Managing surgical demand when needs outstrip resource: qualitative investigation of colorectal cancer surgery provision in the first wave of the COVID-19 pandemic.

BACKGROUND: At the onset of the COVID-19 pandemic, elective surgical provision was severely affected by the need for hospital reorganization to care for critically ill patients. In response, National Health Service (NHS) England issued national guidance proposing acceptable time intervals for postponing different types of surgical procedure. This study reports healthcare professionals' private accounts of the strategies adopted to manage the imbalance of demand and resource, using colorectal cancer surgery as a case study. METHODS: Twenty-seven semistructured interviews were conducted with healthcare professionals between June and November 2020. A key informant sampling approach was used, followed by snowballing to achieve maximum regional variation across the UK. Data were analysed thematically using the constant comparison approach. RESULTS: In the context of considerable resource constraint, surgical teams overcame challenges to continue elective cancer provision. They achieved this by pursuing a combination of strategies: relocating surgical services; prioritizing patients within and across surgical specialties; adapting patient treatment plans; and introducing changes to surgical team working practices. Despite national guidance, prioritization decisions were framed as complex, and the most challenging of the strategies to implement, both practically and emotionally. CONCLUSION: There is a need to better support surgeons tasked with prioritizing patients when capacity exceeds demand.

SARS-CoV2 infectivity is potentially modulated by host redox status.

The current coronavirus disease (COVID-19) outbreak caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV2) has emerged as a threat to global social and economic systems. Disparity in the infection of SARS-CoV2 among host population and species is an established fact without any clear explanation. To initiate infection, viral S-protein binds to the Angiotensin-Converting Enzyme 2 (ACE2) receptor of the host cell. Our analysis of retrieved amino acid sequences deposited in data bases shows that S-proteins and ACE2 are rich in cysteine (Cys) residues, many of which are conserved in various SARS-related coronaviruses and participate in intra-molecular disulfide bonds. High-resolution protein structures of S-proteins and ACE2 receptors highlighted the probability that two of these disulfide bonds are potentially redox-active, facilitating the primal interaction between the receptor and the spike protein. Presence of redox-active disulfides in the interacting parts of S-protein, ACE2, and a ferredoxin-like fold domain in ACE2, strongly indicate the role of redox in COVID-19 pathogenesis and severity. Resistant animals lack a redox-active disulfide (Cys133-Cys141) in ACE2 sequences, further strengthening the redox hypothesis for infectivity. ACE2 is a known regulator of oxidative stress. Augmentation of cellular oxidation with aging and illness is the most likely explanation of increased vulnerability of the elderly and persons with underlying health conditions to COVID-19.

Disability-adjusted life years (DALYs) due to the direct health impact of COVID-19 in India, 2020.

COVID-19 has affected all countries. Its containment represents a unique challenge for India due to a large population (> 1.38 billion) across a wide range of population densities. Assessment of the COVID-19 disease burden is required to put the disease impact into context and support future pandemic policy development. Here, we present the national-level burden of COVID-19 in India in 2020 that accounts for differences across urban and rural regions and across age groups. Input data were collected from official records or published literature. The proportion of excess COVID-19 deaths was estimated using the Institute for Health Metrics and Evaluation, Washington data. Disability-adjusted life years (DALY) due to COVID-19 were estimated in the Indian population in 2020, comprised of years of life lost (YLL) and years lived with disability (YLD). YLL was estimated by multiplying the number of deaths due to COVID-19 by the residual standard life expectancy at the age of death due to the disease. YLD was calculated as a product of the number of incident cases of COVID-19, disease duration and disability weight. Scenario analyses were conducted to account for excess deaths not recorded in the official data and for reported COVID-19 deaths. The direct impact of COVID-19 in 2020 in India was responsible for 14,100,422 (95% uncertainty interval [UI] 14,030,129-14,213,231) DALYs, consisting of 99.2% (95% UI 98.47-99.64%) YLLs and 0.80% (95% UI 0.36-1.53) YLDs. DALYs were higher in urban (56%; 95% UI 56-57%) than rural areas (44%; 95% UI 43.4-43.6) and in men (64%) than women (36%). In absolute terms, the highest DALYs occurred in the 51-60-year-old age group (28%) but the highest DALYs per 100,000 persons were estimated for the 71-80 years old age group (5481; 95% UI 5464-5500 years). There were 4,815,908 (95% UI 4,760,908-4,924,307) DALYs after considering reported COVID-19 deaths only. The DALY estimations have direct and immediate implications not only for public policy in India, but also internationally given that India represents one sixth of the world's population.

Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities.

Management of coronavirus disease 2019 (COVID-19) in India is a top government priority. However, there is a lack of COVID-19 adjusted case fatality risk (aCFR) estimates and information on states with high aCFR. Data on COVID-19 cases and deaths in the first pandemic wave and 17 state-specific geodemographic, socio-economic, health and comorbidity-related factors were collected. State-specific aCFRs were estimated, using a 13-day lag for fatality. To estimate country-level aCFR in the first wave, state estimates were meta-analysed based on inverse-variance weighting and aCFR as either a fixed- or random-effect. Multiple correspondence analyses, followed by univariable logistic regression, were conducted to understand the association between aCFR and geodemographic, health and social indicators. Based on health indicators, states likely to report a higher aCFR were identified. Using random- and fixed-effects models, cumulative aCFRs in the first pandemic wave on 27 July 2020 in India were 1.42% (95% CI 1.19%-1.70%) and 2.97% (95% CI 2.94%-3.00%), respectively. At the end of the first wave, as of 15 February 2021, a cumulative aCFR of 1.18% (95% CI 0.99%-1.41%) using random and 1.64% (95% CI 1.64%-1.65%) using fixed-effects models was estimated. Based on high heterogeneity among states, we inferred that the random-effects model likely provided more accurate estimates of the aCFR for India. The aCFR was grouped with the incidence of diabetes, hypertension, cardiovascular diseases and acute respiratory infections in the first and second dimensions of multiple correspondence analyses. Univariable logistic regression confirmed associations between the aCFR and the proportion of urban population, and between aCFR and the number of persons diagnosed with diabetes, hypertension, cardiovascular diseases and stroke per 10,000 population that had visited NCD (Non-communicable disease) clinics. Incidence of pneumonia was also associated with COVID-19 aCFR. Based on predictor variables, we categorised 10, 17 and one Indian state(s) expected to have a high, medium and low aCFR risk, respectively. The current study demonstrated the value of using meta-analysis to estimate aCFR. To decrease COVID-19 associated fatalities, states estimated to have a high aCFR must take steps to reduce co-morbidities.

Public health interventions slowed but did not halt the spread of COVID-19 in India.

The government of India implemented social distancing interventions to contain the COVID-19 epidemic. However, effects of these interventions on epidemic dynamics are yet to be understood. Rates of laboratory-confirmed COVID-19 infections per day and effective reproduction number (Rt ) were estimated for 7 periods (Pre-lockdown, Lockdown Phases 1 to 4 and Unlock 1-2) according to nationally implemented interventions with phased relaxation. Adoption of these interventions was estimated using Google mobility data. Estimates at the national level and for 12 Indian states most affected by COVID-19 are presented. Daily case rates ranged from 0.03 to 285.60/10 million people across 7 discrete periods in India. From 18 May to 31 July 2020, the NCT of Delhi had the highest case rate (999/10 million people/day), whereas Madhya Pradesh had the lowest (49/10 million/day). Average Rt was 1.99 (95% CI 1.93-2.06) and 1.39 (95% CI 1.38-1.40) for the entirety of India during the period from 22 March 2020 to 17 May 2020 and from 18 May 2020 to 31 July 2020, respectively. Median mobility in India decreased in all contact domains during the period from 22 March 2020 to 17 May 2020, with the lowest being 21% in retail/recreation, except home which increased to 129% compared to the 100% baseline value. Median mobility in the 'Grocery and Pharmacy' returned to levels observed before 22 March 2020 in Unlock 1 and 2, and the enhanced mobility in the Pharmacy sector needs to be investigated. The Indian government imposed strict contact mitigation, followed by a phased relaxation, which slowed the spread of COVID-19 epidemic progression in India. The identified daily COVID-19 case rates and Rt will aid national and state governments in formulating ongoing COVID-19 containment plans. Furthermore, these findings may inform COVID-19 public health policy in developing countries with similar settings to India.

Indian Resuscitation Council (IRC) suggested guidelines for Comprehensive Cardiopulmonary Life Support (CCLS) for suspected or confirmed coronavirus disease (COVID-19) patient.

Management of the recent outbreak of the novel coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) remains challenging. The challenges are not only limited to its preventive strategies, but also extend to curative treatment, and are amplified during the management of critically ill patients with COVID-19. Older persons with comorbidities like diabetes mellitus, cardiac diseases, hepatic impairment, renal disorders and respiratory pathologies or immune impairing conditions are more vulnerable and have a higher mortality from COVID-19. Earlier, the Indian Resuscitation Council (IRC) had proposed the Comprehensive Cardiopulmonary Life Support (CCLS) for management of cardiac arrest victims in the hospital setting. However, in patients with COVID-19, the guidelines need to be modified,due to various concerns like differing etiology of cardiac arrest, virulence of the virus, risk of its transmission to rescuers, and the need to avoid or minimize aerosolization from the patient due to various interventions. There is limited evidence in these patients, as the SARS-CoV-2 is a novel infection and not much literature is available with high-level evidence related to CPR in patients of COVID-19. These suggested guidelines are a continuum of CCLS guidelines by IRC with an emphasis on the various challenges and concerns being faced during the resuscitative management of COVID-19 patients with cardiopulmonary arrest.