Abstract

Traditional, mutually exclusive approaches to racial and ethnic classification obscure important differences within major demographic groups and among multiracial populations. This study offers a novel examination of obstetric and neonatal outcomes among pregnant U.S. military service members, by applying multiple approaches to racial and ethnic classification and presenting disaggregated data. Overall, 235,608 births were identified among pregnant service members from 2010 through 2021. Inclusion of service members who identified with each racial group, whether alone or in combination with any other group, increased the American Indian or Alaska Native and Native Hawaiian or Pacific Islander birth populations by 209.7% and 94.0%, respectively, when compared to mutually exclusive classifications. Prevalences of obstetric outcomes such as cesarean delivery varied among racial and ethnic groups, particularly Asian and Latino populations, for example, Asian Indian, 36.7%; Filipino, 32.3%; Chinese, 26.5%; Puerto Rican, 30.2%; Mexican, 23.2%; and between distinct multiracial populations. Disaggregated estimates ultimately increased visibility of multiracial and Native service members and elucidated patterns indiscernible in aggregated data. Wider adoption of disaggregated racial and ethnic data methods is needed to improve understanding of health outcomes in the Military Health System.

What are the new findings?

Reporting of non-mutually exclusive racial and ethnic groups as well as disaggregated Asian, Hispanic or Latino, and multiracial populations elucidates important differences in obstetric and neonatal outcomes.

What is the impact on readiness and force health protection?

The collection and reporting of disaggregated racial and ethnic data is crucial to promote understanding of populations of multiracial, Native, and national origins serving in the U.S. military. System improvements in access to and  quality of Military Health System obstetric care are needed to address persistent racial disparities and improve force readiness.

Background

Racial and ethnic disparities in adverse obstetric and neonatal outcomes have been widely reported in the U.S. literature.^1-4 Despite concerted efforts to document and attend to disparities, traditional approaches to racial and ethnic classification often obscure important differences within major racial or ethnic groups (e.g., among diverse Asian and Latino populations) and among multiracial populations, resulting in a bias of averages.⁵ Additionally, classification methods that restrict racial and ethnic group counts to individuals identifying as single-race and non-Hispanic or Latino lead to significant suppression of American Indian or Alaska Native (IAN) and Native Hawaiian or Pacific Islander (NHPI) populations: just 23.3% and 39.2% of their national populations, respectively, identified as single-race and non-Hispanic or Latino in the 2020 U.S. Census.⁶

Assessments of racial and ethnic health disparities in the Military Health System (MHS) are limited by many of the aforementioned data concerns.^7-13 A more holistic assessment is crucial given the diversity of the population: in 2022, 26.8% of U.S. military service members identified with a historically racialized group (i.e., AIAN, Asian, Black or African American, NHPI, or multiracial), and 17.3% identified as Hispanic or Latino.¹⁴ The present study used 1) self-reported racial and ethnic data from personnel records and 2) population-level health care claims data to assess the prevalence of obstetric and neonatal outcomes among U.S. service members by disaggregated race and ethnicity. Additionally, prevalence estimates were calculated for each racial and ethnic group using 2 distinct methods of classification: a mutually exclusive and non-mutually exclusive approach.

Methods

Study population

The study population was derived from the U.S. Department of War Birth and Infant Health Research (BIHR) program. The BIHR program is an ongoing surveillance and research effort that identifies live births among military families and captures information on associated pregnancy and infant health outcomes.¹⁵ BIHR data comprise military demographic and personnel data from the Defense Manpower Data Center (DMDC) and administrative medical encounter data from the MHS Data Repository. The data repository includes records for all care paid for by TRICARE, the health care plan for service members, retirees, and their families. Covered care spans medical services received at military and civilian facilities within the U.S. and abroad and is available at no cost to active duty service members and their families.

BIHR data were used to identify all live births occurring from January 2010 through December 2021 among pregnant U.S. military service members. Same-sex multiples were excluded due to difficulties distinguishing their neonatal medical records. The study was approved by the Naval Health Research Center Institutional Review Board (protocol NHRC.1999.0003); informed consent was waived in accordance with criteria set forth by Title 32, Code of Federal Regulations, Part 219.

Measures

Self-reported race and ethnicity data were ascertained from DMDC military personnel records. Values from both the race and ethnicity data fields were considered when assigning race and ethnicity (Supplementary Table 1). The Army and Army Reserve do not allow service members to select multiple categories of race: Multiracial individuals must select a single racial group or “other”. Additionally, all service members can report only 1 ethnicity.

Data were categorized using 2 distinct approaches: 1) a mutually exclusive (‘alone’) and 2) non-mutually exclusive (‘alone or in combination’) approach. The mutually exclusive (‘alone’) approach first identified Hispanic or Latino individuals, and subsequently grouped non-Hispanic individuals into 1 of the following racial categories: AIAN, Asian, Black or African American, NHPI, multiracial, or unknown. If service members selected multiple categories, they were classified as multiracial. The non-mutually exclusive approach identified all individuals identifying with each group, whether alone or in combination with any other group (i.e., including people who would otherwise be classified as multiracial or Hispanic or Latino). For example, if an individual’s self-reported race was “Black or African American” and ethnicity was “Korean,” that individual was categorized as multiracial using the mutually exclusive (‘alone’) approach, and Black or African American, Asian, and Korean using the non-mutually exclusive (‘alone or in combination’) approach.

Risk factors (e.g., age) and indicators of socio-economic disadvantage (e.g., educational attainment, military rank) were identified and treated dichotomously: age at delivery (18-19 years vs. ≥20 years; <35 years vs. ≥35 years), educational attainment (bachelor’s degree or higher vs. less education), and military rank (officer vs. enlisted).

Three obstetric outcomes were ascertained using International Classification of Diseases, 9th and 10th Revisions (ICD-9/ICD-10), diagnosis codes: cesarean delivery, gestational hypertension, and gestational diabetes (Supplementary Table 2). Cesarean deliveries required notation on either the delivery record or the infant birth record. Gestational hypertension cases required record of associated codes on 1 inpatient or 2 outpatient encounters from 20 weeks estimated gestational age (EGA) to 6 weeks postpartum. Gestational diabetes cases required record of associated codes on 1 inpatient or 2 outpatient encounters from 28 weeks EGA to date of delivery. Cases of pre-existing hypertension and pre-existing diabetes in pregnancy or the year prior to pregnancy were excluded from gestational case definitions. Two neonatal outcomes were also ascertained using ICD-9/ICD-10 diagnosis codes in the infant medical record: pre-term birth (<37 weeks EGA) and low birth weight (<2,500 grams).

Analysis

The proportion of live births among pregnant U.S. service members was calculated for each racial and ethnic group, alone and alone or in combination with any other group, overall and stratified by age, educational attainment, and military rank. Estimates were presented in the style of a heat map, with color gradients from dark green (indicating lowest risk or disadvantage) to dark yellow (indicating greatest risk or disadvantage). The prevalence of each outcome, as well as 95% confidence intervals (CIs), were calculated for each racial and ethnic group, alone and alone or in combination with any other group. Prevalence was not reported when the numerator included less than 11 cases. Secondary analyses examined prevalence among specific, mutually exclusive racial and ethnic identity intersections (e.g., AIAN and White). Data management and statistical analyses were performed using SAS Enterprise Guide, version 7.1 (SAS Institute Inc., Cary, NC).

Results

The BIHR program captured 1,353,602 live births among U.S. military families from 2010 through 2021, of which 235,608 occurred to pregnant military service members. Analysis of race and ethnicity as an exclusive classification demonstrated births to White ‘alone’ pregnant service members comprised the plurality (47.7%), followed by Black or African American ‘alone’ (22.6%), Hispanic or Latino (16.0%), multiracial (4.1%), Asian ‘alone’ (3.7%), NHPI ‘alone’ (1.4%), and AIAN ‘alone’ (1.3%) (Table 1). When using a non-mutually exclusive racial and ethnic classification approach, the AIAN birth population increased by 209.7% (from 2,985 to 9,245) and the NHPI group increased by 94.0% (from 3,309 to 6,421).

Service members identifying as AIAN, Black or African American, and NHPI (both alone and alone or in combination) had higher proportions of pregnant service members younger than age 20 years at delivery and lower proportions of those who completed a bachelor’s degree and of officer rank in relation to other groups (Table 2). Patterns were variable among Asian and Latino ethnic groups. Pregnant Filipino service members had lower proportions of college graduates and officers compared with other Asian ethnic groups. Mexican service members demonstrated higher proportions of pregnant service members younger than age 20 years at delivery and lower proportions of those with college education and of officer rank in relation to Cuban and Puerto Rican service members.

The overall prevalence of cesarean delivery, gestational hypertension, and gestational diabetes among all live births was 27.5% (95% CI 27.4, 27.7), 13.4% (95% CI 13.3, 13.6), and 7.3% (95% CI 7.2, 7.4), respectively (Figure 1). As a mutually exclusive group, Black or African American service members had a high prevalence of cesarean delivery (31.9%; 95% CI 31.5, 32.3) and gestational hypertension (15.5%; 95% CI 15.2, 15.8), but a low prevalence of gestational diabetes (6.4%; 95% CI 6.2, 6.7). The prevalence of each outcome varied across Asian alone ethnic groups, but skewed below the overall estimate for gestational hypertension, ranging from 6.7% (95% CI 4.5, 8.8) among Chinese service members to 11.9% (95% CI 10.6, 13.3) among Filipino service members. For gestational diabetes, the prevalence among Asian ‘alone’ ethnic groups skewed above the overall estimate, ranging from 13.4% (95% CI 12.5, 14.4) for the ‘other’ Asian descent population to 18.6% (95% CI 15.5, 21.8) for Korean service members. Among Hispanic and Latinos, Puerto Rican and Cuban service members had high prevalences of cesarean delivery (30.2%; 95% CI 28.6, 31.9 and 34.2%; 95% CI 29.2, 39.3, respectively) compared with the overall prevalence and that among Mexican service members (23.2%; 95% CI 22.3, 24.0); however, gestational diabetes was less prevalent among Cuban service members (5.1%, 95% CI 2.7, 7.4) than Mexican service members (8.4%; 95% CI 7.9, 9.4). Gestational diabetes was higher among AIAN alone service members (9.8%; 95% CI 8.8, 10.9) compared to the population inclusive of multiracial and Hispanic or Latino individuals (8.1%; 95% CI 7.5, 8.7). The prevalence of all obstetric outcomes was low among White service members.

Overall prevalence of pre-term birth and low birth weight was 8.4% (95% CI 8.3, 8.5) and 5.0% (95% CI 4.9, 5.1), respectively (Figure 2). Black or African American service members had higher prevalences of pre-term birth (alone 11.0%; 95% CI 10.7, 11.3) and low birth weight (alone 8.0%; 95% CI 7.8, 8.2) relative to several other racial and ethnic groups. Prevalence estimates among Asian ‘alone’ and Hispanic or Latino ethnic groups revealed wide variations among both neonatal outcomes, although corresponding CIs were widened for some groups, due to smaller sample sizes. For example, pre-term birth ranged from 6.7% (95% CI 4.5, 8.8) among Chinese service members to 11.9% (95% CI 7.5, 16.3) among Asian Indian service members, and low birth weight ranged from 3.2 (95% CI 1.7, 4.8) among Chinese service members to 7.1 (95% CI 3.7, 10.6) among Asian Indian service members. Hispanic or Latino service members had lower prevalences of pre-term birth (7.9%; 95% CI 7.6, 8.1) and low birth weight (4.7%; 95% CI 4.5, 4.9) than the overall estimate, but prevalence was elevated among Puerto Rican service members (pre-term birth 10.1%; 95% CI 9.0, 11.1; low birth weight 5.8%; 95% CI 5.0, 6.6). The inclusion of multiracial and Hispanic or Latino individuals in estimates for AIAN and NHPI groups, as is reflected in the non-mutually exclusive groupings, resulted in a disproportionate increase in cases of adverse neonatal outcomes, although CIs overlapped.

Outcome prevalences also differed at racial and ethnic identity intersections (Table 3). Black or African American ‘alone’, Black or African American and other Hispanic descent, Filipino, and White and Puerto Rican service members had higher estimates of several adverse outcomes. In contrast, service members who identified as White ‘alone’, White and other Hispanic descent, White and Mexican, and Other Hispanic descent ‘alone’ frequently had lower estimates. Despite similar population sizes, there were also differences in the prevalences of cesarean delivery and gestational diabetes for AIAN ‘alone’ versus AIAN and White service members.

Discussion

This study reported the prevalence of selected obstetric and neonatal outcomes among U.S. service members by disaggregated race and ethnicity, revealing varying prevalence within and across racial and ethnic groups. Furthermore, we identified differences between distinct multiracial groups and by using mutually exclusive versus non-mutually exclusive classification structures. These differences are especially important for AIAN and NHPI service members, who are very likely to additionally identify as another race or ethnicity.

We add to a limited body of racial health disparities research conducted among pregnant U.S. military service members. The prevalence of pre-term birth among Black or African American service members was lower than that previously reported using 2003-2014 data (11.0% vs. 11.5%), while low birth weight was more prevalent in the present study (8.0% vs. 7.7%).¹⁵ Prevalence of both neonatal outcomes, as well as of cesarean delivery and gestational hypertension, remained higher among Black or African American service members compared with all other major racial and ethnic groups. These findings underscore the continued relevance of disparities previously identified for neonatal mortality, severe maternal morbidity, and pregnancy-related mortality, and counter suggestions that comprehensive health coverage alone eliminates health disparities.^8-10,16

Disaggregation of the population identifying as Asian or Pacific Islander elucidated marked differences for each group overall and across specific ethnic groups. For low birth weight, overall estimates were 4.1% among NHPI alone service members and 5.2% among Asian alone service members, whereas the aggregated estimate using data from 2003-2014 was 4.9%.¹⁵ Differences were also pronounced for gestational diabetes, with Asian alone service members having a 49.5% increased risk compared with NHPI alone service members. Findings parallel prior work documenting higher risk for gestational diabetes among Asian populations compared with other major racial and ethnic groups, as well as uniquely high risk among Asian Indian, Vietnamese, and Filipino ethnic groups.^17,18 We also noted prevalence estimates for Chinese and Korean service members that were higher than typically reported^17,18; this finding may reflect differences between civilian and active duty populations related to place of birth, childhood socio-economic status, education, and age at delivery. Observed differences among Asian ethnic groups provide further justification for acknowledgment of aggregation as a potential fallacy.⁵

More inclusive, non-mutually exclusive definitions of race and ethnicity proved particularly effective for capturing births among AIAN and NHPI service members, as the numbers of births attributed to these populations increased by 209.7% and 94.0%, respectively, if compared to a mutually exclusive approach. Although meaningful differences in estimates between mutually exclusive and non-mutually exclusive groups were difficult to ascertain due to wide CIs, with the exception of gestational diabetes, point estimates were consistently higher among the NHPI ‘alone or in combination’ population, indicating greater risk for multiracial NHPI populations. For AIAN service members, gestational diabetes was higher among those identifying as AIAN alone. Prior work has shown the AIAN ‘alone’ population experienced increased economic disadvantage and decreased life expectancy relative to the multiracial AIAN population,^19,20 whereas the multiracial AIAN population experienced increased depression and mental distress.²¹ Our findings of disparate estimates by multiracial identity, therefore, contribute further nuance to awareness of Native health in the U.S.

There are some notable limitations with military personnel race and ethnicity data that affected this work. First, in the Services overall, only 1 ethnic identity could be reported; and in the Army and Army Reserve (which accounted for nearly 40% of all births in this cohort), soldiers could not report identity with multiple racial groups. Consequently, the multiracial population was under-estimated. Second, although self-reported, these records remain subject to data entry and administrative errors. Finally, detailed ethnicity is available only for Asian and Hispanic or Latino groups, hampering understanding of diversity among White, Black or African American, and NHPI service members.

The approach to this work, through the presentation of estimates for mutually exclusive and non-mutually exclusive racial and ethnic groups, as well as specific groups comprising a significant proportion of the population, mirrors recommendations in the 2024 Office of Management and Budget guidance for the maintenance, collection, and presentation of racial and ethnic data.²² Our findings underscore that a singular, mutually-exclusive approach to racial and ethnic classification is insufficient for understanding racial health disparities: It disproportionately obscures AIAN and NHPI populations and homogenizes multiracial populations.^23,24 As the U.S. population is increasingly multiracial,²⁵ disaggregation will only grow more pertinent. Ultimately, while application of multiple approaches to racial and ethnic classification may not always be feasible, researchers should consider the implicit biases or  assumptions reflected in their selected approach.²⁶ Greater attention to the collection and reporting of disaggregated racial and ethnic health data will improve understanding of health outcomes within and beyond the MHS.¹³

References

1. Creanga AA, Bateman BT, Kuklina EV, Callaghan WM. Racial and ethnic disparities in severe maternal morbidity: a multistate analysis, 2008–2010. Am J Obstet Gynecol. 2014;210(5):435e1-435e8. doi:10.1016/j.ajog.2013.11.039  
2. Grobman WA, Parker CB, Willinger M, et al. Racial disparities in adverse pregnancy outcomes and psychosocial stress. Obstet Gynecol. 2018;131(2):328-335. doi:10.1097/aog.0000000000002441  
3. Pollock EA, Gennuso KP, Givens ML, Kindig D. Trends in infants born at low birthweight and disparities by maternal race and education from 2003 to 2018 in the United States. BMC Public Health. 2021;21(1):1117. doi:10.1186/s12889-021-11185-x  
4. Martin JA, Osterman M. National Vital Statistics Reports: Shifts in the Distribution of Births by Gestational Age: United States, 2014–2022. U.S. Centers for Disease Control and Prevention. 2024. Accessed Nov. 21, 2024. https://www.cdc.gov/nchs/data/nvsr/nvsr73/nvsr73-01.pdf  
5. Gee GC, Chien J, Sharif MZ, Penaia C, Tran E. East is east...or is it? Racialization of Asian, Middle Eastern, and Pacific Islander persons. Epidemiol Rev. 2023;45(1):93-104. doi:10.1093/epirev/mxad007  
6. U.S. Census Bureau. Hispanic or Latino, and Not Hispanic or Latino by Race. U.S. Dept. of Commerce. 2020. Accessed Feb. 19, 2024. https://data.census.gov/table/decennialdhc2020.p9?q=raceandethnicity&g=010xx00us&y=2020  
7. Hall C, Bukowinski AT, McGill AL, et al. Racial disparities in prenatal care utilization and infant small for gestational age among active duty US military women. Matern Child Health J. 2020;24(7):885-893. doi:10.1007/s10995-020-02941-3  
8. Hall C, Romano CJ, Bukowinski AT, et al. Severe maternal morbidity among women in the U. S. military, 2003–2015. Am J Perinatol. 2024;41(2):150-159. doi:10.1055/s-0041-1740248  
9. Romano CJ, Hall C, Bukowinski AT, Gumbs GR, Conlin AMS. Pregnancy-associated and pregnancy-related deaths in the United States military, 2003–2014. Am J Obstet Gynecol. 2022;227(3):508e1-508e9. doi:10.1016/j.ajog.2022.04.020  
10. Hamilton JL, Shumbusho D, Cooper D, et al. Race matters: maternal morbidity in the Military Health System. Am J Obstet Gynecol. 2021;224(5):512e1-512e6. doi:10.1016/j.ajog.2021.02.036  
11. Ranjit A, Andriotti T, Madsen C, et al. Does universal coverage mitigate racial disparities in potentially avoidable maternal complications? Am J Perinatol. 2021;38(8):848-856. doi:10.1055/s-0040-1701195  
12. Williams VF, Oh GT, Stahlman S, Shell D. Diabetes mellitus and gestational diabetes, active and reserve component service members and dependents, 2008–2018. MSMR. 2020;27(2):8-17. Accessed Jan. 20, 2026. https://www.health.mil/reference-center/reports/2020/02/01/medical-surveillance-monthly-report-volume-27-number-2  
13. Mancuso JD, Young J, Rusiecki J, et al. Advancing health equity in the US military. J Health Dispar Res Pract. 2023;16(1):3.  
14. Office of the Deputy Assistant Secretary of Defense for Military Community and Family Policy. 2022 Demographics: Profile of the Military Community. U.S. Department of Defense. Accessed Jan. 20, 2026. https://www.militaryonesource.mil/data-research-and-statistics/military-community-demographics/archive  
15. Bukowinski AT, Conlin AMS, Gumbs GR, et al. Department of Defense Birth and Infant Health Registry: select reproductive health outcomes, 2003–2014. MSMR. 2017;24(11):39-49. Accessed Jan. 20, 2026. https://www.health.mil/reference-center/reports/2017/01/01/medical-surveillance-monthly-report-volume-24-number-11  
16. Vereen RJ, Nestander M, Haischer-Rollo G, et al. Neonatal mortality and disparities within the Military Health System. J Perinatol. 2023;43:496-502. doi:10.1038/s41372-022-01598-w  
17. Bane S, Abrams B, Mujahid M, et al. Risk factors and pregnancy outcomes vary among Asian American, Native Hawaiian, and Pacific Islander individuals giving birth in California. Ann Epidemiol. 2022;76:128-135e9. doi:10.1016/j.annepidem.2022.09.004  
18. Pu J, Zhao B, Wang EJ, et al. Racial/ethnic differences in gestational diabetes prevalence and contribution of common risk factors. Paediatr Perinat Epidemiol. 2015;29(5):436-443. doi:10.1111/ppe.12209  
19. Huyser KR, Takei I, Sakamoto A. Demographic factors associated with poverty among American Indians and Alaska natives. Race Soc Probl. 2014;6:120-134. doi:10.1007/s12552-013-9110-1  
20. Burnette J, Gregg M. How much did Native American life expectancy drop during COVID-19? Econofact. Jan. 31, 2023. Accessed Feb. 19, 2024. https://econofact.org/how-much-did-native-american-life-expectancy-drop-during-covid-19  
21. Asdigian NL, Bear UR, Beals J, Manson SM, Kaufman CE. Mental health burden in a national sample of American Indian and Alaska Native adults: differences between multiple-race and single-race subgroups. Soc Psychiatry Psychiatr Epidemiol. 2018;53(5):521-530. doi:10.1007/s00127-018-1494-1  
22. U.S. Office of Management and Budget. Statistical Policy Directive No. 15: Standards for Maintaining, Collecting, and Presenting Federal Data on Race and Ethnicity. Updated Nov. 14, 2025. Accessed Jan. 20, 2026. https://spd15revision.gov  
23. Quint J, Matagi C, Kaholokula JK. The Hawai’i NHPI data disaggregation imperative: preventing data genocide through statewide race and ethnicity standards. Hawaii J Health Soc Welf. 2023;82(10 suppl 1):67-72.  
24. Urban Indian Health Institute. Toolkit: Best Practices for American Indian and Alaska Native Data Collection. Updated Aug. 26, 2020. Accessed Feb. 19, 2024. https://www.uihi.org/resources/best-practices-for-american-indian-and-alaska-native-data-collection  
25. Jones N, Marks R, Ramirez R, Ríos-Vargas M. Improved Race and Ethnicity Measures Reveal U.S. Population Is Much More Multiracial: 2020 Census Illuminates Racial and Ethnic Composition of the Country. U.S. Census Bureau, U.S. Dept. of Commerce. Aug. 12, 2021. Accessed Feb. 19, 2024. https://www.census.gov/library/stories/2021/08/improved-race-ethnicity-measures-reveal-united-states-population-much-more-multiracial.html  
26. Lam-Hine T, Forthal S, Johnson CY, Chin HB. Asking multicrit questions: a reflexive and critical framework to promote health data equity for the multiracial population. Milbank Q. 2024;102(2):398-428. doi:10.1111/1468-0009.12696

Author Affiliations

Deployment Health Research Department, Naval Health Research Center, San Diego, CA: Ms. Romano, Dr. Hall, Ms. Burrell, Ms. Bukowinski, Ms. Lanning, Ms. Maduforo, Ms. Magallon, Dr. Khodr, Ms. Gumbs, Dr. Conlin; Leidos, Inc., San Diego, CA: Ms. Romano, Dr. Hall, Ms. Burrell, Ms. Bukowinski, Ms. Lanning, Ms. Maduforo, Ms. Magallon, Dr. Khodr, Ms. Gumbs

Disclaimer

The views expressed in this report are those of the authors and do not necessarily reflect the official policy nor position of the Defense Health Agency, Department of War, nor the U.S. Government.

Report 24-21 was supported by U.S. Navy Bureau of Medicine and Surgery under work unit 60504.

The study protocol was approved by the Naval Health Research Center Institutional Review Board in compliance with all applicable Federal regulations governing the protection of human subjects. Research data were derived from approved Naval Health Research Center Institutional Review Board protocol NHRC.1999.0003.

Dr. Conlin is an employee of the U.S. Government. This work was prepared as part of her official duties. Title 17, U.S. Code Section 105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S. Code Section 101 defines a U.S. Government work as work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties.