TIMELINESS OF INFECTIOUS DISEASES CASE REPORTING IN THE WASHINGTON STATE DISEASE REPORTING SYSTEM

Date
2023
Authors
Ingalls, Kacey Kathleen
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Hurwitz, Eric
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Epidemiology
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Objectives: The objective of this dissertation was to investigate if an increase in the 7-day case rate for COVID-19 cases led to an increase in the 7-day average of time (in days) between the specimen collection date and the investigation create date in the Washington State Disease Reporting System (WDRS) for COVID-19 cases reported through electronic laboratory reporting (ELR), COVID-19 cases manually entered, Hepatitis B cases reported through ELR, and Hepatitis C cases reported through ELR. Additionally, this dissertation analysis will retroactively assign the COVID-19 reinfection case classification to cases between January 2020 and September 2021. MethodsThere were two data sources used for this analysis. Laboratory result data housed in WDRS were requested from the Washington State Department of Health. The second data source used was the public facing Washington State COVID-19 dashboard. All analysis were completed in R. A rolling 7-day average lag time variable was created by subtracting the investigation create date from the specimen collection date. A time series analysis, scatterplots, and Pearson’s Correlations were completed using the 7-day average lag times and case rate data available from the COVID-19 dashboard. All analyses were completed for ELR, manual, year-specific, and wave-specific datasets. To reassign the COVID-19 case classification a total count of laboratory results were created using R and a 90-day rolling cut off. Data were then manually reviewed and cleaned using Excel. ResultsOverall, we found that there was a lag between the specimen collection date and the investigation create date and there was an association with the 7-day COVID-19 case rate average. Magnitude of relationship varied based on year and how data were broken into differing timeframes. There was also a difference seen between disease type and mode of laboratory result, either ELR or manually entered. We also found that there were approximately 7,165 laboratory results that could be considered reinfections in the first year and a half of the pandemic response by applying the updated COVID-19 reinfection case classification. ConclusionsIn conclusion this dissertation outlines a practical application of including a lag time metric into a surveillance system evaluation during a pandemic response. These analyses highlighted there was a significant lag between when tests were completed and when they were received in the surveillance system. Future research should focus on identifying where along the informatics pipeline the lag is occurring so that we can work to improve efficiency and be better prepared to respond to the next pandemic.
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Epidemiology
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142 pages
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