Publications by authors named "Samuel H Preston"

In the United States, estimates of excess deaths attributable to the COVID-19 pandemic have consistently surpassed reported COVID-19 death counts. Excess deaths reported to non-COVID-19 natural causes may represent unrecognized COVID-19 deaths, deaths caused by pandemic health care interruptions, and/or deaths from the pandemic's socioeconomic impacts. The geographic and temporal distribution of these deaths may help to evaluate which explanation is most plausible.

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Racial/ethnic and age disparities in COVID-19 and all-cause mortality during 2020 are well documented, but less is known about their evolution over time. We examine changes in age-specific mortality across five pandemic periods in the United States from March 2020 to December 2022 among four racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Asian) for ages 35+. We fit Gompertz models to all-cause and COVID-19 death rates by 5-year age groups and construct age-specific racial/ethnic mortality ratios across an Initial peak (Mar-Aug 2020), Winter peak (Nov 2020-Feb 2021), Delta peak (Aug-Oct 2021), Omicron peak (Nov 2021-Feb 2022), and Endemic period (Mar-Dec 2022).

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Excess mortality is the difference between expected and observed mortality in a given period and has emerged as a leading measure of the COVID-19 pandemic's mortality impact. Spatially and temporally granular estimates of excess mortality are needed to understand which areas have been most impacted by the pandemic, evaluate exacerbating factors, and inform response efforts. We estimated all-cause excess mortality for the United States from March 2020 through February 2022 by county and month using a Bayesian hierarchical model trained on data from 2015 to 2019.

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Importance: Prior research has established that Hispanic and non-Hispanic Black residents in the US experienced substantially higher COVID-19 mortality rates in 2020 than non-Hispanic White residents owing to structural racism. In 2021, these disparities decreased.

Objective: To assess to what extent national decreases in racial and ethnic disparities in COVID-19 mortality between the initial pandemic wave and subsequent Omicron wave reflect reductions in mortality vs other factors, such as the pandemic's changing geography.

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Background: During the COVID-19 pandemic, the high death toll from COVID-19 was accompanied by a rise in mortality from other causes of death. The objective of this study was to identify the relationship between mortality from COVID-19 and changes in mortality from specific causes of death by exploiting spatial variation in these relationships across US states.

Methods: We use cause-specific mortality data from CDC Wonder and population estimates from the US Census Bureau to examine relationships at the state level between mortality from COVID-19 and changes in mortality from other causes of death.

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Accurate and timely tracking of COVID-19 deaths is essential to a well-functioning public health surveillance system. The extent to which official COVID-19 death tallies have captured the true toll of the pandemic in the United States is unknown. In the current study, we develop a Bayesian hierarchical model to estimate monthly excess mortality in each county over the first two years of the pandemic and compare these estimates to the number of deaths officially attributed to Covid-19 on death certificates.

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This cross-sectional study examines age-specific COVID-19 mortality rates in the US from March 2020 to October 2021 by sex and race and ethnicity.

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Excess mortality is the difference between expected and observed mortality in a given period and has emerged as a leading measure of the overall impact of the Covid-19 pandemic that is not biased by differences in testing or cause-of-death assignment. Spatially and temporally granular estimates of excess mortality are needed to understand which areas have been most impacted by the pandemic, evaluate exacerbating and mitigating factors, and inform response efforts, including allocating resources to affected communities. We estimated all-cause excess mortality for the United States from March 2020 through February 2022 by county and month using a Bayesian hierarchical model trained on data from 2015 to 2019.

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The COVID-19 pandemic in the U.S. has been largely monitored using death certificates containing reference to COVID-19.

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Despite a growing body of literature focused on racial/ethnic disparities in Covid-19 mortality, few previous studies have examined the pandemic's impact on 2020 cause-specific mortality by race and ethnicity. This paper documents changes in mortality by underlying cause of death and race/ethnicity between 2019 and 2020. Using age-standardized death rates, we attribute changes for Black, Hispanic, and White populations to various underlying causes of death and show how these racial and ethnic patterns vary by age and sex.

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This cross-sectional study assesses health care factors associated with excess deaths not assigned to COVID-19 in US counties in 2020.

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Background: Coronavirus Disease 2019 (COVID-19) excess deaths refer to increases in mortality over what would normally have been expected in the absence of the COVID-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to COVID-19. In this study, we take advantage of county-level variation in COVID-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by sociodemographic and health characteristics.

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We use three indexes to identify how age-specific mortality rates in the United States compare to those in a composite of five large European countries since 2000. First, we examine the ratio of age-specific death rates in the United States to those in Europe. These show a sharp deterioration in the US position since 2000.

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Recent unprecedented increases in mortality and morbidity during midlife are often ascribed to rising despair in the US population. An alternative and less often examined explanation is that these trends reflect, at least in part, the lagged effects of the obesity epidemic. Adults in midlife today are more likely to live with obesity and have a greater cumulative exposure to excess adiposity during their lifetime than any previous generation.

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Background: Covid-19 excess deaths refer to increases in mortality over what would normally have been expected in the absence of the Covid-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to Covid-19. In this study, we take advantage of county-level variation in Covid-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to Covid-19 varies across subsets of counties defined by sociodemographic and health characteristics.

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Importance: Vital statistics are the primary source of data used to understand the mortality burden of dementia in the US, despite evidence that dementia is underreported on death certificates. Alternative estimates, drawing on population-based samples, are needed.

Objective: To estimate the percentage of deaths attributable to dementia in the US.

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Importance: Describing potential mortality risk reduction associated with weight loss between early adulthood and midlife is important for informing primary and secondary prevention efforts for obesity.

Objective: To examine the risk of all-cause mortality among adults who lost weight between early adulthood and midlife compared with adults who were persistently obese over the same period.

Design, Setting, And Participants: Combined repeated cross-sectional analysis was conducted using data from the National Health and Nutrition Examination Survey III (1988-1994) and continuous waves collected in 2-year cycles between 1999 and 2014.

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Introduction: Prior studies have identified associations between obesity and numerous conditions that increase risks for chronic pain. However, the impact of obesity on prescription opioid use is not well known. This study investigates the association between obesity and incidence of long-term prescription opioid use.

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Objectives: To identify levels and trends in life expectancy at age 65 (e65) by geographic region and metropolitan status in the United States.

Methods: Using county-level data on population and deaths from the Census and National Center for Health Statistics, we consider spatial inequality in e65 across 4 metropolitan types and 10 geographic regions from 2000 to 2016. We examine whether changes in e65 are driven by mortality developments in metro types or geographic regions, and compare spatial patterns in the United States to mortality trends in other Organization of Economic Cooperation and Development (OECD) countries.

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The impact of rising drug use on US mortality may extend beyond deaths coded as drug-related to include excess mortality from other causes affected by drug use. Here, we estimate the full extent of drug-associated mortality. We use annual death rates for 1999-2016 by state, sex, five-year age group, and cause of death to model the relationship between drug-coded mortality-which serves as an indicator of the population-level prevalence of drug use-and mortality from other causes.

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Objectives: To examine trends in inequality in life expectancy and age-specific death rates across 40 US spatial units from 1990 to 2016.

Methods: We use multiple cause-of-death data from vital statistics to estimate measures of inequality in mortality across metropolitan status and geographic region. We consider trends for 5-year age intervals and examine inequality in cause-specific mortality.

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Background: The incidence and/or diagnosis of a major disease may activate weight change. Patterns of weight change associated with diagnoses have not been systematically documented.

Methods: We use data on adults ages 30+ in the National Health and Nutrition Examination Survey (NHANES) from 1999-2014.

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