True COVID-19 mortality rates from administrative data

Depalo, Domenico

doi:10.1007/s00148-020-00801-6

Cited by 30 publications

(28 citation statements)

References 38 publications

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“… 8 Mortality rates from COVID-19 in Lombardia of Italy were known to be remarkably high. However, after controlling for selection bias, mortality rates ranged between 0.1 and 7.5%, which is far smaller than the 17.5% earlier reported (Depalo 2020 ). …”

contrasting

confidence: 65%

School closures and mental health during the COVID-19 pandemic in Japan

Yamamura

Tsustsui

2021

J Popul Econ

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The spread of the novel coronavirus disease caused schools in Japan to close to cope with the pandemic. In response to the school closures, parents of students were obliged to care for their children during the daytime, when children usually were at school. Did the increase in the burden of childcare influence parents’ mental health? Based on short panel data from mid-March to mid-April 2020, we explore how school closures influenced the mental health of parents with school-aged children. Using a fixed-effects model, we find that school closures led to mothers of students suffering from worse mental health compared to other females, while the fathers’ mental health did not differ from that of other males. This tendency is only observed for less-educated mothers who had children attending primary school, not for those with children attending junior high school nor for more-educated mothers. The contribution of this paper is showing that school closures increased the inequality of mental health between genders and parents with different educational backgrounds.

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contrasting

confidence: 65%

School closures and mental health during the COVID-19 pandemic in Japan

Yamamura

Tsustsui

2021

J Popul Econ

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“…While according toDepalo (2020) and others, death rates might not have been in reality that high, the salient numbers of deaths discussed in the media and related public policy measures might have significantly affected the perceptions, mental health, and behavior of people, as pointed among others byQiu et al (2020);Milani (2020); and.…”

mentioning

confidence: 99%

Culture and mental health resilience in times of COVID-19

Tubadji

2021

J Popul Econ

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This paper aims to clarify the role of culture as a public good that serves to preserve mental health. It tests the evolutionary hypothesis that cultural consumption triggers a microeconomic mechanism for the self-defense of mental health from uncertainty. The COVID-19 pandemic offers a natural experiment of cultural consumption under increased uncertainty. Using primary data from a pilot survey conducted online during the pandemic and applying Probit and Heckman selection models, the study analyzes levels of happiness and propensity to help others. The results suggest that past consumption of culture is associated with higher happiness levels during crises. Moreover, spontaneous cultural practices (such as group singing) during times of uncertainty are associated with an increase in the pro-social propensity to help others. These findings highlight culture as a tool for promoting mental health at the micro level and social capital resilience at the aggregate level.

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“…They found that deaths recorded as directly due to COVID-19 constitute only half of the excess deaths verified in March. In comparison, Depalo ( 2021 ) applied partial identification to administrative data at the municipality level to estimate the number of deaths, the number of infections, and mortality rates from COVID-19 in Lombardy. He calculated that in March 2020, there were between 10,000 and 18,500 deaths, more than in the period 2015–2019.…”

Section: Literature Reviewmentioning

confidence: 99%

Local mortality estimates during the COVID-19 pandemic in Italy

et al. 2021

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Estimates 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.

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True COVID-19 mortality rates from administrative data

Cited by 30 publications

References 38 publications

School closures and mental health during the COVID-19 pandemic in Japan

School closures and mental health during the COVID-19 pandemic in Japan

Culture and mental health resilience in times of COVID-19

Local mortality estimates during the COVID-19 pandemic in Italy

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