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Uniform practices and quality control methods are needed to detect and quantify airborne viruses across sampling and analysis platforms. We compared detection of airborne SARSCoV-2 RNA in residences of individuals with COVID-19 using two commonly used criteria: environmental (at least one SARS-CoV-2-specific gene and internal control amplified by PCR with Ct ≤ 40) and clinical (at least two SARS-CoV-2-specific genes and internal control amplified with Ct ≤ 37). 24-hr total aerosol samples were collected in a self-isolation room and an additional room without manipulating subjects' behavior/activities. Under the environmental criterion, 7/16 samples in primary rooms and 7/15 samples in secondary rooms were positive. Comparable but lower positive sample proportions were observed using the more rigorous clinical criterion: 6/16 primary rooms and 5/15 secondary rooms. A consensus SARS-CoV-2 environmental sampling and analysis framework is needed for comparisons between studies. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
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Different initiatives have been implemented to improve air quality in large cities, such as encouraging travel by sustainable modes of transport, promoting electro-mobility, or the car-free day. However, to date, we have not found statistics that indicate to what extent the concentration levels of particulate matter PM2.5, PM10 and nitrogen oxides (NOx) pollutants decrease as a result of public policy. We used official data from the Chilean Government's national air quality information system (SINCA) for the Santiago metropolitan region and estimated the impact of the confinement by COVID-19 on the ambient concentration average values of NOx gases and particulate matter PM2.5 and PM10, which are the main air pollutants produced by the transport sector after CO2. We found that in general there are significant differences between the average levels of gas emissions for 2020 compared to 2019. In particular, we found that, for the months of total confinement May-July, the monthly average levels decreased between 7% and 19% for particulate matter PM2.5, between 18% and 50% for PM10 and between 34% and 48% for NOx. With the return to the new normality, these improvements in ambient concentration levels may be affected by the increase in private transport trips, due to the reluctance of citizens to return to mass public transport. Our results, therefore, represent the maximum impact that can be expected in reducing ambient concentration levels in the city of Santiago of Chile when a mobility reduction of gasoline vehicles is implemented. Article Highlights The reduction of PM2.5, PM10 and NOx was no more than 7%, 18% and 34%, respectively. The average concentration of PM2.5 decreased by 7-19% compared to previous years. The average concentration of PM10 decreased by 18% and 50% compared to previous years. Concentrating commuting on public transport would help reduce levels of PM(10 )and PM2.5.
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Introduction: Patients with cirrhosis should be immunized against hepatitis A (HAV) & hepatitis B (HBV) as they are high risk for decompensation if infected. Baseline data at the Atlanta VA hepatology clinics demonstrated 75% of veterans with cirrhosis were immune to HAV and 44% to HBV. One fourth of visits failed to address hepatitis vaccines, and 60% of missed opportunities were from providers not discussing it. Our aim was to reduce missed opportunities to initiate HAV and HBV vaccination to <and to confirm immunity with serologies to < over 6 months in the Atlanta VA hepatology clinics. Methods: A process map identifying steps to obtain a vaccine and Pareto charts quantifying rates of commonly identified barriers were used to implement Plan-Do-Study-Act (PDSA) cycles. Approximately every 10th chart was sampled, and P charts were created to assess the average missed opportunity rate before and after our QI initiatives. Results: PDSA 1 focused on educating providers on the importance of vaccination, sharing the baseline data, and instructing how to order vaccines. For PDSA 2, the liver pharmacist monitored the vaccine queue daily to ensure that orders remained active. PDSA 3 involved a “quality inspection” by the attending hepatologist, who alerted the provider if vaccination was not addressed but was warranted. The last PDSA included creating a “Liver Passport” for the veteran that had personalized vaccination, hepatocellular screening, and esophageal variceal screening recommendations. The overall average missed opportunities for HAV & HBV vaccination and serologic confirmation testing decreased from 41% to 29% after four PDSA cycles. Separately, mean missed opportunities for initiating HAV & HBV vaccination decreased from 25% to 19% and assessing immunity with HAV & HBV serologies declined from 16% to 10%.Conclusion: Lack of in-person visits during the COVID-19 pandemic was a significant study limitation early on. Nevertheless, the national discussion surrounding COVID-19 vaccines provided a springboard for our QI initiatives to increasingly address HAV & HBV vaccines and immunity within a short time frame. Future aims are ≥HAV and HBV immunity and addressing all other vaccines recommended in patients with cirrhosis.
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Introduction: Numerous studies have reported increased risk for adverse COVID-19 outcomes in patients with obesity. However, diagnosis through body mass index (BMI) does not acknowledge the health burden associated with adipose tissue dysfunction. We compared the predictive performance of the Edmonton Obesity Staging System (EOSS), a clinical scheme to assess the obesity-related comorbidity, for adverse COVID-19 outcomes to that of BMI. Methods: Multi-center case series of 1071 patients hospitalized for COVID-19 in 11 hospitals adapted as COVID-19 centres in Mexico. We classified patients into 5 EOSS stages, from no obesity-related risk factors in the medical, mental, and functional areas (stage 0), to severe disease in any of those areas (stage 4). We calculated adjusted risk factors and performed survival analyses for mechanical ventilation and death according to EOSS stages and BMI categories. Results: Compared to patients with normal weight, intubation was higher in patients with EOSS stages 2 and 4 (HR 1.42, 95%CI 1.02-1.97 and 2.78, 95%CI 1.83-4.24), and in patients with class II and III obesity (HR 1,71, 95%CI 1.06-2.74, and 2.62, 95%CI 1.65-4.17). Death rates were lower in patients in EOSS stages 0 and 1 (HR 0.62, 95%CI 0.42-0.92) and higher in patients with class III obesity (HR 1.58, 95%CI 1.03-2.42). In the analysis of the group of patients with BMI ≥25 kg/m2, increasing EOSS stages were associated with increased HRs for intubation (EOSS stage 2 1.91, 95%CI 1.35-2.72, EOSS stage 3 1.74, 95%CI 1.06-2.85, and EOSS stage 4 3.75, 95%CI 2.38-5.90);by comparison, BMI group was only associated with intubation in individual with class III (40 kg/m2) (HR 2.24, 95%CI 1.50-3.34). Risk for death was increased in patients with EOSS stages 2 and 4 (EOSS stage 2 1.55, 95%CI 1.10-2.19, and EOSS stage 4 2.19, 95%CI 1.43-3.36) compared to EOSS 0 and 1, and in patients with class II and III obesity, compared to patients with overweight (class II HR 1.52, 95%CI 1.00-2.30 and class III 1.92, 95%CI 1.30-2.84). Conclusion: Risk for adverse COVID-19 outcomes was predicted better by the EOSS category, which was associated with the highest BMI class. Patients with overweight and obesity in EOSS stages 0 and 1 had lower risk for adverse outcomes than patients with normal weight, showing that BMI can assign “healthy” and “sick” category wrongly, and fails to identify pathways to guide chronic-disease management. Patients with EOSS stages 0 and 1 had the highest survival, followed by patients with normal weight, EOSS stage 3 and 2, and EOSS 4 had reduced survival starting from the first days of hospitalization. According to BMI, Class I and II obesity had the longest survival, followed by normal weight and overweight, and class III obesity had the lowest survival. Similarly, in the patients with a BMI ≥25 kg/m2, EOSS stages 0 and 1 had the highest survival, compared to EOSS stage 4, which had significantly lower survival. Class I and II obesity had longer survival than patients with overweight, and the lowest survival occurred in patients with class III obesity.
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In this paper, entropy was studied in non-linear models including exponential, Gompertz, and logistic, to estimate epidemiological parameters of interest in data from confirmed cases of infection by COVID-19 in Peru. The data related to the spread of COVID-19 in Peru comes from the information available on the INS-Peru institutional portal (2020). The Akaike information criterion (AIC) and the residual standard error (ERR) were considered to evaluate the entropy of the models. The estimation of the parameters of the models was carried out using maximum likelihood and by the Bootstrap method. The results showed that the entropy of the models is related to the information generation rate, associated with the differential in the number of tests applied. Entropy severely affected maximum likelihood estimators. The Bootstrap estimators showed better performance against EMV with the estimated peak of confirmed cases. Bootstrap estimators were significantly affected by sample size, especially when n <= 10. The results of this research suggest considering the entropy and the information generation rate (differential in the application of tests for the diagnosis of COVID-19 in Peru), as well as the use of Bootstrap estimators as an alternative to estimate parameters of epidemiological models.