COVID-related dysphonia and persistent long-COVID voice sequelae: A systematic review and meta-analysis.

PURPOSE: Dysphonia is a common symptom due to the coronavirus disease of the 2019 (COVID-19) infection. Nonetheless, it is often underestimated for its impact on human's health. We conducted this first study to investigate the global prevalence of COVID-related dysphonia as well as related clinical factors during acute COVID-19 infection, and after a mid- to long-term follow-up following the recovery. METHODS: Five electronic databases including PubMed, Embase, ScienceDirect, the Cochrane Library, and Web of Science were systematically searched for relevant articles until Dec, 2022, and the reference of the enrolled studies were also reviewed. Dysphonia prevalence during and after COVID-19 infection, and voice-related clinical factors were analyzed; the random-effects model was adopted for meta-analysis. The one-study-removal method was used for sensitivity analysis. Publication bias was determined with funnel plots and Egger's tests. RESULTS: Twenty-one articles comprising 13,948 patients were identified. The weighted prevalence of COVID-related dysphonia during infection was 25.1 % (95 % CI: 14.9 to 39.0 %), and male was significantly associated with lower dysphonia prevalence (coefficients: -0.116, 95 % CI: -0.196 to -0.036; P = .004) during this period. Besides, after recovery, the weighted prevalence of COVID-related dysphonia declined to 17.1 % (95 % CI: 11.0 to 25.8 %). 20.1 % (95 % CI: 8.6 to 40.2 %) of the total patients experienced long-COVID dysphonia. CONCLUSIONS: A quarter of the COVID-19 patients, especially female, suffered from voice impairment during infection, and approximately 70 % of these dysphonic patients kept experiencing long-lasting voice sequelae, which should be noticed by global physicians.

Impact of COVID-19 Pandemic on Emergency Department Volume and Acuity in Low Incidence Area: Taiwan's Experience in Three Hospitals.

Background: The decrease in emergency department (ED) patient visits during the COVID-19 pandemic was reported by various studies. Our study aimed to investigate whether a similar trend can be observed in a country with a low incidence of COVID-19 as well as the impact caused by the pandemic on ED patients in different triage levels and categories. Methods: This multicenter retrospective study collected data from three regional hospitals between March 2019 and December 2020. We evaluated the differences between patient volume, disease severity, and patient composition in ED before and after the COVID-19 pandemic among these hospitals. Results: There was a 23% reduction in ED patient volume in the urban hospital (hospital A) as well as a 16% reduction in suburban hospitals (hospitals B and C) during the pandemic period, respectively. The regression analysis showed a high correlation in the change in monthly patient volume among these hospitals. In terms of severity, there was a 24% reduction in ED visits with high severity levels (Taiwan Triage and Acuity Scale [TTAS] I, II) in hospital A, as well as 16% and 12% in hospitals B and C during the pandemic period, respectively. Similarly, there was a 23% reduction in ED visits with low severity levels (TTAS III, IV, V) in hospital A, as well as 20% and 16% in hospitals B and C during the pandemic period, respectively. In terms of patient types, there was a significant decline in non-traumatic adult patients (19%, 17%, and 10%), and pediatric patients (49%, 50%, and 46%) in hospitals A, B, and C, respectively. Conclusions: Despite the low incidence of COVID-19 in Taiwan, a decrease in total ED visits was still found during the pandemic, especially in non-trauma adult visits and pediatric visits. In addition, ED visits in both high and low severity levels decreased in these regional hospitals.

Incorporating field-based research into remote learning: An assessment of soil lead pollution in different land-use types in Los Angeles.

A research-based course was developed to investigate the legacy of soil lead (Pb) pollution in Los Angeles, California. During the course, undergraduate and graduate students collected a total of 270 soil samples for analyses of metal (loid) concentrations in different land-use types (residential, park, and school). Residential soils had significantly higher Pb concentrations than other land uses (p < 0.01) exceeding the California recommended safety level for soil Pb (80 mg/kg) at the highest frequency (64% of samples), followed by schools (42%) and parks (6.0%). Soil Pb from all 87 census block groups was correlated with battery recycling plant and railroad proximity as geospatial indicators of childhood Pb exposure risk. Meanwhile, census block groups with higher Pb levels were correlated with higher percentages of the following population: those without health insurance, without college degrees, with a lower median household income and income below the poverty line, and ethnic and racial minorities (r = -0.46 to 0.59, p < 0.05). Principal component regression models significantly improved soil Pb estimation over correlation analysis by incorporating sociodemographic, economic, and geospatial risk factors for Pb exposure (R2 = 0.58, p < 0.05). This work provides new insights into how topsoil Pb prevails in various land-use types and their co-occurring sociodemographic, economic, and geospatial risk factors, indicating the need for multi-scalar assessment across urban land uses.

A potential controlling approach on surface ozone pollution based upon power big data

Surface ozone pollution has attracted extensive attention with the decreasing of haze pollution, especially in China. However, it is still difficult to efficiently control the pollution in time despite numbers of reports on mechanism of ozone pollution. Here we report a method for implementing effective control of ozone pollution through power big data. Combining the observation of surface ozone, NO2, meteorological parameters together with hourly electricity consumption data from volatile organic compounds (VOCs) emitting companies, a generalized additive model (GAM) is established for quantifying the influencing factors on the temporal and spatial distribution of surface ozone pollution from 2020 to 2021 in Anhui province, central China. The average R2 value for the modelling results of 16 cities is 0.82, indicating that the GAM model effectively captures the characteristics of ozone. The model quantifies the contribution of input variables to ozone, with both NO2 and industrial VOCs being the main contributors to ozone, contributing 33.72% and 21.12% to ozone formation respectively. Further analysis suggested the negative correlation between ozone and NO2, revealing VOCs primarily control the increase in ozone. Under scenarios controlling for a 10% and 20% reduction in electricity use in VOC-electricity sensitive industries that can be identified by power big data, ozone concentrations decreased by 9.7% and 19.1% during the pollution period. This study suggests a huge potential for controlling ozone pollution through power big data and offers specific control pathways. Supplementary Information The online version contains supplementary material available at 10.1007/s42452-022-05045-5. Article Highlights Surface ozone pollution in central China was investigated during the prevalence of the COVID-19 (2020.1–2021.5) NO2 and industrial VOCs contributing 33.72% and 21.12% to ozone formation Potential controlling pathway was proposed Supplementary Information The online version contains supplementary material available at 10.1007/s42452-022-05045-5.