Monitoring the spatiotemporal epidemiology of Covid-19 incidence and mortality: A small-area analysis in Germany.
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SUBMITTER: Rohleder S
PROVIDER: S-EPMC8139365 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundSocioeconomic conditions affect the dynamics of the Covid-19 pandemic. We analysed the association between area-level socioeconomic deprivation, proportion of non-nationals, and incidence of Covid-19 infections in Germany.MethodsUsing linked nationally representative data at the level of 401 German districts from three waves of infection (January-2020 to May-2021), we fitted Bayesian spatiotemporal models to assess the association between socioeconomic deprivation, and proportion of non-nationals with Covid-19 incidence, controlling for age, sex, vaccination coverage, settlement structure, and spatial and temporal effects. We estimated risk ratios (RR) and corresponding 95% credible intervals (95% CrI). We further examined the deprivation domains (education, income, occupation), interactions between deprivation, sex and the proportion of non-nationals, and explored potential pathways from deprivation to Covid-19 incidence.FindingsCovid-19 incidence risk was 15% higher (RR=1·15, 95%-CrI=1·06-1·24) in areas classified with the highest deprivation quintile (Q5) compared to the least deprived areas (Q1). Medium-low (Q2), medium (Q3), and medium-high (Q4) deprived districts showed 6% (1·06, 1·00-1·12), 8% (1·08, 1·01-1·15), and 5% (1·05, 0·98-1·13) higher risk, respectively, compared to the least deprived. Districts with higher proportion of non-nationals showed higher incidence risk compared to districts with lowest proportion, but the association weakened across the three waves. During the first wave, an inverse association was observed with highest incidence risk in least deprived areas (Q1). Deprivation interacted with sex, but not with the proportion of non-nationals.InterpretationSocioeconomic deprivation, and proportion of non-nationals are independently associated with the incidence of Covid-19. Regional planning of non-pharmaceutical interventions and vaccination strategies would benefit from consideration of area-level deprivation and non-national residency.FundingThe study was funded by the German Ministry of Health (ZMV I 1 - 25 20 COR 410).
| S-EPMC9189383 | biostudies-literature
Project description:ObjectiveStudies have shown an increased incidence of pediatric type 1 diabetes during the COVID-19 pandemic, but the detailed role of SARS-CoV-2 infection in the incidence increase in type 1 diabetes remains unclear. We investigated the spatiotemporal association of pediatric type 1 diabetes and COVID-19 incidence at the district level in Germany.MethodsFor the period from March 2020 to June 2022, nationwide data on incident type 1 diabetes among children and adolescents aged <20 years and daily documented COVID-19 infections in the total population were obtained from the German Diabetes Prospective Follow-up Registry and the Robert Koch Institute, respectively. Data were aggregated at district level and seven time periods related to COVID-19 pandemic waves. Spatiotemporal associations between indirectly standardized incidence rates of type 1 diabetes and COVID-19 were analyzed by Spearman correlation and Bayesian spatiotemporal conditional autoregressive Poisson models.ResultsStandardized incidence ratios of type 1 diabetes and COVID-19 in the pandemic period were not significantly correlated across districts and time periods. A doubling of the COVID-19 incidence rate was not associated with a significant increase in the incidence rate of type 1 diabetes (relative risk 1.006, 95% CI 0.987; 1.019).ConclusionOur findings based on data from the pandemic period indirectly indicate that a causal relationship between SARS-COV-2 infection and type 1 diabetes among children and adolescents is unlikely.
| S-EPMC10791930 | biostudies-literature
Project description:ObjectivesThe first wave of the SARS-CoV-2 pandemic in Germany lasted from week 10 to 23 in 2020. The aim is to provide estimates of excess mortality in Germany during this time.MethodsWe analyzed age-specific numbers of deaths per week from 2016 to week 26 in 2020. We used weekly mean numbers of deaths of 2016-2019 to estimate expected weekly numbers for 2020. We estimated standardized mortality ratios (SMR) and 95% confidence intervals.ResultsDuring the first wave observed numbers of deaths were higher than expected for age groups 60-69, 80-89, and 90+. The age group 70-79 years did not show excess mortality. The net excess number of deaths for weeks 10-23 was +8,071. The overall SMR was 1•03 (95%CI 1•03-1•04). The largest increase occurred among people aged 80-89 and 90+ (SMR=1•08 and SMR=1•09). A sensitivity analysis that accounts for demographic changes revealed an overall SMR of 0•98 (95%CI 0•98-0•99) and a deficit of 4,926 deaths for week 10-23, 2020.ConclusionsThe excess mortality existed for two months. The favorable course of the first wave may be explained by a younger age at infection at the beginning of the pandemic, lower contact rates, and a more efficient pandemic management.
| S-EPMC7501062 | biostudies-literature
Project description:ObjectivesAmong the few studies examining patterns of COVID-19 spread in border regions, findings are highly varied and partially contradictory. This study presents empirical results on the spatial and temporal dynamics of incidence in 10 European border regions. We identify geographical differences in incidence between border regions and inland regions, and we provide a heuristic to characterise spillover effects.Study designObservational spatiotemporal analysis.MethodsUsing 14-day incidence rates (04/2020 to 25/2021) for border regions around Germany, we delineate three pandemic 'waves' by the dates with the lowest recorded rates between peak incidence. We mapped COVID-19 incidence data at the finest spatial scale available and compared border regions' incidence rates and trends to their nationwide values. The observed spatial and temporal patterns are then compared to the time and duration of border controls in the study area.ResultsWe observed both symmetry and asymmetry of incidence rates within border pairs, varying by country. Several asymmetrical border pairs feature temporal convergence, which is a plausible indicator for spillover dynamics. We thus derived a border incidence typology to characterise (1) symmetric border pairs, (2) asymmetric border pairs without spillover effects, and (3) asymmetric with spillover effects. In all groups, border control measures were enacted but appear to have been effective only in certain cases.ConclusionsThe heuristic of border pairs provides a useful typology for highlighting combinations of spillover effects and border controls. We conclude that border control measures may only be effective if the timing and the combination with other non-pharmaceutical measures is appropriate.
| S-EPMC8683116 | biostudies-literature
Project description:The COVID-19 pandemic has kept the world in suspense for the past year. In most federal countries such as Germany, locally varying conditions demand for state- or county-level decisions to adapt to the disease dynamics. However, this requires a deep understanding of the mesoscale outbreak dynamics between microscale agent models and macroscale global models. Here, we use a reparameterized SIQRD network model that accounts for local political decisions to predict the spatiotemporal evolution of the pandemic in Germany at county resolution. Our optimized model reproduces state-wise cumulative infections and deaths as reported by the Robert Koch Institute and predicts the development for individual counties at convincing accuracy during both waves in spring and fall of 2020. We demonstrate the dominating effect of local infection seeds and identify effective measures to attenuate the rapid spread. Our model has great potential to support decision makers on a state and community politics level to individually strategize their best way forward during the months to come.
| S-EPMC8493548 | biostudies-literature
Project description:The coronavirus disease 2019 (COVID-19) pandemic and associated economic crisis have placed millions of US households at risk of eviction. Evictions may accelerate COVID-19 transmission by decreasing individuals' ability to socially distance. We leveraged variation in the expiration of eviction moratoriums in US states to test for associations between evictions and COVID-19 incidence and mortality. The study included 44 US states that instituted eviction moratoriums, followed from March 13 to September 3, 2020. We modeled associations using a difference-in-difference approach with an event-study specification. Negative binomial regression models of cases and deaths included fixed effects for state and week and controlled for time-varying indicators of testing, stay-at-home orders, school closures, and mask mandates. COVID-19 incidence and mortality increased steadily in states after eviction moratoriums expired, and expiration was associated with a doubling of COVID-19 incidence (incidence rate ratio = 2.1; 95% confidence interval (CI): 1.1, 3.9) and a 5-fold increase in COVID-19 mortality (mortality rate ratio = 5.4; CI: 3.1, 9.3) 16 weeks after moratoriums lapsed. These results imply an estimated 433,700 excess cases (CI: 365,200, 502,200) and 10,700 excess deaths (CI: 8,900, 12,500) nationally by September 3, 2020. The expiration of eviction moratoriums was associated with increased COVID-19 incidence and mortality, supporting the public-health rationale for eviction prevention to limit COVID-19 cases and deaths.
| S-EPMC8634574 | biostudies-literature
Project description:ObjectivesAs a global pandemic is inevitable, real-time monitoring of transmission is vital for containing the spread of COVID-19. The main objective of this study was to report the real-time effective reproduction numbers (R(t)) and case fatality rates (CFR) in Europe.MethodsData for this study were obtained mainly from the World Health Organization website, up to March 9, 2020. R(t) were estimated by exponential growth rate (EG) and time-dependent (TD) methods. 'R0' package in R was employed to estimate R(t) by fitting the existing epidemic curve. Both the naïve CFR (nCFR) and adjusted CFR (aCFR) were estimated.ResultsWith the EG method, R(t) was 3.27 (95% confidence interval (CI) 3.17-3.38) for Italy, 6.32 (95% CI 5.72-6.99) for France, 6.07 (95% CI 5.51-6.69) for Germany, and 5.08 (95% CI 4.51-5.74) for Spain. With the TD method, the R value for March 9 was 3.10 (95% CI 2.21-4.11) for Italy, 6.56 (95% CI 2.04-12.26) for France, 4.43 (95% CI 1.83-7.92) for Germany, and 3.95 (95% CI 0-10.19) for Spain.ConclusionsThis study provides important findings on the early outbreak of COVID-19 in Europe. Due to the recent rapid increase in new cases of COVID-19, real-time monitoring of the transmissibility and mortality in Spain and France is a priority.
| S-EPMC7102547 | biostudies-literature
Project description:BackgroundAlthough measles is endemic throughout the World Health Organization European Region, few studies have analysed socioeconomic inequalities and spatiotemporal variations in the disease's incidence.AimTo study the association between socioeconomic deprivation and measles incidence in Germany, while considering relevant demographic, spatial and temporal factors.MethodsWe conducted a longitudinal small-area analysis using nationally representative linked data in 401 districts (2001-2017). We used spatiotemporal Bayesian regression models to assess the potential effect of area deprivation on measles incidence, adjusted for demographic and geographical factors, as well as spatial and temporal effects. We estimated risk ratios (RR) for deprivation quintiles (Q1-Q5), and district-specific adjusted relative risks (ARR) to assess the area-level risk profile of measles in Germany.ResultsThe risk of measles incidence in areas with lowest deprivation quintile (Q1) was 1.58 times higher (95% credible interval (CrI): 1.32-2.00) than in those with highest deprivation (Q5). Areas with medium-low (Q2), medium (Q3) and medium-high deprivation (Q4) had higher adjusted risks of measles relative to areas with highest deprivation (Q5) (RR: 1.23, 95%CrI: 0.99-1.51; 1.05, 95%CrI: 0.87-1.26 and 1.23, 95%CrI: 1.05-1.43, respectively). We identified 54 districts at medium-high risk for measles (ARR > 2) in Germany, of which 22 were at high risk (ARR > 3).ConclusionSocioeconomic deprivation in Germany, one of Europe's most populated countries, is inversely associated with measles incidence. This association persists after demographic and spatiotemporal factors are considered. The social, spatial and temporal patterns of elevated risk require targeted public health action and policy to address the complexity underlying measles epidemiology.
| S-EPMC8086244 | biostudies-literature
Project description:Italy is particularly affected by SARS-CoV-2/COVID-19. Recently, Colombo and Impicciatore compared the deaths in 1084 selected municipalities between 21 February 2020 and 21 March 2020 with deaths in the same time period in 2015 to 2019. We extend analyses of data from the Italian National Institute of Statistics (ISTAT) and calculate SMRs for all causes of death in the nine selected regions of Italy, separately for men and women and summarized. We analyze the effect of covariables by Poisson modelling and discuss the limitations of the current elaborations. We conclude: In agreement with Colombo and Impicciatore, in the particular corona situation, this "mortality excess loupe" - assuming otherwise constant determinants of death - can be a virus-test-independent tool to determine mortality effects of SARS-CoV-2. The current "loupe" is focused on municipalities with increases of more than 20% deaths in March 2020 compared to the average deaths on the same days in 2015-2019. The time window of investigation could be opened before 21 February 2020 to detect masked increases in mortality before the first "COVID-19 death" was ascertained. The current "loupe" conveys pronounced mortality increases also in regions that were not considered to be corona hotspots. In this respect, even in the absence of representative virus test results, mortality data can be important indicators of the distribution or spread of a newly acting factor. Overall, it is advisable to carry out SMR analyses for Germany on a regular basis, differentiated by region, gender, age group and cause of death. Such analyses can contribute to the early detection and evaluation of the severity of a deadly pandemic ("burden of disease") as well as to monitoring the dynamic spread of a factor such as SARS-CoV-2. SMR analyses can also be used to assess and evaluate both desired and undesired effects of measures taken against SARS-CoV-2/COVID-19 - and possibly other epidemics or pandemics.
| S-EPMC7295299 | biostudies-literature
Project description:This paper forecasts the future spread of COVID-19 by exploiting the identified lead-lag effects between different countries. Specifically, we first determine the past relation among nations with the aid of dynamic time warping. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences. Afterwards, the established framework utilizes information about the leading country to predict the Coronavirus spread of the following nation. The presented methodology is applied to confirmed Coronavirus cases from 1 January 2020 to 28 March 2020. Our results show that China leads all other countries in the range of 29 days for South Korea and 44 days for the United States. Finally, we predict a future collapse of the healthcare systems of the United Kingdom and Switzerland in case of our explosion scenario.
| S-EPMC7349853 | biostudies-literature