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dc.contributor.authorParry, Gareth
dc.contributor.authorProvost, Lloyd P.
dc.contributor.authorProvost, Shannon M.
dc.contributor.authorLittle, Kevin
dc.contributor.authorPerla, Rocco J.
dc.date2022-08-11T08:08:11.000
dc.date.accessioned2022-08-23T15:45:27Z
dc.date.available2022-08-23T15:45:27Z
dc.date.issued2021-12-04
dc.date.submitted2022-01-12
dc.identifier.citation<p>Parry G, Provost LP, Provost SM, Little K, Perla RJ. A hybrid Shewhart chart for visualizing and learning from epidemic data. Int J Qual Health Care. 2021 Dec 4;33(4). doi: 10.1093/intqhc/mzab151. PMID: 34865014. <a href="https://doi.org/10.1093/intqhc/mzab151">Link to article on publisher's site</a></p>
dc.identifier.issn1353-4505 (Linking)
dc.identifier.doi10.1093/intqhc/mzab151
dc.identifier.pmid34865014
dc.identifier.urihttp://hdl.handle.net/20.500.14038/27530
dc.description.abstractOBJECTIVE: As the globe endures the coronavirus disease 2019 (COVID-19) pandemic, we developed a hybrid Shewhart chart to visualize and learn from day-to-day variation in a variety of epidemic measures over time. CONTEXT: Countries and localities have reported daily data representing the progression of COVID-19 conditions and measures, with trajectories mapping along the classic epidemiological curve. Settings have experienced different patterns over time within the epidemic: pre-exponential growth, exponential growth, plateau or descent and/ or low counts after descent. Decision-makers need a reliable method for rapidly detecting transitions in epidemic measures, informing curtailment strategies and learning from actions taken. METHODS: We designed a hybrid Shewhart chart describing four 'epochs' ((i) pre-exponential growth, (ii) exponential growth, (iii) plateau or descent and (iv) stability after descent) of the COVID-19 epidemic that emerged by incorporating a C-chart and I-chart with a log-regression slope. We developed and tested the hybrid chart using international data at the country, regional and local levels with measures including cases, hospitalizations and deaths with guidance from local subject-matter experts. RESULTS: The hybrid chart effectively and rapidly signaled the occurrence of each of the four epochs. In the UK, a signal that COVID-19 deaths moved into exponential growth occurred on 17 September, 44 days prior to the announcement of a large-scale lockdown. In California, USA, signals detecting increases in COVID-19 cases at the county level were detected in December 2020 prior to statewide stay-at-home orders, with declines detected in the weeks following. In Ireland, in December 2020, the hybrid chart detected increases in COVID-19 cases, followed by hospitalizations, intensive care unit admissions and deaths. Following national restrictions in late December, a similar sequence of reductions in the measures was detected in January and February 2021. CONCLUSIONS: The Shewhart hybrid chart is a valuable tool for rapidly generating learning from data in close to real time. When used by subject-matter experts, the chart can guide actionable policy and local decision-making earlier than when action is likely to be taken without it.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=34865014&dopt=Abstract">Link to Article in PubMed</a></p>
dc.relation.urlhttps://doi.org/10.1093/intqhc/mzab151
dc.subjectShewhart control chart
dc.subjectcovid-19 pandemic
dc.subjectstatistical process control
dc.subjectstatistical public reporting of healthcare data
dc.subjectEpidemiology
dc.subjectInfectious Disease
dc.subjectStatistics and Probability
dc.subjectVirus Diseases
dc.titleA hybrid Shewhart chart for visualizing and learning from epidemic data
dc.typeJournal Article
dc.source.journaltitleInternational journal for quality in health care : journal of the International Society for Quality in Health Care
dc.source.volume33
dc.source.issue4
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1337&amp;context=covid19&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/covid19/331
dc.identifier.contextkey27296609
refterms.dateFOA2022-08-23T15:45:27Z
html.description.abstract<p>OBJECTIVE: As the globe endures the coronavirus disease 2019 (COVID-19) pandemic, we developed a hybrid Shewhart chart to visualize and learn from day-to-day variation in a variety of epidemic measures over time.</p> <p>CONTEXT: Countries and localities have reported daily data representing the progression of COVID-19 conditions and measures, with trajectories mapping along the classic epidemiological curve. Settings have experienced different patterns over time within the epidemic: pre-exponential growth, exponential growth, plateau or descent and/ or low counts after descent. Decision-makers need a reliable method for rapidly detecting transitions in epidemic measures, informing curtailment strategies and learning from actions taken.</p> <p>METHODS: We designed a hybrid Shewhart chart describing four 'epochs' ((i) pre-exponential growth, (ii) exponential growth, (iii) plateau or descent and (iv) stability after descent) of the COVID-19 epidemic that emerged by incorporating a C-chart and I-chart with a log-regression slope. We developed and tested the hybrid chart using international data at the country, regional and local levels with measures including cases, hospitalizations and deaths with guidance from local subject-matter experts.</p> <p>RESULTS: The hybrid chart effectively and rapidly signaled the occurrence of each of the four epochs. In the UK, a signal that COVID-19 deaths moved into exponential growth occurred on 17 September, 44 days prior to the announcement of a large-scale lockdown. In California, USA, signals detecting increases in COVID-19 cases at the county level were detected in December 2020 prior to statewide stay-at-home orders, with declines detected in the weeks following. In Ireland, in December 2020, the hybrid chart detected increases in COVID-19 cases, followed by hospitalizations, intensive care unit admissions and deaths. Following national restrictions in late December, a similar sequence of reductions in the measures was detected in January and February 2021.</p> <p>CONCLUSIONS: The Shewhart hybrid chart is a valuable tool for rapidly generating learning from data in close to real time. When used by subject-matter experts, the chart can guide actionable policy and local decision-making earlier than when action is likely to be taken without it.</p>
dc.identifier.submissionpathcovid19/331
dc.contributor.departmentDepartment of Population and Quantitative Health Sciences


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