During the SARS-CoV-2 pandemic, heterogeneous mortality patterns have been recorded worldwide, both in terms of cause-specific and all-cause mortality. A plethora of indicators (COVID-19 confirmed mortality, all-cause mortality, cause-specific mortality, non-COVID-19 mortality, excess mortality, and many more) have been used to investigate those patterns.
These indicators have strengths and weaknesses, but they can be used individually or in combination to describe or explain the pandemic’s impact on society at multiple levels. It is therefore of primary importance to analyze mortality patterns by studying their spatial (spatial analysis) and temporal (time-series analysis) distribution, their possible association with explanatory variables (public health policies, non-pharmaceutical interventions, socio-demographic characteristics of the population, vaccination campaigns, and more), and their implications for health policies, with a particular focus for public health policies and practice.
Mortality patterns are mostly related to the large-scale interventions implemented to control the pandemic and/or to pre-existing conditions. An animated debate has risen about whether and to what extent these interventions have been effective in slowing down the pandemic. The question remains open as several studies have revealed differences in the efficacy of the implemented public health interventions worldwide. The main goal of this Research Topic is to publish methodologically sound scientific papers that investigate spatial and temporal patterns of mortality during the SARS-CoV-2 pandemic, their relationship with other factors, and their implication in terms of health policies and future challenges.
Authors are therefore invited to submit studies that analyze publicly available data, aggregated data, data generated by healthcare systems, and other data, comparing mortality patterns between geographical areas, investigating the spatio-temporal patterns and their possible determinants, attempting to assess the relationship between adopted policy and the burden of mortality directly or indirectly linked to COVID-19, or addressing the burden of the pandemic on the population, through methodologically sound study design and analyses.
Authors are invited to submit original articles, reviews and systematic reviews, method articles, conceptual analysis, perspective, and other types of articles that address topics within the overall scope of this Research Topic: to analyze patterns and determinants of all-cause, cause-specific, and excess- mortality during the SARS-CoV-2 pandemic.
Areas to be covered in this Research Topic to reach its scope may include, but are not limited to:
- COVID-19 confirmed mortality
- non-COVID-19 mortality
- all-cause and cause-specific mortality
- excess mortality
Analyses to be used in this Research Topic to reach its scope may include, but are not limited to:
- spatial analyses
- time-series analyses
- panel data analyses
and may use several study designs:
- descriptive analyses
- retrospective and prospective studies
- counterfactual analyses
- quasi-experiment
- natural experiment
During the SARS-CoV-2 pandemic, heterogeneous mortality patterns have been recorded worldwide, both in terms of cause-specific and all-cause mortality. A plethora of indicators (COVID-19 confirmed mortality, all-cause mortality, cause-specific mortality, non-COVID-19 mortality, excess mortality, and many more) have been used to investigate those patterns.
These indicators have strengths and weaknesses, but they can be used individually or in combination to describe or explain the pandemic’s impact on society at multiple levels. It is therefore of primary importance to analyze mortality patterns by studying their spatial (spatial analysis) and temporal (time-series analysis) distribution, their possible association with explanatory variables (public health policies, non-pharmaceutical interventions, socio-demographic characteristics of the population, vaccination campaigns, and more), and their implications for health policies, with a particular focus for public health policies and practice.
Mortality patterns are mostly related to the large-scale interventions implemented to control the pandemic and/or to pre-existing conditions. An animated debate has risen about whether and to what extent these interventions have been effective in slowing down the pandemic. The question remains open as several studies have revealed differences in the efficacy of the implemented public health interventions worldwide. The main goal of this Research Topic is to publish methodologically sound scientific papers that investigate spatial and temporal patterns of mortality during the SARS-CoV-2 pandemic, their relationship with other factors, and their implication in terms of health policies and future challenges.
Authors are therefore invited to submit studies that analyze publicly available data, aggregated data, data generated by healthcare systems, and other data, comparing mortality patterns between geographical areas, investigating the spatio-temporal patterns and their possible determinants, attempting to assess the relationship between adopted policy and the burden of mortality directly or indirectly linked to COVID-19, or addressing the burden of the pandemic on the population, through methodologically sound study design and analyses.
Authors are invited to submit original articles, reviews and systematic reviews, method articles, conceptual analysis, perspective, and other types of articles that address topics within the overall scope of this Research Topic: to analyze patterns and determinants of all-cause, cause-specific, and excess- mortality during the SARS-CoV-2 pandemic.
Areas to be covered in this Research Topic to reach its scope may include, but are not limited to:
- COVID-19 confirmed mortality
- non-COVID-19 mortality
- all-cause and cause-specific mortality
- excess mortality
Analyses to be used in this Research Topic to reach its scope may include, but are not limited to:
- spatial analyses
- time-series analyses
- panel data analyses
and may use several study designs:
- descriptive analyses
- retrospective and prospective studies
- counterfactual analyses
- quasi-experiment
- natural experiment