A taxonomy of health behaviors

Mapping health behaviors: Constructing and validating a commonsense taxonomy of health behaviors



 

Behavioral health insurance choice

Behavioral hazard in health insurance
Can Consumers Make Affordable Care Affordable? The Value of Choice Architecture

Behavioral Economics is still a great promise for health economics. Anyway, in health insurance some materials are already available. Today I'll bring two articles on the choice of health insurance policy.
Some insights:

People do not misuse care only because the price is below the social marginal cost: they also misuse it because of behavioral biases—because they make mistakes. We call this kind of misutilization behavioral hazard . Many psychologies contribute to behavioral hazard. People may overweight salient symptoms such as back pain or underweight non-salient ones such as high blood pressure or high blood sugar. They may be present-biased (Newhouse 2006) and overweight the immediate costs of care, such as copays and hassle-costs of setting up appointments or filling prescriptions. They may simply forget to take their medications or refill their prescriptions. Or they may have false beliefs about the efficacy of care (Pauly and Blavin 2008).

The key message from the first article:

Incorporating behavioral hazard alongside moral hazard changes the fundamental tradeoff between insurance and incentives. With only moral hazard, lowering copays increases the insurance value of a plan but reduces its efficiency by generating overuse. With the addition of behavioral hazard, lowering copays may potentially both increase insurance value and increase efficiency by reducing underuse. This means that having an estimate of the demand response is no longer enough to set optimal copays; the health response needs to be considered as well. This provides a theoretical foundation for value-based insurance design, where copays should optimally be lower both when price changes have relatively small effects on demand and when they have relatively large effects on health. We show that ignoring behavioral hazard can lead to welfare estimates that are both wrong in sign and off by an order of magnitude.

"Avoidable copayments" , that's it. And about the second:

We examine how well people make these choices, how well they think they do, and what can be done to improve these choices. We conducted 6 experiments asking people to choose the most cost-effective policy using websites modeled on current exchanges. Our results suggest there is significant room for improvement. Without interventions, respondents perform at near chance levels and show a significant bias, overweighting out-of-pocket expenses and deductibles. Financial incentives do not improve performance, and decision-makers do not realize that they are performing poorly. However, performance can be improved quite markedly by providing calculation aids, and by choosing a ‘‘smart’’ default. Implementing these psychologically based principles could save  purchasers of policies and taxpayers approximately 10 billion dollars every year.

That's a lot. glups!

Private health insurance subsidies: the case of Ireland

Unwinding the State subsidisation of private health insurance in Ireland

Taxes may distort individual decisions and hence resource allocation. Subsidies may have the same effect. Ireland had large subsidies for private health until 2013.

In Budget 2014, announced in October 2013, the Minister announced that charges for all beds in public hospitals would be levied on insurers from 2014, raising € 30 m in 2014. Also in Budget 2014, the Minister for Finance announced that the amount of health insurance premium subject to tax relief would be capped at €1000 for an adult and € 500 for a child. This was expected to yield € 94 min savings in 2014 and € 127 m per year thereafter.

The article explains the concrete situation and policies. Its impact on one statement:

Despite the fears about the effect thes emeasures would have on the private health insurance market, the measures do not appear to have caused significant damage to this market. This may be partly due to the introduction of Lifetime Community Rating by the Government in May 2015, and consequent moves by insurers to innovate at the lower-priced end of the market in advance of this.

Ireland is the closer market to us, we share similar features.

Big Data at work in healthcare

Novel Predictive Models for Metabolic Syndrome Risk: A “Big Data” Analytic Approach

Reducing Metabolic Syndrome Risk Using a Personalized Wellness Program

Metabolic syndrome is somewhat fuzzily-defined, let's say it's a concept-frame that can lead to a condition like chronic heart disease, stroke and diabetes. The results of the estimates from a big data application provide clear messages about the need for personalized wellness programs. Both issues are covered in the quoted articles. There is no reason to delay its application.
Let's add the key statement:

The ability of the models to produce highly individualized risk profiles for overall risk of metabolic syndrome and by specific risk factors allows for more successful patient engagement in subsequent care management programs. Figure 2 shows 2 different individual risk profiles. Subject ID 423262 was a 46-year-old male with current out-of-range metabolic syndrome risk factors of high-density lipoprotein (HDL) and waist circumference. He had a 92% predicted probability of developing metabolic syndrome within 12 months, and a 73% probability of developing abnormal blood glucose as a third specific metabolic syndrome risk factor during the study period. Subject ID 107975 presented a contrasting profile. He was a 37-year-old male with 2 out-of-range metabolic syndrome risk factors—HDL and triglycerides—but had only a 40% predicted probability of developing metabolic syndrome within 12 months. For this subject, abnormal blood glucose was also the most likely abnormal factor to develop next, but carried only a 26% likelihood.