ABSTRACT
Background/Aims@#Global distribution of dominant liver cancer aetiologies has significantly changed over the past decades. This study analyzed the updated temporal trends of liver cancer aetiologies and sociodemographic status in 204 countries and territories from 1990 to 2019. @*Methods@#The Global Burden of Disease 2019 report was used for statistical analysis. In addition, we performed stratification analysis to five quintiles using sociodemographic index and 21 geographic regions. @*Results@#The crude numbers of liver cancer disease-adjusted life years (DALYs) and deaths significantly increased during the study period (DALYs; 11,278,630 in 1990 and 12,528,422 in 2019, deaths; 365,215 in 1990 and 484,577 in 2019). However, the Age-standardized DALY and mortality rates decreased. Hepatitis B virus (HBV) remains the leading cause of liver cancer DALYs and mortality, followed by hepatitis C virus (HCV), alcohol consumption, and non-alcoholic steatohepatitison-alcoholic fatty liver disease (NASH/NAFLD). Although Age-standardized DALY and mortality rates of liver cancer due to HBV and HCV have decreased, the rates due to alcohol consumption and NASH/NAFLD have increased. In 2019, the population of the East Asia region had the highest Age-standardized DALY and mortality rates, followed by high-income Asia-Pacific and Central Asia populations. Although East Asia and high-income Asia-Pacific regions showed a decrease during the study period, Age-standardized DALY rates increased in Central Asia. High-income North American and Australasian populations also showed a significant increase in Age-standardized DALY. @*Conclusions@#Liver cancer remains an ongoing global threat. The burden of liver cancer associated with alcohol consumption and NASH/NAFLD is markedly increasing and projected to continuously increase.
ABSTRACT
Objective@#Previous studies have shown that meteorological factors may increase COVID-19 mortality, likely due to the increased transmission of the virus. However, this could also be related to an increased infection fatality rate (IFR). We investigated the association between meteorological factors (temperature, humidity, solar irradiance, pressure, wind, precipitation, cloud coverage) and IFR across Spanish provinces ( @*Methods@#We estimated IFR as excess deaths (the gap between observed and expected deaths, considering COVID-19-unrelated deaths prevented by lockdown measures) divided by the number of infections (SARS-CoV-2 seropositive individuals plus excess deaths) and conducted Spearman correlations between meteorological factors and IFR across the provinces.@*Results@#We estimated 2,418,250 infections and 43,237 deaths. The IFR was 0.03% in < 50-year-old, 0.22% in 50-59-year-old, 0.9% in 60-69-year-old, 3.3% in 70-79-year-old, 12.6% in 80-89-year-old, and 26.5% in ≥ 90-year-old. We did not find statistically significant relationships between meteorological factors and adjusted IFR. However, we found strong relationships between low temperature and unadjusted IFR, likely due to Spain's colder provinces' aging population.@*Conclusion@#The association between meteorological factors and adjusted COVID-19 IFR is unclear. Neglecting age differences or ignoring COVID-19-unrelated deaths may severely bias COVID-19 epidemiological analyses.
Subject(s)
Adult , Aged , Aged, 80 and over , Humans , Middle Aged , Young Adult , COVID-19/virology , Meteorological Concepts , Pandemics/statistics & numerical data , SARS-CoV-2/physiology , Spain/epidemiology , WeatherABSTRACT
Since the outbreak of the coronavirus disease 2019 (COVID-19) in December of 2019 in China, the estimation of the pandemics case fatality rate (CFR) has been the focus and interest of many stakeholders. In this manuscript, we prove that the method of using the cumulative CFR is static and does not reflect the trend according to the daily change per unit of time. A proportion meta-analysis was carried out on CFR in every country reporting COVID-19 cases. Based on the results, we performed a meta-analysis for global COVID-19 CFR. Each analysis was performed on two different calculations of CFR: according to calendar date and according to days since the outbreak of the first confirmed case. We thus explored an innovative and original calculation of CFR concurrently based on the date of the first confirmed case as well as on a daily basis. For the first time, we showed that using meta-analyses, according to calendar date and days since the outbreak of the first confirmed case were different. We propose that CFR according to days since the outbreak of the first confirmed case might be a better predictor of the current CFR of COVID-19 and its kinetics.
