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JMIR Public Health Surveill ; 7(6): e28265, 2021 06 03.
Article in English | MEDLINE | ID: covidwho-2197911

ABSTRACT

BACKGROUND: Despite the limitations in the use of cycle threshold (CT) values for individual patient care, population distributions of CT values may be useful indicators of local outbreaks. OBJECTIVE: We aimed to conduct an exploratory analysis of potential correlations between the population distribution of cycle threshold (CT) values and COVID-19 dynamics, which were operationalized as percent positivity, transmission rate (Rt), and COVID-19 hospitalization count. METHODS: In total, 148,410 specimens collected between September 15, 2020, and January 11, 2021, from the greater El Paso area were processed in the Dascena COVID-19 Laboratory. The daily median CT value, daily Rt, daily count of COVID-19 hospitalizations, daily change in percent positivity, and rolling averages of these features were plotted over time. Two-way scatterplots and linear regression were used to evaluate possible associations between daily median CT values and outbreak measures. Cross-correlation plots were used to determine whether a time delay existed between changes in daily median CT values and measures of community disease dynamics. RESULTS: Daily median CT values negatively correlated with the daily Rt values (P<.001), the daily COVID-19 hospitalization counts (with a 33-day time delay; P<.001), and the daily changes in percent positivity among testing samples (P<.001). Despite visual trends suggesting time delays in the plots for median CT values and outbreak measures, a statistically significant delay was only detected between changes in median CT values and COVID-19 hospitalization counts (P<.001). CONCLUSIONS: This study adds to the literature by analyzing samples collected from an entire geographical area and contextualizing the results with other research investigating population CT values.


Subject(s)
COVID-19 Nucleic Acid Testing/statistics & numerical data , COVID-19/epidemiology , Hospitalization/statistics & numerical data , Adult , COVID-19/transmission , Female , Humans , Male , Middle Aged , Pandemics , SARS-CoV-2 , Texas , Time Factors
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