Abstract
Background
Occupational injuries remain an important source of morbidity and mortality in the United States. Recent changes in workforce participation, employment patterns, and workplace conditions may have altered the epidemiology of occupational trauma. This study evaluated national trends in work-related traumatic injuries from 2017 to 2023 using the National Trauma Data Bank.
Methods
We performed a retrospective cross-sectional study using the American College of Surgeons National Trauma Data Bank Participant Use Files from 2017 to 2023. Work-related trauma encounters among patients aged 14 years or older were included. Temporal trends in demographic, injury, and clinical characteristics were assessed using Mann–Kendall trend testing. Multivariable regression models were used to evaluate factors associated with hospital length of stay (LOS) and mortality.
Results
From 2017 to 2023, work-related trauma demonstrated significant demographic shifts. The proportion of occupational injuries increased among patients aged 14–19 years and those aged 60 years or older, female patients, Hispanic patients, and patients with Medicare or self-pay status. Pre- versus post-COVID period was not independently associated with hospital LOS or mortality. Longer hospital LOS was associated with older age, higher occupational hazard category, transportation-related mechanisms, penetrating trauma, major injury severity, multiple injured body regions, comorbidity burden, and positive alcohol or drug testing. Mortality was associated with older age, Black race, Medicare or self-pay status, lower trauma center level designation, penetrating trauma, major injury severity, multiple injured body regions, lower Revised Trauma Score, and comorbidity burden.
Conclusion
Occupational injuries treated at U.S. trauma centers changed meaningfully from 2017 to 2023, with increasing representation among younger workers, older workers, female patients, Hispanic patients, and patients without private insurance. These findings support targeted occupational injury prevention strategies and highlight the need for improved surveillance of vulnerable worker populations.
Keywords: Occupational injuries, Work-related traumatic injuries, Trauma trends, COVID-19, Workers’ compensation
Introduction
Occupational injuries, defined as injury or illness that occurs related to specific occupational demands, represent a substantial percentage of emergency department visits [1]. In 2019, over 285,000 private industry workers presented to a U.S. emergency department with a non-fatal workplace injury that resulted in days away from work [2].
Traumatic occupational injury patterns have evolved in recent years, potentially reflecting the impact of COVID-19-related workplace changes. Many industries experienced substantial changes in working conditions, including altered work hours, transitions to remote work, and increased job insecurity. Prior to the pandemic, only 37% of U.S. workers stated they worked from home at least some of the time. During 2020, the first year of the pandemic era, this figure rose to 82%, and even in 2024, 75% of U.S. workers continue to work from home at least some of the time [3]. COVID-19-related workplace changes provide an opportunity to examine trends in occupational trauma before, during, and after the pandemic.
Historically, major world events have altered occupational injury patterns. The 2008 Global Financial Crisis led to a decline in occupational injuries, specifically in construction, as well as a decrease in injury severity [4]. Additionally, the 1918 Influenza Pandemic led to marked increases in absenteeism from work and school [5]. However, those who remained at work, such as military personnel and healthcare employees, held a higher injury and infection burden. Though injury data reporting was limited during this time, inferences from economic burdens created by the pandemic support this claim [5].
Prior studies using workers’ compensation claims, trauma registries, and emergency department records suggest that while certain types of injuries— such as occupational falls and vehicular trauma—declined during lockdowns, others, including COVID-related claims and mental health conditions, rose sharply [6, 7]. Understanding occupational trauma patterns is essential for prevention, policy, and public health planning, yet few national studies have examined these trends over time.
Using the National Trauma Data Bank (NTDB), this study aimed to evaluate changes in occupational trauma trends, examine demographic and clinical patterns of injury, and identify factors associated with clinical outcomes. Given the complexity of these trends, it is essential to distinguish genuine changes in injury incidence from artifacts introduced by evolving hospital admission practices, underreporting, or shifts in employment patterns [8, 9]. Findings from this analysis will support data-driven resource allocation, inform targeted injury prevention strategies, and guide future research efforts [10, 11].
Methods
This was a retrospective cross-sectional study using the American College of Surgeons (ACS) NTDB. The NTDB Participant Use File (PUF) includes data submitted on pediatric and adult patients treated in Level I through III or undesignated trauma centers [12]. All NTDB data is fully de-identified. This analysis included all work-related trauma encounters for patients aged 14 years or older from 2017 to 2023.
Socio-demographic variables included age, gender, race, ethnicity, and insurance type. Injuries were described by occupational industry, which was further categorized by level of hazard (low, mixed exposure, high) to model the impact on clinical outcomes. Mechanisms of injury are described (excluding isolated burns due to changes in NTDB inclusion criteria during the analysis period). Other variables included trauma type, body region affected, injury severity score (ISS), and facility-level trauma designation. Minor trauma was defined as an ISS 1–15 and major trauma as ISS > 15. The Revised Trauma Score (RTS) was calculated using the Glasgow Coma Scale, systolic blood pressure, and respiratory rate [13]. Encounters were further classified by whether a protective device had been used. Clinical risk factors included whether blood alcohol was positive on admission and drug screen results. Clinical outcomes included hospital length of stay (LOS) and mortality.
The primary objective of the study was to describe temporal trends in occupational injuries. For each year from 2017 to 2023, the number and proportion of occupational injury cases was calculated using the overall total number of cases per calendar year as the denominator. Trends in occupational injury incidence were stratified by patient characteristics, injury patterns, and clinical factors and analyzed using a Mann–Kendall trend test.
The secondary objective was to evaluate the impact of study variables on clinical outcomes for the total analysis cohort. Mean hospital LOS (in days) was analyzed by gamma regression with a log link accounting for a positive, right-skewed distribution. It is interpreted as a multiplicative ratio of LOS for each variable level compared to the reference level while adjusting for all other variables. Results for LOS were reported as adjusted ratios with 95% confidence intervals (CI). Mortality was analyzed using multivariable logistic regression and reported as odds ratios (OR) with 95% CIs.
Both models used prespecified demographic, injury, and clinical variables. When needed, study variables were collapsed for interpretability and stability of regression models. Comorbidities were summed into an overall score (0–18) used for adjusted models, based on NTDB pre‑existing conditions affecting cardiovascular, pulmonary, renal, hepatic, and metabolic systems. Psychiatric conditions, substance use disorders, pregnancy, developmental conditions, and goals‑of‑care variables were excluded. The impact of COVID-19 on occupational injury was included in regression modeling, differentiating before (2017–2019) and after (2020–2023).
For the evaluation of temporal trends in demographic and injury characteristics, missing values were included in the denominator. Missing clinical outcome data was addressed using multiple imputations by chained equations, which incorporated all clinically relevant variables, including injury severity, to preserve associations between potential predictors and mortality [14–16]. To confirm this approach, a sensitivity analysis was performed, showing that using complete-case analysis would yield inflated and unstable estimates. A two-sided p-value of less than 0.05 was considered statistically significant. All analyses were performed with R software, version 4.5.3 (R Foundation for Statistical Computing). This study was determined to be exempt by the BayCare Health System Institutional Review Board.
Results
Significant trends in demographic characteristics associated with occupational injuries were detected during the analysis period. As shown in Table 1, injuries increased over time for the youngest (aged 14–19 years) and oldest (aged ≥ 60 years), while decreasing in other age groups. For females, a significant upward trend occurred from 10.9% to 12.8% from 2017 to 2023, respectively, while there was a downward trend for male patients over time from 89.1% to 87.2% (p = 0.016). Occupational injuries in White patients decreased from 61.3% in 2017 to 52.7% in 2023 (p = 0.003) while injuries increased in Hispanic patients from 24.6% to 30.3% (p = 0.007). Primary payor patterns showed increases in Medicare (3.1% to 4.0%, p = 0.016) and self-pay encounters (13.1% to 14.8%, p = 0.007), with a decline in private insurance (68.2% to 63.8%, p = 0.016).
Table 1.
Frequency of occupational injuries from 2017 through 2023 stratified by demographic variables
| Characteristic | Year | Tau | p-value | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | |||
| n=28,597 | n=31,067 | n=31,674 | n=29,193 | n=31,295 | n=33,259 | n=34,042 | |||
| Age (years) | |||||||||
| 14–19 | 667 (2.3%) | 769 (2.5%) | 829 (2.6%) | 808 (2.8%) | 857 (2.7%) | 917 (2.8%) | 996 (2.9%) | 0.810 | 0.016 |
| 20–29 | 5281 (18.5%) | 5729 (18.4%) | 5785 (18.3%) | 5245 (18%) | 5825 (18.6%) | 6304 (19%) | 6284 (18.5%) | 0.143 | 0.764 |
| 30–39 | 6058 (21.2%) | 6498 (20.9%) | 6607 (20.9%) | 6051 (20.7%) | 6525 (20.8%) | 6907 (20.8%) | 7076 (20.8%) | -0.619 | 0.072 |
| 40–49 | 5817 (20.3%) | 6130 (19.7%) | 6148 (19.4%) | 5885 (20.2%) | 6047 (19.3%) | 6258 (18.8%) | 6560 (19.3%) | -0.714 | 0.035 |
| 50–59 | 6349 (22.2%) | 6885 (22.2%) | 6761 (21.3%) | 6026 (20.6%) | 6480 (20.7%) | 6532 (19.6%) | 6413 (18.8%) | -0.905 | 0.007 |
| 60+ | 4425 (15.5%) | 5056 (16.3%) | 5544 (17.5%) | 5178 (17.7%) | 5561 (17.8%) | 6341 (19.1%) | 6713 (19.7%) | 1.000 | 0.003 |
| Gender | |||||||||
| Male | 25,490 (89.1%) | 27,417 (88.3%) | 27,900 (88.1%) | 25,812 (88.4%) | 27,438 (87.7%) | 29,114 (87.5%) | 29,690 (87.2%) | -0.810 | 0.016 |
| Female | 3107 (10.9%) | 3650 (11.7%) | 3774 (11.9%) | 3381 (11.6%) | 3846 (12.3%) | 4134 (12.4%) | 4343 (12.8%) | 0.810 | 0.016 |
| Non-binary | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 11 (0%) | 11 (0%) | 9 (0%) | 0.507 | 0.180 |
| Race/ethnicity | |||||||||
| Hispanic | 7021 (24.6%) | 7592 (24.4%) | 8022 (25.3%) | 7813 (26.8%) | 8578 (27.4%) | 9367 (28.2%) | 10,317 (30.3%) | 0.905 | 0.007 |
| White | 17,533 (61.3%) | 18,845 (60.7%) | 19,013 (60%) | 17,123 (58.7%) | 17,258 (55.1%) | 17,900 (53.8%) | 17,939 (52.7%) | -1.000 | 0.003 |
| Black | 2454 (8.6%) | 2839 (9.1%) | 2792 (8.8%) | 2552 (8.7%) | 2866 (9.2%) | 3122 (9.4%) | 2837 (8.3%) | 0.143 | 0.764 |
| Asian | 467 (1.6%) | 465 (1.5%) | 504 (1.6%) | 411 (1.4%) | 1405 (4.5%) | 1492 (4.5%) | 1537 (4.5%) | 0.429 | 0.230 |
| American Indian | 123 (0.4%) | 173 (0.6%) | 163 (0.5%) | 159 (0.5%) | 168 (0.5%) | 158 (0.5%) | 167 (0.5%) | -0.143 | 0.764 |
| Pacific Islander | 74 (0.3%) | 80 (0.3%) | 93 (0.3%) | 72 (0.2%) | 89 (0.3%) | 95 (0.3%) | 89 (0.3%) | 0.143 | 0.764 |
| Other | 925 (3.2%) | 1073 (3.5%) | 1087 (3.4%) | 1063 (3.6%) | 931 (3%) | 1125 (3.4%) | 1156 (3.4%) | -0.048 | 1.000 |
| Insurance type | |||||||||
| Private / Commercial | 19,504 (68.2%) | 21,016 (67.6%) | 21,430 (67.7%) | 19,586 (67.1%) | 20,145 (64.4%) | 21,422 (64.4%) | 21,706 (63.8%) | -0.810 | 0.016 |
| Medicaid | 1682 (5.9%) | 1770 (5.7%) | 1726 (5.4%) | 1812 (6.2%) | 2124 (6.8%) | 2196 (6.6%) | 2338 (6.9%) | 0.619 | 0.072 |
| Medicare | 873 (3.1%) | 989 (3.2%) | 1163 (3.7%) | 1020 (3.5%) | 1141 (3.6%) | 1275 (3.8%) | 1367 (4%) | 0.810 | 0.016 |
| Other Government | 645 (2.3%) | 853 (2.7%) | 925 (2.9%) | 834 (2.9%) | 1552 (5%) | 1552 (4.7%) | 1488 (4.4%) | 0.619 | 0.072 |
| Self-Pay | 3746 (13.1%) | 4086 (13.2%) | 4295 (13.6%) | 4113 (14.1%) | 4515 (14.4%) | 4693 (14.1%) | 5047 (14.8%) | 0.905 | 0.007 |
| Not Billed | 70 (0.2%) | 80 (0.3%) | 72 (0.2%) | 73 (0.3%) | 58 (0.2%) | 78 (0.2%) | 89 (0.3%) | 0.048 | 1.000 |
| Other | 2077 (7.3%) | 2273 (7.3%) | 2063 (6.5%) | 1755 (6%) | 1760 (5.6%) | 2043 (6.1%) | 2007 (5.9%) | -0.619 | 0.072 |
Trends in occupational injury characteristics from 2017 to 2023 are shown in Table 2. The construction industry accounted for a high number of occupational injuries, with the proportion of total injuries remaining stable over time. Injuries in the “other services” industry increased from 18.3% in 2017 to 20.9% in 2023 (p = 0.007); this category includes repair and maintenance, personal and laundry services, membership organizations, and private households. In contrast, a significant decrease in injuries occurred in agriculture, forestry, and fishing (7.4% to 6.3%, p = 0.016) and a smaller decrease in finance, insurance, and real estate.
Table 2.
Frequency of occupational injuries from 2017 through 2023 stratified injury, facility, and clinical characteristics
| Characteristic | Year | Tau | p-value | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | ||||
| n=28,597 | n=31,067 | n=31,674 | n=29,193 | n=31,295 | n=33,259 | n=34,042 | ||||
| Occupation industry | ||||||||||
| Low hazard | ||||||||||
| Education and Health Services | 780 (2.7%) | 940 (3%) | 989 (3.1%) | 821 (2.8%) | 890 (2.8%) | 1101 (3.3%) | 1008 (3%) | 0.333 | 0.368 | |
| Finance, Insurance, and Real Estate | 98 (0.3%) | 97 (0.3%) | 94 (0.3%) | 80 (0.3%) | 71 (0.2%) | 81 (0.2%) | 79 (0.2%) | -0.810 | 0.016 | |
| Government | 931 (3.3%) | 1095 (3.5%) | 1179 (3.7%) | 1079 (3.7%) | 944 (3%) | 1062 (3.2%) | 1092 (3.2%) | -0.238 | 0.548 | |
| Information Services | 69 (0.2%) | 46 (0.1%) | 46 (0.1%) | 43 (0.1%) | 84 (0.3%) | 110 (0.3%) | 80 (0.2%) | 0.238 | 0.548 | |
| Professional and Business Services | 774 (2.7%) | 832 (2.7%) | 820 (2.6%) | 731 (2.5%) | 839 (2.7%) | 903 (2.7%) | 966 (2.8%) | 0.333 | 0.368 | |
| Mixed exposure | ||||||||||
| Leisure and Hospitality | 705 (2.5%) | 777 (2.5%) | 794 (2.5%) | 595 (2%) | 765 (2.4%) | 887 (2.7%) | 911 (2.7%) | 0.429 | 0.230 | |
| Retail Trade | 1226 (4.3%) | 1297 (4.2%) | 1392 (4.4%) | 1322 (4.5%) | 1401 (4.5%) | 1452 (4.4%) | 1470 (4.3%) | 0.143 | 0.764 | |
| Wholesale Trade | 297 (1%) | 359 (1.2%) | 302 (1%) | 275 (0.9%) | 362 (1.2%) | 334 (1%) | 367 (1.1%) | 0.048 | 1.000 | |
| Other Services | 5231 (18.3%) | 5887 (18.9%) | 6020 (19%) | 5828 (20%) | 6425 (20.5%) | 6710 (20.2%) | 7099 (20.9%) | 0.905 | 0.007 | |
| High hazard | ||||||||||
| Agriculture, Forestry, Fishing | 2122 (7.4%) | 2091 (6.7%) | 2110 (6.7%) | 2009 (6.9%) | 2014 (6.4%) | 2112 (6.4%) | 2155 (6.3%) | -0.810 | 0.016 | |
| Construction | 10,133 (35.4%) | 10,824 (34.8%) | 11,345 (35.8%) | 10,507 (36%) | 11,065 (35.4%) | 11,579 (34.8%) | 12,102 (35.6%) | -0.048 | 1.000 | |
| Manufacturing | 2405 (8.4%) | 2654 (8.5%) | 2541 (8%) | 2338 (8%) | 2595 (8.3%) | 2812 (8.5%) | 2766 (8.1%) | -0.143 | 0.764 | |
| Natural Resources and Mining | 598 (2.1%) | 632 (2%) | 643 (2%) | 438 (1.5%) | 446 (1.4%) | 522 (1.6%) | 543 (1.6%) | -0.524 | 0.133 | |
| Transportation and Public Utilities | 3228 (11.3%) | 3536 (11.4%) | 3399 (10.7%) | 3127 (10.7%) | 3394 (10.8%) | 3594 (10.8%) | 3404 (10%) | -0.524 | 0.133 | |
| Injury mechanism | ||||||||||
| Falls | 11,840 (41.4%) | 12,879 (41.5%) | 13,281 (41.9%) | 12,094 (41.4%) | 12,673 (40.5%) | 13,898 (41.8%) | 14,608 (42.9%) | 0.333 | 0.368 | |
| MVT | 3462 (12.1%) | 3751 (12.1%) | 3784 (11.9%) | 3322 (11.4%) | 3613 (11.5%) | 4055 (12.2%) | 3953 (11.6%) | -0.238 | 0.548 | |
| Transport (non-MVT) | 957 (3.3%) | 1063 (3.4%) | 1105 (3.5%) | 911 (3.1%) | 1003 (3.2%) | 1091 (3.3%) | 1058 (3.1%) | -0.429 | 0.230 | |
| Struck by/against | 3515 (12.3%) | 3910 (12.6%) | 3969 (12.5%) | 3714 (12.7%) | 3859 (12.3%) | 4000 (12%) | 4340 (12.7%) | 0.143 | 0.764 | |
| Machinery | 2816 (9.8%) | 2878 (9.3%) | 2766 (8.7%) | 2561 (8.8%) | 2698 (8.6%) | 2702 (8.1%) | 2694 (7.9%) | -0.905 | 0.007 | |
| Cut/pierce | 2100 (7.3%) | 2190 (7%) | 2284 (7.2%) | 2317 (7.9%) | 2486 (7.9%) | 2506 (7.5%) | 2667 (7.8%) | 0.429 | 0.230 | |
| Overexertion | 68 (0.2%) | 98 (0.3%) | 98 (0.3%) | 93 (0.3%) | 94 (0.3%) | 123 (0.4%) | 139 (0.4%) | 0.619 | 0.072 | |
| All other | 3773 (13.2%) | 4249 (13.7%) | 4329 (13.7%) | 4107 (14.1%) | 4236 (13.5%) | 4325 (13%) | 4520 (13.3%) | -0.238 | 0.548 | |
| Missing | 66 (0.2%) | 49 (0.2%) | 58 (0.2%) | 74 (0.3%) | 633 (2%) | 559 (1.7%) | 63 (0.2%) | 0.333 | 0.368 | |
| Region | ||||||||||
| Extremities | 16,827 (58.8%) | 18,363 (59.1%) | 18,806 (59.4%) | 17,529 (60%) | 19,146 (61.2%) | 20,201 (60.7%) | 20,721 (60.9%) | 0.810 | 0.016 | |
| External | 15,696 (54.9%) | 17,091 (55%) | 17,309 (54.6%) | 16,190 (55.5%) | 17,307 (55.3%) | 18,350 (55.2%) | 19,115 (56.2%) | 0.524 | 0.133 | |
| Chest | 6573 (23%) | 7020 (22.6%) | 7030 (22.2%) | 6551 (22.4%) | 7369 (23.5%) | 7696 (23.1%) | 7885 (23.2%) | 0.333 | 0.368 | |
| Abdomen | 4356 (15.2%) | 4588 (14.8%) | 4723 (14.9%) | 4281 (14.7%) | 4750 (15.2%) | 5121 (15.4%) | 5175 (15.2%) | 0.238 | 0.548 | |
| Head and neck | 7644 (26.7%) | 8100 (26.1%) | 8153 (25.7%) | 7269 (24.9%) | 7709 (24.6%) | 8184 (24.6%) | 8101 (23.8%) | -1.000 | 0.003 | |
| Face | 3137 (11%) | 3469 (11.2%) | 3627 (11.5%) | 3420 (11.7%) | 3718 (11.9%) | 3967 (11.9%) | 4169 (12.2%) | 1.000 | 0.003 | |
| Trauma type | ||||||||||
| Blunt | 25,021 (87.5%) | 27,307 (87.9%) | 27,810 (87.8%) | 25,473 (87.3%) | 27,012 (86.3%) | 28,987 (87.2%) | 30,132 (88.5%) | -0.143 | 0.764 | |
| Penetrating | 2538 (8.9%) | 2690 (8.7%) | 2786 (8.8%) | 2838 (9.7%) | 3084 (9.9%) | 3143 (9.5%) | 3268 (9.6%) | 0.429 | 0.230 | |
| Other/unspecified | 934 (3.3%) | 980 (3.2%) | 964 (3%) | 770 (2.6%) | 538 (1.7%) | 528 (1.6%) | 534 (1.6%) | -1.000 | 0.003 | |
| Missing | 104 (0.4%) | 90 (0.3%) | 114 (0.4%) | 112 (0.4%) | 661 (2.1%) | 601 (1.8%) | 108 (0.3%) | 0.238 | 0.548 | |
| Injury severity | ||||||||||
| Minor trauma (ISS 1–15) | 24,184 (84.6%) | 26,375 (84.9%) | 26,938 (85%) | 24,762 (84.8%) | 26,305 (84.1%) | 27,946 (84%) | 28,622 (84.1%) | -0.429 | 0.230 | |
| Major trauma (ISS >15) | 4413 (15.4%) | 4692 (15.1%) | 4736 (15%) | 4431 (15.2%) | 4990 (15.9%) | 5313 (16%) | 5420 (15.9%) | 0.429 | 0.230 | |
| Facility-level designation | ||||||||||
| Level I | 13,241 (46.3%) | 14,080 (45.3%) | 14,791 (46.7%) | 13,498 (46.2%) | 14,083 (45%) | 15,347 (46.1%) | 16,235 (47.7%) | 0.048 | 1.000 | |
| Level II | 7114 (24.9%) | 7863 (25.3%) | 8204 (25.9%) | 7310 (25%) | 7765 (24.8%) | 8174 (24.6%) | 8019 (23.6%) | -0.619 | 0.072 | |
| Level III | 1370 (4.8%) | 1612 (5.2%) | 1714 (5.4%) | 1468 (5%) | 1630 (5.2%) | 1821 (5.5%) | 1833 (5.4%) | 0.524 | 0.133 | |
| Missing | 6872 (24%) | 7512 (24.2%) | 6965 (22%) | 6917 (23.7%) | 7817 (25%) | 7917 (23.8%) | 7955 (23.4%) | -0.143 | 0.764 | |
| Protective device | ||||||||||
| None | 22,951 (80.3%) | 25,131 (80.9%) | 25,808 (81.5%) | 24,222 (83%) | 25,629 (81.9%) | 27,243 (81.9%) | 28,147 (82.7%) | 0.714 | 0.035 | |
| Unknown | 2160 (7.6%) | 2154 (6.9%) | 2190 (6.9%) | 1740 (6%) | 2088 (6.7%) | 2136 (6.4%) | 2018 (5.9%) | -0.810 | 0.016 | |
| Lap belt | 1615 (5.6%) | 1809 (5.8%) | 1798 (5.7%) | 1525 (5.2%) | 1734 (5.5%) | 1819 (5.5%) | 1713 (5%) | -0.619 | 0.072 | |
| Shoulder belt | 1303 (4.6%) | 1428 (4.6%) | 1438 (4.5%) | 1264 (4.3%) | 1405 (4.5%) | 1510 (4.5%) | 1399 (4.1%) | -0.524 | 0.133 | |
| Airbag | 1284 (4.5%) | 1318 (4.2%) | 1354 (4.3%) | 1176 (4%) | 1243 (4%) | 1350 (4.1%) | 1360 (4%) | -0.619 | 0.072 | |
| Helmet | 637 (2.2%) | 709 (2.3%) | 688 (2.2%) | 630 (2.2%) | 755 (2.4%) | 916 (2.8%) | 982 (2.9%) | 0.524 | 0.133 | |
| Eye protection | 111 (0.4%) | 147 (0.5%) | 157 (0.5%) | 121 (0.4%) | 95 (0.3%) | 107 (0.3%) | 108 (0.3%) | -0.429 | 0.230 | |
| Clothing | 358 (1.3%) | 410 (1.3%) | 354 (1.1%) | 321 (1.1%) | 332 (1.1%) | 371 (1.1%) | 377 (1.1%) | -0.524 | 0.133 | |
| Gear | 310 (1.1%) | 384 (1.2%) | 330 (1%) | 284 (1%) | 267 (0.9%) | 295 (0.9%) | 330 (1%) | -0.619 | 0.072 | |
| Floatation | 2 (0%) | 6 (0%) | 2 (0%) | 5 (0%) | 1 (0%) | 3 (0%) | 1 (0%) | -0.429 | 0.230 | |
| Other | 248 (0.9%) | 244 (0.8%) | 262 (0.8%) | 240 (0.8%) | 253 (0.8%) | 236 (0.7%) | 228 (0.7%) | -0.714 | 0.035 | |
| Alcohol | ||||||||||
| Not tested | 16,679 (58.3%) | 18,254 (58.8%) | 18,616 (58.8%) | 17,118 (58.6%) | 17,709 (56.6%) | 19,108 (57.5%) | 19,232 (56.5%) | -0.524 | 0.133 | |
| No | 11,095 (38.8%) | 11,935 (38.4%) | 12,168 (38.4%) | 11,101 (38%) | 12,508 (40%) | 12,989 (39.1%) | 13,705 (40.3%) | 0.333 | 0.368 | |
| Yes | 823 (2.9%) | 878 (2.8%) | 890 (2.8%) | 974 (3.3%) | 1078 (3.4%) | 1162 (3.5%) | 1105 (3.2%) | 0.429 | 0.230 | |
| Drugs | ||||||||||
| Not tested | 20,086 (70.2%) | 22,826 (73.5%) | 23,354 (73.7%) | 21,291 (72.9%) | 23,230 (74.2%) | 24,791 (74.5%) | 25,203 (74%) | 0.619 | 0.072 | |
| None | 4899 (17.1%) | 5618 (18.1%) | 5710 (18%) | 5292 (18.1%) | 5324 (17%) | 5602 (16.8%) | 5894 (17.3%) | -0.238 | 0.548 | |
| Amphetamine | 496 (1.7%) | 460 (1.5%) | 416 (1.3%) | 440 (1.5%) | 510 (1.6%) | 493 (1.5%) | 503 (1.5%) | -0.238 | 0.548 | |
| Barbiturate | 151 (0.5%) | 109 (0.4%) | 65 (0.2%) | 53 (0.2%) | 56 (0.2%) | 53 (0.2%) | 61 (0.2%) | -0.810 | 0.016 | |
| Benzodiazepines | 361 (1.3%) | 386 (1.2%) | 343 (1.1%) | 326 (1.1%) | 351 (1.1%) | 355 (1.1%) | 363 (1.1%) | -0.714 | 0.035 | |
| Cannabinoid | 1068 (3.7%) | 1274 (4.1%) | 1302 (4.1%) | 1322 (4.5%) | 1439 (4.6%) | 1557 (4.7%) | 1604 (4.7%) | 1.000 | 0.003 | |
| Cocaine | 420 (1.5%) | 407 (1.3%) | 333 (1.1%) | 334 (1.1%) | 355 (1.1%) | 380 (1.1%) | 400 (1.2%) | -0.238 | 0.548 | |
| Ecstasy | 74 (0.3%) | 68 (0.2%) | 37 (0.1%) | 38 (0.1%) | 51 (0.2%) | 47 (0.1%) | 49 (0.1%) | -0.238 | 0.548 | |
| Methadone | 51 (0.2%) | 63 (0.2%) | 33 (0.1%) | 17 (0.1%) | 24 (0.1%) | 29 (0.1%) | 38 (0.1%) | -0.238 | 0.548 | |
| Methamphetamine | 133 (0.5%) | 181 (0.6%) | 127 (0.4%) | 150 (0.5%) | 199 (0.6%) | 174 (0.5%) | 178 (0.5%) | 0.238 | 0.548 | |
| Opioid | 515 (1.8%) | 533 (1.7%) | 502 (1.6%) | 518 (1.8%) | 520 (1.7%) | 524 (1.6%) | 620 (1.8%) | -0.143 | 0.764 | |
| Oxycodone | 77 (0.3%) | 100 (0.3%) | 113 (0.4%) | 100 (0.3%) | 93 (0.3%) | 113 (0.3%) | 124 (0.4%) | 0.429 | 0.230 | |
| Phencyclidine | 40 (0.1%) | 39 (0.1%) | 20 (0.1%) | 6 (0%) | 11 (0%) | 8 (0%) | 3 (0%) | -0.810 | 0.016 | |
| TCAs | 27 (0.1%) | 43 (0.1%) | 24 (0.1%) | 17 (0.1%) | 11 (0%) | 11 (0%) | 9 (0%) | -0.905 | 0.007 | |
| Other | 71 (0.2%) | 59 (0.2%) | 33 (0.1%) | 40 (0.1%) | 44 (0.1%) | 51 (0.2%) | 86 (0.3%) | 0.143 | 0.764 | |
| Unknown | 1470 (5.1%) | 345 (1.1%) | 351 (1.1%) | 271 (0.9%) | 232 (0.7%) | 218 (0.7%) | 157 (0.5%) | -1.000 | 0.003 | |
ISS = injury severity score; MVT = motor vehicle transport
No trends in mechanism of injury were observed, except for machinery-related injuries, which decreased from 9.8% in 2017 to 7.9% in 2023 (p = 0.007). Injuries to extremities were most frequent and increased from 58.8% in 2017 to 60.9% in 2023 (p = 0.016). Injuries to the face also increased (11% to 12.2%, p = 0.003), whereas those to the head and neck decreased (26.7% to 23.8%, p = 0.003). No significant trends were found in trauma type, trauma severity, or facility level designation. A protective device was not identified in most cases, increasing from 80.3% in 2017 to 82.7% in 2023 (p = 0.035). No specific devices were found to change over time. Alcohol and drug testing were not performed in most cases; this trend was stable from 2017 to 2023. When drug testing was completed, the frequency of cannabinoids steadily increased over time from 3.7% in 2017 to 4.7% in 2023 (p = 0.003). There were small decreasing trends for several other drugs, including benzodiazepines, barbiturates, tricyclic antidepressants, and phencyclidine.
For the total study cohort, the median hospital LOS was 3 days (interquartile range, 2–6 days). The impacts of study variables on LOS are shown in Table 3. Compared to patients aged 14–19 years, LOS increased progressively with age. Patients aged 20–29 years had an adjusted LOS 1.06 times those aged 14–19 years (ratio 1.06, 95% CI 1.03–1.10, p < 0.001). For patients aged ≥ 60, the adjusted LOS was 1.30 times that for aged 14–19 years (ratio 1.30, 95% CI 1.26–1.35, p < 0.001). Compared to male patients, females had a longer LOS (ratio 1.03, 95% CI 1.01–1.05, p = 0.001). Regarding race and ethnicity, compared to White patients, Black, Asian, and Hispanic patients had an increased LOS. With private insurance as the reference, self-pay or not billed encounters, Medicare, and other insurance had shorter LOS while Medicaid and other government insurance had a longer LOS. Compared to patients treated at level I facilities, those treated at level II (ratio 0.90, 95% CI 0.89–0.91, p < 0.001) and level III (ratio 0.65, 95% CI 0.63–0.66, p < 0.001) facilities had a shorter LOS. Whether injuries occurred pre-COVID or post-COVID did not influence LOS.
Table 3.
Impact of demographic, injury, and clinical factors on hospital length of stay
| Characteristic | Adjusted Ratio | 95% Confidence Interval | p-value |
|---|---|---|---|
| Age (years) | |||
| 14-19 | Reference | ||
| 20-29 | 1.06 | 1.03-1.10 | <0.001 |
| 30-39 | 1.12 | 1.08-1.16 | <0.001 |
| 40-49 | 1.17 | 1.13-1.21 | <0.001 |
| 50-59 | 1.23 | 1.19-1.27 | <0.001 |
| 60+ | 1.30 | 1.26-1.35 | <0.001 |
| Gender | |||
| Male | Reference | ||
| Female | 1.03 | 1.01-1.05 | 0.001 |
| Race/ethnicity | |||
| White | Reference | ||
| Black | 1.13 | 1.11-1.15 | <0.001 |
| Asian | 1.70 | 1.65-1.77 | <0.001 |
| Pacific Islander | 0.99 | 0.89-1.09 | 0.815 |
| American Indian | 1.06 | 0.98-1.14 | 0.137 |
| Hispanic | 1.12 | 1.11-1.14 | <0.001 |
| Other | 1.08 | 1.05-1.11 | <0.001 |
| Insurance type | |||
| Private | Reference | ||
| Medicaid | 1.03 | 1.00-1.05 | 0.024 |
| Medicare | 0.96 | 0.93-0.99 | 0.010 |
| Other government | 1.15 | 1.11-1.18 | <0.001 |
| Self-pay/Not billed | 0.86 | 0.84-0.87 | <0.001 |
| Other insurance | 0.97 | 0.95-0.99 | 0.017 |
| Facility-level designation | |||
| Level I | Reference | ||
| Level II | 0.90 | 0.89-0.91 | <0.001 |
| Level III | 0.65 | 0.63-0.66 | <0.001 |
| Missing | 0.93 | 0.92-0.95 | <0.001 |
| Occupation industry | |||
| Low hazard | Reference | ||
| Mixed hazard | 1.08 | 1.06-1.10 | <0.001 |
| High hazard | 1.11 | 1.09-1.13 | <0.001 |
| Injury mechanism | |||
| Falls | Reference | ||
| MVT (all) | 1.10 | 1.08-1.13 | <0.001 |
| Transport (non-MVT) | 1.15 | 1.12-1.19 | <0.001 |
| Struck by/against | 0.83 | 0.82-0.84 | <0.001 |
| Machinery | 0.82 | 0.81-0.84 | <0.001 |
| Cut/pierce | 0.56 | 0.54-0.59 | <0.001 |
| All other | 0.91 | 0.89-0.92 | <0.001 |
| Trauma type | |||
| Blunt | Reference | ||
| Penetrating | 1.05 | 1.00-1.10 | 0.033 |
| Other/unspecified | 1.11 | 1.07-1.15 | <0.001 |
| Missing | 1.35 | 1.27-1.44 | <0.001 |
| Injury severity | |||
| Minor trauma (ISS 1–15) | Reference | ||
| Major trauma (ISS >15) | 2.21 | 2.17-2.24 | <0.001 |
| Body region | |||
| Single region | Reference | ||
| Multiple regions | 1.11 | 1.10-1.13 | <0.001 |
| Revised Trauma Score | 0.89 | 0.88-0.89 | <0.001 |
| Comorbidity Index | 1.10 | 1.09-1.11 | <0.001 |
| Time period | |||
| Pre-COVID (2017-2019) | Reference | ||
| Post-COVID (2020-2023) | 0.99 | 0.98-1.00 | 0.054 |
| Alcohol | |||
| No/Not tested | Reference | ||
| Positive | 1.13 | 1.10-1.16 | <0.001 |
| Drugs | |||
| No/Not tested | Reference | ||
| Positive | 1.21 | 1.18-1.23 | <0.001 |
| Protective device | |||
| None | Reference | ||
| Unknown | 1.03 | 1.01-1.05 | 0.013 |
| Vehicle restraint | 0.90 | 0.86-0.94 | <0.001 |
| Head/eye | 0.94 | 0.90-0.97 | <0.001 |
| Body | 0.98 | 0.94-1.02 | 0.313 |
| Other | 1.06 | 0.99-1.13 | 0.085 |
| Multiple devices | 0.90 | 0.88-0.93 | <0.001 |
ISS = injury severity score; MVT = motor vehicle transport
Compared to low-hazard occupations, those with mixed physical demand (ratio 1.08, 95% CI 1.06–1.10, p < 0.001) and high risk (ratio 1.11, 95% CI 1.09–1.13, p < 0.001) had a longer LOS. Motor vehicle injuries (ratio 1.10, 95% CI 1.08–1.13, p < 0.001) and other transportation injuries (ratio 1.15, 95% CI 1.12–1.19, p < 0.001) had longer LOS compared with falls, whereas a significantly shorter hospital stay was found for other injury mechanisms. For injury type, compared to blunt trauma, penetrating trauma had a longer LOS (ratio 1.05, 95% CI 1.00-1.10, p = 0.033). Patients with ISS > 15 (ratio 2.21, 95% CI 2.17–2.24, p < 0.001) and multisite injuries (ratio 1.11, 95% CI 1.10–1.13, p < 0.001) had longer LOS. This is also reflected by the weighted RTS, and similar with more comorbidities (ratio 1.10, 95% CI 1.09–1.11, p < 0.001), presence of alcohol (ratio 1.13, 95% CI 1.10–1.16, p < 0.001), and other drugs (ratio 1.21, 95% CI 1.18–1.23, p < 0.001). Patients using multiple protective devices had a shorter LOS (ratio 0.90, 95% CI 0.88–0.93, p < 0.001).
The effects of study variables on mortality associated with occupational injury are shown in Table 4. Compared to patients aged 14–19 years, those aged ≥ 40 years had significantly increased odds of mortality, with the highest odds among those aged ≥ 60 years (OR 3.96, 95% CI 2.82–5.55, p < 0.001). Compared to male patients, females had lower odds of mortality (OR 0.65, 95% CI 0.54–0.79, p < 0.001). Black patients had a higher mortality than White patients (OR 1.20, 95% CI 1.02–1.42, p = 0.032). Other race categories were not associated with higher mortality from occupational injury. Compared to patients with private insurance, those with public insurance, particularly Medicare, had higher odds of mortality (OR 1.90, 95% CI 1.58–2.29, p < 0.001). Self-pay/not billed encounters had twice the odds of mortality (OR 2.04, 95% CI 1.76–2.38, p < 0.001). Whether encounters occurred before or after COVID did not influence mortality.
Table 4.
Impact of demographic, injury, and clinical factors on mortality
| Characteristic | Adjusted OR | 95% Confidence Interval | p-value |
|---|---|---|---|
| Age (years) | |||
| 14-19 | Reference | ||
| 20-29 | 1.28 | 0.91-1.80 | 0.164 |
| 30-39 | 1.31 | 0.93-1.85 | 0.116 |
| 40-49 | 1.74 | 1.24-2.45 | 0.001 |
| 50-59 | 2.63 | 1.88-3.67 | <0.001 |
| 60+ | 3.96 | 2.82-5.55 | <0.001 |
| Gender | |||
| Male | Reference | ||
| Female | 0.65 | 0.54-0.79 | <0.001 |
| Race/ethnicity | |||
| White | Reference | ||
| Black | 1.20 | 1.02-1.42 | 0.032 |
| Asian | 0.93 | 0.73-1.19 | 0.566 |
| Pacific Islander | 1.77 | 0.85-3.69 | 0.130 |
| American Indian | 1.49 | 0.80-2.75 | 0.207 |
| Hispanic | 1.09 | 0.98-1.23 | 0.116 |
| Other | 0.94 | 0.72-1.21 | 0.610 |
| Insurance type | |||
| Private | Reference | ||
| Medicaid | 1.21 | 1.01-1.45 | 0.043 |
| Medicare | 1.90 | 1.58-2.29 | <0.001 |
| Other government | 0.83 | 0.65-1.08 | 0.163 |
| Self-pay/Not billed | 2.04 | 1.76-2.38 | <0.001 |
| Other insurance | 1.33 | 1.10-1.62 | 0.004 |
| Facility-level designation | |||
| Level I | Reference | ||
| Level II | 1.15 | 1.03-1.28 | 0.016 |
| Level III | 1.38 | 1.08-1.77 | 0.010 |
| Missing | 1.01 | 0.89-1.13 | 0.925 |
| Occupation industry | |||
| Low hazard | Reference | ||
| Mixed hazard | 0.99 | 0.82-1.18 | 0.880 |
| High hazard | 0.88 | 0.74-1.04 | 0.138 |
| Injury mechanism | |||
| Falls | Reference | ||
| MVT (all) | 1.00 | 0.86-1.16 | 0.993 |
| Transport (non-MVT) | 1.27 | 1.00-1.61 | 0.048 |
| Struck by/against | 1.03 | 0.88-1.21 | 0.702 |
| Machinery | 0.81 | 0.63-1.04 | 0.092 |
| Cut/pierce | 0.20 | 0.12-0.33 | <0.001 |
| All other | 1.13 | 0.91-1.39 | 0.273 |
| Trauma type | |||
| Blunt | Reference | ||
| Penetrating | 2.50 | 1.86-3.35 | <0.001 |
| Other/unspecified | 0.94 | 0.69-1.29 | 0.704 |
| Missing | 1.04 | 0.67-1.61 | 0.872 |
| Injury severity | |||
| Minor trauma (ISS 1–15) | Reference | ||
| Major trauma (ISS >15) | 7.88 | 7.00-8.86 | <0.001 |
| Body region | |||
| Single region | Reference | ||
| Multiple regions | 1.23 | 1.06-1.42 | 0.006 |
| Revised Trauma Score | 0.35 | 0.34-0.36 | <0.001 |
| Comorbidity Index | 1.13 | 1.06-1.20 | <0.001 |
| Time period | |||
| Pre-COVID (2017-2019) | Reference | ||
| Post-COVID (2020-2023) | 0.94 | 0.86-1.04 | 0.226 |
| Alcohol | |||
| No/Not tested | Reference | ||
| Positive | 1.00 | 0.82-1.23 | 0.976 |
| Drugs | |||
| No/Not tested | Reference | ||
| Positive | 0.62 | 0.54-0.72 | <0.001 |
| Protective device | |||
| None | Reference | ||
| Unknown | 1.01 | 0.86-1.18 | 0.931 |
| Vehicle restraint | 1.04 | 0.75-1.43 | 0.823 |
| Head/eye | 0.73 | 0.52-1.02 | 0.069 |
| Body | 1.21 | 0.81-1.80 | 0.361 |
| Other | 1.43 | 0.86-2.40 | 0.170 |
| Multiple devices | 0.67 | 0.53-0.85 | <0.001 |
ISS = injury severity score; MVT = motor vehicle transport
Occupational industry categorized by hazard risk was not significantly associated with mortality. Further, compared with falls, transportation-related injuries had higher mortality (OR 1.27, 95% CI 1.00-1.61, p = 0.048) while cut/pierce injuries had lower mortality (OR 0.20, 95% CI 0.12–0.33, p < 0.001). When compared to encounters at level I trauma facilities, those at level II (OR 1.15, 95% CI 1.03–1.28, p = 0.016) and level III facilities (OR 1.38, 95% CI 1.08–1.77, p = 0.010) had higher odds of mortality. For injury type, compared to blunt trauma, penetrating trauma had an increased odds of mortality (OR 2.50, 95% CI 1.86–3.35, p < 0.001). Severe injuries indicated by an ISS > 15 (OR 7.88, 95% CI 7.00-8.86, p < 0.001) and multisite injuries (OR 1.23, 95% CI 1.06–1.42, p = 0.006) had higher mortality. This is also reflected by the weighted RTS (OR 0.35, 95% CI 0.34–0.36, p < 0.001) and higher comorbidity index (OR 1.13, 95% CI 1.06–1.20, p < 0.001). Patients using multiple protective devices had lower mortality (OR 0.67, 95% CI 0.53–0.85, p < 0.001).
Discussion
Analysis of NTDB data from 2017 to 2023 revealed significant demographic shifts in occupational trauma, including increased representation of patients aged 14–19 and ≥ 60 years, females, Hispanic individuals, and those insured through Medicare or self-pay rather than private insurance.
Increased occupational trauma among patients aged 14–19 years may reflect earlier workforce entry following the COVID-19 pandemic, driven by labor shortages, economic pressures, and growing participation in temporary or “gig” employment. However, engagement in gig employment has also been increasing since the late 2010s. “Gig” employment is categorized as work performed outside the traditional employer-employee relationship, usually within the transportation, delivery, e-commerce, or temporary labor sectors [17]. These workers may experience increased physical and psychosocial stressors as they often work longer hours to generate a consistent income without receiving traditional employment benefits such as health insurance from their employers [18]. Reduced private insurance use among younger workers may reflect loss of employer-sponsored coverage, as temporary, part-time, and gig workers are less likely to receive insurance or workers’ compensation. This may partially account for the shift of health insurance towards self-pay. Younger workers are at increased risk for occupational injury in general because of limited workplace experience, reduced familiarity with safety practices, and less job training or supervision [19].
Reduced employer-sponsored coverage may have led to underreporting minor injuries, while more severe injuries continued to be captured by trauma centers and the NTDB. These findings align with prior research and highlight the need for stronger protections for younger workers.
Among workers aged ≥ 60 years, the increase in serious occupational injuries from their middle-aged counterparts may reflect delayed retirement or prolonged workforce participation coupled with increased physiologic risk with aging [20]. Many older workers have jobs that are physically demanding or expose them to unhealthy or hazardous conditions [21]. Older workers are more likely to have reduced physiologic reserve, mobility, balance, and reaction time compared with younger populations, which, when combined with dangerous working conditions, may contribute to greater injury severity when trauma occurs [20, 22]. The increase in Medicare utilization is consistent with this age group remaining in the workforce beyond traditional retirement age.
Increased Hispanic representation in occupational trauma may be driven by greater participation in higher-risk jobs. Data analyzed from the National Institute of Health shows that injuries and fatalities on construction sites increased by 107.1% among Hispanic workers, compared with 16.5% among non-Hispanic workers [23]. They are twice as likely to suffer from non-fatal workplace injuries, possibly a result of disproportionate employment of Hispanics in higher-risk roles or a lack of training for those who do not speak English as their first language [24].
The increase in serious occupational trauma among workers aged 14–19 years, ≥ 60 years, and Hispanic individuals highlights the need for targeted injury prevention strategies. The absence of a comparable increase among middle-aged workers reflects greater job retention, less transition to gig work, or increased remote employment after COVID-19. Enhanced safety training, workplace supervision, age-specific prevention, and multilingual education may reduce occupational injury risk. The shift away from private insurance toward Medicare and self-pay raises concern for gaps in workers’ compensation coverage and healthcare access following workplace injury.
Occupational injury outcomes varied with patient demographics, injury characteristics, and clinical severity. Median hospital LOS was 3 days, with progressively longer stays among older adults, particularly those aged ≥ 60 years, consistent with age being among the strongest determinants of trauma-related LOS [25]. Female patients had slightly longer LOS but lower mortality than males, which may reflect sex-based differences in discharge disposition and complication rates rather than recovery differences, as literature demonstrates higher mortality, organ failure, and sepsis rates among males after trauma [26]. Higher injury severity, multiple body region involvement, and penetrating mechanism were all associated with both prolonged hospitalization and increased mortality, consistent with large registry studies identifying ISS ≥ 26 as a major mortality threshold [27]. Motor vehicle and transportation-related injuries were associated with longer stays compared with falls, likely reflecting greater complexity in multisystem injuries. Workers in high-risk occupations experienced longer LOS, aligning with evidence that occupational physical demands contribute to injury severity and prolonged recovery [28]. Insurance status was independently associated with mortality, with higher rates among publicly insured and self-pay patients, consistent with national data demonstrating increased mortality among uninsured trauma patients [29]. These findings underscore that LOS and mortality following work-related injury are shaped by interacting demographic, occupational, and injury-specific factors, emphasizing the need for targeted prevention and early identification of high-risk patients.
Several limitations warrant consideration. The NTDB relies on administrative coding, introducing potential misclassification of variables such as injury severity, occupational status, and payor type. Reporting practices and data completeness also vary across facilities. Selection bias is possible because the NTDB includes only patients treated at participating trauma centers, overrepresenting severe injuries and underrepresenting those managed in non-trauma hospitals, urgent care centers, or outpatient settings. Consequently, these findings may not be generalizable to all work-related injuries in the United States. Missingness in trauma registries often reflects hospital characteristics or reporting quality; therefore, selected missing variables were analyzed using a missing-category approach rather than imputation. Nevertheless, the 16–17% missingness in mortality data remains a concern and may bias certain subgroups. Finally, the retrospective design precludes causal inference. Although shifts in injury demographics, severity, and payor patterns were observed following COVID-19, these trends may reflect broader workforce and societal changes. Despite these limitations, the NTDB remains the largest national trauma registry. While some misclassification, reporting variability, and selection bias are unavoidable in large administrative databases, standardized registry definitions, broad trauma center participation, and multivariable risk-adjusted analyses strengthen the reliability of these findings [30].
Conclusion
In this national trauma registry analysis, occupational trauma patterns changed meaningfully from 2017 to 2023, with increasing representation among younger workers, older workers, female patients, Hispanic patients, and patients with Medicare or self-pay status. Although overall injury severity and facility-level designation remained relatively stable, clinical outcomes were strongly associated with age, injury severity, comorbidity burden, mechanism, trauma type, and protective-device use. These findings highlight the need for targeted occupational injury prevention strategies, improved surveillance of vulnerable worker populations, and further research incorporating workforce denominator data to distinguish changes in injury risk from changes in employment and reporting patterns.
Acknowledgements
The content reproduced from the NTDB PUF remains the full and exclusive copyrighted property of the American College of Surgeons. The American College of Surgeons is not responsible for any claims arising from works based on the original data, text, tables, or figures.
Abbreviations
- NTDB
National Trauma Data Bank
- ACS
American College of Surgeons
- PUF
Participant Use File
- ISS
Injury Severity Score
- RTS
Revised Trauma Score
- LOS
Length of Stay
Author contributions
AFP, JC, RO, and SG contributed to the study concept and design and worked on the acquisition of data; JC and RO contributed to the analysis and interpretation of the data; all authors drafted the manuscript; TJ and SG provided critical revision of the manuscript for important intellectual content. The authors read and approved the final manuscript.
Funding
The authors declare that no funding was received for the conduct of this study or the preparation of this manuscript.
Data availability
The data that support the findings of this study are available from the American College of Surgeons (ACS), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of ACS.
Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the Declaration of Helsinki and was exempted by the BayCare Health System Institutional Review Board. The requirement for written informed consent was waived as the obtained data was de-identified.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Chen Chen and Alexnys Fernandez-Pacheco contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the American College of Surgeons (ACS), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of ACS.