A Data-based Re-design of Housing Supports and Services for Aging Adults who Experience Homelessness

This report examines health services use and population dynamics among the aging homeless population in Los Angeles. Evidence suggests that adverse health outcomes lead to homelessness, and the conditions related to homelessness lead to or exacerbate a range of health problems. In addition, the barriers to accessing preventative and primary care while homeless lead to receipt of healthcare only when morbidities are more acute, meaning that there is a disproportionate use of inpatient hospitalization and other costly medical and behavioral health services among persons experiencing homelessness.

As a result, homelessness is expensive for healthcare systems and for society as a whole. Given this, interest in using healthcare systems as a platform to address homelessness has grown in recent years. Strategies include efforts to identify homeless patients in healthcare settings in order to link them with housing and social services; the creation of accountable care organizations that seek to coordinate healthcare and social services for persons experiencing housing instability; and the development of new financing mechanisms geared towards using healthcare dollars to support housing stability.

This paper focuses on healthcare use among older homeless individuals, a group that is particularly vulnerable to adverse health outcomes, using Los Angeles County as a case study to examine future trends in healthcare use among an older homeless population through combining analyses of current healthcare use with projected aging trends among Los Angeles County’s homeless population. In doing so, it addresses the following objectives:

  1. Project aging dynamics for sheltered homeless population using LAHSA HMIS data (2009-15) and demographic forecasting methods
  2. Apply age-group specific healthcare and shelter cost estimates to population projections for likely future cost dynamics
  3. Use cluster analysis to match sheltered sub-populations to different housing interventions and estimate related service costs, and
  4. Draw upon prior research to estimate potential cost offsets associated with housing under different scenarios 5. Compare costs of housing interventions to cost offsets.
Publication Date: 
United States