scholarly journals How COVID-19 affects voting for incumbents: Evidence from local elections in France

2021 ◽  
Author(s):  
Davide Morisi ◽  
Héloïse Cloléry ◽  
Guillaume Kon Kam King ◽  
Max Schaub
Keyword(s):  
Propensity Score ◽  
Excess Mortality ◽  
Indirect Evidence ◽  
Mortality Data ◽  
Local Elections ◽  
Municipal Elections ◽  
Fine Grained ◽  
Dependent Variables ◽  
Left And Right ◽  
Fear And Anxiety

How do voters react to an ongoing natural threat? We address this question by investigating voters’ reactions to the early spread of COVID-19 in the 2020 French municipal elections. Using a novel, fine-grained measure of the circulation of the virus based on excess-mortality data, we find that support for incumbents increased in the areas that were particularly hit by the virus. Incumbents from both left and right gained votes in areas more strongly affected by COVID-19. The results are robust to a placebo test and hold across different methods, including regressions with lagged dependent variables, a differences-in-differences approach and propensity score matching. We also provide indirect evidence for two mechanisms that can explain our findings: an emotional channel related to feelings of fear and anxiety, and a prospective-voting channel, related to the ability of incumbents to act more swiftly against the diffusion of the virus than challengers.

scholarly journals Volcanic Ash as a precursor for SARS-CoV-2 infection among susceptible populations in Ecuador: A satellite Imaging and excess mortality-based analysis

2021 ◽  
pp. 1-37
Author(s):  
Theofilos Toulkeridis ◽  
Rachid Seqqat ◽  
Marbel Torres Arias ◽  
Rodolfo Salazar-Martinez ◽  
Esteban Ortiz-Prado ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Excess Mortality ◽  
Heart Diseases ◽  
Health Determinants ◽  
Mortality Data ◽  
Previous Exposure ◽  
Satellite Imaging ◽  
Fine Grained ◽  
Volcanic Ashes ◽  
Susceptible Populations

Abstract The global COVID-19 pandemic has altered entire nations and their health systems. The greatest impact of the pandemic has been seen among vulnerable populations such as those with comorbidities like heart diseases, kidney failure, obesity or those with worst health determinants like unemployment and poverty. In the current study, we are proposing previous exposure to fine-grained volcanic ashes as a risk factor for developing COVID-19. Based on several previous studies it has been known since the mid-eight-tees of the last century that volcanic ash is most likely an accelerating factor to suffer from different types of cancer including lung or thyroid cancer. Our study postulates, that people who are most likely to be infected during a SARS-CoV-2 widespread wave will be those with comorbidities that are related to previous exposure to volcanic ashes. We have explored 8,703 satellite images from the last 21 years of available data from the NOAA database and correlated them with the data from the national institute of health statistics in Ecuador. Additionally, we provide more realistic numbers of fatalities due to the virus based on excess mortality data of 2020-2021, when compared to previous years. This study would be a very first of its kind combining social and spatial distribution of COVID-19 infections and volcanic ash distribution. The results and implications of our study will also help countries to identify such aforementioned vulnerable parts of the society, if the given geodynamic and volcanic settings are similar.

scholarly journals Local mortality estimates during the COVID-19 pandemic in Italy

2021 ◽  
Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli
Keyword(s):  
Machine Learning ◽  
Excess Mortality ◽  
Control Method ◽  
Local Level ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Mortality Data ◽  
Official Method ◽  
Learning Techniques ◽  
Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

Excess Mortality During the COVID-19 Pandemic in Philadelphia

2021 ◽  
pp. e1-e6
Author(s):  
Megan Todd ◽  
Meagan Pharis ◽  
Sam P. Gulino ◽  
Jessica M. Robbins ◽  
Cheryl Bettigole
Keyword(s):  
Public Health ◽  
Excess Mortality ◽  
Poisson Model ◽  
People Of Color ◽  
Mortality Data ◽  
Registration System ◽  
Vital Registration System ◽  
All Cause Mortality ◽  
Race Ethnicity ◽  
Excess Deaths

Objectives. To estimate excess all-cause mortality in Philadelphia, Pennsylvania, during the COVID-19 pandemic and understand the distribution of excess mortality in the population. Methods. With a Poisson model trained on recent historical data from the Pennsylvania vital registration system, we estimated expected weekly mortality in 2020. We compared these estimates with observed mortality to estimate excess mortality. We further examined the distribution of excess mortality by age, sex, and race/ethnicity. Results. There were an estimated 3550 excess deaths between March 22, 2020, and January 2, 2021, a 32% increase above expectations. Only 77% of excess deaths (n=2725) were attributed to COVID-19 on the death certificate. Excess mortality was disproportionately high among older adults and people of color. Sex differences varied by race/ethnicity. Conclusions. Excess deaths during the pandemic were not fully explained by COVID-19 mortality; official counts significantly undercount the true death toll. Far from being a great equalizer, the COVID-19 pandemic has exacerbated preexisting disparities in mortality by race/ethnicity. Public Health Implications. Mortality data must be disaggregated by age, sex, and race/ethnicity to accurately understand disparities among groups. (Am J Public Health. Published online ahead of print June 10, 2021: e1–e6. https://doi.org/10.2105/AJPH.2021.306285 )

Newspaper news exposure predicts political participation

2017 ◽  
Vol 38 (2) ◽  
pp. 231-244 ◽  
Author(s):  
Esther Thorson ◽  
Scott Swafford ◽  
Eunjin (Anna) Kim
Keyword(s):  
Local Government ◽  
Political Participation ◽  
Media Use ◽  
Political Knowledge ◽  
Local Elections ◽  
Government Structure ◽  
Municipal Elections ◽  
Political News ◽  
Perceived Importance

This study reports a survey of media use, political knowledge, and participation in local elections by people in three small Midwest communities. This study showed that newspaper political news exposure strongly predicted political participation, perceived importance of local municipal elections, and self-reported voting. It did not, however, predict knowledge about local government structure.

scholarly journals Forecast of the COVID-19 trend in India: a simple modelling approach

2021 ◽  
Author(s):  
Haitao Song ◽  
Guihong Fan ◽  
Shi Zhao ◽  
Huichen Li ◽  
Qihua Huang ◽  
...  
Keyword(s):  
Model Simulation ◽  
Current Trend ◽  
Health And Safety ◽  
Wave Pattern ◽  
Mortality Data ◽  
Local Elections ◽  
Sporting Events ◽  
Epidemic Size ◽  
New Variant ◽  
Short Period

Abstract By February 2021, the overall impact of the COVID-19 pandemic in India had been relatively mild in terms of total reported cases and deaths. Surprisingly, the second wave in early April becomes devastating and attracts worldwide attention. On April 30, 2021, India became the first country reporting over 400,000 daily new cases. Multiple factors drove the rapid growth of the epidemic in India and caused a large number of deaths within a very short period. These factors include a new variant with increased transmissibility, a lack of preparations exists national wide, and health and safety precautions poorly implemented or enforced during festivals, sporting events, and state/local elections. Moreover, India's cases and deaths are vastly underreported due to poor infrastructure, and low testing rates. In this paper, we use the COVID-19 mortality data in India and a mathematical model to calculate the effective reproduction number and to model the wave pattern in India. We propose a new approach to forecast the epidemic size and peak timing in India with the aim to inform mitigation in India. Our model simulation matched the reported deaths accurately and is reasonably close to results of serological study. We forecast that the IAR could reach 43% by June 13, 2021 under the current trend, which means 532,629 reported deaths with a 95% CI (552,445, 513,194) ie., double the current total deaths. Our approach is readily applicable in other countries and with other type of data (e.g. excess deaths).

scholarly journals Excess mortality in Guadeloupe and Martinique, islands of the French West Indies, during the chikungunya epidemic of 2014

2018 ◽  
Vol 146 (16) ◽  
pp. 2059-2065 ◽  
Author(s):  
A. R. R. Freitas ◽  
P. M. Alarcón-Elbal ◽  
M. R. Donalisio
Keyword(s):  
Strong Correlation ◽  
Excess Mortality ◽  
Pearson Correlation ◽  
Age Groups ◽  
Epidemiological Data ◽  
Mortality Data ◽  
Surveillance Systems ◽  
Mortality And Morbidity ◽  
The Impact ◽  
Excess Deaths

AbstractIn some chikungunya epidemics, deaths are not completely captured by traditional surveillance systems, which record case and death reports. We evaluated excess deaths associated with the 2014 chikungunya virus (CHIKV) epidemic in Guadeloupe and Martinique, Antilles. Population (784 097 inhabitants) and mortality data, estimated by sex and age, were accessed from the Institut National de la Statistique et des Études Économiques in France. Epidemiological data, cases, hospitalisations and deaths on CHIKV were obtained from the official epidemiological reports of the Cellule de Institut de Veille Sanitaire in France. Excess deaths were calculated as the difference between the expected and observed deaths for all age groups for each month in 2014 and 2015, considering the upper limit of 99% confidence interval. The Pearson correlation coefficient showed a strong correlation between monthly excess deaths and reported cases of chikungunya (R= 0.81,p< 0.005) and with a 1-month lag (R= 0.87,p< 0.001); and a strong correlation was also observed between monthly rates of hospitalisation for CHIKV and excess deaths with a delay of 1 month (R= 0.87,p< 0.0005). The peak of the epidemic occurred in the month with the highest mortality, returning to normal soon after the end of the CHIKV epidemic. There were excess deaths in almost all age groups, and excess mortality rate was higher among the elderly but was similar between male and female individuals. The overall mortality estimated in the current study (639 deaths) was about four times greater than that obtained through death declarations (160 deaths). Although the aetiological diagnosis of all deaths associated with CHIKV infection is not always possible, already well-known statistical tools can contribute to the evaluation of the impact of CHIKV on mortality and morbidity in the different age groups.

scholarly journals An Exploration of Temperature Metrics for Further Developing the Heat-Health Weather Warning System in Hong Kong

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Cho Kwong Charlie Lam ◽  
Margaret Loughnan ◽  
Nigel Tapper
Keyword(s):  
Hong Kong ◽  
Temperature Change ◽  
Excess Mortality ◽  
Extreme Temperature ◽  
Warning System ◽  
Weather Data ◽  
Mortality Data ◽  
Critical Thresholds ◽  
Daily Weather Data ◽  
Heat And Cold Stress

Background. The current weather warning system aims to reduce mortality from heat and cold stress but still has room to be improved in terms of incorporating other temperature metrics. The aim of this study is to determine how extreme temperature affects mortality in Hong Kong. Methods. An ecological study was used; daily weather data were subdivided into seven temperature metrics. Daily detrended mortality data were stratified by disease groups and analysed using seven different metrics for temperature. The temperature metrics were then compared. Results. A diurnal temperature range (DTR) of ≥8°C leading to an increase in median mortality of up to 16% and a mean temperature change between neighbouring days of ≥4°C leading to an increase in median mortality of up to 6% were the critical thresholds for excess mortality in Hong Kong. Conclusions. This study reveals that mean net effective temperature, DTR, and temperature change between neighbouring days are effective to predict excess mortality in Hong Kong.

scholarly journals Pioglitazone and cause-specific risk of mortality in patients with type 2 diabetes: extended analysis from a European multidatabase cohort study

2018 ◽  
Vol 6 (1) ◽  
pp. e000481 ◽  
Author(s):  
Helen Strongman ◽  
Solomon Christopher ◽  
Maila Majak ◽  
Rachael Williams ◽  
Shahram Bahmanyar ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Propensity Score ◽  
Cardiovascular Mortality ◽  
Proportional Hazards ◽  
Cohort Analysis ◽  
Cox Proportional Hazards ◽  
Exposed Group ◽  
Mortality Data ◽  
Exact Matching

ObjectivesDescribe and compare the risk of cardiovascular and non-cardiovascular mortality in patients whose antidiabetic therapy is modified to include pioglitazone compared with an alternative antidiabetic medication at the same stage of disease progression.Research design and methodsThis exploratory linked database cohort analysis used pooled health and mortality data from three European countries: Finland, Sweden and the UK. Propensity score together with exact matching was used to match 31 133 patients with type 2 diabetes first prescribed pioglitazone from 2000 to 2011, to 31 133 patients never prescribed pioglitazone. Exact matching variables were treatment stage, history of diabetes, diabetes complications and cardiovascular disease, and year of cohort entry. Mean follow-up time was 2.60 (SD 2.00) and 2.69 (SD 2.31) years in the pioglitazone and non-pioglitazone-exposed groups, respectively. Crude cause-specific mortality rates were ascertained. Association with pioglitazone use was estimated using Cox proportional hazards models adjusted a priori for country, age, sex, the propensity score quintile and time-dependent variables representing use of antidiabetic drugs. Stepwise testing identified no additional confounders to include in adjusted models.ResultsThe crude mortality rate was lower in the pioglitazone-exposed group than the non-exposed group for both cardiovascular and non-cardiovascular mortality. Adjusted HRs comparing pioglitazone to alternative antidiabetic exposure were 0.58 (95% CI 0.52 to 0.63) and 0.63 (95% CI 0.58 to 0.68) for cardiovascular and non-cardiovascular mortality, respectively. A protective effect associated with pioglitazone was also found for all specific cardiovascular causes.ConclusionsThis analysis suggests that pioglitazone is associated with a decrease in both cardiovascular and non-cardiovascular mortality. Results should be interpreted with caution due to the potential for residual confounding in this exploratory analysis. Further studies, specifically designed to test the association between pioglitazone use and patient-focused outcomes, are suggested.Study registration numberEuropean Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP; EUPAS3626).

scholarly journals Real-time estimation of the influenza-associated excess mortality in Hong Kong

2019 ◽  
Vol 147 ◽  
Author(s):  
Jessica Y. Wong ◽  
Edward Goldstein ◽  
Vicky J. Fang ◽  
Benjamin J. Cowling ◽  
Peng Wu
Keyword(s):  
Hong Kong ◽  
Real Time ◽  
Excess Mortality ◽  
Time Estimation ◽  
Surveillance Data ◽  
Control Measures ◽  
Influenza Surveillance ◽  
Mortality Data ◽  
Real Time Estimation ◽  
The Impact

Abstract Statistical models are commonly employed in the estimation of influenza-associated excess mortality that, due to various reasons, is often underestimated by laboratory-confirmed influenza deaths reported by healthcare facilities. However, methodology for timely and reliable estimation of that impact remains limited because of the delay in mortality data reporting. We explored real-time estimation of influenza-associated excess mortality by types/subtypes in each year between 2012 and 2018 in Hong Kong using linear regression models fitted to historical mortality and influenza surveillance data. We could predict that during the winter of 2017/2018, there were ~634 (95% confidence interval (CI): (190, 1033)) influenza-associated excess all-cause deaths in Hong Kong in population ⩾18 years, compared to 259 reported laboratory-confirmed deaths. We estimated that influenza was associated with substantial excess deaths in older adults, suggesting the implementation of control measures, such as administration of antivirals and vaccination, in that age group. The approach that we developed appears to provide robust real-time estimates of the impact of influenza circulation and complement surveillance data on laboratory-confirmed deaths. These results improve our understanding of the impact of influenza epidemics and provide a practical approach for a timely estimation of the mortality burden of influenza circulation during an ongoing epidemic.

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