Abstract
Purpose:
To evaluate temporal trends and drivers of mortality among younger adults (aged 18-39), from 1999-2021.
Methods:
Observational study using nationally representative United States (US) mortality data from 1999-2021, acquired via the US Centers for Disease Control Wide-ranging Online Data for Epidemiologic Research (WONDER) database. Exposure of interest was cause of death. Primary outcomes were population-level mortality rates and percent increase from 1999-2021. Secondary outcomes were cause-specific and subgroup-specific (sex, race, ethnicity) mortality rates and percent increases.
Results:
From 1999-2021, US younger adults aged 18-39 experienced a 54.1% increase in annual mortality (from 113.4 deaths per 100,000 in 1999 to 174.7 deaths per 100,000 in 2021; Cochran-Armitage p < 0.0001). Prior to COVID, from 1999-2019, younger adults experienced a 10.8% increase in mortality, compared to a 1.5% increase among the broader US population. The top driver of increased younger adult mortality, from 1999 through 2021, was accidental poisoning and exposure to noxious substances. Mortality trends varied by demographic variables, with notable increases among American Indian/Alaskan Native Americans.
Discussion:
US younger adults are suffering from rising premature mortality. Resources should be calibrated to better support this generation.
Keywords: Young adults, premature mortality, health disparities
Implications and Contribution:
Mortality has been rising among United States (US) younger adults aged 18-39, with notable disparities among different demographic groups. This examination of mortality trends among US younger adults from 1999-2021, and the causes of death driving these changes, could allow researchers and public health professionals to better support this generation.
Introduction
The United States (US) has seen trends towards declining life expectancy and rising premature mortality for years,1-3 marking it as an outlier amongst other high-income countries. In fact, the gap in life expectancy between the US and peer high-income countries has been growing, especially in late adolescents and younger adults (under age 40).2 Recent literature has explored specific causes of mortality such as overdose1,2,4 and suicide,5 and their disproportionate impacts on specific demographic subgroups. Although some research has been directed towards age and cohort-specific mortality trends,4,6,7 few studies have addressed the high relative increases in mortality experienced by younger adults in the US – despite research suggesting mortality increases in younger and middle-aged adults are in fact driving the aforementioned declines in life expectancy.2 In particular, there is a paucity of literature exploring increasing all-cause mortality among 25–39-year-olds, who are often grouped into broader categories of “working-age” or “young and middle-aged” adults.2,4,7 While Shiels et al. acknowledged rising premature mortality from 1999 to 2014, particularly among 25–30-year-olds,3 a decade has passed since then and contemporary evidence is needed. Notably, a 2021 review examined declines in US life expectancy but was unable to explore changes in mortality trends in the context of the COVID-19 pandemic, and examined factors contributing to mortality across the life course rather than specifically among younger adults.8 Overall, younger adults remain an understudied population with unique challenges that may influence age-group-specific mortality trends. Furthermore, contemporary evidence is needed to better understand how mortality trends may have changed in the context of the COVID-19 pandemic.
Younger adulthood (ages 18-39) comprises a unique stage in the life course characterized by transition9 and the exploration of health behaviors related to domains like substance use and physical activity.10 Evidence suggests 75% of mental disorders emerge by age 24,11 and anxiety is rising among younger adults.12 Pandemic-related disruptions, including isolation fueled by social distancing efforts, reduced mobility, and a shrinking labor market, may have particularly severe consequences for younger adults, many of whom were entering the workforce at the time of the pandemic’s onset.9,13 This developmental stage is challenging, and reports suggest today’s younger adults are suffering. A recent analysis of the opioid crisis in Ohio found that younger adults ages 25-39 bear the greatest burden of overdose mortality perhaps due to the early onset of substance use, which often begins during the ages of 18-25.14 Millennials are reported to have worsening health, possibly related to increasingly sedentary lifestyles, psychological distress, and increased prevalence of chronic disease, compared to Generation X at the same age.6 Measures of “despair” such as overdose, homicide, and suicidality arerising among young adults of all demographic groups15 and may, in turn, contribute to rising premature mortality.15 The term “despair” remains an inadequately defined construct lacking validated measures and the term, “deaths of despair,” fails to account for macro-level economic and social changes impacting population health.2 However, the decline in well-being among younger adults – often referred to as “despair,” 15 – is troubling. To our knowledge, there is no recent evidence documenting the specific changes in mortality in US younger adults (ages 18-39), the drivers of those changes, and whether mortality rates in younger adults differ by demographic group.
The primary aim of this study is to use US mortality data to evaluate temporal trends in mortality among younger adults (ages 18-39) over the past 22 years (1999-2021) including prior to (≤2019), and during (2020-2021), the COVID-19 pandemic. Our secondary aims are to (1) identify the specific causes of death that are driving temporal changes in mortality, and (2) evaluate if changes in mortality differ by sex, race, and ethnicity.
Methods
Mortality data for 1999-2021 were acquired from the National Center for Health Statistics (NCHS) Underlying Cause of Death data via the US Centers for Disease Control (CDC) Wide-ranging ONline Data for Epidemiologic Research (WONDER) database.16,17 Information from death certificates, including single underlying causes of death, is reported by states to NCHS via the National Vital Statistics System.16,17 The WONDER database defines the “underlying cause” of death in accordance with World Health Organization guidelines, with the specific cause of death adjudicated by a physician.16,17 We examined cause-specific trends (corresponding to the ICD-10 113 causes list18) among all 18–39-year-olds. Causes of particular interest – the 15 causes with the greatest crude mortality rate in 1999 and 2021, respectively, and the 15 causes with the greatest percent increase in mortality between 1999 and 2021 – were examined via analyses stratified by sex, race, and ethnicity (i.e., Hispanic origin). Overarching categories of causes – for instance, “Accidents,” which includes causes such as accidental poisoning and motor vehicle accidents – were explored, but we primarily report on specific, mutually exclusive causes of death to maintain consistency with the ICD-10 113 causes list. Age is entered on death certificates as age at last birthday. Dates of birth and death are entered when available.19 Race and Hispanic origin are reported on death certificates and usually come from a funeral director, informed by the next of kin.19 Studies suggest that self-report and death certificate proxy reports of race vary in accuracy by race. For example, self-report and proxy are in close agreement for White and Black races, but are poor for American Indian or Alaska Native (AI/AN) individuals.20 In cases where Hispanic origin is not reported on the death certificate, the data are coded as "not stated," and are excluded in death rates calculated by Hispanic origin. Sex (referred to as gender in the WONDER database) is typically ascertained from s medical records or physical examination of the body.19 Some states are shifting to allow recording sex as reported by other sources, such as the primary informant or legal documents.21 Only male and female sex are available in the WONDER database.21
The Brown University Institutional Review Board (IRB) indicates that analyses on datasets for which researchers do not have access to individually identifiable information, or access to private information, are not considered human subjects research. Details can be found on the Brown University IRB website.22 Analyses in this paper were performed through querying the CDC WONDER database web portal; researchers did not have access to the data itself.
Statistical Analyses
We examined trends in crude mortality rate and percent change in mortality rate since 1999 by 5-year age groups to provide an estimate of mortality trends among those at different stages in the life course. Analyses stratified by sex, race, and ethnicity compared trends in mortality within the 18–39-year age group. Crude mortality rates were calculated as deaths per 100,000 United States residents, per unit of time. Changes in cause-specific mortality rates were calculated by evaluating net change from baseline data (referent year 1999) through to future years, with a focus on 2019 (before COVID) and 2021 (during COVID). . Analyses were stratified by sex, race and ethnicity. To provide estimates of statistical variance, standard error of the rate is reported. Cochran-Armitage tests for trend23,24 were performed to assess overall trend from 1999 to 2019, as well as 1999 to 2021 (α = 0.05). While the WONDER database only offers age-adjusted rates for ten-year age groupings outside our age range of interest, sensitivity analyses were performed standardizing the available mortality rates using 1999 population estimates. . Additional sensitivity analyses examined key drivers of mortality increases using the WONDER Multiple Cause of Death database,25 to assess for differences in cause-specific trends when allowing multiple causes to be listed for a death. Analyses were performed using SAS version 9.426 and the WONDER system software.27 The WONDER system public domain software is intended for open platform operation, and was developed in Java, XSL and XML.27
Results
While overall US mortality rates remained relatively stable from 1999 to 2019 with evidence of increased mortality rates beginning in 2009 (Figure 1), changes in death rates were sizably different by age. Mortality rates markedly increased for younger adults, particularly ages 25-39, while declining for young children and older adults (Figure 2). Increases in younger adult mortality occurred prior to the COVID pandemic, showing a 10.8% increase from 1999 to 2019 (Cochran-Armitage p < 0.0001), compared to a 1.5% increase in mortality among the broader US population (all ages). Mortality rates in younger adults further accelerated during COVID, demonstrating a 54.1% increase from 1999 to 2021 (Cochran-Armitage p < 0.0001), from 113.4 deaths per 100,000 to 174.7 deaths per 100,000 (Figure 1 and 2, Figure A1). Younger adult increases in mortality between 1999 to 2021 differed by sex, race, and ethnicity, showing the following increases: 49.7% for females; 55.6% for males; 40.2% for Black Americans; 62.3% for White Americans; 87.5% for American Indian/Alaska Native Americans; 32.1% for Asian/Pacific Islander Americans; 59.3% for Hispanic Americans; and 55.9% for non-Hispanic Americans.
Figure 1. Crude mortality rate (per 100,00) for all US residents, 1999-2021.
Error bars represent standard error of the rate.
Figure 2. Percent change in mortality rates since 1999, across five-year age groups in the United States.
Increases in younger adult deaths since 1999 were driven sizably by “deaths of despair” both pre-COVID (up to 2019) and during COVID (2020 and beyond) (Table 1). For instance, overall mortality in this age group increased by 61.3 deaths per 100,000 from 1999 to 2021. During the same period, deaths due to accidental poisoning and exposure to noxious substances increased by 38.3 per 100,000, corresponding to over half of the overall increase. Notably, deaths from COVID infection itself accounted for a substantial amount of the crude mortality rate increase for young adults, but it was not the preeminent driver (14.0 deaths per 100,000 in 2021).
Table 1. Crude mortality rates (per 100,000) for top ICD-10-113 cause of death categories for United States younger adults aged 18-39 y, 1999-2021.
Color coding represents percent change in mortality in comparison to 1999. “U” indicates rate listed as “unreliable” due to small counts.
| 1999 | 2009 | 2019 | 2020 | 2021 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ICD-10 113 Cause List | ICD-10 113 Cause List Code |
Crude Rate (CR) |
Lower 95% CI |
Upper 95% CI |
Crude Rate (CR) |
Lower 95% CI |
Upper 95% CI |
Crude Rate (CR) |
Lower 95% CI |
Upper 95% CI |
Crude Rate (CR) |
Lower 95% CI |
Jpper 95% CI |
Crude Rate (CR) |
Lower 95% CI |
Upper 95% CI |
| #Accidents (unintentional injuries) (V01-X59,Y85-Y86) | GR113-112 | 33.01 | 32.64 | 33.39 | 34.22 | 33.84 | 34.6 | 48.92 | 48.48 | 49.36 | 62.47 | 61.98 | 62.97 | 68.77 | 68.25 | 69.29 |
| Nontransport accidents (W00-X59,Y86) | GR113-117 | 11.51 | 11.29 | 11.73 | 17.42 | 17.15 | 17.69 | 33.01 | 32.65 | 33.37 | 44.15 | 43.73 | 44.57 | 48.67 | 48.23 | 49.11 |
| Accidental poisoning and exposure to noxious substances (X40-X49) | GR113-122 | 5.92 | 5.76 | 6.08 | 13.43 | 13.2 | 13.67 | 29.19 | 28.85 | 29.53 | 39.91 | 39.52 | 40.31 | 44.23 | 43.81 | 44.64 |
| Transport accidents (V01-V99, Y85) | GR113-113 | 21.5 | 21.2 | 21.81 | 16.8 | 16.53 | 17.06 | 15.91 | 15.66 | 16.16 | 18.32 | 18.05 | 18.59 | 20.11 | 19.82 | 20.39 |
| #Intentional self-harm (suicide) (*U03,X60-X84,Y87.0) | GR113-124 | 12.85 | 12.62 | 13.09 | 13.07 | 12.84 | 13.3 | 17.23 | 16.97 | 17.49 | 17.64 | 17.37 | 17.9 | 18.75 | 18.48 | 19.02 |
| Motor vehicle accidents (V02-V04,V09.0,V09.2,V12-V14,V19.0-V19.2,V19.4-V19.6,V20-V79,V80.3-V80.5,V81.0-V81.1,V82.0-V82.1,V83-V86,V87.0-V87.8,V88.0-V88.8,V89.0,V89.2) | GR113-114 | 19.84 | 19.55 | 20.14 | 15.94 | 15.68 | 16.2 | 15.16 | 14.92 | 15.41 | 17.58 | 17.32 | 17.84 | 19.33 | 19.06 | 19.61 |
| #Assault (homicide) (*U01-*U02,X85-Y09,Y87.1) | GR113-127 | 11.27 | 11.05 | 11.49 | 10.68 | 10.47 | 10.89 | 11.55 | 11.34 | 11.77 | 15.47 | 15.22 | 15.71 | 16.13 | 15.87 | 16.38 |
| Assault (homicide) by discharge of firearms (*U01.4,X93-X95) | GR113-128 | 8.27 | 8.08 | 8.46 | 8.47 | 8.28 | 8.65 | 9.89 | 9.69 | 10.08 | 13.52 | 13.29 | 13.75 | 14.34 | 14.1 | 14.58 |
| Major cardiovascular diseases (I00-I78) | GR113-053 | 12.05 | 11.82 | 12.27 | 10.69 | 10.48 | 10.9 | 10.47 | 10.27 | 10.68 | 11.88 | 11.67 | 12.1 | 12.35 | 12.13 | 12.57 |
| All other diseases (Residual) | GR113-111 | 8.49 | 8.3 | 8.68 | 8.14 | 7.95 | 8.32 | 9.97 | 9.77 | 10.17 | 11.88 | 11.66 | 12.09 | 12.54 | 12.31 | 12.76 |
| #Diseases of heart (I00-I09,I11,I13,I20-I51) | GR113-054 | 9.55 | 9.35 | 9.76 | 8.54 | 8.35 | 8.73 | 8.3 | 8.12 | 8.48 | 9.46 | 9.27 | 9.65 | 9.84 | 9.64 | 10.04 |
| Intentional self-harm (suicide) by other and unspecified means and their sequelae (*U03,X60-X71,X75-X84, Y87.0) | GR113-126 | 6.06 | 5.89 | 6.22 | 7.19 | 7.02 | 7.36 | 9.62 | 9.43 | 9.82 | 9.25 | 9.06 | 9.44 | 9.44 | 9.25 | 9.63 |
| #Malignant neoplasms (C00-C97) | GR113-019 | 12.33 | 12.1 | 12.56 | 9.99 | 9.79 | 10.2 | 8.95 | 8.77 | 9.14 | 8.74 | 8.55 | 8.92 | 8.89 | 8.7 | 9.07 |
| Intentional self-harm (suicide) by discharge of firearms (X72-X74) | GR113-125 | 6.79 | 6.62 | 6.97 | 5.88 | 5.72 | 6.04 | 7.61 | 7.43 | 7.78 | 8.39 | 8.21 | 8.57 | 9.31 | 9.11 | 9.5 |
| Other and unspecified infectious and parasitic diseases and their sequelae (A00,A05,A20-A36,A42-A44,A48-A49,A54-A79,A81-A82,A85.0-A85.1,A85.8,A86-B04,B06-B09,B25-B49,B55-B99,U07.1) | GR113-018 | 0.6 | 0.55 | 0.65 | 0.29 | 0.26 | 0.33 | 0.35 | 0.31 | 0.39 | 5.52 | 5.37 | 5.67 | 14.39 | 14.15 | 14.62 |
| #COVID-19 (U07.1) | GR113-137 | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | 5.09 | 4.95 | 5.23 | 13.96 | 13.73 | 14.2 |
| Other heart diseases (I26-I51) | GR113-064 | 4.8 | 4.66 | 4.95 | 4.39 | 4.25 | 4.52 | 4.31 | 4.18 | 4.44 | 4.94 | 4.8 | 5.08 | 5.23 | 5.09 | 5.38 |
| All other forms of heart disease (I26-I28,I34-I38,I42-I49,I51) | GR113-068 | 4.36 | 4.22 | 4.5 | 3.93 | 3.8 | 4.06 | 3.59 | 3.47 | 3.71 | 4.04 | 3.92 | 4.17 | 4.36 | 4.23 | 4.49 |
| #Chronic liver disease and cirrhosis (K70,K73-K74) | GR113-093 | 1.71 | 1.62 | 1.79 | 1.34 | 1.27 | 1.42 | 2.65 | 2.55 | 2.75 | 3.92 | 3.8 | 4.05 | 4.52 | 4.38 | 4.65 |
| Alcoholic liver disease (K70) | GR113-094 | 1.15 | 1.08 | 1.22 | 1.01 | 0.95 | 1.08 | 2.15 | 2.06 | 2.24 | 3.22 | 3.11 | 3.34 | 3.72 | 3.6 | 3.84 |
| Ischemic heart diseases (I20-I25) | GR113-058 | 3.91 | 3.78 | 4.04 | 2.98 | 2.87 | 3.09 | 2.5 | 2.4 | 2.6 | 2.81 | 2.7 | 2.91 | 2.86 | 2.75 | 2.97 |
| Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99) | GR113-110 | 2.82 | 2.71 | 2.93 | 3.1 | 2.99 | 3.22 | 2.32 | 2.23 | 2.42 | 2.67 | 2.57 | 2.77 | 2.85 | 2.74 | 2.95 |
| #Diabetes mellitus (E10-E14) | GR113-046 | 1.61 | 1.52 | 1.69 | 1.53 | 1.45 | 1.61 | 2.02 | 1.93 | 2.1 | 2.66 | 2.56 | 2.76 | 2.79 | 2.69 | 2.9 |
| Events of undetermined intent (Y10-Y34,Y87.2,Y89.9) | GR113-131 | 1.95 | 1.86 | 2.04 | 1.75 | 1.66 | 1.83 | 2.11 | 2.02 | 2.2 | 2.28 | 2.18 | 2.37 | 2.45 | 2.35 | 2.54 |
| Other and unspecified events of undetermined intent and their sequelae (Y10-Y21,Y25-Y34,Y87.2,Y89.9) | GR113-133 | 1.76 | 1.67 | 1.84 | 1.65 | 1.56 | 1.73 | 1.94 | 1.85 | 2.02 | 2.08 | 1.99 | 2.17 | 2.2 | 2.11 | 2.29 |
| Assault (homicide) by other and unspecified means and their sequelae (*U01.0-*U01.3,*U01.5-*U01.9,*U02,X85-X92,X96-Y09,Y87.1) | GR113-129 | 3 | 2.88 | 3.11 | 2.21 | 2.11 | 2.31 | 1.67 | 1.59 | 1.75 | 1.95 | 1.86 | 2.04 | 1.79 | 1.7 | 1.87 |
| All other and unspecified malignant neoplasms (C17,C23-C24,C26-C31,C37-C41,C44-C49,C51-C52,C57-C60,C62-C63,C66,C68-C69,C73-C80,C97) | GR113-043 | 2.2 | 2.1 | 2.29 | 1.86 | 1.77 | 1.95 | 1.86 | 1.77 | 1.94 | 1.84 | 1.75 | 1.92 | 1.92 | 1.83 | 2.01 |
| Other forms of chronic ischemic heart disease (I20,I25) | GR113-061 | 2.3 | 2.2 | 2.39 | 1.81 | 1.73 | 1.9 | 1.54 | 1.47 | 1.62 | 1.67 | 1.59 | 1.75 | 1.66 | 1.58 | 1.74 |
| Other and unspecified nontransport accidents and their sequelae (W20-W31,W35-W64,W75-W99,X10-X39,X50-X59,Y86) | GR113-123 | 2.26 | 2.16 | 2.36 | 1.51 | 1.43 | 1.59 | 1.56 | 1.48 | 1.64 | 1.6 | 1.52 | 1.68 | 1.76 | 1.68 | 1.84 |
| #Cerebrovascular diseases (I60-I69) | GR113-070 | 1.84 | 1.75 | 1.93 | 1.49 | 1.41 | 1.57 | 1.39 | 1.32 | 1.47 | 1.55 | 1.47 | 1.62 | 1.62 | 1.54 | 1.7 |
| Malignant neoplasms of lymphoid, hematopoietic and related tissue (C81-C96) | GR113-037 | 2.61 | 2.5 | 2.71 | 1.99 | 1.9 | 2.09 | 1.37 | 1.29 | 1.44 | 1.26 | 1.19 | 1.33 | 1.23 | 1.16 | 1.3 |
| Malignant neoplasm of breast (C50) | GR113-029 | 1.6 | 1.52 | 1.68 | 1.16 | 1.09 | 1.23 | 1.23 | 1.16 | 1.3 | 1.09 | 1.02 | 1.15 | 1.11 | 1.04 | 1.17 |
| Acute myocardial infarction (I21-I22) | GR113-059 | 1.58 | 1.49 | 1.66 | 1.12 | 1.05 | 1.19 | 0.92 | 0.86 | 0.98 | 1.09 | 1.02 | 1.15 | 1.15 | 1.08 | 1.21 |
| #Human immunodeficiency virus (HIV) disease (B20-B24) | GR113-016 | 6.57 | 6.4 | 6.73 | 2.18 | 2.08 | 2.27 | 0.88 | 0.82 | 0.94 | 0.9 | 0.84 | 0.95 | 0.92 | 0.86 | 0.98 |
| All other forms of chronic ischemic heart disease (I20,I25.1-I25.9) | GR113-063 | 1.6 | 1.52 | 1.68 | 1.13 | 1.06 | 1.2 | 0.76 | 0.7 | 0.81 | 0.78 | 0.72 | 0.83 | 0.75 | 0.7 | 0.81 |
| Hypertensive heart disease (I11) | GR113-056 | 0.65 | 0.6 | 0.71 | 1.03 | 0.96 | 1.09 | 1.27 | 1.2 | 1.34 | 1.47 | 1.4 | 1.55 | 1.46 | 1.38 | 1.53 |
| Accidental drowning and submersion (W65-W74) | GR113-120 | 1.27 | 1.2 | 1.35 | 1.06 | 0.99 | 1.12 | 1 | 0.93 | 1.06 | 1.28 | 1.21 | 1.35 | 1.24 | 1.17 | 1.31 |
| #Influenza and pneumonia (J09-J18) | GR113-076 | 1.05 | 0.98 | 1.12 | 1.79 | 1.71 | 1.88 | 1.03 | 0.97 | 1.1 | 1.24 | 1.17 | 1.31 | 0.87 | 0.81 | 0.92 |
| #Pregnancy, childbirth and the puerperium (O00-O99) | GR113-105 | 0.80 | 0.72 | 0.89 | 1.68 | 1.56 | 1.8 | 1.96 | 1.83 | 2.08 | 2.24 | 2.1 | 2.37 | 2.93 | 2.78 | 3.09 |
| Other complications of pregnancy, childbirth and the puerperium (O10-O99) | GR113-107 | 0.74 | 0.66 | 0.82 | 1.61 | 1.5 | 1.73 | 1.91 | 1.79 | 2.03 | 2.19 | 2.05 | 2.32 | 2.85 | 2.69 | 3 |
| Malignant neoplasms of colon, rectum and anus (C18-C21) | GR113-023 | 0.86 | 0.8 | 0.92 | 0.88 | 0.82 | 0.94 | 0.9 | 0.84 | 0.96 | 1 | 0.94 | 1.06 | 1.01 | 0.95 | 1.08 |
| #Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | GR113-109 | 1.15 | 1.08 | 1.22 | 1.05 | 0.98 | 1.11 | 0.93 | 0.87 | 0.99 | 0.98 | 0.92 | 1.04 | 1.01 | 0.95 | 1.08 |
| Malignant neoplasms of meninges, brain and other parts of central nervous system (C70-C72) | GR113-036 | 1.15 | 1.08 | 1.22 | 1.02 | 0.95 | 1.08 | 0.96 | 0.9 | 1.02 | 0.95 | 0.89 | 1.01 | 0.95 | 0.89 | 1.01 |
| Leukemia (C91-C95) | GR113-040 | 1.36 | 1.28 | 1.43 | 1.14 | 1.07 | 1.21 | 0.88 | 0.82 | 0.94 | 0.81 | 0.76 | 0.87 | 0.79 | 0.74 | 0.85 |
| Atherosclerotic cardiovascular disease, so described (I25.0) | GR113-062 | 0.7 | 0.64 | 0.75 | 0.69 | 0.63 | 0.74 | 0.79 | 0.73 | 0.84 | 0.9 | 0.84 | 0.96 | 0.91 | 0.85 | 0.97 |
| Pneumonia (J12-J18) | GR113-078 | 1.02 | 0.96 | 1.09 | 1.11 | 1.04 | 1.18 | 0.77 | 0.71 | 0.82 | 0.88 | 0.82 | 0.93 | 0.84 | 0.78 | 0.9 |
| #Septicemia (A40-A41) | GR113-010 | 0.77 | 0.71 | 0.83 | 0.91 | 0.85 | 0.97 | 0.82 | 0.76 | 0.87 | 0.86 | 0.8 | 0.92 | 0.96 | 0.9 | 1.02 |
| #Chronic lower respiratory diseases (J40-J47) | GR113-082 | 0.87 | 0.8 | 0.93 | 0.72 | 0.67 | 0.78 | 0.79 | 0.73 | 0.84 | 0.85 | 0.79 | 0.91 | 0.76 | 0.71 | 0.82 |
| Falls (W00-W19) | GR113-118 | 0.92 | 0.85 | 0.98 | 0.73 | 0.67 | 0.78 | 0.76 | 0.7 | 0.81 | 0.76 | 0.71 | 0.82 | 0.85 | 0.79 | 0.91 |
| #Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27) | GR113-097 | 0.65 | 0.59 | 0.7 | 0.72 | 0.66 | 0.77 | 0.68 | 0.63 | 0.73 | 0.75 | 0.7 | 0.81 | 0.81 | 0.76 | 0.87 |
| Renal failure (N17-N19) | GR113-100 | 0.59 | 0.54 | 0.64 | 0.65 | 0.6 | 0.71 | 0.64 | 0.59 | 0.69 | 0.71 | 0.66 | 0.77 | 0.77 | 0.72 | 0.83 |
| Other chronic liver disease and cirrhosis (K73-K74) | GR113-095 | 0.56 | 0.51 | 0.61 | 0.33 | 0.3 | 0.37 | 0.5 | 0.46 | 0.55 | 0.7 | 0.65 | 0.75 | 0.8 | 0.74 | 0.85 |
| Asthma (J45-J46) | GR113-085 | 0.66 | 0.61 | 0.71 | 0.51 | 0.47 | 0.56 | 0.64 | 0.59 | 0.69 | 0.69 | 0.64 | 0.74 | 0.59 | 0.54 | 0.63 |
| Other diseases of respiratory system (J00-J06,J30- J39,J67,J70-J98) | GR113-089 | 0.62 | 0.57 | 0.68 | 0.68 | 0.62 | 0.73 | 0.65 | 0.6 | 0.7 | 0.67 | 0.62 | 0.72 | 0.67 | 0.61 | 0.72 |
| Other disorders of circulatory system (I80-I99) | GR113-075 | 0.24 | 0.21 | 0.27 | 0.34 | 0.3 | 0.38 | 0.41 | 0.37 | 0.45 | 0.54 | 0.49 | 0.58 | 0.49 | 0.44 | 0.53 |
| Heart failure (I50) | GR113-067 | 0.2 | 0.17 | 0.23 | 0.25 | 0.22 | 0.29 | 0.4 | 0.36 | 0.44 | 0.53 | 0.48 | 0.57 | 0.53 | 0.48 | 0.58 |
| Malignant neoplasm of cervix uteri (C53) | GR113-030 | 1.17 | 1.07 | 1.27 | 0.96 | 0.87 | 1.05 | 0.88 | 0.8 | 0.96 | 1.01 | 0.92 | 1.1 | 1.05 | 0.96 | 1.14 |
| #Legal intervention (Y35,Y89.0) | GR113-130 | 0.3 | 0.26 | 0.33 | 0.25 | 0.22 | 0.28 | 0.39 | 0.35 | 0.42 | 0.48 | 0.43 | 0.52 | 0.44 | 0.4 | 0.48 |
| Other diseases of circulatory system (I71-I78) | GR113-072 | 0.41 | 0.36 | 0.45 | 0.34 | 0.3 | 0.38 | 0.45 | 0.41 | 0.5 | 0.46 | 0.41 | 0.5 | 0.45 | 0.41 | 0.49 |
| Water, air and space, and other and unspecified transport accidents and their sequelae (V90-V99,Y85) | GR113-116 | 0.78 | 0.72 | 0.84 | 0.54 | 0.49 | 0.59 | 0.42 | 0.38 | 0.46 | 0.41 | 0.37 | 0.45 | 0.41 | 0.37 | 0.45 |
| #Essential hypertension and hypertensive renal disease (I10,I12,I15) | GR113-069 | 0.22 | 0.19 | 0.25 | 0.3 | 0.27 | 0.34 | 0.32 | 0.28 | 0.35 | 0.4 | 0.36 | 0.44 | 0.43 | 0.39 | 0.47 |
| nfluenza (J09-J11) | GR113-077 | 0.03 | 0.02 | 0.04 | 0.68 | 0.63 | 0.74 | 0.27 | 0.24 | 0.3 | 0.37 | 0.33 | 0.41 | 0.03 | 0.02 | 0.04 |
| Accidental exposure to smoke, fire and flames (X00-X09) | GR113-121 | 0.71 | 0.65 | 0.76 | 0.44 | 0.4 | 0.49 | 0.3 | 0.27 | 0.34 | 0.36 | 0.32 | 0.4 | 0.37 | 0.34 | 0.41 |
| Malignant neoplasms of trachea, bronchus and lung (C33-C34) | GR113-027 | 0.96 | 0.9 | 1.03 | 0.56 | 0.52 | 0.61 | 0.35 | 0.31 | 0.39 | 0.35 | 0.31 | 0.39 | 0.34 | 0.31 | 0.38 |
| Other land transport accidents (V01,V05-V06,V09.1,V09.3-V09.9,V10-V11,V15-V18,V19.3,V19.8-V19.9,V80.0-V80.2,V80.6-V80.9,V81.2-V81.9,V82.2-V82.9,V87.9,V88.9,V89.1,V89.3,V89.9) | GR113-115 | 0.88 | 0.82 | 0.94 | 0.32 | 0.28 | 0.36 | 0.33 | 0.29 | 0.36 | 0.33 | 0.29 | 0.36 | 0.36 | 0.32 | 0.4 |
| Non-Hodgkin lymphoma (C82-C85) | GR113-039 | 0.81 | 0.75 | 0.87 | 0.55 | 0.5 | 0.6 | 0.36 | 0.33 | 0.4 | 0.32 | 0.29 | 0.36 | 0.32 | 0.29 | 0.36 |
| #Anemias (D50-D64) | GR113-045 | 0.39 | 0.35 | 0.43 | 0.34 | 0.3 | 0.38 | 0.3 | 0.26 | 0.33 | 0.31 | 0.28 | 0.35 | 0.32 | 0.29 | 0.36 |
| #Aortic aneurysm and dissection (I71) | GR113-073 | 0.29 | 0.25 | 0.32 | 0.26 | 0.23 | 0.29 | 0.31 | 0.28 | 0.35 | 0.31 | 0.28 | 0.35 | 0.3 | 0.27 | 0.34 |
| Malignant neoplasm of stomach (C16) | GR113-022 | 0.37 | 0.33 | 0.41 | 0.35 | 0.31 | 0.38 | 0.33 | 0.29 | 0.36 | 0.3 | 0.27 | 0.34 | 0.32 | 0.29 | 0.36 |
| #In situ neoplasms, benign neoplasms and neoplasms of uncertain or unknown behavior (D00-D48) | GR113-044 | 0.42 | 0.37 | 0.46 | 0.34 | 0.3 | 0.38 | 0.26 | 0.23 | 0.29 | 0.29 | 0.26 | 0.33 | 0.29 | 0.25 | 0.32 |
| Acute and subacute endocarditis (I33) | GR113-065 | 0.1 | 0.08 | 0.12 | 0.08 | 0.06 | 0.1 | 0.22 | 0.19 | 0.25 | 0.26 | 0.23 | 0.3 | 0.25 | 0.22 | 0.28 |
| Malignant melanoma of skin (C43) | GR113-028 | 0.52 | 0.47 | 0.57 | 0.47 | 0.43 | 0.52 | 0.25 | 0.22 | 0.28 | 0.25 | 0.22 | 0.28 | 0.21 | 0.19 | 0.24 |
| #Pneumonitis due to solids and liquids (J69) | GR113-088 | 0.18 | 0.15 | 0.21 | 0.21 | 0.18 | 0.24 | 0.26 | 0.23 | 0.29 | 0.25 | 0.22 | 0.28 | 0.3 | 0.27 | 0.34 |
| Accidental discharge of firearms (W32-W34) | GR113-119 | 0.44 | 0.39 | 0.48 | 0.25 | 0.22 | 0.28 | 0.2 | 0.17 | 0.23 | 0.24 | 0.21 | 0.27 | 0.22 | 0.19 | 0.25 |
| Malignant neoplasms of liver and intrahepatic bile ducts (C22) | GR113-024 | 0.27 | 0.23 | 0.3 | 0.22 | 0.19 | 0.25 | 0.25 | 0.22 | 0.28 | 0.24 | 0.21 | 0.28 | 0.26 | 0.23 | 0.29 |
| #Complications of medical and surgical care (Y40-Y84,Y88) | GR113-135 | 0.19 | 0.16 | 0.22 | 0.16 | 0.14 | 0.19 | 0.23 | 0.2 | 0.26 | 0.24 | 0.21 | 0.27 | 0.3 | 0.27 | 0.33 |
| Malignant neoplasm of ovary (C56) | GR113-032 | 0.59 | 0.52 | 0.67 | 0.48 | 0.42 | 0.54 | 0.41 | 0.36 | 0.47 | 0.44 | 0.38 | 0.5 | 0.47 | 0.41 | 0.53 |
| Discharge of firearms, undetermined intent (Y22-Y24) | GR113-132 | 0.19 | 0.16 | 0.22 | 0.1 | 0.08 | 0.12 | 0.17 | 0.14 | 0.2 | 0.2 | 0.17 | 0.23 | 0.25 | 0.22 | 0.28 |
| Malignant neoplasm of pancreas (C25) | GR113-025 | 0.26 | 0.23 | 0.3 | 0.22 | 0.19 | 0.25 | 0.21 | 0.18 | 0.23 | 0.19 | 0.17 | 0.22 | 0.21 | 0.18 | 0.23 |
| Acute rheumatic fever and chronic rheumatic heart diseases (I00-I09) | GR113-055 | 0.11 | 0.09 | 0.13 | 0.07 | 0.06 | 0.09 | 0.12 | 0.1 | 0.14 | 0.15 | 0.13 | 0.18 | 0.17 | 0.14 | 0.2 |
| Malignant neoplasms of kidney and renal pelvis (C64-C65) | GR113-034 | 0.19 | 0.16 | 0.22 | 0.16 | 0.14 | 0.19 | 0.15 | 0.13 | 0.18 | 0.14 | 0.12 | 0.17 | 0.14 | 0.12 | 0.17 |
| Other diseases of arteries, arterioles and capillaries (I72-I78) | GR113-074 | 0.12 | 0.1 | 0.14 | 0.08 | 0.06 | 0.1 | 0.14 | 0.12 | 0.17 | 0.14 | 0.12 | 0.16 | 0.15 | 0.13 | 0.17 |
| Other chronic lower respiratory diseases (J44,J47) | GR113-086 | 0.14 | 0.12 | 0.17 | 0.16 | 0.13 | 0.18 | 0.12 | 0.1 | 0.14 | 0.13 | 0.11 | 0.16 | 0.15 | 0.13 | 0.18 |
| #Viral hepatitis (B15-B19) | GR113-015 | 0.34 | 0.3 | 0.38 | 0.15 | 0.13 | 0.18 | 0.12 | 0.1 | 0.14 | 0.12 | 0.1 | 0.15 | 0.11 | 0.09 | 0.13 |
| Malignant neoplasms of lip, oral cavity and pharynx (C00-C14) | GR113-020 | 0.18 | 0.15 | 0.21 | 0.14 | 0.12 | 0.17 | 0.14 | 0.12 | 0.16 | 0.12 | 0.1 | 0.15 | 0.13 | 0.11 | 0.15 |
| Malignant neoplasms of corpus uteri and uterus, part unspecified (C54-C55) | GR113-031 | 0.14 | 0.11 | 0.18 | 0.15 | 0.12 | 0.19 | 0.28 | 0.23 | 0.33 | 0.24 | 0.2 | 0.29 | 0.28 | 0.23 | 0.32 |
| Diseases of pericardium and acute myocarditis (I30-I31,I40) | GR113-066 | 0.14 | 0.12 | 0.17 | 0.12 | 0.1 | 0.14 | 0.1 | 0.08 | 0.12 | 0.11 | 0.08 | 0.13 | 0.09 | 0.07 | 0.11 |
| Malignant neoplasm of esophagus (C15) | GR113-021 | 0.12 | 0.1 | 0.15 | 0.11 | 0.09 | 0.13 | 0.13 | 0.11 | 0.15 | 0.1 | 0.08 | 0.12 | 0.11 | 0.09 | 0.13 |
| Hypertensive heart and renal disease (I13) | GR113-057 | 0.08 | 0.06 | 0.1 | 0.07 | 0.05 | 0.09 | 0.09 | 0.07 | 0.11 | 0.09 | 0.07 | 0.11 | 0.12 | 0.1 | 0.14 |
| #Nutritional deficiencies (E40-E64) | GR113-047 | 0.04 | 0.03 | 0.06 | 0.05 | 0.03 | 0.06 | 0.06 | 0.04 | 0.08 | 0.09 | 0.07 | 0.11 | 0.12 | 0.1 | 0.14 |
| Hodgkin disease (C81) | GR113-038 | 0.36 | 0.32 | 0.4 | 0.26 | 0.22 | 0.29 | 0.09 | 0.07 | 0.11 | 0.08 | 0.07 | 0.11 | 0.08 | 0.06 | 0.1 |
| #Peptic ulcer (K25-K28) | GR113-090 | 0.07 | 0.05 | 0.09 | 0.06 | 0.05 | 0.08 | 0.07 | 0.06 | 0.09 | 0.08 | 0.06 | 0.1 | 0.1 | 0.08 | 0.12 |
| Malnutrition (E40-E46) | GR113-048 | 0.04 | 0.03 | 0.06 | 0.05 | 0.03 | 0.06 | 0.06 | 0.04 | 0.07 | 0.08 | 0.06 | 0.1 | 0.11 | 0.09 | 0.13 |
| Certain other intestinal infections (A04,A07-A09) | GR113-003 | U | 0.01 | 0.02 | 0.06 | 0.05 | 0.08 | 0.09 | 0.07 | 0.11 | 0.08 | 0.06 | 0.1 | 0.1 | 0.08 | 0.12 |
| #Meningitis (G00,G03) | GR113-050 | 0.12 | 0.1 | 0.15 | 0.09 | 0.07 | 0.11 | 0.05 | 0.04 | 0.07 | 0.05 | 0.04 | 0.07 | 0.05 | 0.04 | 0.07 |
| Malignant neoplasm of bladder (C67) | GR113-035 | 0.05 | 0.04 | 0.07 | 0.04 | 0.03 | 0.06 | 0.04 | 0.03 | 0.05 | 0.05 | 0.03 | 0.06 | 0.03 | 0.02 | 0.04 |
| Ĉ#Cholelithiasis and other disorders of gallbladder (K80-K82) | GR113-096 | 0.05 | 0.04 | 0.07 | 0.06 | 0.05 | 0.08 | 0.04 | 0.02 | 0.05 | 0.05 | 0.04 | 0.07 | 0.05 | 0.04 | 0.07 |
| #Tuberculosis (A16-A19) | GR113-004 | 0.06 | 0.05 | 0.08 | 0.04 | 0.03 | 0.05 | 0.03 | 0.02 | 0.04 | 0.04 | 0.03 | 0.05 | 0.04 | 0.03 | 0.05 |
| Other acute ischemic heart diseases (I24) | GR113-060 | 0.04 | 0.03 | 0.05 | 0.05 | 0.04 | 0.07 | 0.03 | 0.02 | 0.05 | 0.04 | 0.03 | 0.06 | 0.05 | 0.04 | 0.07 |
| #Infections of kidney (N10-N12,N13.6,N15.1) | GR113-102 | 0.03 | 0.02 | 0.04 | 0.03 | 0.02 | 0.04 | 0.03 | 0.02 | 0.04 | 0.04 | 0.03 | 0.06 | 0.05 | 0.03 | 0.06 |
| Multiple myeloma and immunoproliferative neoplasms (C88,C90) | GR113-041 | 0.08 | 0.06 | 0.1 | 0.04 | 0.03 | 0.06 | 0.03 | 0.02 | 0.05 | 0.03 | 0.02 | 0.04 | 0.03 | 0.02 | 0.04 |
| Respiratory tuberculosis (A16) | GR113-005 | 0.05 | 0.03 | 0.06 | 0.03 | 0.02 | 0.04 | U | 0.01 | 0.03 | 0.03 | 0.02 | 0.04 | 0.03 | 0.02 | 0.04 |
| Pregnancy with abortive outcome (O00-O07) | GR113-106 | 0.07 | 0.04 | 0.09 | 0.07 | 0.04 | 0.09 | 0.05 | 0.03 | 0.07 | 0.05 | 0.03 | 0.08 | 0.09 | 0.06 | 0.12 |
| #Hernia (K40-K46) | GR113-092 | 0.04 | 0.03 | 0.05 | 0.02 | 0.02 | 0.04 | 0.03 | 0.02 | 0.04 | 0.03 | 0.02 | 0.05 | 0.02 | 0.02 | 0.04 |
| #Enterocolitis due to Clostridium difficile (A04.7) | GR113-136 | #N/A | #N/A | #N/A | U | 0.01 | 0.03 | 0.04 | 0.02 | 0.05 | 0.03 | 0.02 | 0.04 | 0.03 | 0.02 | 0.04 |
| 0-25% increase from 1999-2004 |
| 25-50% increase |
| 50-100% increase |
| 100-200% increase |
| 200-300% increase |
| >300% increase |
| 0-25% decrease from 1999-2004 |
| 25-50% decrease |
| 50-100% decrease |
| 100-200% decrease |
| 200-300% decrease |
| >300% decrease |
The top five pre-COVID drivers of increased crude mortality rates in younger adults from 1999 to 2019, in order of magnitude, were: Accidental poisoning and exposure to noxious substances ( 23.3 increased deaths per 100,000); intentional self-harm (suicide) by other and unspecified means and their sequelae ( 3.6 increased deaths per 100,000);; assault (homicide) by discharge of firearms ( 1.6 increased deaths per 100,000); all other diseases (residual) ( 1.5 increased deaths per 100,000); other complications of pregnancy, childbirth, and the puerperium (1.2 increased deaths per 100,000).
The top five drivers of increased crude mortality rates in younger adults from 1999 through 2021, in order of magnitude, were: Accidental poisoning and exposure to noxious substances ( 38.3 increased deaths per 100,000); other and unspecified infectious and parasitic diseases and their sequelae (including COVID-19; 13.8 increased deaths per 100,000); assault (homicide) by discharge of firearms ( 6.1 increased deaths per 100,000); all other diseases (residual) ( 4.1 increased deaths per 100,000); intentional self-harm (suicide) by other and unspecified means and their sequelae ( 3.4 increased deaths per 100,000). Differences in mortality and drivers of mortality by race, ethnicity, and sex were apparent, but accidental poisoning and exposure to noxious substances remained the greatest driver of mortality for every subgroup examined (Figure 3 and Table 2; Figures A3, A27, and A49). The top five drivers of crude increased mortality rates for each demographic group from 1999 through 2021 are described in Table 2.
Figure 3. Percent change in mortality rates since 1999 across five-year age groups in the United States, stratified by sex, race, and ethnicity.
Table 2. Causes of mortality with greatest subgroup-specific crude rate increase from 1999 to 2021 among US younger adults, ranked.
Subgroup-specific rankings of causes of mortality by increase in crude mortality rate from 1999-2021 are presented in color-coded cells (e.g., #1 driver was Accidental poisoning and exposure to noxious substances for females.)
| Subgroup | ||||||||
|---|---|---|---|---|---|---|---|---|
| Cause | Female | Male | Black | White | American Indian/ Alaska Native |
Asian/ Pacific Islander |
Hispanic | Non- Hispanic |
| Accidental poisoning and exposure to noxious substances | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Other and unspecified infectious and parasitic diseases and their sequelae (includes COVID-19) | 2 | 2 | 3 | 2 | 2 | 2 | ||
| All other diseases (residual) | 3 | 4 | 3 | 3 | 4 | 4 | 4 | |
| Other complications of pregnancy, childbirth, and the puerperium | 4 | |||||||
| Intentional self-harm (suicide) by other and unspecified means and their sequelae | 5 | 5 | 4 | 4 | 3 | 3 | 5 | |
| Assault (homicide) by discharge of firearms | 3 | 2 | 3 | |||||
| Motor vehicle accidents | 4 | |||||||
| Intentional self-harm (suicide) by discharge of firearms | 5 | |||||||
| Alcoholic liver disease | 5 | 2 | 5 | |||||
| Major cardiovascular diseases | 5 | 5 | ||||||
| Other land transport accidents | 2 | |||||||
| All-cause Crude Mortality, 1999 (per 100,000) | 71.9 | 154.1 | 197.7 | 102.3 | 152.6 | 48.0 | 93.3 | 116.3 |
| All-cause Crude Mortality, 2019 (per 100,000) | 78.6 | 171.4 | 177.5 | 122.0 | 161.7 | 48.3 | 94.9 | 133.8 |
| Net change, 1999-2019 | 6.7 | 17.3 | −20.2 | 19.7 | 9.1 | 0.3 | 1.6 | 17.5 |
| All-cause Crude Mortality, 2021 (per 100,000) | 107.6 | 239.9 | 277.2 | 166.0 | 286.2 | 63.4 | 148.6 | 181.5 |
| Net change, 1999-2021 | 35.7 | 85.8 | 79.5 | 63.7 | 133.6 | 15.4 | 55.3 | 65.2 |
See Figures A1-A69 for major drivers of both crude rates and percent increases in causes of mortality in specific demographic groups.
Sensitivity analyses were performed to evaluate if effects differed in crude vs. age-adjusted rates. While the WONDER database does not provide age-adjusted rates for our age group of interest we standardized the availability mortality rates to 1999 population estimates28 for age-adjusted comparisons. As shown in Supplementary Tables B1-B3, there were similar effect sizes in crude and age-adjusted analyses. Additionally, as shown in Supplementary Table B4, there were key similarities in the preeminent drivers of mortality using WONDER Multiple Causes of Death data (e.g., in accidental poisoning and exposure to noxious substances), albeit with a particularly large proportion of deaths attributed to “all other causes (residual)” in the Multiple Causes data.
Discussion
Our findings highlight a troubling rise in mortality among younger adults in the US since 1999, which has been exacerbated by the COVID-19 pandemic. This has sizably been driven by “deaths of despair” including overdose, suicide, and homicide. Subgroup analyses by sex, race, and ethnicity indicate notable differences in the relative burden and drivers of mortality, although accidental poisoning and exposure to noxious substances (which includes overdose) is the preeminent driver for all subgroups examined. We note particularly large increases in mortality among AI/AN populations(133.6 increased deaths per 100,000) and males (85.8 increased deaths per 100,000). Additionally, homicide by firearms disparately impacted males, Black Americans, and non-Hispanic Americans, consistent with recent literature.29 Our findings are consistent with documented increases in premature mortality2,3 and a widening gender gap in US life expectancy30 driven by overdose and suicide.2,30 However, much of the literature highlights mortality trends among the broader population of US adults,1,2,7 with little attention directed toward drastic increases in mortality among younger adults, who comprise an understudied group. Increases in mental illness and distress have been well-documented in this group,12,15 and our study examines the sobering implications of this widespread suffering.15
Understanding the reasons for the dramatic rise in younger adult mortality in the US requires identifying the factors most proximate to causes of mortality (e.g., behaviors, mental health), and the upstream macrostructural factors (e.g., public policies, economic conditions) that influence health. Establishing causal relationships among these explanatory factors is methodologically challenging and affected by issues such as data availability and quality. Nevertheless, given the importance of the issue and the alarming trends observed, we offer some of the leading hypotheses, as follows.
First, the opioid crisis has been an important driver of mortality, influenced in part by FDA approval of OxyContin® and other opioids in the 1990s, with limited initial reporting on adverse effects.2 As evidence emerged on opioid adverse effects, prescribing lessened, but many of those addicted to prescription opioids shifted towards heroin and fentanyl, with well-documented risk for overdose deaths.2 In the context of pandemic-driven social isolation and economic insecurity, substance use rose steeply,31 and younger adults may be particularly vulnerable to substance use driven by increasing loneliness.9,13 Substance use is often initiated between the ages of 18 and 25, although adults aged 25-44 may initiate opioid use for medical or recreational purposes.14 To address the ongoing opioid crisis, a 2021 National Academies of Sciences, Engineering and Medicine report recommended several actions, such as: (1) Research on the effectiveness of behavioral health interventions; on improved methods for delivering mental health and substance use treatment; and on the extent to which inadequate access to care has contributed to rising working-age mortality; (2) Policies to address the larger economic and social strains that made communities which experienced economic decline more vulnerable to substance use ; and (3) Improved regulatory control of prescription drug marketing and dispensing.2
Second, evidence suggests that economic stressors such as stagnating income, fragile safety nets, and high foreclosure rates are associated with rising suicide mortality. For example, regions of historical manufacturing and mining jobs such as the industrial Midwest and Appalachia have experienced outsized economic downturns compared to many other regions of the US. There have also been greater deteriorations in economic conditions among Americans without college degrees.2 Evidence suggests that social supports that can buffer suicidality, such as from community organizations, marriage, or formal institutions like churches, have declined in recent decades, which could also contribute to rising suicide mortality.2 The National Academies recommended research on lethal means of suicide - the use of which may in part explain sex differences in suicide mortality - to better understand what factors might precipitate the use of different modalities.2
Third, the lack of health insurance is unique in the US compared to other high-income countries. Evidence demonstrated that the increased availability of medical insurance through the Affordable Care Act improved mortality outcomes for working age adults. For example, the expansion of Medicaid was associated with a 6 percent reduction in opioid overdose mortality.32 Counties within Medicaid expansion states had an 11% reduction in deaths involving heroin, and a 10% reduction in deaths involving synthetic opioids other than methadone, compared to counties in non-expansion states (although additional evidence is needed to evaluate possible impacts on methadone-related mortality).32 The National Academies’ 2021 report recommended that states which have not yet increased access to Medicaid do so.2
Fourth, several major drivers of younger adult mortality (e.g., overdose, suicide, homicide) are exacerbated by the COVID-19 pandemic, alongside the deaths driven by the disease itself. Evidence suggests that intimate partner violence, to which younger adults may be particularly vulnerable,33 rose during the pandemic,33 as did gun violence.34 Furthermore, the downstream consequences of disruptions to professional and educational plans experienced by many younger adults during the COVID-19 pandemic9 could heighten the economic stressors faced by this group.
Overall, the National Academies recommended investing in research aimed at identifying the macro-level historical and contemporary drivers of health and mortality inequities.2 They also recommended cross-national research aimed at understanding why trends and disparities in working-age mortality have unfolded differently in the US and in other high-income countries. Such research could, in turn, identify best practices for supporting younger adults and addressing structural factors which may be contributing to these disparities.
American Indian/Alaskan Native younger adults have experienced particularly troubling increases in mortality related to “despair,” such as suicide (Figure A12) and accidental poisoning (Figure A3). Disproportionately high overdose and suicide mortality rates amongst young AI/AN individuals may stem from historical trauma, which has been linked to both substance use and suicidality. 35 Furthermore, AI/AN individuals (along with Black Americans) are more likely to be from areas with high levels of community distress (e.g., high unemployment), compared to other groups, contributing to racial disparities in substance use treatment.35 Future research should assess regional disparities in mortality trends and drivers of mortality to better allocate resources to the communities most in need.
There remains ample opportunity to support younger populations, through the above approaches, and others focused on resilience alongside mental and physical health promotion. For example, our group developed a mindfulness-based program adapted for young adults, which improved depression symptoms and loneliness in a randomized controlled trial.36 A recent meta-analysis found significant mental health benefits of mindfulness programs in young adults.37 Mindfulness-based interventions (MBIs) have shown promise in addressing opioid misuse38 and suicidality.39 Improving access to MBIs or other evidence-based programs could provide critical support to younger adults in conjunction with broader structural changes.
Overall, there are several domains, from more proximate domains to macrostructural factors that could improve young adult well-being and reduce premature mortality.
Strengths and Limitations
Key strengths of our study include the use of the WONDER database, which comprises robust, nationally representative mortality data. Examining mortality over the course of two decadesenables the detection of longstanding trends and allows us to contextualize our findings in data both prior to and during the COVID-19 pandemic – indicating that the pandemic is not the sole cause of rising younger adult mortality. Furthermore, we focus on an understudied group, namely younger adults, who are often studied within the context of the larger adult population. Focusing specifically on this group allows us to examine relevant drivers of mortality at a life stage with unique challenges. Drawing attention to the troubling rise in mortality within this group may provide the impetus for further research to elucidate underlying causes of this trend, and to intervene.
Our study has several limitations, particularly related to the categorization of demographic variables within the WONDER dataset. WONDER mortality data prior to 2018 uses bridged race categories, without any designations for multiracial individuals. To maintain consistency while evaluating temporal trends, we were limited to continued use of these categories for 2018-2021 data and could not perform subgroup analyses specific to mortality trends among multiracial individuals. Thus, our analyses may not capture the specific drivers of mortality and needs of multiracial younger adults. Furthermore, the reliability of data regarding race and ethnicity varies among different racial groups, and20 the available sex data is binary.19 While some states have developed processes to report the decedent’s gender identity,21 federal mortality data regarding sexual and gender minorities is limited.21 Beyond these limitations, we note that WONDER mortality data relies on death certificates issued for US residents,16,17 so these analyses do not capture mortality trends amongst undocumented individuals. Additionally, inaccuracies in death certificates – for instance, the underreporting of lawenforcement-related deaths described in the literature40 – could impact the validity of WONDER data.
Our primary analyses provide only crude rates, as age-adjusted rates were unavailable for the population of interest. However, sensitivity analyses standardizing mortality rates to 1999 population estimates,28 suggest crude and age-adjusted rates were similar among younger adults during this time period. Finally, the specification of singular underlying causes of death does not account for the possibility of multiple comorbidities contributing to mortality and is limited to proximate causes. Although our sensitivity analysis suggests similar trends may be noted using the Multiple Causes of Death data (e.g., accidental poisoning remains the preeminent driver of increased mortality), future analyses could more fully assess whether our findings are replicated in these data.
Conclusion
US adults aged 18-39 are suffering from troubling increases in mortality driven largely by “deaths of despair.” This trend predates, but was exacerbated by, the COVID-19 pandemic. Resources should be calibrated to address persisting drivers of mortality and to support this generation, who are the present and future of our nation.
Supplementary Material
Acknowledgements
Work by Shufang Sun was supported by National Institute of Health (K23AT011173). Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number P30MH062294.
Footnotes
Authors report no conflicts of interest.
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