Abstain from Alcohol to Decrease Risk of Atrial Fibrillation

Here is a press release about a study that was presented by has not yet been published. Perhaps the best study design for evaluating the benefits/harms of alcohol.

Lead Exposure in Childhood can lead to Personality Changes

Here is the link to a simplified version

Readmissions and Mortality–Any Difference by Race?

Since the introduction of Hospital Readmission Reduction Program by the CMS, there has been a significant decrease in readmissions for all targetted diseases. However, studies have raised concern that although readmissions are decreasing, there may be an increase in mortality.

A recent study showed that there is no difference in mortality between white and black patients. Authors used interrupted time-series analysis. I enjoyed statistical modelling but wonder if the within-hospital and between hosptial effects were properly accounted for. In other words, the assumption in their modeling is that both effects are the same (unlikely to be true). It is possible that the within and between effects are different and may shed a better light on what is happening at individual hospital level and what is happening across hospitals. Having said that, it is an interesting study with important implications.

Are Machine Learning Tools Better than Standard Tools in Predicting Readmissions

Just saw this study where authors compared standard readmission tools (HOSPITAL score, modified LACE score, and Maxim/RightCare score) with a model developed using machine learning. Authors found that machine learning score (they called it Baltimore Score or B-score) performed much better than standard tools.

While I agree that machine learning tools will likely outperform standard methods. Standard methods are quite a bit of oversimplification of the real life, machine learning tools less so. However, I doubt that authors have got their model right. Two reasons: One, their sample size is relatively small. Two, they have not validated their tool in a new dataset.

Social risk factors adjustment for readmission penalties

I have shown, in my previous work, that social risk factors affect hospital performance. CMS, at th eurging of Congress, will be including including social risk factors in their patient risk models. Here is an interesting paper that examined retrospectively the effect of adjustment for social risk factors on readmission penalties and found that the penalties will drop by almost half for safety-net hospitals. I am certain that these adjustment will bring more fairness in hospital comparisons and will decrease the amount of penalty these safety-net hospitals have to face. 

Obesity and Hospital Readmissions

The link between obesity and readmissions is not as clear cut, despite the fact that almost 40% of older adults in the US are obese. In this relatively large study examining the readmission rates of obese nursinghome patients, authors found that the relationship is perhaps complex and islikely affected by age and co-morbid conditions.

Rebate on Prescription Drugs

Illuminating article; must read. Here are two sentences that capture the essence of this issue: “In 2016, leading products in these classes had total pre-rebate spending in Part D of $2.5 billion (Lantus Solostar) and $4.4 billion (Harvoni). For an individual patient, this would reduce list prices on which their deductibles and co-insurance are calculated from $417 to $142 for patients filling a 1-month supply for Lantus Solostar and from $92 648 to $35 206 for patients filling an 84-day course of treatment with Harvoni, assuming class-level rebates applied to these products.”