Abstract
Purpose:
To provide insight into the current scope of workplace-related eye injuries (WREIs) by describing the demographic profiles and causes of WREIs from the years 2011 through 2020.
Methods:
The US Bureau of Labor Statistics (BLS) dataset on WREIs injuries was used. Descriptive data generated included the frequency of eye injuries, the setting, and demographic data.
Results:
The BLS reported an estimated 237 590 WREIs in the study timeframe. In that time, the incidence fell from 2.4 to 1.7 per 10 000 workers. These injuries commonly occurred in men (77.1%), White individuals (36.3%), those aged 25 to 34 years (26.9%), and those in the service (23.0%) and production (18.5%) industries. On average, WREIs resulted in a median of 2 missed workdays, with only 5.0% missing more than 1 month of work. Between 2019 and 2020, there was a 15.6% reduction in total WREIs in the US but a 39.3% increase in WREIs among healthcare workers.
Conclusions:
Men, White individuals, and younger workers might be at increased risk for WREIs. Public health interventions targeted toward improving access to and the quality of protective equipment in these groups and in fields involved in the primary or secondary sectors of industry and healthcare might be the most cost-effective measure to reduce the impact of WREIs on the US workforce.
Keywords: ocular trauma, open globe, work injury, personal protective equipment
Introduction
Eye injuries are a significant source of morbidity around the world. The World Health Organization Program for the Prevention of Blindness suggests that nearly 55 million eye injuries that restrict activity for more than 1 day occur annually. 1 From 2006 to 2013 in the US, eye trauma was listed as the primary diagnosis for more than 5.6 million emergency department visits. 2
One of the major sources of morbidity from eye trauma is the potential for permanent diminished acuity in this setting. In a study of 11 320 patients in the US Eye Injury Registry, 27% of eyes with a serious injury had a final visual acuity worse than 20/200 despite treatment. 3 Loss of vision also leads to further morbidity from poor psychological outcomes, disability, and reduced quality of life. 4 Eye injuries also stress the healthcare system; from 2006 to 2014, the direct costs associated with treating ocular injuries were estimated to be $9.5 billion. 5
Previous studies have identified the workplace as a common setting for these injuries, and such workplace-related eye injuries (WREIs) can occur in all sectors, affecting individuals across every socioeconomic category.4,6,7 They cause widely variable levels of morbidity, ranging from transient soreness to complete blindness.8,9 Furthermore, WREIs force employees to miss work, creating additional societal costs in the form of missed wages and reduced gross domestic output. Fortunately, WREIs are largely preventable thanks to improvements in the access to and quality of protective equipment, such as face shields and goggles. 10 Further improvements in the availability of protective equipment are possible, however, and targeted interventions aimed at providing access to those at greatest risk for WREIs might help reduce the incidence over time.
Thus, it is important to understand the epidemiology of WREIs to provide insight into where public health interventions can be most effective. Unfortunately, there have been few high-powered studies specifically focusing on WREIs in the US in the past 2 decades. The most recent article was published by Forrest and Cali 11 in 2009, and they used a dataset from 2002.
Workplaces have evolved, and protective technology has improved; therefore, it is important to update our understanding of the impact WREIs have on the US workforce to evaluate the need for further intervention. Toward this goal, this study used workplace injury data from the US Bureau of Labor Statistics (BLS), which includes information from 2011 through 2020, to elucidate the extent and severity of WREIs in the US.
Methods
The Vanderbilt University Medical Center Institutional Review Board exempted this study from review because it did not meet the criteria for human research; therefore, informed consent was not required. No identifiable data were used in the study, which complied with the tenets of the Declaration of Helsinki.
The BLS of the US Centers for Disease Control and Prevention maintains a large database on labor market activity, working conditions, and price changes in the US economy. This includes information on workplace-related injuries and illnesses, which is collected annually via the Survey of Occupational Injuries and Illnesses (SOII) and the Census of Fatal Occupation Injuries. The data reported represent weighted national estimates of the numbers and incidence rates of nonfatal injuries and illnesses for all types of cases based on the information provided by various employer injury and illness logs.
Samples are preselected at the beginning of the calendar year by individual states, and employers are notified at that time of their participation in the survey. The samples are selected to be representative of those working in all private industries, state government, and local government in that state. Data collection is completed in the summer of the corresponding data year, and the data are published online (https://wwwn.cdc.gov/Niosh-whc/source/bls) for public access.
Data tables can be customized based on desired datapoints via the occupational injuries and illnesses profile tool, including by organ or body part affected by the injury. This study used the BLS code for “eye” to construct tables on eye injuries in the US workplace.
The SARS-CoV2 pandemic began forcing US workers out of their workplaces in early 2020. Thus, this study specifically evaluated the changes in WREIs in this period to determine the pandemic’s impact on WREIs. It also looked at the change in the incidence of WREIs in healthcare workers.
Descriptive data generated included the frequency of eye injuries, the setting, and demographics. Data analysis and graph construction were performed in Excel software (Microsoft Corp). Statistical analysis was performed on the reported subcategories and included χ2 analyses for categorical data, single-factor analysis of variance, and CI construction. Results with a P value less than 0.05 and a respective CI that did not include the null hypothesis value of 1.0 were considered statistically significant.
The incidence rates were calculated using the number of injuries divided by the total estimated work hours of US employees and then multiplied by the approximate number of hours worked by 10 000 full-time hours (roughly 20 000 000). Changes over time were calculated as follows: (final year − initial year)/(initial year) × 100%.
Results
Demographic Profiles and Injury Characteristics of WREIs
An estimated total of 237 590 WREIs were reported by the BLS between the years 2011 and 2020 (Figure 1). The average incidence rate of WREIs during this time was 2.2 per 10 000 workers. Table 1 shows the demographic data of workers who had WREIs. Of the injuries, 77.1% occurred in men (P < .001), 36.3% in White workers, and 26.9% in those aged 25 years through 34 years; all 3 factors accounted for the most injuries in each of their respective demographic categories.
Figure 1.
Total number of workplace-related eye injuries by year.
Table 1.
Demographic Characteristics of Workplace-Related Eye Injuries.
| Years, n (%) | Change (%) | ||||||
|---|---|---|---|---|---|---|---|
| Characteristic | 2011–2020 | 2011 | 2019 | 2020 | 2011–2019 | 2019–2020 | 2011–2020 |
| Total | 237 590 | 24 930 | 21 920 | 18 510 | −12.1 | −15.6 | −25.8 |
| Sex | |||||||
| Male | 183 120 (77.1) | 18 940 (76.0) | 16 820 (76.7) | 13 360 (76.7) | −11.2 | −20.6 | −29.5 |
| Female | 53 540 (22.5) | 5880 (23.6) | 5020 (23.2) | 4890 (23.2) | −14.6 | −2.6 | −16.8 |
| Age (y) | |||||||
| <20 | 6540 (2.8) | 520 (2.1) | 570 (2.6) | 780 (4.2) | 9.6 | 36.8 | 50.0 |
| 20–24 | 27 530 (11.6) | 3090 (12.4) | 2370 (10.8) | 1860 (10.0) | −23.3 | −21.5 | −39.8 |
| 25–34 | 63 930 (26.9) | 6400 (25.7) | 5990 (27.3) | 5030 (27.2) | −6.4 | −16.0 | −21.4 |
| 35–44 | 50 780 (21.4) | 6080 (24.4) | 5090 (23.2) | 4060 (21.9) | −16.3 | −20.2 | −33.2 |
| 45–54 | 49 180 (20.7) | 5740 (23.0) | 3920 (17.9) | 3210 (17.3) | −31.7 | −18.1 | −44.1 |
| 55+ | 27 120 (11.4) | 2460 (9.9) | 2930 (13.4) | 2650 (14.3) | 19.1 | −9.6 | 7.7 |
| Race | |||||||
| White | 86 140 (36.3) | 10 550 (42.3) | 6910 (31.5) | 6010 (32.5) | −34.5 | −13.0 | −43.0 |
| Black or African American | 18 150 (7.6) | 2260 (9.1) | 1720 (7.8) | 1540 (8.3) | −23.9 | −10.5 | −31.9 |
| Hispanic or Latino | 41 800 (17.6) | 2970 (11.9) | 3450 (15.7) | 3040 (16.4) | 16.2 | −11.9 | 2.4 |
| Asian | 2840 (1.2) | 240 (1.0) | 270 (1.2) | 220 (1.2) | 12.5 | −18.5 | −8.3 |
| Native Hawaiian or other Pacific Islander | 860 (0.4) | 60 (0.2) | 70 (0.3) | 80 (0.4) | 16.7 | 14.3 | 33.3 |
| American Indian or Alaska Native | 1040 (0.4) | 80 (0.3) | 60 (0.3) | 80 (0.4) | −25.0 | 33.3 | 0.0 |
| Not reported | 86 440 (36.4) | 8730 (35.0) | 9410 (42.9) | 7460 (40.3) | 7.8 | −20.7 | −14.5 |
Table 2 shows the occupational characteristics of those who experienced WREIs. WREIs mostly commonly affected those working in the service (23.0%), production (18.5%), installation/maintenance (14.4%), transportation (13.6%), and construction (12.3%) industries (Figure 2). Individuals who had spent between 1 year and 5 years working for their current employer had more WREIs (34.6%) than those working with their employers for fewer than 3 months (12.3%), 3 months through 11 months (20.8%), and more than 5 years (30.2%). On average, WREIs resulted in a median of 2 missed workdays; the highest percentage of workers (37.2%) missed 1 day, with only 5.0% missing more than 1 month.
Table 2.
Distribution of Occupations Affected by Workplace-Related Eye Injuries.
| Years, n (%) | Change (%) | ||||||
|---|---|---|---|---|---|---|---|
| Characteristic | 2011–2020 | 2011 | 2019 | 2020 | 2011–2019 | 2019–2020 | 2011–2020 |
| Total | 237 590 | 24 930 | 21 920 | 18 510 | −12.1 | −15.6 | −25.8 |
| Occupation | |||||||
| Management, business, and financial | 3280 (1.4) | 310 (1.2) | 290 (1.3) | 260 (1.4) | −6.5 | −10.3 | −16.1 |
| Computer, engineering, and science | 1590 (0.7) | 120 (0.5) | 180 (0.8) | 140 (0.8) | 50.0 | −22.2 | 16.7 |
| Educational instruction and library occupations | 6350 (2.7) | 490 (2.0) | 620 (2.8) | 570 (3.1) | 26.5 | −8.1 | 16.3 |
| Healthcare practitioners and technical | 6670 (2.8) | 710 (2.8) | 560 (2.6) | 780 (4.2) | −21.1 | 39.3 | 9.9 |
| Service | 54 540 (23.0) | 6080 (24.4) | 5440 (24.8) | 4490 (24.3) | −10.5 | −17.5 | −26.2 |
| Sales and related | 8090 (3.4) | 910 (3.7) | 1210 (5.5) | 630 (3.4) | 33.0 | −47.9 | −30.8 |
| Office and administrative support | 8630 (3.6) | 1050 (4.2) | 550 (2.5) | 350 (1.9) | −47.6 | −36.4 | −66.7 |
| Farming, fishing, and forestry | 8080 (3.4) | 600 (2.4) | 660 (3.0) | 650 (3.5) | 10.0 | −1.5 | 8.3 |
| Construction and extraction | 29 240 (12.3) | 3090 (12.4) | 2150 (9.8) | 2510 (13.6) | −30.4 | 16.7 | −18.8 |
| Installation, maintenance, and repair | 34 260 (14.4) | 3830 (15.4) | 2900 (13.2) | 2560 (13.8) | −24.3 | −11.7 | −33.2 |
| Production | 44 050 (18.5) | 4760 (19.1) | 3630 (16.6) | 3080 (16.6) | −23.7 | −15.2 | −35.3 |
| Transportation and material moving | 32 220 (13.6) | 2970 (11.9) | 3730 (17.0) | 2480 (13.4) | 25.6 | −33.5 | −16.5 |
| Length of service with employer | |||||||
| <3 mo | 29 340 (12.3) | 2510 (10.1) | 2430 (10.7) | 2530 (13.7) | −6.8 | 8.1 | 0.8 |
| 3–11 mo | 49 430 (20.8) | 5010 (20.1) | 5320 (24.3) | 4050 (21.9) | 6.2 | −23.9 | −19.2 |
| 1–5 y | 82 290 (34.6) | 8870 (35.6) | 7650 (34.9) | 6410 (34.6) | −13.8 | −16.2 | −27.7 |
| More than 5 y | 71 770 (30.2) | 8090 (32.5) | 6100 (27.8) | 4930 (26.6) | −24.6 | −19.2 | −39.1 |
| Time away from work (d) | |||||||
| 1 | 88 290 (37.2) | 10 110 (40.6) | 7510 (34.3) | 5860 (31.7) | −25.7 | −22.0 | −42.0 |
| 2–10 | 121 690 (51.2) | 11 980 (48.1) | 11 360 (51.8) | 9870 (53.3) | −5.2 | −13.1 | −17.6 |
| 11–30 | 15 850 (6.7) | 1630 (6.5) | 1820 (8.3) | 1730 (9.3) | 11.7 | −4.9 | 6.1 |
| ≥31 | 11 800 (5.0) | 1220 (4.9) | 1230 (5.6) | 1060 (5.7) | 0.8 | −13.8 | −13.1 |
| Median days away from work | — | 2 | 2 | 3 | — | — | — |
Figure 2.
Total number of workplace relayed eye injuries by occupation type.
The leading cause of injury was contact with an object or piece of equipment (64.6%) (P < .05) (Table 3 and Figure 3). Other common events leading to WREIs included exposure to harmful substances or environments (26.0%) and workplace violence (6.6%). The most common sources of WREIs were chemicals (16.2%) (P < .05), machine parts or materials (7.0%), and persons other than the injured party (6.6%). WREIs occurred most frequently in the middle of the workweek, specifically Tuesday (19.5%) and Wednesday (19.5%). They were also most common after the injured party had worked between 2 hours and 4 hours (20.0%) of his or her shift.
Table 3.
Injury Characteristics and Timing of Workplace-Related Eye Injuries.
| Years, n (%) | Change (%) | ||||||
|---|---|---|---|---|---|---|---|
| Characteristic | 2011–2020 | 2011 | 2019 | 2020 | 2011–2019 | 2019–2020 | 2011–2020 |
| Total | 237 590 | 24 930 | 21 920 | 18 510 | −12.1 | −15.6 | −25.8 |
| Event/exposure | |||||||
| Violence and other injuries by persons or animal | 15 660 (6.6) | 1730 (6.9) | 1400 (6.4) | 1200 (6.5) | −19.1 | −14.3 | −30.6 |
| Transportation incidents | 1190 (0.5) | 140 (0.6) | 50 (0.2) | 40 (0.2) | −64.3 | −20.0 | −71.4 |
| Fires, explosions | 520 (0.2) | 30 (0.1) | 30 (0.1) | 70 (0.4) | 0.0 | 133.3 | 133.3 |
| Falls, slips, trips | 3190 (1.3) | 380 (1.5) | 230 (1.0) | 290 (1.6) | −39.5 | 26.1 | −23.7 |
| Exposure to harmful substances or environments | 61 710 (26.0) | 6310 (25.3) | 5910 (27.0) | 4830 (26.1) | −6.3 | −18.3 | −23.5 |
| Contact with object, equipment | 153 460 (64.6) | 16 190 (64.9) | 13 950 (63.6) | 11 980 (64.7) | −13.8 | −14.1 | −26.0 |
| Overexertion and bodily reaction | 960 (0.4) | 70 (0.3) | 300 (1.4) | 60 (0.3) | 328.6 | −80.0 | −14.3 |
| All other | 940 (0.4) | 80 (0.3) | 60 (0.3) | 50 (0.3) | −25.0 | −16.7 | −37.5 |
| Nature of injury | |||||||
| Cuts, lacerations | 13 260 (5.6) | 1440 (5.8) | 1100 (5.0) | 960 (5.2) | −23.6 | −12.7 | −33.3 |
| Punctures (except gunshot wounds) | 5180 (2.2) | 1110 (4.5) | 2560 (1.2) | 190 (1.0) | −76.6 | −26.9 | −82.9 |
| Bruises, contusions | 12 120 (5.1) | 1540 (6.2) | 1130 (5.2) | 900 (4.9) | −26.6 | −20.4 | −41.6 |
| Chemical burns and corrosions | 13 660 (5.7) | 1540 (6.2) | 1770 (8.1) | 1260 (6.8) | 14.9 | −28.8 | −18.2 |
| Heat (thermal) burns | 3260 (1.4) | 270 (1.1) | 1320 (1.9) | 170 (0.9) | 55.6 | −59.5 | −37.0 |
| Multiple traumatic injuries | 1440 (0.6) | 250 (1.0) | 90 (0.4) | 60 (0.3) | −64.0 | −33.3 | −76.0 |
| Soreness, pain | 19 660 (8.3) | 1250 (5.0) | 1680 (7.7) | 1490 (8.0) | 34.4 | −11.3 | 19.2 |
| All other | 184 640 (77.7) | 17 520 (70.3) | 18 600 (84.9) | 18 510 (100.0) | 6.2 | −0.5 | 5.7 |
| Source of injury | |||||||
| Chemicals, chemical products | 38 470 (16.2) | 3870 (15.5) | 4160 (19.6) | 3560 (19.2) | 7.5 | −14.4 | −8.0 |
| Containers | 7450 (3.1) | 630 (2.5) | 700 (3.2) | 630 (3.4) | 11.1 | −10.0 | 0.0 |
| Furniture, fixtures | 3260 (1.4) | 240 (1.0) | 300 (1.4) | 370 (2.0) | 25.0 | 23.3 | 54.2 |
| Machinery | 2840 (1.2) | 220 (0.9) | 220 (1.0) | 150 (0.8) | 0.0 | −31.8 | −31.8 |
| Parts and materials | 16 710 (7.0) | 1560 (15.5) | 1450 (6.6) | 1390 (7.5) | −7.1 | −4.1 | −10.9 |
| Person, injured or ill worker | 1040 (0.4) | 40 (0.2) | 290 (1.3) | 50 (0.3) | 625.0 | −82.8 | 25.0 |
| Person, other than injured or ill workers | 15 690 (6.6) | 1740 (7.0) | 1440 (6.6) | 1200 (6.5) | −17.2 | −16.7 | −31.0 |
| Floors, walkways, ground surfaces | 2360 (1.0) | 310 (1.2) | 180 (0.8) | 210 (1.1) | −41.9 | 16.7 | −32.3 |
| Hand tools | 6570 (2.8) | 750 (3.0) | 430 (2.0) | 530 (2.9) | −42.7 | 23.3 | −29.3 |
| Vehicles | 2510 (1.1) | 210 (0.8) | 230 (1.0) | 110 (0.6) | 9.5 | −52.2 | −47.6 |
| All other | 140 650 (59.2) | 15 360 (61.6) | 12 510 (57.1) | 10 300 (55.6) | −18.6 | −17.7 | −32.9 |
| Day of week | |||||||
| Sunday | 11 980 (5.0) | 1390 (5.6) | 1290 (5.9) | 720 (3.9) | −7.2 | −44.2 | −48.2 |
| Monday | 41 640 (17.5) | 4200 (16.8) | 3510 (16.0) | 3490 (18.9) | −16.4 | −0.6 | −16.9 |
| Tuesday | 46 400 (19.5) | 5190 (20.8) | 4520 (20.6) | 3530 (19.1) | −12.9 | −21.9 | −32.0 |
| Wednesday | 46 320 (19.5) | 5020 (20.1) | 4030 (18.4) | 4120 (22.3) | −19.7 | 2.2 | −17.9 |
| Thursday | 41 350 (17.4) | 4060 (16.3) | 3620 (16.5) | 3040 (16.4) | −10.8 | −16.0 | −25.1 |
| Friday | 32 490 (13.7) | 2910 (11.7) | 3070 (14.0) | 2390 (12.9) | 5.5 | −22.1 | −17.9 |
| Saturday | 17 400 (7.3) | 2150 (8.6) | 1870 (8.5) | 1210 (6.5) | −13.0 | −35.3 | −43.7 |
| Time of day | |||||||
| 12:01 am–4:00 am | 7560 (3.2) | 720 (2.9) | 930 (4.2) | 450 (2.4) | 29.2 | −51.6 | −37.5 |
| 4:01 am –8:00 am | 17 680 (7.4) | 2010 (8.1) | 1530 (7.0) | 1670 (9.0) | −23.9 | 9.2 | −16.9 |
| 8:01 am–12:00 pm | 66 940 (28.2) | 7200 (28.9) | 6220 (28.4) | 4810 (26.0) | −13.6 | −22.7 | −33.2 |
| 12:01 pm–4:00 pm | 56 770 (23.9) | 6070 (24.3) | 4920 (22.4) | 4830 (26.1) | −18.9 | −1.8 | −20.4 |
| 4:01 pm–8:00 pm | 23 610 (9.9) | 2550 (10.2) | 2810 (12.8) | 1660 (9.0) | 10.2 | −40.9 | −34.9 |
| 8:01 pm–12:00 am | 13 390 (5.6) | 1620 (6.5) | 1210 (5.5) | 1150 (6.2) | −25.3 | −5.0 | −29.0 |
| Not reported | 51 630 (21.7) | 4770 (19.1) | 4300 (19.6) | 3940 (21.3) | −9.9 | −8.4 | −17.4 |
| Hours worked | |||||||
| Occurred before shift began | 440 (0.2) | 80 (0.3) | 40 (0.2) | 20 (0.1) | −50.0 | −50.0 | −75.0 |
| <1 | 15 090 (6.4) | 1490 (6.0) | 1300 (5.9) | 1330 (7.2) | −12.8 | 2.3 | −10.7 |
| 1–2 | 20 130 (8.5) | 1780 (7.1) | 2310 (10.5) | 1400 (7.6) | 29.8 | −39.4 | −21.3 |
| 2–4 | 47 610 (20.0) | 6050 (24.3) | 4820 (22.0) | 3540 (19.1) | −20.3 | −26.6 | −41.5 |
| 4–6 | 35 020 (14.7) | 3400 (13.6) | 2820 (12.9) | 2450 (13.2) | −17.1 | −13.1 | −27.9 |
| 6–8 | 36 020 (15.2) | 3790 (15.2) | 3460 (15.8) | 3730 (20.2) | −8.7 | 7.8 | −1.6 |
| 8–10 | 19 640 (8.3) | 1990 (8.0) | 2090 (9.5) | 1300 (7.0) | 5.0 | −37.8 | −34.7 |
| 10–12 | 4930 (2.1) | 450 (1.8) | 370 (1.7) | 340 (1.8) | −17.8 | −8.1 | −24.4 |
| 12–16 | 2000 (0.8) | 420 (1.7) | 100 (0.5) | 90 (0.5) | −76.2 | −10.0 | −78.6 |
| >6 | 190 (0.1) | 20 (0.1) | 30 (0.1) | 20 (0.1) | 50.0 | −33.3 | 0.0 |
| Not reported | 56 530 (23.8) | 5460 (21.9) | 4590 (20.9) | 4300 (23.2) | −15.9 | −6.3 | −21.2 |
Figure 3.
Most commonly reported source of eye injuries in the US.
Changes in WREIs From 2011 Through 2020
From 2011 through 2020, the total number of WREIs decreased by 25.8%. The populations with the greatest decrease among their respective categories were those in administrative or office roles (−66.7%), those aged 45 years through 54 years (−44.1%), White workers (−43.0%), and men (−29.5%). Workers in more labor-intensive roles (eg, service, production, installation, maintenance, repair) also had a decrease in WREIs over the 10-year study period (−57.9%). Those younger than 20 years (+50.0%), Native Hawaiians or Pacific Islanders (+33.3%), Hispanics or Latinos (+2.4%), and those with careers in computer, engineering, or science (+16.7%), educational instruction (+16.3%), healthcare (+9.9%), and farming, fishing, or forestry (+8.3%) had an increase in the number of WREIs from 2011 to 2020.
Changes were also observed in the characteristics of WREIs between 2011 and 2020. The greatest decreases by category included punctures (−82.9%), those working between 12 hours and 16 hours before the WREI occurred (−78.6%), transportation incidents (−71.4%), injuries involving vehicles (−47.6%), those occurring on Sunday (−48.2%), and those occurring between 12:01 am and 4:00 am (−37.5%). None of these categories had greater decreases between 2019 and 2020 than between 2011 and 2019. The greatest increases over the 10-year span were in explosive injuries (133%), those involving furniture or other fixtures (54.2%), those involving other persons (25.0%), and injuries resulting in soreness and pain (19.2%).
Between 2019 and 2020, coinciding with the start of the SARS-CoV2 pandemic in the US, the number of WREIs decreased by a greater amount (−15.6%) than in any other single preceding year in the study period. Those younger than 20 years (36.8%), American Indians or Alaskan natives (33.3%), Native Hawaiians and Pacific Islanders (14.3%), healthcare practitioners and technicians (39.3%), construction and extraction workers (16.7%), and those with fewer than 3 months of experience at their current job (8.1%) experienced more WREIs in 2020 than in 2019.
WREIs in Healthcare Workers
Of the occupations listed by the BLS in the tertiary or quaternary sectors of industry, healthcare workers had the third highest number of WREIs (n = 6670) between 2011 and 2020. In this period, healthcare workers accounted for 2.8% of all WREIs. Notably, there was an increase in the number of WREIs in healthcare workers from 2011 through 2020 (9.9%). However, there was a decrease of 21.1% from 2011 to 2019 but an increase of 39.3% from 2019 to 2020.
Conclusions
The present study used the BLS dataset on workplace-related injuries to provide an update to the current understanding of eye injuries in the US workforce. From the years 2011 through 2020, an estimated 237 590 WREIs were reported by the BLS. Over that timeframe, the incidence of WREIs decreased from 2.4 to 1.7 per 10 000 workers. Although this falls below previously reported incidence rates for eye injuries as a whole (7.0 to 9.5 per 1000),6,12,13 WREIs remain a significant burden on the US population, accounting for more than 475 000 missed days of work and $80 million in lost wages over the 10-year period we studied. Because many of these injuries are preventable,14–16 it is important to determine the qualities of people who have had WREIs to provide targeted interventions to at-risk populations.
When examining the demographics of WREIs, previous studies of eye injuries identified male sex, non-Hispanic White race, and age less than 55 years to be risk factors for eye injuries.3,10,11,16 The present study corroborated these findings, showing that men had significantly more WREIs than women and that non-Hispanic White workers and those aged 25 to 34 years had the most WREIs in their respective categories. These findings can be explained in part by these groups’ representation in the labor force. According to the BLS Current Population Survey, men accounted for 53% of the labor force, White workers for 77.4%, and those aged between 25 years and 34 years for 22.7%; all were the highest in their respective categories (https://www.bls.gov/emp/tables/civilian-labor-force-summary.htm). However, that 77.1% of WREIs were in men and only 36.3% were in White workers suggests that these groups have a disproportionately greater number of WREIs and lesser number of WREIs, respectively.
Other studies have identified similar discrepancies in terms of the sex and racial makeup of workplace injuries. These have postulated that differences in the type of work and the quality of workplace training for men and White workers might serve as explanations.11,17,18 The present study provides evidence to support these claims. For instance, we identified that fields in the primary and secondary sectors of industry, such as production or installation, were associated with increased rates of WREIs. Because men are more likely than women to fill positions in these sectors (https://www.bls.gov/cps/aa2020/cpsaat11.htm), it is reasonable to conclude that their increased rate of WREIs is related to their overrepresentation in these higher risk occupations.
The present study also indirectly supports claims that improved workplace training contributes to the lower rate of workplace injuries in White workers compared with the rate in minority populations. This evidence comes from the observation that Hispanics, although making up approximately 15% to 18% of the workforce between 2011 and 2020 (https://www.bls.gov/cps/demographics.htm#race), accounted for 17% of all WREIs in the study period. In the past, this group has been shown to receive poorer safety training and to be less likely to voice workplace concerns to their employers. 19 However, this study was not designed to determine the causality between the characteristics of the injured and WREIs; therefore, this is conjecture and future studies might be warranted to better elucidate these observations.
The effect of the COVID-19 pandemic is an important factor to consider when examining the trends in WREIs over the past decade, especially in terms of healthcare workers. Although many were forced out of their workplaces to curb the spread of COVID-19 in early 2020, this was one of the few groups designated as “essential workers”. Thus, they were exposed to pandemic-induced stressors such as increased workloads, longer shifts, fatigue, severe personal protective equipment shortages, and worsening mental health outcomes.20–23 These likely contributed to the finding that WREIs increased by 39.3% in this group between the years 2019 and 2020. This is especially striking when one considers that this group experienced an overall decline in WREIs of 21.1% between 2011 and 2019.
Notable trends in WREIs overall were also identified during the study period. For instance, the rate of decline in WREIs between 2019 and 2020 (−15.5%) exceeded the rate of change in any other single year during the 10-year time period and the overall rate of decline between 2011 and 2019 (−12.1%). Previous studies have identified similar and statistically significant declines in work-related injuries after the COVID-19 lockdowns.24–26 The 15.5% decline between 2019 and 2020 seems to be a progression of the 9.9% decline from 2018 to 2019. Potential explanations of the mechanism of the initial downtrend include factors related to the workplace, such as improvements in safety protocols or in the reporting characteristics of employers (eg, delays in reporting injuries to the BLS). Although there are multiple etiologies for the decline, its acceleration was likely secondary to the shift of workers out of their workplaces as a result of the COVID-19 lockdowns.
To our knowledge, this is the first high-powered study of WREIs in the US workforce performed in the past decade. A strength of this study was its ability to capture a consequential number of WREIs, allowing us to provide commentary on the magnitude of and trends in this problem in the US. In addition, the study’s sampling method ensured national representation of the civilian noninstitutional workforce; thus, the overall trends identified in the current study likely mirror the true distribution of WREIs among workers in the US.
Although previous studies used the same dataset we used for similar purposes,27–29 this approach has limitations. This weighted dataset is based on employer records; thus, the published estimates rely on proper recordkeeping and reporting by employers. Moreover, inconsistency in the methodology from year to year could significantly influence the trend over time. Data are collected in a mid-summer month of the year, meaning the WREI collection times did not completely align with the institution of widespread stay-at-home orders during the COVID-19 pandemic. Recent criticisms of the database suggest that it is prone to undercounting workplace injuries and illnesses because of this, a finding that has been corroborated by comparisons between SOII data and workers’ compensation records. 30
The construction of our data tables might have exacerbated the underestimation of WREIs because we only used the code for “eye” to select data to include in our analysis, whereas codes for “face” or “face, unspecified” could have included eye injuries as well. A final limitation of using the dataset is that it is difficult to select a comparison group. As such, we could not determine whether the injury characteristics of WREIs represented truly statistically significant risk factors. Future studies are required to fully characterize the risk factors associated with WREIs in the modern workforce.
Despite these limitations, the present study provides an update to the current understanding of WREIs in the US. Over the 10-year period, the incidence of WREIs decreased from 2.4 to 1.7 per 10 000 workers and accounted for an estimated $80 million in missed income in that time. We determined that men, White race, and those aged 25 years through 34 years had more WREIs between 2011 and 2020 than any other sex, race or ethnicity, or age group, respectively.
We also found that men and Hispanic individuals might have the highest frequency of WREIs, which is likely secondary to these groups’ exposure to fields with more WREIs, such as production, as well as variable workplace safety standards. In addition, although most occupational fields had a decrease in the total number of WREIs between 2019 and 2020, healthcare occupations were among the few that had an increase during that time. Because many of these injuries occur through preventable mechanisms, such as contact between materials or chemicals, future targeted public health initiatives are warranted to decrease the impact of WREIs on the US workplace.
Footnotes
Ethics Approval: Institutional Review Board Exemption was obtained from the Vanderbilt IRB.
Statement of Informed consent: Informed consent was not required for this retrospective study.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr. Sridhar is a consultant to Alcon, Allergan, Dorc, Genentech, and Regeneron. Dr. Durrani is a consultant to Allergan and has received research support from Bausch + Lomb. Dr. Patel has received research support from Alcon.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Supported in part by a Research to Prevent Blindness unrestricted grant to the Vanderbilt Eye Institute. The sponsor or funding organization had no role in the design or conduct of this research.
ORCID iDs: Akash Patel 
https://orcid.org/0000-0003-2500-211X
Sean Berkowitz
https://orcid.org/0000-0002-9763-7192
References
- 1. Negrel AD, Thylefors B. The global impact of eye injuries. Ophthalmic Epidemiol. 1998;5(3):143-169. doi: 10.1076/opep.5.3.143.8364 [DOI] [PubMed] [Google Scholar]
- 2. Ramirez DA, Porco TC, Lietman TM, Keenan JD. Ocular injury in United States emergency departments: seasonality and annual trends estimated from a nationally representative dataset. Am J Ophthalmol. 2018;191:149-155. doi: 10.1016/j.ajo.2018.04.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Kuhn F, Morris R, Witherspoon CD, Mann L. Epidemiology of blinding trauma in the United States Eye injury Registry. Ophthalmic Epidemiol. 2006;13(3):209-216. doi: 10.1080/09286580600665886 [DOI] [PubMed] [Google Scholar]
- 4. Varma R, Vajaranant TS, Burkemper B, et al. Visual impairment and blindness in adults in the United States: demographic and geographic variations from 2015 to 2050. JAMA Ophthalmol. 2016;134(7):802-809. doi: 10.1001/jamaophthalmol.2016.1284 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Mir TA, Canner JK, Zafar S, Srikumaran D, Friedman DS, Woreta FA. Characteristics of open globe injuries in the United States from 2006 to 2014. JAMA Ophthalmol. 2020;138(3):268-275. doi: 10.1001/jamaophthalmol.2019.5823 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Glynn RJ, Seddon JM, Berlin BM. The incidence of eye injuries in New England adults. Arch Ophthalmol. 1988;106(6):785-789. doi: 10.1001/archopht.1988.01060130855039 [DOI] [PubMed] [Google Scholar]
- 7. AlMahmoud T, Al Hadhrami SM, Elhanan M, Alshamsi HN, Abu-Zidan FM. Epidemiology of eye injuries in a high-income developing country: an observational study. Medicine (Baltimore). 2019;98(26):e16083. doi: 10.1097/MD.0000000000016083 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Vats S, Murthy GV, Chandra M, Gupta SK, Vashist P, Gogoi M. Epidemiological study of ocular trauma in an urban slum population in Delhi, India. Indian J Ophthalmol. 2008;56(4):313-316. doi: 10.4103/0301-4738.41413 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Xiang H, Stallones L, Chen G, Smith GA. Work-related eye injuries treated in hospital emergency departments in the US. Am J Ind Med. 2005;48(1):57-62. doi: 10.1002/ajim.20179 [DOI] [PubMed] [Google Scholar]
- 10. Chen SY, Fong PC, Lin SF, Chang CH, Chan CC. A case-crossover study on transient risk factors of work-related eye injuries. Occup Environ Med. 2009;66(8):517-522. doi: 10.1136/oem.2008.042325 [DOI] [PubMed] [Google Scholar]
- 11. Forrest KY, Cali JM. Epidemiology of lifetime work-related eye injuries in the U.S. population associated with one or more lost days of work. Ophthalmic Epidemiol. 2009;16(3):156-162. doi: 10.1080/09286580902738175 [DOI] [PubMed] [Google Scholar]
- 12. McGwin G, Jr., Xie A, Owsley C. Rate of eye injury in the United States. Arch Ophthalmol. 2005;123(7):970-976. doi: 10.1001/archopht.123.7.970 [DOI] [PubMed] [Google Scholar]
- 13. Swain T, McGwin G., Jr. The prevalence of eye injury in the United States, estimates from a meta-analysis. Ophthalmic Epidemiol. 2020;27(3):186-193. doi: 10.1080/09286586.2019.1704794 [DOI] [PubMed] [Google Scholar]
- 14. AlMahmoud T, Elkonaisi I, Grivna M, AlNuaimi G, Abu-Zidan FM. Eye injuries and related risk factors among workers in small-scale industrial enterprises. Ophthalmic Epidemiol. 2020;27(6):453-459. doi: 10.1080/09286586.2020.1770302 [DOI] [PubMed] [Google Scholar]
- 15. Chatterjee S, Agrawal D. Primary prevention of ocular injury in agricultural workers with safety eyewear. Indian J Ophthalmol. 2017;65(9):859-864. doi: 10.4103/ijo.IJO_334_17 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Sun F, Zhou Y, Dong L, Qin H. Relationship between the use and type of eye protection and work-related corneal and conjunctival foreign body injuries. Inj Prev. 2021;27(6):521-526. doi: 10.1136/injuryprev-2020-043958 [DOI] [PubMed] [Google Scholar]
- 17. Veazie MA, Landen DD, Bender TR, Amandus HE. Epidemiologic research on the etiology of injuries at work. Annu Rev Public Health. 1994;15:203-221. doi: 10.1146/annurev.pu.15.050194.001223 [DOI] [PubMed] [Google Scholar]
- 18. Wong TY, Smith GS, Lincoln AE, Tielsch JM. Ocular trauma in the United States army: hospitalization records from 1985 through 1994. Am J Ophthalmol. 2000;129(5):645-650. doi: 10.1016/s0002-9394(99)00448-1 [DOI] [PubMed] [Google Scholar]
- 19. Fernandez-Esquer ME, Fernandez-Espada N, Atkinson JA, Montano CF. The influence of demographics and working conditions on self-reported injuries among Latino day laborers. Int J Occup Environ Health. 2015;21(1):5-13. doi: 10.1179/2049396714Y.0000000083 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Maraqa B, Nazzal Z, Zink T. Palestinian health care workers’ stress and stressors during COVID-19 pandemic: a cross-sectional study. J Prim Care Community Health. 2020;11:2150132720955026. doi: 10.1177/2150132720955026 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Lai J, Ma S, Wang Y, et al. Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Netw Open. 2020;3(3):e203976. doi: 10.1001/jamanetworkopen.2020.3976 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Shaukat N, Ali DM, Razzak J. Physical and mental health impacts of COVID-19 on healthcare workers: a scoping review. Int J Emerg Med. 2020;13(1):40. doi: 10.1186/s12245-020-00299-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Emanuel EJ, Persad G, Upshur R, et al. Fair allocation of scarce medical resources in the time of Covid-19. N Engl J Med. 2020;382(21):2049-2055. doi: 10.1056/NEJMsb2005114 [DOI] [PubMed] [Google Scholar]
- 24. Huber D, Frank R, Crevenna R. The impact of lockdowns during the COVID-19 pandemic on work-related accidents in Austria in 2020. Wien Klin Wochenschr. 2022;134(9-10):391-398. doi: 10.1007/s00508-022-02013-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. van Aert GJJ, van der Laan L, Boonman-de Winter LJM, et al. Effect of the COVID-19 pandemic during the first lockdown in the Netherlands on the number of trauma-related admissions, trauma severity and treatment: the results of a retrospective cohort study in a level 2 trauma centre. BMJ Open. 2021;11(2):e045015. doi: 10.1136/bmjopen-2020-045015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Probert AC, Sivakumar BS, An V, et al. Impact of COVID-19-related social restrictions on orthopaedic trauma in a level 1 trauma centre in Sydney: the first wave. ANZ J Surg. 2021;91(1-2):68-72. doi: 10.1111/ans.16375 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Centers for Disease Control and Prevention. Work-related injuries and illnesses associated with child labor–United States, 1993. MMWR Morb Mortal Wkly Rep. 1996;45(22):464-468. [PubMed] [Google Scholar]
- 28. Centers for Disease Control and Prevention. Nonfatal occupational injuries and illnesses among older workers — United States, 2009. MMWR Morb Mortal Wkly Rep. 2011;60(16):503-508. [PubMed] [Google Scholar]
- 29. Mannan MS, O’Connor TM, Keren N. Patterns and trends in injuries due to chemicals based on OSHA occupational injury and illness statistics. J Hazard Mater. 2009;163(1):349-356. doi: 10.1016/j.jhazmat.2008.06.121 [DOI] [PubMed] [Google Scholar]
- 30. Bureau of Labor Statistics, U.S. Department of Labor. Survey of occupational injuries and illnesses. Handbook of Methods. Accessed September 21, 2022. https://www.bls.gov/opub/hom/soii/home.htm [Google Scholar]