Respiratory Health Among Older Adults in Vietnam: Does Earlier-Life Military Role and War Exposure Matter?

Abstract

Objective

We explore how earlier-life military roles and war trauma associate with later-life respiratory health in Vietnam. Method: The population-based sample aged 60+ is from the 2018 Vietnam Health and Aging Study. Poisson and binary logistic regressions investigate correlates of overall lung health, measured as total number of four conditions, and individual conditions, with focus on earlier-life wartime experiences. Results: Exposure is associated with lung conditions. Overall, a one-standard deviation increase in exposure results in 0.529 more conditions (p ≤ .001). Association varies across military roles and is partially explained by PTSD and smoking. Civilians heavily exposed to war trauma exhibit worse lung health than similarly exposed formal and informal military personnel. Discussion: Earlier-life war exposure is an important predictor of late-adulthood respiratory health in lower- and middle-income countries. Evidence calls for attention to the long-term impacts of war on health among not only formal and informal military personnel but also civilians.

Keywords

Respiratory health war military participation older persons Vietnam

Introduction

This study explores associations between earlier-life wartime involvement and traumatic exposure and respiratory health among older adults in the context of Vietnam. Respiratory illnesses are leading causes of morbidity and mortality worldwide (GBD Chronic Respiratory Collaborators, 2020). Chronic obstructive pulmonary disease (COPD) and asthma alone are responsible for over 3.5 million deaths annually. Respiratory conditions are particularly relevant in lower- and middle-income countries (LMICs) where approximately 90% of COPD-related deaths and 80% of asthma-related deaths occur (WHO, 2019). In these countries, older adults bear a disproportionate burden of lung diseases (GBD 2019 Diseases and Injuries Collaborators, 2020). Given rapid population aging, the world may expect a continually growing segment to be at risk of respiratory illnesses in coming decades, calling for a better understanding of the disease etiology.

War and military participation are frequently neglected yet potentially critical factors in shaping the etiology of late-life health (Settersten, 2006). War exposure affects one’s social interactions, behaviors, and biological characteristics (Bendavid et al., 2021; Levy & Sidel, 2016). Current knowledge about the relationship between war stressors and respiratory health comes primarily from studies based on American male veterans and focuses on relatively short-term impacts. For example, deployment to Iraq and Afghanistan has been associated with increases in self-reported respiratory symptoms during and after deployment among US military personnel (Abraham et al., 2012). A broader literature connects posttraumatic stress disorder (PTSD), a common outcome of war exposure, with respiratory dysfunction (Boscarino, 2006; Spiro et al., 2006). PTSD is also associated with the development or worsening of respiratory illnesses among populations with traumatic experiences in childhood and adulthood (e.g., child abuse, World Trade Center terrorist attack) (Kotov et al., 2015; Spitzer et al., 2011; Waszczuk et al., 2019).

Past research suggests different pathways through which PTSD may mediate the relationship between war stressors and lung illnesses. For instance, individuals with PTSD are likely to experience disruption in their autonomic nervous system and hypothalamic-pituitary-adrenal axis, thus heightening airway inflammation that leads to a decline in pulmonary function (Spitzer et al., 2009). Furthermore, epigenetic changes in immune system genes that are associated with PTSD may also contribute to the pathway (Uddin et al., 2010). Moreover, PTSD has been associated with unhealthy behaviors, such as heavy smoking, binge drinking, physical inactivity, sleep disturbances, and unhealthy diet. Obesity and metabolic dysregulation can in turn have adverse effects on pulmonary physiology and compromise respiratory health (Carroll et al., 2011; Waszczuk et al., 2019).

While most of the research on the association between war and respiratory health comes from American-based studies, during the last half century, wars have mainly concentrated in LMICs where war-damaged institutions and infrastructure are layered upon fragile economies and weakly developed healthcare systems (Ghobarah et al., 2004; Iqbal, 2006). Many survivors from wars in LMICs are now entering old age and are experiencing growing risk of respiratory-related chronic illnesses (Massey et al., 2017). Vietnam provides an unusually informative example. The country underwent major armed conflicts for consecutive decades during the 20th century. Noteworthy is the Vietnam War (also known as the American war by the Vietnamese) during the 1960s and 1970s. This war encompassed one of the most intense aerial bombing campaigns and the most extensive use of military herbicides (e.g., Agent Orange) in modern history (Miguel & Roland, 2011). Survivors of the Vietnam War are among the largest populations in the world today to have lived through brutal conflict, and they are now entering old age, creating a unique opportunity for studying long-term impacts on health and aging.

Plus, older Vietnamese were exposed to stressors in their early adulthood in myriad ways—some directly, while others less directly. A series of draft laws implemented in the 1960s mandated young men to serve in formal military, thus making military service a rite of passage for most Vietnamese men coming of age during the 1960s and 1970s (Teerawichitchainan, 2009). Additionally, nearly 150,000 teenagers, mostly women, were recruited to the Youth Shock Brigade (YSB) and approximately 1.6 million adult civilians to self-defense militia forces (Guillemot, 2009; Pike, 1986). Furthermore, heavy bombing campaigns displaced a wide swathe of the Vietnamese population, while millions were estimated to have been sprayed on directly by military herbicides (Hirschman et al., 1995; Stellman et al., 2003). Civilians who lived in the periphery of warzones were often exposed to war stressors through adverse living conditions such as food and water shortages (Young et al., 2021). Even after the war ended, its impacts may have lingered and continued to shape the life course of many Vietnamese men and women from these cohorts (Teerawichitchainan & Korinek, 2012). For instance, the loss of family members to war violence may create a chain of stressors (e.g., loss of economic and social support) for survivors that could affect well-being in the long run. This suggests a great deal of heterogeneity in how various segments of the Vietnamese population were involved in and experienced wartime trauma.

Emerging evidence has suggested that war stressors in Vietnam are significantly associated with several health outcomes such as frailty and cardiovascular disease (Korinek et al., 2020; Zimmer et al., 2021). No studies to our knowledge have yet explored the linkages between war exposure and late-life respiratory conditions in the context of Vietnam. Yet, respiratory diseases are among the country’s leading causes of death (GBD 2019 Diseases and Injuries Collaborators, 2020; Lâm et al., 2014). Cross-national studies have shown prevalence of chronic respiratory diseases to be higher in Vietnam than other Asian countries (V.N. Nguyen et al., 2015; Regional COPD Working Group, 2003). Smoking has consistently been identified as the prime risk factor for later-life respiratory illnesses in Vietnam, while other risk factors such as the use of biomass fuel, ambient particulate matter, and respiratory infections have received some attention (Duong et al., 2018; Lâm et al., 2014; Marais et al., 2013). Vietnam’s burden of respiratory infections such as tuberculosis is among the world’s highest and disproportionately affects its male and older-aged populations (H.V. Nguyen et al., 2020; WHO, 2021). These findings call for a better understanding of various factors determining respiratory health in late adulthood.

We analyze a general population sample of adults aged 60 and older from the 2018 Vietnam Health and Aging Study and investigate how wartime military role and war trauma exposure in early adulthood are linked to later-life respiratory health. Specifically, we examine the following research questions: To what extent is war exposure in youth and young adulthood related to older persons’ respiratory conditions and overall lung health, controlling for potential confounders? How does the association between war exposure and respiratory health vary by wartime military role among older men and women? How are smoking history and PTSD associated with respiratory illnesses after considering war exposure and military role?

Based on the extant literature, we hypothesize war exposure in youth and young adulthood is significantly correlated with respiratory illnesses in older age. We anticipate that more intense exposure is associated with poorer lung health, increasing the probability of individual conditions and total number of conditions. Furthermore, since one’s wartime military participation is linked to the degree of war trauma exposure as well as risks and resources associated with such roles, we hypothesize the association between war exposure and respiratory conditions varies across military veterans, former YSB/militia members, and civilians. Lastly, we hypothesize smoking status and PTSD is significantly associated with late-life respiratory health, independent of war stressors and military experience.

Data and Methods

Data from the Vietnam Health and Aging Study (VHAS), conducted in 2018, is uniquely suited to our purpose. The VHAS is the first comprehensive population-based survey in Vietnam to examine health outcomes as they vary across forms of war exposure. The VHAS sample comprises 2447 community-dwelling Vietnamese adults aged 60 and above who were randomly selected from regularly updated household registration databases. VHAS respondents were born before 1959, thus reaching youth and young adulthood during the 1960s and 1970s when the Vietnam War took place (circa 1965–1975). The sample is based on a random stratified design that assured sufficient numbers of men and women with and without military experience. The VHAS respondents resided in 12 communes/wards in four districts of northern and central Vietnam. The districts were purposively chosen to capture a spectrum of bombing intensities during the war. The overall response rate was 85%. Detailed information about the VHAS sampling methodology is available elsewhere (Korinek et al., 2019).

The VHAS included a structured survey interview with detailed questions about military experience, war trauma exposure, physical, mental, and functional health, and socioeconomic conditions. Additionally, the VHAS conducted clinic-based biomarker collection including lung function tests. Among those who were interviewed, nearly 96% participated in biomarker collection.

Measurements

Respiratory health

First, we consider a count variable to approximate an overall summary measure of lung health. The VHAS allows us to assess whether or not the respondent has the following four respiratory conditions: COPD, asthma, severe respiratory symptoms (i.e., coughing and/or difficulty in breathing), and low peak expiratory flow (PEF). The first three respiratory conditions were self-reported. For COPD and asthma, we consider both self-reported conditions diagnosed by doctors and those conditions respondents indicated were present but not diagnosed by a doctor. A majority of respondents indicated the conditions of COPD and asthma have been diagnosed by a doctor. Respiratory symptoms refer to the health complaints that the respondent reportedly experienced during the month prior to the survey.

PEF, assessed by a Clement Clarke Airzone peak flow meter, is an average of the peak flow values from three pulmonary function tests that each VHAS respondent performed during the biomarker collection. PEF indicates a person’s maximum airflow achieved during expiration. It is considered a valid approach for measuring respiratory impairment particularly among physically frail or cognitively impaired older adults in primary care settings in LMICs, where spirometry is not readily available (Vaz Fragoso et al., 2007). Respondents with low PEF are defined as those whose age- and gender-standardized z-scores for PEF values are below the 10th percentile, suggesting that they demonstrate low pulmonary function relative to their age and gender (Vaz Fragoso & Gill, 2012).

Individuals with comorbid lung conditions are more likely to require intervention and are at high risk for other related diseases, including lung cancer (McCutchan et al., 2019; Schabert et al., 2021). Additionally, the extant literature supports the use of a sum score of respiratory conditions to indicate severity of respiratory problems (Global Initiative for Chronic Obstructive Lung Disease [GOLD], 2020; Maddox, 2016). For instance, GOLD (2020) recommends that both airflow limitations and self-reported symptoms (e.g., exacerbation, breathing difficulty) should be considered when assessing severity of COPD because persons with mild or moderate COPD may not meet the diagnostic threshold set for COPD during one-time airflow obstruction test (Aaron et al., 2017). Therefore, as a proxy for overall lung health, we consider respondents with greater total number of conditions to have poorer respiratory health than those with fewer. Furthermore, in addition to the summary measure of respiratory conditions, we consider each of the four conditions separately.

Independent variables

The four explanatory variables of greatest interest in this study are wartime military role, war trauma exposure, smoking history, and PTSD symptoms. First, military role is incorporated as a categorial variable indicating whether during wartime the respondent served in formal military, YSB/militia, or was a civilian.

The VHAS included an extensive inventory of potentially traumatic wartime experiences, including subsets of exposure scales commonly used in other studies (Young et al., 2021). These items represent several domains of exposure such as various types of inhospitable living conditions, forced displacement, and witnessing death and injury. Data were also linked geographically to bombing intensity data (Miguel & Roland, 2011). Those that served in the military or YSB/militia were additionally asked about combat experiences such as having worked on dangerous patrols or being shot at. In total, there are 26 individual war exposure items, each of which can be coded by intensity. We code each item as 0 (did not experience), 1 (experienced in lesser intensity), or 2 (experienced in greater intensity) and sum them to create a wartime exposure score. Both civilians and formal and informal military personnel were asked a similar series of exposure questions. However, formal and informal military personnel were asked several additional questions that would only be applicable to those in combat. Therefore, a summative score would be biased since some respondents are at risk of a greater number of exposures than others. To standardize the exposure scores so that they are comparable across wartime military roles, the scores for the civilian sample and the formal military and YSB/militia samples were each normalized separately to have a mean of zero and a standard deviation of one. A list of items and their coding can be found in the Supplemental Materials. Our sensitivity analyses show that results are independent of whether we used the war exposure scale that includes only items common among civilians and non-civilians or whether we used standardized scores that consider the full set of wartime experience inventories in which some items are applicable to only formal and informal military personnel.

Smoking history is incorporated as a categorical variable indicating whether the respondent was a non-smoker (i.e., never smoked), a former smoker, or a current smoker at the time of the survey.

A PTSD symptoms scale (min:0, max:27, α = 0.758) is included as a continuous variable indicating the severity of PTSD symptoms experienced by the respondent in the year prior to the survey. The assessment of PTSD in the VHAS is based on respondents’ experience of a subset of 9 symptoms from the 20-item posttraumatic checklist (PCL-5) (Weathers et al., 2013). Specifically, respondents were asked to indicate whether in the past year they experienced specific forms of stress (e.g., disturbing/unwanted memories of war, irritable/aggressive behavior, negative self-belief, jumpy feeling) and how much the symptoms bothered them (not at all, a little, moderately, a lot). PTSD symptoms scale is estimated by aggregating the severity scores for each PTSD symptom.

Covariates

Our analyses consider a series of socio-demographic characteristics that tend to be associated with later-life health. These includes gender (male = 1, female = 0), age, current marital status (currently married = 1, not married = 0), education (secondary or more = 1, primary or less = 0), and lifetime occupation (agriculture = 1, professional/technical = 2, manufacturing/service = 3), childhood health status (poor/very poor = 1, fair/good/very good = 0), and wealth. The latter variable is constructed using a principal component analysis which estimates a household wealth score based on ownership of household assets (e.g., TV, refrigerator, motorcycle), which is then categorized into quintiles (Filmer & Pritchett, 2001). We also control for other health conditions and disability since individuals with chronic lung diseases often experience comorbid conditions and physical dysfunction (Cazzola et al., 2013; Decramer & Janssens, 2013). Comorbidity index is measured as a summative index of seven self-reported conditions: hypertension, diabetes, cardiovascular disease, cancer, liver disease, stroke, and arthritis. Disability is measured as a dichotomous variable indicating whether the respondent requires assistance in activities of daily living (ADL), including dressing, bathing, eating, getting in/out of bed, and using the toilet. Additionally, we consider respondents’ current district of residence (Bavi = 1, Yen Khanh = 2, Dong Hoi = 3, Bo Trach = 4). Bavi is a semi-rural area near Hanoi (Vietnam’s capital city). Among the four districts, there are socioeconomic advantages to living in Bavi with respect to having better access to doctors and healthcare facilities.

Analytical Approach

We start by describing the sample in terms of war exposure, smoking history, PTSD symptoms, and socio-demographic characteristics and assess how these characteristics vary by gender. We also describe variations of respiratory health conditions among older Vietnamese with different military experience. Next, we utilize Poisson regression models to simultaneously examine determinants of total number of respiratory conditions among older adults. A series of hierarchical/nested regression models are presented, each of which controls for a different set of covariates, which permits us to examine the impact that additional variables have on our main predictors of interest. Additionally, we use binary logistic regression to assess correlates of each respiratory condition (COPD, asthma, severe respiratory symptoms, and low PEF), while controlling for socio-demographic characteristics. In all multivariate analyses, we pay attention to how the interactions between military role and war exposure as well as the interaction between gender and war exposure are associated with respiratory health. Finally, to interpret results intuitively, we use the multivariate findings to estimate predicted total number of respiratory conditions for older men and women with different military roles and varying degrees of war exposure.

For all regression models, we omit missing responses (e.g., “don’t know” and “refuse to answer” responses). There are 333 cases with at least one missing response (13.6% of the sample). Given the number of cases with missing responses is relatively small, we utilize a listwise deletion approach (results are largely similar when a pairwise deletion approach is used) (Allison, 2001). We also apply weights in multivariate analyses to adjust the sample to be gender and military participation representative within communes/wards. In other words, the application of sampling weights makes the VHAS data representative of older persons residing in the 12 communes/wards from which the sample was drawn.

Results

Sample Description

Descriptive statistics for independent variables are presented in Table 1 for the entire sample and separately for men and women. Approximately 40% of the sample participated in formal military, 26% in YSB/militia, and 34% were civilians. Three quarters of men were military veterans compared to 8% of women. Women frequently participated in war by joining the YSB/militia. Slightly over half of women and 13% of men were civilians. Results suggest significant gender differences in smoking history, traumatic exposure, and PTSD symptoms. About 82% of men ever smoked compared to just 2% of women, with 30% of men presently having a smoking habit. Moreover, men reported significantly higher mean scores for war exposure and PTSD symptoms than women. Also shown in the table is descriptive statistics for all the socio-demographic control variables.

Table 1.

Characteristics of Older Persons in the VHAS Sample by Gender.

	All	Male	Female	Differences by gender p value
	(N = 2447)	(N = 1195)	(N = 1252)	Differences by gender p value
War exposure score (Mean, SD)	0.00 (0.98)	0.22 (1.00)	0.00 (0.92)	.000
Military role (%)
Formal military	40.3	74.5	7.6	.000
Youth Shock Brigades (YSB)/Militia	26.1	13.1	38.5	.000
Civilian	33.7	12.5	53.9	.000
Smoking history (%)
Non-smoker	59.1	18.5	97.9	.000
Former smoker	26.0	51.6	1.5	.000
Current smoker	14.9	29.9	0.6	.000
Posttraumatic stress disorder (PTSD) symptoms (Mean, SD)	3.18 (3.67)	3.41 (3.76)	2.96 (3.57)	.003
% Female	51.2	0.0	100.0	—
Age (Mean, SD)	70.33 (8.47)	69.80 (7.96)	70.84 (8.90)	.002
% Currently married	71.6	92.5	51.6	.000
% Secondary education or beyond	18.6	27.2	10.3	.000
Lifetime occupation (%)
Agriculture	69.2	60.8	77.2	.000
Professional/technical	11.3	13.5	9.3	.001
Manufacturing/service	19.5	25.7	13.5	.000
Wealth quintile (%)
Lowest	17.0	12.0	21.7	.000
Second	14.1	13.4	14.7	.353
Third	29.1	31.7	26.6	.006
Fourth	18.5	19.3	17.7	.285
Highest	21.4	23.6	19.2	.009
% Poor childhood health	6.0	5.9	6.0	.969
Comorbidity index (Mean, SD)	1.71 (1.22)	1.75 (1.29)	1.66 (1.15)	.000
% Activity of daily living (ADL) disability	6.5	6.9	6.0	.337
Current location of residence (%)
Bavi	33.4	33.0	33.7	.700
Yen Khanh	33.4	33.6	33.1	.830
Dong Hoi	16.7	16.8	16.5	.849
Bo Trach	16.6	16.7	16.6	.979

Source: The 2018 Vietnam Health and Aging Study.

Table 2 describes prevalence of respiratory health conditions for the total sample and separately by military roles. Mean total number of respiratory conditions is 0.42 for the entire sample, with the differences across military roles demonstrating no statistical significance. COPD is more common than other respiratory conditions. 18% of respondents have COPD, while 7% have asthma, 8% have severe respiratory symptoms, and 11% have low PEF. Results indicate significant differences in COPD and asthma (but not respiratory symptoms and PEF) across wartime military roles. Percentages with the two chronic lung diseases are consistently lower among civilians than formal military and YSB/militia. Pearson’s correlations between all study variables are available in the Supplemental Materials.

Table 2.

Respiratory Conditions among Older Persons by Wartime Military Roles.

	All	Formal military	YSB/Militia	Civilian	Differences by military roles p value
	(N = 2152)	(N = 900)	(N = 555)	(N = 697)	Differences by military roles p value
Number of respiratory conditions (Mean, SD)	0.42 (0.77)	0.44 (0.77)	0.45 (0.78)	0.38 (0.74)	.218
% Chronic obstructive pulmonary disease (COPD)	18.4	20.8	18.4	15.5	.017
% Asthma	6.7	6.6	8.7	5.3	.037
% Severe respiratory symptoms	8.0	8.0	8.9	7.4	.579
% Low peak expiratory flow (PEF)	11.0	10.2	10.8	12.2	.431

Source: The 2018 Vietnam Health and Aging Study.

Multivariate Analyses

Table 3 presents coefficients and robust standard errors from Poisson regression models that examine the correlates of the total number of respiratory conditions. We report 6 additive models. Model 1 considers the total number of respiratory conditions as a function of war exposure, gender, and age. Model 2 adds other socio-demographic covariates. Model 3 incorporates wartime military role. Model 4 and Model 5 adds smoking history and PTSD symptoms, respectively. Lastly, Model 6 incorporates three interaction terms between military role, gender, and war exposure. Statistically significant positive coefficients indicate greater respiratory conditions associated with a particular category relative to the reference category.

Table 3.

Coefficients and Robust Standard Errors from Poisson Regression Models Predicting Number of Respiratory Conditions.

	Model 1		Model 2		Model 3		Model 4		Model 5		Model 6
	Coef.	Robust Std. Err.	Coef.	Robust Std. Err.	Coef.	Robust Std. Err.	Coef.	Robust Std. Err.	Coef.	Robust Std. Err.	Coef.	Robust Std. Err.
War exposure score	0.265 ***	0.047	0.173 ***	0.052	0.185 ***	0.051	0.180 ***	0.051	0.128 *	0.056	0.345 ***	0.109
Military role (civilian = ref)
Formal military					0.118	0.149	0.117	0.150	0.056	0.153	0.085	0.151
YSB/Militia					0.141	0.130	0.135	0.130	0.082	0.134	0.034	0.127
Smoking history (non-smoker = ref)
Former smoker							0.277	0.192	0.293 †	0.190	0.295 †	0.190
Current smoker							0.318	0.226	0.318	0.223	0.323	0.222
Posttraumatic stress disorder (PTSD) symptoms									0.037 **	0.013	0.040 **	0.013
Formal military x War exposure											−0.291 **	0.113
YSB/Militia x War exposure											−0.215 †	0.134
Female x War exposure											−0.190 †	0.113
Female (male = ref)	−0.098	0.107	−0.297 *	0.125	−0.267 †	0.158	−0.025	0.218	−0.045	0.216	−0.064	0.216
Age	0.003	0.006	−0.006	0.007	−0.007	0.007	−0.006	0.007	−0.006	0.007	−0.007	0.007
Married (not married = ref)			−0.088	0.132	−0.102	0.132	−0.099	0.133	−0.096	0.133	−0.110	0.133
Secondary education+ (primary or less = ref)			−0.366 *	0.152	−0.360 *	0.152	−0.361 *	0.152	−0.366 **	0.148	−0.403 **	0.149
Lifetime occupation (agriculture = ref)
Professional/technical			0.042	0.174	0.057	0.173	0.061	0.173	0.095	0.173	0.130	0.173
Manufacturing/service			−0.040	0.158	−0.032	0.159	−0.035	0.159	−0.023	0.158	−0.011	0.156
Wealth quintile (lowest = ref)
Second			−0.405 *	0.162	−0.405 *	0.160	−0.393 *	0.159	−0.387 *	0.160	−0.383 *	0.160
Third			−0.397 **	0.152	−0.396 **	0.151	−0.401 **	0.150	−0.407 **	0.150	−0.424 **	0.150
Fourth			−0.388 *	0.179	−0.382 *	0.177	−0.389 *	0.175	−0.382 *	0.176	−0.362 *	0.174
Highest			−0.723 ***	0.206	−0.716 ***	0.204	−0.713 ***	0.204	−0.702 ***	0.206	−0.694 ***	0.205
Poor childhood health (Not poor = ref)			0.373 *	0.180	0.377 *	0.180	0.378 *	0.180	0.348 †	0.182	0.361 *	0.179
Comorbidity index			0.224 ***	0.038	0.220 ***	0.037	0.218 ***	0.037	0.202 ***	0.039	0.193 ***	0.039
Activity of daily living (ADL) disability (no disability = ref)			0.109	0.243	0.113	0.239	0.124	0.240	0.048	0.244	0.041	0.253
Current location (Bavi = ref)
Yen Khanh			−0.125	0.137	−0.115	0.137	−0.101	0.136	−0.108	0.137	−0.101	0.136
Dong Hoi			0.119	0.188	0.103	0.187	0.109	0.186	0.104	0.183	0.069	0.184
Bo Trach			0.150	0.144	0.122	0.152	0.140	0.150	0.133	0.151	0.143	0.148
Constant	−1.152 *	0.458	−0.301	0.599	−0.337	0.636	−0.659	0.662	−0.727	0.664	−0.657	0.663
Log-likelihood	−1719.206		−1617.114		−1615.671		−1613.551		−1603.942		−1598.002
Number	2152		2115		2115		2115		2113		2113

Source. The 2018 Vietnam Health and Aging Study.

Note. ***Statistically significant at p ≤ .001; **p ≤ .01; *p ≤ .05; †p ≤ .10.

Results demonstrate war exposure to be positively associated with late-life respiratory conditions. The size and statistical significance of its coefficients alter slightly yet remain significant after other covariates are considered. According to Model 6, all else equal, older adults exposed to greater war stressors earlier in life exhibit significantly higher number of lung disorders (b = 0.345, p ≤ .001). Because the coefficients in a Poisson regression are themselves not easily interpretable, in separate calculations we converted these into number of conditions. The coefficient in Model 6 specifically translates into 0.529 more conditions for each one standard deviation increase in war exposure. The clinical implications of this for lung health are quite substantial. Those that might otherwise not have any lung disorders have a good chance of developing one if they were highly exposed, while those with one disorder may have comorbid lung conditions with higher wartime exposure.

Furthermore, the association between war exposure and lung health varies significantly by wartime military role and gender. According to Models 3–5, the association between military role and respiratory health is not statistically significant when military role is considered only as a main effect. In Model 6, however, military role is interacted with war exposure and the significance of the interaction terms suggest that military role functions to determine the effect of war exposure. The interaction between being in the formal military and war exposure is negative (b = −0.291) and statistically significant at p ≤ .01, while the interaction between being in the YSB/militia and war exposure is negative (b = −0.215) and statistically significant at p ≤ .10. While the main effect for war exposure is strongly positive, the interaction means that the strong positive effect exists for the comparison group (civilians) only. The effect of war exposure for formal and informal military personnel diminishes by the degree indicated by the interaction effects. Likewise, results indicate that the interaction between being female and war exposure is negative (b = −0.190) and statistically significant at p ≤ .10. This means the effect of war exposure on women’s respiratory health decreases according to the size of the interaction effect.

PTSD and past smoking status are important predictors of lung health, independent of war exposure and military experience. Results show that these covariates explain some but not all of the relationship between war exposure and respiratory conditions. PTSD symptoms, for instance, are found positively related to respiratory conditions before and after interaction terms are considered. Our supplementary calculations for Model 6 indicate that all else equal, a unit increase in PTSD results in 0.330 more respiratory conditions. Meanwhile, smoking status is represented by two dummy variables, each of which is compared to being a non-smoker. There is a statistically significant association between being a former smoker as opposed to non-smoker and number of respiratory conditions (b = 0.295, p ≤ .10). However, the association is not statistically significant when compared to being a current smoker. Also provided in the table are the results of other covariates.

Next, we examine the correlates of each separate respiratory condition (COPD, asthma, severe respiratory symptoms, and low PEF) based on binary logistic regression models. Table 4 presents coefficients and robust standard errors for independent variables of interest and interaction terms between military role and war exposure. We control for socio-demographic covariates in all models but do not report them in Table 4 since these variables are associated with each respiratory disorder similarly to those presented in Table 3.

Table 4.

Coefficients and Robust Standard Errors from Binary Logistic Regression Models Predicting Chronic obstructive pulmonary disease (COPD), Asthma, Severe Respiratory Symptoms, and Low peak expiratory flow (PEF).

	Chronic obstructive pulmonary disease (COPD)		Asthma		Severe respiratory symptoms		Low peak expiratory flow (PEF)
	Coef.	Robust Std. Err.	Coef.	Robust Std. Err.	Coef.	Robust Std. Err.	Coef.	Robust Std. Err.
War exposure score	0.403†	0.238	0.379	0.317	0.900***	0.245	0.165	0.227
Military role (civilian = ref)
Formal military	0.186	0.210	−0.022	0.481	0.138	0.335	−0.095	0.270
YSB/Militia	0.116	0.196	0.271	0.312	0.143	0.269	−0.313	0.240
Smoking history (non-smoker = ref)
Former smoker	0.660*	0.294	−0.344	0.475	0.220	0.357	0.701	0.526
Current smoker	0.238	0.325	−0.235	0.449	0.435	0.417	1.079†	0.575
Posttraumatic stress disorder (PTSD) symptoms	0.055**	0.020	0.039	0.032	0.060*	0.029	0.008	0.025
Formal military x War exposure	−0.352†	0.236	−0.433	0.325	−0.769**	0.255	−0.045	0.235
YSB/Militia x War exposure	−0.396†	0.219	−0.287	0.274	−0.403†	0.251	0.023	0.229
Female x War exposure	−0.167	0.245	−0.190	0.299	−0.513*	0.253	−0.153	0.228
Socio-demographic characteristics	(Controlled)		(Controlled)		(Controlled)		(Controlled)
Log-likelihood	−919.870		−432.008		−491.798		−602.862
Number	2263		2302		2312		2166

Note. ***Statistically significant at p ≤ .001; **p ≤ .01; *p ≤ .05; †p ≤ .10.

Consistent with the association observed for overall number of respiratory conditions (Table 3), war exposure is significantly linked to increased likelihood of COPD and severe respiratory symptoms. Interaction terms further indicate that the detrimental effects of war exposure on these two lung disorders appear diminished for formal and informal military personnel compared to civilians. Moreover, the interaction between being female and war exposure is statistically significant at p ≤ .05 in the model predicting respiratory symptoms. This suggests that the effect of war exposure on women’s likelihood of severe respiratory symptoms decreases by the degree indicated by the interaction effect. Furthermore, results show that a unit increase in PTSD is associated with a greater likelihood in COPD (b = 0.055, p ≤ .01) and of severe respiratory symptoms (b = 0.060, p ≤ .05). The effect of smoking history is nevertheless limited to the model predicting COPD, whereby being a former smoker is associated with a much higher likelihood of developing COPD compared to being a non-smoker (b = 0.660, p ≤ .05). Lastly, results do not demonstrate war exposure, military role, smoking status, and PTSD to be significantly associated with the likelihood of having asthma and low PEF. The only exception is the marginally significant association between being a current smoker and low PEF.

To interpret the interaction terms more intuitively, we estimate predicted number of respiratory conditions for formal military, YSB/militia, and civilians according to varying degree of war exposure, separately for men and women (Figure 1). Predicted values are calculated based on Model 6 in Table 3, holding other covariates at their observed values. Results demonstrate distinct patterns for males and females. For men, more intense war exposure results in increasing number of respiratory conditions across military roles, but it is most severe for civilians. At the mean and below mean levels of war exposure, older men who were formal military and YSB/militia demonstrate higher predicted number of respiratory conditions than civilians. However, when civilian men are exposed to war trauma at the levels above mean values, they show consistently more respiratory conditions. For women, the pattern of increasing respiratory problems by war exposure can only be observed for civilians, not formal and informal military personnel. Results for individual indicators of respiratory health are largely similar to the overall number of lung conditions, and these can be found in the Supplemental Materials.

Figure 1.

Predicted Number of Respiratory Conditions by Military Roles and Intensity of War Exposure.

Discussion

The foregoing analyses of population-based data from the VHAS indicate that respiratory illnesses among today’s older adults in Vietnam can be partially explained by their war exposure in youth and early adulthood and that such association is independent of common risk factors such as smoking, age, and poverty. More intense war exposure is found to be significantly associated with poorer lung health. Consistent with findings based on the US veteran populations (e.g., Abraham et al., 2012; Garshick et al., 2019), the association is evident in a measure we created for overall number of respiratory conditions, which we believe to be a proxy for overall lung health and diseases, COPD, and severe respiratory symptoms. Next, our study extends the current literature by demonstrating that the association between war exposure and respiratory health is significantly moderated by past military experience in Vietnam where older adults were exposed to a multitude of war stressors and had divergent military roles ranging from formal military to militia and civilian. Among men and women most heavily exposed to war stressors earlier in life, it is civilians who demonstrate worse respiratory health in late adulthood than formal and informal military personnel. Furthermore, our evidence concurs with past research that shows PTSD and smoking status to be associated with worse respiratory health even after considering war exposure and military experience (Boscarino, 2006; Kuh & Ben-Shlomo, 2004; Spiro et al., 2006).

The extant literature suggests that war exposure and military role may be associated with respiratory illnesses through multifactorial, complex pathways. For example, combat may expose veterans to pollutants such as fine bullet fragments and military herbicides that can reduce lung expiratory volumes (Borander et al., 2017; Kang et al., 2006). Moreover, war stressors have been linked to PTSD and unhealthy behaviors such as heavy smoking, which are in turn associated with metabolic dysregulation and increased risks of chronic respiratory diseases (Spitzer et al., 2011). Given that most existing studies focus on veterans from high-income countries, little information is available to explain mechanisms linking war exposure and respiratory health among civilians who live in war zones. Why civilians are particularly affected is therefore not an area of research that has been at all established. Without comparable research elsewhere, we speculate on a few explanations as follows.

Selectivity may explain the observed vulnerabilities in lung health among civilians. For example, formal military and YSB/militia subsamples in the VHAS are likely to experience greater mortality selection than civilians. Military and militia members who were gravely inflicted by war may have already died before the survey was conducted. The mortality selection may thus diminish the observed association between war exposure and respiratory illnesses among formal military and YSB/militia. Apart from mortality selection, formal and informal military personnel may benefit from their military roles. They might have received various training during wartime that could enhance their physical fitness and life-long coping skills, thus making those who survived the war more resilient to the detrimental effects of wartime stressors on health. Furthermore, they may have a greater access to healthcare than civilians that in turn benefits their health status in the long run. For instance, a qualitative study on health-seeking behaviors among patients with chronic lung diseases in Vietnam suggests that military veterans, especially formerly wounded soldiers, have a better access to specialist care compared to others (T.A. Nguyen et al., 2021).

Another noteworthy finding is the absence of statistically significant associations between war exposure, PTSD, and low PEF—the objectively assessed measure of respiratory conditions in this study. Nevertheless, this pattern is not uncommon. Existing studies argue that a low agreement between self-reported and objectively assessed measures of respiratory disorders could be because PTSD is likely associated with subtle changes in airways rather than chronic obstruction of airways (Kotov et al., 2015). Self-reports of respiratory illnesses tend to be more sensitive than one-time pulmonary function tests such as PEF in capturing minor respiratory conditions that have not yet resulted in chronic physiological obstruction of airways (Skloot et al., 2004; Waszczuk et al., 2019).

Study Limitations

Our study extends the literature on the relationship between war trauma and respiratory health in late adulthood by examining this association in the context of LMICs and paying attention to the heterogeneity of war exposure and military roles among older men and women. Nevertheless, we recognize several limitations and point out potential directions for future research. The first notable limitation is selective mortality among formal and informal military personnel in Vietnam, as mentioned earlier in this section. Also, given that the number of women in the formal military role is small relative to men, it is plausible that there is likely more intense selection into military among females. Various types of selection may thus affect health outcomes of interest. Due to our data limitation, it is not plausible to pinpoint whether the observed vulnerabilities among civilians can be explained by selection or whether formal and informal military personnel benefit from being involved in these roles (e.g., military training, healthcare access). We cautiously interpret findings with this limitation in mind.

The cross-sectional nature of the VHAS is another important limitation. While it is highly likely that war exposure occurs before current respiratory illnesses, it is not possible for us to clarify whether recent PTSD (i.e., the symptoms taking place within the year prior to the survey) precede the onset of lung diseases (e.g., COPD, asthma) for many survey respondents since more than half of them were reportedly diagnosed with lung disorders within the past 5 years prior to the survey. Existing studies indicate that conditions associated with impaired lung can cause psychological distress (Carroll et al., 2011). Longitudinal data are therefore required to determine the issue of bi-directionality. Given the second wave of the VHAS is underway, we will be better equipped to address this limitation in the near future.

Furthermore, we are mindful about the lack of some key information in the VHAS to comprehensively examine respiratory health. For example, there is no information about respondents’ exposure to pollutants from burn pits, biomass fuel, ambient particulate matter which have been found to increase risks of chronic respiratory diseases among American veterans or among other LMIC populations (Garshick et al., 2019; Liu et al., 2016). Additionally, the VHAS does not collect information about the exact timing of smoking initiation in relevance to the timing of formal and informal military induction. This restricts us from examining how smoking history mediates the association between war trauma exposure and lung health. Investigation on the roles of smoking in mediating traumatic stress and respiratory illnesses is warranted in future studies, given that the prevalence of smoking among Vietnamese men is among the highest in the world (Lâm et al., 2014). Past research indicates that poor respiratory health among US veterans could be partially explained by the prominence of smoking in the US military culture, particularly for socializing with peers and subsequently for coping with war-related PTSD after discharge (London et al., 2017).

Lastly, recall bias and the self-reported nature of respiratory health measures may pose some limitations to the study. Our war exposure measure may be affected by recall bias since it attempts to gather information about events that took place in early adulthood. Past research nevertheless suggests that this bias may be minimal because individuals tend to recall memories with greater accuracy when they are linked to personal anchor events like war (Hirschman et al., 1995). Evidence also demonstrates that information about early life experiences which has been collected in many aging surveys can be effectively linked to later-life health (Haas, 2008). Moreover, we are cognizant of limitations associated with self-reported measures of respiratory illnesses. Past research, for example, suggests that individuals with psychological distress may perceive more respiratory symptoms (Dales et al., 1998). Furthermore, in LMICs such as Vietnam, chronic lung diseases tend to be underdiagnosed due to significant economic disparity in health access and utilization (T.A. Nguyen et al., 2021). In this study, we complement self-reported measures of respiratory health with a measure of pulmonary function test (PEF). The limited agreement between self-reported measures and PEF regarding their associations with war exposure warrants future investigation to better understand various biases that may influence self-reports of respiratory conditions and lung function tests.

Implications

While there is growing evidence suggesting war trauma as an important predictor of respiratory conditions, existing studies focus almost exclusively on formal military personnel from high-income countries deployed in LMICs (Abraham et al., 2012; Garshick et al., 2019; Spiro et al., 2006). Based on a general population sample of Vietnamese war survivors, our study consistently demonstrates the negative long-term impacts of war exposure on respiratory health in a developing setting. Importantly, we show that it is crucial to recognize that war exposure is a critical determinant of older adults’ health in LMICs and that individuals’ heterogeneous roles in armed conflicts—be it direct participation in battlefields or living in war zones—matter. Therefore, policymakers attempting to address health needs of older Vietnamese should pay attention not only to formal and informal military personnel but also civilians. Essentially, in an assessment of respiratory of older adults’ respiratory health, war-related experiences should be taken into account in clinical settings, especially when almost all Vietnamese exposed to the Vietnam War in the 1960s and 1970s are now entering old age.

Supplemental Material

Supplemental Material - Respiratory Health Among Older Adults in Vietnam: Does Earlier-Life Military Role and War Exposure Matter?

Supplemental Material for Respiratory Health Among Older Adults in Vietnam: Does Earlier-Life Military Role and War Exposure Matter? by Bussarawan Teerawichitchainan, Zachary Zimmer, Timothy Qing Ying Low, and Toan Khanh Tran in Journal of Aging and Health

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute on Aging at the National Institutes of Health (R01 AG052537) and the Isaac Manasseh Meyer Fellowship from the National University of Singapore.

ORCID iDs

Bussarawan Teerawichitchainan

Zachary Zimmer

Timothy Qing Ying Low

Supplemental Material

Supplemental material for this article is available online.

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