381. The Importance of Data Accuracy and Transparency for Policymaking During a Public Health Crisis: A Case Study in the State of Iowa

Abstract Background High-quality data are necessary for decision-making during the SARS-CoV-2 pandemic. Lack of transparency and accuracy in data reporting can erode public confidence, mislead policymakers, and endanger safety. Two major data errors in Iowa impacted critical state- and county-level decision-making. Methods The Iowa Department of Public Health (IDPH) publishes daily COVID-19 data. Authors independently tracked daily data from IDPH and other publicly available sources (i.e., county health departments, news media, and social networks). Data include: number and type of tests, results, hospitalizations, intensive care unit admissions, and deaths at state/county levels. Results Discrepancies were identified between IDPH and non-IDPH data, with at least two confirmed by IDPH: (1) The backdating of test results identified on May 28, 2020. IDPH labeled results as occurring up to four months before the actual test date. IDPH confirmed that if a person previously tested for SARS-CoV-2, a new test result was attributed to the initial test’s date. Corrections on August 19, 2020 increased positivity rates in 31 counties, but decreased the state’s overall rate (9.1% to 7.5%). (2) The selective exclusion of antigen test results noted on August 20, 2020. Antigen testing was included in the total number of tests reported in metric denominators, but their results were being excluded from their respective numerators. Thus, positive antigen results were interpreted as de facto negative tests, artificially lowering positivity rates. Corrections increased Iowa’s positivity rate (5.0% to 14.2%). In July 2020, the Iowa Department of Education mandated in-person K-12 learning for counties with < 15% positivity. These data changes occurred during critical decision-making, altering return-to-learn plans in seven counties. The Center for Medicare and Medicaid Services’ requirements also caused nursing homes to urgently revise testing strategies. Timeline of changes to Iowa state COVID-19 testing through the end of August 2020. Change in positive and overall test results due to IDPH data corrections. These graphs represent the difference in cumulative total reported test results when pulled from the IDPH website on September 29, 2020 compared to data for the same dates when pulled on August 19, 2020 before the announced adjustment. The adjustment and subsequent daily changes in reported data amount to a dramatic change in the number of reported positive cases (A) with an increase of nearly 3,000 cases by April 25, as well as the loss of tens of thousands of data points when tracking total resulted tests (B). Conclusion Data availability, quality, and transparency vary widely across the US, hindering science-based policymaking. Independent audit and curations of data can contribute to better public health policies. We urge all states to increase the availability and transparency of public health data. Disclosures All Authors: No reported disclosures

Conclusion. We conclude that environmental contamination of surfaces in the rooms of COVID-19 patients is nearly universal and persistent. Patients with greater independence are more likely than fully dependent patients to contaminate their immediate environment.
Disclosures. Background. High-quality data are necessary for decision-making during the SARS-CoV-2 pandemic. Lack of transparency and accuracy in data reporting can erode public confidence, mislead policymakers, and endanger safety. Two major data errors in Iowa impacted critical state-and county-level decision-making.
Methods. The Iowa Department of Public Health (IDPH) publishes daily COVID-19 data. Authors independently tracked daily data from IDPH and other publicly available sources (i.e., county health departments, news media, and social networks). Data include: number and type of tests, results, hospitalizations, intensive care unit admissions, and deaths at state/county levels.
Results. Discrepancies were identified between IDPH and non-IDPH data, with at least two confirmed by IDPH: (1) The backdating of test results identified on May 28, 2020. IDPH labeled results as occurring up to four months before the actual test date. IDPH confirmed that if a person previously tested for SARS-CoV-2, a new test result was attributed to the initial test's date. Corrections on August 19, 2020 increased positivity rates in 31 counties, but decreased the state's overall rate (9.1% to 7.5%).
(2) The selective exclusion of antigen test results noted on August 20, 2020. Antigen testing was included in the total number of tests reported in metric denominators, but their results were being excluded from their respective numerators. Thus, positive antigen results were interpreted as de facto negative tests, artificially lowering positivity rates. Corrections increased Iowa's positivity rate (5.0% to 14.2%). In July 2020, the Iowa Department of Education mandated in-person K-12 learning for counties with < 15% positivity. These data changes occurred during critical decision-making, altering return-to-learn plans in seven counties. The Center for Medicare and Medicaid Services' requirements also caused nursing homes to urgently revise testing strategies.
Timeline of changes to Iowa state COVID-19 testing through the end of August 2020.
Change in positive and overall test results due to IDPH data corrections. These graphs represent the difference in cumulative total reported test results when pulled from the IDPH website on September 29, 2020 compared to data for the same dates when pulled on August 19, 2020 before the announced adjustment. The adjustment and subsequent daily changes in reported data amount to a dramatic change in the number of reported positive cases (A) with an increase of nearly 3,000 cases by April 25, as well as the loss of tens of thousands of data points when tracking total resulted tests (B).
Conclusion. Data availability, quality, and transparency vary widely across the US, hindering science-based policymaking. Independent audit and curations of data can contribute to better public health policies. We urge all states to increase the availability and transparency of public health data.
Disclosures. Background. It is estimated that 18% of adults in the U.S. take Vitamin D supplements. Some observational studies suggest that vitamin D supplementation activates the innate immune system and reduces the incidence and severity of viral infections. During the SARS-CoV-2 pandemic, vitamin D supplements were touted as a potential therapy to prevent the disease and/or complications. However, supportive evidence is lacking.
Methods. The National COVID Cohort Collaborative (N3C) enclave is the largest COVID-19 data base with nearly 1.4 million positive patients at 56 sites in the U.S. We performed a retrospective analysis of vitamin D supplementation, either prescribed before or during hospitalization for SARS-CoV-2.
Results. 137,399 people took vitamin D supplements out of 1.4 million. Females prescribed vitamin D outnumbered males by almost 2:1, whereas in non-users there were no sex differences. Most supplement users were older than 50. African Americans constituted 13% of the non-users, but 23% of those prescribed vitamin D. Infected individuals with any vitamin D supplementation, pre-Covid, post-Covid or both, had a 6.66% mortality rate vs 2% mortality in non-users. Similarly, nearly a third of the supplement users were hospitalized compared to 11% in the non-users. The Charlson Co-Morbidity Index was 3.0±3 (SD) in users vs 1.0±2 (SD) in non-users.
Conclusion. 10% of SARS-CoV-2 infected patients were taking vitamin D. They tended to be older, more likely to be African American and have significant co-morbidities. Hospitalization and mortality were higher among those taking Vitamin D in this cohort. Vitamin D is widely used to prevent and treat SARS-CoV-2 but without evidence of efficacy.
Disclosures Background. While pediatric cases of COVID-19 are at low risk for adverse events, schoolchildren should be considered for surveillance as they can become infected at school and serve as sources of household or community transmission. Our team assessed the feasibility of young children self-collecting SARS-CoV-2 samples for surveillance testing in an educational setting.
Methods. Students at a K-8 school were tested weekly for SARS-CoV-2 from September 2020 -June 2021. Error rates were collected from September 2020 -January 2021. Clinical staff provided all students with instructions for anterior nares specimen self-collection and then observed them to ensure proper technique. Instructions included holding the sterile swab while making sure not to touch the tip, inserting the swab into their nostril until they start to feel resistance, and rubbing the swab in four circles before repeating the process in their other nostril. An independent observer timed random sample self-collections from April -June 2021.
Results. 2,590 samples were collected from 209 students during the study period when data on error rates were collected. Errors occurred in 3.3% of all student encounters (n=87). Error rates over time are shown in Figure 1, with the highest rate occurring on the first day of testing (n=20/197, 10.2%) and the lowest in January 2021 (n=1/202, 0.5%). 2,574 visits for sample self-collection occurred during the study period when independent timing data was collected (April -June 2021). Of those visits, 7.5% (n=193) were timed. The average duration of each visit was 70 seconds.

Figure 1. Swab Error Rates Over Time
Conclusion. Pediatric self-collected lower nasal swabs are a viable and easily tolerated specimen collection method for SARS-CoV-2 surveillance in school settings, as evidenced by the low error rate and short time window of sample self-collection during testing. School administrators should expect errors to drop quickly after implementing testing.
Disclosures. All Authors: No reported disclosures