Self-perceptions of mental health among U.S. Veterans and non-Veterans: Behavioral Risk Factor Surveillance System, 2022

Abstract

Introduction:

Veteran and other service member sub-populations have an increased likelihood of mental illness. Diagnosed mental health conditions among Veteran populations have been well described. However, direct self-perceptions of mental health have been under-researched. This study examined the association between Veteran status and self-perceived mental health in U.S. adults to provide better monitoring of frequent mental distress among Veteran populations. Methods: The 2022 Behavioral Risk Factor Surveillance System (BRFSS) cross-sectional telephone survey data were used in this analysis conducted in 2023. Weighted multinomial logistic regression was used to determine the association between Veteran status and reporting no (0), some (1–13), or frequent (≥14) days of mental distress, adjusted by key demographic, socio-economic, and health-related factors.

Results:

A total of 430,653 individuals were included in these analyses. In multivariate analyses, Veteran status was associated with decreased odds of reporting 1–13 poor mental health days (adjusted odds ratio [aOR] = 0.81, 95% CI, 0.77–0.86) compared to non-Veterans but greater odds of reporting ≥14 poor mental health days (aOR = 1.23, 95% CI, 1.15–1.33). Sex, age, marital status, race/ethnicity, education, annual income, employment status, health insurance, rurality, BMI, general health status, comorbid conditions, and poor physical health days were also significantly associated with poor mental health days in the final multivariate model..

Discussion:

Veterans are more likely to suffer from frequent mental distress than non-Veterans when considering demographic, socio-economic, and health-related characteristics. Validated methods for assessing perceived mental health should be standardized for comparable monitoring of mental distress and expansion of Veteran-centred care.

Lay Summary

Many studies investigate the various health conditions that impact Veteran populations and describe patterns of diagnosed mental health conditions such as posttraumatic stress disorder, anxiety, and depression. These high rates of mental health diagnoses may be connected to traumatic experiences, military culture, and other challenges when facing reintegration into civilian life. Even though these mental health diagnoses are known to exist among Veteran communities, not very much is known about how Veterans see their health. Do Veterans view their mental distress as being poor? If so, do they report more days of poor mental health than non-Veterans? In this study, the authors found that Veterans perceive their mental health as poor and are more likely to report a greater number of poor mental health days than non-Veterans. Knowledge of a Veteran’s perceived mental health provides insight into population-level mental distress among Veterans rather than solely diagnosed mental health conditions. Veteran populations will remain, and researchers must continue to improve the understanding of the complexities surrounding health to expand Veteran-centred care.

Introduction

Even though the number of Veterans has decreased in the United States, Veterans still accounted for about 6.2% of the U.S. population in 2022.¹ Veterans’ mental health is shaped by military culture, traumatic experiences, and challenges associated with reintegration into civilian life.^2–7 Mental health is often stigmatized in military culture.^4,8 Those in military service tend to have the mindset to tough it out when experiencing physical or mental illness rather than seeking help.^4,8,9 Therefore, high rates of mental health problems are seen across multiple service member sub-populations.

In the United States, health-related quality of life (HRQoL) studies have been conducted and typically involve the Department of Veterans Affairs (VA) and use of the Veterans Health Study. They focus on assessing a Veteran’s mental, physical, and overall health by scoring various health- and lifestyle-related questions to result in an overall quality of life rating.^10,11 The relationship between Veteran status and mental, physical, and general health is complex, with some evidence indicating an association^12,13 and other evidence suggesting a null relationship.^12,14,15 The relationship between Veterans and mental and physical health may also be dependent on a variety of factors contributing to HRQoL scoring outcomes.¹⁶ These studies, however, fail to consider how Veterans may perceive their health, and some lack comparisons with non-Veteran populations.

Veteran populations are known to have high rates of mental health issues including depression, anxiety, and posttraumatic stress disorder (PTSD).^{7,9–11,15,17–32} Well-explored areas include diagnosed mental health conditions between different military branches,⁹ service time periods,^{3,5,9,17,32,33} deployment status,^3,34 and military service type.¹⁴ Many analyses also quantify mental health diagnoses while focusing on topics such as access to care,¹³ combat exposure,^6,34 and the impact/prevalence of comorbidities.^{12,14,15,17–19,21–23,25,27,35} However, definitions for mental health conditions among Veteran populations are not consistent. Depression, anxiety, and PTSD may be described via formal diagnoses through medical record abstraction, whereas mental distress may be defined by one or more mental health diagnoses or determined by scored health-related questionnaires. Additionally, these studies lack a focus on self-perceived mental health (SPMH) and reflection of a larger Veteran population with mixed experiences.

Few studies incorporate SPMH when investigating Veteran-specific risk factors.^{7,12,13,15,21,24} However, they fail to focus on SPMH and associations between SPMH and other influential socio-demographic and health-related characteristics. Additionally, Veterans reporting frequent mental distress (FMD) have varied over the years: with odds from 2013 to 2016 being relatively low but greater from 2017 to 2018 than non-Veterans.¹⁵ More recent analyses are needed to identify possible trends.

Direct self-perceptions of mental health have been under-researched compared to investigations of mental illness diagnoses in Veteran populations. Furthermore, how the SPMH of Veterans compares with non-Veteran populations is not well known. Awareness of how Veterans perceive their mental health could play an essential role in better understanding population-level mental distress versus diagnosed mental health conditions. Increased knowledge about Veterans’ self-perceived health would impact Veteran-centred care, allowing for improved communication between health care providers and patients. Therefore, the authors aim to establish a clearer link between Veteran status and SPMH in the United States.

Methods

Study sample

Data from the 2022 Behavioral Risk Factor Surveillance System (BRFSS) were used in these analyses conducted in 2023.³⁶ BRFSS data are collected continuously throughout the year.³⁶ BRFSS is a national cross-sectional health-related survey used to collect information about lifestyle factors that may impact health and other health conditions. Random digit dialing techniques are used to conduct surveys by telephone, and data are collected continuously from all 50 states, three U.S. territories, and the District of Columbia. BRFSS uses survey weights to ensure these data are most representative of the U.S. population and minimize selection bias. BRFSS data is publicly available from the U.S. Centers for Disease Control and Prevention (CDC).³⁶

A total of 445,132 individuals either completed or partially completed interviews. Survey participants included non-institutionalized U.S. adults over the age of 18 years. Those who had incomplete or missing information on Veteran status (5,649) or number of poor mental health days (9,067) were excluded, resulting in a total analytic sample size of 430,653. All measurements were self-reported by respondents.

Measures

The primary outcome variable was self-reported poor mental health days. Respondents were asked to report how many days their mental health was not good within the last 30 days. Continuous responses were classified into three levels: no (0), some (1–13), and frequent (≥14) days of mental distress. Previous literature has described FMD and diagnostic markers for depression by reporting a minimum of 14 out of 30 affected days.^37,38

The primary exposure of interest was Veteran status. Veterans were classified as individuals who reported having served as active duty with the U.S. Armed Forces in a regular military, National Guard, or military reserve unit. Responses were dichotomized as “Yes, Veteran” and “No, non-Veteran.”

Socio-demographic covariates included sex, age, race/ethnicity, marital status, education status, annual income, health insurance status, employment status, and rurality. If birth sex was unavailable, self-reported male or female responses were used. Missing race/ethnicity data were imputed as the most common race/ethnicity reported for the matching state region. Health-related covariates included depressive disorder, BMI, poor physical health days, general health status, and comorbid conditions.

An additional category of “Missing/Not Sure/Don’t Know/Refused” was included for annual income (22.1%) and BMI (11.5%). Missing values were negligible (<5%) for the remaining demographic variables.

Statistical analysis

To account for the complex survey design of the BRFSS, all analyses were weighted using survey procedures in SAS Studio 3.82.

Univariate analyses were performed to examine the distribution of the outcome, exposure, and covariate. Chi-square tests and bivariate multinomial regression models were used to examine crude associations between covariates and both the exposure and the outcome.

Multinomial logistic regression was used to examine the relationship between Veteran status and FMD, accounting for potential confounders and effect modifiers. Crude and adjusted odds ratios (OR and aOR) and 95% CIs for mental health status were obtained and reported. All variables listed in the previous section were tested as potential confounders and were selected based on a comprehensive search of the literature.^{12,13,15,17,21,27,39} Age and sex were evaluated as effect modification. Backward elimination with a threshold of p = 0.05 was used for model selection. Age and sex analyses were not conducted, as neither age nor sex proved to be effect modifiers. All variables were significant in the model. However, depressive disorder was removed due to multicollinearity. McFadden’s Pseudo-R-Squared was used to assess model fit (R² = 0.21).

Results

Socio-demographic and health characteristics of the study population are presented by weighted frequencies in Table 1 (n = 439,438). About 10% of the total population were Veterans. Most individuals reported no days of poor mental health (57.4%) followed by 26.8% reporting 1–13, and 15.8% reporting 14–30 days of poor mental health, respectively. Roughly half of the total population was female (51.4%) and were married (50.2%). Just under half were between the ages of 18–44 years. Most of the population identified as white, non-Hispanic (58.6%). Most individuals attended or graduated college (60.6%), and the greatest proportion reported an annual household income of more than $75,000 (32.4%). Most individuals reported having some form of health insurance (87.0%) and residing in urban counties (93.7%). Most of the population reported being employed (57.9%), while 15% reported not being employed; 20.7% were retired, and 6.3% reported being unable to work. Most individuals were overweight or obese (34.1% and 33.4%, respectively). A substantial proportion of the population reported having a depressive disorder (20.7%) and 1–13 or 14–30 days of poor physical health (25.2% and 12.7%, respectively). Most individuals reported excellent/very good/good general health status (82.2%). More than half of the population reported having no chronic conditions (51.6%), while 27.4% of the population reported one chronic condition, 12.5% reported two conditions, and 8.5% reported three or more conditions.

Table 1.

Distribution of socio-demographic and health-related characteristics among U.S. Veteran and non-Veteran adults, 2022 BRFSS

Characteristic	No. (%)* of respondents; n = 430,653
Veteran status
Yes	52,051 (10.0)
No	378,602 (90.0)
Poor mental health days
0	261,687 (57.4)
1–13	109,480 (26.8)
14–30	59,486 (15.8)
Sex
Male	202,104 (48.6)
Female	228,549 (51.4)
Age group, y
18–44	129,755 (45.5)
45–64	144,694 (31.5)
≥65	156,204 (22.9)
Race/ethnicity
White only, non-Hispanic	311,950 (58.8)
Black only, non-Hispanic	33,970 (11.7)
Hispanic	40,967 (18.4)
Other race or multiracial, non-Hispanic	31,168 (11.0)
Marital status
Married	221,533 (50.2)
Divorced/separated	63,924 (12.6)
Widowed	46,064 (7.0)
Never married	95,248 (30.1)
Education
Did not graduate high school	24,319 (11.3)
High school graduate	104,834 (27.5)
Some college	116,616 (30.4)
College graduate	183,143 (30.9)
Annual income
<$25,000	54,363 (13.2)
$25,000 to <$50,000	87,329 (19.7)
$50,000 to <$75,000	58,317 (12.2)
$75,000+	143,273 (32.8)
Missing/not sure/don’t know/refused	87,371 (22.1)
Health insurance
Yes	39,2479 (91.7)
No	21,876 (8.3)
Employment status
Employed	220,935 (57.9)
Not employed	44,178 (15.0)
Retired	133,870 (20.7)
Unable to work	25,587 (6.3)
Rurality
Urban	368,087 (93.7)
Rural	53,353 (6.3)
Depressive disorder
Yes	88,355 (20.7)
No	340,066 (79.3)
BMI
Normal weight	114,512 (26.9)
Underweight	6,560 (1.8)
Overweight	2,137,291 (30.2)
Obese	13,0011 (29.5)
Missing/not sure/don’t know/refused	42,279 (11.5)
Poor physical health days
0	261,027 (62.3)
1–13	105,551 (25.1)
14–30	56,058 (12.6)
General health status
Excellent/very good/good	353,309 (82.2)
Fair/Poor	76,407 (17.8)
Comorbid conditions
No reported conditions	189,214 (51.6)
1 condition	128,451 (27.4)
2 conditions	65,765 (12.5)
≥3 conditions	47,223 (8.5)

^*All percentages are weighted.

Table 2 shows the crude and adjusted odds ratios of the final model. In the multivariate model, Veterans were less likely to report 1–13 poor mental health days (aOR = 0.82, 95% CI, 0.77–0.86) but more likely to report >14 poor mental health days (aOR = 1.23, 95% CI, 1.14–1.33) than non-Veterans.

Table 2.

. Crude odds ratios (ORs) and adjusted ORs (aORs) for self-reported poor mental health days, 2022 Behavioral Risk Factor Surveillance System (n = 430,653).

Characteristic	Crude OR (95% CI)		aOR* (95% CI)
	1–13 days	≥14 days	1–13 days	≥14 days
Veteran status
No	REFERENCE	REFERENCE	REFERENCE	REFERENCE
Yes	0.58 (0.55–0.61)†	0.77 (0.72–0.82)†	0.82 (0.77–0.87)†	1.23 (1.14–1.33)†
Sex
Male	REFERENCE	REFERENCE	REFERENCE	REFERENCE
Female	1.53 (1.49–1.58)†	1.72 (1.66–1.79)†	1.59 (1.53–1.65)†	1.87 (1.78–1.96)†
Age
65+	REFERENCE	REFERENCE	REFERENCE	REFERENCE
45–64	1.65 (1.69–1.88)	1.65 (1.59–1.72)	1.62 (1.53–1.72)	2.04 (1.88–2.21)†
18–44	3.25 (3.13–3.37)†	3.45 (3.28–3.63)	3.38 (3.17–3.60)†	5.83 (5.33–6.39)†
Marital status
Married	REFERENCE	REFERENCE	REFERENCE	REFERENCE
Never married	2.05 (1.98–2.12)†	3.05 (2.92–3.19)†	1.65 (1.58–1.73)†	2.11 (2.00–2.24)†
Widowed	1.21 (1.16–1.26)†	2.31 (2.19–2.43)†	1.17 (1.09–1.26)†	1.42 (1.30–1.55)†
Divorced/separated	0.74 (0.70–0.79)†	1.24 (1.15–1.32)†	1.35 (1.28–1.42)†	1.81 (1.70–1.93)†
Race/ethnicity
White only, non-Hispanic	REFERENCE	REFERENCE	REFERENCE	REFERENCE
Black only, non-Hispanic	0.96 (0.92–1.01)	1.09 (1.02–1.15)†	0.81 (0.77–0.86)†	0.72 (0.67–0.77)†
Hispanic	0.84 (0.80–0.88)†	0.96 (0.90–1.01)	0.74 (0.69–0.78)†	0.59 (0.55–0.64)†
Other race or multiracial, non-Hispanic	1.06 (1.00–1.12)	1.06 (0.99–1.14)	0.79 (0.74–0.84)†	0.59 (0.55–0.64)†
Education
Did not graduate high school	REFERENCE	REFERENCE	REFERENCE	REFERENCE
High school graduate	1.28 (1.19–1.38)†	0.92 (0.86–0.99)†	1.22 (1.12–1.33)†	1.18 (1.07–1.29)†
Some college	1.62 (1.51–1.74)†	0.97 (0.90–1.04)	1.58 (1.45–1.73)†	1.34 (1.22–1.47)†
College graduate	1.72 (1.60–1.84)†	0.58 (0.54–0.62)†	1.80 (1.65–1.97)†	1.15 (1.04–1.27)†
Annual income
<$25,000	REFERENCE	REFERENCE	REFERENCE	REFERENCE
$25,000 to <$50,000	1.04 (0.98–1.10)	0.69 (0.65–0.73)†	1.09 (1.02–1.17)†	1.07 (0.99–1.15)
$50,000 to <$75,000	1.08 (1.02–1.15)†	0.56 (0.52–0.59)†	1.10 (1.02–1.19)†	1.01 (0.93–1.10)
$75,000+	1.12 (1.06–1.18)†	0.37 (0.35–0.39)†	1.12 (1.04–1.20)†	0.81 (0.75–0.88)†
Missing/not sure/don’t know/refused	0.83 (0.78–0.87)†	0.50 (0.47–0.53)†	0.91 (0.85–0.98)†	0.84 (0.77–0.91)†
Health insurance
Yes	REFERENCE	REFERENCE	REFERENCE	REFERENCE
No	0.85 (0.79–0.90)†	1.30 (1.21–1.38)†	0.80 (0.75–0.87)†	1.02 (0.94–1.11)
Employment status
Employed	REFERENCE	REFERENCE	REFERENCE	REFERENCE
Not employed	1.29 (1.23–1.35)†	1.93 (1.82–2.03)†	1.15 (1.09–1.22)†	1.33 (1.25–1.42)†
Retired	0.44 (0.43–0.46)†	0.49 (0.47–0.52)†	0.79 (0.75–0.84)†	0.70 (0.65–0.76)†
Unable to work	1.15 (1.07–1.24)†	3.97 (3.73–4.23)†	1.12 (1.03–1.23)†	1.48 (1.35–1.62)†
Rurality
Urban	REFERENCE	REFERENCE	REFERENCE	REFERENCE
Rural	0.77 (0.73–0.81)†	0.93 (0.88–0.99)†	0.83 (0.78–0.88)†	0.81 (0.75–0.87)†
BMI
Normal weight	REFERENCE	REFERENCE	REFERENCE	REFERENCE
Underweight	1.12 (0.99–1.26)	2.00 (1.74–2.29)†	0.95 (0.82–1.09)	1.25 (1.06–1.47)†
Overweight	0.79 (0.76–0.82)†	0.77 (0.73–0.80)†	0.93 (0.89–0.97)†	0.90 (0.85–0.95)†
Obese	0.92 (0.89–0.96)†	1.20 (1.15–1.26)†	0.95 (0.91–1.00)	1.00 (0.95–1.06)
Missing/not sure/don’t know/refused	0.68 (0.65–0.72)†	0.73 (0.68–0.78)†	0.74 (0.69–0.80)†	0.66 (0.61–0.73)†
Poor physical health days
0	REFERENCE	REFERENCE	REFERENCE	REFERENCE
1–13	3.07 (2.97–3.18)†	3.69 (3.53–3.86)†	2.76 (2.66–2.87)†	2.99 (2.84–3.14)†
14–30	1.95 (1.85–2.05)†	8.69 (8.27–9.14)†	2.14 (2.00–2.28)†	5.73 (5.33–6.16)†
General health status
Excellent/very good/good	REFERENCE	REFERENCE	REFERENCE	REFERENCE
Fair/poor	1.31 (1.26–1.37)† †	4.27 (4.09–4.45)†	1.30 (1.23–1.38)†	2.28 (2.14–2.43)†
Comorbid conditions
No reported conditions	REFERENCE	REFERENCE	REFERENCE	REFERENCE
1 condition	0.96 (0.93–1.00)	1.31 (1.25–1.37)†	1.20 (1.16–1.25)†	1.49 (1.41–1.57)†
2 conditions	0.86 (0.83–0.90)†	1.57 (1.49–1.66)†	1.28 (1.21–1.36)†	1.70 (1.56–1.84)†
≥3 conditions	0.99 (0.94–1.05)	2.53 (2.39–2.68)†	1.52 (1.42–1.64)†	2.09 (1.91–2.29)†

^*Adjusted model excludes depressive disorder.

^†Statistical significance at p < 0.05).

Females were more likely to report poor mental health days than males (1–13 days: aOR = 1.59, 95% CI, 1.53–1.65 and ≥14 days: aOR = 1.87, 95% CI, 1.78–1.96). Younger age appeared to be highly associated with poor mental health days. Those aged 18–44 years and 45–64 years had, respectively, 3.38 (95% CI, 3.17–3.60) and 1.62 (95% CI, 1.53–1.72) times the odds of reporting 1–13 poor mental health days and 5.83 (95% CI, 5.33–6.39) and 2.04 (95% CI, 1.88–2.21) times the odds of reporting ≥14 poor mental health days than those aged 65 years and older.

Those who were never married had the greatest odds of reporting poor mental health days, even more so than widowed and divorced individuals (1–13 days: aOR = 1.65, 95% CI, 1.58–1.73 and ≥14 days: aOR = 2.11, 95% CI, 2.00–2.24).

All race categories had lower odds of reporting poor mental health days than those who identified as white, non-Hispanic only. An education level greater than or equal to high school graduation was associated with higher odds of reporting poor mental health days.

All of those who reported graduating high school or higher had increased odds of reporting 1–13 poor mental health days. When compared with those who did not graduate high school, those who did graduate were 18% more likely to report ≥14 poor mental health days, while those with some college experience were 34% more likely to report ≥14 days of poor mental health, and college graduates were 15% more likely to report ≥14 days of poor mental health.

Income appears to have a greater impact on reporting 1–13 days than ≥14 days of poor mental health. An annual income greater than $25,000 was associated with greater odds of reporting 1–13 days of poor mental health, whereas those with missing annual income had lower odds. An annual household income greater than $75,000 or missing had lower odds of reporting ≥14 poor mental health days.

Those living in rural counties had lower odds of reporting either 1–13 or ≥14 days of poor mental health (1–13 days: aOR = 0.83, 95% CI, 0.78–0.88 and ≥14 days: aOR = 0.81, 95% CI, 0.75–0.87).

Respondents without health insurance were less likely to report 1–13 days of poor mental health (aOR = 0.80, 95% CI, 0.75–0.87). Those who were not employed or unable to work had 1.15 (95% CI, 1.09–1.22) and 1.12 (95% CI, 1.03–1.23) times the odds of reporting 1–13 and ≥14 poor mental health days than those who were employed. Retired people were less likely to report poor mental health days (1–13 days: aOR = 0.79, 95% CI, 0.75, 0.84 and ≥14 days: aOR = 0.70, 95% CI, 0.65–0.76).

Being underweight was associated with increased odds of reporting ≥14 poor mental health days. Poor physical health days appeared to have a significant association with poor mental health days across both categories of 1–13 and 14–30 days. Those who reported 14–30 days of poor physical health had 5.73 times the odds of reporting ≥14 days of poor mental health. Fair/poor general health status was associated with an increased odds of reporting poor mental health days (1–13 days: aOR = 1.30, 95% CI, 1.23–1.38 and ≥14 days: aOR = 2.28, 95% CI, 2.14–2.43). As the number of comorbid conditions increased, so did the odds of reporting poor mental health days across both levels of poor mental health.

Discussion

When controlling for influential socio-demographic and health-related characteristics, Veterans have more FMD than non-Veterans. Veterans reporting more FMD is in agreement with known high rates of Veterans having mental health diagnoses,^17,20 so the expectation to see Veterans having more FMD accords with diagnostic criteria for depression and anxiety diagnoses of ≥14 poor mental health days within the last 30 days.^37,40 Additionally, this observation might be due to Veterans in this study sample having lesser odds of higher education and annual income, as well as increased odds of being not employed, as these have been seen to be associated with poorer reported health.^21,41 Although not investigated in this study, it is possible that treatment might impact these results as well. Military personnel have been known to have less support and seek less treatment because of barriers and stigma regarding mental health during and after service, which could result in worse mental health.^8,20,42,43

The results align with those of previous studies showing a relationship between sex and poor mental health. Females had more FMD in both crude and adjusted models, which might be related to female Veterans having a higher prevalence than males of depressive disorders and other measures of poor mental health.^17,22,23,29 It is also possible that females are less impacted by social and self-stigmas surrounding mental health and are more likely to seek help than males, which may create more ownership of their own mental health status.^29,44 Increased awareness of SPMH may then result in being more willing to share mental health information.

Younger age groups appeared to have greater mental distress than older groups, regardless of the impacts of socio-demographic and health-related characteristics. Previous findings consistently indicate that younger individuals have poorer mental health than older ones.^13,26 Younger populations have also been documented to have worse HRQoL, which could be explained by differences in mental health between older and younger VA populations.¹⁰ This variation between age groups may be due to different war events for different Veteran cohorts and the overall increasing rates of mental health diagnoses among younger populations because of cultural changes, including increases in electronic communications.⁴⁵ It is also possible that the length of time out of military service could lead to more engagement in seeking help for conditions and lower reported mental distress.

Other socio-demographic and health-related characteristics also appeared to impact perceived mental health of Veterans and non-Veterans. The authors’ findings suggest lower odds of some or FMD for those who did not identify as white, non-Hispanic, without health insurance, living in rural areas, and retired. Differences in health outcomes have been identified previously and support the current findings of lesser mental distress among those who do not identify as white, non-Hispanic.¹⁵ Conversely, higher rates of mental health issues are typically seen among those without health insurance and living in rural areas because of limited access to care among civilian populations.¹³ However, consistent with the authors’ findings, Veterans have been observed to have poorer health with better access to care.¹² These differences may arise due to different care techniques and options through VA health care and non-VA health care.

Those who were unable to work or were not employed had greater odds of some or FMD, which is consistent with previous research.⁴¹ The odds of some or FMD is greater for those who are not married. Although this study did not investigate social support systems, this observation could be due to an overall limited social support system, which has been seen to be influential in the mental health status of Veterans.²³

Education and annual income appeared to have varying impacts on mental distress. Higher education level was associated with greater odds of some mental distress but lower odds of FMD. Similarly, increased annual income resulted in lower odds of FMD. These findings are consistent with those of previous studies that demonstrate an association between social determinants of health and risk of poor mental health.^21,46

BMI appeared to impact FMD regardless of other characteristics. Those who were overweight were less likely to have FMD, whereas being obese or underweight was associated with FMD. BMI has been seen to predict lower quality of life and increased depression, which may be why the current results suggest that being underweight or obese is associated with greater odds of FMD. A U-shaped relationship between BMI and weight could explain why overweight was slightly protective of some and FMD, regardless of other factors.⁴⁷

Physical health also appeared to be highly associated with some and FMD. Living with disabilities is challenging, limits some aspects of healthy habits, and can take a great toll on mental health. The interconnectedness of physical health and mental health has been observed frequently, suggesting the importance of tackling physical health to improve mental health.^19,25 Those reporting fair/poor general health status also had a greater number of poor mental health days. Lastly, comorbid conditions are known to be associated with mental health, and this study corroborates these observations.³⁹

Limitations

Factors regarding military service such as deployment status and branch of service were not collected and therefore could not be accounted for in this analysis, which limits the ability to make inferences and impacts generalizability. Socio-demographic and health-related characteristics pertain to the overall population and may not include all Veteran specific risk factors. Because of the nature of the survey, all information, including Veteran status, was self-reported and could not be verified, which could lead to misclassification. The cross-sectional data does not allow for assessing causality. The number of poor mental health days reported by an individual is subjective, and the definition might vary from person to person, resulting in some form of recall bias and self-report bias. Veterans might also under-report poor mental health days due to the stigmatization of mental health in military culture.⁴ Treatment or medication for mental health diagnoses was not described, which could impact the results depending on whether Veterans were or were not undergoing treatment.

Conclusion

Veteran populations are more likely to self-perceive FMD than non-Veterans when socio-demographic and health-related characteristics are considered. The impact that socio-demographic and health-related characteristics have on mental health warrants further investigation in the context of SPMH. Knowledge of Veterans’ perceived mental health provides insight into population-level mental distress rather than solely diagnosed mental health conditions. Veterans’ perceptions of mental health could be influential in conversations related to treatment for diagnosed mental health conditions. Additionally, perceptions of one’s mental health could indicate a risk of developing a diagnosed mental health condition. Validated methods for assessing SPMH should be standardized for comparable monitoring of mental distress and the impact of external influences. Interventions and increased support to minimize risk factors for FMD should also be implemented. Further investigations into specific military experiences not limited to deployment/combat status and social stigma are also important to continue improving methods to address all factors related to mental distress in Veterans.

Biographies

Nicole Frederickson, MPH, is a graduate from the University of Nebraska Medical Center College of Public Health in Omaha, Nebraska. Her research interests include mental and physical health disparities, vaccine-preventable diseases, and cancer epidemiology.

Anthony Blake, MPH, is a director of professional programs at the University of Nebraska Medical Center College of Public Health in Omaha, Nebraska. His interests include environmental and occupational health.

Nicole Kolm-Valdivia, PhD, CHES, MPH, is an assistant professor in the Department of Epidemiology and Assistant Dean at the University of Nebraska Medical Center College of Public Health in Omaha, Nebraska. Her research interests include the intersection of infectious disease and substance use, harm reduction, teenage pregnancy and parenting, health equity, and program evaluation.

Patrick Maloney, PhD, MPH, is an assistant professor in the Department of Epidemiology at the University of Nebraska Medical Center College of Public Health in Omaha, Nebraska, and holds a joint appointment as a senior epidemiologist with the Nebraska Department of Health and Human Services. He has a strong background in applied epidemiology and is interested in global health, humanitarian health, infectious disease epidemiology, vaccine-preventable diseases, and health disparities.

Funding Statement

No funding was received for this article.