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The lack of safe levels of asbestos exposure and the long latency of asbestos-related disease (ARD) makes workers’ health surveillance challenging, especially in lower-income countries. This paper aims to present the recently developed Brazilian system for monitoring workers and general population exposed to asbestos (Datamianto), and to discuss the main challenges and opportunities for workers’ health surveillance. Methods: a descriptive study of the Datamianto development process, examining all the stages of system planning, development, improvement, validation, availability, and training of health services for its use, in addition to presenting the main challenges and opportunities for its implementation. Results: The system was developed by a group of software developers, workers’ health specialists, and practitioners, and it was recently incorporated by the Ministry of Health to be used for workers’ health surveillance. It can facilitate the monitoring of exposed individuals, epidemiological data analysis, promote cooperation between health services, and ensure periodical medical screening guaranteed to workers by labor legislation. Moreover, the system has a Business Intelligence (BI) platform to analyze epidemiologic data and produce near real-time reports. Conclusions: Datamianto can support and qualify the healthcare and surveillance of asbestos-exposed workers and ARD, promoting a better quality of life for workers and improving companies’ compliance with legislation. Even so, the system’s significance, applicability, and longevity will depend on the efforts aimed at its implementation and improvement.

The aim of this study is to compare the mortality rates for typical asbestos-related diseases (ARD-T: mesothelioma, asbestosis, and pleural plaques) and for lung and ovarian cancer in Brazilian municipalities where asbestos mines and asbestos-cement plants had been operating (areas with high asbestos consumption, H-ASB) compared with in other municipalities. The death records for adults aged 30+ years were retrieved from multiple health information systems. In the 2000–2017 time period, age-standardized mortality rates (standard: Brazil 2010) and standardized rate ratios (SRR; H-ASB vs. others) were estimated. The SRRs for ARD-T were 2.56 for men (257 deaths in H-ASB municipalities) and 1.19 for women (136 deaths). For lung cancer, the SRRs were 1.33 for men (32,604 deaths) and 1.19 for women (20,735 deaths). The SRR for ovarian cancer was 1.34 (8446 deaths). Except for ARD-T and lung cancer in women, the SRRs were higher in municipalities that began using asbestos before 1970 than in municipalities that began utilizing asbestos from 1970 onwards. In conclusion, the mortality rates for ARD-T, and lung and ovarian cancer in municipalities with a history of asbestos mining and asbestos-cement production exceed those of the whole country. Caution is needed when interpreting the results of this ecological study. Analytical studies are necessary to document the impact of asbestos exposure on health, particularly in the future given the long latency of asbestos-related cancers.

This study examined inter-observer agreement and diagnostic accuracy in classifying radiographs for pneumoconiosis among Asian physicians taking the AIR Pneumo examination. We compared agreement and diagnostic accuracy for parenchymal and pleural lesions across residing countries, specialty training, and work experience using data on 93 physicians. Physicians demonstrated fair to good agreement with kappa values 0.30 (95% CI: 0.20-0.40), 0.29 (95% CI: 0.23-0.36), 0.59 (95% CI: 0.52-0.67), and 0.65 (95% CI: 0.55-0.74) in classifying pleural plaques, small opacity shapes, small opacity profusion, and large opacities, respectively. Kappa values among Asian countries ranging from 0.25 to 0.55 (pleural plaques), 0.47 to 0.73 (small opacity profusion), and 0.55 to 0.69 (large opacity size). The median Youden's J index (interquartile range) for classifying pleural plaque, small opacity, and large opacity was 61.1 (25.5), 76.8 (29.3), and 88.9 (23.3), respectively. Radiologists and recent graduates showed superior performance than other groups regarding agreement and accuracy in classifying all types of lesions. In conclusion, Asian physicians taking the AIR Pneumo examination were better at classifying parenchymal lesions than pleural plaques using the ILO classification. The degree of agreement and accuracy was different among countries and was associated with background specialty training.

In the original publication [...]

Algoritmos de machine learning têm impactado a área da saúde nos últimos anos. Muita dessa popularidade deve-se aos ganhos de performance preditiva em comparação aos modelos estatísticos tradicionais, já que estes algoritmos conseguem capturar relações não-lineares e complexas, além de permitirem o uso de diferentes tipos de dados. Esta pesquisa objetiva descrever as diferentes técnicas recentes de machine learning e como elas podem ser aplicadas na saúde e segurança do trabalhador (SST). Os resultados da tese estão organizados em três artigos científicos. No primeiro artigo, foi realizada uma revisão bibliográfica para entender o panorama de uso de machine learning na saúde pública e em SST. Foram identificadas e categorizadas aplicações de aprendizagem supervisionada e não-supervisionada, e os principais problemas de pesquisa correspondentes. No segundo artigo, foram aplicados algoritmos de aprendizagem supervisionada para predição de absenteísmo por doença e doença relacionada ao trabalho em professores da rede pública municipal do Estado de São Paulo entre 2014 a 2018 (n=174.294), usando como fonte de dados a Relação Anual de Informações Sociais (RAIS). Cinco algoritmos foram comparados de acordo com o valor da área abaixo da curva (AUC). Todos os algoritmos obtiveram AUROC superior a 0,76. O melhor algoritmo (redes neurais artificiais) obteve AUROC de 0,79, com acurácia de 71,52%, sensibilidade de 72,86% e especificidade de 70,52%. Foi possível realizar predições que forneceram estimativas de risco no ensino infantil, para subsidiar a prevenção de afastamento por morbidades em professores, utilizando dados públicos e anônimos. No terceiro e último artigo, foram desenvolvidos modelos preditivos para identificar, antecipadamente, trabalhadores com risco de diagnóstico positivo para doença pulmonar obstrutiva crônica (DPOC). O estudo utilizou dados da coorte prospectiva do UK Biobank, de indivíduos acompanhados desde 2006, filtrando aqueles que preencheram o questionário de histórico ocupacional (n=120.289). Desses, 1731 (1,4%) foram diagnosticados com DPOC. Ao todo, foram selecionadas 26 variáveis, entre dados demográficos, exames laboratoriais, hábitos e sintomas, para a construção de modelos generalistas para predição de DPOC. Além disso, foi selecionado um subconjunto de participantes (n=7.628) com histórico ocupacional na indústria da construção civil e na mineração com possível exposição a poeira de sílica, para desenvolver modelos especialistas. Desses, 237 (3,11%) tiveram diagnóstico de DPOC. O modelo generalista obteve AUROC de 0.845, e o modelo especialista obteve AUROC de 0.841. As cinco principais variáveis preditoras foram idade, tosse crônica, tabagismo, histórico de asma e expectoração. Os resultados mostram que é possível predizer risco individual de diagnóstico de DPOC na população geral e nos trabalhadores expostos a poeiras minerais utilizando variáveis comumente coletadas na atenção primária. Nesta tese, mostramos a viabilidade de uso de modelos preditivos na saúde do trabalhador tanto para prognóstico quanto para diagnóstico, com boa performance preditiva. Espera-se que este estudo possa contribuir para uma maior adoção de modelos preditivos por pesquisadores em SST, permitindo identificar antecipadamente trabalhadores expostos a riscos ambientais de forma a auxiliar o início de medidas preventivas que inibam ou minimizem os riscos. Machine learning algorithms have gained prominence in the health area in recent years. Much of this popularity is due to predictive performance gains when compared to traditional statistical models, as these algorithms are able to capture non-linear relationships and to handle different types of data. This research aims to describe the different machine learning techniques and how these techniques can be applied in occupational safety and health (OSH). The results are organized into three scientific articles. In the first manuscript, a literature review was carried out to understand the panorama of machine learning use in public health and OSH. Supervised and unsupervised learning algorithms were identified and categorized, and main research problems were listed. In the second article, supervised learning algorithms were developed to predict absenteeism due to illness and work related illness in teachers from all public municipal schools in the State of São Paulo between 2014 and 2018 (n=174.294) available from the Relação Anual de Informações Sociais (RAIS). Five algorithms were compared according to the value of the area under the receiver operating characteristic curve (AUROC). All algorithms obtained AUROC greater than 0.76. The best algorithm (artificial neural networks) obtained an AUROC of 0.79, with an accuracy of 71.52%, sensitivity of 72.86% and specificity of 70.52%. It was possible to make assertive predictions, which provide estimates of risk, providing subsidies for preventing sick leave in teachers using public and anonymous data. In the third and last article, predictive models were developed to identify workers at risk of a positive diagnosis for chronic obstructive pulmonary disease (COPD). The study used data from the UK Biobank prospective cohort from individuals followed since 2006, filtering those who completed the occupational history questionnaire (n=120.294). Of these, 1731 (1.4%) had a positive diagnosis of COPD. In all, 26 variables were selected, including demographic data, laboratory tests, habits and symptoms, for the development of generalist models for the prediction of COPD. In addition, a subset of individuals (n=7628) with an occupational background in the construction and mining industry, with possible exposure to mineral dusts was selected to develop specialized models. Of these, 237 (3.11%) were diagnosed with COPD. The generalist model obtained AUROC of 0.845, and the specialist model, an AUC of 0.841. The five main predictive variables were age, chronic cough, smoking, earlier diagnosis of asthma and chronic sputum. The results show that it is possible to predict individual risk of COPD diagnosis in the general population and in workers exposed to silica dust using variables commonly collected in primary care. In this research, we showed the feasibility of using predictive models in worker health for both prognosis and diagnosis, with good predictive performance. We believe that this study can contribute to a greater adoption of predictive models by OSH researchers, allowing the early identification of workers exposed to risks and the adoption of preventive measures that inhibit or minimize risks.