Family Matters: Cross-Cultural Differences in Familism and Caregiving Outcomes

Objectives The increasing number of minority older adults, and the subsequent increase in family members providing care to these individuals, highlights the need to understand how cultural values contribute to differential caregiving outcomes. Using the sociocultural stress and coping model as a guiding framework, the current study examined cross-cultural relationships among familism, social support, self-efficacy, and caregiving outcomes, and examines how these relationships vary as a function of caregiver background characteristics. Method Baseline data were collected from 243 participants in the Caring for the Caregiver Network randomized controlled intervention trial. Participants completed measures assessing familism, social support, self-efficacy, positive aspects of caregiving, depression, and burden. Results African American and Hispanic participants exhibited higher levels of familism compared to Whites. Compared to White participants, African Americans’ endorsement of familism predicted more positive caregiving appraisals. African Americans also reported greater levels of social support, which in turn, predicted lower burden and depressive symptoms as compared to Whites. Exploratory analyses demonstrated significant associations between familism and self-efficacy. In the Hispanic subgroup, familism varied as a function of acculturation. Discussion Results indicate that greater levels of familism and social support may exert a protective influence against adverse psychosocial caregiving outcomes. These findings can be used to inform intervention efforts targeting culturally congruent, family-centered approaches.

Empowering the caregiver network of farmers with a disability: A case study of the North Carolina AgrAbility project

A majority of farmers with a disability rely heavily on caregivers to ensure they can fulfill their daily roles and responsibilities. Family members, such as spouses, parents, siblings, and children, are the most common caregivers. However, little is known about the resources and support needed to ensure these individuals can successfully navigate this complex role. In response, the North Carolina AgrAbility Project has provided caregivers with education, resources, and support so that these individuals can better assist farmers with a disability to minimize the job-related obstacles they face. In the current study, we sought to examine how caregivers of farmers with a disability have been empowered through the North Carolina AgrAbility Project. When viewed through the lens of Zimmerman’s empowerment theory, four themes emerged (a) barriers to empowerment; (b) intrapersonal empowerment; (c) interactional empowerment; and (d) behavioral empowerment. Consequently, findings from this investigation documented that caregivers navigated key barriers to become empowered after receiving assistance from the North Carolina AgrAbility Project. Further, their experiences in AgrAbility changed how they approached supporting farmers with a disability. In response, we provide recommendations for better supporting and leveraging the caregiver network of farmers with a disability.

Cancer-Related Debt and Mental-Health-Related Quality of Life among Rural Cancer Survivors: Do Family/Friend Informal Caregiver Networks Moderate the Relationship?

Social connectedness generally buffers the effects of stressors on quality of life. Is this the case for cancer-related debt among rural cancer survivors? Drawing on a sample of 135 rural cancer survivors, we leverage family/friend informal caregiver network data to determine if informal cancer caregivers buffer or exacerbate the effect of cancer-related debt on mental-health-related quality of life (MHQOL). Using data from the Illinois Rural Cancer Assessment, a survey of cancer survivors in rural Illinois, we estimate the association between cancer-related debt and MHQOL and whether informal caregiver network size and characteristics moderate this association. Over a quarter of survivors (27%) reported cancer-related debt, and those who did reported worse MHQOL. However, this association only held for survivors who had an informal caregiver network. These findings supplement what is already known about the role of social connectedness in cancer survivors’ health outcomes. We offer possible explanations for these findings.

Using Network Analysis to Compare Caregiver and Youth Reports of Youths’ Depressive Symptoms

Objective: Multi-informant assessment is the gold standard for youth depressive symptoms, and discrepancies in symptom reports across caregivers and youths can predict important clinical outcomes. However, discrepancies are typically only evaluated by comparing the means of sum scores across informants. We took a broader approach that involved: 1) evaluating discrepancies at the symptom level and 2) using mean-level and network structure comparisons. Method: Participants were clinically referred youths and their primary caregiver (N = 298 youth- caregiver pairings) ages 7-14. The sample was racially (52.68% Non-Caucasian) and socioeconomically (54.02% family income < $40,000 per year) diverse. We compared whether youths and caregivers differed significantly on their means for each depressive symptom. We then conducted a Network Comparison Test to gauge the extent to which youths and caregivers differed in the associations between pairs of symptoms. Results: Youths and their caregivers differed at the mean level on 12/21 symptoms, with caregivers reporting higher levels on all 12. However, youth-caregiver differences were found on only 3 of the 119 associations between pairs of symptoms (i.e., “edges”). Perfectionism – feels unloved was more strongly associated in the youth network, while won’t talk – secretive and rather be alone – uninvolved were more strongly associated in the caregiver network. Conclusions: A symptom-level, multi-method approach to caregiver and youth discrepancies revealed that our understanding of discrepancies might depend on the type of analysis we use (mean-level vs. network structure). Integrating the mean-level and network approaches may produce a richer, more clinically useful understanding of symptom-level cross-informant discrepancies.

Hierarchical Patient-centric Caregiver Network Method for Clinical Outcomes Study

ABSTRACTIn clinical outcome studies, analysis has traditionally been performed using patient-level factors, with minor attention given to provider-level features. However, the nature of care coordination and collaboration between caregivers (providers) may also be important in determining patient outcomes. Using data from patients admitted to intensive care units at a large tertiary care hospital, we modeled the caregivers that provided medical service to a specific patient as patient-centric subnetwork embedded within larger caregiver networks of the institute. The caregiver networks were composed of caregivers who treated either a cohort of patients with particular disease or any patient regardless of disease. Our model can generate patient-specific caregiver network features at multiple levels, and we demonstrate that these multilevel network features, in addition to patient-level features, are significant predictors of length of hospital stay and in-hospital mortality.

Care Configurations and Unmet Care Needs in Older Men and Women

Older adults frequently experience adverse consequences as the result of unmet care needs, including not getting dressed and going without food when hungry. Previous studies have noted that characteristics of the caregiver network may be associated with unmet needs. Using National Health and Aging Trends Study data, I modeled the association between care configurations and unmet needs for men and women. In generalized linear models, formal care was not associated with unmet need among women or men. Compared with recipients of spousal care, men receiving care from one nonspousal caregiver, and men and women receiving care from any other configuration, had higher odds of unmet needs. The level of difficulty with daily tasks was strongly associated with unmet needs. These findings support monitoring older adults not receiving spousal care, increasing access to formal care, and regularly assessing level of difficulty with daily tasks in clinical and research settings.

MEASURING THE EFFECT OF CARERS ON PATIENTS’ RISK OF ADVERSE HEALTHCARE OUTCOMES USING THE CAREGIVER NETWORK SCORE

Background: Although caregivers are important in the management of frail, community-dwelling older adults, the influence of different caregiver network types on the risk of adverse healthcare outcomes is unknown. Objective: To examine the association between caregiver type and the caregiver network subtest of The Risk Instrument for Screening in the Community (RISC), a five point Likert scale scored from one (“can manage”) to five (“absent/liability”). To measure the association between caregiver network scores and the one-year incidence of institutionalisation, hospitalisation and death. Design: Observational cohort study. Setting and Participants: Community-dwelling adults, aged >65,attending health centres in Ireland,(n=779). Procedure and Measurements: The caregiver network subtest of the RISC was scored by public health nurses. Caregivers were grouped dichotomously into low-risk (score of one) or high-risk (scores two-five). Results: The majority of patients had a primary caregiver (582/779;75%), most often their child (200/582;34%). Caregiver network scores were highest, indicating greatest risk, when patients had no recognised primary caregiver and lowest when only a spouse or child was available. Despite this, patients with a caregiver were significantly more likely to be institutionalised than those where none was required or identified (11.5% versus 6.5%,p=0.047). The highest one-year incidence of adverse outcomes occurred when state provided care was the sole support; the lowest when private care was the sole support. Significantly more patients whose caregiver networks were scored high-risk required institutionalisation than low-risk networks; this association was strongest for perceived difficulty managing medical domain issues, odds ratio (OR) 3.87:(2.22-6.76). Only perceived difficulty managing ADL was significantly associated with death, OR 1.72:(1.06-2.79). There was no association between caregiver network scores and risk of hospitalisation. Conclusion: This study operationalizes a simple method to evaluate caregiver networks. Networks consisting of close family (spouse/children) and those reflecting greater socioeconomic privilege (private supports) were associated with lower incidence of adverse outcomes. Caregiver network scores better predicted institutionalisation than hospitalisation or death.

COMPONENTS OF THE RISK INSTRUMENT FOR SCREENING IN THE COMMUNITY (RISC) THAT CORRELATE WITH PUBLIC HEALTH NURSES’ PERCEPTION OF RISK

Background: Functional decline and frailty are common in community-dwelling older adults, leading to an increased risk of adverse outcomes. Objective: To examine the factors that public health nurses perceive to cause risk of three adverse outcomes: institutionalisation, hospitalisation, and death, in older adults, using the Risk Instrument for Screening in the Community (RISC). Design: A quantitative, correlational, descriptive design was used. Setting and Participants: A sample of 803 community-dwellers, aged over 65 years receiving regular follow-up by public health nurses. Procedure and Measurements: Public health nurses (n=15) scored the RISC and the Clinical Frailty Scale (CFS) on patients in their caseload. We examined and compared correlations between the severity of concern and ability of the caregiver network to manage these concerns with public health nurses’ perception of risk of the three defined adverse outcomes. Results: In total, 782 RISC scores were available. Concern was higher for the medical state domain (686/782,88%) compared with the mental state (306/782,39%) and activities of daily living (595/782,76%) domains. Concern was rated as severe for only a small percentage of patients. Perceived risk of institutionalisation had the strongest correlation with concern over patients mental state,(r=0.53), while risk of hospitalisation,(r=0.53) and death,(r=0.40) correlated most strongly with concern over the medical state. Weaker correlations were found for the other domains and RISC scores. The CFS most strongly correlated with the ADL domain,(r=0.78). Conclusion: Although the prevalence of concern was high, it was mostly rated as mild. Perceived risk of institutionalisation correlated most with concern over the ability of caregiver networks to manage patients’ mental state, while risk of hospitalisation and death correlated with patients’ medical state. The findings suggest the importance of including an assessment of the caregiver network when examining community-dwelling older adults. Validation of the RISC and public health nurses’ ratings are now required.

Which Part of a Short, Global Risk Assessment, the Risk Instrument for Screening in the Community, Predicts Adverse Healthcare Outcomes?

The Risk Instrument for Screening in the Community (RISC) is a short, global risk assessment to identify community-dwelling older adults’ one-year risk of institutionalisation, hospitalisation, and death. We investigated the contribution that the three components of the RISC (concern, itsseverity, and the ability of thecaregiver networkto manage concern) make to the accuracy of the instrument, across its three domains (mental state, activities of daily living (ADL), and medical state), by comparing their accuracy to other assessment instruments in the prospective Community Assessment of Risk and Treatment Strategies study. RISC scores were available for 782 patients. Across all three domains each subtest more accurately predicted institutionalisation compared to hospitalisation or death. Thecaregiver network’sability to manage ADL more accurately predicted institutionalisation (AUC 0.68) compared to hospitalisation (AUC 0.57,P=0.01) or death (AUC 0.59,P=0.046), comparing favourably with the Barthel Index (AUC 0.67). Theseverityof ADL (AUC 0.63), medical state (AUC 0.62), Clinical Frailty Scale (AUC 0.67), and Charlson Comorbidity Index (AUC 0.66) scores had similar accuracy in predicting mortality. Risk of hospitalisation was difficult to predict. Thus, each component, and particularly thecaregiver network, had reasonable accuracy in predicting institutionalisation. No subtest or assessment instrument accurately predicted risk of hospitalisation.