Recently, Tufts University published a research paper that looked at the effect of price incentives on health cost savings. (Paper: here).
This was brought to my attention via a new headline that read:
"“Prescribing” fruits and veggies would save $100 billion in medical costs"
-Fast Company January 2021: LINK
$100 billion sure seems like a lot! And these are the kinds of statements that get my inner actuary all fired up. Where did this number come from? I will unpack this bit by bit (or maybe bite by bite?) and please allow me a few rants.
RANT 1; The article references the study, but the first link goes to an announcement about the study and not to the study itself. You get that three links later. ANNOYING!
Here's the key tidbit (emphasis added):
The study, published in the the medical journal PLOS Medicine, followed adults between the ages of 35-80 who were enrolled in Medicare and/or Medicaid. It then established two scenarios: one in which Medicare/Medicaid covered the cost of 30% of fruits and vegetables, the other in which it covered fruits, vegetables, seafood, whole grains, plant oils, and other healthy foods.
The results showed that with such subsidies, subjects rely less on healthcare. The first scenario would prevent 1.93 million cardiovascular events (such as heart attacks) and 350,000 deaths, as well as cut healthcare costs by $40 billion. The expanded second scenario would prevent 3.28 million cardiovascular events, 620,000 deaths, and 120,000 cases of diabetes–and save the U.S. system a whopping $100 billion
OK - per usual, we have a headline that is not accurately reflecting the content. (Not faulting the author on that one, usually that is out of their hands)
- The $100B savings is under the second scenario which is more than just fruits and veggies. The savings from the fruits and veggies only plan was $40B. Still a good number, but less than half of the other one!
- The price reduction mechanism is a subsidy, not a prescription. A subsidy will drive different behaviors than prescriptions.
- (RANT) They use the word "would" a lot, and I think that assigns a lot more perceived certainty to a scenario based simulation. "Could" is a better word.
- It is not totally clear (in the article) that this is a SIMULATION. It is a model. It is not an actual experiment. They did NOT study an actual intervention. It is, at the end of the day a very fancy HYPOTHESIS about what COULD be possible. To quote the paper itself "Simulation studies such as this one provide quantitative estimates of benefits and uncertainty but cannot directly prove health and economic impacts."
- The lion's share of the cost savings is from prevention of cardiovascular events. (More on that later). The other savings come from fewer deaths (do these people live forever?) and a tiny little bit of people not getting diabetes. All these numbers are presented without any helpful visualizations. Can we get at least one bar chart?
- Key Assumption 1: Price Sensitivity. How much will purchasing of health foods change due to the subsidy?
- Key Assumption 2: Consumption Levels of Healthy Foods. The researchers developed a baseline and seemed to crank it up assuming all additional healthy food purchased was consumed.
- Key Assumption 3: Effect of Food on Health Outcomes. This effect will depend on the underlying condition being studied (cancer vs. heart attack). For most of this post, I will focus on heart related conditions.
Assumption 1 Price Sensitivity
- A pound of bananas is about $0.60. It would now be $0.42.
- Let's say a pound of Kale is about $2.00. Now it would be $1.40
- If I spend $50 at a grocery store on produce, my grocery bill would be $15 less.
- Instead of a pound of bananas I will by 1.4 pounds of bananas. Which is like 2 more bananas.
- Maybe I'll buy other things instead of more of the same thing.
- From the subsidy, my grocery bill went from $50 to $35 (50 x 0.7).
- So now I'll get more stuff, $35*1.4 = $49. So in this setup almost all my savings went back into more food.
Where did the 1.24 ratio come from anyway? From the research paper:
"The effect of the price change on dietary intakes was derived from a systematic review and meta-analysis of interventional and prospective observational studies of changes in food price in relation to dietary consumption."
- 1.2 is the average for all studies for all food types
- Note that a 'tax' decreases consumption by a factor of 0.6. So the relationship is conditional on change in direction of price, and the effect size is drastically different. This is not a surprising result and is often seen in other applications, like loss aversion in gambling..
- The "study of studies" isolated the dynamic for fruit and veggies and healthy foods only and observed bigger impacts than the overall average. (1.4 for fruits and veggies and 1.6 for all other health foods)
- This could imply that the 1.2 baseline is a touch on the conservative side. But it does fit in the confidence interval for both of the other metrics.
- 14% (95%CI = 11–17%; N = 9);
- 16% (95%CI = 10–23%; N = 10);
- The underlying studies that get to the 1.2 number span age ranges, including children and also spanned geographies, including France, South Africa and New Zealand. Studies based in the US were not always national. Note that the headline result is supposed to apply to adults in the US, and even then, focusing on select populations. So I wonder if we have a sample bias issue here?
- Not every 'subsidy' was isolated, some of them also included educational interventions. This is a type of confounding variable, which means that the consumption changes are not isolated to price only.
Assumption 2 Consumption Effects
Assumption 3: Health Outcomes aka Artichoke Heart Attack
- Gathered data about dietary patterns. They used NHANES data (more on that later).
- Applied their consumption effects (assumptions 1 and 2) to the underlying population to generate modified dietary patterns.
- Fed in the updated population to a predictive model (CVD-PREDICT) that output certain cardiac events over time.
- NHANES for example is known to have various limitations because of the way in which the information was collected. Food intakes are based on recollection. One critique argues that the food intakes people reported would not meet your daily caloric needs. A lot of studies use NHANES, but I don't see much for adjustments in data.
- The 'CVD-Predict' Model similarly relies on data from something called the 'Framingham' study. There is a disclosure on the model validation paper that the predictive algorithm is limited due to issues in underlying data.
Where do we go from here?
- Do we need to introduce government subsidies?
- What role might insurers have?