scholarly journals Total COVID-19 Mortality in Italy: Excess Mortality and Age Dependence through Time-Series Analysis

2020 ◽  
Author(s):  
Chirag Modi ◽  
Vanessa Böhm ◽  
Simone Ferraro ◽  
George Stein ◽  
Uroš Seljak
Keyword(s):  
Time Series ◽  
Lower Bound ◽  
Time Series Analysis ◽  
Excess Mortality ◽  
Large Population ◽  
Mortality Data ◽  
Age Group ◽  
Fatality Rate ◽  
Age Dependence ◽  
Series Analysis

ABSTRACTWe perform a counterfactual time series analysis using two different Data Science methods applied to 2020 mortality data reported from towns in Italy, with data from the previous five years as control. We find an excess mortality that is correlated in time with the COVID-19 reported death rate time series. Our analysis shows good agreement with reported COVID-19 mortality for age<70 years, but an excess in total mortality increasing with age above 70 years, suggesting there is a large population of predominantly old people missing from the official fatality statistics. We estimate that the number of COVID-19 deaths in Italy is 52,000 ± 2000 as of April 18 2020, more than a factor of 2 higher than the official number. The Population Fatality Rate (PFR) has reached 0.22% in the most affected region of Lombardia and 0.57% in the most affected province of Bergamo, which constitutes a lower bound to the Infection Fatality Rate (IFR). We estimate PFR as a function of age, finding a steep age dependence: in Lombardia (Bergamo province) 0.6% (1.7%) of the total population in age group 70-79 died, 1.6% (4.6%) in age group 80-89, and 3.41% (10.2%) in the age group above 90. We combine this with the Test Positivity Rate to estimate the lower bound of 0.84% on the IFR for Lombardia. We observe IFR to trace the Yearly Mortality Rate (YMR) above 60 years, which can be used to estimate the IFR for other regions in the world. We predict an IFR lower bound of 0.5% for NYC and 26% of total COVID-19 mortality arising from the population below 65 years, in agreement with the existing data and several times higher than Lombardia. Combining PFR with the Princess Diamond cruise ship IFR for ages above 70 we estimate the infection rates (IR) of regions in Italy, which peak in Lombardia at 23% (12%-41%, 95% c.l.), and for provinces in Bergamo at 67% (33%-100%, 95% c.l.). This suggests that Bergamo may have reached herd immunity, and that the number of infected people greatly exceeds the number of positive tests, by a factor of 35 in Lombardia∗.

scholarly journals Seasonality in extra-pulmonary tuberculosis notifications in Germany 2004-2014- a time series analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tanja Charles ◽  
Matthias Eckardt ◽  
Basel Karo ◽  
Walter Haas ◽  
Stefan Kröger
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Disease Onset ◽  
Age Groups ◽  
Age Group ◽  
Pulmonary Tb ◽  
Series Analysis ◽  
Disease Site ◽  
Extra Pulmonary

Abstract Background Seasonality in tuberculosis (TB) has been found in different parts of the world, showing a peak in spring/summer and a trough in autumn/winter. The evidence is less clear which factors drive seasonality. It was our aim to identify and evaluate seasonality in the notifications of TB in Germany, additionally investigating the possible variance of seasonality by disease site, sex and age group. Methods We conducted an integer-valued time series analysis using national surveillance data. We analysed the reported monthly numbers of started treatments between 2004 and 2014 for all notified TB cases and stratified by disease site, sex and age group. Results We detected seasonality in the extra-pulmonary TB cases (N = 11,219), with peaks in late spring/summer and troughs in fall/winter. For all TB notifications together (N = 51,090) and for pulmonary TB only (N = 39,714) we did not find a distinct seasonality. Additional stratified analyses did not reveal any clear differences between age groups, the sexes, or between active and passive case finding. Conclusion We found seasonality in extra-pulmonary TB only, indicating that seasonality of disease onset might be specific to the disease site. This could point towards differences in disease progression between the different clinical disease manifestations. Sex appears not to be an important driver of seasonality, whereas the role of age remains unclear as this could not be sufficiently investigated.

scholarly journals Differential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico: a time-series analysis

2018 ◽  
Vol 2 (11) ◽  
pp. e478-e488 ◽  
Author(s):  
Carlos Santos-Burgoa ◽  
John Sandberg ◽  
Erick Suárez ◽  
Ann Goldman-Hawes ◽  
Scott Zeger ◽  
...  
Keyword(s):  
Time Series ◽  
Puerto Rico ◽  
Time Series Analysis ◽  
Excess Mortality ◽  
Series Analysis

scholarly journals Contribution of Alcohol to Hypertension Mortality in Russia

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Y. E. Razvodovsky
Keyword(s):  
Time Series ◽  
Alcohol Consumption ◽  
Mortality Rate ◽  
Time Series Analysis ◽  
Binge Drinking ◽  
Mortality Rates ◽  
Mortality Data ◽  
Effective Measure ◽  
Male And Female ◽  
Series Analysis

Background. Hypertension (HTN) is reported to be the leading contributor to premature death globally. Considerable research evidence suggests that excessive alcohol intake (binge drinking) is an independent risk factor for HTN. It was repeatedly emphasized that binge drinking is a major contributor to a high cardiovascular mortality rate in Russia.Objective. The aim of this study was to examine the aggregate-level relation between alcohol consumption and HTN mortality rates in Russia.Method. Age-standardized sex-specific male and female HTN mortality data for the period 1980–2005 and data on overall alcohol consumption were analyzed by means of ARIMA (autoregressive integrated moving average) time-series analysis. The level of alcohol consumption per capita has been estimated using the indirect method based on alcohol psychoses incidence rate and employing ARIMA time-series analysis.Results. Alcohol consumption was significantly associated with both male and female HTN mortality rates: a 1-liter increase in overall alcohol consumption would result in a 6.3% increase in the male HTN mortality rate and in a 4.9% increase in female HTN mortality rate. The results of the analysis suggest that 57.5% of all male HTN deaths and 48.6% of all female HTN deaths in Russia could be attributed to alcohol.Conclusions. The outcomes of this study provide support for the hypothesis that alcohol is an important contributor to the high HTN mortality rate in the Russian Federation. The findings from the present study have important implications with to regards HTN mortality prevention, indicating that a restrictive alcohol policy can be considered as an effective measure of prevention in countries with a higher rate of alcohol consumption.

scholarly journals Evaluation of the impact of the NICE head injury guidelines on inpatient mortality from traumatic brain injury: an interrupted time series analysis

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e028912 ◽  
Author(s):  
Carl Marincowitz ◽  
Fiona Lecky ◽  
Victoria Allgar ◽  
Trevor Sheldon
Keyword(s):  
Time Series ◽  
Head Injury ◽  
Mortality Rate ◽  
Time Series Analysis ◽  
Interrupted Time Series ◽  
Age Group ◽  
Interrupted Time Series Analysis ◽  
Admission Rates ◽  
Series Analysis ◽  
The Impact

ObjectiveTo evaluate the impact of National Institute for Health and Care Excellence (NICE) head injury guidelines on deaths and hospital admissions caused by traumatic brain injury (TBI).SettingAll hospitals in England between 1998 and 2017.ParticipantsPatients admitted to hospital or who died up to 30 days following hospital admission with International Classification of Diseases (ICD) coding indicating the reason for admission or death was TBI.InterventionAn interrupted time series analysis was conducted with intervention points when each of the three guidelines was introduced. Analysis was stratified by guideline recommendation specific age groups (0–15, 16–64 and 65+).Outcome measuresThe monthly population mortality and admission rates for TBI.Study designAn interrupted time series analysis using complete Office of National Statistics cause of death data linked to hospital episode statistics for inpatient admissions in England.ResultsThe monthly TBI mortality and admission rates in the 65+ age group increased from 0.5 to 1.5 and 10 to 30 per 100 000 population, respectively. The increasing mortality rate was unaffected by the introduction of any of the guidelines.The introduction of the second NICE head injury guideline was associated with a significant reduction in the monthly TBI mortality rate in the 16–64 age group (-0.005; 95% CI: −0.002 to −0.007).In the 0–15 age group the TBI mortality rate fell from around 0.05 to 0.01 per 100 000 population and this trend was unaffected by any guideline.ConclusionThe introduction of NICE head injury guidelines was associated with a reduced admitted TBI mortality rate after specialist care was recommended for severe TBI. The improvement was solely observed in patients aged 16–64 years.The cause of the observed increased admission and mortality rates in those 65+ and potential treatments for TBI in this age group require further investigation.

scholarly journals Estimating COVID-19 mortality in Italy early in the COVID-19 pandemic

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chirag Modi ◽  
Vanessa Böhm ◽  
Simone Ferraro ◽  
George Stein ◽  
Uroš Seljak
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
Time Series Analysis ◽  
Infection Rate ◽  
Mortality Data ◽  
Cruise Ship ◽  
Series Analysis ◽  
Case Ascertainment ◽  
Population Mortality ◽  
Overall Mortality

AbstractEstimating rates of COVID-19 infection and associated mortality is challenging due to uncertainties in case ascertainment. We perform a counterfactual time series analysis on overall mortality data from towns in Italy, comparing the population mortality in 2020 with previous years, to estimate mortality from COVID-19. We find that the number of COVID-19 deaths in Italy in 2020 until September 9 was 59,000–62,000, compared to the official number of 36,000. The proportion of the population that died was 0.29% in the most affected region, Lombardia, and 0.57% in the most affected province, Bergamo. Combining reported test positive rates from Italy with estimates of infection fatality rates from the Diamond Princess cruise ship, we estimate the infection rate as 29% (95% confidence interval 15–52%) in Lombardy, and 72% (95% confidence interval 36–100%) in Bergamo.

scholarly journals Time-series analysis of monthly age-specific numbers of newly registered cases of active tuberculosis in Japan from 1998 to 2013

2016 ◽  
Vol 144 (11) ◽  
pp. 2401-2414 ◽  
Author(s):  
Y. KOHEI ◽  
A. SUMI ◽  
N. KOBAYASHI
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Least Squares Method ◽  
Control Strategies ◽  
Age Groups ◽  
Series Data ◽  
Age Group ◽  
Series Analysis ◽  
Winter Temperatures

SUMMARYWe investigated the seasonality of age-specific tuberculosis (TB) in Japan. To allow the development of TB control strategies for different age groups we used a time-series analysis, including a spectral analysis and least squares method, to analyse the monthly age-specific numbers of newly registered cases of all forms of active TB in Japan from January 1998 to December 2013. The time-series data are reported in 10-year age groups: 0–9, 10–19, …, 70–79, and ⩾80 years. We defined the contribution ratio of the 1-year cycle, Q1, as the contribution of the amplitude of a 1-year cycle to the whole amplitude of the time-series data. The Q1 values in the age groups corresponding to adolescence and middle life (10–39 years) and old age (⩾70 years) were high. The peaks in the active TB epidemics for the ⩾70 years age group occurred in August and September, 1–2 months behind the peaks for the 10–39 years age group (June and July). An active TB epidemic might be attributable to travel by public transport and irregular employment in the 10–39 years age group and immune system suppression by low winter temperatures in the ⩾70 years age group.

scholarly journals Research on the Trend of the Height of Han Students Aged 7-18 in China

2020 ◽  
Author(s):  
Chenlu Hong ◽  
Zhang Rui ◽  
Fei Wu
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Arima Model ◽  
Interdisciplinary Studies ◽  
Trend Test ◽  
Age Group ◽  
Height Difference ◽  
Series Analysis ◽  
Comparison Results ◽  
The Mean

AbstractAimsTo make time series analysis on the 29-year (1985−2014) trends in height among Han Students Aged 7-18 in China and predict their future height from the perspective of interdisciplinary studies of demography and physical education.MethodsThe data were from the findings of seven cross-sectional surveys from the Chinese National Survey on Students’ Constitution and Health (CNSSCH). The mean, standard error, variance analysis, trend test, F test, T test, development growth and growth rate were used to make a descriptive analysis of height trends. The ARIMA model in the time series analysis was applied to predict the height development of Han students in 2024.ResultsThe height of students aged 7-18 substantially increased from 1985 to2014. The mean of the height of boys increased by 3.99 cm, 8.01 cm, 8.31 cm, 8.63 cm, 9.81 cm, 11.62 cm, 10.38 cm, 9.22 cm, 7.50 cm, 5.59 cm, 4.51 cm, and 3.79 cm, respectively. The mean of the height of girls increased by 6.66 cm, 7.36 cm, 8.00 cm, 8.84 cm, 9.60 cm, 8.66 cm, 5.57 cm, 4.65 cm, 3.95 cm, 3.32 cm, 2.87 cm and 3.32 cm, respectively. The increasing disparity of the sex differential in the mean height was also observed. From 1985 to 2014, the mean of height difference between boys and girls aged 7-18 had been increased constantly. Their mean height differences were 3.27 cm, 4.03 cm, 4.64 cm, and 5.13 cm, respectively in 1985, 1995, 2005, and 2014. A narrowing of the urban−rural differential in the mean height was observed. In 1985, on average, urban boys aged 7-18 were 4.18cm higher than rural boys in the same age group. And the height differences between urban and rural boys in the same age group were 3.58 cm, 3.18 cm and 2.41 cm, respectively in 1995, 2005 and 2014. According to comparison results, the mean height of urban girls was greater than that of rural girls, and the height difference between urban and rural girls had been constantly narrowed as well. The results of 4 comparisons showed that the height differences between urban and rural girls were 3.68 cm, 3.12 cm, 2.62 cm, and 1.98 cm, respectively.ConclusionsThere was a general increase in the height of Chinese Han students aged 7-18 in the past 29 years and difference between sex, rural-urban and age have been observed. In 2024, the height of students will continue to grow.

scholarly journals Factors associated with excess all-cause mortality in the first wave of COVID-19 pandemic in the UK: a time-series analysis using the Clinical Practice Research Datalink

2021 ◽  
Author(s):  
Helen Strongman ◽  
Helena Carreira ◽  
Bianca L De Stavola ◽  
Krishnan Bhaskaran ◽  
David A Leon
Keyword(s):  
Time Series ◽  
Learning Disabilities ◽  
Clinical Practice ◽  
Time Series Analysis ◽  
Excess Mortality ◽  
Practice Research ◽  
Clinical Practice Research Datalink ◽  
Series Analysis ◽  
All Cause Mortality ◽  
The Uk

Objectives: Excess mortality captures the total effect of the COVID-19 pandemic on mortality and is not affected by mis-specification of cause of death. We aimed to describe how health and demographic factors have been associated with excess mortality during the pandemic. Design: Time-series analysis. Setting: UK primary care data from practices contributing to the Clinical Practice Research Datalink on July 31st 2020. Participants: We constructed a time-series dataset including 9,635,613 adults (≥40 years old) who were actively registered at the general practice during the study period. Main outcome measures: We extracted weekly numbers of deaths between March 2015 and July 2020, stratified by individual-level factors. Excess mortality during wave 1 of the UK pandemic (5th March to 27th May 2020) compared to pre-pandemic was estimated using seasonally adjusted negative binomial regression models. Relative rates of death for a range of factors were estimated before and during wave 1 by including interaction terms. Results: All-cause mortality increased by 43% (95% CI 40%-47%) during wave 1 compared with pre-pandemic. Changes to the relative rate of death associated with most socio-demographic and clinical characteristics were small during wave 1 compared with pre-pandemic. However, the mortality rate associated with dementia markedly increased (RR for dementia vs no dementia pre-pandemic: 3.5, 95% CI 3.4-3.5; RR during wave 1: 5.1, 4.87-5.28); a similar pattern was seen for learning disabilities (RR pre-pandemic: 3.6, 3.4-3.5; during wave 1: 4.8, 4.4-5.3), for Black or South Asian ethnicity compared to white, and for London compared to other regions. Conclusions: The first UK COVID-19 wave appeared to amplify baseline mortality risk by a relatively constant factor for most population subgroups. However disproportionate increases in mortality were seen for those with dementia, learning disabilities, non-white ethnicity, or living in London.

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