Death on the permafrost: Revisiting the 1918-20 influenza pandemic in Alaska using death certificates.

The 1918-20 influenza pandemic devastated Alaska's Indigenous populations. We report on quantitative analyses of pandemic deaths due to pneumonia and influenza (P&I) using information from Alaska death certificates dating between 1915 and 1921 (n=7,147). Goals include a reassessment of pandemic death numbers, analysis of P&I deaths beyond 1919, estimates of excess mortality patterns overall and by age using intercensal population estimates based on Alaska's demographic history, and comparisons between Alaska Native (AN) and non-AN residents. Results indicate that ANs experienced 83% of all P&I deaths and 87% of all-cause excess deaths during the pandemic. AN mortality was 8.1 times higher than non-AN mortality. Analyses also uncovered previously unknown mortality peaks in 1920. Both subpopulations showed characteristically high mortality of young adults, possibly due to imprinting with the 1889-90 pandemic virus, but their age-specific mortality patterns were different: non-AN mortality declined after age 25-29 and stayed relatively low for the elderly, while AN mortality increased after age 25-29, peaked at age 40-44, and remained high up to age 64. This suggests a relative lack of exposure to H1-type viruses pre-1889 among AN persons. In contrast, non-AN persons, often temporary residents, may have gained immunity before moving to Alaska.

Frailty and survival in the 1918 influenza pandemic.

One of the most well-known yet least understood aspects of the 1918 influenza pandemic is the disproportionately high mortality among young adults. Contemporary accounts further describe the victims as healthy young adults, which is contrary to the understanding of selective mortality, which posits that individuals with the highest frailty within a group are at the greatest risk of death. We use a bioarchaeological approach, combining individual-level information on health and stress gleaned from the skeletal remains of individuals who died in 1918 to determine whether healthy individuals were dying during the 1918 pandemic or whether underlying frailty contributed to an increased risk of mortality. Skeletal data on tibial periosteal new bone formation were obtained from 369 individuals from the Hamann-Todd documented osteological collection in Cleveland, Ohio. Skeletal data were analyzed alongside known age at death using Kaplan-Meier survival and Cox proportional hazards analysis. The results suggest that frail or unhealthy individuals were more likely to die during the pandemic than those who were not frail. During the flu, the estimated hazards for individuals with periosteal lesions that were active at the time of death were over two times higher compared to the control group. The results contradict prior assumptions about selective mortality during the 1918 influenza pandemic. Even among young adults, not everyone was equally likely to die-those with evidence of systemic stress suffered greater mortality. These findings provide time depth to our understanding of how variation in life experiences can impact morbidity and mortality even during a pandemic caused by a novel pathogen.

Missing data in bioarchaeology II: A test of ordinal and continuous data imputation.

OBJECTIVES: Previous research has shown that while missing data are common in bioarchaeological studies, they are seldom handled using statistically rigorous methods. The primary objective of this article is to evaluate the ability of imputation to manage missing data and encourage the use of advanced statistical methods in bioarchaeology and paleopathology. An overview of missing data management in biological anthropology is provided, followed by a test of imputation and deletion methods for handling missing data. MATERIALS AND METHODS: Missing data were simulated on complete datasets of ordinal (n = 287) and continuous (n = 369) bioarchaeological data. Missing values were imputed using five imputation methods (mean, predictive mean matching, random forest, expectation maximization, and stochastic regression) and the success of each at obtaining the parameters of the original dataset compared with pairwise and listwise deletion. RESULTS: In all instances, listwise deletion was least successful at approximating the original parameters. Imputation of continuous data was more effective than ordinal data. Overall, no one method performed best and the amount of missing data proved a stronger predictor of imputation success. DISCUSSION: These findings support the use of imputation methods over deletion for handling missing bioarchaeological and paleopathology data, especially when the data are continuous. Whereas deletion methods reduce sample size, imputation maintains sample size, improving statistical power and preventing bias from being introduced into the dataset.