Journal of Gerontological Nursing

Research Brief 

Sociodemographic Disparities in Adult Child Informal Caregiving Intensity in the United States: Results from the New National Study of Caregiving

Sarah K. Cook, MPH; Steven A. Cohen, DrPH, MPH

Abstract

The objective of the current study was to assess socioeconomic and demographic disparities in caregiving intensity among informal caregivers. Using a randomized, nationally representative sample of 1,014 adult child informal caregivers from Medicare enrollment databases, the associations between informal caregiving intensity and age, race/ethnicity, and income were examined using binary and ordinal logistic regression. Caregiving intensity varied by demographics. Activities of daily living (ADL) caregiving was highest among Black, non-Hispanic caregivers. Instrumental ADL caregiving and number of hours spent caregiving was highest in female and non-White caregivers. Although the overall association between caregiving intensity and income was not significant, when stratified by race/ethnicity, this association was positive for White caregivers and negative for non-White caregivers. Health care providers frequently interact with informal caregivers and should be aware of trends in caregiving and the needs and supports available to ameliorate caregiver burden. To protect caregivers, policies and programs should be designed to promote well-being and mitigate the potential harms of caregiving to health. [Journal of Gerontological Nursing, 44(9), 15–20.]

Abstract

The objective of the current study was to assess socioeconomic and demographic disparities in caregiving intensity among informal caregivers. Using a randomized, nationally representative sample of 1,014 adult child informal caregivers from Medicare enrollment databases, the associations between informal caregiving intensity and age, race/ethnicity, and income were examined using binary and ordinal logistic regression. Caregiving intensity varied by demographics. Activities of daily living (ADL) caregiving was highest among Black, non-Hispanic caregivers. Instrumental ADL caregiving and number of hours spent caregiving was highest in female and non-White caregivers. Although the overall association between caregiving intensity and income was not significant, when stratified by race/ethnicity, this association was positive for White caregivers and negative for non-White caregivers. Health care providers frequently interact with informal caregivers and should be aware of trends in caregiving and the needs and supports available to ameliorate caregiver burden. To protect caregivers, policies and programs should be designed to promote well-being and mitigate the potential harms of caregiving to health. [Journal of Gerontological Nursing, 44(9), 15–20.]

According to a 2015 research report (AARP & National Alliance for Caregiving, 2015), more than 34 million American adults provided informal care to an adult older than 50, with 47% of these individuals caring for a parent. These figures are only expected to rise as the number of Americans older than 65 grows from 47.5 million in 2015 to 98 million by 2060 (Administration on Aging, 2016). Informal caregiving, the unpaid care and support family members and friends voluntarily provide to individuals who are unable to function independently, has numerous benefits to care recipients and society as a whole. These benefits include savings to the national economy, prevention of hospitalization and institutionalization, and allowing older adults to remain in their own homes (Chari, Engberg, Ray, & Mehrotra, 2015). Informal caregivers are estimated to spend more than 30 billion hours per year providing care to individuals who are disabled or chronically ill, with an opportunity cost savings of $522 billion per year that would otherwise be spent on formal care and institutionalization (Chari et al., 2015).

Despite these benefits of informal caregiving to care recipients and the national economy, numerous negative effects associated with caregiving have been documented in the literature. Many studies have demonstrated the negative impacts on caregiver health-related quality of life, including physical and emotional health consequences (e.g., anxiety, depression), that can occur as a result of providing care (AARP & National Alliance for Caregiving, 2015; Cannuscio et al., 2002; Ho, Chan, Woo, Chong, & Sham, 2009; Macneil et al., 2010; Pinquart & Sörensen, 2011; Schultz & Sherwood, 2008). These negative health-related quality of life outcomes are commonly referred to as caregiver stress, strain, or burden. Moreover, informal caregivers providing a high amount of care may be particularly vulnerable to the effects of caregiver strain and may differ from those providing less care in substantial ways, such as their employment status, the type of caregiving duties they provide, and the impact caregiving has on them (AARP & National Alliance for Caregiving, 2015; Jacobs, Laporte, Van Houtven, & Coyte, 2014). The duties caregivers provide may vary by sociodemographic factors, including race and gender, where female and non-White caregivers are more likely to provide higher amounts of informal care than their counterparts (AARP & National Alliance for Caregiving, 2015). However, findings are mixed. Few studies have examined differences in caregiving intensity by socioeconomic and demographic factors using a nationally representative dataset.

There is a critical need to better understand who is providing informal care to aging parents, and how best to provide the support, assistance, and resources caregivers may need. To further the understanding of who is providing informal care, the objectives of the current study were to examine the demographic profiles of informal adult child caregivers in the United States and assess the sociodemographic differences in caregiving duties (i.e., caregiving intensity) among this population of informal caregivers.

Method

Study Population

Data were obtained from the 2011 National Study of Caregiving (NSOC) dataset, a nationally representative sample of informal caregivers. The NSOC identified caregivers of National Health and Aging Trends Study (NHATS) participants who were receiving assistance in self-care, mobility, medical, or household activities. These caregivers were then contacted to participate in a one-time, cross-sectional assessment of caregiving that included questions on caregiving activities, duration, intensity, and demographics. The current analysis focused on adult child caregivers to older adult parents (N = 1,014), a subset of informal caregivers.

Outcome Variables: Caregiving Intensity

Four individual measures of caregiving duties that are most common in the literature were used to assess caregiving intensity: (a) number of activities of daily living (ADL) performed, (b) number of instrumental ADL (IADL) performed, (c) hours of caregiving provided per month, and (d) duration (years) of caregiving. To measure these four intensity domains, items from the NSOC questionnaire assessing aspects of caregiver duties were used. ADL refer to daily self-care activities that are necessary for fundamental functioning. ADL were measured by the number of personal care activities caregivers helped with each month, including eating, bathing, dressing, toileting, and helping care recipients move around. IADL comprise other caregiving activities not necessary for fundamental functioning but which allow an individual to live independently. This domain included the number of IADL caregivers helped their parents with, including medication management, scheduling medical appointments, and other health- and hygiene-related tasks. The remaining two intensity domains were calculated based on the average number of hours spent caregiving in the past 1 month and average number of years providing care. Of each individual intensity domain, the top 25% were considered high-intensity caregivers, whereas the bottom 75% were considered low-intensity caregivers.

To calculate the composite intensity measure, each of the four individual measures of caregiving intensity scored 1 point if considered high intensity and 0 points if considered low intensity. This composite score ranged from 0 (provided no high-intensity care in any of the four individual caregiving measures) to 4 (provided high-intensity care in all four individual caregiver domains).

Exposure Variables: Caregiver Demographics

Four demographic characteristics of caregiver respondents identified in previous studies of caregiving intensity were assessed, including caregiver age, gender, race/ethnicity, and annual household income (Cohen, Cook, Sando, Brown, & Longo, 2017; Fredman, Doros, Ensrud, Hochberg, & Cauley, 2009; Navaie-Waliser et al., 2002; Navaie-Waliser, Spriggs, & Feldman, 2002). Age was categorized in 10-year age groups (<45, 45 to 54, 55 to 64, and ≥65). Race/ethnicity was based on three calculated domains (i.e., non-Hispanic White, non-Hispanic Black, and Other [Hispanic, American Indian, Native Hawaiian, Pacific Island, other non-Hispanic]). Income was categorized in intervals of $25,000 (<$24,999; $25,000 to $49,999; $50,000 to $74,999; and ≥$75,000).

Additional Demographic Confounders and Covariates

Other key confounders and covariates commonly used in studies of differences in caregiver intensity, including caregiver marital status (Brody, Litvin, Hoffman, & Kleban, 1995; Dentinger & Clarkberg, 2002), presence of a child (age <18) living in the home (Cohen et al., 2017; Grundy & Henretta, 2006), and caregiver co-resident status (i.e., care recipient and caregiver reside in same home) (Tennstedt, Crawford, & McKinlay, 1993), were also assessed.

Data Analysis

Univariate and bivariate analyses were used for all primary outcome and exposure variables to assess individual measures of high-intensity caregiving. For the composite measure of high-intensity caregiving, ordinal logistic regression models were used to calculate adjusted odds ratio (OR) and 95% confidence interval (CI), adjusting for covariates. Pairwise deletion was used to handle missing values for each model. SAS 9.3 was used for all analyses.

Results

Demographics of NSOC Adult Child Caregivers

The demographic breakdown for the current sample of adult child caregivers is found in Table 1. Caregivers' average age was 54.6 years (SD = 8.9 years; range = 19 to 77 years), with 69% of respondents being female. Respondents reported an average annual income of $56,582 (SD = $53,401; range = $0 to $500,000). Sixty percent of respondents identified as non-Hispanic White, 31% as non-Hispanic Black, and 9% as another racial/ethnic group. Adult child caregivers spent an average of 85 hours (SD = 122.9 hours; range = 0.5 to 720 hours) per month providing care and had been caring for their parents for an average of 5.6 years (SD = 6.2 years; range = 0 to 44 years).

Descriptive Characteristics for the National Study of Caregiving Sample of Adult Child Caregivers (N = 1,014)

Table 1:

Descriptive Characteristics for the National Study of Caregiving Sample of Adult Child Caregivers (N = 1,014)

Individual Measures of Caregiving Intensity

High ADL caregiving was most prevalent in caregivers ages 45 to 54 (28.1%) and non-Hispanic Black caregivers (33.9%). High IADL caregiving was significantly higher in females (30.3%) than males (20.6%). Compared to White caregivers, high IADL caregiving was significantly higher among non-Hispanic Black caregivers (33.5%) and caregivers of “other” racial/ethnic groups (30.9%). A high number of hours spent caregiving was highest in females (27.9%), non-Hispanic Black caregivers (36.1%), and “other” race/ethnicities (30.8%), and individuals earning <$25,000 per year (37.6%). High years of caregiving was highest in non-Hispanic Black caregivers (33.8%) and caregivers of “other” race/ethnicities (35.7%). All results are displayed in Table 2.

Demographics of High-Intensity Caregivers for Four Measures of Caregiving Intensity (N = 1,014)

Table 2:

Demographics of High-Intensity Caregivers for Four Measures of Caregiving Intensity (N = 1,014)

Composite Measure of Caregiving Intensity

Differences in high-intensity caregiving varied by gender, race, and other sociodemographic factors (Figure). Female caregivers had higher odds of providing high-intensity care than their male caregiver counterparts (OR = 1.43, 95% CI [1.03, 1.99]). The odds of providing high-intensity caregiving were greater for non-White caregivers (i.e., Black and other race/ethnicity) than White caregivers (OR = 1.86, 95% CI [1.30, 2.64]). Co-resident caregivers were more likely to have provided high-intensity caregiving than caregivers not residing with their care recipient (OR = 1.70, 95% CI [1.19, 2.42]). In addition, a negative association between annual income and high-intensity caregiving was observed: as caregivers' annual income decreased, the odds of providing high-intensity caregiving increased significantly (p < 0.001).

Crude and adjusted odd ratios and 95% confidence intervals of high-intensity caregiving by seven sociodemographic factors compared to reference caregivers. Note. Bold indicates p < 0.05. Outcome is the composite measure of high-intensity caregiving.

Figure.

Crude and adjusted odd ratios and 95% confidence intervals of high-intensity caregiving by seven sociodemographic factors compared to reference caregivers. Note. Bold indicates p < 0.05. Outcome is the composite measure of high-intensity caregiving.

Discussion

The current study sought to better understand who is providing high-intensity informal care to an older parent. Findings indicate that there are notable differences in caregiving intensity that vary by caregiver socioeconomic and demographic factors. High-intensity caregiving was most prevalent among caregivers who were female, non-White, low income, and resided with their care recipient. These results are in line with previous studies that report greater caregiving responsibilities among females, racial/ethnic minorities, individuals with shared residence, and low income caregivers (AARP & National Alliance for Caregiving, 2015; Kim, Chang, Rose, & Kim, 2012; Pinquart & Sörensen, 2005). However, the observed associations were dependent on the type of care being provided, such that certain caregivers were more likely to provide high-intensity care in some domains of caregiving than others. Surprisingly, no differences were observed between males and females in high ADL caregiving. Historically, this has not been the case and could be a result of the changing face of informal caregivers or in how caregivers were selected for inclusion in the current sample.

Although the current results largely confirm previous findings, the analysis adds to the body of caregiver research in several ways. First, the analysis uses a nationally representative sample of caregivers previously identified by their care recipients. This is an important distinction from other representative samples where respondents self-identify as caregivers. Second, the focus on adult child caregivers sheds light on who is providing high-intensity care to an aging parent. Investigating adult child caregivers—an important and large subset of caregivers—is important, as they differ from other types of caregivers (e.g., spousal) in significant ways. Examining this group separately is recommended, as significant differences in caregiver characteristics, needs, and burden have been noted (Chappell, Dujela, & Smith, 2014; Pinquart & Sörensen, 2011). Third, the current analysis compares caregivers based on the level of care they provided (high versus low intensity).

Caregiving intensity, whether measured by the type or amount of assistance provided, is associated with various health effects and quality of life outcomes (Schultz & Sherwood, 2008). Numerous studies have shown any type of informal caregiving can result in negative physical and emotional health consequences for caregivers, often referred to as caregiving-related stress or burden (AARP & National Alliance for Caregiving, 2015; Cannuscio et al., 2002; Ho et al., 2009; Macneil et al., 2010; Pinquart & Sörensen, 2011; Schultz & Sherwood, 2008). With noted differences in the intensity of care being provided, the current authors anticipate implications for caregiver health and quality of life that also vary by sociodemographic factors. Research suggests that differences exist in caregiver quality of life among male and female caregivers, caregivers of different racial and ethnic groups, different ages (Anderson et al., 2013; Covinsky et al., 2003; Neugaard, Andresen, McKune, & Jamoom, 2008), and different income levels (Williams, Forbes, Mitchell, Essar, & Corbett, 2003).

Health care professionals should be aware of trends in high-intensity caregiving among informal caregivers. Gerontological nurses may have frequent contact with aging adults and their informal caregivers who are susceptible to or exhibit signs of burnout and quality of life concerns. Nurses' role in facilitative informal caregiving has expanded from primary caregiving to teaching and assisting family members to provide care (Schultz & Sherwood, 2008). Nurses who provide informal caregivers a temporary break from their caregiving responsibilities can significantly improve caregiver health and quality of life (Lopez-Hartmann, Wens, Verhoeven, & Remmen, 2012) and reduce subsequent caregiver burden (Horton-Deutsch, Farran, Choi, & Fogg, 2002).

Nurses are poised to recognize symptoms of distress and burn-out and offer appropriate resources for caregivers in need of additional support. Such support is facilitated through communication between nurses and informal caregivers, building relationships with informal caregivers and creating a culture of trust with the family of the care recipient (Weman & Fagerberg, 2006). However, the availability of health care providers trained to provide such support may depend on the services and resources that are available at the local level and may not be equitable for all informal caregiver populations throughout the United States.

Limitations

When interpreting the current findings, there are several important limitations to note. First, due to the cross-sectional nature of the study, causal relationships between caregiver demographics and high-intensity caregiving were unable to be determined. A second wave of NSOC data will be available within the year and future analyses may be able to determine causality. Second, caregiver employment was not explored, which may impact caregivers' ability to provide different types and levels of care. Third, caregiving responsibility was dichotomized into high and low caregiving intensity, rather than assessed along a gradient. As such, a composite analysis was conducted to assess overall caregiving intensity as a continuous measure composed of multiple types of caregiving (e.g., ADL, IADL, hours per month). Next, all measures were self-reported, which may bias the results toward more socially acceptable responses to the measures examined in the study. Sample weights were not used in the analysis, as the importance of including weights in regression models such as these in which descriptive population parameters are not being estimated are a subject of debate in the survey analysis literature. Lastly, missing data were assumed to be missing at random; therefore, missing values were not imputed and instead were handled through the use of pairwise deletion.

Conclusion

Findings show female and non-White caregivers are more likely to provide high-intensity care; however, the intersection between these two constructs and the influence on outcomes is not well known. What remains to be seen is the impact sociodemographic interactions have on caregiver intensity and caregiver health outcomes. In addition, as the population of aging adults increases, and the face of caregiving evolves, the sociodemographics of caregivers providing high-intensity care are anticipated to change over time, as are their needs and necessary supports. Consequently, policies and programs designed to promote caregiver health and quality of life should consider these important sociodemographic differences to protect and support this vital component of the U.S. health care system.

References

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Descriptive Characteristics for the National Study of Caregiving Sample of Adult Child Caregivers (N = 1,014)

Characteristicn (%)
Age (years)
  <45102 (12.6)
  45 to 54281 (34.7)
  55 to 64323 (31.9)
  ≥65104 (12.8)
Annual income
  $25,000 to $49,999206 (29.7)
  $50,000 to $74,999126 (18.2)
  ≥$75,000204 (29.4)
Gender
  Female704 (69.4)
  Male310 (30.6)
Race/ethnicity
  White, non-Hispanic610 (60.2)
  Black, non-Hispanic313 (30.9)
  Other91 (9)
Marital status
  Married491 (48.4)
  Never married196 (19.3)
  Divorced170 (16.8)
  Widowed60 (5.9)
  Living with partner54 (5.3)
  Separated32 (3.2)
Child age <18 living at home181 (17.9)
Caregiver co-resident383 (37.8)

Demographics of High-Intensity Caregivers for Four Measures of Caregiving Intensity (N = 1,014)

DemographicADLIADLHoursYears
Race/ethnicity
  White, non-Hispanic24.9*23.6*19.0*22.5*
  Black, non-Hispanic33.9*33.5*36.1*33.8*
  Other24.2*30.9*30.8*35.7*
Gender
  Female28.530.3*27.9*26.1
  Male26.120.6*19.7*29.6
Age (years)
  <4524.5*27.523.524.7
  45 to 5428.1*24.620.730.5
  55 to 6418.6*21.119.626.0
  ≥6520.3*17.320.525.0
Income
  <$25,00032.531.637.6*31.6
  $25,000 to $49,99930.624.822.4*27.2
  $50,000 to $74,99929.427.019.8*22.0
  ≥$75,00022.525.513.4*23.1
Authors

Ms. Cook is Research Projects Coordinator II, Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee; and Dr. Cohen is Assistant Professor, Health Studies Program, Department of Kinesiology, University of Rhode Island, Kingston, Rhode Island.

The authors have disclosed no potential conflicts of interest, financial or otherwise. The authors acknowledge the data source used in this analysis, the National Health and Aging Trends Study (NHATS). NHATS is sponsored by the National Institute on Aging (grant NIA U01AG032947) through a cooperative agreement with the Johns Hopkins Bloomberg School of Public Health.

Address correspondence to Sarah K. Cook, MPH, Research Projects Coordinator II, Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN 37203; e-mail: sarah.k.cook@vumc.org.

Received: February 02, 2018
Accepted: June 19, 2018

10.3928/00989134-20180808-05

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