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 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 Effects of covid-19 pandemic on life expectancy and premature mortality in 2020: time series analysis in 37 countries

BMJ ◽  
2021 ◽  
pp. e066768
Author(s):  
Nazrul Islam ◽  
Dmitri A Jdanov ◽  
Vladimir M Shkolnikov ◽  
Kamlesh Khunti ◽  
Ichiro Kawachi ◽  
...  
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Life Expectancy ◽  
Confidence Interval ◽  
Time Series Analysis ◽  
South Korea ◽  
Mortality Data ◽  
Years Of Life Lost ◽  
The Us ◽  
The Difference

Abstract Objective To estimate the changes in life expectancy and years of life lost in 2020 associated with the covid-19 pandemic. Design Time series analysis. Setting 37 upper-middle and high income countries or regions with reliable and complete mortality data. Participants Annual all cause mortality data from the Human Mortality Database for 2005-20, harmonised and disaggregated by age and sex. Main outcome measures Reduction in life expectancy was estimated as the difference between observed and expected life expectancy in 2020 using the Lee-Carter model. Excess years of life lost were estimated as the difference between the observed and expected years of life lost in 2020 using the World Health Organization standard life table. Results Reduction in life expectancy in men and women was observed in all the countries studied except New Zealand, Taiwan, and Norway, where there was a gain in life expectancy in 2020. No evidence was found of a change in life expectancy in Denmark, Iceland, and South Korea. The highest reduction in life expectancy was observed in Russia (men: −2.33, 95% confidence interval −2.50 to −2.17; women: −2.14, −2.25 to −2.03), the United States (men: −2.27, −2.39 to −2.15; women: −1.61, −1.70 to −1.51), Bulgaria (men: −1.96, −2.11 to −1.81; women: −1.37, −1.74 to −1.01), Lithuania (men: −1.83, −2.07 to −1.59; women: −1.21, −1.36 to −1.05), Chile (men: −1.64, −1.97 to −1.32; women: −0.88, −1.28 to −0.50), and Spain (men: −1.35, −1.53 to −1.18; women: −1.13, −1.37 to −0.90). Years of life lost in 2020 were higher than expected in all countries except Taiwan, New Zealand, Norway, Iceland, Denmark, and South Korea. In the remaining 31 countries, more than 222 million years of life were lost in 2020, which is 28.1 million (95% confidence interval 26.8m to 29.5m) years of life lost more than expected (17.3 million (16.8m to 17.8m) in men and 10.8 million (10.4m to 11.3m) in women). The highest excess years of life lost per 100 000 population were observed in Bulgaria (men: 7260, 95% confidence interval 6820 to 7710; women: 3730, 2740 to 4730), Russia (men: 7020, 6550 to 7480; women: 4760, 4530 to 4990), Lithuania (men: 5430, 4750 to 6070; women: 2640, 2310 to 2980), the US (men: 4350, 4170 to 4530; women: 2430, 2320 to 2550), Poland (men: 3830, 3540 to 4120; women: 1830, 1630 to 2040), and Hungary (men: 2770, 2490 to 3040; women: 1920, 1590 to 2240). The excess years of life lost were relatively low in people younger than 65 years, except in Russia, Bulgaria, Lithuania, and the US where the excess years of life lost was >2000 per 100 000. Conclusion More than 28 million excess years of life were lost in 2020 in 31 countries, with a higher rate in men than women. Excess years of life lost associated with the covid-19 pandemic in 2020 were more than five times higher than those associated with the seasonal influenza epidemic in 2015.

scholarly journals Changes in soft drinks purchased by British households associated with the UK soft drinks industry levy: controlled interrupted time series analysis

BMJ ◽  
2021 ◽  
pp. n254 ◽  
Author(s):  
David Pell ◽  
Oliver Mytton ◽  
Tarra L Penney ◽  
Adam Briggs ◽  
Steven Cummins ◽  
...  
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
Time Series Analysis ◽  
Interrupted Time Series ◽  
Soft Drinks ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Market Research Company ◽  
One Year ◽  
The Uk

Abstract Objective To determine changes in household purchases of drinks and confectionery one year after implementation of the UK soft drinks industry levy (SDIL). Design Controlled interrupted time series analysis. Participants Members of a panel of households reporting their purchasing on a weekly basis to a market research company (average weekly number of participants n=22 183), March 2014 to March 2019. Intervention A two tiered tax levied on manufacturers of soft drinks, announced in March 2016 and implemented in April 2018. Drinks with ≥8 g sugar/100 mL (high tier) are taxed at £0.24/L and drinks with ≥5 to <8 g sugar/100 mL (low tier) are taxed at £0.18/L. Drinks with <5 g sugar/100 mL (no levy) are not taxed. Main outcome measures Absolute and relative differences in the volume of, and amount of sugar in, soft drinks categories, all soft drinks combined, alcohol, and confectionery purchased per household per week one year after implementation of the SDIL compared with trends before the announcement of the SDIL. Results In March 2019, compared with the counterfactual estimated from pre-announcement trends, purchased volume of drinks in the high levy tier decreased by 155 mL (95% confidence interval 240.5 to 69.5 mL) per household per week, equivalent to 44.3% (95% confidence interval 59.9% to 28.7%), and sugar purchased in these drinks decreased by 18.0 g (95% confidence interval 32.3 to 3.6 g), or 45.9% (68.8% to 22.9%). Purchases of low tier drinks decreased by 177.3 mL (225.3 to 129.3 mL) per household per week, or 85.9% (95.1% to 76.7%), with a 12.5 g (15.4 to 9.5 g) reduction in sugar in these drinks, equivalent to 86.2% (94.2% to 78.1%). Despite no overall change in volume of no levy drinks purchased, there was an increase in sugar purchased of 15.3 g (12.6 to 17.9 g) per household per week, equivalent to 166.4% (94.2% to 238.5%). When all soft drinks were combined, the volume of drinks purchased did not change, but sugar decreased by 29.5 g (55.8 to 3.1 g), or 9.8% (17.9% to 1.8%). Purchases of confectionery and alcoholic drinks did not change. Conclusions Compared with trends before the SDIL was announced, one year after implementation, the volume of soft drinks purchased did not change. The amount of sugar in those drinks was 30 g, or 10%, lower per household per week—equivalent to one 250 mL serving of a low tier drink per person per week. The SDIL might benefit public health without harming industry. Trial registration ISRCTN18042742.

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 Association of Logic’s hip hop song “1-800-273-8255” with Lifeline calls and suicides in the United States: interrupted time series analysis

BMJ ◽  
2021 ◽  
pp. e067726 ◽  
Author(s):  
Thomas Niederkrotenthaler ◽  
Ulrich S Tran ◽  
Madelyn Gould ◽  
Mark Sinyor ◽  
Steven Sumner ◽  
...  
Keyword(s):  
United States ◽  
Time Series ◽  
Confidence Interval ◽  
Time Series Analysis ◽  
Hip Hop ◽  
The United States ◽  
Full Time ◽  
Public Attention ◽  
Expected Number ◽  
Series Analysis

Abstract Objective To assess changes in daily call volumes to the US National Suicide Prevention Lifeline and in suicides during periods of wide scale public attention to the song “1-800-273-8255” by American hip hop artist Logic. Design Time series analysis. Setting United States, 1 January 2010 to 31 December 2018. Participants Total US population. Lifeline calls and suicide data were obtained from Lifeline and the Centers for Disease Control and Prevention. Main outcome measures Daily Lifeline calls and suicide data before and after the release of the song. Twitter posts were used to estimate the amount and duration of attention the song received. Seasonal autoregressive integrated moving average time series models were fitted to the pre-release period to estimate Lifeline calls and suicides. Models were fitted to the full time series with dummy variables for periods of strong attention to the song. Results In the 34 day period after the three events with the strongest public attention (the song’s release, the MTV Video Music Awards 2017, and Grammy Awards 2018), Lifeline received an excess of 9915 calls (95% confidence interval 6594 to 13 236), an increase of 6.9% (95% confidence interval 4.6% to 9.2%, P<0.001) over the expected number. A corresponding model for suicides indicated a reduction over the same period of 245 suicides (95% confidence interval 36 to 453) or 5.5% (95% confidence interval 0.8% to 10.1%, P=0.02) below the expected number of suicides. Conclusions Logic’s song “1-800-273-8255” was associated with a large increase in calls to Lifeline. A reduction in suicides was observed in the periods with the most social media discourse about the song.

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