Agravos à saúde dos trabalhadores de enfermagem em uma instituição pública de ensino

Objetivo: Identificar os agravos ocorridos com os trabalhadores de enfermagem por meio do Sistema de Monitoramento da Saúde dos Trabalhadores de Enfermagem (SIMOSTE) e descrever as consequências desses agravos. Método: Trata-se de um estudo de natureza quantitativa do tipo exploratório descritivo que foi realizado em um hospital-escola situado na zona oeste do município de São Paulo. Resultados: A partir do SIMOSTE foram registradas 1.847 ocorrências no período de 6 meses. Entre as principais ocorrências destacam-se as licenças médicas, os acidentes de trabalho com e sem afastamentos, os atendimentos psiquiátricos e psicoterapia. Conclusão: Os dados apontam para a necessidade do desenvolvimento de novas ações de vigilância em saúde voltadas para a notificação dos acidentes e das doenças relacionadas ao trabalho, além da prevenção dos agravos.
Objetivo: Identificar los problemas de salud producidos en el personal de enfermería por medio del Sistema de Vigilancia de la Salud de los Trabajadores de Enfermería (SIMOSTE) y describir las consecuencias de esos problemas. Método: Estudio exploratorio cuantitativo y descriptivo realizado en un hospital universitario situado en el oeste de la ciudad de São Paulo. Resultados: En SIMOSTE se registraron 1.847 incidentes ocurridos en un periodo de 6 meses. Entre los eventos más importantes se destacan las licencias médicas, los accidentes laborales con y sin baja laboral, atención psiquiátrica y psicoterapia. Conclusión: Los datos apuntan a la necesidad de desarrollar nuevas acciones de vigilancia en salud dirigidas a la notificación de los accidentes y de las enfermedades profesionales, además de su prevención. 

Objective: To identify the issues occurred with nursing workers through a Health Monitoring System for Nursing Workers (SIMOSTE) and to describe the consequences of those problems. Method: This is a quantitative, exploratory and descriptive study realized in a teaching hospital in the west region of the city of São Paulo. Results: From the SIMOSTE, 1.847 occurrences were registered in a six month period. Within the main occurrences, medical licenses, work related accidents with and without removals; psychiatric consultations and psychotherapy were highlighted. Conclusion: The data points out to the need for the development of new health vigilance actions to notify accidents and illness related to work, besides the prevention of issues.

Health problems of nursing workers in a public educational institution

Objective: To identify the issues occurred with nursing workers through a Health Monitoring System for Nursing Workers (SIMOSTE) and to describe the consequences of those problems. Method: This is a quantitative, exploratory and descriptive study realized in a teaching hospital in the west region of the city of São Paulo. Results: From the SIMOSTE, 1.847 occurrences were registered in a six month period. Within the main occurrences, medical licenses, work related accidents with and without removals; psychiatric consultations and psychotherapy were highlighted. Conclusion: The data points out to the need for the development of new health vigilance actions to notify accidents and illness related to work, besides the prevention of issues

Data-driven cutoff selection for the patient health questionnaire-9 depression screening tool

Importance: Test accuracy studies often use small datasets to simultaneously select an optimal cutoff score that maximizes test accuracy and generate accuracy estimates. Objective: To evaluate the degree to which using data-driven methods to simultaneously select an optimal Patient Health Questionnaire-9 (PHQ-9) cutoff score and estimate accuracy yields (1) optimal cutoff scores that differ from the population-level optimal cutoff score and (2) biased accuracy estimates. Design, Setting, and Participants: This study used cross-sectional data from an existing individual participant data meta-analysis (IPDMA) database on PHQ-9 screening accuracy to represent a hypothetical population. Studies in the IPDMA database compared participant PHQ-9 scores with a major depression classification. From the IPDMA population, 1000 studies of 100, 200, 500, and 1000 participants each were resampled. Main Outcomes and Measures: For the full IPDMA population and each simulated study, an optimal cutoff score was selected by maximizing the Youden index. Accuracy estimates for optimal cutoff scores in simulated studies were compared with accuracy in the full population. Results: The IPDMA database included 100 primary studies with 44 503 participants (4541 [10%] cases of major depression). The population-level optimal cutoff score was 8 or higher. Optimal cutoff scores in simulated studies ranged from 2 or higher to 21 or higher in samples of 100 participants and 5 or higher to 11 or higher in samples of 1000 participants. The percentage of simulated studies that identified the true optimal cutoff score of 8 or higher was 17% for samples of 100 participants and 33% for samples of 1000 participants. Compared with estimates for a cutoff score of 8 or higher in the population, sensitivity was overestimated by 6.4 (95% CI, 5.7-7.1) percentage points in samples of 100 participants, 4.9 (95% CI, 4.3-5.5) percentage points in samples of 200 participants, 2.2 (95% CI, 1.8-2.6) percentage points in samples of 500 participants, and 1.8 (95% CI, 1.5-2.1) percentage points in samples of 1000 participants. Specificity was within 1 percentage point across sample sizes. Conclusions and Relevance: This study of cross-sectional data found that optimal cutoff scores and accuracy estimates differed substantially from population values when data-driven methods were used to simultaneously identify an optimal cutoff score and estimate accuracy. Users of diagnostic accuracy evidence should evaluate studies of accuracy with caution and ensure that cutoff score recommendations are based on adequately powered research or well-conducted meta-analyses

Global Roadkill Data: a dataset on terrestrial vertebrate mortality caused by collision with vehicles

Roadkill is widely recognized as one of the primary negative effects of roads on many wildlife species and also has socioeconomic impacts when they result in accidents. A comprehensive dataset of roadkill locations is essential to evaluate the factors contributing to roadkill risk and to enhance our comprehension of its impact on wildlife populations and socioeconomic dimensions. We undertook a compilation of roadkill records, encompassing both published and unpublished data gathered from road surveys or opportunistic sources. GLOBAL ROADKILL DATA includes 208,570 roadkill records of terrestrial vertebrates from 54 countries across six continents, encompassing data collected between 1971 and 2024. This dataset serves to minimise the collection of redundant data and acts as a valuable resource for local and macro scale analysis regarding rates of roadkill, road- and landscape-related features associated with risk of roadkill, vulnerability of species to road traffic, and populations at risk of local extinction. The objective of this dataset is to promote scientific progress in infrastructure ecology and terrestrial vertebrate conservation while limiting the socio-economic costs

Health problems of nursing workers in a public educational institution

Objective: To identify the issues occurred with nursing workers through a Health Monitoring System for Nursing Workers (SIMOSTE) and to describe the consequences of those problems. Method: This is a quantitative, exploratory and descriptive study realized in a teaching hospital in the west region of the city of São Paulo. Results: From the SIMOSTE, 1.847 occurrences were registered in a six month period. Within the main occurrences, medical licenses, work related accidents with and without removals; psychiatric consultations and psychotherapy were highlighted. Conclusion: The data points out to the need for the development of new health vigilance actions to notify accidents and illness related to work, besides the prevention of issues. &#8233