Sexual and Gender Minority Differences in Likelihood of Being a Caregiver and Levels of Caregiver Strain in a Sample of Older Adults

ABSTRACT

Over the next two decades, the number of caregivers is expected to climb dramatically alongside a rise in older adults, particularly sexual and gender minority (SGM) older adults, yet little research has assessed differences between SGM and non-SGM care partners. Data for these analyses come from the Columbus Healthy Aging Project (N = 79). This study was designed to assess several domains of health among adults aged ≥50 years in Columbus, Ohio, US. Multivariable regression models were used to examine the likelihood of being a care partner, the SGM identity of the primary care recipient, and caregiver strain. In our sample, 227 (28.6%) participants self-identified as care partners for at least one individual. Compared to heterosexuals, gay/lesbian (aOR = 8.38; 95% CI: 5.29, 13.29) participants were more likely to be care partners but did not experience elevated caregiver strain. Bisexual individuals (aIRR = 1.70, 95% CI: 1.11, 2.61) reported greater caregiver strain, while those identifying as a different sexual identity reported lower caregiver strain (aIRR = 0.46, 95% CI: 0.23, 0.96). In turn, caregiver strain was reduced significantly when the care recipient identified as a member of the SGM community (aIRR = 0.67: 95% CI: 0.55, 0.80). These results suggest that SGM care partners may be at risk of unique stressors which may contribute to extant health disparities.

KEYWORDS:

• Care partners
• sexual and gender minorities
• caregiver strain

Introduction

According to the U.S. Census Bureau, within the next two decades the number of older adults is expected to surpass that of children for the first time in the history of the country (U.S. Census Bureau, 2018). Among these, the number of older adults identifying as sexual and gender minorities (SGMs) is expected to rise dramatically to more than six million adults over the age of 50 by the end of the current decade (American Psychological Association [APA], 2013; Fredriksen-Goldsen, 2011). Importantly, these estimates, which have yet to take into account recent reports that the number of SGM adults has risen sharply in the U.S. (Gallup, 2022), largely as a result of rising visibility for SGM adults and advances in anti-discrimination work over the past several decades. As both SGM, cisgendered people, and heterosexual adults age, the number of caregivers (or care partners)—those providing unpaid care to another person—is also expected to rise. In fact, recent data suggest this trend has already begun with the number of caregivers in the U.S. increasing from 43.5 million in 2015 to 53 million in 2020 (The National Alliance for Caregiving, 2020). Taken together, these data indicate that the growing population of older SGM adults will result not only in a greater number of individuals acting as caregivers but also a greater number of individuals in need of care themselves. Importantly, this population of individuals faces unique needs, particularly at the intersection of age- and SGM-related stressors, which may be exacerbated by additional stress related to caregiving. And while some recent work has specifically examined SGM caregivers of people living with dementia J. G. Anderson et al. (2021, 2022), broader research on basic differences between caregivers based on sexual or gender identity remains lacking.

Due to the social and physiological consequences of aging, older SGM adults have been identified as a key population in need of additional research to reduce health disparities (National Academies of Sciences E, and Medicine, 2020). In addition to SGM-related stressors (e.g., microaggressions, internalized stigma), older SGM adults experience stressors related to aging, making it critical to attend to the intersection of not only sexual, but also gender, identity, and age to improve understanding of the health of this key subpopulation of older adults (Fredriksen-Goldsen, Kim, et al., 2013). Some age-related stressors are unique to older SGM adults (e.g., discrimination in assisted living facilities and nursing homes), whereas others are shared by older adults regardless of sexual or gender identity (e.g., social isolation, declines in health). Even when age-related stressors are not unique to SGMs, they are still more common among this population. For example, compared to older cisgendered people and heterosexuals, older SGMs are more likely to have a disability, to be in poorer physical health, and to live alone (Fredriksen-Goldsen et al., 2014; Fredriksen-Goldsen, Emlet, et al., 2013; Gendron et al., 2013). As a result, they often contend with greater social isolation and loneliness (Hwang et al., 2020; Pietrabissa & Simpson, 2020), which, in turn, could lead to increased use of licit and illicit substances to cope with the additional burden of stress (Bresin & Mekawi, 2019; Keough et al., 2015). Furthermore, older SGMs have also described SGM-based discrimination in assisted living facilities (Erdley et al., 2014) and many actively avoid health-care facilities due to fears of discrimination (Ritter & Ueno, 2019). These experiences may lead older SGMs to rely on others in the SGM community for support and caregiving needs, contributing to increased caregiving responsibilities among older SGMs. In fact, it may be possible that those in need of caregiving services seek care specifically from SGM caregivers and, in turn, strain among SGM caregivers may be reduced. No research the authors are aware of has begun to examine this possibility, a gap we will begin to fill here.

To begin to address these gaps, we recruited a sample of older adults in Columbus, Ohio, with a focus on assessing disparities in caregiving between SGM and non-SGM older adults. The aims of the paper are thus to 1) identify basic sociodemographic differences in the likelihood of serving as a caregiver; 2) assess differences in caregiver strain between SGM and cisgendered people/heterosexuals; and 3) examine whether caregiver strain may differ based on the SGM identity of the primary care recipient.

Methods

Study population

Data come from the cross-sectional survey, the Columbus Healthy Aging Project (CHAP). The study was designed to assess several domains of health and potential risk factors among adults aged 50 years and older in Columbus, Ohio, USA. Recruitment occurred throughout the Columbus metropolitan area exclusively via Facebook and Instagram. Two ads were run to recruit participants: one for the general population and a second targeted toward SGMs. Inclusion criteria were as follows: 1) age ≥50 years; 2) residence in Columbus or surrounding suburbs; 3) access to a computer or smartphone to complete the online survey assessment; and 4) a working e-mail address. We demographically matched participants to recruit equal numbers of SGM, cisgendered people, and heterosexual participants while maintaining a racially and ethnically diverse sample that was reflective of Columbus’s own distribution (U.S. Census Bureau, 2019). This was done by pre-defining several groups of individuals (e.g. non-Hispanic White sexual minority women), each with a quota that, once filled by qualified participants, were no longer recruited. All participants were compensated $20 in the form of an Amazon gift card for their time. Study protocols and procedures were approved by The Ohio State University’s Institutional Review Board (2020B0394).

Demographic measures

Self-reported demographic information was collected and included age, sex assigned at birth, gender identity, race, ethnicity, and sexual identity. Age was operationalized as a continuous variable. Sex assigned at birth included two categories: female and male. Race and ethnicity were coded based on participant self-identification as Black, White, Hispanic/Latinx, and a different race (American Indian/Alaska Native, Asian, Native Hawaiian/Other Pacific Islander, Multiracial, or a different race). Sexual identity was operationalized as gay, lesbian, bisexual, heterosexual, or a different identity (e.g., queer, pansexual, unsure/questioning, asexual, or any other sexual identity). Gender identity was operationalized as cisgender woman or man, transgender woman or man, or a different gender identity (e.g., genderqueer, gender non-conforming, non-binary, or any other gender identity). Additionally, those whose assigned sex at birth differed from their current gender identity were coded as transgender as appropriate (e.g., sex assigned female at birth and current gender identity as man would be coded as a transgender man).

Caregiver status

Caregiver or care partner status was assessed by asking participants, “Do you consider yourself to be a caregiver for a family member or friend? That is anyone who cares or looks after the health or well-being of another individual.” The variable was operationalized as a dichotomous yes or no variable. Those who reported being caregivers were also asked whether the primary care recipient identified as a member of the SGM community. This response was recorded as yes, a member of the SGM community; no, not a member of the SGM community; or unknown. In these analyses, if the status of the primary care recipient was unknown they were not included in subsequent analyses, thus the final operationalized variable was dichotomous, yes or no, a member of the SGM community or not a member.

Caregiver strain index

The caregiver strain index (Robinson, 1983) was included to assess stress experienced by individuals during their daily work as caregivers. As a result, only the subset of participants who reported serving as caregivers were asked to complete this portion of the survey. Participants were asked, “Thinking specifically of your work as a caregiver, do any of the following apply to you?” Options included sleep disturbance, inconvenience, physical strain, confinement, familial adjustments, changes to personal plans, emotional adjustments, upset, changes to self-image, work adjustments, financial strain, and feeling of overwhelm. All items were summed and reported/operationalized as a continuous variable. Scores on this scale could range between 0 and 12 and a score of 7 or higher indicating high levels of stress related to caregiving. Cronbach’s alpha for the index was 0.86.

Other measures

Participants were asked to report their current living situation with the following options: living with a spouse or partner, living alone, living with family members or a roommate, residing in a long-term care facility, or a different living situation (e.g., no permanent address, living in a shelter, etc.). The number of children was assessed and operationalized as a continuous measure.

Statistical analyses

Bivariate associations initially examined demographic differences in self-reported caregiver status using chi-squared analyses, Fisher’s exact test, and Student’s t-test, as appropriate. Multivariable logistic regression was then used to assess sociodemographic differences and likelihood of being a caregiver. Particular attention was paid to differences based on sexual identity and gender identity with each variable loaded individually into each model. The second set of multivariable logistic and Poisson regression models examined demographic characteristics in relation to: 1) care partner sexual identity; 2) the caregiver strain index; and 3) the caregiver strain index adjusting for care recipient sexual identity. Poisson models were chosen over linear regression models given the distribution of the outcome variable, the caregiver strain index, which is better approximated by a Poisson distribution. Statistical significance was established at alpha <0.05. All analyses were performed in StataBE 17.0.

Results

Table 1 presents demographic and other variables stratified by self-reported caregiver status. Overall, 227 (28.7%) participants reported serving as caregivers for at least one individual. The mean caregiver strain index score was 6.3 (SD = 3.7), nearing the threshold of 7 which would indicate high levels of stress related to caregiving. Among caregiving participants who were knowledgeable about their primary care recipient’s sexual identity, half (115, 50.7%) reported that the primary care recipient identified as a member of the SGM community, 32 (14.1%) reported not caring for someone part of the SGM community, and 80 (35.2%) did not know the sexual identity of the primary care recipient.

Table 1. Demographic attributes of the Columbus Health Aging Project, Columbus, OH (N = 794).

	Total	Non-Caregivers	Caregivers	p-value^a
	(N = 791)	(n = 564)	(n = 227)
Age, mean (SD)	58.5 (6.3)	58.7 (6.3)	58.3 (6.4)	.216
Gender Identity, n (%)				.001
Cisgender Woman	330 (41.7)	248 (44.0)	82 (36.1)
Transgender Woman	25 (3.2)	11 (2.0)	14 (6.2)
Cisgender Man	357 (45.1)	244 (43.3)	113 (49.8)
Transgender Man	27 (3.4)	24 (4.3)	3 (1.3)
Different Identity	52 (6.6)	27 (6.6)	15 (6.6)
Race and Ethnicity, n (%)				.001
Black	295 (37.3)	205 (36.4)	90 (39.7)
White	410 (51.8)	284 (50.4)	126 (55.5)
Hispanic/Latinx	39 (6.2)	47 (8.3)	2 (0.9)
Multiracial or different race	37 (4.7)	28 (5.0)	9 (4.0)
Sexual Identity, n (%)				<.001
Gay	213 (26.9)	114 (20.2)	99 (43.6)
Lesbian	180 (22.8)	117 (20.7)	63 (27.8)
Bisexual	44 (7.8)	8 (3.5)	52 (6.6)
Heterosexual	315 (39.8)	266 (47.2)	49 (21.6)
Different Identity	31 (3.9)	23 (4.1)	8 (3.5)
Living Situation, n (%)				<.001
With Spouse/Partner	326 (41.2)	247 (43.8)	79 (34.8)
Alone	169 (21.4)	148 (26.2)	21 (9.3)
With Family/Roommate	266 (33.6)	151 (26.8)	115 (50.7)
Long-Term Care	14 (1.8)	8 (1.4)	6 (2.6)
Other	16 (2.0)	10 (1.8)	6 (2.6)
Care Partner Identity, n (%)
Not SGM	–	–	32 (14.1)	–
SGM	–	–	115 (50.7)	–
Unknown			80 (35.2)
Caregiver Strain Index, mean (SD)	–	–	6.3 (3.7)	–

^aCalculated via either chi-square analyses or Student’s t-test, as appropriate.

The overall mean age of the participants was 58.5 years (SD = 6.3). The largest proportion of participants were White (410, 51.8%) followed by Black (295, 37.3%), Hispanic/Latinx (39, 6.2%), and Multiracial or a different race (37, 4.7%). A majority of participants did report being an SGM (477, 60.2%; gay, lesbian, bisexual, or a different identity) followed by a cisgendered heterosexual (315, 39.8%); however, this was expected given the initial study design. Regarding gender identity, 357 (45.1%) identified as cisgender men, 330 (41.7%) as cisgender women, 27 (3.4%) as transgender men, 25 (3.2%) as transgender women, and 52 (6.6%) identified as a different identity. The largest proportion of participants reported living with a spouse or partner (326, 41.2%) followed by those living with family or roommates (266, 33.6%), those living alone (169, 21.4%), those living in long-term care facilities (14, 1.8%), and those in other living situations (16, 2.0%).

Table 2 presents a multivariable logistic regression model examining the association between sociodemographic variables and self-reported status as a caregiver. Relative to White participants, Black (adjusted odds ratio [aOR] = 0.61; 95% CI: 0.40, 0.93) and Hispanic/Latinx participants (aOR = 0.04; 95% CI: 0.01, 0.20) reported lower odds of serving as caregivers. Transgender men (aOR = 0.17; 95% CI: 0.04, 0.73) and those with a different gender identity (aOR = 0.35; 95% CI: 0.16, 0.77) reported lower odds of being caregivers relative to cisgender women. Both gay/lesbian participants (aOR = 8.38; 95% CI: 5.29, 13.29) and those with a different sexual identity (aOR = 3.29; 95% CI: 1.24, 8.72) were significantly more likely to report serving as caregivers compared to heterosexuals and cisgendered people, respectively. Those living alone were significantly less likely (aOR = 0.34; 95% CI: 0.18, 0.62), while those living with family members or roommates were significantly more likely (aOR = 1.82; 95% CI: 1.17, 2.81) to be caregivers, relative to those living with a spouse or partner. Those with children were significantly more likely to report serving as caregivers (aOR = 1.64; 95% CI: 1.44, 1.87).

Table 2. A multivariable logistic regression model assessing the association between various sociodemographic characteristics and self-reported status as a care partner.

	Care Partner Status^a
Characteristic	aOR (95% CI)
Age	1.01 (0.98, 1.04)
Race/Ethnicity
White	Ref
Black	0.61 (0.40, 0.93)*
Hispanic/Latinx	0.04 (0.01, 0.20)***
Different Race/Ethnicity	1.13 (0.46, 2.80)
Gender Identity
Cisgender Women	Ref
Transgender Women	1.33 (0.44, 3.99)
Cisgender Men	0.98 (0.65, 1.48)
Transgender Men	0.17 (0.04, 0.73)*
Different Identity	0.35 (0.16, 0.77)**
Sexual Identity
Heterosexual	Ref
Gay/Lesbian	8.38 (5.29, 13.29)***
Bisexual	2.21 (0.78, 5.74)
Different Identity	3.29 (1.24, 8.72)*
Living Situation
With Spouse/Partner	Ref
Alone	0.34 (0.18, 0.62)***
With Family/Roommate	1.82 (1.17, 2.81)**
Long-Term Care	2.76 (0.68, 11.19)
Other	2.20 (0.66, 7.36)
Number of Children	1.64 (1.44, 1.87)***

*p ≤ .05; **p ≤ .01; **p ≤ .001.

^aUsing logistic regression.

Table 3 presents multivariable logistic and Poisson regression models examining the association between sociodemographic variables and the outcomes: 1) care recipient SGM status; 2) the caregiver strain index; and 3) the caregiver strain index adjusting for care recipient SGM status. The first model utilized logistic regression to examine the association between the sociodemographic attributes of the caregiver and the sexual identity of the primary care recipient. Black participants (aOR = 5.62; 95% CI: 1.76, 17.99) and gay/lesbian participants (aOR = 4.10; 95% CI: 1.00, 16.84) were each significantly more likely to care for SGM individuals relative to White and heterosexual participants, respectively. Participants living alone (aOR = 0.15; 95% CI: 0.02, 0.95), compared to those living with a spouse or partner, were significantly less likely to care for an SGM individual.

Table 3. Multivariable regression models assessing care partner SGM status and care partner strain index among those individuals self-reporting as a caregiver.

	Care Recipient SGM Status^a	Caregiver Strain Index^b
		Model 1	Model 2
Characteristic	aOR (95% CI)	aIRR (95% CI)	aIRR (95% CI)
Age	0.91 (0.81, 1.00)*	0.99 (0.98, 1.00)	1.01 (0.99, 1.02)
Race/Ethnicity
White	Ref	Ref	Ref
Black	5.62 (1.76, 17.99)**	1.11 (0.98, 1.26)	1.47 (1.23, 1.75)***
Hispanic/Latinx	Empty^c	1.03 (0.59, 1.79)	Empty^c
Different Race/Ethnicity	1.20 (0.04, 33.08)	1.31 (1.01, 1.70)*	1.27 (0.84, 1.93)
Gender Identity
Cisgender Woman	Ref	Ref	Ref
Transgender Woman	Empty^d	0.45 (0.32, 0.65)***	0.48 (0.31, 0.75)***
Cisgender Man	0.36 (0.09, 1.38)	1.25 (1.10, 1.43)***	1.17 (0.99, 1.38)
Transgender Man	Empty^d	1.56 (1.02, 2.40)*	1.59 (1.00, 2.52)*
Different Identity	3.41 (0.20, 58.21)	0.83 (0.61, 1.12)	0.85 (0.58, 1.23)
Sexual Identity
Heterosexual	Ref	Ref	Ref
Gay/Lesbian	4.10 (1.00, 16.84)*	1.12 (0.96, 1.30)	1.22 (0.97, 1.54)
Bisexual	0.17 (0.01, 5.33)	1.36 (1.02, 1.81)*	1.70 (1.11, 2.61)*
Different Identity	9.35 (0.27, 320.79)	0.39 (0.22, 0.68)***	0.46 (0.23, 0.96)*
Living Situation
With Spouse/Partner	Ref	Ref	Ref
Alone	0.15 (0.02, 0.95)*	0.99 (0.81, 1.21)	0.92 (0.71, 1.20)
With Family/Roommate	0.81 (0.23, 2.84)	1.10 (0.97, 1.25)	0.89 (0.76, 1.04)
Long-Term Care	0.13 (0.01, 1.82)	0.99 (0.70, 1.41)	0.78 (0.50, 1.23)
Other	0.88 (0.04, 20.13)	1.09 (0.76, 1.57)	1.17 (0.67, 2.03)
Care Recipient Identity
Not SGM	–	–	Ref
SGM	–	–	0.67 (0.55, 0.80)***

*p ≤ .05; **p ≤ .01; **p ≤ .001.

^aUsing logistic regression; reference group is non-SGM individuals.

^bUsing Poisson regression.

^cNo Hispanic/Latinx individuals reported knowing the SGM status of their care recipients.

^dTransgender women and men only reported caring for SGM individuals.

^^Cells empty.

The final set of models (Table 3) first examined the association between sociodemographic variables and the caregiver strain index followed by the addition of adjustment for the primary care recipient’s sexual identity. In the first model, those identifying as a different race/ethnicity reported higher caregiver strain relative to White participants (adjusted incidence rate ratio [aIRR] = 1.31; 95% CI: 1.01, 1.70). Compared to cisgender women, transgender men (aIRR = 1.56; 95% CI: 1.02, 2.40) and cisgender men (aIRR = 1.25; 95% CI: 1.10, 1.43) each reported higher scores on the caregiver strain index, while transgender women reported lower scores (aIRR = 0.45; 95% CI: 0.32, 0.65). Compared to heterosexuals, bisexual caregivers (aIRR = 1.36; 95% CI: 1.02, 1.81) reported higher caregiver strain, while those reporting as a different sexual identity reported lower caregiver strain (aIRR = 0.39; 95% CI: 0.22, 0.68). No significant differences were observed with regard to living situation.

The second model (Table 3) was identical to that described above but also included adjustment for the primary care recipient’s status as a member of the SGM community. Relative to White participants, Black participants reported greater caregiver strain (aIRR = 1.47; 95% CI: 1.23, 1.75), while results among those reporting a different race/ethnicity were no longer significant. As in the previous model, compared to cisgender women, transgender men (aIRR = 1.59; 95% CI: 1.00, 2.52) continued to report higher scores on the caregiver strain index, while transgender women continued to report lower scores (aIRR = 0.48; 95% CI: 0.31, 0.75). Results also continued to hold with regard to sexual identity with bisexual caregivers (aIRR = 1.70; 95% CI: 1.11, 2.61) reporting higher caregiver strain and those reporting as a different sexual identity reporting lower caregiver strain (aIRR = 0.46; 95% CI: 0.23, 0.96). Compared to those who cared for non-SGM individuals, those caring for SGM individuals reported significantly less strain (aIRR = 0.67; 95% CI: 0.55, 0.80). Sensitivity analyses examining an interaction term between care partner sexual identity and care recipient SGM status was non-significant and thus not included in these analyses.

Discussion

In a diverse sample of older adults (age ≥50 years) in Columbus, Ohio, we observed a unique set of findings with regard to self-identified care partners. Key among these were increased odds of gay and lesbian individuals, relative to heterosexuals, to report serving as care partners for at least one individual. Compared to cisgender women, transgender men and those identifying as a different gender were less likely to report serving as care partners. Gay/lesbian and bisexual participants as well as cisgender men reported higher scores on the caregiver strain index. Regarding the SGM status of the care recipient, participants identifying as gay or lesbian were more likely to report caring for another SGM person. Meanwhile, caring for an SGM person significantly reduced caregiver strain, regardless of the sexual identity of the caregiver themselves.

With the exception of a few studies, little work has examined even basic differences between SGM and non-SGM care partners. One such past study by Anderson and Flatt (2018) utilized data from the Caregiving in the U.S. survey of 7,600 adults age 18 or older (J. Anderson & Flatt, 2018). They found SM caregivers to be younger and more racially and ethnically diverse compared to non-SM caregivers. In this analysis, we took a step back from this past study to examine the odds of being a care partner in the first place, observing that gay and lesbian individuals were 7.4-times as likely to serve as care partners, findings similar to other research in this area (Capistrant, 2018). Beyond simply comparing demographic differences of the caregiver, we also assessed the SGM status of the primary care recipient. This set of results suggests that older adults identifying as gay or lesbian were more likely to care for other SGM identified persons in need of care but this was not true for those identifying as bisexual nor those identifying as a different sexual identity.

Where our study overlaps with that of past research is in examining the caregiver strain index, or the amount of stress experienced by those providing care to another individual. The previously mentioned research by Anderson and Flatt (2018) also noted that sexual minority identity was not an independent predictor of caregiver strain, although sexual minority caregivers did report other key differences such as a greater odds of helping with medical nursing tasks (J. Anderson & Flatt, 2018). Our findings are in stark contrast to these as we did observe that SGM status was an independent predictor of caregiver strain such that both gay or lesbian participants reported greater strain than cisgendered people and heterosexuals. The difference in findings between studies, however, is likely attributable to a simple difference in samples. The former work was a national sample among adults 18 years or older, while our own work was conducted only among adults 50 years or older and was a very localized sample. Even so, our results suggest that older SGM care partners may be at unique risk not only of stressors related to their age and SGM identity (Bresin & Mekawi, 2019; Fredriksen-Goldsen et al., 2014; Fredriksen-Goldsen, Emlet, et al., 2013; Gendron et al., 2013; Keough et al., 2015), as past research has demonstrated, but may also be at greater risk of stress related to caregiving. These compounding stressors may only serve to exacerbate extant health disparities (Dyar et al., 2019; Morgan, D’Aquila, et al., 2019; Morgan, Dyar, et al., 2021; Morgan, Hudson, et al., 2021; Morgan, Taylor, et al., 2019; Sherman et al., 2022) among this population and future research should aim to explore targeted interventions among SGM care partners.

Interestingly, we also observed that the sexual identity of the care recipient contributed to differences in caregiver strain. Namely, when caring for an individual who identifies as a member of the SGM community, caregiver strain was reduced by nearly two-and-a-half points or almost a quarter of the entire caregiver strain scale. Sensitivity analyses also did not observe significant effect moderation between care partner sexual identity and care recipient SGM status, suggesting that the reduction in the strain index was not simply attributable to sexual minorities caring for other members of the SGM community. One possible hypothesis for this is that due to greater contact between SGMs and the healthcare system, as a result of their worse health outcomes (Dyar et al., 2019; Sherman et al., 2022), they may be more likely to have greater tolerance or more realistic expectations of their care partner responsibilities resulting in reduced strain among the care partner themselves. Another possibility may be that among SGM caregivers who provide services for non-SGM persons, there may be more inadvertent or knowing microaggressions or engagements of heterosexism against the SGM caregiver that increases overall strain, similar to microaggressions experienced by the broader SGM community (Nadal et al., 2010; Swann et al., 2016). Unfortunately, though, we did not anticipate this finding, so our survey was not set-up to assess any additional variables that may have provided a more nuanced discussion here. Still, future research should aim to develop a better understanding of how factors such as one’s sexual identity may play a role in contributing to or diminishing caregiver strain.

Our study should be considered in light of its limitations. First, these data are cross-sectional, and this is a study; therefore, findings are limited in not being able to draw causal conclusions. Second, this sample is very limited in geography, and thus these results may only reflect the local population of Columbus rather than being representative of older adults across the U.S. We also did not assess the relationship between care partner and the primary care recipient which would have helped clarify differences between cisgendered people, heterosexuals, and sexual and gender minorities. Next, some cell sizes were small (<10) which is not ideal in regression analyses, however, collapsing these groups would serve to erase the identity of those in these groups, a common issue when examining SGM populations. Care should be taken when interpreting those results with special attention paid to the precision of the estimates. Finally, social media recruitment can also lead to biases in the data due to more “active” populations on social media or differences and barriers due to socio-economic status. Future work should aim to focus more specifically on older transgender adults in order to better understand caregiving among this population, particularly as they have a unique set of needs.

Even in light of these limitations, our study demonstrated a novel set of findings among a diverse sample of older adult care partners. First, we noted that those identifying as gay or lesbian were significantly more likely to identify as care partners in the first place, while transgender men and those identifying as a different gender were less likely to be care partners. In contrast to past research, we observed a significant relationship between sexual identity and caregiver strain, with both gay/lesbian and bisexual participants reporting greater strain. Finally, caregiver strain was reduced, on average, by nearly a quarter of the overall scale when care recipients were part of the SGM community. Taken together, these results suggest that SGM care partners may be at risk of yet another unique stressor which may be contributing to health disparities among this population. Future research should aim to assess why care recipient SGM identity impacts overall care partner strain, as well as assessing potential interventions aimed at reducing strain among SGM care partners.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Institute on Drug Abuse [K01DA046716; PI: Dyar]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.