Building a National Capability to Monitor and Assess Medical Countermeasure Use During a Public Health Emergency Going Beyond the Last Mile: Proceedings of a Workshop (2017) / Chapter Skim
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3 Data Needs, Data Sources, and Collection Methodologies for Stakeholder Decision Making
3 Data Needs, Data Sources, and Collection Methodologies for Stakeholder Decision Making
Pages 31-50
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Disseminating data and information. DEFINING AND ANSWERING QUESTIONS TO INFORM DATA NEEDS FOR MCM DISTRIBUTION AND MONITORING Yu called on panelists to describe, from their sector's perspective, key questions that should be asked and the corresponding data needed to inform the monitoring and assessment of MCM use when responding to a PHE.
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From the federal standpoint, Patel said, broad operational questions apply to MCMs in every PHE response that must be answered before a distribution plan can be developed: • (Do) es the "right" product(s)
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Henry "Skip" Francis, director of Data Mining and Informatics Evaluation and Research at the Center for Drug Evaluation and BOX 3-1 Examining the Impact of Safety and Efficacy Profiles on MCM Distribution: Lessons from Anthrax and H1N1 Patel described two scenarios that highlight how a product's safety and ef ficacy profile influences the operational aspects of MCM distribution. In a potential anthrax scenario, two FDA-approved drugs are available with mass dispensing orders available for their distribution.
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Three main categories of data discussed by workshop panelists and participants for answering key operational questions for MCM monitoring and assessment included medical history data, symptomatology data, and data to inform threat containment. Ataher noted that in many PHEs, there may be limited safety and efficacy information available for an MCM.
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. Levy added that data from 911 calls are used by local public health departments when dealing with a variety of incidents, including monitoring for carbon monoxide poisoning, power outages, or potential Ebola patients.
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SOURCE: Lance presentation, June 6, 2017. (see Operational Questions That Drive Data Needs on p.
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Collection from one data source could result in a sequence of events leading to different types of data collection, she noted, including observational studies, patient registries, electronic health data, big data, and clinical studies. In terms of how these different data streams are used, timing of data collection becomes an important issue, said Cullen.
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This system aids both in syndromic surveillance and also establishes baselines, and it has fairly sophisticated analytic tools that can detect deviations.2 Identifying Missing or Unknown Data Scott Proestel, director of the Division of Epidemiology, Office of Biostatistics and Epidemiology at the Center for Biologics Evaluations and Research at FDA, remarked that a key challenge in data collection efforts is capturing data that are missing or unknown. Lee added that, in some cases, data that are not collected can also provide helpful information.
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Avenues such as social media and polling could be helpful to collect these data after MCM distribution, he said. Using Machine Learning and Artificial Intelligence Francis suggested that machine learning strategies or artificial intelligence could identify data sources for MCM monitoring and assessment that would not normally be considered.
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. Workshop panelists and participants discussed some of the many types of data and existing datasets that could be leveraged for monitoring and assessing the outcomes of MCM use, including patient narratives, EHRs, pharmacy databases, federal surveillance systems, big data, and social media.
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Furthermore, some EHR formats force providers to enter data at a level of specificity that may run counter to the care delivery goals in a PHE, noted Francis, because of time constraints at the point of care and in following up on data entries. Groom of the Indian Health Service reiterated that EHR systems were designed for billing, not public health, although they have been evolving in response to Centers for Medicare & Medicaid Services (CMS)
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. Baseline data are critical to interpreting the clinical implications of an emerging infectious disease outbreak, and they could inform adaptive clinical trial designs and allow for some level of generalizability of trial findings beyond the trial setting.
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She referred workshop participants to a recent study of Yelp reviews of foodservice businesses that included reports of food-borne illness (Nsoesie et al., 2014) , and asked whether and how such an approach could be applied to MCM monitoring and assessment: What information could be gleaned from big data, and is this type of data robust enough to inform monitoring and assessment efforts?
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He suggested that pharmacies could potentially be leveraged for mass vaccinations or mass dispensing, as an appropriate system for collecting dispensing data is already in place. Social Media There is untapped potential in patient-collected data, whether it is collected by a mobile health application (see next section)
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For example, opinion polling suggests that the public will accept MCMs that are provided to them, but they will wait to see if they get sick before taking them. He pointed out that the issue of monitoring MCM use is not just what happens after people take MCMs, but also whether they take it at all, a question that could potentially be answered by social media.
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Direct Access to Information Sources Levy pointed out that in a PHE, such as the recent Zika and Ebola outbreaks, information about the threat, MCMs, and the impact on the population comes through surveillance systems, to CDC, then to Public Health Emergency Preparedness awardees, and then to the local-level partners. At each level, nuances are lost, she said, and local health departments do not have the ability to directly question those who performed the research and the data collection.
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Local health departments do not have the funding for sophisticated data collection, nor do they have the personnel to provide the epidemiological oversight needed to answer the questions asked by researchers, said Cooper. She emphasized that most local health systems do not have the capacity to collect the data requested of them for those who wish to analyze the data, and described data collection in MCM dispensing operations as early days.
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There is no one unified system with common standards for collecting and sharing data. Even local health departments within a state, or individual hospitals under the same network, face challenges sharing information, she added.
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Data scientists need to sell the importance of public health preparedness to the politicians who make the policies that influence the design of these products, she added. Data Science Training The most undeveloped resource is not necessarily a particular database or data source, Francis said, but our own expertise in working with the available data and the available IT tools.
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50 BUILDING A NATIONAL CAPABILITY TO MONITOR AND ASSESS MCM USE knowledge and skills gaps, she said, and what might be done at both undergraduate and graduate levels to offer better training and develop a pipeline of individuals with data science and informatics competencies, along with their domain knowledge. Lee reminded participants of NIH training grant opportunities in this area.
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