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1.
Int J Environ Res Public Health ; 20(2)2023 Jan 10.
Article in English | MEDLINE | ID: covidwho-2227659

ABSTRACT

Work is a recognized social determinant of health. This became most apparent during the COVID-19 pandemic. Workers, particularly those in certain industries and occupations, were at risk due to interaction with the public and close proximity to co-workers. The purpose of this study was to assess how states collected work and employment data on COVID-19 cases, characterizing the need for systematic collection of case-based specific work and employment data, including industry and occupation, of COVID-19 cases. A survey was distributed among state occupational health contacts and epidemiologists in all 50 states to assess current practices in state public health surveillance systems. Twenty-seven states collected some kind of work and employment information from COVID-19 cases. Most states (93%) collected industry and/or occupation information. More than half used text-only fields, a predefined reference or dropdown list, or both. Use of work and employment data included identifying high risk populations, prioritizing vaccination efforts, and assisting with reopening plans. Reported barriers to collecting industry and occupation data were lack of staffing, technology issues, and funding. Scientific understanding of work-related COVID-19 risk requires the systematic, case-based collection of specific work and employment data, including industry and occupation. While this alone does not necessarily indicate a clear workplace exposure, collection of these data elements can help to determine and further prevent workplace outbreaks, thereby ensuring the viability of the nation's critical infrastructure.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Pandemics , Social Determinants of Health , Occupations , Industry
2.
International Journal of Environmental Research and Public Health ; 19(9):5112, 2022.
Article in English | ProQuest Central | ID: covidwho-1837299

ABSTRACT

Temporary staffing has an increasing role in world economies, contracting workers and dispatching them to work for leasing employers within countries and across borders. Using Illinois as a case study, co-authors have undertaken investigations to understand the occupational health, safety, and well-being challenges for workers hired through temporary staffing companies;to determine knowledge and attitudes of temp workers and temp staffing employers;and to assess temporary staffing at a community level. Temporary staffing workers in Illinois tend to be people of color who are employed in the most hazardous sectors of the economy. They have a higher rate of injury, are compensated less, and often lose their jobs when injured. Laws allow for ambiguity of responsibility for training, reporting, and compensation between the staffing agency and host employers. Our findings illustrate the ways in which principles of fairness and equity are violated in temporary staffing. Shared responsibility for reporting injuries, providing workers’ compensation insurance, and training workers should be mandated in law and required in contractual language between temporary staffing and host/contracting employers. Monitoring, enforcement, and adjustment of the law based on experience are required to “promote inclusive and sustainable economic growth, employment and decent work for all.

3.
Clin Chest Med ; 41(4): 605-621, 2020 12.
Article in English | MEDLINE | ID: covidwho-896784

ABSTRACT

Computer and information systems can improve occupational respiratory disease prevention and surveillance by providing efficient resources for patients, workers, clinicians, and public health practitioners. Advances include interlinking electronic health records, autocoding surveillance data, clinical decision support systems, and social media applications for acquiring and disseminating information. Obstacles to advances include inflexible hierarchical coding schemes, inadequate occupational health electronic health record systems, and inadequate public focus on occupational respiratory disease. Potentially transformative approaches include machine learning, natural language processing, and improved ontologies.


Subject(s)
Informatics/methods , Lung Diseases/diagnosis , Lung Diseases/prevention & control , Occupational Diseases/diagnosis , Occupational Diseases/prevention & control , Occupational Exposure/adverse effects , Humans , Machine Learning
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