Decline in Perinatal Mortality in the United States, 2017–2019

This report presents trends in perinatal mortality, as well as its components, late fetal and early neonatal mortality, for 2017 through 2019. Also shown are perinatal mortality trends by mother’s age, race and Hispanic origin, and state for 2017–2019.

Cervical cancer mortality among young women in Latin America and the Caribbean: trend analysis from 1997 to 2030

Background Cervical cancer continues to show a high burden among young women worldwide, particularly in low- and middle-income countries. Limited data is available describing cervical cancer mortality among young women in Latin America and the Caribbean (LAC). The purpose of this study was to examine the mortality trends of cervical cancer among young women in LAC and predict mortality rates to 2030. Methods Deaths from cervical cancer were obtained from the World Health Organization mortality database. Age-standardized mortality rates per 100,000 women-years were estimated in women aged 20–44 years using the world standard population for 16 countries (and territories) in LAC from 1997 to 2017. We estimated the average mortality rates for the last 4 years (2014–2017). Joinpoint regression models were used to identify significant changes in mortality trends. Nordpred method was used for the prediction of the mortality rates to 2030. Results Between 2014 and 2017, Paraguay and Venezuela had the highest mortality rates of cervical cancer, whereas Puerto Rico had the lowest rates. Overall, most of the LAC countries showed downward trends of cervical cancer mortality over the entire period. Significant decreases were observed in Chile (Average annual percent change [AAPC]: − 2.4%), Colombia (AAPC: − 2.0%), Cuba (AAPC: − 3.6%), El Salvador (AAPC: − 3.1%), Mexico (AAPC: − 3.9%), Nicaragua (AAPC: − 1.7%), Panama (AAPC: − 1.7%), and Peru (AAPC: − 2.2%). In contrast, Brazil (AAPC: + 0.8%) and Paraguay (AAPC: + 3.7%) showed significant upward trends. By 2030, mortality rates are not predicted to further decrease in some LAC countries, including Argentina, Paraguay, and Venezuela. Conclusions Mortality trends of cervical cancer among young women have large variability in LAC countries. Cervical cancer screening programs have a high priority for the region. Primary and secondary prevention in the community are necessary to accelerate a reduction of cervical cancer mortality by 2030.

Assessing excess mortality in Vienna and Austria after the first year of the COVID-19 pandemic

In Austria, the first confirmed COVID-19 death occurred in early March 2020. Since then, the question as to whether and, if so, to what extent the COVID-19 pandemic has increased overall mortality has been raised in the public and academic discourse. In an effort to answer this question, Statistics Vienna (City of Vienna, Department for Economic Affairs, Labour and Statistics) has evaluated the weekly mortality trends in Vienna, and compared them to the trends in other Austrian provinces. For our analysis, we draw on data from Statistics Austria and the Austrian Agency for Health and Food Safety (AGES), which are published along with data on the actual and the expected weekly numbers of deaths via the Vienna Mortality Monitoring website. Based on the definition of excess mortality as the actual number of reported deaths from all causes minus the expected number of deaths, we calculate the weekly prediction intervals of the expected number of deaths for two age groups (0 to 64 years and 65 years and older). The temporal scope of the analysis covers not only the current COVID-19 pandemic, but also previous flu seasons and summer heat waves. The results show the actual weekly numbers of deaths and the corresponding prediction intervals for Vienna and the other Austrian provinces since 2007. Our analysis underlines the importance of comparing time series of COVID-19-related excess deaths at the sub-national level in order to highlight within-country heterogeneities.

Assessing neo-natal mortality trends in Pakistan: an insight using equity lens

Background Almost 2.5 million neonates died in the first year of life in the year 2017. These account for almost half of the total deaths of children under the age of 5 years. Overall, child mortality has declined over the past two decades. Comparatively, the pace of decline in neonatal mortality has remained much slow. Significant inequalities in health across several dimensions – including wealth, ethnicity, and geography – continue to exist both between and within countries, and these contribute to neonatal mortality. This study aims to quantify the magnitude of inequalities in neonatal mortality trends by wealth quintile and place of residence with province wise segregation. Methods The study was done using raw data from the last three Pakistan Demographic & Health Surveys (2017–18, 2012–13 and 2006–07). The concentration curves were drawn in Microsoft Excel 365 using scatter plot as graph type while the frequencies were calculated using SPSS 24. Results The situation of inequity across provinces and in rural vs urban areas has slightly declined, however, gross inequities continue to exist. Conclusions Presentation of outcomes data, such as neonatal mortality in various wealth quintiles is an effective way to highlight the inequities amongst income groups as it highlights the vulnerable and at-risk groups. In other countries, rural-urban distribution, or ethnic groups may also reflect similar differences and help in identifying high-risk groups.

Allowing for shocks in portfolio mortality models

The COVID-19 pandemic creates a challenge for actuaries analysing experience data that include mortality shocks. Without sufficient local flexibility in the time dimension, any analysis based on the most recent data will be biased by the temporarily higher mortality. Also, depending on where the shocks sit in the exposure period, any attempt to identify mortality trends will be distorted. We present a methodology for analysing portfolio mortality data that offer local flexibility in the time dimension. The approach permits the identification of seasonal variation, mortality shocks and occurred-but-not reported deaths (OBNR). The methodology also allows actuaries to measure portfolio-specific mortality improvements. Finally, the method assists actuaries in determining a representative mortality level for long-term applications like reserving and pricing, even in the presence of mortality shocks. Results are given for a mature annuity portfolio in the UK, which suggest that the Bayesian information criterion is better for actuarial model selection in this application than Akaike’s information criterion.

Lung cancer incidence and mortality trends in the Republic of Belarus (1990-2019)

favorable malignant diseases globally. In the Republic of Belarus, it ranks third (8.2%) in the cancer incidence structure and first (21.5%) in the cancer mortality structure. The object of the study were the lung cancer incidence and mortality trends in the Republic of Kazakhstan in 1990-2019. Methods: The present study included all patients - residents of Belarus, registered in the national cancer registry from 1990 to 2019 with a diagnosis of lung cancer (ICD-10 code: C33-C34). Demographic variables included gender, age, and area of residence (urban or rural). The number of PD cases is presented as absolute values and rough intensity indicators per 100,000 population. Standardized morbidity and mortality rates are calculated using the world standard (World) and are indicated per 100,000 population. Results: In the study period, the standardized incidence rate decreased from 27.5 to 25.6 per 100 000 population (– 7.1%, p<0.01). In males, it decreased from 62.1 to 54.6 per 100 000 males (– 12.1%, p<0.001); in females, it increased from 5.3 to 6.4 per 100 000 females (+20.7%, p<0.05). The standardized lung cancer mortality rate has decreased over the study period from 23.0 to 18.3 per 100 000 population (– 20.4%, p<0.001). In males, it went down from 53.6 to 40.7 (– 24.1%, p<0.001), and in women it changed slightly from 3.6 to 3.7 (+2.8%, p>0.05) per 100 000 of the relevant sex. The average annual increase in standardized mortality decreased eight times faster than the growth in standardized incidence. Conclusion: In the Republic of Belarus, lung cancer incidence is increasing in males and decreasing in females. At that, lung cancer mortality is decreasing. Quality specialized cancer care creates conditions for quicker negative growth of lung cancer standardized mortality vs. incidence

Disparities on Pancreatic Cancer Mortality Trends and Human Development Index (HDI) in Brazil

Pancreatic cancer mortality is greatest in countries with a high and very high Human Development Index (HDI). The aim was to evaluate the pancreatic cancer mortality rates and trends related to HDI in Brazil by state. An ecological study was conducted on pancreatic cancer mortality in Brazilian states between 1979 and 2019. Age-standardized mortality rates (ASMR) and Annual Average Percent Change (AAPC) were calculated. Pearson´s correlation test was applied to compare rates over the 3 decades from 1986-2015 to verify correlation between change in HDI from 1991 to 2010. A total of 209,425 deaths from pancreatic cancer were reported in Brazil from 1979 to 2019. In men, ASMRs ranged from 2.9/100,000 in 1979 to 6.1/100,000 in 2019, with AAPC of 1.5% per year, and in women, ASMR ranged from 2.1/100,000 in 1979 to 4.7/100,000 in 2019, with AAPC of 1.9% per year. Mortality rates and trends increase with higher % of HDI improvement with a correlation between ASMRs and HDI above r>0.80. The mortality trends in pancreatic cancer were uneven increase in Brazil, there was an upward trend in mortality in both genders, but higher among women. Pancreatic cancer mortality trends were higher in those states where there was greatest increase in HDI, regions as North and Northeast. However, mortality rates remain higher in South, Southeast and Central-West of Brazil.

Dying From COVID-19 or With COVID-19: A Definitive Answer Through a Retrospective Analysis of Mortality in Italy (Preprint)

BACKGROUND COVID-19 mortality was associated with several reasons, including conspiracy theories and infodemic phenomena. However, little is known about the potential endogenous reasons for the increase in COVID-19 associated mortality in Italy. OBJECTIVE This study aimed to search the potential endogenous reasons for the increase in COVID-19 mortality recorded in Italy during the year 2020 and evaluate the statistical significance of the latter. METHODS We analyzed all the trends in the timelapse 2011-2019 related to deaths by age, sex, region, and cause of death in Italy and compared them with those of 2020. Ordinary least squares (OLS) linear regressions and ARIMA (p, d, q) models were applied to investigate the predictions of death in 2020 as compared to death reported in the same year. Grubbs and Iglewicz-Hoaglin tests were used to identify the statistical differences between the predicted and observed deaths. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. RESULTS Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (95% confidence intervals range: 620,000 – 695,000) and these values were far from the observed deaths reported (above 750,000). Significant difference in deaths at national level (P = 0.003), and higher male mortality than women (+18% versus +14%, P < 0.001 versus P = 0.01) was observed. Finally, higher mortality was strongly and positively correlated with latitude (R = 0.82, P < 0.001). CONCLUSIONS Our findings support the absence of historical endogenous reasons capable of justifying the increase in deaths and mortality observed in Italy in 2020. Together with the current knowledge on the novel coronavirus 2019, these findings provide decisive evidence on the devastating impact of COVID-19 in Italy. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses. Moreover, given the marked concordance between the predictions of the ARIMA and OLS regression models, we suggest that these models be exploited to predict mortality trends.

Dying from COVID-19 or with COVID-19: a definitive answer through a retrospective analysis of mortality in Italy

Background: COVID-19 mortality was associated with several reasons, including conspiracy theories and infodemic phenomena. However, little is known about the potential endogenous reasons for the increase in COVID-19 associated mortality in Italy. Objective: This study aimed to search the potential endogenous reasons for the increase in COVID-19 mortality recorded in Italy during the year 2020 and evaluate the statistical significance of the latter. Methods: We analyzed all the trends in the timelapse 2011-2019 related to deaths by age, sex, region, and cause of death in Italy and compared them with those of 2020. Ordinary least squares (OLS) linear regressions and ARIMA (p, d, q) models were applied to investigate the predictions of death in the year 2020 as compared to death reported in 2020. Grubbs and Iglewicz-Hoaglin tests were used to identify the statistical differences between the predictors and observed death during the year 2020. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Results: Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during the year 2020 is between 640,000 and 660,000 (95% confidence intervals range: 620,000 - 695,000) and these values were far from the observed deaths reported (n = 750,000). Significant difference in deaths at national level (P = 0.003), and higher male mortality than women (+18% versus +14%, P < 0.001 versus P = 0.01) was observed. Finally, higher mortality was strongly and positively correlated with latitude (R = 0.82, P < 0.001) Conclusions: Our findings suggest that the absence of historical endogenous reasons capable of justifying the increase in deaths and mortality observed in Italy in 2020. Together with the current knowledge on the novel coronavirus 2019, these findings provide decisive evidence on the devastating impact of COVID-19 in Italy. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy theorists. Moreover, given the marked concordance between the predictions of the ARIMA and OLS regression models, we suggest that these models be exploited to predict mortality trends.