scholarly journals Eruption of COVID-19 like illness in a remote village in Papua (Indonesia)

2020 ◽  
Author(s):  
Elco van Burg ◽  
Wijnanda van Burg-Verhage
Keyword(s):  
Healthcare Workers ◽  
Age Distribution ◽  
Case Fatality ◽  
Medical Doctors ◽  
Young Population ◽  
Remote Areas ◽  
Resource Levels ◽  
The Impact ◽  
The Village ◽  
Village Population

Background The COVID-19 pandemic is creating significant challenges for healthcare infrastructure for countries of all development and resource levels. Low-and-middle resource countries face even larger challenges, as their resources are stretched and often insufficient under normal circumstances. A village in the Papuan highlands of Indonesia; small, isolated, accessed only by small plane or trekking has experienced an outbreak typical of COVID-19. Methodology/Principal Findings This description was compiled from patient care records by lay healthcare workers in M20 (a pseudonym) during and after an outbreak and from medical doctors responding to online requests for help. We assume that, for reasons given, the outbreak that has been described was COVID-19. The dense social structure of the village resulted in a rapid infection of 90-95% of the population. Physical distancing and isolation measures were used, but probably implemented suboptimal and too late, and their effect on the illness course was unclear. The relatively young population, with a majority of women, probably influenced the impact of the epidemic, resulting in only two deaths so far. Conclusions/Significance This outbreak pattern of suspected SARS-CoV-2 in a village in the highlands of Papua (Indonesia) presents a unique report of the infection of an entire village population over five weeks. The age distribution, common in Papuan highland villages may have reduced case fatality rate (CFR) in this context and that might be the case in similar remote areas since survival to old age is already very limited and CFR among younger people is lower.

scholarly journals The role of age distribution, time lag between reporting and death and healthcare system capacity on case fatality estimates of COVID-19

2020 ◽  
Author(s):  
Patrizio Vanella ◽  
Christian Wiessner ◽  
Anja Holz ◽  
Gerard Krause ◽  
Annika Moehl ◽  
...  
Keyword(s):  
Time Lag ◽  
Age Distribution ◽  
Case Fatality ◽  
System Capacity ◽  
Time Lags ◽  
Cross Country ◽  
The Difference ◽  
Case Fatality Risk ◽  
The Impact

European countries report large differences in coronavirus disease (COVID-19) case fatality risk (CFR). CFR estimates depend on demographic characteristics of the cases, time lags between reporting of infections and deaths and infrastructural characteristics, such as healthcare and surveillance capacities. We discuss the impact of these factors on the CFR estimates for Germany, Italy, France, and Spain for the COVID-19 pandemic from early March to mid-April, 2020. We found that, first, a large proportion of the difference in CFRs can be attributed to different age structures of the cases. Second, lags of 5-10 days between day of case report and death should be used, since these provide the most constant estimates. Third, for France, Italy, and Spain, intensive care beds occupied by COVID-19 patients were positively associated with fatality risks of hospitalized cases. Our results highlight that cross-country comparisons of crude CFR estimates can be misleading and should be avoided.

scholarly journals Enhanced comparisons of COVID-19 mortality across populations

2020 ◽  
Author(s):  
Chalapati Rao ◽  
Suhail A. Doi ◽  
Gail Williams
Keyword(s):  
Health System ◽  
Age Distribution ◽  
Case Fatality ◽  
The South ◽  
South Korean ◽  
Age Variations ◽  
Incidence And Mortality ◽  
System Factors ◽  
The Impact ◽  
Age Structures

AbstractBackgroundThe reported crude case fatality rate (CFR) for COVID-19 varies considerably across countries. Crude CFRs could by biased by larger proportions of older COVID-19 cases in population data, who are also at increased mortality risk. Such distorted age case structures are a common feature of selective COVID 19 testing strategies in many countries, and they potentially mask underlying differences arising from other important factors such as health system burden.MethodsWe used the method of direct case-age standardisation to evaluate the effects of age variations on CFRs. Data on cases and death by age from Italy, Spain, China, Australia and South Korea were analysed to derive standardised CFRs. Findings were compared across different case age distribution references as standards.ResultsUsing the South Korean case age distribution as a standard, the fivefold higher crude CFR for Italy is reduced to less than two-fold after adjustment, while the crude CFR difference for Spain is virtually eliminated. The adjusted CFR for Australia is the lowest among all countries.DiscussionMortality differences based on crude CFRs are exaggerated by age structures, which are effectively controlled by case age standardization. Residual CFR differences could be attributed to health and health system factors. The South Korean case age distribution is an appropriate reference standard, given its robust case detection and contact tracing program. Till reliable population level indicators of incidence and mortality are available, the age-standardized CFR could be a viable option for international comparison of the impact of the COVID 19 epidemic.SummaryThe knownThere are intense debates around the magnitude of and reasons for wide variations in observed case fatality rates (CFRs) from COVID 19 across countries. Age is commonly speculated as a reason, but this has not been technically quantified or explained.The newThe technique of direct standardization using reference distributions of case age structures eliminates the effects of age on CFR, thus enhancing the comparability as well as understanding of differentialsThe implicationsResidual differences between adjusted CFRs can be used to infer health and health system factors that influence mortality in COVID 19 cases in different populations

Healthcare under pressure: modelling COVID-19 fatalities with multiscale geographically weighted regressions

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zizi Goschin ◽  
Gina Cristina Dimian
Keyword(s):  
Local Level ◽  
Age Distribution ◽  
Case Fatality ◽  
Healthcare Services ◽  
Healthcare Systems ◽  
Content Type ◽  
Case Fatality Ratio ◽  
Factors Of Influence ◽  
The Impact ◽  
Local Healthcare

PurposeThe paper aims to disentangle the factors behind territorial disparities in the coronavirus disease 2019 (COVID-19) case fatality ratio, focusing on the pressure put by the pandemic on healthcare services and adopting a spatial perspective.Design/methodology/approachMultiscale geographically weighted regression (MGWR) models have been used for uncovering the spatial variability in the impact of healthcare services on COVID-19 case fatality ratio, allowing authors to better capture the real spatial patterns at local level. The authors proved that this approach yields better results, and the MGWR model outperforms traditional regression methods. The selected case studies are two of the biggest UE countries, among the first affected by a high incidence of COVID-19 cases, namely Italy and Germany.FindingsThe authors found sizeable regional differences in COVID-19 mortality rates within each of the analysed countries, and the stress borne by local healthcare systems seems to be the most powerful factor in explaining them. In line with other studies, the authors found additional factors of influence, such as age distribution, gender ratio, population density and regional development.Originality/valueThis research clearly indicated that COVID-19 related deaths are strongly associated with the degree of resilience of the local healthcare systems. The authors supply localized results on the factors of influence, useful for assisting the decision-makers in prioritizing limited healthcare resources. The authors provide a scientific argument in favour of the decentralization of the pandemic management towards local authorities not neglecting, however, the necessary regional or national coordination.

scholarly journals Measles in developing countries. Part II. The predicted impact of mass vaccination

1988 ◽  
Vol 100 (3) ◽  
pp. 419-442 ◽  
Author(s):  
A. R. Mclean ◽  
R. M. Anderson
Keyword(s):  
Developing Countries ◽  
Measles Virus ◽  
Age Distribution ◽  
Case Fatality ◽  
Mass Vaccination ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Practical Significance ◽  
Epidemic Period ◽  
The Impact

SummaryA mathematical model is developed to mimic the transmission dynamics of the measles virus in communities in the developing world with high population growth rates and high case fatality rates. The model is used to compare the impacts of different mass vaccination programmes upon morbidity and mortality arising from infection by measles virus. Analyses identify three conclusions of practical significance to the design of optimal vaccination programmes. First, there is no single optimum age at which to vaccinate children for all urban and rural communities in developing countries. For a given community the best age at which to vaccinate depends critically on the age distribution of cases of infection prior to the introduction of control measures. Second, numerical studies predict that the introduction of mass vaccination will induce a temporary phase of very low incidence of infection before the system settles to a new pattern of recurrent epidemics. Mass vaccination acts to lengthen the inter-epidemic period in the postvaccination period when compared with that prevailing prior to control. Third, numerical simulations suggest that two-phase and two-stage vaccination programmes are of less benefit than one-stage programmes (achieving comparable coverage) aimed at young children. The paper ends with a discussion of the needs for: improved programmes of data collection; monitoring of the impact of current vaccination programmes; and the development of models that take account of viral transmission dynamics, host demography and economic factors.

scholarly journals Is group C meningococcal disease increasing in Europe? A report of surveillance of meningococcal infection in Europe 1993–6

1999 ◽  
Vol 122 (1) ◽  
pp. 41-49 ◽  
Author(s):  
M. CONNOLLY ◽  
N. NOAH
Keyword(s):  
Meningococcal Disease ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
Age Distribution ◽  
Case Fatality ◽  
European Countries ◽  
Meningococcal Infection ◽  
Slow Increase ◽  
Set Up ◽  
The Impact

A surveillance system to assess the impact and changing epidemiology of invasive meningococcal disease in Europe was set up in 1987. Since about 1991, contributors from national reference laboratories, national communicable disease surveillance centres and institutes of public health in 35 European countries provided information on all reported cases of meningococcal disease in their country. We describe some trends observed over the period 1993–6. The main findings were: the overall incidence of meningococcal disease was 1·1 per 100000 population but there was some evidence of a slow increase over time and with northern European countries tending to have a higher incidence (Kendall correlation 0·5772, P<0·001), an increasing predominance of serogroup C, and a shift in the age distribution towards teenagers and away from younger children (χ2 test for trend 44·56, P<0·0001), although about half of the cases were under 5 years of age. The overall case fatality rate was 8·3% and the most common serosubtypes were B[ratio ]15[ratio ]P1.7,16 and C[ratio ]2a[ratio ]P1.2,5.

scholarly journals Standardization and Age-Distribution of COVID-19: Implications for Variability in Case Fatality and Outbreak Identification

2020 ◽  
Author(s):  
David N. Fisman ◽  
Amy L. Greer ◽  
Ashleigh R. Tuite
Keyword(s):  
Data Analysis ◽  
Early Stage ◽  
Age Distribution ◽  
Case Fatality ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Apparent Size ◽  
Older Individuals ◽  
Indirect Standardization ◽  
The Impact

AbstractBackgroundEpidemiological data from the COVID-19 pandemic has demonstrated variability in attack rates by age, and country-to-country variability in case fatality ratio (CFR).ObjectiveTo use direct and indirect standardization for insights into the impact of age-specific under-reporting on between-country variability in CFR, and apparent size of COVID-19 epidemics.DesignPost-hoc secondary data analysis (“case studies”), and mathematical modeling.SettingChina, global.InterventionsNone.MeasurementsData were extracted from a sentinel epidemiological study by the Chinese Center for Disease Control (CCDC) that describes attack rates and CFR for COVID-19 in China prior to February 12, 2020. Standardized morbidity ratios (SMR) were used to impute missing cases and adjust CFR. Age-specific attack rates and CFR were applied to different countries with differing age structures (Italy, Japan, Indonesia, and Egypt), in order to generate estimates for CFR, apparent epidemic size, and time to outbreak recognition for identical age-specific attack rates.ResultsSMR demonstrated that 50-70% of cases were likely missed during the Chinese epidemic. Adjustment for under-recognition of younger cases decreased CFR from 2.4% to 0.8% (assuming 50% case ascertainment in older individuals). Standardizing the Chinese epidemic to countries with older populations (Italy, and Japan) resulted in larger apparent epidemic sizes, higher CFR and earlier outbreak recognition. The opposite effect was demonstrated for countries with younger populations (Indonesia, and Egypt).LimitationsSecondary data analysis based on a single country at an early stage of the COVID-19 pandemic, with no attempt to incorporate second order effects (ICU saturation) on CFR.ConclusionDirect and indirect standardization are simple tools that provide key insights into between-country variation in the apparent size and severity of COVID-19 epidemics.FundingThe research was supported by a grant to DNF from the Canadian Institutes for Health Research (2019 COVID-19 rapid researching funding OV4-170360).

scholarly journals Epidemiology and Trends of Pertussis among Infants: United States, 2000–2015

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S5-S5 ◽  
Author(s):  
Catherine Bozio ◽  
Tami Skoff ◽  
Tracy Pondo ◽  
Jennifer Liang
Keyword(s):  
Negative Binomial ◽  
Severe Disease ◽  
Age Distribution ◽  
Age Groups ◽  
Case Fatality ◽  
Annual Average ◽  
Young Infants ◽  
Hospitalization Rates ◽  
The Impact ◽  
Over Time

Abstract Background Pertussis, a cyclic respiratory disease, causes the greatest morbidity and mortality among infants, particularly those too young to be vaccinated. Following a resurgence of pertussis in the 1990s, a recommendation was made in 2012 to vaccinate during every pregnancy in order to prevent infant disease. We describe pertussis trends from 2000–2015 among U.S. infants aged &lt;1 year. Methods We analyzed infant pertussis cases reported through the National Notifiable Diseases Surveillance System from 2000 to 2015. Incidence rates (cases per 100,000 population) among various age groups (&lt;2, 2– &lt;4, 4– &lt;6, and 6–&lt;12 months) were calculated using National Center for Health Statistics population estimates as denominators. Negative binomial regression was used to estimate the annual average percent change with a linear trend; P &lt; 0.05 was significant. Results From 2000 to 2015, 48,909 infant pertussis cases and 255 deaths were reported; infants aged &lt;2 months accounted for 38.7% of cases. The age distribution of infant cases was stable from 2000 to 2009 but changed from 2010 to 2015 (Fig. 1), as the proportion of cases aged 4–&lt;12 months increased annually on average by 4.7% (P &lt; 0.001). Annual incidence was highest among &lt;2 month olds; however, rates increased among older infants (Fig. 2): 7% average annual increase among infants aged 4–&lt;6 months and 11% among infants aged 6–&lt;12 months (P &lt; 0.001 for each). The proportion of infants hospitalized decreased over time in each age group (P &lt; 0.001 for all) with the largest annual average declines among 4–&lt;6 (−5.1%) and 6–&lt;12 month (−5.9%) olds. For all age groups, hospitalization rates were relatively stable, but non-hospitalization rates increased (P &lt; 0.05 for all). The case–fatality ratio (CFR) was highest among &lt;2 month olds (1.6%); CFRs decreased over time among &lt;2 and 2–&lt; 4 month olds (P &lt; 0.05 for each). Conclusion Pertussis incidence remains highest among infants aged &lt;4 months, although the age distribution appears to be changing. Decreasing proportions of infants hospitalized may suggest a true decline in disease severity or an increase in reporting of less severe disease. Ongoing monitoring of infant pertussis is needed to better understand the impact of vaccinating pregnant women to prevent pertussis in young infants. Disclosures All authors: No reported disclosures.

scholarly journals A deeper look at COVID-19 CFR: health care impact and roots of discrepancy

2020 ◽  
Author(s):  
Amirhoshang Hoseinpour Dehkordi ◽  
Reza Nemati ◽  
Pouya Tavousi
Keyword(s):  
Intensive Care ◽  
Age Distribution ◽  
Estimation Method ◽  
Case Fatality ◽  
Quantitative Measure ◽  
Age Group ◽  
Population Distributions ◽  
Reported Data ◽  
Care Capacity ◽  
The Impact

AbstractIntensive care capacity and proper testing play a paramount role in the COVID-19 Case Fatality Rate (CFR). Nevertheless, the real impact of such important measures has not been appreciated due to the lack of proper metrics. In this work, we have proposed a method for estimating a lower bound for the number of positive cases by using the reported data on the oldest age group and the regions’ population distributions. The proposed estimation method improved the expected similarity between the age-distribution of positive cases and regions’ population. Further, we have provided a quantitative measure for the impact of intensive care on the critical cases by comparing the CFR among those who did and did not receive intensive care. Our findings showed that the chance of living among non-ICU receivers is less than half of ICU receivers (∼24% vs ∼60%).

scholarly journals The Impact of Socioeconomic and Demographic Variables on Poverty: A Village Study

2009 ◽  
Vol 14 (1) ◽  
pp. 39-68 ◽  
Author(s):  
Imran Sharif Chaudhry ◽  
Shahnawaz Malik ◽  
Abo ul Hassan Abo ul Hassan
Keyword(s):  
Rural Poverty ◽  
Demographic Factors ◽  
Demographic Variables ◽  
Primary Data ◽  
Final Conclusion ◽  
Remote Areas ◽  
Rural Pakistan ◽  
Econometric Approach ◽  
The Impact ◽  
The Village

Poverty is a complex phenomenon based on a network of interlocking economic, social, political, and demographic factors. An understanding of the extent, nature, and determinants of rural poverty is a precondition for effective public policy to reduce poverty in rural Pakistan. The present study attempts to analyze the impact of socioeconomic and demographic characteristics of households on poverty, using primary data collected in the village of Betti Nala in Tehsil Jatoi, district Muzaffargarh in southern Punjab. We have used two distinct approaches: (i) a poverty profile, and (ii) an econometric approach in our empirical analysis. The results show that household size, dependency on household, participation, landholdings, and number of livestock have a significant impact on poverty incidence. Our final conclusion is that efforts should be made to improve socioeconomic factors in general and demographic factors in particular to alleviate rural poverty in remote areas of Pakistan, while land should be allotted to landless households.

scholarly journals How many lives can be saved? A global view on the impact of testing, herd immunity and demographics on COVID-19 fatality rates

2020 ◽  
Author(s):  
Miguel Sanchez-Romero ◽  
Vanessa di Lego ◽  
Alexia Prskawetz ◽  
Bernardo L Queiroz
Keyword(s):  
Herd Immunity ◽  
Age Distribution ◽  
Case Fatality ◽  
Population Characteristics ◽  
Seir Model ◽  
Testing Strategies ◽  
The Impact ◽  
Testing Policies ◽  
Different Levels

In this work, we assess the global impact of COVID-19 showing how demographic factors, testing policies and herd immunity are key for saving lives. We extend a standard epidemiological SEIR model in order to: (a) identify the role of demographics (population size and population age distribution) on COVID-19 fatality rates; (b) quantify the maximum number of lives that can be saved according to different testing strategies, different levels of herd immunity, and specific population characteristics; and (d) infer from the observed case fatality rates (CFR) what the true fatality rate might be. Different from previous SEIR model extensions, we implement a Bayesian Melding method in our calibration strategy which enables us to account for data limitation on the total number of deaths. We derive a distribution of the set of parameters that best replicate the observed evolution of deaths by using information from both the model and the data.

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