Age Differences in Experiences of Pandemic-Related Health and Economic Challenges Among Adults Aged 55 and Older

Abstract

Background and Objectives

The oldest adults faced the highest risk of death and hospitalization from coronavirus disease 2019 (COVID-19), but less is known about whether they also were the most likely to experience pandemic-related economic, healthcare, and mental health challenges. Guided by prior research on vulnerability versus resilience among older adults, the current study investigated age differences in economic hardship, delays in medical care, and mental health outcomes among adults aged 55 and older.

Research Design and Methods

Data were from the COVID-19 module and Leave Behind Questionnaire in the 2020 Health and Retirement Study (HRS). We estimated linear probability models to examine differences in experiences of pandemic-related economic and health challenges by age group (55–64, 65–74, 75+) with and without controls for preexisting sociodemographic, social program, health, and economic characteristics from the 2018 HRS. Models accounting for differential mortality also were estimated.

Results

Adults aged 65–74 and 75+ experienced fewer economic and mental health challenges and those aged 75+ were less likely to delay medical care than adults aged 55–64. Age gradients were consistent across a broad range of measures and were robust to including controls. For all age groups, economic challenges were less common than delays in medical care or experiences of loneliness, stress, or being emotionally overwhelmed.

Discussion and Implications

Even though the oldest adults were at the greatest risk of death and hospitalization from COVID-19, they experienced fewer secondary pandemic-related challenges. Future research should continue to explore the sources of this resilience for older adults.

People aged 50 and older make up about one third of the national population, but account for 71% of coronavirus disease 2019 (COVID-19)-related hospitalizations, 76% of intensive care unit admission (U.S. Center for Disease Control and Prevention, n.d.-a), and 93% of COVID-19 deaths (U.S. Center for Disease Control and Prevention, n.d.-b). Beyond COVID-19 deaths, one third of adults aged 55+ delayed medical care during the pandemic and 20% of these older adults believed that these delays harmed their health (Zhong et al., 2022). Food insecurity among adults aged 60+ increased (Ziliak, 2021) and those who experienced food insecurity were more likely to delay or forgo medical care because of cost concerns (Bertoldo et al., 2022). Anxiety about developing COVID-19 (Pearman et al., 2021) and expected income declines as a result of COVID-19 (Whitehead, 2021) were positively associated with perceived stress among adults aged 60+.

Among adults age 50+, there is a strong age gradient in COVID-19 deaths and hospitalizations, with 57% of deaths in this age group occurring among those aged 75 and older and 65% of hospitalizations in this age group occurring among those aged 65 and older (U.S. Center for Disease Control and Prevention, n.d.-a, n.d.-b). The age disadvantage in COVID-19 deaths and hospitalizations among the oldest adults have remained throughout the pandemic. Yet, whether age differences among adults aged 50+ in physical vulnerability during the COVID-19 pandemic translated into age gaps in vulnerability to other pandemic-related hardships remains unknown. Theoretical predictions on the magnitude and direction of age gradients in other pandemic-related hardships among adults aged 50+ are ambiguous because age exerts both positive and negative effects on the emotional and financial resources to cope with adverse experiences.

On the one hand, old age is often characterized as a time of loss through deteriorating health, losing friends or family members, and declining cognitive functioning, all of which diminish older adults’ capacity to use effective coping strategies in the face of adverse life events. Thus, although adverse life events reduce one’s sense of control in life at any age, the erosive effect is particularly pronounced among the oldest adults (Cairney & Krause, 2008). Emotional well-being at older ages is particularly threatened when stressors are sustained and affect multiple life domains (Charles, 2010; Sun & Sauter, 2021). Thus, we might expect increased health risks, decreased access to healthcare, the loss of personal and social resources during the pandemic, and the sustained and systematic impacts of the pandemic to have made the oldest adults more vulnerable to adverse mental health outcomes during the pandemic relative to adults in late middle age.

The oldest adults also may have been more financially vulnerable to pandemic-related economic shocks because they have less education, are more likely to be female, and are more likely to be unmarried, all of which are correlated with financial fragility (Lusardi et al., 2011). They are also less likely to have been eligible for expanded unemployment insurance (UI) during the pandemic and have lower take-up rates for Supplemental Nutrition Assistance Program (SNAP) benefits (Ziliak, 2021). These programs were a crucial part of the public safety net during the pandemic and so we might expect the oldest adults to be more vulnerable to economic hardship. Finally, the oldest adults have more interactions with the healthcare system and thus might be more likely to report delaying care during the pandemic.

On the other hand, older adults are resilient. Older adults are better than younger adults at emotional regulation because they accrue experience and knowledge from years lived (Charles, 2010). Due to shorter subjective time horizons, older adults are more adept at regulating emotional states to prioritize well-being in the present relative to future goals (Carstensen, 2006). Evidence from prior physical disasters supports the theory that older adults may be more psychologically resilient even if they are more vulnerable in their physical health (Strough et al., 2023). These factors point to fewer mental health challenges among older adults relative to those in late middle age.

The oldest adults also may have faced fewer economic challenges during the pandemic because they rely on sources of income such as pensions and Social Security, which are more stable (Mitchell et al., 2022). Evidence from the Great Recession suggests that older adults were more financially resilient than younger adults (Pfeffer et al., 2013). Those aged 65+ also may have been less likely to delay medical care because health insurance through Medicare is not tied to employment and thus would not be disrupted by a spell of unemployment. Resilience at older ages also affects the way in which hardships affect mental health. A study conducted by Mirowsky and Ross (2001) showed that both new and persistent economic hardship were associated with lower levels of depressive symptoms for older adults relative to younger adults, in support of the age-as-maturity hypothesis (Mirowsky & Ross, 1992). How the pandemic affected this push-and-pull between vulnerability and resilience at older ages remains an open question.

Empirical evidence on age differences among adults in late middle age and older in experiences of pandemic-related challenges is limited and mixed. For mental health outcomes, some studies showed that during the pandemic, loneliness (Choi, Farina, et al., 2022), anxiety (Zhu & Upenieks, 2022), and depression (Bruine de Bruin, 2021) declined with age among older adults, while other studies found no age gradient in perceived isolation and loneliness among older adults (Peng & Roth, 2022), or evidence that concerns about COVID-19 increased with age (Lin & Liu, 2022). Economic hardship during the pandemic was lower at older ages (Choi, Carr, et al., 2022; Clark et al., 2021; Clark & Mitchell, 2022; Ziliak, 2021), but this age variation in economic hardship has received considerably less attention, perhaps because it is often indicated only by statistical controls for age. At older ages, adults also were less likely to delay medical care (Zhong et al., 2022).

As the summary of past research suggests, many studies have documented the influence of COVID-19 on older adults’ well-being, but these studies provide only piecemeal evidence of age differences among adults in late middle age and older in pandemic-related challenges, despite the clear age gradient in death and hospitalization from COVID-19. When evidence exists, it is usually across a single domain, such as economic well-being, or mental health, or access to care, or may be discerned only from estimates of age coefficients where age is included as a control variable. Existing studies that focus on age differences in outcomes during the pandemic examine them between younger and older adults but not among adults in late middle age and older (Barber & Kim, 2021; Birditt et al., 2021; Carney et al., 2021; Zhu & Upenieks, 2022). To date, no study has provided the rich descriptive picture necessary to understand age differences among adults aged 50+ in experiences of pandemic-related challenges. We lack evidence across multiple domains of well-being to inform understanding of how the pandemic affected age gradients in the vulnerability or resilience of older adults beyond the increased risks to physical health posed by the disease itself.

To address these limitations, we use data from the 2020 Health and Retirement Study (HRS), a nationally representative sample, with interviews collected between March 2020 and May 2021, to assess the unique challenges that the first year of the pandemic posed to the economic well-being, access to healthcare, and mental health of Americans aged 55+ and whether there are age differences among these adults in their experiences during the pandemic. The pandemic-related economic challenges we consider include missing credit card and other bill payments and not being able to afford food. We also examine reports of delaying medical care. For mental health, we examine loneliness, lack of in-person contact, too much time with household members, feeling emotionally overwhelmed, and stress. We use the panel structure of the data to control for a rich set of preexisting demographic, economic, and health characteristics that affect economic and health outcomes, and we assess whether these controls reduce age gradients in pandemic challenges. We also use information on deaths between waves to evaluate whether differential mortality by age affects our conclusions.

Method

Data came from the HRS, a nationally representative survey of noninstitutionalized individuals aged 51 and older in the United States, which is sponsored by the National Institute on Aging and is conducted by the University of Michigan. The HRS began interviewing a cohort of individuals aged 51–61 in 1992 and reinterviews have been conducted every other year. Starting in 1998, additional cohorts were added to the study to make the sample representative of individuals aged 51 and older. The most recent refresher sample was added in 2016, making the sample in 2020 representative of individuals aged 55 and older.

Interviewing for the 2020 HRS started in March 2020 but the COVID-19 module on experiences during the pandemic that we used in our analysis was not added to the survey until May 2020 (HRS, 2020a). We also used the 2020 Leave Behind Questionnaire (LBQ), which included a battery of psychological functioning measures (HRS, 2020b). The LBQ is a part of each biennial wave of the HRS; all respondents are randomly split into two groups, each of which receives the LBQ to complete every other wave. The 2020 LBQ was sent out starting May–June 2020 and included a set of COVID-related questions on mental health. Control variables including sociodemographic characteristics, social program benefits, health, and economic resources of respondents were taken from the 2018 HRS core interview (HRS, 2018). The data are ideal for this study because the COVID-19 module and LBQ provide rich information on self-reported economic hardship, delays in medical care, and mental health challenges during the ongoing pandemic, and because the panel design of the core HRS data facilitated adjusting for differences in respondents’ sociodemographic characteristics, social program benefits, health, and economic resources prior to the pandemic. All analyses were weighted using the 2020 HRS person weights to account for the unequal probability of selection and nonresponse (Ofstedal et al., 2011).

Analytic Sample

In total, 15,723 respondents were interviewed in the 2020 HRS core interview. We limited the sample to study participants who responded to both the 2018 and 2020 HRS core interviews (n = 14,257), because we adjusted for prepandemic differences. We also excluded respondents who were interviewed before the COVID-19 module became available (i.e., prior to May 2020, n = 5,091), who resided in nursing homes in either 2018 or 2020 (n = 189), who were younger than age 55 in 2020 (n = 492), and those whose race information was missing (n = 7) or who identified themselves as other in the race category (n = 423). Nonresponse on any outcome variables were listwise deleted (n = 120), yielding a final sample of 7,935 respondents. Of them, 3,154 were aged 55–64, 2,439 were aged 65–74, and 2,342 were aged 75 and older. The main source of selection in our analytic sample was the timing of the interview. Thus, our estimates of economic hardship and delays in medical care, when weighted, represent adults aged 55 and older who were interviewed from May 2020 to May 2021.

Information on mental health outcomes comes from the 5,961 respondents who were randomly assigned to the 2020 LBQ; 3,943 answered and returned the questionnaire. Nonresponse on any outcome variables were listwise deleted (n = 255), yielding 3,688 respondents. Of these, 1,250 were aged 55–64, 1,228 were aged 65–74, and 1,210 were aged 75 and older. The response rates for the 2020 core interview and the 2020 LBQ are 74% (HRS, 2023) and 62% (authors’ calculation based on HRS technical guidance), respectively. Nonresponse is higher for the LBQ than the core interview, likely because the LBQ is conducted by mail. The results in Supplementary Table 1 indicate that, in general, LBQ respondents were more likely to be older, non-Hispanic White, college-educated, partnered, not in the labor force, and had fewer children, more income and wealth, and better health than those who were eligible to respond but did not. Because advantaged older adults were more likely to respond to the LBQ, the estimates of mental health challenges we report may be lower than in the overall population.

We use information on deaths between the 2018 and 2020 waves of the HRS to assess whether differential mortality alters our conclusions about age differences in pandemic-related challenges. Among respondents who met our sample criteria, 815 were identified by the HRS Tracker File as known or presumed to have died between 2018 and 2020.

Measures

Economic hardships came from the COVID-19 module. Respondents were asked if they had experienced any of the following challenges since the beginning of the pandemic: missing any regular payments on rent or mortgage; credit cards or other debt; or other regular payments such as utilities or insurance; not paying medical bills; or not having enough money to buy food. For each item, a value of 1 was assigned if the respondent reported experiencing the hardship and 0 otherwise. We also created a summary indicator for experiencing any economic hardship (1 = yes, 0 = no).

Delayed medical care also was gauged in the COVID-19 module by asking respondents whether they delayed medical care since March 2020 (1 = yes, 0 = no).

Mental health measures came from the LBQ. Respondents were asked how often since the COVID-19 pandemic they felt lonely, emotionally overwhelmed, or stressed; did not get enough in-person contact with people outside their household; and shared too much time with other people in their household. We dichotomized these outcomes by recoding responses of hardly ever or never to take on a value of 0 and responses of often or sometimes to take on a value of 1. Because the experiences of mental health challenges during the pandemic were quite common, we created a summary indicator of multiple mental health challenges if the respondent reported experiencing more than one of these challenges (1 = yes, 0 = no).

In our analyses, we controlled for a comprehensive set of preexisting characteristics that are associated with age and economic and health problems, including sociodemographic characteristics, social programs, health conditions and behaviors, and economic resources, all observed in the 2018 core interview. We added each set of characteristics sequentially, building to a full model with all preexisting characteristics, to assess whether they affect the age differences (55–64, 65–74, 75+) in pandemic-related hardship. Sociodemographic characteristics included gender, race–ethnicity (non-Hispanic White, non-Hispanic Black, U.S.-born Hispanic, and foreign-born Hispanic), educational attainment, whether the respondent lived alone, partnership status, number of children, and whether at least one of the respondent’s children lived within 10 miles of the respondent. Social programs included Social Security disability insurance (SSDI) payments, Social Security payments (retirement, spouse, or widow benefits), UI income, and simulated 2020 COVID-19 Economic Impact Payments (EIPs), as well as whether the respondent or spouse/partner was enrolled in Medicaid. Medicaid receipt, Social Security and SSDI payments, and simulated 2020 EIPs were based on data from the 2018 core interview, and UI was based on the 2020 COVID-19 module. Health conditions and behaviors consisted of self-rated health, number of chronic conditions, number of sleeping problems, number of depressive symptoms, number of doctor’s visits, body mass index, physical activity level, level of chronic pain, average number of drinks per week, and numbers of activities of daily living and instrumental activities of daily living limitations. Economic resources included employment status, homeownership, household income, and wealth. Supplementary Table 2 indicates how each of these variables was coded and Supplementary Table 3 displays the means of all control variables by age group.

Analytic Strategy

We conducted three sets of analyses. First, we calculated weighted proportions to describe economic and health challenges among the population aged 55+ and how these varied by age groups (55–64, 65–74, 75+). Second, using linear probability regressions, we estimated the differences across age groups in economic hardship, delays in medical care, and mental health challenges during the pandemic while adding controls for preexisting sociodemographic characteristics, social program benefits, health conditions and behaviors, and economic resources, sequentially, with the final model including all control variables. The 2018 measures were chosen as controls to avoid using endogenously or jointly determined characteristics. For the same reasons, where possible, we simulated sources of governmental support during the pandemic based on preexisting characteristics. Because the 2020 HRS was in the field until May 2021, we controlled for month of interview to take account of any time trends in hardship over the course of the pandemic. We also examined the results using logit models (see Supplementary Tables 6 and 7) and our conclusions are robust to this alternative specification. In the final analyses that we present, we used a Heckman selection model (1979) to assess whether differential mortality affected the age gradients for each outcome, where the exclusion restrictions from the outcome equations were specific health conditions (whether the respondent has ever been told by a doctor that they have cancer, psychiatric problems, arthritis, diabetes, hypertension, lung disease, heart disease, heart attack, and stroke), whether the respondent smokes, and the respondent’s subjective longevity (see the list of variables used as exclusion restrictions in Supplementary Table 2).

Results

Table 1 displays the weighted proportions of the outcome variables for all age groups and by age. Superscript a indicates the tests of statistical significance of differences in proportions between those aged 65–74 (or age 75+) and those aged 55–64. Superscript b indicates the tests of statistical significance of differences in proportions between those aged 65–74 and aged 75+. Panel A presents results for experiencing economic hardship during the pandemic. Economic hardship was not that common among adults aged 55+, with less than 7% of adults experiencing each type of hardship and 14% experiencing any type of hardship. For each measure of economic hardship, there was a clear negative age gradient, with less hardship reported by adults aged 65–74 than by those aged 55–64. For all but one measure (could not afford food), adults aged 75+ experienced less hardship than those aged 65–74. As shown in Panel B, delaying medical care was more common than experiencing economic hardship. Nearly one third of those aged 55+ reported delaying medical care after the onset of the pandemic. There also was a negative age gradient for delays in medical care. Less than one quarter of respondents in the oldest age group (75+) reported delaying medical care compared to one third of respondents aged 65–74 and 36% of respondents aged 55–64.

Panel C of Table 1 presents results for experiencing mental health challenges. Mental health challenges were more prevalent than economic hardship and delays in medical care. Slightly less than half of adults aged 55+ reported experiencing loneliness during the first year of the pandemic, 69% of older adults stated not enough in-person contact with people outside their household, 27% said they shared too much time with other people in their households, 45% reported feeling emotionally overwhelmed, and 60% were stressed. In total, two thirds of older adults reported experiencing multiple mental health challenges during the pandemic. Among all adults aged 55+, experiencing multiple mental health challenges was twice as common (=0.66/0.32) as experiencing delays in medical care and nearly five times more common (=0.66/0.14) than experiencing any economic hardship.

Except for loneliness and not having enough in-person contact, all mental health challenges had a negative age gradient, that is, were less common among adults aged 65+ than those aged 55–64. Reports of not enough in-person contact was the only hardship that those aged 65+ were more likely to experience (6 percentage points more common than for those aged 55–64). Adults aged 75+ and those aged 65–74 were similarly likely to experience mental health challenges except that those aged 75+ were 9 percentage points less likely to feel stressed.

Figure 1 presents the regression-adjusted age differences (and 95% confidence intervals) in experiences of any economic hardship, delayed medical care, or multiple mental health challenges during the pandemic using the full set of controls. After adjusting for all observed preexisting characteristics, adults aged 65–74 were 6.8 percentage points less likely to experience any economic hardship, and 6.2 percentage points less likely to experience multiple mental health challenges relative to those aged 55–64. There was no statistically significant difference in delayed medical care between adults aged 65–74 and those 55–64. Adults aged 75+ were 10.3 percentage points less likely to experience any economic hardship, 10.5 percentage points less likely to delay medical care, and 9.8 percentage points less likely to experience multiple mental health challenges than those aged 55–64. These differences were all statistically significant.

Table 2 presents more detailed regression-adjusted evidence on age differences for each outcome of interest and when control variables are added sequentially, culminating in a model including all controls. Marginal effects of age (shown in columns 1–4) were measured relative to the reference group of those aged 55–64 (see Supplementary Table 4 for full regression results). The final column shows tests of statistical significance of differences in marginal effects between those aged 65–74 and 75+. Panel A displays the results for economic hardships. For every measure of economic hardship and regardless of the controls used, adults aged 65–74 and 75+ were less likely to experience each hardship relative to those aged 55–64. The age gradients are stable across specifications although they generally decline somewhat in magnitude when controls for economic resources prior to the pandemic were included in Model 4. Adults aged 65+ had higher levels of wealth but lower levels of income than those aged 55–64 (see Supplementary Table 3) and these differences were controlled in Model 4. The results in the last column of the table indicate that for this final model the age differences between those aged 65–74 and 75+ were statistically significant at the 10% level for each outcome and at the 5% level for all outcomes except missing rent or mortgage payments.

Results in Panel B of Table 2 indicate that delaying medical care was less common among those aged 75+ relative to those aged 55–64 and that the difference is stable across different model specifications, but somewhat decreased in magnitude when preexisting health conditions were added in Model 3. The average difference in delaying medical care between those aged 55–64 and 65–74 shown in Table 1 was reduced after controlling for a broad set of characteristics and did not reach statistical significance (see Table 2 Panel B and Figure 1). This suggests that preexisting sociodemographic characteristics, social programs, health conditions, and economic resources are sources of age differences in delays in medical care among those under age 75 during the first year of the pandemic (see Supplementary Table 4 for full regression results).

Panel C of Table 2 presents the results for age differences in mental health challenges during the pandemic using data from the LBQ (Supplementary Table 5 contains full results for these regressions). We found that loneliness declined with age (differences statistically significant at the 10% level for ages 65–74 and 5% level for ages 75+) after adding all controls in Model 4. As shown in Table 1, without these controls there were no age differences for this outcome. Adjusting for economic characteristics, in particular, increased age differences in loneliness. We found that the unadjusted age differences in not having enough in-person contact at older ages shown in Table 1 were not robust to controlling for socioeconomic characteristics and other factors. As shown in Table 2, the age gradient for this outcome was small and not statistically significant. Reporting spending too much time with household members was 9.2 percentage points less common for those aged 75+ compared to those aged 55–64 and the gap is relatively stable across specifications. Differences in this outcome between those aged 65–74 and 55–64 were not statistically significant.

Consistent with Table 1, there were clearer age differences in feeling emotionally overwhelmed and stressed. With the full set of controls, adults aged 65–74 (aged 75+) were 7.4 (10.5) percentage points less likely to report feeling emotionally overwhelmed, and 5.5 (12.1) percentage points less likely to report feeling stressed than their younger counterparts aged 55–64. Differences in feelings of stress between those aged 65–74 and aged 75+ also were statistically significant.

One concern with the age gradients we have shown is that deaths among the most vulnerable older adults between the 2018 and 2020 waves may bias our estimates. In the most extreme example, older adults who were most vulnerable to COVID-19 also may have been particularly vulnerable to economic challenges, delays in medical care, and poor mental health outcomes, and they may have died of the disease prior to being interviewed. This would bias our findings in the direction of finding fewer pandemic-related challenges for older adults and reduce the age gradients between older adults aged 55–64 and older adults aged 65+. Table 3 (columns 1 and 2) displays marginal effects relative to those aged 55–64 from a Heckman selection model to correct for the potential selection bias in the differential survival of those observations used to estimate these outcomes for Model 4 with the full set of controls. The final column shows the tests of statistical significance of differences in marginal effects between those aged 65–74 and 75+. Panel A displays the results for economic hardship. We find that age differences in experiences of economic hardship between those aged 65–74 and 75+ relative to 55–64 are robust to correcting for mortality selection, although the differences between these two older groups are slightly smaller and not always statistically significant. The results in Panel B indicate that our conclusions on age differences in delaying medical care are robust to correcting for mortality selection, as are those in Panel C for experiencing mental health hardships.

Discussion

Our study is the first to specifically examine age differences among adults aged 55+ in experiences of a broad set of challenges during the pandemic, including economic hardship, access to medical care, and mental health. We do so in the context of theoretical uncertainty about whether we would expect the pandemic to have exacerbated vulnerabilities at older ages or whether the emotional and financial resilience of older adults persisted in the face of pandemic-related shocks.

Our focus on multiple aspects of hardship for adults aged 55+ in the midst of the pandemic yields two important findings. First, there was a strong negative age gradient to nearly all economic and mental health challenges during the pandemic. We show that the gradient previously documented for young versus older adults extends within older adulthood. In the bivariate relationships, adults aged 65–74 and those aged 75 and older were less likely than their counterparts aged 55–64 to experience hardship of all forms including missing payments, delaying medical care, and experiencing mental health challenges. We find that most of the age differences in experiences of hardship were not accounted for by prepandemic sociodemographic, social program, health, and economic characteristics. In models with an extensive set of controls, relative to those aged 55–64, experiences with all of the measures of economic hardships declined with age. Moreover, compared to adults aged 55–64, those aged 75+ were less likely to delay medical care, report loneliness, or spend too much time with household members, and those aged 65+ were less likely to report feeling emotionally overwhelmed or stressed. Our findings extend past research on delays in medical care (Zhong et al., 2022), loneliness (Choi, Farina, et al., 2022), and anxiety and depression (Bruine de Bruin, 2021) by showing that lower levels of hardship for those aged 75 and older are consistent across a wide range of economic and mental health challenges and access to medical care.

Our finding of age gradients across the many COVID-19-related hardships we consider suggests that despite the risks posed by COVID-19 to the physical health of the oldest adults, they were more resilient to the challenges posed by the pandemic economically and in terms of their mental health than their counterparts in late middle age and they were less likely to delay medical care during the pandemic. Though our findings lend support to increased resilience at older ages, the exact sources of this resilience among older adults, especially in the context of the pandemic, remain an important topic for future research. That said, our findings that controls for prepandemic income from social insurance programs, Medicaid coverage, and pandemic stimulus payments did not change the age gradients that we found suggest that prepandemic access to social programs is not the reason for age differences in resilience to the secondary effects of the pandemic. Controls for prepandemic economic resources reduced, but did not eliminate, the age gradients in experiences of economic hardship, a finding that is consistent with other evidence on pandemic-related financial fragility (Clark & Mitchell, 2022). Similarly, controls for preexisting health conditions reduced but did not eliminate age gaps in delaying medical care, suggesting that those with poor health were less likely to delay their care compared to older adults with fewer preexisting health conditions but that age differences in health conditions do not fully explain the age differences in delays in medical care.

Our work shows that more research is needed to identify age differences in the causal effect of pandemic-related social insurance programs like expanded UI, SNAP benefits, and changes to the Medicaid program on economic hardship, mental health outcomes, and delays in medical care among adults in late middle age and older. Because the size of the government response to the COVID-19 pandemic was so much larger than in prior recessions or natural disasters, we hesitate to draw broader conclusions on age gradients in mental health, healthcare access, or economic hardship during times of crisis.

Second, we show that among adults aged 55 and older, mental health challenges and delays in medical care were much more prevalent during the pandemic than economic hardships. Two thirds of adults aged 55+ reported multiple mental health challenges. In contrast, only 14% of adults aged 55+ experienced at least one economic hardship and 32% reported delaying medical care. Although experiences of most mental health challenges were less common for those aged 65+ than for those aged 55–64, 62% of adults aged 75+ experienced multiple mental health challenges during the pandemic. The consequences of the COVID-19 pandemic for a wide range of aspects of mental health were not limited to adults of working age, even though economic challenges among older adults were less common than for working-age adults. Economic hardships often bring about stress. One avenue for future research would be to examine whether pandemic-related economic challenges are associated with the mental health challenges that older adults faced and how this relationship varies by age.

Our analysis has several limitations. First, our results are limited to the first year of the pandemic (May 2020 to May 2021). In the time since May 2021, mask and vaccination mandates for public indoor spaces have largely disappeared and vaccines have become more widely available, which may have improved mental health outcomes, particularly for isolated older adults. The labor market also has improved quite dramatically since May 2021, which may have reduced economic insecurity, especially among those aged 55–64. Second, although we examined a wide set of economic and mental health indicators, the questions in the COVID-19 module did not include respondents’ self-attribution of changes in physical health. Given the devastating immediate health impact of the virus on older adults, there may be long-term effects of COVID-19, that is, “long COVID,” on physical health that we have not captured. Finally, mental health outcomes come from the LBQ, which has somewhat more advantaged respondents relative to the HRS sample overall.

Despite these limitations, our analysis clearly shows that there were substantial differences in the adverse effects of COVID-19 by age for older adults, with the oldest adults being less likely to experience economic and mental health challenges as well as delays in medical care. Perhaps family members buffered the most vulnerable older adults from the most severe impacts of the pandemic on economic well-being and mental health by providing additional time and money help. Those aged 55–64 may be more vulnerable to economic and mental health challenges because they may be more directly affected by changes in the labor market and the strains experienced by their younger adult children and grandchildren, than those who are older. Finally, while in-person contact between older adults and their family and friends fell during the pandemic, other forms of social contact were stable or actually increased (Freedman et al., 2022), which may have buffered emotional resilience of older adults. Future studies should build on our finding that prepandemic characteristics do not account for age differences in resilience by exploring other explanations for why older adults were more able to cope with pandemic-related health and economic challenges.

Funding

This work was supported by the National Institute on Aging (grant numbers R03AG073799 and U01AG079065). E. E. Wiemers acknowledges support from the Center for Aging and Population Studies at Syracuse University which receives funding from the National Institute on Aging (P30AG066583). I.-F. Lin acknowledges support from the Center for Family and Demographic Research, Bowling Green State University, which receives core funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD050959). V. J. Hotz acknowledges support from the Center for Population Health and Aging at Duke University which receives funding from the National Institute on Aging (P30AG034424). J. A. Seltzer acknowledges support from the California Center for Population Research which receives core funding from the Eunice Kennedy Shriver National Institution of Child Health and Human Development (P2CHD041022).

Conflict of Interest

None.

Data Availability

Data and code for replication purposes are available on request from the authors. This study is not preregistered.

Acknowledgments

The authors acknowledge the helpful comments of the editor and anonymous referees as well as comments on earlier versions of this work presented at the 2023 Population Association of America Annual Meeting and the 2023 Syracuse University COVID-19 and Policy Conference.

Author Contributions

Emily Wiemers (Conceptualization [equal], Formal analysis [supporting], Funding acquisition [lead], Methodology [equal], Project administration [equal], Writing—original draft [equal]), I-Fen Lin (Conceptualization [equal], Formal analysis [supporting], Funding acquisition [supporting], Methodology [equal], Project administration [equal], Writing—original draft [equal]), Anna Wiersma Strauss (Data curation [equal], Formal analysis [equal], Visualization [lead], Writing—original draft [supporting], Writing—review & editing [supporting]), Janecca Chin (Data curation [equal], Formal analysis [equal], Writing—original draft [supporting], Writing—review & editing [supporting]), V. Joseph Hotz (Conceptualization [supporting], Funding acquisition [supporting], Writing—review & editing [supporting]), and Judith Seltzer (Conceptualization [supporting], Funding acquisition [supporting], Writing—review & editing [supporting])

References