Statistical Inferences and Analysis based on the COVID-19 data from the United States

This paper investigates the mortality statistics of the COVID-19 pandemic from the United States perspective. Using empirical data analysis and statistical inference tools, we bring out several exciting and important aspects of the pandemic, otherwise hidden. Specific patterns seen in demo-graphics such as race/ethnicity and age are discussed both qualitatively and quantitatively. We also study the role played by factors such as population density. Connections between COVID-19 and other respiratory diseases are also covered in detail. The temporal dynamics of the COVID-19 outbreak and the impact of vaccines in controlling the pandemic are also looked at with sufficient rigor. It is hoped that statistical inference such as the ones gathered in this paper would be helpful for better scientific understanding, policy preparation and thus adequately preparing, should a similar situation arise in the future.

Determinants of the Underlying Causes of Mortality during the First Wave of COVID-19 Pandemic in Saudi Arabia: A Descriptive National Study

Since the emergence of the COVID-19 pandemic, the mortality statistics are constantly changing globally. Mortality statistics analysis has vital implications to implement evidence-based policy recommendations. This study aims to study the demographic characteristics, patterns, determinants, and the main causes of death during the first half of 2020, in the Kingdom of Saudi Arabia (KSA). Methodology: A retrospective descriptive study targeted all death (29291) registered in 286 private and governmental health settings, from all over KSA. The data was extracted from the ministry of health’s death records after the ethical approval. The International Classification of Diseases (ICD-10) and WHO grouping, were used to classify the underlying causes of deaths. The collected data were analyzed using the appropriate tables and graphs. Results: 7055 (24.9%) died at the middle age (40–59 y), and 19212(65.6%) were males, and 18110 (61.8%) were Saudi. The leading causes of deaths were non-communicable diseases (NCDs) 15340 (62.1%), mainly Cardiovascular diseases (CVDs) 10103(34. 5%). There was a significant relationship between the main causes of deaths and sex (p< 0.05) and nationality (p = 0.01). Conclusion: NCDs mainly CVDs are the leading cause of death. The Covid-19 mortalities were mainly in males, and old age >55y. The lockdown was associated with a reduction in the NCDs and Road traffic accidents mortalities.

COVID-19 In United States: Statistical Analysis and Inferences From the Empirical Data

This paper investigates the mortality statistics of the COVID-19 pandemic from the United States perspective. Using empirical data analysis and statistical inference tools, we bring out several exciting and important aspects of the pandemic, otherwise hidden. Specific patterns seen in demo- graphics such as race/ethnicity and age are discussed both qualitatively and quantitatively. We also study the role played by factors such as population density. Connections between COVID-19 and other respiratory diseases are also covered in detail. The temporal dynamics of the COVID-19 outbreak and the impact of vaccines in controlling the pandemic are also looked at with suf- ficient rigor. It is hoped that statistical inference such as the ones gathered in this paper would be helpful for better scientific understanding, policy prepa- ration and thus adequately preparing, should a similar situation arise in the future.

Accounting for Pandemic: Better Numbers for Management and Policy

Infection, hospitalization and mortality statistics have played a pivotal role in forming social attitudes and support for policy decisions about the 2020-21 SARS-CoV-2 (COVID-19) pandemic. This article raises some questions on some of the most widely-used indicators, such as the case fatality rate, derived from these statistics, recommending replacing them with information based on regular stratified statistical sampling, coupled with diagnostic assessment. Some implications for public health policies and pandemic management are developed, opposing individualistic and holistic approaches.

Mortality estimates for WHO SEAR countries: problems and prospects

Cause-specific mortality estimates for 11 countries located in the WHO’s South East Asia Region (WHO SEAR) are generated periodically by the Global Burden of Disease (GBD) and the WHO Global Health Estimates (GHE) analyses. A comparison of GBD and GHE estimates for 2019 for 11 specific causes of epidemiological importance to South East Asia was undertaken. An index of relative difference (RD) between the estimated numbers of deaths by sex for each cause from the two sources for each country was calculated, and categorised as marginal (RD=±0%–9%), moderate (RD=±10%–19%), high (RD=±20%–39%) and extreme (RD>±40%). The comparison identified that the RD was >10% in two-thirds of all instances. The RD was ‘high’ or ‘extreme’ for deaths from tuberculosis, diarrhoea, road injuries and suicide for most SEAR countries, and for deaths from most of the 11 causes in Bangladesh, DPR Korea, Myanmar, Nepal and Sri Lanka. For all WHO SEAR countries, mortality estimates from both sources are based on statistical models developed from an international historical cause-specific mortality data series that included very limited empirical data from the region. Also, there is no scientific rationale available to justify the reliability of one set of estimates over the other. The characteristics of national mortality statistics systems for each WHO SEAR country were analysed, to understand the reasons for weaknesses in empirical data. The systems analysis identified specific limitations in structure, organisation and implementation that affect data completeness, validity of causes of death and vital statistics production, which vary across countries. Therefore, customised national strategies are required to strengthen mortality statistics systems to meet immediate and long-term data needs for health policy and research, and reduce dependence on current unreliable modelled estimates.

896Quantifying cause-related mortality incorporating multiple causes: challenges and opportunities

Key contact person Dr Grace Joshy, Fellow, Research School of Population Health, Australian National University. Focus and outcomes for participants Mortality statistics are typically based on a single underlying cause of death (UCoD), derived from multiple conditions on the death certificate, and have provided critical evidence for policy and practice for over a century. There have been radical shifts in patterns of death in the past couple of decades; deaths in older ages are increasingly from chronic and degenerative diseases. The relevance of assuming that a single disease is causing the death is diminishing, especially with an aging population structure and increasing life expectancy. This symposium will enable participants to understand the complexities associated with mortality reporting/coding, strengths and limitations of available statistical methods for using multiple causes of death (MCoD) and the importance of quantifying mortality incorporating MCoD. Rationale for the symposium, including for its inclusion in the Congress The use of a single UCoD rather than MCoD means that vast amounts of potentially useful data are largely ignored, which is likely to bias mortality estimates (including under- and over-reporting of the importance of certain causes of death). Despite global recognition of the urgent need to better integrate data on MCoD into mortality statistics, use of these data are challenging and limited. Complexities arise from the way mortality information is reported on death certificates and coded to form mortality collections; limited understanding of available statistical methods also adds to the complexity. International Classification of Diseases 10th Revision (ICD-10) has been translated into 43 languages and it is being used by over 100 countries to report mortality data, a primary indicator of health status. The 2018 release of the 11th revision of the International Classification of Diseases, enriching data on multiple parameters including comorbidity, confers further urgency and a unique opportunity to optimise the use of MCoD in mortality reporting. The World Congress of Epidemiology 2020 will provide a unique platform for wider discussions on the challenges and opportunities for using MCoD data. The symposium will provide a deeper understanding and enhanced the use of MCoD data. The speakers are engaged in cutting-edge NHMRC-funded research on mortality incorporating MCoD and development of novel statistical methods. Presentation program The symposium will feature presentations from six speakers. Names of presenters James Eynstone-Hinkins, Lauren Moran, Saliu Balogun, Karen Bishop, Margarita Moreno-Betancur, Grace Joshy

Pandemic excess mortality in Spain, Sweden, and Switzerland during the COVID-19 pandemic in 2020 was at its highest since 1918

Estimating excess mortality allows quantification of overall pandemic impact. For recent decades, mortality data are easily accessible for most industrialized countries, but only a few countries have continuous data available for longer periods. Since Spain, Sweden, and Switzerland were militarily neutral and not involved in combat during both world wars, these countries have monthly all-cause mortality statistics available for over 100 years with no interruptions. We show that during the COVID-19 pandemic in 2020, Spain, Sweden and Switzerland recorded the highest aggregated monthly excess mortality (17%, 9% and 14%) since the 1918 influenza pandemic (53%, 33% and 49%), when compared to respective expected values. For Sweden and Switzerland, the highest monthly spikes in 2020 almost reached those of January 1890. These findings emphasize the historical dimensions of the ongoing pandemic and support the notion of a pandemic disaster memory gap.

Technological Progress and Health Convergence: The Case of Penicillin in Postwar Italy

Throughout history, technological progress has transformed population health, but the distributional effects of these gains are unclear. New substitutes for older, more expensive health technologies can produce convergence in population health outcomes but may also be prone to elite capture and thus divergence. We study the case of penicillin using detailed historical mortality statistics and exploiting its abruptly timed introduction in Italy after WWII. We find that penicillin reduced both the mean and standard deviation of infectious disease mortality, leading to substantial convergence across disparate regions of Italy. Our results do not appear to be driven by competing risks or confounded by mortality patterns associated with WWII.