dards are the route to having the right size and shape of boxes to collect your data. This understanding makes it not only easier to move but also allows you to know where to get the stu you need whenever you need it. The Standard for Aquatic Facility Environ- ments–Data (SAFE-D) model demonstrates this idea. Each piece of data is defined to support collecting whatever information you need from swimming pool inspections, regardless of your regulations, location, or jurisdiction. Setting up standards for each of the services we provide would require a simi- lar focus on the science—and not any single regulation or way to get the service com- pleted. If all fields of environmental health had standard data dictionaries that had com- mon definitions, we could advance our abil- ity to assess our data against other data, such as comparing socioeconomic factors, demo- graphics, and public policies. Cost Benefits The reduced cost of setting up or chang- ing your data system based on a standard dictionary comes from the fact that most,
if not all the pieces, are already defined. As such, system developers and programmers can focus on making the user interface and reports as needed, and not have to reinvent the basic structure of the backend database. Using standardized data will also prevent cost and time overruns by helping you define the scope of your data project. Having a standard data dictionary and relationship structure to start with limits the customization, which is often the problem with project scope issues. If you have your existing data already boxed up in a standardized system, it is far easier and less costly to convert it to another system should you find the need to change database systems or contractors. Most often, the greatest cost in time and funds in chang- ing systems is the conversion of your data, if that is possible at all.
to modernize data across the federal and state public health landscape (www.cdc.gov/sur veillance/data-modernization/index.html). Even this massive federal eort to build a standard way to share data so that we can better identify disease threats is struggling with the fact that local oces do not have a data standard or have conflicting views of what should limit data sharing. As our data are related to primary prevention that only overlaps with the rest of public health when outbreaks happen, we are not a central part of this initiative. So, we have a decision to make: Do we chase this DMI ship that is already sailing or do we prepare for our own journey that focuses on the needs of environmental health operations? Either way, now is the time to use the political winds to drive our eorts. Corresponding Author: Tim Callahan, Direc- tor, Evaluation and Support Program, Envi- ronmental Health Section, Georgia Depart- ment of Public Health, 200 Piedmont Avenue SE, East Tower, Suite 486, Atlanta, GA 30334. Email: tim.callahan@dph.ga.gov.
Get on Board or Build Our Own Ship?
The rest of public health is seeing huge investments in the Data Modernization Ini- tiative (DMI), an initiative from the Centers for Disease Control and Prevention that aims
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October 2023 • Journal of Environmental Health
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