1508Comparison of two waves of the COVID-19 pandemic in Rural Bikaner: A record based analysis

Abstract Background In comparison to the slow progression of first wave of the COVID-19 pandemic, by March 2021, the second wave appeared to be much more aggressive with many more cases. We aimed to evaluate reported COVID-19 epidemiology data to better understand the pandemic's progression in Rural Bikaner. Methods A record-based descriptive analysis study between first and second covid-19 waves, on the variables of interest including positivity rates, case fatality rate, demographic profile of positive patients. Results the daily reported cases at the peak of the first wave in rural area in 2020 was 20-25 cases per day and in second wave number of cases was increased double to first wave. Death rate and severity was also increased in second wave. In second wave the mean age of positive patients was decreased. Conclusions In our analysis rural area had a more severe second wave of the COVID-19 pandemic than the first and highlights the importance of examining multiple epidemiological variables down to the regional and country levels over time. These country-specific and regional results informed the implementation of continent-wide initiatives and supported equitable distribution of supplies and technical assistance. Monitoring and analysis of these data over time are essential for continued situational awareness, especially as Member States attempt to balance controlling COVID-19 transmission with ensuring stable economies and livelihoods. Key messages Demographic profile of rural area plays a key role in spread of Covid-19 cases.

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Evolution of case fatality rates in the second wave of coronavirus in England: effects of false positives, a Variant of Concern and vaccination.

10.1101/2021.04.14.21255385 ◽

2021 ◽

Author(s):

James A Ackland ◽

Graeme J Ackland ◽

David J Wallace

Keyword(s):

False Positive ◽

Age Groups ◽

Case Fatality ◽

Potential Effect ◽

Time To Death ◽

New Variant ◽

Government Data ◽

The Uk ◽

Second Wave ◽

Over Time

Objective: To track the statistical case fatality rate (CFR) in the second wave of the UK coronavirus outbreak, and to understand its variations over time. Design: Publicly available UK government data and clinical evidence on the time between first positive PCR test and death are used to determine the relationships between reported cases and deaths, according to age groups and across regions in England. Main Outcome Measures: Estimates of case fatality rates and their variations over time. Results: Throughout October and November 2020, deaths in England can be broadly understood in terms of CFRs which are approximately constant over time. The same CFRs prove a poor predictor of deaths when applied back to September, when prevalence of the virus was comparatively low, suggesting that the potential effect of false positive tests needs to be taken into account. Similarly, increasing CFRs are needed to match cases to deaths when projecting the model forwards into December. The growth of the S gene dropout VOC in December occurs too late to explain this increase in CFR alone, but at 33% increased mortality, it can explain the peak in deaths in January. On our analysis, if there were other factors responsible for the higher CFRs in December and January, 33% would be an upper bound for the higher mortality of the VOC. From the second half of January, the CFRs for older age groups show a marked decline. Since the fraction of the VOC has not decreased, this decline is likely to be the result of the rollout of vaccination. However, due to the rapidly decreasing nature of the raw cases data (likely due to a combination of vaccination and lockdown), any imprecisions in the time-to-death distribution are greatly exacerbated in this time period, rendering estimates of vaccination effect imprecise. Conclusions: The relationship between cases and deaths, even when controlling for age, is not static through the second wave of coronavirus in England. An apparently anomalous low case-fatality ratio in September can be accounted for by a modest 0.4% false-positive fraction. The large jump in CFR in December can be understood in terms of a more deadly new variant B1.1.7, while a decline in January correlates with vaccine roll-out, suggesting that vaccine reduce the severity of infection, as well as the risk.

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Visualising regional disparities in the risk of COVID-19 at different phases of lockdown in England

Environment and Planning A Economy and Space ◽

10.1177/0308518x20984165 ◽

2021 ◽

pp. 0308518X2098416

Author(s):

Yu-Wang Chen ◽

Lei Ni ◽

Dong-Ling Xu ◽

Jian-Bo Yang

Keyword(s):

United Kingdom ◽

Sharp Increase ◽

Regional Disparities ◽

Local Authorities ◽

The North ◽

Second Wave ◽

Over Time

Since late January 2020 when the first coronavirus case reached England, United Kingdom, the coronavirus disease 2019 (COVID-19) has spread rapidly and widely across all local authorities (LAs) in England. In this featured graphic, we visualise how COVID-19 severity changes nationally and locally from 30 January to 23 November 2020. The geo-visualisation shows that there have been large regional disparities in the severity of the outbreak, and the epicentres have shifted from Greater London, Leicester, to the North of England and remained in the North during pre-lockdown, post-lockdown, easing lockdown and second national lockdown phases. We further find that the increase in the testing capacity may partially explain the sharp increase in the confirmed cases during the second wave of the pandemic. However, the disparities in the severity of COVID-19 (i.e., confirmed cases and deaths) among LAs in England become more significant over time. It further sheds light on the necessity of establishing decisive and timely responses to cope with local pandemic situations.

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Estimating effective infection fatality rates during the course of the COVID-19 pandemic in Germany

BMC Public Health ◽

10.1186/s12889-021-11127-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Christian Staerk ◽

Tobias Wistuba ◽

Andreas Mayr

Keyword(s):

Age Distribution ◽

Age Groups ◽

Case Fatality ◽

Time Dependent ◽

Temporary Increase ◽

International Studies ◽

Age Group ◽

Fatality Rate ◽

Substantial Fraction ◽

Second Wave

Abstract Background The infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID-19) is one of the most discussed figures in the context of this pandemic. In contrast to the case fatality rate (CFR), the IFR depends on the total number of infected individuals – not just on the number of confirmed cases. In order to estimate the IFR, several seroprevalence studies have been or are currently conducted. Methods Using German COVID-19 surveillance data and age-group specific IFR estimates from multiple international studies, this work investigates time-dependent variations in effective IFR over the course of the pandemic. Three different methods for estimating (effective) IFRs are presented: (a) population-averaged IFRs based on the assumption that the infection risk is independent of age and time, (b) effective IFRs based on the assumption that the age distribution of confirmed cases approximately reflects the age distribution of infected individuals, and (c) effective IFRs accounting for age- and time-dependent dark figures of infections. Results Effective IFRs in Germany are estimated to vary over time, as the age distributions of confirmed cases and estimated infections are changing during the course of the pandemic. In particular during the first and second waves of infections in spring and autumn/winter 2020, there has been a pronounced shift in the age distribution of confirmed cases towards older age groups, resulting in larger effective IFR estimates. The temporary increase in effective IFR during the first wave is estimated to be smaller but still remains when adjusting for age- and time-dependent dark figures. A comparison of effective IFRs with observed CFRs indicates that a substantial fraction of the time-dependent variability in observed mortality can be explained by changes in the age distribution of infections. Furthermore, a vanishing gap between effective IFRs and observed CFRs is apparent after the first infection wave, while an increasing gap can be observed during the second wave. Conclusions The development of estimated effective IFR and observed CFR reflects the changing age distribution of infections over the course of the COVID-19 pandemic in Germany. Further research is warranted to obtain timely age-stratified IFR estimates, particularly in light of new variants of the virus.

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A retrospective cohort study of 27,049 polytraumatized patients age 60 and above: identifying changes over 16 years

European Geriatric Medicine ◽

10.1007/s41999-021-00546-9 ◽

2021 ◽

Author(s):

Y. Kalbas ◽

M. Lempert ◽

F. Ziegenhain ◽

J. Scherer ◽

V. Neuhaus ◽

...

Keyword(s):

Length Of Stay ◽

Injury Severity ◽

Descriptive Analysis ◽

Patient Characteristics ◽

Severe Trauma ◽

Diagnostic Tools ◽

Trauma Patients ◽

Treatment Algorithms ◽

Injured Patients ◽

Over Time

Abstract Purpose The number of severely injured patients exceeding the age of 60 has shown a steep increase within the last decades. These patients present with numerous co-morbidities, polypharmacy, and increased frailty requiring an adjusted treatment approach. In this study, we establish an overview of changes we observed in demographics of older severe trauma patients from 2002 to 2017. Methods A descriptive analysis of the data from the TraumaRegister DGU® (TR-DGU) was performed. Patients admitted to a level one trauma center in Germany, Austria and Switzerland between 2002 and 2017, aged 60 years or older and with an injury severity score (ISS) over 15 were included. Patients were stratified into subgroups based on the admission: 2002–2005 (1), 2006–2009 (2), 2010–2013 (3) and 2014–2017 (4). Trauma and patient characteristics, diagnostics, treatment and outcome were compared. Results In total 27,049 patients with an average age of 73.9 years met the inclusion criteria. The majority were males (64%), and the mean ISS was 27.4. The proportion of patients 60 years or older [(23% (1) to 40% (4)] rose considerably over time. Trauma mechanisms changed over time and more specifically low falls (< 3 m) rose from 17.6% (1) to 40.1% (4). Altered injury patterns were also identified. Length-of-stay decreased from 28.9 (1) to 19.5 days (4) and the length-of-stay on ICU decreased from 17.1 (1) to 12.7 days (4). Mortality decreased from 40.5% (1) to 31.8% (4). Conclusion Length of stay and mortality decreased despite an increase in patient age. We ascribe this observation mainly to increased use of diagnostic tools, improved treatment algorithms, and the implementation of specialized trauma centers for older patients allowing interdisciplinary care.

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Trender i insidens og mortalitet av kreft i Norge

Norsk Epidemiologi ◽

10.5324/nje.v11i2.554 ◽

2009 ◽

Vol 11 (2) ◽

Author(s):

Steinar Tretli ◽

Trude Eid Robsahm ◽

Elisabeth Svensson

Keyword(s):

Case Fatality ◽

English Summary ◽

Classification Rules ◽

Font Size ◽

Treatment Results ◽

Mortality Trends ◽

Calendar Time ◽

Incidence And Mortality ◽

Short Time ◽

Over Time

ENGLISH SUMMARYTretli S, Robsahm TE, Svensson E. Time trends in cancer incidence and mortality in Norway.Nor J Epidemiol 2001; 11 (2): 177-185.The aim of this study is to decribe the trends in incidence and mortality of cancer by calendar time.Most types of cancer, except those with high case fatality short time after the diagnosis, demonstrate alarger increase in incidence than in mortality over time. For persons below 70 years of age during theperiod 1931-95 the mortality rate has been close to constant. Obviously, the mortality of lung andstomach cancer has changed over time, however, these have changed in different direction and almostlevelled out. In this paper, it is discussed how registration routines, classification rules, treatment resultsand the basis of the diagnosis can influence the incidence and mortality trends.

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Premature Mortality in Canada

University of Toronto Journal of Public Health ◽

10.33137/utjph.v1i1.33812 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Catherine Liang ◽

Emmalin Buajitti ◽

Laura Rosella

Keyword(s):

Cause Of Death ◽

Descriptive Analysis ◽

Premature Mortality ◽

Mortality Rates ◽

Vital Statistics ◽

Income Quintile ◽

Mortality Trends ◽

Premature Deaths ◽

Central Canada ◽

Over Time

Introduction: Premature mortality (deaths before age 75) is a well-established metric of population health and health system performance. In Canada, underlying differences between provinces/territories present a need for stratified mortality trends. Methods: Using data from the Canadian Vital Statistics Database, a descriptive analysis of sex-specific adult premature deaths over 1992-2015 was conducted by province, census divisions (CD), socioeconomic status (SES), age, and underlying cause of death. Premature mortality rates were calculated as the number of deaths per 100,000 individuals aged 18 to 74, per 8-year era. SES was measured using the income quintile of the neighbourhood of residence. Absolute and relative inequalities were respectively summarized using slope and relative indices of inequality, produced via unadjusted linear regression of the mortality rate on income rank. Results: Premature mortality in Canada declined by 21% for males and 13% for females between 1992-1999 and 2008-2015. The greatest reductions were in Central Canada, while Newfoundland saw notable increases. CD-level improvements appeared mostly in the southern half of Canada. As of 2008-2015, Newfoundland, Nova Scotia, and Nunavut had the highest mortality rates. Low area-level income was associated with higher mortality. SES inequalities grew over time. Newfoundland’s between-quintile differences rose from 1292 to 2389 deaths per 100k males, or 1.33 to 2.12-fold, and 586 to 1586 per 100k females, or 1.24 to 1.74-fold. In 2008-2015, mortality rates of the bottom quintile in Manitoba and Saskatchewan were more than 2.5 times those of the top. Mortality increased with age, and varied regionally. Low mortality in Central Canada and BC, and high mortality in the Territories were consistent across eras and sexes. Cause of death distributions shifted with age and sex, with more external deaths in younger males. Conclusion: Improvements were seen in adult premature mortality rates over time, but were unequal across geographies. Evidence exists for growing socioeconomic disparities in mortality.

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Web Application for Statistical Tracking and Predicting the Evolution of Active Cases with the Novel Coronavirus (SARS-CoV-2)

International Journal of Modeling and Optimization ◽

10.7763/ijmo.2021.v11.780 ◽

2021 ◽

pp. 70-74

Author(s):

Iulia Clitan ◽

◽

Adela Puscasiu ◽

Vlad Muresan ◽

Mihaela Ligia Unguresan ◽

...

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Web Application ◽

Point Of View ◽

The Novel ◽

End User ◽

First Case ◽

Novel Coronavirus ◽

Second Wave ◽

Over Time

Since February 2020, when the first case of infection with SARS COV-2 virus appeared in Romania, the evolution of COVID-19 pandemic continues to have an ascending allure, reaching in September 2020 a second wave of infections as expected. In order to understand the evolution and spread of this disease over time and space, more and more research is focused on obtaining mathematical models that are able to predict the evolution of active cases based on different scenarios and taking into account the numerous inputs that influence the spread of this infection. This paper presents a web responsive application that allows the end user to analyze the evolution of the pandemic in Romania, graphically, and that incorporates, unlike other COVID-19 statistical applications, a prediction of active cases evolution. The prediction is based on a neural network mathematical model, described from the architectural point of view.

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Quantifying the relationship between lockdowns, mobility, and effective reproduction number (Rt) during the COVID-19 pandemic in the Greater Toronto Area

BMC Public Health ◽

10.1186/s12889-021-11684-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Christopher Dainton ◽

Alexander Hay

Keyword(s):

Public Health ◽

Secular Trend ◽

Reproduction Number ◽

Time Lag ◽

Correlation Coefficients ◽

Greater Toronto Area ◽

Global Mobility ◽

The Relationship ◽

Second Wave ◽

Over Time

Abstract Background The effectiveness of lockdowns in mitigating the spread of COVID-19 has been the subject of intense debate. Data on the relationship between public health restrictions, mobility, and pandemic growth has so far been conflicting. Objective We assessed the relationship between public health restriction tiers, mobility, and COVID-19 spread in five contiguous public health units (PHUs) in the Greater Toronto Area (GTA) in Ontario, Canada. Methods Weekly effective reproduction number (Rt) was calculated based on daily cases in each of the five GTA public health units between March 1, 2020, and March 19, 2021. A global mobility index (GMI) for each PHU was calculated using Google Mobility data. Segmented regressions were used to assess changes in the behaviour of Rt over time. We calculated Pearson correlation coefficients between GMI and Rt for each PHU and mobility regression coefficients for each mobility variable, accounting for time lag of 0, 7, and 14 days. Results In all PHUs except Toronto, the most rapid decline in Rt occurred in the first 2 weeks of the first province-wide lockdown, and this was followed by a slight trend to increased Rt as restrictions decreased. This trend reversed in all PHUs between September 6th and October 10th after which Rt decreased slightly over time without respect to public health restriction tier. GMI began to increase in the first wave even before restrictions were decreased. This secular trend to increased mobility continued into the summer, driven by increased mobility to recreational spaces. The decline in GMI as restrictions were reintroduced coincides with decreasing mobility to parks after September. During the first wave, the correlation coefficients between global mobility and Rt were significant (p < 0.01) in all PHUs 14 days after lockdown, indicating moderate to high correlation between decreased mobility and decreased viral reproduction rates, and reflecting that the incubation period brings in a time-lag effect of human mobility on Rt. In the second wave, this relationship was attenuated, and was only significant in Toronto and Durham at 14 days after lockdown. Conclusions The association between mobility and COVID-19 spread was stronger in the first wave than the second wave. Public health restriction tiers did not alter the existing secular trend toward decreasing Rt over time.

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Descriptive Analysis: Television Uses among Community in Rural Area, Sarawak

International Journal of Academic Research in Business and Social Sciences ◽

10.6007/ijarbss/v11-i11/11605 ◽

2021 ◽

Vol 11 (11) ◽

Author(s):

Ribka Alan ◽

Md Salleh Hassan ◽

Jusang Bolong ◽

Mohd Nizam Osman ◽

Philip Lepun ◽

...

Keyword(s):

Rural Area ◽

Descriptive Analysis

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Customers’ Awareness and Attitude in Purchasing Herbal Products in Kuala Terengganu

Journal Of Agrobiotechnology ◽

10.37231/jab.2021.12.1s.266 ◽

2021 ◽

Vol 12 (1S) ◽

pp. 14-20

Author(s):

Nurul Nabilah Ramli ◽

Nalini Arumugam

Keyword(s):

Descriptive Analysis ◽

Chi Square ◽

Statistical Tool ◽

Herbal Products ◽

Demographic Profile ◽

Job Opportunities ◽

Gender Education ◽

Buying Behaviour ◽

Chi Square Analysis ◽

Scale Design

The growth of the herbal industry in Malaysia has sparked the economy and it is identified as one of the new incomes to the country. This industry is expected to bring more job opportunities to the citizen in Malaysia. Demand for herbal products also has been increasing for the past few years. This study aims to contribute the general understanding of customer awareness and attitude in purchasing herbal products and show the relationship between demographic profile and customer awareness and attitude in purchasing herbal products. This study was conducted in Kuala Terengganu and the online Google form was used as study instrument for data collection. There were around 150 respondents that answered the questionnaire. The questionnaire was divided into 2 sections. The first section which is section A, discusses the socio-demographic profile of the respondents. The second part which is section B were in Likert-scale design to obtain the customers’ awareness and attitude in purchasing herbal products. Data that were collected in this study were analysed using a statistical tool which is Statistical Package for Social Science (SPSS). The Descriptive analysis was used to provide simple summaries of the respondents while the Cross-tab with Chi-Square analysis was chosen to be used to test the statistical independence of the variables. From this study, the variable Age is the only demographic factor that shows a significant relationship towards the customers’ awareness and attitude in purchasing herbal products. Other demographic factors such as Gender, Education, Occupation, and Income shows independent and no relationship towards the customers’ awareness and attitude in purchasing herbal products. This study could be beneficial to researchers and marketers in understanding customer buying behaviour.

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