A History of Heart Attack and Experiencing Frequent Mental Distress Among US Veterans and Adult Civilians: A 2019 National Level Cross-Sectional Study

Abstract

Introduction

Frequent mental distress (FMD) is poor mental health for ≥14 days in the past month. Prevalence and risk for depression and suicide are higher among US veterans (USV) than US civilians (USC). Limited research has been done among USV regarding FMD. Anyone can experience mental distress without being clinically depressed—examining FMD more broadly captures health burden of poor mental state. This study’s purpose was to examine the association between having a history of heart attack (HHHA) and FMD among USV vs USC.

Methods

This cross-sectional study used the 2019 Behavioral Risk Factor Surveillance System (n = 274 352) data. Weighted and adjusted logistic regression models were conducted overall and by USV/USC status.

Results

HHHA increases weighted adjusted odds (WAO) of FMD. Among insured not obese USV with HHHA, the WAO of FMD were 1.4x significantly greater (P < .05) than among insured not obese USV without HHHA. Among uninsured obese USC with HHHA, the WAO of FMD were 3.2x significantly greater (P < .0001) than among uninsured obese USC without HHHA, and significantly lower among USV.

Conclusions

Study findings suggest a distinction in FMD among USV/USC with HHHA. Understanding this association can inform policy for FMD screening post-heart attack as another potential intervention to prevent/reduce suicide among USV/USC.

Keywords

veterans military civilians heart attack frequent mental distress BRFSS

“We hypothesize the odds of experiencing FMD will be significantly higher among US adults with HHHA compared to adults without HHHA.”

Introduction

In 2018 in the United States (US) there were approximately 18 million veterans, representing 7% of the US population at that time.¹ The prevalence and risk for post-traumatic stress disorder,^2,3 traumatic brain injury,^4,5 suicide,⁶ and depression^7,8 are higher among US veterans (USV) than US civilians (USC). While much needed attention is given to address these public health crises, limited research has been done among USV regarding the general experience of mental distress beyond these specific issues.

Frequent mental distress (FMD) is a health-related quality of life (HRQOL) measure defined as poor mental health—including feeling stressed or depressed—for any 14 or more days in the past month.⁹ While depression is projected to be the leading cause of disability in America by 2030,¹⁰ and FMD includes feelings of depression in its construct, anyone can experience stress and problems with emotions without necessarily being clinically depressed. Therefore, examining FMD, over clinical depression alone, more broadly captures the experience and burden of poor mental state on one’s HRQOL.^11,12 The use of HRQOL measures like FMD provides a more holistic assessment of self-perceived health and disease burden and is correlated with disease morbidity and mortality.^11,13 Good health is a multi-dimensional state, is more than “merely the absence of disease,”¹⁴ and includes functioning and flourishing in all aspects of life¹⁵ including being free of mental distress.

Heart disease, which can result in a heart attack (HA), remains the leading cause of death in the US.¹⁶ While heart disease prevalence among USV remained around 6% between 2014-2019,¹⁷ USV had higher prevalence of having a history of HA (HHHA) (7.1% vs 3.3%, respectively), and over 2 times significantly greater odds (adjusted odds ratio (AOR): 2.26, 95% confidence interval (CI): 1.41, 3.62) of HHHA than USC.¹⁸ The risk factors for having a HA—including race, gender, socioeconomic factors, and health behaviors—are well-established in the general population.^19-22 Mental distress is a risk factor for developing chronic diseases, such as coronary heart disease (CHD), and poorer associated outcomes therefrom like having or dying from HA.^23,24 Among US adults, the pathways between HA and mental distress—specifically depression—are bidirectional in nature.^25,26 This creates a vicious cycle where individuals find themselves struggling with mental distress and adverse health behaviors contributing to poor health outcomes manifesting as increased symptoms and physical and emotional distress.^27,28

While the body of literature is growing, risk factors for experiencing FMD are not nearly as well studied as those of heart disease but are similar—including minority race, socioeconomic factors (e.g., lower income), health behaviors (e.g., lack of physical activity), and health conditions including HA²⁹—although only investigated among certain states^30,31 or specific subgroups of adults.^32,33 A population seldom examined for experiencing FMD is USV. While the existing literature is limited, previous studies have found the prevalence and odds of FMD among USV to be higher than national estimates for the general population.^24,34,35

Mental distress and its relationship with the heart is complex. Underutilized mental health services,³⁶ especially among USV,³⁷ suggest measuring depression alone may inadequately capture the effect of HHHA on mental wellbeing. While previous research has examined risk factors for HA and separately for FMD among USV, to our knowledge, there are no studies to date that explore the association between HHHA and experiencing FMD among USV compared to USC. The purpose of this study is to examine the association between HHHA and FMD among USV vs USC, and to answer 3 research questions:

1. Are US adults with HHHA more likely to experience FMD than adults without HHHA?

2. Does this association differ by US veteran status?

3. Are there any effect modifiers of this association within each of the 2 groups (USV and USC)?

We hypothesize the odds of experiencing FMD will be significantly higher among US adults with HHHA compared to adults without HHHA. We also hypothesize this association will be stronger among USV than USC.

Methods

The 2019 Behavioral Risk Factor Surveillance System (BRFSS) publicly-available and de-identified data was used for this study. The BRFSS is a cross-sectional, US nationally representative, annual telephone survey.³⁸ All BRFSS questionnaires, data, methods, sample selection, including the weighting procedure and technical information, and reports are available at https://www.cdc.gov/brfss/.

Measures

Veteran Status

Veteran status was based on responses to the 2019 BRFSS question, “Have you ever served on active duty in the United States Armed Forces, either in the regular military or in a National Guard or military reserve unit?” with possible answers “Yes” or No.”

Exposure of Interest: Having a History of Heart Attack (HHHA)

The exposure of interest is HHHA, derived from the 2019 BRFSS Chronic Health Conditions core section question that asks, “Has a doctor, nurse, or other health professional ever told you that you had a heart attack also called a myocardial infarction?” with possible answers “Yes” or “No.” This variable has demonstrated good validity and reliability.³⁹ In the 2019 BRFSS questionnaire, HHHA is considered a distinct event from CHD diagnosis.

Outcome of Interest: Experiencing Frequent Mental Distress (FMD)

The outcome under investigation is FMD, derived from the 2019 BRFSS Healthy Days core section question that asks respondents to indicate the number of days their mental health was not good in the last 30 days at the time of the survey. A cut-off point of 14 or more days was used to dichotomize FMD based on clinical markers for depression.⁴⁰ This threshold to cross-sectionally measure and longitudinally track FMD has been commonly used by the CDC and other researchers for decades,^11,12,41 has been validated for use among US adults,⁴² and has high test/retest³⁹ and strong intra-rater reliability.⁴³ The poor mental health days question has demonstrated good construct and criterion validity.^11,44

Identifying Confounders

Confounders of interest were identified based on previous literature and then entered into a directed acyclic graph (DAG).⁴⁵ After running the DAG (Figure 1), the independent variables identified as the minimum sufficient adjustment set needed for estimating the total effect of HHHA on FMD include: age (in years; range 18 to 79 years) 11^19,20,29; race/ethnicity (white, non-Hispanic; black, non-Hispanic; other, non-Hispanic; and Hispanic) 22^20,30,31; sex (male/female) 22^20,33; completed education (less than high school graduate; high school graduate; some college or technical school; and college graduate or more) 22^21,29; employment (unable to work; out of work; homemaker/student/retired; and employed for wages/self-employed) 22^29,33; household income (<$25,000; $25,000 to <$50,000; and ≥$50,000) 22^20,32; marital status (never married; separated/divorced/widowed; and married/unmarried couple) 22^29,46; CHD (yes/no if ever told by doctor/other health professional they had angina/CHD) 22^21,33; high blood pressure (yes/no if ever told by doctor/other health professional they have high blood pressure) 44^47,48; diabetes (yes/no; 2019 BRFSS does not distinguish between type 1/type 2 diabetes) 11^19,22,33; obesity (Body Mass Index (BMI) ≥ 30 kg/m²) 11^19,29; health insurance (yes/no to having any kind of health care coverage: health insurance, prepaid plans e.g., HMOs, or government plans e.g., Medicare, Indian Health Service) 33^30,49; heavy drinking (yes/no, adult men having >14 drinks/week, adult women having >7 drinks/week) 22^22,33; physical activity for leisure (yes/no to doing leisure physical activity/exercise e.g., running/calisthenics/gardening/walking during the past 30 days at the time of the survey) 22^22,30; and smoking status (never smoker; former smoker; current some day smoker; and current every day smoker).^19,29

Figure 1.

Directed Acyclic Graph (DAG): HHHA and FMD.

Final Study Sample

The initial sample size from the 2019 BRFSS was 418 268 US adults. Pregnant women (at the time of the survey), individuals ages 80 and above, and individuals with “missing”/“refused”/“don’t know” responses to any variables included in analyses were excluded from this study. Pregnant women were excluded due to the “scientific complexities”⁵⁰ and physiological changes experienced during pregnancy that cannot otherwise be controlled for; and respondents ages 80 years and above were excluded due to their ages being imputed to 80 years. The final study sample for this study was 274 352 adult US residents (Figure 2).

Figure 2.

Consort diagram of final study sample.

Statistical Analyses

Weighted median and interquartile range are reported for continuous variables; and weighted prevalence and 95% CIs are reported for categorical variables. We evaluated the simple association between each categorical variable and FMD status with Rao-Scott Chi-square tests separately for USV and for USC. To investigate the relationship between HHHA and FMD, weighted adjusted logistic regression models were conducted for the overall final study sample and then by veteran status while adjusting for confounders previously specified. We tested for interaction effects and where significant, corresponding subgroup analyses were performed. All analyses were conducted in 2022 using SAS OnDemand for Academics (SAS Institute Inc., SAS 3.8 (Enterprise Edition), Cary, NC, USA). SAS survey procedures (e.g., PROC SURVEYLOGISTIC) were used to account for the complex sampling design. Differences were considered statistically significant at P < .05 using two-tailed tests.

Results

Characteristics of the Final Study Sample

Overall, 13.87% (95% CI: 13.61-14.14%) of US adults in the final study sample (n = 274 352) had the outcome under investigation—FMD—compared to 12.71% (95% CI: 11.95-13.47%) among USV and 14.01% (95% CI: 13.72-14.29%) among USC. Among all individuals in the final sample, 3.71% (95% CI: 3.58-3.83%) had the exposure of interest—HHHA—compared to 8.01% (95% CI: 7.50-8.53%) among USV and 3.21% (95% CI: 3.08-3.33%) among USC. While the proportion of USV experiencing FMD was slightly lower than among USC, the weighted prevalence of HHHA was more than two and a half times higher among USV than USC. As shown in Table 1, a larger proportion of USV with HHHA were experiencing FMD (16.62%) than USV without HHHA (12.37%), but this difference in weighted FMD prevalence by HHHA status is more apparent among USC (23.35% and 13.70%, respectively) (Table 2). USV in the sample were older than USC (median age 55.25 vs 44.41, respectively). Nearly twice the proportion of USC in the sample were uninsured (12.27%) compared to USV (6.17%). USV and USC were comparable on being obese (34.60% vs 32.44%), performing physical activity for leisure in the past month (77.59% vs 75.89%), and indulging in heavy drinking (7.57% vs 7.17%).

Table 1.

Descriptive Statistics for US Veterans (USV).

	Overall (N = 31 034)	Weighted		Experiencing Frequent Mental Distress (FMD)				P Value
	Overall (N = 31 034)			Yes (N = 3843)		No (N = 30 191)
	Unweighted Counts			Weighted		Weighted
	Unweighted Counts	Median	IQR^a	Median	IQR	Median	IQR
Age at survey	34 034	55.25	38.22-68.60	48.95	32.97-62.06	56.30	39.23-69.16	<.0001^b

	Overall (N = 34 034)	Weighted		Experiencing Frequent Mental Distress (FMD)				P Value
	Overall (N = 34 034)			Yes (N = 3843)		No (N = 30 191)
	Unweighted Counts			Weighted		Weighted
	Unweighted Counts	Percent	95% CI for Percent	Percent	95% CI for Percent	Percent	95% CI for Percent
Ever had a heart attack
Yes	3363	8.01	7.50-8.53	16.62	14.22-19.01	83.38	80.99-85.78	.0003^c
No	30 671	91.99	91.47-92.50	12.37	11.57-13.17	87.63	86.83-88.43	.0003^c
Angina/coronary heart disease
Yes	3315	7.19	6.75-7.64	17.11	14.65-19.58	82.89	80.42-85.35	<.0001^c
No	30 719	92.81	92.36-93.25	12.37	11.57-13.17	87.63	86.84-88.43	<.0001^c
High blood pressure
No	17 046	57.34	56.32-58.35	11.89	10.79-13.00	88.11	87.00-89.21	<.0001^c
Yes, taking medication	14 283	33.85	32.93-34.77	12.17	11.17-13.16	87.83	86.84-88.83
Yes, not taking medication	2705	8.81	8.21-9.41	20.10	17.22-22.97	79.90	77.03-82.78
Health care coverages
Yes	32 489	93.83	93.22-94.43	11.96	11.22-12.70	88.04	87.30-88.78	<.0001^c
No	1545	6.17	5.57-6.78	24.04	19.33-28.76	75.96	71.24-80.66	<.0001^c
Gender
Female	3816	12.76	11.99 - 13.52	19.03	16.04-22.02	80.97	77.98-83.96	<.0001^c
Male	30 218	87.24	86.48-88.01	11.79	11.04-12.53	88.21	87.47-88.96	<.0001^c
Race/Ethnicity
White, non-Hispanic	27 491	72.01	70.93-73.09	12.01	11.28-12.74	87.99	87.26-88.72	.0439^c
Black, non-Hispanic	2480	12.56	11.76-13.36	13.49	11.29-15.70	86.51	84.30-88.71
Other, non-Hispanic	2498	5.95	5.38-6.52	17.47	12.76-22.17	82.53	77.83-87.23
Hispanic	1565	9.48	8.63-10.33	14.00	10.10-17.90	86.00	82.10-89.90
Income
>$25,000	5902	16.08	15.33-16.83	22.52	20.50-24.53	77.48	75.46-79.50	<.0001^c
$25,000 to >$50,000	8565	23.81	22.91-24.71	15.90	13.89-17.90	84.10	82.09-86.11
$50,000 or more	19 567	60.11	59.08-61.13	8.82	8.04-9.61	91.18	90.39-91.96
Employment status
Employed for wages/self-emp	15 561	56.48	55.47-57.50	10.92	9.87-11.97	89.08	88.03-90.13	<.0001^c
Homemaker/student/retired	15 366	34.57	33.63-35.50	9.67	8.63-10.71	90.33	89.29-91.37
Out of work	966	3.21	2.82-3.60	27.36	21.88-32.84	72.64	64.16-78.12
Unable to work	2141	5.74	5.29-6.20	40.46	36.51-44.40	59.54	55.60-63.49
Education
Less than high school	857	3.73	3.32-4.15	19.25	14.88-23.62	80.75	76.38-85.12	<.0001^c
High school graduate	8514	27.60	26.64-28.56	14.16	12.51-15.82	85.84	84.18-87.49
Some college/technical school	11 253	38.70	37.64-39.77	13.77	12.48-15.06	86.23	84.94-87.52
College graduate or more	13 410	29.96	29.08-30.84	9.19	8.21-10.17	90.81	89.83-91.79
Marital status
Married/unmarried couple	21 654	64.68	63.65-65.70	10.32	9.50-11.14	89.68	88.86-90.50	<.0001^c
Never married	3350	13.24	12.42-14.06	16.54	13.52-19.56	83.46	80.44-86.48
Separated/divorced/widowed	9030	22.08	21.22-22.94	17.41	15.82-19.01	82.59	80.99-84.18
Obese
Yes	12 193	34.60	33.62-35.57	15.01	13.78-16.23	85.99	83.77-86.22	<.0001^c
No	21 841	65.40	64.43-66.38	11.49	10.53-12.46	88.51	87.54-89.47	<.0001^c
PA for leisure
Yes	25 790	77.59	76.74-78.44	10.77	9.95-11.59	89.23	88.41-90.05	<.0001^c
No	8244	22.41	21.56-23.26	19.43	17.63-21.22	80.57	78.78-82.37	<.0001^c
Diabetes
Yes	6330	15.36	14.65-16.06	14.69	13.07-16.31	85.31	83.69-86.93	.0092^c
No	27 704	84.64	83.94-85.35	12.35	11.50-13.20	87.65	86.80-88.50	.0092^c
Heavy drinker
Yes	2316	7.57	6.99-8.15	21.79	17.80-25.78	78.21	74.22-82.20	<.0001^c
No	31 718	92.43	91.85-93.01	11.97	11.23-12.71	88.03	87.29-88.77	<.0001^c
Smoking status
Current, every day	4114	12.42	11.75-13.10	22.48	20.02-24.94	77.52	75.06-79.98	<.0001^c
Current, some days	1432	4.87	4.41-5.34	21.40	17.23-25.57	78.60	74.43-82.77
Former	14 112	37.86	36.88-38.85	11.85	10.78-12.93	88.15	87.07-89.22
Never	14 336	44.84	43.79-45.90	9.78	8.63-10.93	90.22	89.07-91.37

^aCI = confidence interval; IQR = interquartile range.

^bBased on unadjusted weighted logistic regression.

^cBased on the Rao-Scott Chi-square test, which is the design adjusted equivalent of the Pearson Chi-square test.

Table 2.

Descriptive Statistics for US Civilians (USC).

	Overall (N = 240 318)	Weighted		Experiencing Frequent Mental Distress (FMD)				P Value
	Overall (N = 240 318)			Yes (N = 30 653)		No (N = 209 665)
	Unweighted Counts			Weighted		Weighted
	Unweighted Counts	Median	IQR	Median	IQR	Median	IQR
Age at survey	240 318	44.41	31.27-58.64	39.46	27.53-54.24	45.33	31.89-59.31	<.0001^a

	Overall (N = 240 318)	Weighted		Experiencing Frequent Mental Distress (FMD)				P Value
	Overall (N = 240 318)			Yes (N = 30 653)		No (N = 209 665)
	Unweighted Counts			Weighted		Weighted
	Unweighted Counts	Percent	95% CI for Percent	Percent	95% CI for Percent	Percent	95% CI for Percent
Ever had a heart attack
Yes	10 258	3.21	3.08-3.33	23.35	21.63-25.06	76.65	74.94-78.37	<.0001^b
No	230 060	96.79	96.66-96.92	13.70	13.41-13.99	86.30	86.01-86.59	<.0001^b
Angina/coronary heart disease
Yes	10 417	3.10	2.98-3.21	22.59	21.07-24.12	77.41	75.88-78.93	<.0001^b
No	229 901	96.90	96.79-97.03	13.73	13.44-14.02	86.27	85.98-86.55	<.0001^b
High blood pressure
No	151 623	70.00	69.66-70.35	12.81	12.48-13.14	87.19	86.86-87.52	<.0001^b
Yes, taking medication	70 433	22.03	21.73-22.33	14.90	14.32-15.48	85.10	84.52-85.68
Yes, not taking medication	18 262	7.97	7.76-8.19	22.06	20.88-23.23	77.94	76.77-79.12
Health care coverage
Yes	219 516	87.73	87.44-88.02	13.42	13.12-13.71	86.58	86.29-86.88	<.0001^b
No	20 802	12.27	11.98-12.56	18.24	17.24-19.24	81.76	80.76-82.76	<.0001^b
Gender
Female	138 308	53.18	52.78-53.58	16.21	15.80-16.61	83.79	83.39-84.20	<.0001^b
Male	102 010	46.82	46.42-47.22	11.51	11.12-11.90	88.49	88.09-88.88	<.0001^b
Race/Ethnicity
White, non-Hispanic	182 967	63.22	62.82-63.62	14.26	13.95-14.57	85.74	85.43-86.05	.0319^b
Black, non-Hispanic	17 590	11.03	10.76-11.29	14.51	13.61-15.42	85.49	84.58-86.39
Other, non-Hispanic	17 533	8.54	8.27-8.82	13.21	12.10-14.31	86.79	85.68-87.90
Hispanic	22 228	17.21	16.86-17.56	13.16	12.26-14.06	86.84	85.94-87.74
Income
>$25,000	56 901	24.37	24.03-24.71	23.10	22.43-23.77	76.90	76.23-77.57	<.0001^b
$25,000 to >$50,000	54 436	21.69	21.36-22.02	14.94	14.35-15.54	85.06	84.46-85.65
$50,000 or more	128 981	53.94	53.54-54.34	9.52	9.17-9.88	90.48	90.12-90.83
Employment status
Employed for wages/self-emp	140 971	64.24	63.86-64.62	11.23	10.90-11.56	88.77	88.44-89.10	<.0001^b
Homemaker/student/retired	72 692	24.45	24.12-24.79	12.03	11.47-12.60	87.97	87.40-88.53
Out of work	9606	4.95	4.77-5.14	26.48	24.79-28.16	73.52	71.84-75.21
Unable to work	17 048	6.36	6.18-6.53	39.94	38.58-41.31	60.06	58.69-61.42
Education
Less than high school	14 326	11.11	10.80-11.42	19.16	17.96-20.37	80.84	79.73-82.04	<.0001^b
High school graduate	59 029	26.04	25.68-26.39	15.83	15.26-16.39	84.17	83.60-84.74
Some college/technical school	67 183	31.17	30.79-31.54	15.86	15.32-16.40	84.14	83.60-84.68
College graduate or more	99 780	31.68	31.35-32.02	8.88	8.54-9.22	91.12	90.78-91.46
Marital status
Married/unmarried couple	138 654	56.97	56.58-57.37	10.28	9.95-10.62	89.72	89.38-90.05	<.0001^b
Never married	44 008	25.21	24.85-25.58	18.98	18.30-19.65	81.02	80.34-81.69
Separated/divorced/widowed	57 656	17.82	17.54-18.09	18.89	18.22-19.56	81.11	80.44-81.78
Obese
Yes	81 737	32.44	32.07-32.80	16.67	16.15-17.18	83.33	82.82-83.85	<.0001^b
No	158 581	67.56	67.20-67.93	12.73	12.39-13.07	87.27	86.93-87.61	<.0001^b
PA for leisure
Yes	181 185	75.89	75.55-76.23	12.23	11.91-12.55	87.77	87.45-88.09	<.0001^b
No	59 133	24.11	23.77-24.45	19.60	18.97-20.22	80.40	79.78-81.03	<.0001^b
Diabetes
Yes	29 686	10.02	9.79-10.25	18.78	17.75-19.80	81.22	80.20-82.25	<.0001^b
No	210 632	89.98	89.75-90.21	13.48	13.18-13.77	86.52	86.23-86.82	<.0001^b
Heavy drinker
Yes	16 362	7.17	6.96-7.37	20.09	18.74-21.44	79.91	78.56-81.26	<.0001^b
No	223 956	92.83	92.63-93.04	13.54	13.25-13.82	86.46	86.18-86.75	<.0001^b
Smoking status
Current, every day	25 546	10.70	10.47-10.92	27.43	26.43-28.43	72.57	71.57-73.57	<.0001^b
Current, some days	10 490	4.95	4.77-5.14	22.02	20.43-23.60	77.98	76.40-79.57
Former	61 378	22.80	22.48-23.12	14.04	13.47-14.61	85.96	85.39-86.53
Never	142 904	61.55	61.17-61.93	11.02	10.68-11.36	88.98	88.64-89.32

*CI = confidence interval; IQR = interquartile range.

^aBased on unadjusted weighted logistic regression.

^bBased on the Rao-Scott Chi-square test, which is the design adjusted equivalent of the Pearson Chi-square test.

Weighted and Adjusted Logistic Regression Results

Based on the DAG, we adjusted for age, sex, race, income, education, employment status, marital status, obesity, heavy drinking, smoking, physical activity for leisure, diabetes, health insurance status, CHD, and high blood pressure. As shown in Table 3, the overall weighted and adjusted odds (WAO) of experiencing FMD among US adults with HHHA were 1.28 times significantly greater (95% CI: 1.13, 1.45) than the WAO of FMD among US adults without HHHA. While the WAO of FMD among USV with HHHA were 1.11 times greater than the WAO among USV without HHHA, this association was not statistically significant (95% CI: .85, 1.45). However, the WAO of FMD among USC with HHHA were 1.30 times significantly greater (95% CI: 1.13, 1.49) than the WAO of FMD among USC without HHHA.

Table 3.

Weighted Unadjusted/Adjusted Logistic Regression results of FMD by HHHA Within Subgroups.

	Total Unweighted n	Unweighted n with Outcome (FMD) (Unweighted Count, Prevalence)	Weighted/Unadjusted		Weighted/Adjusted
	#	#	WUOR	95% CI	WAOR	95% CI
Overall	274 352	34 496 (12.57%)	1.78	1.63, 1.94****	1.288	1.13, 1.456****
US Veterans (USV)	34 034	3843 (11.29%)	1.41	1.167 1.70***	1.11	.85, 1.45
Insured/not obese	20 850	2036 (9.76%)			1.45	1.04, 2.03*
Insured/obese	11 639	1500 (12.89%)			.92	.59, 1.45
Not insured/not obese	991	194 (19.58%)			.66	.25, 1.75
Not insured/obese	554	113 (20.40%)			.33	.13, .84*
US Civilians (USC)	240 318	30 653 (12.75%)	1.92	1.74, 2.12****	1.30	1.13, 1.49***
Insured/not obese	144 693	15 649 (10.81%)			1.16	.95, 1.42
Insured/obese	74 823	11 149 (14.90%)			1.29	1.04, 1.61*
Not insured/not obese	13 888	2417 (17.40%)			1.65	1.01, 2.71*
Not insured/obese	6914	1438 (20.80%)			3.23	1.94, 5.38****

WUOR: Weighted unadjusted odds ratio; WAOR: Weighted adjusted odds ratio controlling for age, race, sex, education, employment, income, marital status, CHD, high blood pressure, diabetes, heavy drinking, physical activity, and smoking status; 95% CI: 95% confidence intervals for odds ratios; *P < .05; **P < .01; ***P < .001; ****P < .0001

The current study found health insurance and obesity status to be effect modifiers (P < .05) of the weighted adjusted association between HHHA and FMD. Table 3 shows results from the weighted, adjusted logistic regression model by veteran, health insurance, and obesity status. Among insured, not obese USV, the WAO of FMD among those with HHHA were 1.45 times significantly greater (95% CI: 1.04, 2.03) than the WAO of FMD among insured, not obese USV without HHHA. Although positive, the WAO of FMD among insured, not obese USC with HHHA were non-significant (WAO = 1.16; 95% CI: .95, 1.42). Among uninsured, obese USC, the WAO of FMD among those with HHHA were 3.23 times significantly greater (95% CI: 1.94, 5.38) than the WAO of FMD among uninsured, obese USC without HHHA. However, among uninsured, obese USV, the WAO of FMD were 67% significantly lower (WAO = .33; 95% CI: .13, .84) among USV with HHHA.

Discussion

To our knowledge, this is the first study to investigate the association between HHHA and FMD among USV and USC using a large, nationally representative sample of US adults. Results indicated HHHA increases the odds of FMD among US adults, providing support for our first hypothesis. Previous literature is limited, but this finding is consistent with one study among Hawaii adults that found HHHA was associated with FMD.³⁰ We found higher prevalence of HHHA among USV than USC which is consistent with previous literature,¹⁸ but found FMD prevalence to be lower among USV than USC differing from previous literature.²⁴ Contrary to our second hypothesis, study results indicated the odds of FMD among USV with HHHA were non-significantly greater than the odds of FMD among USV without HHHA. Whereas the odds of FMD among USC with HHHA were significantly greater than the odds of FMD among USC without HHHA (Table 3). We found health insurance and obesity status to be effect modifiers in the weighted adjusted association between HHHA and FMD. The WAO of FMD were nearly one and half times significantly greater among insured, not obese USV with HHHA. Although positive, the WAO of FMD among insured, not obese USC with HHHA were non-significant. The WAO of FMD were over 3 times significantly greater among uninsured obese USC with HHHA. However, among uninsured, obese USV with HHHA, the WAO of FMD were 67% significantly lower.

To better understand the differences in findings between USV and USC, we consider both sociological and psychological interpretations. From a sociological perspective, the military is a distinct institution⁵¹ with a specific set of norms, nomenclature, and social structures that are shared among all its members.⁵² There is a shared understanding and application of a warrior ethos embodied in the Soldier’s Creed⁵³ that always places the mission first and is built on a foundation of self-discipline and mastery, physical and mental toughness, etc., which is instilled in all recruits through basic training, regardless of branch of service (Army, Navy, etc.).^53,54 There are key differences between the military institution and the general US culture. The military molds its members into a cohesive unit, minimizing individual differences,⁵⁴ which is in juxtaposition with the diverse and multi-cultural^55,56 America of civilians who embrace individualism—a core value in American culture.⁵⁷ Therefore, the finding that the WAO of FMD were significantly higher among USC than USV with HHHA may be partially explained by the training USV received emphasizing mental toughness, offering protection against the experience of FMD.⁵⁸ Conversely, insured not obese USV with HHHA had significantly greater WAO of FMD than USV without HHHA. Based on the values instilled in military personnel (e.g., self-discipline/mastery), HHHA may be especially distressing considering the veteran continued to practice self-discipline and mastery by taking care of their health. Psychologically, this finding echoes the basis for terror management theory (TMT) which asserts individuals utilize both unconscious and conscious methods to cope with the anxiety induced by the awareness of one’s mortality and inevitable death.⁵⁹ Unconscious methods are based on one’s view of self as valuable in, and meaningful to, society regarding certain attributes (e.g., bravery/intelligence/beauty) and social roles (e.g., soldier/parent/friend). They provide symbolic protection against the recognition of one’s mortality by establishing the individual as a significant part of “an eternal death-transcending reality.”⁵⁹ Conscious methods push away the threat of death as something in the distant future and involve rational efforts to reduce or deny “one’s vulnerability and emphasizing one’s prospects for longevity.”⁵⁹ In other words, one eats well, exercises, maintains a healthy weight, sees the doctor, etc. so one can tell themselves they have reasonably done all the right things to push death as far away as possible. This expectation of positive health outcomes stems from the just-world belief hypothesis (JWB) asserting individuals with this belief think people get what they deserve—if they do good, they will be rewarded, and vice versa.^60,61

Considering a veteran who has served their country and has practiced self-discipline and mastery to maintain their health (e.g., healthy weight); HHHA breaks through both conscious and unconscious defenses as well as challenges one’s belief in a just world—they did everything right, didn’t they? On the other hand, when viewed through the lens of TMT and JWB, this study’s finding that uninsured obese USV with HHHA had significantly lower WAO of FMD suggest the veteran’s conscious defenses may argue they could have done more to prevent a HA so they may not experience FMD.

Study Limitations and Strengths

Limitations of this study include BRFSS data being cross-sectional, limiting the ability to determine temporality/causation in the association between HHHA and experiencing FMD. Also, FMD is based on self-reported data and is not a clinical diagnosis, and therefore may not accurately reflect the severity or type of mental health condition experienced by the study participants.

Strengths of this study include the large and nationally representative sample of US resident adults. To our knowledge this is the first study to date that assesses the association between HHHA and currently experiencing FMD among USV and USC.

Future Research

Future research should explore the potentially diminishing healthy warrior effect. Shrestha, et al⁶² found ideal heart health—including blood pressure and BMI—was less prevalent in active-duty military (Army) personnel than a corresponding subset of noninstitutionalized USC. Hoerster, et al⁶³ found male USV have poorer short- and long-term health outcomes and behaviors than male USC, specifically a higher prevalence of diabetes, obesity, current smoking, heavy alcohol consumption, and lack of physical activity, which are risk factors for HA. A growing body of literature has demonstrated negative life stressors are risk factors and precipitating circumstances for suicide, which can be a consequence of FMD,⁶⁴ and refer to physical health, including HA,^65-67 as well as psychosocial factors, such as mental distress.^68-70 There is no single cause for suicide, and this public health crisis calls for any and all precipitating circumstances—including HHHA and FMD—to be examined and addressed, especially among USV who have higher suicide rates than USC.⁶

Conclusion

Results of this study indicate that HHHA increases the odds of FMD among US adults overall but varies by health insurance and obesity status among USV and USC. This study’s findings contribute new knowledge to the literature about USV. Based on these findings, screening for FMD should be increased among US adults—especially among HA survivors and USV who have a higher HA prevalence than USC. Understanding this association can help inform interventions post-HA to reduce the experience of FMD—including screening and counseling—and may provide another potential area of intervention to prevent and reduce suicide by identifying and preventing FMD post-HA among USV and USC alike.

Footnotes

Acknowledgments

Research reported in this publication was supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under Grant no. P20GM103442. IDeA did not have any role in study design; collection, analysis, and interpretation of data; writing the report; and the decision to submit the report for publication.

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) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Jessica N. Passini

S. Cristina Oancea

References

Vespa

. Those who served: America’s veterans from world war I to the war on terror—American community survey report. 2020. Published June 2020. Accessed November 1, 2021. https://www.census.gov/content/dam/Census/library/publications/2020/demo/acs-43.pdf

Reisman

. PTSD Treatment for Veterans: what’s working, what’s new, and what’s next. P T. 2016;41(10):623-634.

Norris

Slone

. Understanding research on the epidemiology of trauma and PTSD. PTSD Research Quarterly. 2013;24:2-3. https://www.ptsd.va.gov/publications/rq_docs/V24N2-3.pdf

Defense and Veterans Brain Injury Center . Department of defense (DoD) numbers for traumatic brain injury report. 2017. Updated March 29, 2017. Accessed November 1, 2021. https://www.biami.org/wp-content/uploads/2017/09/DoD-TBI-Worldwide-Totals_2000-2016_Feb-17-2017_v1.0_2017-04-06.pdf

CDC, NIH, DoD, and VA Leadership Panel . Report to congress on traumatic brain injury in the United States: understanding the public health problem among current and former military personnel. 2013. Published June 2013. Accessed November 1, 2021. https://www.cdc.gov/traumaticbraininjury/pdf/report_to_congress_on_traumatic_brain_injury_2013-a.pdf

U.S. Department of Veterans Affairs . National veteran suicide prevention annual report. 2021. Published September 2021. Accessed November 5, 2021. https://www.mentalhealth.va.gov/docs/data-sheets/2021/2021-National-Veteran-Suicide-Prevention-Annual-Report-FINAL-9-8-21.pdf

Liu

Collins

Wang

Xie

Bie

. The prevalence and trend of depression among veterans in the United States. J Affect Disord. 2019;245:724-727. doi: 10.1016/j.jad.2018.11.031

Trivedi

Post

Sun

, et al. Prevalence, comorbidity, and prognosis of mental health among US veterans. Am J Public Health. 2015;105(12):2564-2569.

Centers for Disease Control and Prevention (CDC) . Health-related quality of life (HRQOL): methods and measures. 2018. Updated October 31, 2018. Accessed August 15, 2021. https://www.cdc.gov/hrqol/methods.htm

10.

Mathers

Loncar

. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3(11):e442. doi:10.1371/journal.pmed.0030442

11.

Slabaugh

Shah

Zack

, et al. Leveraging health-related quality of life in population health management: the case for healthy days. Popul Health Manag. 2017;20(1):13-22. doi:10.1089/pop.2015.0162

12.

Centers for Disease Control and Prevention (CDC) . Measuring healthy days. 2020. Published November 2020. Accessed March 30, 2021. https://www.cdc.gov/hrqol/pdfs/mhd.pdf

13.

Idler

Benyamini

. Self-rated health and mortality: a review of twenty-seven community studies. J Health Soc Behav. 1997;38(1):21-37. https://www.jstor.org/stable/2955359

14.

World Health Organization (WHO) . Constitution. 2021. Accessed August 15, 2021.

15.

Healthy People . 2020. Foundation Health measure report HRQOL and wellbeing. Published November 16, 2021. Accessed March 30, 2021. https://www.healthypeople.gov/sites/default/files/HRQoLWBFullReport.pdf

16.

Murphy

Kochanek

Arias

. Mortality in the United States, 2017. NCHS Data Brief. 2018;328:1-8. Published November 2018. Accessed 2018.https://www.cdc.gov/nchs/data/databriefs/db328-h.pdf

17.

U.S. Army Public Health Center . 2020 health of the Force report. 2021. Updated June 14, 2021. Accessed October 17, 2021. https://phc.amedd.army.mil/topics/campaigns/hof

18.

Hinojosa

. Cardiovascular disease among United States military veterans: evidence of a waning healthy soldier effect using the National Health Interview Survey. Chronic Illn. 2020;16(1):55-68. doi:10.1177/1742395318785237

19.

Sagris

Antonopoulos

Theofilis

, et al. Risk factors profile of young and older patients with myocardial infarction. Cardiovasc Res. 2021;118(10):2281-2292. doi:10.1093/cvr/cvab264

20.

Acosta

Leasure

Both

, et al. Cardiovascular health disparities in racial and other underrepresented groups: initial results from the All of Us research program. J Am Heart Assoc. 2021;10:e021724. doi:10.1161/JAHA.121.021724

21.

Kelli

Mehta

Tahhan

, et al. Low educational attainment is a predictor of adverse outcomes in patients with coronary artery disease. J Am Heart Assoc. 2019;8:e013165. doi:10.1161/JAHA.119.013165

22.

Yusuf

Hawken

Ounpuu

, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case control study. Lancet 2004 Sep 11-17;364:937-952. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(04)17018-9/fulltext

23.

De Hert

Detraux

Vancampfort

. The intriguing relationship between coronary heart disease and mental disorders. Dialogues Clin Neurosci. 2018;20(1):31-40. doi:10.31887/DCNS.2018.20.1/mdehert

24.

DiBiasio

Clark

Rosenthal

. Correlates of frequent mental distress among active and former military personnel. J Mil Veterans. 2014;18:22.

25.

Whooley

Wong

. Depression and cardiovascular disorders. Annu Rev Clin Psychol 2013;9:327-354. doi:10.1146/annurev-clinpsy-050212-185526

26.

Thomas

Kalaria

O’Brien

. (2004). Depression and vascular disease: what is the relationship? J Affect Disord. 79(1-3):81-95. doi:10.1016/S0165-0327(02)00349-x

27.

American Heart Association . How does depression affect the heart? 2021. Updated June 22, 2021. Accessed August 15, 2021. https://www.heart.org/en/healthy-living/healthy-lifestyle/mental-health-and-wellbeing/how-does-depression-affect-the-heart

28.

Chaddha

Robinson

Kline-Rogers

Alexandris-Souphis

Rubenfire

Mental health and cardiovascular disease. Am J Med. 2016;129(11):1145-1148. doi:10.1016/j.amjmed.2016.05.018

29.

Charara

El Bcheraoui

Kravitz

Dhingra

Mokdad

. Mental distress and functional health in the United States. Prev Med. 2016;89:292-300. doi:10.1016/j.ypmed.2016.06.011

30.

Lim

Davis

Siriwardhana

Aggarwal

Hixon

Chen

. Racial/ethnic differences in health-related quality of life among Hawaii adult population. Health Qual Life Outcomes. 2020;18:380. doi:10.1186/s12955-020-01625-4

31.

Adakai

Sandoval-Rosario

, et al. Health disparities among American Indians/Alaska Natives—Arizona, 2017. MMWR. 2018;67(47):1314-1318.

32.

Cree

Okoro

Zack

Carbone

. Frequent mental distress among adults, by disability status, disability type, and selected characteristics — United States, 2018. MMWR. 2020;69(36):1238-1243.

33.

Ford

Mokdad

, et al. Gender differences in coronary heart disease and health-related quality of life: findings from 10 states from the 2004 Behavioral Risk Factor Surveillance System. J Womens Health. 2008;17(5):757-768. doi:10.1089/jwh.2007.0468

34.

Blosnich

Gordon

Bossarte

. Suicidal ideation and mental distress among adults with military service history: results from 5 US states, 2010. Am J Publ Health. 2017;104(Suppl 4):S595-S602. doi:10.2105/AJPH.2014.302064

35.

Luncheon

Zack

. Health-related quality of life among US veterans and civilians by race and ethnicity. Prev Chronic Dis. 2012;9:E108. doi:10.5888/pcd9.110138

36.

Center for Behavioral Health Statistics and Quality (CBHSQ) . Results from the 2017 national survey on drug use and health: detailed tables; table 8.33B-received mental health services in past year among persons aged 18 or older, by past year level of mental illness and demographic characteristics: percentages, 2016 and 2017. 2018. Accessed August 15, 2021. https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHDetailedTabs2017/NSDUHDetailedTabs2017.htm#tab8-33A

37.

Hom

Stanley

Schneider

Joiner

. A systematic review of help-seeking and mental health service utilization among military service members. Clin Psychol Rev. 2017;53:59-78. doi: 10.1016/j.cpr.2017.01.008

38.

Centers for Disease Control and Prevention (CDC) . The BRFSS data user guide. 2013. Published August 15, 2013. Accessed March 30, 2021. https://www.cdc.gov/brfss/data_documentation/pdf/userguidejune2013.pdf

39.

Pierannunzi

Balluz

. A systematic review of publications assessing reliability and validity of the Behavioral Risk Factor Surveillance System (BRFSS), 2004-2011. BMC Med Res Methodol. 2013;13(49):49. http://www.biomedcentral.com/1471-2288/13/49

40.

American Psychiatric Association . Diagnostic and statistical manual of mental disorders. 5th ed. American Psychiatric Association; 2013. doi:10.1176/appi.books.9780890425596

41.

Cook

Owen

Bender

, et al. Self-reported frequent mental distress among adults—United States, 1993-1996. MMWR (Morb Mortal Wkly Rep). 1998;47(16):325-331.

42.

Moriarty

Zack

Kobau

. The centers for disease control and prevention's healthy days measures-population tracking of perceived physical and mental health over time. Health Qual Life Outcomes. 2003;1:37. https://hqlo.biomedcentral.com/articles/10.1186/1477-7525-1-37

43.

Andresen

Catlin

Wyrwich

Jackson-Thompson

. Retest reliability of surveillance questions on health related quality of life. J Epidemiol Community Health. 2003;57(5):339-343. doi:10.1136/jech.57.5.339

44.

Newschaffer

Jackson-Thompson

Counte

. Validation of behavioral risk factor surveillance System (BRFSS) HRQOL measures in a statewide sample. 1998. Published may 1998. Accessed October 23, 2021. https://www.cdc.gov/hrqol/pdfs/validationreport.pdf

45.

Suttorp

Siegerink

Jager

Zoccali

Dekker

. Graphical presentation of confounding in directed acyclic graphs. Nephrol Dial Transplant. 2015;30(9):1418-1423. doi:10.1093/ndt/gfu325

46.

Wong

Kwok

Narain

, et al. Marital status and risk of cardiovascular diseases: a systematic review and meta-analysis. Heart. 2018;104(23):1937-1948. doi:10.1136/heartjnl-2018-313005

47.

Ojike

Sowers

Seixas

, et al. Psychological distress and hypertension: results from the national health interview survey for 2004-2013. Cardiorenal Med. 2016;6:198-208. doi:10.1159/000443933

48.

Pan

Cai

Cheng

Dong

Yan

. Association between anxiety and hypertension: a systematic review and meta-analysis of epidemiological studies. Neuropsychiatric Dis Treat. 2015;11(11):1121-1130. doi:10.2147/NDT.S77710

49.

Song

Wang

Chen

Yang

Zhang

Wang

. The association between health insurance and all-cause, cardiovascular disease, cancer and cause-specific mortality: a prospective cohort study. Int J Environ Res Publ Health. 2020;17(5):1525. doi:10.3390/ijerph17051525

50.

Blehar

Spong

Grady

Goldkind

Sahin

Clayton

. Enrolling pregnant women: issues in clinical research. WHI. 2013;23(1):e39-e45. doi:10.1016/j.whi.2012.10.003

51.

Nuciari

. Models and explanations for military organization: An updated reconsideration. In: Caforio

eds Handbook of the Sociology of the Military. Handbooks of Sociology and Social Research. Springer; 2007:chap 4. Accessed February 12, 2022. doi:

10.1007/0-387-34576-0_4

52.

Straub

Loch

Evaristo

Karahanna

Srite

. Toward a theory-based measurement of culture. J Glob Inf Manag. 2002;10(1):13-23. doi:10.4018/jgim.2002010102

53.

Riccio

Sullivan

Klein

, et al. Warrior Ethos: analysis of the concept and initial development of applications. 2004. Published September 2004. Accessed February 12, 2022. https://apps.dtic.mil/sti/citations/ADA428065

54.

Redmond

Wilcox

Campbell

, et al. A brief introduction to the military workplace culture. Work. 2015;50(1):9-20. 10.3233/WOR-141987.

55.

Jensen

Jones

Rabe

, et al. 2020 U.S. population more racially and ethnically diverse than measure in 2010. 2021. Published August 12, 2021. Accessed March 22, 2022. https://www.census.gov/library/stories/2021/08/2020-united-states-population-more-racially-ethnically-diverse-than-2010.html

56.

Kottak

. Ethnicity and Race. Cultural Anthropology: Appreciating cultural diversity. 15th ed. McGraw-Hill; 2013:chap 6.

57.

Kohls

. The values Americans live by. Meridian House International; 1984.

58.

Lin

Mutz

Clough

Papageorgiou

. Mental toughness and individual differences in learning, educational and work performance, psychological well-being, and personality: a systematic review. Front Psychol. 2017;8:1345. doi:10.3389/fpsyg.2017.01345

59.

Pyszczynski

Greenberg

Solomon

. A dual-process model of defense against conscious and unconscious death-related thoughts: an extension of terror management theory. Psychol Rev. 1999;106(4):835-845. doi:10.1037/0033-295X.106.4.835

60.

Bulut

. Examining belief in a just world, religious worldviews, and self-esteem within the framework of terror management theory: mortality salient and nonmortality salient organizations. Omega. 2021;83(1):121-141. 10.1177/0030222820928148

61.

Lerner

Miller

. Just world research and the attribution process: looking back and ahead. Psychol Bull. 1978;85(5):1030-1051. doi:10.1037/0033-2909.85.5.1030

62.

Shrestha

Vie

, et al. Comparison of cardiovascular health between US army and civilians. J Am Heart Assoc. 2019;8:e009056. doi:10.1161/JAHA.118.009056

63.

Hoerster

Lehavot

Simpson

, et al. Health and health behavior differences: U.S. military, veteran, and civilian men. Am J Prev Med. 2012;43(5):483-489.

64.

Chen

Roberts

. Negative life events and suicide in the national violent death reporting System. Arch Suicide Res. 2021;25(2):238-252. doi:10.1080/13811118.2019.1677275

65.

Onyeka

Maguire

Ross

O’Reilly

. Does physical ill-health increase the risk of suicide? A census-based follow-up study of over 1 million people. Epidemiol Psychiatr Sci. 2020;29:e140. doi:10.1017/S2045796020000529

66.

Erlangsen

Stenager

Conwell

. Physical diseases as predictors of suicide in older adults: a nationwide, register-based cohort study. Soc Psychiatry Psychiatr Epidemiol. 2015;50:1427-1439. doi:10.1007/s00127-015-1051-0

67.

Larsen

Agerbo

Christensen

Søndergaard

Vestergaard

. Myocardial infarction and risk of suicide: a population-based case-control study. Circulation. 2010;122:2388-2393. doi:10.1161/CIRCULATIONAHA.110.956136

68.

Batty

Kivimaki

Bell

, et al. Psychosocial characteristics as potential predictors of suicide in adults: an overview of the evidence with new results from prospective cohort studies. Transl Psychiatry. 2018;8:22. doi:10.1038/s41398-017-0072-8

69.

Liu

Miller

. Life events and suicidal ideation and behavior: a systematic review. Clin Psychol Rev. 2014;34:181-192. doi:10.1016/j.cpr.2014.01.006

70.

Stone

Simon

Fowler

, et al. Vital Signs: trends in state suicide rates — United States, 1999-2016 and circumstances contributing to suicide — 27 states. MMWR (Morb Mortal Wkly Rep). 2018;67:617-624. doi:10.15585/mmwr.mm6722a1