Descomposición de metano (de la Casa-Lillo, 2002)

Knowing that the underlying blood sugar evolution governs an individual’s diabetes state tran- sition and that body mass is highly correlated with the onset of diabetes and risks of other compli- cations, I discuss the key determinants of those production processes.

Blood sugar evolution

state in the period, and her medical and non-medical care inputs chosen in the current period. The simulated contemporaneous effects, both direct and total effects, of some key contributors to the A1c evolution are displayed in Table 6.5. I focus my discussion on the direct effect when the direct effect and total effect are identical.

Several mechanisms of blood sugar evolution that are consistent with our hypotheses are dis- covered. First, diabetes state plays an important role in influencing an individual’s blood sugar level: an individual who is diagnosed with diabetes has higher blood sugar levels (measured by the A1c test readings) than those without diabetes. For example, an individual with diabetes but no medical treatment has a higher A1c value by 0.55 units (or 9.6 percent) than an individual who is tested but not diagnosed with diabetes. In addition, among people with diabetes, those receiving insulin shots or oral medication have higher blood sugar levels than those receiving no medical treatment. Second, doctor visits have protective effects on an individual’s blood sugar level in terms of direct effect. Specifically, an individual with a high level of doctor visits has a lower A1c reading by 0.10 units (or 1.6 percent) than an individual with no doctor visits. However, after taking into account the effect of doctor visits on lifestyle and screening behaviors, individuals with a high number of doctor visits have higher A1c readings by 1.4 percent. Third, body mass (BMI) influences blood sugar levels. On average, an individual with a BMI of 35 (i.e., who is obese) has a higher A1c reading by 0.13 units (2.2 percent) than an individual with a BMI of 23 (i.e., who has normal weight). Lastly, lifestyle behaviors also impact the evolution of blood sugar levels. A vigorous level of exercise leads to a lower blood sugar level. If an individual binge drinks, her A1c value falls by 0.09 units for direct effect and 0.15 units for total effect. This result is consistent with some medical research that claims “while moderate amounts of alcohol may cause blood sugar to rise, excess alcohol can actually decrease your blood sugar level” (WebMD Medical).3

Table 6.5: Simulations: Impacts of key determinants on blood sugar evolution

Contemporaneous Marginal Effect Direct Effecta Total Effectb Case 1 Case 2- Case 1 Case 1 Case 2- Case 1 Case 1→Case 2 (Level) ∆Value ∆Percent (Level) ∆Value ∆Percent Doctor visits: none→high 5.996 -0.10 -1.6 5.777 0.08 1.4

(0.0003) (0.0020)

Exercise: none→mild 5.917 0.02 0.4 5.884 -0.01 -0.2

(0.0003) (0.0004)

Exercise: mild→moderate 5.938 -0.02 -0.3 5.872 0.00 0.1

(0.0002) (0.0003)

Exercise: moderate→vigorous 5.921 -0.02 -0.4 5.875 -0.02 -0.3

(0.0001) (0.0004)

Smoke→No smoke 5.941 -0.03 -0.4 5.825 0.05 0.8

(0.0002) (0.0004)

Binge drink→No binge drink 5.834 0.09 1.6 5.728 0.15 2.7

(0.0001) (0.0003)

No test→Test and no diabetes 5.695 0.02 0.3 5.696 0.01 0.3

(0.0002) (0.0002)

Test and no diabetes→Diabetes without med 5.711 0.55 9.6 5.711 0.55 9.7

(0.0006) (0.0007)

Diabetes without med→diabetes with med 6.259 0.31 4.9 6.262 0.31 4.9

(0.0002) (0.0003)

Diabetes with med→diabetes with insulin 6.568 0.62 9.5 6.571 0.62 9.4

(0.0002) (0.0003)

BMI: 23→35c 5.860 0.13 2.2 5.761 0.13 2.3

(0.0006) (0.0008)

a: Direct effect is the effect of change in specified variable on the blood sugar level (or A1c reading).

b: Total effect measures both direct effects of the change in specified variable as well as the indirect effects of that change on blood sugar test, diagnosis of diabetes, and hospital nights.

c: The value 23 is the average body mass index among all observations with normal BMI (i.e.,18.5<BMI<24.9) and the value 35 is the average BMI among all observations with obese BMI (i.e., BMI>30).

Note: All explanatory variables are in Table E13-14 in Appendix E. Simulations use the estimation results from the FIML/DFRE multiple equation model with 50 replications of the estimation sample. Standard errors (reported in parentheses) are bootstrapped with 1,000 draws.

Body mass production

Body mass production also depends on an individual’s diabetes state and her medical and non- medical inputs in the current period. Additionally, since body mass may reflect an individual’s diet or nutrition behavior that we do not observe directly from data, the production process may also depend on the health information associated with a blood sugar test. The simulated direct and total marginal effects are shown in Table 6.6. The direct effect and total effect are identical for most changes, so I also focus my discussion on the direct effects.

First, lifestyle behaviors are significant predictors of body mass (as measured by BMI) produc- tion. A moderate or vigorous level of exercise leads to lower BMI values. For example, compared to having a mild level of exercise, having a moderate level of exercise lowers an individual’s BMI by 0.13 units (or 0.5 percent), holding all other variables constant. On average, smoking reduces an individual’s BMI by 0.48 units. This result is consistent with the finding that current smokers have significantly lower BMI than never smokers using the National Health and Nutrition Examination Surveys (NHANES) (Plurphanswat and Rodu, 2014).

Second, diabetes states are also important contributors to body mass through medical treatment and information effects. Compared to an individual who is tested but not diagnosed with diabetes, an individual who is diagnosed with an early stage of diabetes (i.e., diabetes without medical treatment) has a lower BMI by 0.15 units (or 0.5 percent). The negative effects of the diagnosis of early stage diabetes may reflect that an individual responds to this health information and consumes a better diet to control her body mass. However, having oral medication or insulin shots as a treatment for diabetes increases an individual’s BMI. This result is consistent with studies showing that weight gain is a common phenomenon among people taking insulin treatments. The marginal effects also suggest that individuals are not likely to change diet behavior and respond to health information associated with a test but no diagnosis of diabetes. However, we do find them to respond to this health information by increasing exercise levels. Lastly, individuals with higher longevity expectations have lower body mass.

Table 6.6: Simulations: Impacts of key determinants on body mass production

Contemporaneous Marginal Effect Direct Effecta _{Total Effect}b Case 1 Case 2 - Case 1 Case 1 Case 2 - Case 1 Case 1→Case 2 (Level) ∆Value ∆Percent (Level) ∆Value ∆Percent Exercise: none→mild 28.410 0.08 0.3 28.405 0.09 0.3

(0.0003) (0.0012)

Exercise: mild→moderate 28.488 -0.13 -0.5 28.493 -0.13 -0.5

(0.0002) (0.0010)

Exercise: moderate→vigorous 28.359 -0.09 -0.3 28.363 -0.09 -0.3

(0.0002) (0.0014)

Smoke→No smoke 27.956 0.48 1.7 27.966 0.47 1.7

(0.0003) (0.0013)

Binge drink→No binge drink 28.412 -0.04 -0.1 28.415 -0.04 -0.1

(0.0001) (0.0008)

No test→Test and no diabetes 28.358 0.00 0.0 28.369 0.00 0.0

(0.0064) (0.0064)

Test and no diabetes→Diabete without med 28.363 -0.15 -0.5 28.367 -0.16 -0.6

(0.0008) (0.0009)

Diabetes without med→Diabetes with med 27.209 0.19 0.7 28.209 0.20 0.7

(0.0008) (0.0010)

Diabetes with med→Diabetes with insulin 28.402 0.39 1.4 28.408 0.37 1.3

(0.0011) (0.0014)

Survival prob 50%→100% 28.395 -0.03 -0.1 28.391 -0.02 -0.1

(0.0001) (0.0009)

a: Direct effect is the effect of change in the specified variable on the body mass index value.

b: Total effect measures both direct effect of change in the specified variable as well as the indirect effect of that change on body mass through testing behavior, diabetes diagnosis and states, and hospital nights.

Note: All explanatory variables are in Table E15-16 in Appendix E. Simulations use the estimation results from the FIML/DFRE multiple equation model with 50 replications of the estimation sample. Standard errors (in parentheses) are bootstrapped with 1,000 draws.

CHAPTER 7 POLICY SIMULATIONS

Using the estimated data generating process, I simulate an individual’s behaviors and health outcomes over time in order to evaluate potential policy interventions. That is, I forward simu- late the behaviors and outcomes of individuals assuming that an implemented policy successfully changes the targeted behaviors of individuals. I then compare the simulated values to the ones for the baseline scenario (i.e., without the policy intervention). In this chapter, I discuss four policy simulations: two policies aiming to improve screening behavior (a health anxiety improve- ment program and a mandatory screening program for 60 years olds) and two policies aiming to improve population health (a diabetes prevention program and a wellness program that improve exercise behavior).