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1. Dynamics of racial disparities in all-cause mortality during the COVID-19 pandemic

   https://doi.org/10.1073/pnas.2210941119  

   Aschmann, Hélène E.; Riley, Alicia R.; Chen, Ruijia; Chen, Yea-Hung; Bibbins-Domingo, Kirsten; Stokes, Andrew C.; Glymour, M. Maria; Kiang, Mathew V. (October 2022, Proceedings of the National Academy of Sciences)

   As research documenting disparate impacts of COVID-19 by race and ethnicity grows, little attention has been given to dynamics in mortality disparities during the pandemic and whether changes in disparities persist. We estimate age-standardized monthly all-cause mortality in the United States from January 2018 through February 2022 for seven racial/ethnic populations. Using joinpoint regression, we quantify trends in race-specific rate ratios relative to non-Hispanic White mortality to examine the magnitude of pandemic-related shifts in mortality disparities. Prepandemic disparities were stable from January 2018 through February 2020. With the start of the pandemic, relative mortality disadvantages increased for American Indian or Alaska Native (AIAN), Native Hawaiian or other Pacific Islander (NHOPI), and Black individuals, and relative mortality advantages decreased for Asian and Hispanic groups. Rate ratios generally increased during COVID-19 surges, with different patterns in the summer 2021 and winter 2021/2022 surges, when disparities approached prepandemic levels for Asian and Black individuals. However, two populations below age 65 fared worse than White individuals during these surges. For AIAN people, the observed rate ratio reached 2.25 (95% CI = 2.14, 2.37) in October 2021 vs. a prepandemic mean of 1.74 (95% CI = 1.62, 1.86), and for NHOPI people, the observed rate ratio reached 2.12 (95% CI = 1.92, 2.33) in August 2021 vs. a prepandemic mean of 1.31 (95% CI = 1.13, 1.49). Our results highlight the dynamic nature of racial/ethnic disparities in mortality and raise alarm about the exacerbation of mortality inequities for Indigenous groups due to the pandemic. 

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2. Impact of Vaccination and Nonpharmaceutical Interventions With Possible COVID-19 Viral Evolutions Using an Agent-Based Simulation

   https://doi.org/10.1016/j.focus.2023.100155  

   Lee, Serin; Zabinsky, Zelda B.; Wasserheit, Judith N.; Ross, Jennifer M.; Chen, Shi; Liu, Shan (February 2024, AJPM Focus)

   The objective is to understand the role of emerging variants, vaccination, and NPI policies on COVID-19 infections and deaths. We aim to identify scenarios in which COVID-19 can be managed such that the death rate from COVID-19 becomes comparable with the combined annual mortality rate from influenza and pneumonia. As a case study for a large urban area, we simulate COVID-19 transmission in King County, Washington, (greater Seattle) using an agent- based simulation model. Calibrated to local epidemiological data, our study uses detailed synthetic population data and includes interactions between vaccination and specific NPIs while considering waning immunity and emergence of variants. Virus mutation scenarios include 12 combinations of infectivity, disease severity, and immune evasiveness. A highly effective pancoronavirus vaccine that works against all strains is considered an optimistic scenario. Our findings highlight the potential benefits of pancoronavirus vaccines that offer enhanced and longer-lasting immunity. We emphasize the crucial role of nonpharmaceutical interventions in reducing COVID-19 deaths regardless of virus mutation scenarios. Owing to highly immune evasive and contagious SARS-CoV-2 variants, most scenarios in this study fail to reduce the mortality of COVID-19 to the level of influenza and pneumonia. However, our findings indicate that periodic vaccinations and a threshold nonpharmaceutical intervention policy may succeed in achieving this goal. This indicates the need for caution and vigilance in managing a continuing COVID-19 epidemic. 

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3. Investigating associations between COVID-19 mortality and population-level health and socioeconomic indicators in the United States: A modeling study

   https://doi.org/10.1371/journal.pmed.1003693  

   Kandula, Sasikiran; Shaman, Jeffrey (July 2021, PLOS Medicine)

   Turner, Richard (Ed.)

   Background With the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines. Methods and findings County-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran’s I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R 2 ); and (ii) effect estimates of each predictor. Adjusting for case rates, the selected indicators individually explain 24%–29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R 2 . Mortality is estimated to increase by 43 per thousand residents (95% CI: 37–49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34–44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R 2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors. Conclusions Significant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines. 

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4. Ensemble machine learning of factors influencing COVID-19 across US counties

   https://doi.org/10.1038/s41598-021-90827-x  

   McCoy, David; Mgbara, Whitney; Horvitz, Nir; Getz, Wayne M.; Hubbard, Alan (December 2021, Scientific Reports)

   Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the causal agent for COVID-19, is a communicable disease spread through close contact. It is known to disproportionately impact certain communities due to both biological susceptibility and inequitable exposure. In this study, we investigate the most important health, social, and environmental factors impacting the early phases (before July, 2020) of per capita COVID-19 transmission and per capita all-cause mortality in US counties. We aggregate county-level physical and mental health, environmental pollution, access to health care, demographic characteristics, vulnerable population scores, and other epidemiological data to create a large feature set to analyze per capita COVID-19 outcomes. Because of the high-dimensionality, multicollinearity, and unknown interactions of the data, we use ensemble machine learning and marginal prediction methods to identify the most salient factors associated with several COVID-19 outbreak measure. Our variable importance results show that measures of ethnicity, public transportation and preventable diseases are the strongest predictors for both per capita COVID-19 incidence and mortality. Specifically, the CDC measures for minority populations, CDC measures for limited English, and proportion of Black- and/or African-American individuals in a county were the most important features for per capita COVID-19 cases within a month after the pandemic started in a county and also at the latest date examined. For per capita all-cause mortality at day 100 and total to date, we find that public transportation use and proportion of Black- and/or African-American individuals in a county are the strongest predictors. The methods predict that, keeping all other factors fixed, a 10% increase in public transportation use, all other factors remaining fixed at the observed values, is associated with increases mortality at day 100 of 2012 individuals (95% CI [1972, 2356]) and likewise a 10% increase in the proportion of Black- and/or African-American individuals in a county is associated with increases total deaths at end of study of 2067 (95% CI [1189, 2654]). Using data until the end of study, the same metric suggests ethnicity has double the association as the next most important factors, which are location, disease prevalence, and transit factors. Our findings shed light on societal patterns that have been reported and experienced in the U.S. by using robust methods to understand the features most responsible for transmission and sectors of society most vulnerable to infection and mortality. In particular, our results provide evidence of the disproportionate impact of the COVID-19 pandemic on minority populations. Our results suggest that mitigation measures, including how vaccines are distributed, could have the greatest impact if they are given with priority to the highest risk communities. 

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5. Biocultural perspectives on bioarchaeological and paleopathological evidence of past pandemics

   https://doi.org/10.1002/ajpa.24647  

   Zuckerman, Molly K.; Tribble, Anna Grace; Austin, Rita M.; DeGaglia, Cassandra M.; Emery, Taylor (January 2023, American Journal of Biological Anthropology)

   Kim, Andrew; Agarwal, Sabrina (Ed.)

   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. 

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