Caregiving in a Pandemic: COVID-19 and the Well-Being of Family Caregivers 55+ in the United States
Abstract
Little is known about the effects of Coronavirus disease 2019 (COVID-19) on older family caregivers. Using data from a national sample of 2,485 U.S. adults aged ≥55, we aimed to describe the magnitude of disruptions to family care arrangements during the initial wave of the COVID-19 pandemic, and the associations between these disruptions and the mental health outcomes (depression, anxiety, loneliness, and self-rated health) and employment outcomes (job loss or furlough, hours or wages reduced, transition to work-from-home) of family caregivers. We found that COVID-19 disrupted over half of family caregiving arrangements, and that care disruptions were associated with increased depression, anxiety, and loneliness among caregivers, compared with both noncaregivers and caregivers who did not experience disruptions. Family caregivers who experienced pandemic-related employment disruptions were providing more care than caregivers who did not experience disruptions. These findings highlight the impact of the pandemic on an essential and vulnerable health care workforce.
Introduction
Most Americans take on multiple, unpaid caregiving responsibilities over the courses of their lives. Beyond regular childcare, more than one in 10 adults in the United States provide care to a spouse, elderly parent, or other relative, and more than one in three grandparents regularly care for their grandchildren (David et al., 2019; Freedman & Wolff, 2020; National Alliance for Caregiving, & AARP, 2020). Furthermore, most older adults with disabilities rely on care provided by family or friends, a trend that has accelerated in recent decades (Van Houtven, Konetzka, et al., 2020). Beginning in March 2020, the Coronavirus disease 2019 (COVID-19) pandemic complicated caregiving arrangements for families with care needs (Friedman et al., 2021; Van Houtven, Boucher & Dawson, 2020). Nursing homes across the country became epicenters of COVID-19 transmission and mortality, while schools and child care services shut down (Barnett & Grabowski, 2020; Chen et al., 2020; Conlen et al., 2020). Non-coresident family and formal care arrangements of all types became not only logistically more difficult due to pandemic control measures, but posed a direct health threat as older adults, who are both recipients and providers of family care, are at high risk of severe COVID-19 morbidity and mortality (AP-NORC Center for Public Affairs Research, 2020; Centers for Disease Control and Prevention [CDC], 2020).
Family caregivers take on a range of care tasks and responsibilities in a variety of settings, including in their own home, in the care recipient’s home, or an institutional setting such as a nursing home. They provide uncompensated care to aging relatives, disabled children, and friends and neighbors with whom they may have no legal relationship (National Academies of Sciences, Engineering, and Medicine, 2016). Many care arrangements involve multiple caregivers, which may include formal caregivers, or services such as day care (David et al., 2019; Freedman & Wolff, 2020; National Alliance for Caregiving, & AARP, 2020; Van Houtven, Boucher & Dawson, 2020). Tasks can range from running errands and providing companionship to administering complicated medical care (Freedman & Wolff, 2020; Wolff et al., 2016). Prior to COVID-19, the substantial physical, mental, and economic costs of family caregiving had been well-documented (Bom et al., 2019; Coe & Van Houtven, 2009; Hirst, 2005; Schmitz & Westphal, 2017; Van Houtven et al., 2013). Recognizing these costs, as well as the potential health benefits and implicit dollar value of the services provided by family caregivers (Chari et al., 2015), federal efforts such as the National Family Caregiver Support Program and the Veterans Affairs Caregiver Support Program were designed to provide information, service referrals, counseling and support groups, training, and, in some cases, financial compensation to family caregivers (Van Houtven et al., 2019; Wolff et al., 2016). However, many family caregivers have limited or no access to services and supports, as the provision of respite services such as meal delivery and adult day care, financial compensation, and paid family caregiving leave has remained largely at the discretion of state legislatures (Dawson et al., 2020; Feinberg, 2018).
The piecemeal structure of the U.S. long-term care system is likely to have left families with caregiving needs vulnerable to sudden and widespread disruptions induced by the COVID-19 pandemic. However, little is known about how the pandemic has impacted the provision of family care and the consequences for family caregivers as they contend with such disruptions. In this article, we use data from the COVID-19 Coping Study, a national sample of 2,485 U.S. adults aged ≥55, to provide the first comprehensive assessment of the magnitude of disruptions to family caregiving arrangements during the initial wave of COVID-19 among family caregivers aged 55 and above, and we document the associations between these disruptions and the mental health, self-rated health, and employment outcomes of family caregivers.
COVID-19 may have had substantial acute impacts on the well-being of middle-aged and older family caregivers. Adults aged 55 and above provide important family caregiving, while facing age-based elevated risks for COVID-19 morbidity and mortality (CDC, 2020). Prior to the pandemic, older caregivers were more likely to themselves be in poor health, to take on more intensive caregiving roles, and to experience poor physical and mental health as a result of caregiving (Navaie-Waliser et al., 2002; Pinquart & Sörensen, 2007). The physical isolation necessitated by COVID-19 coupled with potentially significant changes in caregiving roles may compound the risks of adverse mental health outcomes such as anxiety, depression, and loneliness in this population (Beach et al., 2021; Czeisler et al., 2020; Kobayashi et al., 2021; Lyons et al., 2015; National Alliance for Caregiving, & AARP, 2020; Park, 2021). Furthermore, prior to the pandemic, over half of family caregivers in the United States were balancing paid work and family care responsibilities (Freedman & Wolff, 2020). Those family caregivers who are approaching or working beyond retirement age may face difficult trade-offs during the COVID-19 pandemic, as they may experience increased or different caregiving roles and expectations about future care needs (Van Houtven et al., 2013). As the population ages, family caregiving will continue to play an increasingly central role in the national health care system. In this study, we provide evidence that formal supports for family caregiving, including for the sizable number of aging family caregivers who are simultaneously engaged in paid work, will be critical amid recovery from COVID-19 (Friedman et al., 2021; joebiden.com, 2020).
New Contributions
The COVID-19 pandemic has dramatically disrupted an already fragile and piecemeal U.S. caregiving infrastructure, but virtually nothing is known about the effects of COVID-19 on older family caregivers and their care arrangements. Using data from the COVID-19 Coping Study, a national sample of 2,485 U.S. adults aged ≥55, we aimed to describe the magnitude of disruptions to family care arrangements during the initial wave of the COVID-19 pandemic (April-May 2020), and the associations between these disruptions and the mental health outcomes (depression, anxiety, loneliness, and self-rated health) and employment outcomes (job loss or furlough, hours or wages reduced, transition to work-from-home) of family caregivers.
We newly identified that over half of middle-aged and older U.S. family caregivers experienced disruptions to their ongoing care provision in the first wave of the COVID-19 pandemic. Non-coresident care arrangements were more likely to be disrupted, and caregivers who experienced disruptions were disproportionately younger, female, and Black or Hispanic. Using adjusted and population-weighted regression, we found that care disruptions were associated with increased depression, anxiety, and loneliness among caregivers, compared with both noncaregivers and caregivers who did not experience disruptions. Caregivers who were providing more care than usual during the pandemic were more likely to have their employment affected than noncaregivers, primarily in the forms of job loss or furlough or a work-from-home transition. These findings inform how the COVID-19 pandemic has impacted key family support systems, while identifying middle-aged and older family caregivers as an essential health care workforce who are vulnerable to mental health and economic consequences during the pandemic.
Conceptual Framework
Family caregiving arrangements are extremely diverse. The COVID-19 pandemic may have impacted family caregiving through several pathways, depending on the parameters of the caregiving arrangement. Individuals who are the sole caregiver for a coresident care recipient may have experienced no change in their caregiving arrangements, whereas coresident caregivers who relied on additional paid or unpaid help prior to the pandemic may have needed to provide more care than usual due to restrictions on in-person contact. Non-coresident caregivers who provided care in person outside of their own homes may have needed to reduce their caregiving activities or stop providing care altogether to reduce risk of virus transmission and adhere to state or local restrictions. Finally, changes in caregiving needs or arrangements during the pandemic may have induced some to take on new caregiving roles that they did not hold before.
The caregiver stress model conceptualizes caregiver stress as a product of both direct challenges in caregiving (primary stressors) and strains in roles and activities outside of caregiving (secondary stressors) (Pearlin et al., 1990). We hypothesize that changes in caregiving roles act as primary stressors on caregivers, increasing caregivers’ appraisals of their burden, and leading to worse mental health outcomes in the short term. Taking on additional or new caregiving roles may cause distress to caregivers with respect to the additional care burden and social isolation. Conversely, the inability to provide care to a dependent or loved one may also adversely affect the mental health of the caregiver. The lack of support and isolation experienced by the dependent can be distressing to the caregiver who feels responsibility for them, or if the caregiver relies on the caregiving role for social interaction, as can be the case with grandparents caring for grandchildren. These mechanisms may also operate in the opposite direction: Caregivers who experience poor mental health because of the pandemic may be more likely to change their caregiving arrangements, as they may find themselves too distressed or worried about viral transmission risks to provide care, or to rely on secondary caregivers.
For working caregivers, changes in employment circumstances can lead to caregiving disruptions, acting as either primary or secondary stressors. Although a layoff or a reduction in wages may impose economic hardships (secondary stressor), a transition to working from home or a change in schedule may directly impact caregiving by facilitating more or new care or making caregiving more challenging (primary stressor). Similarly, a new or additional caregiving burden may also adversely affect employment, depending on the caregiving scale or arrangement in place.
Data and Study Method
Data Sources
Data were from the baseline of the COVID-19 Coping Study, a national cohort of U.S. adults aged ≥55 years (Kobayashi et al., 2021). Data collection began on April 17, 2020, approximately 2 weeks after much of the country adopted intensive social distancing and shelter-in-place orders to mitigate the spread of the SARS-CoV-2 virus, and ended on May 15, 2020. The sampling frame was drawn from an existing online research panel maintained by the professional survey company, Dynata. Sampling quotas were used for age, gender, race, ethnicity, and education that matched the general U.S. population aged ≥55 based on CDC Wide-ranging ONline Data for Epidemiologic Research (WONDER; Kobayashi et al., 2021). Data were collected through an online questionnaire designed to take approximately 17 min on computer, tablet, and smartphone interfaces. The questionnaire included questions about regular caregiving responsibilities prior to the pandemic, caregiving disruptions experienced as a result of the pandemic, employment prior to the pandemic, employment disruptions experienced as a result of the pandemic, and a series of validated research scales to evaluate depressive symptoms, anxiety symptoms, loneliness, and self-rated health (Fisher et al., 2005; Lewinsohn et al., 1997; Radloff, 1977; Russell, 1996). We merged these data with county-level information on COVID-19 case rates and deaths compiled by The New York Times and distributed by the Inter-university Consortium for Political and Social Research (ICPSR), and 2019 county-level population data from the Census Bureau (New York Times, 2020; U.S. Census Bureau, 2019). The University of Michigan Health Sciences and Behavioral Sciences Institutional Review Board approved the COVID-19 Coping Study protocol (HUM00179632; Kobayashi et al., 2021).
Study Sample
Our eligible sample included all COVID-19 Coping Study respondents recruited through Dynata (N = 2,485). Some analyses were restricted to respondents who were caregivers prior to the start of COVID-19 (N = 535), and those who were employed prior to the start of COVID-19 (N = 808).
Outcomes and Measures
We identified a caregiver as anybody who reported having regular caregiving responsibilities in their “usual life, before the COVID-19 (coronavirus) pandemic.” This definition included care provided to spouses, parents, children, grandchildren, other relatives, or nonrelatives with long-term illness or disability. We used reports of relationships with household members to identify which caregivers were caring for coresidents. Among respondents with prepandemic caregiving obligations, we identified three (mutually exclusive) categories of disrupted caregivers: those who reported providing more care or took on an additional caregiving role as a result of the pandemic (N = 124), those who reported providing less care as a result of the pandemic (N = 47), and those unable to provide care as a result of the pandemic (N = 55). In our regression analysis, we collapse these latter two categories into a single group. We also identified 52 respondents who were not caregivers before the pandemic but reported taking on caregiving roles in response to the pandemic.
To evaluate the associations between caregiving, caregiving disruptions, and mental and self-rated health, we constructed the following four binary outcomes: high depressive symptoms (defined as scoring ≥3 on the eight-item Center for Epidemiological Studies–Depression Scale; CES-D), high anxiety symptoms (defined as scoring ≥10 on the five-item Beck Anxiety Inventory), high loneliness (defined as scoring ≥6 on the three-item UCLA Loneliness Scale), and fair or poor self-rated health (based on a 5-point Likert-type scale). These cutoffs were selected using existing clinically validated thresholds (Fisher et al., 2005; Lewinsohn et al., 1997; Radloff, 1977; Russell, 1996). Our results are robust to using continuous versions of these scales (Supplemental Table S3). To evaluate the associations between caregiving, caregiving disruptions, and self-reported effects of COVID-19 on employment, we included binary indicators for experiencing each of any employment change, a job loss or furlough, reduced hours or income, or working from home.
Statistical Analysis
First, we calculated descriptive statistics comparing prepandemic caregivers and noncaregivers on a range of demographic characteristics and county-level COVID-19 cases and death rates. We then explored demographic factors associated with experiencing caregiving disruptions (defined as providing more care, providing less care, being unable to provide care, and newly becoming a caregiver).
Next, we estimated covariate-adjusted regressions to determine the relationships between each of (a) caregiver status and (b) caregiving disruptions by type with the mental and self-rated health outcomes using the following linear probability model1:
(1)
Here,
is the mental health outcome for individual
living in county
in state
,
is a binary indicator for prepandemic caregiver status (yes, no), and
are mutually exclusive binary indicators for if caregiving was disrupted by the pandemic by disruption type. We combine into one category respondents who reported providing less care and who reported being unable to provide care due to the small sample sizes in those group.
is a binary indicator for the respondent took on a new caregiving role during the pandemic (having no reported prepandemic caregiving role). The coefficient
captures the covariate-adjusted mean difference in the probability of each outcome, expressed as percentage points, between noncaregivers and caregivers who did not experience disruptions. The coefficient
captures the difference for prepandemic caregivers providing more care relative to noncaregivers, the coefficient
captures the difference for prepandemic caregivers providing less care or unable to provide care relative to noncaregivers, and the coefficient
captures the difference for new caregiving relative to noncaregivers.
All models included a vector of individual controls,
, which were age, age2, marital status, race/ethnicity (non–Hispanic White, non–Hispanic Black, Hispanic, Other), education (less than high school, high school, some college, college degree or higher), housing tenure (home owned outright, home owned with mortgage, renter in a market rental, renter in subsidized housing, living rent-free, other), being employed prepandemic (yes, no), health history (previous physician diagnosis of each of high blood pressure, diabetes, heart disease, asthma, chronic obstructive pulmonary disease, cancer, depression, anxiety, other physical or mental health condition), use of a mobility aid (yes, no), and a 5-point prepandemic social isolation index that incorporated frequency of contact with each of children, other family, friends, participation in social clubs or organizations, and living alone (Steptoe et al., 2013). We included state fixed effects
to control for any unmeasured differences between U.S. states that may have influenced the relationship between caregiving and the outcomes of interest, such as long-term care policies and COVID-19 responses, as well as
, the average confirmed COVID-19 cases and deaths per 1,000 population members in the respondent’s county of residence over the 4-week survey period. Standard errors were clustered at the state level. All models were weighted to the U.S. general population aged ≥55 based on 2018 American Community Survey data on age, sex, race, ethnicity, education, and marital status (Kobayashi et al., 2021). As we show in Supplemental Tables S1 and S2, our main results are robust to excluding these weights.
| Characteristics | Caregivers (unweighted N = 535) |
Noncaregivers (unweighted N = 1,950) |
||
|---|---|---|---|---|
| % | 95% CI | % | 95% CI | |
| Caregiving characteristics | ||||
| Any caregiving | 22.7 | [21.1, 24.4] | ||
| Caring for | ||||
| Spouse | 47.4 | [43.2, 51.7] | — | — |
| Parent | 26.9 | [23.1, 30.7] | — | — |
| Child | 21.6 | [18.1, 25.1] | — | — |
| Grandchild | 19.2 | [15.9, 22.6] | — | — |
| Other | 10.3 | [7.70, 12.9] | — | — |
| Coresident | 61.4 | [57.3, 65.6] | — | — |
| Multiple recipients | 19.6 | [16.2, 22.9] | — | — |
| Descriptive characteristics | ||||
| Age (M) | 66.2 | [65.4, 66.9] | 68.2 | [67.8, 68.6] |
| Female | 58.0 | [53.8, 62.2] | 52.7 | [50.5, 54.9] |
| Non–Hispanic White | 68.0 | [64.1, 72.0] | 73.8 | [71.9, 75.8] |
| Non–Hispanic Black | 10.0 | [7.50, 12.6] | 10.2 | [8.90, 11.6] |
| Hispanic | 14.3 | [11.4, 17.3] | 9.30 | [8.00, 10.6] |
| Married | 67.4 | [63.4, 7.13] | 56.2 | [54.0, 58.4] |
| College degree | 28.8 | [24.9, 32.6] | 28.5 | [26.5, 30.5] |
| Number of chronic health conditions (M) | 0.96 | [0.86, 1.05] | 0.75 | [0.70, 0.79] |
| Previously diagnosed with depression | 14.9 | [11.9, 17.9] | 10.7 | [9.3, 12.1] |
| Previously diagnosed with anxiety | 18.8 | [15.5, 22.1] | 10.7 | [9.3, 12.1] |
| Pre-COVID-19 employment characteristics | ||||
| Employed (any) | 34.8 | [30.8, 38.9] | 29.9 | [27.9, 32.0] |
| Retired | 44.5 | [40.3, 48.7] | 55.8 | [53.6, 58.0] |
| County-level COVID-19 burden | ||||
| Cases per 1,000 (M) | 2.80 | [2.43, 3.17] | 2.81 | [2.63, 2.99] |
| Deaths per 1,000 (M) | 0.14 | [0.12, 0.16] | 0.14 | [0.13, 0.15] |
| COVID-19 mental health and employment | ||||
| Screen high for depression | 0.41 | [0.37 0.45] | 0.28 | [0.26 0.30] |
| Screen high for anxiety | 0.36 | [0.32 0.40] | 0.25 | [0.23 0.26] |
| Screen high for loneliness | 0.35 | [0.31 0.39] | 0.29 | [0.27 0.31] |
| Fair or poor self-reported health | 0.23 | [0.19 0.27] | 0.20 | [0.18 0.22] |
| Employment affected by COVID-19 | 0.75 | [0.69 0.81] | 0.73 | [0.69 0.76] |
| Lost job or layoff | 0.20 | [0.14 0.25] | 0.25 | [0.21 0.28] |
| Reduced hours or wages | 0.29 | [0.23 0.35] | 0.23 | [0.19 0.26] |
| Working from home | 0.33 | [0.26 0.39] | 0.29 | [0.25 0.32] |
Source. Authors’ calculations using data from the COVID-19 Coping Study and The New York Times COVID-19 database.
Note. All estimates are population weighted using survey weights. All variables are binary except age, number of health conditions (which ranges from 0 to 8), and COVID-19 cases per 1,000 and deaths per 1,000. CI = confidence interval.
For analysis of employment outcomes, we restricted the sample to respondents who reported working prior to the pandemic (N = 808) and dropped the control variable for prepandemic employment status. We used the same covariate-adjusted linear probability models to estimate the relationships between caregiving status and self-reported effects of the COVID-19 pandemic on employment (each of job loss or furlough, reduction in wages or hours worked, and transition to working from home, vs. no effect on employment, as well as an indicator for any vs. no employment disruption). Finally, in Supplemental Table S4, we repeated this analysis further restricting our sample to exclude respondents working in health, health-related, or personal care professions (N = 106) to minimize any possible overlap with paid caregivers. We do not exclude these respondents from the main analysis because there is evidence that nearly two thirds of formal caregivers also provide unpaid care to family members (Van Houtven, DePasquale, & Coe, 2020).
Results
Descriptive Statistics
In this national sample of Americans aged 55 and above, 23% (535/2,485) reported having caregiving responsibilities prior to the COVID-19 pandemic (Table 1). Among caregivers, 47% (95% confidence interval [CI] = [43%, 51%]) provided care to a spouse, 27% (95% CI = [23%, 31%]) provided care to parents or parents-in-law, 22% (95% CI = [18%, 25%]) provided care to children, and 19% (95% CI = [16%, 23%]) provided care to grandchildren. More than 60% (95% CI = [57%, 66%]) of respondents reported caring for coresident recipients, and 20% (95% CI = [16%, 23%]) reported more than one caregiving role.
Caregivers were, on average, 2 years younger than noncaregivers (66.2 vs. 68.2 years) and were also more likely to be female (58% vs. 53%), less likely to be White (68% vs. 74%), and more likely to be Hispanic (14% vs. 9%) than noncaregivers (Table 1). Caregivers and noncaregivers reported similar levels of education, but caregivers were more likely to be married (67% vs. 56%) and to be diagnosed with a health condition than noncaregivers (Table 1). Caregivers were more likely to be participating in the labor market prior to the pandemic than noncaregivers (Table 1). Caregivers were almost 5 percentage points more likely to be employed (35% vs. 29%), and less likely to identify as retired (45% vs. 56%) than noncaregivers (Table 1). We observed no differences in county-level COVID-19 cases or deaths between caregivers and noncaregivers.
During the first few months of the pandemic, caregivers were more likely to screen high for depression, anxiety, and loneliness than noncaregivers, but were not more likely to report worse self-rated health (Table 1). Caregivers and noncaregivers reported similar rates of COVID-19–related employment disruptions. Nearly three quarters of both caregivers and noncaregivers reported that their employment was affected by COVID-19, and noncaregivers were somewhat more likely to report job loss or layoff whereas caregivers were more likely to report reduced hours or wages (Table 1).
Table 2 compares characteristics of caregivers, according to their experiences of caregiving disruptions during COVID-19. The pandemic disrupted caregiving routines for more than half of the caregivers in this sample (Table 2). More than 30% (N = 124) of prepandemic caregivers reported more or new care responsibilities because of the pandemic, and 20% provided less care (N = 47) or were unable to provide care (N = 55) (Table 2). More than 2% (52/2,485) of the full sample newly became caregivers because of the pandemic.
| Characteristics | No disruptions (N = 292) | More care (N = 124) | Less care (N = 47) | Unable to care (N = 55) | New caregiver (N = 52) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| M | 95% CI | M | 95% CI | M | 95% CI | M | 95% CI | M | 95% CI | |
| Caregiving characteristics | ||||||||||
| Spouse | 0.58 | [0.52, 0.64] | 0.43 | [0.35, 0.50] | 0.42 | [0.28, 0.57] | 0.15 | [0.06, 0.25] | 0.17 | [0.07, 0.28] |
| Parent | 0.23 | [0.17, 0.28] | 0.28 | [0.21, 0.35] | 0.31 | [0.17, 0.44] | 0.41 | [0.28, 0.55] | 0.30 | [0.17, 0.42] |
| Child | 0.23 | [0.18, 0.29] | 0.26 | [0.19, 0.33] | 0.21 | [0.09, 0.33] | 0.08 | [0.01, 0.15] | 0.10 | [0.02, 0.18] |
| Grandchild | 0.11 | [0.07, 0.15] | 0.22 | [0.15, 0.28] | 0.33 | [0.20, 0.47] | 0.42 | [0.29, 0.55] | 0.29 | [0.16, 0.41] |
| Other | 0.09 | [0.05, 0.12] | 0.08 | [0.04, 0.12] | 0.13 | [0.03, 0.23] | 0.19 | [0.09, 0.30] | 0.25 | [0.13, 0.37] |
| Coresident | 0.70 | [0.64, 0.75] | 0.67 | [0.60, 0.75] | 0.46 | [0.31, 0.60] | 0.23 | [0.12, 0.34] | 0.10 | [0.02, 0.18] |
| Multiple recipients | 0.17 | [0.13 0.22] | 0.23 | [0.16, 0.30] | 0.28 | [0.15, 0.40] | 0.20 | [0.09, 0.31] | 0.18 | [0.08, 0.29] |
| Descriptive statistics | ||||||||||
| Age | 67.9 | [66.7, 69.2] | 64.9 | [63.6, 66.2] | 64.3 | [61.8, 66.7] | 63.7 | [62.3, 65.2] | 64.6 | [62.3, 66.9] |
| Female | 0.51 | [0.45, 0.57] | 0.57 | [0.50, 0.65] | 0.77 | [0.65, 0.89] | 0.74 | [0.62, 0.86] | 0.56 | [0.42, 0.69] |
| Non–Hispanic White | 0.77 | [0.72, 0.82] | 0.56 | [0.48, 0.64] | 0.63 | [0.49, 0.77] | 0.69 | [0.56, 0.81] | 0.68 | [0.55, 0.80] |
| Non–Hispanic Black | 0.08 | [0.04, 0.11] | 0.14 | [0.08, 0.19] | 0.13 | [0.03, 0.23] | 0.04 | [−0.01, 0.10] | 0.11 | [0.02, 0.19] |
| Hispanic | 0.11 | [0.07, 0.15] | 0.18 | [0.12, 0.24] | 0.21 | [0.09, 0.32] | 0.15 | [0.05, 0.24] | 0.17 | [0.07, 0.28] |
| Married | 0.70 | [0.64, 0.75] | 0.67 | [0.60, 0.75] | 0.70 | [0.57, 0.83] | 0.63 | [0.50, 0.76] | 0.56 | [0.42, 0.70] |
| College degree | 0.29 | [0.23, 0.34] | 0.27 | [0.20, 0.34] | 0.40 | [0.26, 0.54] | 0.32 | [0.19, 0.44] | 0.30 | [0.18, 0.43] |
| Number of health conditions | 1.48 | [1.32, 1.64] | 1.92 | [1.68, 2.15] | 1.22 | [0.84, 1.59] | 2.01 | [1.52, 2.51] | 1.57 | [1.23, 1.91] |
| Previously diagnosed with depression | 0.12 | [0.08, 0.16] | 0.17 | [0.11, 0.23] | 0.07 | [0.01, 0.14] | 0.32 | [0.20, 0.44] | 0.18 | [0.08, 0.29] |
| Previously diagnosed with anxiety | 0.14 | [0.10, 0.18] | 0.24 | [0.17, 0.31] | 0.16 | [0.06, 0.27] | 0.29 | [0.16, 0.40] | 0.25 | [0.13, 0.37] |
| Pre-COVID-19 employment characteristics | ||||||||||
| Employed (any) | 0.31 | [0.25, 0.36] | 0.40 | [0.32, 0.48] | 0.46 | [0.32, 0.61] | 0.35 | [0.22, 0.48] | 0.33 | [0.20, 0.46] |
| Retired | 0.50 | [0.44, 0.56] | 0.40 | [0.32, 0.48] | 0.32 | [0.19, 0.46] | 0.36 | [0.23, 0.49] | 0.50 | [0.36, 0.63] |
| County-level COVID-19 burden | ||||||||||
| Cases per 1,000 | 2.20 | [1.85, 2.56] | 3.12 | [2.37, 3.86] | 3.17 | [1.94, 4.40] | 3.92 | [2.10, 5.73] | 3.34 | [2.38, 4.31] |
| Deaths per 1,000 | 0.11 | [0.09, 0.13] | 0.16 | [0.12, 0.21] | 0.16 | [0.08, 0.23] | 0.22 | [0.11, 0.33] | 0.14 | [0.10, 0.18] |
| COVID-19 mental health and employment | ||||||||||
| Screen high for depression | 0.27 | [0.22, 0.33] | 0.53 | [0.45, 0.61] | 0.52 | [0.37, 0.66] | 0.57 | [0.43, 0.70] | 0.44 | [0.30, 0.57] |
| Screen high for anxiety | 0.22 | [0.17, 0.27] | 0.52 | [0.44, 0.60] | 0.47 | [0.33, 0.62] | 0.45 | [0.32, 0.59] | 0.39 | [0.26, 0.52] |
| Screen high for loneliness | 0.25 | [0.20, 0.30] | 0.44 | [0.36, 0.52] | 0.42 | [0.28, 0.56] | 0.51 | [0.38, 0.64] | 0.44 | [0.30, 0.57] |
| Fair or poor self-reported health | 0.18 | [0.14, 0.23] | 0.34 | [0.27, 0.42] | 0.08 | [0.00, 0.16] | 0.24 | [0.13, 0.36] | 0.28 | [0.15, 0.40] |
| Employment affected | 0.63 | [0.52, 0.73] | 0.88 | [0.80, 0.96] | 0.75 | [0.57, 0.92] | 0.79 | [0.63, 0.96] | 0.92 | [0.79, 1.05] |
| Lost job or layoff | 0.13 | [0.05, 0.20] | 0.21 | [0.11, 0.30] | 0.33 | [0.14, 0.52] | 0.26 | [0.08, 0.44] | 0.38 | [0.14, 0.61] |
| Reduced hours or wages | 0.30 | [0.20, 0.40] | 0.35 | [0.23, 0.46] | 0.13 | [0.00, 0.27] | 0.26 | [0.08, 0.44] | 0.13 | [-0.04, 0.29] |
| Working from home | 0.26 | [0.16, 0.35] | 0.43 | [0.31, 0.54] | 0.33 | [0.14, 0.52] | 0.32 | [0.12, 0.51] | 0.49 | [0.24, 0.73] |
Source. Authors’ calculations using data from the COVID-19 Coping Study and The New York Times COVID-19 database.
Note. All means are weighted using survey weights. All variables are binary except the number of health conditions, which ranges from 0 to 8, and cases per 1,000 and deaths per 1,000. CI = confidence interval.
Caregivers who experienced any disruptions were less likely to be caring for spouses, and more likely to be caring for parents or grandchildren than those who did not experience disruptions (Table 2). Caregivers who had coresident care arrangements prior to the pandemic were less likely to be disrupted than those in non-coresident care arrangements, as were those caring for only one individual (Table 2). Among caregivers with disruptions, those providing more care were likely to be caring for coresident recipients, including spouse and children, whereas those providing less care were more likely to be caring for non-coresident recipients, including parents and grandchildren. New caregivers were most likely to start caring for another relative or nonrelative, both captured by the “other” category and the least likely to care for a coresident recipient. These patterns are consistent with the COVID-19 pandemic, making prepandemic non-coresident caregiving arrangements more challenging because of logistical or virus transmission risk concerns.
There were also significant sociodemographic differences between caregivers who did and did not experience disruptions. Caregivers who experienced any kind of disruptions were younger, more likely to be female, and less likely to be White than those who did not experience disruptions (Table 2). Caregivers who provided more care were nearly twice as likely to be Black and two thirds more likely to be Hispanic than caregivers who did not experience disruptions. New caregivers were also more likely to be Black and Hispanic. Caregivers who experienced disruptions, as well as new caregivers were more likely to have ever been diagnosed with a health condition, including depression or anxiety, than caregivers who did not experience disruptions (Table 2). Caregivers who experienced disruptions were more likely to be employed and less likely to be retired than those who did not experience disruptions, whereas new caregivers had very similar employment rates to those who did not experience disruptions (Table 2). Finally, caregivers who experienced disruptions and new caregivers lived in counties with higher COVID-19 cases and death rates than those who did not experience disruptions (Table 2).
Caregiving Status, Disruptions, and Mental and Self-Rated Health
Unadjusted comparisons of mental and self-rated health during the first months of the pandemic suggested that caregivers experienced worse mental health outcomes (Table 1) but that this was driven mostly by worse outcomes among caregivers experiencing disruptions (Table 2). We next compared mental and self-rated health outcomes across noncaregivers, and new caregivers by disruption type using covariate-adjusted, population-weighted linear probability models. The results from estimating Equation 1 on mental and self-rated health are presented graphically in Figure 1. Each set of bars presents the point estimate and confidence intervals from a separate regression model that includes four indicators for caregiving disruptions (none, more care, less or no care, and new caregiver).
Figure 1. Associations Between Pre–COVID-19 Caregiver Status, Types of COVID-19-Related Caregiving Disruptions, and Mental and Self-Rated Health in a National Sample of Americans Age 55+ Between April 17 and May 15, 2020.
Source. Authors’ calculations using data from the COVID-19 Coping Study and The New York Times COVID-19 database.
Note. Sample includes all survey respondents. Each color corresponds to a single regression model of health outcomes on indicator variables representing noncaregivers (reference category), caregivers without disruptions, and caregivers with disruptions by type, as described by Equation 1. The models adjusted for the following covariates: age, age2, marital status, race/ethnicity, education, housing tenure, prepandemic employment status, previous chronic condition diagnoses (high blood pressure, diabetes, heart disease, asthma, chronic obstructive pulmonary disease, cancer, depression, anxiety, other medical condition), use of mobility aids, a prepandemic social isolation index, average county-level COVID-19 cases/1,000 and deaths/1,000. All models included state fixed effects. Robust standard errors are clustered at the state level. Error bars represent 95% confidence intervals.
There were no differences in the mental and self-rated health outcomes between caregivers who did not experience disruptions and noncaregivers (Figure 1). However, prepandemic caregivers who experienced disruptions were more likely to screen positive for each of depression, anxiety, and loneliness than both noncaregivers and caregivers who reported no disruptions (Figure 1). Caregivers who provided more (less) care were 20.5 (17.0), 21.4 (17.5), and 15.7 (18.3) percentage points more likely to screen positive for each of depression, anxiety, and loneliness, respectively, compared with noncaregivers. New caregivers were also somewhat more likely to screen positive for anxiety, depression, and loneliness than both noncaregivers and caregivers not experiencing disruptions, though these results are not statistically significant at conventional levels, perhaps due to the small size of this group. There was no difference in the reporting of fair or poor self-rated health according to caregiving status or caregiving disruptions (Figure 1).
Caregiving Status, Disruptions, and Employment Outcomes
More than one third (206/535) of caregivers and 623 noncaregivers in this sample worked prior to the pandemic. Among employed noncaregivers, 3% (17/623) took on new caregiving roles in the first months of the pandemic. In unadjusted comparisons, both caregivers experiencing disruptions and new caregivers reported higher rates of employment disruptions during the pandemic (Table 2). As visualized in Figure 2, among those employed prior to COVID-19, caregivers who did not experience care disruptions were 11.1 percentage points less likely to report a job loss or furlough, though 9.5 percentage points more likely to report that hours or wages were affected than noncaregivers (this latter estimate is significant at the 10% level). Caregivers who provided more care as a result of the pandemic were also 20 percentage points more likely than noncaregivers to have their employment affected, primarily in the forms of job loss or furlough or a work-from-home transition. Caregivers who provided less or no care because of the pandemic were 13.9 percentage points more likely to experience a job loss or furlough than noncaregivers (Figure 2). New caregivers had similar employment patterns to those providing more care: They were 15.3 percentage points more likely to report that their employment was affected, primarily in the form of job loss or furlough, compared with noncaregivers, though they were less likely to report reduced hours or wages, similar to those providing less care (we interpret these somewhat noisy results cautiously, given the very small sample size in this subgroup). When we reran these models excluding caregivers who were employed in a health, health-adjacent, or personal care profession prior to the pandemic, the relationship between providing less or no care and job loss became smaller in magnitude (7.7 percentage points vs. 13.9 percentage points) and was imprecisely estimated. The remainder of the estimates were negligibly impacted, indicating that employment in formal care is not responsible for the observed positive relationship between providing more care and employment disruptions (Supplemental Table S3).
Figure 2. Associations Between Pre-COVID-19 Caregiver Status, COVID-19-Related Caregiving Disruptions by Type, and Employment Outcomes in a National Sample of Americans Aged 55+, April 17 to May 15, 2020.
Source. Authors’ calculations using data from the COVID-19 Coping Study and The New York Times COVID-19 database.
Note. Sample includes respondents employed prior to COVID-19. Each color corresponds to a single regression model of employment outcomes on indicator variables representing noncaregivers (reference category), caregivers without disruptions, and caregivers with disruptions by type, as described by Equation 1. The models adjusted for the following covariates: age, age2, marital status, race/ethnicity, education, housing tenure, previous chronic condition diagnoses (high blood pressure, diabetes, heart disease, asthma, chronic obstructive pulmonary disease, cancer, depression, anxiety, other medical condition), use of mobility aids, a prepandemic social isolation index, average county-level COVID-19 cases/1,000 and deaths/1,000. All models included state fixed effects. Robust standard errors are clustered at the state level. Error bars represent 95% confidence intervals.
Discussion
As COVID-19 fundamentally changes an already fragile caregiving landscape in the United States, understanding the pressure that the pandemic puts on family care is key to addressing future population care needs as the population ages. In this national sample of Americans aged ≥55 during the first wave of the COVID-19 pandemic, we identified that half of respondents with caregiving responsibilities prior to the pandemic experienced disruptions to their caregiving. Non-coresident care arrangements were more likely to be disrupted, and caregivers who experienced disruptions were disproportionately younger, female, and Black or Hispanic. Caregivers who experienced disruptions had elevated symptoms of depression, anxiety, and loneliness compared with noncaregivers, as well as caregivers who did not experience disruptions. Caregivers in the labor force who experienced disrupted care arrangements, especially those providing more care, were also likely to experience disrupted employment, particularly in the forms of job loss or furlough or a work-from-home transition.
Our findings are consistent with prepandemic evidence demonstrating that caregiving, particularly transitions in and out of caregiving roles and perceived caregiving burden, can negatively impact the mental and emotional health of family caregivers (Bom et al., 2019; Coe & Van Houtven, 2009; Feinberg et al., 2011; Lyons et al., 2015; Pearlin et al., 1990; Riffin et al., 2017). Our results are also consistent with findings showing that family caregivers in the United States experienced worse mental and physical health than noncaregivers during the first wave of the COVID-19 pandemic (Beach et al., 2021; Czeisler et al., 2020; Park, 2021). We add evidence that COVID-19–related disruptions to caregiving provided by middle-aged and older adults are associated with a mental health symptom burden and are often experienced alongside employment disruptions.
Our findings are also consistent with recent studies showing the disproportionate impacts of COVID-19 on racial/ethnic minorities and women, who are overrepresented among both professional and family caregivers as well as frontline and essential workers (Dawson et al., 2020; Grooms et al., 2021; Van Houtven, DePasquale & Coe, 2020). Dramatically higher COVID-19–related mortality and morbidity among Blacks and Hispanics may have led to more disruption and lower caregiver well-being through both the primary stress of caring for an especially vulnerable family member and a secondary stress of increased exposure to risk in essential, frontline occupations among working caregivers and their families (Dorn et al., 2020; Grooms et al., 2021). These stresses build on and compound documented disparities in caregiver outcomes, as racial and ethnic minority caregivers report worse physical and mental health over time and provide more intensive care for recipients with higher rates of disability and functional limitations (Knight & Sayegh, 2010; Pinquart & Sörensen, 2005; Rote et al., 2019).
The associations we observed between the provision of more care during the pandemic and job disruptions are consistent with research indicating that pandemic-related school and day care closures have placed significant pressure on the productivity and labor supply of parents, particularly mothers (Ewing-Nelson, 2020; Handwerker et al., 2020). A lack of childcare has been a key driver in the disproportionate impact of the COVID-19–related recession on women, and these changes are likely to be permanent (Dingel et al., 2020). We expand upon these studies, finding that middle-aged and older family caregivers who experienced employment disruptions, including transitions to working from home, in the first months of the pandemic were also providing more care than they did before the pandemic. American workers who are approaching or working beyond retirement age are already at risk of employment discrimination, of leaving the labor market during recessions, and of adjusting caregiving roles in response to labor market conditions (Coile et al., 2014; Mommaerts & Truskinovsky, 2020; Neumark et al., 2018). It is possible that family care needs during the COVID-19 crisis may similarly lead to the early labor force exit of some middle-aged and older workers.
Our study cannot identify the directions or mechanisms of the relationships between caregiving disruptions, mental health, and employment. Caregivers who provided more care may have found themselves overwhelmed, whereas those providing less care may have felt distress at not being able to support their loved ones (Herships, 2021; Savla et al., 2020). Although we adjusted for self-reported previous physician-diagnosed anxiety and depression, our findings could also be explained if preexisting mental health symptoms led some caregivers to proactively change their caregiving arrangements due to fear of COVID-19 transmission. This interpretation highlights how the pandemic may have placed additional burden on the most vulnerable caregivers. Future research is necessary to understand the mechanisms behind these associations.
Although our sample has coverage across all 50 U.S. states and the District of Columbia, and aligns with national estimates for major sociodemographic groups, it is not a probability-based sample and does not represent non-internet users. Non-internet users may differ from internet users on many dimensions, including resources and educational attainment. In the context of our research question and methodology, our observed estimates would be affected by selection bias if participation in the study is driven by factors that are uncorrelated with the sociodemographic factors incorporated in our sampling weights but correlated with the caregiving disruptions as well as outcomes under study (Rothman et al., 2013). Although we believe this scenario is unlikely, we recommend caution when interpreting results beyond internet users. Although our results are associational, we took care to adjust for potential common causes of caregiving disruptions and mental health and employment outcomes. However, there may be residual confounding due to aspects of socioeconomic conditions not captured by education, housing tenure, or pre-COVID-19 employment status, such as income or wealth, or by other unmeasured confounders. In addition, our data do not provide fine-grained descriptions of the specific types of care provided, whether the caregiver was paid or otherwise compensated, or the intensity or frequency of care, except to specify “regular” care. We performed robustness checks excluding individuals employed in health care or health care–adjacent professions, which showed similar results to the main analysis. Our sample size also precluded us from examining the relationship between caregiving disruptions and mental health by the identity of the care recipient caregiving role, though our results do not change when we include this as a control variable.
Strengths of this study include its large national sample of middle-aged and older Americans, and its timeliness in collecting rich data on caregiving, employment, mental health, and other social, economic, and health-related factors during the first wave of the COVID-19 pandemic. Although existing and ongoing surveys of family caregiving and COVID-19 compare caregivers with noncaregivers (Beach et al., 2021; Czeisler et al., 2020; Park, 2021) or follow caregivers longitudinally, our study identified COVID-19-related caregiving disruptions and compared disrupted caregivers with those who did not experience disruptions as well as with noncaregivers. Findings from our research may inform hypotheses for future research on longer term disruptions to family care provision, as well as the potential persistence or trajectories of mental health and economic situations of middle-aged and older family caregivers as the COVID-19 pandemic continues. Indeed, an AP/NORC survey of adult caregivers from August 2020, 3 months after this survey was conducted, found roughly similar percentages of caregivers with care disruptions, suggesting that the impacts of COVID-19 on families with caregiving needs were both immediate and persistent (AP-NORC Center for Public Affairs Research, 2020).
The COVID-19 pandemic has simultaneously highlighted both the national reliance on family care and caregivers’ deep vulnerability to disruptions under the current system. Although some U.S. states took advantage of emergency waivers to expand access to supports including telehealth, meal delivery, and financial compensation, these programs are temporary and only available to Medicaid-eligible care recipients with significant functional limitations (Fox-Grage et al., 2020). Many working caregivers found themselves without access to paid family leave or sick leave. The Families First Coronavirus Response Act (FFCRA) provided emergency paid leave only to parents caring for children or those caring for somebody with a COVID-19 diagnosis, ignoring the obligations of those caring for nonchild family members such as spouses (Feinberg, 2018; Waldfogel & Liebman, 2019). Some previously employed caregivers who lost work while managing changes to their care arrangements may have relied on the Coronavirus Aid, Relief, and Economic Security (CARES) Act pandemic unemployment compensation benefits, which expired in December 2020. No systematic approach was taken to provide testing, training, or personal protective equipment to family caregivers, and few states prioritized caregivers in early vaccine priority groups (Friedman et al., 2021; Halley & Mangurian, 2021; Van Houtven, Boucher & Dawson, 2020). Our study points to the urgent need to explicitly consider family caregivers as essential workers as well as integral members of primary care medical teams, and to provide them with respite to support their care provision, mental health, and engagement in the labor force where appropriate. More research is needed to understand the impact of the pandemic on their immediate and long-term well-being.
Conclusion
We newly identified that over half of middle-aged and older U.S. family caregivers experienced disruptions to their ongoing care provision in the first wave of the COVID-19 pandemic. These disruptions were associated with elevated symptoms of depression, anxiety, and loneliness for caregivers, and were often experienced alongside employment disruptions. Caregivers who were most vulnerable to experiencing care disruptions were disproportionately younger, female, Black, and Hispanic, highlighting disparities in the mental health and economic impacts of the pandemic on middle-aged and older family caregivers in the United States. These findings inform how the COVID-19 pandemic has impacted key family support systems, while identifying middle-aged and older family caregivers as an important demographic that is vulnerable to mental health and economic consequences during the pandemic.
Acknowledgments
We thank Norma Coe, Vicki Freedman, Corina Mommaerts, Meredith Slopen, Courtney Van Houtven, and participants in the 2020 APPAM Annual Conference and the 2021 NBER COVID-19 and Health Research Conference for helpful comments and suggestions.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
ORCID iD
Yulya Truskinovsky https://orcid.org/0000-0002-7308-6496
Footnote
1. Although all our outcomes were binary, we used linear probability models (ordinary least squares [OLS]) instead of logistic models because linear probability models produce marginal effects that are directly interpretable and comparable across models with different covariates (Norton & Dowd, 2018). In Supplemental Tables S1 and S2, we include the percent of predicted values that fall outside the 0–1 range and reproduce our analysis using nonlinear regression models (logit).
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Article first published online: January 10, 2022
Issue published: October 2022
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PubMed: 35001714
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This article was published in Medical Care Research and Review.
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