Wastewater-based epidemiology in hazard forecasting and early-warning systems for global health risks.

With the advent of the SARS-CoV-2 pandemic, Wastewater-Based Epidemiology (WBE) has been applied to track community infection in cities worldwide and has proven succesful as an early warning system for identification of hotspots and changingprevalence of infections (both symptomatic and asymptomatic) at a city or sub-city level. Wastewater is only one of environmental compartments that requires consideration. In this manuscript, we have critically evaluated the knowledge-base and preparedness for building early warning systems in a rapidly urbanising world, with particular attention to Africa, which experiences rapid population growth and urbanisation. We have proposed a Digital Urban Environment Fingerprinting Platform (DUEF) - a new approach in hazard forecasting and early-warning systems for global health risks and an extension to the existing concept of smart cities. The urban environment (especially wastewater) contains a complex mixture of substances including toxic chemicals, infectious biological agents and human excretion products. DUEF assumes that these specific endo- and exogenous residues, anonymously pooled by communities' wastewater, are indicative of community-wide exposure and the resulting effects. DUEF postulates that the measurement of the substances continuously and anonymously pooled by the receiving environment (sewage, surface water, soils and air), can provide near real-time dynamic information about the quantity and type of physical, biological or chemical stressors to which the surveyed systems are exposed, and can create a risk profile on the potential effects of these exposures. Successful development and utilisation of a DUEF globally requires a tiered approach including: Stage I: network building, capacity building, stakeholder engagement as well as a conceptual model, followed by Stage II: DUEF development, Stage III: implementation, and Stage IV: management and utilization. We have identified four key pillars required for the establishment of a DUEF framework: (1) Environmental fingerprints, (2) Socioeconomic fingerprints, (3) Statistics and modelling and (4) Information systems. This manuscript critically evaluates the current knowledge base within each pillar and provides recommendations for further developments with an aim of laying grounds for successful development of global DUEF platforms.

Disease severity and patient recovery in COVID-19: an observational study comparing first and second wave admissions in London

P97 Table 1Demographics and clinical characteristics of participants at hospital admission and follow up for wave 1 and 2 admissions Wave 1 Wave 2 p-value N = 400 N = 400 Demographics and Lifestyle Age (years) (Median, IQR) 61 (50 -74) 61 (51 - 74) 0.59 Male gender (N,%) 247 (61.8%) 237 (59.3%) 0.47 Ethnicity (White) (N,%) 200 (50.0%) 195 (48.8%) 0.001* Smoking status – Never smoker (N,%) 215 (53.8%) 219 (54.8%) 0.58 BMI (kg/m2) (Median, IQR) 26.8 (24.1 - 29.4) 27.7 (24.3 - 31.6) 0.015 Underlying clinical status Clinical Frailty Score (Median, IQR) 2 (2, 4) N = 332 3 (2, 3) N = 384 0.001 Shielding Status (N,%) Extremely vulnerable HCP issued letter 32 (10.1%) 23 (7.2%) 39 (11.2%) 5 (1.4%) 0.001 Covid Admission Severity Parameters Total number of symptoms (Median, IQR) 4 (3 - 6) 3 (2 - 3) <0.0001 NEWS2 score (Median, IQR) 5 (2 - 7) N = 372 4 (3 - 6) N = 379 0.60 TEP status – For full escalation (N,%) 284/365 (77.8%) 361/400 (90.3%) <0.0001 Maximum respiratory support (N,%) CPAP NIV N= 377 10 (2.7%) 2 (0.5%) N = 400 32 (8.0%) 5 (1.3%) <0.0001 Received anti-viral or immunosuppressive drugs (N,%) 23/374 (6.2%) 127/400 (31.8%) <0.0001 ITU admission (N,%) 62/377 (16.5%) 43/400 (10.8%) 0.02 Intubation (N,%) 49/364 (13.5%) 19/400 (4.8%) <0.0001 Pulmonary Embolus (N,%) 22/360 (6.1%) 24/395 (6.1%) 0.98 Follow-up Outcomes N = 322 N = 365 Mental Health Outcomes PHQ2 score ≥ 3 (N,%) 47 (15.4%) 34 (9.9%) 0.04 TSQ score ≥ 5 (N,%) 44 (14.9%) 12 (3.3%) <0.0001 Physical Recovery and Symptoms Not returned to work (N,%) 76 (24.8%) 114 (33.6%) 0.03 Improved Sleep quality (N,%) 168 (61.5%) 265 (78.4%) <0.0001 Improved Fatigue (N,%) 241 (87.6%) 307 (88.7%) 0.91 Improved Cough (N,%) 194 (69.5%) 291 (84.8%) <0.0001 Improved Breathlessness (N,%) 213 (76.1%) 311 (89.6%) <0.0001 Total Number of Symptoms (Median, IQR) 1 (0 - 2) N=314 0 (0 – 1) N=364 Radiology outcomes (N,%) Normalised Significantly Improved Not significantly improved Worsened N=309 211 (68.3%) 55 (17.8%) 2 (0.7%) 30 (9.7%) N=279 187 (67.0%) 65 (23.3%) 13 (4.7%) 14 (5.0%) <0.0001 *p value likely attributable to differences in unknown ethnicityConclusionThese data suggest second wave pa ients, although frailer, presented with fewer symptoms and experienced improved hospital admission trajectory. They demonstrated improved self-reported mental health and physical recovery outcomes despite earlier follow-up, possibly attributed to improved in-hospital treatment. Supporting recovery remains a clinical priority given many patients had not returned to work.ReferenceSaito S, et al. First and second COVID-19 waves in Japan: comparison of disease severity and characteristics. J Infect. 2021;82(4):84-123.

Do persistent chest radiograph changes Correlate with ongoing respiratory symptoms in patients recovering from covid-19 Pneumonia?

Introduction and Objectives In May 2020, BTS published guidelines on radiological follow-up for patients with COVID-19 pneumonia, advising an initial repeat chest radiograph at 12 weeks to assess resolution.1 It is unclear whether persistent chest radiograph changes are clinically significant. Our aim was to assess whether there is a correlation between post-COVID chest radiograph appearances and ongoing respiratory symptoms. Methods Inpatients at two trust hospital sites diagnosed with COVID-19, either clinically or from a positive nasopharyngeal swab, were followed-up via telephone approximately 6-8 weeks post-discharge. Patients were offered a chest radiograph and blood tests if abnormal and a symptomatic assessment via a proforma. Patients subjectively rated their degree of breathlessness, cough and fatigue using a numerical rating scale. Chest radiograph reports were coded by consultant radiologists as per BSTI guidelines2 and grouped into 'improvers' (PCVCX0/1) and 'non-improvers' (PCVCX2/3 i.e. static or worsening appearances). Patients who had both an initial and follow-up chest radiograph, and who completed a proforma were included for retrospective analysis Introduction and Objectives In May 2020, BTS published guidelines on radiological follow-up for patients with COVID-19 pneumonia, advising an initial repeat chest radiograph at 12 weeks to assess resolution.1 It is unclear whether persistent chest radiograph changes are clinically significant. Our aim was to assess whether there is a correlation between post-COVID chest radiograph appearances and ongoing respiratory symptoms. Methods Inpatients at two trust hospital sites diagnosed with COVID-19, either clinically or from a positive nasopharyngeal swab, were followed-up via telephone approximately 6-8 weeks post-discharge. Patients were offered a chest radiograph and blood tests if abnormal and a symptomatic assessment via a proforma. Patients subjectively rated their degree of breathlessness, cough and fatigue using a numerical rating scale. Chest radiograph reports were coded by consultant radiologists as per BSTI guidelines2 and grouped into 'improvers' (PCVCX0/1) and 'non-improvers' (PCVCX2/3 i.e. static or worsening appearances). Patients who had both an initial and follow-up chest radiograph, and who completed a proforma were included for retrospective analysis.

The impact of smoking on symptom and radiological severity at COVID-19 follow up

Background The relationship between smoking and COVID-19 disease severity is uncertain;one meta-analysis found smoking increases the risk of developing severe COVID-19 two-fold.1 No previous study has reported whether smokers have worse outcomes at follow-up. We hypothesised that smokers admitted to hospital with COVID-19 would have a greater symptom and radiological severity at follow-up. Methods We prospectively followed up swab-positive COVID-19 patients in two hospitals discharged between 03.05.20 and 19.06.20. Telephone calls were conducted 8-10 weeks post discharge. Demographics, co-morbidities, smoking history and symptom burden data were collected. Symptom burden was quantified using a numerical rating scale for breathlessness, cough and fatigue. Patients were offered a follow-up chest radiograph (CXR) if abnormal on discharge Results 782 patients were reviewed post-discharge, median (IQR) time to review: 63 (54-79) days. Smoking history was obtained for 537 patients. Outcomes for 181 (34%) current/ex-smokers were compared to 356 (66%) never-smokers. Table 1 demonstrates baseline characteristics and symptom burden between groups at follow-up. Never-smokers were significantly younger (59.5±16.3 vs. 65.1±15.5 years, p<0.001) and more likely to be from ethnic minority groups (51.4% vs 34.8%, p<0.001). Ex/current smokers had significantly increased self-reported breathlessness (1 (0-3) vs 0 (0-2);p=0.037) and higher Medical Research Council (MRC) dyspnoea score (2 (1-3) vs 1(1-2);p=0.013). They were less likely to have returned to work (30% vs 51%;p=0.013). Regression analyses demonstrated no significant impact of age and ethnicity on self-reported breathlessness (p=0.317) but demonstrated a significant impact of age on the MRC score (p<0.001). There were no significant differences in CXR findings at follow-up. Conclusion In this large clinical cohort, ex/current smokers had significantly increased self-reported breathlessness at follow-up. These results should be interpreted with caution as the burden of breathlessness prior to admission is unknown. Interestingly, there were no significant differences in other symptoms, nor any differences in radiology findings. Further work is required to understand the mechanisms underlying these findings in order to mitigate the effect of COVID-19 in current/ex smokers. We should continue to routinely and optimally treat current smokers for their tobacco dependence.

S58 The impact of smoking on symptom and radiological severity at COVID-19 follow up

S58 Table 1Patient characteristics for smokers and never smokers with follow up dataVariable (%)Never-smokersEx/current SmokersP - valueN356181-DemographicsAge *(years)60 ± 1665 ± 16<0.0001Male Sex (%)212 (60)122 (67)0.076Black, Asian, Minority Ethnic (BAME) (%)183 (51)63 (35)<.0001ComorbiditiesHypertension155 (44)68 (38)0.382Ischaemic Heart Disease32 (9)25 (14)0.088Diabetes93 (26)41 (23)0.392Respiratory Background65 (18)45 (25)0.073Hospital AdmissionAdmission NEWS2 Score4 (2–6)5 (2–6.25)0.838Intensive Care Admission39 (11)24 (13.3)0.462Symptom Burden at follow upBreathlessness rating 0–100 (0–2)1 (0–3)0.037Cough rating 0–100 (0–1)0 (0–1)0.594Fatigue rating 0–102 (0–5)2 (0–5)0.933How close to 100% do they feel90 (75–100)90 (75–100)0.969Returned to Work92/179 (51)40/133 (30)0.013MRC Dyspnoea scale (1–5)1 (1–2)2 (1–3)0.005CXR at follow upNormal (%)86/120 (72)43/69 (62)0.184Significantly improved (%)25/120 (21)19/69 (28)0.294Unchanged (%)01/69 (2)0.186Significantly Worsened (%)9/120 (8)6/69 (5)0.770*normally distributed parametric data with mean and standard deviationAll other scale data was non-parametric with median and interquartile ranges shownAbbreviations: NEWS2 (National Early Warning Score 2);MRC (Medical Research Council)ConclusionIn this large clinical cohort, ex/current smokers had significantly increased self-reported breathlessness at follow-up. These results should be interpreted with caution as the burden of breathlessness prior to admission is unknown. Interestingly, there were no significant differences in other symptoms, nor any differences in radiology findings. Further work is required to understand the mechanisms underlying these findings in order to mitigate the effect of COVID-19 in current/ex smokers. We should continue to routinely and optimally treat current smokers for their tobacco dependence.ReferenceZhao Q., et al. J Med Virol 2020 Apr 15.

P9 Do persistent chest radiograph changes correlate with ongoing respiratory symptoms in patients recovering from COVID-19 pneumonia?

P9 Table 1Differences in symptom burden in patients with improved vs. non-improved chest radiographsDemographicChest radiograph appearancep-valueImprovers n=356 (93%)Non-improvers n=26 (7%)Age (years)58.9 ± 14.763.23 ± 12.70.103BMI (kg/m2)27.7 ± 5.4326.7 ± 4.500.331Male sex (n,%)224 (62.9)21 (80.8)0.154BAME (n,%)165 (46.3)10 (38.5)0.268Current or ex-smoker (n,%)112 (31.4)9 (34.6)0.008Underlying respiratory disease (n,%)62 (17.4)3 (11.5)0.737ITU admission (n,%)41 (11.5)10 (38.5)<0.001Symptoms Breathlessness*1 (0 – 3)2 (0 – 4)0.010Cough*0 (0 –1)0 (0 – 2.5)0.090Fatigue*2 (0 – 5)2 (0.5 – 4.5)0.773How close back to 100% of usual do you feel?*90 (80 – 100)85 (75 – 97.5)0.500MRC dyspnoea scale*1 (1 – 2)1 (1 – 3)0.021*non-parametric data presented as median and IQR, all other data are presented as mean ± standard deviationConclusionsIn our cohort, patients recovering from COVID-19 pneumonia with a ‘non-improver’ chest radiograph are more likely to have been admitted to ITU and remain breathless at follow-up. We conclude that ‘non-improver’ chest radiographs at follow-up are an indicator of who may have ongoing respiratory pathology. These patients can thus be prioritised for further respiratory investigation.ReferencesBritish Thoracic Society (2020). Guidance on Respiratory Follow Up of Patients with a Clinico-Radiological Diagnosis of COVID-19 Pneumonia. https://www.brit-thoracic.org.uk/document-library/quality-improvement/covid-19/resp-follow-up-guidance-post-covid-pneumonia/British Society of Thoracic Imaging (2020). Post-COVID-19 CXR Report Codes. https://www.bsti.org.uk/media/resources/files/BSTI_PostCOVIDCXRtemplatefinal.28.05.201.pdf

Validation of the British Society of Thoracic Imaging guidelines for COVID-19 chest radiograph reporting.

AIM: To validate the British Society of Thoracic Imaging issued guidelines for the categorisation of chest radiographs for coronavirus disease 2019 (COVID-19) reporting regarding reproducibility amongst radiologists and diagnostic performance. MATERIALS AND METHODS: Chest radiographs from 50 patients with COVID-19, and 50 control patients with symptoms consistent with COVID-19 from prior to the emergence of the novel coronavirus were assessed by seven consultant radiologists with regards to the British Society of Thoracic Imaging guidelines. RESULTS: The findings show excellent specificity (100%) and moderate sensitivity (44%) for guideline-defined Classic/Probable COVID-19, and substantial interobserver agreement (Fleiss' k=0.61). Fair agreement was observed for the "Indeterminate for COVID-19" (k=0.23), and "Non-COVID-19" (k=0.37) categories; furthermore, the sensitivity (0.26 and 0.14 respectively) and specificity (0.76, 0.80) of these categories for COVID-19 were not significantly different (McNemar's test p=0.18 and p=0.67). CONCLUSION: An amalgamation of the categories of "Indeterminate for COVID-19" and "Non-COVID-19" into a single "not classic of COVID-19" classification would improve interobserver agreement, encompass patients with a similar probability of COVID-19, and remove the possibility of labelling patients with COVID-19 as "Non-COVID-19", which is the presenting radiographic appearance in a significant minority (14%) of patients.