA). This approach—developed from systems engineering—measures how different treatments perform across a variety of attributes and explicitly asks the decision maker to weigh these different decisions. I helped to create MCDA modules for the Innovation and Value Initiative’s IVI-RA and IVI-NSCLC Value Tools. There are a variety of different types of MCDA approaches including: ELimination and Choice Expressing Reality (ELECTRE), Multi-Attribute Utility Theory (MAUT), Analytic Hierarchy Process (AHP), Measuring Attractiveness by a Categorical Evaluation Technique (MACBETH), Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) and many others.
Phelps and Madhavan also describe how MCDA could be
incorporated into health care decision-making.
Conduct direct comparisons of MCDA and CEA to
see where the approaches produce similar answers and where the approaches
produce different answers.
Building the MCDA capability through more
commonly available data resources, increased number of researchers with MCDA
skills, increased validation of tools and improving human factor dimensions
(e.g., ease of use, comprehensibility).
Understanding how different voting methods
perform when used to implement MCDA models in group settings.
MCDA may be especially useful when some key attributes of
MCDA-informed value include cost or benefits received by society, but that are
not captured by individual decision making.
The answer is not raw brain processing power, but rather it is our ability to learn from others. At least that is the argument from Joseph Henrich’s book The Secret of our Success. Slate Star Codex has an excellent book review. Consider the following graph comparing human, chimpanzee and orangutan intelligence.
Another interesting quotation from the book:
While hunters reach their peak strength and speed in their twenties, individual hunting success does not peak until around age 30, because success depends more on know-how and refined skills than on physical prowess.
I have not read this book, but certainly some interesting points to consider for your weekend reading.
Life expectancy in Canada has stopped increasing for the first time in more than four decades, due largely to soaring overdose deaths in the Western provinces. In British Columbia, the province hit hardest by these deaths, life expectancy fell for a second year in a row, decreasing by 0.3 years for men and 0.1 years for women from 2016 to 2017, according to Statistics Canada. In Alberta, the life expectancy for men fell by 0.24 years, and for women 0.1 years, over the same period…More than 10,300 people died of apparent opioid-related deaths across Canada between January, 2016, and September, 2018. In Vancouver’s Downtown Eastside, the hardest-hit neighbourhood in the country, life expectancy for men dropped by four years to 69.6 from 73.5 in the period from 2010-12 to 2016-17, according to Vancouver Coastal Health.
According to the CIA World Factbook, life expectancy in Canada is 81.9 years, which is still higher than the U.S. which is at 80.0 years.
Why have a physician do a job when a nurse could do it? Why have a nurse do it if a home health aide could do it? Clearly, the latter group in each sentence is lower cost; however, the former group may provide higher quality care. The balance of who can do what in terms of trading off quality and cost clearly varies by task, but one key issue with scope of work restrictions is that they limit flexibility and individual professional judgement. Consider the case of a seriously ill patient with primary progressive multiple sclerosis as outlined in this Health Affairs article by Michael Ogg:
Nurse practice acts in each state regulate tasks that must be performed by registered nurses instead of being delegated to agency employed home health aides. Although my state had relaxed its regulations, allowing home health aides to administer medications, MyPACE did not yet permit them to do so. My nurse told me that if I needed twice-daily medications, they might have to put me in a nursing home instead of sending the home nurse twice a day.
Recommending certain specialties provide certain tasks does make sense. But in many situations, mandating that they do so is problematic as it prevents providing creative health care solutions to the patients who need help the most.
Case-mix-adjusted total care costs were 6–7 percent lower for NP [nurse practitioner] and PA [physician assistant] patients than for physician patients, driven by more use of emergency and inpatient services by the latter. We found that use of NPs and PAs as primary care providers for complex patients with diabetes was associated with less use of acute care services and lower total costs.