Given the importance of body mass and lifestyle behaviors to an individual’s short- and long- term health outcomes, many health policies and programs are targeting individuals’ lifestyle be- haviors. For example, workplace wellness programs offer many activities to improve employees’ lifestyle behaviors. According to the 2016 annual survey conducted by Kaiser Family Foundation and the Health Research & Educational Trust, 46 percent of small firms and 83 percent of large firms offer a program in at least one of these areas: smoking cessation, weight management (e.g., on-site fitness programs or facilities), and behavioral or lifestyle coaching.
This policy simulation aims to evaluate the effects of a successful wellness program that estab- lishes a high (i.e., vigorous) level of exercise among individuals. Specifically, it examines whether the high level of exercise can (1) increase diabetes screening; (2) reduce type-2 diabetes and pre- diabetes; and (3) reduce adverse health shocks and improve longevity. The policy simulation results are reported in Table 7.4.
The simulation results suggest that the wellness program improves individuals’ health out- comes in several dimensions. First, it reduces the average body mass by 0.8 percent, which comes with the largest reduction in the share of obese observations, by 3.1 percent. Second, individuals are less likely to develop type-2 diabetes or pre-diabetes. The wellness program lowers the rate of diabetes by 1.1 percent and the probability of pre-diabetes by 0.9 percent. Further more, it also reduces the rate of undiagnosed diabetes by 1.3 percent. Individuals have lower medical care con- sumption, fewer adverse health outcomes, and live longer. Specifically, individuals are 3.1 percent less likely to have a high level of doctor visits, 7.0 percent less likely to have a night in the hospital, and the average nights in the hospital is 8.4 percent lower if any hospital night happens. The death rate is 8.2 percent lower. Meanwhile, the wellness program increases the probability of not having a blood sugar test over a two-year period by 19.9 percent, and it leads to an increase in the rate of unknown pre-diabetes by 19.5 percent.
Table 7.4: Policy simulation: A wellness program to improve exercise level
Baseline Policy Simulation Percent change (%)
Variable Mean Mean Mean
Doctor visits None 0.07 0.07 4.7 Low 0.65 0.66 0.8 High 0.28 0.27 −3.1 Smoking 0.13 0.13 0.3 Binge drinking 0.10 0.10 0.3
No blood sugar test (if no diagnosed) 0.18 0.21 19.9
BMI value 27.10 26.89 −0.8
Underweight/Normal 0.39 0.40 2.8
Overweight 0.27 0.27 −0.1
Obese 0.34 0.33 −3.1
Prob of eventual diabetes (ind level) 0.30 0.30 −1.1 Prob of undiagnosed diabetes 0.08 0.08 −1.3 Prob of diagnosed diabetes 0.22 0.22 −1.1 Without med observations 0.03 0.03 −0.5 With oral medication 0.13 0.13 −0.7 With insulin shot 0.06 0.06 −2.1 Prob of eventual prediabetes 0.25 0.25 −0.9
Prob of unknown 0.05 0.06 19.5
Prob of having any hospital nights 0.26 0.24 −7.0 Number of hospital nights (if any) 7.18 6.58 −8.4
Death 0.03 0.02 −8.2
Total number of observations 3,775,467 Total number of individuals 1,076,400
Note: Simulations use the estimation results from the FIML/DFRE multiple equation model with 50 replications of the estimation sample.
CHAPTER 8 CONCLUSION
This study evaluates the roles of many contributors to an individual’s type-2 diabetes screening behavior. The various contributors nested in the model are: monetary and time costs of doctor visits and test, the marginal effectiveness of different types of medical and non-medical inputs for controlling blood sugar level, an imperfect perception of true health, life expectancy, and health anxiety. This empirical study is the first to quantify the impact of health anxiety, which represents the anticipatory disutility of taking a screening test and serves as an explanation for the puzzle of health information avoidance. To evaluate all contributors simultaneously, I develop a dynamic, stochastic model of an individual’s decisions about doctor visits (at which a blood sugar test may or may not be administered), health-related lifestyle behaviors, and employment where underlying disease state governs diabetes stage and individuals have imperfect information about their true health (if untested). Solving the optimization problem, I derive the demand behaviors as a function of information available to the individual at the beginning of the period. The demand equations are jointly estimated with observed medical care behaviors, health outcomes, and expectations processes using a maximum likelihood method. The joint estimation procedure allows for common unobservables that are modeled as random effects with discrete distributions and influence several outcomes within a period and/or over time.
Using data from HRS, I find that health anxiety, monetary costs, time costs, and health and longevity expectations are each significant and important contributors to an individual’s blood sugar testing behavior. Specifically, a reduction in health anxiety (of wo standard deviations) has the second largest impact on reducing avoidance of a test conditional on the level of doctor visits. Lowering health anxiety has the same impact as giving everyone Medicare plus any private health insurance coverage. Using a broader definition of health anxiety, I find that a health anxious
individual is less likely to receive a diabetes screening test by both reducing the number of doctor visits and avoiding the test during visits. Instead of assuming that screening tests always have a positive benefit, I also model and examine the impact of screening tests on endogenous lifestyle behaviors and health productions. I find that a diagnosis of early stage diabetes leads individuals to exercise more and to have better diets that in turn, reduce body mass.
Using the estimated data-generating process, I evaluate four policy experiments through sim- ulations. I find that a policy that reduces health anxiety improves population screening behavior, leading to a lower rate of people living with unknown diabetes, but it does not improve diabetes prevalence over time. A mandatory screening program at age 60 slightly improves the screening behavior after age 60, but induces this group of people to rely more heavily on medical care con- sumption instead of changing lifestyle behaviors. Two policies that promote exercise levels reduce diabetes prevalence in the population, reduce individual medical care consumption, and improve population longevity over time. However, the higher exercise level reduces the screening rate, which leads to an increase in the percentage of people living with unknown prediabetes.
There are several extensions to explore in future research. First, as my study focuses on dia- betes, it would be policy significant to apply the developed framework to other expanding chronic conditions, such as Alzheimer’s disease and cancer, that may benefit from early diagnosis. Given that the information value and treatment value of these conditions may differ from that for diabetes, it would be interesting to analyze the impact of different contributors on individuals’ screening be- havior and the behavioral changes that proceed and follow screening. Second, in my study, health anxiety is identified by an individual’s pessimism level after controlling for many other potential explanations for non-participation in diabetes screening. One caveat of this identification strategy is that it may only account for that part of health anxiety measured by pessimism. With better data containing direct measures of health anxiety, future studies could examine the full effect of health anxiety on an individual’s screening behavior. Third, physicians play a critical role in the screening decisionmaking process. My study uses a partial equilibrium model that accounts for the effect of physicians using given state-level medical care supply-side factors. With more detailed measures of physician characteristics and the interactions between physicians and patients, future studies
would be able to incorporate and model the supply side behaviors. A general equilibrium model may suggest more efficient policy prescriptions for physicians as well as consumers interacting in the medical care market.
APPENDIX A
PROOFS IN THEORETICAL MODEL
Proof of Lemma 3.1
Lemma 3.1If the individual chooses not to test (b = 0), her optimal lifestyle choice (a∗) is:
a∗ = 0 ifπ < Ω+ΦΦ 1 ifπ ≥ Φ Ω+Φ
Figure A.1: Optimal lifestyle choice
Proof
For an individual with a given subjective belief,π, who chooses not to test (b= 0), her expected utility of choosinga= 0is:
E[u(s, a= 0)|π] =πu(1,0) + (1−π)u(0,0) =−πΩ + (1−π) (A.1)
and the expected utility of choosinga= 1is:
E[u(s, a= 1)|π] =πu(1,1) + (1−π)u(0,1) = (1−π)(1−Φ) (A.2)
Therefore, the individual choosesa= 0if and only if
π < Φ
Ω + Φ
and she choosesa= 1if and only if
E[u(s, a= 0)|π]≤E[u(s, a= 1)|π] (A.4)
π ≥ Φ
APPENDIX B
ALTERNATIVE MODELS TO EXPLAIN INFORMATION AVOIDANCE
B.1 Theory 1: Information averse