The Role of Age Distribution, Time Lag Between Reporting and Death and Healthcare System Capacity in Case Fatality Estimates of COVID-19

Abstract Background: 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. Methods: We used publicly available data from official reports of the national health authorities of Germany, Italy, France, and Spain on COVID-19. These include age-specific numbers of cases and deaths for different dates, which we used to compute age-standardized CFR ratios using a standard European population for standardization. Moreover, we investigated the impact of different potential time lags on the estimation of the CFR using data published by the European Centre for Disease Prevention and Control (ECDC). Finally, we described the association between case fatality and the intensive care bed capacity.Results: We found that age-standardized CFR estimates increased from the beginning of March to mid-May 2020 in all included European countries. In Germany, CFRs are lower than in other countries. However, the differences are much larger when comparing the crude risks rather than the age-adjusted risks. Thus, the different age distribution of the cases account for a major proportion of the reported differences. Case fatality estimates using time lags of 1-10 days converged in all countries over time, however, there is no optimal time lag to assess the CFR during the pandemic. Time lags that provided the most constant estimates and approach best the observed CFR after the pandemic ranged from 5-10 days in different countries and at different time points during the pandemic. For the association between intensive care bed capacity and fatality we found that days with a high need for intensive care beds were positively correlated with daily hospitalization fatality in France, Italy, and Spain, but not in Germany. Conclusions: Our results highlight that cross-country comparisons of crude CFR estimates can be misleading and should be avoided. However, to adjust for potential sources of bias more disaggregated data and information on surveillance and health care capacities are needed. Filling these gaps and harmonizing data across European countries will facilitate further analysis.

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

10.1101/2020.05.16.20104117 ◽

2020 ◽

Cited By ~ 1

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.

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

Epidemiology and Infection ◽

10.1017/s0950268898001848 ◽

1999 ◽

Vol 122 (1) ◽

pp. 41-49 ◽

Cited By ~ 86

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.

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

10.1101/2020.04.22.20071498 ◽

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%).

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

10.21203/rs.3.rs-23962/v1 ◽

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

Abstract Intensive 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%).

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Analysis of Covid-19 Data for Eight European Countries and the United Kingdom Using a Simplified SIR Model

10.21203/rs.3.rs-97697/v1 ◽

2020 ◽

Author(s):

Gyan Bhanot ◽

Charles DeLisi

Keyword(s):

United Kingdom ◽

Time Lag ◽

Case Fatality ◽

Sir Model ◽

Control Measures ◽

European Countries ◽

Daily Number ◽

The United Kingdom ◽

The Impact ◽

Infective Period

Abstract Background: As the SARS-Cov-2/Covid-19 pandemic continues to ravage the world, it is important to understanding the characteristics of its spread and possible correlates for control to develop strategies of response. Methods: Here we show how a simple Susceptible-Infective-Recovered (SIR) model applied to data for eight European countries and the United Kingdom (UK) can be used to forecast the descending limb (post-peak) of confirmed cases and deaths as a function of time, and predict the duration of the pandemic once it has peaked, by estimating and fixing parameters using only characteristics of the ascending limb and the magnitude of the first peak. Results: The predicted and actual case fatality ratio, or number of deaths per million population from the start of the pandemic to when daily deaths number less than five for the first time, was lowest in Norway (predicted: 44 5 deaths/million; actual: 36 deaths/million) and highest for the United Kingdom (predicted: 578 +/- 65 deaths/million; actual 621 deaths/million). The inferred pandemic characteristics separated into two distinct groups: those that are largely invariant across countries, and those that are highly variable. Among the former is the infective period, TL = 16.3 2.7 days, the average time between contacts, TR = 3.8+/- 0.5 days and the average number of contacts while infective R = 4.4 +/- 0.5. In contrast, there is a highly variable time lag TD between the peak in the daily number of confirmed cases and the peak in the daily number of deaths, ranging from lows of TD = 2,4 days for Denmark and Italy respectively, to highs of TD = 12, 15 for Germany and Norway respectively. The mortality fraction, or ratio of deaths to confirmed cases, was also highly variable, ranging from low values 3%, 5% and 5% for Norway, Denmark and Germany respectively, to high values of 18%, 20% and 21% for Sweden, France, and the UK respectively. The probability of mortality rather than recovery was a significant correlate of the duration of the pandemic, defined as the time from 12/31/2019 to when the number of daily deaths fell below 5. Finally, we observed a small but detectable effect of average temperature on the probability of infection per contact, with higher temperatures associated with lower infectivity. Conclusions: Our simple model captures the dynamics of the initial stages of the pandemic, from its exponential beginning to the first peak and beyond, with remarkable precision. As with all epidemiological analyses, unanticipated behavioral changes will result in deviations between projection and observation. This is abundantly clear for the current pandemic. Nonetheless, accurate short-term projections are possible, and the methodology we present is a useful addition to the epidemiologist's armamentarium. Our predictions assume that control measures such as lockdown, social distancing, use of masks etc. remain the same post-peak as before peak. Consequently, deviations from our predictions are a measure of the extent to which loosening of control measures have impacted case-loads and deaths since the first peak and initial decline in daily cases and deaths. Our findings suggest that the two key parameters to control and reduce the impact of a developing pandemic are the infective period and the mortality fraction, which are achievable by early case identification, contact tracing and quarantine (which would reduce the former) and improving quality of care for identified cases (which would reduce the latter).

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Small-scaled association between ambient temperature and campylobacteriosis incidence in Germany

Scientific Reports ◽

10.1038/s41598-020-73865-9 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Julia Oberheim ◽

Christoph Höser ◽

Guido Lüchters ◽

Thomas Kistemann

Keyword(s):

Climate Change ◽

Ambient Temperature ◽

Time Lag ◽

Daily Minimum Temperature ◽

Time Lags ◽

Recent Part ◽

The Impact ◽

Incidence Increase ◽

Observation Period

Abstract Campylobacteriosis is the leading bacterial cause of human diarrheal illness worldwide. Campylobacteriosis incidence exhibits seasonality and has been attributed to ambient temperature. However, the role of ambient temperature on campylobacteriosis remains poorly understood. To examine the impact of ambient temperature on local campylobacteriosis in Germany, weekly incidences on NUTS-3 level were analysed using a novel small-scaled approach, regression and time lags. Campylobacteriosis incidence correlated positively with temperatures between − 5 and 28 °C. The sigmoid regression model estimated an incidence increase of 0.52 per 5 °C temperature rise in the observation period. The weekly average of daily minimum temperature was most significant at a time lag of two weeks and showed the steepest incidence increase of 0.13 per 1 °C temperature increase in a temperature corridor of 5.1 to 12.2 °C. The impact of average minimum temperatures on campylobacteriosis incidence is crucial, likely to be indirect and especially relevant in the recent part of the infection chain. Vectors or human behaviour are presumably more directly linked with temperature than the pathogen’s microbiology and should be examined. These variables outweigh the direct temperature-pathogen relationship when the whole chain of infection is considered. In the context of climate change, campylobacteriosis is likely to increase in Germany due to an increased temperature effect.

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Population age structure only partially explains the large number of COVID-19 deaths at the oldest ages

10.1101/2020.04.09.20056341 ◽

2020 ◽

Cited By ~ 1

Author(s):

Anthony Medford ◽

Sergi Trias-Llimós

Keyword(s):

Old Age ◽

Age Structure ◽

Age Distribution ◽

Case Fatality ◽

European Countries ◽

Population Age Structure ◽

Country Comparison ◽

Cross Country ◽

Age Structures

AbstractTo date any attention paid to the age shape of COVID-19 deaths has been mostly in relation to attempts to understand the differences in case fatality rates between countries. The aim of this paper is to explore differences in age distribution of deaths from COVID-19 among European countries which have old age structures. We do this by way of a cross-country comparison and put forward some reasons for potential differences.

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Enhanced comparisons of COVID-19 mortality across populations

10.1101/2020.06.14.20131318 ◽

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

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

10.1101/2020.05.19.20106740 ◽

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.

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

Kybernetes ◽

10.1108/k-07-2021-0548 ◽

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.

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