Abstract
Background
Health disparities in cardiovascular disease (CVD) risk factors persist among racially, ethnically, and sex diverse civilian populations. Little is known about whether these disparities persist in US military populations. The aim of this study was to examine and describe differences in CVD risk factors by sex, race, and ethnicity among US service members and veterans of Operation Enduring Freedom/Operation Iraqi Freedom conflicts.
Method
Sex, racial, and ethnic differences in CVD risk factors (self-reported diagnoses of hypertension, high cholesterol, and diabetes, obese body mass index, and current smoking) were examined in 2014-2016 among 103,245 service members and veterans (age [years], M=40, SD=11; 70.3% men; 75.7% non-Hispanic White adults) enrolled in the Millennium Cohort Study. A series of ordinal regressions were conducted sequentially adjusting for sociodemographic, military, behavioral, and psychosocial factors.
Results
Overall, 59% of participants reported at least 1 CVD risk factor. Men had greater odds of screening positive for CVD risk factors than did women. Non-Hispanic Asian or Pacific Islander adults had lower odds of screening positive for CVD risk factors than did non-Hispanic White adults. Although unadjusted models showed non-Hispanic Black, Hispanic/Latino, and non-Hispanic American Indian/Alaska Native individuals had greater odds of screening positive for CVD risk factors, these associations were attenuated or reversed after adjusting for sociodemographic, military, behavioral, and/or psychosocial factors.
Conclusion
Due to differences in CVD risk by sex, race, and ethnicity among service members and veterans, studies are needed to understand the potential mechanisms that explain persistent disparities. Implementation of CVD prevention interventions should be considered.
Keywords: Cardiovascular Disease, Disparities, Race and Ethnicity, Service Members, Veterans
Introduction
Cardiovascular disease (CVD) is a leading cause of death in the United States, and sex, racial, and ethnic disparities in CVD prevalence, mortality, and risk factors within the US general population are widely recognized.1 Explanatory models, such as the National Institute on Minority Health and Health Disparities (NIMHD) Minority Health and Health Disparities Research Framework,2 have emphasized social and environmental determinants of health, such as unequal access to health care, socioeconomic status, or discrimination as mechanisms contributing to disparities. CVD disparities research among the US military population is comparatively limited; however, prior research suggests sex-, racial-, and ethnic-based inequalities in paygrade, career advancement, and traumatic experiences (e.g., combat exposures) in the military exist, which may, in accordance with the NIMHD Research Framework, influence CVD disparities in this population.3 Prior research among pre-9/11 veterans suggest that non-Hispanic White veterans have elevated risk for CVD compared with other racial and ethnic groups.4,5 However, studies of service members and veterans who served post-9/11 in support of operations Enduring Freedom, Iraqi Freedom, or New Dawn suggest that male non-Hispanic Black veterans have a higher risk of CVD mortality.6
Frequencies of traditional CVD risk factors (i.e., hypertension, diabetes, obesity, high cholesterol, and smoking) differ by sex, race, and ethnicity and thus contribute to a disproportionate CVD burden among certain groups.7 Although research is limited on CVD risk among service members, a recent study reported that 18% of all active duty service members had at least 1 of the following CVD risk factors: obesity, high cholesterol, hypertension, abnormal blood glucose, and diabetes mellitus.7 Hypertension is the most common health condition diagnosed among veterans (37%).8 The prevalence of diabetes among US veterans is approximately 21%,9 with variations by race, ethnicity, and sex. Among veterans with diabetes, non-Hispanic Black and Hispanic/Latino adults are most likely to have uncontrolled diabetes.10 Higher proportions of service members (>30%)11 and veterans (30%) smoke compared with the US population (15%).12 Among veterans, Hispanic/Latino adults (34%) are more likely to use tobacco products than are other racial and ethnic groups.12 The prevalence of obese body mass index (BMI) (i.e., ≥30 kg/m2) among veterans is high (41%),13 with variations by sex, race, and ethnicity,14 and recent research shows that approximately 20% of service members have an obese BMI, costing the military more than a $1 billion per year.15 In some research, 50% of veterans reported having high cholesterol,16 with women and non-Hispanic Black veterans at greatest risk.17
Although there has been prior research in military populations, there are few studies examining CVD risk among both service members and veterans and few studies examining the specific burden of CVD risk by sex, race, and ethnicity among those who served in recent military conflicts. The aim of this study was to examine and describe differences in CVD risk factors by sex, race, and ethnicity among post-9/11 service members and veterans enrolled in the Millennium Cohort Study.
Methods
Study Population
The Millennium Cohort Study is a population-based longitudinal study of military personnel sponsored by the Department of Defense and Department of Veterans Affairs (VA). From 2001 to 2013, 4 panels of participants (n=201,619) were enrolled into the study, with a fifth panel enrolled in 2020 (n=58,609). Study participants for each panel were recruited from random samples of rosters of current US service members (i.e., active duty, Reserve, and National Guard personnel) maintained by the US Defense Manpower Data Center (DMDC), the central Department of Defense source for personnel, manpower, training, and financial databases. Enrolled participants represent all service branches and components and are surveyed approximately every 3-5 years after initial enrollment,18 including after they leave military service, with additional follow-up surveys planned through 2068. For this analysis, the eligible group was drawn from participants enrolled in the first 4 panels who completed the 2014-2016 follow-up survey (n=103,245), of which 11,475 had missing CVD risk data and 61 had missing sex, race, and ethnicity data. After taking into account additional missing covariate information, complete data were available on 68.9% (n=71,156) of the group. The study protocol was approved by participating institutional review boards in compliance with all applicable federal regulations governing the protection of human subjects.
Measures
All measures were assessed from the 2014-2016 survey unless otherwise specified.
Sex, Race, Ethnicity, and Age
Sex, race, ethnicity, and birth date were obtained from the DMDC database at study enrollment. These demographic characteristics are self-reported by individuals when they enroll into military service. Race and ethnicity were combined into the following categories: non-Hispanic American Indian or Alaska Native, non-Hispanic Asian or Pacific Islander, non-Hispanic Black, non-Hispanic White, Hispanic/Latino, or multiracial. Sex was recorded with binary options of male or female. Age was calculated using each participant’s survey completion date and birth date.
CVD Risk Factors
Participants reported whether a doctor or other health professional had told them they had any of the following conditions in the past 3 years: diabetes, hypertension, or high cholesterol requiring medication. BMI was calculated from self-reported height and weight, and a BMI of >30 kg/m2 was considered obese. Smoking status was based on lifetime cigarette use and quit attempts, and smoking status was coded as currently smoking versus no longer smoking/never smoked. A CVD risk composite score was created by counting the number of CVD risk factors present (diabetes, hypertension, high cholesterol, current smoking, and obesity), which were coded as 0, 1, or 2+ CVD risk factors.19
Sociodemographic Characteristics
Education level, marital status, and health insurance coverage were self-reported. When survey data were missing, education and marital status were backfilled using administrative data from the DMDC.
Military-Related Characteristics
Paygrade, service branch, and service status (e.g., active duty, Reserve/National Guard, or veteran) or last known status for veterans were obtained from DMDC data at the time of survey completion. Deployment and combat experience were ascertained by combining DMDC deployment dates and self-reported exposure to 13 combat experiences (e.g., feeling that you were in great danger of being killed or receiving small arms fire) and categorized as never deployed, deployed without combat, deployed with low combat, or deployed with medium or high combat experience.20
Behavioral Characteristics
Participants screened positive for risky drinking of alcohol when they reported more than the recommended weekly drinking limits: 14 drinks per week or 5 drinks per occasion for men and 7 drinks per week or 4 drinks per occasion for women.21 Sleep duration was measured as self-reported hours of sleep in an average 24-hour period over the past month, categorized as insufficient sleep (<7 hours/day), healthy sleep (7-9 hours/day), and long sleep (>9 hours/day).22 Fast food consumption was assessed with an item that asked about the number of times fast food was eaten per week and dichotomized at ≥1 time/week.23 Physical activity was measured as self-reported minutes of moderate and vigorous activity in a typical week, categorized as meeting US national guidelines (versus not meeting them) of at least 75 minutes/week of activity of vigorous intensity, at least 150 minutes/week of activity of moderate intensity, or an equivalent combination of the 2.24 Sedentary time was assessed with an item from National Health and Nutrition Examination Survey that asks about the average number of hours per day spent sitting and watching TV or videos or using a computer, and scores were recorded as the number of hours per day.24
Psychosocial Characteristics
Posttraumatic stress disorder (PTSD) was ascertained from the PTSD Check List—Civilian Version25; participants screened positive for probable PTSD when they reported moderate or higher for at least 1 intrusive reexperiencing symptom, 3 avoidance symptoms, and 2 hyperarousal symptoms. Depression was assessed using the 8-item Patient Health Questionnaire (PHQ), with other anxiety syndromes measured using an additional 7 items in the PHQ26; participants screened positive for major depressive disorder when they responded with “more than half the days” or “nearly every day” to ≥5 items, of which 1 item was depressed mood or anhedonia. Anxiety syndrome was recorded when participants responded “more than half the days” for ≥4 of the 6 items, 1 of which was “feeling nervous, anxious on edge, or worrying a lot about different things.” Seven items assessing stressful life events (e.g., death of a family member or divorce) were summed and coded as 0, 1, or 2+ events. Adverse childhood experiences were assessed with 4 items (neglect and physical, sexual, and emotional abuse) from the Juvenile Victimization Questionnaire27 and were summed. Social support was measured using a single PHQ item26: “During the last 4 weeks, how much have you been bothered by having no one to turn to when you have a problem?” Three unit morale items assessing unit support, perceptions of leadership, and sense of military community were aggregated into a mean score, stemming from the Deployment Risk and Resilience Inventory.28
Analysis
Descriptive statistics for sociodemographics, military factors, individual CVD risk factors, and composite CVD risk score stratified by sex, race, and ethnicity are reported in Tables 1-4. Hierarchical ordinal logistic regression models based on a Monte Carlo integration method to account for missing data were used to examine the association of CVD risk scores with sets of covariates entered sequentially: sex, age, and race and ethnicity (block 1), block 1 variables and sociodemographic factors (block 2), blocks 1 and 2 variables and military factors (block 3), blocks 1, 2, and 3 variables and behavioral factors (block 4), and blocks 1, 2, 3, and 4 variables and psychosocial factors (block 5). Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) and coefficients of determination (R2) are reported in Table 5. Statistical interactions between sex, race, and ethnicity in unadjusted models were initially examined and found to be nonsignificant and were not included in adjusted analyses (P>.05). Sensitivity analyses were conducted comparing adjusted models using complete case analyses (n=71,156) with the primary adjusted models using missing data analysis techniques (n=103,245). No substantive differences were noted; results presented in Table 5 thus account for missing data. All analyses were performed using SPSS version 28.029 and MPlus version 8.6.30
Table 1.
Cardiovascular risk factors by sex, race and ethnicity
| CVD risk factor | Number (%) or mean (SD) |
||||||||
|---|---|---|---|---|---|---|---|---|---|
| Overall | Male | Female | NHW | NHB | H/L | AA/PI | MR | AI | |
| Diabetesa | 4256 (4.2) | 3317 (4.7) | 941 (3.1) | 2771 (3.6) | 802 (7.7) | 362 (2.0) | 213 (5.1) | 65 (5.3) | 43 (3.5) |
| Obese BMIa | 28,695 (28.3) | 21,589 (30.2) | 7119 (23.7) | 21,115 (27.4) | 3769 (36.2) | 2298 (31.5) | 804 (19.1) | 343 (28.1) | 366 (29.3) |
| Hypertensiona | 19611 (19.4) | 15917 (22.3) | 3701 (12.3) | 13,934 (18.1) | 3191 (30.7) | 1253 (17.2) | 783 (18.6) | 246 (20.1) | 204 (16.3) |
| Current smokinga | 29,331 (30.3) | 21,681 (31.8) | 7638 (26.6) | 23,925 (32.3) | 1707 (17.8) | 1917 (28.3) | 1031 (26.2) | 306 (26.5) | 425 (35.6) |
| High cholesterola | 16,669 (16.9) | 13,873 (19.5) | 2806 (9.3) | 12,567 (16.4) | 1953 (18.9) | 1157 (16.0) | 633 (15.1) | 213 (17.5) | 146 (11.7) |
| Risk compositea | |||||||||
| 0 | 41,793 (41.1) | 26,925 (37.6) | 14,894 (41.1) | 31,653 (41.0) | 3990 (38.1) | 3009 (41.2) | 2098 (49.8) | 545 (44.5) | 498 (39.6) |
| 1 | 34,315 (33.7) | 24,103 (33.7) | 10,239 (33.7) | 26,417 (34.2) | 3329 (31.8) | 2495 (34.1) | 1251 (29.5) | 371 (30.3) | 452 (36.0) |
| 2+ | 15,662 (25.2) | 20,594 (28.8) | 5074 (16.8) | 19,198 (24.8) | 3155 (30.1) | 1807 (24.7) | 886 (20.9) | 310 (25.3) | 306 (24.4) |
| Risk countb | 0.97 (1.06) | 1.07 (1.01) | 0.74 (.97) | 0.96 (1.05) | 1.09 (1.13) | 0.96 (1.04) | 0.82 (1.03) | 0.96 (1.11) | 0.94 (.99) |
CVD, cardiovascular disease, NHW, non-Hispanic White; NHB, non-Hispanic Black; H/L, Hispanic or Latino; AA/PI, Asian or Pacific Islander, non-Hispanic; MR, multiracial; AI, American Indian or Alaska Native, non-Hispanic; BMI, body mass index
Values are number (%)
Values are mean (SD)
Table 4.
Behavioral and psychosocial factors by sex, race and ethnicity
| Factora | Number (%) or mean (SD) |
||||||||
|---|---|---|---|---|---|---|---|---|---|
| Overall | Male | Female | NHW | NHB | H/L | AA/PI | MR | AI | |
| Risky alcohol drinkingb | 37,257 (38.2) | 29,196 (42.6) | 8077 (27.9) | 29,772 (40.2) | 2440 (25.1) | 2740 (40.0) | 1215 (30.5) | 383 (33.0) | 507 (42.1) |
| Sedentary time (hours/day)c | 5.28 (3.49) | 5.23 (3.44) | 5.41 (3.58) | 5.25 (3.44) | 5.61 (3.69) | 5.00 (3.50) | 5.54 (3.67) | 5.28 (3.49) | 5.17 (3.35) |
| Fast foodb | 77,897 (77.6) | 56,229 (79.6) | 21,712 (72.8) | 58,640 (76.7) | 8271 (8039) | 5803 (81.0) | 3278 (78.9) | 924 (76.4) | 981 (79.2) |
| Meeting PA guidelinesb | 48,286 (59.5) | 33,917 (59.6) | 14,400 (59.2) | 37,412 (60.3) | 4445 (54.5) | 3279 (57.0) | 1973 (59.7) | 553 (57.1) | 624 (61.1) |
| Sleep duration, (hours/night)b | |||||||||
| ≤6 | 55,001 (55.8) | 39,122 (56.4) | 15,911 (54.3) | 39,933 (53.0) | 6748 (68.3) | 4376 (63.1) | 2486 (61.5) | 728 (61.5) | 730 (59.6) |
| 7-9 | 41,387 (42.0) | 29,020 (41.9) | 12,392 (42.3) | 33,852 (44.9) | 2808 (28.4) | 2375 (34.3) | 1469 (36.3) | 432 (36.5) | 451 (36.8) |
| ≥10 | 2215 (2.2) | 1200 (1.7) | 1016 (3.5) | 1557 (2.1) | 320 (3.2) | 183 (2.6) | 87 (2.2) | 24 (2.0) | 44 (3.6) |
| ACESc | 0.80 (1.09) | 0.68 (0.99) | 1.06 (1.24) | 0.76 (1.07) | 0.84 (1.11) | 0.99 (1.16) | 0.88 (1.10) | 0.88 (1.10) | 1.02 (1.22) |
| Stress eventsb | |||||||||
| 0 | 76,122 (78.0) | 55,268 (80.4) | 20,898 (72.2) | 59,074 (79.1) | 7091 (72.8) | 4695 (72.4) | 3238 (81.3) | 893 (76.9) | 861 (71.6) |
| 1 | 16,554 (17.0) | 10,784 (15.7) | 5782 (20.0) | 12,132 (16.2) | 1999 (20.5) | 1407 (20.5) | 575 (14.4) | 204 (17.6) | 231 (19.7) |
| 2+ | 4926 (5.0) | 2676 (3.9) | 2252 (7.8) | 3455 (4.6) | 651 (6.7) | 482 (7.0) | 168 (4.2) | 65 (5.6) | 105 (8.7) |
| Lack of social supportb | 28,441 (28.5) | 18,595 (26.5) | 9864 (33.2) | 20,406 (26.8) | 3495 (34.5) | 2547 (35.9) | 1157 (28.0) | 399 (33.2) | 437 (35.2) |
| PTSDb | 12,498 (12.7) | 8512 (12.2) | 3992 (13.6) | 8755 (11.6) | 1614 (16.3) | 1252 (17.9) | 492 (12.1) | 166 (14.1) | 219 (17.9) |
| Depressive symptomsb | 9087 (9.0) | 5967 (8.4) | 3122 (10.4) | 6295 (8.2) | 1251 (12.1) | 895 (12.5) | 359 (8.6) | 127 (10.5) | 160 (12.9) |
| Anxiety symptomsb | 6595 (6.6) | 4035 (5.7) | 2562 (8.6) | 4742 (6.2) | 823 (8.0) | 610 (8.5) | 200 (4.8) | 99 (8.2) | 121 (9.7) |
| Unit cohesionc | 3.60 (0.97) | 3.67 (1.01) | 3.43 (1.07) | 3.62 (1.03) | 3.56 (1.07) | 3.50 (1.08) | 3.57 (.99) | 3.53 (1.03) | 3.44 (1.07) |
NHW, non-Hispanic White; NHB, non-Hispanic Black; H/L, Hispanic or Latino; AA/PI, Asian or Pacific Islander, non-Hispanic; MR, multiracial; AI, American Indian or Alaska Native, non-Hispanic
Fast food, one or more occasions per week; PA, physical activity; ACES, adverse childhood experiences; PTSD, post-traumatic stress disorder
Values are number (%)
Values are mean (SD)
Table 5.
Regression estimates of cardiovascular disease risk by race, ethnicity, and sex, adjusting for individual, psychosocial, military, and behavioral factors (N=103,245)
| Factor | OR (95% CI) |
||||
|---|---|---|---|---|---|
| Model 1, R2=.12 | Model 2, R2=.15 | Model 3, R2=.19 | Model 4, R2=.21 | Model 5, R2=.22 | |
| Race and ethnicity (ref: White, non-Hispanic) | |||||
| American Indian or Alaska Native, non-Hispanic | 1.30 (1.17, 1.44) | 1.19 (1.07, 1.32) | 1.16 (1.04, 1.29) | 1.14 (1.03, 1.27) | 1.10 (0.99, 1.23) |
| Asian or Pacific Islander, non-Hispanic | 0.85 (0.80, 0.90) | 0.87 (0.82, 0.93) | 0.92 (0.87, 0.98) | 0.91 (0.86, 0.97) | 0.91 (0.85, 0.94) |
| Black, non-Hispanic | 1.22 (1.18, 1.27) | 1.16 (1.11, 1.21) | 1.10 (1.05, 1.14) | 1.06 (1.01, 1.10) | 1.06 (1.02, 1.10) |
| Hispanic/Latino | 1.12 (1.08, 1.18) | 1.04 (0.99, 1.09) | 0.99 (0.95, 1.04) | 0.97 (0.93, 1.01) | 0.94 (0.90, 0.98) |
| Multiracial | 0.94 (0.85, 1.05) | 0.94 (0.84, 1.04) | 0.96 (0.86, 1.08) | 0.94 (0.84, 1.05) | 0.91 (0.82, 1.02) |
| Sex (ref: male) | |||||
| Female | 0.61 (0.60, 0.63) | 0.66 (0.64, 0.68) | 0.60 (0.59, 0.62) | 0.64 (0.62, 0.66) | 0.60 (0.58, 0.62) |
Bold values represent estimates that are significant at P<.05 and 95% confidence intervals (CIs) that do not contain 1. All odds ratios (ORs) and CIs are rounded to the second decimal place. The cardiovascular disease (CVD) risk outcome includes a sum variable of the number of CVD risk factors present (diabetes, hypertension, high cholesterol, current smoking, and obesity), coded as 0, 1, or 2+ CVD risk factors. Model 1 adjusts for age; model 2 adjusts for model 1 variables in addition to sociodemographic variables (education, marital status, and insurance); model 3 adjusts for model 1 and 2 variables in addition to military factors (paygrade, service branch, service status, and deployment/combat exposure); model 4 adjusts for model 1, 2, and 3 variables in addition to behavioral factors (alcohol risky drinking, sedentary time, fast food, physical activity, and sleep duration); model 5 adjusts for model 1, 2, 3, and 4 variables in addition to psychosocial factors (adverse childhood experiences, stress events, lack of social support, unit cohesion, posttraumatic stress disorder, depression, and anxiety)
Table 2.
Sociodemographic factors by sex, race and ethnicity
| Factor | Number (%) or mean (SD) |
||||||||
|---|---|---|---|---|---|---|---|---|---|
| Overall | Male | Female | NHW | NHB | H/L | AA/PI | MR | AI | |
| Age (years)a | 40.2 (11.1) | 40.8 (11.3) | 38.8 (10.4) | 40.4 (11.3) | 41.7 (10.7) | 38.3 (10.0) | 37.8 (9.7) | 40.4 (10.2) | 37.1 (10.3) |
| Sexb | |||||||||
| Male | 72,535 (70.3) | — | — | 56,934 (72.9) | 6000 (55.6) | 5096 (68.2) | 2879 (66.4) | 831 (66.3) | 795 (62.4) |
| Female | 30,649 (29.7) | — | — | 21,137 (27.1) | 4785 (44.4) | 2371 (31.8) | 1454 (33.6) | 423 (33.7) | 479 (37.6) |
| Educationb | |||||||||
| ≤High school | 6348 (6.2) | 5464 (7.5) | 887 (2.9) | 4811 (6.2) | 571 (5.3) | 564 (7.6) | 244 (5.6) | 56 (4.5) | 102 (8.0) |
| Some college | 27,677 (26.8) | 20,908 (28.8) | 6779 (22.1) | 20,303 (26.0) | 3112 (28.9) | 2448 (32.8) | 1045 (24.1) | 318 (25.4) | 451 (35.4) |
| Associate’s degree | 16,719 (16.2) | 11,209 (15.4) | 516 (18.0) | 12197 (15.6) | 2001 (18.6) | 1351 (18.1) | 713 (16.5) | 240 (19.1) | 217 (17.0) |
| Bachelor’s degree | 27,417 (26.6) | 18,398 (25.4) | 9032 (29.4) | 20,800 (26.6) | 2756 (25.6) | 1933 (25.9) | 1291 (29.8) | 319 (25.4) | 318 (25.0) |
| Graduate/professional | 25,023 (24.3) | 16,590 (22.9) | 8462 (27.6) | 19,960 (25.6) | 2345 (21.7) | 1171 (15.7) | 1040 (24.0) | 321 (25.6) | 186 (14.6) |
| Marital statusb | |||||||||
| Married | 71,781 (69.6) | 54,299 (74.8) | 17,524 (57.1) | 56,356 (72.2) | 6008 (55.7) | 4893 (65.5) | 2835 (65.4) | 880 (72.0) | 809 (63.5) |
| Never married | 14,455 (14.0) | 8691 (12.0) | 5769 (18.8) | 9871 (12.6) | 2129 (19.7) | 1105 (14.8) | 973 (22.5) | 164 (13.1) | 213 (16.7) |
| No longer married | 16,948 (16.4) | 9579 (13.2) | 7383 (24.1) | 11,844 (15.2) | 2648 (24.6) | 1469 (19.7) | 525 (12.1) | 210 (16.7) | 252 (19.8) |
| Insuranceb | 93,928 (96.7) | 66,159 (96.7) | 27,827 (96.6) | 72,134 (97.0) | 9276 (95.9) | 6488 (95.2) | 3798 (96.1) | 1121 (97.6) | 1111 (93.0) |
NHW, non-Hispanic White; NHB, non-Hispanic Black; H/L, Hispanic or Latino; AA/PI, Asian or Pacific Islander, non-Hispanic; MR, multiracial; AI, American Indian or Alaska Native, non-Hispanic
Values are mean (SD)
Values are number (%)
Table 3.
Military factors by sex, race and ethnicity
| Factor | Number (%) |
||||||||
|---|---|---|---|---|---|---|---|---|---|
| Overall | Male | Female | NHW | NHB | H/L | AA/PI | MR | AI | |
| Paygrade | |||||||||
| Junior enlisted | 5862 (5.7) | 3390 (4.7) | 2747 (8.1) | 4381 (5.6) | 644 (6.0) | 466 (6.2) | 218 (5.0) | 61 (4.9) | 92 (7.2) |
| NCO | 48,670 (47.2) | 33,175 (45.7) | 15,514 (50.6) | 34,808 (44.6) | 5587 (51.8) | 4560 (61.1) | 2349 (54.2) | 568 (45.3) | 798 (62.6) |
| Senior NCO | 19,977 (19.4) | 15,746 (21.7) | 4234 (13.8) | 14,819 (19.0) | 2869 (26.6) | 1275 (17.1) | 550 (12.7) | 283 (22.6) | 181 (14.2) |
| Junior commissioned or warrant officer | 12,254 (11.9) | 8344 (11.5) | 3916 (12.8) | 10,800 (12.9) | 757 (7.0) | 577 (7.7) | 618 (14.3) | 125 (10.0) | 97 (7.6) |
| Senior commissioned or warrant officer | 16,421 (15.9) | 11,914 (16.4) | 4538 (14.8) | 13,983 (17.9) | 928 (8.6) | 589 (7.9) | 598 (13.8) | 217 (17.3) | 106 (8.3) |
| Service branch | |||||||||
| Army | 46,174 (44.7) | 32,083 (44.2) | 14,092 (45.9) | 33,895 (43.5) | 5909 (54.8) | 3727 (49.9) | 1780 (41.1) | 462 (36.8) | 401 (31.5) |
| Navy, Coast Guard | 18,712 (18.1) | 12,922 (17.8) | 5841 (19.1) | 13,329 (17.1) | 1735 (16.1) | 1823 (24.4) | 1021 (23.6) | 268 (24.1) | 536 (42.1) |
| Marines | 7658 (7.4) | 6685 (9.2) | 973 (3.2) | 5971 (7.6) | 545 (5.1) | 690 (9.2) | 292 (16.7) | 71 (5.7) | 89 (7.0) |
| Air Force | 30,640 (29.7) | 20,879 (28.8) | 9770 (31.8) | 24,876 (31.9) | 2596 (24.1) | 1227 (16.4) | 1240 (28.6) | 453 (36.1) | 248 (19.5) |
| Service status | |||||||||
| Active | 26,290 (25.5) | 19,722 (27.2) | 6568 (21.4) | 19,019 (24.4) | 2899 (26.9) | 2091 (28.0) | 1505 (34.7) | 415 (33.1) | 355 (27.9) |
| Reserved, National Guard | 16,750 (16.2) | 11,117 (15.3) | 5633 (18.4) | 12,609 (16.2) | 1570 (14.6) | 1329 (17.8) | 858 (19.8) | 178 (14.2) | 188 (14.8) |
| Veteran | 60,205 (58.3) | 41,730 (57.5) | 18,475 (60.2) | 46,433 (59.5) | 6316 (58.6) | 4047 (54.2) | 1970 (45.5) | 661 (15.7) | 731 (57.4) |
| Deployment experience | |||||||||
| Never deployed | 86,348 (86.5) | 59,396 (84.8) | 27,006 (90.4) | 65,436 (86.3) | 9175 (88.0) | 6219 (86.6) | 3434 (83.5) | 1025 (86.4) | 1059 (86.4) |
| Deployed, no combat | 5304 (5.3) | 4068 (5.8) | 1240 (4.2) | 4031 (5.3) | 495 (4.8) | 360 (5.5) | 287 (7.0) | 80 (6.7) | 51 (4.2) |
| Deployed, low combat | 4326 (4.3) | 3315 (4.7) | 1011 (3.4) | 3297 (4.3) | 378 (3.7) | 327 (4.6) | 227 (5.5) | 40 (3.4) | 57 (4.6) |
| Deployed, medium or high combat | 3891 (3.9) | 3290 (4.7) | 604 (2.0) | 3061 (4.0) | 290 (2.8) | 275 (3.8) | 164 (4.0) | 42 (3.5) | 59 (4.8) |
NHW, non-Hispanic White; NHB, non-Hispanic Black; H/L, Hispanic or Latino; AA/PI, Asian or Pacific Islander, non-Hispanic; MR, multiracial; AI, American Indian or Alaska Native, non-Hispanic; NCO, noncommissioned officer
Results
Among participants, 70% were male, and 1.2% identified as non-Hispanic American Indian or Alaska Native, 4.2% as non-Hispanic Asian or Pacific Islander, 10.5% as non-Hispanic Black, 75.7% as non-Hispanic White, 7.2% as Hispanic/Latino, and 1.2% as multiracial. Across all participants, 59% reported at least 1 CVD risk factor. A higher proportion of non-Hispanic Asian or Pacific Islander adults (50%) reported no CVD risk factors, whereas 38% of non-Hispanic Black, 39% of non-Hispanic American Indian or Alaska Native, 41% of non-Hispanic White, 41% of Hispanic/Latino, and 45% of multiracial adults reported no CVD risk factors (Table 1). Due to the small number of cases of CVD (1.2% history of coronary heart disease [CHD] and 0.6% history of stroke), regression models were not developed with these CVD outcomes.
The initial age-adjusted model (block 1) showed significant associations between race, ethnicity, and sex and CVD risk factor scores. Those who identified as non-Hispanic American Indian or Alaska Native (AOR=1.30; 95% CI: 1.17-1.44), non-Hispanic Black (AOR=1.22; 95% CI: 1.18-1.27), or Hispanic/Latino (AOR=1.12; 95% CI: 1.08-1.18) had higher odds of reporting more CVD risk factors than did their non-Hispanic White counterparts, whereas non-Hispanic Asian and Pacific Islander individuals (AOR=0.85; 95% CI: 0.80-0.90) had lower odds. Women (AOR=0.61; 95% CI: 0.60-0.63) had lower odds of reporting more CVD risk factors than did men (Table 5).
Compared with non-Hispanic White individuals, non-Hispanic American Indian or Alaska Native individuals had significantly higher odds of reporting more CVD risk factors when adjusting for sociodemographic, military, and behavioral factors; however, this association was attenuated and became nonsignificant after adjusting for psychosocial factors. After adjusting for sociodemographic, military, behavioral, and psychosocial factors, the relative odds of reporting more CVD risk factors among non-Hispanic Black individuals were attenuated but remained significant. Additionally, the magnitude of the association between sex and CVD risk factors persisted even after adjusting for all covariates. Contrary to model 1, Hispanic/Latino individuals had significantly lower odds of reporting more CVD risk factors in the fully adjusted model compared with non-Hispanic White individuals (Table 5).
Discussion
Results from this study suggest that CVD risk disparities persist within active service member and veteran populations. Men were more likely to report greater CVD risk than women, even after adjustment. Previous studies show that men have higher CVD risk than women.1 Sex differences in CVD and CVD mortality risk may stem from biological mechanistic pathways and may be largely dependent on age.31 More definitive research on sex-by-age longitudinal changes in CVD risk is needed.
Non-Hispanic Asian and Pacific Islander adults were less likely to report CVD risk factors than were non-Hispanic White adults, somewhat mirroring national data from the American Heart Association showing that Asian American adults are most likely to have ideal cardiovascular health.1
Non-Hispanic Black adults were more likely to report CVD risk factors than were their non-Hispanic White counterparts, even after adjusting for other factors. Studies are needed to understand potential mechanisms (e.g., discrimination or increased stress exposure32) that may explain persistent health disparities among non-Hispanic Black service members and veterans. Both non-Hispanic American Indian/Alaskan Native and Hispanic/Latino individuals were more likely to report more CVD risk factors than were non-Hispanic White adults; however, these differences were attenuated or reversed after accounting for covariates. Future studies are needed to examine the potential mediating role of social determinants of health on CVD and other chronic disease outcomes among military and veteran populations.
In the current study, 97% of the participants had health insurance coverage. This is in contrast to the civilian literature documenting pronounced racial and ethnic differences in access to and utilization of health care.33 Universal access can help to eliminate some health disparity outcomes34; however, in this study, racial and ethnic disparities in CVD outcomes existed even after accounting for access to health care.
Military rank and career advancement are not equally distributed across racial and ethnic groups. In 2020, 92% of the highest ranking paygrade were non-Hispanic White individuals compared with 5% non-Hispanic Black and <1% Hispanic/Latino individuals. In contrast, in the lowest ranking paygrade, 49% were non-Hispanic White, 21% were non-Hispanic Black, and 22% were Hispanic/Latino individuals.35 Higher military rank is associated with increased education and income and less severe combat experiences,3 which may explain differences in observed CVD risk factors.
There are a few notable limitations of this analysis. Data used in the current study were self-reported and may be subject to recall error. However, self-reported data allow for ascertainment of CVD risk for those who are not eligible or not receiving care in the Military Health System or Veterans Health Administration. The analysis of 2014-2016 survey data allowed for a more representative snapshot of post-9/11 service members and veterans; however, we may be underestimating long-term CVD that can develop over the life course, especially among this relatively young and healthy cohort. Only 1% of responders at 2014-2016 self-reported having a stroke and/or CHD diagnosis. Further longitudinal research is needed to understand the health trends as this cohort ages. Despite these limitations, the Millennium Cohort Study remains one of the most robust, largest, and longest running study of contemporary service members and veterans; these findings increase understanding of the burden of cardiovascular disease risk among military personnel.
Conclusion
Results from this study of a population-based sample of service members and veterans demonstrate that CVD disparities across sex, race, and ethnicity persist even after accounting for sociodemographic, military, psychosocial, and behavioral factors. Future research is needed to understand potential mechanisms that explain persistent disparities and to examine long-term trends in CVD and CVD risk among post-9/11 service members and veterans. Promotion of behavioral and psychosocial interventions for CVD prevention efforts should be considered.
Acknowledgments
In addition to the authors, the Millennium Cohort Study team includes Anna Baccetti, MPH; Jennifer N. Belding, PhD; Satbir K. Boparai, MBA; Marvin A. Brown Jr; Nathan Carnes, PhD; Rebecca A. Consigli; Toni Rose Geronimo-Hara, MPH; Judith Harbertson, PhD; Lauren Jackson, BS; Isabel G. Jacobson, MPH; Cynthia A. LeardMann, MPH; Crystal L. Lewis, EdD; David Moreno Ignacio; Erin L. Richard, PhD; Anna C. Rivera, MPH; Beverly D. Sheppard; Daniel W. Trone, PhD; Javier Villalobos, MS; Jennifer L. Walstrom; and Yunnuo Zhu, MPH. The authors also appreciate contributions from the Deployment Health Research Department and Leidos, Inc. We greatly appreciate the contributions of the Millennium Cohort Study participants and are greatly indebted to the team of founding investigators (Dr. Gregory Gray, Dr. Margaret A.K. Ryan, Dr. Edward Boyko, Dr. Rick Riddle, Dr. Timothy Wells, Dr. Paul Amoroso, Dr. Tomoko Hooper, and Dr. Gary Gackstetter) whose foresight laid the framework for the Study’s continued success. The Millennium Cohort Study is funded through the Military Operational Medicine Research Program, the Defense Health Program, and the VA under work unit no. 60002. R.P.R., SF.C., F.R.C., and K.N.C. are military service members or employees of the US government. This work was prepared as part of their official duties. Title 17, USC §105 provides that copyright protection under this title is not available for any work of the US government. Title 17, USC §101 defines a US government work as that prepared by a military service member or employee of the US government as part of that person’s official duties. The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Department of the Navy, DoD, VA, or the US government. The study protocol was approved by the Naval Health Research Center Institutional Review Board (IRB) in compliance with all applicable federal regulations governing the protection of human participants. Research data were derived from an approved Naval Health Research Center IRB protocol, NHRC.2000.0007. The study protocol was approved by participating organizations’ IRBs in compliance with all applicable federal regulations governing the protection of human subjects. Informed consent was obtained from all participants included in the study.
Footnotes
Conflict of Interest: No conflicts of interest or financial disclosures reported by authors.
Author Contributions: Concept and design: Castañeda, Carey, Rull, Sharifian; Acquisition, analysis, or interpretation of data: Roesch, Kolaja, Sharifian; Drafting of the manuscript: Castañeda, Kolaja, Sharifian, Roesch; Critical revision of the manuscript for important intellectual content: Castañeda, Kolaja, Sharifian, Roesch, Rull, Carey, Carlton, Seay; Statistical expertise: Roesch, Sharifian, Kolaja, Carey, Rull; Acquisition of funding: Rull; Administrative, technical, or material support: Rull, Carey; Supervision: Rull.
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