Asymptomatic/mildly symptomatic coronavirus disease 2019 (COVID-19) patients produce a considerable amount of virus and transmit severe acute respiratory syndrome virus 2 (SARS-CoV-2) through close contact. Preventing in-hospital transmission of SARS-CoV-2 is challenging, since symptom-based screening protocols may miss asymptomatic/mildly symptomatic patients. In particular, dental healthcare workers (HCWs) are at high risk of exposure, as face-to-face contact and exposure to oral secretions is unavoidable. We report exposure of HCWs during dental procedures on a mild symptomatic COVID-19 patient. A 32-year-old male visited a dental clinic at a tertiary care hospital. He experienced mild cough, which started three days before the dental visit, but did not report his symptom during the entrance screening. He underwent several dental procedures and imaging for orthognathic surgery without wearing a mask. Seven HCWs were closely exposed to the patient during dental procedures that could have generated droplets and aerosols. One HCW had close contact with the patient during radiologic exams, and seven HCWs had casual contact. All HCWs wore particulate filtering respirators with 94% filter capacity and gloves, but none wore eye protection or gowns. The next day, the patient experienced dysgeusia and was diagnosed with COVID-19 with high viral load. All HCWs who had close contact with the patient were quarantined for 14 days, and polymerase chain reaction and antibody tests for SARS-CoV-2 were negative. This exposure event suggests the protective effect of particulate filtering respirators in dental clinics. The recommendations of different levels of personal protective equipment (PPE) for dental HCWs according to the procedure types should be established according to the planned procedure, the risk of COVID-19 infection of the patient, and the outbreak situation of the community. Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Wuhan, China, in December 2019, there has been a global outbreak of coronavirus disease 2019 (COVID-19) ,4,5. WheHealthcare workers (HCWs) at dental clinics are at high risk of unexpected exposure. Regular dental treatments lead to close face-to-face contact with patients not wearing masks. Frequent utilization of vibrating devices may produce aerosols, and body fluids such as blood and saliva can spatter into the eyes. Despite the precautions taken, it is impossible to entirely prevent the production of droplets and aerosols during dental procedures . HoweverPatient A was a 32-year-old man. On 11 May 2020, he visited a dental clinic at a tertiary care medical center  for a consult prior to orthognathic surgery. After being made aware of SARS-CoV-2 exposure at the dental clinic due to Patient A being diagnosed with COVID 19, we immediately performed thorough contact tracing of the COVID-19 patient. All exposed HCWs, patients, and visitors were identified. We evaluated the degree of contact, such as exposure situation and time, adequacy of PPE, and the presence of any COVID-19-related symptoms. The exposed persons were divided into quarantine (close and unprotected exposure) and active monitoring (distant or protected exposure) groups . QuarantRT-PCR assay  was performed according to the manufacturer’s instructions. The primer and probe sequences were described in a previous study . Each saTo detect anti-SARS-CoV-2 IgM and IgG antibodies among exposed HCWs, we used a FIA kit . The FIA IgM and IgG kit used the automated fluorescent lateral flow immunoassay method, using the AFIAS-6 analyzer system . This asThe ELISA total antibody kit  detects total antibody against nucleocapsid protein and receptor-binding domain (RBD) of the spike protein of SARS-CoV-2 using the sandwich immunoassay method. An optical density (OD) ratio < 1.0 was interpreted as negative, ≥0.9 to <1.0 as borderline, and ≥1.0 as positive. All tests were performed according to the manufacturer’s instructions.Although Patient A experienced mild cough three days before the hospital visit, he did not report this symptom during the entrance screening at the hospital. He had a consultation with a dental surgeon and necessary dental procedures as preparation for orthognathic surgery in the dental clinic from 13:30 to 14:45. He underwent chest X-ray, blood sampling, and facial computed tomography (CT) scanning from 14:45 to 15:20. After that, he returned to the dental department for orthodontic treatment. The first step was pre-consultation examination in the oral and maxillofacial surgery department, which included dental impressions, intraoral photographs, extraoral photographs, dental X-rays, wax bite taking, and face-bow transfer. The second step was face-to-face consultation with a professor and one assistant. The third step was orthodontic treatment in the orthodontic department, including a dental bonding procedure and orthodontic wire change. During these dental procedures, Patient A did not wear a face mask. After all the procedures were over, he was given a verbal summary of future treatment procedures. He left the clinic at 16:40. The next morning, the patient developed dysgeusia and his dry cough worsened. He underwent RT-PCR for SARS-CoV-2. On May 13, 2020, he was confirmed to have SARS-CoV-2 infection. The low Ct value on RT-PCR (22.38 for RdRp and 22.52 for E genes) suggested high viral load.A total of 48 persons were identified as exposed, including 15 HCWs. Eight HCWs were quarantined because they had contact with Patient A when he was not wearing a mask. The demographics and epidemiological characteristics of the exposed HCWs are summarized in All procedures are listed in All HCWs under quarantine wore a particulate filtering respirator with 94% filter capacity  of Korea, according to the criteria equivalent to the European Standard for FFP2 respirator), but did not wear eye protection or gowns. Patient A removed his face mask for various dental procedures. Since Patient A was not suspected to have COVID-19, each procedure was performed under normal safety precautions. Hand hygiene before and after patient contact or invasive procedures is an infection control policy that has been of the utmost importance since before the COVID-19 pandemic. This is the infection control policy emphasized at our hospital as well. However, the exposed dental HCWs could not accurately remember whether or not hand hygiene was done. They wore white gowns on scrubs, and these scrubs and gowns were only worn inside the hospital. Ventilation in the treatment room was performed 7.5 times per hour, and 70% of air was recirculated through a high efficiency particulate air (HEPA) filter.Patient A was not wearing a mask during CT scanning, so the radiologic technician was also quarantined. One HCW complained of myalgia shortly after exposure, but improved within two days.All HCWs under quarantine and active monitoring underwent RT-PCR for SARS-CoV-2 four to five days from the date of exposure, and quarantined HCWs were tested again before the end of quarantine. The results of the RT-PCR were all negative. Tests for anti-SARS-CoV-2 antibodies were performed on quarantined HCWs on the 52nd day from exposure. All serologic test results, including FIA IgM and IgG and ELISA total antibody, were negative.There are currently no data available to assess the risk of SARS-CoV-2 transmission and the prevalence of subclinical seroconversion of SARS-CoV-2 in a dental setting . This isRoutine application of particulate filtering respirators, such as N95 (US standard), FFP2 (European standard), KN95 , or KF94 , to protect HCWs from respiratory viruses is controversial. A recent randomized controlled trial resulted in no significant difference between the effectiveness of N95 respirators and surgical masks in preventing influenza infection among participants routinely exposed to respiratory illnesses in a hospital setting . HoweverAt the time of the exposure event reported herein, community transmission of SARS-CoV-2 was not a serious concern in the Republic of Korea. Although a cluster associated with clubs in Itaewon occurred two weeks before, most cases were traced and the number of new daily reported cases was <50 . As a reAnother reason why SARS-CoV-2 spread did not occur at our dental clinic is probably the difference in the degree of droplet and aerosol production depending on dental departments. Moreover, some questions about the aerosol generated during dental procedures remain unresolved. The US CDC regarded the aerosol generated during medical and dental procedures to be an equivalent degree of risk. However, there is an opinion that aerosols generated during dental procedures mainly by water and air-spray cannot be considered to have the equivalent risk as aerosols generated in tracheal or nasopharyngeal procedures because they would substantially dilute the viral presence . Furthermore, unlike the periodontal department and the conservative dentistry and prosthesis department, the use of high-speed handpieces is relatively low in the oral and maxilla-facial surgery and orthodontic department, especially for treatments related to orthognathic surgery. Patient A did not undergo typical dental procedures using a high-speed handpiece. All procedures were in preparation for orthognathic surgery. As shown in In the case of non-emergent issues, if a patient suspected to have COVID-19 visits a dental clinic, it is recommended that the dentist defers treatment for at least 14 days. Orthognathic surgery for treating jaw deformities is not a life-threatening condition that must be treated urgently. One study recommended deferral of orthognathic surgery until the COVID-19 pandemic situation has settled . HoweverOur study had several limitations. As this was a dental procedure in the department of oral and maxillofacial surgery, production of droplets and aerosols would have been minimal. Only eight HCWs were quarantined. As there was no security camera in the dental clinic, the level of exposure was based on individual recollections. Since this study based its data on a single case report that includes one patient who mainly received a consultation-type appointment without any use of high-speed handpiece, as mentioned above, it might not be representative of all scenarios.Among HCWs exposed during dental procedures on a mildly symptomatic COVID-19 patient, there were no SARS-CoV-2 infections. Within the limitation of a case report, the usage of particulate filtering respirators in dental clinic could play a major role in preventing transmission. It is a case showing an effective preventive effect of a high-efficiency mask with ear loop that is relatively easy to wear for a long time (relative to the N95) that could considered for application to the routine practice of a dental clinic.


Even after adjusting for confounding factors, PRA-S remained predictive for all-cause death in the multivariable model with a hazard ratio of 2.14 . We conclude that high PRA-S is associated with poor prognosis in patients with HFpEF, regardless of RAASi treatment, which could ultimately result in hyperactivated RAAS and consecutive negative effects on the cardiovascular and renal system, leading to poor outcome in patients with HFpEF.Drugs which interact with the renin angiotensin aldosterone system (RAAS) aim to reduce the negative effects of angiotensin (Ang) II. Treatment with these drugs anticipate a compensatory up-regulation of renin; however, it has been shown that there is a large variability in circulating plasma renin (PRA), even in patients with optimal medical therapy in patients with heart failure (HF) with reduced ejection fraction (HFrEF). Our aim was to measure plasma renin activity (PRA-S), its response to RAAS inhibitor (RAASi) therapies and its effects on outcome in patients with HF with preserved ejection fraction (HFpEF). For this purpose, 150 HFpEF patients were included into a prospective single-center registry. Equilibrium (eq) angiotensin metabolites were measured from serum samples using mass spectroscopy. PRA-S (eqAng I + eqAng II) was calculated and compared in respect to the primary endpoint defined as all-cause death. PRA-S in patients with RAASi therapy was not significantly higher than in patients without RAASi ( The renin angiotensin aldosterone system (RAAS) is a well-studied cascade, which acts as a regulator of blood pressure and fluid homeostasis. In the RAAS pathway, angiotensin (Ang) I is generated by enzymatic cleavage from hepatic angiotensinogen via renin secreted by the juxtaglomerular cells of the kidney. It is then converted to Ang II by the angiotensin converting enzyme (ACE) . An overFor many years, the significance of the RAAS has been appreciated in the pathophysiology of heart failure (HF). Drugs which interact with the RAAS have become an important pillar of HF therapies in patients with reduced ejection fraction (HFrEF) ,4,5. HigIn principle, the administration of RAAS inhibitors (RAASi) such as ACE-inhibitors (ACEi) or angiotensin-receptor-blockers (ARBs) leads to a significant change in plasma angiotensin profiles, driven by direct pharmacodynamic drug effects. However, it is essential to understand that these changes in return cause a compensatory up-regulation of renin via a renal feedback mechanism involving renal angiotensin II type 1 receptor (AT1R) signaling . An effePlasma RAAS components have repeatedly been described as predictors of outcome in HF patients . AlthougThis study prospectively examined the effects of RAAS-associated biomarkers on outcome in patients with HFpEF. Consecutive patients were included into a prospective clinical registry at the Division of Cardiology of the Medical University of Vienna, a national HFpEF referral center at our high-volume HF outpatient clinic. All patients gave written informed consent before study inclusion. The protocol is in line with the Declaration of Helsinki and was approved by the local Ethics Committee (EK #796/2010). Baseline assessment included clinical examination, the documentation of patient history and drug therapy. Imaging and laboratory testing were performed, including the measurement of serum equilibrium levels of angiotensins and aldosterone. Following baseline tests, mineralocorticoid antagonists (MRAs) were initiated in all patients during follow-up visits, if potassium and creatinine levels allowed.Transthoracic echocardiography (TTE) was used to confirm the diagnosis of HFpEF and was performed by board-certified physicians on high-end machines  in accordance with current guidelines ,17. TTE 2) or LV hypertrophy . Patients were not included, when they had significant coronary artery disease, confirmed by left heart catheterization and defined as a visual stenosis of more than 50% in one of the main vessels and/or over 70% in a distal vessel.The diagnosis of HFpEF was made according to current consensus statements of the European Society of Cardiology  and the Clinical outcomes were ascertained by follow-up at our dedicated HFpEF outpatient clinic as well as clinical records or phone calls in the case of patient immobility. The primary outcome parameter was defined as death by any cause. When an event occurred, local and external medical records were carefully screened and the cause of death was reviewed by a clinical adjudication committee of board—certified cardiology specialists .Blood sampling was performed at baseline and analyzed according to local laboratory standards. In addition to routinely performed laboratory parameters, serum samples were drawn at study baseline. Samples were centrifuged at 1300 rpm for 10 min and frozen at −80°C for biomarker assessment.®, C18, Waters, Milford, MA, USA) operating in line with a XEVO TQ-S triple quadruple mass spectrometer  in multiple reaction monitoring mode. Internal standards were used to correct for analyte recovery across the sample preparation procedure in each individual sample. Analyte concentrations were calculated from integrated chromatograms considering the corresponding response factors determined in appropriate calibration curves in serum matrix, on the condition that integrated signals exceeded a signal-to-noise ratio of 10. The lower level of quantification (LLOQ) for serum equilibrium Ang I, Ang II and aldosterone were 5 pmol/L, 2 pmol/L and 14 pmol/L, respectively. Based on measured values for eqAng I, eqAng II and aldosterone, angiotensin-based markers for renin activity (PRA-S), ACE activity (ACE-S) and adrenal AT1-receptor function (AA2-Ratio) were calculated as shown in Equilibrium concentrations of Ang I (eqAng I), Ang II (eqAng II) and aldosterone levels were measured following ex vivo equilibration and subsequent stabilization of conditioned serum samples as described previously ,21. Stabp-value < 0.05 was considered statistically significant. IBM SPSS version 24  was used for all statistical analyses.Categorical variables are shown as numbers and percentages, and continuous variables are expressed in median and interquartile range. A Patients were divided by median PRA-S, calculated as (eqAng I+ eqAng II) for baseline assessment. Baseline characteristics of the total study population, as well as for the aforementioned groups were computed using one-way ANOVA or the Chi-square test as appropriate. Angiotensin profiles were displayed for patient groups receiving different RAAS-inhibiting therapies and compared using Mann–Whitney U-test. To depict angiotensin concentrations and compare them between different groups of RAAS-inhibiting therapy, graphs were created using the median of each angiotensin concentration.For Kaplan–Meier analysis, “high versus low” PRA groups were categorized according to median PRA-S. To assess the effects of PRA-S on outcomes, Kaplan–Meier plots were calculated for the endpoint all-cause death. Univariable Cox regression analysis was performed for all parameters. To account for possible confounding effects, a multivariable stepwise-forward model was applied by entering predictive parameters separately for clinical, laboratory and imaging parameters from the univariable model at a significance level of 0.05.n = 2), reduced left ventricular ejection fraction (n = 2) and severe aortic stenosis (n = 1) during follow-up, resulting in a final patient population of 150 patients (n = 17) or ARB and MRA (n = 18). An additional 18 patients (12.0 %) were only on MRA at the time of assessment. Between December 2010 and December 2018, a total of 159 patients were considered eligible for this study. Four patients were excluded due to hemolytic serum samples, which could not be analyzed. Further, five patients were excluded due to diagnosis of cardiac amyloidosis (patients . Of thesp = 0.714). Patients treated with MRA at baseline had higher levels of aldosterone, Ang I and Ang II, and therefore also higher levels of PRA-S. Drug effects were clearly reflected by serum levels of eqAng I and eqAng II . In mostn = 140, 93.3%) and more than half of the total study population presented with atrial fibrillation . Patients with higher PRA-S presented with lower blood pressure and more pronounced renal impairment. Median NT-pro BNP levels were markedly elevated among the total study population  and there was no difference between the PRA-S groups.Baseline characteristics of the total study population, as well as for PRA-S groups are shown in Patients with higher PRA-S had higher serum eqAng I and eqAng II and lower ACE-S, as well as eqAng 1–5. However, among patients without RAAS inhibitors, we observed patients with elevated PRA-S, while others showed low PRA-S , indicatn = 4), sepsis (n = 2), pulmonary embolism (n = 2), iatrogenic causes (n = 2) and liver failure (n = 1). An additional six deaths were verified; however, the cause of death could not be ascertained, due to missing documentation and the inability to contact relatives of the deceased individuals. There was no difference in outcomes regarding therapy with ACEi, ARBs or no RAASi . Our data show that higher levels of eqAng II, as well as higher PRA-S are associated with poor outcome  patients died and 48 (32.0%) were hospitalized for HF. Of all deaths, 13 (43.3%) were classified as cardiac. Eleven deaths were attributed to non-cardiac causes. These included end-stage cancer  may not be inhibited efficiently in some patients receiving ACEi or ARB. This could be caused by a compensatory up-regulation of the RAAS system, ultimately leading to higher levels of Ang II than would be necessary to prevent adverse outcomes. It is likely that complex RAAS-mediated interactions of the cardio–renal system, which may also be triggered by inadequate dosage of RAASi, could lead to an overshoot of renin activity. This could ultimately lead to an increase in pro-fibrotic signals and could further promote myocardial stiffness and impaired relaxation. This could be especially detrimental in HFpEF, as myocardial fibrosis is a central mechanism in the pathogenesis of this disease. Assessment of individual PRA-S, ACE-S and AA2-Ratio values on treatment by RAAS Triple-A analysis may be key to finding the optimal dose in this patient population, assuring sustained pharmacodynamic efficacy and adequate individualized dosage of therapeutic RAAS blockade.Several limitations exist regarding the characteristics of the study population and methodology. We are aware of the limitations that go along with the single-center design of this study. On the other hand, advantages of this study lie in the consistency of diagnostic workup and treatment and follow-up. Due to the relatively small size of our study cohort, the number of clinical events is limited. The limited number of events in the present study does not allow extensive Cox regression analysis. Our multivariable Cox regression model included five variables compared to 30 outcome events. Therefore, these results must be interpreted with caution, as low event per variable ratios may affect type I errors and may cause bias away from the null hypothesis . FurtherThe present study shows that high levels of eqAng II and PRA-S, even despite RAASi treatment, are associated with poor prognosis in patients with HFpEF. This may ultimately result in negative effects on the cardiovascular and renal system.


The serial interval and effective reproduction number for coronavirus disease (COVID-19) are heterogenous, varying by demographic characteristics, region, and period. During February 1–July 13, 2020, we identified 4,080 transmission pairs in Georgia, USA, by using contact tracing information from COVID-19 cases reported to the Georgia Department of Public Health. We examined how various transmission characteristics were affected by symptoms, demographics, and period (during shelter-in-place and after subsequent reopening) and estimated the time course of reproduction numbers for all 159 Georgia counties. Transmission varied by time and place but also by persons’ sex and race. The mean serial interval decreased from 5.97 days in February–April to 4.40 days in June–July. Younger adults (20–50 years of age) were involved in most transmission events occurring during or after reopening. The shelter-in-place period was not long enough to prevent sustained virus transmission in densely populated urban areas connected by major transportation links. Coronavirus disease (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). After it was first reported in Wuhan, China, in December 2019, COVID-19 spread rapidly across the world as an ongoing global pandemic. As of July 9, 2021, most confirmed COVID-19 cases  and deaths  in the world were in the United States (Transmission of COVID-19 varies by region (t (the expected number of cases directly caused by any single infectious person). Rt has been shown to vary strongly; some case-patients have caused superspreading events  and the effective reproduction number RAfter the first case of COVID-19 was reported in the state of Georgia on March 2, 2020, a series of events and interventions followed (Appendix Table 1). On April 3, state officials announced a shelter-in-place order, requiring all residents and visitors to remain in their residence and take every possible precaution to limit social interactions. On April 24, officials allowed some businesses to reopen, and on April 30 the shelter-in-place order was lifted. On June 1, state officials further relaxed restrictions. During June–July 2020, as new COVID-19 cases continued to surge in Georgia and other states, knowing how shelter-in-place and the subsequent reopening events affected the transmission of SARS-CoV-2 in different regions became crucial.t  Institutional Review Board determined that this analysis was exempt from the requirement for review and approval, and informed consent was not required. This activity was reviewed by the Centers for Disease Control and Prevention and was consistent with their applicable policy and with federal law.GDPH provided data for all 118,491 confirmed COVID-19 cases in all 159 counties of Georgia during February 1–July 13, 2020. Available data included demographic characteristics , clinical characteristics , and social contacts (contacts between confirmed case-patients and if cases were part of a confirmed outbreak) . Transmission pairs with serial intervals >15 days were dropped because such long intervals are unlikely, as shown in previous studies  in each county independently by estimating the transmission probability matrix.We estimated probabilities of transmission between any pairs of case-patients in an outbreak by using a transmission probability matrix method  . These missing symptom-onset dates were imputed according to dates of first specimen collection if available or dates of laboratory report if not .t approaching the present date are biased. Because one of our study goals was to examine the timing and magnitude of the first 2 waves of SARS-CoV-2 transmission in Georgia (and not to nowcast transmission), we removed Rt estimates of the most recent 4 weeks (June 16–July 13) from the analysis. SARS-CoV-2 transmission in Georgia seemed to include multiple waves and varied considerably among counties. The time-varying average Rt estimates were smoothed by using LOESS regression, and local maximums/minimum were identified for each individual county. On the basis of our review of the epidemic curves and curves, we defined the following 5 transmission patterns:The most recent data are incomplete because not all incident cases have been reported and not all persons have become symptomatic. Therefore, estimates of Rt >1) during the shelter-in-place period. Consequently, numbers of cases remained high and increased rapidly at reopening.Consistent spreading: there was sustained transmission of SARS-CoV-2 (Rt <1) during the shelter-in-place period and a new surge in cases (1 ≤ Rt <2) after reopening.Two strong waves: a first wave of early transmission was followed by a slowdown (Rt <1).Strong first wave: there was a considerable number of cases during the initial period of the outbreak. During the shelter-in-place period, spreading was controlled; after reopening no new surge in cases occurred (Rt ≥2) after reopening.Strong second wave: there were few cases during the early transmission period, but new cases surged : SARS-CoV-2 transmission was rare.We generated maps to spatially examine the spreading of the COVID-19 first wave. We evaluated the effect of shelter-in-place, reopening, and further reopening by the trend of reproduction numbers before and after those events in different regions of Georgia.On the basis of 4,080 tracked pairs of primary and linked secondary case-patients in Georgia (Appendix Table 3), we estimated the serial interval distribution as a gamma distribution with a mean (10th–90th percentile) of 4.99 (1.32–9.71) days. Generally, the serial interval was longer when outcomes for primary case-patients were severe, such as hospitalization, undergoing ventilation, having an abnormal chest radiograph result, or death as final outcome (Appendix Table 4). Specific signs/symptoms in primary case-patients  did not shorten serial intervals. Serial intervals did not differ across demographic categories . The mean (10th–90th percentile) serial interval was 5.97 (1.65–11.50) days in February–April, 5.03 (1.41–9.65) days in May, and 4.40 (1.18–8.52) days in June–July . The aveTo study the variation in transmission by demographic characteristic , the observed frequencies in transmission pairs can be shown in a matrix  and elderly persons (>60 years) was observed less often, suggesting that transmission occurred more frequently between persons of similar ages .ts were >1 and then decreased until late April and early May, considered the first wave in Georgia. Rt usually decreased to a  local minimum during the shelter-in-place period and started to increase again as the second wave began. As during the first wave, Rts peaked and then started to decrease again during the second wave (t was much higher in the first wave (≈3.5) than in the second wave (≈1.7).During February and March, Rond wave . Althougt at these extremes varied among counties. The peak dates for the first wave in counties with cumulative case numbers was >200 cases by July 13, 2020 (t during the shelter-in-place period (April 3–April 30)  and the magnitude of R13, 2020 , panel April 30) . After rConsistent spreading occurred in Georgia counties around some major cities  and counties along interstate highways , panel BDuring February–July, the estimated serial intervals for onset of COVID-19 symptoms in the state of Georgia seemed to become shorter . Such a The serial interval estimation could also be affected by changing testing practices and contact tracing over the duration of the pandemic. COVID-19 testing capacity and contact tracing ability in Georgia were limited during earlier stages of the pandemic; thus, identification and isolation of COVID-19 case-patients and their close contacts were often delayed. With improved testing capacity, symptomatic case-patients were tested more promptly and isolated more quickly, which led to fewer exposures during their infectious periods. Rapid isolation and contact tracing could truncate transmission and lead to shorten serial intervals. More recent data collected when testing and contact tracing have improved are less likely to be affected by delayed testing and isolation. Contraction of serial intervals continued into May through early July, so the changes may still be explained at least partly by increased prevalence and increased contact rates .Transmission of a respiratory infection such as COVID-19 depends on behavioral factors and in particular on social contacts. Studies of contact behavior have shown that persons tend to have social contact with peers of similar age and demographic backgrounds . The traA primary case-patient who was male was more likely to transmit infection to a female contact than to a male contact. Female case-patients were infected by male case-patients across a wide range of ages , panel BSimilar to the serial interval, transmission patterns also changed as the pandemic continued. The major contribution to spreading SARS-CoV-2 shifted over time to the younger generation. This shift could be caused by elderly persons becoming more careful to protect themselves from infection by taking measures such as staying at home, wearing face masks in public spaces, and observing good hand hygiene. At the same time, younger persons might have been less compliant with quarantine measures and more likely to attend indoor gatherings such as parties or to have visited bars, gyms, and clubs while not wearing face masks.Previous pandemics, such as the 1918 influenza and the 2009 swine influenza (H1N1) pandemics, caused multiple waves of infections , panel Ats reached a local minimum in most counties. However, before reopening, Rts were still >1 in many counties even at the local minimum, indicating continued disease spread (During the shelter-in-place period (April 3–April 30), SARS-CoV-2 transmission slowed and Re spread , panel BThus far, the second wave has been heterogenous in time and magnitude in different counties. Local prevalence was different at the time of reopening, and counties where prevalence was high  experienced a stronger second wave. Counties not connected by major transportation links  often also saw a second wave of COVID-19 but on a relatively small scale. Some counties that experienced an early and intense first wave  did not experience a second wave. Possibly, inhabitants of those counties were more compliant with the prevention and control measures.A limitation of our study is that although data were available for >100,000 cases, clinical information and contacts with a confirmed case-patient were missing on some records. Absence of clinical information may depend on several factors. For example, reporting rates tend to be lower and clinical information more frequently missing for case-patients with mild or no symptoms than for case-patients with severe symptoms. A subgroup analysis showed similar distributions of serial intervals for transmission pairs with complete clinical information and transmission pairs with missing clinical information. This finding lends credibility to the assumption that the absence of clinical information does not affect the overall serial interval.Data on tracked pairs were not missing at random because contact tracing is voluntary and its capacity was limited during the early stages of the pandemic. Tracked pairs were more likely to be recorded when they involved known contacts. Identifying transmission links in public spaces or within clusters of cases remains challenging.t estimates could safely be ignored.In this study, presymptomatic transmission leading to negative serial intervals was ignored because infectors could rarely be determined by exposure information or travel history. On the basis of the sensitivity analysis , the influence of a small proportion of negative serial intervals on Rt is underestimated by the same amount. Both the numbers of primary case-patients (transmitting infection) and the numbers of secondary case-patients (acquiring infection) are underestimated, so the estimated rate of increase is likely to be less affected  persons, but this underestimation does not imply that RIn conclusion, transmission of SARS-CoV-2 in Georgia changed over time during February–July 2020. The mean serial interval decreased from 5.97 days in February–April to 4.40 days in June–July. The younger population (20–50 years of age) was involved in most transmission events during or after reopening subsequent to the shelter-in-place period. By mid-July, 2 waves of SARS-CoV-2 transmission were apparent, separated by the shelter-in-place period in Georgia. Transmission was more intense in counties around major cities and along interstate highways. These transmission patterns can be used to help predict and guide states in COVID-19 prevention and control according to population and region.Supplemental results for study of severe acute respiratory syndrome coronavirus 2 transmission in Georgia, USA, February 1–July 13, 2020.


AimThe aim of the study was to compare the clinical characteristics and outcomes  of patients with and without diabetes with confirmed COVID-19. MethodsThis retrospective study evaluated clinical and laboratory variables in adult inpatients from Brighton and Sussex University Hospitals NHS Trust with laboratory-confirmed COVID-19 between March 10, 2020, and June 30, 2020. Univariate and multivariate analyses were performed to compare the outcomes of patients with and without diabetes. ResultsOver 457 patients were included in this study (140 with diabetes and 317 without diabetes), of which 143 (31.9%) died. The median age was 80 years and were predominantly males (59.1%). Baseline characteristics at the time of COVID-19 diagnosis demonstrated that the patients with diabetes were younger than those without diabetes (p=0.008). Mortality increased with age. There was no difference in adverse outcomes in those with and without diabetes. However, subgroup analysis of patients aged ≤60 years demonstrated a significantly increased mortality in those with diabetes (p=0.016). Patients with diabetes had an increased length-of-stay compared to those without diabetes, which was more evident in those aged ≤60 years.ConclusionAge is the most important predictor of mortality. Patients with diabetes did not have increased mortality from COVID-19, which is likely due to their younger age in our cohort. More patients with diabetes stayed in the hospital longer than seven days than those without diabetes. The world is presently undergoing a significant health crisis due to coronavirus disease 2019 (COVID-19). COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The World Health Organisation declared COVID-19 as a pandemic on March 11, 2020 .Several studies have demonstrated a higher proportion of patients with diabetes are admitted to hospital due to COVID-19 ,5. Data This study reports the outcomes of patients admitted with COVID-19 at Brighton Sussex University Hospitals NHS Trust (BSUH), UK. This study evaluates the impact of diabetes on mortality, intensive care admission, mechanical ventilation, and length-of-stay (LoS) of hospitalized patients with COVID-19.Study designThis retrospective cross-sectional observational study was conducted in BSUH, which comprises of two hospitals: Royal Sussex County Hospital and Princess Royal Hospital. BSUH serves a population of approximately 500,000 people. All adult patients (≥18-years) hospitalized between March 10, 2020, and June 30, 2020, with laboratory-confirmed COVID-19 [a reverse-transcriptase-polymerase-chain-reaction (RT-PCR) SARS-CoV-2 assay of a nasopharyngeal swab], were included in the analysis. Patients were excluded from the analysis if they were discharged from the emergency department, transferred to another center (predominantly occurring in the early stages of the COVID-19 pandemic in the UK), or there was insufficient clinical information. Ethics approval was not required as the data were collected anonymously as part of routine healthcare delivery and service improvement analysis.Data collectionClinical and laboratory data were extracted from electronic hospital records. The clinical information collected were age, biological sex, ethnicity, socio-economic status, pre-existing diabetes or no pre-existing diabetes, type of diabetes (type 1 diabetes mellitus or type 2 diabetes mellitus), co-morbidities , length-of-stay, admission to intensive care unit (ITU), requiring mechanical ventilation and mortality. The laboratory parameters collected were glycated hemoglobin (HbA1c), estimated glomerular filtration rate (eGFR), and admission venous blood gas (VBG) glucose. Ward-based capillary glucose readings were reviewed to evaluate inpatient glycaemic control for patients with diabetes.Diabetes was defined as an HbA1c ≥6.5% (≥48 mmol/mol) on admission or an established diagnosis of diabetes prior to hospitalization. Socio-economic status was based on English Indices of Multiple Deprivation for the postcode 2019) [ [9]. IndStatistical analysisCategorical variables were reported as percentages. Continuous variables were reported as a median and inter-quartile range (IQR) as they were not normally distributed. Variables were compared between patients with diabetes and without diabetes. Furthermore, we compared ITU admissions, mechanical ventilation, and length of stay (LoS) between patients based on diabetes status and age ≤60 and >60 years. We also compared age between ethnic groups.In patients with diabetes, individual variables were assessed according to mortality outcomes. For continuous variables, the Mann-Whitney U test was used for non-normally distributed variables, whereas Pearson’s chi-squared test (χ²) was used for categorical variables.Logistic regression analysis was undertaken to calculate the odds for all-cause 28-day mortality in both the whole cohort and in the diabetes subset. Variables with a p-value <0.05 in the univariate analysis and those thought to be clinically related to the associations examined were included in the multivariate analysis.All-cause mortality was calculated as time, in days, between the first positive RT-PCR SARS-CoV-2 swab date and the date of death within 28 days of a positive COVID-19 test. Follow-up is defined from the date of positive RT-PCR SARS-CoV-2 swab to death or within 28 days of a positive COVID-19 test (whichever comes first). This is based on the Public Health England criteria used to calculate COVID-19 daily death . SurvivaOverall resultsDuring the study period, 550 patients were identified to have tested positive for SARS-CoV-2. After pre-protocol exclusions, 457 patients were included in the analysis Figure . BaselinBaseline characteristics in patients with diabetes and without diabetesPatients with diabetes were significantly younger and with more co-morbidities than those without diabetes Table . There wThere was no significant difference in mortality outcomes and median LoS in patients with and without diabetes. However, the distribution of LoS demonstrated more patients without diabetes were discharged within a week of admission.Admission blood glucose was significantly higher in those with diabetes (9.2 mmol/L) compared to those without diabetes . There was a significant difference in the number of patients with an eGFR<60 in patients with diabetes than those without diabetes .The number of patients requiring ITU admission and mechanical ventilation and length-of-stay outcomes of COVID-19 patients based on age (≤60 years compared to >60 years) and diabetes status are provided in Table There was no significant difference in LoS , ITU admissions, and mechanical ventilation in patients aged >60 years in those with and without diabetes.Characteristics in COVID-19 patients with diabetes and mortality outcomeThere was no significant difference in the mortality outcomes of COVID-19 patients with diabetes Table  in relatRisk factors associated with mortalityTable Univariate analysis Table  of patieSurvival analysisKaplan-Meier survival curves demonstrated no difference in survival in hospitalized COVID-19 patients with diabetes compared to those without diabetes Figure . HoweverThis retrospective study described clinical characteristics of patients with and without diabetes, and COVID-19 admitted to a tertiary care hospital in the United Kingdom. Around a third (140 out of 457) of the patients with COVID-19 had diabetes. This study demonstrated that patients with diabetes did not have an increased risk of death, ITU admission, or mechanical ventilation. This was despite patients with diabetes having more co-morbidities and a larger proportion of BAME background. This lack of difference is likely due to the age difference between diabetes and non-diabetes groups. Subgroup analysis revealed significantly increased mortality in patients with diabetes who were aged ≤60 years compared to those without diabetes, though the numbers involved in this subgroup analysis included very few deaths.Characteristics of all patients admitted with COVID-19This study showed that most of the patients admitted were male 59.1%) and elderly (median age of 80 years). There were fivefold more patients aged >60 years compared to those aged ≤60 years. Furthermore, age >60 years was associated with increased mortality. These findings are consistent with current worldwide evidence , includi9.1% and 2 at the time of COVID-19 diagnosis was found in 44.1% of the patients (patients with chronic kidney disease [CKD] and acute kidney injury were both included), and this was associated with increased mortality. This supports recently published data that CKD and reduced admission eGFR were associated with increased mortality [Hypertension, followed by heart disease was the most commonly encountered co-morbidities amongst the hospitalized COVID-19 patients in this study. Neither was associated with increased mortality, admission to ITU, or mechanical ventilation which is in keeping with recent literature ,16. An eortality ,19. Angiortality . The memortality .Prevalence of diabetes in patients with COVID-19Current evidence has demonstrated patients with COVID-19 and diabetes are more likely to develop severe COVID-19 . 30.6% oOutcomes of COVID-19 in patients with diabetesEven though 29.4% of the total deaths were in patients with diabetes, there was no association with increased mortality. Patients with diabetes were significantly younger than those without diabetes, which may be why there was no difference in mortality between the two groups. Univariate analysis in the diabetes group showed that age was the most significant factor predicting mortality. However, multivariate analysis in the diabetes group could not be undertaken. There is conflicting data in the literature as to whether diabetes is associated with increased mortality in COVID-19 ,25. AlthThe proportion of patients in the ≤60 and >60 years age groups was similar in diabetes and non-diabetes groups. All patients ≤60 years old who died from COVID-19 had diabetes, although this represented only three patients. In patients ≤60 years of age with diabetes, two out of three deaths occurred in the BAME population. However, regression analysis did not show BAME background was a risk factor for increased mortality. This is likely due to younger patients with a BAME background. There was no difference in mortality in patients with and without diabetes in >60 years of age.It is well known that diabetes is associated with longer LoS in hospitalized patients . We demoCOVID-19 patients with diabetes and co-morbiditiesOur study showed that patients with diabetes had significantly more co-morbidities . However, these co-morbidities were not associated with increased mortality in patients with COVID-19 and diabetes. eGFR was associated with increased mortality in the overall analysis of all patients. Our data are supported by current evidence with regards to HTN, heart disease, and eGFR ,19.Impact of glycemia in patients with COVID-19 and diabetesPoor glycaemic control in patients with COVID-19 has been associated with worse outcomes, including mortality ; howeverWe demonstrated that the median blood glucose in patients with diabetes were acceptable. Hyperglycaemia has been reported with COVID-19 in patients with and without diabetes and is associated with worse outcomes, including increased mortality . This isLimitationsOur study has some limitations. First, due to the retrospective study design, body mass index was not available, which is an important independent risk factor for mortality in COVID-19 . Second,In conclusion, diabetes was only associated with increased mortality in patients ≤60 years old. However, due to the small sample size, the results of the subgroup ≤60 years old should be interpreted with caution. There was no association between glycaemic control, prior to and during hospitalization, and mortality. Patients with diabetes had longer hospital stays, which may imply greater disease severity. Older age was a significant risk factor for increased mortality in COVID-19. Prospective studies of patients with diabetes hospitalized with COVID-19 are required to determine the impact of glycemic emergencies and glycemic control on outcomes. Additional studies from other regions of the UK are needed to address whether there are regional differences in risks for mortality in patients with COVID-19.


There is an increasing need for Mental Health Promotion (MHP) among adolescents, especially in developing countries with limited resources and rapid socio-demographic transition. With the growing burden of mental health problems among adolescents  and their preferences to seek help from their peers, improving Mental Health Literacy (MHL) and behaviours for First Aid in Mental Health (MH-FA) becomes crucial to promote their mental health.Schools are ideal settings for reaching the vulnerable adolescents. The proposed study evaluates the effectiveness of a classroom-based teacher-led integrated school mental health intervention called SUMS (MHP + MHL + MH-FA). The study will involve a pragmatic, cluster-randomised waitlist-controlled design to evaluate the effectiveness of SUMS intervention using schools as unit-of-randomisation. The study will be conducted in Srinivaspura taluka (Sub-district) of Kolar district  of Karnataka in collaboration with a multi-disciplinary expert team from NIMHANS , Bangalore-India and Department of Education, Government of Karnataka, India. A total of 8 schools (400 students studying in 6–8 grade) from Srinivaspura taluka will be randomised into intervention and waitlist control group. The intervention group will receive SUMS intervention through 10–15 h of classroom sessions. The primary outcome is the improvement in positive mental health literacy, as measured by the Mental Health-Promoting Knowledge (MHPK-10) scale. Changes in MH-FA knowledge and intentions, Mental health stigma, help-seeking and resilience are assessed as secondary outcomes. Data will be collected at baseline, 6-weeks, 6-months and 12-months post-intervention. The waitlist-control schools will receive the interventions at the end of the 12-month follow-up assessment in intervention-schools.This is the first study to integrate Mental Health Literacy with Mental Health Promotion and behaviours for First Aid in Mental Health to promote mental health well-being among adolescent school children in India. With a need to build a more substantial evidence base on School Mental Health Promotion approaches in developing countries, the study findings will have implications for implementing and operationalising Health and Wellness Ambassador initiative in India.ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=35724&EncHid=&userName=sums).Clinical Trials Registry - India, CTRI/2019/07/020394. Registered prospectively on 29 July 2019. ( In recent years, there is an increasing concern about adolescents’ mental health, especially in developing countries. An estimated 10–20% of children and adolescents are affected by mental health problems worldwide [Today, suicide is one of the leading cause of preventable deaths, and depression is one of the leading cause of morbidity among adolescents . Based oMental health literacy (MHL) is defined as “knowledge and attitudes regarding mental health that aid in recognition, management and prevention of mental health issues” . Low levGiven the critical role of education in health literacy, schools become an ideal setting for reaching vulnerable adolescents for improving mental health literacy. This is because adolescents spend most of their time in schools, and health is intrinsically linked with education. Furthermore, many risks and protective factors for adverse mental health outcomes are operated in the school context. Presently, the evidence base for educational interventions targeting adolescents for mental health literacy and mental health promotion is limited . Even thIn India, adolescent mental health assumes a great relevance where adolescents aged 10–19 years constitute about 22% of the population . With inDeveloping personal skills for both students and teachers has been emphasised for successful mental health interventions  Among vahttps://mhlcurriculum.org/) where a curriculum resource guide was developed through desk review and expert inputs. The curriculum resource guide contained content and delivery methods for each SUMS intervention . This curriculum resource guide was adapted to the local context through key informant interviews and intervention designing workshops conducted among school teachers, students, block education officers and subject experts.With this in the background, we developed a classroom-based teacher-led integrated school mental health intervention called SUMS (MHP + MHL + MH-FA) based on “School Friendly” mental health literacy approach and “School-Based Integrated Pathway to Care Model” developed by Kutcher et al. . Like MHIn this context, we have undertaken a cluster randomised controlled trial to evaluate the effectiveness of SUMS  intervention  for adolescents, integrated into the classroom and delivered by teachers. We hypothesise that SUMS intervention delivered by teachers will lead to significant improvement in mental health knowledge, positive attitudes toward mental illness, and behaviours for First Aid in Mental Health as compared to ongoing regular school approach among school-going adolescent children. The ongoing regular school approach was used as comparator to reflect real-life scenario.This study’s primary objective is to assess the effectiveness of evidence-informed integrated school mental health intervention (SUMS) in promoting mental health knowledge, positive attitudes toward mental illness, and behaviours for First Aid in Mental Health among adolescent school children. Furthermore, we will explore SUMS intervention’s potential influence on a range of adolescent health-related outcomes such as academic achievement, absenteeism rate, educational stress, suicidal ideation, bullying, and substance use. Additionally, we will explore the implementation process of the intervention and identify contextual factors associated with outcomes.The study will use a pragmatic, cluster randomised waitlist-controlled design with schools as unit-of-randomisation to evaluate the effectiveness of SUMS intervention. The study will follow consort guidelines for cluster randomised trial Fig. .Fig. 1Thttps://kolar.nic.in/en/) in collaboration with a multi-disciplinary expert team from NIMHANS , Bangalore-India and Department of Education, Government of Karnataka, India. Kolar district is the Public Health Observatory for Department of Epidemiology, NIMHANS.The study will be conducted in Srinivaspura taluka (Sub-district) of Kolar district  of Karnataka , Higher-Primary (6–8 grades) and High-schools (9–10 grades). The study will be conducted in higher-primary public schools in Srinivaspura taluka of Kolar District, Karnataka. Higher-primary public schools usually include children from ages 13 to 15 years. These grades were chosen because this is the period where life skills curriculum were introduced and this period in the life span also represents the point of departure for the rapid increase in psychiatric diagnoses that occurs prior to age 25 years .The students from grades 6 and 7 will be included for the present study so that the students in grade-7 will have 12-month follow-up when they are in class-8.All children enrolled in class 6th and 7th grade of the participating higher-primary public schools will be eligible for the present study.(1) Children who did not give informed assent (2) Children whose parents/guardians did not provide written informed consent (3) Children who were absent for the baseline assessment even after two attempted visits (4) children aged> 15 years at enrolment.SUMS intervention (MHP + MHL + MH-FA) is a classroom-based teacher-led integrated school mental health intervention that will promote positive mental health, mental health literacy and behaviours for First Aid in Mental Health among school-going adolescent school children. As described earlier, SUMS intervention takes the form of a classroom-ready resource (the curriculum resource guide) designed to be delivered by usual classroom teachers to students in grades 6 and 7 (ages 12 to 15 years). The curriculum resource guide contains 10 modules categorised under 3 units . The ten modules are as follows: Positive mental health, Promoting Resilience, Promoting Mindfulness, Life Skills Education, Mental Health Literacy, Understanding Mental Health and Mental Illness, Stigma of Mental Illness, Information on specific mental illnesses, seeking help and finding support, First Aid in Mental health and strategy for mental health first Aid . Any suggestions by Block Education Officers will be duly accommodated to improve the feasibility .The following core outcomes will be used to assess the intervention. All outcome measures will be measured at the individual student level.Positive mental health literacy will be measured using a 10-item Mental Health-Promoting Knowledge (MHPK-10) scale. It is a valid and reliable one-dimensional instrument measuring knowledge of factors promoting good mental health among adolescents. The MHPK-10 had demonstrated good internal validity and test-retest reliability (r = 0.74) among adolescents. For each item, respondents rate items on 1–5 likert-type scale, the extent to which they are correct or wrong. The score range is 10–50, with a higher score indicating a higher positive mental health literacy [The primary outcome, literacy .This will be measured using a 35-item univariate scale, Mental Health Literacy Scale (MHLS). The scale will be adapted for use among adolescents. The score range is 35–160, with a higher score indicating a higher mental health literacy .This will be measured using the 24-item Peer Mental Health Stigmatization Scale (PMHSS). For the present study, the 8-item personal stigma subscale will be used. Τhe scale demonstrated adequate discriminant validity, internal consistency (α = 0.75) and test-retest reliability in adolescents . will be measured using the Reported and Intended Behaviour Scale (RIBS). Scores on the RIBS range from 4 to 20, with higher scores indicating more positive attitudes. The RIBS has a test-retest reliability of 0.75, and a Cronbach’s α of 0.86 .This will be assessed using a 24-item Inventory of Attitudes towards Seeking Mental Health Services (IASMHS) scale. It consists of three subscales; Psychological Openness, Help-seeking Propensity, and Indifference to Stigma. The total help-seeking score is calculated as a sum of the subscale totals with higher scores indicate more favourable attitudes towards help-seeking .Kessler psychological distress scale (K6) with good internal consistency  will be used. The total score ranges from 0 to 24 with a higher score indicating higher distress [A 6-item self-administered distress .A 25-item Connor-Davidson Resilience Scale (CD-RISC) will be used. The CD-RISC is a valuable measure to assess resilience among adolescents in low-income settings. Psychometric properties of the Kannada version of CD-RISC were established in a sample of adolescent girls from low-income settings. The total score ranges from 0 to 100, with higher scores reflecting greater resilience . will measure socio-demographic details and contain case vignettes/questions to assess MH-FA knowledge and intentions to provide MH-FA (proxy measures of behaviour change).A compelling body of evidence points to the potential influence of SUMS intervention on a range of adolescent health-related outcomes such as academic achievement, absenteeism rate, educational stress, suicidal ideation, bullying and substance use, which will be explored.The pre-validated study instruments will be translated into local language and back-translated for conceptual equivalence. For pre-testing, a sample size of 42 is estimated to establish reliability of at least 0.85, against acceptable value of 0.7  for a minimum of 6 items . The traα = 0.05) with 85% power. For the above assumption, the design effect varied from 1.5 to 3.5 for ICC (ρ) values from 0.01 to 0.05, respectively [Positive mental health literacy measured using the 10-item Mental Health-Promoting Knowledge (MHPK-10) scale is considered primary outcome for the present study . Howeverectively . We experandomly selected using computer-generated random-number by an independent researcher. The selected schools will then be assigned to the intervention, and waitlist-control condition (receiving intervention after the post-assessment in intervention-group) by the independent researcher through the random-allocation sequence. To ensure allocation concealment, randomly selected schools will be assigned unique study numbers that will not be known to the independent researcher at the central office. The sequence will be delivered through sealed, opaque envelopes.From the eligible higher-primary school-list obtained from the education department of Srinivaspura taluka, eight schools will be The selected-schools will be invited to participate in the study, and briefing-meetings will be conducted to inform principals of selected-schools regarding the study. Information on any initiatives/activities related to target outcomes in the selected-schools will be obtained. Local consortium and local stakeholders for education will be interacted to ensure minimal refusal of the selected schools. In case of refusal, the refusing school will be replaced with another school from the eligible school-list. A due care will be taken to prevent possible contamination so that no control-schools will be selected within 5 km radius of intervention-schools. An alternate covariate-constrained randomisation will be considered only if we do not reach an acceptable baseline balance through simple-randomisation.Given the nature and complexity of intervention & its delivery, it will not be possible to completely blind or conceal the intervention from the teachers, students, and field-staff. The trial will be open-label. Nonetheless, the study-statistician will be blinded till the database is locked for final-analysis.Enrolment will start after obtaining assent and parental-consent from the eligible students. In the case of multiple sections with > 60 students in each grade, one section will be randomly selected from each grade. The intervention will be provided to all students in the selected grades irrespective of their number. The teachers will be free to provide intervention for other sections, but data will not be collected.For the schools randomised to the intervention arm, the teachers nominated by their designated superior officials will be trained on the curriculum resource guide content in a 3-day workshop. The teachers who provide consent to participate in the research, complete the training, stay in the selected-schools for the study period and deliver the intervention will be selected. Following baseline assessment of participating students, the trained teachers will then proceed with the implementation of SUMS Intervention Curriculum Resource Guide, which requires approximately 10–15 h of classroom time over two months. In the intervention delivery, the teachers will be given pedagogical flexibility but will be instructed to adhere to the standardised core content, recommended lesson plans and procedures to maintain fidelity Table . BoosterControl-schools will continue to provide regular teaching as usual of the existing course to their students. The control group will receive the interventions at the end of the 12-month follow-up assessment in intervention-schools. The teachers in the waitlist control arm will be trained just one month before their scheduled intervention to prevent contamination.An information letter detailing the intervention program and data collection along with consent-form will be sent to the parents/guardians of participating class for parental-consent. Parental-consent will be recorded with a signature/thumbprint. Existing school communication channels  will be used to maximise the parental-consent for child participation. Parents will be provided an opportunity to contact the field-team to discuss the study and their participation. An attempt will be made to meet the non-responding parents to obtain their consent. Children without parental consent in the selected class will be permitted to attend intervention sessions, but data will not be collected from them.The students may withdraw their participation at any point in time during the study. An attempt will be made to document the reasons for withdrawal.Data will be collected using self-administered questionnaire. Students will complete the survey during class time in the presence of trained project assistant/ teacher to assist the children with reading, writing and answering any queries. Data will be collected at 4-timepoints from both intervention and control schools: baseline and 6-weeks, 6-months and 12-months post-intervention  among students/teachers during the final stages of the trial to explore the implementation of the intervention and identify contextual factors associated with outcomes. Teachers will be provided with questionnaires to evaluate the Curriculum Guide and to provide feedback on their experience.p-value< 0.05, and Bonferroni’s correction will be made for multiple testing (if any).The study tools/measures will be examined for internal consistency. Demographic and baseline characteristics will be summarised using descriptive statistics. Principal-analyses will be intention-to-treat which will take into account clustering by the school and repeated measures. To assess the effectiveness of interventions, outcomes will be compared between two trial arms using linear-mixed-model analysis for continuous outcomes and generalised linear mixed models or generalised estimating evaluation (GEE) for binary outcomes. The analysis will include all participants with at least one outcome measurement at 6 weeks post-intervention. A per-protocol analysis will also be conducted along with intention-to-treat analysis to examine the influence of missing data patterns. Multiple imputation will be attempted for missing values. The analysis will be adjusted for potential confounders that are identified a-priori during the study. Subgroup analysis will be performed for important socio-demographic variables. Statistical significance will be set at For qualitative analysis, all the verbatims of interviews/group discussions will be transcribed and translated into English for thematic analysis.Interim analyses is planned for the follow-up data collected at 6-weeks post-intervention and will be submitted to Data Safety & Monitoring Board (DSMB) for further review Table .The DSMB will be asked to advise stopping the trial if they have proof beyond doubt that intervention is futile. The trial’s stopping-rule will be based on evidence-of-futility in > 70% of outcome measures (both primary &secondary) at interim-analyses and critical feedback from the local stakeholders.thIEC (BS&NS DIV.)/2019 on 2019-07-17. The trial was registered at Clinical Trials Registry-India with identification code: CTRI/2019/07/020394 dated 2019-07-29. Due to COVID-19 Pandemic and school closures, recruitment for the trial is anticipated after March 2022.The trial was reviewed and funded by Indian Council of Medical Research (Id: 2017–4697 version F1 dated 02 September 2019). The trial was reviewed and approved by the Institute Ethics Committee, NIMHANS  in Kolar district, Karnataka. The activities included situational analysis, numerous consultation workshops with teachers and various training programmes organised in-collaboration with the education department in Kolar. Following interactive discussions with NIMHANS experts and a national-level symposium, a road-map and draft action plan for integrated CAMH services in a district was finalised. The present trial is a part and extension of the action plan developed for promoting CAMH. Though the mental health intervention domains (MHP+ MHL + MH-FA) are closely interlinked with considerable cross-cutting areas of action, we intentionally compartmentalised it for better focus and delivery. With a need to build a more substantial evidence base on effective School Mental Health Promotion approaches in low-income countries, the study builds on the facilitating role of school climate in promoting mental health and related outcomes among adolescents and evaluates its effectiveness through a robust and systematic methodology.As cultural aspects are likely to influence the effectiveness of SUMS interventions, the trial results should be interpreted within the given cultural context. As this study is being conducted in a relatively small geographical area, a larger multi-centre trial is required to better understand SUMS interventions’ replicability and scalability, especially in different cultural settings.India has recently launched the Health and Wellness Ambassador initiative under the School Health Programme of Ayushman Bharat Mission . This in


Human rhinovirus (HRV) is the predominant etiological agent of the common cold in children and adults. A recent study showed that the inhibitory effect of face masks on viral shedding of HRV was less prominent than that on other respiratory viruses. Considering that most Chinese people have worn face masks in public area since the outbreak of coronavirus disease 2019, we aimed to find out whether HRV prevailed among children in 2020 and demonstrate the details of the epidemiological features of HRV under such a special circumstance.We summarized the incidences of various respiratory virus infections in patients who visited the Children’s Hospital of Fudan University during 2018–2020, and genotyped HRV positive nasopharyngeal specimens collected from 316 inpatients and 72 outpatients that visited the hospital in 2020.There was a major prevalence of HRV among children in the latter half of 2020, with a clear seasonality that HRV-As prevailed in summer while HRV-Cs in autumn. HRV-As were more prone to cause severe lower respiratory tract infections (LRTI), while HRV-Cs were closely associated with childhood wheezing. The predominant genotypes were A11, A28, A47, A82, A101, C40 and C43. Notably, A21, A82 and A101 took up larger proportions in severe cases than in non-severe cases.Our findings described a major prevalence of HRVs among children in 2020, which highlight the unique transmitting pattern of HRV and help to narrow the targets for antiviral strategies. Human rhinoviruses (HRVs) are the leading cause of upper respiratory tract infections (URTIs) since its first isolation in the 1950s . HoweverPicornaviridae family, and are single-stranded, positive-sense RNA viruses, indicating that it contains the sense strand of RNA as their genome which can be readily translated into proteins [HRVs belong to the proteins . The genproteins . About 1proteins . So far,proteins .After the outbreak of coronavirus disease 2019 (COVID-19), most people have developed the habit of wearing face masks in public area in order to inhibit the transmission of respiratory pathogens. However, Leung et al. quantified the amount of respiratory viruses in exhaled breath of participants with acute respiratory illnesses and found that wearing medical face masks significantly reduced the RNA level of influenza viruses and coronaviruses (OC43 and NL63) in respiratory droplets or aerosols, but not in HRVs , suggest5′UTR (263 bp) of HRVs was used to detect HRV RNA. The remaining viruses and mycoplasma were detected using an immunofluorescence assay kit . Briefly, nasopharyngeal aspirates were centrifuged and the cell pellet was fixed in acetone. A mixture of fluorescein-labeled monoclonal antibodies directed against the target viruses were added onto the cells, followed by an incubation of 30 min at 37 °C. A Mounting Fluid containing glycerol was added onto the stained cells and then a coverslip was placed on the prepared cells. The cells were examined using a fluorescence microscope . Isolation and culture of bacterial and fungal pathogens were carried out according to the routine microbiology examination and diagnosis. Bacterial and fungal strains were identified using VIETEK automated bacterial analyzer (France) or MALDI-TOF/MS mass spectrometry .A total of 316 nasopharyngeal aspirates from inpatients with lower respiratory tract infection (LRTI) hospitalized in the Children’s Hospital of Fudan University in Shanghai from June 2020 to November 2020 were collected in this study. All the inpatients were diagnosed with LRTI supported by symptoms and radiographic changes and were defined as HRV positive after routine screening for common respiratory viruses including respiratory syncytial virus (RSV), adenovirus (AdV), influenza A and B viruses (IAV and IBV), parainfluenza virus type 1 (PIV-1), PIV-2, PIV-3, human rhinoviruses (HRV) and human metapneumovirus (MPV). For HRV screening, RNA from respiratory samples were extracted using a magnetic beads-based nucleic acid extraction system NP968-C  according to the manufacturer’s instruction. Then a one-step real time quantitative polymerase chain reaction (RT-qPCR) kit  with primers targeting the A total of 703 nasopharyngeal swabs from outpatients with URTI who visited the hospital during June 2020 to November 2020 were collected randomly and screened for HRV by RT-qPCR. The randomization was done as follows: first, one staff member covered all the information of the patients on the swabs with a blank tag paper. Then another staff member was asked to choose the swabs randomly to avoid biases in patients’ gender, age, and illness.LRTIs are illnesses that affect the respiratory system below the throat. The severity-based classification of the patients was performed by experienced clinicians according to the World Health Organization (WHO)’s latest definition of severe LRTI cases , 9. BrieVP4/VP2 regions (540 bp) of HRVs as reviewed in a previously published paper [For genotyping, the extracted RNA were reverse transcribed and amplified using a nested RT-PCR strategy. HRV molecular subtyping was performed using primers targeting the ed paper . To incred paper . Brieflyed paper , 13. The2 test or Fisher’s exact test. Independent group t-test was used for the comparison of means for continuous variables that were normally distributed. The Mann–Whitney U test was used for continuous variables not normally distributed. All statistical analyses were performed using GraphPad Prism software. Two-sided p-values of less than 0.05 were considered statistically significant.Proportions for categorical variables were compared using the χA total of 4481 patients were tested positive for at least one of the following viruses as RSV, AdV, IAV, IBV, PIV-1, PIV-2, PIV-3, MPV and HRV in the Children’s Hospital of Fudan University from June 2018, when the hospital started the HRV test for patients, to December 2020. Most respiratory viruses were barely detected after the outbreak of COVID-19  were successfully genotyped. We also randomly collected 703 nasopharyngeal swabs from outpatients with URTI during the same period of the inpatients, among which 10.2% (72/703) were determined as HRV positive and 90.3% (65/72) were successfully genotyped.The genetic variability of HRV genotypes in our data was very wide, as shown in the phylogenetic tree Fig. . A totalTo find out the association between HRV and clinical characteristics, we collected the clinical information of patients including symptoms, co-infections and underlying diseases. Among the three species, HRV-Bs seemed to infect more females than males, although the number of HRV-Bs were small Table . NotablyAfterwards we classified the patients into three groups according to their disease severity, including the outpatients with URTI, the inpatients with non-severe LRTI or severe LRTI Table . No signp = 0.0004), A82  and A101 . To be noted, A21 was the only genotype that was more frequently detected in severe LRTI cases (3 cases) than in non-severe LRTI cases (1 case), despite that the total number of non-severe LRTI cases was almost ten times of the severe LRTI cases.The percentage of severe cases in HRV positive cases showed an increase in October and November 2020 Fig. A, despitTo further investigate the viral load-associated factors, we collected the Ct values of HRV positive samples. In our data, the patients co-infected with other respiratory viruses showed comparable viral loads with those with HRV mono-infection Fig. A. Also, HRV infections were mainly transmitted via aerosols generated by coughing, sneezing and nose , which iThe majority of people wear disposable medical masks in public during the COVID-19 pandemic as WHO recommended, given that medical masks could help block large-particle droplets, splashes, sprays, or splatter that may contain viruses or bacteria . But as Generally speaking, non-enveloped viruses  are more heat-resistant and could survive longer in a dry and acidic environment than enveloped respiratory viruses , which largely increases their chances of spread . Also, iZhao et al.’s paper based on the respiratory samples of children in Shanghai during 2013–2015 shared some similar findings with ours, such as the age/gender preferences of HRV and the seasonality of HRV-C . But HRVIn consistence with our findings, several studies also reported that HRV-Cs are more commonly associated with early childhood asthma than the other two species , 28–30, It was reported that the patients co-infected with other respiratory viruses showed higher viral loads than those with HRV mono-infection , but it There is a growing understanding on the pathogenesis of viral and bacterial coinfections. For instance, viral infection in the respiratory tract could induce airway damage, promote bacterial adherence, decrease mucociliary clearance and impair the immune system, all of which facilitate bacterial co-infection , 41. ConCollectively, our findings described the details of the HRV prevalence among children in 2020, which is worthy of our reflection on the distinct transmitting pattern of HRVs. Moreover, our data suggested that the antiviral strategies to reduce HRV-related morbidity in high-risk children should focus on HRV-As and HRV-Cs. In a word, our findings add to the knowledge of the epidemiological features of HRV among children and underline the necessity to control HRV infection despite of the use of face masks.


Sandy coastlines adjacent to tidal inlets are highly dynamic and widespread landforms, where large changes are expected due to climatic and anthropogenic influences. To adequately assess these important changes, both oceanic  and terrestrial  processes that govern the local sediment budget must be considered. Here, we present novel projections of shoreline change adjacent to 41 tidal inlets around the world, using a probabilistic, reduced complexity, system-based model that considers catchment-estuary-coastal systems in a holistic way. Under the RCP 8.5 scenario, retreat dominates (90% of cases) over the twenty-first century, with projections exceeding 100 m of retreat in two-thirds of cases. However, the remaining systems are projected to accrete under the same scenario, reflecting fluvial influence. This diverse range of response compared to earlier methods implies that erosion hazards at inlet-interrupted coasts have been inadequately characterised to date. The methods used here need to be applied widely to support evidence-based coastal adaptation. LECZ are heavily utilised for a wide range of activities including navigation, tourism, agriculture, marine/ecosystem resources and services, waste disposal, and recreational activities8. Some of the most heavily utilised LECZ areas are fronted by open sandy coasts, which comprise about one-third of the world's coastline9. They are complex morphodynamic systems that are commonly expected to erode during the twenty-first century due to rising sea levels and reduced sediment supply11. A majority of these sandy coasts are interrupted by tidal inlets18—i.e., inlet-interrupted coasts19, forming an important sub-set of open sandy coasts.The Low Elevation Coastal Zone (LECZ), defined globally as the areas within 10 m of mean sea-level22. This means that climate-change impacts  and anthropogenic activities  are likely to strongly influence the future behaviour of inlet-interrupted coasts. Such changes will inevitably lead to severe socio-economic impacts and generate adaptation needs along these heavily populated and utilised coastal zones25. This study was therefore undertaken with the overarching aim of deriving hitherto lacking projections of how this important and globally widespread type of coasts may evolve over the twenty-first century, taking account of both oceanic and terrestrial influences.Both oceanic  and terrestrial  processes contribute to the long-term (50–100 year) evolution of inlet-interrupted coasts26. Another complicating factor is the significant uncertainties associated with the anthropogenic and climate-change impacts that affect the future evolution of inlet-interrupted coasts29. Consequently, in addition to the uncertainties associated with the model techniques employed , projections of inlet-interrupted coastline changes will also inherit the uncertainties associated with the climate-related drivers , storm conditions, precipitation) and anthropogenic activities considered . Therefore, it is important to understand and quantify the uncertainties associated with coastline change projections, which requires probabilistic as opposed to deterministic approaches. In view of these considerations, most existing coastal impact approaches are inadequate for supporting long-term planning and management of inlet-interrupted coasts, reflecting their: (1) partial rather than holistic consideration of sediment budgets in CEC system behaviour26, and (2) inability to provide long-term projections (particularly probabilistic projections) due to high computational demands and inherent model limitations 30. The Generalised-Scale-aggregated Model for Inlet-interrupted Coasts (G-SMIC)) used here overcomes these shortcomings and provides probabilistic assessments of the long-term evolution of inlet-interrupted coasts. To keep the problem tractable, the G-SMIC applications undertaken here account only for input uncertainties  and do not account for model uncertainties.The behavioural dependence of inlet-interrupted coasts on both oceanic and terrestrial processes poses a major complication for long-term modelling of inlet-interrupted coasts. To adequately resolve the governing physical processes requires a holistic representation of Catchment-Estuary-Coastal (CEC) systems in a model22, which is a substantial extension of the SMIC18, computes long-term shoreline change on inlet-interrupted coasts through the superposition of two main components: (1) shoreline change due to variations in sediment volume exchange between an estuary and the adjacent inlet-interrupted coast , annual cumulative runoff (Q), change in global mean sea-level 9 at those locations. In addition to this, shoreline hindcasts were undertaken at eight more CEC systems, covering the full range of estuary surface areas considered in this study. The model hindcasts compare well with observed shoreline change rates presented by Luijendijk et al. (2018)9, with an average deviation of ~ 16% 31.Here, we apply G-SMIC at 41 CEC systems located in different climatic regions and geomorphic settings across the world to obtain shoreline change projections (including associated uncertainties) over the twenty-first century under the four IPCC Representative Concentration Pathways 11, 4 systems from the enhanced UK estuary database32, 8 systems from the New South Wales  estuary database33, 3 systems from the original G-SMIC application22, 3 systems from the previous SMIC application18, and one system in Liberia35. The location of the selected CEC systems, which cover North America, South and Central America, Europe, Africa, Asia, and Australasia, are shown in Fig. The 41 CEC systems used in this study were selected based on data availability. These comprise 22 systems from the DIVA (Dynamic and Interactive Vulnerability Assessment) databaseth, 50th, and 90th percentile values of The projected change in total sediment volume exchange . The magnitudes of mentclass2pt{minimmentclass2pt{minim37, increasing fluvial sediment supply to the coast.Of the 41 CEC systems considered here, only four systems are projected to export sediment . These are Columbia River (USA), Gambia River , St. Paul (Liberia), and Kalutara (Sri Lanka). A closer inspection of the relative contributions of the three governing processes considered in G-SMIC Eq. , shows tWe further analyse the relative contributions of basin infilling and fluvial sediment supply to the projected The projected 50th percentile values of mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimThe projected shoreline position changes along the coastlines adjacent to the 41 CEC systems by 2091–2100, relative to present-day, are shown in Fig. G-SMIC derived median projections of shoreline position change indicate that the combined effect of climate change  and anthropogenic influences will induce shorelines adjacent to 90% of the systems to retreat over the twenty-first century under any RCP, with shorelines at 46% (RCP 2.6) and 68% (RCP 8.5) of the systems retreating more than 100 m by the end of the century.Median projections of shoreline retreat across all sites projected to erode range from 6 to 632 m across the 4 RCPs. However, it should be noted that there are large uncertainties associated with these projections, with the largest and smallest ranges of projected shoreline position change for an individual site being 116 m and 2 m, respectively . The largest range (10th–90th percentile) of shoreline position change by the end-century period is projected at the Muni system for all RCPs . The minimum range of around 2 m is projected at Rio Chone, Bega River, Gambia, and Tweed River CEC systems for RCP 2.6, 4.5, 6.0, and 8.5, respectively. The large ranges in projections Table  emphasisThe difference between projections for mid- and end-century is only in the magnitude of shoreline retreat for 39 of the 41 CEC systems with projected increases  ranging between 44 and102% and 58 and 157% across the systems under RCP 2.6 and RCP 8.5, respectively. At the two remaining CEC systems (Gambia and St Paul), shoreline retreat is projected for the mid-century period (relative to present-day), while shoreline progradation is projected for the end-century period (relative to present-day).10 that presented global projections of shoreline retreat/progradation over the twenty-first century under RCP 4.5 and RCP 8.5 ’s projections do not fully account for inlet influences, and how they may change in the future, that are addressed in G-SMIC. A fundamental difference between the two sets of projections occurs at the Columbia, Gambia, and St. Paul systems, where G-SMIC projects shoreline progradation, whereas Vousdoukas et al. (2020)10 project shoreline retreat. This is likely because G-SMIC considers fluvial sediment supply and future changes therein , while the approach adopted by Vousdoukas et al. (2020)10 does not consider this component of the sediment budget which is important, especially at CEC systems.We compare the median shoreline position changes projected at the 41 CEC systems for 2091–2100 in this study with the findings of a recent study by Vousdoukas et al. (2020).5 Table . Vousdou.5 Table . The com18. It is also noteworthy that three of the 41 CEC systems where G-SMIC projects shoreline progradation for the end-century period, the corresponding Bruun rule predictions indicate shoreline retreat. These discrepancies between projections re-iterate the inappropriateness of using the Bruun rule alone in the vicinity of tidal inlets and underline the importance of considering CEC systems in a holistic manner when deriving projections of future shoreline position change along inlet-interrupted coasts.Finally, G-SMIC projected median shoreline position change are compared with those derived only through the use of the Bruun rule alone 18 for small tidal inlets, was substantially extended for general application at CEC systems by Bamunawala et al. (2020)21. In G-SMIC applications, long-term shoreline change at inlet-interrupted coasts is represented as the superposition of two main components: (1) shoreline change due to variation in sediment volume exchange between an estuary and its adjacent inlet-interrupted coast 18 as expressed in Eq. (The variation of sediment volume exchange between an estuary system and its adjacent inlet-interrupted coast (mentclass2pt{minim2), and 'fac' (0 < fac < 1) is a factor that accounts for the morphological response lag. Following the SMIC18 and G-SMIC22 applications, this factor is taken as 0.5 in all the simulations. The negative sign in Eq. , and T , catchment lithology (L) that accounts for its soil type and erodibility, a reservoir trapping-efficiency factor (I) is expressed as:Projected changes in climate and anthropogenic activities will change fluvial sediment supply to the coasts46. However, Balthazar et al.44 demonstrate that this 47. Following previous G-SMIC applications at three case studies22, HFPI data is used in this study to represent human-induced erosion factor (The BQART model uses nation-wide GDP/per capita and population density to approximate the human-induced erosion factor (T), annual cumulative runoff (Q), change in global mean sea-level  (see under Model inputs associated with climate change and human activities). T and Q inputs are then stochastically generated from the fitted joint-probability distributions . Annual stochastic model inputs of global mean sea-level change  are directly related to increases in temperature, here, a direct relationship between T and 52. To achieve this direct relationship, percentiles of each annual mean temperature value are calculated. These percentiles and fitted triangular distributions of 22, CEC system settings by 2019 are considered as the reference conditions. Climatic conditions over the last decade  are used to obtain the reference T and Q values, to eliminate biased representation of the baseline climate that would likely have occurred if temperature and runoff values for only 2019 were used as the baseline.First, the stochastic model inputs are created for the study period considered (2020–2100). Here, per RCP, annual joint probability distributions are fitted between 18, where the change in total sediment volume exchange (D is the depth of closure (m). The extent of The above computed stochastic and other model inputs Table  are then10, expressed as:E is a correction factor for the Bruun effect that varies randomly within a fitted triangular distribution of minimum, maximum, and median values of 0.1, 1.0, and 0.75, respectively, 54, and SLR is the projected sea-level rise. When considering long-term shoreline change, the active profile should ideally span from the base of the shoreface to the back of overwash deposition, if present54. However, due to the lack of local measurements at the CEC systems considered, the global estimates of active profile slopes presented by Athanasiou et al. (2019)55 are used in this study. An illustration of the method adopted to determine the shoreline position change by 2100 (relative to present-day) is presented in Figs. Subsequently, the shoreline retreat due to the Bruun effect is superimposed on 18. In addition, G-SMIC projections assume that any sediment exported from an estuary is coarse enough to remain in the high-energy nearshore system. However, fluvial sediment loads in some systems include finer sediment, which can contribute to sediment budgets for estuaries but not shorelines. In sediment exporting systems that contain a high percentage of fine fluvial sediments, a proportion of the exported sediment volume may be lost to the sea. In such situations, the shoreline progradation (or the offsetting of the Bruun effect) computed by G-SMIC is likely to be an overestimate. Furthermore, the model application presented here assumes that there are no physical obstructions  to shoreline retreat at the CEC systems considered, which is often not the case for developed coastlines. Such physical obstructions to shoreline retreat are present at about 60% of the CEC systems considered here. Given these assumptions, in essence, what G-SMIC computes is the “potential” shoreline position change. However, to improve readability, ‘shoreline position change’ is used throughout the manuscript when referring to ‘potential shoreline position change’.The change in shoreline position computed by G-SMIC assumes no scarcity of sediment supply from the coast or the catchment. However, this assumption may not hold in some situations: e.g., when a basin consists of a stiff clay bed, when the nearshore zone consists of non-erodible rock covered with a thin layer of sand, when large volumes of sand enter embayed beaches due to episodic headland bypassing22. Therefore, if it is desired to compute absolute shoreline positions in a future period, it is necessary to superimpose the reference rate of shoreline changes9, arising from the sediment balance during the reference period, to those obtained from G-SMIC applications. G-SMIC will only provide an absolute shoreline position in a future period when the projected rate of shoreline change is much larger than those under reference conditions.It is important to note that the shoreline changes calculated in the G-SMIC model represent the change in shoreline position due to future variations in fluvial sediment supply, river discharge, and sea level56. These river basin extents are then used in conjunction with one arc-second resolution digital elevation model obtained from USGS Earth Explorer tool57 and Human FootPrint Index data47 to determine the catchment relief (R) and human-induced erosion factor  for the selected systems are obtained from the global map presented together with the BQART model development46. The depth of closure and the active coastal-profile slope values at the CEC systems are obtained from a published global dataset55. The estuary surface-area values presented in DIVA11, UK32, and NSW33 estuary datasets were checked against a published global estuary dataset60 to ensure the reliability of the estuary surface-area values used in the model applications, as this parameter affects two of the three components  for the linear regression model development are obtained by combining three estuary databases from UK32, USA62, and New South Wales 33. The final combined dataset used for regression model development contained 324 estuary systems.Connecting river catchment areas of the selected inlet-estuary systems are determined from the river network and basin information given by Lehner et al. (2008)63). Time series of minimum, median and maximum projections of global mean sea-level rise per RCP are obtained from Nicholls et al. (2014)50. Annual stochastic model inputs of human-induced erosion factor , 15 (median), and 20 (maximum) percent of the reference condition. It should be noted that different methods of translating present-day HFPI into the future would result in diverse projections of fluvial sediment supply from the river catchments. A dedicated study investigating different socio-economic pathways, including changes in land use pattern, reservoir sediment trapping, and the introduction of flood control/water diversion structures, could provide more detailed representations of these increased human-induced impacts on the environment.Catchment wide mean annual temperature and annual cumulative runoff values for the period 2010–2100 are obtained from four GCMs . The selection of the above four GCMs is based on the availability of temperature and runoff projections for all RCPs over the 2010–2100 interval, the spatial resolution of the GCM outputs  takes about 15 min on a standard 4-core laptop.Supplementary Information.