Historical Mortality Data Research Articles

Artificial Intelligence in Life Expectancy Prediction: A Paradigm Shift for Annuity Pricing and Risk Management

This article explores the transformative potential of artificial intelligence (AI) in predicting life expectancy and its far-reaching implications for the annuities market. Traditional actuarial models, often reliant on demographic data and historical trends, face limitations in accuracy and personalization. We present a novel approach leveraging machine learning algorithms, including neural networks, decision trees, and ensemble methods, alongside natural language processing and deep learning techniques. Our AI-driven models integrate diverse data sources, including medical histories, genetic information, lifestyle factors, and socio-economic indicators, to provide more accurate and individualized life expectancy predictions. Using a dataset of anonymized health records and historical mortality data, we demonstrate that our AI models significantly outperform traditional actuarial tables in predicting individual mortality risks. The enhanced predictive power of these models has substantial implications for the annuities market, enabling insurers to price products more precisely, manage longevity risk more effectively, and optimize reserve capital. Moreover, consumers benefit from fairer pricing and personalized product offerings. This article underscores the need for regulatory framework revisions to accommodate AI-driven methodologies in actuarial practices. We also discuss ethical considerations, data privacy concerns, and the challenge of model interpretability, highlighting areas for future research to ensure responsible deployment of AI in life expectancy predictions and annuity pricing

Dunking Pancreaticojejunostomy With Braun's Jejunojejunostomy: Perioperative Outcomes of a Hundred Pancreaticoduodenectomies.

Background The optimal surgical technique for pancreaticojejunostomy (PJ) following pancreaticoduodenectomy (PD) is still debated. Dunking and duct-to-mucosa PJ are the most commonly adopted techniques. Incorporating Braun's jejunojejunostomy (JJ) could reducetheincidence and severity of delayed gastric emptying (DGE). This retrospective descriptive study seeks to clarify the outcomes of dunking PJ with Braun's JJ in 100 PD patients. Methodology We retrospectively reviewed 100 patients who underwent PDs in a single unit of the Department of Surgical Gastroenterology of Tribhuvan University Teaching Hospital from October 2012 to February 2023. Demographic and historical data, indications, procedure-related data, complications, and mortality data were collected and analyzed. Results The mean age was 50.96 ± 14.97 years, and 64 (64%) were males. The most common indication was ampullary carcinoma (53, 53%) followed by distal cholangiocarcinoma (18, 18%) and pancreatic ductal adenocarcinoma (7, 7%). Operative time was 5.83 ± 1.09 hours, intraoperative blood loss was 515 ± 194 mL, and the average time for PJ and Braun's JJ was 22 ± 6 and 15 ± 3 minutes, respectively. Soft pancreas was encountered in 52 (52%) patients and clinically significant postoperative pancreatic fistula (Grade B, C) was seen in 23 (23%). Postpancreatectomy hemorrhage was seen in 21 (21%) patients (Grades A: 3, B: 12, C: 6). DGE (Grade B) occurred in two (2%) patients, and bile leak was observed in 4% of patients (Grades A: 1, B: 2, C: 1). Major complications (Clavien-Dindo ≥IIIA) occurred in 24%, and 11 patients died. Conclusions The dunking technique is easily adaptable, less time-consuming, and can be performed in the pancreas of any texture or duct size but is associated with an increased incidence of post-pancreatectomy hemorrhage. Incorporation of Braun's anastomosis lowers DGE, allows early initiation of feeding, and reduces complication rates.

Health disparity viewed from public historic cancer mortality data in the US: Advocacy for gastrointestinal cancers, for tailored prevention measures, and for inter-agency population data harmonization.

152 Background: We searched public datasets released by CDC to answer the question ”Can we have a bird’s-eye view of health disparity in cancer care in the US?” By looking into historic data, we hope to identify not only health disparity reflected by the different cancer mortality rates for major cancer types, but also signs of missing data. Methods: We analyzed 2016-2020 US Cancer Statistics (USCS) cancer burden data to assess cancer disparity as reflected by rates of death in different races or ethnicities. Since the 2022 AACR report on cancer in Asian Americans raised concerns, we then looked into the “Single Race 15” grouping for the underlying cause-of-death data in WONDER@CDC to count liver cancer deaths (ICD10: C22) 2018-2021 among 6 Asian minority groups with population data from 2021 Census ACS data. 2020 GLOBOCAN estimated liver cancer mortality data were also compared. With anecdotal stories suggesting immigrants may seek end-of-life care in the birth country to access culture-concordant services, we looked into how cancer deaths abroad was reported. Results: Gastrointestinal (GI) cancers emerged as an area of cancer health disparity from USCS data. 5 GI cancers (colon and rectum, pancreas, liver and bile duct, stomach, and esophagus) contributed to 1/4 of US cancer deaths, and cancers in the pancreas, liver and bile duct, and esophagus still have poor outcomes. Black non-hispanic Americans are more likely to die from cancers of colon/rectum or pancreas. All minority races or ethnicities are more likely to die from cancers in liver and bile duct, or stomach than white non-Hispanic Americans. Cancers in colon and rectum, liver and bile duct, or stomach also cause high numbers of death in US under-50 population. Liver cancer mortality of US East Asian alone groups shown in the table were lower than GLOBOCAN estimated ASR of 16.1, with the greatest discrepancy in the Chinese (9.1 vs 17.2). Difference between the Vietnamese and Asian Indians may reflect that liver cancer is #1 cause of death in Vietnam, or HepB infection is low in India. Census data excluded expat US citizens, and consulate reports of death abroad have neither cause of death nor race/ethnicity data. Conclusions: We identified gastrointestinal cancers as a focus for cancer health disparity advocacy. Divergent risks for liver cancer in Asian Americans call for tailored prevention measures. Cross-agency death abroad data harmonisation to include race/ethnicity information will complete views of cancer mortality data in US. [Table: see text]

A competing risks machine learning study of neutron dose, fractionation, age, and sex effects on mortality in 21,000 mice

This study explores the impact of densely-ionizing radiation on non-cancer and cancer diseases, focusing on dose, fractionation, age, and sex effects. Using historical mortality data from approximately 21,000 mice exposed to fission neutrons, we employed random survival forest (RSF), a powerful machine learning algorithm accommodating nonlinear dependencies and interactions, treating cancer and non-cancer outcomes as competing risks. Unlike traditional parametric models, RSF avoids strict assumptions and captures complex data relationships through decision tree ensembles. SHAP (SHapley Additive exPlanations) values and variable importance scores were employed for interpretation. The findings revealed clear dose–response trends, with cancer being the predominant cause of mortality. SHAP value dose–response shapes differed, showing saturation for cancer hazard at high doses (> 2 Gy) and a more linear pattern at lower doses. Non-cancer responses remained more linear throughout the entire dose range. There was a potential inverse dose rate effect for cancer, while the evidence for non-cancer was less conclusive. Sex and age effects were less pronounced. This investigation, utilizing machine learning, enhances our understanding of the patterns of non-cancer and cancer mortality induced by densely-ionizing radiations, emphasizing the importance of such approaches in radiation research, including space travel and radioprotection.

Leveraging browse and grazing forage estimates to optimize index-based livestock insurance

African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy.

A bird's-eye view of disparity from public historic cancer mortality data in the US: Advocacy for gastrointestinal cancers and for inter-agency data harmonization.

e13723 Background: We searched into public datasets released by CDC to answer a question raised during an NCI Health Disparity Workshop: "Can we have a bird’s-eye view of health disparity in cancer care in the US?" By looking into the historic data reported, we hope to identify not only health disparity reflected by the different cancer mortality rates for major cancer types, but also what might be missing from the data-reporting. Methods: We analyzed 2016-2020 US Cancer Statistics (USCS) cancer burden data to assess cancer disparity as reflected by rates of death in different races or ethnicities. We then looked into the recently available “Single Race 15” grouping for the underlying cause-of-death data in WONDER@CDC to count cancer deaths 2018-2021 in California comparing cancer mortality among 7 Asian minority groups, including Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, and other Asians. We then matched census data for these Asian minority groups published by Census 2020. Results: We identified gastrointestinal (GI) cancers as an area of cancer health disparity. 5 GI cancers (colon and rectum, pancreas, liver and bile duct, stomach, and esophagus) contributed to a quarter of cancer deaths, and cancers in the pancreas, liver and bile duct, and esophagus still have very poor outcomes. Based on USCS data, Black non-Hispanic Americans are more likely to die from cancers of colon/rectum or pancreas. All minority races or ethnicities are more likely to die from cancers in liver and bile duct, or stomach than white non-Hispanic Americans. Cancers in colon and rectum, liver and bile duct, or stomach also cause high numbers of death in the under-50 US population. Since the 2022 AACR report on cancer in Asian Americans raised concerns on liver and stomach cancers, we counted cancer deaths in recent 4 years for different Asian groups in California that numbered about 7 million (alone or in combination) in 2020 Census. This includes 1.8 million Chinese, 1.7 million Filipino, 900k Asian Indian, 800k Vietnamese, 560k Korean, 470k Japanese, and other Asian ethnicities. Chinese has the highest numbers of deaths (2.8k) in 4 years from these 5 GI cancers followed by Filipino (2k), Vietnamese (1.2k), Korean (1.1k), Japanese (800), other Asians (1.1k), and Asian Indian (< 400). The lower cancer deaths in Asian Indians may be associated with their younger median age based on Census. With anecdotal stories suggesting immigrants may seek end-of-life care in their birth country to access more culture-concordant services, we looked into how cancer deaths abroad were reported. The data appeared missing. Conclusions: We identified gastrointestinal cancers as focuses for cancer health disparity advocacy. Harmonized consulate report of death abroad to include detailed race/ethnicity information will complete view of cancer mortality data in the US.

Nonlinear Modeling of Mortality Data and Its Implications for Longevity Bond Pricing

Human mortality has been improving faster than expected over the past few decades. This unprecedented improvement has caused significant financial stress to pension plan sponsors and annuity providers. The widely recognized Lee–Carter model often assumes linearity in its period effect as an integral part of the model. Nevertheless, deviation from linearity has been observed in historical mortality data. In this paper, we investigate the applicability of four nonlinear time-series models: threshold autoregressive model, Markov switching model, structural change model, and generalized autoregressive conditional heteroskedasticity model for mortality data. By analyzing the mortality data from England and Wales and Italy spanning the years 1900 to 2019, we compare the goodness of fit and forecasting performance of the four nonlinear models. We then demonstrate the implications of nonlinearity in mortality modeling on the pricing of longevity bonds as a practical illustration of our findings.

Estimation, Comparison, and Projection of Multifactor Age–Cohort Affine Mortality Models

Affine mortality models, developed in continuous time, are well suited to longevity risk applications including pricing and capital management. A major advantage of this mortality modeling approach is the availability of closed-form cohort survival curves, consistent with the assumed time dynamics of mortality rates. This article makes new contributions to the estimation of multifactor continuous-time affine models including the canonical Blackburn-Sherris, the arbitrage-free Nelson-Siegel (AFNS), and the Cox-Ingersoll-Ross (CIR) mortality models. We discuss and address numerical issues with model estimation. We apply the estimation methods to age–cohort mortality data from five different countries, providing insights into the dynamics of mortality rates and the fitting performance of the models. We show how the use of maximum likelihood with the univariate Kalman filter turns out to be faster and more robust compared to traditional estimation methods that heavily use large matrix multiplication and inversion. We present graphical and numerical goodness-of-fit results and assess model robustness. We project cohort survival curves and assess the out-of-sample performance of the models for the five countries. We confirm previous results by showing that, across these countries, although the CIR mortality model fits the historical mortality data well, particularly at older ages, the canonical and AFNS affine mortality models provide better out-of-sample performance. We also show how these affine mortality models are robust with respect to the set of age–cohort data used for parameter estimation. R code is provided.

Estimating excess mortality during the COVID-19 pandemic from a population-based infectious disease surveillance in two diverse populations in Kenya, March 2020-December 2021.

Robust data on the impact of the COVID-19 pandemic on mortality in Africa are relatively scarce. Using data from two well-characterized populations in Kenya we aimed to estimate excess mortality during the COVID-19 pandemic period. The mortality data arise from an ongoing population-based infectious disease surveillance (PBIDS) platform, which has been operational since 2006 in rural western Kenya (Asembo, Siaya County) and an urban informal settlement (Kibera, Nairobi County), Kenya. PBIDS participants were regularly visited at home (2-3 times a year) by field workers who collected demographic data, including deaths. In addition, verbal autopsy (VA) interviews for all identified deaths are conducted. We estimated all-cause and cause-specific mortality rates before and during the height of the COVID-19 pandemic, and we compared associated mortality rates between the periods using incidence rate ratios. Excess deaths during the COVID-19 period were also estimated by modelling expected deaths in the absence of COVID-19 by applying a negative binomial regression model on historical mortality data from January 2016. Overall and monthly excess deaths were determined using the P-score metric. Spearman correlation was used to assess whether there is a relationship between the generated P-score and COVID-19 positivity rate. The all-cause mortality rate was higher during the COVID-19 period compared to the pre-COVID-19 period in Asembo [9.1 (95% CI, 8.2-10.0) vs. 7.8 (95% CI, 7.3-8.3) per 1000 person-years of observation, pyo]. In Kibera, the all-cause mortality rate was slightly lower during the COVID-19 period compared to the pre-COVID-19 period [2.6 (95% CI, 2.2-3.2 per 1000 pyo) vs. 3.1; 95% CI, 2.7-3.4 per 1000 pyo)]. An increase in all-cause mortality was observed (incidence rate ratio, IRR, 1.16; 95% CI, 1.04-1.31) in Asembo, unlike in Kibera (IRR, 0.88; 95% CI, 0.71-1.09). The notable increase in mortality rate in Asembo was observed among persons aged 50 to 64 years (IRR, 2.62; 95% CI, 1.95-3.52), persons aged 65 years and above (5.47; 95% CI, 4.60-6.50) and among females (IRR, 1.25; 95% CI, 1.07-1.46). These age and gender differences were not observed in Kibera. We observed an increase in the mortality rate due to acute respiratory infection, including pneumonia (IRR, 1.45;95% CI, 1.03-2.04), and a reduction in the mortality rate due to pulmonary tuberculosis (IRR, 0.22; 95% CI, 0.05-0.87) among older children and adults in Asembo. There was no statistically significant change in mortality rates due to leading specific causes of death in Kibera. Overall, during the COVID-19 period observed deaths were higher than expected deaths in Asembo (P-score = 6.0%) and lower than expected in Kibera (P-score = -22.3%).Using well-characterized populations in the two diverse geographic locations, we demonstrate a heterogenous impact of the COVID-19 pandemic on all-cause and cause-specific mortality rates in Kenya. We observed more deaths than expected during the COVID-19 period in our rural site in western Kenya contrary to the urban site in Nairobi, the capital city in Kenya.

Future Temperature-Related Deaths in the U.S.: The Impact of Climate Change, Demographics, and Adaptation.

Mortality due to extreme temperatures is one of the most worrying impacts of climate change. In this analysis, we use historic mortality and temperature data from 106 cities in the United States to develop a model that predicts deaths attributable to temperature. With this model and projections of future temperature from climate models, we estimate temperature-related deaths in the United States due to climate change, changing demographics, and adaptation. We find that temperature-related deaths increase rapidly as the climate warms, but this is mainly due to an expanding and aging population. For global average warming below 3°C above pre-industrial levels, we find that climate change slightly reduces temperature-related mortality in the U.S. because the reduction of cold-related mortality exceeds the increase in heat-related deaths. Above 3°C warming, whether the increase in heat-related deaths exceeds the decrease in cold-related deaths depends on the level of adaptation. Southern U.S. cities are already well adapted to hot temperatures and the reduction of cold-related mortality drives overall lower mortality. Cities in the Northern U.S. are not well adapted to high temperatures, so the increase in heat-related mortality exceeds the reduction in cold-related mortality. Thus, while the total number of climate-related mortality may not change much, climate change will shift mortality in the U.S. to higher latitudes.

Mortality postponement and compression at older ages in human cohorts.

A key but unresolved issue in the study of human mortality at older ages is whether mortality is being compressed (which implies that we may be approaching a maximum limit to the length of life) or postponed (which would imply that we are not). We analyze historical and current population mortality data between ages 50 and 100 by birth cohort in 19 currently-industrialized countries, using a Bayesian technique to surmount cohort censoring caused by survival, to show that while the dominant historical pattern has been one of mortality compression, there have been occasional episodes of mortality postponement. The pattern of postponement and compression across different birth cohorts explain why longevity records have been slow to increase in recent years: we find that cohorts born between around 1900 and 1950 are experiencing historically unprecedented mortality postponement, but are still too young to break longevity records. As these cohorts attain advanced ages in coming decades, longevity records may therefore increase significantly. Our results confirm prior work suggesting that if there is a maximum limit to the human lifespan, we are not yet approaching it.

Short-term forecast of U.S. COVID mortality using excess deaths and vector autoregression

We analyze overall mortality in the U.S. as a whole and several states in particular in order to make conclusions regarding timing and strength of COVID pandemic effect from an actuarial risk analysis perspective. No effort is made to analyze biological or medical characteristics of the pandemic. We use open data provided by CDC, U.S. state governments and Johns Hopkins University. In the first part of the paper, we suggest time series analysis (ARIMA) for weekly excess U.S. mortality in 2020 as compared to several previous years’ experience in order to build a statistical model and provide short-term forecast based exclusively on historical mortality data. In the second half of the paper, we also analyze weekly COVID cases, hospitalizations and deaths in 2020 and 2021. Two midwestern states, Minnesota and Wisconsin, along with geographically diverse Colorado and Georgia, are used to illustrate global and local patterns in the COVID pandemic data. We suggest vector autoregression (VAR) as a method of simultaneous explanatory and predictive analysis of several variables. VAR is a popular tool in econometrics and financial analysis, but it is less common in problems of risk management related to mortality analysis in epidemiology and actuarial practice. Efficiency of short-term forecast is illustrated by observing the effect of vaccination on COVID development in the state of Minnesota in 2021.

The effects of night-time warming on mortality burden under future climate change scenarios: a modelling study

The health impacts of climate warming are usually quantified based on daily average temperatures. However, extra health risks might result from hot nights. We project the future mortality burden due to hot nights. We selected the hot night excess (HNE) to represent the intensity of night-time heat, which was calculated as the excess sum of high temperature during night time. We collected historical mortality data in 28 cities from three east Asian countries, from 1981 to 2010. The associations between HNE and mortality in each city were firstly examined using a generalised additive model in combination with a distributed lag non-linear model over lag 0-10 days. We then pooled the cumulative associations using a univariate meta-regression model at the national or regional levels. Historical and future hourly temperature series were projected under two scenarios of greenhouse-gas emissions from 1980-2099, with ten general circulation models. We then projected the attributable fraction of mortality due to HNE under each scenario. Our dataset comprised 28 cities across three countries (Japan, South Korea, and China), including 9 185 598 deaths. The time-series analyses showed the HNE was significantly associated with increased mortality risks, the relative mortality risk on days with hot nights could be 50% higher than on days with non-hot nights. Compared with the rise in daily mean temperature (lower than 20%), the frequency of hot nights would increase more than 30% and the intensity of hot night would increase by 50% by 2100s. The attributable fraction of mortality due to hot nights was projected to be 3·68% (95% CI 1·20 to 6·17) under a strict emission control scenario (SSP126). Under a medium emission control scenario (SSP245), the attributable fraction of mortality was projected to increase up to 5·79% (2·07 to 9·52), which is 0·95% (-0·39 to 2·29) more than the attributable fraction of mortality due to daily mean temperature. Our study provides evidence for significant mortality risks and burden in association with night-time warming across Japan, South Korea, and China. Our findings suggest a growing role of night-time warming in heat-related health effects in a changing climate. The National Natural Science Foundation of China, Shanghai International Science and Technology Partnership Project.

A New Class of Counting Distributions Embedded in the Lee–Carter Model for Mortality Projections: A Bayesian Approach

The Lee–Carter model, the dominant mortality projection modeling in the literature, was criticized for its homoscedastic error assumption. This was corrected in extensions to the model based on the assumption that the number of deaths follows Poisson or negative binomial distributions. We propose a new class of families of counting distributions, namely, the ABM class, which belongs to a wider class of natural exponential families. This class is characterized by its variance functions and contains the Poisson and the negative binomial distributions as special cases, offering an infinite class of additional counting distributions to be considered. We are guided by the principle that the choice of distribution should be made from a pool of distributions as large as possible. To this end, and following a data mining approach, a training set of historical mortality data of the population could be modeled using the ABM’s rich choice of distributions, and the chosen distribution should be the one that proved to offer superior projection results on a test set of mortality data. As an alternative to parameter estimation via the singular value decomposition used in the classical Lee–Carter model, we adopted Bayesian estimation, harnessing the Markov Chain Monte Carlo methodology. A numerical study demonstrates that when fitting mortality data using this new class of distributions, while traditional distributions may provide desirable projections for some populations, for others, alternative distributions within the ABM class can potentially produce superior results for the entire population or particular age groups, such as the oldest-old.

Increasing trees and high-albedo surfaces decreases heat impacts and mortality in Los Angeles, CA

There is a pressing need for strategies to prevent the heat-health impacts of climate change. Cooling urban areas through adding trees and vegetation and increasing solar reflectance of roofs and pavements with higher albedo surface materials are recommended strategies for mitigating the urban heat island. We quantified how various tree cover and albedo scenarios would impact heat-related mortality, temperature, humidity, and oppressive air masses in Los Angeles, California, and quantified the number of years that climate change–induced warming could be delayed in Los Angeles if interventions were implemented. Using synoptic climatology, we used meteorological data for historical summer heat waves, classifying days into discrete air mass types. We analyzed those data against historical mortality data to determine excess heat-related mortality. We then used the Weather Research and Forecasting model to explore the effects that tree cover and albedo scenarios would have, correlating the resultant meteorological data with standardized mortality data algorithms to quantify potential reductions in mortality. We found that roughly one in four lives currently lost during heat waves could be saved. We also found that climate change–induced warming could be delayed approximately 40–70 years under business-as-usual and moderate mitigation scenarios, respectively.

A linear mixed model to estimate COVID-19-induced excess mortality.

The Corona Virus Disease (COVID‐19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID‐19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down‐weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5‐year weekly average, the linear mixed model is forecasting the year‐specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.

Epidemiological features of the first Unusual Mortality Event linked to cetacean morbillivirus in the South Atlantic (Brazil, 2017–2018)

AbstractSince the 1980s, cetacean morbillivirus (CeMV) has caused mass mortality events worldwide. However, no epizootics had been recorded in the South Atlantic, until an unusual mortality event (UME) linked to Guiana dolphin cetacean morbillivirus (GD‐CeMV) began in Ilha Grande Bay, southeastern Brazil, in November 2017. In a five‐month period, the UME spread to neighboring Sepetiba Bay and accounted for the death of at least 277 Guiana dolphins (Sotalia guianensis). Prevalence of morbillivirus positive dolphins, as estimated from RT‐PCR diagnostics, was 92.3% (24/26) in Ilha Grande Bay and 91.9% (57/62) in Sepetiba Bay. Females had higher mortality rates during the UME (1.5:1), in contrast with historical mortality data from both bays that showed a 2:1 male to female death ratio. Calf mortality rates also increased in both bays. These results suggest that females and calves were more vulnerable to morbilliviral infection. Herein, we discuss possible explanations for such sex‐biased death pattern during the UME and their implication for the conservation of endangered Guiana dolphins. We also speculate about the origin and spread of morbillivirus in the South Atlantic Ocean.

City-level vulnerability to temperature-related mortality in the USA and future projections: a geographically clustered meta-regression

SummaryBackgroundExtreme heat exposure can lead to premature death. Climate change is expected to increase the frequency, intensity, and duration of extreme heat events, resulting in many additional heat-related deaths globally, as well as changing the nature of extreme cold events. At the same time, vulnerability to extreme heat has decreased over time, probably due to a combination of physiological, behavioural, infrastructural, and technological adaptations. We aimed to account for these changes in vulnerability and avoid overstated projections for temperature-related mortality. We used the historical observed decrease in vulnerability to improve future mortality estimates.MethodsWe used historical mortality and temperature data from 208 US cities to quantify how observed changes in vulnerability from 1973 to 2013 affected projections of temperature-related mortality under various climate scenarios. We used geographically structured meta-regression to characterise the relationship between temperature and mortality for these urban populations over the specified time period. We then used the fitted relationships to project mortality under future climate conditions.FindingsBetween Oct 26, 2018, and March 9, 2020, we established that differences in vulnerability to temperature were geographically structured. Vulnerability decreased over time in most areas. US mortalities projected from a 2°C increase in mean temperature decreased by more than 97% when using 2003–13 data compared with 1973–82 data. However, these benefits declined with increasing temperatures, with a 6°C increase showing only an 84% decline in projected mortality based on 2003–13 data.InterpretationEven after accounting for adaptation, the projected effects of climate change on premature mortality constitute a substantial public health risk. Our work suggests large increases in temperature will require additional mitigation to avoid excess mortality from heat events, even in areas with high air conditioning coverage in place.

Using excess deaths and testing statistics to determine COVID-19 mortalities

Factors such as varied definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US from January 2020 until February 2021 is 9% higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find statistically insignificant or even negative excess deaths for at least most of 2020 in places such as Germany, Denmark, and Norway.

Could Historical Mortality Data Predict Mortality Due to Unexpected Events?

Research efforts focused on developing a better understanding of the evolution of mortality over time are considered to be of significant interest—not just to the demographers. Mortality can be expressed with different parameters through multiparametric prediction models. Based on the Beta Gompertz generalized Makeham (BGGM) distribution, this study aims to evaluate and map four of such parameters for 22 countries of the European Union, over the period 1960–2045. The BGGM probabilistic distribution is a multidimensional model, which can predict using the corresponding probabilistic distribution with the following parameters: infant mortality (parameter θ), population aging (parameter ξ), and individual and population mortality due to unexpected exogenous factors/events (parameters κ and λ, respectively). This work focuses on the random risk factor (λ) that can affect the entire population, regardless of age and gender, with increasing mortality depicting developments and trends, both temporally (past–present–future) and spatially (22 countries). Moreover, this study could help policymakers in the field of health provide solutions in terms of mortality. Mathematical models like BGGM can be used to achieve and highlight probable cyclical repetitions of sudden events (such as Covid-19) in different time series for different geographical areas. GIS context is used to map the spatial patterns of this estimated parameter as well as these variations during the examined period for both men and women.