COVID-19 Surveillance at the Texas Department of State Health Services
A blend of modern data systems and low-tech data products was best for internal surveillance and sharing the story of the pandemic in Texas.
In this third year of the COVID-19 pandemic, public health practitioners, and specifically those who managed COVID-19 surveillance, data processing, and data dissemination, continue to reflect upon processes and products that worked well, could be improved upon, or could be utilized in a future pandemic or public health emergency response.
In the Center for Health Statistics at the Texas Department of State Health Services, the COVID-19 Data Team produced several data products for surveillance and data dissemination over the course of the COVID-19 pandemic – an ArcGIS dashboard and website with data files updated daily (now weekly) for public download and use, as well as three internal surveillance tools, which we describe in our article “Creating Novel Surveillance Data Products for Briefing Health Department Leadership and Elected Officials During the COVID-19 Pandemic in Texas.”
Though the department began the pandemic with a fragmented system of data collection and processing, teams from across divisions worked together to stand up data infrastructure and refine the COVID-19 data pipeline. Though modern data management systems are key to efficiency, timely reporting of data, and reducing errors, the data products that proved most useful for internal departmental surveillance and for telling the story of the pandemic in Texas in external briefings and presentations were fairly low-tech and provided basic visualizations that could be understood across audiences.
The three surveillance products and associated methods we describe include (1) a COVID-19 data book displaying case, fatality, hospitalization, and testing data by county and statewide by day, (2) graphs and data files displaying new daily COVID-19 fatalities among residents of long-term care facilities in Texas, and (3) graphs and data files comparing COVID-19 cases and hospitalizations between the four COVID-19 waves in Texas.
We interviewed health department leadership regarding use cases, improvement, and generalizability to other areas and assembled a table of strengths, limitations, and lessons learned from each. The COVID-19 data book displaying all the different types of COVID-19 related data was considered to be the most useful because of its comprehensiveness and its format as an informal Excel™ “database” of COVID-19 data; however, it required manual input and revision, making it time and labor-intensive to produce. Graphing new weekly COVID-19 fatalities among residents of long-term care facilities allowed us to tell the story of vaccine effectiveness in a vulnerable population by visualizing the decline in fatalities among older adults over the fatality trend graph for the Texas population as a whole. Lastly, visualizing the four waves of COVID-19 in Texas by week for new confirmed cases and for current COVID-19 patients in hospitals assisted preparedness efforts, helping staff determine the behavior of the current wave in relation to previous waves and to plan for resources and response.
Each surveillance product served a valuable purpose during the pandemic. The visualizations were used in multiple presentations to different audiences because of the clear story they told about the nature of the pandemic at the state level. The data book included a wealth of state-, Trauma Service Area- and county-level data that could be used by staff throughout the agency to inform programming and public health practice. Though each of the three products we describe in the article had their challenges in production and their limitations depending on audience and setting, they complemented the publicly available data products and provided a valuable resource for a diverse group of stakeholders. Lastly, their low-tech design makes them potentially useful for other public health jurisdictions with less robust information technology infrastructure and data science capacity that could recreate these surveillance products using our templates with publicly available data.
Leah Chapman is a Postdoctoral Research Fellow at the Center for Health Inclusion, Research and Practice at Merrimack College and the Harvard T.H. Chan School of Public Health. She completed her PhD in Nutrition at the Gillings School of Global Public Health at the University of North Carolina at Chapel Hill. She formerly worked as a COVID-19 Research Specialist at the Texas Department of State Health Services Center for Health Statistics.
Kai Cobb is a Research Specialist on the COVID-19 Data Dissemination Team at the Texas Department of State Health Services Center for Health Statistics. She completed her MPH in Epidemiology at the Arnold School of Public Health at the University of South Carolina and her B.S. in Bioinformatics at Claflin University.
Emily Hall is a Research Data Manager and Analyst with the Dell Medical School at the University of Texas at Austin in the Department of Women’s Health. She formerly led the Advanced Analytics Team at the Texas Department of State Health Services Center for Health Statistics.