Excess mortality in Israel associated with COVID-19 in 2020–2021 by age group and with estimates based on daily mortality patterns in 2000–2019

Peretz, Chava; Rotem, Naama; Keinan-Boker, Lital; Furshpan, Avner; Green, Manfred S.; Bitan, Michal; Steinberg, David M.

doi:10.1093/ije/dyac047

Cited by 17 publications

(16 citation statements)

References 19 publications

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“…Generally, this result replicated restricting to different demographic groups and a shorter pre-intervention interval of five rather than 20 years. Additionally, these results are consistent with previous studies [15,16] which also found an increase of about 10-12% in all-cause mortality rates in Israel during the Covid-19 period, even though both used a different statistical model and other study covariates. That is, the results are robust to the chosen statistical model.…”

Section: Discussionsupporting

confidence: 91%

Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research

Travis-Lumer

Goldberg

Levine

2022

Emerg Themes Epidemiol

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Background Interrupted time series (ITS) analysis is a time series regression model that aims to evaluate the effect of an intervention on an outcome of interest. ITS analysis is a quasi-experimental study design instrumental in situations where natural experiments occur, gaining popularity, particularly due to the Covid-19 pandemic. However, challenges, including the lack of a control group, have impeded the quantification of the effect size in ITS. The current paper proposes a method and develops a user-friendly R package to quantify the effect size of an ITS regression model for continuous and count outcomes, with or without seasonal adjustment. Results The effect size presented in this work, together with its corresponding 95% confidence interval (CI) and P-value, is based on the ITS model-based fitted values and the predicted counterfactual (the exposed period had the intervention not occurred) values. A user-friendly R package to fit an ITS and estimate the effect size was developed and accompanies this paper. To illustrate, we implemented a nation population-based ITS study from January 2001 to May 2021 covering the all-cause mortality of Israel (n = 9,350 thousand) to quantify the effect size of Covid-19 exposure on mortality rates. In the period unexposed to the Covid-19 pandemic, the mortality rate decreased over time and was expected to continue decreasing had Covid-19 not occurred. In contrast, the period exposed to the Covid-19 pandemic was associated with an increased all-cause mortality rate (relative risk = 1.11, 95% CI = 1.04, 1.18, P < 0.001). Conclusion For the first time, the effect size in ITS: was quantified, can be estimated by end-users with an R package we developed, and was demonstrated with data showing an increase in mortality following the Covid-19 pandemic. ITS effect size reporting can assist public health policy makers in assessing the magnitude of the entire intervention effect using a single, readily understood measure.

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Section: Discussionsupporting

confidence: 91%

Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research

Travis-Lumer

Goldberg

Levine

2022

Emerg Themes Epidemiol

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“…In addition, androgens, especially testosterone, are considered a possible risk factor [ 66 ]. Populations older than 60 years had higher excess mortality, and several country studies also indicate the same results [ 42 , 45 , 67 , 68 ]. We believe that the senior population has lower physical function and immunity compared to younger people, and they are more susceptible to the neo-crown virus during the pandemic [ 69 , 70 ].…”

Section: Discussionmentioning

confidence: 65%

“…After exclusion, 439 studies were eligible for full-text review. The final meta-analysis comprised 20 eligible studies ( Figure 1 ), References [ 15 , 21 , 24 , 25 , 26 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ] of which 79 countries were included in the study ( Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

Global Excess Mortality during COVID-19 Pandemic: A Systematic Review and Meta-Analysis

et al. 2022

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Background: Currently, reported COVID-19 deaths are inadequate to assess the impact of the pandemic on global excess mortality. All-cause excess mortality is a WHO-recommended index for assessing the death burden of COVID-19. However, the global excess mortality assessed by this index remains unclear. We aimed to assess the global excess mortality during the COVID-19 pandemic. Methods: We searched PubMed, EMBASE, and Web of Science for studies published in English between 1 January 2020, and 21 May 2022. Cross-sectional and cohort studies that reported data about excess mortality during the pandemic were included. Two researchers independently searched the published studies, extracted data, and assessed quality. The Mantel–Haenszel random-effects method was adopted to estimate pooled risk difference (RD) and their 95% confidence intervals (CIs). Results: A total of 79 countries from twenty studies were included. During the COVID-19 pandemic, of 2,228,109,318 individuals, 17,974,051 all-cause deaths were reported, and 15,498,145 deaths were expected. The pooled global excess mortality was 104.84 (95% CI 85.56–124.13) per 100,000. South America had the highest pooled excess mortality [134.02 (95% CI: 68.24–199.80) per 100,000], while Oceania had the lowest [−32.15 (95% CI: −60.53–−3.77) per 100,000]. Developing countries had higher excess mortality [135.80 (95% CI: 107.83–163.76) per 100,000] than developed countries [68.08 (95% CI: 42.61–93.55) per 100,000]. Lower middle-income countries [133.45 (95% CI: 75.10–191.81) per 100,000] and upper-middle-income countries [149.88 (110.35–189.38) per 100,000] had higher excess mortality than high-income countries [75.54 (95% CI: 53.44–97.64) per 100,000]. Males had higher excess mortality [130.10 (95% CI: 94.15–166.05) per 100,000] than females [102.16 (95% CI: 85.76–118.56) per 100,000]. The population aged ≥ 60 years had the highest excess mortality [781.74 (95% CI: 626.24–937.24) per 100,000]. Conclusions: The pooled global excess mortality was 104.84 deaths per 100,000, and the number of reported all-cause deaths was higher than expected deaths during the global COVID-19 pandemic. In South America, developing and middle-income countries, male populations, and individuals aged ≥ 60 years had a heavier excess mortality burden.

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“…Our method of comparison of mortality with previous 3-years’ average allowed analysis by population group, age groups, for a vaccinated cohort, and similar comparison of non-COVID-19 mortality. By calculating rate ratios and comparing corresponding months, we controlled for changes in population size and for seasonality, although not for decreasing mortality trend and influenza morbidity, included in the more refined statistical model of Peretz et al [ 2 ]. However, they noted a smaller decrease in mortality in recent years, so this effect is likely to be small.…”

Section: Discussionmentioning

confidence: 99%

“…In a previous paper on excess mortality in Israel between March and October, 2020, [ 1 ], the excess mortality was presented by month, age, population group and locality for the first two waves. A recent paper by Peretz et al [ 2 ] analyzed total excess mortality in Israel by age groups until the end of March 2021, using a predictive model based on 19 years of mortality data (2000–2019) and including weather data, influenza counts, and yearly population sizes, and comparing excess mortality with that attributed to COVID-19. They found 12% higher excess for the whole period analyzed, with varying excesses in different age groups over age 19.…”

Section: Introductionmentioning

confidence: 99%

Mortality during the first four waves of COVID-19 pandemic in Israel: March 2020–October 2021

Haklai

Goldberger

Gordon

2022

Isr J Health Policy Res

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Background The COVID-19 pandemic caused four waves of infection in Israel until October 2021. Israel was the first country to offer vaccinations to all the adult population followed by boosters. This study addresses how mortality rates reflect the effects of the pandemic. Methods Total mortality rates and rates of mortality without COVID-19 deaths (non-COVID-19 mortality) between March 2020 and October 2021 were compared with the average pre-COVID-19 rates in 2017–2019 by month, population group and by age group. In addition, a cohort vaccinated at least once by 31 March 2021 was followed up for mortality in the following seven months compared to the corresponding months in 2017–2019. Results A small number of excess deaths was found in the first wave and a greater excess in the following waves. The monthly mortality rate ratio was highest in October 2020, 23% higher than the average in 2017–2019, followed by August 2021 (22%), September 2021 (20%) and September 2020 (19%). Excess mortality in the Arab population was greater than for Jews and Others, and they had 65% and 43% higher mortality in September and October, 2020, 20–44% higher mortality between December 2020 and April 2021 and 33%, 45% and 22% higher mortality in August, September and October 2021, respectively. In most months of the pandemic, the non-COVID-19 mortality rates were not significantly different from those in 2017–2019. However, between November 2020 and March 2021, they were significantly lower for the total population and Jews and Others. They were significantly higher for the total population only in August 2021, and particularly for the Arab population. Non-COVID-19 mortality was also lower for most sex/age groups over the total study period. In a cohort of 5.07 million Israeli citizens vaccinated at least once by 31 March, 2021, age adjusted and age specific mortality rates for the following 7 months were lower than the average rates in 2017–2019 for these months, Conclusion Israel has seen significant excess mortality during the COVID-19 pandemic, particularly in the Arab sector. Following lockdowns and administration of vaccinations excess mortality was reduced, and no excess mortality was seen amongst the vaccinated in the months after the vaccination campaign. These findings highlight the importance of public health measures such as mandating mask wearing and population vaccinations to control infection and reduce mortality.

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Excess mortality in Israel associated with COVID-19 in 2020–2021 by age group and with estimates based on daily mortality patterns in 2000–2019

Cited by 17 publications

References 19 publications

Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research

Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research

Global Excess Mortality during COVID-19 Pandemic: A Systematic Review and Meta-Analysis

Mortality during the first four waves of COVID-19 pandemic in Israel: March 2020–October 2021

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