Title: Biocultural perspectives on bioarchaeological and paleopathological evidence of past pandemics
Objectives: Pandemics have profoundly impacted human societies, but until rela- tively recently were a minor research focus within biological anthropology, especially within biocultural analyses. Here, we explore research in these fields, including molecular anthropology, that employs biocultural approaches, sometimes integrated with intersectionality and ecosocial and syndemic theory, to unpack relationships between social inequality and pandemics. A case study assesses the 1918 influenza pandemic's impacts on the patient population of the Mississippi State Asylum (MSA). Materials and Methods: We survey bioarchaeological and paleopathological litera- ture on pandemics and analyze respiratory disease mortality relative to sex, age, and social race amongst patient deaths (N = 2258) between 1912 and 1925. Logistic regression models were used to assess relationships between cause of death and odds of death during the pandemic (1918–1919).
Results: Findings include substantial respiratory mortality during the pandemic, including from influenza and influenza syndemic with pneumonia. Older patients (40–59 years, 60+ years) had lower odds (p < 0.01) of dying from respiratory disease than younger patients, as did female patients compared to males (p < 0.05). Age pat- terns are broadly consistent with national and state trends, while elevated mortality amongst Black and/or African American patients may reflect intersections between gender roles and race-based structural violence in the Jim Crow South.
Discussion: Future work in biological anthropology on past pandemics may benefit from explicit incorporation of biocultural frameworks, intersectionality, and ecosocial and syndemic theory. Doing so enables holistic analyses of interactions between social context, social inequality and pandemic outcomes, generating data informative for public health responses and pandemic preparedness. more »« less
van Doren, Taylor P.; Kelmelis, Saige(
, American Journal of Biological Anthropology)
AbstractBackground
Research on the 1918 influenza pandemic often focuses exclusively on pandemic years, reducing the potential long‐term insights about the pandemic. It is critical to frame the 1918 pandemic within the underlying population dynamics, health, and sociocultural context to understand what factors contributed to pandemic mortality and survivorship, with respect to observed inequality, and consequences of the pandemic.
Materials & Methods
Individual death records and censuses from The Rooms Provincial Archives and Memorial University of Newfoundland Digital Archives for three major causes of death—influenza and pneumonia; tuberculosis; and pooled bronchitis, measles, and whooping cough—were collected for three periods in the early 20th century: pre‐pandemic (1909–11), pandemic (March 1918–Janaury 1919), and post‐pandemic (1933–1935). We calculated pooled age‐standardized mortality rates and changes in pre‐ to post‐pandemic mortality rates by region. We fit Kaplan–Meier and Cox proportional hazards models to each period, controlling for age, cause of death, and region.
Results
Pandemic mortality was higher than that of pre‐ and post‐pandemic periods. Post‐pandemic mortality was significantly lower than pre‐pandemic mortality in all regions, except Western Newfoundland. Survival was lowest during the pandemic and increased significantly post‐pandemic (p < 0.0001), with no significant differences among regions during the pandemic (p = 0.32). Significant differences in survivorship in 1933–1935 were driven by increasing differences in survivorship for P&I among the regions more than other causes of death.
Discussion
Myopic perspectives of pandemics can obscure our understanding of observed outcomes. Inequalities in respiratory disease mortality are evident in pre‐ and post‐pandemic periods, but these would have been missed in investigations of the pandemic period alone.
Villanustre, Flavio; Chala, Arjuna; Dev, Roger; Xu, Lili; LexisNexis, Jesse Shaw; Furht, Borko; Khoshgoftaar, Taghi(
, Journal of Big Data)
Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper.
van Doren, Taylor P.; Sattenspiel, Lisa(
, American Journal of Physical Anthropology)
AbstractObjectives
The selective mortality hypothesis of tuberculosis after the 1918 influenza pandemic, laid out by Noymer and colleagues, suggests that acute exposure or pre‐existing infection with tuberculosis (TB) increased the probability of pneumonia and influenza (P&I) mortality during the 1918 influenza pandemic, leading to a hastened decline of TB mortality in post‐pandemic years. This study describes cultural determinants of the post‐pandemic TB mortality patterns in Newfoundland and evaluates whether there is support for this observation.
Materials and methods
Death records and historical documents from the Provincial Archives of Newfoundland and Labrador were used to calculate age‐standardized island‐wide and sex‐based TB mortality, as well as region‐level TB mortality, for 1900–1939. The Joinpoint Regression Program (version 4.8.0.1) was used to estimate statistically significant changes in mortality rates.
Results
Island‐wide, females had consistently higher TB mortality for the duration of the study period and a significant shift to lower TB mortality beginning in 1928. There was no similar predicted significant decline for males. On the regional level, no models predicted a significant decline after the 1918 influenza pandemic, except for the West, where significant decline was predicted in the late‐1930s.
Discussion
Although there was no significant decline in TB mortality observed immediately post‐pandemic, as has been shown for other Western nations, the female post‐pandemic pattern suggests a decline much later. The general lack of significant decrease in TB mortality rate is likely due to Newfoundland's poor nutrition and lack of centralized healthcare rather than a biological interaction between P&I and TB.
Malnutrition has been reported in the literature to be adversely associated with outcomes in paediatric malignancies. Our objective in this paper was to evaluate malnutrition as a potential predictor for adverse outcomes in febrile neutropenia associated with haematological malignancies.
Methods
A prospective observational study was performed in a tertiary care teaching hospital of Kolkata, India. Forty‐eight participants, suffering from haematological malignancy, were included. Participants were included if they experienced at least one episode of febrile neutropenia. For children aged <5 years, weight for height, height for age and weight for age were used as criteria for defining malnutrition, while body mass index for age was used in children ≥5 years. A total of 162 episodes of febrile neutropenia were studied.
Results
Thirty patients (30/48, 62.5%) included in the study had malnutrition. In bivariate analyses at patient level, there is a strong association between malnutrition and death (odds ratio (OR) 7.286, 95% confidence interval (CI) 0.838–63.345, one‐tailedP = 0.044), and life‐threatening complications show a moderate trend towards significance (OR 3.333, 95% CI 0.791–14.052, one‐tailedP = 0.084). Survival functions were significantly different between malnourished and non‐malnourished children (log rank test χ2 = 4.609, degree of freedom = 1,P = 0.032). Wasting was associated with life‐threatening complications in children aged <5 years (OR 14, 95% CI 1.135–172.642, one‐tailedP = 0.036). Logistic regression analyses at episode level revealed that phase of treatment and respiratory system involvement were significant predictors of death, while malnutrition was not.
Conclusion
Malnutrition may be a potential predictor of mortality in febrile neutropenia.
Marginalized racial and ethnic groups in the United States were disproportionally affected by the COVID-19 pandemic. To study these disparities, we construct an age-and-race-stratified mathematical model of SARS-CoV-2 transmission fitted to age-and-race-stratified data from 2020 in Oregon and analyze counterfactual vaccination strategies in early 2021. We consider two racial groups: non-Hispanic White persons and persons belonging to BIPOC groups (including non-Hispanic Black persons, non-Hispanic Asian persons, non-Hispanic American-Indian or Alaska-Native persons, and Hispanic or Latino persons). We allocate a limited amount of vaccine to minimize overall disease burden (deaths or years of life lost), inequity in disease outcomes between racial groups (measured with five different metrics), or both. We find that, when allocating small amounts of vaccine (10% coverage), there is a trade-off between minimizing disease burden and minimizing inequity. Older age groups, who are at a greater risk of severe disease and death, are prioritized when minimizing measures of disease burden, and younger BIPOC groups, who face the most inequities, are prioritized when minimizing measures of inequity. The allocation strategies that minimize combinations of measures can produce middle-ground solutions that similarly improve both disease burden and inequity, but the trade-off can only be mitigated by increasing the vaccine supply. With enough resources to vaccinate 20% of the population the trade-off lessens, and with 30% coverage, we can optimize both equity and mortality. Our goal is to provide a race-conscious framework to quantify and minimize inequity that can be used for future pandemics and other public health interventions.
Zuckerman, Molly K., Tribble, Anna Grace, Austin, Rita M., DeGaglia, Cassandra M., and Emery, Taylor. Biocultural perspectives on bioarchaeological and paleopathological evidence of past pandemics. Retrieved from https://par.nsf.gov/biblio/10418772. American Journal of Biological Anthropology . Web. doi:10.1002/ajpa.24647.
Zuckerman, Molly K., Tribble, Anna Grace, Austin, Rita M., DeGaglia, Cassandra M., & Emery, Taylor. Biocultural perspectives on bioarchaeological and paleopathological evidence of past pandemics. American Journal of Biological Anthropology, (). Retrieved from https://par.nsf.gov/biblio/10418772. https://doi.org/10.1002/ajpa.24647
Zuckerman, Molly K., Tribble, Anna Grace, Austin, Rita M., DeGaglia, Cassandra M., and Emery, Taylor.
"Biocultural perspectives on bioarchaeological and paleopathological evidence of past pandemics". American Journal of Biological Anthropology (). Country unknown/Code not available. https://doi.org/10.1002/ajpa.24647.https://par.nsf.gov/biblio/10418772.
@article{osti_10418772,
place = {Country unknown/Code not available},
title = {Biocultural perspectives on bioarchaeological and paleopathological evidence of past pandemics},
url = {https://par.nsf.gov/biblio/10418772},
DOI = {10.1002/ajpa.24647},
abstractNote = {Objectives: Pandemics have profoundly impacted human societies, but until rela- tively recently were a minor research focus within biological anthropology, especially within biocultural analyses. Here, we explore research in these fields, including molecular anthropology, that employs biocultural approaches, sometimes integrated with intersectionality and ecosocial and syndemic theory, to unpack relationships between social inequality and pandemics. A case study assesses the 1918 influenza pandemic's impacts on the patient population of the Mississippi State Asylum (MSA). Materials and Methods: We survey bioarchaeological and paleopathological litera- ture on pandemics and analyze respiratory disease mortality relative to sex, age, and social race amongst patient deaths (N = 2258) between 1912 and 1925. Logistic regression models were used to assess relationships between cause of death and odds of death during the pandemic (1918–1919). Results: Findings include substantial respiratory mortality during the pandemic, including from influenza and influenza syndemic with pneumonia. Older patients (40–59 years, 60+ years) had lower odds (p < 0.01) of dying from respiratory disease than younger patients, as did female patients compared to males (p < 0.05). Age pat- terns are broadly consistent with national and state trends, while elevated mortality amongst Black and/or African American patients may reflect intersections between gender roles and race-based structural violence in the Jim Crow South. Discussion: Future work in biological anthropology on past pandemics may benefit from explicit incorporation of biocultural frameworks, intersectionality, and ecosocial and syndemic theory. Doing so enables holistic analyses of interactions between social context, social inequality and pandemic outcomes, generating data informative for public health responses and pandemic preparedness.},
journal = {American Journal of Biological Anthropology},
author = {Zuckerman, Molly K. and Tribble, Anna Grace and Austin, Rita M. and DeGaglia, Cassandra M. and Emery, Taylor},
editor = {Kim, Andrew and Agarwal, Sabrina}
}
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