Trends in Non‐Hispanic White Mortality in the United States by Metropolitan‐Nonmetropolitan Status and Region, 1990–2016

The first decades of the twenty-first century have been a challenging period for American mortality. Life expectancy in the United States ranked 30th in the world in 2010 and is much lower than in other high-income countries (World Health Organization 2017). Between 2010 and 2016, US life expectancy fell further behind other developed countries, increasing by only 0.08 years, the smallest 5-year increase since 1970 (Ho and Hendi 2018). These relatively slow mortality declines occurred against a background in which US mortality in the 1990s and 2000s was already high by the standards of other OECD countries (Ho and Preston 2010; Crimmins et al. 2011; Ho 2013; Institute of Medicine and National Research Council 2013; Palloni and Yonker 2016). At the same time, there have been large and growing geographic and socioeconomic inequalities in health and mortality within the United States (Fenelon 2013; Wang et al. 2013; Hendi 2015, 2017; Chetty et al. 2016; Montez, Sasson, and Hayward 2016a). Several recent studies of the national-level mortality stagnation have documented adverse mortality trends among middle-aged non-Hispanic whites (Kochanek, Arias, and Bastian 2016a; Squires and Blumenthal 2016; Case and Deaton 2017), particularly among women (Astone, Martin, and Aron 2015; Gelman and Auerbach 2016; Kochanek et al. 2016b) and those with lower levels of education (Hendi 2017) and income (Chetty et al. 2016). Case and Deaton (2015, 2017) drew attention to the role that “deaths of despair”—consisting of accidental poisoning (linked to the epidemic of prescription opioids and heroin), suicide, and chronic liver disease—play in mortality increases among non-Hispanic whites. Elevated mortality from these causes of death is especially concentrated among individuals with low levels of education (see also Ho 2017; Kochanek et al. 2016b). However, these “deaths of despair” cannot fully explain the slowdown in mortality declines, since the adverse trends persist even after eliminating mortality from these causes (Squires and Blumenthal 2016; Monnat 2018; Rigg, Monnat, and Chavez 2018). Other causes of death are also hypothesized to be important contributors to stagnating mortality declines. Case and Deaton (2017) show that between 1999 and 2015, declines in cardiovascular disease and cancer mortality at ages 50–54 were relatively slow among non-Hispanic whites in the United States compared to other OECD countries. One study estimates that rising obesity has reduced the annual rate of decline in US death rates at ages 40–84 by 0.5–0.6 percentage points between 1986 and 2011 (Preston, Vierboom, and Stokes 2018). A parallel literature has highlighted widening geographic inequalities in mortality in the period leading up to the recent mortality stagnation. Mortality improvements in Appalachia and the South, particularly the East South Central Division, have lagged behind other regions (Fenelon 2013; Wang et al. 2013), a pattern that has been partly linked to behavioral risk factors such as smoking and obesity (Fenelon 2013; Singh and Siahpush 2014; Dwyer-Lindgren et al. 2016; Dwyer-Lindgren et al. 2017; Mokdad et al. 2017; Roth et al. 2017). Trends in all-cause and cause-specific mortality rates have also varied considerably among US states (Chetty et al. 2016; Montez, Sasson, and Hayward 2016a) and counties (Ezzati et al. 2008; Dwyer-Lindgren et al. 2016; Roth et al. 2017). These studies typically find that although some counties, states, and regions have experienced improvements in life expectancy, others have experienced more moderate gains or even declines over the past few decades (Murray et al. 2006; Cullen, Cummins, and Fuchs 2012; Wang et al. 2013; Chetty et al. 2016). Other studies have documented widening mortality differences between metropolitan and nonmetropolitan areas since the 1980s (Cosby et al. 2008; Cossman et al. 2010; James 2014; James and Cossman 2017; Moy et al. 2017). Singh and Siahpush (2014) examined data through 2009 and found that rural, nonmetropolitan areas made slower progress in life expectancy in the preceding decades than urban, metropolitan areas. More recently, Stein et al. (2017) focused on mortality trends at ages 25–64 between 1999 and 2015 by age, race/ethnicity, cause of death, and level of urbanization. They documented increasing death rates for non-Hispanic whites, mainly outside large urban areas, with suicide, poisoning, and liver disease contributing to these adverse trends. Death rates were the highest in rural areas for all racial/ethnic groups. Similarly, James and Cossman (2017) documented growing urban-rural disparities in age-adjusted mortality rates for both whites and Blacks since the mid-1980s. Percent poor, region, and emergency room visits and medical doctors per 1,000 were significant predictors of age-adjusted death rates among whites in most metropolitan-nonmetropolitan subcategories in 2012, but not among Blacks, suggesting that factors that predict the metropolitan-nonmetropolitan mortality disparities vary by race/ethnicity. In this article, we build on this prior research by providing a comprehensive examination of trends in non-Hispanic white mortality between 1990 and 2016 by metropolitan-nonmetropolitan status and region. Prior studies have often focused on finer levels of geographic variation, for example, counties or states, whereas our study examines 40 different geographic areas consisting of 10 broad geographic regions cross-classified by four metropolitan-nonmetropolitan categories. This cross-classification allows us to incorporate the substantial amount of variation within states (e.g., across metro-nonmetro categories) while identifying shared factors at the regional level that may be driving mortality trends. We focus on non-Hispanic whites because their mortality trends were particularly adverse in the last decade and differ from those of non-Hispanic Blacks and Hispanics. In contrast to non-Hispanic whites, mortality has continued to decline among Hispanics and non-Hispanic Blacks, although death rates remain substantially higher for Blacks than for whites (Harper, MacLehose, and Kaufman 2014; Murphy et al. 2017; Stein et al. 2017). The underlying mechanisms driving mortality trends for non-Hispanic whites are fundamentally different from those driving trends for other racial/ethnic groups, as suggested by James and Cossman (2017). Thus, we limit the scope of this article to non-Hispanic whites. This article is an effort to unite the literature on adverse mortality trends among non-Hispanic whites at the national level with the literature on growing geographic inequalities in mortality. Our goals are to carefully document life expectancy trends in each of the 40 geographic areas described above, building upon previous research in several ways. First, we extend analyses to 2016 and include all age groups. Second, we estimate the contributions of four key age groups to changes in life expectancy at birth between 1990 and 2016 by metropolitan-nonmetropolitan status and region. Third, we examine the contribution of 14 broad, cause of death categories to these changes, highlighting categories that are strongly linked to behavioral factors and access to health care. In the Discussion section, we consider several potential explanations of the patterns we describe. We use the 1990–2016 Multiple Cause of Death data files (provided by the National Center for Health Statistics under a data user agreement) to tabulate deaths by age, sex, race/ethnicity, cause of death, county, and year. To estimate person-years of exposure, we use the public-use Census bridged-race population estimates by age, sex, race/ethnicity, county, and year. These data are combined to estimate age-specific death rates for all causes combined and for 14 specific, cause of death categories described below. Death rates are estimated for non-Hispanic white men and women by age, year, metropolitan-nonmetropolitan category, and geographic region. For parsimony, we use “white men” and “white women” to refer to non-Hispanic white men and non-Hispanic white women, respectively, from this point forward. We focus on 14 mutually exclusive and exhaustive cause of death categories (described in Table A-1). Several of these categories are closely linked to behavioral factors: alcohol-attributable deaths and deaths from drug overdose, HIV/AIDS, homicide, suicide, lung cancer, respiratory diseases, and diabetes. Two categories, screenable cancers and influenza/pneumonia, were chosen as indicators of access to and quality of health services. Cardiovascular disease, which is the leading cause of death and influenced by both health behaviors and health care system variables, constitutes another category. We also consider the role of a composite category, “deaths of despair” (the aggregation of alcohol-attributable, drug overdose,1 and suicide mortality), which has been hypothesized to play a key role in adverse mortality trends among whites in recent years (Case and Deaton 2015, 2017; Ho 2017). In addition, we separate out mental and nervous system disorders, a category that includes Alzheimer's disease and is of emerging importance (Xu et al. 2016; Ho and Hendi 2018). The remaining categories are the ill-defined causes category, which is used for nonspecific causes of death and accounts for a relatively small proportion of overall deaths, and the residual category. To classify counties by metro-nonmetro status, we use the codes developed by the United States Department of Agriculture (USDA) Economic Research Service (ERS), which were modified and made available by the National Center for Health Statistics2 (https://www.cdc.gov/nchs/data_access/urban_rural.htm). We use four categories: large central metros, their suburbs (“large metro suburbs”), small/medium metros, and nonmetro areas (definitions of these categories are provided in Table 1). Results from preliminary investigations separating nonmetro counties by whether they were adjacent to metro areas were very similar for the two groups, so they were combined in the final analyses. To maintain consistency over time, we use the counties’ metropolitan category as of 2013 (preliminary analyses showed only minor differences if we used earlier classification schemes). Our 10 broad geographic regions are based on nine Census divisions and Appalachia, as defined by the Appalachian Regional Commission. Appalachia includes all of West Virginia and counties from 12 other states. The Appalachian counties are excluded from their overlapping Census divisions (definitions of these regions are provided in Table 1). When counties are cross-classified by region and metropolitan-nonmetropolitan category, we identify 40 distinct geographic units. Our main measure of mortality is life expectancy at birth. We start by examining trends in life expectancy at birth and age-group contributions to changes in life expectancy across metro–nonmetro categories at the national level. Next, we investigate whether the metro-nonmetro mortality patterns observed at the national level also hold within regions. We focus on three periods (1990–1992, 2009–2011, and 2014–2016) and the change between 1990–1992 and 2014–2016 and between 2009–2011 and 2014–2016. Data are pooled across three-year periods to create more stable estimates. In estimating the life tables, nax values are produced using graduation (Preston, Heuveline, and Guillot 2001). Mortality estimates for ages 85 and older are corrected using a variant of the procedure outlined in Horiuchi and Coale (1982) to account for differences across geographic areas in the age distributions of people aged 85 and older. The variant involves the use of a parametric smoothing procedure (as opposed to direct computation) to estimate growth rates and mortality above age 85. Next, we estimate broad age group contributions (0–24, 25–44, 45–64, and 65+) to changes in life expectancy at birth using Arriaga's (1984) decomposition. We focus on these four age groups because they are socially and economically meaningful: people aged 0–24 capture the young and school-aged population; people aged 25–44 have typically left school and entered the workforce, and are not yet at the ages where chronic diseases dominate; people aged 45–64 are often labeled middle aged and, while still in the workforce, are more subject to chronic diseases; and people aged 65 and older are usually out of the workforce. In addition, the 45–64 age group overlaps with the age groups that were the focus of several prior studies of midlife mortality. Third, we investigate the contribution of the specific cause of death categories described above to trends in life expectancy using Arriaga's (1984) decomposition. We estimate these contributions for the four metro-nonmetro categories at the national level, and then for each of the 40 geographic areas (metro-nonmetro categories cross-classified with region). All analyses are performed separately by sex, and all decompositions sum to 100 percent of the change in life expectancy over time for a specific geographic area. In the Discussion section, we introduce five variables that are potentially linked to the trends we observe and examine the correlation between trends in these variables and trends in life expectancy between 1990–1992 and 2014–2016 by the 40 region/metro-nonmetro categories. The five variables measure are as follows: educational attainment (percentage of college graduates from the 1990 US Census Summary files and 5-year 2011–2015 American Community Survey), physician availability (active, nonfederal physicians per 1,000 population from the Area Health Resource Files [US Health Resources & Services Administration 2018]), obesity (percentage of population aged 20 and older with body mass index greater than 30 kg/m2 from the University of Wisconsin [2018]), transfer dependency (transfers as a percentage share of personal income from the Bureau of Economic Analysis 2018), and net in-migration rate for the working-age population (cumulative net in-migration rate for people aged 22.5–62.5, estimated using data from the NCHS and the Census Bureau and indirect methods detailed in the Appendix).3 Table 2 presents changes in life expectancy between 1990 and 2016 by metro-nonmetro status. Life expectancy levels themselves are presented in Table A-2 in the Appendix. It is clear from Table 2 that the United States has experienced growing geographic inequality in life expectancy gains for white men and women over this period. This divergence has been driven by more rapid increases in life expectancy in large central metros and slower improvements elsewhere. White male life expectancy increased 5.09 years in large central metros, compared to 3.45 years in large metro suburbs, 2.81 years in small/medium metros, and 2.25 years in nonmetro areas. Large central metros are now among the areas with the highest white male life expectancy in the country. This ascendance is particularly noteworthy because large central metros had the lowest life expectancy levels in 1990–1992. The gain in large central metros was 2.3 times greater (5.09/2.25) than that in nonmetro areas, which had the second lowest life expectancy levels in 1990–1992. Among white women, life expectancy differences by metro-nonmetro status were relatively small in 1990–1992. However, by 2014–2016, substantial variation emerged, with the largest gains recorded in large central metros (2.98 years), followed by large metro suburbs (2.23 years), small/medium metros (1.24 years), and nonmetro areas (0.20 years) (Table 2). These differences are particularly striking for large central metros compared to nonmetros, with gains in white female life expectancy that were 14.9 times greater (2.98/0.20) in large central metros than in nonmetro areas. The most recent period stands out for its stark metro-nonmetro differences (Table 2, bottom panel). Between 2009–2011 and 2014–2016, large central metros and large metro suburbs continued to experience gains in life expectancy, but small/medium metros and nonmetros experienced life expectancy declines. Thus, while the metro-nonmetro gradient widened between 1990–1992 and 2009–2011 due to quicker gains among large central metros, the gradient has since widened due to a combination of gains in large metros and their suburbs and declines in small/medium metros and nonmetro areas. The pattern of life expectancy gains was similar for men and women, but women experienced smaller gains than men, leading to a narrowing of sex differences in life expectancy. By 2014–2016, the sex difference ranged from 4.65 years in large metro suburbs to 4.96 in nonmetro areas, down from 5.87 years in large metro suburbs and 7.01 years in nonmetro areas in 1990–1992 (Table A-2). There are important variations in the contribution of different age groups to life expectancy trends across the metro-nonmetro categories. In large central metros, all age groups contributed to gains in life expectancy between 1990 and 2016 (Table 2, top panel). In the other three areas, however, mortality increased at ages 25–44 for both white men and women and additionally at ages 45–64 for white women in nonmetro areas. Between 2009–2011 and 2014–2016, mortality increased in the 25–44 age group for white men and women in all four categories, including large central metros. Furthermore, in all areas except large central metros, mortality increased at ages 45–64 (Table 2, bottom panel). The national trends in life expectancy by metro-nonmetro status are also observed within most regions of the country, with important regional variation. Figure 1 and Table 3 present trends in life expectancy between 1990–1992 and 2014–2016 for the 40 areas, representing the four metro-nonmetro categories and the 10 geographic regions. Life expectancy levels by metro-nometro areas and region are shown in Table A-3 in the Appendix. The spatial pattern that was evident for the nation as a whole in Table 2—greatest life expectancy gains in large central metros and smallest life expectancy gains in nonmetros—is, with minor exceptions, maintained within each of the 10 regions. At the same time, regional variations are also evident: the Middle Atlantic and Pacific regions stand out as having particularly rapid life expectancy gains for white men in large central metros, on the order of 7.13 and 6.11 years, respectively. White men in nonmetro areas of the Appalachian, East South Central, and West South Central regions experienced the smallest gains, amounting to 1.42–1.80 years. Change in non-Hispanic white life expectancy at birth (in years) by metropolitan-nonmetropolitan category and region, 1990–1992 and 2014–2016 NOTE: APP = Appalachia, ENC = East North Central, ESC = East South Central, MA = Middle Atlantic, MTN = Mountain, NE = New England, PAC = Pacific, SA = South Atlantic, WNC = West North Central, WSC = West South Central. A similar pattern is observed for white women, with the largest gains occurring in large central metros in the Middle Atlantic (4.66 years) and Pacific (4.00 years) regions. Nonmetro areas experienced the smallest gains in female life expectancy, especially in the East North Central, West North Central, and South Atlantic regions, with declines in life expectancy observed in nonmetro areas of the Appalachian, East South Central, and West South Central regions. In all 40 areas, white life expectancy gains white life expectancy we the combination to the men in large central metros in the Middle Atlantic 7.13 years of life expectancy this period whereas women in nonmetros in the West South Central and East South Central regions a in life expectancy between 1990–1992 and 2014–2016. the of large central metros and the relatively of nonmetros within each region, striking patterns across both region and metro category. For example, large central metros nonmetros within the Appalachian and East South Central regions. However, life expectancy gains in large central metros in these two regions were smaller than gains in nonmetros of the Middle The life expectancy gains in nonmetros of the Middle Atlantic those in large central metros of the Appalachian and East South Central regions by years for men and years for contributions to gains in life expectancy between 1990–1992 and 2014–2016 by metro-nonmetro status and region are shown in and and Table the patterns observed for the nation as a whole are in of the 40 areas. Mortality increases among white men and women aged 25–44 were in large metro suburbs, small/medium metros, and nonmetro areas, with the most increases typically occurring in Appalachia and New Mortality improvements at ages 65 and above made the largest contributions to gains in life expectancy in all 40 geographic areas. contributions (in years) to changes in non-Hispanic white male life expectancy at birth by metropolitan-nonmetropolitan category and region, 1990–1992 to 2014–2016 NOTE: APP = Appalachia, ENC = East North Central, ESC = East South Central, MA = Middle Atlantic, MTN = Mountain, NE = New England, PAC = Pacific, SA = South Atlantic, WNC = West North Central, WSC = West South Central. contributions (in years) to changes in non-Hispanic white female life expectancy at birth by metropolitan-nonmetropolitan category and region, 1990–1992 to 2014–2016 NOTE: APP = Appalachia, ENC = East North Central, ESC = East South Central, MA = Middle Atlantic, MTN = Mountain, NE = New England, PAC = Pacific, SA = South Atlantic, WNC = West North Central, WSC = West South Central. When we consider life expectancy trends between 2009–2011 and 2014–2016 (Table and and the adverse mortality trends in the 25–44 age group are even more In all four metro categories and across region, mortality at ages 25–44 contributed to life expectancy trends in this most recent period. These contributions were particularly large in the New and Appalachian regions for both men and 45–64 also contributed to life expectancy trends in areas, and more so among women than among We observed a metro-nonmetro gradient with to these age mortality at ages 25–44 relatively more in large central metros and their suburbs, while mortality at ages 45–64 contributed relatively more to trends in small/medium metros and contributions (in years) to changes in non-Hispanic white male life expectancy at birth by metropolitan-nonmetropolitan category and region, 2009–2011 to 2014–2016 NOTE: APP = Appalachia, ENC = East North Central, ESC = East South Central, MA = Middle Atlantic, MTN = Mountain, NE = New England, PAC = Pacific, SA = South Atlantic, WNC = West North Central, WSC = West South Central. contributions (in years) to changes in non-Hispanic white female life expectancy at birth by metropolitan-nonmetropolitan category and region, 2009–2011 to 2014–2016 NOTE: APP = Appalachia, ENC = East North Central, ESC = East South Central, MA = Middle Atlantic, MTN = Mountain, NE = New England, PAC = Pacific, SA = South Atlantic, WNC = West North Central, WSC = West South Central. These adverse mortality trends among whites to declines in life expectancy at birth between 2009–2011 and 2014–2016 for both in all nonmetro areas except in the Middle Atlantic and regions. Among men, life expectancy also in small/medium metros in out of 10 regions, in large metro suburbs in four out of 10 regions, and in large central metros in two out of 10 regions. experienced life expectancy declines in small/medium metros in regions, in large metro suburbs in two regions, and in large central metros in two regions. The by whites this most recent period were by sex, region, and metropolitan category, but the most were in nonmetropolitan areas. Table and Figure present cause of death contributions to the change in life expectancy by metro-nonmetro status between 1990 and of death with values in Table to life expectancy while those with values to life expectancy In all four areas, in cardiovascular disease mortality made the largest contributions to improvements in life expectancy. Mortality from causes of death to health such as screenable and and HIV/AIDS, also contributed to life expectancy gains in all four metro-nonmetro categories, but these contributions were the smallest in nonmetro areas. in mortality from to the of were more important for men than for women, and their was particularly large for men in large central metros, where made the second largest contribution to life expectancy gains, after cardiovascular disease et al. et al. Among causes of death closely to health lung cancer mortality made important contributions to life expectancy increases among men, but much smaller contributions among For women in nonmetro areas, lung cancer mortality increased over Mortality from respiratory diseases, which are also to increased among women, with the largest increases recorded in nonmetro areas. Mortality from drug overdose, suicide, and causes of death, which “deaths of despair” category, increased and contributed to life expectancy across the metro-nonmetro categories. was the most important among these and made a contribution to life expectancy declines among men than Among men, drug made the largest in large metro suburbs, followed by small/medium metros, nonmetros, and large central metros. Among women, the were greater in large metro suburbs, small/medium metros, and nonmetros than in large central metros. causes were a minor in all metro categories. One of the most important contributors to life expectancy were mental and nervous system disorders, including Alzheimer's This category was particularly important for women, large contributions to life expectancy trends in all metro-nonmetro areas. The pattern of cause-specific contributions across the metro-nonmetro at the national level is across regions and with a few key In all 40 areas, in cardiovascular disease mortality made the largest contribution to increases in life expectancy at birth. This was observed for both men and in lung cancer mortality were evident in all regions and all metro-nonmetro categories among men, but were small or among women, especially in nonmetro areas. One is the Pacific region, where women experience in lung cancer mortality in all metro-nonmetro categories. was the increases in respiratory disease mortality contributed to life expectancy among women in all regions, with the largest observed in nonmetro areas. in respiratory disease mortality were particularly large among women in nonmetro areas of the Appalachian, East South Central, West South Central, West North Central, and South Atlantic regions, where they contributed to a in life expectancy at birth. contributions to changes in life expectancy at birth (in years) by metropolitan-nonmetropolitan category and region, non-Hispanic white men, 1990–1992 to 2014–2016 NOTE: 1 = Large central 2 = Large metro 3 = = contributions to changes in life expectancy at birth (in years) by metropolitan-nonmetropolitan category and region, non-Hispanic white women, 1990–1992 to 2014–2016 NOTE: 1 = Large central 2 = Large metro 3 = = both region and metro-nonmetro categories, the contributions of causes of death to medical care (e.g., screenable HIV/AIDS, and were similar to those documented for the as a In all regions, these causes contributed to an increase in life expectancy. The contribution of was particularly large in large central metros in the Middle Atlantic years), South Atlantic years), and Pacific years) regions among men, whereas among women, made a contribution only in large central metros of the Middle Atlantic cancers and and made contributions to life expectancy improvements among women than among Furthermore, the cause of death categories contributing to adverse life expectancy trends in Table and Figure 1 are also in adverse life expectancy trends in all regions across metro-nonmetro categories. There is some especially among men, that suicide to contributions to life expectancy in nonmetros and small/medium metros than in large central metros and their is a key to life expectancy for men in all 40 areas, the largest contributions in Appalachia and New