Excess natural-cause mortality in US counties and its association with reported COVID-19 deaths

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. We developed a Bayesian hierarchical model to produce monthly estimates of excess natural-cause mortality for US counties over the first 30 mo of the pandemic. From March 2020 through August 2022, 1,194,610 excess natural-cause deaths occurred nationally [90% PI (Posterior Interval): 1,046,000 to 1,340,204]. A total of 162,886 of these excess natural-cause deaths (90% PI: 14,276 to 308,480) were not reported to COVID-19. Overall, 15.8 excess deaths were reported to non-COVID-19 natural causes for every 100 reported COVID-19 deaths. This number was greater in nonmetropolitan counties (36.0 deaths), the West (Rocky Mountain states: 31.6 deaths; Pacific states: 25.5 deaths), and the South (East South Central states: 26.0 deaths; South Atlantic states: 25.0 deaths; West South Central states: 24.2 deaths). In contrast, reported COVID-19 death counts surpassed estimates of excess natural-cause deaths in metropolitan counties in the New England and Middle Atlantic states. Increases in reported COVID-19 deaths correlated temporally with increases in excess deaths reported to non-COVID-19 natural causes in the same and/or prior month. This suggests that many excess deaths reported to non-COVID-19 natural causes during the first 30 mo of the pandemic in the United States were unrecognized COVID-19 deaths.

Sociodemographic Differences in E-Cigarette Uptake and Perceptions of Harm

INTRODUCTION

This study aimed to evaluate socioeconomic and racial/ethnic differences in e-cigarette uptake and harm perceptions about e-cigarettes among adults who smoke cigarettes in the U.S.

METHODS

Five waves of the U.S. Population Assessment of Tobacco and Health Study (2013-2019) were used to assess transitions from exclusive cigarette smoking to (1) exclusive e-cigarette use, (2) dual use, and (3) nonuse of either product (N=7,172). Analyses (conducted in 2022) estimated differences in transitions and e-cigarette harm perceptions by race/ethnicity, income, and education.

RESULTS

Hispanic (OR=0.32; 95% CI=0.18, 0.54) and Black (OR=0.38; 95% CI=0.22, 0.65) adults were less likely than White adults to transition from exclusive cigarette to exclusive e-cigarette use after 1 year. Adults with a bachelor's degree (versus those with less than high school) (OR=2.57; 95% CI=1.49, 4.45) and adults making ≥$100,000/year (versus those making <$10,000) (OR=3.61; 95% CI=2.10, 6.22) were more likely to transition from exclusive cigarette to exclusive e-cigarette use. Hispanic and Black adults and those with lower income and education were more likely to perceive e-cigarettes as equally or more harmful than cigarettes, which in turn was associated with lower odds of transitioning from exclusive cigarette smoking to exclusive e-cigarette use (OR=0.62; 95% CI=0.47, 0.81).

CONCLUSIONS

Adults who were Hispanic, were Black, and/or had lower SES were less likely to use e-cigarettes to quit cigarettes. Findings provide preliminary evidence that differences in harm perceptions may contribute to disparities in e-cigarette transitions.

Monthly excess mortality across counties in the United States during the COVID-19 pandemic, March 2020 to February 2022

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. An estimated 1,179,024 excess deaths occurred during the first 2 years of the pandemic (first: 634,830; second: 544,194). Overall, excess mortality decreased in large metropolitan counties but increased in nonmetropolitan counties. Despite the initial concentration of mortality in large metropolitan Northeastern counties, nonmetropolitan Southern counties had the highest cumulative relative excess mortality by July 2021. These results highlight the need for investments in rural health as the pandemic's rural impact grows.

COVID-19 Mortality by Race and Ethnicity in US Metropolitan and Nonmetropolitan Areas, March 2020 to February 2022

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.

Design, Setting, and Participants

This cross-sectional study was conducted using data from the US Centers for Disease Control and Prevention for COVID-19 deaths from March 1, 2020, through February 28, 2022, among adults aged 25 years and older residing in the US. Deaths were examined by race and ethnicity across metropolitan and nonmetropolitan areas, and the national decrease in racial and ethnic disparities between initial and Omicron waves was decomposed. Data were analyzed from June 2021 through March 2023.

Exposures

Metropolitan vs nonmetropolitan areas and race and ethnicity.

Main Outcomes and Measures

Age-standardized death rates.

Results

There were death certificates for 977 018 US adults aged 25 years and older (mean [SD] age, 73.6 [14.6] years; 435 943 female [44.6%]; 156 948 Hispanic [16.1%], 140 513 non-Hispanic Black [14.4%], and 629 578 non-Hispanic White [64.4%]) that included a mention of COVID-19. The proportion of COVID-19 deaths among adults residing in nonmetropolitan areas increased from 5944 of 110 526 deaths (5.4%) during the initial wave to a peak of 40 360 of 172 515 deaths (23.4%) during the Delta wave; the proportion was 45 183 of 210 554 deaths (21.5%) during the Omicron wave. The national disparity in age-standardized COVID-19 death rates per 100 000 person-years for non-Hispanic Black compared with non-Hispanic White adults decreased from 339 to 45 deaths from the initial to Omicron wave, or by 293 deaths. After standardizing for age and racial and ethnic differences by metropolitan vs nonmetropolitan residence, increases in death rates among non-Hispanic White adults explained 120 deaths/100 000 person-years of the decrease (40.7%); 58 deaths/100 000 person-years in the decrease (19.6%) were explained by shifts in mortality to nonmetropolitan areas, where a disproportionate share of non-Hispanic White adults reside. The remaining 116 deaths/100 000 person-years in the decrease (39.6%) were explained by decreases in death rates in non-Hispanic Black adults.

Conclusions and Relevance

This study found that most of the national decrease in racial and ethnic disparities in COVID-19 mortality between the initial and Omicron waves was explained by increased mortality among non-Hispanic White adults and changes in the geographic spread of the pandemic. These findings suggest that despite media reports of a decline in disparities, there is a continued need to prioritize racial health equity in the pandemic response.

Differences Between Reported COVID-19 Deaths and Estimated Excess Deaths in Counties Across the United States, March 2020 to February 2022

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. Overall, we estimated that 268,176 excess deaths were not reported as Covid-19 deaths during the first two years of the Covid-19 pandemic, which represented 23.7% of all excess deaths that occurred. Differences between excess deaths and reported COVID-19 deaths were substantial in both the first and second year of the pandemic. Excess deaths were less likely to be reported as COVID-19 deaths in the Mountain division, in the South, and in nonmetro counties. The number of excess deaths exceeded COVID-19 deaths in all Census divisions except for the New England and Middle Atlantic divisions where there were more COVID-19 deaths than excess deaths in large metro areas and medium or small metro areas. Increases in excess deaths not assigned to COVID-19 followed similar patterns over time to increases in reported COVID-19 deaths and typically preceded or occurred concurrently with increases in reported COVID-19 deaths. Estimates from this study can be used to inform targeting of resources to areas in which the true toll of the COVID-19 pandemic has been underestimated.

Monthly excess mortality across counties in the United States during the Covid-19 pandemic, March 2020 to February 2022

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. An estimated 1,159,580 excess deaths occurred during the first two years of the pandemic (first: 620,872; second: 538,708). Overall, excess mortality decreased in large metropolitan counties, but increased in nonmetro counties, between the first and second years of the pandemic. Despite the initial concentration of mortality in large metropolitan Northeast counties, beginning in February 2021, nonmetro South counties had the highest cumulative relative excess mortality. These results highlight the need for investments in rural health as the pandemic's disproportionate impact on rural areas continues to grow.

Geographic and Temporal Patterns in Covid-19 Mortality by Race and Ethnicity in the United States from March 2020 to February 2022

Prior research has established that American Indian, Alaska Native, Black, Hispanic, and Pacific Islander populations in the United States have experienced substantially higher mortality rates from Covid-19 compared to non-Hispanic white residents during the first year of the pandemic. What remains less clear is how mortality rates have changed for each of these racial/ethnic groups during 2021, given the increasing prevalence of vaccination. In particular, it is unknown how these changes in mortality have varied geographically. In this study, we used provisional data from the National Center for Health Statistics (NCHS) to produce age-standardized estimates of Covid-19 mortality by race/ethnicity in the United States from March 2020 to February 2022 in each metro-nonmetro category, Census region, and Census division. We calculated changes in mortality rates between the first and second years of the pandemic and examined mortality changes by month. We found that when Covid-19 first affected a geographic area, non-Hispanic Black and Hispanic populations experienced extremely high levels of Covid-19 mortality and racial/ethnic inequity that were not repeated at any other time during the pandemic. Between the first and second year of the pandemic, racial/ethnic inequities in Covid-19 mortality decreased-but were not eliminated-for Hispanic, non-Hispanic Black, and non-Hispanic AIAN residents. These inequities decreased due to reductions in mortality for these populations alongside increases in non-Hispanic white mortality. Though racial/ethnic inequities in Covid-19 mortality decreased, substantial inequities still existed in most geographic areas during the pandemic's second year: Non-Hispanic Black, non-Hispanic AIAN, and Hispanic residents reported higher Covid-19 death rates in rural areas than in urban areas, indicating that these communities are facing serious public health challenges. At the same time, the non-Hispanic white mortality rate worsened in rural areas during the second year of the pandemic, suggesting there may be unique factors driving mortality in this population. Finally, vaccination rates were associated with reductions in Covid-19 mortality for Hispanic, non-Hispanic Black, and non-Hispanic white residents, and increased vaccination may have contributed to the decreases in racial/ethnic inequities in Covid-19 mortality observed during the second year of the pandemic. Despite reductions in mortality, Covid-19 mortality remained elevated in nonmetro areas and increased for some racial/ethnic groups, highlighting the need for increased vaccination delivery and equitable public health measures especially in rural communities. Taken together, these findings highlight the continued need to prioritize health equity in the pandemic response and to modify the structures and policies through which systemic racism operates and has generated racial health inequities.

Excess all-cause mortality across counties in the United States, March 2020 to December 2021

Official Covid-19 death counts have underestimated the mortality impact of the Covid-19 pandemic in the United States. Excess mortality, which compares observed deaths to deaths expected in the absence of the pandemic, is a useful measure for assessing the total effect of the pandemic on mortality levels. In the present study, we produce county-level estimates of excess mortality for 3,127 counties between March 2020 and December 2021. We fit two hierarchical linear models to county-level death rates from January 2015 to December 2019 and predict expected deaths for each month during the pandemic. We compare observed deaths to these estimates to obtain excess deaths for each county-month. An estimated 936,911 excess deaths occurred during 2020 and 2021, of which 171,168 (18.3%) were not assigned to Covid-19 on death certificates as an underlying cause of death. Urban counties in the Far West, Great Lakes, Mideast, and New England experienced a substantial mortality disadvantage in 2020, whereas rural counties in these regions had higher mortality in 2021. In the Southeast, Southwest, Rocky Mountain, and Plains regions, there was a rural mortality disadvantage in 2020, which was exacerbated in 2021. The proportion of excess deaths assigned to Covid-19 was lower in 2020 (76.3%) than in 2021 (87.0%), suggesting that a larger fraction of excess deaths was assigned to Covid-19 later in the pandemic. However, in rural areas and in the Southeast and Southwest a large share of excess deaths was still not assigned to Covid-19 during 2021.

SIGNIFICANCE

Deaths during the Covid-19 pandemic have been primarily monitored through death certificates containing reference to Covid-19. This approach has missed more than 170,000 deaths related to the pandemic between 2020 and 2021. While the ascertainment of Covid-19 deaths improved during 2021, the full effects of the pandemic still remained obscured in some regions. County-level estimates of excess mortality are useful for studying geographic inequities in the mortality burden associated with the pandemic and identifying specific regions where the full mortality burden was significantly underreported (i.e. Southeast). They can also be used to inform resource allocation decisions at the federal and state levels and encourage uptake of preventive measures in communities with low vaccine uptake.

County-level estimates of excess mortality associated with COVID-19 in the United States

The COVID-19 pandemic in the U.S. has been largely monitored using death certificates containing reference to COVID-19. However, prior analyses reveal that a significant percentage of excess deaths associated with the pandemic were not directly assigned to COVID-19. In this study, we estimated a generalized linear model of expected mortality based on historical trends in deaths by county of residence between 2011 and 2019. We used the results of the model to generate estimates of excess mortality and excess deaths not assigned to COVID-19 in 2020 for 1470 county sets in the U.S. representing 3138 counties. Across the country, we estimated that 438,386 excess deaths occurred in 2020, among which 87.5% were assigned to COVID-19. Some regions (Mideast, Great Lakes, New England, and Far West) reported the most excess deaths in large central metros, whereas other regions (Southwest, Southeast, Plains, and Rocky Mountains) reported the highest excess mortality in nonmetro areas. The proportion assigned to COVID-19 was lowest in large central metro areas (79.3%). Regionally, the proportion of excess deaths assigned to COVID-19 was lowest in the Southeast (81.6%), Southwest (82.6%), Far West (83.7%), and Rocky Mountains (86.7%). Across the regions, the number of excess deaths exceeded the number of directly assigned COVID-19 deaths in most counties. The exception to this pattern occurred in New England, which reported more directly assigned COVID-19 deaths than excess deaths in metro and nonmetro areas. Many county sets had substantial numbers of excess deaths that were not accounted for in direct COVID-19 death counts. Estimates of excess mortality at the local level can inform the allocation of resources to areas most impacted by the pandemic and contribute to positive behavior feedback loops, such as increases in mask-wearing and vaccine uptake.

County-level estimates of excess mortality associated with COVID-19 in the United States

The COVID-19 pandemic in the U.S. has been largely monitored using death certificates containing reference to COVID-19. However, prior analyses reveal that a significant percentage of excess deaths associated with the pandemic were not directly assigned to COVID-19. In this study, we estimated a generalized linear model of expected mortality based on historical trends in deaths by county of residence between 2011 and 2019. We used the results of the model to generate estimates of excess mortality and excess deaths not assigned to COVID-19 in 2020 for 1470 county sets in the U.S. representing 3138 counties. Across the country, we estimated that 438,386 excess deaths occurred in 2020, among which 87.5% were assigned to COVID-19. Some regions (Mideast, Great Lakes, New England, and Far West) reported the most excess deaths in large central metros, whereas other regions (Southwest, Southeast, Plains, and Rocky Mountains) reported the highest excess mortality in nonmetro areas. The proportion assigned to COVID-19 was lowest in large central metro areas (79.3%). Regionally, the proportion of excess deaths assigned to COVID-19 was lowest in the Southeast (81.6%), Southwest (82.6%), Far West (83.7%), and Rocky Mountains (86.7%). Across the regions, the number of excess deaths exceeded the number of directly assigned COVID-19 deaths in most counties. The exception to this pattern occurred in New England, which reported more directly assigned COVID-19 deaths than excess deaths in metro and nonmetro areas. Many county sets had substantial numbers of excess deaths that were not accounted for in direct COVID-19 death counts. Estimates of excess mortality at the local level can inform the allocation of resources to areas most impacted by the pandemic and contribute to positive behavior feedback loops, such as increases in mask-wearing and vaccine uptake.

•

438,386 excess deaths occurred in 2020, among which 87.5% were assigned to COVID-19.

•

There was substantial heterogeneity in excess death rates across counties.

•

The mortality impact of the Covid-19 pandemic was effectively hidden in many counties.

•

The percent of excess deaths assigned to COVID-19 was lowest in the South and West.

•

New England uniquely reported more direct COVID-19 deaths than excess deaths.

Loneliness, social isolation, and all-cause mortality in the United States

Social isolation and loneliness are both established risk factors for mortality, but it remains unclear how these two conditions interact with each other. We used data from 3975 adults aged 25-74 years who completed self-administered questionnaires (SAQs) for the Midlife in the United States (MIDUS) National Study Wave 2 (2004-2006). Loneliness was measured by asking participants how often they felt lonely. A shortened version of the Berkman-Syme Social Network Index that captured partnerships, friends/family, religious participation, and other forms of social connection was used to assess social isolation. Follow-up for all-cause mortality was censored at the end of 2016. We used progressively adjusted Cox proportional hazard models to examine the mortality risks of loneliness, social isolation, the components of social isolation, and combinations of loneliness and isolation. We adjusted for sociodemographic characteristics in our first models and then added health behaviors and physical and mental health conditions in subsequent models. In the minimally adjusted model, loneliness was associated with higher mortality risk (HR, 1.34; 95% CI, 1.22-1.47), but the association was not significant in the fully adjusted model. Social isolation was significantly associated with mortality in the minimally adjusted model (HR, 1.24; 95% CI, 1.15-1.34) and the fully adjusted model (HR, 1.13; 95% CI, 1.04-1.23). Having infrequent contact with family or friends was the component of isolation that had the strongest association with mortality. Contrary to prior literature, which has found either no interaction or a synergistic interaction between isolation and loneliness, we identified a significant, negative interaction between the two measures, indicating that loneliness and social isolation may partially substitute for one another as risk factors for mortality. Both are associated with a similar increased risk of mortality relative to those who express neither; we find no evidence that the combined effect is worse than experiencing either by itself.

Post-Consumer textile waste and disposal: Differences by socioeconomic, demographic, and retail factors

The amount of post-consumer textile waste (PCTW) generated annually in the United States has increased nearly ten-fold since the 1960s to exceed more than 34 billion pounds annually. Of the waste generated, 66% is sent to landfills, 19% is combusted with energy recovery, and only 15% is recycled. When left to decompose in landfills, PCTW decomposes, producing harmful leachates and greenhouse gases including methane. In this study, we used publicly available data from 67 counties in the state of Florida from 2014 to 2019 to assess how PCTW generation and recycling behaviors differ by area-level demographic, socioeconomic, and retail characteristics. We also used publicly available data on landfills to determine whether these same factors were associated with having more landfills per capita in a county. This study provides preliminary evidence that people living in areas with higher incomes, that are more racially segregated, and that have more clothing stores generate significantly more textile waste than people in other areas. In contrast, there were more landfills per capita in areas with lower incomes and fewer landfills per capita in areas that were more racially segregated.Textile recycling occurred at relatively uniform rates across counties. Taken together, these findings support the understanding that textile waste represents an issue of environmental injustice; wealthier communities contribute more PCTW to landfills, which are more commonly located in communities with lower socioeconomic status. Multipronged solutions are needed to produce relevant behavior change, including efforts and policies that seek to reduce textile consumption and increase textile recycling at the individual and societal level.

Association of Health Care Factors With Excess Deaths Not Assigned to COVID-19 in the US

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

COVID-19 and excess mortality in the United States: A county-level analysis

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.

METHODS AND FINDINGS

In this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct COVID-19 and all-cause mortality occurring in US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more COVID-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black, and 59.6% non-Hispanic White. A total of 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and COVID-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to COVID-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than COVID-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to COVID-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to COVID-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics.

CONCLUSIONS

In this study, we found that direct COVID-19 death counts in the US in 2020 substantially underestimated total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.

Assessing the Impact of the Covid-19 Pandemic on US Mortality: A County-Level Analysis

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.

Methods and Findings.

In this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct Covid-19 and all-cause mortality occurring in U.S. counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a ten week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more Covid-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black and 59.6% non-Hispanic White. 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and Covid-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to Covid-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than Covid-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to Covid-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of Covid-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to Covid-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics.

Conclusions.

In this study, we found that direct Covid-19 death counts in the United States in 2020 substantially underestimated total excess mortality attributable to Covid-19. Racial and socioeconomic inequities in Covid-19 mortality also increased when excess deaths not assigned to Covid-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.

Estimates of the Association of Dementia With US Mortality Levels Using Linked Survey and Mortality Records

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.

Design, Setting, and Participants

A prospective cohort study of the Health and Retirement Study of noninstitutionalized US individuals with baseline exposure assessment in 2000 and follow-up through 2009 was conducted. Data were analyzed from November 2018 to May 2020. The sample was drawn from 7489 adults aged 70 to 99 years interviewed directly or by proxy. Ninety participants with missing covariates or sample weights and 57 participants lost to follow-up were excluded. The final analytic sample included 7342 adults.

Exposure

Dementia and cognitive impairment without dementia (CIND) were identified at baseline using Health and Retirement Study self- or proxy-reported cognitive measures and the validated Langa-Weir score cutoff.

Main Outcomes and Measures

Hazard ratios relating dementia and CIND status to all-cause mortality were estimated using Cox proportional hazards regression models, accounting for covariates, and were used to calculate population-attributable fractions. Results were compared with information on cause of death from death certificates.

Results

Of the 7342 total sample, 4348 participants (60.3%) were women. At baseline, 4533 individuals (64.0%) were between ages 70 and 79 years, 2393 individuals (31.0%) were between 80 and 89 years, and 416 individuals (5.0%) were between 90 and 99 years; percentages were weighted. The percentage of deaths attributable to dementia was 13.6% (95% CI, 12.2%-15.0%) between 2000 and 2009. The mortality burden of dementia was significantly higher among non-Hispanic Black participants (24.7%; 95% CI, 17.3-31.4) than non-Hispanic White participants (12.2%; 95% CI, 10.7-13.6) and among adults with less than a high school education (16.2%; 95% CI, 13.2%-19.0%) compared with those with a college education (9.8%; 95% CI, 7.0%-12.5%). Underlying cause of death recorded on death certificates (5.0%; 95% CI, 4.3%-5.8%) underestimated the contribution of dementia to US mortality by a factor of 2.7. Incorporating deaths attributable to CIND revealed an even greater underestimation.

Conclusions and Relevance

The findings of this study suggest that the mortality burden associated with dementia is underestimated using vital statistics, especially when considering CIND in addition to dementia.

Racial/Ethnic Differences in Associations of Non-cigarette Tobacco Product Use With Subsequent Initiation of Cigarettes in US Youths

Introduction

Understanding which non-cigarette tobacco products precede smoking in youth across different racial/ethnic groups can inform policies that consider tobacco-related health disparities.

Methods

We used nationally representative, longitudinal data from the Population Assessment of Tobacco and Health Study waves 1–4. The sample was a dynamic cohort of cigarette-naïve youth aged 12–17 years. Mixed-effects models were used to assess non-cigarette product (e-cigarette, cigar product, or other product) use with cigarette use over 1-year intervals.

Results

Of the 28 788 observations pooled across waves 1–4, respondents were 48.7% non-Hispanic white, 13.9% non-Hispanic black, and 23.1% Hispanic. Odds of cigarette initiation over 1-year follow-up were higher among youth with prior use of e-cigarettes (odds ratio [OR], 2.76; 95% confidence interval [CI], 2.21–3.45), cigars (OR, 2.00; 95% CI, 1.42–2.80), or other products (OR, 1.66; 95% CI, 1.28–2.14) compared to never users. At the population level, 20.6% of cigarette initiation was attributable to e-cigarette use among white youth and 21.6% among Hispanic youth, while only 3.5% of cigarette initiation was attributable to e-cigarette use among black youth. In contrast, 9.1% of cigarette initiation for black youth was attributable to cigar use compared to only 3.9% for both white and Hispanic youth.

Conclusions

Prior use of e-cigarettes, cigars, and other non-cigarette products were all associated with subsequent cigarette initiation. However, white and Hispanic youth were more likely to initiate cigarettes through e-cigarette use (vs. cigar or other product use), while black youth were more likely to initiate cigarettes through cigar use (vs. e-cigarette or other product use).

Implications

Our findings suggest that previous studies on effects of non-cigarette tobacco products may overlook the critical role of cigar products as a pathway into cigarette smoking among US youth, particularly black youth. While our data support the importance of e-cigarette use as a pathway into smoking, regulatory actions aimed at addressing youth e-cigarette use alone may contribute to disparities in black versus white tobacco use and further exacerbate inequities in tobacco-related disease. Thus, contemporary policy development and discourse about the effects of non-cigarette tobacco products on cigarette initiation should consider cigar and other non-cigarette products as well as e-cigarettes.

Increases in BMI and chronic pain for US adults in midlife, 1992 to 2016

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. Prior work has demonstrated a link between obesity and mortality risk at midlife, but the mechanisms remain unclear. Pain may represent one important pathway linking obesity to mortality trends. Pain is a debilitating condition that has increased significantly over recent decades and is associated with both morbidity and mortality, including suicide and opioid-related mortality. Evidence suggests obesity and pain may be linked, but there is little evidence of an association at the population level. In this paper, we examine to what extent increases in overweight and obesity explain the rising trends in chronic pain observed among middle-aged adults in the US from 1992 to 2016. We assess trends in both mild/moderate nonlimiting pain and severe and/or limiting pain. In doing so, we draw attention to one mechanism through which overweight/obesity may have contributed to recent population health trends. Our analysis found that increases in BMI from 1992 to 2016 may account for up to 20% of the upward trend in mild/moderate nonlimiting pain and 32% of the trend in severe and/or limiting pain for women, and 10% and 19% of the trends respectively for men.

•

We study the contribution of overweight and obesity to recent trends in pain among middle-aged adults.

•

Overweight and obesity accounted for 32.1% of increases in severe or limiting pain among women and 19.0% among men.

•

Overweight and obesity explained a larger share of the increase in severe than mild/moderate pain.

•

The study highlights the importance of obesity prevention to decrease the prevalence of pain.

Association of Weight Loss Between Early Adulthood and Midlife With All-Cause Mortality Risk in the US

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. The data analysis was conducted from February 10, 2019, to April 20, 2020. Individuals aged 40 to 74 years at the time of survey (baseline) were included in the analyses (n = 24 205).

EXPOSURES

Weight history was assessed by self-reported weight at age 25 years, at 10 years before baseline (midlife: mean age, 44 years; interquartile range, 37-55), and measured weight at baseline. Body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) at each time was categorized as normal (18.5-24.9), overweight (25.0-29.9), and obese (≥30.0). Weight change patterns were assessed from age 25 years (early adulthood) to 10 years before baseline (midlife).

MAIN OUTCOMES AND MEASURES

Incident all-cause mortality using linked data from the National Death Index.

RESULTS

Of the 24 205 participants, 11 617 were women (49.0%) and 11 567 were non-Hispanic White (76.9%). The mean (SD) BMI was 29.0 (6.1) at baseline. During a mean (SD) follow-up of 10.7 (7.2) years, 5846 deaths occurred. Weight loss from obese to overweight was associated with a 54% (hazard ratio, 0.46; 95% CI, 0.27-0.77) reduction in mortality risk compared with individuals with stable obesity between early adulthood and midlife. An estimated 3.2% (95% CI, 1.6%-4.9%) of early deaths could have been avoided if those who maintained an obese BMI instead lost weight to an overweight BMI by midlife. Overall, an estimated 12.4% (95% CI, 8.1%-16.5%) of early deaths may be attributable to having weight in excess of the normal BMI range at any point between early and mid-adulthood.

CONCLUSIONS AND RELEVANCE

In this study, weight loss from obesity to overweight between early adulthood through midlife appeared to be associated with a mortality risk reduction compared with persistent obesity. These findings support the importance of population-based approaches to preventing weight gain across the life course and a need for greater emphasis on treating obesity early in life.

Obesity and Incident Prescription Opioid Use in the U.S., 2000-2015

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.

METHODS

Fifteen panels of the Medical Expenditure Panel Survey from 2000 to 2015 were pooled to generate a sample of civilian non-institutionalized adults aged 30-84 years who were prescription opioid-naïve for approximately 9 months. Incident long-term prescription opioid use was defined as reporting use at 2 of 3 interviews during a 15-month follow-up. BMI was reported at baseline. Analyses were completed in 2019.

RESULTS

Among opioid-naïve adults (n=89,629), obesity was strongly associated with incident long-term prescription opioid use. The association increased at progressively higher BMI values, with 24% elevated odds (95% CI=7%, 44%) in adults with overweight (25-29.9 kg/m²) and 158% increased odds (95% CI=106%, 224%) among adults with Class III obesity (40-49.9 kg/m²). These associations grew with higher-dosage opioids. Of the reasons for opioid use, joint pain, back pain, injury, and muscle/nerve pain contributed the most to the excess use observed among adults with obesity. At the population level, 27.0% of incident long-term prescription opioid use (95% CI=19.0%, 34.8%) was attributable to adults having a BMI above normal weight (25-49.9 kg/m²).

CONCLUSIONS

These findings suggest that obesity has contributed to prescription opioid use in the U.S. Future investments in chronic pain reduction may benefit from increased integration with obesity prevention and treatment.

Association of Obesity With Prescription Opioids for Painful Conditions in Patients Seeking Primary Care in the US

IMPORTANCE

Prior studies have identified an association between obesity and prescription opioid use in the US. However, the pain conditions that are factors in this association remain unestablished.

OBJECTIVE

To investigate the association between obesity and pain diagnoses recorded by primary care clinicians as reasons for prescription of opioids.

DESIGN, SETTING, AND PARTICIPANTS

A cross-sectional study including 565 930 patients aged 35 to 64 years with a body mass index (BMI) measurement recorded in 2016 was conducted. Electronic health records of patients seen by primary care clinicians in the US in the multipayer athenahealth network from January 1, 2015, to December 31, 2017, were reviewed, and data were analyzed from March 1 to September 15, 2019.

MAIN OUTCOMES AND MEASURES

Any prescription of opioids in the 365 days before or after the first BMI measurement in 2016 were identified. All International Classification of Diseases, Ninth Revision, and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, claims within 7 days before each opioid prescription were captured and classified using a pain diagnosis typologic system. Weight was categorized as underweight (BMI, 18.5-19.9), normal weight (BMI, 20.0-24.9), overweight (BMI, 25.0-29.9), obese I (BMI, 30-34.9), obese II (BMI, 35.0-39.9), obese III (BMI, 40.0-49.9), and obese IV (BMI, 50.0-80.0).

RESULTS

Among 565 930 patients, 329 083 (58.1%) were women. A total of 125 093 patients (22.1%) were aged 35 to 44 years, 199 384 patients (35.2%) were 45 to 54 years, and 241 453 patients (42.7%) were 55 to 64 years. A total of 177 631 patients (31.4%) were overweight and 273 135 patients (48.2%) were obese at baseline. Over 2 years, 93 954 patients (16.6%) were prescribed opioids. The risk of receiving prescription opioids increased progressively with BMI (adjusted relative risk for overweight: 1.08; 95% CI, 1.06-1.10; obese I: 1.24; 95% CI, 1.22-1.26; obese II: 1.33; 95% CI, 1.30-1.36; obese III: 1.48; 95% CI, 1.45-1.51; and obese IV, 1.71; 95% CI, 1.65-1.77). The percentage of patients with opioid prescriptions attributable to an overweight or obese BMI was 16.2% (95% CI, 15.0%-17.4%). Prescription opioids for management of osteoarthritis (relative risk for obese vs normal weight, 1.90; 95% CI, 1.77-2.05) and other joint disorders (relative risk, 1.63; 95% CI, 1.55-1.72) both had stronger associations with obesity than the mean for any pain diagnosis (relative risk, 1.33; 95% CI, 1.31-1.36). Osteoarthritis, other joint disorders, and other back disorders comprised a combined 53.4% of the absolute difference in prescription of opioids by obesity.

CONCLUSIONS AND RELEVANCE

Joint and back disorders appear to be the most important diagnoses in explaining the increased receipt of opioid prescriptions among patients with obesity. Addressing the opioid crisis will require attention to underlying sources of demand for prescription opioids, including obesity, through its associations with pain.

Trends in Prescription Analgesic Use Among Adults With Musculoskeletal Conditions in the United States, 1999-2016

IMPORTANCE

Monitoring trends in prescription analgesic use among adults with musculoskeletal conditions provides insight into how changing prescribing practices, guidelines, and policy measures may affect those who need pain management.

OBJECTIVE

To evaluate trends in prescription opioid use and nonopioid analgesic use among adults with functional limitations attributable to musculoskeletal conditions.

DESIGN, SETTING, AND PARTICIPANTS

This repeated cross-sectional study uses data from the National Health and Nutrition Examination Study from 1999 to 2016. Data were analyzed from January to July 2019. The participants were adults aged 30 to 79 years who reported functional limitations due to back or neck problems and/or arthritis or rheumatism.

MAIN OUTCOMES AND MEASURES

Any use of a prescription opioid or exclusive use of a prescription nonopioid analgesic.

RESULTS

In this population of 7256 adults with 1 or more functional limitations attributable to a musculoskeletal condition (4226 women [59.9%]; 3508 [74.4%] non-Hispanic white individuals; median [interquartile range] age, 63 [53-70] years), opioid use and exclusive nonopioid analgesic use exhibited approximately reciprocal patterns of change from 1999 to 2016. Opioid use increased significantly (difference in prevalence for 2015-2016 vs 1999-2000, 7.2%; 95% CI, 1.3% to 13%; P for trend = .002), and exclusive use of nonopioid analgesics decreased significantly (difference in prevalence for 2015-2016 vs 1999-2000, -13%; 95% CI, -19% to -7.5%; P for trend < .001) during this period. The increase in any opioid use was driven by long-term rather than short-term use. A crossover in the prevalence of opioid use and exclusive use of nonopioid analgesics occurred between 2003 and 2006, after which opioid use was more prevalent. Between 2013 and 2016, decreases in opioid use were observed among men (difference in prevalence for 2015-2016 vs 2013-2014, -11%; 95% CI, -21% to 1.8%) and participants with less than a high school education (difference, -15%; 95% CI, -24% to -6.1%). During this same period, exclusive nonopioid analgesic use also decreased markedly across the population (difference, -5.3%; 95% CI, -9.1% to -1.5%).

CONCLUSIONS AND RELEVANCE

The substitution of opioids for nonopioid analgesics between 2003 and 2006 may have occurred as evidence emerged on the cardiovascular risks associated with nonopioid analgesics. Reductions in opioid use between 2013 and 2016 were most substantial among those with low socioeconomic status, who may encounter barriers in accessing alternatives. Despite those decreases, opioid use remained more prevalent in 2015 to 2016 than in 1999 to 2000, suggesting a potentially long tail for the opioid epidemic.

The contribution of obesity to prescription opioid use in the United States

The prevalence of obesity has grown rapidly over the past several decades and has been accompanied by an increase in the prevalence of chronic pain and prescription opioid use. Obesity, through its association with pain, may represent an important contributor to opioid use. This cross-sectional study investigated the relationship between obesity and prescription opioid use among adults aged 35 to 79 years using data from the National Health and Nutrition Examination Survey (NHANES, 2003-2016). Relative to normal weight, body mass indices in the overweight {odds ratio (OR), 1.11 (confidence interval [CI], 0.88-1.39)}, obese I (OR, 1.26 [CI, 1.01-1.57]), obese II (OR, 1.69 [CI, 1.34-2.12]), and obese III (OR, 2.33 [CI, 1.76-3.08]) categories were associated with elevated odds of prescription opioid use. The association between excess weight and opioid use was stronger for chronic opioid use than for use with a duration of less than 90 days (P-value, <0.001). We estimated that 14% (CI, 9%-19%) of prescription opioid use at the population level was attributable to obesity, suggesting there might have been 1.5 million fewer opioid users per year under the hypothetical scenario where obese individuals were instead nonobese (CI, 0.9-2.0 million users). Back pain, joint pain, and muscle/nerve pain accounted for the largest differences in self-reported reasons for prescription opioid use across obesity status. Although interpretation is limited by the cross-sectional nature of the associations, our findings suggest that the obesity epidemic may be partially responsible for the high prevalence of prescription opioid use in the United States.