Emergency room visits for work-related injuries: characteristics and associated factors - Capitals and the Federal District, Brazil, 2011

Mascarenhas, Márcio Dênis Medeiros; Freitas, Mariana Gonçalves de; Monteiro, Rosane Aparecida; Silva, Marta Maria Alves da; Malta, Déborah Carvalho; Gómez, Carlos Minayo

doi:10.1590/1413-81232015203.16842014

Cited by 11 publications

(9 citation statements)

References 10 publications

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“…Já a taxa de incidência de acidentes de trabalho no agronegócio paranaense, foi de 72,73 acidentes para cada 1000 trabalhadores, entre 2012 e 2018, maior que a taxa identificada em Ontário no Canadá, que registrou 58 acidentes para cada 1000 trabalhadores (Pickett et al, 1995), e a da Finlândia 62/1000 (Karttunen & Rautiainen, 2013 Quanto ao perfil dos acidentes, a evidência de maior proporção destes agravos envolvendo homens jovens corrobora com outros achados de estudos nacionais (Begnini & Almeida, 2015;Mascarenhas et al, 2015;Moreira et al, 2015;Scussiato et al, 2013). Isso pode estar relacionado com o fato de que a população masculina, jovem e em idade produtiva, geralmente desempenha serviços mais perigosos e que demandam maior força física, o que demonstra a exigência de se promover ações de prevenção de acidentes com esta população (Mascarenhas et al, 2015;Scussiato et al, 2013).…”

Section: Discussionunclassified

Acidentes de trabalho em segmentos do agronegócio paranaense

Ruths

Souza

Staduto

et al. 2021

RSD

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Acidentes de trabalho se vinculam a diversificação dos processos produtivos e ao perfil de produção, logo pesquisas sobre o tema devem considerar as especificidades das atividades envolvidas. Apesar do número crescente de trabalhos sobre a temática, ainda há carência de estudos que avaliem especificamente o setor do agronegócio, portanto, objetivou-se analisar os acidentes de trabalho nos segmentos de insumos, primário e agroindustrial do agronegócio, notificados no Paraná, no período de 2012 a 2017. Desenvolveu-se um estudo epidemiológico, descritivo e quantitativo, com base nas Comunicações de Acidentes de Trabalho (CAT), registradas no Instituto Nacional de Seguridade Social (INSS). Foram comunicadas 51.071 CAT relacionadas ao agronegócio no Estado, a taxa de incidência foi de 72,73 acidentes a cada mil trabalhadores. No período estudado, houve diminuição do número de CAT em todos os segmentos analisados. Destacaram-se os acidentes de trabalho no abate de suínos, aves e outros pequenos animais, foram 12.923 CAT registadas. A ocupação mais vulnerável foi a de alimentador de linha de produção, com 10.230 acidentes. A parte do corpo mais atingida foram os dedos 15.907 (31,15%), e, dentre os agentes causadores, as facas e fações estavam entre os principais, causando 4.782 (9,36%) lesões. Este estudo reforça a importância da análise dos acidentes de trabalho no agronegócio paranaense, de modo que, espera-se contribuir com evidências que auxiliem na prevenção destes agravos nos segmentos analisados.

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Section: Discussionunclassified

Acidentes de trabalho em segmentos do agronegócio paranaense

Ruths

Souza

Staduto

et al. 2021

RSD

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“…In addition, the NHS was performed with a significant sample of the Brazilian population and the results used here provide subsidies for the causal explanation of diseases that can lead to illnesses and risks of accidents to which workers are subjected. In Brazil, the mortality rate due to accidents at work reaches 13.2 per 100,000 insured workers (Mascarenhas et al, 2015). In addition, work accidents represent about 7.3% of the benefits paid for by the Social Security Institute (Malta et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, associated diseases can impact workers in the long run. Examples include cancer, asthma or pneumonia in retired people who worked in environments with polluted and toxic air (Mascarenhas et al, 2015).…”

Section: Physical and Psychosocial Stress And Healthrelated Factorsmentioning

confidence: 99%

“…In the mid-1980s and 1990s, a new phenomenon emerged: the musculoskeletal disorders, repetitive strain injuries (RSIs), and work-related musculoskeletal disorders (WMSDs), that affected workers in various segments (Mascarenhas et al, 2015). The use of manual force and the adoption of inappropriate postures in performing labor activities are some of the factors that cause such affections.…”

Section: Physical and Psychosocial Stress And Healthrelated Factorsmentioning

confidence: 99%

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Workplace Accidents in Brazil: Analysis of Physical and Psychosocial Stress and Health-Related Factors

Soares

Gelmini

Brandão³

et al. 2018

RAM, Rev. Adm. Mackenzie

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Purpose: To identify the influence of physical and psychosocial stress and health-related factors on the occurrence of work-related accidents, based on the National Health Survey (NHS) conducted in Brazil in 2013, through the execution of a retest in the Brazilian context, according to the model elaborated by Rommel, Varnaccia, Lahmann, Kottner, and Kroll (2016). Originality/value: Brazil has a high rate of workplace accidents. This retest, applied to the Brazilian context based on the German model of Rommel et al. (2016), allows obtaining information concerning the factors that may increase the chances of workplace accidents. Design/methodology/approach: Study with secondary data collected from the National Health Survey (Fiocruz, 2013). The model of Rommel et al. (2016) was used with the set of four blocks of variable factors. Logistic regression explored the physical, psychosocial, and health-related stress factors that may be more likely to occur in the workplace. Findings: 2.8% of the individuals have been involved in some work accident. Model 5 is the most explanatory of occupational accidents and factors such as high school level of education (OR = 2.082), full-time work (OR = 4.814), having three or more jobs (OR = 2.593), exposure to radiation, asthma (OR = 4.880), and alcohol consumption (OR = 4.620), according to each block typified, which most significantly increase the chances for the occurrence of work accidents. Managers and legislators should observe the factors that increase the chances of an accident occurring in order to develop prevention policies or actions.

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“…Meanwhile, the emergency department (ED) is the main unit to manage urgent injuries. It serves as an excellent venue for disease surveillance (6,(9)(10)(11).However, healthcare professionals typically have to handle an overwhelming number of patients at the ED. This situation makes it di cult to e ciently assess occupational injuries; thus an automatic screening system to facilitate clinical evaluation is needed (12,13).…”

mentioning

confidence: 99%

Novel Surveillance of Occupational Injury Powered by Machine Learning Using Chief Complaint at Emergency Triage

Weng

Wei²,

Chan

et al. 2020

Preprint

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Background: Underreporting of occupational injuries remains a global health issue, and warrants social awareness. To assist surveillance of occupational injuries, we developed an automatic screening model using chief complaint at emergency department to improve detection and reporting of occupational injuries.Methods: A total of 181,843 emergency visits aged 15 to 65 year-old were included at a medical center in Taiwan from April, 2015 to March, 2018. The retrospective cohort composed of 9,307 cases of occupational injuries and 172,536 controls. We applied the first 30 months as training dataset, and the following 6 months for prospective testing. Natural Language Processing (NLP) was applied to analyze patients’ chief complaints. The sentences were processed by JiebaR, a Chinese text segmentation technique, reviewed by two occupational physicians, and transformed by a word-embedding model using Glove. Logistic regression was conducted to predict suspected cases of occupational injuries.Results: The prediction model using the chief complaint alone can achieve an overall AUC of 0.936 [95% confidence interval (CI): 0.931 to 0.941], with high sensitivity (93.1%, 95% CI: 91.8 to 94.4) and adequate specificity (84.8%, 95% CI: 84.4 to 85.2). Patients in the most urgent or severe conditions showed the highest accuracy around 90%. We also observed increasing trends of referral to specialists of occupational medicine, and reimbursement rate.Conclusions: Interdepartmental coordination and integration of machine learning may augment the detection of occupational injuries at the emergency triage and improve the reporting, compensation, and prevention of occupational injuries.Contributions to the literaturel This novel surveillance powered by supervised machine learning can help to identify occupational injuries at the emergency triage, and it achieves 93.1% sensitivity and 84.8% specificity.l Along with the system implementation, there are trends of increasing reimbursement rate in the National Labor Insurance and increasing case referrals to specialists of occupational medicine.l Combining the use of Artificial Intelligence (AI) with conventional medical information, such as chief complaints, may provide an effective surveillance system for occupational injuries at hospitals. l Effective screening of labors who suffer from occupational injuries will promote Total Worker Health.

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Emergency room visits for work-related injuries: characteristics and associated factors - Capitals and the Federal District, Brazil, 2011

Abstract: Work-related (PR = 3.37, 95% CI 2.80 to 4.05) and injuries due to perforating object (PR = 3.01,. The results support the surveillance of external causes and direct public policies to promote occupational health.

Cited by 11 publications

References 10 publications

Acidentes de trabalho em segmentos do agronegócio paranaense

Acidentes de trabalho em segmentos do agronegócio paranaense

Workplace Accidents in Brazil: Analysis of Physical and Psychosocial Stress and Health-Related Factors

Novel Surveillance of Occupational Injury Powered by Machine Learning Using Chief Complaint at Emergency Triage

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