Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2024 Jan 3.
Published in final edited form as: Soc Sci Med. 2023 Mar 10;323:115772. doi: 10.1016/j.socscimed.2023.115772

Societal impact on older persons’ chronic pain: Roles of age stereotypes, age attribution, and age discrimination

Becca R Levy a,b,*, Robert H Pietrzak a,c,d, Martin D Slade e
PMCID: PMC10763575  NIHMSID: NIHMS1944073  PMID: 36965204

Abstract

Rationale:

In view of the severity and prevalence of chronic pain, combined with the limited success of long-term treatments, there is the need for a more expansive understanding of its etiology. We therefore investigated over time three societal-based potential determinants of chronic pain that were previously unexamined in this connection: negative age stereotypes, age attribution, and age discrimination.

Methods:

The cohort consisted of 1373 Americans aged 55 and older, who participated in four waves of the National Health and Resilience in Veterans Study, spanning seven years.

Results:

Consistent with the hypotheses, negative age stereotypes as well as age discrimination predicted chronic pain, and age attribution acted as a mediator between the negative age stereotypes and chronic pain. In a subset of participants who were free of chronic pain at baseline, those who had assimilated negative age stereotypes were 32% more likely to develop chronic pain in the next seven years than those who had assimilated positive age stereotypes.

Conclusion:

Our finding that the three societal-based and modifiable predictors contributed to chronic pain refutes the widely held belief that chronic pain experienced in later life is entirely and inevitably a consequence of aging.

Keywords: Age stereotypes, Age attribution, Age discrimination, Pain, Ageism, Aging, Culture, Older persons

1. Introduction

Chronic pain, which is defined as lasting longer than six months, afflicts one out of five older Americans (Domenichiello and Ramsden, 2019). Existing treatments often have limited long-term success (Reid et al., 2015; Tennant, 2015). To develop more effective treatments for chronic pain, it would be helpful to expand our understanding of its etiology. The current study focused on three societal-based and modifiable potential determinants that previously had not been systematically investigated in connection with the experience of chronic pain: age stereotypes, age attribution, and age discrimination. The assumption that society impacts the pain experience was stated in a classical formulation: “Pain sensitivity can evoke responses beyond those appropriate, and out of all proportion to the danger of the situation,” due to, among other causes, “the attitude of the society in which the individual finds himself (Hardy et al., 1952, pp. 386–387).

A number of studies support the premise that there is a society-pain relationship (e.g., Gatchel et al., 2007 Rahim-Williams et al., 2012). For example, it was found that in Japan, 15% of women in menopause experienced pain, compared to the United States where 38% of women in menopause experienced pain; this difference is particularly noteworthy in view of the finding that the American women were substantially more likely than the Japanese women to take pain medications—62% and 14%, respectively (Lock, 1994). A complementary study showed that older Asians who immigrated to the United States tended to experience significantly greater pain than those who remained in their country of origin (Kim et al., 2020).

Although these previous studies did not consider societal determinants in general or age stereotypes, age attribution, and age discrimination in particular, they may help to explain the pattern of pain findings. For Asian society is characterized by Confucianism and a collectivist outlook that contribute to empowering views of and behavior towards older persons, in contrast to the demeaning views of and behavior towards older persons that are often found in the United States (Chung and Lin, 2012; Levy, 2022). There is, then, a link between society and the potential determinants. Whether there is, in turn, a link between these societal-based determinants and chronic pain is the question that the current study sought to answer.

The framework we used for the current study is the Stereotype Embodiment theory (SET) that posits negative age stereotypes are assimilated from the surrounding society in childhood with reinforcement throughout the lifespan and, after becoming self-relevant in later life, can have a harmful influence on a wide array of health conditions (Levy, 2009; Levy et al., 2009a). This theory has been supported by several meta-analyses (e.g., Tully-Wilson et al., 2021). Associating aging with chronic pain is one of several societal-based negative age stereotypes that contribute to viewing old age as a time of debilitation (Levy et al., 2019). That is, negative age stereotypes about pain often cluster with those related to other aspects of perceived attenuated aging health (Sarkisian et al., 2005). These factors led to our examining whether negative age stereotypes contribute to the chronic pain of older persons.

According to SET, one of the pathways along which negative age stereotypes influence health is psychological; it includes age attribution (i.e., blaming health problems, such as chronic pain, on aging rather than on extenuating circumstances), which might act as a mediator between age stereotypes and chronic pain. This tendency to blame age automatically has been found to be widespread among older individuals in the United States (e.g., Stewart et al., 2012). If this causal thinking is found to be a mediator, it would be consistent with the conceptualization by Fritz Heider, “Our reaction to a disagreeable experience, for instance of pain, is greatly influenced by the attribution to a source …” (1944, p. 367).

The first part of this mediator pathway, between negative age stereotypes and age attribution, was expected because studies have demonstrated that negative age stereotypes can lead to maladaptive thought processes, such as a perception that the detriments of life outweigh its benefits (Levy et al., 2000). The second part of the pathway, between age attribution and chronic pain, is also supported by research. Characterizing bad events as having stability (long-lasting), globality (far-reaching), and internality (coming from within oneself) is known collectively as “pessimistic explanatory style,” which can lead to a number of adverse health events (Peterson et al., 1988; Wise and Rosqvist, 2006). Because aging may be viewed as stable, global, and internal, it would follow that blaming age for bad events could lead to worse health outcomes (Levy et al., 2011; Stewart et al., 2012), among them the experience of chronic pain.

Additionally, the current study examined whether encountering age discrimination institutionally or interpersonally may elevate the experience of pain. According to SET, this discrimination is among the societal sources of age beliefs that can detrimentally impact aging health (Levy, 2009, Levy et al., 2022). In a qualitative study, it was found that some healthcare professionals act in age-discriminatory ways by dismissing their older patients’ chronic pain as inevitable (Makris et al., 2015). That study did not consider whether ageism predicted level of pain, as was done in the current study.

A clue that age discrimination contributes to elevated pain experiences among older persons can be found in research that indicates racism can heighten these experiences for Black individuals (Akinlade, 2020; Edwards et al., 2001). A range of mechanisms for this racism-pain linkage has been identified, including stress (Brown et al., 2018; Burgess et al., 2009).

We hypothesized that among older persons: (1) negative age stereotypes will predict chronic pain; (2) age attribution will act as a mediator between negative age stereotypes and chronic pain; and (3) age discrimination will predict chronic pain.

2. Methods

2.1. Participants

The cohort consisted of 1373 participants in the National Health and Resilience in Veterans Study (NHRVS). The NHRVS sample was drawn from the KnowledgePanel, a survey cohort of over 50,000 American households that is maintained by Ipsos (formerly GfK Knowledge Networks). Our study comprised four waves of data spanning seven years. This nationally-representative baseline survey was conducted in 2011, wave two in 2013, wave three in 2015, and wave four in 2018.

Inclusion criteria consisted of: served in the United States armed forces, military reserves, or National Guard; aged 55 years or older at baseline (when age stereotypes tend to become self-relevant; Levy, 2009, Levy et al., 2020); and had measures of the predictors and the outcome of chronic pain.

The average age of participants was 68 (SD = 7.88), with ages ranging from 55 to 96. They had an average of three chronic health conditions at baseline. Most of the participants were married (79.32%), attended college (85.42%), had incomes of $60,000 or more (51.06%), and were white (87.63%).

Participants with more-negative age stereotypes and more-positive age stereotypes, based on splitting the sample into those below and those equal to or above the mean, did not significantly differ by marital status, race, depression, combat exposure, or number of lifetime traumas. Those with more-negative age stereotypes were: older (68.47 vs. 67.47; t = 3.07, p = .002); more likely to be male (χ2 = 16.95, p < .001); less educated (84.53% college vs. 86.55% college; χ2 = 12.18, p = .007); less likely to have an annual income of $60,000 or more (48.75% vs. 53.99%; χ2 = 6.39, p = .011); more likely to be depressed (6.15% vs. 2.63%; χ2 = 8.48, p = .004); and had more chronic conditions (3.12 vs. 2.71; t = 5.13, p < .001).

Participants with and without chronic pain did not significantly differ by age, marital status, or gender. Those with chronic pain were: less educated (82.54% college vs. 86.13% college; χ2 = 10.00, p = .019); less likely to have an annual income of $60,000 or more (43.10% vs 53.01%; χ2 = 14.63, p < .001); less likely to be white (83.84% vs. 88.55% χ2 = 7.66, p = .006); more likely to have been exposed to combat (44.40% vs. 34.73%; χ2 = 14.99, p < .001) more likely to be depressed (10.18% vs 3.38%; χ2 = 19.29, p < .001); had more chronic conditions (4.69 vs 2.51; t = 21.35, p < .001); and more lifetime traumas (4.38 vs. 2.92; t = 9.17, p < .001).

To be conservative, all of these variables were included as covariates in all of the models to determine whether negative age stereotypes and age discrimination predicted chronic pain above and beyond these factors.

2.2. Assessments

2.2.1. Predictor: Negative age stereotypes

At baseline, negative age stereotypes were assessed using a three-item version of the Expectations Regarding Aging (ERA) questionnaire (Sarkisian et al., 2005) that consisted of the items with the strongest factor loadings on the physical health, mental health, and cognitive health age-stereotype domains of the parent scale: “Every year that people age, their energy levels go down”; “It’s normal to be depressed when you are old”; and “Forgetfulness is a natural occurrence just from growing old” (Levy et al., 2019; Sarkisian et al., 2005). This short-form version has been found to be valid (Levy et al., 2019).

We included the ERA as a continuous variable. Responses to the items were scored from 0 = definitely false to 3 = definitely true, and summed. Total scores ranged from 0 to 9, with a higher score indicating more-negative age stereotypes.

In support of the generalizability of our cohort, we found that the negativity of age stereotypes, measured with the ERA, closely resembled a sample of matched-aged non-veteran Americans (N = 319) that was also assessed in an internet survey. The ERA means of the two groups did not significantly differ, t = 1.53, p = .13, with the veteran sample having a mean of 4.32 and the non-veteran sample having a mean of 4.51. We also compared the ERA scores of participants within the veteran sample and found that those who had been exposed to combat did not significantly differ from those who had not been exposed to it, t = 0.06, p-.95.

2.2.2. Secondary predictor: Lifetime age discrimination

We assessed ageism with the 20-item Ageism Survey that records frequency of experiencing various acts of age discrimination, including: I was denied employment because of my age; and I was treated with less dignity and respect because of my age (Palmore, 2001). Participants indicated whether they had experienced each event, with possible responses ranging from 1 = never to 5 = all the time. This measure, which has been found to be valid and reliable (Palmore, 2001), was assessed at the fourth wave. In our sample, 67.82% reported experiencing one or more incidents of lifetime age discrimination.

2.2.3. Mediator: Age attribution

The measure of age attribution, assessed with two vignettes, has been previously validated (Levy et al., 2009a). In the first vignette, participants were asked: “If you wake up in the morning with an ache in your leg,” how much it would be due to an age attribution (“I seem to be getting old”). In the second vignette, participants were asked: “If you misplaced your keys,” how much it would be due to an age attribution (“I am losing my memory”). Responses were on 6-point Likert scales ranging from 1 = strongly disagree to 6 = strongly agree. Consistent with a previous study, we averaged the items to assess the general tendency to think of age as the cause of health challenges (Levy et al., 2009a), so that a higher score indicates a stronger tendency to blame aging for a bad health event. Participants tended to blame aging (M = 3.61, SD = 1.16).

2.2.4. Outcome: Chronic pain

Chronic pain was assessed with the Medical Conditions Checklist adapted from the structured interview instrument used in the National Epidemiologic Survey on Alcohol and Related Conditions (Goldstein et al., 2012), which asked, “In the past two years, has a doctor or other healthcare professional ever told you that you have any of the following medical conditions?” The item we selected for the current study was, “Chronic pain, such as low back pain, neck pain or fibromyalgia.” At baseline, 33.79% reported they had been informed that they had chronic pain. At the fourth wave, this had increased to 40.33%. These prevalence rates are similar to those found in other studies (e.g., Domenichiello and Ramsden, 2019).

2.2.5. Covariates

The covariates selected have been previously found to impact the predictors (i.e., age stereotypes, age attribution, and age discrimination) as well as the outcome (i.e., chronic pain) (Levy, 2022; Reid et al., 2015). These covariates consisted of the following variables measured at baseline: age, depression, education, income, marital status, medical conditions, sex, race, combat exposure, and number of lifetime traumas. Age was measured as a continuous variable; race was dichotomized into white or person of color; education was assessed as less than high school, high school, some college, and bachelor’s degree or higher; income was assessed as an annual income of less than $60,000 or equal to or more than $60,000; medical condition was based on the number that a health professional told participants they had; combat exposure was assessed with a single Yes-No item; and depression was assessed with the Patient Health Questionnaire-2, using the usual clinical cutoff of greater than or equal to 3 (Lowe et al., 2005). Number of lifetime traumas was assessed with the Trauma History Screen (Carlson et al., 2005), which has high reliability and validity, significantly predicting PTSD symptomatology and incidence (Hooper et al., 2011). (See Table for description of variables and correlations between them.)

Table.

Correlation Matrix of Baseline Variables

Variables Mean (SD) or % 1 2 3 4 5 6 7 8 9 10 11 12
1. Negative SA 4.32 (1.70) 1.00
2. Married 79.32% .01 1.00
3. College 85.42% −.06 −.02 1.00
4. Income 51.06% −.07 .21 .23 1.00
5. Combat 36.48% .00 .04 .03 .05 1.00
6. POC 12.37% −.03 −.07 −.02 −.01 .04 1.00
7. Pain 33.79% .11 −.04 −.04 −.08 .08 .06 1.00
8. Female 5.51% −.10 −.15 .04 −.03 −.14 .07 .00 1.00
9. Age 68.03 (7.88) .09 .02 −.01 −.10 −.02 −.12 −.04 −.19 1.00
10. Depressed 4.63% .11 −.06 .03 −.04 −.01 .04 .13 .06 −.09 1.00
11. # of Conditions 2.94 (1.96) .13 −.01 −.01 −.09 .09 .02 .41 .01 .13 .11 1.00
12. # of Traumas 3.21 (2.63) .02 −.04 .07 −.03 .23 .11 .18 .03 −.18 .09 .26 1.00
Open in a new tab

We adjusted for baseline chronic pain in all models, other than the one in which we examined new cases of chronic pain as the outcome in a subsample of participants free of chronic pain at baseline.

2.3. Analytic plan

We conducted analyses to examine whether the sample differed by level of age-stereotype negativity and whether participants had chronic pain at baseline. To examine the first hypothesis, we conducted three multiple regression analyses which took advantage of the maximum amount of time participants were followed. In a first analysis, using a logistic regression model, we evaluated whether negative age stereotypes at baseline predicted the outcome of chronic pain in the fourth wave, adjusting for baseline chronic pain as well as all covariates. In a second analysis, also using a logistic regression model, we started with the subset of participants (n = 900) without chronic pain at baseline, and again examined whether negative age stereotypes at baseline predicted the outcome of new chronic pain in the fourth wave, adjusting for all covariates. In a third analysis, we conducted a survival analysis using a proportional hazards model, with the subset of participants who were free of chronic pain at baseline, to determine whether negative age stereotypes at baseline predicted shorter time to new onset of chronic pain, adjusting for all covariates. For this analysis, we measured time to chronic-pain onset by recording whether each participant developed new chronic pain in the second, third, or fourth waves.

To examine the second hypothesis, we conducted a series of linear regression analyses, with baseline age stereotypes acting as the independent variable, age attribution acting as the presumed mediator, and chronic pain (measured in the fourth wave) acting as the outcome variable. We followed the procedure for identifying a mediator suggested by Kenny et al. (1998).

To examine the third hypothesis, we conducted a logistic-regression analysis to determine whether age discrimination measured at baseline predicted chronic pain in the fourth wave.

Lastly, we conducted a sensitivity analysis to examine whether use of pain medication impacted the pattern of results. For this analysis, we reran all of the models, adjusting for pain medication taken at baseline.

In all analyses, the predictor variables of age stereotypes, age attribution, and age discrimination were examined as continuous variables. To illustrate the incidence model of the first hypothesis (see Fig. 1), we dichotomized the age-stereotype variable into those below the mean and those at or above the mean of 4.32 to examine time-to-develop chronic pain over the following seven years. For the Figure, those below the mean were labeled the positive-age-stereotype group because 417 (73.20%) of the participants completely rejected at least two of the three negative-age-stereotype items.

Fig. 1.

Fig. 1.

Open in a new tab

Negative Age Stereotypes Predict New Cases of Chronic Pain over Time

Note: This figure is based on a proportional-hazards model that adjusted for the covariates of age, education, income, marital status, medical conditions, sex, race, combat exposure, depression, and number of lifetime traumas.

Although there were few missing data points in our sample (2.16%, all of which were within categorical variables), to prevent list-wise deletion of participants with any missing data, we utilized the missing-indicator method for categorical missing data (Zhuchkova and Rotmistrov, 2022). This involved inserting a response level of “unknown” for these missing data points. As with imputation, it allows all other values of the participant to be utilized in the models. We checked that this process did not change the pattern of significant findings, by rerunning the models in a way that allowed for list-wise deletion of the participants that had a missing value. All of the significant patterns remained the same.

We also determined that multicollinearity was not an issue for our sample. None of the correlations among variables met the most frequently used multicollinearity-diagnostic cutoff for pairwise-correlation coefficients of 0.80. In fact, none of the predictors met the more-conservative multicollinearity-diagnostic cutoff for pairwise-correlation coefficients of 0.50 (Vatcheva et al., 2016). (See Table 1)

All analyses were conducted with SAS version 9.4 (SAS Institute Inc., Cary, NC). The p-values are presented as one-tailed tests (Cumming, 2012) because we hypothesized a direction of the effects based on prior studies that found negative age stereotypes, age attribution, and age discrimination led to worse health outcomes (Levy, 2022; Levy et al., 2020; Tully-Wilson et al., 2021). To be conservative, we included all covariates in all models.

3. Results

Consistent with our first hypothesis, negative age stereotypes predicted chronic pain. This was supported by three models. In the first model, those with negative age stereotypes at baseline were significantly more likely to experience chronic pain in the fourth wave than those with positive age stereotypes at baseline, adjusting for baseline chronic pain as well as all covariates, β = .09, OR = 1.09, p = .04. In the second model, with the subset of participants who were free of chronic pain at baseline, those with negative age stereotypes at baseline were significantly more likely to develop new chronic pain over the next seven years than those with positive age stereotypes at baseline, adjusting for all covariates, β = .09, OR = 1.09, p = .04. In the third model, participants with negative age stereotypes at baseline developed chronic pain significantly faster than those with positive age stereotypes at baseline, adjusting for all covariates, HR = 1.13 (95% CI: 1.04, 1.23), p = .006. The negative-age-stereotype group had 32% higher incidence of chronic pain than the positive-age-stereotype group. (See Figure.)

Consistent with our second hypothesis, age attribution acted as a mediator between negative age stereotypes and chronic pain. Age attribution met the criteria for a full mediator, as proposed by Kenny et al. (1998). Fulfilling the first criterion, the independent variable (i.e., negative age stereotypes) predicted the outcome of chronic pain, β = 0.01, p = .04, η2 = 0.002. Fulfilling the second criterion, the independent variable significantly predicted the presumed mediator (i.e., age attribution), β = 0.18, p < .001, η2 = 0.067. Fulfilling the third criterion, the influence of the presumed mediator on the outcome of chronic pain was significant, β = 0.02, p = .04, η2 = 0.003 when we included the path between the independent variable and the outcome. Fulfilling the fourth criterion, when the mediator was included in the model, along with the independent variable, the coefficient associated with the path between the independent variable and chronic pain was no longer significant, β = 0.006, p = .19, η2 = 0.001. The Sobel test also confirmed that age attribution acted as a mediator in the model (Z = −3.11, p = .002).

Consistent with our third hypothesis, age discrimination predicted chronic pain, after adjusting for all covariates, β = 0.50, p = .02, OR = 1.65. In a model comparing the contribution of the predictor and covariates to chronic pain, it was found that the impact of age discrimination on chronic pain was greater than the impact of age, number of chronic conditions, combat exposure, income, or race (when all variables were normalized).

When we ran all the models in the sensitivity analysis with use of pain medication added as a covariate, the pattern of significant results remained.

4. Discussion

The origin of the word “pain” is poena, which in Latin means penalty or punishment (The Oxford English Dictionary, 2022). It suggests pain was seen as having roots in society. This ancient insight fits the current study’s findings. For societal-based negative age stereotypes predicted chronic pain, as hypothesized. Among participants who were free of chronic pain at baseline, those who had assimilated negative age stereotypes were 32% more likely to develop chronic pain in the following seven years than those who had assimilated positive age stereotypes (See Fig. 1.).

The process by which negative age stereotypes influenced chronic pain likely involved a feedback loop. This would entail the pain experience being amplified by stereotypes that associate it with aging. In turn, the amplification would reinforce these stereotypes. Consequently, the pain experience would be further amplified.

Through influencing the level of pain experienced, negative age stereotypes may adversely affect additional health conditions. To illustrate, a previous study showed a significantly greater recovery rate from severe disability among older persons who held more-positive age stereotypes, in contrast to those with more-negative age stereotypes (Levy et al., 2012). Pain was not considered in that study, but if those with more-positive age stereotypes experienced less of it, their physical recovery might have been expedited.

As hypothesized in the current study, we identified two additional factors that contributed to chronic pain. Specifically, age attribution acted as a pathway by which negative age stereotypes predicted chronic pain; age discrimination also predicted it. There is a likelihood that these predictors are linked, because age discrimination tends to convey a deleterious message that associates old age with incapacity; when this message, in the form of a negative age stereotype, is internalized, it can shape age attributions (Levy et al., 2009a).

The predictors had a robust impact. In a sensitivity analysis, the pattern of significant results remained when we included pain medication in the models. Further, in models with all variables normalized, each of the three predictors contributed more to chronic pain than age.

False assumptions that characterize negative age stereotypes provide a common theme for uniting the three predictors: (a) negative age stereotypes focus on debilitation, including the false assumption that chronic pain is experienced by all older persons (Davis et al., 2002; Levy, 2009, Levy et al., 2009b; Reid et al., 2015); (b) age attributions, derived from negative age stereotypes, can take the form of the false assumption that bad health events, such as chronic pain, are entirely due to aging (Levy et al., 2009b; Stewart et al., 2012); and (c) age discrimination is justified by the false assumption that older adults are unworthy of being treated in the same manner as younger adults (Butler, 2010; Levy, 2022).

4.1. Strengths and limitations

An apparent limitation of the current study is that our sample of older individuals was composed of veterans. Although it may seem they are not representative of the larger population, a comparison of our sample with age-matched non-veterans showed there was no significant difference between the two groups on the age-stereotype measure. There was not even a significant difference on this measure when veterans with and without combat exposure were compared. This primacy of age stereotypes is consistent with their being ingrained long before military service and remaining stable (Levy, 2009, Levy et al., 2009b; Levy et al., 2015).

Among the strengths of our study, we were able to show, over time, three important modifiable determinants of chronic pain for the first time. Additionally, our dataset was large and had very little missing data. Another strength was that all our participants had access to pain medications through the Veterans’ Administration which provides them to veterans throughout the United States; whereas, in the non-veteran population, access to pain medication can vary considerably by state (Davis et al., 2019). This allowed us to confirm that our predictors, rather than pain-medication access, or its absence, led to the hypothesized patterns of results.

4.2. Future research

Future research could examine whether chronic pain is affected when older persons not only hold negative age stereotypes, but also stereotypes arising from their membership in one or more other marginalized groups. That is, studies could consider whether the impact of negative age stereotypes on chronic pain will be mitigated by the coping techniques perhaps learned from a lifetime of holding marginalizing stereotypes holding other marginalizing stereotypes, or whether the latter stereotypes will compound negative age stereotypes and, therefore, magnify chronic pain. Although previous research has shown that female and Black individuals tend to experience higher levels of pain (Akinlade, 2020; Edwards et al., 2001; Macchia and Oswald, 2021) and that the confluence of prejudice directed at their groups can increase stress (Perez et al., 2023), those studies have not considered the potential intersecting impacts of age, sex, and race stereotypes on the pain experience. Filling this gap would be beneficial.

5. Conclusions

The central finding of the current study, that culture-based modifiable risk factors (i.e., negative age stereotypes, age attribution, and age discrimination) influenced chronic pain, refutes the stereotype that this pain is entirely and inevitably a consequence of aging. The stereotype is widely held in the medical world (Davis et al., 2011; Makris et al., 2015) and carries a risk of reinforcing or initiating the same misconception among patients. Our finding, therefore, could benefit healthcare professionals as well as older patients. Further, it indicates the need for interventions aimed at the societal level to reduce age discrimination and at the individual level to reduce negative age stereotypes.

Author credit

We thank the participants in our study. We also thank Michael Lombardo for creating Fig. 1. This study was supported by grants (R01AG067533 and U01AG032284) from the National Institute on Aging to the first author.

Data availability

The authors do not have permission to share data.

References

  1. Akinlade O, 2020. Taking Black pain seriously. N. Engl. J. Med. 383, e68. 10.1056/NEJMpv2024759. [DOI] [PubMed] [Google Scholar]
  2. Brown TT, Partanen J, Chuong L, Villaverde V, Chantal Griffin A, Mendelson A, 2018. Discrimination hurts: the effect of discrimination on the development of chronic pain. Soc. Sci. Med. 204, 1–8. 10.1016/j.socscimed.2018.03.015. [DOI] [PubMed] [Google Scholar]
  3. Burgess DJ, Grill J, Noorbaloochi S, Griffin JM, Ricards J, van Ryn M, Partin MR, 2009. The effect of perceived racial discrimination on bodily pain among older African American men. Pain Med. 10, 1341–1352. 10.1111/j.1526-4637.2009.00742.x. [DOI] [PubMed] [Google Scholar]
  4. Butler R, 2010. The Longevity Revolution. The Benefits and Challenges of Living a Long Life. PublicAffairs, New York. [Google Scholar]
  5. Carlson E, Palmieri P, Smith S, Kimerling R, Ruzek J, Burling T, 2005. The trauma history screen (THS). Available from: http://www.ptsd.va.gov. [DOI] [PMC free article] [PubMed]
  6. Chung C, Lin Z, 2012. A cross-cultural examination of the positivity effect in memory: United States vs. China. Int. J. Aging Hum. Dev. 75, 31–44. [DOI] [PubMed] [Google Scholar]
  7. Cumming G, 2012. Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta Analyses. Routledge, New York. [Google Scholar]
  8. Davis GC, Hiemenz ML, White TL, 2002. Barriers to managing chronic pain of older adults with arthritis. J. Nurs. Scholarsh. 34, 121–126. 10.1111/j.1547-5069.2002.00121.x. [DOI] [PubMed] [Google Scholar]
  9. Davis MM, Bond LA, Howard A, Sarkisian CA, 2011. Primary care clinician expectations regarding aging. Gerontol. 51, 856–866. 10.1093/geront/gnr017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Davis CS, Lieberman AJ, Hernandez-Delgado H, Suba C, 2019. Laws limiting the prescribing or dispensing of opioids for acute pain in the United States: a national systematic legal review. Drug Alcohol Depend. 194, 166–172. 10.1016/j.drugalcdep.2018.09.022. [DOI] [PubMed] [Google Scholar]
  11. Domenichiello AF, Ramsden CE, 2019. The silent epidemic of chronic pain in older adults. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 93, 284–290. 10.1016/j.pnpbp.2019.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Edwards RR, Doleys DM, Fillingim RB, Lowery D, 2001. Ethnic differences in pain tolerance: clinical implications in a chronic pain population. Psychosom. Med. 63 10.1097/00006842-200103000-00018, 316–23. [DOI] [PubMed] [Google Scholar]
  13. Gatchel RJ, Peng YB, Peters ML, Fuchs PN, Turk DC, 2007. The biopsychosocial approach to chronic pain: scientific advances and future directions. Psychol. Bull. 133, 581–624. 10.1037/0033-2909.133.4.581. [DOI] [PubMed] [Google Scholar]
  14. Goldstein RB, Dawson DA, Chou SP, Grant BF, 2012. Sex differences in prevalence and comorbidity of alcohol and drug use disorders: results from wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. J. Stud. Alcohol Drugs 73, 938–950. 10.15288/jsad.2012.73.938. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hardy JD, Wolff HG, Goodell H, 1952. Pain Sensations and Reactions. Williams & Wilkins, Baltimore. [Google Scholar]
  16. Heider F, 1944. Social perception and phenomenal causality. Psychol. Rev. 51, 358–374. 10.1037/h0055425. [DOI] [Google Scholar]
  17. Hooper LM, Stockton P, Krupnick JL, Green BL, 2011. Development, use, and psychometric properties of the trauma history questionnaire. J. Loss Trauma 16, 258–283. 10.1080/15325024.2011.572035. [DOI] [Google Scholar]
  18. Kenny DA, Kashy DA, Bolger N, 1998. Data analysis in social psychology. In: Gilbert DT, Fiske ST, Lindzey G (Eds.), The Handbook of Social Psychology. McGraw-Hill, New York, pp. 233–265. [Google Scholar]
  19. Kim HJ, Chang SJ, Park H, Choi SW, Juon H, Lee K, Hyunju Ryu H, 2020. Intra-ethnic differences in chronic pain and the associated factors: an exploratory, comparative design. J. Nurs. Scholarsh. 52, 89–396. 10.1111/jnu.12564. [DOI] [PubMed] [Google Scholar]
  20. Levy BR, 2009. Stereotype embodiment: a psychosocial approach to aging. Curr. Dir. Psychol. Sci. 18, 332–336. 10.1111/j.1467-8721.2009.01662.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Levy BR, 2022. Breaking the Age Code: How Your Age Beliefs Determine How Long and Well You Live. HarperCollins, New York. [Google Scholar]
  22. Levy BR, Ashman O, Dror IE, 2000. To be or not to be: the effects of aging stereotypes on the will to live. Omega 40, 409–420. 10.2190/Y2GE-BVYQ-NF0E-83VR. [DOI] [PubMed] [Google Scholar]
  23. Levy BR, Ashman O, Slade MD, 2009a. Age attributions and aging health: contrast between the United States and Japan. J. Gerontol. B Psychol. Sci. Soc. Sci. 64, 335–338. 10.1093/geronb/gbp002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Levy BR, Zonderman AB, Slade MD, Ferrucci L, 2009b. Age stereotypes held earlier in life predict cardiovascular events in later life. Psychol. Sci. 20, 296–298. 10.1111/j.1467-9280.2009.02298.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Levy BR, Chung P, Canavan M, 2011. Impact of explanatory style and age stereotypes on health across the lifespan. In: Fingerman KL, Berg CA, Smith J, Antonucci TC (Eds.), Handbook of Life-Span Psychology. Springer, New York, pp. 437–456. [Google Scholar]
  26. Levy BR, Slade MD, Murphy TE, Gill TM, 2012. Association between positive age stereotypes and recovery from disability in older persons. JAMA 308, 1972–1973. 10.1001/jama.2012.14541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Levy BR, Slade MD, Chung PH, Gill TM, 2015. Resiliency over time of elders’ age stereotypes after encountering stressful events. J. Gerontol. B Psychol. Sci. Soc. Sci. 70, 886–890. 10.1093/geronb/gbu082. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Levy BR, Chung PH, Slade MD, Van Ness PH, Pietrzak RH, 2019. Active coping shields against negative aging self-stereotypes contributing to psychiatric conditions. Soc. Sci. Med. 228, 25–29. 10.1016/j.socscimed.2019.02.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Levy BR, Slade MD, Chang E, Kannoth S, Wang S, 2020. Ageism amplifies cost and prevalence of health conditions. Gerontol. 60, 174–181. 10.1093/geront/gny131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Levy BR, Chang ES, Lowe SR, Provolo N, Slade MD, 2022. Impact of media-based negative and positive age stereotypes on older individuals’ mental health. J. Gerontol. B Psychol. Sci. Soc. Sci. 77, e70–e75. 10.1093/geronb/gbab085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lock M, 1994. Menopause in cultural context. Exp. Gerontol. 29, 307–317. 10.1016/0531-5565(94)90011-6. [DOI] [PubMed] [Google Scholar]
  32. Lowe B, Kroenke K, Grafe K, 2005. Detecting and monitoring depression with a two-item questionnaire (PHQ-2). J. Psychosom. Res. 58, 163–171. [DOI] [PubMed] [Google Scholar]
  33. Macchia L, Oswald AJ, 2021. Physical pain, gender, and the state of the economy in 146 nations. Soc. Sci. Med. 287 10.1016/j.socscimed.2021.114332. [DOI] [PubMed] [Google Scholar]
  34. Makris UE, Higashi RT, Marks EG, Fraenkel L, Sale JE, Gill TM, Reid MC, 2015. Ageism, negative attitudes, and competing co-morbidities–why older adults may not seek care for restricting back pain: a qualitative study. BMC Geriatr. 15, 39. 10.1186/s12877-015-0042-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Palmore EB, 2001. The ageism survey: first findings. Geront 41, 572–575. 10.1093/geront/41.5.572. [DOI] [PubMed] [Google Scholar]
  36. Perez AD, Dufault SM, Spears EC, Chae DH, Woods-Giscombe CL, Allen AM, 2023. Superwoman schema and John Henryism among African American women: an intersectional perspective on coping with racism. Soc. Sci. Med. 316, 115070 10.1016/j.socscimed.2022.115070. [DOI] [PubMed] [Google Scholar]
  37. Peterson C, Seligman ME, Vaillant GE, 1988. Pessimistic explanatory style is a risk factor for physical illness: a thirty-five-year longitudinal study. J. Pers. Soc. Psychol. 55, 23–27. 10.1037/0022-3514.55.1.23. [DOI] [PubMed] [Google Scholar]
  38. Rahim-Williams B, Riley JL, Williams AK, Fillingim RB, 2012. Quantitative review of ethnic group differences in experimental pain response: do biology, psychology, and culture matter? Pain Med. 13, 522–540. 10.1111/j.1526-4637.2012.01336.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Reid MC, Eccleston C, Pillemer K, 2015. Management of chronic pain in older adults. BMJ 13, 350. 10.1136/bmj.h532 h532. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Sarkisian CA, Steers WN, Hays RD, Mangione CM, 2005. Development of the 12-item expectations regarding aging survey. Geront 45, 240–248. 10.1093/geront/45.2.240. [DOI] [PubMed] [Google Scholar]
  41. Stewart TL, Chipperfield JG, Perry RP, Weiner B, 2012. Attributing illness to ‘old age:’ consequences of a self-directed stereotype for health and mortality. Psychol. Health 27, 881–897. 10.1080/08870446.2011.630735. [DOI] [PubMed] [Google Scholar]
  42. Tennant F, 2015. Chronic pain patients who fail standard treatment. Pract. Pain Manag. 15. [DOI] [PubMed] [Google Scholar]
  43. The Oxford English Dictionary online, 2022. Oxford University Press. December. (Accessed 20 January 2023). Accessed. [Google Scholar]
  44. Tully-Wilson C, Bojack R, Millear PM, Stallman HM, Allen A, Mason J, 2021. Self-perceptions of aging: a systematic review of longitudinal studies. Psychol. Aging 36, 773–789. 10.1037/pag0000638. [DOI] [PubMed] [Google Scholar]
  45. Vatcheva KP, Lee M, McCormick JB, Rahbar MH, 2016. Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiology 227. 10.4172/2161-1165.1000227. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Wise D, Rosqvist J, 2006. Explanatory style and well-being. In: Thomas JC, Segal DL, Hersen M (Eds.), Comprehensive Handbook of Personality and Psychopathology, 1. Personality and Everyday Functioning. John Wiley & Sons Inc., New York, pp. 285–305. [Google Scholar]
  47. Zhuchkova S, Rotmistrov A, 2022. How to choose an approach to handling missing categorical data: (un) expected findings from a simulated statistical experiment. Qual. Quantity 56, 1–22. 10.1007/s11135-021-01114-w. [DOI] [Google Scholar]

Associated Data

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

Data Availability Statement

The authors do not have permission to share data.

RESOURCES