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Application of Bayesian spatial-temporal models for estimating unrecognized COVID-19 deaths in the United States.

ABSTRACT: In the United States, COVID-19 has become a leading cause of death since 2020. However, the number of COVID-19 deaths reported from death certificates is likely to represent an underestimate of the total deaths related to SARS-CoV-2 infections. Estimating those deaths not captured through death certificates is important to understanding the full burden of COVID-19 on mortality. In this work, we explored enhancements to an existing approach by employing Bayesian hierarchical models to estimate unrecognized deaths attributed to COVID-19 using weekly state-level COVID-19 viral surveillance and mortality data in the United States from March 2020 to April 2021. We demonstrated our model using those aged ≥85 years who died. First, we used a spatial-temporal binomial regression model to estimate the percent of positive SARS-CoV-2 test results. A spatial-temporal negative-binomial model was then used to estimate unrecognized COVID-19 deaths by exploiting the spatial-temporal association between SARS-CoV-2 percent positive and all-cause mortality counts using an excess mortality approach. Computationally efficient Bayesian inference was accomplished via the Polya-Gamma representation of the binomial and negative-binomial models. Among those aged ≥85 years, we estimated 58,200 (95% CI: 51,300, 64,900) unrecognized COVID-19 deaths, which accounts for 26% (95% CI: 24%, 29%) of total COVID-19 deaths in this age group. Our modeling results suggest that COVID-19 mortality and the proportion of unrecognized deaths among deaths attributed to COVID-19 vary by time and across states.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC8730676 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn the United States, Coronavirus Disease 2019 (COVID-19) deaths are captured through the National Notifiable Disease Surveillance System and death certificates reported to the National Vital Statistics System (NVSS). However, not all COVID-19 deaths are recognized and reported because of limitations in testing, exacerbation of chronic health conditions that are listed as the cause of death, or delays in reporting. Estimating deaths may provide a more comprehensive understanding of total COVID-19-attributable deaths.MethodsWe estimated COVID-19 unrecognized attributable deaths, from March 2020-April 2021, using all-cause deaths reported to NVSS by week and six age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) for 50 states, New York City, and the District of Columbia using a linear time series regression model. Reported COVID-19 deaths were subtracted from all-cause deaths before applying the model. Weekly expected deaths, assuming no SARS-CoV-2 circulation and predicted all-cause deaths using SARS-CoV-2 weekly percent positive as a covariate were modelled by age group and including state as a random intercept. COVID-19-attributable unrecognized deaths were calculated for each state and age group by subtracting the expected all-cause deaths from the predicted deaths.FindingsWe estimated that 766,611 deaths attributable to COVID-19 occurred in the United States from March 8, 2020-May 29, 2021. Of these, 184,477 (24%) deaths were not documented on death certificates. Eighty-two percent of unrecognized deaths were among persons aged ≥65 years; the proportion of unrecognized deaths were 0•24-0•31 times lower among those 0-17 years relative to all other age groups. More COVID-19-attributable deaths were not captured during the early months of the pandemic (March-May 2020) and during increases in SARS-CoV-2 activity (July 2020, November 2020-February 2021).InterpretationEstimating COVID-19-attributable unrecognized deaths provides a better understanding of the COVID-19 mortality burden and may better quantify the severity of the COVID-19 pandemic.FundingNone.

Project description:The behavior and fate of pharmaceutical ingredients in coastal marine ecosystems are not well understood. To address this, the spatial and temporal distribution of 15 high-volume pharmaceuticals were measured over a 1-yr period in Narragansett Bay (RI, USA) to elucidate factors and processes regulating their concentration and distribution. Dissolved concentrations ranged from below detection to 313?ng/L, with 4 pharmaceuticals present at all sites and sampling periods. Eight pharmaceuticals were present in suspended particulate material, ranging in concentration from below detection to 44?ng/g. Partitioning coefficients were determined for some pharmaceuticals, with their range and variability remaining relatively constant throughout the study. Normalization to organic carbon content provided no benefit, indicating other factors played a greater role in regulating partitioning behavior. Within the upper bay, the continuous influx of wastewater treatment plant effluents resulted in sustained, elevated levels of pharmaceuticals. A pharmaceutical concentration gradient was apparent from this zone to the mouth of the bay. For most of the pharmaceuticals, there was a strong relationship with salinity, indicating conservative behavior within the estuary. Short flushing times in Narragansett Bay coupled with pharmaceuticals' presence overwhelmingly in the dissolved phase indicate that most pharmaceuticals will be diluted and transported out of the estuary, with only trace amounts of several compounds sequestered in sediments. The present study identifies factors controlling the temporal and spatial dynamics of dissolved and particulate pharmaceuticals; their partitioning behavior provides an increased understanding of their fate, including bioavailability in an urban estuary. Environ Toxicol Chem 2017;36:1846-1855. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

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Application of Bayesian spatial-temporal models for estimating unrecognized COVID-19 deaths in the United States.

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