YOUR ASSOCIATION
PRESIDENT’S MESSAGE
Environmental Health Data—Can We Make More Powerful Decisions?
Tom Butts, MSc, REHS
W hile we serve our communities, we work in a more and more con- nected environment where shar- ing information and data are required. Doing this sharing in a consistent way, with limited data manipulation, supports better decision making. Environmental public health sys- tems have historically collected a variety of community-, program-, and project-related information and data. These important and potentially useful data have often been placed in spreadsheets, custom databases, or en- terprise software systems designed around workflow, workload management, and ensur- ing regulatory compliance. The information is sometimes publicly available but often kept behind one or more layers of “protection.” There are changes that have occurred with some information that the public and consumers actively sought to access. Retail food inspections are a great example of how demand from the for-profit world has made these data more available and widely used. Initially, big data players (e.g., Yelp and oth- ers) worked to gather these data to add to the information that they provided to their customers and system users who were con- sumers. Now, many (maybe even most) retail food inspections are available on a state or county website, or even shared via social media in near real time. We still have a wide range of data modifiers that are added (e.g., color codes, category descriptors, scoring systems) that often require significant expla- nations and caveats. Community members should be encour- aged to check and understand the narratives or scores of their favorite eateries and patron-
There are many local, state, and national eorts to use program information and data to improve food safety, assure safe practices are adopted, and document regulatory compli- ance. These data are also used on a much more limited basis for academic research, which I suspect is in part due to the wide range of ways the data are collected and the limits around data access. When artificial intelligence (AI) use grows and taps into this information and data, how—for better or worse—will environ- mental health programs, consumers, the pri- vate sector, and even academia be impacted? • Data analysis and decision support: AI algorithms can process large volumes of data quickly and accurately, helping pro- fessionals analyze regulatory requirements, identify patterns, and make informed deci- sions based on the data. It could help with workload analysis and program funding. • Compliance monitoring and risk assess- ment: AI can assist in monitoring and ensuring compliance with regulations by analyzing data from various sources and identifying any anomalies or noncompli- ance activities. It can flag potential issues for further investigation, which can reduce the burden of manual monitoring and increase the eectiveness of regulatory oversight. This process could also assist with workload analysis, fee-for-service justification, or early outbreak risk factor identification that could be addressed with targeted educational outreach. Next, let us consider air quality informa- tion and data. As we work to address air qual- ity impacts from national or international sources, transportation, and point sources
ize those establishments with higher ratings. When data reveal recurring issues in certain establishments, does it prompt targeted inter- ventions or increase consumer interest? I have certainly seen these instances occur. Food safety inspections are not only a formality but also a tool for continuous improvement. Another element of retail food safety data that is of particular interest and importance is the growing reference to one set of stan- dards. It is a program where a national model exists (i.e., the Food and Drug Administra- tion model Food Code ). Data can become more powerful if they are uniform. By con- sistently applying one set of standards, a step toward data standardization is possible. Vari- ous versions of the Food Code from 1995 to 2022 have been adopted in most states (Food and Drug Administration, 2023). These data sources are, however, still fraught with a wide range of implementation models (i.e., vary- ing adoption of the Food Code or state and local variances from the Food Code for local, regional, or governance reasons). As such, there is room for improvement. The eective collection and use of data are crucial for both public health and environmental health initiatives.
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Volume 86 • Number 4
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