Alzheimer’s disease and related dementia (AD/dementia) represent a looming public health crisis, affecting roughly 5 million people in the U.S. and 11% of older adults. As with other chronic conditions, racial/ethnic and socio-economic disparities exist in the prevalence and burden of illness. However, less is known about how disparities in access to care influence the care trajectories – i.e., the scope, frequency and sequence of services used across healthcare settings – of those with AD/dementia. This retrospective, longitudinal cohort study will address this gap, using network analysis to examine care trajectories and outcomes in a racially and socioeconomically diverse sample of community-dwelling individuals with AD/dementia and cognitive impairment. The cohort consists of home health care patients served by the Visiting Nurse Service of New York (VNSNY), a large, non-profit home care provider. Data will include clinical assessments for roughly 89,000 patients admitted to home care during 2010-2012 linked with 4 years of Medicare and Medicaid claims data and geographic data on neighborhood services. Using a systems science framework, the study will use innovative network analytics to capture complex patterns in care trajectories that may contribute to adverse outcomes in a racially and socioeconomically diverse sample of individuals with these two conditions. The long-term goal of this research is to identify and, in subsequent studies, test potential home- and community-based interventions that may improve access to services and outcomes for individuals who may be underserved or at heightened risk for unmet healthcare needs.

Faculty Advisor

Project timeline

Research position can start immediately.

  • Start date: 1/15/2018
  • End date: 08/31/2018
  • Number of hours per week of research expected: 20 in spring semester, up to 40 in summer semester

Candidate requirements

  • Skill sets: BS in a computational field or related skills. Prefer experience with large data sets or experience with data visualization. The student will use data mining and relevant data science techniques (e.g., visualization in Tableau) to investigate ways in which the multi-modal, integrated data can be processed automatically and scaled to reveal new knowledge and enhance access to relevant information.
  • Student eligibility (as of Spring 2018): freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: NOT eligible
  • Other comments: this is a funded student RA position so if a student is hired prior to summer institute would have to negotiate how stipend would be arranged.