Machine learning in Medicine

Machine Learning and Evidence-Based Medicine

While waiting for the new book by Eric Topol: Deep Medicine, let's have a look at this article, and at this summary table:

 Xavier Miserachs

Health spending in late life

Predictive modeling of U.S. health care spending in late life

In US, it is said that a quarter of public expenditure for the elderly (Medicare) is spent in the last 12 months of life. Really what happens is that the last year is only close to 10% of the whole lifetime health spending. Anyway, a new article in Science highlights commmon misunderstandings on such figure and disentangles the fundamentals.

These common interpretations of end-of-life spending flirt with a statistical fallacy: Those who endup dying are not the same as those who were sure to die. Ex post, spending could appear concentrated on the dead, simply because we spend more on sicker individuals who have higher mortality—even if we never spent money on those certain to die within the year. Empirically, this suggests using predicted mortality, rather than ex post mortality, to assess end of-life spending.

Less than 5% of spending is accounted for by individuals with predicted mortality above 50%. The simple fact that we spend more on the sick—both on those who recover and those who die—accounts for 30 to 50% of the concentration of spending on the dead. 

Crucial conclusion:

In sum, although spending on the ex post dead is very high, we find there are only a few individuals for whom, ex ante, death is near certain. Moreover, a substantial component of the concentration of spending at the end of life is mechanically driven by the fact that those who end up dying are sicker, and spending, naturally, is higher for sicker individuals. Of course, we do not— and cannot—rule out individual cases where treatment is performed on an individal for whom death is near certain. But our findings indicate that such individuals are not a meaningful share of decedents. These findings suggest that a focus on end-of life spending is not, by itself, a useful way to identify wasteful spending. Instead, researchers must focus on quality of care for very sick patients.

Good article.

PS Eight years ago I made this presentation on estimates of costs of late life. The summary in this post (in catalan)

Club des Belugas - Never think twice

Regulating alcohol marketing

Policy Approaches for Regulating Alcohol Marketing in a Global Context: A Public Health Perspective

The article says:

The range of policy options for alcohol marketing restrictions includes four main categories: no restrictions, voluntary regulation or self-regulation, partial restrictions (e.g., on content, time and place, or particular audiences), and complete bans.

Unfortunately you'll not find a clear assessment of the impact of these policies. Only anecdotical facts. Therefore, no prescriptions can be made with sounding evidence. My impression is that somebody should care about the current advertising strategies that are very far from what WHO considered as comercials some years ago.

Conceptual framework on the growth of alcohol corporations, exposure to alcohol marketing, and alcohol-related public health problems.