Generational Shifts in Young Adult Cardiovascular Health? Millennials and Generation X in the United States and England

Abstract

Objectives

To compare cardiovascular (CV) risks/conditions of Millennials (born 1981–1996) to those of Generation X (Gen X; born 1965–1980) at ages 20–34 years, across 2 countries (United States, England), by gender.

Methods

Using data from the National Health and Nutrition Examination Survey (United States) and Health Survey for England, we estimated weighted unadjusted and adjusted gender-specific proportions of CV risk factors/conditions, separately for Millennials and Generation X in each country. We also further calculated sex-specific generational differences in CV risk factor/conditions by income tercile and for individuals with normal body weight.

Results

Millennials in the United States were more obese compared to their Gen X counterparts and more likely to have diabetes risk but less likely to smoke or have high cholesterol. Millennials in England had higher diabetes risk but similar or lower rates of other CV risk/conditions compared to their Gen X counterparts. Generational changes could not be fully attributed to increases in obesity or decreases in income.

Discussion

We expected that Millennial CV risk factors/conditions would be worse than those of Gen X, particularly in the United States, because Millennials came of age during the Great Recession and a period of increasing population obesity. Millennials generally fared worse than their Gen X counterparts in terms of obesity and diabetes risk, especially in the United States, but had lower rates of smoking and high cholesterol in both countries. Secular trends of increasing obesity and decreased economic opportunities did not appear to lead to uniform generational differences in CV risk factors.

Cardiovascular (CV) disease, the leading cause of death in the United States (Heron, 2019), often starts in young adulthood (Blane et al., 1996; Gray et al., 2011; Pletcher et al., 2008) and early onset is strongly associated with later-life morbidity and mortality (for a review, see Bucholz et al., 2018). In particular, CV risk factors in early adulthood, including obesity, high cholesterol, and high blood pressure, are associated with later-life CV disease and mortality, independent of later-life risk factors (Hirko et al., 2015; Yano et al., 2018; Zhang et al., 2019).

Millennials in the United States (commonly defined as people born between 1981 and 1996), who represent the largest living adult generation (Fry, 2020), confronted a unique set of economic challenges as they came of age in the aftermath of the Great Recession (Horowitz et al., 2020) and during a period of rising income inequality (Horowitz et al., 2020) and educational debt (Bialik & Fry, 2019; Cramer et al., 2019). Millennials, particularly those with relatively low wages (Fry, 2017), have been more likely to live with their parents for extended periods of time compared to young adults in previous generations.

Young adults in England have experienced many of the same economic challenges as faced by those in the United States (Duffy et al., 2017). In the years after the Great Recession, the English have faced a period of significant fiscal austerity, with well-documented decreases in economic quality of life for Millennials as compared to previous generations alongside general decreases in income and increases in housing costs (Gustafsson, 2019). In addition, Millennials were the first generation in England to experience debt associated with higher education on a scale comparable to that of Millennials in the United States (Murphy et al., 2017), and educational debt disproportionately burdens young adults from lower-income families. The consistent, strong, and well-known linkages between socioeconomic status and health portend worse health outcomes for this generation of young adults compared to previous generations in both countries, particularly among individuals with lower incomes.

Lower socioeconomic status has been associated with increased CV risk factors, even for young adults, in both the United States and England (Martinson, 2012). Millennials in both countries tend to have fewer economic opportunities than previous generations, which may lead to generational differences in CV risk factors and conditions. The unique set of economic challenges faced by Millennials after the Great Recession may have contributed to such differences, as evidence suggests that macroeconomic fluctuations (and the Great Recession in particular) have led to increases in obesity, fasting glucose, and hypertension (Granados, 2008; Patel et al., 2019; Seeman et al., 2018). Financial hardship, unemployment, and potential downstream effects of those situations (e.g., adverse health behaviors and physiological responses) could be pathways between macroeconomic shifts and CV disease (Patel et al., 2019).

At the same time, there has been a stagnation of improvements in CV and metabolic population health in the United States, which has contributed to decreases in life expectancy (Mehta et al., 2020; National Academy of Sciences, 2021; Preston et al., 2018) and can in large part be explained by increasing rates of obesity (National Academy of Sciences, 2021; Preston et al., 2018). These trends have been particularly pronounced for working-age adults (National Academy of Sciences, 2021). Previous studies have shown that CV health is generally worse in the United States than in England (Banks et al., 2006; Choi et al., 2020; Martinson et al., 2011), a country often compared to the United States on health indicators, even in early adulthood (Martinson, 2012; Martinson et al., 2011). In fact, certain indicators of CV disease in England have been improving; for example, rates of ischemic heart disease and stroke have been declining in the United Kingdom (Scholes & Mindell, 2018). However, population overweight and obesity in England have been increasing; for example, adult overweight in England has been increasing in parallel with the United States in recent decades but with lower rates overall (Whitacre & Burns, 2010).

These trends suggest that Millennials would be uniquely at risk of developing CV risk factors and conditions in young adulthood, particularly those with lower incomes, those who are obese, and perhaps those who are residents of the United States. Recent reports on insured individuals in the United States (Blue Cross Blue Shield, 2019) and the overall population in the United Kingdom (Duffy et al., 2017) suggest that general well-being and some health behaviors of the Millennial generation compare unfavorably to those of Generation X (Gen X, commonly defined as those born between 1965 and 1980). In addition, DePew and Gonzales (2020) found that Millennials in the United States have worse mental and self-rated physical health than their Gen X counterparts. These patterns of decreasing well-being are echoed in a recent study examining declines in physiological status and mental health across U.S. birth cohorts, starting with the Boomer generation and increasing with Gen X (Zheng & Echave, 2021); that study suggests that obesity is a risk factor for increasing metabolic syndrome in more recent cohorts. However, how Millennials compare to previous generations on indicators of CV health has not been explored, perhaps because the youngest Millennials are still only 24 years old.

In this study, we address this knowledge gap by comparing well-measured CV risk factors and conditions of Millennials in young adulthood (ages 20–34 years) to those of the immediately preceding generation, Gen X, at the same ages—overall and by income and body mass index (BMI) status—using nationally representative data from both the United States and England. Beyond providing important nationally representative information for each of the two countries, the comparison across countries provides important first-order facts about whether there appear to be generalized differences in Millennial versus Gen X CV risk factors and conditions in young adulthood or whether findings for the United States appear to be distinctly American phenomena. The international comparison also will highlight the extent to which any changes in CV risk factors and conditions between the two generations differ across contexts with relatively poor CV health (the United States) and generally better CV health (England) in their populations—that is, if the cohort differences in CV conditions and risk factors between countries are associated with the more general country-level state of CV health.

Method

Data

We use health data from the National Health and Nutrition Examination Survey (NHANES) for the United States and the Health Survey for England (HSE). Both data sets are large, nationally representative health surveys that include health data from detailed questionnaires, body measurements, and blood samples (NatCen Social Research & University College London, 2019; National Center for Health Statistics, 2018). These data sets are particularly useful for addressing the aims of this paper because of comparability over time when pooling waves of data, but also because they are quite comparable with each other as has been demonstrated in previous research (Banks et al., 2006; Martinson, 2012; Martinson et al., 2011).

The NHANES is a comprehensive health survey conducted continuously by the National Center for Health Statistics since 1999 and released biennially (National Center for Health Statistics, 2018). We used publicly available data from 1999 through 2006 for Gen X and from 2013 through 2018 for Millennials. Of the 97,807 participants from the relevant waves of the NHANES, we had a total sample of 7,842 young adults aged 20–34 years old that were Gen X (4,309) and Millennial (3,533). Sample sizes varied by health measure as some questions were only asked of select respondents.

The HSE is conducted annually by the Joint Health Surveys Unit of the National Centre for Social Research and the University College London (NatCen Social Research & University College London, 2019). We used publicly available data from 2000 through 2006 for Gen X (excluding Wave 2004 that focused on racial and ethnic health) and from 2013 through 2017 for Millennials. The total number of respondents from these survey waves was 154,964, and our sample was 14,672 after selecting respondents aged 20–34 in the Gen X (9,653) and Millennial (5,019) generations. Some of the biomarker data were only collected for a subsample of respondents, so sample sizes varied by health measure.

Measures

We consider established CV body measurements and health behaviors that constitute health risks defined by ideal CV health (ICH) as documented in the Goals and Metrics Committee of the Strategic Planning Task Force of the American Heart Association (AHA; Lloyd-Jones et al., 2010) and that are comparable across the NHANES and HSE. ICH was originally constructed using measures included in the continuous NHANES (National Center for Health Statistics, 2018). The AHA’s ICH measure is based on seven CV health metrics, namely healthy diet, exercise, smoking status, BMI, cholesterol, blood pressure, and blood glucose. In our study, due to limitations in harmonizing cross-national data sets, we are able to examine five of those risk factors or related conditions: smoking status, BMI, cholesterol, blood pressure, and glycated hemoglobin (HbA1c). As BMI is highly correlated with the two risk factors we could not assess comparably across generations (exercise and diet), we are confident that we are assessing key health risk factors that are important for long-term CV health.

We use two BMI variables in both data sets: continuously and to categorize BMI into normal weight, overweight, obese, and underweight in accordance with the Centers for Disease Control and Prevention (CDC) guidelines (CDC, 2020). We also look at the extent of the change in CV risk factors and conditions between generations among normal-weight individuals in some analyses. We categorize cigarette use as a binary variable where respondents either are a current smoker or never smoked or had quit smoking. Following the guidelines for ICH, total cholesterol levels of 200 or higher are defined as a risk factor for CV health. Blood pressure is measured according to mean values of systolic blood pressure (SBP) and diastolic blood pressure (DBP) readings in all three data sets. We define blood pressure of SBP greater than 120 or DBP greater than 80 as a risk factor for CV health for young adults (Lawrence et al., 2018). HbA1c is used as a measure of diabetes risk. Diabetes risk is measured by the level of HbA1c greater than 5.7 (Lawrence et al., 2017). Fasting blood glucose is not available in the HSE, but the HbA1c has been used to examine diabetes risk in several international comparative studies for younger adults (Martinson, 2012; Martinson et al., 2011).

Our primary exposure was generational status, defined as Gen X (born in years 1965–1980) or Millennial (born in years 1981–1996). Birth year was calculated by subtracting age in years at the time of survey from the year the survey was administered. Given our focus on young adults, we only included individuals in our sample who were 20–34 years old at the time of the survey in each of the two generations. Additionally, for the HSE in survey years 2015 through 2017, age was available only categorically, so this also influenced our decision to only include individuals who were categorized as ages 20 through 34 in both data sets. We also limited the age range in NHANES as well in order to be consistent across data sets within years, despite age being available continuously in all waves of the NHANES. Thus, the overall sample is slightly younger than the full range of Millennial ages based on birth year. On average, young adults in our sample were 27 years old in each of the generations (for both generations in NHANES and for waves with continuous age in the HSE).

In certain analyses, we included sociodemographic factors that have been used in previous comparative studies using the NHANES and HSE (Martinson, 2012; Martinson & Reichman, 2016; Martinson et al., 2011). For income, we calculated income in terciles. First, we adjusted household income to the most recent year of data, 2018, using country-specific consumer price indices (International Monetary Fund, 2021). Then, we adjusted household income for household size using the Organisation for Economic Co-Operation and Development (OECD) equivalency scale (OECD, 2008). Finally, we created weighted terciles within our country-specific samples of young adults aged 20–34, because income tends to be lower for young adults than middle age or older adults in both countries. Because education was measured differently in the two countries, we were only able to use measures of high school diploma or less (or equivalent) versus more than a high school diploma (or equivalent). For race–ethnicity, we included the categories that were relevant and available for each country—White, Black, Hispanic, or other for the United States and White, Black, Asian (Indian, Pakistani, or Bangladeshi background), or other for England. Nativity status was not available in the waves of the HSE used for this analysis, but we were able to include foreign-born versus native-born status in analyses for the United States, where the foreign-born health advantage is well-documented.

We also included comparable measures of health behaviors and health contexts in certain analyses. While obesity was examined as a CV risk factor, we also controlled for BMI in categories as well as stratified by normal BMI for some analyses of diabetes, high cholesterol, and hypertension, to assess the contribution of BMI to generational differences in conditions or indicators for which obesity is a risk factor. We used a comparable measure of high alcohol use, defined as drinking alcohol 3 or more days per week, versus low or moderate alcohol use defined as drinking alcohol 2 days per week or less. Health insurance was only measured for the United States as health care access is universally provided in England through the National Health Service, and we included a dichotomous measure of any health insurance or no health insurance in some U.S. analyses.

Analysis

We used Stata SE 16 to conduct all analyses using the svy suite of commands as required by the sampling design of both data sets (StataCorp, 2019). Given recent studies finding large gender differences in CV conditions, risk factors, and disparities (Loucks et al., 2007; Martinson et al., 2016; Mensah et al., 2005; Zhang & Wang, 2004), all analyses were conducted separately for men and women. After harmonizing the variables as detailed in the previous section, we estimated weighted gender-specific proportions of each health condition or risk factor at ages 20–34 years, separately for Millennials and Gen X in each country (see Figure 1 and Table 2). We then adjusted the weighted proportions, first for BMI in categories, then for income, and then for the full set of comparable sociodemographic and health factors described in the Measures section. The proportions reported are predicted probabilities calculated using the margins command in Stata. These results comparing the adjusted proportions when adjusting for BMI, income, and the full set of controls are presented in Table 2.

Figure 1.

Unadjusted cardiovascular risk factors presented as proportions with confidence intervals separately for Millennial and Gen X young adult men and women in the United States and England. HbA1c = glycated hemoglobin; US = United States.

Table 2.

Model Comparison of Cardiovascular Risk Factor Proportions With No Controls (Same as Figure 1), Adjusted by BMI, Adjusted by Income, and Adjusted by All Sociodemographic Control Variables for Gen X and Millennials (Ages 20–34)

	United States						England
	Gen X men	Millennial men	Sig.	Gen X women	Millennial women	Sig.	Gen X men	Millennial men	Sig.	Gen X women	Millennial women	Sig.
Obesity
No controls	0.25	0.36	***	0.31	0.36	**	0.18	0.18		0.19	0.21	*
BMI only	—	—		—	—		—	—		—	—
Income only	0.25	0.36	***	0.31	0.36	**	0.19	0.18		0.19	0.20
All controlsa	0.18	0.35	***	0.21	0.33	**	0.19	0.19		0.18	0.22	**
Smoking
No controls	0.35	0.25	***	0.26	0.19	***	0.36	0.29	***	0.33	0.23	***
BMI only	0.35	0.25	***	0.26	0.19	***	0.37	0.30	***	0.33	0.23	***
Income only	0.36	0.25	***	0.26	0.19	***	0.37	0.29	***	0.33	0.22	***
All controls	0.39	0.28	***	0.40	0.25	***	0.36	0.29	***	0.33	0.24	***
High blood pressure
No controls	0.51	0.46	+	0.18	0.17		0.81	0.65	***	0.46	0.22	***
BMI only	0.52	0.45	**	0.18	0.16		0.79	0.65	***	0.46	0.23	***
Income only	0.51	0.46	+	0.18	0.17		0.81	0.65	***	0.46	0.22	***
All controls	0.52	0.48		0.19	0.16		0.80	0.65	***	0.46	0.24	***
High total cholesterol
No controls	0.36	0.26	***	0.32	0.23	***	0.49	0.32	***	0.37	0.19	***
BMI only	0.37	0.25	***	0.33	0.23	***	0.46	0.31	***	0.37	0.20	***
Income only	0.36	0.26	***	0.33	0.23	***	0.49	0.32	***	0.36	0.20	***
All controls	0.38	0.25	***	0.30	0.21	**	0.46	0.33	***	0.37	0.21	***
Diabetes risk
No controls	0.06	0.10	**	0.04	0.09	***	0.03	0.07	**	0.03	0.05	*
BMI only	0.07	0.09	**	0.04	0.09	***	0.03	0.07	***	0.03	0.05	*
Income only	0.06	0.10	**	0.04	0.09	***	0.04	0.07	**	0.03	0.05	*
All controls	0.05	0.08	*	0.03	0.06	+	0.04	0.08	**	0.03	0.05	+

Notes: BMI = body mass index.

^aControl variables include income, race/ethnicity, education, marital status, alcohol consumption, and BMI (in all models except obesity).

Data sources: NHANES for United States and HSE for England.

Differences between Gen X and Millennials: ***p < .001. **p < .01. *p < .05. ⁺p < .10.

Next, we calculated gender- and country-specific generational differences in CV conditions and risk factors by income tercile and for those with normal-weight BMI status (Table 3). This analysis allows us to assess if specific income groups may be driving differences in CV risk factors and conditions between the generations. By examining changes for those of normal-weight BMI status, we can assess if increases in overweight and obesity between the generations are a major factor in changes in Millennial CV risk factors and conditions, or if these changes are also present for individuals with a healthy BMI. Finally, for diabetes risk, high blood pressure, and high cholesterol (United States only), we conducted supplemental analyses using broader measures that were based not only on biomarker data, but also on having ever been diagnosed with the condition, regardless of current biomarker data. These results are included in Supplementary Appendix Table 1.

Table 3.

Unadjusted Cardiovascular Risk Factors Presented as Proportions and Stratified by Income Status and by Normal Weight

	United States						England
	Gen X men	Millennial men	Sig.	Gen X women	Millennial women	Sig.	Gen X men	Millennial men	Sig.	Gen X women	Millennial women	Sig.
Obesity
Low income	0.23	0.28	+	0.37	0.40		0.20	0.22		0.23	0.27	*
Medium income	0.30	0.38	*	0.35	0.38		0.19	0.18		0.21	0.21
High income	0.23	0.40	***	0.21	0.30	*	0.17	0.15		0.13	0.13
Normal weight	—	—	—	—	—	—	—	—	—	—	—	—
Smoking
Low income	0.45	0.34	***	0.36	0.26	***	0.50	0.42	**	0.47	0.32	***
Medium income	0.36	0.24	***	0.26	0.20	**	0.37	0.28	***	0.29	0.22	***
High income	0.26	0.16	***	0.16	0.11	+	0.29	0.18	***	0.24	0.12	***
Normal weight	0.39	0.28	***	0.28	0.16	***	0.42	0.33	***	0.33	0.21	***
High blood pressure
Low income	0.49	0.40	**	0.15	0.15		0.81	0.63	***	0.44	0.21	***
Medium income	0.51	0.48		0.20	0.20		0.81	0.66	***	0.48	0.23	***
High income	0.52	0.50		0.17	0.16		0.81	0.65	***	0.44	0.24	***
Normal weight	0.41	0.31	**	0.12	0.09	*	0.74	0.54	***	0.35	0.15	***
High total cholesterol
Low income	0.39	0.23	***	0.27	0.22	+	0.47	0.34	*	0.37	0.15	***
Medium income	0.37	0.25	***	0.36	0.23	***	0.51	0.28	***	0.36	0.20	***
High income	0.34	0.29		0.34	0.23	**	0.49	0.33	***	0.37	0.23	***
Normal weight	0.24	0.14	***	0.28	0.17	***	0.36	0.22	***	0.29	0.14	***
Diabetes risk
Low income	0.09	0.12	+	0.05	0.12	***	0.04	0.12	*	0.05	0.05
Medium income	0.06	0.10	**	0.05	0.08	**	0.04	0.06		0.04	0.07	*
High income	0.04	0.08	**	0.03	0.09	***	0.02	0.05	+	0.02	0.05
Normal weight	0.02	0.04	*	0.01	0.02	+	0.02	0.05	*	0.03	0.04

Notes: Data sources: NHANES for United States and HSE for England.

Differences between Gen X and Millennials: ***p < .001. **p < .01. *p < .05. ⁺p < .10.

Results

Sample Description

Table 1 presents characteristics of Millennials versus Gen X in both the United States and England. Noteworthy differences between the generations include differences in the proportions identifying as White, with Millennials less likely than Gen X to identify as White in the United States and England. In the United States and England, Millennial adults were more likely to identify as Other race/ethnicity, and in England Millennial adults were also more likely to identify as Asian.

Table 1.

Sample Characteristics for Gen X (1999–2006) and Millennials (2013–2018) at Ages 20–34

	United States		England
	Gen X	Millennials	Gen X	Millennials
	n = 4,309	n = 3,533	n = 9,653	n = 5,019
Sex
Female	51.3	49.5	52.1	50.6
Male	48.7	50.5	47.9	49.4
Race
White	64.4	57.6*	89.5	84.9***
Black	12.5	13.0	2.4	3.0+
Hispanic/Asiana	17.8	18.4	5.6	8.8***
Other	5.3	11.0***	2.5	3.2*
Education
High school diploma or less	42.7	34.3***	72.1	60.9***
> High school diploma	57.3	65.7***	27.9	39.1***
Income (mean)b	31,663	31,894	50,907	41,996
High income	31.4	32.7	37.6	27.6***
Middle income	35.8	32.2*	36.2	37.3
Low income	32.7	35.1	26.2	35.2***
Marital status
Married	59.0	51.4***	39.7	27.6***
Not married	41.0	48.6***	60.3	73.4***
Alcohol use
<3 drinks/week	67.3	74.1*	68.8	83.9***
3+ drinks/week	32.7	25.9*	31.3	16.1***
Insurance status
Insured	71.9	77.6**	—	—
Uninsured	28.1	22.4**	—	—
Nativity status
Foreign-born	18.1	16.7	—	—
U.S.-born	81.9	83.3	—	—

Notes:

^aHispanic ethnicity in the United States and Asian race in England.

^bPresented in 2018 dollars in the United States and pounds in England.

Data sources: NHANES for United States and HSE for England.

Differences between Gen X and Millennials: ***p < .001. **p < .01. *p < .05. ⁺p < .10.

In both countries, Millennials were significantly more educated than Gen X on average. In the United States they had slightly higher mean incomes after adjusting for the Consumer Price Index (CPI), but in England they had significantly lower incomes. Millennials were more likely to be in the lowest income tercile compared to those in Gen X in both countries, and the difference was significant in England. A clear generational shift is apparent for marital status in both countries as Millennials are significantly less likely to be married than their Gen X counterparts. In both countries, weekly alcohol consumption is less frequent for Millennials compared to Gen X, and this pattern was particularly notable in England following public health shifts on alcohol consumption between the generations (Ng Fat et al., 2018).

Main Results

Figure 1 shows the gender-specific proportions of specific CV conditions or risk factors by generation in the United States and England (results also shown in the first row for each measure in Table 2). Obesity was significantly more prevalent among Millennials than Gen X in the United States (Figure 1A). The difference was particularly notable for U.S. men, with Millennial men in the United States almost 50% more likely to be obese than Gen X men. We found that while Millennial women in England were also more likely to be obese than their Gen X counterparts, the shifts were not as large as in the U.S. women. Millennial men in England had the same obesity prevalence as Gen X men. In both countries smoking was significantly less prevalent among Millennials than among Gen X, for both genders (Figure 1B).

For high blood pressure, there was a general pattern of lower rates for Millennials compared to Gen X in both countries, but not statistically different in the United States (Figure 1C). In England, Millennials were significantly less likely to have high blood pressure. For high total cholesterol, Millennials had lower prevalence compared to Gen X, for both genders in both countries (Figure 1D).

When considering HbA1c to characterize diabetes risk, Millennials had consistent disadvantages compared to Gen X, with higher rates of high HbA1c in both countries (Figure 1E). In the United States, the associations were strong and statistically significant, with Millennial men and women having over twice the prevalence of diabetes risk compared to their Gen X counterparts. The patterns were similar in England, but the overall rates of diabetes risk were lower than in the United States.

In Table 2, we present the unadjusted proportions presented from Figure 1 in the first row for each outcome, as well as proportions separately adjusting for BMI in categories, then income, and finally all sociodemographic and health factors. With just a few exceptions, overall the adjustments did not substantively alter the unadjusted generational differences in CV conditions and risk factors, suggesting that changes across generations in BMI, income, and sociodemographic and health factors such as marriage status or alcohol consumption do not explain generational differences in CV conditions and risk factors.

Stratified Results

Table 3 presents the unadjusted proportions of CV risk factors and conditions stratified by income tercile and then for normal-weight respondents only to further assess the extent to which changes in income or BMI across generations may be driving changes between Millennials and Gen X. We find that patterns in CV risk factors and conditions across generations varied notably by socioeconomic status. In the United States, generational differences in obesity favoring Gen X were largely confined to medium- and (especially) high-income individuals. In contrast, Millennial women in England with low incomes had a significantly higher prevalence of obesity compared to their Gen X counterparts. We find no significant differences in obesity between Gen X and Millennial men in that country.

We find significant declines in smoking for all stratified groups in both countries, though it is also notable that smoking continues to have a strong income gradient for Millennials, with lower-income men and women reporting higher levels of smoking. For high blood pressure, while there were no statistically significant differences between the generations in the United States overall, the stratified analyses reveal lower prevalence among low-income Millennial men compared to low-income Gen X men. In England, the Millennial advantage is present for men and women across the income spectrum. Millennial advantages in high blood pressure were present for normal-weight men and women in both countries and suggest that improvements in high blood pressure across generations in the United States would have been larger if obesity had not trended upward from Gen X to the Millennial generation. Men with low and medium incomes were responsible for the Millennial male advantage in total cholesterol in the United States, while in England the Millennial male advantages in total cholesterol were significant for all incomes, though larger among medium- and higher-income men. Millennial female advantages in total cholesterol were significant for medium- and higher-income women in the United States and for women of all incomes in England. Significant decreases in total cholesterol between the generations were also found across the board for normal-weight individuals in both countries.

For diabetes risk or elevated HbA1c, the results in Figure 1 indicated much larger prevalence among Millennials as compared to Gen X in the United States and England (though lower levels overall in that country). While this pattern was present at all income levels, the Millennial disadvantage was larger for medium- and high-income men than for lower-income men in the United States. Among normal-weight men and women in the United States, the Millennial disadvantage was statistically significant only for men, though the prevalence was very low for this subgroup. In England, we found that increases in diabetes risk for the Millennial generation are significant only for low-income men and medium-income women, and like the United States, we found a significant increase in diabetes risk for normal-weight Millennial men over Gen X. No such differences existed in diabetes risk for women of normal weight in England. Overall, the stratified results suggest that the increase in obesity from Gen X to the Millennial generation is a major factor underlying the Millennial disadvantage in diabetes risk in the United States. However, in England, it is less clear, because we did not find an increase for obesity for the same income groups that saw an increase in diabetes risk across the two generations.

Sensitivity Analysis

In sensitivity analysis presented in Supplementary Appendix Table 1, we examined whether a diagnosis of a CV risk factor or condition may account for differences between the generations. For example, if Millennials were more likely to receive medical care that results in medication use early in adulthood for conditions such as high blood pressure, high total cholesterol (available in United States only), and diabetes risk, this may play a role in the differences we found. The sensitivity analysis showed, however, that results were similar when we included the measure of high HbA1c or a diagnosis of diabetes as they were for the high HbA1c only. Our results were also robust when we considered that younger generations may have been more likely to have a high blood pressure or cholesterol diagnosis resulting in medication use to control these risk factors for CV disease. In summary, we found no evidence that early diagnosis may be driving either generational increases or decreases in the rates of diabetes risk, high blood pressure, and high cholesterol presented from biomarker and body measurement data in the United States or England.

Discussion

Millennials in the United States, particularly men, were significantly more obese compared to their Gen X counterparts at the same ages. In contrast, Millennial men in England were no more likely to be obese than their Gen X counterparts, and though Millennial women were more likely to be obese, differences between Millennials and Gen X were not as large as in the United States. For both generations, the English had lower rates of obesity overall compared to Americans, which has been described in previous work (Martinson et al., 2011). Millennials were more likely to have high diabetes risk than Gen X in both countries, but again the overall rates were lower in England than in the United States. It is noteworthy that diabetes risk also increased in England between Gen X and Millennials, despite no increase in obesity for men and a smaller increase in obesity for women than in the United States. The increasing rate of diabetes risk measured as HbA1c in young adults is concerning for the metabolic health of Millennials as they age.

However, the news was not all bad for Millennials when it came to their CV risks and conditions. In the United States, Millennials were less likely than Gen X to smoke and have high cholesterol, and the two generations had similar rates of high blood pressure. English Millennials had an advantage over Gen X not only for smoking and cholesterol (like the United States), but also for high blood pressure.

We expected that CV risk factors and conditions would be worse for Millennials than Gen Xers in both countries, particularly in the United States given the economic retrenchment when Millennials came of age in tandem with the large increases in rates of obesity in the population. We found little evidence that CV risk factors uniformly worsened across generations in either country or that any generational shifts in CV risk factors or conditions could be attributed to increases in obesity or decreases in income. The one major exception is the increase in diabetes risk in both countries, where at least in the United States, it appears that increases in population obesity play a significant role.

Overall, while U.S. Millennials were more obese than their Gen X counterparts, it did not appear that BMI explained the Millennial disadvantages in other CV risk factors or conditions. Adjusting for income also did not change the patterns. In some cases, the patterns differed by income, but no consistent pattern emerged. Thus, it does not appear that the secular trends of increasing obesity and decreased economic opportunities led directly to worse CV risk factors and conditions among Millennials compared to Gen X in either country. However, it is possible those trends have played roles that will only become apparent over time (e.g., that obesity will lead to increases in high blood pressure and cholesterol later in life) or that the adverse effects were offset by health benefits accruing from reduced rates of smoking. Moreover, it is important to consider that the findings from this study pertain exclusively to CV risk factors and conditions; previous research found that Millennials were more likely than Gen Xers to report worse self-rated health in the United States and suggests that Millennials may fare worse than Gen Xers when considering other physical health outcomes or indicators of mental health (Blue Cross Blue Shield, 2019; DePew & Gonzales, 2020).

In sensitivity analysis, we also looked to see if differences in the likelihood of diagnosis of CV risk factors and conditions, which may result in medication use that could moderate our findings, were present in either country. We did not find evidence that diagnosis has played a role in explaining the differences between generations in either country, perhaps because it is unlikely that our young adult sample aged 20–34 years was systematically screened or treated for the CV risk factors we explored in this study. In the United States, routine cholesterol screening is recommended for men aged 35 and older and women aged 45 and older and diabetes screening is recommended beginning at age 40 for men and women (US Preventive Services Task Force, 2016, 2021). In England, routine cholesterol and diabetes screening is recommended at age 40 and older for both sexes (NHS, 2019; National Institute for Health and Care Excellence, 2016).

Other explanations for generational differences in CV risk factors and conditions that we were not able to directly explore are worth noting. The U.S. adult population has seen secular decreases in high total cholesterol in tandem with increases in high HDL cholesterol over the past 20 years (Carroll & Fryar, 2020). In addition, in the most recent decade, while the United States has not seen population shifts in meeting aerobic exercise recommendations, it has seen a shift toward more time spent on sedentary behaviors (Du et al., 2019).

This study has several limitations. We were unable to further stratify by age within each generation, because starting in 2015 the HSE provided ages only in categories. We were also limited in our ability to study the full spectrum of potential CV health risk factors including C-reactive protein and triglycerides, which are available consistently in the NHANES but not in all waves of the HSE. Body weight history is not consistently available in our data, so we were unable to account for this in our analysis. It is also worth noting that though our study focuses on generations as described by Pew Research Center (2015), many demographers recommend moving away from some conventionally named generations to describe cohorts or individuals born in specific decades (Cohen, 2021). Finally, sample size limitations prevented us from stratifying analyses by race and ethnicity.

Despite these limitations, this study was able to uniquely examine CV risk factors and conditions in young adulthood and variations in those risks across two generations (Millennials and Gen X), by gender, and across two countries (United States and England). The use of biomarker and body measurement data, along with measures of self-reported health and smoking, provides a comprehensive picture of CV risk factors and conditions in young adulthood—a stage of life during which CV risks are typically not monitored as closely as in later adulthood—in each of the two countries.

Funding

Funding for this research was provided by the National Institute on Aging (P30AG012846), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2C HD042828) , the National Center for Advancing Translational Sciences (UL1TR003017), the U.S. Department of Health and Human Services/Health Resources and Service Administration (U3DMD32755), and the Robert Wood Johnson Foundation through its support of the Child Health Institute of New Jersey (grant 74260). This paper was published as part of a supplement sponsored by the University of Michigan with support from the National Institute on Aging (P30AG012846).

Conflict of Interest

None declared.

Acknowledgments

An earlier version of this paper was presented at the 2021 Annual Meeting of the TRENDS Network, May 13–14. The views expressed are those of the authors alone and do not represent those of their employers or any funding agency.