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Early-Life Military Exposures and Functional Impairment Trajectories Among Older Male Veterans: The Buffering Effect of Psychological Resilience

Early-Life Military Exposures and Functional Impairment Trajectories Among Older Male Veterans:... Abstract Objectives Drawing on the life course framework and theoretical concept of resilience, we examine the impact of early-life service-related exposures (SREs) on later-life functional impairment trajectories among older U.S. male veterans. We conceptualize resilience as a psychological resource potentially moderating the lasting negative consequences of traumatic military exposures. Method Using the 2013 Veterans Mail Survey linked to the Health and Retirement Study 2006–2014 Leave Behind Questionnaire and RAND Data File (v.N), we estimate latent growth curve models of functional impairment trajectories. Results SRE to death has a persistent positive effect on functional limitations and activities of daily living limitations. Psychological resilience significantly moderates this association, such that veterans maintaining higher levels of resilience in the face of adverse exposures have considerably less functional impairment over time compared to their counterparts with low levels of resilience. Discussion Our findings point to the importance of psychological resilience in later life, especially within the realm of traumas occurring in early life. We discuss implications for current military training programs, stressing the importance of research considering individual resources and processes that promote adaptation in the face of adverse life events. ADLs, Functional impairment, HRS, Latent growth curves Over half of older men and more than 80% of those over age 85 in the United States are veterans (Newport, 2012). The literature demonstrates that military experiences during early adulthood provide a critical influence on life course trajectories, and may play a particularly salient role in shaping health as we age (Settersten, 2006). Among early-life experiences, the military is unique in its association with both positive and negative long-term outcomes (London & Wilmoth, 2016; Wilmoth & London, 2013). Key to these effects is specific service-related exposures (SREs), especially traumatic events such as combat or exposure to death (Taylor, Ureña, & Kail, 2015; Ureña, Taylor, & Kail, 2017; Wilmoth, London, & Parker, 2010). However, little research has examined the circumstances under which these potentially traumatic exposures translate to lasting impacts on physical health trajectories among older veterans. The negative consequences of traumatic life events on health may be counteracted or buffered by maintained psychological resources. Such a resource is psychological resilience, which appears to shape the ways early-life adversities and traumas influence well-being over the life course (Smith & Hayslip, 2012). In short, a resilient individual “embodies the personal qualities that enable them to thrive in the face of adversity” (Connor & Davidson, 2003). Psychological resilience is important for understanding the connection between military involvement and later-life health because current military training and infrastructure is designed to bolster recovery from adversity and traumas stemming from service, in part by strengthening psychological resources that may benefit individuals as they age (Meredith et al., 2011). With a few exceptions (e.g., Manning, Carr, & Kail, 2016), much of the research documenting resilience is based on simple associations between adversity and positive outcomes. However, a growing number of training programs have emerged within military environments because resilience is increasingly recognized as an individual resource that can be cultivated and leveraged to produce better outcomes in the face of trauma (Meredith et al., 2011). Knowing whether, how, and under what circumstances resilience as an individual psychological resource promotes adaptive functioning may help inform development of these programs (Lerner et al., 2012; Smith & Hayslip, 2012). The purpose of the current study is to examine how exposures to SREs in early adulthood influence functional impairment trajectories in later life for male veterans, and determine whether psychological resilience in later life buffers the negative consequences of SREs for physical health. The Life Course Framework and the Military The life course framework explains the ways in which specific contexts shape individual lives (Elder, 1994) and emphasizes how short-term transitions and “turning points” lead to long-term, age-graded trajectories unfolding over individual lives (Elder, 1994). Military service is such a turning point (Sampson & Laub, 1996), marking a distinct transition that potentially results in a deviation from an existing path with positive and/or negative consequences based on historical time and place of service, age upon entry, individual characteristics, and social context (Settersten, 2006). Although Elder’s work (1986) suggests that service at earlier ages may be more protective in stabilizing the life course, other research suggests that exiting the military at an early age generates a health disadvantage in later life (Hardy & Reyes, 2016). Even today, military enlistment occurs before age 25, which is prior to most major adult milestones (U.S. Army, 2013). Overall the research suggests that military service, as a turning point, has the potential to shape trajectories across adulthood and later life. Under the best circumstances, the military can provide education, skills, resources, and opportunities for upward mobility, particularly if there is minimal disruption to formative life experiences such as marriage and education (London & Wilmoth, 2016). However, the military also introduces physical and psychological risks arising from combat, injury and disability that damage health and wellbeing (Bennett & McDonald, 2013). Most of today’s older veterans served during conscription and wartime eras (Wilmoth & London, 2016), with military-related traumas or adversities experienced in young adulthood when individuals are also vulnerable to long-term physical and mental health consequences (Rindfuss, 1991; Spiro, Schnurr, & Aldwin, 1997). Research suggests that exposure to particularly heavy or traumatic combat, including exposure to the dead and wounded, has the greatest negative impacts on physical and mental health (Taylor et al., 2015; Ureña et al., 2017; Elder & Clipp 1989; Marcellino & Tortorello, 2015). Thus, despite the potentially positive effects of military involvement during early adulthood, we hypothesize that exposure to traumatic combat, especially exposure to the dead, dying, and wounded, will relate to increases in functional impairment during later life for older male veterans. Adversity, Resilience, and Military Exposures Some individuals exposed to traumatic events experience substantial and lasting negative outcomes, while others experience no long-term consequences. Life course scholars propose that we need to understand how and under what circumstances early adversity, such as exposure to SREs, relate to differential outcomes as individuals age (Ferraro, Schafer, & Wilkinson, 2016; Willson, Shuey, & Elder, 2007). Various theories describe the processes by which the negative effects of adversity accumulate over the life course (e.g., Ferraro & Shippee, 2009; O’Rand, 1996; Thoits, 2010), noting that early-life events tend to have lasting and cumulative impacts on individuals. In the case of military service, adverse childhood experiences such as financial hardship and child abuse are often associated with military enlistment (Bennett & McDonald, 2013; Blosnich, Dichter, Cerulli, Batten, & Bossarte, 2014). Any injury, disability, or trauma sustained in service during young adulthood may impose subsequent disadvantage in areas such as the labor force and social relationships, all of which affect health over time (MacLean, 2013). As a result, research seeking to understand how military experiences in early life contribute to later-life health must consider both upstream (childhood) and downstream (post-service) factors. Just as the military may influence negative and/or positive outcomes among individuals, processes of adversity in this context may have varied outcomes. For instance, military stressors are positively associated with both post-traumatic stress disorder (PTSD) and post-traumatic growth (PTG; Park, Aldwin, Fenster, & Snyder, 2008). Although particularly traumatic combat, including exposure to the dead, is linked to negative health outcomes (Marcellino & Tortorello, 2015; Taylor et al., 2015; Ureña et al., 2017), older veterans also report that military service provided coping skills, self-discipline, and appreciation for life (Elder & Clipp, 1989). One reason some individuals report positive or mixed effects of traumatic events may be because they generally maintain higher psychological “resilience” resources over time, which may translate to fewer negative long-term outcomes (Spiro, Settersten, & Aldwin, 2016). The counterintuitive effects of individuals exposed to adversities who experience positive outcomes has raised attention to the concept of resilience in life course research (Bonanno et al., 2012; Pruchno & Carr, 2017; Seligman, 2012). Resilience has primarily appeared in studies surrounding childhood adversity and traumas (Rutter, 1979), but has also been an important and burgeoning concept in the literature surrounding trauma-exposed adults, specifically regarding military service and combat trauma (Bonanno et al., 2012; Marcellino & Tortorello, 2015). Although there is no consistent definition or operationalization of resilience used in research on veterans today (Meredith et al., 2011), we generally conceptualize resilience as the capacity to navigate adversity through positive adaptation in a manner that protects health, well-being, and life satisfaction (Manning, 2013). Initial empirical evidence conceived of resilience as the absence of negative outcomes or presence of positive outcomes among those experiencing substantial adversity or trauma (Smith & Hayslip, 2012). However, scholars now seek to better understand personal attributes of resilience and processes of adaptation, that is, mobilizing resources to produce favorable outcomes (Lerner et al., 2012). In thinking about the role of resilience in the lives of older veteran men serving during early adulthood, it is important to recognize different potential pathways involved in cultivating resilience. For older veterans, the military may have facilitated opportunities to accumulate psychological resources that could be leveraged or further cultivated throughout their lives. However, some older veterans may have been resilient prior to service, and therefore experienced fewer negative and more positive effects. Still others may have psychological resilience that was eroded by trauma or adversity as they aged. It is likely that all of these pathways are at play. Smith and Hayslip (2012) posit that resilience is an individual characteristic shaped by social contexts, and that individuals engage in a dynamic process to mobilize resources in order to cope and adapt throughout life. Thus, resilience incorporates agency in the activation of internal and external resources necessary for an adaptive process (Schafer, Shippee, & Ferraro, 2009). Marcellino and Tortorello (2015) document how U.S. Marines “construct” resilience in choices and interactions with others that promote coping, meaning-making, flexibility and other attributes characterizing resilient individuals. Steps to promote resilience in the current All Volunteer Force are increasingly implemented across branches (Marcellino and Tortorello, 2015; Meredith et al., 2011; Seligman, 2012), where the goal of more psychologically fit soldiers hinges on cultivation of these attributes and how they may work to promote favorable outcomes over time in the face of trauma. The data in this study (i.e., Health and Retirement Study [HRS] data) do not allow us to explore the causal pathways by which individuals become more or less resilient in early life, or if and how much resilience changes over time in response to life course stressors and supports. Rather, we are concerned with the resilience of veterans who survived into later life, and how this resource patterns the relationship between SREs and functional impairment trajectories. We operationalize resilience as a multidimensional personal attribute (Manning et al., 2016), in line with other established instruments designed to measure personal resilience resources (Connor & Davidson, 2003; Wagnild & Young, 1993) among veterans and first responders (Kuwert, Knaevelsrud, & Pietrzak, 2014; Lee, Ahn, Jeong, Chae, & Choi, 2014). In line with other literature (Schafer et al., 2009), we conceptualize this resource as relatively stable in later life, but not an inherent trait that is completely fixed across the life course. A series of sensitivity analyses examining changes in resilience in our sample suggest that our conception of its general stability at this life stage is sound (see Supplementary Appendix A). Under this operationalization, psychological resilience in later life is primarily found to moderate the impact of earlier adversities on future negative outcomes (Manning et al., 2016; Smith & Hayslip, 2012), suggesting trauma-exposed veterans reporting higher levels of resilience in later life will have better health than their counterparts with lower levels of resilience. Thus, we hypothesize that net of other early-life factors, resilience will have an independent negative association with later-life functional impairment and that it will buffer (i.e., moderate) the effects of SREs. Method Data Two data files were linked to the 2013 Veterans Mail Survey (VMS) for analyses, the RAND HRS Data File (v.N) and the biennial HRS data for the 2006–2012 Leave Behind Questionnaire (LBQ) and childhood variables. The HRS includes a sample of 12,652 people born between 1931 and 1941 and their spouses, with biennial follow-ups and cohort replenishment resulting in a 16-year panel study focused on health and socioeconomic factors of older Americans (HRS, 2012). The 2013 VMS is a supplement focused on military and related experiences among a subsample of HRS respondents who reported serving in the active military in any wave of the HRS (N = 1,874 HRS veterans). We linked these records to the RAND HRS Data file (v.N), retaining respondents that merged across data sets (N = 1,860). The RAND file was used to access functional impairment outcomes between 2006 and 2014 along with other health-related and demographic variables. This analytic sample was then linked to the HRS LBQ to establish resilience measures. The LBQ is a psychosocial questionnaire beginning in 2006, surveying an alternating half of HRS respondents in each wave such that for every two-wave period, the entire HRS sample is surveyed. We used the 2006–2012 years of the LBQ to establish each respondent’s first report on the resilience measure, a substantial majority of which (over 80%) reported by 2008. We then used the RAND file to establish functional impairment trajectories for each individual where the baseline wave was the wave at first resilience report with up to four waves following. We further limited the VMS and linked HRS and LBQ sample to only men (N = 1,796), and those with valid reports of the functional impairment outcomes at baseline (N = 1,692). Since the focus of our paper is on early-life influences, we further restricted the sample to veterans who initiated military service by the age of 25, more than 97% of the male VMS sample (N = 1,545), and dropped those less than age 50 at baseline (N = 1,535). Missingness on covariates (N = 96, 6%) yielded a final analytic sample size of 1,439. Missing cases in the outcome variables over time were handled with a full information maximum likelihood estimator (FIML), which allows missingness due to unbalanced panel data and attrition. Outcome Measures We examined the association of SREs with functional impairment trajectories using two outcome measures, functional limitations (FLs) and activities of daily living (ADLs), because these measures are associated with military-related experiences in this sample (Taylor et al., 2015). We chose FLs and ADLs since they represent a continuum of severity in the disablement process. The measures were taken from the 2006–2014 RAND HRS file (RAND, 2014), with every individual contributing up to four waves. FLs were measured as a summed six-item index including self-reports of difficulty with any of the following activities: walking one block; climbing one flight of stairs; lifting or carrying 10 pounds; picking a dime up off the ground; stooping, kneeling, or crouching; and pushing or pulling large objects (2006–2014 average Kuder-Richardson coefficient of reliability = 0.73). Difficulties in FLs tap into earlier stages of disablement (Katz, Ford, Moskowitz, Jackson, & Jaffe, 1963). ADLs were measured as the summed index of five dichotomous items indicating difficulty with the following tasks: walking across the room; getting dressed; taking a bath or shower; eating; and getting in or out of bed (2006–2014 average Kuder-Richardson coefficient of reliability = 0.64 in the HRS). Difficulties with ADLs are more severe manifestations of functional impairment, often emerging later than FLs (Katz et al., 1963). Explanatory Variables The main predictors of interest surround potentially traumatic SREs. Two measures were taken from the VMS. Exposure to combat was measured by a question asking whether the respondent ever served in a combat or war zone (not exposed = 0). As an indicator of heavy or traumatic combat, we used exposure to the dead and wounded, measured with the question: During your military service, were you ever exposed to dead, dying, or wounded people? (not exposed = 0). Since we are interested in the ability of psychological resilience as a resource to moderate the impact of SREs, we utilized the simplified resilience score (SRS) established by Manning, Carr, and Kail (2016) from the psychosocial battery of items in the LBQ. The SRS is guided by the Wagnild and Young Resilience Scale (RS; 1993), incorporating five psychosocial domains (a) perseverance or the ability to keep going despite major setbacks; (b) equanimity, or being able to adjust to change, often with humor; (c) meaningfulness or purpose in life; (d) self-reliance or recognition of one’s one inner strengths; and (e) existential aloneness or the realization that some experiences must be faced alone (Wagnild & Young, 1993; Zeng & Shen, 2010). The SRS is a shortened version of the original SRS using 12 items from the LBQ with high reliability (α = 0.85) and similar to the measure used in the 2011 National Health and Resilience in Veterans Study (Kuwert et al., 2014). The items ask respondents how strongly they agree or disagree with the following statements: (1) I feel it is impossible for me to reach the goals that I would like to strive for; (2) So far, I have gotten the important things I want in life; (3) If something can go wrong for me, it will; (4) I am satisfied with my life; (5) I feel that what happens in life is often determined by factors beyond my control; (6) I can do the things that I want to do; (7) The future seems hopeless to me and I can’t believe that things are changing for the better; (8) When I really want to do something, I usually find a way to succeed at it; (9) In most ways, my life is close to ideal; (10) I can do just about anything I set my mind to; (11) There is really no way I can solve the problems I have; and (12) I have a sense of direction and purpose in life. Items 1, 3, 5, 7, and 11 were reverse coded and values were standardized to range between 0 and 1 since items varied in their original ranges. The standardized items were then summed with observed values ranging from 2.4 to 12 (baseline α = 0.84 in the analytic sample). Sensitivity analyses conducted to establish the SRS’s efficacy, stability, and temporal relationship with functional impairment in our study are described in detail in Supplementary Appendix A. Background demographic control variables included race (white = 1, other = 0), the age at baseline in years, and the highest recorded year of parent’s educational attainment. Missing values on the parental education variable (5%) were imputed using regression-based techniques. We also wanted to establish the effects of resilience net of other childhood adversity, military-related variables, and other potential confounders. We included childhood measures, drawn from the biennial HRS data and the LBQ between 2006 and 2012, including whether the respondent ever reported physical child abuse, parents with substance abuse problems, or whether they suffered from a disability in childhood (including concussions and severe head injuries before age 16). before age 16). We included three military service control variables (from the VMS) to reduce further confounding: self-reported duration of military service in years to handle the impact of longer/repeated exposures or deployments, self-reported VA service-connected disability rating (yes = 1) to handle injury or disability sustained directly from service and highest rank achieved (officer = 1). Other control variables included educational attainment in years, marital status (married = 1) at baseline, and the inverse hyperbolic sine of total non-housing wealth at baseline. Analysis We utilized latent growth curves (LGCs) to examine associations between SREs, resilience, and functional impairment in later life among veteran men based on their ability to capture long-term levels and change in our outcomes over a considerable time span. Using a structural equation modeling (SEM) framework, we established individual-level trajectories of FLs and ADLs, respectively, for up to four waves for every individual between 2006 and 2014. We further assessed how individuals varied from the average trajectory based on SREs, and how this variation differs by level of resilience net of confounders. The unconditional, Level 1 equation used for the baseline trajectories of FLs and ADLs is: yit=αyi+βyiλyt+εyit where yit is a vector of repeated measures, αyi is a vector of latent intercepts, βyi is a vector of latent slopes, λyt is a vector of fixed/freed loadings representing time, and εyit is a vector of disturbance terms assumed to have equal variances. The conditional, Level 2 equation used for the three nested models that include covariates is: αyi=μαy+γαy1x1i+ζyαi βyi=μβy+γβy1x1i+ζyβi Baseline models were used to establish significant levels and change of the outcomes over time. Results from the baseline models suggest that linear trajectories fit the data well for both outcomes (factor loadings = 0, 1, 2, 3), with significant variability in the randomly estimated intercepts and slopes (models not shown). We originally estimated nested models examining demographic variables and SREs alone (Model 1), adding in psychological resilience and the interaction (Model 2) prior to the full model with all covariates (final model). Because the results from Models 1 and 2 remained very stable, we present the final model with a brief discussion of Models 1 and 2 in the text. All models were estimated in Mplus v. 6.12 (Muthén & Muthén, 2004). Results Descriptive Statistics Table 1 presents unweighted descriptive statistics for the variables in our analyses. Our sample was 69 years old on average at baseline and 85% white. Given their ages, a substantial minority of these veterans would have served during WWII and Korea, but the most common war era represented in our sample was the Vietnam War (41%). Twenty-nine percent of the sample reported exposure to combat, and 27% reported exposure to the dead and wounded. Although similar percentages of veterans experienced these exposures, additional analyses suggest SREs were quite varied, with 69% of combat veterans also exposed to the dead and 73% of those exposed to the dead also serving in combat or war zones. The average resilience score at baseline was 9.5, suggesting generally high psychological resilience levels among older veterans. We also tested associations between SREs and resilience, with no significant associations in bivariate relationships. Among the childhood variables, small but substantial percentages reported history of child abuse, family history of substance abuse, or disability in childhood. Among service-related covariates, 10% of the sample reported being an officer, 16% reported a VA disability rating, and the average length of service was 5 years. Functional impairment generally increased in our sample over time. FLs increased from 0.85 items at baseline to more than 1 item on average at Wave 4. ADLs also increased on average from 0.17 items at baseline to 0.20 items in Wave 4. Table 1. Unweighted Descriptive Statistics (Mean and SD) Mean/% SD N Age at baseline 69.06 8.83 1,439 White 84.71% — 1,439 Parent’s education (years) 11.03 3.13 1,439 Combat exposure 28.77% — 1,439 Exposure to the dead 27.24% — 1,439 Psychological resilience 9.51 1.66 1,439 Child abuse 7.71% — 1,439 Family substance abuse 19.53% — 1,439 Childhood disability 16.26% — 1,439 Rank (officer) 10.01% — 1,439 VA disability rating 16.33% — 1,439 Education (years) 13.59 2.4 1,439 Service duration (years) 5.03 6.6 1,439 Married (baseline) 76.58% — 1,439 Wealth (inverse hyperbolic sine) 10.96 5.5 1,439 FL (Wave 1) 0.85 1.26 1,439 FL (Wave 2) 0.88 1.3 1,315 FL (Wave 3) 1.08 1.44 1,208 FL (Wave 4) 1.07 1.41 651 ADL (Wave 1) 0.17 0.56 1,439 ADL (Wave 2) 0.16 0.57 1,315 ADL (Wave 3) 0.23 0.65 1,208 ADL (Wave 4) 0.20 0.63 651 Mean/% SD N Age at baseline 69.06 8.83 1,439 White 84.71% — 1,439 Parent’s education (years) 11.03 3.13 1,439 Combat exposure 28.77% — 1,439 Exposure to the dead 27.24% — 1,439 Psychological resilience 9.51 1.66 1,439 Child abuse 7.71% — 1,439 Family substance abuse 19.53% — 1,439 Childhood disability 16.26% — 1,439 Rank (officer) 10.01% — 1,439 VA disability rating 16.33% — 1,439 Education (years) 13.59 2.4 1,439 Service duration (years) 5.03 6.6 1,439 Married (baseline) 76.58% — 1,439 Wealth (inverse hyperbolic sine) 10.96 5.5 1,439 FL (Wave 1) 0.85 1.26 1,439 FL (Wave 2) 0.88 1.3 1,315 FL (Wave 3) 1.08 1.44 1,208 FL (Wave 4) 1.07 1.41 651 ADL (Wave 1) 0.17 0.56 1,439 ADL (Wave 2) 0.16 0.57 1,315 ADL (Wave 3) 0.23 0.65 1,208 ADL (Wave 4) 0.20 0.63 651 Note: ADL = activities of daily living; FL = functional limitations. View Large Table 1. Unweighted Descriptive Statistics (Mean and SD) Mean/% SD N Age at baseline 69.06 8.83 1,439 White 84.71% — 1,439 Parent’s education (years) 11.03 3.13 1,439 Combat exposure 28.77% — 1,439 Exposure to the dead 27.24% — 1,439 Psychological resilience 9.51 1.66 1,439 Child abuse 7.71% — 1,439 Family substance abuse 19.53% — 1,439 Childhood disability 16.26% — 1,439 Rank (officer) 10.01% — 1,439 VA disability rating 16.33% — 1,439 Education (years) 13.59 2.4 1,439 Service duration (years) 5.03 6.6 1,439 Married (baseline) 76.58% — 1,439 Wealth (inverse hyperbolic sine) 10.96 5.5 1,439 FL (Wave 1) 0.85 1.26 1,439 FL (Wave 2) 0.88 1.3 1,315 FL (Wave 3) 1.08 1.44 1,208 FL (Wave 4) 1.07 1.41 651 ADL (Wave 1) 0.17 0.56 1,439 ADL (Wave 2) 0.16 0.57 1,315 ADL (Wave 3) 0.23 0.65 1,208 ADL (Wave 4) 0.20 0.63 651 Mean/% SD N Age at baseline 69.06 8.83 1,439 White 84.71% — 1,439 Parent’s education (years) 11.03 3.13 1,439 Combat exposure 28.77% — 1,439 Exposure to the dead 27.24% — 1,439 Psychological resilience 9.51 1.66 1,439 Child abuse 7.71% — 1,439 Family substance abuse 19.53% — 1,439 Childhood disability 16.26% — 1,439 Rank (officer) 10.01% — 1,439 VA disability rating 16.33% — 1,439 Education (years) 13.59 2.4 1,439 Service duration (years) 5.03 6.6 1,439 Married (baseline) 76.58% — 1,439 Wealth (inverse hyperbolic sine) 10.96 5.5 1,439 FL (Wave 1) 0.85 1.26 1,439 FL (Wave 2) 0.88 1.3 1,315 FL (Wave 3) 1.08 1.44 1,208 FL (Wave 4) 1.07 1.41 651 ADL (Wave 1) 0.17 0.56 1,439 ADL (Wave 2) 0.16 0.57 1,315 ADL (Wave 3) 0.23 0.65 1,208 ADL (Wave 4) 0.20 0.63 651 Note: ADL = activities of daily living; FL = functional limitations. View Large Trajectory Models Results for the final conditional models for FLs and ADLs are included in Table 2, respectively. As mentioned previously, nested models were originally estimated to examine main and interactive effects of SREs and psychological resilience prior to the inclusion of childhood factors, other service-related variables, and controls. These models showed effects that were very stable across models and were similar to those observed in the final model. We therefore discuss support for our hypotheses by referencing the final models, with all covariates. Overall, we find support for the first hypothesis. Although both combat and exposure to the dead and wounded were independently associated with the outcomes in bivariate models, when both SREs are included in the models, exposure to the dead and wounded significantly impacts FL and ADL trajectories but combat exposure does not. This is consistent with the findings of Taylor and colleagues (2015). Specifically, exposure to the dead increases the intercept of FL trajectories by 1.08 items and the intercept of ADL trajectories by 0.45 items, respectively. There are, however, no significant impacts on either slope. This suggests that exposure to the dead and wounded has a lasting but stable relationship with increased functional impairment trajectories in later life for older veterans. Table 2. Latent Growth Curve Models of Functional Limitations and Activities of Daily Living (N = 1,439) Functional limitations Activities of daily living N = 1,439 FLα FLβ ADLα ADLβ Age at baseline 0.02*** 0.01*** 0.00 0.00 White −0.25** 0.13** −0.08 0.01 Parent’s education (years) 0.00 0.00 0.01 −0.00 Combat exposure −0.01 −0.01 −0.03 0.03 Exposure to the dead 1.08** −0.11 0.45* 0.11 Psychological resilience −0.15*** −0.02 −0.05*** −0.00 Resilience * death −0.09* 0.01 −0.04* −0.01 Child abuse 0.00 0.07 0.09 −0.01 Family substance abuse 0.12 0.05 0.02 0.02 Childhood disability 0.21** −0.01 0.09* −0.04* Rank (officer) 0.00 −0.00 0.02 0.05 VA disability rating 0.37*** 0.05 0.10* 0.00 Education (years) −0.05*** −0.00 −0.02* −0.00 Service duration (years) −0.01 0.00 0.00 0.00 Married −0.07 −0.01 −0.02 −0.01 Wealth −0.02 0.00 −0.00 0.00 Intercept 1.97*** −0.28 0.58*** −0.10 Var. 0.83*** 0.04*** 0.18*** 0.02*** χ2 (df) 69.74 (37) 65.83 (37) p Value 0.00 0.00 TLI 0.98 0.97 CFI 0.99 0.98 RMSEA 0.03 0.02 Functional limitations Activities of daily living N = 1,439 FLα FLβ ADLα ADLβ Age at baseline 0.02*** 0.01*** 0.00 0.00 White −0.25** 0.13** −0.08 0.01 Parent’s education (years) 0.00 0.00 0.01 −0.00 Combat exposure −0.01 −0.01 −0.03 0.03 Exposure to the dead 1.08** −0.11 0.45* 0.11 Psychological resilience −0.15*** −0.02 −0.05*** −0.00 Resilience * death −0.09* 0.01 −0.04* −0.01 Child abuse 0.00 0.07 0.09 −0.01 Family substance abuse 0.12 0.05 0.02 0.02 Childhood disability 0.21** −0.01 0.09* −0.04* Rank (officer) 0.00 −0.00 0.02 0.05 VA disability rating 0.37*** 0.05 0.10* 0.00 Education (years) −0.05*** −0.00 −0.02* −0.00 Service duration (years) −0.01 0.00 0.00 0.00 Married −0.07 −0.01 −0.02 −0.01 Wealth −0.02 0.00 −0.00 0.00 Intercept 1.97*** −0.28 0.58*** −0.10 Var. 0.83*** 0.04*** 0.18*** 0.02*** χ2 (df) 69.74 (37) 65.83 (37) p Value 0.00 0.00 TLI 0.98 0.97 CFI 0.99 0.98 RMSEA 0.03 0.02 Notes: Tucker-Lewis Index (TLI); Comparative Fit Index (CFI); Root Mean Square Error of Approximation (RMSEA). *p ≤ .05. **p ≤ .01. ***p ≤ .001. View Large Table 2. Latent Growth Curve Models of Functional Limitations and Activities of Daily Living (N = 1,439) Functional limitations Activities of daily living N = 1,439 FLα FLβ ADLα ADLβ Age at baseline 0.02*** 0.01*** 0.00 0.00 White −0.25** 0.13** −0.08 0.01 Parent’s education (years) 0.00 0.00 0.01 −0.00 Combat exposure −0.01 −0.01 −0.03 0.03 Exposure to the dead 1.08** −0.11 0.45* 0.11 Psychological resilience −0.15*** −0.02 −0.05*** −0.00 Resilience * death −0.09* 0.01 −0.04* −0.01 Child abuse 0.00 0.07 0.09 −0.01 Family substance abuse 0.12 0.05 0.02 0.02 Childhood disability 0.21** −0.01 0.09* −0.04* Rank (officer) 0.00 −0.00 0.02 0.05 VA disability rating 0.37*** 0.05 0.10* 0.00 Education (years) −0.05*** −0.00 −0.02* −0.00 Service duration (years) −0.01 0.00 0.00 0.00 Married −0.07 −0.01 −0.02 −0.01 Wealth −0.02 0.00 −0.00 0.00 Intercept 1.97*** −0.28 0.58*** −0.10 Var. 0.83*** 0.04*** 0.18*** 0.02*** χ2 (df) 69.74 (37) 65.83 (37) p Value 0.00 0.00 TLI 0.98 0.97 CFI 0.99 0.98 RMSEA 0.03 0.02 Functional limitations Activities of daily living N = 1,439 FLα FLβ ADLα ADLβ Age at baseline 0.02*** 0.01*** 0.00 0.00 White −0.25** 0.13** −0.08 0.01 Parent’s education (years) 0.00 0.00 0.01 −0.00 Combat exposure −0.01 −0.01 −0.03 0.03 Exposure to the dead 1.08** −0.11 0.45* 0.11 Psychological resilience −0.15*** −0.02 −0.05*** −0.00 Resilience * death −0.09* 0.01 −0.04* −0.01 Child abuse 0.00 0.07 0.09 −0.01 Family substance abuse 0.12 0.05 0.02 0.02 Childhood disability 0.21** −0.01 0.09* −0.04* Rank (officer) 0.00 −0.00 0.02 0.05 VA disability rating 0.37*** 0.05 0.10* 0.00 Education (years) −0.05*** −0.00 −0.02* −0.00 Service duration (years) −0.01 0.00 0.00 0.00 Married −0.07 −0.01 −0.02 −0.01 Wealth −0.02 0.00 −0.00 0.00 Intercept 1.97*** −0.28 0.58*** −0.10 Var. 0.83*** 0.04*** 0.18*** 0.02*** χ2 (df) 69.74 (37) 65.83 (37) p Value 0.00 0.00 TLI 0.98 0.97 CFI 0.99 0.98 RMSEA 0.03 0.02 Notes: Tucker-Lewis Index (TLI); Comparative Fit Index (CFI); Root Mean Square Error of Approximation (RMSEA). *p ≤ .05. **p ≤ .01. ***p ≤ .001. View Large In support of the second hypothesis, higher levels of resilience have a negative relationship with functional impairment. For each unit increase in the resilience measure there is a 0.15 decrease in the latent intercept of FLs and a 0.05 decrease in the latent intercept of ADLs. This suggests that net of combat exposures and other characteristics, resilience has a lasting impact on functional impairment. Further, the interaction term was significant and negative for the latent intercept in both models, suggesting that the long-term health effects of exposure to death are buffered by higher levels of resilience. Rather than decreasing magnitudes in the effects of exposure to death in nested models (not shown), the inclusion of resilience and the interaction term increase the main effects, suggesting that negative consequences associated with this exposure are higher among those with low levels of resilience in later life. Figures 1 and 2 plot these results for individuals at the 25th (8.50) and 75th (10.83) percentile in their resilience scores, holding all covariates at their means. Figures 1 and 2 suggest that for FLs and ADLs, those with higher levels of resilience remain at low levels of functional impairment over time, with little difference between those experiencing and not experiencing exposure to death. For ADLs, the model estimates a crossover among those with high resilience, but sensitivity analyses suggest that predicted ADL values among those with high resilience are similar for those with and without exposure to the dead. In contrast, those with exposure to death and low levels of resilience have the highest levels of FLs and ADLs over time. Additional models were tested to address various forms of selection bias in our study, described in detail in Supplementary Appendix B. Figure 1. View largeDownload slide Estimated trajectories of functional limitations (FLs). Figure 1. View largeDownload slide Estimated trajectories of functional limitations (FLs). Figure 2. View largeDownload slide Estimated trajectories of activities of daily living (ADLs). Figure 2. View largeDownload slide Estimated trajectories of activities of daily living (ADLs). Discussion Although connections between early-life traumas and later-life outcomes are well established, researchers are increasingly focused on mechanisms and pathways involved in these associations. Pruchno and Carr (2017) argue for an increased understanding of why some individuals are resilient to challenges they face across the life course and, more specifically, “how we can enable people to be resilient.” We examine early-life stressors in a specific context, the military, to understand the potentially buffering role psychological resilience plays in the connection between SREs in early life and functional impairment in later life. We find that exposure to death has a significant and persistent effect for functional impairment trajectories, and that psychological resilience has a substantial and significant moderating effect, such that individuals with exposure to death but higher levels of resilience maintain considerably lower functional impairment over time compared to their counterparts with low levels of resilience. Resilience is also protective of functional impairment changes in models accounting more closely for temporal order (see Supplementary Appendix A). Resilience is particularly salient for aging because the capacity to adapt to stressors usually slows with age (Hadley, Kuchel, & Newman, 2017), and while some individuals succumb to traumas and adversities, others recover or flourish (Carr et al., 2017; Wilson-Genderson, Pruncho, & Heid, 2017). Understanding this phenomenon more broadly is important since it may directly or indirectly promote health and functioning in older adults who have experienced previous or recent disadvantage or trauma. Life course scholars urge researchers to examine resilience as both a personal attribute and a process including the activation of resources and, ultimately, adaptation to adversity (Schafer, 2009; Lerner et al., 2012). Although the concept and measurement of resilience is debated in the literature, we argue that our measure of this resource is in line with other measures and the aim of the U.S. military to promote psychological fitness and resilience through increasing psychological and social resources prior to deployment (Meredith et al., 2011). Although we cannot know the life course trajectories of psychological resilience among veterans before and directly after service using our data, prior research demonstrates that veterans recognize the military’s ability to strengthen individual resources like those measured here, even while putting them in harm’s way (Elder & Clipp, 1989; Marcellino & Tortorello, 2015). Indeed, additional analyses of the VMS sample compared to an age and gender matched comparison group suggest that veterans have higher levels of resilience than their civilian counterparts, while some measures of health and well-being are lower (models not shown). Our findings have direct implications for older veterans and current service men and women, for whom the roles of peacekeeper and warrior intertwine within a cumulative stress environment (Boermans, Delahaij, Korteling, & Euwema, 2012). Although most soldiers face challenges, the majority do not develop lasting problems, thus making favorable responses to SREs a possible by-product of psychological resilience, at least in part. Learning more about such responses to SREs is important because it illuminates specific pathways to successful adaptation. Of particular importance is the reinforcement and activation of resilience, recovery, and sustainability (Boermans et al., 2012). While we cannot distinguish whether the military or specific SREs imparted, strengthened, or weakened resilience among our veterans—or whether they entered the military more or less resilient—our findings suggest that psychological resilience plays an important role between early-life SREs and health that reaches into and throughout later life. Practice-based methods of increasing resilience are found to be effective (meta-analysis; Boermans et al., 2012), where service members actively enact and integrate skills that promote adaptation. With increasing emphasis on the potential to strengthen this resource through military training (Meredith et al., 2011), effective strategies to bolster resilience in military personnel could have lasting effects for life course trajectories within an institution that inherently places individuals at risk of trauma. Limitations and Future Directions We should note that among traumatic SREs considered here, we did not examine the most extreme form of military trauma: prisoner of war (POW; King et al., 2015), as the variable was unavailable to maintain confidentiality. Consistent with recent research on service personnel, our conceptualization of resilience as a resource focuses on internal attributes without including social support and related external resources. We argue that in disentangling the processes of resource activation, external resources should be considered separately but alongside psychological resilience in future research. Although we document the buffering effect of psychological resilience here in the specific context of the military, we urge future researchers to empirically consider these multifaceted and multidimensional resources as part of a process of adaptation applicable to a number of stressors and traumas (Schafer, 2009; Smith & Hayslip, 2012). Stemming from this is a better understanding of how health, well-being, and psychological resilience interplay over the life course prior to later life. Although sensitivity analyses (not shown) suggest our results are not driven by health selection into higher levels of resilience, earlier life investigations of this resource and health at younger ages are paramount for future research. In addition, although we conducted sensitivity analyses to address the bias introduced by missingness over the study period, we cannot know about the selective nature of those veterans surviving to report in later life. Although previous research suggests that respondents of the 2013 VMS look similar to all male veteran respondents in the 2010 HRS, the VMS is slightly healthier and more advantaged (Taylor, Ureña, & Kail, 2015) which may influence the generalizability of our findings. Additionally, future research should examine what mechanisms explain the buffering effects found here in relation to physical health, and especially mental health as it is tied closely over time to functional impairment at late ages. Funding S. Min was supported by the National Science Foundation Research Fellowship Program under Grant No. 2016-1449440. Any opinions, findings, and conclusions or recommendations expressed in this study are those of the authors and do not necessarily reflect the view of the National Science Foundation. Conflict of Interest None reported. Acknowledgments M. G. Taylor designed the study, performed analyses, and wrote the paper. S. 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Early-Life Military Exposures and Functional Impairment Trajectories Among Older Male Veterans: The Buffering Effect of Psychological Resilience

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Abstract

Abstract Objectives Drawing on the life course framework and theoretical concept of resilience, we examine the impact of early-life service-related exposures (SREs) on later-life functional impairment trajectories among older U.S. male veterans. We conceptualize resilience as a psychological resource potentially moderating the lasting negative consequences of traumatic military exposures. Method Using the 2013 Veterans Mail Survey linked to the Health and Retirement Study 2006–2014 Leave Behind Questionnaire and RAND Data File (v.N), we estimate latent growth curve models of functional impairment trajectories. Results SRE to death has a persistent positive effect on functional limitations and activities of daily living limitations. Psychological resilience significantly moderates this association, such that veterans maintaining higher levels of resilience in the face of adverse exposures have considerably less functional impairment over time compared to their counterparts with low levels of resilience. Discussion Our findings point to the importance of psychological resilience in later life, especially within the realm of traumas occurring in early life. We discuss implications for current military training programs, stressing the importance of research considering individual resources and processes that promote adaptation in the face of adverse life events. ADLs, Functional impairment, HRS, Latent growth curves Over half of older men and more than 80% of those over age 85 in the United States are veterans (Newport, 2012). The literature demonstrates that military experiences during early adulthood provide a critical influence on life course trajectories, and may play a particularly salient role in shaping health as we age (Settersten, 2006). Among early-life experiences, the military is unique in its association with both positive and negative long-term outcomes (London & Wilmoth, 2016; Wilmoth & London, 2013). Key to these effects is specific service-related exposures (SREs), especially traumatic events such as combat or exposure to death (Taylor, Ureña, & Kail, 2015; Ureña, Taylor, & Kail, 2017; Wilmoth, London, & Parker, 2010). However, little research has examined the circumstances under which these potentially traumatic exposures translate to lasting impacts on physical health trajectories among older veterans. The negative consequences of traumatic life events on health may be counteracted or buffered by maintained psychological resources. Such a resource is psychological resilience, which appears to shape the ways early-life adversities and traumas influence well-being over the life course (Smith & Hayslip, 2012). In short, a resilient individual “embodies the personal qualities that enable them to thrive in the face of adversity” (Connor & Davidson, 2003). Psychological resilience is important for understanding the connection between military involvement and later-life health because current military training and infrastructure is designed to bolster recovery from adversity and traumas stemming from service, in part by strengthening psychological resources that may benefit individuals as they age (Meredith et al., 2011). With a few exceptions (e.g., Manning, Carr, & Kail, 2016), much of the research documenting resilience is based on simple associations between adversity and positive outcomes. However, a growing number of training programs have emerged within military environments because resilience is increasingly recognized as an individual resource that can be cultivated and leveraged to produce better outcomes in the face of trauma (Meredith et al., 2011). Knowing whether, how, and under what circumstances resilience as an individual psychological resource promotes adaptive functioning may help inform development of these programs (Lerner et al., 2012; Smith & Hayslip, 2012). The purpose of the current study is to examine how exposures to SREs in early adulthood influence functional impairment trajectories in later life for male veterans, and determine whether psychological resilience in later life buffers the negative consequences of SREs for physical health. The Life Course Framework and the Military The life course framework explains the ways in which specific contexts shape individual lives (Elder, 1994) and emphasizes how short-term transitions and “turning points” lead to long-term, age-graded trajectories unfolding over individual lives (Elder, 1994). Military service is such a turning point (Sampson & Laub, 1996), marking a distinct transition that potentially results in a deviation from an existing path with positive and/or negative consequences based on historical time and place of service, age upon entry, individual characteristics, and social context (Settersten, 2006). Although Elder’s work (1986) suggests that service at earlier ages may be more protective in stabilizing the life course, other research suggests that exiting the military at an early age generates a health disadvantage in later life (Hardy & Reyes, 2016). Even today, military enlistment occurs before age 25, which is prior to most major adult milestones (U.S. Army, 2013). Overall the research suggests that military service, as a turning point, has the potential to shape trajectories across adulthood and later life. Under the best circumstances, the military can provide education, skills, resources, and opportunities for upward mobility, particularly if there is minimal disruption to formative life experiences such as marriage and education (London & Wilmoth, 2016). However, the military also introduces physical and psychological risks arising from combat, injury and disability that damage health and wellbeing (Bennett & McDonald, 2013). Most of today’s older veterans served during conscription and wartime eras (Wilmoth & London, 2016), with military-related traumas or adversities experienced in young adulthood when individuals are also vulnerable to long-term physical and mental health consequences (Rindfuss, 1991; Spiro, Schnurr, & Aldwin, 1997). Research suggests that exposure to particularly heavy or traumatic combat, including exposure to the dead and wounded, has the greatest negative impacts on physical and mental health (Taylor et al., 2015; Ureña et al., 2017; Elder & Clipp 1989; Marcellino & Tortorello, 2015). Thus, despite the potentially positive effects of military involvement during early adulthood, we hypothesize that exposure to traumatic combat, especially exposure to the dead, dying, and wounded, will relate to increases in functional impairment during later life for older male veterans. Adversity, Resilience, and Military Exposures Some individuals exposed to traumatic events experience substantial and lasting negative outcomes, while others experience no long-term consequences. Life course scholars propose that we need to understand how and under what circumstances early adversity, such as exposure to SREs, relate to differential outcomes as individuals age (Ferraro, Schafer, & Wilkinson, 2016; Willson, Shuey, & Elder, 2007). Various theories describe the processes by which the negative effects of adversity accumulate over the life course (e.g., Ferraro & Shippee, 2009; O’Rand, 1996; Thoits, 2010), noting that early-life events tend to have lasting and cumulative impacts on individuals. In the case of military service, adverse childhood experiences such as financial hardship and child abuse are often associated with military enlistment (Bennett & McDonald, 2013; Blosnich, Dichter, Cerulli, Batten, & Bossarte, 2014). Any injury, disability, or trauma sustained in service during young adulthood may impose subsequent disadvantage in areas such as the labor force and social relationships, all of which affect health over time (MacLean, 2013). As a result, research seeking to understand how military experiences in early life contribute to later-life health must consider both upstream (childhood) and downstream (post-service) factors. Just as the military may influence negative and/or positive outcomes among individuals, processes of adversity in this context may have varied outcomes. For instance, military stressors are positively associated with both post-traumatic stress disorder (PTSD) and post-traumatic growth (PTG; Park, Aldwin, Fenster, & Snyder, 2008). Although particularly traumatic combat, including exposure to the dead, is linked to negative health outcomes (Marcellino & Tortorello, 2015; Taylor et al., 2015; Ureña et al., 2017), older veterans also report that military service provided coping skills, self-discipline, and appreciation for life (Elder & Clipp, 1989). One reason some individuals report positive or mixed effects of traumatic events may be because they generally maintain higher psychological “resilience” resources over time, which may translate to fewer negative long-term outcomes (Spiro, Settersten, & Aldwin, 2016). The counterintuitive effects of individuals exposed to adversities who experience positive outcomes has raised attention to the concept of resilience in life course research (Bonanno et al., 2012; Pruchno & Carr, 2017; Seligman, 2012). Resilience has primarily appeared in studies surrounding childhood adversity and traumas (Rutter, 1979), but has also been an important and burgeoning concept in the literature surrounding trauma-exposed adults, specifically regarding military service and combat trauma (Bonanno et al., 2012; Marcellino & Tortorello, 2015). Although there is no consistent definition or operationalization of resilience used in research on veterans today (Meredith et al., 2011), we generally conceptualize resilience as the capacity to navigate adversity through positive adaptation in a manner that protects health, well-being, and life satisfaction (Manning, 2013). Initial empirical evidence conceived of resilience as the absence of negative outcomes or presence of positive outcomes among those experiencing substantial adversity or trauma (Smith & Hayslip, 2012). However, scholars now seek to better understand personal attributes of resilience and processes of adaptation, that is, mobilizing resources to produce favorable outcomes (Lerner et al., 2012). In thinking about the role of resilience in the lives of older veteran men serving during early adulthood, it is important to recognize different potential pathways involved in cultivating resilience. For older veterans, the military may have facilitated opportunities to accumulate psychological resources that could be leveraged or further cultivated throughout their lives. However, some older veterans may have been resilient prior to service, and therefore experienced fewer negative and more positive effects. Still others may have psychological resilience that was eroded by trauma or adversity as they aged. It is likely that all of these pathways are at play. Smith and Hayslip (2012) posit that resilience is an individual characteristic shaped by social contexts, and that individuals engage in a dynamic process to mobilize resources in order to cope and adapt throughout life. Thus, resilience incorporates agency in the activation of internal and external resources necessary for an adaptive process (Schafer, Shippee, & Ferraro, 2009). Marcellino and Tortorello (2015) document how U.S. Marines “construct” resilience in choices and interactions with others that promote coping, meaning-making, flexibility and other attributes characterizing resilient individuals. Steps to promote resilience in the current All Volunteer Force are increasingly implemented across branches (Marcellino and Tortorello, 2015; Meredith et al., 2011; Seligman, 2012), where the goal of more psychologically fit soldiers hinges on cultivation of these attributes and how they may work to promote favorable outcomes over time in the face of trauma. The data in this study (i.e., Health and Retirement Study [HRS] data) do not allow us to explore the causal pathways by which individuals become more or less resilient in early life, or if and how much resilience changes over time in response to life course stressors and supports. Rather, we are concerned with the resilience of veterans who survived into later life, and how this resource patterns the relationship between SREs and functional impairment trajectories. We operationalize resilience as a multidimensional personal attribute (Manning et al., 2016), in line with other established instruments designed to measure personal resilience resources (Connor & Davidson, 2003; Wagnild & Young, 1993) among veterans and first responders (Kuwert, Knaevelsrud, & Pietrzak, 2014; Lee, Ahn, Jeong, Chae, & Choi, 2014). In line with other literature (Schafer et al., 2009), we conceptualize this resource as relatively stable in later life, but not an inherent trait that is completely fixed across the life course. A series of sensitivity analyses examining changes in resilience in our sample suggest that our conception of its general stability at this life stage is sound (see Supplementary Appendix A). Under this operationalization, psychological resilience in later life is primarily found to moderate the impact of earlier adversities on future negative outcomes (Manning et al., 2016; Smith & Hayslip, 2012), suggesting trauma-exposed veterans reporting higher levels of resilience in later life will have better health than their counterparts with lower levels of resilience. Thus, we hypothesize that net of other early-life factors, resilience will have an independent negative association with later-life functional impairment and that it will buffer (i.e., moderate) the effects of SREs. Method Data Two data files were linked to the 2013 Veterans Mail Survey (VMS) for analyses, the RAND HRS Data File (v.N) and the biennial HRS data for the 2006–2012 Leave Behind Questionnaire (LBQ) and childhood variables. The HRS includes a sample of 12,652 people born between 1931 and 1941 and their spouses, with biennial follow-ups and cohort replenishment resulting in a 16-year panel study focused on health and socioeconomic factors of older Americans (HRS, 2012). The 2013 VMS is a supplement focused on military and related experiences among a subsample of HRS respondents who reported serving in the active military in any wave of the HRS (N = 1,874 HRS veterans). We linked these records to the RAND HRS Data file (v.N), retaining respondents that merged across data sets (N = 1,860). The RAND file was used to access functional impairment outcomes between 2006 and 2014 along with other health-related and demographic variables. This analytic sample was then linked to the HRS LBQ to establish resilience measures. The LBQ is a psychosocial questionnaire beginning in 2006, surveying an alternating half of HRS respondents in each wave such that for every two-wave period, the entire HRS sample is surveyed. We used the 2006–2012 years of the LBQ to establish each respondent’s first report on the resilience measure, a substantial majority of which (over 80%) reported by 2008. We then used the RAND file to establish functional impairment trajectories for each individual where the baseline wave was the wave at first resilience report with up to four waves following. We further limited the VMS and linked HRS and LBQ sample to only men (N = 1,796), and those with valid reports of the functional impairment outcomes at baseline (N = 1,692). Since the focus of our paper is on early-life influences, we further restricted the sample to veterans who initiated military service by the age of 25, more than 97% of the male VMS sample (N = 1,545), and dropped those less than age 50 at baseline (N = 1,535). Missingness on covariates (N = 96, 6%) yielded a final analytic sample size of 1,439. Missing cases in the outcome variables over time were handled with a full information maximum likelihood estimator (FIML), which allows missingness due to unbalanced panel data and attrition. Outcome Measures We examined the association of SREs with functional impairment trajectories using two outcome measures, functional limitations (FLs) and activities of daily living (ADLs), because these measures are associated with military-related experiences in this sample (Taylor et al., 2015). We chose FLs and ADLs since they represent a continuum of severity in the disablement process. The measures were taken from the 2006–2014 RAND HRS file (RAND, 2014), with every individual contributing up to four waves. FLs were measured as a summed six-item index including self-reports of difficulty with any of the following activities: walking one block; climbing one flight of stairs; lifting or carrying 10 pounds; picking a dime up off the ground; stooping, kneeling, or crouching; and pushing or pulling large objects (2006–2014 average Kuder-Richardson coefficient of reliability = 0.73). Difficulties in FLs tap into earlier stages of disablement (Katz, Ford, Moskowitz, Jackson, & Jaffe, 1963). ADLs were measured as the summed index of five dichotomous items indicating difficulty with the following tasks: walking across the room; getting dressed; taking a bath or shower; eating; and getting in or out of bed (2006–2014 average Kuder-Richardson coefficient of reliability = 0.64 in the HRS). Difficulties with ADLs are more severe manifestations of functional impairment, often emerging later than FLs (Katz et al., 1963). Explanatory Variables The main predictors of interest surround potentially traumatic SREs. Two measures were taken from the VMS. Exposure to combat was measured by a question asking whether the respondent ever served in a combat or war zone (not exposed = 0). As an indicator of heavy or traumatic combat, we used exposure to the dead and wounded, measured with the question: During your military service, were you ever exposed to dead, dying, or wounded people? (not exposed = 0). Since we are interested in the ability of psychological resilience as a resource to moderate the impact of SREs, we utilized the simplified resilience score (SRS) established by Manning, Carr, and Kail (2016) from the psychosocial battery of items in the LBQ. The SRS is guided by the Wagnild and Young Resilience Scale (RS; 1993), incorporating five psychosocial domains (a) perseverance or the ability to keep going despite major setbacks; (b) equanimity, or being able to adjust to change, often with humor; (c) meaningfulness or purpose in life; (d) self-reliance or recognition of one’s one inner strengths; and (e) existential aloneness or the realization that some experiences must be faced alone (Wagnild & Young, 1993; Zeng & Shen, 2010). The SRS is a shortened version of the original SRS using 12 items from the LBQ with high reliability (α = 0.85) and similar to the measure used in the 2011 National Health and Resilience in Veterans Study (Kuwert et al., 2014). The items ask respondents how strongly they agree or disagree with the following statements: (1) I feel it is impossible for me to reach the goals that I would like to strive for; (2) So far, I have gotten the important things I want in life; (3) If something can go wrong for me, it will; (4) I am satisfied with my life; (5) I feel that what happens in life is often determined by factors beyond my control; (6) I can do the things that I want to do; (7) The future seems hopeless to me and I can’t believe that things are changing for the better; (8) When I really want to do something, I usually find a way to succeed at it; (9) In most ways, my life is close to ideal; (10) I can do just about anything I set my mind to; (11) There is really no way I can solve the problems I have; and (12) I have a sense of direction and purpose in life. Items 1, 3, 5, 7, and 11 were reverse coded and values were standardized to range between 0 and 1 since items varied in their original ranges. The standardized items were then summed with observed values ranging from 2.4 to 12 (baseline α = 0.84 in the analytic sample). Sensitivity analyses conducted to establish the SRS’s efficacy, stability, and temporal relationship with functional impairment in our study are described in detail in Supplementary Appendix A. Background demographic control variables included race (white = 1, other = 0), the age at baseline in years, and the highest recorded year of parent’s educational attainment. Missing values on the parental education variable (5%) were imputed using regression-based techniques. We also wanted to establish the effects of resilience net of other childhood adversity, military-related variables, and other potential confounders. We included childhood measures, drawn from the biennial HRS data and the LBQ between 2006 and 2012, including whether the respondent ever reported physical child abuse, parents with substance abuse problems, or whether they suffered from a disability in childhood (including concussions and severe head injuries before age 16). before age 16). We included three military service control variables (from the VMS) to reduce further confounding: self-reported duration of military service in years to handle the impact of longer/repeated exposures or deployments, self-reported VA service-connected disability rating (yes = 1) to handle injury or disability sustained directly from service and highest rank achieved (officer = 1). Other control variables included educational attainment in years, marital status (married = 1) at baseline, and the inverse hyperbolic sine of total non-housing wealth at baseline. Analysis We utilized latent growth curves (LGCs) to examine associations between SREs, resilience, and functional impairment in later life among veteran men based on their ability to capture long-term levels and change in our outcomes over a considerable time span. Using a structural equation modeling (SEM) framework, we established individual-level trajectories of FLs and ADLs, respectively, for up to four waves for every individual between 2006 and 2014. We further assessed how individuals varied from the average trajectory based on SREs, and how this variation differs by level of resilience net of confounders. The unconditional, Level 1 equation used for the baseline trajectories of FLs and ADLs is: yit=αyi+βyiλyt+εyit where yit is a vector of repeated measures, αyi is a vector of latent intercepts, βyi is a vector of latent slopes, λyt is a vector of fixed/freed loadings representing time, and εyit is a vector of disturbance terms assumed to have equal variances. The conditional, Level 2 equation used for the three nested models that include covariates is: αyi=μαy+γαy1x1i+ζyαi βyi=μβy+γβy1x1i+ζyβi Baseline models were used to establish significant levels and change of the outcomes over time. Results from the baseline models suggest that linear trajectories fit the data well for both outcomes (factor loadings = 0, 1, 2, 3), with significant variability in the randomly estimated intercepts and slopes (models not shown). We originally estimated nested models examining demographic variables and SREs alone (Model 1), adding in psychological resilience and the interaction (Model 2) prior to the full model with all covariates (final model). Because the results from Models 1 and 2 remained very stable, we present the final model with a brief discussion of Models 1 and 2 in the text. All models were estimated in Mplus v. 6.12 (Muthén & Muthén, 2004). Results Descriptive Statistics Table 1 presents unweighted descriptive statistics for the variables in our analyses. Our sample was 69 years old on average at baseline and 85% white. Given their ages, a substantial minority of these veterans would have served during WWII and Korea, but the most common war era represented in our sample was the Vietnam War (41%). Twenty-nine percent of the sample reported exposure to combat, and 27% reported exposure to the dead and wounded. Although similar percentages of veterans experienced these exposures, additional analyses suggest SREs were quite varied, with 69% of combat veterans also exposed to the dead and 73% of those exposed to the dead also serving in combat or war zones. The average resilience score at baseline was 9.5, suggesting generally high psychological resilience levels among older veterans. We also tested associations between SREs and resilience, with no significant associations in bivariate relationships. Among the childhood variables, small but substantial percentages reported history of child abuse, family history of substance abuse, or disability in childhood. Among service-related covariates, 10% of the sample reported being an officer, 16% reported a VA disability rating, and the average length of service was 5 years. Functional impairment generally increased in our sample over time. FLs increased from 0.85 items at baseline to more than 1 item on average at Wave 4. ADLs also increased on average from 0.17 items at baseline to 0.20 items in Wave 4. Table 1. Unweighted Descriptive Statistics (Mean and SD) Mean/% SD N Age at baseline 69.06 8.83 1,439 White 84.71% — 1,439 Parent’s education (years) 11.03 3.13 1,439 Combat exposure 28.77% — 1,439 Exposure to the dead 27.24% — 1,439 Psychological resilience 9.51 1.66 1,439 Child abuse 7.71% — 1,439 Family substance abuse 19.53% — 1,439 Childhood disability 16.26% — 1,439 Rank (officer) 10.01% — 1,439 VA disability rating 16.33% — 1,439 Education (years) 13.59 2.4 1,439 Service duration (years) 5.03 6.6 1,439 Married (baseline) 76.58% — 1,439 Wealth (inverse hyperbolic sine) 10.96 5.5 1,439 FL (Wave 1) 0.85 1.26 1,439 FL (Wave 2) 0.88 1.3 1,315 FL (Wave 3) 1.08 1.44 1,208 FL (Wave 4) 1.07 1.41 651 ADL (Wave 1) 0.17 0.56 1,439 ADL (Wave 2) 0.16 0.57 1,315 ADL (Wave 3) 0.23 0.65 1,208 ADL (Wave 4) 0.20 0.63 651 Mean/% SD N Age at baseline 69.06 8.83 1,439 White 84.71% — 1,439 Parent’s education (years) 11.03 3.13 1,439 Combat exposure 28.77% — 1,439 Exposure to the dead 27.24% — 1,439 Psychological resilience 9.51 1.66 1,439 Child abuse 7.71% — 1,439 Family substance abuse 19.53% — 1,439 Childhood disability 16.26% — 1,439 Rank (officer) 10.01% — 1,439 VA disability rating 16.33% — 1,439 Education (years) 13.59 2.4 1,439 Service duration (years) 5.03 6.6 1,439 Married (baseline) 76.58% — 1,439 Wealth (inverse hyperbolic sine) 10.96 5.5 1,439 FL (Wave 1) 0.85 1.26 1,439 FL (Wave 2) 0.88 1.3 1,315 FL (Wave 3) 1.08 1.44 1,208 FL (Wave 4) 1.07 1.41 651 ADL (Wave 1) 0.17 0.56 1,439 ADL (Wave 2) 0.16 0.57 1,315 ADL (Wave 3) 0.23 0.65 1,208 ADL (Wave 4) 0.20 0.63 651 Note: ADL = activities of daily living; FL = functional limitations. View Large Table 1. Unweighted Descriptive Statistics (Mean and SD) Mean/% SD N Age at baseline 69.06 8.83 1,439 White 84.71% — 1,439 Parent’s education (years) 11.03 3.13 1,439 Combat exposure 28.77% — 1,439 Exposure to the dead 27.24% — 1,439 Psychological resilience 9.51 1.66 1,439 Child abuse 7.71% — 1,439 Family substance abuse 19.53% — 1,439 Childhood disability 16.26% — 1,439 Rank (officer) 10.01% — 1,439 VA disability rating 16.33% — 1,439 Education (years) 13.59 2.4 1,439 Service duration (years) 5.03 6.6 1,439 Married (baseline) 76.58% — 1,439 Wealth (inverse hyperbolic sine) 10.96 5.5 1,439 FL (Wave 1) 0.85 1.26 1,439 FL (Wave 2) 0.88 1.3 1,315 FL (Wave 3) 1.08 1.44 1,208 FL (Wave 4) 1.07 1.41 651 ADL (Wave 1) 0.17 0.56 1,439 ADL (Wave 2) 0.16 0.57 1,315 ADL (Wave 3) 0.23 0.65 1,208 ADL (Wave 4) 0.20 0.63 651 Mean/% SD N Age at baseline 69.06 8.83 1,439 White 84.71% — 1,439 Parent’s education (years) 11.03 3.13 1,439 Combat exposure 28.77% — 1,439 Exposure to the dead 27.24% — 1,439 Psychological resilience 9.51 1.66 1,439 Child abuse 7.71% — 1,439 Family substance abuse 19.53% — 1,439 Childhood disability 16.26% — 1,439 Rank (officer) 10.01% — 1,439 VA disability rating 16.33% — 1,439 Education (years) 13.59 2.4 1,439 Service duration (years) 5.03 6.6 1,439 Married (baseline) 76.58% — 1,439 Wealth (inverse hyperbolic sine) 10.96 5.5 1,439 FL (Wave 1) 0.85 1.26 1,439 FL (Wave 2) 0.88 1.3 1,315 FL (Wave 3) 1.08 1.44 1,208 FL (Wave 4) 1.07 1.41 651 ADL (Wave 1) 0.17 0.56 1,439 ADL (Wave 2) 0.16 0.57 1,315 ADL (Wave 3) 0.23 0.65 1,208 ADL (Wave 4) 0.20 0.63 651 Note: ADL = activities of daily living; FL = functional limitations. View Large Trajectory Models Results for the final conditional models for FLs and ADLs are included in Table 2, respectively. As mentioned previously, nested models were originally estimated to examine main and interactive effects of SREs and psychological resilience prior to the inclusion of childhood factors, other service-related variables, and controls. These models showed effects that were very stable across models and were similar to those observed in the final model. We therefore discuss support for our hypotheses by referencing the final models, with all covariates. Overall, we find support for the first hypothesis. Although both combat and exposure to the dead and wounded were independently associated with the outcomes in bivariate models, when both SREs are included in the models, exposure to the dead and wounded significantly impacts FL and ADL trajectories but combat exposure does not. This is consistent with the findings of Taylor and colleagues (2015). Specifically, exposure to the dead increases the intercept of FL trajectories by 1.08 items and the intercept of ADL trajectories by 0.45 items, respectively. There are, however, no significant impacts on either slope. This suggests that exposure to the dead and wounded has a lasting but stable relationship with increased functional impairment trajectories in later life for older veterans. Table 2. Latent Growth Curve Models of Functional Limitations and Activities of Daily Living (N = 1,439) Functional limitations Activities of daily living N = 1,439 FLα FLβ ADLα ADLβ Age at baseline 0.02*** 0.01*** 0.00 0.00 White −0.25** 0.13** −0.08 0.01 Parent’s education (years) 0.00 0.00 0.01 −0.00 Combat exposure −0.01 −0.01 −0.03 0.03 Exposure to the dead 1.08** −0.11 0.45* 0.11 Psychological resilience −0.15*** −0.02 −0.05*** −0.00 Resilience * death −0.09* 0.01 −0.04* −0.01 Child abuse 0.00 0.07 0.09 −0.01 Family substance abuse 0.12 0.05 0.02 0.02 Childhood disability 0.21** −0.01 0.09* −0.04* Rank (officer) 0.00 −0.00 0.02 0.05 VA disability rating 0.37*** 0.05 0.10* 0.00 Education (years) −0.05*** −0.00 −0.02* −0.00 Service duration (years) −0.01 0.00 0.00 0.00 Married −0.07 −0.01 −0.02 −0.01 Wealth −0.02 0.00 −0.00 0.00 Intercept 1.97*** −0.28 0.58*** −0.10 Var. 0.83*** 0.04*** 0.18*** 0.02*** χ2 (df) 69.74 (37) 65.83 (37) p Value 0.00 0.00 TLI 0.98 0.97 CFI 0.99 0.98 RMSEA 0.03 0.02 Functional limitations Activities of daily living N = 1,439 FLα FLβ ADLα ADLβ Age at baseline 0.02*** 0.01*** 0.00 0.00 White −0.25** 0.13** −0.08 0.01 Parent’s education (years) 0.00 0.00 0.01 −0.00 Combat exposure −0.01 −0.01 −0.03 0.03 Exposure to the dead 1.08** −0.11 0.45* 0.11 Psychological resilience −0.15*** −0.02 −0.05*** −0.00 Resilience * death −0.09* 0.01 −0.04* −0.01 Child abuse 0.00 0.07 0.09 −0.01 Family substance abuse 0.12 0.05 0.02 0.02 Childhood disability 0.21** −0.01 0.09* −0.04* Rank (officer) 0.00 −0.00 0.02 0.05 VA disability rating 0.37*** 0.05 0.10* 0.00 Education (years) −0.05*** −0.00 −0.02* −0.00 Service duration (years) −0.01 0.00 0.00 0.00 Married −0.07 −0.01 −0.02 −0.01 Wealth −0.02 0.00 −0.00 0.00 Intercept 1.97*** −0.28 0.58*** −0.10 Var. 0.83*** 0.04*** 0.18*** 0.02*** χ2 (df) 69.74 (37) 65.83 (37) p Value 0.00 0.00 TLI 0.98 0.97 CFI 0.99 0.98 RMSEA 0.03 0.02 Notes: Tucker-Lewis Index (TLI); Comparative Fit Index (CFI); Root Mean Square Error of Approximation (RMSEA). *p ≤ .05. **p ≤ .01. ***p ≤ .001. View Large Table 2. Latent Growth Curve Models of Functional Limitations and Activities of Daily Living (N = 1,439) Functional limitations Activities of daily living N = 1,439 FLα FLβ ADLα ADLβ Age at baseline 0.02*** 0.01*** 0.00 0.00 White −0.25** 0.13** −0.08 0.01 Parent’s education (years) 0.00 0.00 0.01 −0.00 Combat exposure −0.01 −0.01 −0.03 0.03 Exposure to the dead 1.08** −0.11 0.45* 0.11 Psychological resilience −0.15*** −0.02 −0.05*** −0.00 Resilience * death −0.09* 0.01 −0.04* −0.01 Child abuse 0.00 0.07 0.09 −0.01 Family substance abuse 0.12 0.05 0.02 0.02 Childhood disability 0.21** −0.01 0.09* −0.04* Rank (officer) 0.00 −0.00 0.02 0.05 VA disability rating 0.37*** 0.05 0.10* 0.00 Education (years) −0.05*** −0.00 −0.02* −0.00 Service duration (years) −0.01 0.00 0.00 0.00 Married −0.07 −0.01 −0.02 −0.01 Wealth −0.02 0.00 −0.00 0.00 Intercept 1.97*** −0.28 0.58*** −0.10 Var. 0.83*** 0.04*** 0.18*** 0.02*** χ2 (df) 69.74 (37) 65.83 (37) p Value 0.00 0.00 TLI 0.98 0.97 CFI 0.99 0.98 RMSEA 0.03 0.02 Functional limitations Activities of daily living N = 1,439 FLα FLβ ADLα ADLβ Age at baseline 0.02*** 0.01*** 0.00 0.00 White −0.25** 0.13** −0.08 0.01 Parent’s education (years) 0.00 0.00 0.01 −0.00 Combat exposure −0.01 −0.01 −0.03 0.03 Exposure to the dead 1.08** −0.11 0.45* 0.11 Psychological resilience −0.15*** −0.02 −0.05*** −0.00 Resilience * death −0.09* 0.01 −0.04* −0.01 Child abuse 0.00 0.07 0.09 −0.01 Family substance abuse 0.12 0.05 0.02 0.02 Childhood disability 0.21** −0.01 0.09* −0.04* Rank (officer) 0.00 −0.00 0.02 0.05 VA disability rating 0.37*** 0.05 0.10* 0.00 Education (years) −0.05*** −0.00 −0.02* −0.00 Service duration (years) −0.01 0.00 0.00 0.00 Married −0.07 −0.01 −0.02 −0.01 Wealth −0.02 0.00 −0.00 0.00 Intercept 1.97*** −0.28 0.58*** −0.10 Var. 0.83*** 0.04*** 0.18*** 0.02*** χ2 (df) 69.74 (37) 65.83 (37) p Value 0.00 0.00 TLI 0.98 0.97 CFI 0.99 0.98 RMSEA 0.03 0.02 Notes: Tucker-Lewis Index (TLI); Comparative Fit Index (CFI); Root Mean Square Error of Approximation (RMSEA). *p ≤ .05. **p ≤ .01. ***p ≤ .001. View Large In support of the second hypothesis, higher levels of resilience have a negative relationship with functional impairment. For each unit increase in the resilience measure there is a 0.15 decrease in the latent intercept of FLs and a 0.05 decrease in the latent intercept of ADLs. This suggests that net of combat exposures and other characteristics, resilience has a lasting impact on functional impairment. Further, the interaction term was significant and negative for the latent intercept in both models, suggesting that the long-term health effects of exposure to death are buffered by higher levels of resilience. Rather than decreasing magnitudes in the effects of exposure to death in nested models (not shown), the inclusion of resilience and the interaction term increase the main effects, suggesting that negative consequences associated with this exposure are higher among those with low levels of resilience in later life. Figures 1 and 2 plot these results for individuals at the 25th (8.50) and 75th (10.83) percentile in their resilience scores, holding all covariates at their means. Figures 1 and 2 suggest that for FLs and ADLs, those with higher levels of resilience remain at low levels of functional impairment over time, with little difference between those experiencing and not experiencing exposure to death. For ADLs, the model estimates a crossover among those with high resilience, but sensitivity analyses suggest that predicted ADL values among those with high resilience are similar for those with and without exposure to the dead. In contrast, those with exposure to death and low levels of resilience have the highest levels of FLs and ADLs over time. Additional models were tested to address various forms of selection bias in our study, described in detail in Supplementary Appendix B. Figure 1. View largeDownload slide Estimated trajectories of functional limitations (FLs). Figure 1. View largeDownload slide Estimated trajectories of functional limitations (FLs). Figure 2. View largeDownload slide Estimated trajectories of activities of daily living (ADLs). Figure 2. View largeDownload slide Estimated trajectories of activities of daily living (ADLs). Discussion Although connections between early-life traumas and later-life outcomes are well established, researchers are increasingly focused on mechanisms and pathways involved in these associations. Pruchno and Carr (2017) argue for an increased understanding of why some individuals are resilient to challenges they face across the life course and, more specifically, “how we can enable people to be resilient.” We examine early-life stressors in a specific context, the military, to understand the potentially buffering role psychological resilience plays in the connection between SREs in early life and functional impairment in later life. We find that exposure to death has a significant and persistent effect for functional impairment trajectories, and that psychological resilience has a substantial and significant moderating effect, such that individuals with exposure to death but higher levels of resilience maintain considerably lower functional impairment over time compared to their counterparts with low levels of resilience. Resilience is also protective of functional impairment changes in models accounting more closely for temporal order (see Supplementary Appendix A). Resilience is particularly salient for aging because the capacity to adapt to stressors usually slows with age (Hadley, Kuchel, & Newman, 2017), and while some individuals succumb to traumas and adversities, others recover or flourish (Carr et al., 2017; Wilson-Genderson, Pruncho, & Heid, 2017). Understanding this phenomenon more broadly is important since it may directly or indirectly promote health and functioning in older adults who have experienced previous or recent disadvantage or trauma. Life course scholars urge researchers to examine resilience as both a personal attribute and a process including the activation of resources and, ultimately, adaptation to adversity (Schafer, 2009; Lerner et al., 2012). Although the concept and measurement of resilience is debated in the literature, we argue that our measure of this resource is in line with other measures and the aim of the U.S. military to promote psychological fitness and resilience through increasing psychological and social resources prior to deployment (Meredith et al., 2011). Although we cannot know the life course trajectories of psychological resilience among veterans before and directly after service using our data, prior research demonstrates that veterans recognize the military’s ability to strengthen individual resources like those measured here, even while putting them in harm’s way (Elder & Clipp, 1989; Marcellino & Tortorello, 2015). Indeed, additional analyses of the VMS sample compared to an age and gender matched comparison group suggest that veterans have higher levels of resilience than their civilian counterparts, while some measures of health and well-being are lower (models not shown). Our findings have direct implications for older veterans and current service men and women, for whom the roles of peacekeeper and warrior intertwine within a cumulative stress environment (Boermans, Delahaij, Korteling, & Euwema, 2012). Although most soldiers face challenges, the majority do not develop lasting problems, thus making favorable responses to SREs a possible by-product of psychological resilience, at least in part. Learning more about such responses to SREs is important because it illuminates specific pathways to successful adaptation. Of particular importance is the reinforcement and activation of resilience, recovery, and sustainability (Boermans et al., 2012). While we cannot distinguish whether the military or specific SREs imparted, strengthened, or weakened resilience among our veterans—or whether they entered the military more or less resilient—our findings suggest that psychological resilience plays an important role between early-life SREs and health that reaches into and throughout later life. Practice-based methods of increasing resilience are found to be effective (meta-analysis; Boermans et al., 2012), where service members actively enact and integrate skills that promote adaptation. With increasing emphasis on the potential to strengthen this resource through military training (Meredith et al., 2011), effective strategies to bolster resilience in military personnel could have lasting effects for life course trajectories within an institution that inherently places individuals at risk of trauma. Limitations and Future Directions We should note that among traumatic SREs considered here, we did not examine the most extreme form of military trauma: prisoner of war (POW; King et al., 2015), as the variable was unavailable to maintain confidentiality. Consistent with recent research on service personnel, our conceptualization of resilience as a resource focuses on internal attributes without including social support and related external resources. We argue that in disentangling the processes of resource activation, external resources should be considered separately but alongside psychological resilience in future research. Although we document the buffering effect of psychological resilience here in the specific context of the military, we urge future researchers to empirically consider these multifaceted and multidimensional resources as part of a process of adaptation applicable to a number of stressors and traumas (Schafer, 2009; Smith & Hayslip, 2012). Stemming from this is a better understanding of how health, well-being, and psychological resilience interplay over the life course prior to later life. Although sensitivity analyses (not shown) suggest our results are not driven by health selection into higher levels of resilience, earlier life investigations of this resource and health at younger ages are paramount for future research. In addition, although we conducted sensitivity analyses to address the bias introduced by missingness over the study period, we cannot know about the selective nature of those veterans surviving to report in later life. Although previous research suggests that respondents of the 2013 VMS look similar to all male veteran respondents in the 2010 HRS, the VMS is slightly healthier and more advantaged (Taylor, Ureña, & Kail, 2015) which may influence the generalizability of our findings. Additionally, future research should examine what mechanisms explain the buffering effects found here in relation to physical health, and especially mental health as it is tied closely over time to functional impairment at late ages. Funding S. Min was supported by the National Science Foundation Research Fellowship Program under Grant No. 2016-1449440. Any opinions, findings, and conclusions or recommendations expressed in this study are those of the authors and do not necessarily reflect the view of the National Science Foundation. Conflict of Interest None reported. Acknowledgments M. G. Taylor designed the study, performed analyses, and wrote the paper. S. 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Published: Jun 14, 2019

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