It is apparent as we move toward value-based care and payments, that health care is dependent on so much more than what we would consider care. It’s not all up to the provider nor up to the individual patient, there’s a wide network of costs and influences from genetics to nutrition.
As we move toward digital health and digital payments, the relationships between spending, environment, and other health determinants are becoming clearer, affecting the choices we make at any moment. Behavioral choices are often driven by the social determinants of health, the cultural and economic contexts (including geography) of our day-to-day decisions.
Many things, of course, influence health and outcomes and our need for care, including, genetics, behavioral choices (smoking, drugs, alcohol, unprotected sex, obesity, preventative care, exercise, taking prescribed medications, sugar intake and nutrition), access to care, capabilities to care for oneself and many other risks.
While we tend to think in terms of science and individuals controlling outcomes, that’s at the very least a bit of hubris on the part of science. Zip codes were recently declared better at predicting outcomes than genetic codes (hat tip to Cyndy Nayer).
And these social influences are becoming better understood, because we are getting better at measuring them, with access to better data, as a byproduct of ubiquitous connectivity (although extent of connectivity is often correlated with zip code as well). We often assume that it’s all up to the individual, but most of what we do is a combination of many things including marketing, education, costs, and culture. As we spend more time online, those influences become both greater and more measurable. Tremendous value will be seen once we understand these decisions and why people make them, including social, economic and geographic influences in the context of vast networks of influences.
The impact numbers of personal choice and behavior related to health and health care spending, when you dig in, are pretty staggering, and perhaps, devastating for our financial outlook.
“Consumption of junk food (for example a Twinkie or a sugary drink) is akin to a financial exchange where short-term gains are privatized and long-term costs are socialized in the form of horrific health outcomes. The metabolic donkeys – consumers – pay relatively little money and turn a blind eye to the health consequences of their food choices – instead hoisting the fantastic profits of companies like Monster and opting for a shortened, diseased life.”
In the Forbes article, Munro estimates that sugar may be costing the U.S. healthcare system $1 trillion. That’s 25% of healthcare’s overall $4 trillion. Estimates are that Americans eat 70 lbs of sugar a year. Even at a rather high price of $1 a pound (commodity prices are around 15 cents per pound), that’s only about $25 billion that we spend on sugar as a country for the ingredient itself (certainly we pay much more for it when it comes in a soda or Monster beverage, or myriad of other products). So the costs of sugar to the healthcare system are on the order of 40 times higher than the price of sugar itself. Sugar, or a cigarette, is very small down payment on future health costs.
Prices and financial incentives are too often left out of the equation because we haven’t found the right mix. Offering salads at McDonald’s might not work, we don’t go to McDonald’s for salads, wrong context. Low-income women, on the other hand, might be incentivized to buy and eat vegetables, and at least in limited contexts, we do see that vouchers like this can work.
Carolyn Dimitri, an applied economist at New York University, tested whether farmer’s markets vouchers would not only encourage low-income women to buy and eat more vegetables using vouchers and measuring with surveys. They found that vouchers not only encouraged the purchasing, but also the consumption of more vegetables.
According to Pacific Standard’s write-up of the article, “..this suggests that disadvantaged families may eat fewer vegetables not because of preferences or education but because of access…(and possibly) economic scarcity and its psychological effects.”
To truly understand the health system, not just the healthcare system, we’ll need to understand decisions and incentives around food. Patient engagement has direct effects on health outcomes and health spending, as has been shown many times. How closely tied is nutrition to outcomes? Certainly it’s more long-term, but we need to understand correlations and causations much sooner.
Could providers or payers benefit by providing nutritional vouchers? Is there an app or technological solution that works for reducing sugar intake?
This is one area of mobile health and app development we hear little about, despite the fact that diabetes, prediabetes, and metabolic syndrome affect more than 40% of Americans, or over 100 million people. These are Americans that will have long-term health consequences and costs.
Why aren’t we doing more to help? Is it just too hard? Is our sugar addiction just too strong? What will Apple do now that they are including Healthkit in IOS8? What can Stikk do to improve on sugar intake?
This may be one of the most difficult, but also one of the most valuable, quests in healthcare.
Who else stands to benefit from reducing the $1 trillion in sugar-related health spending? How quickly can nutritions steer some of that money, much larger than that spent on sugar, toward better health and better nutritional decisions?
Moving just a little bit of the money we spend on sugar and on sugar-related diseases will pay enormous dividends in quality of life and cost of care. At VivaPhi, we’re rolling with the Center of Health Engagement, driving new incentive programs to drive better engagement and better health. Have an idea for how to create these kinds of incentives for healthier choices? We want to hear them.
Latest posts by Leonard Kish (see all)
- Data Liberación Gets Personal, EXTREME Interoperability - June 23, 2015
- Health Data Planes, Trains and Automobiles: Think Like a Retailer - May 5, 2015
- The Death of Interoperability: Is it Time for One Record? - March 25, 2015