Building Public Health Surveillance 3.0: Emerging Timely Measures of Physical, Economic, and Social Environmental Conditions Affecting Health.

In response to rapidly changing societal conditions stemming from the COVID-19 pandemic, we summarize data sources with potential to produce timely and spatially granular measures of physical, economic, and social conditions relevant to public health surveillance, and we briefly describe emerging analytic methods to improve small-area estimation. To inform this article, we reviewed published systematic review articles set in the United States from 2015 to 2020 and conducted unstructured interviews with senior content experts in public heath practice, academia, and industry. We identified a modest number of data sources with high potential for generating timely and spatially granular measures of physical, economic, and social determinants of health. We also summarized modeling and machine-learning techniques useful to support development of time-sensitive surveillance measures that may be critical for responding to future major events such as the COVID-19 pandemic. (Am J Public Health. 2022;112(10):1436-1445. https://doi.org/10.2105/AJPH.2022.306917).

Validation of a neighborhood-level COVID Local Risk Index in 47 large U.S. cities.

OBJECTIVES: To present the COVID Local Risk Index (CLRI), a measure of city- and neighborhood-level risk for SARS COV-2 infection and poor outcomes, and validate it using sub-city SARS COV-2 outcome data from 47 large U.S. cities. METHODS: Cross-sectional validation analysis of CLRI against SARS COV-2 incidence, percent positivity, hospitalization, and mortality. CLRI scores were validated against ZCTA-level SARS COV-2 outcome data gathered in 2020-2021 from public databases or through data use agreements using a negative binomial model. RESULTS: CLRI was associated with each SARS COV-2 outcome in pooled analysis. In city-level models, CLRI was positively associated with positivity in 11/14 cities for which data were available, hospitalization in 6/6 cities, mortality in 13/14 cities, and incidence in 33/47 cities. CONCLUSIONS: CLRI is a valid tool for assessing sub-city risk of SARS COV-2 infection and illness severity. Stronger associations with positivity, hospitalization and mortality may reflect differential testing access, greater weight on components associated with poor outcomes than transmission, omitted variable bias, or other reasons. City stakeholders can use the CLRI, publicly available on the City Health Dashboard (www.cityhealthdashboard.com), to guide SARS COV-2 resource allocation.

The relationship between nosocomial infections and coronavirus disease 2019 in the neurosurgery unit: Clinical characteristics and outcome (preprint)

Purpose The purpose of this study was to evaluate how the “COVID-19 prevention and control measures” changed nosocomial infections in neurosurgery.Methods To explore changes in nosocomial infections in neurosurgery during the COVID-19 pandemic, the clinical data of inpatients of neurosurgery from January 1, 2020, to April 30, 2020 (COVID-19 era) were first analyzed and then compared with the same period in 2019 (pre-COVID-19 era). We also analyzed data from May 1, 2020, and December 31, 2020 (post-COVID-19 era) at the same time in 2019 (second pre-COVID-19 era).Results The nosocomial infection rate was 7.85% (54/688) in the pre-COVID-19 era and 4.30% (26/605) in the COVID-19 era (P = 0.011). Between the pre-COVID-19 and COVID-19 eras, the respiratory system infection rate was 6.1% vs. 2.0% (P < 0.001) and the urinary system was 1.7% vs. 2.0% (P = 0.837). Between the pre-COVID-19 and COVID-19 eras, the proportion of respiratory system and urinary infections in total nosocomial infections was 77.78% (42/54) vs. 46.15% (12/26) and 22.22% (12/54) vs. 46.15% (12/26), respectively, (P = 0.006). Between the second pre-COVID-19( ) and post-COVID-19 eras, the proportion of respiratory system and urinary infections in total nosocomial infections was 53.7% (44/82) vs. 40.6% (39/96) and 24.4% (20/82) vs. 40.6% (39/96), respectively, (P = 0.022).Conclusions The COVID-19 pandemic reduced the incidence of nosocomial infection in neurosurgery, and the main reduction was in respiratory infection, while the proportion of urinary infections in total nosocomial infections increased significantly.

Racial/Ethnic and Income Disparities in Neighborhood-Level Broadband Access in 766 US Cities, 2017- 2019 (preprint)

Broadband access is an essential social determinant of health, the importance of which has increased due to the ongoing COVID-19 outbreak. Using data from the City Health Dashboard and American Community Survey, we found that in 2017, 30% of urban households in 766 large US cities still had no access to high-speed broadband internet. After controlling for household income, broadband access in majority Black and Hispanic neighborhoods was 10-15% lower than in White or Asian majority neighborhoods. In 2019, lack of broadband access in urban households decreased to 28%, but substantial broadband disparities still persist in these cities.

Risk factors associated with PCR repositivity in patients with COVID-19 after recovery in Guangzhou, China: a retrospective cohort study (preprint)

Some patients retested positive for SARS-CoV-2 following negative testing results and discharge. However, the potential risk factors associated with redetectable positive test results in a large sample of patients who recovered from COVID-19 have not been well estimated. A total of 745 discharged COVID-19 patients were enrolled between January 30, 2020, and September 9, 2020, in Guangzhou, China. Data on the clinical characteristics, comorbidities, drug therapy, RT-PCR testing, and contact modes to close contacts were collected. Patients who tested positive for SARS-CoV-2 after discharge (positive retest patients) were confirmed by guidelines issued by China. The repositive rate in different settings was calculated. Among 745 discharged patients, 157 (21.1%; 95% CI, 18.2% to 24.0%) retested positive, of which 55 (35.0%) were asymptomatic, 15 (9.6%) had mild symptoms, 83 (52.9%) had moderate symptoms and 4 (2.6%) had severe symptoms at the first admission. The median time from discharge to repositivity was 8.0 days (IQR, 8.0 to 14.0 days). Most positive retest patients were without clinical symptoms, and lymphocyte cell counts were higher than before being discharged. The likelihood of repositive testing for SARS-CoV-2 RNA was significantly higher among patients who were younger age (OR, 3.88; 95% CI, 1.74 to 8.66, 0 to 17 years old), had asymptomatic severity (OR, 4.36; 95% CI, 1.47 to 12.95) and did not have clinical symptoms (OR, 1.89; 95% CI, 1.32 to 2.70, without fever). We found that the positive retest rate of COVID-19 was relatively high, and these patients tested positive again with a median of 8.0 to 14.0 days after discharge. Positive retest results were mainly observed in young patients without severe clinical symptoms. These findings suggest that a significant proportion of patients could carry viral fragments for a long time, and effective management, such as a prolonged quarantine phase for discharged patients, is necessary.

SARS-CoV-2 encoded microRNAs are involved in the process of virus infection and host immune response.

The outbreak of COVID-19 caused by SARS-CoV-2 is spreading worldwide, with the pathogenesis mostly unclear. Both virus and host-derived microRNA (miRNA) play essential roles in the pathology of virus infection. This study aims to uncover the mechanism for SARS-CoV-2 pathogenicity from the perspective of miRNA. We scanned the SARS-CoV-2 genome for putative miRNA genes and miRNA targets and conducted in vivo experiments to validate the virus-encoded miRNAs and their regulatory role on the putative targets. One of such virus-encoded miRNAs, MR147-3p, was overexpressed that resulted in significantly decreased transcript levels of all of the predicted targets in human, i.e., EXOC7, RAD9A, and TFE3 in the virus-infected cells. The analysis showed that the immune response and cytoskeleton organization are two of the most notable biological processes regulated by the infection-modulated miRNAs. Additionally, the genomic mutation of SARS-CoV-2 contributed to the changed miRNA repository and targets, suggesting a possible role of miRNAs in the attenuated phenotype of SARS-CoV-2 during its evolution. This study provided a comprehensive view of the miRNA-involved regulatory system during SARS-CoV-2 infection, indicating possible antiviral therapeutics against SARS-CoV-2 through intervening miRNA regulation.

Maximum chest CT score is associated with progression to severe illness in patients with COVID-19: A retrospective study from Wuhan, China (preprint)

Background: The Coronavirus Disease 2019 (COVID-19) pandemic is a world-wide health crisis. Limited information is available regarding which patients will experience more severe disease symptoms. We evaluated hospitalized patients who were initially diagnosed with moderate COVID-19 for clinical parameters and radiological feature that showed an association with progression to severe/critical symptoms. Methods: : This study, a retrospective single-center study at the Central Hospital of Wuhan, enrolled 243 patients with confirmed COVID­19 pneumonia. Forty of these patients progressed from moderate to severe/critical symptoms during follow up. Demographic, clinical, laboratory, and radiological data were extracted from electronic medical records and compared between moderate- and severe/critical-type symptoms. Univariable and multivariable logistic regressions were used to identify the risk factors associated with symptom progression. Results: : Patients with severe/critical symptoms were older (p<0.001) and more often male (p=0.046). A combination of chronic obstructive pulmonary disease (COPD) and high maximum chest computed tomography (CT) score was associated with disease progression. Maximum CT score (>11) had the greatest predictive value for disease progression. The area under the receiver operating characteristic curve was 0.861 ( 95% confidence interval: 0.811-0.902). Conclusions: : Maximum CT score and COPD were associated with patient deterioration. Maximum CT score (>11) was associated with severe illness.

Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study (preprint)

Key pointsO_ST_ABSQuestionC_ST_ABSHow do nomograms and machine-learning algorithms of severity risk prediction and triage of COVID-19 patients at hospital admission perform? FindingsThis model was prospectively validated on six test datasets comprising of 426 patients and yielded AUCs ranging from 0.816 to 0.976, accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off probability values for low, medium, and high-risk groups were 0.072 and 0.244. MeaningThe findings of this study suggest that our models performs well for the diagnosis and prediction of progression to severe or critical illness of COVID-19 patients and could be used for triage of COVID-19 patients at hospital admission. IMPORTANCEThe outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality for severely and critically ill patients. However, the availability of validated nomograms and the machine-learning model to predict severity risk and triage of affected patients is limited. OBJECTIVETo develop and validate nomograms and machine-learning models for severity risk assessment and triage for COVID-19 patients at hospital admission. DESIGN, SETTING, AND PARTICIPANTSA retrospective cohort of 299 consecutively hospitalized COVID-19 patients at The Central Hospital of Wuhan, China, from December 23, 2019, to February 13, 2020, was used to train and validate the models. Six cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 2020, were used to prospectively validate the models. MAIN OUTCOME AND MEASURESThe main outcome was the onset of severe or critical illness during hospitalization. Model performances were quantified using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTSOf the 299 hospitalized COVID-19 patients in the retrospective cohort, the median age was 50 years ((interquartile range, 35.5-63.0; range, 20-94 years) and 137 (45.8%) were men. Of the 426 hospitalized COVID-19 patients in the prospective cohorts, the median age was 62.0 years ((interquartile range, 50.0-72.0; range, 19-94 years) and 236 (55.4%) were men. The model was prospectively validated on six cohorts yielding AUCs ranging from 0.816 to 0.976, with accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off values of the low, medium, and high-risk probabilities were 0.072 and 0.244. The developed online calculators can be found at https://covid19risk.ai/. CONCLUSION AND RELEVANCEThe machine learning models, nomograms, and online calculators might be useful for the prediction of onset of severe and critical illness among COVID-19 patients and triage at hospital admission. Further prospective research and clinical feedback are necessary to evaluate the clinical usefulness of this model and to determine whether these models can help optimize medical resources and reduce mortality rates compared with current clinical practices.

Modes of contact and risk of transmission in COVID-19 among close contacts (preprint)

Background Rapid spread of SARS-CoV-2 in Wuhan prompted heightened surveillance in Guangzhou and elsewhere in China. Modes of contact and risk of transmission among close contacts have not been well estimated. Methods We included 4950 closes contacts from Guangzhou, and extracted data including modes of contact, laboratory testing, clinical characteristics of confirmed cases and source cases. We used logistic regression analysis to explore the risk factors associated with infection of close contacts. Results Among 4950 closes contacts, the median age was 38.0 years, and males accounted for 50.2% (2484). During quarantine period, 129 cases (2.6%) were diagnosed, with 8 asymptomatic (6.2%), 49 mild (38.0%), and 5 (3.9%) severe to critical cases. The sensitivity of throat swab was 71.32% and 92.19% at first to second PCR test. Among different modes of contact, household contacts were the most dangerous in catching with infection of COVID-19, with an incidence of 10.2%. As the increase of age for close contacts and severity of source cases, the incidence of COVID-19 presented an increasing trend from 1.8% (0-17 years) to 4.2% (60 or over years), and from 0.33% for asymptomatic, 3.3% for mild, to 6.2% for severe and critical source cases, respectively. Manifestation of expectoration in source cases was also highly associated with an increased risk of infection in their close contacts (13.6%). Secondary cases were in general clinically milder and were less likely to have common symptoms than those of source cases. Conclusions In conclusion, the proportion of asymptomatic and mild infections account for almost half of the confirmed cases among close contacts. The household contacts were the main transmission mode, and clinically more severe cases were more likely to pass the infection to their close contacts. Generally, the secondary cases were clinically milder than those of source cases.