Laura M. Koehly is Senior Investigator and Chief, Social and Behavioral Research Branch at the National Human Genome Research Institute. Her research focuses on developing and applying social network methods to the study of complex social systems, such as families and communities. In order to better understand the impact of the interpersonal environment on behaviors, Laura Koehly also develops statistical methods to examine the perspectives of all members within a family system, thereby considering the social context in which at-risk individuals live.
In the United States, approximately 66 million informal (unpaid) caregivers provide care to someone who is ill, disabled or are experiencing loss of function associated with aging; approximately 4.7 million perform such roles in Germany. These caregivers may be adult children, spouses, parents, or other social network members. Caregiving research has traditionally engaged a single-informant, primary caregiver approach to characterize the caregiving network composition and function. However, multiple family members are affected by caregiving and may experience it differently. In the current talk, we examine the added value of the multi-informant approach to characterize the social landscape of caregiving within the context of Alzheimer’s disease and related dementia (ADRD). Our data come from the Caregiving Roles and Expectations Networks (CaRENet) Project in which 72 informants from 30 families enumerated network members and indicated caregiving roles for each. We observe both within family and between family variability with respect to caregiving roles and expectations. These results provide evidence for moving beyond a sole primary caregiver model, suggesting the need to move towards a multi-informant approach when designing caregiving studies and interventions. In addition, network-level factors derived from such an approach may be important to family adaptation and caregiver well-being.