The Gender Gap in Life Expectancy in the United States and Deaths of Despair: Trends from 1979 to 2022

Rodrigo González-Velastín; Christine R Schwartz

doi:10.1111/padr.70028

. Author manuscript; available in PMC: 2025 Dec 11.

Published before final editing as: Popul Dev Rev. 2025 Sep 29:10.1111/padr.70028. doi: 10.1111/padr.70028

The Gender Gap in Life Expectancy in the United States and Deaths of Despair: Trends from 1979 to 2022

Rodrigo González-Velastín ¹, Christine R Schwartz ¹

PMCID: PMC12692230 NIHMSID: NIHMS2115480 PMID: 41383528

Abstract

The extent to which women outlive men in the U.S. has fluctuated over the 20th century, with periods of equalization, stagnation, and increase. Women’s life expectancy advantage declined for roughly four decades but resurged after 2012. This coincided with an increase in deaths of despair (deaths due to suicide, alcohol, and drugs), for which rates are higher among men than women. We decompose the gender gap in life expectancy from 1979 to 2022 in the U.S. by cause of death and find that deaths of despair explain the vast majority of the resurgence of women’s life expectancy advantage since 2012, while its contribution to trends before 2012 is small relative to cardiovascular disease, lung cancer, and other causes of death. Drug-related mortality drives almost all of the post-2012 growth for White, Black, and Hispanic Americans alike, although its contribution is much higher for those without a college degree. Over the longer term, we show that deaths of despair have significantly offset the equalization of the gender gap in life expectancy since 1979. Our paper contributes to the literature by providing new evidence on the role of anomic social processes as recent drivers of gender disparities in life expectancy.

INTRODUCTION

The tendency for women to have lower mortality than men around the world is widely recognized (e.g., Beltrán-Sánchez et al., 2015; Zarulli et al., 2018). In the United States, women have outlived men on average for at least the last 100 years, but the gender gap in life expectancy has fluctuated. As shown in Figure 1 (Panel A), after a small rise and fall prior to 1920, women’s life expectancy advantage at birth and at other ages began to grow steadily until roughly 1970, at which point it began to fall. Beginning around 2010, the fall in women’s advantage plateaued and then began to increase in 2012, reaching a difference at birth in 2022 of 5.4 years (a 13.5% increase since 2012).

Figure 1. — *Sources:* 1880, 1900 Haines (1998, pp. Series I pp. 157, Series III pp. 165); 1910, 1920, 1930 Bell and Miller (2005, pp. 30, Table 6); 1933 to 2022 Human Mortality Database (HMD).

What are the sources of trends in the gender gap in life expectancy in the U.S.? Past literature has focused on three sources: the historic importance of gender differences in infant mortality and the subsequent shift away from this (Drevenstedt et al., 2008; Read et al., 1997; Zarulli et al., 2021), the gendered consequences of shifting adult mortality from infectious diseases to chronic diseases, such as cardiovascular disease (Beltrán-Sánchez et al., 2015; Lawlor et al., 2001), and shifts in gendered patterns of smoking (Preston et al., 2011; Preston & Wang, 2006).

Studies on age patterns of mortality show that, until the mid-20th century, higher mortality among male infants was the main factor contributing to women’s survival advantage in several countries, including the U.S. (Drevenstedt et al., 2008; Zarulli et al., 2021). From 1900 to 1970, women’s survival advantage increased, in part, because infant mortality declined more rapidly for female than male infants (Turner et al., 2020; Woodbury, 1936). This was due to a gradual shift from infant deaths caused by infectious diseases to those resulting from perinatal conditions (e.g., prematurity, birth defects), for which female infants had a greater survival advantage (Drevenstedt et al., 2008). In recent times, however, the evidence suggests that higher male mortality at older ages (age 60+) is the primary source to women’s survival advantage in the U.S. and several European countries (Feraldi & Zarulli, 2022; Zazueta-Borboa et al., 2023).

The increase in women’s life expectancy advantage from 1900 to 1970 also coincides with the more general shift away from infectious disease to chronic disease mortality (Beltrán-Sánchez et al., 2015). The literature suggests a strong association between the increase in excess male mortality during the 20th century and the rise of mortality due to cardiovascular disease, which increased to a greater extent for men than for women over this period (Beaglehole, 1999; Enterline, 1961; Nikiforov & Mamaev, 1998). However, men have experienced sustained reductions in heart disease mortality in the U.S. since the mid-1970s, which has progressively reduced the gender gap attributable to this cause (Lawlor et al., 2001).

Scholars have also shown that smoking disproportionately affected men’s mortality over the first half of the 20th century, contributing to an increase in women’s life expectancy advantage through the 1950s (Preston et al., 2011; Preston & Wang, 2006). The main explanation for this is the gender difference in smoking uptake. While men born between 1890 and 1919 had high smoking rates, these rates began to increase for women born in the 1920s. By 1955, smoking had a strong impact on men’s mortality but virtually no effect on women’s. However, after the 1950s, women’s smoking-related mortality escalated as men’s began to fall, which contributed to the reduction of women’s advantage in life expectancy since roughly mid-century (Preston & Wang, 2006).

A recent study identified a striking resurgence of women’s life expectancy advantage in the U.S. between 2010 and 2021, after decades of decline (Yan et al., 2024). The study attributed the primary cause as men’s disproportionate mortality due to COVID-19 and unintentional injuries, with drug poisonings making a significant contribution. Notably, this surge also corresponds with the documented rise in “deaths of despair” (henceforth, DOD)—deaths resulting from drugs, alcohol, and suicide (Case & Deaton, 2021a). However, research has not linked these two phenomena explicitly, nor has it examined whether the contribution of DOD to the recent uptick in the gender gap varies by education level or race/ethnicity.

The DOD literature has generally emphasized the similarity of trends in DOD by gender.¹ Yet, research shows that DOD are more prevalent among men (Elo et al., 2019; Geronimus et al., 2019; Ho & Hendi, 2018; Masters et al., 2017; Woolf & Schoomaker, 2019). In addition, scholars who have examined the contribution of DOD to gender differences in mortality have not focused on the gender gap in life expectancy, but instead different measures and have analyzed short periods of time. For example, studies have examined annual trends in age-standardized death rates from suicide, drug use, and alcohol use among Black and White Americans, separately by gender (Tilstra et al., 2021); the contribution of suicide, alcohol-related causes, and drug overdose deaths to changes in non-Hispanic White life expectancy by sex and geographic setting between 1990 and 2016 (Woolf & Schoomaker, 2019); the impact of suicide mortality on men’s and women’s life expectancy in 2011 and 2015 (Sagna et al., 2020); variation in the rate of alcohol-induced deaths for men and women in the U.S. between 2000 and 2016 (Spillane et al., 2020); the contribution of DOD to the total years of life lost by gender in the U.S. between 1990 and 2015 (Geronimus et al., 2019); and the contribution of drug overdoses to men’s and women’s life expectancies between 1990 and 2017 (Ho, 2020). Although it may be that DOD affects men and women similarly, we can better judge whether this is indeed the case by quantifying the changing contribution of DOD to the gender gap in life expectancy and relative to other causes of death.

Likewise, literature on the gender gap in life expectancy has not considered the impact of the rise in DOD. Instead, this literature has focused on identifying the causes of death and age groups that contribute most to explaining gender differences, usually using a cross-national comparative approach.² Several studies have decomposed the gender gap in life expectancy, identifying infant mortality, heart disease, cancers, and external causes of death as relevant contributors to the female advantage in life expectancy over the 20^th century (Chisumpa & Odimegwu, 2018; Feraldi & Zarulli, 2022; Feraldi et al., 2023; Gómez-Redondo & Boe, 2005; Luy & Wegner-Siegmundt, 2015; Meslé, 2004; Sundberg et al., 2018; Tarkiainen et al., 2012; Trias-Llimós & Janssen, 2018; Zazueta-Borboa et al., 2023). These studies point out, however, that the relative contribution of each of these causes of death varies substantially across countries, suggesting that the gender gap in life expectancy is produced by the interplay of epidemiological, historical, and contextual factors. This variation in empirical trends sheds light on why there is no consensus on which causes of death are most important in explaining trends in the gender gap in life expectancy.

Research decomposing the gender gap in life expectancy in the U.S. is scarce and has not specifically focused on DOD. Past studies have examined the contributions of specific causes of death separately to gender differences in life expectancy (e.g., Ho, 2020), have identified major causes cross-nationally (Feraldi & Zarulli, 2022; Feraldi et al., 2023), or have examined much shorter time spans, e.g., a single decade or a single year (Acciai & Firebaugh, 2017; Yan et al., 2024).³ Thus, it is not possible to evaluate the changing contribution of DOD to gender gaps in life expectancy in the U.S. on the basis of prior research. This reflects that the gender gap in life expectancy and DOD literatures are largely separate, although the narrative of anomic deaths as one of the main forces undermining improvements in life expectancy has gained prominence in academic and policy debates.

In addition, we stratify our analysis by educational attainment and race/ethnicity to provide more detailed estimates. Both life expectancy and deaths of despair have become increasingly stratified along these lines in recent decades. Research shows that the gender gap in life expectancy can differ markedly across racial and educational lines due to unequal exposure to social, economic, and health-related risks (Case & Deaton, 2023; Montez et al., 2019; Tilstra et al., 2021). For instance, deaths of despair are particularly prevalent among White individuals without a college degree (Case & Deaton, 2015), although their rise has also affected other segments of the population. Disaggregating these patterns allows us to examine whether recent trends are consistent or heterogeneous across social groups.

In this paper, we address the long-term contributions of DOD to the gender gap in life expectancy in the U.S. between 1979 and 2022, and we and analyze the extent to which the increase in women’s life expectancy advantage observed in recent years is the result of trends in DOD versus other causes that have been highlighted as very significant factors in the past such as cardiovascular disease and lung cancer. In doing so, we separate DOD into its three constituent parts—deaths due to suicide, drugs, and alcohol—and also analyze whether the contributions of infant mortality and chronic and infectious diseases as drivers of the gender gap in life expectancy have waned. Lastly, by performing our analysis by education (college degree status) and race/ethnicity, we also contribute to understanding the extent to which the gender gap in life expectancy is a socially patterned phenomenon in the U.S. These results allow us to quantify the extent to which DOD has affected men’s and women’s life expectancy similarly and contextualize the size of these impacts relative to the impact of other causes of death over the longer-term.

DATA & METHODS

To decompose trends in the gender gap in life expectancy for the population, by college degree (BA) status, and by race/ethnicity, we use published period life tables for men and women from the Human Mortality Database for 1933–2022 (HMD) for the population, the Human Life Table Database for 1978–2021 (HLD) and the National Center for Health Statistics for 2022 for estimates by race/ethnicity, and cause of death counts from the Multiple-Cause-of-Death Mortality Data (National Center for Health Statistics, 1979–2022). Data exist to show trends in the overall gender gap in life expectancy since at least 1880 (as in Figure 1) using published period life tables, but our decomposition into causes of death for the population covers 1979 to 2022 as this is when data on causes of death is most standardized and historically comparable. Note that our time series includes the COVID-19 pandemic. As we show below, by 2022, pre-COVID-19 trends in gender gaps in life expectancy appear to have more or less resumed. We show our main results through 2019 (before the onset of the pandemic) for comparison in Appendix Table S3. The main conclusions of our results hold regardless of whether we end our analysis in 2022 or 2019, although the role of higher male mortality from the pandemic can still be seen in 2022.

We categorize underlying causes of death according to the International Classification of Diseases (ICD), spanning the 9^th and 10^th revisions (1979–1998 and 1999–2022, respectively) and estimate age- and year-specific proportions dying from the following causes: deaths of despair (subclassified into deaths due to drugs, alcohol, and suicide), cancer (other than lung cancer), cardiovascular disease, lung cancer, accidents and homicides, infant mortality, infectious diseases, and all other causes.^4,5

Given substantial debate about the category “deaths of despair” (e.g., Ruhm, 2018; Shanahan & Copeland, 2021; Simon & Masters, 2021) it is worth noting how we approach this term. We regard DOD as a useful analytic category that captures a class of anomic deaths from drugs, alcohol, and suicide. However, we do not claim that these deaths are necessarily caused by psychological despair, especially given the complexity of this concept and the lack of consistent empirical evidence supporting this idea (Copeland et al., 2020; Ruhm, 2024; Shanahan & Copeland, 2021; Shanahan et al., 2019). However, we use the term given its ubiquity in the literature and follow the recommendations of its critics by examining deaths due to drugs, alcohol, and suicide separately as well as together (Masters et al., 2017; Simon & Masters, 2021; Tilstra et al., 2021) and, in addition, examine other causes of death.

To decompose the gender difference in life expectancy by year $y$ and cause $i$ between ages $x$ and $x + n ({}_{n}{∆_{x y}^{i}})$ , we use the following equation (adapted from Preston et al., 2000, p. 84 eq. 4.9):

{}_{n}{∆_{x y}^{i}} = {}_{n}{∆_{x y}} \frac{{}_{n}{R_{x y}^{i}} (W) * {}_{n}{m_{x y}} (W) - {}_{n}{R_{x y}^{i}} (M) * {}_{n}{m_{x y}} (M)}{{}_{n}{m_{x y}} (W) - {}_{n}{m_{x y}} (M)}

where ${}_{n}{∆_{x y}}$ is the contribution of all-cause mortality differences in age group $x$ to $x + n$ to differences in life expectancies, ${}_{n}{R_{x y}^{i}}$ are the proportion of deaths from cause $i$ in year $y$ in age group $x$ to $x + n$ for women ( $W$ ) and men ( $M$ ), and ${}_{n}{m_{x y}}$ are all-cause age specific death rates in age group $x$ to $x + n$ in year $y$ for women ( $W$ ) and men ( $M$ ).

The contribution of each cause $i$ to the overall gender difference in life expectancy at birth in year $y$ is the sum of the contributions across age groups:

∆_{y}^{i} = \sum_{x} {}_{n}{∆_{x y}^{i}}

and summing the contributions of each cause $i$ to the gender difference gives the total gender difference in life expectancy at birth:

∆_{y} = e_{0 y}^{0} (W) - e_{0 y}^{0} (M) = \sum_{x} \sum_{i} {}_{n}{∆_{x y}^{i}}

Thus, the proportion of the overall gender difference in life expectancy at birth that is due to cause $i$ in year $y$ is:

\frac{∆_{y}^{i}}{∆_{y}}

Single-year age-specific death rates for the population of men and women from 1979–2022 ( ${}_{n}{m_{x y}}$ ) are from published life tables (HMD). Because some age, sex, year, and cause of death combinations have small sample sizes, we estimate the proportion of deaths, ${}_{n}{R_{x y}^{i}}$ using wider age categories (<1 year, 1–4, 5–9,…80–84, 85+) and assume that the proportions of deaths are constant within these intervals.

We further decompose the gender gap in life expectancy by educational attainment, categorizing the population based on BA/non-BA status. Due to the limited availability on causes of death by education prior to 1990, we conduct these analyses beginning in 1990. For the analyses by BA status, instead of estimating life expectancy at birth, we estimate life expectancy at age 25 as is conventional given that, by this age, most people have finished their formal education. We construct period life tables by sex and single years of age between ages 25 and 84, stratified by BA status, utilizing numerator counts of death estimates from the Multiple-Cause-of-Death Mortality Data and denominator estimates derived from the 1991 to 1999 Current Population Survey and the 1990, 2000 to 2022 U.S. Census/American Community Survey following the methodology outlined by Case and Deaton (2021b). Because variability in the CPS and Census/ACS denominators creates problems estimating life tables by BA status at older ages, we calculate year- and sex-specific BA to non-BA mortality ratios for all individuals aged 85 and older from the Multiple-Cause-of-Death Mortality Data and apply these to the single age- and year-specific death rates from the Human Mortality Database for the BA- and non-BA-specific life tables. Then, we apply BA-status-specific proportions of deaths by cause to the BA/non-BA life tables by gender as described above for the population to decompose gender differences in life expectancy within BA-status categories.

We use a similar method to decompose the gender gap in life expectancy at birth by race/ethnicity. However, rather than constructing life tables by race/ethnicity from the Multiple-Cause-of-Death Mortality Data, we use published period life tables by race/ethnicity from the Human Life-Table Database for 1990–2021 (HLD) and the National Center for Health Statistics for 2022 (Arias et al., 2025). Period life tables are available for Black and White Americans for 1979, 1989, and annually from 1999 to 2006. From 2007 to 2022, period life tables are available for non-Hispanic Black, non-Hispanic White, and Hispanic Americans⁶. Thus, in the life table data, the definition of race/ethnicity changes between 2006 and 2007, but there is little evidence of a discontinuity in the results, as we show below. Similarly, in the cause-of-death data (National Center for Health Statistics, 1979–2022), our analyses by race identify White and Black Americans from 1979 through 2006, corresponding to the life table data. Beginning in 2007, we use information on Hispanic origin to classify race/ethnicity into three groups: non-Hispanic Black, non-Hispanic White, and Hispanic. Beginning in 2021, the cause-of-death data contains single- and multiple-race categories. Thus, starting in 2021, the race/ethnicity categorization in the cause-of-death records is non-Hispanic (single-race) White, non-Hispanic (single-race) Black, and Hispanic⁷.

RESULTS

The gender gap in life expectancy has fluctuated across the 20th century (Figure 1, Panel A), but the dominant trend has been an increase in women’s life expectancy advantage through about 1970, followed by decline through 2012. More recently, the decades-long decline in women’s life expectancy advantage reversed, and women’s life expectancy advantage increased from 4.75 years in 2012 to 5.39 years in 2022 (see proposed online appendix Table S1 for numbers corresponding to Figure 1). One also sees the disproportionate impact of COVID-19 mortality on men between 2020 and 2021, but the gender gap then declined in 2022 resuming to what appears to be closer to pre-pandemic trends. Panel A of Figure 1 also shows that trends are similar when life expectancy is calculated at age 25 or between ages 25 to 69, although the levels differ. Because of the similarity of the trends, we focus on life expectancy at birth for the population and by race/ethnicity as this has the advantage of incorporating sex differences in infant mortality as well as older ages, but life expectancy at age 25 by BA status given education ultimate unknown at younger ages.

To get a better sense of the magnitude of these trends, Panel B of Figure 1 shows the gender gap in life expectancy at birth in relative terms (percentage difference compared to 2012). It shows that the recent rise in the gap represents a 13.5% increase since 2012, not large relative to the fact that the gender gap was more than 60% higher in the early 1970s, but still notable given that this is a reversal of a 40-year decline.

Panel C of Figure 1 gives context for the changes in the gender gap in life expectancy at birth by showing trends in life expectancy over this period for men and women separately. It shows that the large increase in the gender gap in life expectancy at birth from 1920 through the early 1970s arose from a slower increase in life expectancy for men relative to women, whereas the decline in the gender gap after the 1970s is evident in the slower pace of women’s increased life expectancy compared with men’s. We also see that the resurgence of women’s life expectancy advantage between 2012 and 2019, prior to the COVID-19 pandemic, was the result of the plateau in men’s life expectancy but the continued increase in women’s life expectancy. The larger decline in men’s life expectancy during the pandemic is also evident.

How do trends in the gender gap in life expectancy vary by education and race/ethnicity? Figure 2 shows gender differences in life expectancy at birth for the overall population since 1979 (also shown in Figure 1, Panel A), which is the year we are able to begin the decomposition analysis for the total population, at age 25 by BA status, and at birth by race/ethnicity for the years which these data are available. Panel A of Figure 2 shows that women’s life expectancy advantage at age 25 has been larger among those without BAs than for those with BAs for the entire period, and there is no clear divergence by education across time. Instead, the largest difference in the gender gap between those with and without a BA was in 1993, when women’s life expectancy exceeded men’s by 7.1 years among those without a BA and by 3.9 years among those with a BA. In 2022, differences in the gender gap by BA status were smaller, at 5.8 years for those without a BA and 3.2 years for those with (see proposed online appendix Table S2 for numbers corresponding to Figure 2). It is notable, however, that both those with and without a BA alike have witnessed the resurgence of women’s life expectancy advantage in around 2012.

Panel B of Figure 2 shows the gender gap in life expectancy at birth among White, Black, and Hispanic Americans. It shows that the gender gap among White and Hispanic Americans is quite similar to the population average, although the gender gap in life expectancy at birth was magnified in particular for the Hispanic during the COVID-19 pandemic. Across these years, the gender gap is larger among Black Americans. This is consistent with the high mortality of Black men relative to other groups and has been studied in the context of factors that affect Black men and women alike such as structural racism, individual-level discrimination, and institutional disadvantages in terms of education, housing, the labor market, health care, and experiences with the criminal justice system among other factors (Bailey et al., 2017; Brown & Homan, 2024; O’Brien et al., 2020; Williams et al., 2019). Like the results by BA status, however, we see a resurgence in women’s life expectancy advantage around 2012 across each of the race/ethnic groups we examine here, although this resurgence is more pronounced among Black Americans. Thus, the general decline in women’s life expectancy advantage in the 1980s, 1990s, and 2000s was a broadly shared phenomenon across education and race/ethnic groups, as is the resurgence around 2012.

How does variation in cause of death contribute to trends in gender differences in life expectancy? Figure 3 shows trends in the gender difference in life expectancy at birth for the population from 1979 to 2022 (also shown in Figure 1) by the portion of the difference due to the ten causes of death we delineate. Table 1 quantifies the extent to which these causes of death can account for trends over the entire time series (between 1979 and 2022 for the population), for the period of the reduction in women’s advantage (1979 to 2012), and for the period since its resurgence (2012 to 2022). There are three components to Table 1. First, it shows the overall change in years, e.g., the gender gap in life expectancy declined by −2.90 years between 1979 and 2012 and increased by 0.64 years between 2012 and 2022. Second, it shows the change in years attributable to each cause of death, e.g., −1.76 years of the total decline in the gap is attributable to a reduction in the gender gap in cardiovascular disease mortality; changes in cancer (other than lung) widened the gender gap over this period by 0.24 years. Third, it shows the percentage of the total change attributable to each cause, e.g., 60.7% of the total decline between 1979 and 2012 is due to cardiovascular disease [(−1.76/−2.90)*100]; changes in cancer (other than lung) increased the size of the gap by 8.4%.

Table 1.

Decomposition of Changes in the Gender Gap in Life Expectancy

	1979–2012		2012–2022		1979–2022

Population at age 0	Years	%	Years	%	Years	%

Change in gender gap	−2.90	100	0.64	100	−2.26	100
Due to:
Cardiovascular disease	−1.76	60.7	−0.05	−8.0	−1.81	80.2
Cancer (except lung)	0.24	−8.4	−0.15	−23.0	0.10	−4.2
Lung cancer	−0.48	16.6	−0.18	−27.5	−0.66	29.1
Accidents & homicides	−0.76	26.3	0.16	24.7	−0.60	26.7
DOD:Drug-related	0.17	−5.9	0.54	84.3	0.71	−31.6
DOD:Alc-related	0.00	−0.1	0.06	9.8	0.07	−2.9
DOD:Suicide	0.08	−2.8	0.05	7.2	0.13	−5.6
Infant mortality	−0.15	5.1	−0.01	−1.7	−0.16	7.0
Infectious diseases	0.01	−0.2	0.21	33.5	0.22	−9.8
Other	−0.25	8.7	0.00	0.7	−0.25	11.0
By education at age 25	1990–2012		2012–2022		1990–2022			1990–2012		2012–2022		1990–2022

No BA	Years	%	Years	%	Years	%	BA	Years	%	Years	%	Years	%

Change in gender gap	−1.86	100	0.69	100	−1.17	100	Change in gender gap	−1.64	100	0.32	100	−1.31	100
Due to:							Due to:
Cardiovascular disease	−0.97	52.3	−0.09	−13.8	−1.07	91.0	Cardiovascular disease	−0.97	59.4	0.02	6.2	−0.95	72.4
Cancer (except lung)	0.15	−8.3	−0.20	−28.6	−0.04	3.6	Cancer (except lung)	0.09	−5.6	−0.05	−16.3	0.04	−3.0
Lung cancer	−0.42	22.8	−0.23	−32.8	−0.65	55.4	Lung cancer	−0.24	14.5	−0.08	−25.3	−0.32	2.42
Accidents & homicides	−0.21	11.3	0.20	29.8	−0.01	0.5	Accidents & homicides	0.00	0.0	0.01	3.5	0.01	−0.8
DOD:Drug-related	0.12	−6.3	0.69	100.9	0.81	−6.92	DOD:Drug-related	0.03	−1.8	0.09	26.5	0.11	−8.7
DOD:Alc-related	0.05	−2.6	0.07	9.7	0.12	−9.9	DOD:Alc-related	0.02	−1.5	0.04	11.4	0.06	−4.6
DOD:Suicide	0.03	−1.6	0.06	8.4	0.09	−7.5	DOD:Suicide	0.03	−1.8	0.00	0.0	0.03	−2.2
Infectious diseases	−0.44	23.6	022	32.4	−0.22	18.4	Infectious diseases	−0.57	34.7	0.22	67.5	−0.35	26.7
Other	−0.17	8.9	−0.04	−6.0	−0.21	17.6	Other	−0.03	2.0	0.09	26.5	0.05	4.0
By race/ethnicity at age 0	1979–2012		2012–2022		1979–2022			1979–2012		2012–2022		1979–2022

White	Years	%	Years	%	Years	%	Black	Years	%	Years	%	Years	%

Change in gender gap	−2.79	100	0.38	100	−2.42	100	Change in gender gap	−1.82	100	1.26	100	−0.56	100
Due to:							Due to:
Cardiovascular disease	−1.81	64.9	−0.07	−17.4	−1.88	77.7	Cardiovascular disease	−0.21	11.3	0.01	0.7	−0.20	35.2
Cancer (except lung)	0.28	−10.0	−0.13	−35.5	0.14	−6.0	Cancer (except lung)	0.10	−5.7	−0.24	−18.7	−0.13	23.4
Lung cancer	−0.47	16.8	−0.17	−44.6	−0.64	26.4	Lung cancer	−0.34	18.9	−0.26	−20.8	−0.61	108.2
Accidents & homicides	−0.77	27.4	0.02	4.3	−0.75	31.0	Accidents & homicides	4.06	58.4	0.57	45.2	−0.49	87.9
DOD:Drug-related	0.23	−8.2	0.44	117.9	0.67	−27.8	DOD:Drug-related	0.08	−4.4	0.94	74.9	1.02	−182.6
DOD:Alc-related	0.01	−0.4	0.06	15.7	0.07	−2.9	DOD:Alc-related	−0.14	7.7	0.01	0.8	−0.13	23.3
DOD:Suicide	0.16	−5.8	0.04	10.7	0.20	−8.4	DOD:Suicide	−0.01	0.6	0.09	7.3	0.08	−14.4
Infant mortality	−0.16	5.6	−0.01	−2.8	−0.17	6.9	Infant mortality	−0.08	4.4	−0.06	−4.5	−0.14	24.6
Infectious diseases	−0.02	0.7	0.21	54.8	0.19	−7.8	Infectious diseases	0.04	−2.5	0.11	8.5	0.15	−27.1
Other	−0.25	8.9	−0.01	−3.1	−0.26	10.8	Other	−0.20	11.1	0.08	6.5	−0.12	21.5
	2012–2022		2007–2022

Hispanic	Years	%	Years	%

Change in gender gap	0.78	100	0.70	100
Due to:
Cardiovascular disease	−0.08	−10.1	−0.01	−1.6
Cancer (except lung)	−0.24	−30.8	−0.14	−20.6
Lung cancer	−0.16	−20.1	−0.17	−24.8
Accidents & homicides	0.23	30.0	−0.07	−9.4
DOD:Drug-related	0.66	85.1	0.65	93.0
DOD:Alc-related	0.07	8.7	0.05	7.8
DOD:Suicide	0.07	9.3	0.05	7.1
Infant mortality	−0.01	−1.4	−0.02	−32
Infectious diseases	0.32	41.5	0.30	43.6
Other	−0.09	−12.2	0.06	8.2

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From Figure 3, it is clear that the majority of the reduction in women’s life expectancy advantage since 1979 is due to a reduction in the contribution of cardiovascular disease. Table 1 shows that men’s faster heart disease mortality reduction relative to women’s (Lawlor et al., 2001), accounts for 80% of the reduction in the gender gap in life expectancy between 1979 and 2022. The narrowing of the gender gap in accident and homicide mortality and lung cancer mortality, corresponding to men’s more dramatic smoking cessation compared with women’s (Preston & Wang, 2006), also represent major contributions to the overall decline since 1979, accounting for 27% and 29%, respectively.

Figure 3 and Table 1 also show that a remarkable 84% of the resurgence of women’s life expectancy advantage between 2012 and 2022 is from deaths classified as due to drugs. This finding aligns with evidence indicating that overdose mortality for opioids and stimulant drugs is significantly higher among men than women in the U.S. (Butelman et al., 2023; Han et al., 2021; Wilson, 2020). The rise of the other categories of deaths of despair—alcohol-related deaths and suicide—also contributed to the increase in women’s life expectancy advantage since 2012 (10% and 7% of the increase, respectively), but their contributions are dwarfed when compared to the contribution of drug-related deaths. To put the magnitude of the contribution of DOD over the longer-term in perspective, across the entire period since 1979, men’s life expectancy gains relative to women’s from their faster declines in lung cancer deaths (29% of the decline) were completely erased by their increased relative mortality from DOD (−40% of the decline from the three sub-causes combined).

In terms of proportionate contributions, as the relative contribution of cardiovascular disease to the gender gap in life expectancy has waned, DOD has played a proportionally larger role in accounting for the gender gap in life expectancy. Table 2 shows that in 1979, 43% of women’s advantage in life expectancy at birth was attributable to their lower cardiovascular disease mortality but this contribution declined to 27% in 2022. By contrast, only 7% of the gender difference in 1979 was due to DOD, but this rose to 27% in 2022, more than tripling. The proportionate contribution of infant mortality to the gender difference has decreased over this period from 3% to 1% of the total difference. Given that infant mortality rates are very low in the modern era, they also have a very small impact, even though they represent many person-years of life lost. Comparing 2019 to 2022, most of the causes of death have similar distributions, but there is a noticeable increase in the importance of infectious diseases, which likely reflects the lingering impact of COVID-19 mortality. Considering the entire period, our results support the idea that the underlying mortality causes of women’s life expectancy advantage have shifted in the U.S. away from chronic diseases toward social anomic deaths, especially those from drugs.

Table 2.

Percentage Contribution of Causes of Death to Gender Gap in Life Expectancy

Population at age 0	1979	2012	2019	2022

Gender gap (years)	7.654	4.752	5.035	5.393
% Due to:
Cardiovascular disease	42.8	31.8	30.7	27.1
Cancer (except lung)	3.9	11.3	9.7	7.2
Lung cancer	102	6.2	3.6	2.2
Accidents & homicides	19.8	15.9	15.9	16.9
DOD:Drug-related	0.3	4.2	9.5	13.7
DOD:Alc-related	2.8	4.6	4.8	5.2
DOD:Suicide	4.1	8.2	8.8	8.1
Infant mortality	3.0	1.8	1.7	1.4
Infectious diseases	2.4	4.1	3.0	7.6
Other	10.7	11.9	12.4	10.5
By education at age 25
No BA	1990	2012	2019	2022	BA	1990	2012	2019	2022

Gender gap (years)	6.986	5.127	5.454	5.814	Gender gap (years)	4.481	2.845	3.123	3.167
% Due to:					% Due to:
Cardiovascular disease	38.8	33.9	32.3	28.3	Cardiovascular disease	48.7	42.6	42.0	38.9
Cancer (except lung)	6.7	12.1	10.1	7.3	Cancer (except lung)	5.9	12.6	12.0	9.6
Lung cancer	11.6	7.6	4.5	2.8	Lung cancer	7.9	4.1	2.0	1.2
Accidents & homicides	12.3	12.7	13.7	14.7	Accidents & homicides	5.2	8.2	7.9	7.7
DOD:Drug-related	1.0	3.7	10.0	15.2	DOD:Drug-related	0.3	1.5	3.5	4.0
DOD:Alc-related	3.1	5.1	5.4	5.7	DOD:Alc-related	2.0	3.9	3.9	4.7
DOD:Suicide	4.7	7.0	7.5	7.2	DOD:Suicide	3.9	7.1	6.8	6.4
Infant mortality	0.0	0.0	0.0	0.0	Infant mortality	0.0	0.0	0.0	0.0
Infectious diseases	9.5	4.4	3.2	7.7	Infectious diseases	15.6	4.7	3.9	11.1
Other	12.2	13.4	13.4	11.1	Other	10.5	15.3	18.1	16.5
By race/ethnicity at age 0
White	1979	2012	2019	2022	Black	1979	2012	2019	2022

Gender gap (years)	7.456	4.663	4.925	5.040	Gender gap (years)	7.984	6.164	6.755	7.424
% Due to:					% Due to:
Cardiovascular disease	44.4	32.2	31.0	28.5	Cardiovascular disease	25.8	30.1	29.1	25.1
Cancer (except lung)	3.7	11.9	10.6	8.4	Cancer (except lung)	5.7	9.0	6.3	4.3
Lung cancer	10.0	5.8	3.3	2.1	Lung cancer	10.2	7.6	4.4	2.8
Accidents & homicides	18.5	13.1	12.3	12.4	Accidents & homicides	32.3	24.6	26.3	28.1
DOD:Drug-related	0.3	5.4	10.6	13.8	DOD:Drug-related	0.6	2.1	8.5	14.5
DOD:Alc-related	2.6	4.5	4.7	5.3	DOD:Alc-related	3.7	2.5	2.4	2.3
DOD:Suicide	4.3	10.4	11.1	10.4	DOD:Suicide	2.8	3.4	4.0	4.1
Infant mortality	3.0	1.5	1.6	1.2	Infant mortality	3.5	3.2	1.9	1.9
Infectious diseases	2.4	3.5	2.5	7.3	Infectious diseases	3.7	5.5	3.9	6.0
Other	10.7	11.8	12.3	10.6	Other	11.7	11.8	13.1	10.9
Hispanic	2007	2012	2019	2022

Gender gap (years)	5.117	5.032	5.336	5.813
% Due to:
Cardiovascular disease	28.4	30.3	29.5	24.8
Cancer (except lung)	9.4	11.5	9.1	5.8
Lung cancer	5.6	5.4	3.4	2.0
Accidents & homicides	20.9	15.4	15.7	17.3
DOD:Drug-related	3.5	3.2	8.8	14.2
DOD:Alc-related	7.6	7.5	7.7	7.6
DOD:Suicide	4.8	4.4	5.2	5.1
Infant mortality	1.5	1.3	1.4	0.9
Infectious diseases	6.5	6.2	4.3	11.0
Other	11.7	14.9	14.8	11.3

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How do these results vary by BA status? Overall, patterns are similar to the general population. Panels A and B of Figure 4 show the outsized role of cardiovascular disease in accounting for the decline in the gender gap in life expectancy for both those with and without Bas since 1990. Over the entire period, men’s improvement in cardiovascular disease mortality relative to women’s explains 91% of the gender gap decline among those without a BA and 72% for those with a BA (Table 1). It is also notable that for both groups, lung cancer deaths contributed to a reduction in the gender gap in life expectancy, although this accounts for more of the total decline from 1990 to 2022 for those without a BA compared with those with a BA (55% versus 24%, respectively).

One of the largest differences by BA status evident in Figure 4 is the much larger contribution of drug-related deaths to the increase in women’s mortality advantage among those without a BA since 2012 compared to those with a BA. As shown in Table 1, if changes in drug-related mortality were the only changes in mortality to have occurred, the gender gap in life expectancy at age 25 for those without a BA would have been 0.9% larger than what was observed. This is because the increase due to drug-related deaths was offset by other changes, such as the reduction of the gender gap in lung cancer, which reduced the increase in the gender gap by −33%. These offsetting trends explain why drug-related mortality accounts for 100.9% of the increase in the gender gap in life expectancy for those without a BA. By comparison, drug-related mortality explained only 27% of the increase for those with a BA. Women’s life expectancy advantage increased for both those with and without a BA over this period, but the main cause for those without a BA was clearly drug-related mortality whereas for those with a BA it was a combination of factors, with infectious disease (68%) and other causes of death (27%, e.g., Alzheimer disease, Parkinson disease) playing a larger role alongside drug-related mortality.

There are other notable differences by BA status. In particular, the decline in women’s mortality advantage with respect to lung cancer has played a larger role in accounting for declines in the gender gap between 1990 and 2022 for those without a BA (55%) compared to those with a BA (24%). Conversely, between 1990 and 2022, a reduction in gender differences in infectious disease mortality explains more of the decline in women’s life expectancy advantage among those with BAs (27%) compared with those without BAs (18%).

In terms of shifts in the proportionate contributions of causes of death to the gender gap in life expectancy, Table 2 shows that chronic diseases account for a higher proportion of the gender gap in life expectancy for BAs, while DOD is a more significant category for those without BAs and has become increasingly so. In 2022, DOD contributed much more to the gender gap for those without a BA (28%) than with a BA (15%). The internal composition of the DOD category also varies. In 2022, drug-related mortality was clearly the component of DOD with the largest contribution for non-BAs (15%), while for those with a BA, suicides accounted for the largest contribution (6%) with drug-related deaths accounting for only 4% of the gap. Thus, the increasing importance of DOD and drug-related mortality is more pronounced among the less educated. Meanwhile, chronic diseases like cardiovascular disease and cancers (except lung) continue to contribute proportionately more to the gender gap in life expectancy for those with BAs. By 2022, these causes accounted for 50% of women’s life expectancy advantage among those with a BA and 38% for those without a BA. The contribution of lung cancer has waned over time for both groups, but this decline has been steeper for BAs. Accidents and homicides contributed proportionately more to the gap for those without a BA in 2022 than for BAs (15% versus 8%) and the relative contribution of this cause has remained roughly constant since 1990.

Turning to trends by race/ethnicity, Figure 5 shows large differences in the role of cardiovascular disease. Although women’s mortality advantage has declined for both White and Black Americans since 1979, as for the population as a whole, we see that the composition of this change differs. Table 1 shows that, among White Americans, 78% of the decline between 1979 and 2022 is attributed to a decline in gender differences in deaths from cardiovascular disease but this cause accounts for only 35% of the decline among Black Americans, as Black women are disproportionately affected by cardiovascular disease in the U.S., and research has identified a slower decline in cardiovascular disease mortality among Black individuals compared to White individuals since 1973 (Ogunniyi et al., 2022; Vaughan et al., 2015, p. 4 Figure 1). By contrast, for Black Americans, the primary drivers of the declining gender gap in life expectancy were a narrowing of gender differences in mortality from accidents and homicides (88% of the decline) and lung cancer (108% of the decline, that is, there would have been no decline in the gender gap but instead an 8% increase if this was the only component of change), compared with smaller contributions of accidents and homicides and lung cancer for Whites (31% and 26%, respectively).

Like the population, from 2012 to 2022, the gender gap in life expectancy began to widen again for both White and Black populations, and for both the rise was predominantly driven by drug-related deaths, which contributed 75% to the increase for Black Americans and 118% for White Americans. Alcohol-related deaths and suicide account for a smaller fraction of the resurgence of women’s life expectancy advantage among Black individuals (0.8% and 7%) compared to White individuals (16% and 11%, respectively). Given data availability, we examine trends for Hispanic Americans over a shorter time period, but similar trends are evident as for the general population. In particular, since 2012, drugs have been the main driver of the resurgence in women’s life expectancy advantage, accounting for 85% of the increase. The contribution of alcohol-related deaths and suicide to the increase in women’s life expectancy advantage among the Hispanic population is relatively low (9% and 9%).

In terms of changes in the proportionate contributions of causes of death by race/ethnicity, Table 2 shows that the primary contributors of the gender gap in life expectancy have changed differently for White and Black Americans. In particular, the proportionate contribution of cardiovascular disease for White individuals has decreased steadily (44% in 1979 to 29% in 2022), but it has remained relatively constant for Black Americans (26% in 1979 to 25% in 2022). The contribution of cardiovascular disease for Hispanic Americans declined slightly between 2007 and 2022 (28% to 25%). For White, Black, and Hispanic Americans alike, all other causes of death, with the exception of DOD and infectious diseases, either decreased in their proportionate contribution to the gender gap or were relatively stable. Among all groups, the increase in the proportionate contribution of DOD to the gender gap in life expectancy has been large. There is some variation in the proportionate contribution of DOD in 2022 among race/ethnic groups, with DOD accounting for a larger percentage of the gender gap among White and Hispanic Americans (30% and 27%, respectively) than among Blacks Americans (21%). For each race/ethnic group, the increasing contributions of drug-related mortality are large. These changes reflect a shift toward anomic causes of death as major contributors to women’s life expectancy advantage, while causes such as cardiovascular disease, lung cancer, and accidents and homicides are increasingly becoming less significant in relative terms.

CONCLUSION

After declining for more than 30 years, we show that women’s life expectancy advantage resurged around 2012 in the United States. This resurgence was broadly shared across different groups, including for those with and without college degrees, and White, Black, and Hispanic Americans. We bring together the literature on deaths of despair (DOD) with the literature on gender gaps in life expectancy to decompose the sources of change in the U.S. gender gap in life expectancy from 1979 to 2022, with a special focus on DOD.

Our approach provides important context for understanding the extent to which DOD have affected men and women similarly (Case & Deaton, 2021a). We show that the reversal of the four-decade decline in women’s life expectancy advantage is entirely due to higher male DOD mortality with drug-related deaths making up the vast majority of the increase. Suicides and alcohol-related deaths have also contributed to this reversal, although to a lesser extent. This is true for the population as a whole, for those without a BA degree, and for White, Black, and Hispanic Americans. The only group for which DOD does not explain the majority of the rise is college graduates, for whom the growing gender gap in infectious diseases contributed more than DOD to the increase of the gender gap. These results align with previous findings indicating that DOD are more prevalent among disadvantaged populations and that those with a BA have been relatively insulated from DOD (Beseran et al., 2022; Geronimus et al., 2019).

Until now, past research on recent drivers of the gender gap in life expectancy in the U.S. has primarily focused on the effects of changes in cardiovascular disease and smoking (e.g., Lawlor et al., 2001; Pinkhasov et al., 2010; Preston et al., 2011; Preston & Wang, 2006). Our study corroborates the importance of these causes. We estimate that 80% of the decline in women’s life expectancy advantage was attributable to changes in cardiovascular disease and 29% to changes in lung cancer rates between 1979 and 2022. But we show the rising importance of a different source of women’s life expectancy advantage, men’s greater excess mortality from DOD. The increase in women’s life expectancy advantage from DOD has been large enough to completely wipe out men’s mortality improvement from their relatively faster decline in lung cancer mortality. Past studies have examined the contributions of specific causes of death separately to gender differences in life expectancy (e.g., Ho, 2020; Pinkhasov et al., 2010; Woolf & Schoomaker, 2019), have identified major causes cross-nationally (Feraldi & Zarulli, 2022; Feraldi et al., 2023), or have examined much shorter time spans (Acciai & Firebaugh, 2017; Yan et al., 2024), but our findings put trends in gender differences in mortality into context in the U.S. and suggest the large role of DOD in accounting for recent trends.

Our findings show that drug-related deaths, alcohol-related deaths, and suicides have internal cohesiveness as a category with respect to their impact on variation in the gender gap in life expectancy. At least for this outcome, these results counter arguments that DOD lack internal consistency (Masters et al., 2017; Simon & Masters, 2021). Although it is true that drug mortality has a disproportionate weight among the three DOD subcategories, suicides and alcohol have contributed to the gender gap in the same direction as drugs, albeit in more modest magnitudes.

That women’s life expectancy advantage occurred for both Americans with and without BAs alike and for each of the three race/ethnic groups we examine, but for different reasons, deserve further study. In other areas of demography, the concept of a “multiphasic demographic response” has been proposed to get at the idea that the same structural pressures may produce common outcomes in different ways. Davis (1963) introduced this idea with respect to fertility to suggest that there are many paths societies can take to achieve the outcome of low fertility. It is interesting to speculate about whether the commonality in overall trends in the gender gap in life expectancy, but the variation in specific sources of changes across groups, is such a “multiphasic demographic response” pointing to increased gender divergence in the recent era. The current period is also one of increasing gender gaps in political orientation in the U.S. (Box-Steffensmeier et al., 2004; Hansen & Dolan, 2023; Scarborough et al., 2019) as well as women’s growing advantage in educational attainment (England et al., 2020).

In this article, we examine ten groups of causes of death, but it is well known that there are interactions between conditions and individuals often suffer from multiple co-morbidities (Trias-Llimós & Permanyer, 2023). For example, drug and alcohol abuse increase the risk of cardiovascular disease (Nishimura et al., 2020; Thylstrup et al., 2015; Whitman et al., 2017) and motor vehicle accidents (Callaghan et al., 2013), among other causes of death. Thus, the higher prevalence of DOD among men may also imply that the underlying causes of DOD may contribute to the widening of the gender gap in life expectancy through alternative mechanisms. Another aspect we have not explored is how age of death contributes to our results. For example, DOD may contribute more to the recent resurgence of women’s life expectancy advantage than other factors if these deaths occur earlier in people’s lives thus impacting overall life expectancy to a greater extent. In addition, while we focus on differences in average life expectancy between men and women, another important feature of mortality is variability within groups, that is, the extent to which there is variability in ages at death among men versus among women. Recent research has made important advances separating the impact of age effects versus differences in death rates on life expectancy and lifespan (Zazueta-Borboa et al., 2023) variability (Permanyer & Vigezzi, 2024; Trias-Llimós et al., 2023) and these methods could be applied to the contribution of DOD to gender differences in life expectancy as well.

Our article shows the changing nature of the gender gap in life expectancy and highlights how it is socially patterned. Evidence from a variety of areas of life demonstrates how identifying and describing gender differences in outcomes is an important way of identifying potential root causes and policy solutions (DiPrete & Buchmann, 2013; Hsin, 2018; Östlin et al., 2006; Rieker & Bird, 2005). A key finding is that DOD have emerged as a crucial element in understanding of men’s growing life expectancy disadvantage compared with women since 2012. This phenomenon is relatively new and deserves further attention. Overall, the major contributors to the gender gap in life expectancy in the United States have shifted away from cardiovascular disease, lung cancer, and infant mortality towards other forms of cancer and social anomic deaths.

Our findings highlight the need for greater efforts to reduce men’s premature mortality due to DOD. While medical advances to prevent and treat drug abuse and overdose are essential, policy responses are also needed. Expanding access to mental health and addiction treatment, along with addressing economic insecurity and poverty, which are strongly linked to DOD in the United States (Knapp et al., 2019; Monnat, 2018; Peters et al., 2020), are key strategies. These measures would help reduce these highly socially patterned deaths and, because they disproportionately affect men, contribute to narrowing the gender gap in life expectancy.

Supplementary Material

NIHMS2115480-supplement-1.pdf^{(758.1KB, pdf)}

Footnotes

For example, when describing mortality among non-Hispanic White Americans aged 45 to 55, Case and Deaton (2021a, p. 58) state that “the epidemic is affecting men and women in nearly equal numbers. This is true for each component—suicide, drug overdose, and alcoholic liver disease.” While they emphasize the qualitative similarity of the trends, they also show elsewhere (Figure 4.2) that DOD mortality rates remain substantially higher for men than for women. Our analysis complements theirs by quantifying the magnitude of these gender differences over time and placing them in the broader context of past trends and other causes of death.

Another area of research investigates gender differences in lifespan variation, which have been found to exhibit distinct patterns compared to differences in life expectancy (see Zazueta-Borboa et al., 2023).

The article by Yan (et al. 2024) is closest to our own. They primarily examine the contribution of “unintentional injuries,” within which DOD are captured, to the gender gap in life expectancy between 2010–2021. They separately examine key sub-components of unintentional injuries, finding that drug-related deaths were especially important. However, they do not examine DOD as a category and their time horizon is just eleven years.

⁴

Deaths of despair are from drugs (ICD-9: 292, 304, 305.2–305.9, 850–858, 980; ICD-10: F11-F16, F18–19, X40-X44, Y10-Y14); alcohol (ICD-9: 291, 303, 305.0, 571.0–571.3, 571.5, ICD-10: F10, K70, K74.6, G31.2, X45, Y15); or suicide (ICD-9: 950–959, ICD-10: X60-X84, Y870). Neoplasms (ICD-9: 140–239 excluding 162; ICD-10: C00 – D48 excluding C33-C34). Cardiovascular diseases (ICD-9: 390–459, ICD-10: I00-I99). Mortality from malignant neoplasms of the trachea, bronchus, and lung will be used as a proxy for smoking-attributable deaths (ICD-9: 162, ICD-10: C33-C34). Accidents (ICD-9: E800-E848, E880-E888, ICD-10: V01-V99, Y85-Y86, W00-W20) and homicides (ICD-9: E960-E969, ICD-10: X85-Y09, Y87.1). All deaths from all causes below age 1 were classified as infant mortality. Infectious diseases (ICD-9: 001–139, 460–487, ICD-10: A00-B99, G14, N70-N73, P37.3, P37.4, H65-H66, J00-J22, P23, U07.1).

⁵

The most common causes of death in the ‘other’ category in 2022 were, in order, Alzheimer disease, unspecified (11%, ICD-10 G309); Chronic obstructive pulmonary disease, unspecified (11%; ICD-10 J449); Unspecified dementia (9%, ICD-10 F03); Senile degeneration of brain, not elsewhere classified (4%, ICD-10 G311); Parkinson disease (4%, ICD-10 G20).

⁶

Life tables by Hispanic ethnicity are not available before 2007. In earlier years, many individuals of Hispanic background were likely classified as White, which may have slightly inflated life expectancy estimates for the White population. The separation of Hispanic origin in life table data beginning in 2006 may help explain the decline observed in the gender gap between 2006 and 2007 for the White population in Appendix S2. However, the long-term trends discussed in the paper remain consistent.

⁷

Our code and instructions for generating our results can be accessed on the Open Science Framework (OSF) upon publication of this article.

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NIHMS2115480-supplement-1.pdf^{(758.1KB, pdf)}

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[R47] Ogunniyi MO, Mahmoud Z, Commodore-Mensah Y, Fleg JL, Fatade YA, Quesada O, Aggarwal NR, Mattina DJ, Moraes De Oliveira GM, & Lindley KJ (2022). Eliminating disparities in cardiovascular disease for Black women: JACC review topic of the week. Journal of the American College of Cardiology, 80(18), 1762–1771. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] Östlin P, Eckermann E, Mishra US, Nkowane M, & Wallstam E (2006). Gender and health promotion: a multisectoral policy approach. Health promotion international, 21(suppl_1), 25–35. [DOI] [PubMed] [Google Scholar]

[R49] Permanyer I, & Vigezzi S (2024). Cause-of-Death Determinants of Lifespan Inequality. Demography, 61(2), 513–540. 10.1215/00703370-11245278 [DOI] [PubMed] [Google Scholar]

[R50] Peters DJ, Monnat SM, Hochstetler AL, & Berg MT (2020). The opioid hydra: Understanding overdose mortality epidemics and syndemics across the rural‐urban continuum. Rural sociology, 85(3), 589–622. [Google Scholar]

[R51] Pinkhasov R, Shteynshlyuger A, Hakimian P, Lindsay G, Samadi D, & Shabsigh R (2010). Are men shortchanged on health? Perspective on life expectancy, morbidity, and mortality in men and women in the United States. International journal of clinical practice, 64(4), 465–474. [DOI] [PubMed] [Google Scholar]

[R52] Preston SH, Glei DA, Wilmoth JR, & Crimmins E (2011). Contribution of Smoking to International Differences in Life Expectancy. International Differences in Mortality at Older Ages: Dimensions and Sources, 105–131. [Google Scholar]

[R53] Preston SH, Heuveline P, & Guillot M (2000). Demography: Measuring and Modeling Population Processes. Blackwell Publishers. [Google Scholar]

[R54] Preston SH, & Wang H (2006). Sex mortality differences in the United States: The role of cohort smoking patterns. Demography, 43(4), 631–646. [DOI] [PubMed] [Google Scholar]

[R55] Read JS, Troendle JF, & Klebanoff MA (1997). Infectious disease mortality among infants in the United States, 1983 through 1987. American Journal of Public Health, 87(2), 192–198. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R56] Rieker PP, & Bird CE (2005). Rethinking gender differences in health: why we need to integrate social and biological perspectives. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 60(Special_Issue_2), S40–S47. [Google Scholar]

[R57] Ruhm CJ (2018). Deaths of despair or drug problems? (24188). National Bureau of Economic Research. https://www.nber.org/system/files/working_papers/w24188/w24188.pdf [Google Scholar]

[R58] Ruhm CJ (2024). “Despair” and Death in the United States (32978). National Bureau of Economic Research. https://www.nber.org/papers/w32978 [Google Scholar]

[R59] Sagna AO, Kemp M, DiNitto D, & Choi N (2020). Impact of suicide mortality on life expectancy in the United States, 2011 and 2015: age and sex decomposition. Public health, 179, 76–83. [DOI] [PubMed] [Google Scholar]

[R60] Scarborough WJ, Sin R, & Risman B (2019). Attitudes and the stalled gender revolution: Egalitarianism, traditionalism, and ambivalence from 1977 through 2016. Gender & Society, 33(2), 173–200. [Google Scholar]

[R61] Shanahan L, & Copeland WE (2021). Psychiatry and deaths of despair. JAMA psychiatry, 78(7), 695–696. [DOI] [PMC free article] [PubMed] [Google Scholar]

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The Gender Gap in Life Expectancy in the United States and Deaths of Despair: Trends from 1979 to 2022

Rodrigo González-Velastín

Christine R Schwartz

Abstract

INTRODUCTION

Figure 1. Trends in the Gender Gap in Life Expectancy and in Life Expectancy by Gender.

DATA & METHODS

RESULTS

Figure 2. Gender Gap in Life Expectancy by BA Status and Race/Ethnicity.

Figure 3.

Table 1.

Table 2.

Figure 4.

Figure 5. Decomposition of Gender Difference in Life Expectancy at Birth by Race/Ethnicity.

CONCLUSION

Supplementary Material

Footnotes

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Cite

Add to Collections

PERMALINK

The Gender Gap in Life Expectancy in the United States and Deaths of Despair: Trends from 1979 to 2022

Rodrigo González-Velastín

Christine R Schwartz

Abstract

INTRODUCTION

Figure 1. Trends in the Gender Gap in Life Expectancy and in Life Expectancy by Gender.

DATA & METHODS

RESULTS

Figure 2. Gender Gap in Life Expectancy by BA Status and Race/Ethnicity.

Figure 3.

Table 1.

Table 2.

Figure 4.

Figure 5. Decomposition of Gender Difference in Life Expectancy at Birth by Race/Ethnicity.

CONCLUSION

Supplementary Material

Footnotes

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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