Table 1.

Articles assessing differences and disparities in ageism

Reference	Data source & study design	Sampling methods & sample characteristics	Ageism measure & types assessed	Groups compared	Main findings on differences/ disparities*
Abdou et al. [23]	HRS special module Data collected: 2012 Observational self-report survey study	Random subsample from nationally representative sample N=1479 Ages: ≥50, mean 65.9	Healthcare stereotype threat attributed to age Concern about ageist stereotypes	Age, in years	Older age associated with greater odds of reporting any age-related healthcare stereotype threat
Allen et al. [13]	NPHA Data collected: 2019 Observational self-report survey study	Nationally representative household sample N=2012 Ages: 50-80, mean 60.4	Everyday Ageism Scale Age stereotypes Age discrimination Internalized ageism	Age group of 50-64 v. 65-80	More ageism reported by 65-80 age group (v. 50-64)
Allen et al. [2]	NPHA Data collected: 2019 Observational self-report survey study	Nationally representative household sample N=2035 Ages: 50-80, mean 62.6	Everyday Ageism Scale [13] Age stereotypes Age discrimination Internalized ageism	Objective of identifying sociodemographic differences in ageism Age group of 50-64 v. 65-80; sex; race/ethnicity (Hispanic, non-Hispanic Black, non-Hispanic White, Other/Multiracial); marital status (married/partnered v. not); education ≤HS, some college, ≥BA/BS); annual household income (<$60k v. ≥$60k); employment status (employed v. not); metro area; region (West, Midwest, South, Northeast)	More ageism reported by 65-80 age group (v. 50-64), non-Hispanic White and Hispanic adults (v. non-Hispanic Black), those with less education and lower income, not employed, and living in rural areas and in the Midwest (v. Northeast) No differences by sex, marital status, and other pairwise race/ethnic, higher education, and region comparisons
Choi et al. [24]	HRS Data collected: 2016 Observational self-report survey study	Nationally representative sample N=5914 Ages: 50-98, mean 76.4	Attitude Toward Own Aging subscale from Philadelphia Geriatric Center Morale Scale [25] Everyday Discrimination Scale attributed to age (or age & other reasons) [26] Internalized ageism Age discrimination	Sex	No sex differences in amount of reported internalized ageism or report of any age-based discrimination.
Chopik & Giasson [27]	Project Implicit Demo Site Data collected: 2002-2015 Experimental study combined with observational self-reported survey data	Convenience sample, predominantly US (77%) N=61,732 older adults (8.8% of sample) Ages: ages ≥50	Implicit Preference for Young People Compared to Old People (Implicit Association Test [28]) Explicit Preference for Young People Compared to Old People [29] Acknowledgement of own ageist prejudices Internalized ageism	Objective of identifying sociodemographic differences in ageism Age, in years	Within the older adult portion of the sample (ages ≥50), implicit ageism trended up and both explicit ageism and acknowledgement of ageist prejudices trended down with increasing age†
Giasson et al. [30]	HRS Data collected: 2010/2012 Observational self-report survey study	National sample, not weighted to be nationally representative N=13,623 Ages: 50-101, mean 67.4	Everyday Discrimination Scale due to age (or age & other reasons) [26] Attitude Toward Own Aging subscale from Philadelphia Geriatric Center Morale Scale [25] Age discrimination Internalized ageism	Objective of identifying sociodemographic differences in ageism Age groups (50s, 60s, 70s, ≥80); sex; race (White v. non-White); marital status (married v. not); education (≤12 years v. >12 years); household wealth quintiles; employment status (working v. not)	Older age groups more likely (v. 50s age group) to attribute any discrimination to age in unadjusted and adjusted models Older age groups, men, not being married, less education, and less wealth (2 of 4 lower quintiles) associated with attributing any discrimination to age in models adjusting for sociodemographics, health, and psychological characteristics; race and employment status were not Association between reporting any age discrimination and internalized ageism moderated by age group, such that younger age groups reporting any age discrimination were more likely to report high levels of internalized ageism
Halpin et al. [31]	Emory University Senior Mentoring Program data Data collected: 2013-2014 Observational self-report survey (evaluation) study	Purposive sample of program enrollees in Georgia N=101 Ages: 64-99, mean 77.6	Concerns/anxiety about ageism-based scenarios subscale (modified) from Age-Based Rejection Sensitivity Questionnaire [32] Concern about discrimination	Age, in years; sex; race (White v. non-White); marital status (single, married, divorced/separated, widowed); household composition (lives with family, alone, other); education (<HS, HS, Associates degree, BA/BS, Graduate degree); employment status (FT, PT, unemployed, retired); religion (Christian, Jewish, other, none); religiosity (service attendance frequency)	Higher levels of concern/anxiety over ageism associated with being non-White and lower religiosity in models adjusting for sociodemographics and previous scores of concern and experiences with ageism. Age, sex, marital status, household composition, education, work status, and religion were not
Hooker et al. [33]	HRS Data collected: 2008 Observational self-report survey study	Nationally representative sample N=4,467 Ages: 51-96, mean 69.5	Everyday Discrimination Scale due to age (or age & other reasons) [26] Age discrimination	Age, in years; age groups (51-64, 65-79, ≥80); sex; race (Black, White, Other); education (≤HS v. >HS); household wealth quintiles	Report of any age discrimination was associated with older age and less education and wealth before and after adjusting for sociodemographics, health factors, and self-perceptions of aging. It was not associated with sex or race.
Maxfield et al. [34]	Data collected: 2020 Observational self-report survey study	Convenience sample, predominantly US (77%) N=1878 Ages: ≥50, mean 63.1	Age-based healthcare stereotype threat Concern about ageism stereotypes	Age, in years; sex; race (White v. all others); ethnicity (Hispanic v. not)	Those reporting any age-based healthcare stereotype threat were more likely to be older and female. No differences by race or ethnicity
McConatha et al. [35]	Data collected: 2018-2019 Observational self-report survey study	Convenience sample from mid-sized university in Pennsylvania N=117 Ages: 50-79, mean 59.6	Workplace age discrimination scale [36] Workplace generational inclusiveness subscale from Workplace Intergenerational Climate Scale [37] Negative aging meta-stereotypes scale [38] Age discrimination Age stereotypes	Faculty v. administration/staff	Mean scores on all three ageism measures did not vary by job type (faculty v. administration/staff)
Menkin et al. [39]	Worth the Walk program data Data collected: Observational self-report survey (evaluation) study	Purposive sample of hypertensive program enrollees in California N=229 Ages 60-96, mean 73.9	Expectations Regarding Aging [40] Internalized ageism	Objective of identifying sociodemographic differences in ageism Age, in years; sex; race/ethnicity (African American, Hispanic, Korean, Chinese); education (≤8th grade, 9-12th, ≥some college)	Chinese participants reported the lowest age expectations, followed by Korean, Hispanic, and African American; all pairwise differences were significant except Chinese v. Korean in unadjusted models. Pattern remained consistent after adjusting for sociodemographics, social and health variables, except African American v. Hispanic no longer differed. Being male and less education were associated with lower age expectations but age was not in adjusted models.
Phibbs & Hooker [41]	HRS special module Data collected: 2012 Observational self-report survey study	Nationally representative community residing sample N=1416 Ages: 50-102, mean 65.7	Age-based healthcare stereotype threat Concern about ageist stereotypes	Sex; race (White, Black, Other); ethnicity (Hispanic v. not); education (<HS, HS, some college/BA/BS, graduate/professional degree)	Rates of any ageism stereotype threat did not differ by sex, race, or ethnicity. Rates differed by education, such that those more education were more likely to report ageism stereotype threat
Sabik et al. [42]	Data collected: 2008 Observational self-report survey study	Convenience sample of University of Michigan alumnae, predominantly US N=244 Ages: early 60s, mean 63.4	Perceptions of age discrimination Age discrimination	Race (European American v. African American)	Perceptions of age-based discrimination scores did not differ by race
Smith et al. [43]	Experimental study	Convenience sample from Massachusetts N=166 Ages: 56-90, mean 70.6	Stereotyped written passage on age-related declines in memory read before memory test Concern about ageist stereotypes	Objective of identifying whether sociodemographics moderated ageism-health association Age, in years; education, in years; employment status (employed v. retired)	Being retired was associated with more memory errors following priming with age stereotypes, while being employed was associated with fewer. Buffering effect of more education eliminated when age-discrimination priming was present. Age did not modify association between ageism and errors
Steward & Hasche [44]	HRS Data collected: 2016 Observational self-report survey study	Nationally representative community residing sample N=4561 Ages: ≥50, mean 67.7	Self-perceptions of aging (adapted from Attitude Toward Own Aging subscale from Philadelphia Geriatric Center Morale Scale [25] and Berlin Aging Study [45]) Internalized ageism	Age, in years; sex; race (African American v. White); ethnicity (Hispanic v. not); education (<HS v. ≥HS)	Less positive self-perceptions of aging correlated with older age, being White (v. African American), and less education. Perceptions did not differ by sex or Hispanic ethnicity. In models adjusting for sociodemographics and other health-related factors, less positive self-perceptions of aging were associated with older age, male, White, non-Hispanic, and less education
Steward et al. [46]	Data collected: 2019-2021 Observational self-report survey study	Convenience sample from US Mountain West N=165 Ages: ≥50, mean 68.2	Internalized positive and negative age stereotype subscales from the Self-Stereotypes of Aging Scale [47] Internalized ageism	Age, in years; sex; race (White v. non-White); education (≤BA/BS, professional/graduate degree); employment (working v. not)	Internalized positive and negative age stereotypes subscales not correlated with age, sex, race, education, or employment. Also not associated in models adjusting for other social and health factors
Syme & Cohn [48]	Observational self-report survey study	Convenience sample with US IP addresses N=972 Ages: 50-83, mean 56.6	Aging sexual stigma (single item from Aging Sexual Knowledge and Attitudes Scale [49]) Internalized ageism	Objective of identifying whether sociodemographics moderated ageism-health association Age group (50-59 v. ≥60); sex	Associations between aging sexual stigma and both sexual activity and intimacy did not differ by sex or age group in analyses adjusting for sociodemographics, health, and partnership variables
Templeton et al. [50]	Observational self-report survey study	Convenience sample of female US physicians N=155 Ages: 60-87, mean 70.4	Age-based discrimination and age-based verbal abuse or bullying Age discrimination	Career stage (early, mid, late)	Report of any and frequent age-based discrimination highest among late career physicians, followed by early career, with mid-career physicians least likely to report this.† Report of any and frequent age-based verbal abuse and bullying highest among early career physicians, followed by late career, with mid-career physicians least likely to report this†
Vale et al. [51]	Observational self-report survey study	Convenience sample of younger and older US adults N=335 (older adult n=171, 51%) Older adult ages: 60-80	Ambivalent Ageism Scale [52] Internalized ageism	Sex; race (White v. non-White); education (HS, some college, BA/BS, graduate/professional degree); state (Ohio v. not); political affiliation (Democrat, Independent, Republican, other)	More ageism reported by non-White, less educated, not from Ohio, and Republican or Independent. Ageism did not differ by sex
Weiss & Weiss [53]	MIDUS Data collected: 2004-2009 Observational self-report survey study	Subsample of nationally representative community residing sample N=389 Ages: 61-86, mean 70.8	Essentialist beliefs about cognitive aging (3 items from Personality in Intellectual Aging Contexts Scale [54] Internalized ageism	Age, in years; sex; level of education; subjective social status (MacArthur ladder)	Higher ageism scores were correlated with lower levels of education and social status. Ageism was not correlated with age or sex. Social status moderated the association between ageism and cortisol/stress reactivity, such that low social status was associated with increased reactivity while high social status was not, both before and after accounting for demographic, health, and other covariates.
Wilson et al. [55]	PSID Data collected: 2009-2015 Observational study of employment data	Nationally representative sample of older workers N=1908 Ages: 55-67, mean 60	Occupational downward mobility among those ≥55 as proxy for age-based discrimination within employment Age discrimination	Objective of identifying sociodemographic differences in ageism Sex; race (White v. African American); privileged occupational categories (managers, professionals, blue collar supervisors, skilled/technical)	Among older workers, African Americans have higher rates of downward mobility from 4 different privileged occupations than Whites. Odds of within sex downward mobility from each privileged occupation category higher in African American males than African American females.† Odds of within sex downward mobility highest among African American managers, followed by professionals, skilled/technical, and blue collar supervisors.†

Notes: BA/BS = bachelors of arts or sciences degree, FT = full time, HRS = Health and Retirement Study, HS = high school, MIDUS = Midlife in the US study, NPHA = National Poll on Healthy Aging, PSID = Panel Study on Income Dynamics, PT = part time

^*All reported differences were statistically significant with p<.05, unless accompanied by † symbol.

^†Statistical differences not assessed.