A comparative study of wave 1 and 2 mortality factors among COVID-19 patients in a level 3 medical college hospital in Saharanpur India

Background: In COVID-19, second wave death rate climbed in several states of India including Uttar Pradesh with relatively high number of casualties as compared to first wave. The literature lacks scientific reason behind this. Aims and Objectives: The present study aimed toward detection of mortality factors for COVID-19 patients from a Level 3 Medical College Hospital in both waves in Western UP District in India so as to find a better treatment strategies for COVID-19 patients for possible next 3rd COVID-19 wave. Materials and Methods: This study is aimed to find any difference in Wave 1 and 2 mortality factors among COVID-19 patients in a Level 3 Medical College Hospital in western up district Saharanpur in India from April 1, 2020 to Sep 30, 2021. Results: Although there were more number of deaths in Wave 2 (n=537) as compared to Wave 1 (n=172), deaths even after treatment of COVID-19 were more in wave 1 (72%) as compared to wave 2 (67.9%), but it was not statistically significant (P>0.05). In COVID -19, Wave 1 most of the patients were elderly (aged >60 years) (40.3%) whereas in Wave 2 45-60 years age group were more affected (40.5%). Presence of more than 2 comorbidities was also seen more in wave 1 (37.1%) as compared to Wave 2 (23.8%). In COVID-19, both Waves (1 and 2) the most common co-morbidity was Type2DM (52% and 36.4%, respectively), but DM was found to be more in COVID+ve patients of Wave 1 as compared to wave 2 COVID+ve patients whereas any CARDIAC abnormality comorbidity was found more in Wave 2 patients as compared to Wave 1 (14.2% vs. 12.1%). Conclusion: Possibly COVID-19 virus operated differently at Cardio-respiratory system leading to increased mortality in COVID-19 patients in Wave 2. [ FROM AUTHOR] Copyright of Asian Journal of Medical Sciences is the property of Manipal Colleges of Medical Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Comparison of Outcomes of In-Centre Haemodialysis Patients between the 1st and 2nd COVID-19 Outbreak in England, Wales, and Northern Ireland: A UK Renal Registry Analysis.

INTRODUCTION: This retrospective cohort study compares in-centre haemodialysis (ICHD) patients' outcomes between the 1st and 2nd waves of the COVID-19 pandemic in England, Wales, and Northern Ireland. METHODS: All people aged ≥18 years receiving ICHD at 31 December 2019, who were still alive and not in receipt of a kidney transplant at 1 March and who had a positive polymerase chain reaction test for SARS-CoV-2 between 1 March 2020 and 31 January 2021, were included. The COVID-19 infections were split into two "waves": wave 1 from March to August 2020 and wave 2 from September 2020 to January 2021. Cumulative incidence of COVID-19, multivariable Cox models for risk of positivity, median, and 95% credible interval of reproduction number in dialysis units were calculated separately for wave 1 and wave 2. Survival and hazard ratios for mortality were described with age- and sex-adjusted Kaplan-Meier plots and multivariable Cox proportional models. RESULTS: 4,408 ICHD patients had COVID-19 during the study period. Unadjusted survival at 28 days was similar in both waves (wave 1 75.6% [95% confidence interval [CI]: 73.7-77.5], wave 2 76.3% [95% CI 74.3-78.2]), but death occurred more rapidly after detected infection in wave 1. Long vintage treatment and not being on the transplant waiting list were associated with higher mortality in both waves. CONCLUSIONS: Risk of death of patients on ICHD treatment with COVID-19 remained unchanged between the first and second outbreaks. This highlights that this vulnerable patient group needs to be prioritized for interventions to prevent severe COVID-19, including vaccination, and the implementation of measures to reduce the risk of transmission alone is not sufficient.

Dynamic Panel Data Modeling and Surveillance of COVID-19 in Metropolitan Areas in the United States: Longitudinal Trend Analysis.

BACKGROUND: The COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence. OBJECTIVE: The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level. METHODS: Using a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: Minneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week. CONCLUSIONS: Three of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.

SARS-CoV-2 Surveillance System in Canada: Longitudinal Trend Analysis.

BACKGROUND: The COVID-19 global pandemic has disrupted structures and communities across the globe. Numerous regions of the world have had varying responses in their attempts to contain the spread of the virus. Factors such as public health policies, governance, and sociopolitical climate have led to differential levels of success at controlling the spread of SARS-CoV-2. Ultimately, a more advanced surveillance metric for COVID-19 transmission is necessary to help government systems and national leaders understand which responses have been effective and gauge where outbreaks occur. OBJECTIVE: The goal of this study is to provide advanced COVID-19 surveillance metrics for Canada at the country, province, and territory level that account for shifts in the pandemic including speed, acceleration, jerk, and persistence. Enhanced surveillance identifies risks for explosive growth and regions that have controlled outbreaks successfully. METHODS: Using a longitudinal trend analysis study design, we extracted 62 days of COVID-19 data from Canadian public health registries for 13 provinces and territories. We used an empirical difference equation to measure the daily number of cases in Canada as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: We compare the week of February 7-13, 2021, with the week of February 14-20, 2021. Canada, as a whole, had a decrease in speed from 8.4 daily new cases per 100,000 population to 7.5 daily new cases per 100,000 population. The persistence of new cases during the week of February 14-20 reported 7.5 cases that are a result of COVID-19 transmissions 7 days earlier. The two most populous provinces of Ontario and Quebec both experienced decreases in speed from 7.9 and 11.5 daily new cases per 100,000 population for the week of February 7-13 to speeds of 6.9 and 9.3 for the week of February 14-20, respectively. Nunavut experienced a significant increase in speed during this time, from 3.3 daily new cases per 100,000 population to 10.9 daily new cases per 100,000 population. CONCLUSIONS: Canada excelled at COVID-19 control early on in the pandemic, especially during the first COVID-19 shutdown. The second wave at the end of 2020 resulted in a resurgence of the outbreak, which has since been controlled. Enhanced surveillance identifies outbreaks and where there is the potential for explosive growth, which informs proactive health policy.

SARS-CoV-2 Wave Two Surveillance in East Asia and the Pacific: Longitudinal Trend Analysis.

BACKGROUND: The COVID-19 pandemic has had a profound global impact on governments, health care systems, economies, and populations around the world. Within the East Asia and Pacific region, some countries have mitigated the spread of the novel coronavirus effectively and largely avoided severe negative consequences, while others still struggle with containment. As the second wave reaches East Asia and the Pacific, it becomes more evident that additional SARS-CoV-2 surveillance is needed to track recent shifts, rates of increase, and persistence associated with the pandemic. OBJECTIVE: The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk, persistence, and weekly shifts, to better understand country risk for explosive growth and those countries who are managing the pandemic successfully. Existing surveillance coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed. We provide novel indicators to measure disease transmission. METHODS: Using a longitudinal trend analysis study design, we extracted 330 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in East Asia and the Pacific as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: The standard surveillance metrics for Indonesia, the Philippines, and Myanmar were concerning as they had the largest new caseloads at 4301, 2588, and 1387, respectively. When looking at the acceleration of new COVID-19 infections, we found that French Polynesia, Malaysia, and the Philippines had rates at 3.17, 0.22, and 0.06 per 100,000. These three countries also ranked highest in terms of jerk at 15.45, 0.10, and 0.04, respectively. CONCLUSIONS: Two of the most populous countries in East Asia and the Pacific, Indonesia and the Philippines, have alarming surveillance metrics. These two countries rank highest in new infections in the region. The highest rates of speed, acceleration, and positive upwards jerk belong to French Polynesia, Malaysia, and the Philippines, and may result in explosive growth. While all countries in East Asia and the Pacific need to be cautious about reopening their countries since outbreaks are likely to occur in the second wave of COVID-19, the country of greatest concern is the Philippines. Based on standard and enhanced surveillance, the Philippines has not gained control of the COVID-19 epidemic, which is particularly troubling because the country ranks 4th in population in the region. Without extreme and rigid social distancing, quarantines, hygiene, and masking to reverse trends, the Philippines will remain on the global top 5 list of worst COVID-19 outbreaks resulting in high morbidity and mortality. The second wave will only exacerbate existing conditions and increase COVID-19 transmissions.

Pharmacy response to COVID-19: lessons learnt from Canada.

When the first wave of COVID-19 hit in March 2020, health care professionals across Canada were challenged to quickly and efficiently adapt to change their work practices in these unprecedented times. Pharmacy professionals, being some of the very few front-line health care workers who remained accessible in person for patients, had to rapidly adopt critical changes in their pharmacies to respond in the best interest of their patients and their pharmacy staff. As challenging and demanding as such changes were, they provided pharmacists with invaluable lessons that would be imperative as the country enters a potentially more dangerous second wave. This article seeks to identify and summarize opportunities for improvement in pharmacy as learnt from the pandemic's first wave. Such areas include but are not limited to handling of drug shortage and addressing drug hoarding and stockpiling, providing physical and mental support for staff, timing of flu vaccine and COVID-19 screening/testing, collaboration between different health care sites as well as collaboration with patients and with other health care professionals, telemedicine and willingness to adopt innovative ideas, need for more staff training and more precise research to provide accurate information and finally the need for more organizational and workplace support. Learning from what went well and what did not work in the early stages of the pandemic is integral to ensure pharmacy professionals are better prepared to protect themselves and their patients amidst a second and possibly subsequent waves.