Injury trends, costs, and lost time in an Australian meat processing facility: a retrospective analysis

Abstract

Background

Meat processing is one of Australia’s highest-risk industrial sectors, yet contemporary epidemiological data describing workplace injury patterns and associated economic burden are limited. This study investigated workplace injury trends, compensation costs, and time lost in an Australian red-meat processing facility over seven years and described temporal injury patterns in relation to the timing of pre-employment assessments (PEAs) and physiotherapy-led early intervention programmes, without attempting to evaluate their effectiveness.

Methods

A retrospective descriptive analysis was conducted using organisational injury, compensation, and workforce data from 2018 to 2024. Injuries were categorised as burns, critical injury, foreign body, laceration, musculoskeletal disorder, or soft tissue. Descriptive statistics summarised injury frequency, claim costs, and time lost, and temporal patterns were visualised. Costs were reported in 2024 Australian dollars (AUD). No inferential statistical tests or causal analyses were performed.

Results

A total of 273 workplace injuries were recorded. Lacerations (48.6%) and work-related musculoskeletal disorders (27.7%) were most common. Cumulative compensation costs exceeded AUD $3.1 million and total time lost surpassed 3.6 million hours. Cost increases during 2020–2021 aligned with COVID-19-related operational disruptions. Injury counts decreased from 73 cases in 2023 to 44 cases in 2024; this temporal change is reported for context only and no inference about intervention impact is made.

Conclusions

Workplace injuries in meat processing impose substantial operational and economic impacts, driven largely by high-frequency laceration and musculoskeletal-related claims. This descriptive analysis characterises seven-year patterns in injury types, costs, and time lost at a single facility; it does not assess intervention effects. Prospective or controlled studies are needed to evaluate the effectiveness and scalability of PEAs and early intervention strategies in high-risk industrial settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-026-27562-3.

Introduction

The Australian meat processing industry is among the nation’s highest-risk occupational sectors, characterised by repetitive manual handling, cold environments, sharp-tools, and high production demands [1]. Nationally, approximately 3.5 per cent of employed Australians reported a work-related injury or illness in the 12 months to 2021–22, with 66 per cent requiring time away from work [2]. However, these averages mask the disproportionate burden within meat processing, which records serious injury claim rates several times higher than manufacturing. In this manuscript, ‘red-meat operations’ refers to slaughter, boning, trimming and packing of bovine/ovine species conducted within ‘meat-processing’ facilities; the terms are used in this context.

General manufacturing reports approximately 9.6 serious injury claims per million hours worked, while meat processing facilities report rates between 19 and 29 claims per million hours [2, 3]. Workers are exposed to hazards such as continuous knife use, low-temperature environments, carcass lifting, and labour-intensive manual processes. Comparatively, other high-risk sectors such as agriculture (11.1), construction (up to 16.9), and healthcare (10.2) also report high injury rates, although none exhibit the persistent and elevated rates characteristic of meat processing [2, 4].

Within red-meat operations, lacerations and work-related musculoskeletal disorders (WMSDs) dominate incident reports, together accounting for more than 70% of injuries. Manual handling tasks such as lifting carcasses, shifting heavy product, and stacking boxes contribute to around 45% of claims, while knife-related activities, including cutting, boning, and trimming, account for approximately half of all incidents [2]. National data further indicate that traumatic joint, ligament, and muscle or tendon injuries represent about 50% of claims and 36% of total costs, whereas lacerations account for 29% of claims and 21% of costs [4]. These figures highlight the need for targeted safety interventions and policy attention.

Pre-employment assessments (PEAs) and early intervention programs are increasingly employed as preventive strategies to address the industry’s high injury rates. PEAs evaluate an individual’s physical capacity, mobility, and overall suitability for physically demanding roles, supporting the principle of “person–job fit” and reducing the likelihood of early-tenure injuries [5]. In the meat processing, PEAs, follow a two-stage process. Stage one involves a self-report questionnaire covering lifestyle factors, medical and injury history, immunisation status, and potential exposure to communicable diseases. It also includes questions about previous injuries, surgeries, or medical conditions that may affect work capacity or increase injury risk. Stage two consists of a one-on-one physiotherapy assessment, including grip strength, rotator cuff strength, spinal flexion, squatting, lifting (from ground and overhead), pushing strength, range of motion, and hearing via audiometry. The assessment determines whether the individual has sufficient physical capacity for the intended role and identifies any pre-existing conditions that may increase injury risk. Physiotherapy led functional assessments are particularly effective in identifying workers at higher risk of musculoskeletal strain, outperforming traditional medical-only screening models. These assessments typically evaluate strength, flexibility, endurance, and task-specific capabilities such as lifting, carrying, repetitive cutting, and prolonged standing, providing a more accurate prediction of injury susceptibility in physically demanding environments [6, 7].

Complementary early-intervention programs such as physiotherapy triage, ergonomic risk modification, and task-readiness training can further reduce injury risk and accelerate recovery where injuries occur [8–10]. Evidence suggests that employees who undergo functional screening experience approximately 33 per cent fewer injury claims than unscreened cohorts, while non-screened employees show higher rates of compensation and lost-time claims [3].

This study explores workplace injury patterns, associated costs, and time lost within an Australian meat processing facility over a seven-year period (2018–2024). Drawing on longitudinal operational and demographic data, it describes trends in injury frequency, severity, and economic impact, with particular attention to WMSDs and lacerations the two most common injury categories. The timing of the introduction of a pre-employment assessment (PEA) program and physiotherapy-based early intervention strategy is reported to contextualise observed trends, without attempting to evaluate their effectiveness.

Methods

Study design

This study used a retrospective descriptive design to analyse injury and workforce data from two sites within one company in the Australian red-meat processing sector over a seven-year period (2018 to 2024). The aim was to identify patterns in injury frequency, severity, associated costs, and time lost due to injury-related absences. A secondary aim was to describe temporal trends in relation to the documented timing of PEAs and early intervention programmes as contextual information only.

Descriptive statistical methods were used to summarise injury counts, claim costs, and hours lost. Temporal trends were visualised using line and bar charts, and scatterplots were used to explore the relationship between claim costs and time lost. No inferential statistical tests, pre/post comparisons, or time-series models were conducted, and no effect sizes were estimated, as the study was designed to describe observed patterns rather than to establish causal relationships or attribute changes to interventions. The timing of organisational initiatives is reported to aid interpretation of trends, not to imply causality.

Data sources

Data were extracted from multiple organisational sources including, occupational health and safety (OHS) logs, human resources reports, and financial records. Data were obtained from two sites within the same company, with standardised definitions, reporting procedures, and compensation processes applied across both sites. The OHS logs provided detailed information on incident type, date of occurrence, severity, and associated claim costs. Human resource reports supplied workforce demographic data, including total employee numbers, new hires by year, and site-specific employment figures. Financial records included compensation costs and wage liabilities related to injury claims. Organisational documentation confirmed consistent data collection and stable classification methods across the seven-year period at both sites. Documentation on the implementation and scope of PEAs and physiotherapy led early intervention strategies was reviewed to provide contextual information on organisational practices rather than to support causal analysis.

Variables

Injury events were classified into six categories: burns, critical injuries, foreign body incidents, lacerations, WMSDs, and soft tissue injuries. Burns typically involved thermal exposure during processing tasks. Critical injuries represented severe cases such as traumatic head injuries or amputations, foreign body incidents resulted from embedded particles requiring medical removal. Lacerations were primarily knife-related cuts and puncture wounds, while WMSDs included strains, sprains, and overuse injuries associated with repetitive manual handling. Soft tissue injuries encompassed bruises and minor trauma without structural damage.

Injury coding was completed based solely on the injury descriptions documented in the organisational OHS system, using the company’s standardised classification framework, which was applied consistently across both sites throughout the study period. Ambiguous cases were resolved using predefined organisational rules (e.g., when multiple injuries were recorded, the primary injury requiring clinical intervention was coded). Repeat injuries among the same employee were treated as separate events if occurring on different dates or after documented recovery, consistent with organisational reporting requirements.

The workforce was predominantly male (approximately 95%), with female representation around 5%. The average age of employees was approximately 35 years.

The primary variables analysed included injury frequency, cost metrics, time lost, and workforce demographics. Injury frequency was assessed by tallying the number of recorded incidents annually, stratified by injury category. Cost metrics included both total and mean claim costs, disaggregated by year and injury type to identify financial trends. Time lost was measured by summing the aggregate hours of absence attributed to injury-related leave. Workforce demographics, including employee counts and turnover, were used to contextualise injury rates and assess organisational stability.

Data analysis

Descriptive statistics summarised injury counts, category distribution, claim costs, and time lost. Event counts were aggregated annually and stratified by injury type. Cost analysis involved calculating total and mean claim costs per injury category and year. Given the right-skewed distribution of claim costs, we present distributional summaries including median and interquartile range (IQR) alongside mean, minimum, and maximum. Means are reported as descriptive context only due to their sensitivity to high-cost outliers. Measures of variability, including standard deviation, minimum, and maximum values, were computed to identify high-cost outliers. Time lost was assessed by summing hours of absence per injury category and year, enabling evaluation of productivity impacts. Temporal trends were visualised using bar and line plots, while scatterplots explored the relationship between claim costs and time lost. A heatmap was also developed to illustrate the average cost per injury category across the study years. Injury incidence rates were calculated by dividing the number of injury events by the corresponding employee-year exposure and multiplying by 100 to express the rate per 100 employee-years. Employee exposure was calculated using the formula:

Exact 95% confidence intervals for these rates were estimated using the Poisson distribution, following the method described by Garwood [11]. These rate calculations and confidence intervals were used solely to describe temporal patterns and were not intended for inferential comparison or evaluation of organisational interventions. All analyses were conducted using Microsoft Excel. Results were validated through cross-checking with original organisational records to ensure accuracy and consistency.

Ethical considerations

All data were anonymised prior to analysis. Injury records did not include individual level identifiers such as ethnicity, age, or sex. The study relied exclusively on existing organisational records and did not involve direct interaction with employees, thereby minimising ethical risk. Ethics approval was granted by the University of Newcastle Human Research Ethics Committee (H-2024–0321). Written informed consent was gained prior to data analysis. The research was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and aligned with the National Health and Medical Research Council (NHMRC) National Statement on Ethical Conduct in Human Research [12].

Results

Event trends

A total of 273 injuries were recorded between 2018 and 2024. Injury frequency increased markedly over the study period, rising from 7 cases in 2018 to 35 in 2019, and peaking at 73 cases in 2023 and decreased to 44 cases in 2024. Potential contributors were not analysed and cannot be inferred from these descriptive data. Incidence rates per 100 employee-years are reported where employee-year exposure was available (2020–2024): 2020 = 2.82 (95% CI: 1.74–4.30), 2021 = 5.58 (3.91–7.73), 2022 = 9.28 (7.03–12.03), 2023 = 13.01 (10.20–16.36), and 2024 = 4.74 (3.44–6.36). Employee-year exposure was not retained in extractable form for 2018–2019; therefore, incidence rates could not be calculated for those years and are not reported.

Increases in injury counts and costs were observed during 2020–2021, coinciding with the COVID-19 pandemic period. Potential operational or workforce contributors were not analysed and cannot be inferred from these descriptive data. The timing of organisational initiatives (pre-employment assessments and physiotherapy-led early intervention) is reported for context only; no inference is made about their impact. Figure 1 presents annual injury counts (2018–2024) and incidence rates for years with available denominator data (2020–2024).

Fig. 1.

Annual workplace injury counts and total compensation costs (2018–2024). Line graph showing the annual number of recorded injury events alongside cumulative compensation costs in Australian dollars. Injuries peaked in 2023, while compensation costs showed marked increases during 2020–2021

[4] These trends are illustrated in Fig. 1, which shows annual injury counts alongside total claim costs.

Cost analysis

The cumulative financial burden of workplace injuries exceeded $3.1 million across the study period. Work-related musculoskeletal disorders and lacerations were the costliest injuries, together accounting for more than $1.63 million over half of total compensation costs. Work-related musculoskeletal injuries were particularly expensive due to extended recovery times, physiotherapy requirements, and wage liabilities during prolonged absences. Lacerations, although generally less severe, imposed significant costs due to their high frequency and occasional need for surgical intervention.

Cost spikes in 2020 and 2021 aligned with the pandemic period, although the specific contributors to these increases were not analysed and cannot be inferred from the descriptive data. Critical injuries, including traumatic head injuries and amputations, represented extreme outliers, with individual claims exceeding $685,000 dollars in 2021. In contrast, soft tissue injuries and burns, while less frequent, showed cost variability when complications or extended recovery occurred. These descriptive patterns identify injury categories associated with higher average costs and greater cost variability but do not evaluate the impact of preventive strategies. These trends are shown in Fig. 2, which depicts average event cost by injury category and year, and in Table 1, which provides summary statistics for claim costs. Because claim cost distributions were right-skewed, Table 1 presents medians, while full interquartile ranges (Q1–Q3) are provided in Supplementary Table S1.

Fig. 2.

Mean compensation cost per injury by category (2018–2024). Bar chart displaying mean claim cost for each injury category across the study period. Work-related musculoskeletal disorders had the highest mean cost per event

Table 1.

Summary statistics of event costs by year and category

Year	Category	n	Mean (AUD)	SD (AUD)	Median (AUD)	Min (AUD)	Max (AUD)
2018	Critical Injury	1	1,051		1,051	1,051	1,051
2018	Foreign body	1	2,116		2,116	2,116	2,116
2018	Laceration	1	1,765		1,765	1,765	1,765
2018	Musculoskeletal Disorder	3	44,158	73,225	2,313	1,451	128,709
2018	Soft tissue	1	1,475		1,474	1,474	1,474
2018	Total	7	19,840	48,009	1,765	1,051	128,709
2019	Critical Injury	2	1,749	1,260	1,749	858	2,640
2019	Laceration	9	1,587	1,352	1,060	281	4,037
2019	Musculoskeletal Disorder	18	6,187	13,893	2,001	605	59,977
2019	Soft tissue	6	7,265	13,777	1,660	1,011	35,353
2019	Total	35	4,935	11,412	1,733	281	59,977
2020	Burns	2	1,170	5	1,169	1,166	1,173
2020	Foreign body	1	528		527	527	527
2020	Laceration	6	533	374	421	214	1,241
2020	Musculoskeletal Disorder	7	75,126	171,233	10,716	281	462,618
2020	Soft tissue	5	4,720	7,067	741	83	16,751
2020	Total	21	26,455	100,263	741	83	462,618
2021	Critical Injury	8	87,158	241,899	1,333	0	685,806
2021	Laceration	16	28,433	81,407	864	0	329,693
2021	Musculoskeletal Disorder	4	5,785	5,298	5,327	880	11,602
2021	Soft tissue	8	9,682	16,854	510	0	48,399
2021	Total	36	34,800	124,451	1,057	0	685,806
2022	Burns	2	95	134	94	0	189
2022	Critical Injury	7	11,205	28,406	794	0	75,614
2022	Foreign body	2	350	495	349	0	699
2022	Laceration	20	3,579	8,117	598	0	32,551
2022	Musculoskeletal Disorder	13	3,437	7,579	1,053	0	28,164
2022	Soft tissue	13	2,688	2,989	1,328	423	9,985
2022	Total	57	4,044	11,449	677	0	75,614
2023	Burns	1	67		66	66	66
2023	Critical Injury	10	27,844	60,635	979	0	181,197
2023	Foreign body	2	1,896	1,313	1,896	967	2,824
2023	Laceration	32	3,193	6,913	1,125	0	36,446
2023	Musculoskeletal Disorder	7	6,027	5,506	8,200	154	14,497
2023	Soft tissue	21	4,353	10,150	908	0	42,350
2023	Total	73	7,097	24,117	1,082	0	181,197
2024	Burns	2	4,981	4,933	4,981	1,493	8,468
2024	Critical Injury	2	1,032	1,021	1,032	310	1,754
2024	Foreign body	1	923		922	922	922
2024	Laceration	23	3,518	4,700	1,371	0	15,200
2024	Musculoskeletal Disorder	9	2,942	4,283	775	0	11,834
2024	Soft tissue	7	16,590	25,654	1,855	97	66,707
2024	Total	44	5,374	11,491	1,379	0	66,707
Total	All	273	11,374	55,861	1,094	0	685,806

SD Standard Deviation, IQR Interquartile Range. Claim cost distributions were right-skewed; therefore, medians are presented in Table 1 and full interquartile ranges (Q1–Q3) are provided in Supplementary Table S1. Means are reported as descriptive context only due to their sensitivity to high-cost outliers. All monetary values are expressed in 2024 AUD

Time lost

Total time lost due to injury surpassed 3.63 million hours over the study period, reflecting both direct absenteeism and indirect productivity effects associated with restricted duties and workforce reallocation. Work-related musculoskeletal disorders accounted for approximately 1.30 million h (largest share), lacerations for ~ 935,098 h, critical injuries for ~ 1,003,481 h, soft-tissue injuries for ~ 347,091 h, burns for ~ 27,712 h, and foreign-body incidents for ~ 19,839 h.

Lacerations contributed around 935,000 h, while typically resulting in shorter absences, some cases showed higher time-loss values, producing occasional spikes. Critical injuries, though rare, were highly disruptive and contributed disproportionately to time lost. The highest cumulative time lost occurred during 2020–2021; potential operational or workforce contributors were not analysed and cannot be inferred from these descriptive data. In 2024 a reduction in total time lost was observed; no inference is made about organisational initiatives. The relationship between claim cost and time lost is illustrated in Fig. 3, and category-specific trends are shown in Fig. 4.

Fig. 3.

Relationship between compensation cost and time lost across injury categories. Scatter plot illustrating the association between total claim costs and cumulative time lost (hours) by injury category. Higher cost categories corresponded with greater time lost

Fig. 4.

Injury type distribution by year (2018–2024). Stacked bar chart showing the annual distribution of injury categories. Lacerations and work-related musculoskeletal disorders consistently accounted for the majority of reported injuries

Injury profile

Analysis of injury types revealed that lacerations (n = 107, 48.6 percent) and WMSDs (n = 61, 27.7 percent) were the most common, together comprising over two thirds of all recorded incidents. Soft tissue injuries were the third most frequent (n = 34, 15.5 percent), followed by burns (n = 8, 3.6 percent), foreign body incidents (n = 6, 2.7 percent), and critical injuries (n = 4, 1.8 percent). Although infrequent, critical injuries such as amputations and traumatic head injuries contributed disproportionately to total costs and time lost due to their severity and prolonged recovery. These distributions are summarised in Table 1 and visualised in the heatmap of average cost per injury category across study years (Fig. 4).

Cost trends highlighted the financial burden of both high-frequency and severe injuries. In 2021 critical injuries had the highest mean claim cost, exceeding AUD $87,000 per case. Lacerations, although more frequent, averaged over AUD $28,000 per claim, demonstrating that routine injuries can still be costly. Work-related musculoskeletal disorders showed a sharp increase in mean cost in 2020, reaching AUD $75,126, although the specific factors contributing to this increase were not analysed and cannot be inferred from the descriptive data. Soft-tissue injuries, while less frequent, also demonstrated cost variability, with mean expenses rising to AUD $16,590 in 2024, indicating variability in outcomes but without implying causal factors.

As a descriptive analysis, these findings identify high-frequency and high-cost injury categories but do not evaluate the effectiveness of preventive or organisational interventions.

Discussion

Injuries decreased from 73 cases in 2023 to 44 in 2024; however, given the descriptive design, this temporal change is not interpreted for causation or intervention impact. The timing of organisational initiatives (e.g., PEAs and physiotherapy-led early intervention) is reported for context only [8, 13].

However, causality cannot be inferred due to the retrospective design. Other factors such as improved staffing stability, operational recovery, and reporting practices may have contributed. Regression to the mean following a high-injury year in 2023 is also possible. Accordingly, prospective, multi-site evaluations are needed to confirm effectiveness and to adjust for potential confounding.

The strong association between claim costs and time lost underscores that prolonged recovery drives financial burden, particularly for severe injuries. Descriptively, categories with higher costs also contributed substantial time lost (e.g., WMSDs and lacerations), and rare critical injuries were disproportionately burdensome; these patterns are presented without causal attribution.

Increases in 2020–2021 were observed in counts, costs, and time lost, but potential contributors were not modelled and cannot be inferred from these data.

Work-related musculoskeletal disorders and lacerations remain dominant injury types, which identifies priority areas for descriptive surveillance and future evaluative research. Integrating ergonomic redesign, knife-handling training, and task-specific conditioning into onboarding and ongoing programs may offer additional benefit.

Given the study’s descriptive nature, policy implications and return-on-investment claims are beyond scope. Future work should evaluate the effectiveness and cost-effectiveness of specific interventions (e.g., functional screening, ergonomic redesign, task-specific training) using prospective or controlled designs.

This study relied on retrospective organisational data from two sites within one company, which may limit generalisability. Employee-year exposure denominators were unavailable for 2018–2019, so incidence rates are reported for 2020–2024 only; counts are provided throughout for all years. The absence of inferential statistical analysis and potential confounding factors such as workforce turnover and management changes restrict causal interpretation. Additionally, injury reporting practices may have varied over time, influencing observed trends. Future research should address these limitations through multi-site, prospective designs and incorporate qualitative insights into [10, 14].

Long-term monitoring and high-quality evaluative studies (e.g., stepped-wedge, interrupted time-series, or cluster-randomised designs) are warranted to test the effectiveness, scalability, and cost-effectiveness of organisational programs in high-risk industrial settings.

While the present study does not evaluate intervention effects, future work should formally assess the effectiveness, scalability, and cost-effectiveness of specific organisational programs (e.g., functional screening, ergonomic redesign, task-specific training) using prospective or controlled designs, accompanied by longitudinal monitoring to assess durability of effects.

Conclusion

This study highlights the significant burden of workplace injuries in the Australian meat processing industry, with WMSDs and lacerations emerging as the most frequent and costly categories. These injuries accounted for the majority of compensation costs and productivity losses, as reflected in cumulative costs of approximately AUD $3.1 million and total time loss of 3.6 million hours across 2018–2024. These findings describe the distribution of injury types, associated costs, and time lost at two sites within one company, without causal attribution.

A decline in injuries was observed in 2024; however, given the descriptive design, this temporal change is not interpreted for causation or intervention impact. The timing of organisational initiatives (e.g. pre-employment assessments and physiotherapy-led early intervention) is noted for context only.

Future research should focus on prospective or controlled evaluations (e.g., stepped-wedge, interrupted time-series, or cluster randomised designs) to determine the effectiveness, scalability, and cost-effectiveness of organisational programs in high-risk industrial settings. Where possible, complete employee-year denominators should be retained to enable rate estimation across all years; in the present dataset, incidence rates were reported for 2020–2024 only. Such designs, accompanied by long-term monitoring, can better inform policy and implementation.

Authors’ contributions

BR conceptualised the study, conducted the analysis, and drafted the manuscript. AH, JR, and MS contributed to critical revision of the manuscript and provided supervision and methodological guidance. All authors reviewed and approved the final manuscript.

Funding

The authors received no specific funding for this work.

Data availability

The datasets generated and analysed during the current study are not publicly available due to commercial and confidentiality agreements with the participating organisation. Data may be made available from the corresponding author on reasonable request and with permission from the organisation.

Declarations

Ethics approval and consent to participate

Ethics approval was granted by the University of Newcastle Human Research Ethics Committee (H-2024–0321), that all data were anonymised prior to analysis, that the study relied exclusively on existing organisational records with no direct interaction with employees, and that written informed consent was obtained prior to data analysis. We have also clarified that the research was conducted in accordance with the ethical principles of the Declaration of Helsinki and aligned with the National Health and Medical Research Council (NHMRC) National Statement on Ethical Conduct in Human Research. Clinical trial number: not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.