Incidences and Trends of Cardiovascular Determinants and Diagnoses in Active Duty Service Members

By Sophie Rose Vincent , Michael Anthony Schlenk , Kristin Alyssa Horan and Brian Alexander Moore In   Issue Volume 32 Number 4 Doi No https://doi-ds.org/doilink/09.2024-81354438/JMVH

Abstract

Obesity, alcohol use and hypertension place military service members at a greater risk of developing cardiovascular disease (CVD). The current study utilised the Defense Medical Epidemiology Database (DMED) data to conduct a retrospective cohort study on the incidence rate trends of CVD and six risk factors (per 10 000) in active duty service members from 2016 to 2021. The average incidence rates of CVD diagnoses in active service members decreased except for angina. Specifically, aortic aneurysm and tear, atherosclerosis, peripheral vascular disease (PVD), stroke and heart attack incidence rates decreased (-31.94%, -29.91%, -19.58%, -9.36%, and -3.49%, respectively). However, incidence rates of angina increased by 14.77%. When examining CVD and risk factors, the incidence rate of inappropriate diet increased (54.58%). The remaining risk factors, such as diagnoses of overweight/obesity, diabetes mellitus (types 1 & 2), hypertension, high cholesterol and tobacco use (-21.42, -39.31%, -30.08%, -24.41%, and -36.85%, respectively) decreased between the years 2016 and 2021. The decrease in incidence rates of CVD warrants further investigation into the explanations for this decline. The large overrepresentation of specific demographics signals a warranted need to increase screening efforts for those at increased risk.

Keywords: Cardiovascular disease, military, active duty service members, risk factors

Cardiovascular disease

The leading cause of death in the United States is cardiovascular disease (CVD),1 characterised by a group of illnesses associated with the heart or blood vessels (2). The categories of CVD are coronary heart disease, cerebrovascular disease, peripheral arterial disease, deep vein thrombosis, and pulmonary embolism.2 Coronary heart disease, cerebrovascular disease and peripheral arterial disease are diseases of the blood vessels supplying the heart, brain, arms and legs, respectively.2 Deep vein thrombosis is blood clots from the legs dislodging and moving towards the heart and lungs.2 One aspect that precedes CVD diagnosis is poor cardiovascular health.

Cardiovascular health

Poor cardiovascular health not only heightens the risk of developing CVD but also impacts life expectancy.3 In the general population, common factors that influence CVH include blood pressure, cholesterol, fasting plasma blood glucose, physical activity, diet, smoking and body mass index (BMI).4 Many conditions can play a role in moderating the severity of cardiovascular health. These risk factors are sorted into different classifications, such as modifiable, nonmodifiable, exogenous and endogenous.5 These categories include genetic, occupational, environmental and behavioural factors. This study primarily focuses on behavioural risk factors such as physical inactivity, tobacco use, alcohol consumption and poor diet.2

Cardiovascular health and disease in the military

Cardiovascular disease impacts the general population in addition to active duty service members..6 This disease negatively affects service members’ physical health and mission readiness, and contributes to low job productivity,7 thereby placing more lives at risk due to the inability to perform their duties.8

In addition to health concerns highlighted among the general population, military service members may encounter unique occupational stressors (i.e., poor living conditions9 and combat exposure10), which are associated with increased cardiac diagnoses. For example, a study of 8727 traumatically injured service members showed that they were more likely to develop hypertension, diabetes and coronary artery disease when compared to uninjured patients.9 Additionally, posttraumatic stress disorder (PTSD) diagnoses have been significantly associated with CVD10 and drastically increased in the post-9/11 environment.11

With respect to risk factors, US Army personnel exhibit other risk factors and worsening CVH, such as unhealthy blood pressure12 and increased smoking use compared to civilians.12,13 This is concerning as a report from the Department of Defense identified that approximately 13% of service members above the age of 20 are obese and only 24.7% were a ‘heathy weight’,14 decreasing military readiness and increasing their likelihood of discharge,15 thereby increasing immediate and long-term care costs. Therefore, the primary aim of this study is to examine the incidence rates of CVD diagnoses and behavioural risk factors. A secondary objective is to investigate which categories of service members are most at risk of  behavioural risk factors.

Method

The current report is a retrospective cohort-based study utilising the Defense Medical Epidemiology Database (DMED) data. This publicly available database uses the International Classification of Disease ten (ICD-10) codes. In the present study, we ICD-10 codes used for atherosclerosis (I70, I25.1, I25.81, I25.7), heart attack (I21), stroke (I63), aortic aneurysm and tear (I71), peripheral vascular disease (PVD, I73.89, I73.9) and angina (I20) for all service members between 2016 and 2021. Additionally, the ICD-10 codes utilised for overweight/obesity (E66), hypertension (I10), type 1 diabetes mellitus (E10), type 2 diabetes mellitus (E11), high cholesterol (E78.0, E78.1, E78.2, E78.5), tobacco use (Z72.0), and inappropriate diet and eating habits (Z72.4). The diagnostic criteria for inappropriate diet and eating habits are not explicitly stated; however, the ICD code excludes eating disorders, lack of adequate food and malnutrition. It also excludes any socioeconomic or life management difficulty related to poor eating habits.16

Variables

The demographics included in the DMED dataset contained service members’ branch (Army, Marines, Air Force and Navy), sex (male, female), marital status (married, nonmarried), age (<20, 20–24, 25–29, 30–34, 35–39, >40), rank (junior enlisted, senior enlisted, junior warrant/commissioned officers and senior commissioned officers) and race (White, Black, Other). Space Force data was not included due to its recency of establishment. Coast Guard data was not included in the dataset due to their alignment with the Department of Homeland Security rather than their full-time assignment to the Department of Defense.

Statistical analysis

This report calculated incidence rates per 10 000 service members for each CVD diagnosis and known risk factors for active duty service members between 2016 and 2021. Additionally, the single sample chi-square analyses examined any significant differences between expected and observed CVD diagnoses and risk factor among demographic groups. Expected and observed cases are relative to population density rather than normative clinical expectations. Moreover, we calculated standardised residuals for each demographic group to contextualise the significance of the differences. Finally, we calculated the percentage change in CVD diagnoses and associated risk factors from 2016 to 2021.

Results

Representativeness by demographic variables

Between the years of 2016 and 2021, the largest density of active duty service members were between the ages of 20–24 (32%), male (83%), junior enlisted (43%), white (69%), married (52%), and serving in the Army (36%). Tables 1 to 4 present the demographic outcomes of each demographic variable.

Except for angina, the average incidence rates of CVD in active service members between 2016 and 2021 decreased. Specifically, aortic aneurysm and tear, atherosclerosis, PVD, stroke and heart attack incidence rates decreased (-31.94%, -29.91%, -19.58%, -9.36%, and -3.49%, respectively). Incidence rates of angina increased by 14.77%. The full range of incidence rates and diagnosis changes are in Tables 1 to 4. When examining risk factors, the incidence rate of inappropriate diet increased (54.58%), with those diagnoses most commonly occurring in the Navy. However, the remaining risk factors, such as overweight/obesity, diabetes mellitus (types 1 & 2), hypertension, high cholesterol and tobacco use (-21.42%, -39.31%, -30.08%, -24.41%, and -36.85%, respectively) decreased between the years 2016 and 2021.

Sex

Except for angina, chi-square analyses presented significant (p = < 0.001) differences between CVD and risk factor diagnoses by sex. Males presented with fewer cases than expected when examining overweight/obesity diagnoses (S/R = -34.38), but overrepresentation in the incidence of high cholesterol (S/R = 22.27). In comparison, females presented with fewer diagnoses of hypertension and high cholesterol than expected (S/R = -30.64, S/R = -53.00, respectively). Females presented with more cases than expected in overweight/obesity (S/R = 81.84) diagnoses. Additional information is available in Tables 1 to 4.

Age

Significant (p < 0.001) differences between CVD and risk factor diagnoses were observed when examining age groups. Service members within the age group of >40 presented with more cases than expected in atherosclerosis, angina, PVD and aortic tear and aneurysm (S/R = 133.79, S/R = 53.95, S/R = 30.67, S/R = 43.97, respectively). Similarly, this trend held for diabetes, hypertension, and high cholesterol (S/R = 126.77, S/R = 242.29, S/R = 307.90, respectively). All service members within the age groups of <20 and 20–24 presented fewer cases for all CVD and most risk factors than expected (S/Rs ranging between -1.42 and -139.83). Additional information is available in Tables 1 to 4.

Marital status

Significant differences (p < 0.001) between CVD and risk factors diagnoses were observed when examining marital status. All service members categorised as married presented with more cases for all of the CVD diagnoses than expected (S/Rs ranging between 13.0 [angina] and 47.01[atherosclerosis]). Married service members were significantly overrepresented in hypertension and high cholesterol (S/R = 146.93, S/R = 158.12, respectively)—unmarried service members presented with fewer cases of both CVD (S/Rs ranging between -12.77 [PVD] and -41.67 [atherosclerosis]) and risk factors (S/Rs ranging between -2.99 [inappropriate diet] and ‑140.16 [high cholesterol]) than expected. With regard to the evaluation of risk factors, the greatest values consisted of unmarried service members with hypertension and high cholesterol (S/R = -130.24, S/R = -140.16, respectively). Additional information is available in Tables 1 to 4.

Race

There were significant differences (p < 0.05) between CVD and risk factors diagnosis by race. White service members were underrepresented in incidence rates of diabetes, hypertension and overweight/obesity (S/R = -32.40, S/R = -47.17, S/R = -10.23, respectively) than expected. Black service members presented with more diagnoses of diabetes, hypertension and overweight/obesity (S/R = 47.74, S/R = 82.61, S/R = 25.21, respectively) than expected. Additional information is available in Tables 1 to 4.

Rank

Significant (p < 0.001) differences between CVD and risk factor diagnoses were observed. When examining rank, service members classified as junior enlisted and warrant and commissioned officers were underrepresented among all CVD diagnoses and a few risk factors. Specifically, junior enlisted presented with fewer cases than expected in hypertension and high cholesterol (S/R = -129.99, S/R = -150.10, respectively). Conversely, non-commissioned and senior commissioned officers were overrepresented in all CVD diagnoses. Senior enlisted and senior officers presented with more cases than expected among hypertension (S/R = 99.15, S/R = 93.63, respectively) and high cholesterol diagnoses (S/R = 80.29, S/R = 176.84, respectively). Non-commissioned officers presented more diagnoses than expected in all the risk factors except inappropriate diet (S/R = -0.71). Additional data is available in Tables 1 to 4.

Service branch

Chi-square analyses exhibited significant (p = < 0.001) differences between CVD and risk factor diagnosis by branch. Service members in the Army were overrepresented for diagnoses of hypertension, tobacco use, high cholesterol, diabetes and overweight/obesity (S/R = 20.13, S/R = 15.81, S/R = 18.22, S/R = 6.27, S/R = 33.23, respectively). Service members in the Navy presented with more diagnoses than expected for diabetes (S/R = 19.98). Service members in the Air Force had a higher number of diagnoses of angina than expected (S/R = 15.34). Marines presented with fewer diagnoses than expected for diabetes, hypertension and high cholesterol (S/R = -26.23, S/R = -51.51, S/R = -55.57, respectively). Additional data is available in Tables 1 to 4.

Table 1. Demographics of service members with CVD and CVD risk factors with an increasing incidence between 2016 and 2021.

Demographics Military density Angina
N = 5300
ICD-10
(I20)		 Inappropriate diet
N = 1322
ICD-10
(Z72.4)
IR S/R IR S/R
Overall incidence (per 10,000) 6.75 1.68
% Change from 2016 to 2021 14.77% 54.58%
Sex X2(1, 5300) = .78
p = 0.376		 X2(1, 1322) = 366.95;
p = < 0.001**
Female 17% 6.24 0.82 3.43 17.66
Male 83% 6.85 -0.34 1.34 -7.41
Age, years X2(5, 5300) = 3731.78
p = < 0.001**		 X2(5, 1322) = 19.91;
p = 0.001*
< 20 7% 2.29 -10.39 1.61 1.55
20–24 32% 3.48 -20.15 1.58 -1.42
25–29 23% 4.04 -14.12 1.54 -1.55
30–34 16% 6.00 -1.87 1.53 -0.66
35–39 12% 9.85 10.24 1.86 0.77
40+ 10% 24.80 53.95 2.47 3.47
Marital status X2(1, 5300) = 1379.08
p = < 0.001**		 X2(1, 1322) = 20.29;
p = < 0.001**
Married 52% 9.06 27.79 1.63 3.37
Nonmarried 48% 14.77 -24.63 3.69 -2.99
Race/Ethnicity X2(2, 5300) = 175.28
p = < 0.001**		 X2(2, 1322) = 7.12;
p = 0.029*
White 69% 6.02 -6.91 1.61 -1.40
Black 17% 9.06 10.73 1.90 2.15
Other 14% 7.46 3.52 1.73 0.73
Service branch X2(3, 5300) = 393.48
p = < .001**		 X2(3, 1322) = 54.01;
p = < .001**
Army 36% 7.31 0.07 1.49 -3.99
Navy 25% 4.66 -8.56 2.08 5.96
Air Force 25% 9.38 15.34 1.67 0.43
Marine Corps 14% 4.46 -9.21 1.49 -1.55
Military pay grade X2(3, 5300) = 1035.54
p = < .001**		 X2(3, 1322) = 30.27;
p = < .001**
Jr. Enlisted 43% 3.85 -19.75 1.83 2.65
Sr. Enlisted 39% 9.40 14.84 1.73 -0.71
Jr. Officer 11% 4.35 -5.87 0.85 -4.71
Sr. Officer 7% 14.06 19.77 1.84 0.74

Note: Military composition percentages provided by the DMED. ‘Jr. Enlisted’ includes pay grades E-1 to E-4; ‘Sr. Enlisted’ includes pay grades E-5 to E-9; ‘Jr. Officer’ includes pay grades O1/W1-O3/W3; ‘Sr. Officer’ includes pay grades O4/W4-O9/W5. The DMED provides marital status as Married, Single or Other. As there is no way to meaningfully discriminate between ‘single’ or ‘other’, this variable was dichotomised to improve interpretation. DMED data above represents the incidence rate change between 2016 and 2021. *Indicates significance at p = < 0.001**
Abbreviation: DMED = Defense Medical Epidemiology Database; IR = Incidence Rate; S/R = Standardised residuals

Table 2. Demographics of service members with CVD and risk factors with a decreasing incidence rate between 2016 and 2021.

Demographics Military density Cerebrovascular infarction
N = 2734
ICD-10
(I63)		 PVD
N = 1436
ICD-10
(I73.89, I73.9)		 Myocardial infarction
N = 2200
ICD-10
(I21)
IR S/R IR S/R IR S/R
Overall incidence (per 10 000) 3.48 1.83 2.80
% Change from 2016 to 2021 -9.63% -19.58% -3.49%
Sex X2(1, 2734) = 61.91;
p = < 0.001**		 X2(1, 1436) = 88.93;
p = < 0.001**		 X2(1, 2200) = 84.55;
p = < 0.001 **
Female 17% 4.28 7.25 2.64 8.69 1.35 -8.48
Male 83% 3.33 -3.05 1.67 -3.65 3.09 3.56
Age, years X2(5, 2734) = 1726.39;
p = < 0.001**		 X2(5, 1436) = 1240.49;
p = < 0.001**		 X2(5, 2200) = 1912.33;
p = < 0.001**
< 20 7% 0.83 -9.07 0.46 -6.43 0.81 -7.47
20–24 32% 1.50 -17.05 0.74 -12.96 1.28 -14.55
25–29 23% 2.15 -9.65 1.17 -6.66 1.49 -10.58
30–34 16% 3.77 2.79 1.62 -1.06 2.36 -2.10
35–39 12% 6.20 12.94 2.87 6.74 4.66 9.82
40+ 10% 11.45 32.94 7.13 30.67 10.91 37.84
Marital status X2(1, 2734) = 746.19;
p = < 0.001**		 X2(1, 1436) = 370.77;
p = < 0.001**		 X2(1, 2200) = 704.55;
p = < 0.001**
Married 52% 4.71 20.44 2.45 14.41 3.91 19.86
Nonmarried 48% 7.87 -18.12 4.24 -12.77 5.69 -17.61
Race/Ethnicity X2(2, 2734) = 45.33;
p = < 0.001**		 X2(2, 1436) = 20.14;
p = < 0.001**		 X2(2, 2200) = 219.63;
p = < 0.001**
White 69% 3.30 -2.59 1.75 -1.68 2.32 -7.03
Black 17% 4.42 6.13 2.28 4.09 4.63 12.98
Other 14% 3.23 -1.01 1.69 -0.78 2.95 1.31
Service branch X2(3, 2734) = 43.26;
p = < 0.001**		 X2(3, 1436) = 75.75;
p = < 0.001**		 X2(3, 2200) = 86.78;
p = < 0.001**
Army 36% 3.66 -0.96 2.23 3.08 3.50 4.54
Navy 25% 3.51 2.72 1.33 -3.76 2.65 0.76
Air Force 25% 3.74 2.61 2.19 4.22 2.69 -0.35
Marine Corps 14% 2.53 -5.31 1.07 -5.85 1.51 -8.09
Military pay grade X2(3, 2734) = 675.96;
p = < 0.001**		 X2(3, 1436) = 502.73;
p = < 0.001**		 X2(3, 2200) = 483.51;
p = < 0.001**
Jr. Enlisted 43% 1.73 -16.72 0.81 -13.53 1.47 -14.22
Sr. Enlisted 39% 4.84 10.52 2.50 7.11 4.12 11.72
Jr. Officer 11% 2.87 -0.86 1.58 -0.09 1.78 -3.95
Sr. Officer 7% 7.69 16.88 4.97 16.40 5.51 11.33

Note: Military composition percentages provided by the DMED. ‘Jr. Enlisted’ includes pay grades E-1 to E-4; ‘Sr. Enlisted’ includes pay grades E-5 to E-9; ‘Jr. Officer’ includes pay grades O1/W1-O3/W3; ‘Sr. Officer’ includes pay grades O4/W4-O9/W5. The DMED provides marital status as Married, Single or Other. As there is no way to meaningfully discriminate between ‘single’ or ‘other’, this variable was dichotomised to improve interpretation. DMED data above represents the incidence rate change between 2016 and 2021. *Indicates significance at p = < 0.001**
Abbreviation: DMED = Defense Medical Epidemiology Database; PVD = Peripheral Vascular Disease; IR = Incidence Rate; S/R = Standardized Residuals

Table 3. Demographics of service members with CVD and risk factors of moderate incidence rate decreases between 2016 and 2021.

Demographics Military density AAD
N = 1259
ICD-10
(I71)		 Atherosclerosis
N = 6955
ICD-10
(I70, I25.1,
I25.81, I25.7)		 High cholesterol
N = 78195
ICD-10
(E78.0-E78.2, E78.5)		 Overweight/Obesity
N = 51824
ICD-10
(E66)
IR S/R IR S/R IR S/R IR S/R
Overall incidence (per 10 000) 1.61 8.88 99.77 66.17
% Change from 2016 to 2021 -31.94% -29.91% -24.41% -21.42%
Sex X2(1, 1259) = 48.12;
p = < 0.001**		 X2(1, 6955) = 203.00;
p = < 0.001**		 X2(1, 78195) = 3305.04;
p = < 0.001**		 X2(1, 51824) = 7879.16;
p = < 0.001**
Female 17% 0.78 -6.40 4.78 -13.14 45.91 -53.00 115.95 81.84
Male 83% 1.77 2.69 9.69 5.52 110.41 22.27 56.35 -34.38
Age, years X2(5, 1259) = 2397.75;
p = < 0.001**		 X2(5, 6955) = 20985.42;
p = < 0.001**		 X2(5, 78195) = 137556.69;
p = < 0.001**		 X2(5, 51824) = 709.75;
p = < 0.001**
< 20 7% 0.22 -7.25 0.89 -18.00 2.90 -66.37 32.70 -21.98
20–24 32% 0.34 -16.01 1.57 -39.08 12.10 -139.83 65.68 -2.21
25–29 23% 0.59 -10.85 2.23 -30.12 37.74 -83.89 65.64 -1.13
30–34 16% 1.35 -1.58 4.24 -16.14 85.92 -10.14 64.62 2.55
35–39 12% 2.49 5.94 11.86 8.20 218.68 107.96 81.24 14.08
40+ 10% 8.58 43.97 58.52 133.79 487.07 307.90 79.00 3.95
Marital status X2(1, 1259) = 531.35
p < 0.001**		 X2(1, 6955) = 3946.73;
p = < 0.001**		 X2(1, 78195) = 44648.86;
p = < 0.001**		 X2(1, 51824) = 4406.69;
p = < 0.001**
Married 52% 2.36 17.25 13.92 47.01 156.83 158.12 74.53 49.68
Nonmarried 48% 3.20 -15.29 16.26 -41.67 175.60 -140.16 140.13 -44.03
Race/Ethnicity X2(2, 1259) = 10.58;
p = 0.005*		 X2(2, 6955) = 53.47;
p = < 0.001**		 X2(2, 78195) = 1366.58;
p = < 0.001**		 X2(2, 51824) = 765.66;
p = < 0.001**
White 69% 1.72 1.81 8.52 -3.33 96.30 -9.87 63.09 -10.23
Black 17% 1.36 -2.12 9.00 0.86 89.21 -10.90 82.93 25.21
Other 14% 1.37 -1.68 10.45 6.45 129.00 33.92 60.73 -5.06
Service branch X2(3, 1259) = 28.22;
p = < 0.001**		 X2(3, 6955) = 222.12;
p = < 0.001**		 X2(3, 78195) = 3847.45;
p = < 0.001**		 X2(3, 51824) = 2363.44;
p = < 0.001**
Army 36% 1.85 1.44 11.14 8.42 118.96 18.22 88.15 33.23
Navy 25% 1.63 1.85 7.78 -1.54 90.01 -1.44 46.96 -24.02
Air Force 25% 1.57 -0.01 8.68 0.09 111.91 20.62 50.11 -24.89
Marine Corps 14% 1.03 -4.77 5.38 -12.20 46.63 -55.57 71.96 7.91
Military pay grade X2(3, 1259) = 1434.81;
p = < 0.001**		 X2(3, 6955) = 9765.08;
p = < 0.001**		 X2(3, 18897) = 60222.12;
p = < 0.001**		 X2(3, 51824) = 1791.95;
p = < 0.001**
Jr. Enlisted 43% 0.44 -16.67 1.83 -42.80 17.10 -150.10 73.20 19.38
Sr. Enlisted 39% 1.97 3.53 11.37 11.14 152.73 80.29 71.76 3.59
Jr. Officer 11% 1.32 -0.65 5.62 -7.03 86.92 0.22 31.03 -32.48
Sr. Officer 7% 7.65 33.82 45.73 88.09 348.69 176.84 45.99 -18.66

Note: Military composition percentages provided by the DMED. ‘Jr. Enlisted’ includes pay grades E-1 to E-4; ‘Sr. Enlisted’ includes pay grades E-5 to E-9; ‘Jr. Officer’ includes pay grades O1/W1-O3/W3; ‘Sr. Officer’ includes pay grades O4/W4-O9/W5. The DMED provides marital status as Married, Single or Other. As there is no way to meaningfully discriminate between ‘single’ or ‘other’, this variable was dichotomised to improve interpretation. DMED data above represents the incidence rate change between 2016 and 2021. *Indicates significance at p = < 0.05*; p = < 0.001**
Abbreviation: AAD = Aortic aneurysm and dissection; DMED = Defense Medical Epidemiology Database; IR = Incidence Rate; S/R = Standardized Residuals

Table 4. Demographics of service members with CVD and CVD risk factors with large increasing incidence between 2016 and 2021.

Demographics Military density Diabetes mellitus (1 & 2)
N = 13010
ICD-10
(E10,E11)		 Hypertension
N = 106493
ICD-10
(I10)		 Tobacco use
N = 21624
ICD-10
(Z72.0)
IR S/R IR S/R IR S/R
Overall incidence (per 10 000) 16.63 136.21 27.65
% Change from 2016 to 2021 -39.31% -39.08% -36.85%
Sex X2(1, 13010) = 25.71;
p < 0.001**		 X2(1, 106493) = 1104.17;
p = < 0.001**		 X2(1, 21624) = 182.63;
p = < 0.001 **
Female 17% 13.55 -4.67 94.19 -30.64 19.76 5.23
Male 83% 17.24 1.96 144.49 12.87 29.22 -12.46
Age, years X2(5, 13010) = 20821.01;
p = < 0.001**		 X2(5, 106493) = 95054.95;
p = < 0.001**		 X2(5, 21624) = 1145.17;
p = < 0.001**
< 20 7% 2.66 -22.49 12.11 -71.50 10.41 -19.43
20–24 32% 4.58 -47.13 40.42 -130.86 22.81 -15.33
25–29 23% 6.90 -32.17 82.78 -61.74 28.02 0.76
30–34 16% 11.56 -11.78 144.87 15.61 32.88 14.58
35–39 12% 30.10 29.21 267.83 100.30 38.66 17.36
40+ 10% 81.02 126.77 495.13 242.29 33.93 4.22
Marital status X2(1, 13010) = 5108.14;
p = < 0.001**		 X2(1, 106493) = 38550.66;
p = < 0.001**		 X2(1, 21624) = 2481.80;
p = < 0.001**
Married 52% 23.95 53.48 192.97 146.93 32.27 37.28
Nonmarried 48% 34.87 -47.41 286.66 -130.24 61.54 -33.05
Race/Ethnicity X2(2, 13010) = 3702.57;
p = < 0.001**		 X2(2, 106493) = 9236.03;
p = < 0.001**		 X2(2, 21624) = 189.32;
p = < 0.001**
White 69% 11.04 -32.40 113.44 -47.17 29.62 7.56
Black 17% 33.08 47.74 217.24 82.61 22.87 -9.85
Other 14% 23.52 19.32 147.41 13.68 24.02 -5.93
Service branch X2(3, 13010) = 1182.99;
p = < 0.001**		 X2(3, 106493) = 3154.54;
p = < 0.001**		 X2(3, 21624) = 481.68;
p = < 0.001**
Army 36% 19.51 6.27 161.37 20.13 34.94 15.81
Navy 25% 20.71 19.98 130.17 7.56 24.86 -1.08
Air Force 25% 14.06 -7.52 138.56 6.28 22.65 -11.64
Marine Corps 14% 6.37 -26.23 78.25 -51.51 22.62 -9.75
Military pay grade X2(3, 13010) = 5562.09;
p = < 0.001**		 X2(3, 106493) = 35622.01;
p = < 0.001**		 X2(3, 21624) = 2147.07;
p = < 0.001**
Jr. Enlisted 43% 5.29 -50.01 51.54 -129.99 24.46 -9.01
Sr. Enlisted 39% 27.81 42.28 212.77 99.15 38.75 30.36
Jr. Officer 11% 9.90 -11.14 105.15 -11.25 11.05 -24.63
Sr. Officer 7% 36.30 33.90 291.77 93.63 11.00 -23.18

Note: Military composition percentages provided by the DMED. ‘Jr. Enlisted’ includes pay grades E-1 to E-4; ‘Sr. Enlisted’ includes pay grades E-5 to E-9; ‘Jr. Officer’ includes pay grades O1/W1-O3/W3; ‘Sr. Officer’ includes pay grades O4/W4-O9/W5. The DMED provides marital status as Married, Single or Other. As there is no way to meaningfully discriminate between ‘single’ or ‘other’, this variable was dichotomised to improve interpretation. DMED data above represents the incidence rate change between 2016 and 2021. *Indicates significance at p = < 0.05*; p = < 0.001**
Abbreviation: DMED = Defense Medical Epidemiology Database; IR = Incidence Rate; S/R = Standardized Residuals

Discussion

This study examines the most prominent cardiovascular risk factors and diagnoses17 in active duty service members between 2016 and 2021. To our knowledge, this is the first study to investigate incidence rates of cardiovascular health and diagnoses among US active duty service members across the largest service branches.

Cardiovascular disease and demographics

The identified incidence of CVD decreased between 2016 and 2021, except for angina. A possible reason for this growth, when investigating the rise in angina, may be misdiagnosis. The main symptom of angina is chest pain.18 However, it can also be present in different diseases and disorders. Therefore, a provider may attribute chest pain to angina instead of other medical problems with the same symptoms. For example, chest pain is a symptom when participating in strenuous activity. Most service members diagnosed with CVD are senior enlisted and senior commissioned officers, >40 years of age, black or married. One possible consideration is that older service members assigned sedentary tasks may be more likely to become physically inactive. Similarly, when progressing in rank, service members are not faced with physically strenuous jobs that require them to maintain a healthy diet and remain physically active. Finally, there is often a stigma associated with help-seeking behaviours in the service. Thus, it could be that older service members are waiting until they are closer to retirement to seek care. One recent report highlighted the racial gap in CVD death rates, where black Americans experienced 800 000 more cardiovascular-related deaths in comparison to the white American population.19 Considering this, cultural or genetic components could contribute to the increased incidence of varied diagnoses.

Cardiovascular health and demographics

With respect to behavioural risk factors, inappropriate diet diagnoses have increased over time. One study supports this increase based on its results reporting that only 29% of service members reported eating fruit once a day, and overall, only 3% met the Health People 2010 objectives for vegetables, fruits, and grains.20,21 Concerning diabetes, a previous study on the incidence rates of type 2 diabetes in active duty between 2006 and 2015 found similar findings22 as those reported here. The most overrepresented demographic was service members who were 40 or older.22 Concerning obesity/overweight diagnoses, the rate increased by 8% between 1995 and 2008.23 The treatments of obesity24,25 and the development of newly crafted interventions in the active service surrounding diet, physical activity, and weight management may be a reason for the 21.42% decrease in the current study. Additional examination of the Army’s holistic health and fitness program effectiveness in reducing CVD and risk factors may be warranted and, where appropriate, extended to other branches. Additionally, this program may benefit ageing service members by focusing on at-risk members through tailored CVD-relevant programming.

Military implications

The higher-than-expected rates of risk factor diagnosis in the Army may be present due to the Army being the largest branch or increased access to care compared to other branches. One thing to note that warrants further investigation is the low incidence of inappropriate diet in comparison to the increased incidence of high cholesterol and overweight/obesity. Theoretically, all three diagnoses should fluctuate in unison. However, available diagnosis data implies that poor diet is underreported or, at a minimum, a secondary diagnosis that is not well captured. There may be underdiagnoses due to the lack of nutritionist or nutrition-related knowledge available to service members and leadership.27

Limitations

This study encompassed the most common CVD diagnoses but did not include all potential CVD (i.e., rheumatic heart disease and arrhythmia). This created a limitation as to saying the results address all possible CVD. Second, the DMED data consists of de-identified group-level data provided for a single ICD code, thus preventing us from exploring comorbid conditions, symptoms and prior military experience. Additionally, we cannot definitively state which diagnostic criteria were used in each case, as any medical provider with access to a patient’s electronic health record can input a diagnosis. Thus, we are unable to examine diagnoses in isolation. Therefore, the incidence rates may be higher than reported due to the cases of comorbid conditions excluded from the dataset. Finally, the healthy worker effect, a particular selection bias, can also play a role due to programs28 that regulate physical health in the military, thereby increasing bias in the present sample.

Strengths

The strengths of this study include the vastness of the data used. The data encompasses the four largest military branches; therefore, reports about the incidence of CVD and risk factors in the military can be broadly reported. Additionally, specific results on each demographic provide insights to better address certain groups most at risk of CVD and should be used to inform future interventions. Finally, the data consists of first-time diagnoses, eliminating the possibility of reoccurring diagnoses and impacts of simultaneous comorbidities.

Conclusion

A retrospective cohort-based analysis was conducted using DMED data among active duty service members. There were decreases in CVD and the corresponding determinants, signalling the effectiveness of interventions in the military. Additionally, the analyses showed that specific demographics are overrepresented or unrepresented across various diagnoses. The findings necessitate an investigation into the reasons for the decline and continuing implementation of the interventions impacting CVD among service members, as well as investigating strategies to lessen incidence rates of preceding risk factors for CVD. These results highlight the importance of creating more tailored screening processes to ensure the proper diagnosis of CVD and the risk factors in overrepresented demographics. Future directions for this study consist of performing more detailed analyses with more comprehensive databases (i.e., the USARIEM SPHERE) to report the estimated risks of diagnoses for each demographic. Overall, these findings do not align with what is known regarding CVD and its risk factors in the general population. There is a consistent rise in CVD and most of its determinants in the general population.29 With this rise, civilians should implement prevention interventions where feasible.

 

Corresponding Author: Brian Alexander Moore, bmoore50@kennesaw.edu
Authors: S R Vincent1, M A Schlenk2, K A Horan1, B A Moore1
Author Affiliations:
1 Kennesaw State University
2 University of Central Florida

References

  1. Xu J, Murphy S, Kochanek K, Arias E. Mortality in the United States, 2021 [Internet]. National Center for Health Statistics (U.S.); 2022 Dec [cited 2024 Feb 16]. Available from: https://stacks.cdc.gov/view/cdc/122516
  2. World Health Organization. Cardiovascular diseases (CVDs) [Internet]. World Health Organization; 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
  3. Ma H, Wang X, Xue Q, et al. Cardiovascular Health and Life Expectancy Among Adults in the United States. Circulation. 2023 Apr 11;147(15):1137–46.
  4. Lloyd-Jones DM, Hong Y, Labarthe D, et al. Defining and Setting National Goals for Cardiovascular Health Promotion and Disease Reduction: The American Heart Association’s Strategic Impact Goal Through 2020 and Beyond. Circulation. 2010 Feb 2;121(4):586–613.
  5. Maculewicz E, Pabin A, Dziuda Ł, Białek M, Białek A. Selected Exogenous (Occupational and Environmental) Risk Factors for Cardiovascular Diseases in Military and Aviation. J Clin Med. 2023 Dec 4;12(23):7492.
  6. US Army Public Health Center. Health of the Force: Create a Healthier Force for Tomorrow [Internet]. US Army; 2015. Available from: https://api.army.mil/e2/c/downloads/419337.pdf
  7. Gharasi-Manshadi M, Meskarpour-Amiri M, Mehdizadeh P. Lost productivity among military personnel with cardiovascular disease. J R Army Med Corps. 2018 Aug;164(4):235–9.
  8. Magee C, Haigney MC. Cardiovascular Screening in the U.S. Military: Time to Reconsider the Electrocardiogram. Mil Med. 2020 Aug 14;185(7–8):e1039–45.
  9. Stewart IJ, Poltavskiy E, Howard JT, et al. The Enduring Health Consequences of Combat Trauma: a Legacy of Chronic Disease. J Gen Intern Med. 2021 Mar;36(3):713–21.
  10. Edmondson D, Cohen BE. Posttraumatic Stress Disorder and Cardiovascular Disease. Prog Cardiovasc Dis. 2013 May;55(6):548–56.
  11. Judkins JL, Moore BA, Collette TL, Hale WJ, Peterson AL, Morissette SB. Incidence Rates of Posttraumatic Stress Disorder Over a 17‐Year Period in Active Duty Military Service Members. J Trauma Stress. 2020 Dec;33(6):994–1006.
  12. Shrestha A, Ho TE, Vie LL, et al. Comparison of Cardiovascular Health Between US Army and Civilians. J Am Heart Assoc. 2019 Jun 18;8(12):e009056.
  13. Hoerster KD, Lehavot K, Simpson T, McFall M, Reiber G, Nelson KM. Health and Health Behavior Differences. Am J Prev Med. 2012 Nov;43(5):483–9.
  14. Frances M. Barlas, William Bryan Higgins, Jacqueline C. Pflieger, Jacqueline C. Pflieger. 2011 Health related behaviors survey of active duty military personnel. Virginia: Fairfax; 2013.
  15. Packnett ER, Niebuhr DW, Bedno SA, Cowan DN. Body mass index, medical qualification status, and discharge during the first year of US Army service. Am J Clin Nutr. 2011 Mar;93(3):608–14.
  16. Centers for Disease Control and Prevention. ICD-10-CM Coding Tool [Internet]. [cited 2024 Jul 1]. Available from: https://icd10cmtool.cdc.gov/?fy=FY2024
  17. NHS. Cardiovascular disease [Internet]. [Internet]. [cited 2024 Jul 1]. Available from: https://www.nhs.uk/conditions/cardiovascular-disease/
  18. Mayo Clinic Staff. Angina - Symptoms and causes [Internet]. Mayo Clinic; 2022. Available from: https://www.mayoclinic.org/diseases-conditions/angina/symptoms-causes/syc-20369373#:~:text=Angina%20is%20a%20symptom%20of,weight%20lying%20on%20the%20chest
  19. Arun AS, Sawano M, Lu Y, et al. Excess Cardiovascular Mortality Among Black Americans 2000-2022: A JACC Report Card. J Am Coll Cardiol. 2024 Jun;S0735109724075466.
  20. Smith TJ, Dotson LE, Young AJ, et al. Eating Patterns and Leisure-Time Exercise among Active Duty Military Personnel: Comparison to the Healthy People Objectives. J Acad Nutr Diet. 2013 Jul;113(7):907–19.
  21. Chukwura CL, Santo TJ, Waters CN, Andrews A. ‘Nutrition is out of our control’: soldiers’ perceptions of their local food environment. Public Health Nutr. 2019 Oct;22(15):2766–76.
  22. Tyler L. Collette, Jason L. Judkins, Morgan Gettle, Brian A. Moore, Michelle Lee. A Retrospective, Epidemiological Review of Type 2 Diabetes Mellitus in a Military Population. Med J. 2022;17.
  23. Reyes-Guzman CM, Bray RM, Forman-Hoffman VL, Williams J. Overweight and Obesity Trends Among Active Duty Military Personnel. Am J Prev Med. 2015 Feb;48(2):145–53.
  24. Gravina D, Keeler JL, Akkese MN, et al. Randomized Controlled Trials to Treat Obesity in Military Populations: A Systematic Review and Meta-Analysis. Nutrients. 2023 Nov 14;15(22):4778.
  25. Sanderson PW, Clemes SA, Biddle SJH. The Correlates and Treatment of Obesity in Military Populations: A Systematic Review. Obes Facts. 2011;4(3):229–37.
  26. Malkawi AM, Meertens RM, Kremers SPJ, Sleddens EFC. Dietary, physical activity, and weight management interventions among active-duty military personnel: a systematic review. Mil Med Res. 2018 Dec;5(1):43.
  27. Jayne JM, Bujak BK, Torres-McGehee TM, et al. Role of Drill Sergeants in Nutrition Behaviors of Soldiers in Basic Combat Training. J Acad Nutr Diet. 2019 Jan;119(1):69–75.
  28. Defense Technical Information Center. DoD Instruction 1308.03: DoD Military Cyberspace Activities. [Internet]. [cited 2024 Jul 1]. Available from: https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodi/130803p.pdf
  29. Mohebi R, Chen C, Ibrahim NE, et al. Cardiovascular Disease Projections in the United States Based on the 2020 Census Estimates. J Am Coll Cardiol. 2022 Aug;80(6):565–78.

Acknowledgements

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