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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2024 Sep 16;79(11):gbae157. doi: 10.1093/geronb/gbae157

Socioeconomic Inequalities in Frailty Distribution: A Cross-National Comparison of the United States and England

Rachel Z Wilkie 1,✉, Jennifer A Ailshire 2
Editor: Kenzie Latham-Mintus
PMCID: PMC11520401  PMID: 39283971

Abstract

Objectives

The objective of this study is to examine differences in socioeconomic gradients (i.e., education, income, and wealth) in frailty by gender in the United States and England.

Methods

We used harmonized data from the Health and Retirement Study and the English Longitudinal Study of Ageing in 2016. Frailty status was determined from measured and self-reported signs and symptoms in 5 domains: unintentional weight loss, exhaustion, low physical activity, slow walking speed, and weakness. Respondents were classified as robust (no signs or symptoms of frailty), prefrail (signs or symptoms in 1–2 domains), or frail (signs or symptoms in 3 or more domains). Gender-stratified multinomial logistic regression models were used to assess the relationship between educational attainment, household income, and household wealth with the risk of frailty and prefrailty, with and without covariates. We also calculated the slope index of inequalities on the predicted probabilities of frailty by income and wealth quintiles.

Results

We found socioeconomic gradients in prefrailty and frailty by education, income, and wealth. Furthermore, the educational gradient in frailty was significantly steeper for U.S. women compared to English women, and the income gradient was steeper for U.S. men and women compared to English men and women. The between-country differences were not accounted for by adjusting for race/ethnicity and behavioral factors.

Discussion

Socioeconomic gradients in prefrailty and frailty differ by country setting and gender, suggesting contextual factors such as cultural norms, healthcare access and quality, and economic policy may contribute to the effect of different measures of socioeconomic status on prefrailty and frailty risk.

Keywords: Cross-cultural comparisons, Health inequalities, Socioeconomic status

Physical frailty is a common clinical syndrome characterized by loss of physiological reserve and increased risk of adverse outcomes, such as falls, delirium, hospital or nursing home admission, and premature death (Clegg et al., 2013; Ding et al., 2017; Lohman et al., 2020). Prefrailty is a preclinical state consisting of fewer frailty signs and symptoms, and is associated with an increased risk of progression to frailty, but is also potentially reversible (Hoogendijk et al., 2019). Women (Ahrenfeldt et al., 2019) and those of low socioeconomic position (Franse et al., 2017; Stolz et al., 2017) have the greatest risk of frailty. The prevalence of frailty and prefrailty increases with age: Around 30% of those aged over 80 years and 45% of those aged over 90 years are frail (O’Caoimh et al., 2021). This presents an important public health problem in the context of global aging; the burden of frailty on vulnerable individuals, family caregivers, and health and social care systems will continue to increase in rapidly aging populations worldwide (Hoogendijk et al., 2019; Ringer et al., 2017). Understanding how inequalities in frailty are distributed is essential to designing policies and programs to prevent frailty and improve the quality of life for older people with frailty.

Studies show that frailty prevalence varies markedly by country, suggesting that contextual factors, such as cultural norms, and health, social, and educational systems, contribute to frailty incidence and inequalities (Ailshire & Carr, 2021; Chi, 2011; O’Caoimh et al., 2021). Furthermore, Tom et al. (2013), found that women aged 55–75 in the United States had higher frailty prevalence compared to women in Europe. However, cross-national studies exploring how inequalities in frailty differ between countries, are currently limited to the European context (Romero-Ortuno et al., 2014; Stolz et al., 2017).

The Current Study

High-income countries such as the United States and England are experiencing population aging and rising socioeconomic inequalities, and, therefore, will experience increasing prevalence of frailty coupled with increasing cost and burden to health and social care systems as the older population increases (Office for National Statistics, 2021; United Nations, 2020; Vespa et al., 2020). This study aims to explore how socioeconomic inequalities in frailty are distributed among older adults in the United States and England using data from two nationally representative surveys of older adults: the U.S. Health and Retirement Study (HRS) and the English Longitudinal Survey of Ageing (ELSA). We focus on two research questions. First, are there gradients in frailty, using multiple measures of SEP, including educational attainment, household income, and household wealth, in the United States and England? Second, how do socioeconomic gradients in frailty in older adults differ between the United States and the United Kingdom? We examine socioeconomic gradients separately by gender due to known differences in frailty prevalence between men and women. Exploring how socioeconomic gradients in frailty differ between the United States and the United Kingdom will provide important insights into how the social and economic context of each country plays a role in determining the conditions for frailty and poor health at older ages.

The United States and England are prime examples of high-income, highly developed countries with substantial socioeconomic inequalities in health (Banks, Marmot, et al., 2006; Marmot, 2005; Weinstein et al., 2017) that likely lead to disparate levels of frailty. However, the United States has higher rates of chronic disease, disease markers, and lower life expectancy compared with England (Avendano et al., 2009; Martinson et al., 2011), and thus is expected to also have a higher frailty prevalence.

Single-country studies of frailty inequalities in the United States and the England have demonstrated significant socioeconomic gradients in frailty by education (Hirsch et al., 2006; Niederstrasser et al., 2019; Zimmer et al., 2021), income (Bandeen-Roche et al., 2015; Hirsch et al., 2006; Watts et al., 2019), and wealth (Niederstrasser et al., 2019; Zimmer et al., 2021) in both countries. However, to our knowledge, there have been no cross-national comparisons of inequalities in frailty between the United States and England, so we do not know how inequalities differ between the two countries. Cross-national studies of inequalities in other related health outcomes, such as disability, chronic illness, and mortality, between the United States and England, have shown inconsistent findings. Some studies have found evidence for steeper inequalities in the United States. For example, studies by Banks, Marmot, et al. (2006) and Choi et al. (2020) found steeper income and education gradients in self-reported chronic illness in the United States compared to England. Furthermore, Avendano et al. (2009) found a steeper wealth gradient in disability and chronic illness in the United States compared to England and Europe. In contrast, other studies have found no significant differences in socioeconomic gradients between the United States and England for health outcomes such as disability, mortality, and chronic illness (Makaroun et al., 2017; Martinson et al., 2011; Zaninotto et al., 2020). Our study aims to fill this gap in the literature by examining multiple SES gradients in frailty to fully characterize potential cross-national differences in frailty inequalities between the two countries.

While the United States and England share some similarities in predominant culture, attitudes, religion, and language, there are important differences between the two countries which may lead to differences in frailty inequalities within each country, including health behavior and healthcare funding and provision (Banks, Marmot, et al., 2006; Martinson, 2012; Papanicolas et al., 2019). There is a higher prevalence of obesity in the United States compared to the United Kingdom (of which England is a constituent country) and a higher prevalence of smoking and heavy drinking in the United Kingdom compared to the United States (OECD, 2023). Healthcare in the United States is largely privatized with a subsidized federal healthcare program, Medicare, for people aged 65 and over or those with a disability (Kaiser Family Foundation, 2019). This means that for those under 65 years, access to affordable healthcare could vary markedly by socioeconomic position leading to poorer health across the life course for the most disadvantaged and wide inequalities in frailty prevalence. In England, healthcare for all residents is provided by the National Health Service (NHS), which is tax-funded and provided free to the user (Papanicolas et al., 2019). Yet, while healthcare is freely available to all, the NHS in England has experienced significant challenges in the past decade in the form of extreme financial pressures, healthcare worker shortages, and austerity cuts (Papanicolas et al., 2019), which may affect the availability, accessibility, and quality of care and widen inequalities in frailty in England.

Method

Data

We used a cross-sectional design to compare inequalities in frailty distribution among older adults in the United States and England. Data for this research study were obtained from two nationally representative longitudinal studies of aging: the HRS in the United States and the ELSA in England. The HRS includes adults aged 51 years and over, and their spouses, with interviews occurring every two years since 1992 (Sonnega et al., 2014). Similarly, ELSA includes adults aged 50 years and older, and their spouses, with interviews taking place every 2 years since 2002 (Steptoe et al., 2013). ELSA was designed to be comparable to the HRS through similar sampling methods, wave frequency, and question content (Zaninotto et al., 2020). Informed consent was obtained from all individual participants in both HRS and ELSA. We used the harmonized ELSA and HRS data files available from the Gateway to Global Aging Data, Produced by the Program on Global Aging, Health & Policy, University of Southern California (The Program on Global Aging, Health, and Policy, 2020).

We used data collected between 2016 and 2017, corresponding to Wave 13 of HRS and Wave 8 of ELSA, which was the most recent available wave where both HRS and ELSA collected data on physical measures. In HRS, 50% of participants were randomly selected to participate in the physical measures subsample which included grip strength, walking speed, and body weight measurements. Similarly, in ELSA, approximately 50% were selected for the physical measures subsample which included grip strength and body weight measurements taken by a qualified nurse.

Our sample consisted of community-dwelling participants over 50 years of age who were selected for the 2016/2017 physical measures subsample in HRS or a nurse visit in ELSA. We excluded respondents who lived in a nursing home or participated via proxy interview because they were ineligible for physical measurement modules. After accounting for missingness in the outcome and explanatory measures, the final sample size was 11,300, with 7,896 participants from HRS, and 3,404 participants from ELSA. A flow chart of sample selection is shown in Supplementary Figure 1. We used sample weights to account for differential nonresponse and selection probability, ensuring the representativeness of the survey to each country.

Outcome Measure

Our outcome in this study is physical frailty, as measured by the Fried frailty phenotype, a measure that is valid across cultures with high predictive capability for adverse health outcomes in older populations (Bouillon et al., 2013; Fried et al., 2001). The frailty phenotype is measured using signs and symptoms in five domains: unintentional weight loss, exhaustion, low physical activity, weakness, and slow walk speed (Fried et al., 2001). A detailed description of the frailty phenotype criteria is outlined in Supplementary Table 1. We defined the five domains as follows: (a) unintentional weight loss was defined as greater than 10% loss of body weight between 2012 and 2016 or body mass index (BMI) below 18.5 kg/m2; (b) exhaustion was defined as responding “yes” to either question of “struggling to get going” or “felt like everything was an effort”; (c) low physical activity was defined as no participation in light or moderate activity, or participation in vigorous activity less frequently than once per week; (d) weakness was defined using gender and BMI-specific grip-strength cut-offs or being unable to complete the grip strength test for health reasons; and (e) slow walk speed was defined using a standardized cut-off of walking speed below 0.6 m/s or having a health condition preventing completion of the walking test (Fried et al., 2001; Studenski et al., 2003). Because participants under 65 years in HRS and under 60 years in ELSA were not eligible for walking speed tests, we used self-reported difficulty walking one block (HRS) or 100 yards (ELSA) as a proxy for slow walk speed for participants below age 65. We calculated Cohen’s kappa statistics to determine the level of agreement between self-reported walking difficulty with measured walking speed among those who had both measures, finding fair-moderate agreement (United States: 0.25, England: 0.44). We also calculated the kappa statistic between the frailty phenotype using self-reported walking difficulty and the standard frailty phenotype, finding excellent agreement (United States: 0.75, England: 0.84). This suggests that self-reported walking difficulty is a good proxy for walking speed in a frailty measure. We used this approach so we could include the younger-old in analyses since the most disadvantaged likely experience frailty at younger ages.

We used a standard approach to categorizing respondents into one of three frailty categories: robust, prefrail, and frail (Fried et al., 2001). Respondents with no signs or symptoms of frailty were classified as robust. Those with signs or symptoms in one or two domains were classified as prefrail, and those with signs or symptoms in three or more domains were classified as frail. Participants were excluded from the analytic sample if they had any missing responses which could result in misclassification of frailty status. For example, a respondent with signs or symptoms in three frailty domains and one missing domain would not be excluded as signs or symptoms in the missing domains would not change their frailty status, while a respondent with signs or symptoms in one or two domains with at least one missing domain would be excluded as they could be misclassified as robust or prefrail.

Explanatory Measures

We used three harmonized measures of socioeconomic status: educational attainment, household income, and household wealth. Educational attainment was categorized into three: lower secondary or below, high school graduate, and some college or above. Household income was defined as the combined income for the participant and their spouse, including pension income, in U.S. dollars for HRS and pounds sterling for ELSA, adjusted for household size by dividing the reported household income by the square root of the number of household members. Household wealth was defined in HRS and ELSA as the total nonhousing wealth less debts. Household income and wealth were quintiled by country and gender.

Covariates

We included age (50–64, 65–79, or 80 years and over) and marital status (married/partnered, separated/widowed, divorced, or never married) as covariates in all models. Sociodemographic and behavioral covariates included race/ethnicity (White race/ethnicity or all other race/ethnicities), smoking (currently smokes, previously smoked, or never smoked), obesity (BMI below 30 or BMI of 30 and above), and excess alcohol intake (below 7 drinks per week or 7 drinks or more per week). We also include a measure of retirement (not retired or retired/partially retired) in additional income models.

Statistical Analysis

We first examined the distribution of sample characteristics (age group, marital status, education, race/ethnicity, smoking status, obesity, and alcohol excess) by gender and country using t-tests and Pearson’s Chi-squared statistics for tests of differences.

We performed multinomial logistic regression to estimate odds ratios (ORs) for prefrail and frail status compared with robust status stratified by gender and country. We ran separate models for each socioeconomic indicator adjusting for age and marital status. We also ran models of the interaction with income and retirement to examine if retirement status moderates income inequalities in frailty. Finally, we conducted additional models adjusting for race/ethnicity and behavioral variables associated with frailty. ORs and 95% confidence intervals (95% CIs) for each model were obtained. Findings were considered significant using z-scores and an alpha-level cut-off of 0.05. All models were estimated with robust standard errors to account for clustering within households.

We used the Karlson–Holm–Breen method for logistic regression models to decompose the variance in SES gradients explained by race/ethnicity and behavioral covariates (Karlson et al., 2012). The method calculates the percent change in ORs between a reduced model and a fully adjusted model, accounting for rescaling, and the percent contribution of each potential confounding variable on the percent change.

To compare socioeconomic gradients across countries we calculated predicted probabilities of frailty and prefrailty using gender-stratified models, adjusted for age and marital status, with a country interaction. We calculated the difference in predicted probability, and 95% CI, between the lowest and highest socioeconomic categories for education, income, and wealth, using adjusted Wald tests to assess differences in predicted probabilities between countries. Additionally, we calculated the slope indices of inequality (SII) of the predicted probabilities of frailty and prefrailty for income and wealth gradients. The SII is an absolute measure of inequality that uses a regression model to summarize the difference in an indicator between the most and least advantaged groups, while also taking the values of the middle groups into account (Schlotheuber & Hosseinpoor, 2022). An SII of zero indicates no inequality, positive values indicate a higher prevalence of the indicator among those who are more advantaged, and a negative value suggests that the indicator is concentrated among those who are more disadvantaged (Schlotheuber & Hosseinpoor, 2022). The significance of the country difference for SIIs was determined by nonoverlapping confidence intervals.

Analyses were performed using Stata version 17 and R Studio.

Results

Sample Characteristics

Detailed sample characteristics are displayed by country and gender in Table 1. Overall, U.S. men and women were younger and had higher BMI compared to English men and women. English women had a higher prevalence of smoking, and higher marriage rate compared to U.S. women.

Table 1.

Demographic Characteristics (Percent) of Participants in the United States (HRS) and England (ELSA) by Gender (2016)

Variable Women Test of difference Men Test of difference
United States England United States England
Age
 50–64 years 57.0 46.2 *** 59.5 50.2 ***
 65–79 years 32.4 38.2 31.6 36.3
 80 years and over 10.5 15.6 8.8 13.5
Marital status
 Married or partnered 57.9 67.6 *** 74.5 78.4 ***
 Separated or divorced 17.8 13.2 13.3 9.0
 Widowed 15.9 14.5 5.0 6.4
 Never married 8.5 4.6 7.2 6.1
Education
 Lower secondary 11.1 30.5 *** 11.0 24.8 ***
 High school 31.6 34.5 30.7 24.9
 College or above 57.3 35.0 58.3 50.3
Race/ethnicity
 Non-Hispanic white 74.5 97.2 *** 74.2 94.8 ***
 All other races 25.5 2.8 25.8 5.2
Obesity
 BMI < 30 53.2 66.5 53.8 69.7
 BMI 30 or over 46.8 33.5 *** 46.2 30.3 ***
Excess alcohol
 7 or fewer drinks per week 91.5 82.5 80.0 64.0
 More than 7 drinks per week 8.5 17.5 *** 20.0 36.0 ***
Smoking status
 Nonsmoker 50.4 42.1 *** 39.5 33.5 ***
 Past smoker 36.1 48.0 43.7 56.3
 Current smoker 13.5 9.9 16.8 10.2
No. observations 4539 1881 3357 1523
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Note: BMI = body mass index.

*** p < .001; **p < .01; *p < .05; +p < .1.

Frailty Domains

The prevalence of deficits in the five frailty domains is described in Supplementary Table 2. We found that U.S. men and women had a significantly higher prevalence of low physical activity compared to English men and women (p < .001). English women had a higher prevalence of weakness compared to U.S. women (p = .03). U.S. men also had a significantly higher prevalence of exhaustion compared to English men (p = .002).

Logistic Regression Results

Table 2 shows results from multinomial logistic regression models for each measure of socioeconomic status with frail and prefrail versus robust status, adjusted for age and marital status. Having lower secondary education was associated with a significantly higher risk of frailty compared with college education or higher for all groups. This effect was largest for U.S. women (OR 9.6, p < .001) and U.S. men (OR 8.3, p < .001) followed by English men (OR 6.7, p < .001) and English women (OR 3.6, p < .001).

Table 2.

Multinomial Logistic Regression Models Predicting Frail and Prefrail Status by SES in the United States and England, Adjusted for Age and Marital Status

Variable Women Men
United States England United States England
OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Frail versus robust
 Education (ref: college or above)
  Lower secondary 9.6 *** (6.5, 14.2) 3.3 *** (2.1, 5.1) 8.3 *** (5.3, 12.9) 6.7 *** (3.9, 11.3)
  High school 3.1 *** (2.3, 4.1) 1.6 (1.0, 2.5) 2.6 *** (1.8, 3.7) 2.2 ** (1.3, 3.7)
 Quintiles of income (ref: Q5, highest)
  Q1, lowest 23.7 *** (13.7, 40.9) 10.3 *** (5.0, 21.0) 21.5 *** (12.4, 37.3) 19.8 *** (6.9, 56.6)
  Q2 10.9 *** (6.4, 18.5) 7.5 *** (3.8, 14.8) 9.3 *** (5.6, 15.6) 7.0 *** (2.6, 18.9)
  Q3 5.5 *** (3.2, 9.3) 5.9 *** (3.1, 11.2) 3.4 *** (2.0, 5.8) 5.7 *** (2.1, 15.3)
  Q4 2.2 ** (1.3, 3.7) 3.7 *** (1.8, 7.8) 2.4 ** (1.3, 4.3) 3.7 * (1.3, 10.2)
 Quintiles of wealth (ref: Q5, highest)
  Q1, lowest 6.8 *** (4.3, 10.6) 12.0 *** (6.3, 22.7) 6.9 *** (4.2, 11.4) 21.2 *** (10.3, 43.4)
  Q2 10.4 *** (6.6, 16.4) 7.2 *** (3.9, 13.3) 9.6 *** (5.8, 15.8) 6.4 *** (3.2, 12.5)
  Q3 5.5 *** (3.5, 8.5) 2.5 ** (1.3, 4.6) 3.8 *** (2.4, 6.0) 3.0 ** (1.5, 6.0)
  Q4 1.6 * (1.1, 2.4) 1.9 * (1.0, 3.5) 1.3 (0.8, 2.1) 1.1 (0.5, 2.3)
Prefrail versus robust
 Education (ref: college or above)
  Lower secondary 3.2 *** (2.3, 4.5) 2.2 *** (1.6, 3.1) 3.6 *** (2.5, 5.1) 3.0 *** (2.1, 4.5)
  High school 1.7 *** (1.4, 2.0) 1.3 (0.9, 1.7) 1.7 *** (1.3, 2.1) 1.6 * (1.1, 2.4)
 Quintiles of income (ref: Q5, highest)
  Q1, lowest 3.7 *** (2.6, 5.2) 3.6 *** (2.3, 5.7) 5.5 *** (3.7, 8.2) 5.8 *** (3.5, 9.7)
  Q2 2.5 *** (1.8, 3.4) 2.4 *** (1.6, 3.7) 3.5 *** (2.5, 4.8) 2.0 ** (1.2, 3.2)
  Q3 2.0 *** (1.5, 2.7) 1.6 * (1.1, 2.3) 2.2 *** (1.7, 3.0) 2.0 ** (1.2, 3.3)
  Q4 1.3 * (1.0, 1.7) 1.6 * (1.1, 2.5) 1.6 ** (1.2, 2.1) 1.5 (0.9, 2.4)
 Quintiles of wealth (ref: Q5, highest)
  Q1, lowest 2.2 *** (1.7, 3.0) 3.6 *** (2.3, 5.7) 2.7 *** (1.9, 3.7) 9.0 *** (5.5, 14.9)
  Q2 2.9 *** (2.1, 4.0) 2.5 *** (1.7, 3.9) 4.4 *** (3.0, 6.4) 3.1 *** (2.0, 5.0)
  Q3 2.0 *** (1.5, 2.6) 1.6 * (1.1, 2.2) 2.0 *** (1.4, 2.8) 2.5 *** (1.6, 3.8)
  Q4 1.2 (0.9, 1.6) 1.5 * (1.0, 2.2) 1.1 (0.8, 1.4) 1.7 * (1.1, 2.6)
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Note: SES = socioeconomic status.

*** p < .001; **p < .01; *p < .05; +p < .1.

Using income as the socioeconomic measure, we found a significantly higher risk of frailty among those in the lowest quintile compared to those in the highest quintile; this was highest among U.S. women (OR 23.7, p < .001), followed by U.S. men (OR 21.5, p < .001), English men (OR 19.8, p < .001), and English women (OR 10.3, p < .001). Furthermore, nonretired participants in the lowest income quintile had a significantly higher OR for frailty compared to retired participants among English women (p = .03) and U.S. men (p = .020), suggesting that retirement status significantly moderates the relationship between income and frailty risk in these groups (full model shown in Supplementary Table 3).

For wealth, the odds of frailty were significantly higher among those in the lowest quintile compared to those in the highest quintile, with the largest ORs for English men (OR 21.2, p < .001), followed by English women (OR 12.0, p < .001), U.S. women (OR 7.0, p < .001), and U.S. women (OR 6.9, p < .001).

Similar to the results for frail status, we found strongly significant socioeconomic gradients in prefrailty risk for each socioeconomic measure; however, the ORs were lower overall.

Table 3 shows OR and percent difference between the main models adjusted for age and marital status only (M1), models additionally adjusted for race/ethnicity (M2), and models adjusted for all covariates, including health behaviors (M3). Adjusting for race/ethnicity did not explain much of the SES gradients in models for England, but reduced ORs by between 4% and 17% for U.S. models. Health behaviors accounted for between 9% and 30% of reductions in OR but varied by socioeconomic outcome and group. The highest reduction in ORs was generally in English women, who also had the lowest ORs in each model. The percent of the total reductions accounted for by each behavioral covariate is shown in Supplementary Table 4 and Supplementary Figure 2. In the United States, smoking accounted for greater proportions of the reduction in ORs compared to English men and women. Women and men in England had a larger contribution of obesity to the reduction in ORs compared to U.S. men and women in the education model only. For women in both countries, obesity accounted for a larger proportion of reduction in ORs compared to men in the income and wealth models. The fully adjusted model results (M3) are shown in Supplementary Tables 5A–C.

Table 3.

Difference Between Nested Logit Models for Frail Versus Robust Status, Calculated Using the Karlson–Holm–Breen (KHB) Method

Variable Women Men
United States England United States England
Difference Difference Difference Difference
A: Education (lower secondary vs. college or above)
M1a–M2b (OR) 1.24*** 1.00 1.43*** 1.10
M1–M2 (%) 9.8 0.8 17.0 2.9
M2–M3c (OR) 1.25** 1.46* 1.35** 1.45*
M2–M3 (%) 10.5 30.6 16.9 18.2
B: Household income (Q1 vs. Q5)
M1–M2 (OR) 1.18* 0.98 1.24* 1.12
M1–M2 (%) 5.1 −1.0 6.6 3.9
M2–M3 (OR) 1.45** 1.81* 1.33* 1.78*
M2–M3 (%) 12.4 22.5 8.9 21
C: Household wealth (Q1 vs. Q5)
M1–M2 (OR) 1.09* 1.00 1.15 1.06
M1–M2 (%) 4.3 1.2 6.8 1.9
M2–M3 (OR) 1.38** 1.79* 1.26* 1.67*
M2–M3 (%) 16.7 22.3 11.8 16.8
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Notes: The odds ratio (OR) for the difference is calculated by dividing the OR for the smaller model (e.g., M1) by the OR for the larger model (e.g., M2). Values above 1 indicate that the smaller model has a larger OR compared to the larger model and values below 1 indicate that the smaller model has a smaller OR compared to the larger model. A value of 1 indicates no difference between models.

aM1: Main models are adjusted for age and marital status only.

bM2: M1 + race.

cM3: M2 + behavioral covariates (smoking, alcohol, obesity).

*** p < .001; **p < .01; *p < .05; +p < .1.

Figure 1 shows the predicted probability of frailty and prefrailty by education, income, and wealth for men and women in each country, demonstrating variation in gradients in the frailty risk between each group. For education, the predicted probability of frailty was higher for women with lower secondary education in the United States compared to women with the same educational attainment in England. U.S. women in the lowest quintile of income had higher predicted probabilities of frailty compared to English women in the lowest income quintile, and U.S. men in the second quintile of income had a higher predicted probability of frailty compared to English men. Finally, the predicted probability of frailty is higher in the United States compared to England for men and women in the second and third quintile of wealth, but there were no between-country differences for those in the lowest wealth quintile. For each of the three measures, the predicted probability of frailty in the most advantaged socioeconomic groups was very similar across countries.

Figure 1.

Alt Text: Line graphs showing socioeconomic gradients in the predicted probabilities of frailty and prefrailty for men and women by education, income, and wealth.

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Predicted probability of frailty (A) and prefrailty (B) by educational attainment, household income, and household wealth. Predicted probabilities are estimated from multinomial logistic regression models adjusted for age and marital status.

The differences in predicted probabilities of prefrailty between countries are smaller overall, although there are some significant differences: U.S. women with lower secondary or high school education had higher predicted probabilities of prefrailty compared to their English counterparts, and there were significant country differences for the second and third quintiles of wealth, with U.S. participants having steeper inequalities than English participants. Predicted probabilities from adjusted models are shown in Supplementary Figure 3.

Table 4 shows the difference in predicted probabilities of frailty between the lowest and highest socioeconomic groups by country and gender. U.S. women had a significantly larger gradient in the predicted probability of frailty compared to English women for lowest versus highest education (p < .001) and lowest versus highest household income quintiles (p < .001). Among men, U.S. participants had a larger gradient in predicted probability of frailty compared to English men by income (p = .03) and a borderline difference for education (p = .07). The wealth gradients in predicted probabilities of frailty were not significantly different between countries (women: p = .31; men: p = .20). Furthermore, the income SII was significantly different between countries in both men and women and there were no significant country-differences in the wealth SII. Notably, adjusting for additional covariates did not account for the differences in predicted probability between countries (see Supplementary Table 6).

Table 4.

Differences in Predicted Probability of Frailty Between Lowest Versus Highest SES Groups, and SII for Frailty Probability Across SES Quintiles

Variable Women Men
United States England Test United States England Test
Value (95% CI) Value (95% CI) Value (95% CI) Value (95% CI)
Frail versus robust model
 Difference
  Education (lower secondary vs. college or above) 0.37 (0.29, 0.45) 0.14 (0.08, 0.19) (a) 0.32 (0.23, 0.40) 0.20 (0.11, 0.28)
  Household income (Q1 vs. Q5) 0.48 (0.40, 0.56) 0.23 (0.16, 0.31) (a) 0.44 (0.35, 0.54) 0.26 (0.15, 0.36) (a)
  Household Wealth (Q1 vs. Q5) 0.24 (0.18, 0.30) 0.27 (0.20, 0.34) 0.24 (0.16, 0.31) 0.28 (0.20, 0.37)
 SII
  Household income 0.60 (0.51, 0.68) 0.28 (0.19, 0.37) (b) 0.54 (0.44, 0.64) 0.32 (0.20, 0.43) (b)
  Household wealth 0.38 (0.31, 0.44) 0.36 (0.28, 0.44) 0.35 (0.27, 0.43) 0.38 (0.29, 0.47)
Prefrail versus Robust model
 Difference
  Education (lower secondary vs. college or above) 0.22 (0.14, 0.30) 0.13 (0.05, 0.22) 0.25 (0.16, 0.34) 0.22 (0.11, 0.32)
  Household income (Q1 vs. Q5) 0.23 (0.14, 0.32) 0.22 (0.12, 0.33) 0.31 (0.21, 0.41) 0.33 (0.20, 0.45)
  Household Wealth (Q1 vs. Q5) 0.13 (0.05, 0.20) 0.22 (0.12, 0.32) 0.19 (0.11, 0.27) 0.40 (0.29, 0.52) (a)
 SII
  Household income 0.37 (0.30, 0.45) 0.32 (0.21, 0.42) 0.48 (0.39, 0.57) 0.42 (0.31, 0.54)
  Household wealth 0.28 (0.21, 0.36) 0.33 (0.22, 0.43) 0.39 (0.31, 0.47) 0.52 (0.41, 0.62)
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Notes: SES = socioeconomic status; SII = slope indices of inequality.

“(a)” indicates significance of between-country difference determined using Wald test (p < .05).

“(b)” indicates significance of between-country difference determined by nonoverlapping confidence intervals.

For prefrailty, there was a significant country difference between the predicted probabilities of prefrailty for the lowest and highest wealth quintiles for men (p < .001). There were no other significant differences.

Discussion

Cross-national studies of socioeconomic inequalities in countries at a similar level of development further an understanding of how factors such as income, wealth, and education contribute to health advantage or disadvantage in later life in different contexts. We examined the association between socioeconomic factors and prefrailty and frailty among women and men in the United States and England, two high-income countries that also have high socioeconomic inequality.

We found large socioeconomic gradients in frailty and prefrailty for men and women in both countries. These findings are consistent with previous evidence for socioeconomic inequalities in frailty distribution in many settings (Majid et al., 2020). Using education as a socioeconomic measure, we found that the education gradient for frailty was significantly steeper for U.S. women compared to English women. This finding suggests that the educational attainment is a weaker determinant of health among older women in England; English women have a much lower educational attainment compared to U.S. women and their health could be traditionally more related to the education of their spouse or partner. This finding emphasizes the important role of country context in the relationship between gender, education, and health.

Our finding that U.S. men and women have steeper income inequalities in frailty compared to English men and women indicates that income may mean something different for frailty inequalities in each context. This finding is driven by the high predicted probability of frailty among U.S. participants in the lowest two income quintiles, which suggests that low-income U.S. older adults have lower access to the resources and activities needed to prevent frailty.

Similar to our study findings, two studies using HRS and ELSA found steeper income and education gradients in chronic illness for older adults in the United States compared to England for both genders combined (Banks, Marmot, et al., 2006; Choi et al., 2020). Other studies of different health outcomes in the United States and England found no significant differences in education and wealth inequalities between the two countries (Makaroun et al., 2017; Martinson, 2012; Zaninotto et al., 2020). Our findings may differ from these studies because frailty is a pre-disease syndrome and could be impacted differently by socioeconomic position than other outcomes.

There are many cultural and structural factors that may contribute to steeper education and income inequalities in the United States and England. Racial and ethnic inequalities are persistent in the United States and could contribute to higher frailty inequalities in the United States. Usher et al., (2021) found wide inequalities in frailty between racial/ethnic groups in the United States with non-Hispanic black populations having higher frailty prevalence, which was not accounted for by income, obesity, or chronic disease burden. Furthermore, many health-related behaviors are cultural and vary across countries: our findings agree with evidence for higher obesity rates and lower physical activity among older adults in the United States and higher excess alcohol intake among older adults in England (Banks, Marmot, et al., 2006). However, the country differences in education and income gradients persisted after controlling for race/ethnicity and behavioral risk factors, suggesting that other unmeasured factors are responsible for these differences.

One important difference between the two countries is healthcare access and affordability. Although U.S. citizens over the age of 65 receive Medicare (Kaiser Family Foundation, 2019), which may reduce health inequalities for those over 65, access to healthcare prior to 65 is socioeconomically patterned: many socioeconomically disadvantaged groups in the United States do not have access to affordable, quality, and timely healthcare, including preventative healthcare, which could contribute to health inequalities later in life (Kim & Richardson, 2012). Furthermore, early life factors have been linked to differences in health inequalities across countries and may have a larger influence on later-life health in the United States compared to England (Vanhoutte & Nazroo, 2016).

We did not find any significant between-country differences in inequalities in predicted probabilities of frailty between the lowest and highest wealth quintiles. This is in contrast to a study by Zimmer et al. (2021) which found that wealth contributes a greater advantage to duration of life without frailty compared to education in the United States compared to England. In both countries, wealth likely allows access to more resources available for health, such as quality housing and food, and can mitigate the impact of lost income due to disability and retirement.

No single socioeconomic measure completely captures socioeconomic advantage or disadvantage across the life course (Braveman et al., 2005). For example, educational attainment is established relatively early in life and can impact future income and wealth, knowledge, perspectives, and health literacy (Liefbroer & Zoutewelle-Terovan, 2021; Shavers, 2007). Furthermore, the relationship between education and health and socioeconomic trajectories may differ by context (Liefbroer & Zoutewelle-Terovan, 2021). Conversely, income is a more current and adaptive socioeconomic measure that could be influenced by short-term factors such as ill health or retirement (Shavers, 2007).

Finally, wealth provides a cumulative picture of income, inheritance, and socioeconomic advantage across the life course, which can be related to social class and saving or spending patterns as well as lifetime income (Braveman et al., 2005; Shavers, 2007). However, wealth may not be a good measure of socioeconomic status in all contexts: in our study, many participants in the lowest wealth quintile, especially in the United States, had very high negative wealth, which could have led to misclassification of socioeconomic status, as someone with high debts may not be highly disadvantaged. This would align with our findings that the predicted probability of frailty was higher in U.S. participants compared to English participants in the second quintile of wealth but not the first quintile.

Strengths, Limitations, and Future Directions

This study has some important strengths and limitations. One strength of our study is our analysis using separate models for men and women in each country, allowing identification of differing social gradients within and between genders. Differential patterning of gender and frailty in the United States and England emphasizes the importance of gender-sensitive research in understanding health inequalities at older ages to further an understanding of the social and biological mechanisms, which increase the vulnerability of women to poorer health across the life course. Other strengths of this study include the use of multiple measures of socioeconomic status, which allows us to compare socioeconomic gradients in frailty between countries of differing social, economic, and cultural environments; and the use of nationally representative harmonized data to enable comparisons between the United States and England.

One limitation of our study is our use of cross-sectional data which limits our ability to establish temporal associations between frailty and socioeconomic factors, especially using current measures of socioeconomic status, such as income. Poor health in midlife or the early development of frailty could affect an individual’s employment and income trajectories throughout mid and later life, or result in drawing down on assets, which could lead to temporal bias in the measurement of socioeconomic status using income and wealth. Another limitation of this study is that observed differences in frailty by socioeconomic status could be confounded by many unmeasured structural, environmental, behavioral, or biological factors. Future cross-national research in frailty should attempt to take into account the political, historical, and cultural contexts of the populations studied in order to identify contextual factors that improve or worsen health at older ages. A final limitation is the lack of a gold-standard frailty definition or measure which could result in potential systematic differences between countries. By using a phenotype measure which includes primarily objective measures such as weight, grip strength, and walking speed we aimed to minimize cultural or systematic differences in frailty measurement, but there still could be bias in our measure. This limitation raises an important priority for future research to determine a gold-standard frailty measure that is validated for use in cross-country comparisons.

Conclusion

Cross-national comparisons allow us to understand socioeconomic gradients between and within countries. The United States and England are countries with important similarities and differences in sociocultural context and healthcare systems, lending them to be useful comparative settings. To our knowledge, this is the first study to compare socioeconomic gradients in frailty and prefrailty between the United States and England. Our study found significant socioeconomic inequalities in frailty among older men and women in the United States and England. Our findings suggest that gradients in frailty vary by country, gender, and the socioeconomic measure used, highlighting the importance of examining how country- and gender-specific contexts contribute to inequalities in frailty and other health outcomes among older adults. As populations continue to age in the United States and England, policies that target the unequal distribution of wealth, income, and education within society should be a priority to reducing inequalities in frailty. Reducing socioeconomic inequalities in frailty in the United States and England requires addressing broader societal issues related to healthcare access and quality, economic policy, neighborhood access, and living conditions, which can have a significant impact on the health of older adults in both England and the United States.

Supplementary Material

gbae157_suppl_Supplementary_Materials
gbae157_suppl_supplementary_materials.docx (8.3MB, docx)

Contributor Information

Rachel Z Wilkie, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA.

Jennifer A Ailshire, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA.

Funding

This work was supported by the National Institute on Aging (R01AG030153). The views expressed are those of the authors and not necessarily those of the National Institute of Aging. The Harmonized HRS and Harmonized ELSA were developed by the Gateway to Global Aging team, with funding from the National Institute on Aging (R01AG030153).

Conflict of Interest

None.

Data Availability

Data used in this study is publicly available to registered users. We used the RAND HRS data, created by the RAND Corporation (https://www.rand.org/well-being/social-and-behavioral-policy/portfolios/aging-longevity/dataprod/hrs-data.html), and the Harmonized HRS C and Harmonized ELSA data, created by the Gateway to Global Aging (https://g2aging.org). The RAND HRS and Harmonized HRS C data files and codebooks are available from https://hrs.isr.umich.edu. The Harmonized ELSA data file and codebook are available from https://www.elsa-project.ac.uk/.

Author Contributions

R. Z. Wilkie helped to plan the study, carried out the data analysis, and wrote the paper. J. A. Ailshire provided overall supervision, helped to plan the study, helped with the data analysis, and contributed to manuscript revisions.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

gbae157_suppl_Supplementary_Materials
gbae157_suppl_supplementary_materials.docx (8.3MB, docx)

Data Availability Statement

Data used in this study is publicly available to registered users. We used the RAND HRS data, created by the RAND Corporation (https://www.rand.org/well-being/social-and-behavioral-policy/portfolios/aging-longevity/dataprod/hrs-data.html), and the Harmonized HRS C and Harmonized ELSA data, created by the Gateway to Global Aging (https://g2aging.org). The RAND HRS and Harmonized HRS C data files and codebooks are available from https://hrs.isr.umich.edu. The Harmonized ELSA data file and codebook are available from https://www.elsa-project.ac.uk/.

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