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The Long-Term Consequences of Vietnam-Era Conscription and Genotype on Smoking Behavior and Health

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Abstract

Research is needed to understand the extent to which environmental factors moderate links between genetic risk and the development of smoking behaviors. The Vietnam-era draft lottery offers a unique opportunity to investigate whether genetic susceptibility to smoking is influenced by risky environments in young adulthood. Access to free or reduced-price cigarettes coupled with the stress of military life meant conscripts were exposed to a large, exogenous shock to smoking behavior at a young age. Using data from the Health and Retirement Study (HRS), we interact a genetic risk score for smoking initiation with instrumented veteran status in an instrumental variables (IV) framework to test for genetic moderation (i.e. heterogeneous treatment effects) of veteran status on smoking behavior and smoking-related morbidities. We find evidence that veterans with a high genetic predisposition for smoking were more likely to have been smokers, smoke heavily, and are at a higher risk of being diagnosed with cancer or hypertension at older ages. Smoking behavior was significantly attenuated for high-risk veterans who attended college after the war, indicating post-service schooling gains from veterans’ use of the GI Bill may have reduced tobacco consumption in adulthood.

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Notes

  1. The HRS (Health and Retirement Study) is sponsored by the National Institute on Aging (Grant Number NIA U01AG009740) and is conducted by the University of Michigan.

  2. The RAND HRS Data file (Version N, 2014) is an easy to use longitudinal data set based on the HRS data. It was developed at RAND with funding from the National Institute on Aging and the Social Security Administration, Santa Monica.

  3. See “Genetic risk score” section for QC specifics.

  4. In 1994 and 1996, the smoking questions were fielded to current smokers only. However, since questions about former smoking behavior were asked in 1992, and a new cohort was not added until 1998, data on past smoking behavior is available for the majority of participants.

  5. In general, self-reports of smoking behavior have been shown to be a reliable measure over time despite mounting social stigma (Hatziandreu et al. 1989) and findings from biochemical validation studies suggest self-reported usage is a valid estimate of smoking status in the population (Fortmann et al. 1984; Patrick et al. 1994).

  6. We also explored outcomes related to lung disease and lung function but failed to find any significant effects. Results are available from the authors upon request.

  7. \(Mean \; arterial \; pressure \; \left( {MAP} \right) \cong \frac{2}{3} \times DBP + \frac{1}{3} \times SBP.\)

  8. The results of the Vietnam draft lottery are available at: http://www.sss.gov/lotter1.htm.

  9. Specifically, we regress veteran status on draft eligibility and a constant with controls for month of birth. Men born between 1948 and 1952 were 15.7 % points more likely to serve, while men born between 1950 and 1952 were 15.3 % points more likely to serve. Results are available from the authors upon request.

  10. Genotyping was performed on the HRS sample using the Illumina Human Omni-2.5 Quad beadchip (HumanOmni2.5-4v1 array). The median call-rate for the 2006–2008 samples is 99.7 %.

  11. Clumping takes place in two steps. The first pass is done in fairly narrow windows (250 kb) for all SNPs (the p value significance thresholds for both index and secondary SNPs is set to 1) with a liberal LD threshold (R2 = 0.5). In a second pass, SNPs remaining after the first prune are again pruned in broader windows (5000 kb) but with a more conservative LD threshold (R2 = 0.2).

  12. GxE interaction models with the CPD GRS are available from the authors upon request.

  13. The first condition is easy to verify, and standard first stage statistics (partial R2 and F-statistic) for the significance of the instruments in the HRS sample show draft eligibility and its interaction with the GRS are robust predictors of veteran status and its interaction with the GRS (tables are available upon request). The exclusion restriction, or second condition, cannot directly be tested. In this study, a violation of the exclusion restriction could occur if the stress of having a low draft number triggered smoking behavior. Heckman (1997) shows the IV estimator is not consistent if heterogeneous behavioral responses to the treatment—or military service in this case—are correlated with the instrument (i.e. draft eligibility). However, past research has provided convincing counterfactuals that suggest the exclusion restriction holds. For example, Angrist (1990) found no significant relationship between earnings and draft eligibility status for men born in 1953 (where draft eligibility was defined using the 1952 lottery cutoff of 95). Since the 1953 cohort was assigned RSNs but never called to service, if the draft affected outcomes directly, we would expect outcomes to vary by draft eligibility for this cohort.

  14. A mechanical failure in the implementation of the first round of the lottery (balls with the days of the year were not mixed sufficiently after having been dumped in a month at a time) resulted in a disproportionately high probability of being drafted for those born in the last few months of the year (Fienberg 1971). This could bias estimates if those born later in the year differ in important ways from those born at other times during the year. For example, studies have shown health varies with season of birth.

  15. In a typical linear regression model with an interaction term, the interaction term and each of the corresponding main effects are included as separate terms (e.g. “draft”, “GRS x draft” and “GRS”). Here, because we are using a 2SLS approach, and the “GRS” term is highly correlated with “GRS x draft”, we model the main effect of the GRS as “nodraft x GRS” to strengthen the correlation between the “GRS x veteran” and the “GRS × draft” terms in the first stage. Using the GxE interaction term for draft ineligible non-veterans instead of the main effect of the GRS does not change the meaning of this term, which can still be interpreted as the marginal effect or slope for men who were not drafted and who did not serve. However, it does change the interpretation of \(\updelta_{2}\), which now represents the marginal effect for draft eligible veterans instead of the difference between the marginal effects for draft eligible veterans and draft ineligible non-veterans.

  16. The IV estimates of effects of military service using the draft lottery capture the effect of military service on “compliers”, or men who served because they were draft eligible but who would not otherwise have served. It is not, therefore, an estimate of the effect of military service on men who volunteered. See Angrist and Pischke (2008) for a more detailed discussion of the interpretation of the LATE for the Vietnam-era draft.

  17. Post hoc power analysis was conducted for the GxE coefficients using the software package G*Power (Faul et al. 2009). The sample size of 631 was used for statistical power analysis on a multivariate linear regression equation with 83 predictors at the conventional, two-tailed 0.05 significance level (\(\alpha = 0.05)\).

  18. The partial R2 or Cohen’s effect size for the GxE coefficient was estimated from an OLS regression of Eq. 4. To minimize any potential bias in the estimation of the effect sizes due to the endogeneity of self-reported veteran status, we model the GxE interaction terms in the OLS model using draft eligibility status instead of self-reported veteran status.

  19. Specifically, we estimate the total difference between veterans and non-veterans, or the total treatment effect, by adding the marginal treatment effects for veterans (“Veteran” + “GRS x Veteran”) and subtracting them from the marginal treatment effect for draft ineligible non-veterans (“GRS × Non-Veteran”). To ensure the accuracy of the standard errors, this is done using a post-estimation linear combination.

  20. We note that due to low sample sizes at higher values of the GRS, IV estimates three or more standard deviations away from the mean may not accurately predict total treatment effects.

  21. The exogenous downstream effect of educational attainment might be compromised if the high correlation between draft eligibility and schooling is a reflection of draft avoidance behavior rather than military service—i.e. if it is a capturing the effect of men with low draft lottery numbers who “beat the draft” by obtaining educational deferments. Angrist and Chen (2011) find small but statistically significant positive effects of service on educational attainment for white men born between 1948 and 1952. Weighing this against the sharp decline in educational deferments during the draft lottery period, they argue there is little evidence to support the claim that increases in schooling among draft eligible men are due to draft avoidance behavior.

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Funding

This study was funded by the Russell Sage Foundation (grant number 83-15-29).

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  1. Population Studies Center, University of Michigan, 426 Thompson St, Ann Arbor, MI, 48104, USA

    Lauren Schmitz

  2. Departments of Sociology, Public Policy, and Medicine, New York University, 295 Lafayette St. 4th Floor, New York, NY, 10012, USA

    Dalton Conley

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Schmitz, L., Conley, D. The Long-Term Consequences of Vietnam-Era Conscription and Genotype on Smoking Behavior and Health. Behav Genet 46, 43–58 (2016). https://doi.org/10.1007/s10519-015-9739-1

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