Utility of prehospital call center ambulance dispatch data for COVID-19 cluster surveillance: A retrospective analysis.

INTRODUCTION: Cluster surveillance, identification, and containment are primary outbreak management techniques; however, adapting these for low- and middle-income countries is an ongoing challenge. We aimed to evaluate the utility of prehospital call center ambulance dispatch (CCAD) data for surveillance by examining the correlation between influenza-like illness (ILI)-related dispatch calls and COVID-19 cases. METHODS: We performed a retrospective analysis of state-level CCAD and COVID-19 data recorded between January 1 and April 30, 2020, in Telangana, India. The primary outcome was a time series correlation between ILI calls in CCAD and COVID-19 case counts. Secondarily, we looked for a year-to-year correlation of ILI calls in the same period over 2018, 2019, and 2020. RESULTS: On average, ILI calls comprised 12.9% (95% CI 11.7%-14.1%) of total daily calls in 2020, compared to 7.8% (95% CI 7.6%-8.0%) in 2018, and 7.7% (95% CI 7.5%-7.7%) in 2019. ILI call counts from 2018, 2019, and 2020 aligned closely until March 19, when 2020 ILI calls increased, representing 16% of all calls by March 23 and 27.5% by April 7. In contrast to the significant correlation observed between 2020 and previous years' January-February calls (2020 and 2019-Durbin-Watson test statistic [DW] = 0.749, p < 0.001; 2020 and 2018-DW = 1.232, p < 0.001), no correlation was observed for March-April calls (2020 and 2019-DW = 2.012, p = 0.476; 2020 and 2018-DW = 1.820, p = 0.208). In March-April 2020, the daily reported COVID-19 cases by time series significantly correlated with the ILI calls (DW = 0.977, p < 0.001). The ILI calls on a specific day significantly correlated with the COVID-19 cases reported 6 days prior and up to 14 days after (cross-correlation > 0.251, the 95% upper confidence limit). CONCLUSIONS: The statistically significant time series correlation between ILI calls and COVID-19 cases suggests prehospital CCAD can be part of early warning systems aiding outbreak cluster surveillance, identification, and containment.

Validity of at-home rapid antigen lateral flow assay and artificial intelligence read to detect SARS-CoV-2.

BACKGROUND: The gold standard for COVID-19 diagnosis-reverse-transcriptase polymerase chain reaction (RT-PCR)- is expensive and often slow to yield results whereas lateral flow tests can lack sensitivity. METHODS: We tested a rapid, lateral flow antigen (LFA) assay with artificial intelligence read (LFAIR) in subjects from COVID-19 treatment trials (N = 37; daily tests for 5 days) and from a population-based study (N = 88; single test). LFAIR was compared to RT-PCR from same-day samples. RESULTS: Using each participant's first sample, LFAIR showed 86.2% sensitivity (95% CI 73.6%-98.8) and 94.3% specificity (88.8%-99.7%) compared to RT-PCR. Adjusting for days since symptom onset and repeat testing, sensitivity was 97.8% (89.9%-99.5%) on the first symptomatic day and decreased with each additional day. Sensitivity improved with artificial intelligence (AI) read (86.2%) compared to the human eye (71.4%). CONCLUSION: LFAIR showed improved accuracy compared to LFA alone. particularly early in infection.

External validation of the 4C Mortality Score for hospitalised patients with COVID-19 in the RECOVER network.

OBJECTIVES: Estimating mortality risk in hospitalised SARS-CoV-2+ patients may help with choosing level of care and discussions with patients. The Coronavirus Clinical Characterisation Consortium Mortality Score (4C Score) is a promising COVID-19 mortality risk model. We examined the association of risk factors with 30-day mortality in hospitalised, full-code SARS-CoV-2+ patients and investigated the discrimination and calibration of the 4C Score. This was a retrospective cohort study of SARS-CoV-2+ hospitalised patients within the RECOVER (REgistry of suspected COVID-19 in EmeRgency care) network. SETTING: 99 emergency departments (EDs) across the USA. PARTICIPANTS: Patients ≥18 years old, positive for SARS-CoV-2 in the ED, and hospitalised. PRIMARY OUTCOME: Death within 30 days of the index visit. We performed logistic regression analysis, reporting multivariable risk ratios (MVRRs) and calculated the area under the ROC curve (AUROC) and mean prediction error for the original 4C Score and after dropping the C reactive protein (CRP) component. RESULTS: Of 6802 hospitalised patients with COVID-19, 1149 (16.9%) died within 30 days. The 30-day mortality was increased with age 80+ years (MVRR=5.79, 95% CI 4.23 to 7.34); male sex (MVRR=1.17, 1.05 to 1.28); and nursing home/assisted living facility residence (MVRR=1.29, 1.1 to 1.48). The 4C Score had comparable discrimination in the RECOVER dataset compared with the original 4C validation dataset (AUROC: RECOVER 0.786 (95% CI 0.773 to 0.799), 4C validation 0.763 (95% CI 0.757 to 0.769). Score-specific mortalities in our sample were lower than in the 4C validation sample (mean prediction error 6.0%). Dropping the CRP component from the 4C Score did not substantially affect discrimination and 4C risk estimates were now close (mean prediction error 0.7%). CONCLUSIONS: We independently validated 4C Score as predicting risk of 30-day mortality in hospitalised SARS-CoV-2+ patients. We recommend dropping the CRP component of the score and using our recalibrated mortality risk estimates.

Key concepts in clinical epidemiology: reporting on the accuracy of continuous tests.

Many clinical diagnostic tests, such as the joint fluid white blood cell count, produce results on a continuous scale, rather than a mere positive or negative. The accuracy of such tests is often reported as a positive and negative likelihood ratio at each of several potential cutoff points (e.g., ≥25,000/µL vs. not, ≥50,000/µL vs. not; ≥100,000/µL vs. not).  This Key Concepts article reviews the definition of a likelihood ratio and explains why the practice of dichotomizing the test is problematic. Instead, it proposes that such continuous scales be divided into multiple intervals (e.g., 0-25,000, >25,000-50,000, >50,000-100,000, >100,000) and each interval be given its own likelihood ratio.  This practice not only aligns with clinical common sense and practice but also enables a more accurate estimate of the updated risk of disease, given a pre-test risk.

SARS COV-2 anti-nucleocapsid and anti-spike antibodies in an emergency department healthcare worker cohort: September 2020 - April 2021.

BACKGROUND: Emergency department (ED) workers have an increased seroprevalence of SARS-CoV-2 antibodies. However, breakthrough infections in ED workers have led to a reduced workforce within a strained healthcare system. By measuring levels of IgG antibodies to the SARS-CoV-2 nucleocapsid and spike antigens in ED workers, we determined the incidence of infection and described the course of antibody levels. We also measured the antibody response to vaccination and examined factors associated with immunogenicity. METHODS: We conducted a prospective cohort study of ED workers conducted at a single ED from September 2020-April 2021. IgG antibodies to the SARS-CoV-2 nucleocapsid antigen were measured at baseline, 3, and 6 months, and IgG antibodies to the SARS-CoV-2 spike antigen were measured at 6 months. RESULTS: At baseline, we found 5 out of 139 (3.6%) participants with prior infection. At 6 months, 4 of the 5 had antibody results below the test manufacturer's positivity threshold. We identified one incident case of SARS-COV-2 infection out of 130 seronegative participants (0.8%, 95% CI 0.02-4.2%). In 131 vaccinated participants (125 BNT162b2, 6 mRNA-1273), 131 tested positive for anti-spike antibodies. We identified predictors of anti-spike antibody levels: time since vaccination, prior COVID-19 infection, age, and vaccine type. Each additional week since vaccination was associated with an 11.1% decrease in anti-spike antibody levels. (95% CI 6.2-15.8%). CONCLUSION: ED workers experienced a low incidence of SARS-CoV-2 infection and developed antibodies in response to vaccines and prior infection. Antibody levels decreased markedly with time since infection or vaccination.

Lack of association between either outpatient or inpatient glycemic control and COVID-19 illness severity or mortality in patients with diabetes.

INTRODUCTION: To evaluate whether outpatient insulin treatment, hemoglobin A1c (HbA1c), glucose on admission, or glycemic control during hospitalization is associated with SARS-CoV-2 (COVID-19) illness severity or mortality in hospitalized patients with diabetes mellitus (DM) in a geographical region with low COVID-19 prevalence. RESEARCH DESIGN AND METHODS: A single-center retrospective study of patients hospitalized with COVID-19 from January 1 through August 31, 2020 to evaluate whether outpatient insulin use, HbA1c, glucose on admission, or average glucose during admission was associated with intensive care unit (ICU) admission, mechanical ventilation (ventilator) requirement, or mortality. RESULTS: Among 111 patients with DM, 48 (43.2%) were on outpatient insulin and the average HbA1c was 8.1% (65 mmol/mol). The average glucose on admission was 187.0±102.94 mg/dL and the average glucose during hospitalization was 173.4±39.8 mg/dL. Use of outpatient insulin, level of HbA1c, glucose on admission, or average glucose during hospitalization was not associated with ICU admission, ventilator requirement, or mortality among patients with COVID-19 and DM. CONCLUSIONS: Our findings in a region with relatively low COVID-19 prevalence suggest that neither outpatient glycemic control, glucose on admission, or inpatient glycemic control is predictive of illness severity or mortality in patients with DM hospitalized with COVID-19.

SARS-CoV-2 IgG Seropositivity and Acute Asymptomatic Infection Rate among Firefighter First Responders in an Early Outbreak County in California.

Objective: Firefighter first responders and other emergency medical services (EMS) personnel have been among the highest risk healthcare workers for illness during the SARS-CoV-2 pandemic. We sought to determine the rate of seropositivity for SARS-CoV-2 IgG antibodies and of acute asymptomatic infection among firefighter first responders in a single county with early exposure in the pandemic. Methods: We conducted a cross-sectional study of clinically active firefighters cross-trained as paramedics or EMTs in the fire departments of Santa Clara County, California. Firefighters without current symptoms were tested between June and August 2020. Our primary outcomes were rates of SARS-CoV-2 IgG antibody seropositivity and SARS-CoV-2 RT-PCR swab positivity for acute infection. We report cumulative incidence, participant characteristics with frequencies and proportions, and proportion positive and associated relative risk (with 95% confidence intervals). Results: We enrolled 983 out of 1339 eligible participants (response rate: 73.4%). Twenty-five participants (2.54%, 95% CI 1.65-3.73) tested positive for IgG antibodies and 9 (0.92%, 95% CI 0.42-1.73) tested positive for SARS-CoV-2 by RT-PCR. Our cumulative incidence, inclusive of self-reported prior positive PCR tests, was 34 (3.46%, 95% CI 2.41-4.80). Conclusion: In a county with one of the earliest outbreaks in the United States, the seroprevalence among firefighter first responders was lower than that reported by other studies of frontline health care workers, while the cumulative incidence remained higher than that seen in the surrounding community.