scholarly journals Incidence and Case-Fatality Ratio of COVID-19 infection in relation to Tobacco Smoking, Population Density and Age Demographics in the USA: could Particulate Matter derived from Tobacco Smoking act as a Vector for COVID-19 transmission?

2020 ◽  
Author(s):  
Yves Muscat Baron
Keyword(s):  
Particulate Matter ◽  
Population Density ◽  
Tobacco Smoking ◽  
Case Fatality ◽  
The State ◽  
Smoking Ban ◽  
Age Group ◽  
Case Fatality Ratio ◽  
Significant Difference ◽  
The Usa

ABSTRACTBACKGROUNDTobacco smoking is known to increase the risk for bacterial and viral respiratory infections and this also applies to second-hand smoking. Smoking has been shown to increase the severity of COVID-19 infection and the consequent risk for intra-tracheal ventilation in smokers. Tobacco smoking exposes the user and nearby individuals to very high concentrations of particulate matter in a short period of time. Genes appertaining to COVID-19 have been found adherent to particulate matter. Particulate matter has been shown to travel beyond the social distance of 2 metres up to 10 metres. COVID-19 related mortality has been linked to elevated atmospheric levels of the particulate matter, PM2.5. The aim of the study was to observe the incidence of infection rate and case fatality ratios in the USA, comparing States with partial bans on tobacco smoking, to States with more restrictive smoking regulation, exploring a possible link between smoke-related particulate matter and COVID-19 transmission.METHODOLOGYTwo groups of USA States, differentiated by the degree of smoking legislative restrictions, had a number of variables compared. The incidence of COVID-19 infection, case-fatality ratio and testing frequency were obtained from the John Hopkins Coronavirus Resource Centre. The degree of smoking bans in the USA States was obtained from the websites of the Nonsmokers Rights Foundation. The percentage of the State population which smokes was collected from the Centres of Disease Control database. Population density, Body Mass Index and population percentages of individuals 65+/75+years were obtained from databases concerning USA demographics.RESULTSWith the available data there was no significant difference in COVID-19 testing prevalence between the partial smoking ban group and the more restrictive regulated group. The incidence of COVID-19 infection in the States with limited bans on tobacco smoking was 2046/100,000 (sd+/-827) while the infection incidence in States with more restrictive rulings on tobacco smoking was 1660/100,000 (sd+/-686) (p<0.038). The population percentage of smokers in States with minor limitations to smoking was 18.3% (sd+/-3.28), while States with greater smoking restrictions had a smoking population percentage of 15.2% (sd+/-2.68) (p<0.0006).The two populations of both groups did not differ numerically (p<0.24) and numbered 157,820,000 in the partial smoking ban group and 161,439,356 in the more restrictive group. Population density correlated significantly with the case-fatality ratio (R=0.66 p<0.0001), as did the 75+year age group (R=0.29 p<0.04). Reflecting the possibility of trans-border transmission, the smoking status of adjacent partial smoking ban States may influence the COVID-19 incidence of bordering States (e.g. Utah) even if the smoking regulations of the latter were stricter than the former.Other factors that could impact the COVID-19 pandemic in the USA such as the State case-fatality ratio, population density, population percentage with elevated body mass index and the percentage of the state population aged 65years or above did not show any significant difference between both groups of States.CONCLUSIONStates in the USA with high levels of tobacco smoking and limited regulation had significantly higher rates of COVID-19 infection incidences than States with greater smoking restrictions. Population density and the age group of 75+years, showed a positive significant correlation with the case-fatality ratio. Besides the adverse effects of tobacco smoking on pulmonary defences, it would be interesting to explore the possibility of infection transmission via coronavirus-laden particulate matter from exhaled fumes derived from tobacco smoking.

scholarly journals Brief Epidemiological Report on Chikungunya Outbreak in Bihar, India in 2017: Implications for Control

2019 ◽  
pp. 1-6
Author(s):  
Ragini Mishra ◽  
Navin Mishra
Keyword(s):  
Disease Surveillance ◽  
Age Groups ◽  
Case Fatality ◽  
State Level ◽  
Health Department ◽  
The State ◽  
Control Measures ◽  
Age Group ◽  
The Status ◽  
Almost All

Aims: The present study was done to identify the epidemiology of the disease outbreak in Bihar in 2017 and suggest remedial measures for the prevention of possible future outbreaks of Chikungunya. Study Design:  Daily reports on Chikungunya were collected in prescribed format from the District Surveillance Unit, Integrated Disease Surveillance Programme (IDSP) that included case details from Govt. Medical Colleges and various Private Hospitals in the State. Place and Duration of Study: Index case of Chikungunya was reported in Bihar, India on 15 Feb 2017. After that, few scattered cases were reported till 23 Aug 2017. Cases started increasing from 24 Aug 2017 onwards. From 15 Feb till 31 Dec 2017, total 1223 cases were reported from 32 districts in Bihar. Methodology: The cases were analysed concerning time, place and person. Daily reporting on the health conditions of the cases and the status of the control measures like fogging and larvicidal spray in the affected area was monitored at the State level. Results: Case Fatality Rate (CFR) due to the disease was Nil in the State. The outbreak peak laid from 3-Nov to 12-Nov when 218 cases were reported. Out of 1223 cases, 100% cases were ELISA confirmed. Almost all age groups were affected, but the frequency was greater in the age group 21-30 (25%)> 31-40 (21%)>11-20 (19%). Males (61%) were more affected than females (39%). Out of the total 1223 cases, 100% of the cases were reported from Govt. institutions. State Health Department, Govt. of Bihar took many measures to limit the outbreak, and through strengthening the surveillance and response activities, transmission of the disease was curtailed in the State.     Conclusion: Patna district was most affected followed by Nalanda and Vaishali. Young adults of age group 21-30 were most affected. Males were more affected than females.

scholarly journals Epidemiological characteristics of salmoneilosis in Vojvodina

2005 ◽  
Vol 58 (3-4) ◽  
pp. 136-141 ◽  
Author(s):  
Vladimir Petrovic ◽  
Slavica Stefanovic ◽  
Predrag Djuric
Keyword(s):  
Case Fatality ◽  
Age Group ◽  
Salmonella Spp ◽  
Common Cause ◽  
Case Fatality Ratio ◽  
Epidemiological Method ◽  
Salmonella Infections ◽  
Egg Products ◽  
Epidemiological Characteristics ◽  
Annual Mortality

Introduction Salmonella infections are the most common food-born diseases transmitted from animals to humans. Epidemics of salmonellosis occur after eating improperly cooked contaminated foods. Material and methods A descriptive epidemiological method was used to analyze epidemiological characteristics of salmonellosis in Vojvodina in the period 1978-2003. Results During this period there were 26 851 cases of salmonellosis (mean annual incidence of 51.1/100 000 and mean annual mortality of 0.9/100 000). Mean annual case fatality ratio during this period was 0.1%. The specific incidence was highest in 12-24 month-old children (251/100 000), and lowest in the oldest age group (13.9/100 000). Lethality was highest in children younger than 1 year, and persons older than 50 years (84%). The number of cases registered in food-born epidemics makes 41.7% of all registered cases. A mayority of 772 registered epidemics were small epidemics among families, relatives and friends. The great epidemics with large number of infected persons were due to an industrial bakery with 1713 ill persons and a public restaurant with 311 ill persons during the 80 's. An epidemic was due to sandwiches from an industrial bakery distributed through the whole territory of Vojvodina with the highest incidence of salmonellosis in 1987 (137.7/100 000). The most common serotype was S. enteritidis (69.7%), but 68 more serotypes were isolated. The most common cause of epidemics were eggs and egg products (76.2%). Salmonella spp. was isolated from food in small number of epidemics (less than 20%), because epidemics were discovered usually 2 or 3 days after their beginning. .

scholarly journals Demography and dynamics of mountain nyala Tragelaphus buxtoni in the Bale Mountains National Park, Ethiopia

2010 ◽  
Vol 56 (6) ◽  
pp. 660-669 ◽  
Author(s):  
Yosef Mamo ◽  
Michelle A. Pinard ◽  
Afework Bekele
Keyword(s):  
Population Density ◽  
National Park ◽  
Group Composition ◽  
Anthropogenic Factors ◽  
Line Transect ◽  
Age Group ◽  
Adult Males ◽  
Population Decrease ◽  
Transect Sampling ◽  
Significant Difference

Abstract We studied the population dynamics of endangered mountain nyala Tragelaphus buxtoni between 2003-2005 in the Bale Mountains National Park. Line-transect sampling and total count methods were used to gather data on demographics and movement patterns. The population's age-group composition was 58% adults, 25% sub-adults, 9% juveniles, 5% calves and 3% unidentified with a female-male sex ratio of 2:1. Population density was found to be significantly different between the two sub-populations (Dinsho Sanctuary and Gaysay/Adelay). A significant difference was found for age-group composition across the two sub-populations except adult females, sub-adult males and calves. The Dinsho sub-population was an isolated group. Separation and containment of the mountain nyala population could have negatively affected their ability to search for habitat requirements and mates from distant areas. The population varied between 830-908 individuals (95% CI), a reduction of 45% from earlier reports. However, the mean population density increased due to contraction of the species' habitat range. We observed a population decrease of 2%-5% per year over the course of our study. Many of the assessed demographic parameters did not significantly change over the three years. This suggests that the decrease in nyala population was not due to random variations in reproduction. Anthropogenic factors such as competition with livestock for forage, habitat encroachment and poaching by the local people might have been partly responsible for the depleted population in our study areas.

scholarly journals Geographical Variation in COVID-19 Cases, Prevalence, Recovery and Fatality Rate by Phase of National Lockdown in India, March 14-May 29, 2020

2020 ◽  
Author(s):  
Ankita Srivastava ◽  
Vandana Tamrakar ◽  
Moradhvaj ◽  
Saddaf Naaz Akhtar ◽  
Krishna Kumar ◽  
...  
Keyword(s):  
Infection Rate ◽  
Recovery Rate ◽  
Tamil Nadu ◽  
Geographical Variation ◽  
Case Fatality ◽  
State Level ◽  
The State ◽  
Geographical Pattern ◽  
Case Fatality Ratio ◽  
Age And Sex

AbstractBackgroundSince the COVID-19 pandemic hit Indian states at varying speed, it is crucial to investigate the geographical pattern in COVID-19. We analyzed the geographical pattern of COVID-19 prevalence and mortality by the phase of national lockdown in India.MethodUsing publicly available compiled data on COVID-19, we estimated the trends in new cases, period-prevalence rate (PPR), case recovery rate (CRR), and case fatality ratio (CFR) at national, state and district level.FindingsThe age and sex are missing for more than 60 percent of the COVID-19 patients. There is an exponential increase in COVID-19 cases both at national and sub-national levels. The COVID-19 infected has jumped about 235 times (from 567 cases in the pre-lockdown period to 1,33,669 in the fourth lockdown); the average daily new cases have increased from 57 in the first lockdown to 6,482 in the fourth lockdown; the average daily recovered persons from 4 to 3,819; the average daily death from 1 to 163. From first to the third lockdown, PPR (0.04 to 5.94), CRR (7.05 to 30.35) and CFR (1.76 to 1.89) have consistently escalated. At state-level, the maximum number of COVID-19 cases is found in the states of Maharashtra, Tamil Nadu, Delhi, and Gujarat contributing 66.75 percent of total cases. Whereas no cases found in some states, Kerela is the only state flattening the COVID-19 curve. The PPR is found to be highest in Delhi, followed by Maharastra. The highest recovery rate is observed in Kerala, till second lockdown; and in Andhra Pradesh in third lockdown. The highest case fatality ratio in the fourth lockdown is observed in Gujarat and Telangana. A few districts viz. like Mumbai (96.7); Chennai (63.66) and Ahmedabad (62.04) have the highest infection rate per 100 thousand population. Spatial analysis shows that clusters in Konkan coast especially in Maharashtra (Palghar, Mumbai, Thane and Pune); southern part from Tamil Nadu (Chennai, Chengalpattu and Thiruvallur), and the northern part of Jammu & Kashmir (Anantnag, Kulgam) are hot-spots for COVID-19 infection while central, northern and north-eastern regions of India are the cold-spots.ConclusionIndia has been experiencing a rapid increase of COVID-19 cases since the second lockdown phase. There is huge geographical variation in COVID-19 pandemic with a concentration in some major cities and states while disaggregated data at local levels allows understanding geographical disparity of the pandemic, the lack of age-sex information of the COVID-19 patients forbids to investigate the individual pattern of COVID-19 burden.Major highlights of the studyThe new cases of COVID-19 have increased exponentially since the second lockdown phase in India. There is consistent improvement in the recovery rate (CRR is 7.1 percent in pre-lockdown to 44.0 percent in fourth lockdown period) with a low level of CFR (1.87 percent as of May 29st 2020).At the state level, the most vulnerable states for the COVID-19 crisis are the state of Maharashtra, Tamil Nadu, Delhi, and Gujarat contributing 66.75 percent of total cases.The PPR is found to be highest in Delhi, followed by Maharastra. While the highest recovery rate is observed in Kerala, the highest case fatality ratio in the fourth lockdown is observed in Gujarat and Telangana. The top 10 hotspot districts in India account for 58.3 percent of the new cases. Among them, Mumbai has the highest infection rate of 96.77 per 100 thousand, followed by Chennai with 63.66 per 100 thousand, and Ahmedabad with 62.04 per 100 thousand.The information on age and sex are missing for more than 60 percent of the patients.

scholarly journals Environment Friendly Car: A study of Consumer Awareness with special reference to Maharashtra State

2011 ◽  
Vol 2 (2) ◽  
pp. 92-98 ◽  
Author(s):  
Nitin Joshi
Keyword(s):  
Special Reference ◽  
Geographical Area ◽  
The State ◽  
Age Group ◽  
Consumer Awareness ◽  
Environment Friendly ◽  
Significant Difference

The aim of the study is to understand the behaviour of the customer in the state of Maharashtra which is one of the most developed states of India. The study is being carried out to understand the customer awareness on environment friendly car (EFC). The objective of the study is to understand the awareness levels and create awareness of the EFC so that the efforts of the manufacturing the green car will be achieved. SPSS version 17.0 has been used for analysis of the data. 500 respondents have been asked to fill in a questionnaire. The study has been done keeping in mind age group and the geographical area of the respondents. With reference to the age group, it is observed that there is no significant difference in the awareness levels but with reference to the geography, it is observed that there is a significant difference in the awareness levels with reference to the EFC.

Association of lung cancer incidence and COVID-19 case fatality with income level in Ohio.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 110-110
Author(s):  
Rohan Desai ◽  
Raoul Wadhwa ◽  
Jacob Scott
Keyword(s):  
Lung Cancer ◽  
Cancer Incidence ◽  
Respiratory Diseases ◽  
Case Fatality ◽  
Income Level ◽  
The State ◽  
Median Income ◽  
County Level ◽  
Lung Cancer Incidence ◽  
Case Fatality Ratio

110 Background: Social determinants of health (SDOH) have an impact of health and quality-of-life. However, most models of clinical disease and risk calculators do not incorporate SDOH. Many diseases have residual, unexplained risk after known risk factors have been incorporated. SDOH is important component of this residual risk and resources should be allocated to study their relation to disease. Here, we investigate the role of SDOH on the incidence and severity of respiratory diseases by evaluating the association of income level with lung cancer incidence and COVID-19 case fatality ratio in the state of Ohio. Methods: Socioeconomic data for the state of Ohio was manually collected. Lung cancer incidence was acquired from the National Cancer Institute’s online portal. COVID-19 case fatality ratios were downloaded from Johns Hopkins University’s GitHub repository. All three datasets contained complete information for each of Ohio’s 88 counties and were merged by county name. The first two datasets are up-to-date as of 2019, while the COVID-19 data was up-to-date as of 22 May 2021. Data manipulation, visualization, and statistical inference was conducted in the R programming language. Results: Median county-level income across Ohio was $52,722 (IQR: 47,273-59,166). Median county-level lung cancer incidence was 68.4 per 100,000 population (IQR: 62.0-80.6) and median COVID-19 case fatality ratio was 1.98% (IQR: 1.65-2.36). Data for counties with highest and lowest median income are shown in table. Lung cancer incidence and COVID-19 case fatality ratio were not significantly correlated ( n = 88, p > 0.05). Median income was significantly correlated with both, lung cancer incidence ( r = -0.49, p < 0.001) and COVID-19 case fatality ratio ( r = -0.41, p < 0.001). This implies that 24% of the variation in lung cancer incidence and 17% of the variation in COVID-19 case fatality ratio in counties across Ohio can be explained by median county income. Conclusions: SDOH is significantly associated with the incidence and severity of infectious and oncologic respiratory diseases. This association is strengthened since the disease variables themselves are uncorrelated, decreasing the likelihood of confounding. COVID-19 incidence was not used because of uncertainty in the mechanism of SDOH affecting transmission. However, theories suggest that oncologic incidence and infectious disease treatment are affected by SDOH. We validate these theories and encourage the incorporation of SDOH into clinical care and public health. Future directions include testing the association between income level and lung cancer severity in clinical datasets from medical institutions in Ohio.[Table: see text]

scholarly journals Trends of SARS-CoV-2 infection worldwide: Role of population density, age structure, and climate on transmission and case fatality

2020 ◽  
Author(s):  
Tewodros B Endailalu ◽  
Fitsum W Hadgu
Keyword(s):  
Linear Regression ◽  
Population Density ◽  
Regression Model ◽  
Linear Regression Model ◽  
Reproduction Number ◽  
Case Fatality ◽  
Multiple Linear Regression Model ◽  
Disease Spread ◽  
Statistical Software ◽  
Case Fatality Ratio

Introduction The disease transmission pattern of COVID19 is highly heterogeneous, which suggests that the pandemic maybe driven by complex factors, which may include habitat suitability, region specific human mobility, and transmission related to susceptibility. The purpose of this study was to examine the effects of different spatial and demographic factors on COVID19 transmission and case fatality worldwide. Methods We assessed SARS CoV2 virus transmission and COVID19 related fatalities in 50 countries in all continents of the globe. Data from the COVID19 data repository of the Johns Hopkins Center for Systems Science and Engineering, the European center for disease control and prevention, and the World Health Organization were used to obtain the daily number of cases and organize the incidence data. Disease spread, was assessed using the reproduction number of the disease across the sampled countries. R0 package of R statistical software was used to estimate the reproduction number of the COVID19 using the exponential growth method. After computing the reproductive number of each country in the study, a multiple linear regression model was fitted by taking the R0 value as dependent variable, and latitude and population density as an independent variable. Disease severity was analyzed using the case fatality ratio of COVID19. The proportion of deaths to the total numbers of cases was meta analyzed using metaphor package of R statistical software using random effect inverse variance weighting to come up with the case fatality ratio. Results We found no statistically significant association between disease spread and latitude or population density. The regression model analysis that accounted for age, population density and latitude showed that age distribution remains an important driver shaping the current distribution of COVID19 cases. The relative frequency of people above 65 years old was positively correlated with the cumulative numbers of COVID19 cases as well as case fatality ratio in each country. The multiple linear regression model fitted between CFR and the three major covariates showed that, the demographic distribution of the sampled countries is strongly associated with the case fatality ratio. An increase in the elderly population proportion by 1 percent was associated with an increase in CFR by 0.32. Correlation with proportion of populations over 65 years old is concordant with the previous findings relationship between case fatality ratio and patient age. Conclusion This analysis provides important information that can inform the decisions of local and global health authorities. Particularly, as our study confirms that death and severity of COVID19 are associated with age, in countries with the biggest outbreaks, strategies must be employed to ensure that high risk groups, such as old people received adequate protection from COVID19.

scholarly journals 445. Correlative Factors for State to State Differences in the Prevalence and Case Fatality Rates of SARS-CoV-2, COVID-19 Infections in the United States of America

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S290-S291
Author(s):  
Amanda J Kamar ◽  
Meghana Doniparthi ◽  
Nhan Nhan ◽  
Ying Zhou ◽  
James A Colton ◽  
...  
Keyword(s):  
African American ◽  
Population Density ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Chief Executive ◽  
Fatality Rate ◽  
American Population ◽  
Hispanic American ◽  
African American Population ◽  
The Usa

Abstract Background Individual States of the USA have ethnic, economic, community health and education differences that influence the prevalence and outcomes of COVID-19 infection. We hypothesized that Statewide differences in the prevalence and fatality rates of COVID-19 infections are dependent on factors that may be determined by mathematical modeling. Methods Two separate statistical regression models were developed using COVID-19 case prevalence and case fatality rates functioning as dependent variables. We obtained data from the prevalence and deaths from COVID-19 cases for each state in the USA that was posted at 4 PM Central Standard Time on April 29, 2020 from the Worldometer website. Publicly available databases were utilized to obtain data for the independent variables in the model. Results Models are represented as follows: Statewide COVID-19 Prevalence Model Log (Statewide COVID_19 case prevalence) = 1.847* (100–250 individuals/mile2) +3.0025*(250+ individuals/mile2) + 1.021* (% African American population) +1.029* (% Hispanic American population +2.164 *(% adults aged 85+) Model results are shown in Table 1. Statewide COVID-19 Case Fatality Rate Model Log (Statewide COVID_19 case fatality rate) =2.194* (100–250 individuals/mile2) +2.758* (250+ individuals/mile2) +1.031* (% African American population) + 1.032* (% Hispanic American population) + 0.942 (% Native American population)+ 1.108 (% Asian American population) + 2.275 (% adults aged 85+) Model results are shown in Table 2. Table 1: COVID-19 Statewide Prevalence Model Table 2: COVID-19 Statewide Case Fatality Model Conclusion Higher State population density (See Figure 1 and Figure 2) and higher State populations of elderly persons correspond to increased prevalence and case-fatality rates of COVID-19 infections. Statewide data also shows health disparities for COVID-19 infections in Hispanic Americans, African Americans, and Asian Americans. Paradoxically, States with larger populations of Native Americans who have known poor outcomes from COVID-19 infection demonstrate a decrease in case-fatality rates, suggesting a large effect of healthcare inequality in this population. Figure 1: ANOVA one-way analysis of the association between COVID-19 prevalence and population density Figure 2: ANOVA one-way analysis of the association between COVID-19 death prevalence and population density Disclosures Eli D. Ehrenpreis, MD, FACG, AGAF, E2Bio Consultants (Board Member, Chief Executive Officer)E2Bio Life Sciences (Shareholder, Chief Executive Officer)Level Ex, Inc. (Consultant)

scholarly journals Age Pattern of Premature Mortality under varying scenarios of COVID-19 Infection in India

2020 ◽  
Author(s):  
Sanjay K Mohanty ◽  
Umakanta Sahoo ◽  
Udaya S Mishra ◽  
Manisha Dubey
Keyword(s):  
Case Fatality ◽  
Attributable Mortality ◽  
Age Group ◽  
Disability Adjusted Life Years ◽  
Registration System ◽  
Case Fatality Ratio ◽  
Disability Adjusted Life ◽  
Life Years ◽  
Working Age ◽  
Community Infection

AbstractBackgroundIndia is vulnerable to community infection of COVID-19 due to crowded and poor living condition, high density, slums in urban areas and poor health care system. The number of COVID 19 infection has crossed 300,000 with over 7,500 deaths despite a prolonged period of lock down and restrictions in public spaces. Given the likely scale and magnitude of this pandemic, it is important to understand its impact on the age pattern of mortality under varying scenarios.ObjectiveThe main objective of this paper is to understand the age pattern of mortality under varying scenarios of community infection.Data and MethodsData from the Sample Registration System (SRS), covidi19india.org and country specific data from worldmeter is used in the analyses. Descriptive statistics, case-fatality ratio, case fatality ratio with 14 days delay, abridged life table,years of potential life lost (YPLL) and disability adjusted life years (DALY) is used.ResultsThe case fatality ratio (CFR) with 14 days delay for India is at least twice higher (8.0) than CFR of 3.4. Considering 8% mortality rate and varying scenario of community infection by 0.5%, 1% and 2%, India’s life expectancy will reduce by 0.8, 1.5 and 3.0 years and potential life years lost by 12.1 million, 24.3 million and 48.6 million years respectively. A community infection of 0.5% may result in DALY by 6.2 per 1000 population. Major share of PYLL and DALY is accounted by the working ages.ConclusionCOVID-19 has a visible impact on mortality with loss of productive life years in working ages. Sustained effort at containing the transmission at each administrative unit is recommended to arrest mortality owing to COVID-19 pandemic.What is known?The case fatality rate associated with COVID-19 is low in India compared to many other countries. The mortality level is higher among elderly and people with co-morbidity.ContributionThe case fatality ratio is illusive in the sense that the same with 14 days delay for India is at least twice higher (8.0). The COVID-19 attributable mortality has the potential to reduce the longevity of the population. Unlike developed countries, about half of the COVID-19 attributable mortality would be in the working age group of 45-64 years. With any level of community infection, the years of potential life lost (YPLL) and disability adjusted life years (DALY) world be highest in the working age group (45-64 years).

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