Cross-Disciplinary Consultancy to Enhance Predictions of Asthma Exacerbation Risk in Boston
Ensor, Katherine B.
Levy, Jonathan I.
Streichert, Laura C.
Burkom, HowardNote: Order does not necessarily reflect citation order of authors.
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CitationReid, M., J. Gunn, S. Shah, M. Donovan, R. Eggo, S. Babin, I. Stajner, et al. 2016. “Cross-Disciplinary Consultancy to Enhance Predictions of Asthma Exacerbation Risk in Boston.” Online Journal of Public Health Informatics 8 (3): e199. doi:10.5210/ojphi.v8i3.6902. http://dx.doi.org/10.5210/ojphi.v8i3.6902.
AbstractThis paper continues an initiative conducted by the International Society for Disease Surveillance with funding from the Defense Threat Reduction Agency to connect near-term analytical needs of public health practice with technical expertise from the global research community. The goal is to enhance investigation capabilities of day-to-day population health monitors. A prior paper described the formation of consultancies for requirements analysis and dialogue regarding costs and benefits of sustainable analytic tools. Each funded consultancy targets a use case of near-term concern to practitioners. The consultancy featured here focused on improving predictions of asthma exacerbation risk in demographic and geographic subdivisions of the city of Boston, Massachusetts, USA based on the combination of known risk factors for which evidence is routinely available. A cross-disciplinary group of 28 stakeholders attended the consultancy on March 30-31, 2016 at the Boston Public Health Commission. Known asthma exacerbation risk factors are upper respiratory virus transmission, particularly in school-age children, harsh or extreme weather conditions, and poor air quality. Meteorological subject matter experts described availability and usage of data sources representing these risk factors. Modelers presented multiple analytic approaches including mechanistic models, machine learning approaches, simulation techniques, and hybrids. Health department staff and local partners discussed surveillance operations, constraints, and operational system requirements. Attendees valued the direct exchange of information among public health practitioners, system designers, and modelers. Discussion finalized design of an 8-year de-identified dataset of Boston ED patient records for modeling partners who sign a standard data use agreement.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:31731653
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